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Federated learning for 5G-enabled infrastructure inspection with UAVs
Alexios Lekidis1
Electricity infrastructures include assets that require frequent maintenance, as they are exposed into heavy use, in order to produce energy that satisfies customer demands. Such maintenance is currently performed by specialized personnel that is scaffolding to spot damages or malfunctioning equipment. Scaffolding is time-consuming and incurs accident risks. To tackle this challenges, grid operators are gradually using Unmanned Aerial Vehicles (UAVs). UAV trajectories are observed by a centralized operation center engineers for identifying electrical assets. Moreover, asset identification can be further automated through the use of Artificial Intelligence (AI) models. However, centralized training of AI models with UAV images may cause inspection delays when the network is overloaded and requires Cloud environments with enough processing power for model training on the operation center. This imposes privacy concerns as sensitive data is stored and processed externally from the infrastructure facility. This article proposes a federated learning method for UAV-based inspection that leverages a Multi-access Edge Computing platform installed in edge nodes to train UAV data and improve the overall inspection autonomy. The method is applied for the inspection of the Public Power Corporation's Innovation Hub. Experiments are performed with the proposed method as well as with a centralized AI inspection method and demonstrate the federated learning benefits in reliability, AI model processing time and privacy conservation.
Electricity infrastructure inspection costs have increased over the later years, as they are labour-intensive, time consuming and require experienced linesman or operators to identify faults before they cause severe infrastructure damage. Moreover, they are often assigned to third-party personnel, which does not provide guarantees for the inspection quality. Recent work Jenssen and Roverso (2018), has focused on the reduction of the overall cost and automation of the inspection procedure through the use of Unmanned Aerial Vehicles (UAVs). UAVs can access hard-to-reach places faster, with very low cost and with less risk than manual inspections.
UAV inspection allows to check for cracks and corrosions, missing insulators and detect thermography as well as wiring problems around towers and power lines. Usually, the inspection is achieved by electricity operation center personnel and operators, that investigate the transmitted UAV footage and upon the detection of failures instruct operators to resolve the problems on-site. Identifying the electrical infrastructure, respective assets and faults though is time-consuming as it requires manual effort from the operators. To this end, Artificial Intelligence (AI) techniques are gradually employed to automate asset detection and hence reduce the inspection time (Lekidis et al. 2022). Coupled with 5G technologies they offer real-time latency that meets the requirements of UAV-based infrastructure inspection (3rd Generation Partnership Project 2019).
However, since the AI models are trained in the operation center with real-time video obtained from the UAVs, the identification of electrical assets depends to the use of the 5G Core Network (5GCN) (3rd Generation Partnership Project 2019) resources for data exchange and service orchestration. As such resources are deployed in a central operation center this does not offer networking scalability (Satyanarayanan 2017). Instead, if the models are deployed and executed in edge nodes, networking issues would be avoided as training would be distributed using local UAV data. This also avoids a single-point-of-failure for the automated UAV inspection in case a fault or a cyber-attack occurs in the operation center (Litchfield et al. 2016). Furthermore, as training of the AI models is performance and memory intensive, Cloud environments are usually employed. The use of such environments though, imposes privacy issues as the sensitive company data are leaving the infrastructure facility.
The recent emergence of Federated Learning (FL) (AbdulRahman et al. 2020) allows edge nodes to receive configurations and parameters from the operation center whilst performing AI network training locally. Specifically, the operation center Cloud environment first defines a global model with learning parameters. Each worker downloads the global model, computes the model update by using its local UAV data and then offloads the computed local update back to the operation center. Afterwards, the operation center combines all local model updates and constructs a new improved global model. Furthermore, FL ensures privacy as the data does not leave the electrical infrastructure facility (Li et al. 2020).
In this article we introduce an FL method for automating the UAV inspection. The method is based on the use of edge nodes, running an edge platform that is used for interacting and offloading the computation from the UAVs. Moreover, data and control commands in the method are exchanged through the use of 5G Network Function Virtualization (NFV) technologies, such as network slicing and Multi-access (or Mobile as termed earlier) Edge Computing (ETSI: GR MEC 017 2018). Additionally, since UAV inspection applications have real-time requirements, the formed 5G network slice belongs to the Ultra-Reliable Low-Latency Communication (URLLC) category as defined by the third generation partnership (3GPP) Release 15 (3rd Generation Partnership Project 2019). This category is identified by its reliability and low message latency requirements. Finally, the FL method is illustrated for the inspection of Public Power Corporation's (PPC) research center, called Innovation Hub. The experiments illustrate the method benefits in comparison with the centralized UAV-based inspection (Lekidis et al. 2022). In terms of concrete contributions the article builds on the following:
FL mechanisms for electricity infrastructure inspection by UAVs in urban areas.
MEC platform for electricity asset identification and fault detection using FL models.
Automated update of the FL models through an interaction between the MEC platform and PPC's operation center.
The rest of the article is organized as follows. Section Background provides an overview of the UAV-based inspection phases, the AI models that are used for the inspection as well as an introduction to FL. Section Methodology provides an overview of the UAV-based inspection approach using the FL method and the automation mechanisms for the interaction between the MEC platform of each edge node and PPC's operation center. In section Autonomous UAV inspection using federated learning the methodology is applied in PPC's Innovation Hub and experiments are conducted to demonstrate FL benefits in comparison with a centralized AI method for electrical infrastructure inspection. Finally, section Conclusion provides conclusions and perspectives for future work.
In this section we provide an overview of the proposed electricity infrastructure inspection method, an introduction to Long Short Term Memory (LSTM) networks (Yu et al. 2019) that are used for infrastructure asset and fault detection as well as a brief description of the FL approach.
UAV-based infrastructure inspection
The time-critical requirements of the UAV-based infrastructure are ensured through the use of Network Function Virtualization (NFV) technologies and specifically the establishment of a URLLC network slice between the UAVs, the edge nodes and a operation center facility. To form the URLLC slice the operation center also includes an NFV Management and Orchestration (MANO) (Mijumbi et al. 2016) Virtual Network Function (VNF) for lifecycle management and orchestration, 5GCN (3rd Generation Partnership Project 2019) and Radio Access Network (RAN) VNFs for data exchange as well as and User Plane Function (UPF) VNF for processing the user traffic. Additionally, the UAVs are also included in the network slice. In Fig. 1 illustrate the inspection method overview.
Specifically, the UAVs are controlled by edge nodes, which are also used for interpreting and mapping in real-time the UAV location and flight plans. In the chosen architecture each edge node is considered as a Commercial-of-the-Self hardware platform that is configured with a MEP in fixed ground locations (i.e. base stations). Moreover, through the FL method (Section Methodology) the edge nodes forward the model configurations and parameters from the local model to update the global model in the operation center. Forwarding is based on cellular 5G connectivity and proper routing mechanisms applied from one edge node to another.
UAV-based inspection of electricity infrastructures
The UAVs communicate with the edge nodes by using a dedicated antenna allowing cellular 5G connectivity (Lekidis et al. 2022). Edge nodes also include traffic routing functions to prevent potential collisions during take-off and landing. Furthermore, they aggregate data from multiple UAVs that are used for aerial inspections of electricity infrastructures. Edge nodes reduce the distance of the communication loop from the UAV to the operation center as well as allow faster data-based decision making. This is accomplished through the encapsulation of the UAV data into common Internet of Things (IoT) protocol formats, such as the Constrained Application Protocol (CoAP) (Lekidis and Katsaros 2018).
Long short term memory networks
Long Short Term Memory networks LSTMs), first introduced by Hochreiter and Schmidhuber (1997), are a special kind of Recurrent Neural Network (RNN) (Medsker and Jain 2001), capable of learning long-term dependencies. All recurrent neural networks have the form of a chain of repeating modules of neural network. In standard RNNs, this repeating module will have a very simple structure, such as a single tanh layer. Moreover, traditional RNN models generally experience a vanishing gradient problem which impedes learning of long data sequences. This is because when the gradient becomes smaller, the RNN parameter updates become intangible, which hinders the learning process.
LSTMs also have a chain-based structure, with the main difference lying on the repeating module. Instead of having a single neural network layer, they constitute of four layers interacting in a very special way. In Fig. 2 the pink circles represent pointwise operations, like vector addition, while the yellow boxes are learned neural network layers. Lines merging denote concatenation, while a line forking denote its content being copied and the copies going to different locations.
LSTM layers and primitive operators
An important part of LSTMs is the cell state, that is represented by the horizontal line running through the top of the diagram through \(h_{t-1}\) to \(h_t\) in Fig. 2. The cell state runs straight down the entire chain and allows information to flow along it unchanged. The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates. Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation. The sigmoid layer outputs numbers between zero and one, describing how much of each component should be let through. A value of zero allows to let nothing through, while a value of one allows to let everything through.
An LSTM has three gates, namely, forget gate, input gate, and output gate, to protect and control the cell state. These three gates solve the vanishing gradient problem of RNNs by collectively controlling which information in the cell state to forget, given new information entered the network, and which information to be mapped to the network output. Furthermore, based on their architecture and layers, LSTM AI models are very effective in capturing dynamic temporal correlations. Such correlations are present in electricity infrastructures allowing the asset identification and detection of faults with high accuracy.
FL is a recently introduced AI approach, which aims at training a model across multiple local datasets, contained in decentralized edge nodes or servers. Specifically, the edge nodes store data samples locally and do not perform exchange to central external serves. This also aids in addressing critical issues such as data privacy, security and access rights to heterogeneous sources. The FL approach counters the challenges that are faced in (a) traditional centralized learning techniques where all data are forwarded to a centralized server and (b) classical distributed AI techniques, which assume that the local data are identically distributed and have the same size. The general FL design involves training AI models on data samples locally and exchanging parameters (e.g., weights in a RNN) among those local models to generate a global model. FL algorithms may either (1) employ a centralized server that orchestrates the various steps of the algorithm and serves as a main synchronization reference, or (2) they may also be peer-to-peer, where no centralized server exists. Due to the presence of a synchronization reference, the former are usually more preferable in large-scale deployments for controlling the asynchronous data exchange through edge nodes. The FL process with a centralized server is divided into multiple rounds, each consisting of four steps:
Local training all local edge nodes compute training gradients or parameters and send locally trained model parameters to the central server.
Model aggregation the central server performs secure aggregation of the uploaded parameters from all the local edge nodes without learning any local information.
Parameter broadcasting the central server broadcasts the aggregated parameters to the every local edge node.
Model update all local edge nodes update their respective models with the received aggregated parameters and examine updated models' performance. After several local training and update exchanges between the central server and its associated local edge nodes, it is possible to achieve a global optimal learning model.
The main considerations that are restraining FL usage in many applications are the following points: (1) heterogeneity of distributed devices that may cause security implications, (2) biased training dataset considerations from the individual devices (Kairouz et al. 2021) and (3) coordination of many devices during training, which is highly expensive in terms of communication resources. However, to address point (1), employed devices include a Network-based Intrusion Detection System for early-stage detection of cyber-attacks (Lekidis et al. 2022) and for point (2) the formed 5G network slices for UAV-based inspections and the multiple inspection datasets ensure that data training is fair. Finally for point (3), the introduction of 5G NFV technologies aids in meeting the real-time requirements of critical applications, such as electricity infrastructure inspection and fault detection.
In this section we describe the techniques for automated UAV-based inspection with edge nodes relying on the FL method (section Background). Initially, we focus on describing the MEC platform running on the edge nodes and afterwards we illustrate the automated interaction for the UAV-based inspection using the FL method.
Mobile edge platform overview
UAVs have resource constraints at processing and storage level. Nevertheless, often the data that are gathered from the sensors require processing, before an actual verdict is reached that will lead to autonomous actuation actions. Additionally, storing the data locally at the device level may lead into overflow in memory or storage resources. A common solution to these issues that was followed till recently was the presence of a Cloud environment deployed in a virtualized or physical server of the operation center (Lekidis et al. 2022). However, industrial applications are characterized by real-time and critical operation that requires low latency, which cannot be provided when communicating with Cloud platforms. Hence, a gradual shift is currently observed towards edge platforms, in order to provide a computational and storage layer to this architecture.
The MEC initiative (ETSI: GR MEC 017 2018) allows to extend the network slices by edge resources and services, such as MEC platform, and applications, the UPF, the RAN or even Cloud-native compute, network or storage functions. MEC ensures network scalability by distributing the processing from the centralized architecture of the Cloud platform to the edge that is located closer to the user. This allows faster response to user requests, since computations, data aggregation and analytics are handled within user proximity. A scheme that is currently followed is the presence of a dedicated management entity on the edge for the resource lifecycle management, which includes instantiation, decommissioning and other functionalities. Such entity is called Mobile Edge Platform (MEP) and provides distributed processing and storage capabilities that reduce the network management complexity.
The MEP architecture is illustrated in Fig. 3 and it is deployed in each edge node. The architecture follows the standardized interfaces and components that are defined by the ETSI MEC Industry Specification Group (ISG) (ETSI: GR MEC 017 2018). Additionally, it also includes a Virtualization Infrastructure Manager (VIM) (ETSI: GR MEC 017 2018), which interacts with the NFV MANO to receive instructions for the configuration of the VNFs and virtual links in each edge node. It allows to extend the 5G network slice for providing latency and performance improvements in UAV-based inspection as well as collision avoidance capabilities in UAV missions.
Mobile edge platform architecture
Initially, a resource interface component is used for data exchange with the UAVs. The interface is based on an extension of the Linux Foundation Fledge framework,Footnote 1 which is also offered as VNF using the virtualization environment offered by Linux EVE.Footnote 2 This environment offers isolation for the execution of applications in the mobile edge. The containers are managed by a lightweight version of Kubernetes, namely K3S,Footnote 3 that is used both as a Mobile Edge Orchestrator (MEO) and as a VIM. Moreover, the entire processing and data exchange services are running on the MEP platform, which also contains the UPF. Additionally, the MEP platform also provides accurate geolocation and trajectory data for the UAVs, using constant communication with GPS satellites.
The communication with UAV-based protocols is facilitated through the Fledge resource interface that is included in each MEP (Fig. 3). Finally, every MEP is programmed to regulate the data exchange frequency, in order to maintain minimal edge resource utilization that leads to an extended battery lifetime for autonomous operation.
Federated learning incorporated in infrastructure inspection
The FL method provides a high-level of automation since each MEP is able to interact with the NFV MANO to provide autonomous operation for the system, as depicted in Fig. 4. The MANO that is employed the Open-Source NFV MANO (OSM)Footnote 4 for the orchestration of 5G network slices. Moreover, FL ensures privacy, since the data remain at the edge level and are not stored in cloud platforms. Moreover, a potential failure to the Cloud environment leads to a loss of data, processing and management capabilities and hence a degradation of UE services and applications. A cause of failure is a potential overload or even a targeted cyber-attack. With a decentralized architecture a failure in the Cloud environment of the operation center can avoid such degradation as UE's services and applications may be served by the nearest edge entity. Through the integration of 5G networks and MEC the FL models are updated and re-trained seamlessly with local data and footage from the infrastructure that is obtained from the UAVs.
Each MEP is also able to interact with the NFV MANO to provide autonomous operation for the system, as depicted in Fig. 4.
Automated interaction between Edge and NFV MANO through FL APIs
The interaction is enabled by the LSTM models that are using the FL method, in order to be trained and executed on the edge level. The reasoning behind the choice of FL lies in the presence of multiple edge Points-of-Presence (PoP) in different distributed locations, each one including a MEP platform. The MEP platforms use FL to train locally the LSTM models and receive the parameters and configurations from the Cloud environment where the NFV MANO is deployed, in order to perform infrastructure inspection closer to the UAV's using their local data. Furthermore, the FL method allows to improve the efficiency and provide a high-level of network automation for the UAV-based infrastructure inspection method. This is accomplished by performing data processing and caching in each edge PoP.
Moreover, the FL method provides automation in the formation and management of 5G network slices. In this case, the MEP receives configuration instructions from the NFV MANO for network slice instantiation or extension on the edge level. Then, supervised training techniques are used to translate high-level intents from NFV MANO into concrete instructions on how to deploy and instantiate FL LSTM models in each edge PoP. Overall, the procedure that is followed is divided into three individual steps.
Initially intent-based policies are specified, in order to receive the parameters and configurations based on which the LSTM models that will be deployed and executed in each edge PoP for infrastructure asset identification and fault detection using the UAV video data. The use of intents allows to hide complexity, technology- and vendor-specific details. Intents are described in natural language and are translated into configurations through Natural Language Processing algorithms (Chowdhary 2020). Specifically, these algorithms are trained to receive input in form of textual description of the desired service characteristics and then produce a domain specific encoding corresponding to the original intent. This process follows a step sequence: (1) the intents are pre-processed, (2) keywords are extracted and translated into meaningful actions and then (3) aggregated and validated for lack of conflicts.
As a second step, appropriate Application Programming Interfaces (APIs) on the MEP are used to receive the intents and the LSTM model configuration and parameters that are used for the inspection of the specific infrastructure (Fig. 4). To this end, the ETSI MEC ISG has provided an initial set of API'sFootnote 5 to facilitate this interaction. The APIs are registered and discovered over the Mp1 reference point defined in ETSI MEC architecture (ETSI: GR MEC 017 2018). Then, the associated LSTM models and VNFs are instantiated and connected using virtual links based on the intent translation of the previous step.the intents are translated into edge configurations for deploying and instantiating the LSTM models.
The third step concerns the training of the LSTM models. This is accomplished by each MEP using local data from the UAVs and deployed models based on the intents that originate from the NFV MANO. Then, the LSTM models are executed to identify electricity infrastructures and respective assets. During the inspection and if the envisaged inspection accuracy is not achieved, the FL update service synchronizes with the MEC FL API (Fig. 4) to re-calibrate the LSTM models with different parameters and configurations that will provide accuracy improvements.
Autonomous UAV inspection using federated learning
We have deployed and tested the presented method on a fleet of hexacopter UAVs, which were used to inspect the PPC's Innovation Hub in Kantza, Greece. The aerial image of the infrastructure is illustrated in Fig. 5.
Aerial view of PPC's Innovation Hub from the UAVs
The UAVs are of Foxtech - RHEA 160 typeFootnote 6 and are illustrated in the left part of Fig. 6, whereas the right part shows the process of programming them for conducting the electricity infrastructure inspection missions. Furthermore, the edge nodes used a configuration of a dual-core CPU with 2.0 GHz frequency, 4 GB RAM, 28 GB disk, an antenna and a SIM card slot for 5G connectivity.
UAV programmed for the inspection
The URLLC slice is formed using the OSM NFV MANO entity, which afterwards provides instructions to the K3s platform, allowing the configuration and instantiation of the edge VNFs. For the edge nodes we have configured the MEC platform, the UPF and application modules of the MEC platform as docker containers, managed as pods by K3s that is acting as a VIM (section Methodology). Additionally, in the K3s deployment we have also included an nginxFootnote 7 container to provide analytics and load balancing capabilities.
Two sets of experiments were performed with (1) a centralized inspection method where the LSTM models are deployed and executed in a Cloud environment of the operation center, using the approach presented in Lekidis et al. (2022), and (2) the FL method that is proposed in section Methodology. The main difference of the two methods is that in the FL method the models are deployed and executed at the edge nodes through the MEP platform. Moreover, in the centralized method training phase was performed in the Cloud environment and had an average time of 3 h and 2 min, whereas with the FL method it lasted in average 2 h and 23 min. The results depicted high accuracy for the detection of missing insulator stems for the two methods as illustrated in Fig. 7.
Detection of missing insulator stems using federated learning
Table 1 illustrates Key Performance Indicator (KPI) metrics for both the FL method and the centralized inspection method. The metrics include (1) average processing time for the discovery of assets and faults from the UAVs as a measure of performance, (2) reliability of the inspection method as a percentage for the trustworthiness of the inspection method in terms of the network slice and the involved resources and (3) fault discovery rate as a percentage of the faults discovered divided by the actual ones that actually happened.
Table 1 KPI metrics for UAV-based inspection with and without federated learning
As depicted from the table, FL provides several benefits in the UAV-based inspection method by (1) minimizing the processing time for the identification of infrastructure assets and the detection of faults to 3 min, (2) improving the reliability by 14.6% in comparison with the centralized AI inspection. Finally, the fault discovery rate depends on the data gathered by the UAVs and since the same data re used, the small difference originates from the faster detection time from execution of the models in the edge.
This article presents a UAV-based electricity infrastructure inspection method using FL-oriented AI models deployed in edge computing nodes. The models that are employed for infrastructure inspection and fault detection are based on LSTM networks. Moreover, they are executed through an newly introduced MEP platform that distributes the computation and storage closer to the UAVs. The MEP platform also interacts with the Cloud environment on PPC's operation center through dedicated APIs, ensuring a high level of automation. To this end, the NFV MANO of the Cloud platform provides high-level intents that are translated into concrete instructions on the model update, deployment and execution at the edge. The method is illustrated for the inspection of PPC's Innovation Hub, where experiments are conducted for both centralized inspection and the proposed FL method. The two methods are compared with KPIs focusing on processing time for the discovery of assets and faults from the UAVs, method reliability and fault discovery rate.
As a part of our future work, we plan to develop a dedicated micro-service for each MEP, in order to enable further automation in the network slice which will be extended to the UAV-based inspection as well. Such service will also address interoperability concerns, by providing a more efficient MEP response to resource/service discovery queries about each edge PoP service-layer and/or resource-layer status (available VNF services/resources, AI models as well as active and historical service bindings). Moreover, we will investigate novel network slice isolation policies (Schneider et al. 2018) to enable the optimal sharing of the 5G infrastructure resources based on the directions of the ETSI Zero Touch Network and Service Management (ZSM) Working Group (ETSI 2020).
The datasets generated and/or analysed during the current work are available upon request.
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This work has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101016941 (5G-INDUCE).
Public Power Corporation S.A., Athens, 10432, Greece
Alexios Lekidis
Correspondence to Alexios Lekidis.
Lekidis, A. Federated learning for 5G-enabled infrastructure inspection with UAVs. Energy Inform 5, 66 (2022). https://doi.org/10.1186/s42162-022-00254-z
Electricity infrastructure inspection
Multi-access edge computing
5G NFV
Network slicing


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Q: Is it okay to use itinerary operators in python without assignment? So, consider the case that I have to add values to an array based upon conditions.
I realized using ternary operators without assignment works as well.
Consider the case:
req_array = []
req_dict={'a':1,'b':2,'c':3}

I have to add values to req_array based upon condition. There can be 2 ways:
if req_dict.get("a"):
    req_array.append(req_dict['a'])
if req_dict.get("d"):
    req_array.append(req_dict['d'])
if req_dict.get("c"):
    req_array.append(req_dict['c'])

The other way of doing this is:
req_array.append(req_dict['a']) if req_dict.get('a') else None
req_array.append(req_dict['d']) if req_dict.get('d') else None
req_array.append(req_dict['c']) if req_dict.get('c') else None

Both of them works correctly. But, I was curious as to what is the recommended approach in these cases?

A: I'd view using the conditional operator for side-effects with roughly the same disgust as using list comprehensions for side-effects. These are expressions intended to produce something, and you're using them to do other things while ignoring the thing produced (always None in this case).
If you really need to one-line it, you can always do
if req_dict.get('a'): req_array.append(req_dict['a'])

which, while still not great style (two lines are more readable), is perfectly legal, less verbose (add :, remove  else None), and doesn't imply the production of an unused value even when the if fails. With 3.8, the walrus operator even saves you from performing lookup twice, while still allowing you to one-line it:
if val := req_dict.get('a'): req_array.append(val)

A: As you noted, both options work, so it's a matter of personal style, mostly.
Personally, I find such ternary expression harder to read - they start with stating something will be appended to a list, but as you read on you find out it may not due to some condition. I would definitely got with the explicit if approach.

A: There are many ways to achieve this depending on what exactly you are looking for.
Is condition always the same? Then you could define a list of keys and iterate over it.
keys = ['a', 'b', 'c']
req_array = []
req_dict={'a':1,'b':2,'c':3}
for key in keys:
    val = req_dict.get(key)
    if val:  # your condition here
        req_array.append(val)

Or one liner:
req_array += [req_dict[key] for key in keys if req_dict.get(key)]

There are many appropriate ways. Some are just a matter of preference.
If conditions are different for all of the keys, then i would go for explicit if as this is kind of more readable and the patter is vissually immediately recognizable.

redpajama_set_name: RedPajamaStackExchange


Our boondocking camp during this year's County Fair.
What's more Americana than the County Fair? I didn't really understand this when I lived in the very urban area of Los Angeles, CA. There, to me, the County Fair was the place to go shopping, eat fried food, and drink beer. And, don't get me wrong, those activities are still very much part of the County Fair experience.
But after relocating to a rural county where farming is a way of life, families raise animals, and a great majority of the kids are in Future Farmers of America (FFA) or 4H, I see the County Fair a bit differently now. In addition to the fried food and beer, I now spend a majority of my Fair time wandering through the animal barns and the various buildings displaying the contest entries. Flowers, vegetables, quilts, paintings, photos, poetry, baked goods (especially "ugly cake" open just to kids)… it's all there. I love looking for the blue ribbon entries and reading the tag to see if it belongs to one of my friends. Once I entered my cookies and won two blue ribbons!
I really love the animal barns. It takes a while because, as I said, there are a lot of kids in 4H and they spend all year raising an animal (or several) to bring to fair. Some are "show animals" - like most of the rabbits. Many are "meat animals" that are sold at the auction on Saturday afternoon. These kids put in a lot of time and effort all year long, learning to care for an animal, and then - for the auction animals - have to see them sold off at the end. There is an incredible mix of emotions - pride and sadness primarily - in getting a good price for your animal and knowing the animal you raised will become someone's dinner.
Oh, and the racetrack. My pulse has always quickened at the sound of race cars gearing up for a spin around the track. And our County Fair does not disappoint. Each night of the Fair, a different race style is highlighted. One night is mud bogs, another demolition derby, and the greatest of all - the boat races. Nope, I'm not saying the fairgrounds are on the lake and we watch boats race. No, siree! These worn-out old boats are towed by (barely) running vehicles, without trailers, on a wetted and soaped track. They slip and slide and slam into the walls and each other. It's ridiculously fun to watch. The victor is whoever wins the applause-o-meter when time is up.
"Hey," you might be saying, "I came here to read about camping." Yes, yes, I'm getting to that.
Unlike the County Fairs in southern California, where Fairs last for several weeks, our little County Fair is held on Labor Day weekend, Thursday through Sunday. The 4H families arrive on Wednesday, transporting the animals to the barns and setting them up for showing. About 50-60 families bring their tents, trailer, or motorhomes and set up to spend the weekend caring for the animals and enjoying the Fair.
I am an active member of our local Theatre Company. For years before I was involved, the Fair has hired the Theatre Company to provide various customer services through the weekend. One of those services is Camp Host to the 4H camp. Last year and this year, I jumped at the chance for that job. I mean, combine Fair with camping? What could be better?!?
I, too, arrive on Wednesday and my camp site is nearest the entry gate. With the help of other day-time volunteers, we monitor the gate to make sure anyone driving in is supposed to be driving in. When it's quiet enough, I like to ask what animal each kid has raised, how they did in the judging and auction, and whether they'll come back next year. I'll have a beer with the parents, get invited to "smoked brisket night," and marvel at how many people I know from "real life" that I discover are also 4H parents.
The 4H "campground" is usually just a big empty lot, so there are no hookups, and the spaces are chalked out on the ground before arrival. It's a boondocker's delight! Luckily, the fairgrounds are right in town, and it's a short drive from home, so driving with a full water tank isn't bad. I am also lucky that my site is along a fence line with some trees, so I get shade. We still haven't decided that we need to buy a generator, so a wonderful friend loaned me one so I could run the air conditioning. I only had to do that for two short sessions; the weekend was quite pleasant! I pack food for breakfasts (I get to use my stove-top toaster!) and snacks, but let's face it - I'm not about to cook dinner when I can eat FAIR FOOD!
Monday morning we have some breakfast and pack up for the short drive home. Thanks to the local yacht club (hey, I said the fairgrounds weren't on the lake, I didn't say the town wasn't) there is a free dump station in town so we visit that on the way home.
Well, one thing that could be better would be scheduling nap time each afternoon, after staying up to lock the gates at midnight and getting up to open them before the local kids start arriving to feed their animals at 5:00! I'm a bit sleep deprived on this lovely Labor Day. I'll have to plan better in the future. So, if you'll excuse me, I'm going to go take a nap.


Please upgrade Moment.js to version 2.6.0, which has many bug fixes and several new features. In particular, Moment.js was using an incorrect language code for Vietnamese (vn instead of vi); upgrading should fix the issue in which Hovercards were showing timestamps like "Sửa đổi lần cuối 9 days ago".
As a side note, it needs to be easier for MediaWiki's very thorough translators to find out that timestamps are being translated in an obscure third-party project on GitHub.
bzimport set Reference to bz63873.
Ugh. Yet another one? :< Please file a separate bug for that.
> third-party project on GitHub.
moment.js is part of core, so changing component.
Sadly there is no way to access CLDR in JS, so moment.js was merged to make up for that.
Changelog for 2.6.0 is at https://gist.github.com/ichernev/10544682 , none of it seems to be a breaking change.


This activity would be most effective if delivered in three separate 30-40 minute periods, with groups of no more than 12 students, and followed by a period of "free" or outdoor play.
Before beginning this activity, make sure the children have read Tar Beach and Aunt Harriet�s Underground Railroad by Faith Ringgold. The children can either read the books on their own or it can be a read-aloud activity.
copies of "Quilt Design Sheets" from the Ed Online/NTTI lesson plan, "Sew, You Want to Quilt?"
Gather the children in their usual meeting place. Ask three of them to stand together in a line. Face one forward, one backward and one forward. Next, ask another child to join the line and decide which way to face. Have the children take turns doing this. For younger children, you may need to give initial verbal clues, such as "front, back, front�" When this exercise is finished (approximately 3 minutes), ask, "What have we created?" The children will probably say "a design" or "a pattern."
Transition: If you think the children need to stretch before continuing, have them stand up and "shake" their limbs and then ask them to sit.
Ask children what other things have patterns. Younger children may need prompts, such as "What do you sleep on?" (sheets) or "What do you play checkers on?" (checkerboard of black and red squares).
4th and 5th graders should be able to think of patterns on their own. These can be jotted on the board or on chart paper. Encourage the children by asking questions such as "Can you think of another one?" or "Do you think we can get to 10 items?"
You can also bring out a "bag" of things with patterns � a small flag, a dinner napkin, a picture of a turtle shell, or bumblebee and then give verbal clues to their identity. These can be added to the list you started in step 3.
Explain to the children that in the next session they will create their own designs and patterns.
Transition: Before sending the children to the next part of their afternoon, it might be fun to have them create their own clapping games. If anyone knows the "hand jive," have him or her do it with the children. Younger children might enjoy doing a popular clapping game, like Ms. Mary Mack, first with words and then without. Have them listen for the pattern when they are clapping without words. Then, have the children create their own patterns!
Re-introduce Faith Ringgold�s books, explaining that today we are going to "read" them differently. Say to the children, "We are going to read them for patterns." If multiple copies of these books are available, children can meet in small groups of 3-4 to find and discuss the different patterns. (If only one copy is available, this can be a whole group activity.) Be sure to keep the discussion moving quickly, so that children do not become restless.
In your discussions with the children, note the many intricate patterns presented in Ringgold's work. Point out the borders in Tar Beach, the magnificent quilt on page 17 and the quilt on page 15 of Aunt Harriet's Underground Railroad.
Start a discussion about quilts: Do any children have them at home? Were they made by a family member or store bought? Discuss how quilts can be made from "memorable" scraps and how their designs can tell a story.
For all age groups, this activity would be most effective if children work in groups of four. Each group will work on the same design, with each child receiving his own worksheet. Circulate among the groups while the children color the design, asking them to focus on the geometric shapes they see in their design. Ask: How many triangles do you see? Do the triangles make another shape? What is the name of this second shape?
For younger children, you may want to leave the title of the design -- i.e., Windmill -- on the sheet. For 4th and 5th graders, you may want to omit this information and have students identify the shape themselves.
Each group can appoint a spokesperson to share their design with whole group.
Next, show other samples of quilt designs and ask groups what they think they are representing.
Now it is time for children to design and tile their own quilt squares. Give each child a ruler, pencil and crayons/colored pencils.
The quilt squares will be completed in two steps: design and completion. (Children will start their pencil designs in this session and then finish them in the next session.) As much flexibility as possible should be exercised. Children should be able to work at their own comfortable pace.
Transition: Have children share their completed designs with whole group. 4th and 5th graders may want to ask their classmates what they think their designs represent before disclosing their titles. Children can then mount their designs (with your help) on large foam board or burlap for completion of a group quilt.
To develop this activity even further, these squares can be used as a quilt border, with the center of the quilt incorporating a literacy/writing activity. For example, children can reproduce in pictures and words excerpts of Faith Ringgold�s books. Memory quilts, beginning with, "I remember when�" are also popular and lead to great discussions.
Depending on your agency�s technological capability, all sites noted in the classroom lesson plan can educate both staff and children. For example, if you plan on introducing author Faith Ringgold for the first time, staff will benefit from learning more about her work through the Web sites.
Children can experience hands-on musical patterning by visiting the CYBERCHASE Web site at http://www.pbskids.org/cyberchase/games/patterns/patterns.html.
This AFTERSCHOOL EXCHANGE activity was developed by Cathy Smith, Deputy Director of the Riverdale Neighborhood House, based on the Ed Online / NTTI Lesson Plan "Sew, You Want to Quilt?"


What do "brave" (adj) and "courage" (n) really mean?
For me, if someone is brave, it has always meant that they face certain dangers or situations, even though doing so is something that scares the living daylights out of them. For instance, if a normally timid person, one who would normally run away from danger, were to risk his life to save a child being attacked by a dog, I would consider that person (usually considered a wimp) to be very brave.
Which I think is silly, because a lot of people I would consider brave, did their brave acts despite having fear.
the quality of mind or spirit that enables a person to face difficulty, danger, pain, etc., without fear; bravery.
Which means that to be brave, one cannot have fear. In direct opposition to M-W, which says (in a roundabout way) that withstanding fear is brave.
So, this has lead me to be confused as to whether or not I use the word "brave" in the same sense as others. What do the words "brave", and "courage" really mean?
Courage is reckoned the greatest of all virtues; because, unless a man has that virtue, he has no security for preserving any other.
Samuel Johnson, quote provided by Vincent McNabb in the comments.
C.S. Lewis, sourced on Wikiquote.
To my mind, at the testing point implies a test. There's no test if you don't feel fear. I like the M-W definition you give. Note, though, that the Cambridge dictionary says that bravery is defined by not showing fear. It says nothing about not experiencing fear.
Off the top of my head, I'll say that both a brave person and a courageous person may experience fear. The brave person won't show the fear; the courageous person might. The brave are, then, a subset of the courageous. Take that with a grain of salt: I just made it up, but it sounds roughly right to me.
"Courage is resistance to fear, mastery of fear - not absence of fear. Except a creature be part coward it is not a compliment to say it is brave."
I interpret that to mean that doing something when you have no fear doesn't make you brave. Depending on the situation it may make you foolish or foolhardy, but not brave. Your initial definition works for me in that sense.
What does 'Brace yourself' really mean?
What does "tool" really mean?
Is magnanimity the right word for courage, insight and willingness to overlook or forgive?
What does "and how" really mean?
What does bimonthly really mean?
What can "impolitic" really mean?
What does 'coincide' really mean?
What does "professional grade" really mean?
If craven means "contemptibly lacking in courage," what do "craven desires" and "craven idols" mean?


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About Kim Levine
Kim's passion for storytelling has spanned two decades and multiple platforms including publishing (The Spectator) Television (ABC, CBS, NBC, National Geographic and Comedy Central) and the digital realm (Mercedes, Ferrari and Gap.) Wunderlust has defined her global journey with stops in London, NY, LA, Paris, Rome and now Mallorca, with her equally global son Santo.
Behind the Curtain: Demystifying Video Production (Myth #2)
By Kim Levine|2016-10-13T16:37:50-04:00August 17th, 2016|
In this series of posts, we will shatter some of the misconceptions around video production and hopefully inspire you to reach out for help to create content around your own authentic message. MYTH #2: THE MAGIC FORMULA (Hint, there isn't one) [su_youtube_advanced url="https://www.youtube.com/watch?v=i9eBGUYK2lQ" controls="alt" showinfo="no" rel="no" modestbranding="yes"] [Go here for MYTH #1: BUDGET] Many people [...]
By Kim Levine|2016-10-13T16:37:50-04:00August 4th, 2016|
[su_youtube_advanced url="https://www.youtube.com/watch?v=JvWZrVM46b8" controls="alt" showinfo="no" rel="no" modestbranding="yes"] We all know visual storytelling and video content is one of the most powerful marketing tools to spread your message on the internet. (If you don't believe us, just ask Mark Zuckerberg.) But how do you go about actually creating that content? Where do you start? In this series [...]

redpajama_set_name: RedPajamaCommonCrawl


Thursday 4 June 2009 | Posted by Kishi | Dips,Guess what?
Sick and throat has been bad, yet I eat everything! Poor doctors…hehehe!
Skippy – the old classic American one.
Fab India -organic peanut butter I recommend only if you are in that health mode.
Store bought peanut butter is high on salt and contains added saturated fats, which are not hale and hearty. Here's a truly fast and unproblematic recipe to make peanut butter at home.
Take roasted, unsalted peanuts. For first timers- go for ones that are already roasted and shelled. Shortly, when you are more practised, you can shell the peanuts at home and roast them in the oven. Perfect during winter times when we get lovely peanuts and cheap too! Leave the red skin on if you like the taste. Now put the peanuts in the food processor. Add the oil and the salt. After starting, check after a minute or so. If you like yours chunky like me don't grind it for too long. But if you prefer a smooth silky paste then grind it longer till it's downy. If the butter seems too thick, add some more oil to it.
There- your homemade peanut butter is all set to eat. You can stock up to eat afterwards as well.
Enjoy your treat and let me know how it turned out!


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Q: Unable to cast object of type Castle.Proxies.INavigationServiceProxy to Prism.Common.IPageaware I have been using a mock of INavigationService in order to conduct some unit tests and I get an error when the application tries to verify the current URI path. The line of code that verifies this is the following:
if (_navigationService.GetNavigationUriPath().Contains("Register"))
                RegisterFlag = true;

And here is the mock of the INavigationService:
Mock<INavigationService> _navServ;
...
        [SetUp]
        public void Setup()
        {
            Properties.Api = new MockService();
            _navServ = new Mock<INavigationService>();
        }

When i debug any of my tests i get the error "Unable to cast object of type Castle.Proxies.INavigationServiceProxy to Prism.Common.IPageaware" on the first line of code i present.
I would like to understand why this happens and what I can do to fix this issue.

A: If you look at the code of GetNavigationUriPath (which you should really have done, as the stack trace of the exceptions points to it), you see that you have to setup the Page-property of your navigation-service mock (see the docs of moq):
_navServ = new Mock<INavigationService>();
_navServ.As<IPageAware>().Setup( x => x.Page ).Returns( /* your current page */ );


The best entrepreneurs balance brilliant business ideas with a rigorous commitment to serving their customers' needs. If you read nothing else on entrepreneurship and startups, read these 10 articles by experts in the field. We've combed through hundreds of articles in the Harvard Business Review archive and selected the most important ones to help you build your company for enduring success. Leading experts and practitioners such as Clayton Christensen, Marc Andreessen, and Reid Hoffman provide the insights and advice that will inspire you to: Understand what makes entrepreneurial leaders tick; Know what matters in a great business plan; Adopt lean startup practices such as business model experimentation; Be prepared for the race for scale in Silicon Valley; Better understand the world of venture capital--and know what you'll get along with VC funding; Take an alternative approach to entrepreneurship: buy an existing business and run it as CEO. This collection of articles includes "Hiring an Entrepreneurial Leader," by Timothy Butler; "How to Write a Great Business Plan," by William A. Sahlman; "Why the Lean Start-Up Changes Everything," by Steve Blank; "The President of SRI Ventures on Bringing Siri to Life," by Norman Winarsky; "In Search of the Next Big Thing," an interview with Marc Andreessen by Adi Ignatius; "Six Myths About Venture Capitalists," by Diane Mulcahy; "Chobani's Founder on Growing a Start-Up Without Outside Investors," by Hamdi Ulukaya; "Network Effects Aren't Enough," by Andrei Hagiu and Simon Rothman; "Blitzscaling," an interview with Reid Hoffman by Tim Sullivan; "Buying Your Way into Entrepreneurship," by Richard S. Ruback and Royce Yudkoff; and "The Founder's Dilemma," by Noam Wasserman.


Mr. Baier is a Forensic Document Examiner Certified by SAFE (CFDE). He has AS, BS, and MA degrees. As a certified New York State law enforcement instructor Bob has trained law enforcement in New York and other states in document examination, identity theft, and bogus checks. Mr. Baier has been a primary instructor for the Jamaican Constabulary Force, Document Examination Department.
Mr. Baier has been honored to appear on live radio nationally and on the television shows "Inside Edition", "Law Talk TV" and the Discovery/Smithsonian channel which aired in the US, Canada, Europe and Asia. He has been featured in his alumni magazines for both high school and college and was featured in Orange Magazine of NY in 2009. Mr. Baier has spoken at National and International Conferences in, Portland, OR, Washington, DC, Boca Raton, Fl, Montego Bay, Jamaica, two times in Maryland and in Dallas TX.
Mr. Baier has rendered an opinion on more than 1,450 cases in 26 states and three other countries. He has testified in court in high profile cases valued at $100,000,000, $125,000,000 and was flown to Switzerland to testify in a case valued at $238,000,000.
Increase your confidence for a Jury Trial, from the second you enter the courtroom. What you should and should not do.
Learn the biggest mistake you can make in examining sets of initials.
Learn what you must do according to Albert Osborn in every examination that most document examiners do not do. Doing it was crucial in my solving this case.
What are the two most important requests you can make regarding an opposing document examiner in a case?
What are the ways you can assist your client's attorney regarding the opposing document examiner?
What can you do when many of your exemplars are thrown out at trial and your exhibits are already prepared, on-site and based on those exemplars?


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Gustave-Léonard Van der Mensbrugghe (Gent, 13 februari 1835 - Melle, 20 oktober 1911) was een Belgische natuurkundige.

Biografie
Van der Mensbrugghe volgde middelbaar onderwijs aan het Atheneum van Gent, waar hij in 1852 de wiskundeprijs won. Vervolgens ging hij studeren aan de Universiteit Gent, waar hij in 1859 met onderscheiding promoveerde tot doctor in de natuurkundige en wiskundige wetenschappen. In datzelfde jaar werd hij voorlopig repetitor aan de School voor Burgerlijke Bouwkunde (nu Faculteit Ingenieurswetenschappen en Architectuur), waar hij experimentele natuurkunde, wiskundige natuurkunde en industriële natuurkunde doceerde. Op 30 september 1861 werd hij definitief benoemd.

Vanaf 1856 was Van der Mensbrugghe de assistent van Joseph Plateau, in 1871 trouwde hij met dienst dochter Alice. Tijdens lezingen stond Van der Mensbrugghe de blinde Joseph Plateau bij door de demonstraties uit te voeren. Dit zorgde voor een unieke samenwerking. Van der Mensbrugghe stond bekend als een begenadigd lesgever en een scherp observator.

In 1872 mocht hij de cursus wiskundige natuurkunde doceren aan de Faculteit Wetenschappen. Vier jaar later kreeg hij als buitengewoon hoogleraar ook nog de cursussen algemene wiskundige natuurkunde, grondige wiskundige natuurkunde en astronomische bewegingsleer toegewezen. In 1880 werd hij gewoon hoogleraar. Van 1883 tot 1890 gaf hij ook de lessen fysische aardrijkskunde en kosmografie aan de Vlaamse normaalscholen. Deze laatste cursussen mocht hij vanaf 1890 ook in het Nederlands geven aan de Faculteit Letteren en Wijsbegeerte. Tussen 1887 en 1888 was Van der Mensbrugghe secretaris van de Academische Raad, van 1900 tot 1903 was hij rector van de universiteit. In 1905 ging hij met emeritaat.

Wetenschappelijke erfenis
Van der Mensbrugghe heeft vooral wetenschappelijk werk verricht rond deze onderwerpen: experimenteel onderzoek van de oppervlaktespanning bij vloeistoffen in evenwicht, de toepassing van de principes van de thermodynamica op fenomenen waarbij de oppervlaktespanning optreedt en diverse kritische observaties en syntheses.

Zie ook
 Joseph Plateau

Externe links
  UGentMemorie-pagina van Gustave Van Der Mensbrugghe (Universiteit van Gent)

Belgisch natuurkundige
Belgisch uitvinder
Hoogleraar aan de Universiteit Gent
Rector van de Universiteit Gent
Belgisch persoon in de 19e eeuw
Mensbrugghe, G

redpajama_set_name: RedPajamaWikipedia


Welcome to MTA Solicitors, unfortunately, we are experiencing system issues at the current time. We hope to resolve these as soon as possible, so please attempt to contact us later. We apologise for the inconvenience caused.
We offer a broad range of legal services to individuals and businesses.
MTA is a multi-practice law firm with the ability to represent Commercial and Private Clients from anywhere in England and Wales. We are a fully regulated firm of solicitors, governed by the Solicitors Regulation Authority.


On Tuesday January 18th, my wife Lisa and I had the opportunity to attend the VIP Silent Auction "Drop-Off" party for the Taste of the NFL, a charity event hosted by Heritage Auction Galleries' Heather Walther - Director of Business Development, and Celebrity Chef Kent Rathbun, of Abacus, Jasper's, Rathbun's Blue Plate Kitchen, and Zea Woodfire Grill (Chef Rathbun also upset Bobby Flay to win Food TV's Iron Chef, in February of 2008).
The "Drop-Off" party was held at Chef Rathbun's Swanky Uptown Digs, the Aqua Lounge at The Flats on Park Ln., as a precursor to the Heritage Galleries-hosted auction that will be held on February 6th at the $500 to $600 per-plate Taste of the NFL dinner, at the Ft. Worth Convention Center. The event raises money for food banks across the country and the dinner is prepared by top chefs, from each city in which the NFL has a team based. The evening culminates in a concert by country music star Martina McBride, and this year the charity hopes to raise a record $1,000,000; a lofty, but reachable goal to be sure.
Several of Heritage's Board members were in attendance including Paul Minshull and Todd Imhof, as well as Dallas artists Andy Morris who kindly donated a stunning painting and Kay Miller Wyne who created the wonderful collage piece "Cheers to Twenty Years," to be used as the cover art for the event. Also on hand from Heritage were Noah Fleisher, Director of Public Relations, and his lovely wife Lauren, Debbie Rexing, Vice President of Marketing, and Cosignment Directors Diedre Buchmoyer and Mark Jordan.
Kay Miller Wyne (left) with "Cheers to Twenty Years"
Cocktail appetizers included flaked smoked salmon on crostinis with lemon mouse and capers, mini brisket tacos in multi colored shells, savory parmesan toast rounds, and chicken pesto on butter crackers. Sweet offerings included the best version of a Reese's Peanut Butter Cup that I've ever had; a dark chocolate cup filled with peanut butter mouse and topped with something akin to mini cocoa puffs, and Key lime tartlets. Some nice wines and a good strong drink made from Buffalo Trace bourbon, ginger ale, and grapefruit juice were the libations for the evening.
A good time was had by all and we saw a number of classy items dropped off for the auction, including a couple of paintings, few antiques, and sundry objets d'art. If the pre-party is any indication of the event to come, I expect some great food to be had, with nothing but glowing reviews, and more than $1,000,000 in the food bank kitty!
I'd like to thank potographer Tessa MacLeod for allowing me the use of all of these great images. Please look her up if you need a roaming photographer for any event. Her website is http://www.tessamacleodphotography.net/.


Welcome to the website of the Michigan North American Versatile Hunting Dog Association. Please look around and get to know us! We hope to see you at one of our events.
Reminder to all club members: Chapter dues need to be renewed by March 1, or they will be increased by $5.00.


Plastic welding is the most common application of ultrasonic assembly. To perform ultrasonic plastic welding, a vibrating tip is brought into contact with one of the work pieces as shown in Figure 1–1. Pressure is applied and ultrasonic energy travels through the material, increasing the kinetic energy (or heat) at the contact point of the two parts. The heat melts a molded ridge of plastic on one of the pieces and the molten material flows between the two surfaces. When the vibration stops, the material solidifies forming a permanent bond.
A housing which contains the transducer which converts electrical energy into mechanical vibrations.
A horn to transfer the mechanical vibrations from the transducer to the parts to be welded. A basic ultrasonic probe is shown in Figure 1–2. As indicated, the horn is secured to the transducer with a threaded stud. The transducer housing also has a connector for attaching the high voltage coaxial cable which delivers the high–frequency electrical signal for exciting the transducer. This signal is supplied by a separate ultrasonic generator.
The transducer supplies the ultrasonic vibrations by means of piezoelectric converters which transform electrical energy into mechanical movement. Power applied to the transducer at 20kHz can range from less than 50 Watts up to 3000 Watts.
The transducer is made from a number of polycrystalline ceramic elements separated by thin metal plates, clamped together under high pressure. When an alternating voltage (dV/dt) is applied to the converters (or ceramics), a corresponding electric field (dE/dt) is produced which results in a variation in thickness (dL/dt) of the ceramic elements. This variation in thickness induces a pressure wave (dP/dt). Because the molecules or atoms of a solid are elastically bound to one another, the pressure wave results in a wave propagating through the material which is reflected by the ends the metal mass of the converter.
See Figure 1–8 for a graphical representation of this. When the length of the assembly is tuned to its frequency of excitation, it resonates and becomes a source of standing waves. A typical transducer without its housing is shown in Figure 1–3. The output amplitude from a 20kHz transducer is only about 20 microns (0.0008 inches), so this amplitude needs to be amplified by the horn (and possibly a booster) to do useful work.
It transfers the ultrasonic mechanical vibrational energy (originating at the transducer) to the plastic parts through direct physical contact, and localizes the energy in the area where the melt is to occur.
It amplifies the vibrational amplitude to provide the desired tip amplitude for the thermoplastic and weld process requirements.
It applies the pressure necessary to form the weld once the joint surfaces are melted.
The ultrasonic horn is precision machined and designed to vibrate at either 15kHz, 20kHz, 30kHz, 40kHz, 50kHz or 70kHz. Figure 1–4 shows five aluminum alloy horns ranging from 15kHz to 50kHz. The higher the frequency, the shorter the acoustic wavelength, and consequently the smaller the horn. Notice that the 30Khz horn is only half the length of the 15kHz horn. The tuning of an ultrasonic horn is accomplished using electronic frequency measurement. Inherent variations in material composition prevent tuning by dimensional machining alone. Horns are usually manufactured from high–strength aluminum alloys or titanium. Both metals have excellent acoustical properties to transmit the ultrasonic energy with very little attenuation.
There are many different ultrasonic horn shapes and styles depending upon the process requirements. Factors which influence the horn design are the materials to be welded and the method of assembly. The gain of the ultrasonic horn is determined by its profile. Figure 1–5 shows four different gain profiles. The input vibration amplitude to a horn from a 20kHz transducer is only about 20 microns. This is not enough to generate enough friction to achieve a melt temperature for most thermoplastics. Therefore the horn must amplify the mechanical vibration so that the amplitude is sufficient to melt the thermoplastic. The amplitude at the tip of the horn typically ranges from 30 to 125 microns (1.2 to 5.0 thousandths of an inch) at 20kHz.
An optional threaded tip can also be used when the application calls for staking, a swagging profile or a pointed spot weld. In Figure 1–1, one of the plastic parts had a small ridge used to initiate the melt process. Here in Figure 1–6, the tip provides the energy director since there is only one piece to be melted in a staking operation. Replaceable tips are not commonly used in high–volume production environments. For long–term or high–wear production, a horn with a custom machined tip coated with chrome, carbide or titanium nitride will provide excellent wear resistance.
As the frequency increases, vibration amplitude typically decreases. Higher frequencies are used for seaming of thin materials and delicate parts that do not require a lot of amplitude. Since the horns become smaller at higher frequencies, closer spacing can also be achieved. Some factors to consider for high–frequency (e.g. 40kHz) ultrasonic welding versus lower–frequency (e.g. 20kHz) ultrasonic welding are listed here.
Wear on the horn is greater at high frequencies.
Clean and flat mating surfaces between the horn, booster and transducer are more critical as the frequency increases. At 40kHz, surface flatness specifications are between 0.0005″ and 0.001″ (13 to 25 microns).
The primary function of a booster is to alter the gain (i.e. output amplitude) of the probe. A booster is amplifying if its gain is greater than one and reducing if its gain is less than one. Gains at 20kHz typically range from less than one–half to about three. A booster designed to be mounted in a fixture between the transducer and horn is shown in Figure 1–7. This is commonly referred to as a probe stack. Since the horn cannot be clamped, only the transducer and booster can be secured. Therefore a secondary function (and sometimes sole purpose) of a booster is to provide an additional mounting location without altering the gain when the probe stack is secured in a press. The neutral (1:1) or coupling booster is added between the transducer and horn and mounted in the press by its mounting ring which is placed at the nodal point (where the standing wave has minimal amplitude). See Figure 1–8 for a graphical representation. Note that the maximum stress occurs at the nodal points.


text:
Biden Announces that Beaten O'Rourke Will Be His Gun Control Czar
March 3, 2020 staff
AnDallas, TX–At a rally in Dallas on Monday night, Beto O'Rourke officially endorsed Joe Biden for president. In the last two days, Pete Buttigig and Amy Klobuchar also endorsed Biden, so that's not groundbreaking news.
But what Biden said about O'Rourke has folks on the right taking notice.
Check out this video:
Joe Biden promises Beto O'Rourke, who pushed for gun confiscation for legal gun owners, will "be the one who leads" his gun control effort pic.twitter.com/qjgft8wFkt
— Steve Guest (@SteveGuest) March 3, 2020
Here's Biden's statement, in case that was hard to hear:
"I want to make something clear — I'm gonna guarantee you, this is not the last you're seeing of this guy — you're gonna take care of the gun problem with me, you're gonna be the one who leads this effort. I'm counting on you, I'm counting on you, we need you badly."
Folks, this is all a dog and pony show. We all remember the insanely–almost laughably–outlandish things O'Rourke was saying during his own campaign.
Why wasn't he more careful, more veiled in his goals to smash our gun rights? Because he was playing a role. See, Bet's campaign moved the entire conversation on gun control to the left. FAR left. O'Rourke torpedoed his own presidential campaign by promising gun confiscation, door to door police raids, and more. Why would he do that?
Because he succeeded in making the other anti-gun candidates look more moderate. After all, Joe Biden is at least proposing a mandatory buyback, instead of just taking your guns like O'Rourke.
See how that worked? O'Rourke moved the conversation to the left to give the other candidates political cover…and now he's getting his reward. What does he get? He is apparently going to be Joe Biden's gun control czar! That's another political step up for a twice-loser who doesn't hold any office at all.
It's a Power Game, And American Voters Aren't Invited
That's how the left plays the game. Biden can't appeal to most of the radical elements of the left, so they're stacking his campaign full of those lunatics to broaden his appeal. They've got the pro-LGTBQ element with Pete Buttigig's endorsement. They add in the radical anti-gun crowd's votes with O'Rourke–and the promise that O'Rourke will be in charge of gun control policy! They're all already radically pro-abortion, so no extras needed there.
And that's how it goes. These people aren't in opposition to each other. They're all working together to destroy America. To take away our rights–and if they can't take them from us, they're making sure our Grandkids don't have them. They want to tear everything our American forefather's built down, and put up a leftist hellhole in its place.
We, for one, aren't going to sit back and let that happen.
betoButtigigczarDallasjoe bidenO'Rourke
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Next Post:Michael Bloomberg Drops Out Of Presidential Race After Crushing Defeat On Super Tuesday


text:
LeaguesEnglish Premier LeagueFEATURED COLUMNLATEST ARTICLESNewcastle United
Newcastle United Are Ready To Move In For This Gifted Playmaker: One For The Future?
In a recent report, Globo Esporte claimed that Newcastle United are ready to move in for Flamengo playmaker Matheus Franca. It has been mentioned that the Magpies are eyeing a move for the Brazilian talent as they look to bolster their attack in the long run.
Franca's Impressive Rise In Brazilian Football
Franca came through the youth ranks of Flamengo before establishing himself as an important member of their senior team. The 18-year-old has found the back of the net six times in 25 senior appearances for the Brazilian club so far in various competitions.
The gifted attacker has seen most of his game time come off the bench but has caught the eye with his performances on the attacking third of the field as he is averaging 1.4 shots, 0.5 key passes and 0.9 dribbles per game in the Brasileirao. He has even been tidy when distributing the ball in the opponent's half after making 77.4% of his attempted passes in the Brazilian top-flight (stats via whoscored).
His current contract at Flamengo is set to run out in the summer of 2027. Therefore, the Magpies will have to step up their efforts if they want to sign Franca next year.
St. James' Park
Newcastle United Ready To Move In For Franca: Is He A Prospect For The Future?
Franca is a technically gifted footballer who can strike the ball with power and accuracy from long range. He has got the vision to create some decent chances for his teammates in the final third and can make a few good runs with the ball toward the opponent's penalty box.
The Brazilian sensation is primarily an attacking midfielder but can also fill in as a winger on either flank if his manager tells him to do so. However, he is quite inexperienced at the highest level and might struggle to adapt to the physical nature and high intensity of Premier League football.
At 18, Franca has already established himself as one of the most important members of Flamengo's senior team. Hence, the Magpies will do well to lure him to St. James' Park in 2023.
Franca is already good enough to feature in Eddie Howe's match-day squad and could go on to prove himself to be a prolific attacker in the coming seasons. However, he is still quite raw and would need time to settle into life at the Tyneside club if Howe can find a way to secure his services.
Matheus Franca
Previous articleArsenal Are Among Clubs Hoping To Land This Talented Midfielder: Right Fit For Arteta?
Next articleManchester City Are Not Willing To Loan This Talented Striker Out: Good Move By Pep?


It's a good thing that we don't always get what we deserve, isn't it? And it's a good thing that God's carrying out his plan doesn't depend on our doing what we're supposed to do.
Here's a true statement that should bring us joy: God is faithful . . . even when we aren't.
In the first part of Genesis 12 God called Abraham and promised to use him to make a huge difference in the world, with the culminating promise that through him all the families of the earth would be blessed. After receiving this promise Abraham's very first action was to leave the land God had promised him and travel down to Egypt where he promptly brought plagues on the people there. Instead of bringing blessings, he brought plagues.
An inauspicious beginning to Abraham's walk with God, no doubt.
And yet at the end of the story that we'll study tomorrow, God has protected and blessed Abraham, in spite of the patriarch's poor choices.
One of the takeaways for us is that this is the kind of God we serve: a God who blesses us in spite of ourselves. He's faithful, even when we aren't.
The Bible is a book filled with stories of faithlessness on the part of God's people, yet God accomplishes his purposes anyway. Ultimately, God faithfully offers himself to bless the world. The sacrifice of Christ is the most beautiful expression of faithfulness that we've ever seen.
God is always faithful to his promises. He's a God who accomplishes his purposes . . . even when we (like Abraham) don't act as we should.


text:
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3139 properties returned. Page 432 of 628
Property #: 5325 St Joseph County, MI Hunting Lease
St Joseph County, MI
Max Hunters: 6
This hunting lease is leased and not available. This is a large farm that has everything including crops, creeks, swamps and hardwoods. Don't let the photo fool you as there are over 80 acres of cover and timber on this property. Also, the property to the north has been managed and under QDMA for quite awhile now. If you are looking for a larger property in SW Michigan then this might just be th...
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Property #: 5351 Fulton County, IN Hunting Lease
Fulton County, IN
This hunting lease is leased and not available. The secluded field on the far south side of this farm is intriguing. This lease includes the northwest quadrant of a large tract of woods. The treeline running north and south has a nice stream running through it as well. This property has it all: cover, food, water and a travel corridor. You don't have to hunt huge tracts of land to be successful...
Property #: 5364 Wright County, MN Hunting Lease
Wright County, MN
This hunting lease is leased and not available. This 85-acre lease in Wright County Minnesota is an �Archery Only� lease. This gem is located less than an hour from the Twin Cities. The landowner wants to hunt ducks a time or two on this lease and will work with hunters. In past years some nice deer have come out of this area. The owner stated he has seen excellent turkey numbers in the past few...
Property #: 5370 De Kalb County, IL Hunting Lease
De Kalb County, IL
This hunting lease is leased and not available. *****Archery Lease Only*****
Here is a very nice farm being offered for lease, located in DeKalb County, IL. The Kishwaukee River runs directly through the center. This is truly urban hunting at its best! Farm is made up of timber, CRP, row crops, bedding and water. The CRP is in a tree program and the trees are around 15 to 20 feet tall. Waist...
Property #: 5386 Jasper County, IN Hunting Lease
Jasper County, IN
This hunting lease is leased and not available. This one is pretty straight forward. A 5 acre section of woods sits in the far southeast corner of this property. From the aerial, I didn't expect much, but on my walk around this property I saw a decent amount of sign. The south property line is a bushy fence row and from the time I stepped out of the truck, I was on tracks. Deer are clearly using...
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text:
Honey West (1965-1966)
Starred Anne Francis as female private detective Honey West and John Ericson as her partner, Sam Bolt. The series was based on a series of noels created by Gloria and Forrest E.Fickling under the pseudonym "G.G. Fickling" in the late 1950s. Honey West was one of the first female private eyes to ever appear on television.was intended to be the American equivalent of the popular characters Emma Peel and Cathy Gale in the British series The Avengers. Anne Francis first played West in the second season episode of Burke's Law, entitled "Who Killed the Jackpot?", broadcast on April 21, 1965, this led to this series being commissioned as a spin-off.
Anne Francis (1930-2011) as Honey West
John Ericson (1926- ) as Sam Bolt
Irene Hervey (1909-1998) as Aunt Meg


text:
package extraction

import (
	"errors"
	"net/http"
	"testing"
)

func testDiscriminatorHTTPHeader(t testing.TB) {
	var scenarios = []struct {
		input  map[string]string
		output Processor
		err    error
	}{
		{
			output: nil,
			err:    errors.New("received illegal and nil header"),
		},
		{
			input:  map[string]string{"Content-Type": "application/json", "X-Prometheus-API-Version": "0.0.0"},
			output: nil,
			err:    errors.New("unrecognized API version 0.0.0"),
		},
		{
			input:  map[string]string{"Content-Type": "application/json", "X-Prometheus-API-Version": "0.0.1"},
			output: Processor001,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `application/json; schema="prometheus/telemetry"; version=0.0.0`},
			output: nil,
			err:    errors.New("unrecognized API version 0.0.0"),
		},
		{
			input:  map[string]string{"Content-Type": `application/json; schema="prometheus/telemetry"; version=0.0.1`},
			output: Processor001,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `application/json; schema="prometheus/telemetry"; version=0.0.2`},
			output: Processor002,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `application/vnd.google.protobuf; proto="io.prometheus.client.MetricFamily"; encoding="delimited"`},
			output: MetricFamilyProcessor,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `application/vnd.google.protobuf; proto="illegal"; encoding="delimited"`},
			output: nil,
			err:    errors.New("unrecognized protocol message illegal"),
		},
		{
			input:  map[string]string{"Content-Type": `application/vnd.google.protobuf; proto="io.prometheus.client.MetricFamily"; encoding="illegal"`},
			output: nil,
			err:    errors.New("unsupported encoding illegal"),
		},
		{
			input:  map[string]string{"Content-Type": `text/plain; version=0.0.4`},
			output: Processor004,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `text/plain`},
			output: Processor004,
			err:    nil,
		},
		{
			input:  map[string]string{"Content-Type": `text/plain; version=0.0.3`},
			output: nil,
			err:    errors.New("unrecognized API version 0.0.3"),
		},
	}

	for i, scenario := range scenarios {
		var header http.Header

		if len(scenario.input) > 0 {
			header = http.Header{}
		}

		for key, value := range scenario.input {
			header.Add(key, value)
		}

		actual, err := ProcessorForRequestHeader(header)

		if scenario.err != err {
			if scenario.err != nil && err != nil {
				if scenario.err.Error() != err.Error() {
					t.Errorf("%d. expected %s, got %s", i, scenario.err, err)
				}
			} else if scenario.err != nil || err != nil {
				t.Errorf("%d. expected %s, got %s", i, scenario.err, err)
			}
		}

		if scenario.output != actual {
			t.Errorf("%d. expected %s, got %s", i, scenario.output, actual)
		}
	}
}

func TestDiscriminatorHTTPHeader(t *testing.T) {
	testDiscriminatorHTTPHeader(t)
}

func BenchmarkDiscriminatorHTTPHeader(b *testing.B) {
	for i := 0; i < b.N; i++ {
		testDiscriminatorHTTPHeader(b)
	}
}

redpajama_set_name: RedPajamaGithub


text:
«Мастер перевоплощения» — кинокомедия 2002 года от компании Happy Madison. Считается одной из худших комедий всех времён, имеет рейтинг в 1 % на Rotten Tomatoes.

Сюжет
Смешной, тощий парень в очках по имени Фисташио, однажды узнает, что может превращаться в разные личности. Теперь он и его Очаровательная ассистентка должны спасти родителей Фисташио и заодно мир от богатого и алчного коллекционера древностей.

В ролях

В эпизодических ролях-камео в фильме появлялись Майкл Джонсон, Джессика Симпсон, Бо Дерек, Джесси Вентура, Патриция Де Леон, Берглинд Айси.

Ссылки

Фильмы на английском языке
Фильмы США 2002 года
Кинокомедии США
Кинокомедии 2002 года
Фильмы Happy Madison
Фильмы Columbia Pictures
Фильмы, спродюсированные Адамом Сэндлером


Bulk metallic glasses (BMGs) maybe be considered to share some of the same inherent trade-offs as engineering ceramics. While BMGs typically exhibit high yield strengths, and while some have surprising fracture toughness, they exhibiting little to no tensile ductility, and fail in a brittle manner under uniaxial loading. Speaking broadly, there are two complimentary approaches to improving on these shortcomings: 1) create bulk metallic glass matrix composites (BMGMCs) and 2) improve the properties of a monolithic BMG. The structure of this thesis mirrors this division, with chapters 2-7 focusing on creating and processing amorphous metal matrix composites, and chapter 8 focusing on modifying the properties of a monolithic BGM by altering its configurational state through irradiation.


text:
NBA Playoff Preview: Rockets vs. Clippers
Martin Soaries
(Rockets vs. Clippers has the potential to be just as exciting as the Clippers' first-round series).
We've got a fascinating Western Conference semifinal match up in Rockets-Clippers that features two teams that can prove a lot to themselves and the basketball world by advancing to the Conference Finals. The 3-seed Clippers have never made it that far in the postseason. Neither has their point guard Chris Paul. The 2-seed Rockets are on their own journey of self-worth behind James Harden and a revitalized Dwight Howard. These are two offensive-minded teams with firepower, young stars, and a desire for high pace. Here are some other notes for this series:
– In the four regular season meetings between the Rockets and Clippers (split 2-2) Dwight Howard did not play in one game.
– Chris Paul's shooting percentage against the Rockets (37 percent) was his worst against any other conference opponent. James Harden's shooting percentage against the Clippers (36 percent) was his worst against any other conference opponent.
– Chris Paul comes into the series with a hamstring injury suffered in Game 7 against the Spurs.
– Every time the Rockets have won a best-of-7 series in five games or less, they've reached the NBA Finals (beat Mavericks in 5 in the first round).
Keys for the Rockets:
(Martin)
– Attack the Clipper bigs: Front court depth is still a glaring weakness for the Clippers. Houston has to attack Blake and DeAndre with their forward rotation of Terrence Jones and Josh Smith, if not to simply try and wear them out. Those guys have to be active on both ends.
– Take care of the ball: This doesn't just mean not committing too many live ball turnovers, but also taking good shots, because the Clippers can treat bad shots and bad misses as a turnover in their transition offense.
– Use your length/athleticism: The Clippers were just faster and more athletic than their first-round opponent. The Rockets can match them in that regard, but they have a particular advantage with the length of their wings and forwards. They'll need to emphasize that defensively to slow the Clippers down and make them uncomfortable.
(BJ)
– Get DeAndre Jordan into foul trouble: Just as Martin highlighted, it's extremely important for the Rockets' bigs to be engaged and active in this series. Los Angeles' frontcourt depth is relatively thin, especially at the center position where Spencer Hawes backs up DeAndre Jordan. In Houston's first round series against Dallas, you couldn't tell that Dwight Howard was hampered by a knee injury for majority of the season. He played hard, anchored the paint defensively and looked spry, which does not bode well for the Clippers. Howard should look to continue his interior dominance this series and get Jordan into foul trouble, as the Clippers have no one else that can handle Howard.
– Win the battle of the benches: Both of these teams have sporadic second-units, but Houston's bench mob was very effective and consistent against the woeful defense of the Mavericks in the first round. At their peak, the Clippers can resemble a juggernaut defensively, so it's important for the Rockets to get that same production, or close to it from Josh Smith, Corey Brewer and Pablo Prigioni.
– Attack Chris Paul: Chris Paul is nursing a tender hamstring, so it's very important the Rockets put pressure on him and make him defend. Defending Jason Terry isn't what one would consider the most daunting task, but Paul must be attentive on the defense end or "The Jet" can get loose from three-point land. Terry should look to be very aggressive against CP3.
Keys for the Clippers:
– Make Harden work: This goes for both sides of the ball. You have to double Harden when you get the opportunity and make others beat you, not by giving them open shots, but by forcing them to make plays.
– Be organized: The Clippers have the most efficient offense in the league. When they're in the flow of their offense it's hard to stop their rhythm. If this series comes down to execution it should be to the Clippers' advantage.
Contain in transition: The Rockets are a heady transition team. Corey Brewer is always looking to cherry-pick. They're sneaky in the open floor and unlike their first-round opponent, the Clippers have a team that can run with them.
– Get the ball out of Harden's hands: As Martin eluded to, the Rockets struggle to produce offense if the ball isn't in Harden's palms. If Josh Smith, Corey Brewer and Jason Terry are the one's that beat you, you live with that, but not with "The Beard" stirring up the pot and cooking the Clippers perimeter defenders.
– Three-point defense: The Rockets are a historically great three-point shooting team, so the Clippers are going to have to do their best to keep them from behind the arc. Closing out hard shouldn't be an issue, especially with seven-foot defensive behemoth DeAndre Jordan in the paint lurking.
– Blake Griffin's mid-range game: When Blake Griffin makes his mid-range jumper, the Clippers become very hard to beat. However, Griffin tends to go through dry-spells from mid-range, and the Clippers can't afford a drought with an injured Chris Paul trekking up and down the floor.
X-factors:
Martin's Rockets: Corey Brewer, Jason Terry, Terrence Jones
Clippers: J.J. Redick, Matt Barnes, Jamal Crawford
BJ's: Rockets: Jason Terry, Terrence Jones, Josh Smith
Clippers: Matt Barnes, Austin Rivers, Jamal Crawford.
Both: Whoever wins the "Hack-A-____" battle.
Series determined by:
Martin – Chris Paul's health, three-point shooting, individual defense on James Harden, and the front court battle.
BJ – Chris Paul's hamstring, Clippers' legs after a quick turnaround from their seven-game war with the Spurs, and bench productivity.
Predictions:
Martin – Rockets in 7.
BJ – Rockets in 6.
Previous articleDanny Green Thinks Spurs Won't Be The Same
Next articleBeno Udrih on Knicks' interest in Marc Gasol: 'They're not going to get him'
Martin is the Founder, Chief Editor, and Head Skills Development Trainer for Basketball Society. He has work experience in digital media and marketing, radio, and journalism. Currently, he does freelance work as a videographer and content creator. He has been featured as a writer on sites such as Def Pen, TV Film News, All Hip-Hop, and more. Martin played high school basketball at South Brunswick High School (NJ) where he graduated in 2007. He is a 1,000-point scorer at SBHS and an All-Middlesex County performer as a 3-year varsity starter. He helped lead SBHS to their first-ever Central Jersey Group 4 sectional state championship in 2007. Martin played college basketball at Eastern University, where he graduated (BA, Communications) in 2012. Martin was a four-year starter and a 1,000-point scorer at EU. Follow Martin on Twitter @Marsoaries and on Instagram @martin_soaries


148 * Build a FILEPATHS_W structure for a given file operation.
185 * Map a file callback parameters from W to A and call the A callback.
270 * Retrieve the source file information for a given file.
351 * Retrieve the destination dir for a given section.
374 * Extract a file from a .cab file.
1012 * Recursively create all directories in the path.


text:
Amalfi: Sarah Brightman Love Songs es un álbum de la cantante soprano Sarah Brightman estrenado el 8 de julio de 2009; actualmente está solamente disponible en Japón. Esta producción fue patrocinada por Fuji Television y es la banda sonora de la película que lleva el nombre Amalfi: Megami No 50-Byou. El álbum es disco de oro en Japón por vender 100 000 copias.

Lista de temas

El disco es una recopilación de canciones de los álbumes anteriores de Brightman.

Time to Say Goodbye (solo version) - Classics
La Wally - Classics
Il Mio Cuore Va - Eden, Diva
O Mio Babbino Caro - Classics
Anytime, Anywhere - Eden
Attesa - Symphony
Lascia Ch'io Pianga - Eden, Classics
Nella Fantasia - Eden, Diva
La Luna - La Luna
Solo Con Te - La Luna
Ave Maria (Schubert) - Classics
Until The End Of Time - Harem
Storia D'Amore - Symphony
Jesu, Joy Of Man's Desiring - A Winter Symphony
La Califfa - La Luna
Serenade - La Luna
How Fair This Place - La Luna
Amazing Grace - A Winter Symphony
Nessun Dorma - Eden

La película Amalfi es una producción especial de Fuji Television por su 50º aniversario. Esta es la primera película japonesa que ha sido grabada enteramente en una localización de Italia.

Referencias

Sarah Brightman Official Website http://www.sarah-brightman.com/

Álbumes de 2009
Álbumes de Sarah Brightman