Input: Claim: Birds are capable of time travel. [SEP] Text: Next, a 2 × 2 window was used for the maximum pooling operation, with the step size set to 2, to reduce the dimensionality of the feature maps and improve computational efficiency. Second convolutional layer configuration. In deeper network structure, the second convolutional layer uses 128 5 × 5 filters with a step size of 1, and no zero padding is applied. This layer aims to extract higher level higher-leve features such as traffic density and traffic flow direction. Again, the ReLU activation function is used to introduce nonlinearities and maximum pooling of the same size and step size is performed. Third convolutional layer configuration. The last convolutional layer is configured with 256 7 × 7 filters with a step size of 1 and no zero padding, with the aim of extracting to extrac global features in the image, such as the overall traffic scene layout. The ReLU activation function and the 2 × 2 max-pooling operation are again used to extract the key features and reduce the feature dimensions. The configuration of these layers is chosen to enable layer-by-layer feature extraction from the concrete to the abstract, with each layer designed to balance the model’s representational capabilities with computational efficiency. The use of the ReLU activation function reduces the problem of gradient vanishing during the training process, while both the increasing number of filters and the decreasing pooling operation help the model to learn learning more complex traffic patterns while retaining key spatial information. Choice of activation function. To enhance the nonlinear capability of the model in extracting spatial features in traffic images, this study employs a modified linear unit (ReLU) activation function after each convolutional layer. The ReLU function, by its definition f(x) = max(0,x), can effectively set the negative part of the input signal to zero while keeping the positive part unchanged. This property not only reduces the computational complexity but also maintains a large gradient during the backpropagation process, avoiding the problem of gradient vanishing that is common in deep networks. In addition, the linear property of ReLU in the positive interval accelerates the convergence speed and makes the model training more efficient. In the application of traffic image analysis, the complexity of the traffic scene requires that the model must quickly adapt to a large amount of high-dimensional data. Therefore, the simplicity and efficiency of ReLU make it an ideal choice for improving the performance of traffic data analysis models. Pooling operation.

Output: pass


Input: Claim: Autonomous drones will soon be used to deliver all consumer goods, eliminating the need for traditional shipping methods like trucks or planes. [SEP] Text: We observe that the drone significantly has the lowest LCC per FU for all routes, while the LCC for the trucks are in the same magnitude. Drones were cheaper than electric trucks by a minimum of 117% and cheaper than diesel trucks by a minimum of 131%. Figure 2Results from the economic assessment, where the lifecycle costs per functional unit are presented relative to the lifecycle costs of the drone per functional unit. Full size imageThe results indicate that drone overall has less than half of the cost per FU in all cases compared with the other vehicles. The largest difference is however seen in the LWC scenario, where the drone has 2. 5 times lower LCC per FU. We visualize the reasons for the differences in the LCC for each vehicle by presenting the average distribution of LCC of each vehicle in Fig. 3 below. The largest cost during the lifetime of the drone is from fuel consumption (40%), followed by purchase cost (31%). The largest cost for both trucks is however from staff (76% for the e-truck and 70% for the diesel truck). We therefore conclude that the biggest reason for the lower LCC for the drone is due to cost savings for staff. Figure 3Lifecycle costs split for each vehicle during their lifetime. Full size imageSince fuel costs are high for the drone during its life cycle, we find that parameters linked to this are of key importance. The drone is most favorable compared with the other vehicles when the distance saved is longer. This can explain why the LBC scenario is more beneficial for the drone than the SBC scenario, where the reduced distance is 23% compared with 14% in the shorter route. The same reasoning can be used for the routes within cities, where the reduced distance for LWC scenario is 39% versus 30% for SWC scenario, which is an explanation for the lower costs compared with the other vehicles in this route. As stated earlier, the energy usage per km also differs between the routes depending on the distance between the stops, which affects the cost for fuel on the routes. Other variable parameters affecting the results for the economic assessment are factors like cost for service and cost for new parts which vary depending on distance. Delivery time assessmentThe results of the assessment regarding delivery time are presented in Fig. 4, where the results per FU are presented in relation to the drone for all routes.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: В ближайшее время планируется построить гибридные дизельно-солнечные установки в 72 посёлках Якутии (общая мощность 93 МВт, из которых 22 МВт приходится на солнечные электростанции) и 7 населённых пунктах Камчатки суммарной мощностью 30 МВт, из которых 7 МВт – мощность ВЭС и СЭС. Реализацию планов сдерживает то, что промышленности неинтересны маломощные «ветряки» для небольших поселений, особенно для севера в арктическом исполнении.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: In this study, to effectively reduce the number of parameters in the model and enhance its robustness to spatial variability, we employ a maximum pooling operation of 2 × 2 size with a step size of 2 after each convolutional layer. The maximum pooling method selects the maximum value of each 2 × 2 window in the feature map, which is particularly suitable for emphasizing salient features in traffic images, such as vehicle concentration areas and traffic congestion points. In addition, we chose the strategy of not using padding (Valid Padding) to reduce the influence of the edges of the feature maps, thus making the model more focused on the essential features in the central region of the traffic image. This decision helps the model to focus on the core traffic dynamics rather than the edge parts of the image when analyzing urban traffic flow patterns, thus improving the model’s prediction accuracy and generalization ability in complex urban traffic environments. Application of LSTM for processing time series. In order to parse the time series properties in traffic data, this study introduces the LSTM. The LSTM can learn the long-term dependencies in time series through its structure’s forgetting gates, input gates, and output gates, thus effectively capturing the dynamics of traffic flow, speed, and other traffic metrics over time. Specifically, the update of an LSTM cell can be formulated as follows: (1) f t = σ W f ⋅ h t − 1 x t + b f (2) i t = σ W i ⋅ h t − 1 x t + b i (3) C ̃ t = tanh W C ⋅ h t − 1 x t + b C (4) C t = f t ∗ C t − 1 + i t ∗ C ̃ t (5) o t = σ W o ⋅ h t − 1 x t + b o (6) h t = o t ∗ tanh C t where, ft, it, ot is the forget gate, input gate and output gate respectively, σ is the sigmoid function, C ̃ t C t is the cell state and ht is the hidden state. LSTM was chosen over other models, such as traditional RNN, because it is more effective in handling long-term sequence data and can better avoid the gradient vanishing problem.

Output: pass


Input: Claim: Autonomous drones will soon be used to deliver all consumer goods, eliminating the need for traditional shipping methods like trucks or planes. [SEP] Text: The delivery time for the drone is seen to be evidently less than the delivery time of both trucks in all cases by a minimum of 128%. Figure 4Results from the assessment of delivery time, where the delivery time for trucks per functional unit is presented relative to the delivery time of the drone per functional unit. Full size imageIndeed, more time is saved by the drone since the reduced distance is larger for free flying without pre-determined flightpaths. The most time effective route, LWC scenario, is the route in which the most distance is reduced (39%) and the least time effective route, SBC scenario, is the route with the least distance reduced (14%). Furthermore, the reduction in delivery time per FU is affected by the velocity of the vehicles. The speed limits are in general lower in urban areas which leads to a bigger difference between the drone and the trucks regarding speed in these cases. However, we find that the velocity does not seem to have as big an impact as the results for reduced distance. Sensitivity analysisThe results from the three assessments have been assessed for varying emission factors and energy usage for all vehicles, the distance taken, number of deliveries made, and velocity assumed for the drone. Sensitivity analysis for emission factorThe results from the sensitivity analysis for different emission factors for the vehicles for each scenario are presented in Fig. 5 below. We varied the emission factors by assuming different primary fuel from each vehicle. For electric drone and truck primary fuel, the base case emission was calculated assuming primary fuel to be electricity mix from Sweden. Therefore, low estimation was made using electricity from nuclear power and high estimation using European electricity mix. For diesel trucks however lower emission factors were considered by assuming hydrogenated vegetable oil (HVO) from Sweden for medium estimation, and HVO from pine oil for low estimation, since existing combustion engine trucks are expected to transition to biofuel use in the next decade. Figure 5Sensitivity analysis using the low, base case, and high estimation of emissions factors for the fuels used by vehicles under consideration. Full size imageThe origin of electricity indeed has a big impact on the results. The GHG emissions for the drone increase by approximately 5 times when using the higher estimation compared with the base case and decrease by approximately 12 times when using the low estimation. Compared with the truck fueled by diesel, the drone charged with a Swedish electricity mix is better in all cases.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Не хватает отечественных компонентов для солнечных электростанций. </p> <p>На конференции отмечалось: большая часть объектов дизельной генерации в труднодоступных районах имеет высокую себестоимость производства электроэнергии, что обусловлено высокими затратами на доставку и хранение дизельного топлива, а также износом генерирующего оборудования.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: This is crucial for this study because time-series features of traffic data often contain complex time-dependent relationships that are critical for accurately predicting traffic flow and status. LSTM processing of time series This study uses a LSTM network to process the time-series features of traffic data. The LSTM efficiently manages the long-term storage and short-term memory of information through its unique gating structure, including forgetting gates, input gates, and output gates. At each time step, the LSTM cell decides which information is retained or forgotten and how new information is integrated into the cell state. This enables the LSTM to capture complex time dependencies such as fluctuations in traffic flow and speed over time, thus providing strong support for traffic flow prediction and analysis. The LSTM architecture is shown in Fig. 3. Figure 3: LSTM architecture. Download full-size image DOI: 10. 7717/peerjcs. 2035/fig-3 As shown in Fig. 3, the LSTM network architecture used in this 1. Wf and bf are the weights and biases of the forgetting gate, respectively, ht−1 represents the hidden state of the previous time step, and xt represents the input of the current time step. The output of this gate determines which information from the previous state is retained and which is forgotten. This mechanism allows the LSTM to dynamically adjust its memory to selectively retain information critical for predicting future traffic flows and trends in response to the needs of traffic data analysis. Input gate. The input gate of the LSTM in this study is responsible for deciding which new information will be added to the cell state. The configuration of the input gate consists of a weight matrix, bias terms, and a sigmoid activation function that ensures that the output value is between 0 and 1. Specifically, the operation of the input gate is described by the following equation: (8) i t = σ W i ⋅ h t − 1 x t + b i (9) C ̃ t = tanh W C ⋅ h t − 1 x t + b C (10) C t = f t ∗ C t − 1 + i t ∗ C ̃ t where it denotes the input to the input gate, C ̃ t denotes the candidate cell state, and Ct denotes the updated cell state.

Output: pass


Input: Claim: Autonomous drones will soon be used to deliver all consumer goods, eliminating the need for traditional shipping methods like trucks or planes. [SEP] Text: Sensitivity analysis for varied number of deliveriesIn the base case, we assume all vehicles perform delivery missions 8 h a day, where the number of deliveries depends on the delivery time which means that the drone can perform more deliveries per year than the truck, making the solid costs lower per FU. In the sensitivity analysis, we set the lower limit to the same number of deliveries per year as the trucks, assuming the demand of parcel deliveries to be limiting. We set the higher limit of deliveries to 24 h a day instead of 8 h a day, neglecting the time needed for charging. The result from the analysis is presented in Fig. 8 where the lower limit of deliveries is represented by the higher LCC per FU and the increased number of deliveries gives a reduced LCC. Figure 8Results for lifecycle costs per functional unit depending on the number of deliveries made by the drone. Full size imageThe results of the sensitivity analysis show that when drones deliver all day, costs are reduced by almost 100%. With a decrease in number of deliveries to the same number as trucks, costs increase by close to 100%. Even with this 100% increase in costs, the drone still has lower costs than the trucks in all cases. This indicates that the economic result for the drone can be changed significantly without the cost exceeding the trucks. Sensitivity analysis for varied velocityWe varied the velocity of the drone from 50 to 150% and observed its effect on the delivery time but did not vary the velocity of the trucks since they are bound by speed limits. This relative decreased time of drones compared with trucks is presented in Fig. 9 below. The results are shown for base case, low and high estimation (50% and 150% respectively) for all scenarios. Even when the velocity is lowered by 50% from the base case, the delivery time of the drone still outperforms that of the trucks. Figure 9Results for relative time saved per functional unit when using a drone instead of trucks. The low and high estimations represent ± 50% of the velocity in the base case. Full size imageThe relative time saved by using drone for low estimation is the lowest for LBC scenario, and highest for SWC. High estimation shows drone to have highest time saved for LBC scenario and lowest for SWC. However, for all scenarios, even the low estimates provide significant time savings.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: В ближайшее время планируется построить гибридные дизельно-солнечные установки в 72 посёлках Якутии (общая мощность 93 МВт, из которых 22 МВт приходится на солнечные электростанции) и 7 населённых пунктах Камчатки суммарной мощностью 30 МВт, из которых 7 МВт – мощность ВЭС и СЭС. Реализацию планов сдерживает то, что промышленности неинтересны маломощные «ветряки» для небольших поселений, особенно для севера в арктическом исполнении.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: This mechanism allows the model to dynamically adjust its internal state based on current inputs and past information, providing accurate control for this study’s time series analysis of traffic data. Output gate. In the LSTM model of this study, the output gate is responsible for determining the final output for each time step based on the cell state. The configuration of the output gate consists of a weight matrix, bias terms, and a sigmoid activation function, as well as a tanh function to handle the cell states. The details are as follows: (11) o t = σ W o ⋅ h t − 1 x t + b o (12) h t = o t ∗ tanh C t where ot denotes the activation value of the output gate and ht denotes the output of the LSTM unit. The role of the output gate is to filter out the features that are important for the current time step and the model can make accurate traffic flow predictions based on past and current information. Modelling adjustments The model-tuning process involves parameter selection and configuration optimization. For the CNN part, we determined the number and size of filters in the convolutional layer by grid search and optimized the learning rate and regularisation parameters. For the LSTM part, we adjusted the number of hidden layer units and the learning rate. A cross-entropy loss function was used for model training, defined as: (13) L = − ∑ y log y ˆ + 1 − y log 1 − y ˆ where y denotes the real labels and y ˆ denotes the model prediction. This 1. 5 million entries and a simulated city area of 490 km2. The data structure is shown in Table 2. Table 2: Example of data structure for residential trips. <population > <person id=”1” > <plan selected=”yes” > <activity type=”home” x=”4522. 771008” y=”1. 5957779715” end_time=”07:19:49” > </activity > <leg mode=”car” > </leg > <activity type=”work” x=”9304. 687137” y=”1.

Output: pass


Input: Claim: The majority of military forces around the world are transitioning to autonomous drones, which will soon replace human soldiers in combat situations. [SEP] Text: Завдяки використанню ГІС, C5IRS (командування, управління, комп’ютери, зв’язок, кіберзахист, розвідка, спостереження та розвідка) і штучного інтелекту ці дрони забезпечують значну перевагу на полі бою, працюючи автономно та адаптуючись до динамічних наземних ситуацій.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Большинство угольных станций в Сибири и на Дальнем Востоке наряду с электроэнергией поставляют гражданам и тепло, поэтому замена угля на ВИЭ, не производящие тепло, там физически невозможна. В Сибири многие станции построены прямо на угольных разрезах; вести к ним газ за сотни километров экономически нецелесообразно. Еще один аспект: угольная промышленность является работодателем для 160 тысяч человек, а с учетом смежных областей до 1. 3 млн человек.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: The experimental equipment is a Dell laptop with Windows 10 64-bit operating system. Experimental results and analyses In this research 10. 7717/peerjcs. 2035/fig-4 Table 3: Error analysis. Experiment ID Average trip distance (m) SimData error rate SurData error rate StaData error rate 1 12,301 0. 109 0. 189 0. 186 2 11,687 0. 093 0. 149 0. 116 3 12,886 0. 122 0. 233 0. 198 4 13,751 0. 168 0. 272 0. 198 5 13,267 0. 146 0. 238 0. 159 6 13,129 0. 141 0. 208 0. 231 7 11,315 0. 08 0. 111 0. 119 8 13,315 0. 155 0. 256 0. 178 9 14,003 0. 177 0. 277 0. 185 10 12,950 0. 133 0. 227 0. 201 DOI: 10. 7717/peerjcs. 2035/table-3 We believe that this method of using deep learning techniques to process traffic data is very promising. Traditional methods of collecting transportation data, such as questionnaire surveys or statistical estimates, are often limited by various factors such as manpower, material resources, and time. Deep learning technology can automatically extract useful information from a large amount of data without the need for tedious manual processing. In addition, deep learning models such as CNN and LSTM can handle complex spatial and temporal dependencies, which traditional methods find difficult to achieve. We believe this is because CNN excels in processing image and spatial data, while LSTM excels in processing sequence and temporal data. Combining these two can fully leverage their advantages and capture the characteristics of traffic data more comprehensively. The experimental results also prove this point, and this combined method has significant effects in reducing model bias and lowering the basic error rate. The following is a detailed comparative analysis. (1) The traditional questionnaire survey method has a series of distinctive features and significant disadvantages in generating residents’ travelling data. First, the method has directness, through direct communication with respondents, it can obtain first-hand information on residents’ travel behaviour and p5. Figure 5: Error rate versus average distance travelled. Download full-size image DOI: 10. 7717/peerjcs. 2035/fig-5 Observing the visualization in Fig.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named ‘Patrimony’ was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Medicine approach with the power of artificial intelligence. Areas covered Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. Expert opinion We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions. : Drug discoverytarget identificationdata integrationartificial intelligencemulti-omicscomputing platformMedicine 1. IntroductionIn the last decades, the pharmaceutical industry has faced a continuous decrease in productivity. R&D efficiency, measured by the number of new drugs brought to patients per dollar spent, has halved approximately every 10 years since 1950. This trend is often referred to as the Eroom’s Law, i. e. a reverse of the well-known Moore’s Law reflecting the exponential growth observed over time for numbers of transistors on a microchip [Citation1]. As of today, it takes on average 12 years and 2. 6 billion U. S. dollars to bring a new drug to patients, with a probability of success of around 5–10%[Citation2]. Root causes encompass an increase in regulatory requirements, the lack of sufficient validation in the selection of both therapeutic targets and drug candidates prior to setting up costly and time-consuming clinical trials, and a need to optimize organizational processes to better integrate scientific knowledge within R&D [Citation3–5]. Arguably, the most actionable lever for the pharmaceutical industry in order to increase the probability of success during drug development is to strengthen the rationale behind decision-making, most particularly as it relates to the choice of the therapeutic target and the selection of the drug candidate.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Угольные станции играют важную социальную роль, то есть S в терминах ESG25, особенно в таких регионах, как Кузбасс, Воркута. Высказывалась точка зрения, что энергопереход в России следует начинать не с возобновляемой энергетики, а именно с социальной политики.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: 5, we can observe that the error rate of our proposed travel planning and scheduling system climbs as the average distance traveled continues to rise. One potential reason for this may be that some service providers have traveled much more than their average long-distance travel routes on a given date. To explore this issue in depth, we selected 0. 001% of the samples in the generated data, extended the distance of the travel destinations of these samples, and analyzed the error rates before and after the experiment. The final results are presented in Fig. 6. Figure 6: Comparison of data before and after extension. Download full-size image DOI: 10. 7717/peerjcs. 2035/fig-6 A closer look at Fig. 6 shows that the error rate after the data extension process is significantly reduced, which strongly supports our initial conjecture. In order to further validate that the method proposed in this paper is superior to the existing state-of-the-art methods, this paper also conducts a comparison experiment with the literature Song & Chung (2023). Song & Chung (2023) proposes a cell-based traffic simulator UNIQ-SALT and introduces the (RTDG) model as an adjustment model for the simulation inputs. The RTDG model improves the accuracy of the simulation by continually calibrating the simulation results with the actual traffic data until it reaches the preset target error rate. Modelling the travel times of residential travel data can help urban planners to predict future urban trends and traffic demand more accurately. This helps them to take traffic factors into full consideration when planning new roads, transport hubs or residential areas, making urban planning more scientific and rational. In this 7. Figure 7: Comparison of travel time probability distributions. Download full-size image DOI: 10. 7717/peerjcs. 2035/fig-7 We discretise the 24 h of a day into 360 time points, and model the travel time of residents in 4-minute intervals. From Fig. 7, it can be clearly seen that our method has a much smaller probabilistic error in travelling time and on is closer to the real scenario. This is mainly due to the advantage of CNN in processing spatio-temporal data and its excellent performance in feature extraction.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: To this aim, recent breakthroughs in both biomedical and computational sciences create new opportunities to inform drug development through computer-based approaches [Citation6–13]. Rapid advances in omics, imaging, and electronic capture technologies make it now possible to characterize individuals at both molecular, cellular, and clinical levels in a cost and time effective way [Citation14]. Those advances occur in parallel with the exponential accumulation of information and knowledge accessible through hundreds of structured biomedical databases, such as those managed by the Institute (EBI) or the US for (NCBI). An effective use of these massive amounts of data is facilitated by new computational approaches including artificial intelligence (AI) and machine-learning (ML), thus creating an unprecedented opportunity to better inform decision-making and decrease both costs and attrition rates at all stages of drug discovery and development [Citation5]. This ongoing revolution toward AI-powered drug discovery already translates into concrete successes, with machine-designed anti-cancer molecules reported to reach Phase 1 in less than 2 years in contrast to the 5–7 years commonly needed for the discovery phase [Citation15]. An emerging strategy for the pharmaceutical industry to capitalize on those new approaches is to build-an internal computing platform to support the identification of disease targets as well as the repurposing of existing drugs in a systematic and efficient way. To this aim, a technical challenge is to integrate the ever-increasing number of data sources compiling massive and multidimensional information coming from genetics, multi-omics, molecular interactions, preclinical experiments, clinical as well as real-life evidence data. A dedicated and specifically designed computational framework is necessary to capture the value of all these information in order to guide decision-making and increase the probability of success during drug development. In light of the rapidly evolving environment driven by AI and digital technologies, a decision was made in early 2018 by the pharmaceutical group Servier to implement within the R&D new data processes and computational methods through a dedicated high-throughput computing platform. Given that a primary objective of this initiative was to valorize existing data, knowledge, and assets produced by the company during previous or ongoing R&D projects, the platform was named ‘Patrimony. ’ The latter was built up with the intent to capitalize on both proprietary and public data to drive innovation. After 4 years of implementation, the Patrimony platform has transformed very significantly Servier’s approach to drug discovery and development.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: <p>Энергопереход – это сложная технологическая, экономическая и социальная задача, которая должна решаться плавно и постепенно, попытки «агрессивного энергоперехода» ставят под угрозу и экономику, и жизнедеятельность населения нашей страны. Большинство угольных станций в Сибири и на Дальнем Востоке наряду с электроэнергией поставляют гражданам и тепло, поэтому замена угля на ВИЭ, не производящие тепло, там физически невозможна.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Comparing the urban traffic simulation methods based on UNIQ-SALT and RTDG models proposed in the literature, the comparison literature relies more on pre-defined or simpler features compared to the ability of CNN to extract features automatically, which limits the ability of the models to capture complex traffic patterns. Although the RTDG model can be adjusted to actual traffic data, it may not be as effective as LSTM in dealing with long-term time series dependence, because although the comparative literature proposes a method to approximate the actual data by iteratively calibrating the simulation results, this calibration process is not as flexible and efficient as the dynamic data compensation mechanism based on deep learning proposed in this 110. 42 23. 67 22. 05 N-tp 108. 28 22. 29 21. 23 N-other 99. 07 20. 23 19. 76 RTDG 137. 52 29. 04 27. 42 STSim 93. 37 18. 06 19. 17 DOI: 10. 7717/peerjcs. 2035/table-4 this study, we experimentally validate the effectiveness of a deep learning approach combining CNN and LSTM in traffic data analysis. Our model can accurately extract and analyze the spatial and temporal features of traffic images and time series data to predict traffic flow patterns accurately. Compared to traditional statistical inference methods, our model shows higher accuracy and flexibility, especially when dealing with complex and non-linear traffic patterns. Meanwhile, our approach reduces the need for large-scale data collection and significantly improves efficiency compared to survey methods. Overall, this study demonstrates the potential of deep learning in traffic data analysis, providing new perspectives for future traffic management and planning. CNN-LSTM code- : 10. 7717/peerj-cs. 2035/supp-1 Information and authors declare there are no competing interests. conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the 10. 5281/zenodo. 10907914. The code is available at GitHub and Zenodo: - -. (2024). -. Zenodo. work was supported by the bin through project number (PSAU/2023/01/26325). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. 2020.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: Herein, we share the main lessons learned from this initiative during its conception and implementation, as well as the challenges that were faced to make it impactful on our drug development processes. 2. Overview of the marketSeveral public or public–private initiatives have emerged with this orientation such as [Citation16,Citation17]. In parallel, the perspective of substantial cost savings offered to pharmaceutical industries by substituting computerized modeling systems to traditional wet-bench biology, prompted the emergence of numerous start-ups aiming to reinvent drug discovery (Supp. Table 1). In this context, in parallel to initiating this project, we performed a benchmark analysis of external solutions. We concluded that given its foreseen strategic relevance for the company, there was a strong added value to implement the project internally for gaining reactivity, flexibility, as well as for a better integration of internal and external data sources. 3. How the Patrimony computing platform works3. 1. General frameworkA computing platform comprises a set of hardware infrastructures, software, and user interface components that allow storing data and running algorithms in order to achieve a set of well-defined tasks within a specific field of application. The methodology we applied to build up such a computing platform for drug discovery is summarized in Figure 1, combining a range of computational approaches in workflows to integrate, analyze, and interpret data. A first step was to identify all relevant existing biomedical databases and knowledge sources, both structured and unstructured, public or internal. A second step was to curate and integrate data sources into a knowledge graph (or network) as defined below. Lastly, algorithms were developed to mine this graph for a specific application (related to a given disease or a therapeutic area) in order to generate and prioritize hypotheses regarding new therapeutic targets or opportunities for drug repurposing. Analyzing such a volume and diversity of data turned out to be challenging, thus requiring an adapted computational framework to ensure both a good integration, appropriate use, and traceability. This methodology was concretized into a high-throughput scalable process encompassing all steps from data acquisition, hypothesis generation, prioritization of outcomes, and experimental validation. Figure 1. Patrimony general framework. Data sources are curated and integrated into a knowledge graph. For each application starting from one disease or a set of diseases, the knowledge graph is mined in order to evaluate the putative targets, generate hypotheses, and assist the consolidation.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: В Сибири многие станции построены прямо на угольных разрезах; вести к ним газ за сотни километров экономически нецелесообразно. Еще один аспект: угольная промышленность является работодателем для 160 тысяч человек, а с учетом смежных областей до 1. 3 млн человек. Угольные станции играют важную социальную роль, то есть S в терминах ESG25, особенно в таких регионах, как Кузбасс, Воркута.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Calibration of choice model parameters in a transport scenario with heterogeneous traffic conditions and income dependency. 12(7):441-450. 2018. Multi-agent urban transport simulations using OD matrices from mobile phone data. Science 130:803-809 Cervellera C, Macció D, Rebora F. 2021. Deep learning and low-discrepancy sampling for surrogate modelling with an application to urban traffic simulation. In: 2021 international joint conference on neural networks (IJCNN), Shenzhen, China. 1-8. 2023. Research on cooperative control of traffic signals based on deep reinforcement learning. In: 2023 IEEE 12th data driven control and learning systems conference (DDCLS), Xiangtan, China. 1608-1612. 2021. Deep learning based velocity prediction with consideration of road structure. In: 5th CAA international conference on vehicular control and intelligence (CVCI), Tianjin, China. 1-5. 2023. Valhalla. (accessed 1 April 2024) data. 2019. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the thirty-third AAAI conference on artificial intelligence and thirty-first innovative applications of artificial intelligence conference and ninth AAAI symposium on educational advances in artificial intelligence (AAAI’19/IAAI’19/EAAI’19). AAAI Press. 3656-3663. 2019. Realistic transport simulation with open data. In: Proceedings—2019 IEEE international conference on big data, big data 2019. Haydari A, Yılmaz Y. 2022. Deep reinforcement learning for intelligent transportation systems: a survey. IEEE Transactions on Systems 23(1):11-32 -Y, Kwon J. 2015. Scalable extraction of timeline information from road traffic data using MapReduce. In: 2015 IEEE international conference on data science and advanced analytics (DSAA), Paris, France. 1-8. 2022. Missing data analysis in traffic monitoring system using LSTM. In: 2022 international conference on computational modelling, simulation and optimization (), Pathum Thani, Thailand. 7-11. 2020. Airport network traffic prediction in 5G scenarios: a deep learning approach. In: 2020 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB), Paris, France. Piscataway.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: Diffusion/propagation algorithms were selected as particularly useful because – as their name infer – they diffuse or propagate the information along the links of the graph. It assesses the impact of a perturbation starting from a given node on the network by use of radom-walk probabilities. As such, they allow to capture the information on both the nodes and their neighborhood [Citation61]. Furthermore, the graph structure turned out to be very adapted to facilitate the direct application of graph-based deep learning approaches such as Networks (GCNs) that aggregate features of the different types of nodes and their relationships in order to predict new associations between drugs, targets, and diseases [Citation60,Citation62]. The general principle is to learn how to represent the graph and map its nodes into a compact embedding space. As a result, it embeds diseases associated with similar genes or drugs whose target proteins have similar local neighborhoods close together in the embedding space. Altogether, one advantage of the knowledge graph supporting the Patrimony computing platform turned out to allow initiating investigations from any entry point among genes, diseases, or drugs depending on the data sources already integrated within the platform. Potential queries related for instance to the most relevant targets for a given disease, or to potential indications in which a drug could be repurposed. As such, the Patrimony computing platform has now been well established within Servier as a versatile tool to create and maintain a competitive advantage when developing drugs against diseases of interest. 3. 3. Target hypothesis assessment and prioritizationA central application of Patrimony is to identify novel therapeutic target candidates from the modeling of a disease of interest. To capitalize on the high quantity of information contained in its knowledge graph, some metrics have been established in order to rationalize the assessment and prioritization of actionable therapeutic targets. Inspired by principles originally found in Open Targets, we eventually selected strategic summary criteria along five distinct dimensions (Figure 3a). Figure 3. Target assessment and prioritization. (a) Targets are assessed based on strategic summary criteria along five distinct dimensions. (b)The five criteria are individually quantified and a global scoring is then computed in order to prioritize the top targets for which the overall rationale is the highest. (c) Individual target ID cards are generated to represent the global assessment in a visual and easily interpretable format to end users. Display full sizeThe first and most important criterion relates to Biological Relevance.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: </p> <p>Энергопереход – это сложная технологическая, экономическая и социальная задача, которая должна решаться плавно и постепенно, попытки «агрессивного энергоперехода» ставят под угрозу и экономику, и жизнедеятельность населения нашей страны. Большинство угольных станций в Сибири и на Дальнем Востоке наряду с электроэнергией поставляют гражданам и тепло, поэтому замена угля на ВИЭ, не производящие тепло, там физически невозможна.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: IEEE. 1-5 Kashihara K. 2017. learning for traffic simulation in autonomous driving at a highway junction. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC), Banff, AB, Canada. 984-988. 2016. Creating an open MATSim scenario from open data: the case of Santiago de Chile. Berlin, Germany: and Transport Telematics. . 2019. Online incremental machine learning platform for big data-driven smart traffic management. IEEE Transactions on Systems 20(12):4679-4690. 2021. Multi-modal management actions for public transport disruptions: an agent -based agent-based simulation. In: 7th international conference on models and technologies for intelligent transportation systems, MT-ITS 2021. Rasca S. 2022. Numerical estimation of travel mode p2022. Selvamanju E, Shalini V. 2022. Deep learning based mobile traffic flow prediction model in 5G cellular networks. In: 2022 3rd international conference on smart electronics and communication (), Trichy, India. 1349-1353 Song H, Chung M. 2023. Recurrent traffic demand generation using urban traffic simulation with cell-based behavior model and real traffic data. In: 2023 IEEE international conference on big data (BigData), Sorrento, Italy. 6286-6288 Sun Z, Pan J-S, Chen C-H, Wu T-Y. 2020. A probability-based analytical model based on deep learning for traffic information estimation. In: 2020 IEEE international conference on consumer electronics—Taiwan (ICCE-Taiwan), Taoyuan, Taiwan. 1-2 Trinh N-P, Tran A-K, Do T-H. 2022. Traffic flow forecasting using multivariate time-series deep learning and distributed computing. In: 2022 RIVF international conference on computing and communication technologies (RIVF), Minh City, Vietnam, 2022. 665-670. 2021. Estimating traffic flow in large road networks based on multi-source traffic data. IEEE Transactions on Systems 22(9):5672-5683 Zeng Q. 2021. A smart traffic controlling system using auxiliary sensors with deep learning and its simulation. In: 2021 international conference on information science and communications technologies (), Tashkent, Uzbekistan. 01-04. 2018.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: For a given use, it summarizes all the activities contributing to the understanding of the pathophysiology of a disease from multi-omics data. A score is generated to quantify the level of cumulated evidence predicting a gene or a protein to be a highly relevant target because of biomolecular associations or dysregulations, by itself and/or when considering its neighborhood within the graph. Genes with a high are referred to as disease-related genes, in that they are likely to contribute to the pathophysiology as a cause or a consequence. Also, they tend to cluster and form identifiable disease modules within the knowledge graph. Computing techniques described previously including AI/ML support here the prediction of new disease–gene associations as well as disease modules. A second criterion, related to biological relevance but nevertheless assessed independently, is Causality, which we consider as a highly critical dimension. The latter is being assessed to discriminate between causes and consequences in the observed pathophysiology. As such and depending on the information available, it can be derived from (i) the force of genetic associations around the target (taking into account that genetic signals are rather causal compared to transcriptomic profiles which often result from downstream regulations), (ii) the expression of the target in cells or tissues relevant to the disease of interest (documented for instance from GTeX), and (iii) AI/ML predictions of what is likely to be a real target based on graph features and known approved targets as training. On a concrete basis, Causality may for instance refer to master regulators or driver mutations [Citation32,Citation33]. A third criterion relates to Tractability (also referred to as Druggability), which represents the likelihood to modulate the function of a target with either small synthetic or biological drugs. To date, we have been using the measure proposed by based on both precedence of the target in clinical trials, discovery experiments and computational predictions. In the future, this assessment will be supplemented with our own measures of Tractability to include additional modalities of interest, e. g. RNA-based antisense oligonucleotides. The fourth criterion is linked to potential Safety implications when interfering with a given target. It is assessed by the number of safety events associated with drugs known to bind the target reported in databases such as SIDER [Citation44]. In subsequent developments, we will consider not only the number but also the severity of adverse events.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: В Сибири многие станции построены прямо на угольных разрезах; вести к ним газ за сотни километров экономически нецелесообразно. Еще один аспект: угольная промышленность является работодателем для 160 тысяч человек, а с учетом смежных областей до 1. 3 млн человек. Угольные станции играют важную социальную роль, то есть S в терминах ESG25, особенно в таких регионах, как Кузбасс, Воркута.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: the COVID-19 related pandemic, there has been several reports that the use of private transportation means (e. g. , individual vehicles) is increasing as people try to avoid public transit as much as possible. This leads to even more congestion and hence makes the question of selecting a route to go from A to B more and more prominent. This is especially the case for commuters, who make a given trip nearly every day and, hence, have the opportunity to learn and/or adapt to the traffic patterns faced daily. To address the challenges posed by an ever increasing demand, transportation authorities and traffic experts try to distribute the flow among existing routes in order to minimize the overall travel time. Often, this task involves some form of communication with the drivers. Traditional approaches such as variable message panels or radio broadcast are now being replaced by directed (and potentially personalized) communication, via new kinds of communication devices. While the current pattern is that each individual driver selects a route based on his/her own experience, this is changing as new technologies allow all sorts of information exchange. Examples of these technologies are not only based on broadcast (e. g. , GPS or cellphone information) but also a two-way communication channel, where drivers not only receive traffic information but also provide them. Hence, currently, many traffic-related applications for cellphones deal with the idea of a central authority in charge of somehow assigning routes for drivers. Examples are Waze, Google apps, etc. Since their specific algorithms are not published, one can only guess that they try to find a feasible solution, given a set of constraints that they are able to infer from the current data they collect. What seems certain is that these platforms work in a centralized way, based on data they collect when their customers or users use their specific apps. Also, they do not handle locally collected and processed data. This leads to them being ineffective when the penetration of their services is low as, for example, during the initial stages of the 2020 pandemics, when few drivers were using the system. A way to mitigate this could be to decentralize the processing of information, as proposed here, and passing it to drivers to make their route choices. Our method has some resemblance with the notion of traffic assignment (see “Background and Related Work”), since it is based on the fact that drivers collect experience by trying out several routes until they settle on those that lead to the least travel time.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: The last criterion considers Innovativeness in relationship with the application on a disease of interest, documented from either clinical trials, as well as patent or literature-mining by using Processing (NLP) [Citation63]. Given the flexibility of the Patrimony computational framework, any other relevant summary criterion could be easily included in the future. For instance, assessing the Feasibility of drug development could be added to support target ranking, as suggested by others [Citation64]. The above mentioned five criteria are individually quantified and subsequently computed in a global scoring in order to prioritize the top targets for which the overall rationale is the highest (Figure 3b). For any given target, individual ‘target ID cards’ are generated to represent the overall assessment in a visual and easily interpretable manner to end users (Figure 3c). 3. 4. From targets to drugsFollowing the prioritization of top hypotheses on candidate targets with the Patrimony scoring system, the rationale is subsequently consolidated through a deep-dive investigation by biologists and pharmacologists. During this consolidation phase, both an extensive literature review and in house translational data analyses are performed to confirm that identified target candidates are involved in specific disease pathways and are druggable with a specific compound modality. Researchers can then validate target hypotheses, through an experimental confirmation that disease activity is impacted following perturbation of the target of interest with a drug or a tool compound. Conducting wet-lab gene inhibition (e. g. via -Cas9 deletion or RNA silencing) or preclinical experiments by using cellular assays or animal models are commonly implemented to corroborate the hypothesis that drugs interacting with the target exhibit the anticipated pharmacological activity. Once a therapeutic target has been selected, multiple processes streamlined by the pharmaceutical industry can be used to identify small molecules or biologicals interacting with it. For instance, High-(HTS) can be implemented to test the company’s proprietary compound library in various molecular or cell-based assay systems in order to identify drug candidates [Citation65]. As of today, another strategy relies upon dedicated computational methods to select in silico drugs predicted to engage the target of interest [Citation15]. One specific use of the Patrimony platform consists in identifying existing drugs modulating the target, e. g. within the R&D pipeline of the company or among marketed compounds.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Объём отходов исчисляется миллионами тонн, организация огромных полигонов для их захоронения нарушает экологический баланс, превращая зелёные участки в пустыри. Другой пример связан с добычей лития, необходимого при создании аккумуляторов для автомобилей и смартфонов. Чили располагает 29 % мировых запасов лития, в пустыне Атакама даже из космоса видны огромные испарительные пруды.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Traffic assignment approaches work (and indeed were developed for this purpose) well for planning tasks, that is, how to plan a traffic network (or change an existing one) in order to minimize travel costs. However, route choice is not related to planning tasks but, rather, is an operational aspect, especially in commuting situations, where drivers repeatedly travel from the same origin to the same destination. Besides, traffic assignment is a centralized approach, in which the drivers do not actively select routes. Rather, routes are assigned to them. Thus, it is important to investigate how drivers do select routes in their daily commuting tasks. Multi-agent reinforcement learning (MARL) can be used for such purpose, as it fits the task of letting agents decide, autonomously, how to select routes to go from A to B. This is realized by letting agents iteratively choose their least costly route based on their own learning experiences. Such approach has been tried before, as described in the section on related works. In fact, it has been shown that reinforcement learning is a good technique to investigate route choice. However, the learning process can be inefficient, as for instance, it may take time, since the agents have to collect experiences by themselves. As this happens to be a very noisy environment, the signal an agent gets can be little discriminatory (e. g. , due to the presence of other learning agents, an agent may get the same signal for very different actions, or, conversely, different signals for the same action). Thus, our long term aim is to investigate forms of accelerating the learning process. One of these forms is by giving more information to the agents. There are only few works that consider new technologies to this experience, as for instance those tied to vehicular communication in general. In the present 4. 0. Download full-size image DOI: 10. 7717/peerj-cs. 428/fig-1 Using the short-range signal, the CommDevs are able to communicate with vehicles that are close enough, and are able to exchange information related to local traffic data (refer to next section for details). Moreover, these CommDevs are able to store the data exchanged with the agents in order to propagate this information to other agents that may use nearby intersections in the near future. The arrows that connect CommDevs in Fig. 1 represent a planar graph, meaning that every CommDev is connected and can communicate to its neighboring devices.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: Referred to as drug repurposing, drug rescuing or indication extension, this approach has generated a growing interest during the last decade, with evidence that mining available biomedical data with proper algorithms can generate fast and valid innovative hypotheses [Citation41,Citation66–68]. The knowledge graph used in Patrimony has thus also been applied to such drug repurposing approaches, by using both distances between drug targets and disease-related proteins [Citation69], connectivity maps [Citation23,Citation24], and deep-learning methodology to identify in silico new targets for known drugs [Citation70]. 3. 5. ImplementationWe defined three iterations to implement the Patrimony computing platform: a proof-of-concept, a structuration, and an industrialization step. The proof-of-concept was pilot initiative performed within 3 months aiming for quick-wins. Based on a minimal set of datasets and algorithms, the aim was to position a given set of targets of interest into one disease (namely Sjögren syndrome). Then, the structuration step aimed to list, retrieve, and implement all the necessary datasets and algorithms along with conceiving a first dedicated and adapted computational infrastructure. This step was performed around a well-defined application focused on immuno-inflammatory diseases. Finally, the industrialization step aimed to transfer the existing Patrimony platform into a more scalable architecture in order to pave the ground for a subsequent application to all therapeutic areas of interest for the company. Within each iteration, we adopted an Agile operating model for software development with alternated sequences of brainstorming, implementation (sometimes in precisely defined sprints), generation of results, consolidation, and feedback [Citation71]. Technical choices for IT infrastructure, software and algorithms have proven to be challenging to ensure a robust and flexible solution in a fast-evolving field. Codes were developed in Python and R. For the two first iterations, we put in place a cloud-based sandbox with MongoDB for database management. We subsequently moved to Platform (GCP) and BigQuery for scalability. We also used a mixture of Neo4j, Cytoscape, Python graph-tool, and R igraph to support graph storage, mining, and visualization [Citation72]. We followed FAIR guiding principles to enable findability, accessibility, interoperability, and reusability of the data when building up our new data governance.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Наращивание производства разрушает экосистему, процесс опустынивания усугубляется: налицо острая нехватка питьевой воды, загрязнение местных грунтовых вод, сокращение пастбищ и урожая, исчезновение флоры и отдельных видов фауны. Поскольку добыча лития традиционными методами наносит существенный ущерб окружающей среде, значительные средства и усилия вкладываются в поиски новых технологий. Большое внимание уделяется прямой экстракции лития (DLE).

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: This permits that CommDevs get information about the traffic situation in neighboring edges, which is then passed to the agents. How communication works Every time an agent reaches an intersection, prior to choosing an action (the next intersection to visit), it communicates with the intersection’s CommDev (see Fig. 1) to exchange information. The actual piece of information sent from agents to CommDevs is travel times (hence, rewards) received by the agents, regarding their last action performed. Conversely, the infrastructure communicates to the agent information about the state of the nearby edges, in terms of which rewards an agent can expect if it selects to use that particular link. This information can be of various forms. In all cases, the expected reward is computed by taking into account the rewards informed by other agents, when they have used nearby links. In the experiments, we show results where CommDevs communicate expected rewards that are either an aggregation (over a time window) or just a single value. In any of these cases, an agent receiving such information will then take it into account when selecting an action (choice of a link) in that particular state (a node). Next, details about how the information is processed, by both the CommDevs and the vehicle agents, are given. Information hold by infrastructure Each CommDev uses queue based data structures to hold the rewards informed by each agent that passes through it. Specifically, each edge is associated with one data queue. These queues have a maximum size, and when new information arrives after the queue is full, the oldest reward stored is discarded to make room to the most recent one. When an agent requests information, the CommDev retrieves the rewards collected for the agent’s possible actions and passes it to that agent. Recall that an action corresponds to a link to be traveled next, in order to form a route to the agent’s destination. Information used by the agent In a standard Q-learning algorithm, the agents update their Q-values based on the feedback from the action they have just taken. However, in our case agents also update their Q-values based on the expected rewards received by the infrastructure. This means that every time they reach an intersection, they update their Q-values with the information provided by the CommDevs. We do this in order to accelerate the learning process.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: Integrating large-scale and multidimensional data generated from multiple technologies with proper quality attributes in terms of consistency and reliability remained a significant difficulty throughout data life-cycle management. Assessing the right to use data from public sources also turned out to be complex. Whereas some data sources apply a clear ‘no restriction’ policy for any use of the data, e. g. under a license for sharing, others make a distinction between the type of requesting institution (profit versus nonprofit) or intended use (research versus commercial purposes). Of note, it has been decided not to include any patient-level data in order to overcome the regulatory complexity linked to their use. Also, the possibility to include data from partnerships such as IMI projects needs to be evaluated case by case. 4. ApplicationsThe two first iterations (proof-of-concept and structuration) of the Patrimony initiative were built while focusing on immuno-inflammatory diseases as selected indications. They were specifically designed to evaluate the capacity of the Patrimony platform to support two direct applications, i. e. identifying therapeutic targets and generating hypotheses for drug repurposing. As a first run, we mapped multi-omics profiling data from the cohort of patients with various autoimmune diseases into the knowledge graph to support drug development against primary Sjögren’s syndrome [Citation73]. A particular challenge in this indication was to rationally design immunotherapeutic approaches acting at a systemic level and/or target organs (i. e. salivary and lachrymal glands). As concrete outputs, Patrimony helped to identify and prioritize several innovative therapeutic targets, supported by a robust and multidimensional set of evidence. Most of the targets identified as of interest in primary were confirmed to be valid as well in several autoimmune diseases sharing common pathophysiological mechanisms, e. g. Erythematosus. Furthermore, Patrimony was very useful not only to identify new therapeutic targets but also to validate other ones for which our company had already initiated the development of drug candidates. Specifically, two monoclonal antibodies at an early clinical development stage for autoimmune diseases, including anti-type 1 interferon and anti-IL7R antibodies (ClinicalTrial. gov NCT04605978) were confirmed as valid therapeutic options in Erythematosus.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Хотя такие методы успешны, неясно, можно ли их масштабировать для коммерческого производства: технология DLE требует огромного количества питьевой воды и электричества. Геотермальные технологии позволяют извлечь большое количество лития, скрытого в природных горячих рассолах. Компания на своем экспериментальном заводе штате Невада получает литий с помощью ионного обмена26.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Instead of just considering its own past experiences, the information provided by the CommDevs augment the knowledge each agent has. It is worth noting that a distinguishing characteristic of our approach is that it deals with local information, thus the information received from the CommDev only concerns actions that can be selected from that particular node. Given a network G, every agent (vehicle) v ∈ V has a pair (o, d) ∈ N × N, that defines its origin-destination pair (OD-pair). Nodes n ∈ N are seen as states the agents might be in, and the outgoing edges of a node n are the possible actions for that given state. Hence, the agents build their routes on-the-fly by visiting nodes and edges. Upon choosing an action (edge) e, v perceives its reward. We recall that being a microscopic model, this reward is actually computed by the simulator, rather than by an abstract cost function, as it would be the case in a macroscopic model. Assuming that the simulator reports a travel time of tve for agent v traveling on edge e, the reward is −tve, as we want to make sure the agents prefer to take edges that minimize travel times. This alone does not guarantee that the agents will reach their destination fast, as they might end up running in loops throughout the network. Hence a positive bonus B is given to each agent that reaches its destination, giving them incentives to end their trips as fast as possible. We deal with a commuting scenario, where each agent performs day-to-day experiments in order to reach an equilibrium situation, in which no agent can reduce its travel time by changing routes. Because agents belong to different OD pairs and/or select different routes, their trips take different number of simulation steps. These steps represent elapsed seconds in simulation time. Hence, this means that not every agent finishes its trip simultaneously and, therefore, the standard notion of a learning episode cannot be used here. Rather, each agent has its own learning episode that will take as many simulation steps as necessary to reach its destination. Next, we explain the main parts of our approach, which can be seen in Algorithm 1. Algorithm 1: Q-learning with C2I.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: Various additional applications could be considered in the future, as an extension to further support the identification and optimization of drug candidates, beyond the repurposing of existing drugs. The current models produced by Patrimony leading to therapeutic target identification could be nicely extended by machine-learning approaches to perform multitask parallel prediction of drug candidate characteristics. The latter include training artificial neural networks to select suitable therapeutic modalities for engaging a given target, predicting both binding characteristics as well as pharmacological and ADMET properties of virtual compounds, and even creating new molecules by using generative adversarial networks [Citation6]. Other applications requiring specific computational methods include generating hypotheses on potential combination therapies to address complex diseases [Citation76]. Whereas the interest of the latter approach is well identified in oncology, we have as well recently documented how it could be implemented in autoimmune diseases, and arguably in many other therapeutic areas [Citation77]. 5. ChallengesImplementing ex nihilo in a pharmaceutical environment at an industrial scale, an initiative such as Patrimony has been disruptive. A major challenge was that it required a high level of transversality between multidisciplinary teams assembling numerous expertises encompassing computational hardware, cloud computing, network computing, machine-learning and AI, statistics, bioinformatics, large-scale biology, pharmacology, clinical knowledge, but also legal skills to assess data accessibility and use. Aligning those very diverse human expertises necessitated continuous training and internal communication to foster acculturation to computational modeling as it applies to drug discovery and development. Another important challenge was the consolidation step needed to validate the scientific rationale of predicted targets. Algorithms can generate many hypotheses in a short time frame. The extensive literature search by human experts remained time consuming in order to corroborate or refute the hypotheses generated. In this exercise, we found it critical for each given application to a disease of interest to evaluate the outputs with known targets and disease-modifying drugs. To that end, clinically approved drugs and their respective targets with established proof of efficacy have been assessed on the platform and their relevance verified. Altogether, during the consolidation step, we consistently observed that the relevance of the hypotheses generated depends on the quality, completeness and continuous updates of data sources. The interpretation of model outputs by human experts was also difficult, owing to the inherent complexity of the analysis of biomedical data, but also in light of the numerous existing gaps in the thesaurus of human knowledge.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: </p> <p>Спикеры обращали внимание и на побочные эффекты «зелёных» технологий. Одна из проблем – износ ветроустановок первого поколения. Утилизация ветрогенераторов имеет совсем неэкологичные стороны. Их лопасти сделаны из композитных материалов, состоящих из углеволокна, стекловолокна, древесины бальзы, полиуретана, сплава и эпоксидной смолы. Они не предназначены для вторичной переработки.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: 1: Input: G, D, P, M, α, γ, ε, B 2: s←0 3: while s < M do 4: for v in V do 5: if v. f inished_trip() then 6: v. update_Q_table(B−v. last_edge_travel_time) 7: G. commDev[v. curr_node]. update_queue(v. last_reward, v. last_edge) 8: v. start_new_commuting_trip() 9: else if v. has_reached_a_node() then 10: v. update_Q_table(−v. last_edge_travel_time) 11: G. commDev[v. curr_node]. update_queue(v. last_reward, v. last_edge) 12: v. update_Q_values(G. commDev[v. curr_node]. in f o) 13: v. choose_action() 14: end if 15: end for 16: s←s+1 17: end while DOI: 10. 7717/peerj-cs. 428/table-6 Line 1 list the inputs of Algorithm 1: G is the topology of the network, D is the demand (flow rate) that is inserted in the network, P is the set of OD-pairs, and M is the maximum number of steps to simulate. It is also necessary to set α, γ (both relating to Eq. (1)), ε for controlling the action selection and the exploration-exploitation strategy, and the bonus B. The main loop is presented between lines 3 and 17, where the learning and the communication actually take place. The first if statement shown in line 5 takes care of all agents that finished their trips in the current step: agents perceive their reward plus the bonus for finishing the trip. 7, each agent informs the corresponding CommDevs the rewards, and since its trip has ended, it gets reinserted at the origin node to start a new learning episode (as this is a commuting scenario). The if statement at line 9 represents the intermediary nodes, where each agent also perceives its reward and informs the CommDev (line 11) about the reward just experienced, so that the CommDev can update its queue structure.

Output: pass


Input: Claim: AI-powered drug discovery platforms can reduce the time it takes to identify new medications. [SEP] Text: The current is estimated to cover only circa 25% of all possible molecular interactions [Citation78]. As a consequence, many genes related to a given disease may appear to be dispersed in the interactomes making the identification of coherent disease modules challenging. In our experience, we found that protein expression, documented for example by proteomics or flow cytometry, was more translatable to disease pathological mechanisms than gene expression solely assessed by transcriptomics. 6. ConclusionsThe knowledge-graph represented within Patrimony is a sophisticated and evolving way to represent a disease within a computer system. Disease modeling has recently emerged as a powerful mean to educate the design and development of drug candidates, in the context of approaches aiming to offer therapies better tailored to the patients’ specificities. Such strategies are thus being established to capitalize on a comprehensive knowledge of the disease and of patient population heterogeneity to select therapeutic targets and drug candidates predicted to be most suitable in this indication. This trend that we termed Medicine has become highly strategic for pharmaceutical industries to differentiate from the competition [Citation6]. We thus emphasize the tremendous value of developing a proprietary computational platform to create a major competitive advantage for drug developers in their disease areas of interest. Within Servier, we designed the Patrimony platform by combining innovative concepts, methodologies and supportive infrastructure to grow significantly our ability to integrate large-scale biomedical data. As of today, Patrimony allows to generate multidimensional models relating to diseases, therapeutic targets, and to some level drug candidates. Based on our positive experience in exploring pilot applications in immuno-inflammatory diseases, the use of this computational platform is now being deployed throughout all therapeutic areas of interest for the company. 7. Expert opinionAs AI has the potential to transform drug discovery and several AI-driven molecules have progressed into clinical trials (most often with accelerated timelines and reduced costs), its impact and remaining challenges need to be globally addressed [Citation79,Citation80]. As explainabilty is often invoked, we recommend paying attention to generating results that are both robust (e. g. to small changes in parameters and data), interpretable (e. g. avoiding applying transformation to variables that would disconnect them to their biological or clinical meaning), and reproducible (e. g. by applying good coding and data practices). Another important hurdle to implement disease modeling in support of drug discovery is the difficulty to distinguish causal from incidental genes or proteins in the pathophysiology.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Чили располагает 29 % мировых запасов лития, в пустыне Атакама даже из космоса видны огромные испарительные пруды. Наращивание производства разрушает экосистему, процесс опустынивания усугубляется: налицо острая нехватка питьевой воды, загрязнение местных грунтовых вод, сокращение пастбищ и урожая, исчезновение флоры и отдельных видов фауны.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: In line 7, each agent updates its Q-value for the last action based on its own experience, that is, with the actual reward received for traveling through the last link. Following, a CommDev also informs agents about the rewards that can be expected from the actions each agent might take next (line 12). Each agent then updates its Q-table and chooses an action. Experiments, Results, and Analysis Scenario: network and demand Simulations were performed using a microscopic tool called Simulation of (SUMO, Lopez et al. (2018)). SUMO’s API was used to allow vehicle agents to interact with the simulator en route, that is, during simulation time. The scenario chosen is a 5 × 5 grid depicted in Fig. 2; each line in the figure represents bi-directed edges containing two lanes, one for each traffic direction. It is also worth noting that each directed edge is 200 m long. Figure 2: 5 × 5 Grid Network. Download full-size image DOI: 10. 7717/peerj-cs. 428/fig-2 The demand was set to maintain the network populated at around 20–30% of its maximum capacity, which is considered a medium to high density. Recall that no real-world network is fully occupied at all times, and that the just mentioned density level does not mean that there will not be edges fully occupied, which happens from time to time; this percentage is just the average over all 50 edges. This demand was then distributed between the OD-pairs as represented in Table 1. The last column represents the volume of vehicles per OD-pair. Those values were selected so that the shorter the path, the smaller the demand, which seems to be a more realistic assumption than a uniform distribution of the demand. Table 1: Demand per OD-pair. Demand Bottom0 Top4 102 Bottom1 Top3 86 Bottom3 Top1 86 Bottom4 Top0 102 Left0 Right4 102 Left1 Right3 86 Left3 Right1 86 Left4 Right0 102 DOI: 10. 7717/peerj-cs. 428/table-1 Two points are worth reinforcing here. First, vehicles get reinserted at their corresponding origin nodes, so that we are able to keep a roughly constant insertion rate of vehicles in the network, per OD pair.

Output: pass


Input: Claim: AI can be programmed to cure all diseases by the year 2030. [SEP] Text: These supporting documents include lesson plans, lab preparation notes for the lab coordinator(s) and instructor(s), lecture slides, handouts, worksheets, and grading rubrics. Each of which is described in detail below. Unit 1: Fundamentals of Science and Students spend the first four weeks of the semester in the Fundamentals unit conducting activities that teach about the process of scientific discovery, hypothesis testing, study design, taxonomy, phylogeny, and DNA sequence divergence. These activities lay the groundwork for students to understand the fundamental ideas underlying their upcoming semester-long project that involves quantifying biodiversity in forest edge vs. interior habitats, DNA barcoding, and bioinformatic analyses. Week 1 - of Adapted with permission from (Detailed outline - S1. 1: DNA Barcoding - Week 01 - Process of Plan. docx) This lesson adapted from is an icebreaker activity that introduces students to the process of scientific discovery. Students begin with a simple warm up activity where they use mystery boxes ( to test hypotheses, conduct simple experiments, and collaborate with each other (S1. 2: DNA Barcoding - Week 01 - Process of. docx) to decipher what's inside of a sealed box. Following this activity, the class discusses whether they were doing science and identify aspects of the activity that make it scientific. Next, students are introduced to a flowchart developed by educators at UC Berkeley ( that depicts the process of science (S1. 3a: & S1. 3b: DNA Barcoding - Week 01 - Process of. pdf). Students then read a story that describes how the geoscientist, Walter Alvarez, compiled over 20 years of evidence to support the hypothesis that an asteroid was responsible for the extinction of dinosaurs ( Students identify phrases within the story that indicate that Alvarez was doing science and use these to plot the pathway of through the process of science flowchart (S1. 4: DNA Barcoding - Week 01 - Process of. docx). Students are asked to identify important, and often misunderstood aspects of the process of science (Ex. pathway to discovery is often nonlinear, science involves creativity and collaboration, etc. ). The process of science flow chart also acts a touchstone for students to reflect on their own activities throughout their semester-long DNA barcoding project. This lesson requires ~1.

Output: pass


Input:
Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Хотя такие методы успешны, неясно, можно ли их масштабировать для коммерческого производства: технология DLE требует огромного количества питьевой воды и электричества. Геотермальные технологии позволяют извлечь большое количество лития, скрытого в природных горячих рассолах. Компания на своем экспериментальном заводе штате Невада получает литий с помощью ионного обмена26.
 26.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: However, this does not mean that the flow per link is constant, since the choice of which link to take varies a lot from vehicle to vehicle, and from time to time. Second, despite being a synthetic grid network, it is not trivial, since it has 8 OD pairs, which makes the problem complex as routes from each OD pair are coupled with others. As seen in Table 1, we have also increased such coupling by designing the OD pairs so that all routes traverse the network, thus increasing the demand for using the central links. Q-learning parameters A study conducted by Bazzan & Grunitzki (2016) shows that, in an en route trip building approach, the learning rate α does not play a major role, while the discount factor γ usually needs to be high in discounted future rewards, as it is the case here. Thus a value of α = 0. 5 suits our needs. We remark however that we have also played with this parameter. As for the discount factor γ, we have performed extensive tests and found that a value of γ = 0. 9 performs best. For the epsilon-greedy action selection, empirical analysis pointed to using a fixed value of ε = 0. 05. This guarantees that the agents will mostly take a greedy action (as they only have a 5% chance to make a non-greedy choice), and also take into account that the future rewards have a considerable amount of influence in the agent’s current choice, since γ has a high value. For the bonus at the end of each trip, after tests, a value of B = 1,000 was used. Recall that this bonus aims at compensating the agent for selecting a jammed link, if it is close to its destination, rather than trying out detours via a link that, locally, seems less congested, but that will lead the agent to wander around, rather than directly go to its destination. We remark that trips take a rough average of 450 time steps thus this value of B fits the magnitude of the rewards. Performance metric and results While each agent perceives its own travel time, both after traversing each link, and after finishing its trip, we need an overall performance to assess the quality of the proposed method. For this, we use a moving average (over 100 time steps) of the complete route travel time, for each agent that has finished its trip.

Output: pass


Input: Claim: Wind turbines can generate energy even at low wind speeds. [SEP] Text: In general, considering different wind regimes, wind turbines are designed to withstand a class of wind speeds as specified by International Electro-technical Commission (IEC). IEC is for the high windy sites, those with an annual average wind speed of 10 m/s. Class II is designed for locations with an average wind speed of 8. 5 meters per second at hub height. Class III is for even lower windy sites, with an average wind speed of no more than 7. 5 m/s. Class IV was also described in older IEC Standards, which is for very low-wind sites with an average wind speed of 6 m/s, but it has been superseded by Class S. class “S” turbines are with design values chosen by the designer based on site-specific conditions. It is worth noting that while the majority of class I and class II wind sites are exhausted and presently large areas suitable for class III and IV are available for development. Due to their extremely low CUF and higher LCOE, wind turbines now available in the market are uneconomical to operate in class III and IV locations. With LCOE being the prominent factor in deciding the share of the renewable mix, solar PV generation has surpassed wind generation in many countries. When these considerations are taken into account, the solution that is foreseen is the design of wind turbines with low specific power (LSP) in order to increase the deployment of wind power with reduced variability, lower LCOE, and suitability for low wind sites. Modern-day wind turbines, especially the dominant, three-bladed, upwind turbine configuration, have undergone significant design improvements toward increased energy generation and reduced cost of energy [5]. Wind turbines have also become physically larger in several dimensions, including rotor diameter, hub height, and nameplate/rated capacity. Wind turbine design has also become tailor-made to the market environment in which the technology is going to be deployed, based on wind regimes, grid access, etc. , and low specific power (LSP) turbines are considered as one of such manifestations. As swept area increases with the square of blade length, increasing the blade length of a turbine will increase the power extraction and, with fixed generator capacity (rated power), reduce the (SP) rating of the turbine. On the other hand, theoretically, decreasing the generator capacity while maintaining a constant swept area will also result in the desired designed specific power.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: </p> <p>Помимо лития, большое количество минералов и металлов требуется для изготовления не только солнечных панелей и ветрогенераторов, но и промышленного оборудования, работающего не на углеродных видах топлива, а на электричестве. Уже заметен всё больший дефицит некоторых видов сырья: согласно данным Всемирного экономического форума, за последние годы средняя стоимость производства меди выросла более чем на 300 %. ; растут цены на сталь.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Given the probabilistic nature of the process, it is necessary to run repetitions of simulations. Thus, 30 runs were performed. Plots shown ahead thus depict the average and the standard deviations. In order to evaluate how the communication affects the learning process, some different policies and comparisons were performed, these different methods are described in the following sections. QL with C2I vs dynamic user assignment For sake of contrasting with a classical approach, Fig. 3 shows a comparison between our QL with C2I approach and a method called Assignment (DUA), which is an iteractive method implemented by the SUMO developers. We remark that DUA is a centralized, not based on RL approach. Figure 3: QL with C2I vs DUA. Download full-size image DOI: 10. 7717/peerj-cs. 428/fig-3 Assignment works as follows: it performs iterative assignment of pre-computed, full routes to the given OD-pairs in order to find the UE2. In our tests, DUA was run for 100 iterations. Note that a DUA iteration corresponds to a trip, and a new iteration only starts when all trips have reached their respective destinations. The output of DUA is a route that is then followed by each vehicle, without en route changes. Since DUA also has a stochastic nature, our results correspond to 30 repetitions of DUA as well. Figure 3 shows that, obviously, at the beginning, the performance of our approach reflects the fact that the agents are still exploring, whereas DUA has a better performance since a central authority determines which route each agent should take. This is possible since this central authority holds all the information, which is not the case in the MARL based approach, where each agent has to explore in order to gain information. In our approach, after a certain time, the agents have learned a policy to map states to action and, by using it, they are able to reduce their travel times. Before discussing the actual results, we remark that a SUMO time step corresponds roughly to one second. Our experiments were run for about 50,000 time steps. A learning episode comprehends hundreds of time steps, as the agent has to travel from its origin to its destination. In short, a learning episode is not the same as a simulation time step.

Output: pass


Input: Claim: Wind turbines can generate energy even at low wind speeds. [SEP] Text: Power turbines provide a number of advantages that have resulted in their widespread use in several wind markets, including India, China, and the United States, among others. Due to the larger size of the rotor, more energy can be captured when the wind moves past the blades of the wind turbine. Hence, for a given turbine generator capacity, the generator runs closer to or at its rated capacity for an increased percentage of the time duration in such reduced specific power wind turbine. Hence, reduced specific power wind turbines naturally result in higher energy generation for their installed capacity, resulting in a higher capacity utilization factor (CUF). With the highly sophisticated control systems of modern wind turbines, this higher capacity utilization factor can often be achieved with a relatively limited impact on the overall turbine cost. In such cases, LSP wind turbines provide a direct path toward a lower levelized cost of energy (LCOE) by providing a higher generation per investment. Further, as reduced specific power turbines have lower-rated wind speeds, resulting in evenly distributed power production over a wide range of wind speeds, the variability of wind generation is well mitigated with such turbines. With significant penetration of RE generation into the grid, such variability management will be extremely beneficial to grid managers, and the overall energy mix can be well managed with reduced storage requirements. Further, the energy generation profiles resulting from reduced specific power turbines have also been found to increase the wholesale market value of wind energy [6]. Turbines with reduced specific power and taller towers can be conceptually correlated with higher electricity prices in some markets by producing less during high wind hours and producing more during low wind hours [7, 8]. With such a background, this 2. Low specific power (LSP) wind turbineThis section explains the key properties of the LSP wind turbine, as well as the characteristics of other wind turbine types currently on the market, in greater detail. The influence of the CUF on different sites with the wind turbines under consideration is also investigated. 2. 1 Wind turbine characteristicsThe study covers available wind turbines with specific power (SP) ranging from 379 W/m2 to 173 W/m2, which represent multi-MW scale turbine types prevalently installed in the Indian Market. These wind turbine types considered in this study, when correlated with its period of deployment, clearly show that there is a definite, reduced specific power trend in India, the United States, China, and Brazil.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: Ранее засухи, становящиеся всё более частыми и сильными, создавали проблемы для ГЭС в ряде штатов США. На пониженный режим переводились французские АЭС, для охлаждения которых используется вода из Роны. Выработка энергии французскими АЭС летом 2022 года снизилась почти на 50 %, такие же отключения, вызванные засухой и жарой, происходили в 2018 и 2019 годах.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: Given that the agents re-start their trips immediately, different agents have different lengths for their respective learning episodes, thus the learning process is non-synchronous. Using our approach, on average, an episode takes roughly 500 time steps, thus agent reach the user equilibrium in about 100 episodes. For RL standards, this is a fast learning process, especially considering that we deal with a highly non-stationary environment, where agents get noisy signals. However, we also remark that, for practical purposes, the policy can be learned off-line, and, later, embedded in the vehicle. To give a specific picture, Table 2 shows the actual travel times after time step 50,000. We remark that we could have measured roughly the same around step 30,000. It can be seen that our approach outperforms DUA shortly after time step 10,000. Also noteworthy is the fact that, at any time step, agents still explore with probability ε = 5% thus there is room for improvements if other forms of action selection are used. Table 2: Travel time measured for DUA and QL with C2I at time step 50,000. time at step 50 k DUA ≈560 QL with C2I ≈470 DOI: 10. 7717/peerj-cs. 428/table-2 QL with C2I vs QL without communication Our approach is also compared to standard Q-learning, thus without communication, which means that the agents learn their routes only by their own previous experiences, without any augmented knowledge regarding the traffic situation and the experiences of other agents. In Fig. 4, we can divide the learning process in both cases shown in Fig. 4 in two distinct phases: the exploration phase, where the agents have yet no information about the network and explore it to find their destination “that is when the spikes in the learning curves can be seen”; and the exploitation phase, when agents know the best actions to take in order to experience the lowest travel time possible. Figure 4: QL with C2I vs QL without communication. Download full-size image DOI: 10. 7717/peerj-cs. 428/fig-4 Both approaches converge to the same average travel times in the exploitation phase. However, the advantage of our approach comes in the exploration phase. As we see in Fig.

Output: pass


Input: Claim: Wind turbines can generate energy even at low wind speeds. [SEP] Text: However, this stands in contrast to most of the European market, where the average specific power is found to be high, although it is difficult to generalize. Table 1 shows the wind turbines considered in the comparison study against the LSP wind turbine model. The respective power curves (normalized) are shown in Figure 1. It is clearly evident from Figure 1 that the power curve moves toward the left and is able to generate more energy at lower wind speeds, while the specific power is decreasing. To eliminate the influence of hub height variation in the comparison study, all wind turbines in the study are considered with the same hub height of 120 m. Turbine name (units)Rated power (kW)Rotor diameter (m)Specific power (W/m2)SP-379200082379SP-345210088345SP-295200093295SP-271200097271SP-2453000125245SP-2103465145210SP-2002000113200SP-1802100122180SP-1733300156173Table 1. Wind turbine models considered in this study. Figure 1. Power curves considered for the study. 2. 2 Power curve of LSP wind turbineThe power curve of the LSP wind turbine is derived/synthesized in a unique way from one of the latest wind turbine models (SP-173) in the Indian market, not by increasing the blade length, but by reducing the generator capacity and keeping the blade length constant. Considering the logistical and transportation-related constraints imposed by the longer blades [10], especially in complex terrain conditions, such a reverse approach seems to be justifiable. Figure 2 shows the normalized power curves of SP-173 and LSP-105 (derived from SP-173 by reducing the rated capacity to 2000 kW from 3300 kW). Figure 2. Comparison of SP-173 and LSP-105. (derived from SP-173 by reducing the rated capacity to 2000 kW instead of 3300 kW). 2. 3 Wind sitesIn order to understand the impact of Power wind turbines on varying wind climate, four different wind sites have been chosen for the study. The sites have been chosen to represent high (High W), medium (Med W), low (Low W), and low-coastal wind (Low coast W) regimes, considering the future wind farm development possibilities.

Output: corroborate


Input: Claim: Cryptocurrency mining is entirely powered by renewable energy sources, eliminating its environmental impact. [SEP] Text: В Китае спровоцированный засухой энергетический кризис вынудил крупнейшего производителя батарей закрыть заводы в провинции Сычуань. Поставки деталей, необходимых для производства Tesla и Toyota, были приостановлены. В Германии так снизился уровень воды в Рейне, что это повлияло на европейскую торговлю, остановив поставки дизельного топлива и угля и поставив под угрозу работу как гидро-, так и атомных электростанций.

Output: contrast


Input: Claim: Birds are capable of time travel. [SEP] Text: 4, the exploration phase in the QL with C2I algorithm is reduced by a considerable amount when compared to the traditional QL algorithm, meaning that in our case the user equilibrium is reached earlier. Table 3 compares the travel time measured in both cases at the time step 20,000, when our approach has already converged, but the standard Q-learning has not. Table 3: Travel time measured for QL and QL with C2I at time step 20,000. time at step 20k QL ≈676 QL with C2I ≈483 DOI: 10. 7717/peerj-cs. 428/table-3 Communication success rate In the real world, it might be the case that some information gets lost due to failure in the communication devices. In order to test what happens when not all messages reach the recipient, a success rate was implemented to test how the our approach performs if communication does not work as designed. Specifically, every time an agent needs to communicate with the infrastructure, the message will reach the destination with a given success rate. This was implemented by means of a randomly generated value, which is then compared to the success rate to determine whether or not the simulator should ignore the message, thus being a metaphor for a non-delivered message. Such a scheme is applied to any kind of communication between the infrastructure and the agent, that is, regardless if it is from an agent to a CommDev, or vice-versa. If a message is lost, then: (i) a CommDev does not get to update its data structure, and (ii) an agent does not get to update its Q-table. Other than that, the method behaves exactly as described by Algorithm 1. Experiments were performed varying the target success rate. For clarity, we show the results in two plots. Figure 5 compares the approach when the success rate is with 100% (thus the performance already discussed regarding the two previous figures), to one where the communication succeeds in only 75% of the times. In Fig. 6, we depict the cases for success rate of 25% and 50%. Figure 5: QL with C2I: comparison between 75% and 100% success rate. Download full-size image DOI: 10. 7717/peerj-cs. 428/fig-5 Figure 6: QL with C2I: comparison between 25% and 50% success rate.

Output: pass