Miracle-dz/UniFAR-FTUs huggingface dataset:
Beam Avoidance for Human Safety in Radiative Wireless Power Transfer

Prior studies show that exposure to radio-frequency electromagnetic field (RF-EMF) waves can increase the chance of developing health-related problems. Being exposed to even a small amount of RF-EMF wave emitted from a wireless power transfer (WPT) system for a long time may raise the risk of long-term health effects. Due to this potential human safety issue that may arise from the WPT system, in the future, people may be hesitant to use the RF-based WPT system to charge their electrical devices. To overcome such an issue, we provide an analytical study of a WPT system comprising a single power beacon and multiple target receivers with human bodies in close proximity. We employ a phased antenna array as the transmit and receive antennas to increase the energy transfer efficiency, and model the WPT system as a quadratically constrained quadratic optimization program (QCQP). In this work, we propose two adaptive beam-steering algorithms that maximize the received power on the target receivers while limiting the energy beam to the area where human bodies are detected. In the first proposed algorithm, we apply the semidefinite relaxation (SDR) method to approximate the QCQP solution by relaxing the rank-one constraint. The second proposed algorithm is an optimized adaptive beam-steering method developed by constructing the corresponding dual form of the QCQP problem and solving it through the hierarchical-iterative approach, in which we combine the eigenvalue decomposition and the projected subgradient method to obtain the optimal antenna array weight. Based on the analytical results, both proposed algorithms successfully generate the optimal antenna array weight that steers the main beam toward the target receivers while maintaining the RF-EMF radiation exposure toward the human body below the safety limit. Extensive simulation results are provided for verifying the validness of the proposed algorithms and comparing the performance of these two algorithms. Through testbed implementation, we have shown that the experimental results exhibit good agreement with the simulation results and confirmed the validity of the the proposed algorithm.
Investigating Frontal Neurovascular Coupling in Response to Workplace Design-Related Stress

This research seeks to examine the impact of workstation types on the coupling of neural and vascular activities of the prefrontal cortex (PFC). The design of the workstations was found to impair the performance, physical and mental health of employees. However, the mechanism underlying cognitive activity involved during workstation design-related stress effects in the PFC has not been fully understood. We used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) to simultaneously measure electrical activity and hemoglobin concentration changes in the PFC. The multimodal signal was collected from 23 healthy adult volunteers who completed the Montreal imaging stress task in ergonomic and non-ergonomic workstations. A supervised machine learning method based on temporally embedded canonical correlation analysis (tCCA) was utilized to obtain the association between neural activity and local changes in hemoglobin concentrations to enhance localization and accuracy. The results showed deactivation in alpha power rhythms and oxygenated hemoglobin, as well as declined activation pattern of the fused data in the right PFC at the non-ergonomic workstation. Additionally, all participants at the non-ergonomic workstation experienced a substantial rise in salivary alpha-amylase activity in comparison with the ergonomic workstation, indicating the existence of high-stress levels. The proposed tCCA approach obtains excellent results in discriminating workstation types achieving accuracies of 98.8% and a significant improvement of 8.0% ( $p < 0.0001$ ) and 9.4% ( $p < 0.0001$ ) over EEG-only and fNIRS-only, respectively. Our study suggests the use of functional neuroimaging in designing the workplace as it provides critical information on the causes of workplace-related stress.
Computational and Experimental Study of Time-Averaged Characteristics of Positive and Negative DC Corona Discharges in Point-Plane Gaps in Atmospheric Air

The use of stationary solvers instead of approximate solution methods or time-dependent solvers, which are standard tools in gas discharge modeling, allows one to develop a very fast and robust numerical model for studying the time-averaged characteristics of dc corona discharges. Such an approach is applied to dc corona discharges in point-plane gaps in ambient air. A wide range of currents of both voltage polarities and various gap lengths are investigated, and the simulation results are validated by comparing the computed current–voltage characteristics and spatial distributions of the radiation intensity with experimental results. Specific features of the numerical and experimental results at both polarities are discussed.
Neue ternäre Germanide des Lithiums mit Lanthanoiden im Fe2P‐Typ: LiErGe, LiHoGe, LiNdGe und LiSmGe

In den Systemen LiMGe (M Er, Ho, Nd, Sm) wurden die Verbindungen LiErGe, LiHoGe, LiNdGe und LiSmGe dargestellt und rontgenorgraphisch untersucht. Sie kristallisieren hexagonal (Raumgruppe P62m) im Fe2P-Typ. Einkristalluntersuchungen wurden fur LiErGe durchgefuhrt. Die Strukturen der Verbindungen werden vergleichend diskutiert. Ternary Germanides of Lithium and Rare-Earth-Metals with Fe2P-Type Structure: LiErGe, LiHoGe, LiNdGe and LiSmGe In the systems LiMGe (M Er, Ho, Nd, Sm) the compounds LiErGe, LiHoGe, LiNdGe and LiSmGe were prepared. They crystallize hexagonally (space group P-62m), and a C22-(Fe2P-type) lattice was found. The crystal structure of LiErGe was determined by X-ray single crystal investigation. Structural features of the compounds are discussed in comparison.
Numerical Determination of Vacuum Electronic Device Stability

We present a new approach to the study of the stability of vacuum electronic devices (VEDs) using the Naval Research Laboratory (NRL) large-signal code TESLA-Z. The approach combines a precomputed complex impedance matrix, <inline-formula> <tex-math notation="LaTeX">$\hat {Z}$ </tex-math></inline-formula>, describing the structure with a TESLA-Z computed admittance matrix, <inline-formula> <tex-math notation="LaTeX">$\hat {Y}_{ \boldsymbol {bt}}$ </tex-math></inline-formula>, characterizing the electron beam and beam tunnel. The gain matrix <inline-formula> <tex-math notation="LaTeX">$\hat {G}$ </tex-math></inline-formula> for a given device then can be found as the product of the reduced <inline-formula> <tex-math notation="LaTeX">$Z$ </tex-math></inline-formula>-matrix of the structure and admittance matrix <inline-formula> <tex-math notation="LaTeX">$\hat {Y}_{ \boldsymbol {bt}}$ </tex-math></inline-formula> of the beam tunnel. Subsequent analysis of the eigenvalues of the gain matrix <inline-formula> <tex-math notation="LaTeX">$\hat {G}$ </tex-math></inline-formula> using Nyquist’s method determines the stability of the device. We discuss the details of the new algorithm and illustrate its application to an experimental G-band serpentine TWT.
Colored Resource-Oriented Petri Net Based Ladder Diagrams for PLC Implementation in Reconfigurable Manufacturing Systems

A typical example of discrete event systems (DESs) is a reconfigurable manufacturing system (RMS) whose can be changed and become more complicated during its operation. Thus, an effective tool needs to be developed to create both high- and low-level DES implementation. The ladder diagram (LD) is a common programming method for controlling DESs. Petri nets (PNs) represent the most important graphical and mathematical tool that can be used to provide an integrated solution for the design, modeling, analysis, control, and implementation of DESs. Various types of PN-based LDs are developed for DESs, which have high structural complexity of the LDs. Therefore, an approach that can facilitate the elimination of the LDs’ structural complexity needs to be developed. The main objective of this paper is to develop a two-step approach for LDs implementation for RMSs with dynamic changes. First, a colored resource-oriented Petri net (CROPN)-based algorithm is proposed for a rapid and valid configurations of an RMS. Second, a ladder diagram colored resource-oriented Petri net (LDCROPN) is developed to convert the CROPN into an LD. The developed approach is evaluated using examples in the literature. Moreover, the results demonstrate the effectiveness of the developed approach for LD implementation under RMS specification changes.
State-of-the-art and Future Directions for Using Augmented Reality Head Mounted Displays for First Aid Live Training

Mixed reality and serious games are often advocated as promising technologies in supporting training and increasing the skills necessary to deal with new, complex, and often unexpected situations. This paper explores the feasibility of an Augmented Reality-(AR) based cyber-physical solution to complement live training for the treatment of serious wound injuries after accidents. Based on an experimental study set-up and examining the technology and the involved stakeholders’ experiences, this paper explores current possibilities and limitations for using a Microsoft HoloLens Head Mounted Display (HMD) for first aid trauma training.
MDC-Kace: A Model for Predicting Lysine Acetylation Sites Based on Modular Densely Connected Convolutional Networks

Lysine acetylation (Kace) is a conservative protein posttranslational modification (PTM) closely related to various metabolic diseases. Therefore, Kace sites identification is of great significance for investigating metabolic disease treatments. Existing studies have shown that protein structural properties contain highly useful structural information, which provides a strong basis for identifying PTMs. During the feature learning process, features at different levels are complementary, and taking them into consideration can effectively improve the quality of the features. However, existing deep-learning methods used protein sequence-level information as input without considering the protein structural properties. Furthermore, only high-level features were focused on, resulting in considerable information loss and weakening the prediction results. Therefore, we propose a novel deep-learning model based on modular densely connected convolutional networks (MDC) for Kace sites prediction, called MDC-Kace. MDC-Kace introduces the protein structural properties and combines them with the original protein sequence and the amino acid physicochemical properties to construct the site’s feature space. Then, modular densely connected convolutional networks are used to capture the information of features at different levels and reduce information loss and crosstalk during the feature learning step. We add a squeeze-excitation layer to evaluate the importance of different features and improve the network abstraction ability to identify potential Kace sites. The experimental results of ten-fold cross-validation and independent testing in human, Mus musculus and Escherichia coli datasets showed that our MDC-Kace model outperforms the existing Kace site predictors and can predict potential Kace sites effectively. MDC-Kace can be available at https://github.com/lianglianggg/MDC-Kace.
EchoBERT: A Transformer-Based Approach for Behavior Detection in Echograms

Monitoring fish welfare has become increasingly important for salmon farmers. Current approaches require manual labor and physical inspection or interpretation of video. Echo sounders make real-time monitoring of the entire fish population over time possible. However, current approaches for automatic interpretation of echograms mainly focus on species classification and therefore fail to appropriately encode the spatiotemporal properties contained within the data. Other approaches are primarily aimed at the feeding process and require a human-in-the-loop. Transformer-based approaches have been shown to better handle long sequences than Long Short-Term Memory networks in recent Natural Language Processing research. We therefore introduce EchoBERT - Echo Bidirectional Encoder Representation Transformer, a transformer-based approach for behavior detection in farmed Atlantic salmon (Salmo salar, Salmonidae), using the spatiotemporal properties contained in echograms. The model interprets the spatiotemporal dynamics of echograms through attention mechanisms to classify fish behavior. We compare EchoBERT to a traditional sequence modeling approach on the task of detecting behavior indicative of pancreas disease in a six-fold cross-validation study using data from 6 distinct farming cages. We show that EchoBERT shows a strong correlation between model predictions and true labels, indicated by a Matthew’s Correlation Coefficient score of 0.694 ± 0.178 using an ensemble approach, compared to 0.626 ± 0.084 for traditional sequence models. We also find that EchoBERT is capable of detecting disease indicators over a month prior to detection using standard procedures. Our results show that EchoBERT has high potential for automatic behavior detection through unintrusive methods suitable for applications in aquaculture. The source code is available at: https://gitlab.com/hakonma/echobert.
A Review on Transverse Flux Permanent Magnet Machines for Wind Power Applications

Transverse flux permanent magnet (TFPM) machines are a potential candidate for direct-drive wind power application due to high torque and power densities. The operating principle of TFPM machines is firstly described with various topologies available in literature, with the placement of magnets as a criterion for the classification of TFPM machine topologies. This review includes characteristics, power factor improvement, cogging torque minimization, material consideration, modelling techniques, and scalability. Different wind turbine concepts with direct-drive machines, control techniques and power converter topologies are also considered in this paper.
Multidimensional Fibrillatory Waves Analysis for Improved Electrical Cardioversion Outcome Prediction in Persistent Atrial Fibrillation

The European Society of Cardiology guidelines recommend electrical cardioversion (ECV) as a rhythm control strategy in persistent atrial fibrillation (AF). Although being able to initially restore sinus rhythm in most patients, mid- and long-term AF recurrence rates are high. In this context, anticipation of ECV outcome is interesting to rationalize the management of AF patients. To this end, several parameters have been recently proposed for atrial activity (AA) characterization, such as fibrillatory wave amplitude (FWA), dominant frequency (DF) and sample entropy (SEn). These indices have revealed promising results, but have been mainly computed from lead Vl, thus discarding spatial information from the remaining leads. Hence, this work explores whether a multidimensional extension of these parameters can improve ECV outcome prediction. Results showed that multidimensional parameters provided more balanced values of sensitivity and specificity than unidimensional ones. While FWA and DF showed similar discriminant ability among both approaches, multivariate SEn improved the discriminant ability of its univariate version by 5%, thus predicting 80% of the ECV procedures correctly. Consequently, whereas multivariate extension of linear parameters did not reveal new predictive information, multidimensional entropy analysis was able to quantity novel AA dynamics, which have been helpful in improving ECV outcome prediction.
A Novel Two-Step Strategy Based on White-Balancing and Fusion for Underwater Image Enhancement

Images captured from underwater environment always suffer from color distortion, detail loss, and contrast reduction due to the medium scattering and absorption. This paper introduces an enhancement approach to improve the visual quality of underwater images, which does not require any dedicated devices or additional information more than the native single image. The proposed strategy consists of two steps: an improved white-balancing approach and an artificial multiple underexposure image fusion strategy for underwater imaging. In our white-balancing approach, the optimal color-compensated approach is determined by the sum of the Underwater Color Image Quality Evaluation (UCIQE) and the Underwater Image Quality Measure (UIQM). We get an optimal white-balanced version of the input by combining the well-known Gray World assumption and the optimal channel-compensated approach. In our artificial multiple underexposure image fusion strategy, first the gamma-correction operation is adopted to generate multiple underexposure versions. Then we propose to use ‘contrast’, ‘saturation’, and ‘well-exposedness’ as three weights, to be blended into the well-known multi-scale fusing scheme. Images enhanced by our strategy have a better visual quality than some state-of-the-art underwater dehazing techniques, through our validation with a wide range of qualitative and quantitative evaluations.
The formation of ultracold rubidium molecules using ultrafast photoassociation

The establishment of robust laser-cooling techniques for the formation of ultracold atoms has provided a test-bed for low-temperature science, with scattering events changing character from incoherent thermal interactions to coherent quantum mechanical events. A natural extension is the pursuit of ultracold molecules in prescribed low-energy internal states. Atomic cooling techniques, however, do not generalize to the molecular regime due to the complex energy-level structure afforded by its extra degrees of motion. An indirect approach to ultracold molecule formation - photoassociation using ultrafast laser pulses - is the focus of this thesis. A broadband field associates atom pairs into a localized molecular wavepacket that evolves within the attractive excited-state potential. A suitably timed dump pulse may thus be applied to stabilize population into deeply bound ground vibrational states. This strategy may be generalized to any species whose spectroscopy matches the pulse spectrum, and offers a coherent population transfer scheme that does not require precise knowledge of the system. This thesis presents experiments using high-energy photoassociation pulses applied to ultracold rubidium atoms. The pulses quench the background ground-state molecular population but form bound dimers within the excited state. A pump-probe experiment was designed to chart the excited-state dynamics; however, the oscillations predicted by theoretical calculations were not evident in the molecular signal. The nature of the dynamics is expected to be strongly dependent on the initial state of the atom pairs addressed by the ultrafast pulse: a bound molecular population provides an additional candidate to free atoms. A spectroscopic measurement characterizes these bound molecules and identifies their formation mechanism. A subsequent experiment provides evidence that the predominant contributor to the pump-probe signal is the unbound initial population. The consequences with regard to both the observation of excited-state dynamics and the subsequent application of a dump pulse are discussed.
Applying Cloud Based Machine Learning on Biosensors Streaming Data for Health Status Prediction

The healthcare big data including medical history, Physician reports, prescription, parents and family historical diseases, laboratories, and scan reports can help in disease detection and prediction process. The article presents an overview of the recent technologies and methods in the medical area to get the benefits of cloud systems, data science, and machine algorithms. The paper presents also how can current technologies like Spark can be used to employ streaming data for healthcare applications. Big medical data analysis is a big area of research and the article shows some advanced analysis impact on disease detection and predictions. The proposed system employed an optimized Support Vector Machine classifier with performing the parameter tuning to increase the accuracy of the classification, and efficiency. The proposed system uses wearable devices and sensors to get the data of heart rate, diabetes, and blood pressure of the users to analyze and predict heart diseases with the help of the user healthcare profile on the cloud system.
Characterizing Service Access Patterns under Heterogeneous Clients

Service providers usually maintain multiple kinds of service clients, such as desktop Web pages, mobile Web pages, and mobile native apps, to satisfy different user requirements. Understanding how users consume services via these heterogeneous clients is important for service providers to efficiently manage back-end resources. However, little has been known about such facts. To bridge the knowledge gap, in this paper, we conduct the first empirical study on service access patterns via heterogeneous clients. Our study is based on two large-scale real-world datasets of launching events from both app and Web clients of 986 services, involving a sample of 60 million users within 7 days. We analyze the characteristics of service access patterns from both spatial view and temporal view. We explore how users consume online services through different service clients, and to what extent their usage behavior patterns vary when consuming the same service. Our findings provide better understandings for heterogeneous service clients that can facilitate the development, configuration, deployment and maintenance of service back-end.
Application of Repetitive Control in Electric Spring

With the increasing of the grid-connected capacity of renewable energy such as wind energy and solar energy, its instability, intermittence and uncontrollability have a serious impact on the stable operation of power system, such as voltage fluctuation, frequency flicker, etc. Therefore, Electric Spring (ES) system has become a current research hotspot. Aiming at the deficiencies of the resonance control strategy, a control method that combines quasi-proportional resonance (QPR) control and repetitive control is proposed. The quasi proportional resonance control is used to realize the tracking without static error, and the internal model principle of repetitive control is used to suppress the periodic disturbance of the grid voltage, so as to improve the power quality of the grid. Compared with the traditional quasi proportional resonant control, the introduction of repetitive control can effectively improve the steady-state accuracy, reduce the system harmonic and enhance the ability of anti-harmonic interference. The modeling of ES and the design method of repetitive controller parameters are discussed in detail. The simulation model is built using MATLAB / Simulink, and the ES experimental platform is built with dSPACE as the control core, the results verify the feasibility and effectiveness of the proposed control strategy.
Broadband Planar Low Noise Amplifier for WLAN Applications

In this paper, a broadband Planar Low Noise Amplifier using GaAs HEMT transistor is presented. The single-stage LNA is designed on an FR-4 substrate. Electromagnetic EM simulation is used to take into account the effects of couplings between the lines. The simulation tool used is the Advanced Design System (ADS). LNA is adapted at input and output with a series of open stubs lines to have a minimal noise figure NF. This LNA is polarized with the drain-source voltage of 1V; provide an almost invariant gain of 13.4±1.3 dB with a minimum noise of 0.9±0.1dB. The amplifier is characterized by very high linearity. The input compression point found is −0.2dBm and the third-order input intercept point is 9.8dBm at 4GHz. The consumption power Pdc is very low of the value 56 mW, the total die area is 5.1x2cm2 and the ratio of figure-of-merit is 36.58.