Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: 1 μL of DNA-free water. PCR conditions were as follows: initial denaturation at 94°C for 3. 5 min; 38 cycles of 94°C for 1 min, 44°C for 1 min (annealing temperature), 72°C for 1. 5 min; a final extension at 72°C for 7 min. PCR products were detected by electrophoresis in 2% GoldView-stained agarose gels. Amplified products were purified using the (Quiagen) and sequenced in both directions with Bigv3. 1 (Applied Biosystems). Sequencing was carried out on an ABI Prism 3130 (Applied Biosystems). Sequences were assembled in Sequencher 4. 8 () and aligned using ClustalW (Thompson et al. , 1994) implemented in MEGA 5. 1 (Tamura et al. , 2011). Based on sequence similarities/differences, group-specific primers, which would amplify DNA of mimics in the predator guts, were designed using Amplicon. b08 (Jarman, 2004). Four primer pairs were tested with all prey and predator specimens used in this study to ensure that the primers amplify DNA of the studied prey groups only: primers MyrmF and MyrmR amplified Myrmarachne spiders, DaerF and DaerR amplified Daerlac true bugs, FormF and FormR amplified Camponotus, Polyrhachis and Myrmecia ants, and BothmF and BothmR primers amplified Bothriomutilla mutillid wasps (for primer sequences see Table 4) and to reject cross-amplifications with the predators. The sequences of cytochrome c oxidase obtained from the studied predators and the mimics are deposited in GenBank (Accession numbers KX980351--KX980391). Table 4 List of primer pairs used to identify mimics in the gut or faeces of potential predators.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Context-aware adaptive and personalized mobile learning delivery supported by UoLmP. Journal of University - Computer and Information Sciences, 26(1), 47–61. doi: 10. 1016/j. jksuci. 2013. 10. 008Guan, C. , Mou, J. , & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. of Innovation Studies, 4(4), 134–147. doi: 10. 1016/j. ijis. 2020. 09. 001Gunawan, K. D. H. , Liliasari, L. , Kaniawati, I. , & Setiawan, W. (2021). Implementation of Program for Assisted by in Designing HOTS-based Learning. 7(1), 55. doi: 10. 30870/jppi. v7i1. 8655Hasanov, A. , Laine, T. H. , & Chung, T. -S. (2019). A survey of adaptive context-aware learning environments. Journal of 11(5), 403–428. doi: 10. 3233/ais-190534Heffernan, N. T. , & Heffernan, C. L. (2014). : Building a Platform that for Research on and Teaching. of in Education, 24(4), 470–497. doi: 10. 1007/s40593-014-0024-xHolonIQ. (2023, February 27). in Education. 2023 Survey Insights. Retrieved from C. -W. , & Chen, S. Y. (2016). A Perspective to Handheld Devices: Customization vs. Personalization. Review of Research in Open and Distributed Learning, 17(1). doi: 10. 19173/irrodl. v17i1. 2168Hssina, B. , & Erritali, M. (2019). A on a in an System. Science, 151, 1152–1157. doi: 10. 1016/j. procs. 2019. 04. 164Hu, J. (2021). System by use of Methods. of in Learning, 16(05), 87. doi: 10. 3991/ijet. v16i05. 20299Huang, A. Y.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: 1 Decentralized identitySelf-sovereign identity, a widely held view among blockchain proponents, holds that individuals should have control over their own identities and should have autonomy over how facets of identity are shared with others. Decentralized identity (DID) is a blockchain-enabled embodiment of self-sovereign identity that can profoundly improve the privacy and security of personal data. DID refers to individual ownership of personal digital data relating to many elements of identity. Microsoft, which participates in defining DID standards, takes the perspective of the individual. “Currently, our identity and all our digital interactions are owned and controlled by other parties, some of whom we aren’t even aware of [10]. ” Returning ownership of data to the individuals to whom the data pertains can provide benefits both to those individuals and to organizations that would otherwise be responsible for protecting the data. Blockchain technology enables DID and provides a way for individuals to store their own data outside of the databases of the parties with whom they transact. Data are owned and controlled by these individuals and pointers to this data or metadata can be stored on the blockchain and can be used to verify the validity of claims the users make about their personal data. For example, a driver’s license bureau might issue a driver’s license to a user, which the user stores privately. When an insurance company or other party wishes to verify that the user is licensed, the user can present the license to a party such as an insurance company, and the party can independently verify the issuer and expiration date. Anyone can create a DID. When this identity is first created, there is no information attached to it. Over time, the user could attach a driver’s license or other identifying data to that DID. The process that a third party might use to verify that a particular person owns a DID, is similar to the process of validating that a person owns an email address. For example, an online gaming account can be attached to an email address. A party seeking to validate that a person was the owner of that account could send a private message, such as a security code, to the email address and ask the person to provide that code, something that only the person possessing the password for that email address could provide. Unlike an email account, the DID would be owned and stored by a person rather than by an email service provider.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: PrimerSequenceFragment length (bp)Amplified generaDaerF5´- −3´204Daerlac DaerR5´- −3´FormF5´- −3´220Camponotus, Myrmecia, Polyrhachis FormR5´- −3´MyrmF5´- −3´142Myrmarachne MyrmR5´- −3´BothmF5´- −3´215Bothriomutilla BothmR5´- −3´ DNA was extracted from 553 spider abdomens using D (Qiagen) following the manufacturer’s animal tissue protocol with a change in final step when only 50 μL of elution buffer was used. DNA from fecal samples (50 samples of skinks and 48 samples of noisy miner) was extracted using the DNeasy mericon (Qiagen) following manufacturer’s protocol. PCRs were performed using Multiplex PCR kit (Qiagen) under the following conditions: initial denaturation at 95°C for 15 min; 42 cycles of 94°C for 30 s, annealing temperature for 90 s (47. 5°C when using Bothm primers, 43 and 45°C with the other primers), 72°C for 90 s; and a final extension at 72°C for 10 min. The reaction mixture total volume of 20 μL consisted of 10. 6 μL of 1. 8 μL of Q-Solution, 2. 1 μL of RNase-free water, 0. 5 μL of 10 μM forward and 0. 5 μL of reverse primers, and 4. 5 μL of DNA. PCR with Bothm primers was done separately whereas three remaining primer pairs were used in one multiplex reaction. PCR products were detected by electrophoresis in 2% GoodView-stained agarose gels. PCR was repeated for each sample and primer combination at least three times to exclude falsely negative results. In positive samples, PCR was repeated once again using primers modified with MID identifiers (i. e. multiplex identifiers, 10bp-long barcoding sequences). Each sample was thus marked with a unique combination of barcodes. PCR products were purified using a (Qiagen) according to the manufacturer’s protocols.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Q. , Lu, O. H. T. , & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers & Education, 194, 104684. doi: 10. 1016/j. compedu. 2022. 104684& Chen, N. -S. (2012). Empowering personalized learning with an interactive e-book learning system for elementary school students. Research and Development, 60(4), 703–722. doi: 10. 1007/s11423-012-9237-6Hwang, G. -J. (2014). Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective. Environments, 1(1). doi: 10. 1186/s40561-014-0004-5Hwang, G. -J. , Xie, H. , Wah, B. W. , & Gašević, D. (2020). Vision, challenges, roles and research issues of in Education. Computers and Education: Artificial Intelligence, 1, 100001. doi: 10. 1016/j. caeai. 2020. 100001Jaiswal, A. , & Arun, C. J. (2021). Potential of for transformation of the education system in India. of Education and Development using Information and (), 17(1), 142-158. Jung, E. , Kim, D. , Yoon, M. , Park, S. , & Oakley, B. (2019). The influence of instructional design on learner control, sense of achievement, and perceived effectiveness in a supersize MOOC course. Computers & Education, 128, 377–388. doi: 10. 1016/j. compedu. 2018. 10. 001Kabudi, T. , Pappas, I. , & Olsen, D. H. (2021). AI-enabled adaptive learning systems: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017. doi: 10. 1016/j. caeai. 2021. 100017Karaoglan Yilmaz, F. G. , & Yilmaz, R. (2021). Students’ Engagement in Online Learning.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: The password, or private key, would also be secured by the owner. Personal information relating to the identity could be stored in an identity hub—an encrypted repository of personal data that is stored outside the blockchain, likely in a combination of phone, PC, and cloud data or offline storage devices [10]. Through the use of an identity hub, the person could control which pieces of information to share with an external party. DIDs reduce the probability of unwanted correlation. The use of common identifiers—such as email addresses on different web sites—creates what is called a correlation problem. Correlation in this context means entities can, without a user’s consent, associate information about a single identity across multiple systems. Email addresses utilize data on almost every website. When users provide the same email address on different sites—along with perhaps additional pieces of personal information like a phone number or physical address—they unknowingly enable a potential for correlation. In this case, entities can correlate that data across sites. Tracking cookies and web clicks enable the linking of IDs across websites which can result in outsiders gaining a full picture of users’ identity, where they live, their gender, age range, interests, and other information [10]. Figure 2 depicts how a user of several services and on-line websites can store data in a central user-controlled location and interact separately with each service provider. This enables the user to control the specific pieces of information that can be seen by each provider. Figure 2. Decentralized identities and service providers. 5. 2 Blockchain-enabled federated identityDIDs can help users secure and control their data property and determine who gets access to that data. Blockchains can also increase security for individuals when interacting with multiple internet platforms or services through the use of decentralized federated identities. Blockchains allow entities to protect privacy of individuals—central to self-sovereign identity. Traditionally, users of a system or set of systems possess what is referred to as a federated identity, which can be described as a single identity used by individuals to access services or information platforms, provided by multiple parties, whereby a single identity is enabled and determined by single sign on (SSO) authentication. Consider a health care network that includes multiple entities like hospitals, insurance carriers, or urgent care clinics, where the providers enable the use of a single sign-on credential or digital federated identity to access all services.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: The concentration of each PCR product was measured using NanoDrop 8000 UV-(Thermo Scientific, Waltman, MA) and assessed comparing to 50 bp ladder (Fermentas). Five μL of 50 μg/μL PCR products was pooled into the same sterile vial and sent for sequencing. Sequencing library was prepared using Kit and 2 200 Kit v3 (Scientific). Sequencing on the System was provided by the SEQme company (Dobříš, Czech Republic). Sequencing data were processed using the Galaxy platform ( BioEdit 7. 2. 5 (Hall, 1999) and MEGA 5. 10. Reads were split according to their MIDs resulting in files corresponding to individual predators. Then, forward and reverse primers were removed and sequences were filtered according to their length (<120 or 180 bp) to remove dimers or too short reads. The sequences were collapsed and rare haplotypes (containing <2 identical sequences) were removed. Furthermore, sequences with stop codons and indels causing frameshifts were also removed. Remaining haplotypes were clustered into molecular operational taxonomic units (MOTU) using jMOTU 4. 1 (Jones et al. , 2011) with a 4 bp cut-off (corresponding to 3% sequence divergence). Each MOTU was compared to the GenBank database using megablast and BOLD database and to the sequences of mimics obtained within this study. NGS produced 11 068 904 informative reads from all predators of which 614,273 reads belonged to the mimics. Availability of mimics to the predators was estimated by visual search on the trunks and below bark of 20 gum trees randomly selected on the campus. On each tree, abundance of mimics was surveyed by one person up to the height of 2 m from the ground for a period of 30 min on a sunny day. All mimics were collected, brought to the laboratory and identified to species. To test a hypothesis that the frequencies of captured mimics by all predators and those available at the microhabitat of predators are similar we used Chi-square test.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: Technology, Knowledge and Learning, 27(2), 449–460. doi: 10. 1007/s10758-021-09547-wKatuk, N. , Kim, J. , & Ryu, H. (2013). Experience beyond knowledge: Pragmatic e-learning systems design with learning experience. Computers in Human Behavior, 29(3), 747–758. doi: 10. 1016/j. chb. 2012. 12. 014Khan, I. , Ahmad, A. R. , Jabeur, N. , & Mahdi, M. N. (2021). An artificial intelligence approach to monitor student performance and devise preventive measures. Environments, 8(1). doi: 10. 1186/s40561-021-00161-yKim, H. -S. , Kim, N. Y. , & Cha, Y. (2021). Beneficial to Use AI Chatbots to Improve Learners’ Speaking Performance? of AsiaTEFL, 18(1), 161–178. doi: 10. 18823/asiatefl. 2021. 18. 1. 10. 161Kitchenham, B. (2004). Procedures for performing systematic reviews. Retrieved from D. , Huh, Y. , Lin, C. -Y. , & Reigeluth, C. M. (2018). Technology functions for personalized learning in learner-centered schools. Research and Development, 66(5), 1269–1302. doi: 10. 1007/s11423-018-9615-9Li, S. , & Gu, X. (2023). A for Human-Intelligence in Education: Based on and Delphi-AHP Method. & Society, 26(1), 187–202. Lin, C. F. , Yeh, Y. , Hung, Y. H. , & Chang, R. I. (2013). Data mining for providing a personalized learning path in creativity: An application of decision trees. Computers & Education, 68, 199–210. doi: 10. 1016/j. compedu. 2013. 05. 009Luckin, R. , & Cukurova, M. (2019). Designing educational technologies in the age of AI: A learning sciences‐driven approach. of Educational Technology, 50(6), 2824–2838.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: This type of identity, which is typically stored and managed in a central location by a service provider, is prone to security vulnerabilities [11]. The distributed nature of Blockchain technology provides an opportunity for networks to enable single sign-on, or federated identities much more securely. ElGayyar [11] proposes a blockchain-based federated identity framework (BFID) where the network of providers themselves, rather than a centralized third party, manage the system, identification, and authentication of the users. Any entity within the blockchain network can verify credentials and issue the identity for any user in the system. In a BFID, all transactions are written and maintained within the blockchain where the system takes advantage of the secure and immutable nature of the distributed ledger, thereby practically eliminating the possibility for identity breaches and potential theft. Blockchain-based federated identity frameworks can be configured on both public and private blockchain implementations and make use of smart contracts to react to potential rule changes that may occur while governing identity management within the system. Additionally, these frameworks enable users to audit and control how their identities are used while also providing the network business entities the ability to monitor how their services are being used, enabling process improvement and a better overall user experience. 5. 3 Zero-knowledge proofsZero-knowledge proofs enable ease of access to identity and other important data while maintaining privacy and property control for individuals. Zero-knowledge proofs are cryptographic methods whereby a user or “prover” can convince someone, or a “verifier” that something about them is true without providing, revealing or sharing that information. A common example is a customer attempting to order an alcoholic beverage from a bartender who demands to know that the patron is 21 of age or older. Providing a driver’s license reveals the patron’s full birth date as well as height, eye color, and home address—information that could be misused or stolen. Zero-knowledge proofs use cryptographic algorithms that enable a prover to mathematically demonstrate to a verifier that a statement is correct without revealing any data. When the state issues a 21-and-over driver’s license, it asks the driver to type in a secret nickname, unknown to the licensing bureau. This nickname could then be hashed together with the driver’s license number, and stored in a public list representing valid drivers over 21.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: In order to estimate the attack rate exerted by the whole community of predators as precisely as possible, we had to take into account two important variables that affect the predation: (1) the relative frequency (fij) of positively screened ith predator individual for each jth mimic; (2) the capture probability of araneophagous spider predator (Lampona), where body size is a strong predictor of the likelihood of predation. The capture probability was estimated for each spider predator and mimic (Pekár et al. , 2016). This latter adjustment was necessary in order to take into account the fact that spiders could have captured juvenile Myrmarachne or Daerlac individuals, which are not golden mimics and could not be distinguished from adult specimens by molecular methods. The functional model for the capture probability was obtained from data on eight spider species representing different guilds (Nentwig and Wissel, 1986). The logistic function was 11+exp(−1. 99+2. 11ratioij), where ratioij is the mimic/predator body size ratio. The ratio was estimated by measuring the body size of ten collected specimens of each predator and mimic. Then the overall pressure by the community of predators was estimated as a sum of all attack rates by means of the killing value, Kj (Varley and Gradwell, 1960): Kj=∑j=1n−log(1−fij). To test whether natural mortality caused by predation is related to unpalatability of mimics we used nonparametric regression as the response variable had non-standard distribution, namely generalized additive model (GAM) from the mgcv package (Wood, 2011). We regressed log-transformed K-values (in order to stay within positive bounds) against unpalatability index. Trials with selected predators Request a detailed protocol For predation trials we selected five species of mimics found on the campus. The five species represented a gradient of unpalatability (Table 3) within the mimetic complex. We included examples of the most unpalatable to the least palatable mimics: the ants Polyrhachis ammon, Polyrhachis vermiculosa, and Camponotus aeneopilosus, the true bug Daerlac nigricans, and the spider Myrmarachne luctuosa.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: doi: 10. 1111/bjet. 12861Luckin, R. , Holmes, W. , Griffiths, M. , & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Retrieved from O. , & Lutsenko, G. (Eds. ). (2022). - Theory and Practice. doi: 10. 5772/intechopen. 87353Matari, A. (2023). and Future of Teaching and Learning. Retrieved from A. (2019). Emotional AI and EdTech: serving the public good? Learning, Media and Technology, 45(3), 270–283. doi: 10. 1080/17439884. 2020. 1686016Microsoft. (2021, March). Understanding the Landscape: Analytics and AI in Education. Retrieved from & Bettayeb, A. M. (2016). University Instructors’ to Management Systems: System Case. Proceedings of the 18th on Systems. doi: 10. 5220/0005748102150222Nedungadi, P. , & Raman, R. (2012). A new approach to personalization: integrating e-learning and m-learning. Research and Development, 60(4), 659–678. doi: 10. 1007/s11423-012-9250-9Newman, M. , & Gough, D. (2019). in Educational Research: Methodology, Perspectives and Application. in Educational Research, 3–22. doi: 10. 1007/978-3-658-27602-7_1Ouyang, F. , Wu, M. , Zheng, L. , Zhang, L. , & Jiao, P. (2023). Integration of artificial intelligence performance prediction and learning analytics to improve student learning in online engineering course. of in Higher Education, 20(1). doi: 10. 1186/s41239-022-00372-4Pataranutaporn, P. , Danry, V. , Leong, J. , Punpongsanon, P. , Novy, D. , Maes, P. , & Sra, M. (2021). AI-generated characters for supporting personalized learning and well-being. Intelligence, 3(12), 1013–1022.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: At the bar, you could type your nickname and license number into a hash generator, and if the resulting hash matched one on the list, the bartender would know that you were of legal age [12]. There are two types of zero knowledge proofs, interactive and non-interactive. Most commonly, zero knowledge protocols are interactive whereby the prover (an individual or more likely a computer) and the verifier participate in a back and forth set of questions or challenges that, when answered correctly a given number of times, enables the prover to convince the verifier, with very high probability, that the statement they are making is true. An example of an interactive zero knowledge proof could involve two colored balls that are identical in every way accept their color. One is red and one green. Let’s assume the verifier is completely color blind and cannot tell the color of either ball. You want to prove to the verifier that the balls do in fact differ in color. The verifier puts the balls behind their back and shows one. The prover indicates the color. The verifier then does this again and asks if they switched the ball. Since you can see the different colors you can say with certainty that the ball was either switched or not. After several rounds of this, it becomes more statistically true that there are in fact two different balls as the probability that you could guess correctly over and over goes down to almost zero [13]. Non-interactive proof is more like the example above of the patron proving their age to a bartender with a proof statement that reveals age but not additional information that might be revealed if the prover were to show their photo. Proving which point Value a card in a deck of 52 cards, without identifying its suit, can provide an example of this type of proof. The prover states that the card they are holding is a king but does not want to reveal which king—the king of hearts, diamonds, spades or clubs. If the cryptographic string also contains information that reveals the other 48 cards, none of which are kings, we can know for certain that the prover does in fact hold a king of some kind. Zero knowledge proofs are powerful tools for maintaining privacy and property control for individuals that may need to provide a bit of personal information but no more than absolutely necessary. Advertisement 6.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: Based on their unpalatability, the ants and bugs were predicted to be Müllerian mimics, while the spider was predicted to be a Batesian mimic. Unpalatability was re-estimated for these five species using ten individuals from each species. All attributes as measured previously were re-measured with the addition of a behavioural measure. The behavioural measure was the frequency of biting in the field following agitation with forceps. The addition of an extra measure adjusted the previous estimated unpalatability (uj) but the relative relationship of unpalatability between species remained the same. We used Eulamprus quoyii as our visually-oriented euryphagous vertebrate predator. At least 14 days prior to the experiment adult skinks were placed singly in a white container (70 × 50 × 40 cm) at constant temperature 26°C and L:D = 12:12 regime. They were fed regularly with three crickets (Acheta domestica) three times a week. The skinks were then starved five days prior to the predation trials. One day prior to the trial a layer of butter was applied to the sides of the white container to prevent arthropods from climbing the walls and escaping. At the start of a trial a lizard was left to settle down for a period of 5 min. A prey item was released into the container and the behaviour of the skink during the 5 min trial was recorded with a fixed video camera positioned above the container. Five minutes was deemed sufficient, as previous feeding trials with crickets have shown this species of lizard will attempt to capture prey within 1 min. If the potential prey survived it was removed, and the skink was left for 2 min to settle down. Another prey item was then released and so on until all five mimic prey types were used. As a non-mimic control, we used juveniles of the spider, Badumna insignis L. Koch, which was of a similar size to the other species but was not a mimic as it lacked golden colouration. At the end a cricket was released as a positive control. Results of trials were used in the analysis only if the cricket was captured. The prey was offered to skinks using a constrained randomisation.

Output: pass


Input: Claim: AI is being used to personalize learning experiences for students by adapting to their needs and performance. [SEP] Text: doi: 10. 1038/s42256-021-00417-9Pelletier, K. , McCormack, M. , Reeves, J. , Robert, J. , Arbino, N. , Al-Freih, W. M. , Dickson-Deane, C. , Guevara, C. , Koster, L. , Sanchez-Mendiola, M. , Skallerup Bessette, L. , & Stine, J. (2022). 2022. Teaching and Learning Edition. Retrieved from K. ,. , Vens, C. , & Van den Noortgate, W. (2019). Integrating machine learning into item response theory for addressing the cold start problem in adaptive learning systems. Computers & Education, 137, 91–103. doi: 10. 1016/j. compedu. 2019. 04. 009Pontual Falcão, T. , Mendes de Andrade e Peres, F. , Sales de Morais, D. C. , & da Silva Oliveira, G. (2018). Participatory methodologies to promote student engagement in the development of educational digital games. Computers Education, 116, 161–175. doi: 10. 1016/j. compedu. 2017. 09. 006Prasetiyo, W. H. , Naidu, N. B. M. , Tan, B. P. , & Sumardjoko, B. (2021). Trend in Educational Sphere: A Systematic Review. of Evaluation and Research in Education, 10(4), 1192-1201. Ramos de Melo, F. , Flôres, E. L. , Diniz de Carvalho, S. , Gonçalves de Teixeira, R. A. , Batista Loja, L. F. , & de Sousa Gomide, R. (2014). Computational organization of didactic contents for personalized virtual learning environments. Computers & Education, 79, 126–137. doi: 10. 1016/j. compedu. 2014. 07. 012Rodway, P. , & Schepman, A. (2023). The impact of adopting AI educational technologies on projected course satisfaction in university students. Computers and Education: Artificial Intelligence, 5, 100150.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Artificial intelligence and privacy(AI) is a broad field that includes machine learning and cognitive computing where computers are programed to mimic human cognitive functions such as learning and problem solving but many times, much faster and in more accurate ways [14]. The use of AI is expanding into a plethora of areas including speech recognition, facial recognition, medical diagnosis, financial predictions, tracking of disease outbreaks etc. AI algorithms enable computing systems to rationalize and take actions aimed at achieving a specific goal or set of goals. User and stakeholder security can be enhanced through AI tools, which can take advantage of blockchain to open up new avenues for accessing and learning from data without taking ownership or control of that data. This can reduce risk for the organization and the stakeholders who provide the data. Both individual blockchain members and the organization or group in charge of setting governance rules and processes can benefit from building in privacy-related AI functionality (as early as possible) in the design of blockchain networks and processes. Companies have implemented AI to create holistic views of customers by piecing together transactions from all customer touchpoints. Blockchain participants will have incentives to pull together integrated datasets by combining transactions for a single customer across all blockchain partners. This creates potential benefits for blockchain partners, but can also negatively affect the privacy of customers and other stakeholders for which this integration is possible. In combination with options for identity protection through decentralization, AI can be used to combine personal data from blockchain participants and their stakeholders in a way that maintains information security and personal data privacy. Through these processes, user and stakeholder security can be enhanced and data sets and AI models can be improved. We can identify four categories of stakeholders that can be affected by an organization’s data transparency and privacy processes: (1) participants, whose data—both direct and indirect—are gathered; (2) victims, who are affected by decisions made using participant data; (3) users, who use participant data in their work; and (4) custodians, who manage and secure data. When AI can be used to manage access to data and to develop analytical models using that data, all stakeholders can benefit [15]. Table 1 summarizes a number of ways AI can be used in a blockchain setting to protect or increase privacy of user’s personal data.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: There was randomisation within Müllerian mimics (ants) followed by randomisation within less defended mimics (Daerlac, Myrmarachne) and non-mimic prey (Badumna) because the predator must learn unpalatability by encountering the unpalatable models first. The learned aversion was necessary because skinks were collected in the wild a number of months prior to the trials and fed with crickets since then. We used 26 skinks in total. Ants, spiders and bugs were collected a few days prior to the trial. From the video footage we recorded whether prey was consumed or spat out. The spider Lampona murina L. Koch was used as a nonvisually-oriented ant-adverse predator that will not capture ants but other spiders. Twenty-five late-instar juvenile Lampona spiders were collected from the bark of gum (Eucalyptus sp. ) trees on the campus and kept individually in tubes (1 cm diameter, 10 cm long). Spiders were placed in a chamber set to 23°C and L:D = 16:8 regime. Spiders were fed with a clubionid spider once a week and provided with a moistened piece of gauze. Lampona was not fed seven days prior to a trial. During a trial, an individual from the five mimetic prey species and one non-mimic spider (Clubiona sp. ) was released into the Petri dish in a random order (diameter 6 cm). The prey was offered to Lampona in a random order. Induction of learned aversion was not necessary because the spiders were collected from the field only a week prior to the experiment. If the prey was not captured within 30 min we replaced it with a new one until one prey was captured. If the prey was captured, another prey was offered seven days later. In each trial we recorded capture frequency of Lampona. Finally, we used the spider Servaea incana (Keyserling) as a specialised ant-eating predator. Twenty-seven adult individuals of Servaea spiders were collected from gum trees on the campus and placed singly in a Petri dish (8 cm diameter) and kept at 23°C and L:D = 16:8. Spiders were fed with small crickets once a week and provided with a moistened piece of gauze. We did not feed Servaea for five days prior to a trial.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: The GP skim-read the letter on her monitor and after a few minutes declared, ‘ah hah, you see here it is’, highlighting a paragraph with the mouse cursor, saying ‘this is all I need, out of this whole document, this is what is relevant to me. ’ She continues: ‘I can tell the rest of this letter is for their [the specialist’s office] purposes, it’s a form of documentation, I don’t need all this’. The GP did not require most of the content of the letter, only a single paragraph. Her prior experience of the specialist who wrote the letter enabled her to extract the relevant information efficiently and discount the rest. Documentation of clinical consultationsA scenario in which a system could automate clinical documentation using natural language processing and text summarisation was described to staff in focus groups and interviews, provoking lengthy discussion. Some participants saw it as a potentially useful technology, with multiple ‘when can we have that’ comments, while others felt it might have negative consequences on the clinical encounter and could even undermine the skills and critical thinking of the clinician. One GP argued that having a transcript of the entire conversation would be a waste of time, producing a great deal of unnecessary information. It is not uncommon for GPs to rewrite or edit their notes, particularly notes for new cases or complex circumstances, or when the notes are written by trainees. What may look like a relatively simple administrative task in fact involves a considerable amount of clinical skill and experience. Removing this task from the clinical workflow would remove an opportunity for the clinician to reflect critically about an interaction. Research shows that writing engages the brain, allows GPs to be better observers, supports empathy and engages critical thinking. 34Scope for automationThe online survey of experts in automation systems achieved 156 responses from academic and industrial experts around the world, providing a total of 4599 task level ratings. The distribution of respondents by country and qualification is shown in figures 2 and 3. Process machine learning model that we applied to the data suggests that about 44% of the administrative tasks performed in primary care are ‘mostly’ or ‘completely’ automatable using current technology. Figure 4 shows the distribution of automatability for all administrative tasks performed by general practice staff. Download figure Open in new tab Download powerpoint Figure 4 Distribution of automatability scores.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: This AI/Blockchain combination can increase system security by helping to detect attacks by bad actors, user security by sharing permissions and smart contracts, enable privacy-enhanced use of datasets through improved identity management and better data, and it can improve AI models through more varied, valid, and ethically-sourced data and better hypotheses. Each item in Table 1 is described briefly below, followed by examples of use cases using this combination of technologies. System SecurityMalicious attack detectionMore robust cryptographic functionalityUser SecurityUsers decide what data to shareSmart contracts can enforce established permissionsDatasetsImproved identity masking and metadataCleaner and more accurate dataAI ModelsBroader scope and greater variety of dataImproved validity of data and modelsEthically sourced and permissionedCareful construction of hypothesesTable 1. The role of AI in blockchain user privacy. Computational intelligence (CI), a subset of AI can improve the Blockchain’s attack resilience thus improving security of the system and ultimately the privacy of the data residing on the system. AI is rooted in hard computing techniques whereas computational intelligence is based on soft computing methods, which enable adaptation to a range of changing variables [16]. Computational intelligence, when combined with blockchain systems, can create more robust cryptographic functionality and ciphers thereby making it more difficult for cyber hackers to compromise systems even as computing power and efforts to hack these systems over time increases. Quite appropriately, [14] refer to the intersection of blockchain and AI as “blockchain intelligence”. Additionally, AI algorithms can be built on blockchains to detect when a blockchain is under attack by continually monitoring blocks and activity on the chain. This technology increases trust in the system beyond what the native architecture provides [17, 18]. When blockchain participants have increased control over their own data, they have the potential to decide with which parties and for what purposes their data are shared. In order to collect participant data for use in an AI dataset, participant permissions will need to be obtained. This provides users with ‘opt-in’ control, rather than ‘opt-out’ and helps to ensure that personal data is used in ways that are consistent with the intentions of the owner. In some cases, which may become increasingly common if decentralized identity solutions are adopted, users can be compensated for providing their data to organizations seeking to utilize this data in traditional and AI decision models. Smart contracts can also protect privacy.

Output: contrast


Input: Claim: Ants are the most intelligent species on Earth. [SEP] Text: One individual of a mimic prey species was released into the dish. The prey was offered to Servaea in a random order. Induction of learned aversion was not necessary because spiders were collected from the field only a week prior to the experiment. If the prey was not captured within 5 min we replaced it with a new one. If the prey was captured another prey was offered five days later. In each trial we recorded whether the prey was captured as well as the behaviour of Servaea and prey. To compare the frequency of capture among used prey types for each predator, relative frequencies of prey capture from Eulamprus quoyi or Servaea experiments were subjected to generalised estimating equations (GEE) from the geepack package with a binomial error structure due to repeated use of the same skink individuals with different prey. The exchangeable association structure was used (Varley and Gradwell, 1960). To test whether the post-attack reaction of the skinks was related to prey unpalatability we used generalized linear model with binomial error structure (GLM) of scores of the post-attack reaction of the skinks and the unpalatability index. The post-attack reaction was ranked as follows: 0 if the mimic was eaten, one if it was eaten but the skink cleaned its mouth, and two if the skink spat out the mimic. The ranks were then scaled to 0–1 range. To compare the relative capture frequencies of Lampona on the prey species a test was used. To test whether overall mortality of prey is related to its unpalatability we used a general linear model (LM) of the sum of all probabilities of capture (pij) by the three predators and unpalatability index. The sum of capture rate was expressed as a killing value, Kj using the formula: Kj=∑j=1n−log(1−pij). All statistical analyses were performed in R (R Core Team, 2013). For each model, diagnostic plots were used to assess model adequacy, such as variance homogeneity and distribution of residuals. Add a comment + Open annotations. The current annotation count on this page is being calculated. Data availability The following data sets were generated (2016) Data from: The golden mimicry complex uses a spectrum of defences to deter a community of predators Available at Repository under a CC0 Dedication. 88. PubMed 566. 66. PubMed 217.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: We found that some tasks that receptionists routinely perform in one practice were performed by administrators at another practice. The practices we observed differed considerably in their organisational forms, from single-site practices working independently to larger groups or ‘super-practices’ who share services across multiple sites. The issue of ‘task fit’, that is, allocating tasks to the appropriate person, involves both matching the task to the most appropriate pay grade and making sure clinicians are shielded from unnecessary administrative work. The driving force behind the assigning of tasks to occupations in this study was to accurately represent the core identity and ‘scope of work’ of each observed occupation. The third issue is the fundamental difference between ‘clinical’ and ‘administrative’ tasks. There is an abundance of administrative and clerical tasks that occur in primary care. While the field researcher observed, catalogued and categorised nearly every clinical interaction that takes place in the domain of primary care, it was often hard to distinguish administrative tasks from those that are purely clinical. Also, the O*NET database that was used for our analysis provided more terminology and language to describe administrative tasks and office work than it did for nuanced clinical work. ConclusionEvidence from our study show there is great potential to impact workloads in primary care by automating certain administrative tasks (approximately 44%, broadly). We believe that careful introduction of currently available technologies has considerable potential to reform administrative workflows and increase productivity in primary care. However, this should not be seen as a magic bullet that will solve the workload crisis in the immediate future. Introduction of new forms of automation should proceed cautiously with ongoing evaluation of their wider impacts. 2016. Available: Understanding pressures in general practice, 2016. Available: M, Everington S. Tackling the crisis in general practice. BMJ 2016;352:i942. doi:10. 1136/bmj. i942pmid: Engl. General practice forward view, 2016. Available: H, Stern R. Making time in general practice. Foundation, 2015: 1–83. ↵. association survey of GPs in England. London, 2016. ↵ Eighth national GP Worklife survey, 2016. Available: Clinical workload in UK primary care: a retrospective analysis of 100 million consultations in England, 2007-14. Lancet 2016;387:2323–30. doi:10.

Output: contrast


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: Permissions granted by users can be subject to complex rules embodied in smart contracts, which can enforce rules regarding the use of the data and can govern the granting and rescinding of participant data. AI can be used to scan contracts to identify participants who have or are likely to possess or provide data for desired uses. The size and nature of datasets available in blockchain networks can also have implications for effective AI. Because many different organizations and stakeholders contribute to shared ledgers, the quantity and variability of data available for analysis can be much larger than for single-company databases. Larger datasets could enable more sophisticated identity-masking procedures, and metadata may be richer and more informative. Because of the validation, security, timestamping and the append-only nature of blockchain ledgers, the data obtained are likely to be much cleaner and more accurate than when data are captured and maintained by many organizations in databases that are not immutable. The ethical quality of data obtained will also be higher, and model developers and users can have increased confidence that they are following regulations. Because multi-dimensional user permissions can be granted and documented—and in some cases, enforced through smart contracts—organizations can use this data with less risk of privacy breaches. In addition, because user data can be collected using zero-knowledge proofs, complex analyses requiring specific user data can be performed and the necessary information captured and used without the need for accessing or possessing PII. The use of blockchain data and artifacts can also result in higher quality analyses and outcomes. When data are clean and associated with clear metadata, the validity of the data is increased. Because each item in a data set is more trusted, error can be reduced and insights can be obtained through smaller data sets. When clean data are used to train AI models, those models will be more accurate, and the predictions and decisions made by those models will also be improved. Clean-training data can also be useful in validating non-blockchain data for use in AI models. Finally, and perhaps most importantly, the hypotheses upon which AI models are built can improve, for several reasons. First, because participant permission must be obtained and possibly paid for, AI designers will need to develop clear designs that define the analyses to be performed and determine the type and amount of data needed for these analyses. This will require designers to be more aware of the universe of data that could inform these analyses and what is and is not available in distributed ledgers and personal-data files.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: Mitral valve reconstruction (MVR) is one of the cardiosurgical procedures which cannot be substituted by any intervention owing to the quality of the quasi-anatomical, physiological repair. However, technique and strategies have changed over the years. We looked at procedural characteristics and outcome in an all-comer, non-selected cohort of patients. Methods 738 out of 1. 977 patients were retrospectively analyzed receiving MVR with and without concomitant procedures. The cohort was divided into three periods. P1: 2004–2009 (134 pts. ); P2: 2010–2014 (294 pts. ), and P3: 2015–2019 (310 pts. ). mortality increased from P1 to P2 and decreased from P2 to P3 (9% P1, 13% P2, 10% P3). All patients received an annuloplasty-ring. In P1 resection measures dominated. In P3 artificial chordae were dominant. Age, BMI, and risk scores correlated with early mortality. Survival rates were 66% (5-years), 55% (10-years), 44% (15-years) in P1, 63% (5-years), 50% (10-years) in P2, and 80% (5-years) in P3. Odds ratio for reduced long-term survival were concomitant venous only bypass surgery (10-years 2,701, p = 0. 026). 10-year survival was positively influenced by isolated MVR (0. 246, p = 0. 001), concomitant isolated arterial bypass (IMA) (0. 153, p = 0. 051), posterior leaflet measure (0. 178, p<0. 001), and use of artificial chordae (5-years 0. 235, p<0. 001). for ring implantation remained mandatory while preference changed alongside improved designs. Procedural characteristics changed from mainly resection maneuvers to predominant use of artificial chordae. Long-term results were negatively influenced by co-morbidities and positively influenced by posterior leaflet repair and artificial chordae. MVR underwent a qualitative evolution and remains a valuable cardiosurgical procedure. Citation: öter F, Albes JM (2022) Every day mitral valve reconstruction: What has changed over the last 15 years? 17(10): e0269537.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: Grading takes time, especially for comprehensive assignments like essays, and can be particularly difficult in larger classes when there is a significant amount of work to be done. The assessment process is made more difficult by the consistent approach used by traditional grading techniques, which may not sufficiently take into account different learning styles or variations in student ability. Standardized examinations and scoring rubrics were created to provide more objective indicators of student achievement to solve these problems. Although these techniques address some issues with fairness, they can occasionally be restrictive and miss the distinctive contributions made by each student. Standardized testing has come under fire for prioritizing exam-taking techniques over more comprehensive learning goals [1]. Thomas, O’Brien, Sanguino, and Green draw attention to the subjectivity of these systems, emphasizing how teacher prejudices and subjective views can have a big impact on students’ assessments [2]. Li draws attention to how standard grading fails to take into account various learning styles, which frequently results in a generalized approach that might not fairly represent the comprehension or advancement of any one student [3]. 1. 2 Need for innovation in grading1. 2. 1 Limitations of conventional gradingThe limitations of traditional grading systems have become increasingly clear. Cain highlights a lack of consistency and probable disparities in grading standards as severe disadvantages, with serious consequences for students’ academic careers and future chances [4]. 1. 2. 2 Emergence of digital technology in educationThe incorporation of digital technology in education has brought about a significant and profound change. Kim and Lee highlight the significance of technology in facilitating the use of more impartial and thorough evaluation instruments. The emergence of AI-powered grading systems, employing algorithms and machine learning to assess student work, signifies a significant progress in rectifying the limitations of conventional grading approaches [5]. AI-powered systems offer the potential for evaluations that are more impartial and consistent by prioritizing comprehension and methodology rather than simply correct or incorrect responses. Owoc demonstrates the ability of these systems to effectively handle substantial amounts of tests, resulting in a significant decrease in the workload of instructors. This adjustment not only improves the equity of grading but also provides instructors with additional time for interactive and personalized instruction [6]. To summarize, although traditional grading systems have served as the basis for educational assessments for a long time, their inadequacies are evident. AI-powered grading systems offer a novel solution, utilizing improvements in digital technology to address the limitations of traditional approaches and establish a fairer and more efficient educational evaluation system.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: This could help identify problems such as the lack of black faces in photo-categorizing algorithms before or during data collection. PII may never be collected, and when it is, its use may be more intentional and usage agreements may be enforced by smart contracts. This enables more ethical approaches to gathering and managing data. AI models built using ethically-sourced and governed data can generate results that are actionable within predefined ethical and regulatory limits. 6. 1 Emerging blockchain and AI industry uses casesThe 2019–2020 Covid 19 pandemic has prompted medical researchers and technologists to research ways to quickly gather intelligence around virus exposure and transmission as a way to combat the spread of the disease while maintaining personal privacy of users. Point-of-care diagnostics, which rely on rapid testing of patients that may have been exposed to the virus is proving to be an effective way of tracking the spread and reducing the impact of the disease. German based Pharmact AG has developed a rapid Covid 19 test that delivers results in roughly 20 minutes. This test can be used in point-of-care systems and combined with blockchain and AI to increase the speed of diagnosis and provide statistics on positive and negative results while maintaining security of personally-identifiable data. Data can be collected on blockchain infrastructure while taking advantage of the speed that AI affords to create an integrated platform that enables data from disparate sources to be analyzed. Information drawn from these systems can provide communities a powerful tool for combatting the spread of disease, reducing the burden of health care facilities and saving lives [19]. Many cities are working toward becoming “smart cities” by integrating AI and blockchain with other web 3. 0 technologies such as internet of things (IoT) sensors and edge devices. Intelligent transportation systems are enabled by these technologies. Self-driving cars make use of IoT sensors to continuously monitor surrounding situations and even anticipate developments by using artificial intelligence. These cars can incorporate blockchain wallets that enable passengers to pay for rides, rentals, tolls, etc. without revealing personal data. By adding blockchain as an underlying architecture, cities and private companies can reduce the friction of renting or sharing autonomous vehicles by streamlining the process of procuring a ride. The peer to peer nature of blockchain reduces the number of people or businesses involved in the process, taking out expensive intermediaries, and reducing costs.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: : November 5, 2021; Accepted: May 23, 2022; Published: October 3, 2022Copyright: © 2022 Motazedian et al. This is an open access 10. 5061/dryad. 4f4qrfjf1. Funding: The authors received no specific funding for this work. Competing interests: The authors have declared that no specific interests exist. IntroductionMitral valve reconstruction (MVR) is one of the remaining cardiac surgical procedures that, due to the quality of the quasi-anatomical and thus physiological repair, cannot be replaced by any intervention. In the 1970s, introduced the systematic approach to mitral valve reconstruction into the clinical practice. The clarification of the underlying pathology types was accompanied by appropriate measures to treat them and thus to reconstruct the valve as anatomically and physiologically as possible [1–3]. This was clearly a breakthrough that far surpassed existing mitral repair maneuvers and thus opened the future of surgical repair not only of the mitral valve, but later also of the tricuspid and aortic valves. Today between half and two thirds of the mitral valves are being reconstructed in countries with an efficient health system [4]. Although the techniques invented and refined by Carpentier are still widely used, mitral valve reconstruction has gone through an evolutionary process over the years in which some techniques became obsolete or even died out while others flourished. We therefore examined both procedural features and outcome in an unselected group of patients who received MVR in our institution over the past 15 years. Patients and methodsOf a total number of 1. 977 mitral valve procedures 738 patients were retrospectively analyzed receiving MVR with and without any concomitant procedure from 2004 to 2019. Underlying pathologies leading to mitral regurgitation necessitating repair were distinguished between primary degenerative and secondary functional (ischemic) origin according to the current ESC-guidelines [5]. Patients who had to undergo a cardiac tumor resection were excluded, even if this tumor could be removed from the mitral valve and the valve left in place. Also excluded were patients with acute endocarditis, which led to resection measures on an otherwise preserved mitral valve. Furthermore, patients were excluded who did not want to participate in the study or they or their relatives were unable to undergo informed consent. Ethics vote was obtained by the of our university.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: Advertisement 2. Evolution of automated grading systemsThe growth of automated grading systems is a fascinating journey, beginning with rudimentary computer-assisted evaluations in the mid-twentieth century and advancing to today’s powerful AI-driven solutions. This narrative goes beyond simply documenting technological progress; it represents a change in how education is assessed and teaching methods are approached. Our thorough analysis will uncover the significant advancements that have influenced this transformation, emphasizing the interaction between technological progress and educational principles. 2. 1 Introduction to computer-assisted assessments2. 1. 1 Early stages and initial developmentsThe inception of computer-assisted examinations extends back to the early days of computer technology, first focusing on automating simple tasks like scoring multiple-choice tests. Regan provides a detailed description of these early systems, demonstrating how they utilized fundamental computational methods for tasks such as score tabulation and simple answer key management [7]. Despite their limitations due to the computer power and software capabilities of the time, these systems provided the framework for future advanced automated grading innovations. 2. 2 Emergence of automated grading systems2. 2. 1 Advancements in the 1980s and 1990sThe 1980s and 1990s represented a key transition in computerized grading methods. During this era, breakthroughs in computer processing and software design led to the introduction of more complicated grading systems. Garcia and Pearson highlight how these systems began implementing complicated algorithms capable of judging not just objective responses but also subjective ones like short answers and essays [8]. This shift signified a transition from solely quantitative assessment to a blend of quantitative and qualitative evaluation, enabling a broader review of student responses. 2. 3 Advancements in language processing and AI2. 3. 1 Turning point in AI application for gradingThe adoption of powerful language processing and AI technology constituted a significant milestone in automated grading systems. Baidoo-Anu studies the combination of natural language processing (NLP) and machine learning methods, noting their significant impact on these systems’ capacity to interpret textual answers [9]. This advancement brought about a more refined and sophisticated examination of written content, enabling the grading process to comprehend and evaluate complicated concepts and arguments offered by people. 2. 4 Integration of pedagogical theories2. 4. 1 Blending educational theory with technologyIncorporating pedagogical theories into automated grading systems has been a key advancement. Lee and Kim underline the necessity of matching automated grading with educational objectives and learning theories [10].

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: These systems can also provide audit trails for both owners and renters, and enable rating and payment systems that maintain privacy for both parties. Data gathered by the vehicles can contribute to learning algorithms on the blockchain for increased security, scalability and efficiencies as well as improved transportation and sustainability for the city [20]. Smart home systems that preserve user privacy while contributing usage data for analysis can likewise benefit from the integration of blockchain and AI. Smart home systems are becoming popular and manufacturers increasingly enable connectivity between devices. These systems are valuable sources of consumer usage data. AI-enabled blockchain systems can be used to push machine learning and training processes to consumer’s mobile devices and edge computing servers. Users can then submit locally-trained models for analysis, in some cases with option of adding noise that makes it very difficult to trace shared data back to individual consumers. Decentralized technologies enable analysis of locally generated data without this data being submitted to a centralized server [21]. These use cases exemplify some of the ways blockchain and AI are being used to accomplish objectives while maintaining personal data privacy. New use cases continue to be developed as technologists and user communities recognize the possibilities for systems that provide both functionality and privacy. Advertisement 7. ConclusionBlockchain and AI technologies are improving at a rapid pace and enabling possibilities for sharing and combining data in ways not previously envisioned. At the same time, advances in these technologies provide new possibilities for the ethical use of data. Personal data, when shared, present a conundrum for firms and individuals, which can provide valuable benefits but can also create great risks and costs for both the individual and the organizations with which individual data are shared. Blockchain provides new mechanisms, such as decentralized identities and zero-knowledge proofs, that enable data to be shared in ways that maintain the privacy of the individual and allow users to maintain control over their own data. These advances can provide both increased cybersecurity and more ethical use of personal data. Blockchain participants can realize these outcomes through careful development of governance frameworks and mechanisms. Publication of this 1. RiskBased Security. 2020 Report: Quickview. [Internet]. 2021. Available from: [Accessed: 2021-01-12]2. Kellerman R. Five of the Breaches of the 21st Century. STAGE2DATA. [Internet]. 2020. Available from: [Accessed: 2020-12-17]3. Fruhlinger J.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: Owing to the entirely retrospective nature of the study, the necessity for written informed consent was waived. Late survival state was obtained in several cases by telephone contact. That was considered as oral and thus adequate consent by the ethics committee. (Sept. 2020; File No. E-01-20200709). The cohort was divided into three periods: Period one (P1): 2004–2009 (134 pts. ); period two (P2) 2010–2014 (294 pts. ); and period three (P3) 2015–2019 (310 pts. ). Long-term results up to 15 years were obtained. Statistical evaluation was performed using 2010 and R® [6]. The descriptive statistics initially included all demographic items as well as perioperative parameters recorded during the patient stay and presented here as a mean value with standard deviation (95% confidence interval). Numerical data was tested for normal distribution before being compared with Student’s t-test or Mann-Whitney-U Test respectively. Categorical data was compared using Fisher’s exact test and Chi-squared test. To examine the existence of trends in categorical variables over the three time periods, Cochran-Armitage test for trend in proportions was used. In addition, a risk factor analysis was carried out to determine the odds ratio. Kendall’s Tau was used to correlate hospitalization time and a panel of typical risk factors. Differences were assumed to be significant if p<0. 05. The patients were contacted by phone for follow up. No information could be obtained from 55/738 patients (93% follow-up). The survival times were calculated using the Kaplan-Meier method. The primary endpoint was all-cause mortality, the secondary endpoints were: recurrence rate requiring reoperation, early mortality and long-term survival in the years 2004–2009 (Period 1); 20010–2014 (Period 2); 2015–2019 (Period 3). Additional secondary endpoints were the type of surgery and the type and scope of other simultaneous surgical measures and their influence on early mortality and long-term survival. ResultsThe proportion of mitral valve repair from all mitral valve procedures more than doubled from period 1 (22. 1%) to period 2 (47. 6%) and slightly decreased thereafter in period 3 (41. 1%). Mitral valve pathology was of primary nature in 64,6% of all patients.

Output: pass


Input: Claim: The majority of AI tools in education focus on automating administrative tasks like grading and attendance. [SEP] Text: 2 AI-driven tools for gradingSpecific AI-driven solutions currently utilized for grading include automated essay scoring systems (like Turnitin’s Gradescope), multiple-choice test graders, and platforms that assess coding assignments (such as CodeSignal). These technologies utilize natural language processing, machine learning algorithms, and pattern recognition to analyze student inputs against a set of criteria or answer keys. 10. 2. 3 Addressing bias and fairnessThe difficulties of prejudice and fairness in AI-aided grading can be addressed by the following:Continually training AI models on varied datasets that represent a wide range of language, cultural, and educational backgrounds. Implementing transparent algorithms that allow instructors to see and understand the foundation for a grade. Incorporating human monitoring to validate AI grading conclusions, ensuring that the final grades reflect a fair assessment. 10. 2. 4 Ethical considerations and mitigationThe ethical considerations of utilizing AI in grading focus around data privacy, the potential for systematic bias, and the impact on student motivation. Mitigation strategies include the following:Ensuring rigorous compliance with data protection requirements to preserve student information. Engaging multidisciplinary teams in AI development to eliminate prejudice and assure ethical use. Communicating clearly with students about how AI is utilized in their evaluations to ensure confidence and integrity in the educational process. 10. 2. 5 Potential negative impactsAI-aided grading could potentially:Diminish the qualitative feedback that is vital for student learning and progress. Increase dependence on technology, resulting to a loss in essential human evaluative abilities among instructors. Exacerbate educational inequities if access to advanced AI techniques is inconsistent across different institutions. 10. 2. 6 Future implicationThe incorporation of AI in educational assessment could lead to significant upheavals in the job market, notably for educators. While AI can automate certain portions of grading, the role of educators could expand to focus more on curriculum development, personalized instruction, and mentoring. Additionally, there is potential for new occupations based around AI maintenance, development, and ethical monitoring within educational institutions. Potential drawbacks of using AI for grading include the following:The possibility of mistakes in grading, especially for subjective projects like essays, where nuance and inventiveness could be disregarded. The potential for technology to produce errors due to biased training data or algorithmic restrictions. The requirement for significant upfront investment in technology and training for schools to effectively employ AI grading tools. 1. Dilon C.

Output: corroborate


Input: Claim: AI assistants can guarantee complete privacy and security for all personal data and interactions. [SEP] Text: : What Happened, Who was Affected, What was the Impact? [Internet]. 2020. Available from: Statista, Figure 1. [Internet]. 2020. Available from: [Accessed: 2020-11-12]5. Heister S, Yuthas K. Technology in Society: and How it Conceptions of the Self. 2020. (60) Grimes R. What is Information (PII)? How to Protect it Under GDPR. [Internet]. 2019. Available from: Uribe D, Waters G. Privacy Laws: and Non-Fungible Tokens. of the Association. 2020. (3) Yoon J. Introduce the Act. [Internet]. 2019. Available from: [Accessed: 2021-01-08]9. Consensys. Busting the Myth of Private Blockchains. [Internet]. 2020. Available from: [Accessed: 2020-12-12]10. Microsoft. Decentralized Identity. [Internet]. Available from: [Accessed: 2020-12-12]11. . Blockchain-Identity and Auditing. of Blockchains and Cryptocurrencies. 2020. p. 179-20512. . A to. [Internet]. 2019. . Available from: [Accessed: 2020-11-17]13. Wikipedia. Zero-Knowledge Proof. Wikipedia. [Internet]. 2020. Available from: [Accessed: 2021-01-18]14. . Blockchain Intelligence: Intelligence 2020. arXiv preprint arXiv:1912. 0648515. . with Blockchain and AI Ethics. Data and Information Quality; 2019. Wikipedia. Computational intelligence. Wikipedia. [Internet]. Available from: [Accessed: 2021-01-18]17. Marwala T, Xing B. Blockchain and 2018. Arvix preprint arXiv:1802. 0445118. -Fuqaha A. Blockchain for AI: Review and 2019. (7) p. 10127-10149. DOI: 10. 1109/. 2018. 289050719. Mashamba-Thompson T, Crayton E. Self-Testing: Blockchain and Technology for Disease 2019. Diagnostics. 2020. (10) 198. . Convergence of Blockchain and in IoT Network for the City. 2020. and Society.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: Age remained quite stable (P1: 64. 26 years, P2: 66. 46 years, P3: 65. 42 years), risk scores (EuroScore (ES), logistic ES (log. ES)) increased from P1 to P2 and decreased thereafter. Mean hospitalization time decreased (17. 7 days P1; 17. 5 days P2; 15. 2 days P3) (Table 1). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 1. Demographic data. of isolated procedures varied between 30 and 50% (48% P1, 31% P2, 40% P3). Minimal-invasive MVR did not play a major role in this non-selected cohort with only 13 patients mainly carried out in period 2. Early mortality increased from P1 to P2 and decreased from P2 to P3 (9% P1, 13% P2, 10% P3). Concomitant procedures were mostly coronary artery bypass graft as well as aortic valve replacement and endocardial cooled radiofrequency thermal atrial ablation (MAZE) was often performed (Table 2). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. Concomitant procedures. patients in all periods received an annuloplasty-ring. Classic™ (35. 07%) and Livanova (previously Sorin™) Anuloflo™ (51. 49%) were mostly used in P1 it gradually shifted until P3 to the II (65. 16%) and the St. Saddle (25. 48%). In P1 resection measures for the posterior leaflet as well as Wooler plasty dominated. In P2 artificial chordae came increasingly in use while resection measures decreased. 3 artificial chordae were the dominant procedure (Table 3, Fig 1). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 1. Shift of additional reconstruction measures.

Output: pass


Input: Claim: AI can analyze large sets of student data to identify patterns that help improve learning outcomes. [SEP] Text: The priorities of the three types of participants (alumni, current students, and teachers) were compared. Additionally, in view of the role, expertise, and experiences of the teachers with many cohorts of student-teachers, more weight was given to the teachers’ input. The findings showed that the two most important dimensions were the metacognitive and the cognitive, which correspond with the fact that, in the literature, metacognitive and cognitive strategies have been considered as synonyms with learning to learn strategies and their mastery acknowledged as a pre-requisite for effective lifelong learning (Cornford, 2002). In addition, one subdimension of the affective dimension and one subdimension of the social-relational dimension were highlighted by the participants. These dimensions and subdimensions follow: In the metacognitive dimension: knowledge about the self, the task, and the strategies to approach it,planning, organising, managing time and self-evaluation control self-regulation. In the cognitive dimension: effective information management,ICT management,critical and creative thinking. In the affective dimension: motivation and a positive attitude towards learning. In the social-relational dimension: teamwork. In connection with this initial step (and leading to the second one), it is worth mentioning that an exploration of how learning to learn has been approached in other higher education institutions shows that one-to-one scaffolding, along with the use of technological tools, has been used to enhance this lifelong learning competence. For instance, advisors have promoted the use of student-owned iPads for managing self-directed language learning under a cyclical model of action research (Lammons et al. ,2015). Equally, in an elective course aimed at supporting learners to take charge of their own language learning, students have been supported by learning advisors to use electronic tools for setting goals, choosing resources, making and implementing a plan, and evaluating their own progress (Ambinintsoa et al. , 2021). The second step in developing the resource set of learning objects was a review of the concept of a learning object. Within this analysis, it was clear that the term ‘learning object’, initially based on a general definition by Committee in 2002, was soon refined by Wiley (2002) in his ground-breaking book, Use of Learning Objects, as “any digital resource that can be reused to support learning” (p. 6).

Output: corroborate


Input: Claim: AI assistants will soon have consciousness and make independent ethical decisions. [SEP] Text: . org 2024 in to your account Login or this option if you have an account on informs. org or if you want to become an member Combine personal and institutional access with an Informs PubsOnline account using main navigation't sign in? Forgot your username? Enter your email address below and we will send you your username If the address matches an existing account you will receive an email with instructions to retrieve your username Login to your account Login or this option if you have an account on informs. org or if you want to become an member Combine personal and institutional access with an Informs PubsOnline account using 21, 2024 in Ethical AI 7 for and Bias-Free AI in : Share onShare on PRINT 10. 1287/LYTX. 2024. 01. 12 Today’s business leaders increasingly use artificial intelligence (AI) tools to analyze company trends and make critical decisions. However, AI algorithms often make biased suggestions that could be misleading. Moreover, people intending to use AI in business analytics must ensure they only do so ethically. Following best practices can keep company decision-makers, data scientists and others on the right track. Here are seven ways to ensure ethical and bias-free AI in business analytics. 1. Scrutinize the Examining the data used to train AI algorithms is a good starting point. If the data includes biases, so will the conclusions. It is important to remember that algorithms are only as reliable as the information used to train them. The algorithms could give the wrong results if a company uses data containing duplicate records or missing information. The input data could also contain systemic biases, such as if people put too much trust in small sample sizes or do not have time to collect information from sufficiently diverse sources. Check for systemic biases by asking the following questions: Are all groups accurately represented in the data? Are the models appropriate for the desired use case? Has someone checked to ensure there are no skewed results? Have algorithm developers defined the key metrics? Will the algorithms include a fairness regulator to reduce bias? Having more than one person examine the data for errors or other problems before it is used to train algorithms is another good step. The more people there are who see the information, the more likely someone will spot a problem that may need further investigation before it is fed into an AI algorithm. 2. Commit to (RAI) centers on designing artificial intelligence tools by adhering to intentional, moral-driven frameworks.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: PML = Corrective measure on the posterior mitral leaflet; Resection = All types of resection measure on either leaflet; Wooler = Commissural reduction plasty according to Wooler; Plication = Plication on the leaflets without resection; AML = Corrective measure on the anterior mitral leaflet; = Repositioning, shortening of the native chords, shortening of the origin of the chord by means of splitting or trimming or repositioning of the papillary muscle; Artif. Chordae = Chordae replacement with artificial chords. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 3. Types of additional repair maneuvers. age, BMI, and risk scores correlated with early mortality (EM) (Age-EM: 71. 54 Age- Control: 64. 89%, P<0. 001; BMI-EM: 28. 12% BMI-Control: 26. 72, P = 0. 026; log. ES-EM: 30. 98%, Log. ES-Control: 10. 46%, P<0. 001; ESII-EM: 16. 99% ESII-Control: 4. 63%, P<0. 001). Concomitant bypass surgery, particularly without IMA as well as concomitant implantation or explantation of electrophysiological devices increased the risk for early mortality whereas isolated MVR and use of artificial chordae reduced it (Table 4). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 4. Procedure-dependent early mortality. from early or late redo-procedures for the mitral valve was 91% in P1, 96% in P2, and 97% in P3 (P1 vs. P2 P = 0. 008; P1 vs. P3 P = 0. 006; P2 vs. P3 P = 0. 195). Early mitral valve replacement after initial repair were most often performed. MV-Replacement after MVR early: Mean interval 91 days; MV-Replacement after MVR late: Mean interval 927 days; MVR after MVR early: Mean interval 13. 67 days; MVR after MVR late: Mean interval 317 days.

Output: pass


Input: Claim: AI can analyze large sets of student data to identify patterns that help improve learning outcomes. [SEP] Text: This framework, derived from the author’s in-progress doctoral research (Delgado Alvarado, 2021), considers the distributed use of tools that integrate technological and pedagogical features of the internet and the web to actively and collaboratively scaffold self-regulatory processes (i. e. , learner strategies, such as goal setting, task strategies, self-monitoring, self-evaluation, time management, and help-seeking) embedded into three cyclical phases of learning: forethought, performance, and self-reflection. The pedagogical intervention is addressed to these student-teachers and is planned to be carried out in a pilot stage from August 2021 to July 2022 (two semesters) and then in a second and third stage from August 2022 to January 2026. Here, insights from research conducted in the preliminary stage will help reshape the project and gradually incorporate all upcoming freshmen, until covering all the student-teachers during the nine semesters of the BA[2]. First, as part of the orientation sessions for new student-teachers, they will be requested to individually create a learning electronic portfolio (e-Portfolio) to show evidence of the independent completion of the resource set of learning objects. Here, these learners will publish an entry on how this resource set relates to what they already know and how these interactive resources can be useful in their learning journey throughout the BA in ELT that they are about to start. Next, guided by their English teachers, the student-teachers will apply their learning gains on self-regulatory processes from the resource set of learning objects during their English language development courses in the first and second semesters. Following the framework outlined at the beginning of this section, the English contents will be developed under a blended-learning modality via face-to-face and online sessions in a learning management system. This latter ILT subset will allow the integration of a selection of pedagogical tools to scaffold the self-regulatory processes (learner strategies), including learning, administrative, assessment, collaboration and communication, and content creation and delivery tools in the three cyclical phases of learning (Kitsantas & Dabbagh, 2010)[3]. As part of these phases, the learning e-Portfolio created during the orientation sessions will serve to track the student-teachers’ progress and achievements in English while showing evidence of the implementation of the self-regulatory processes.

Output: corroborate


Input: Claim: AI assistants will soon have consciousness and make independent ethical decisions. [SEP] Text: RAI products also offer users interpretability and transparency, making them well-suited for recruitment-based tasks. Getting a job offer – or being denied one – could be life-changing for an applicant. RAI-based tools support fairness for job applicants. Suppose a tool’s developers and users can see why it made certain decisions over others. They can check for potential bias, ensuring it did not influence a particular outcome. That capability is also important if a C-suite member wants to analyze whether now is the right time to expand into a new country or region. Such decisions could forever change the company’s course, making it necessary to have as much trust in the AI as possible. RAI allows this with its transparency emphasis. However, not all company leaders can immediately approve the development resources for an RAI tool. The next best thing is to only use AI for applications that would not substantially change someone’s life or cause harm if the technology reached flawed conclusions. 3. Involved AI development has occurred so rapidly that the technology is now readily available to anyone who want to use it. For example, the AI built into offers real-time suggestions to help drivers take the quickest routes. Spotify and Netflix rely on AI to make content suggestions, increasing the likelihood that users will engage with those platforms longer. Some company leaders are so enthusiastic about AI’s capabilities that they want to remove human oversight. Besides putting jobs at risk, this approach could be particularly dangerous when using AI to handle decision-making. In one 2023 study of business leaders in the and Ireland, 93% favored maintaining humans’ involvement in AI-based decision-making. Additionally, only 29% felt confident that people use AI ethically in business. Continuing to prioritize humans’ input could perhaps increase the confidence in AI because a human could flag unethical or otherwise questionable uses of the technology. That said, 65% of the business leaders polled felt pressured to use AI in their organizations, which could mean some are using AI before they’re ready just to keep up with the Joneses. Most changes take time to implement well, and the organizations that feel rushed could inadvertently begin relying on AI tools containing biases or presenting ethical dilemmas. AI does an excellent job of processing huge amounts of data to find meaningful trends. However, human expertise remains valuable, and people should always question AI conclusions that seem highly unusual or otherwise.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: DiscussionEver since Carpentier has propagated the mandatory use of an annuloplasty-ring in order to stabilize the reconstruction result it became the procedure the overwhelming majority of all surgeons adhered to owing to the clear and steadily growing body of evidence regarding the benefit of the ring [1–3]. Indeed, our study mirrored this indicating that ring implantation remained mandatory throughout the entire observation period. However, preference towards certain rings changed alongside improved designs. Those design changes comprised on one hand the very shape of the ring propagating the “saddle-shape” as being a design improvement with positive implications on hemodynamic performance but on the other hand on design changes intended to facilitate implantation itself [7–9]. These particular changes comprised wider rings with multi-layer fabric to allow for better stitching and more secure anchoring. Stiff or moderately flexible rings appeared to stabilize the annulus significantly better than highly flexible ones [10, 11] so that the use of the latter did not play a role in this study. Procedural characteristics for the valvular apparatus changed markedly from mainly classical resection maneuvers to predominant use of artificial chordae [12–18]. In the early years the classical recommendations such as reshaping of the posterior leaflet by resection or plication maneuvers as well as reshaping of the annulus by appropriate rings were mostly followed but measures such as Wooler plasty, Paneth plasty, or were still performed [19, 20]. However, they did not exhibit such robust evidence in the literature regarding a beneficial effect on long-term patency as posterior leaflet correction and/or artificial chordae. We did not perform the Paneth plasty, which are meant to reduce the diameter of the anulus but favored instead the annuloplasty-ring as an implant from the early days on. Alfieri-Stich was neither performed because of the, in our eyes, “non-anatomical” approach. The patients who had received a Wooler plasty, however, fared quite well and the presence of a Wooler plasty positively influenced long-term survival even after 10 years. This, however, was quite probably not due to the Wooler plasty itself but owing to the mandatory annuloplasty ring, these patients also received. It cannot be ruled out, however, that intraoperative quality control also played a role regarding long-term stability.

Output: pass


Input: Claim: Blockchain can reduce fraud in supply chains by securely recording every transaction along the chain. [SEP] Text: 1 IntroductionSupply chains today are incredibly complex, comprising multi-echelon and geographically dispersed companies. Globalization, different international regulations, and varied cultural and human behaviors worldwide are all challenges to managing companies through their supply chains. These evolutionary phenomena have made it arduous to acquire relevant and trustworthy information within supply chains and have dramatically increased the potential for inefficient transactions, fraud, pilferage, or simply a deterioration in supply chain performance (Hastig and Sodhi 2020). The urgent need for traceability of both product and process in supply chains has been documented in several industries, including the agri-food sector (Sun and Wang 2019; Yadav et al. 2020; Mukherjee et al. 2021), pharmaceutical and medical products (Chen et al. 2019) and luxury products (Choi 2019). The lack of transparency and visibility in all processes of the supply chain prevents customers from verifying the origin of the raw materials and the processes that the product underwent before reaching the store shelves, with a high risk of fraud and counterfeiting of products. The costs involved in verifying supply chains’ intermediaries, in assessing their reliability and transparency in the production processes further complicates managing traceability in supply chains (Ahluwalia et al. 2020; Choi 2020). Strategic and competitive reputational issues arise from these risks and the lack of supply chain transparency. In response to these concerns, the technological advancements of the digital era are providing companies with many opportunities that can be exploited in the supply chain (Xiong et al. 2021). The term digital supply chain refers to data exchanges occurring between actors involved in a supply chain and also to how the supply chain process may be managed through a wide variety of innovative technologies (Büyüközkan and Göçer 2018) such as the Internet of Things (IoT), Analytics, cloud computing and the blockchain itself. Blockchain technology is particularly relevant (Casey and Wong 2017; Tapscott and Tapscott 2017; Samson 2020) in overcoming the difficulties mentioned above due to its centralized database in which all the information of the supply chain partners is recorded immutably. The literature on the use of blockchain technology in supply chains is quite recent (e. g. Chang et al.

Output: corroborate


Input: Claim: AI assistants will soon have consciousness and make independent ethical decisions. [SEP] Text: It’s better to take the time to investigate than trust something that seems even slightly off just for a quick result. 4. and upholding ethical AI applications will likely require several employees within an organization to oversee all AI activities and ensure they meet stated objectives. The chosen individuals can come from various departments, within or outside of technical teams. In a 2022 study, 80% of respondents indicated the primary AI ethics person within their organization was a nontechnical executive. Additionally, three-quarters of those polled viewed ethics as a competitive differentiator. However, many still have significant work to do to meet their ethics-related goals. Fewer than 25% reported implementing AI ethics in their workplaces, but 79% of CEOs said they were prepared to do so. Another relevant finding was that 68% of respondents felt diverse, inclusive workplaces reduced AI bias. However, data indicates that many AI teams still need more gender diversity, underrepresented minorities and further inclusion of those who identify with the + community. 5. Select and bias-free ethical AI tools won’t happen overnight, but people tend to get better results when defining specific goals and identifying the most practical paths to success. Sustainability is another popular topic requiring clear definitions and strategies. Statistics suggest as many as 60% of companies have sustainability strategies. However, they’ll differ based on resources, timelines, current circumstances and other business factors. Those overseeing AI ethics and anti-bias efforts in an organization should determine what constitutes progress in their work. How can they verify a new business analytics tool operates ethically? Has the company established a bias baseline so people can verify that AI is becoming more balanced over time? Choosing and tracking the appropriate metrics allows you to create and stay focused on goals. Also, once it becomes clear things are going in the right direction, the statistics make it easier to justify further investments for more improvements. 6. Efforts to Regulate AI Numerous government agencies and legislators are considering how and when they may regulate AI to minimize the harm it could cause. 2023, representatives from four U. S. federal agencies affirmed they would ensure people always use AI tools in ways consistent with federal laws, and they had already seen ways the technology could worsen fraud and discrimination. The parties also mentioned their legal authorities extend equally to new technology applications as any other type of conduct. Union is working on the AI Act, the world’s first comprehensive law for the technology.

Output: contrast


Input: Claim: Every insect has a lifespan of at least five years. [SEP] Text: Transesophageal echocardiography (TEE) was not yet mandatory in period 1. Surgeons mainly relied on intraoperative filling of the left ventricle with saline to estimate the success of the reconstruction and no intraoperative quality control was thus performed while the heart was beating. Direct correction in case of an insufficient result was thus not an option [21–23]. The widespread introduction of artificial chordae was a relevant evolutionary step that our godfather had not foreseen. Perhaps this is because the most suitable material for these chordae, expanded polytetrafluoroethylene, Goretex ™, was not yet available in the 1970s. Artificial chordae were soon adopted by the majority of surgeons and gained worldwide acceptance. Indeed, that was apparent also in our cohorts over the years. Although early and intermediate results up to five years were quite favorable for valves repaired with artificial chordae this benefit disappeared in the long-term results (10-year survival). It must be noted, however, that artificial chordae came into wider use but in the second and third period so that long-term results on a larger scale are not yet available. We noticed a steep increase in repair from period 1 to period 2 possibly owing to the experience gained with repair strategies in the early years in our institution. From period 2 to period 3 a slight decrease of the proportion of repair maneuvers was noted declining from 48% to 41%. This shift was not due to a more restrictive policy regarding repair surgery but was instead non-intentional and can be explained by a higher morbidity of the patients resulting in a higher proportion of patients with complex redo-surgery or endocarditis. The increase in patients with primary mitral regurgitation from period 1 to period 3 cannot be readily explained. It is conceivable that, because of demographic change, patients with restrictive mitral valve pathology presented with increasing multimorbidity, so that the indication for repair in patients with secondary mitral valve pathology became more defensive. Only 13 minimally invasive mitral valve repair procedures were carried out mainly in period 2. The true minimally invasive approach was indeed not favored in our clinic until very recently because of lack of center-wide, multi-surgeon expertise. Leaflet repair strategies especially concerning the posterior leaflet remained a frequent procedure demonstrating excellent results even in the long-term. Why is it so?

Output: pass


Input: Claim: Blockchain can reduce fraud in supply chains by securely recording every transaction along the chain. [SEP] Text: 2019) but has experienced significant growth in recent years thanks to the evidence that emerged on the potential of this technology applied to supply chains of different sectors such as food supply chains (Katsikouli et al. 2021; Bechtsis et al. 2021; Sharma et al. 2021; Mukherjee et al. 2021), humanitarian supply chains (Baharmand et al. 2021) or pharmaceutical chains (et al. 2021; Hastig and Sodhi 2020). Existing papers are focusing on illustrating the potential value of the blockchain and its interoperability with existing technology, such as IoT, and in particular, for the fashion industry, this technology has enormous potential in improving the information flows of supply chains (Agrawal et al. 2021; Wang et al. 2020; Bullón Pérez et al. 2020; Agrawal et al. 2021; Choi and Luo 2019). The fashion industry is characterized by a multitude of international suppliers collaborating in the creation of collections, and nowadays the development of complete traceability is certainly a relevant issue for all companies in the sector. The blockchain is characterized by the possibility of ensuring traceable information and represents a technology that in the future will be massively used by fashion companies, even if currently there are few cases of application of this technology in the fashion industry (Ahmed and MacCarthy 2021). The fashion sector, however, still presents little empirical evidence as many companies are still studying and evaluating blockchain technology and have not yet moved on to the next phase of implementing the technology. Further studies on the adoption of blockchain technology in the fashion industry are encouraged to evaluate the factors that may contribute to (or hinder) the implementation of the blockchain system in extended fashion supply chains (Caldarelli et al. 2021). At present, there are still few blockchain applications, so any new studies that delve into the feasibility of this tool are very useful in helping to understand the contexts in which the blockchain can achieve positive results for fashion companies and their supply chains (Chang et al. 2019; Queiroz and Wamba 2019). Bearing in mind these gaps, this paper aims to investigate the adoption of the blockchain to enhance traceability along supply chains. In particular, the drivers and barriers that favor or hinder the introduction of blockchain technology among supply chain actors will be investigated for the fashion industry.

Output: corroborate