Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Governance in Healthcare: A Systematic Literature Review

: The typical structure of the healthcare sector involves (specialist) intertwined practices co-occurring in formal or informal networks. These practices must answer to the concerns and needs of all related stakeholders. Multimorbidity and the need to share knowledge for scientific development are among the driving factors for collaboration in healthcare. To establish and keep up a permanent collaborative link, it takes effort and understanding of the network characteristics that must be governed. It is not hard to find practices of Network Governance (NG) in a variety of industries. Still, there is a lack of insight in this subject, including knowledge on how to establish and maintain an effective healthcare network. Consequently, this study's research question is: How is network governance organized in the healthcare sector? A systematic literature study was performed to select 80 NG articles. Based on these publications the characteristics of NG are made explicit. The findings demonstrate that combinations of governance style (relational versus contractual governance) and governance structure (lead versus shared governance) lead to different network dynamics. Furthermore, the results show that in order to comprehend how networks in the healthcare sector emerge and can be regulated, it is vital to understand the current network type. Additionally, it informs us of the governing factors.
Learning-Based Inverse Kinematics Identification of the Tendon-Driven Robotic Manipulator for Minimally Invasive Surgery

It is well-known that the tendon-driven robotic manipulator plays an important role in robotic-assisted minimally invasive surgery (MIS). However, due to the intrinsic nonlinearities, uncertainties, slack and hysteresis introduced by the tendon-driven actuation, the tendon-driven robotic manipulator is difficult to model and control when compared with the traditional actuation styles. To serve the modeling purpose, in this paper, the deep-learning-based intelligent modeling of inverse kinematics in the snake-like tendon-driven surgical instrument is presented. In the proposed approach the Deep Recurrent Neural Network (DRNN) with Long Short-Term Memory (LSTM) architecture is adopted to memorize and identify the nonlinear inverse kinematics of the tendon-driven surgical instrument through the history of the motor and tip positions. To collect highly reliable data to train the DRNN, the experiment to generate training data is carefully designed with the consideration of the stainless tendon characters and motor limitations. During the designed controller movements, the kinematics data is obtained by recording the motor positions and the tip positions. Besides, it is noticed that there are correlations of the sequential data samples, which could significantly reduce the modeling accuracy. To remove the correlations and improve the modeling performance, the correlations of the sequential data samples are removed by modifying the training processes. Modeling results and detailed discussions verified the effectiveness of the proposed approach.
Generic and Universal Local Cryptocurrency: LCoin

Blockchains are finding evermore applications. One underused application of blockchains is local currencies. Local currencies are currencies that circulate in a restricted area in purpose of growing the local economy by forcing local spending. We introduce the concept of geographical demurrage: money loses of its value the farther away it is spent. We construct four generic local cryptocurrencies: a simple one mimicking local paper money; a second that restricts spending to the dedicated geographical area; a third that utilizes geographical demurrage for maintaining the system, and a fourth that lifts the geographical restrictions and maintains geographical demurrage, thus creating a universal local cryptocurrency: a currency that loses value correspondingly to the distance between its point of reception and point of spending. So without the need to restrict spending to a given geographical zone, the currency will always encourage local spending, no matter where it is spent; yielding a universal local cryptocurrency we name LCoin.
Estimation of Beef Marbling Standard for Live Cattle Using Multi-input CNN with Ultrasound Images and Individual Cattle Information

There is great interest among livestock farmers in estimating the value of live beef cattle prior to slaughter. In this paper, we attempt to design a network structure to estimate the Beef Marbling Standard (BMS) from ultrasonic images of live beef cattle and individual information such as producer and father cattle. We demonstrated that our multi-input neural network with ultrasound images and individual information obtained a high accuracy in BMS estimation.
Potential Benefits of Fix-Based Ground Delay Programs to Address Weather Constraints

The objective of this analysis is to evaluate the performance of increasingly precise airport ground delay program (GDP) procedures under a range of weather forecasting capabilities. The GDP procedures include both current (airport-based) programs and future (airport arrival fix-based) programs. The weather forecasting capabilities assume various temporal and spatial forecast errors relative to a baseline forecast. A general modeling framework is developed for studying weather impacts on GDPs and is applied as a single-day case study of Chicago O’Hare International Airport (ORD). The framework includes a weather severity index used to aggregate weather impact. The analysis demonstrates the payoff for using more precise GDP flow control mechanisms than are used today to exploit improved weather forecasting capabilities expected in the future.
Comparative Analysis of Optimization Algorithms Applied to Monkeypox Classification

An optimizer is a function that modifies the attributes of a neural network by changing the epoch's weights and learning rates during training to ensure that the loss function is at a minimum. The loss function computes the error or loss between the actual and desired output and reflects how well the model predicts given a data point. The role of these optimizers is to help improve accuracy and training speed of the model. An alarming rate of monkeypox cases (more than 85,000) have been reported to the Center for Disease Control coming from geographical areas not previously known to have cases of monkeypox. Prompt diagnosis of monkeypox is extremely daunting as the clinical presentation is very similar to other viral diseases such as scarlet fever, smallpox, and roseola. The aims of this research study are (1) to investigate the diagnostic capability of convolutional neural networks in the evaluation of monkeypox skin lesions and (2) to ascertain the appropriate optimization algorithm which generated the optimum diagnostic performance. InceptionV3, Efficient NetB3, VGG 16 and DenseNet169 were applied to the monkeypox skin images dataset to appraise the presence of monkeypox. Several optimization algorithms were evaluated in terms of diagnostic capability to recognize monkeypox. These included Adam, AdaMax, Adagrad, AdaDelta, SGD and RMSprop. For all the models, Adam and AdaMax generated superior performance yielding the highest Matthews Correlation Coefficient (MCC). SGD had the lowest diagnostic performance for all the CNN models. Overall, the topmost performing CNN model with Adam optimizer was achieved by InceptionV3 with the highest performance metrics of 95% accuracy, 95% sensitivity, 96% specificity, 95% precision, 95% F1-score and MCC of 0.9100. The findings suggest that CNN models can be beneficial complementary support tools for improved surveillance and control of monkeypox especially in times of disease outbreaks where swift recognition is crucial to institute treatment measures and quarantine of patients. A working cooperation of monkeypox experts and machine learning modelers is imperative to attain the target objective curbing community spread of monkeypox.
DSE-VAE: an Interpretable Fault Data Generation Method for the Traction Motors

Due to the non-intuitive high-level semantics of fault samples, such as the fault type and operating condition, traditional unsupervised methods are not suitable for fault data augmentation in traction motors. To this end, a new method termed disentangled semantic embedding in variational autoencoder(DSE-VAE) is proposed to learn the interpretable representation of fault samples. In DSE-VAE, a regularization term is constructed by introducing attribute labels to bridge the latent space and semantics. In addition, the mutual information between different attributes is minimized to achieve attribute disentanglement. Extensive experiments are conducted on the hardware-in-the-loop (HIL)real-time platform. Experimental results suggest that the proposed DSE-VAE could learn an interpretable fault sample generation.
Towards Interoperability of Edge Datacentre in the Energy Community with IEC 61850 Modelling

Green modular datacentres are a new class of datacentres which can reduce the carbon footprints of the datacentre industry which accounts for close to 1% of total energy use worldwide. Green modular datacentres can operate as a prosumer and contribute energy to the energy grid through participation in the energy market as a part of the energy community. This paper investigates the interoperability of the datacentre in the energy community by proposing an IEC 61850 Logical Device model for a green modular datacentre. The applicability of the datacentre model is demonstrated through the re-implementation of the Energy Management System of an edge datacentre installation with the proposed IEC 61850 Logical Device model and its logical implementation in IEC 61499.
A New AC/DC Converter with Controllable Short-Circuit Current for DC Microgrid

In DC microgrid, the uncontrollable DC link short-circuit current seriously shortens the life of the electrolytic capacitors in conventional AC/DC converters. To solve the issue, a new AC/DC converter with controllable DC link short-circuit current is proposed. Simulation results have verified the performance of the proposed converter.
Digitization of Industrial Environments Through an Industry 4.0 Compliant Approach

About a decade after the introduction of Industry 4.0 (I4.0) as a paradigm oriented towards the digitization of industrial environments, centered on the concept of industrial Cyber-physical Systems (CPS) to enable the development of intelligent and distributed industrial systems, many companies around the world are still not immersed in this digital transformation era. This transition is not straightforward and requires the aligned with the novel technologies, architectures and standards to migrate entire traditional systems into I4.0 systems. In this context, this paper presents an approach to perform the digitization of non-I4.0 components/systems into I4.0 through an approach based on the Asset Administration Shell (AAS), which is a standardized digital representation of an asset. This approach enables to hold the asset information throughout its lifecycle, provides a standard communication interface with the asset, and is based on a set of modules that are combined with the AAS to provide novel functionalities for the asset, e.g., monitoring, diagnosis and optimization. Moreover, this approach adopts Multi-agent Systems (MAS) to provide mainly autonomy and collaborative capabilities to the system. The agents are able to get information from the AASs, making intelligent decisions and perform distributed tasks following interaction strategies, e.g., collaboration, negotiation and self-organization. The feasibility of the proposed approach was tested by digitizing a small-scale production system comprising several assets.
Borehole Fiber-Optic Cables for Monitoring Ocean Dynamics: Case Study from Australia

The interaction between ocean waves and the seafloor produces microseisms which propagate through the solid Earth and may be used to monitor distant storms, local wave climate, seafloor sediments, and bottom biomes. We show that onshore boreholes equipped with fiber-optic (FO) cables enable advanced analysis of microseisms, and thus provide a promising tool for oceanographic remote-sensing applications. Contrary to traditional surface seismometers, borehole FO cables allow distributed acoustic sensing (DAS), whereby seismic amplitude may be sampled continuously with depth, providing greater information and signal-processing advantages. DAS systems may easily be installed in boreholes, and given the sheer number of boreholes in near coastal areas around Australia, may provide an efficient, cost-effective, and real-time network for monitoring and studying ocean dynamics, coastal wave impacts, and climate change.
Customer-Centric Business Process Discovery Using Process Mining Techniques

The research is carried out in the area of customer journey analysis within the field of process mining. The aim of this research is to explore the applicability of process mining techniques in discovering and improving customer-centric business processes. Process mining is considered as a tool to extract nontrivial and useful information from process execution logs. These logs, stored in information systems, capture data about customer interactions with the service. The paper presents an approach that involves analysing the customer journey data in form of event log using process discovery, variants analysis, and performance mining techniques.
Intelligent Flame Detection of Heating Furnace Based on Improved YOLO v5*

The detection of the flame combustion status of a heating furnace is an important link in determining its working status and ensuring the safety of petrochemical production. To improve the efficiency of flame combustion detection and timely processing in chemical heating furnaces, this article made specific improvements to the YOLO v5 model from five different aspects: firstly, to address the problem of insufficient sample data, a data augmentation strategy was adopted; Secondly, MobileNetv3 lightweight network is introduced to replace the C3 module in the backbone network, to reduce the parameters and computation; Continuing with SIOU_Loss Loss function replaces the original CIOU_Loss, increased angle loss; Fourthly, changing the NMS in YOLO v5 to soft-NMS has improved detection performance in dense areas; Finally, the method of knowledge distillation is adopted to improve the accuracy of detection. This article uses actual flame images of a heating furnace to compare our algorithm with other detection algorithms. The experimental results show that compared to other detection algorithms, the average accuracy of the improved YOLOv5 reaches 99.38%. Compared with the most advanced YOLOv8 algorithm, the parameter quantity is reduced by 47.70%, and the computational cost is reduced by 45.00%. This confirms that the improved YOLOv5 algorithm can effectively achieve faster and more accurate flame detection.
Cooperative Trajectory Prediction of UAVs via Generative Adversarial Networks

This paper addresses the cooperative trajectory prediction problem of multiple UAVs in air combat. By using the historical trajectory information of each UAV and its neighbors, a novel GAN-CI algorithm is designed based on generate adversarial networks. The algorithm is used to generate multiple prediction trajectories through a generative adversarial network, and the input information is analyzed and processed by a cooperative information interaction module. It is shown that the algorithm improves the average prediction accuracy by 45% over the generative adversarial network without cooperative interaction information and 38% over the Long Short-Term Memory algorithm with pooling module.
Operation Output-Feedback Predictive Control Based on Model Order Reduction and Predictive Optimization in Industrial Processes

Modern industrial processes are essentially complex multivariable systems with high interaction among variables. Moreover, in modern process control, it is imperative not only to track a setting point for the controlled object but also the performance indicators for the entire operation process, where the optimal operational feedback control of multivariable industrial processes is derived. In this paper, we propose an operation output-feedback predictive control (OOFPC) method for multivariable industrial processes based on model order reduction and model predictive optimization. Firstly, causality networks are employed to extract potential correlations between variables to reduce the high-dimensional feature space. Subsequently, a setpoint compensator is developed within the framework of model predictive control (MPC), taking into account both performance indicators and operational constraints. Finally, closed-loop control with stable adjustment of the controlled object is achieved by utilizing proportional-integral-derivative (PID) controllers. Feedback correction and rolling optimization enable optimal setpoint tracking and stable closed-loop control. Experiments on a grinding process control case demonstrate the effectiveness of the proposed control strategy.
Bundle Detection based on Graph Convolutional Network Considering Categorical Hierarchical Relationship

Item bundling is a promising sales strategy for e-commerce services, and researchers have been actively investigating techniques to create bundles that can enhance user satisfaction. A fundamental approach to creating a bundle involves detecting a set of highly relevant items as a bundle that users interact with during a single session. Previous studies have addressed the bundle detection task using non-personalized data mining methods, focusing on the observation of co-occurring items, however, these methods cannot capture the relationships and influences among items due to the discrete processing of items. In this paper, we propose a novel bundle detection method using the Graph Convolutional Network (GCN) to capture the hierarchical relationships among item categories. By utilizing GCN, our method can detect bundles that exhibit infrequent yet significant semantic and potential connections. We validate the effectiveness of our approach through comprehensive quantitative and qualitative evaluations using multiple real-world datasets.
Real-Time Precision Prediction of 3-D Package Thermal Maps via Image-to-Image Translation

Thermal optimization plays a crucial role in the design of advanced packaging technologies. Due to the large number of thermal simulations needed for optimal design, reductions in simulation run-time are critical. Here, we cast the temperature solution process into an image-to-image translation problem. We model the power generation map, conductivity map, and boundary conditions into separate channels of an image. We then generate temperature solutions by training a conditional image generative model, composed of a U-Net shaped generator and a discriminator, using deep neural networks (NNs). The resultant NN model exhibits superior accuracy for unseen inputs. Speed wise, it enables near real-time design, providing a 1663x and 14,885x speedup over a sparse matrix optimized finite element method (FEM) and ABAQUS® respectively.
Attention-PVS for Domestic Hot Water Consumption Forecasting in Individual Household

Short-term hot water consumption forecasting in an individual household is important to realize energy-efficient hot water management while meeting users' comfort. However, consumption traits in an individual household contain much irregularity, and that gives additional challenges to accurate forecasting. This paper proposes an Attention-PVS model to tackle this issue, which focuses on consumption values with similar past consumption trends. In this study, verification experiments were conducted on a real-household hot water consumption dataset collected by Electricity of France. The model was evaluated with its consumption forecasting error with MAE, MSE, and RMSE metrics and computational costs. As for the computational cost, experiments were conducted on NVIDIA Jetson Nano (Jetson) to validate the applicability to embedded systems. The results revealed that the proposed model performs consumption forecasting with comparable accuracy to other state-of-the-art models. Additionally, while LSTM scored lower error than the proposed model, Attention-PVS performed training and inference on Jetson in shorter times than LSTM.
Decentralized Wireless (DeWi): Which Perspectives for Blockchain-Based Mobile Networks?

The convergence of telecommunications and blockchain have paved the way for a new generation of networks, called “Decentralized Wireless” (DeWi). These networks leverage blockchain technology and cryptoeconomics to deliver wireless coverage and connectivity through decentralized peer-to-peer networks, built and operated by individuals. In this paper, we present an overview of the DeWi sector, including its background, operation, enablers, and ecosystem participants. We also delve further into cellular DeWi, outlining the potential business opportunities challenges to address and future directions for this category of DeWi.
Constraint-Behavior Contracts: A Formalism for Specifying Physical Systems

Contract-based design (CBD) is a system development methodology that addresses the ever-increasing complexity and heterogeneity of cyber-physical system design problems. In CBD, systems and subsystems are represented by assume-guarantee contracts, formal specifications that make a distinction between supported environments and guarantees offered by a component. While the assume-guarantee formalism offers compact encoding of specifications, the port directions, i.e., the classifications of ports as input and output ports, implied by its semantics make it difficult to express properties of physical systems and subsystems where the relation between inputs and outputs is implicit. In this paper, we propose constraint-behavior contracts to specify physical components without the need to classify ports. The operations and relations between constraint-behavior contracts are defined to facilitate system reasoning without port directions. The capability of constraint-behavior contracts to integrate with assume-guarantee contracts gives the user the choice of a formalism to use at different abstraction layers. A case study based on an Unmanned Aerial Vehicle design problem shows that the proposed constraint-behavior contracts can facilitate system verification by expressing physical components, reducing the number of contracts, and providing an intuitive encoding of contracts.
Implantable Medical Devices for Wireless Optical Neuromodulation and Neural Recording: Energy-Efficient Integrated Circuit and System Design

Since the development of the first cardiac pacemaker implant in 1958 <xref ref-type="bibr" rid="ref1">[1]</xref>, implantable medical devices (IMDs) have assumed a critical role across a broad spectrum of clinical applications <xref ref-type="bibr" rid="ref2">[2]</xref>, <xref ref-type="bibr" rid="ref3">[3]</xref>, <xref ref-type="bibr" rid="ref4">[4]</xref>, <xref ref-type="bibr" rid="ref5">[5]</xref>. These innovative devices facilitate diagnosis, deliver therapeutic interventions, and promote regenerative processes. Examples abound, from the widely recognized deep brain stimulator to drug delivery systems, vital sign monitoring devices, and cochlear implants. In addition, the significance of IMDs extends into the realm of research, where they have revolutionized scientific approaches and methodologies, particularly in behavioral neuroscience. Cutting-edge IMDs designed for neural interfaces can support advanced neural stimulation and neural recording. Such neural interface IMDs have made significant contributions to deepening our fundamental understanding of brain functions and advancing the development of therapies for various brain injuries and disorders, such as dystonia, depression, and Parkinson’s disease <xref ref-type="bibr" rid="ref6">[6]</xref>, <xref ref-type="bibr" rid="ref7">[7]</xref>, <xref ref-type="bibr" rid="ref8">[8]</xref>.