Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Human Activity Recognition with IMU and Encoder Sensors in Wearable Exoskeleton Robot via Deep Learning Networks

Wearable exoskeleton robots have become a promising technology for supporting human motions in multiple tasks. Activity recognition in real-time provides useful information to enhance the robot’s control assistance for daily tasks. This work implements a real-time activity recognition system based on the activity signals of an inertial measurement unit (IMU) and a pair of rotary encoders integrated into the exoskeleton robot. Five deep learning models have been trained and evaluated for activity recognition. As a result, a subset of optimized deep learning models was transferred to an edge device for real-time evaluation in a continuous action environment using eight common human tasks: stand, bend, crouch, walk, sit-down, sit-up, and ascend and descend stairs. These eight robot wearer’s activities are recognized with an average accuracy of 97.35% in real-time tests, with an inference time under 10 ms and an overall latency of 0.506 s per recognition using the selected edge device.
Device-to-device Communications at the Terahertz Band: Open Challenges for Realistic Implementation

One of the key parameters that plays a major role in enabling the data rate requirements is spectrum or bandwidth, which is scarce and expensive. Therefore, spectrum management policies are required to optimize its usage to meet all the requirements and the promised data rate growth. Another strategy to deal with spectrum scarcity is to move toward higher frequency bands (terahertz bands), which are expected in the next 6G communication standard. It is therefore important to develop not only new techniques that enable efficient dynamic spectrum access and sharing at such bands, but also suitable channel models for the terahertz bands. Meanwhile, offloading mechanisms are very promising for cellular networks where a plethora of options have been proposed in the research arena in terms of device-to-device, licensed assisted access, or WiFi offloading, among others; but their behavior, when operated at high frequencies (terahertz band) remains unclear. Therefore, this article will tackle two technologies that will shape future networks: terahertz channel modeling/communications and offloading mechanisms.
Assessment of Plate Elements on Bending and Vibrations of Composite Structures

This article assesses classical and refined finite plate elements on bending and vibrations of layered composites and sandwich structures. To this purpose, recent authors' findings have been extended to dynamics. About 20 plate finite elements have been implemented and compared: classical ones based on displacement assumptions are compared to advanced mixed elements which are formulated on the basis of Reissner's mixed variational theorem. Finite elements which preserve the independence of the number of independent variables from the numbers of the N l layers (equivalent single-layer models) as well as those elements in which the number of the unknown variables remains N l -dependent (layer-wise models) are both considered. Linear up to fourth-order expansions in the thickness direction are treated for the unknown stress and displacement variables. Sandwich beams and cross-ply as well as angle-ply composites plates have been analyzed. Simply supported as well as clamped edges have been considered. Finite-element results have been implemented and compared, where available, to analytical closed form solutions. Mostly the fundamental circular frequency has been used as a test bed to assess the whole implemented multilayered elements.
Coupled-mode theory for optical waveguides: an overview

The coupled-mode theory (CMT) for optical waveguides is reviewed, with emphasis on the analysis of coupled optical waveguides. A brief account of the recent development of the CMT for coupled optical waveguides is given. Issues raised in the debates of the 1980’s on the merits and shortcomings of the conventional as well as the improved coupled-mode formulations are discussed. The conventional coupled-mode formulations are set up in a simple, intuitive way. The rigorous CMT is established on the basis of a linear superposition of the modes for individual waveguides. The cross-power terms appear logically as a result of modal nonorthogonality. The cross power is necessary for the self-consistency of the CMT for dissimilar waveguides. The nonorthogonal CMT, though more complicated, yields more-accurate results than the conventional orthogonal CMT for most practical applications. It also leads to the prediction of cross talk in directional couplers. The conventional orthogonal CMT is, however, reliably accurate for describing the power coupling between two weakly coupled, nearly identical waveguides. For dissimilar waveguides, a self-consistent orthogonal CMT can be derived by a redefinition of the coupling coefficients, and it predicts the coupling length and therefore the power exchange between the waveguides accurately if the two waveguides are far apart. Three typical coupler configurations—the uniform, the grating-assisted, and the tapered—are examined in detail. The accuracy, scope of validity, limitations, and extensions of the coupled-mode formulations are discussed in conjunction with each configuration. To verify the arguments in the discussions, comparisons with the exact analytical solutions and the rigorous numerical simulations are made.
A Survey on Cross-Chain Technology: Challenges, Development, and Prospect

With the continuous development of blockchain technology, many blockchain applications, such as digital currencies in the form of tokens, are deployed. However, due to the lack of data and value inter-blockchain transmission methods, these chain applications form many data islands. Therefore, cross-chain technology is proposed and applied in distributed transaction platforms, digital finance, e-government and many other different fields. Although cross-chain technology just started, the cross-chain technology and its applications are gradually improving to meet the development needs of blockchain system. At the same time, they have already realized asset transfers across heterogeneous blockchains and platforms, and some projects have realized cross-chain information exchange. At present, cross-chain technology includes the following typical methods: Hash-locking, Notary scheme, Distributed private key control, Sidechains/Relays and Tight coupling. This paper analyzes the above methods from the aspect of techniques, security requirements, and technical limitation, and compares their applicability. With public blockchains and consortium blockchains, this paper analyzes their problems and challenges in cross-chain technology and introduces the main approaches of existing solutions. By analyzing the existing cross-chain technologies and problems, this paper further introduces eight popular cross-chain projects and their application prospects. Finally, this paper summarizes and proposes the future research topics and development trends of cross-chain technology.
Social and Emotional Loneliness in Older Community Dwelling-Individuals: The Role of Socio-Demographics

Background: Social determinants have a major influence on individuals’ health, and among them, loneliness has an important impact on the health of the elderly. Objectives: The aims were to determine loneliness and its social and emotional components in a sample of elderly people and to assess its prevalence and associations with sociodemographic variables. Methods: Analytical, cross-sectional, observational research was carried out based on a population over 60 years of age in Valencia (Spain). Loneliness was assessed with the De Jong-Gierveld Loneliness Scale. Results: Five-hundred and thirty community-dwelling individuals participated. The mean age of the sample was 72.7 years (84.2% women); 36.2% suffered from moderate loneliness and 6.6% suffered from extreme loneliness. The sociodemographic variables most significantly related to loneliness were being single, separated, or divorced (p < 0.01). Among widowers, loneliness was inversely associated with years of widowhood (p < 0.01). Having sons/daughters was a significant protective factor (p < 0.05), while having grandchildren or siblings did not have a significant influence. The ability to walk and smartphone and video call use were not associated with loneliness. Conclusions: There is a high prevalence of unwanted loneliness in community-dwelling individuals, and some social factors play an important role. Interventions against loneliness among older people are a priority for welfare and public health.
Reproducing Aplysia R-15 Bursting Neurodynamics on a Neuromorphic Microchip

We have implemented Aplysia R-15 dynamics, a neuron known for its intrinsic bursting activity and neuromodulatory properties, on a digitally programmable, analog, continuous-time neuromorphic emulation platform called NeuroDyn. To achieve this, we extend the dynamic range of NeuroDyn by using the generalized Hodgkin-Huxley (HH) model to incorporate multiple time scales in membrane dynamics and gating variable kinetics. In addition, the multiscale neurodynamics was mapped into the chip via compensating for its nonlinearities, which allowed for accurate mapping of Aplysia R-15 bursting neurodynamics from the software model to its hardware implementation.
Performance analysis of optical CDMA with prime codes

An ‘extended’ prime code is introduced for optical code-division multiple access (CDMA). The probability distribution of the interference generated by crosscorrelation functions of the original and extended prime codes are derived analytically. The performances of both codes are then analysed using a Gaussian approximation. Finally, the extended prime code is shown to be preferable to the original code.
Adaptive J-Wave Detection Architecture for Online BCG-Complex Recognition on FPGA

In this paper a novel hardware architecture for high-accuracy and near-sensor BCG complex recognition is presented. Main contribution of our work is the implementation of an adaptive method for J-wave peak detection on FPGA enabling reliable online waveform monitoring. Also, to further increase the overall signal quality, Chebyshev-based filtering is installed, leading to a smoother signal progression. The evaluation results highlight our approach as a well suited solution for online BCG complex recognition in resource constraint environments, as detection rates between 95.31% and 100% can be achieved considering human bodies in a resting position.
Natural Resonant Frequency of a Prolate Acoustical Resonator

Natural resonant frequencies of prolate acoustical resonators with either perfectly soft or perfectly rigid boundaries are calculated by locating the zeros of Rmn(1) (h,ξ) and Rmn(1)′ (h,ξ). These resonances are normalized to those of the corresponding modes in a spherical cavity with the same volume and are presented for various ratios of major to minor axes.
Optimal basins of attraction in randomly sparse neural network models

The size of the basin of attraction for randomly sparse neural networks with optimal interactions is calculated. For all values of the storage ratio, alpha =p/C<2, where p is the number of random uncorrelated patterns and C is the connectivity, the basin of attraction is finite, while for alpha <0.42, the basin of attraction is (almost) 100%.
Depth aspect images for robust object recognition

In order to gain generality, robustness and efficiency in search, a novel search is proposed based on a representation called `Depth aspect image' is proposed as a controllable two-dimensional representation of local depth distribution used in cooperation with a distinct `Voxel framing', which enables effective reference coordination without any prominent features, such as vertices or edges. A robust statistical estimator called `Least quantile of residuals' is furthermore introduced for robust matching, which can be utilized for both depth matching and model verification. Since the proposed method is of model-based approach with possible views of local structures, the computation cost for matching has to be reduced by introducing random sampling and an effective hashing. Experiments with real scenes show the effectiveness of the proposed method.
Antimicrobial Activity of P-113 against Bacterial Flora of the Human Oral Cavity

Periodontitis is a chronic disease associated with dental biofilm formation by bacteria. The primary clinical signs include plaque formation, gingivitis, gingival bleeding, and attachment loss. P-113, a 12-amino acid antimicrobial peptide, is derived from Histatin 5 (Hst5) secreted by the human parotid and submandibular salivary glands and has been reported to reduce plaque formation, gingivitis, and gingival bleeding. This study aimed to investigate the effect of P-113 on periodontal disease-associated bacteria and the relative abundance of bacteria using real-time polymerase chain reaction (real-time PCR) and Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Saliva samples were collected from participants for analyses. Real-time PCR results showed significant changes in the abundance of the periodontal disease-associated bacteria, Fusobacterium nucleatum and Tannerella forsythia, after the use of the P-113 mouthwash. After culturing in an anaerobic environment, MALDI-TOF MS analysis was performed. The results showed that the relative abundance of Streptococcus mitis decreased and that of beneficial Streptococcus salivarius increased after the use of the P-113 mouthwash. These results indicate the potential of P-113 mouthwash as a treatment that can influence the proportion of pathogens and probiotics to establish equilibrium in the salivary environment.
Sense of mastery and attitude towards illness: Examining longitudinal benefits of a medical specialty camp for youth with sickle cell disease

Medical specialty camps can play an important role in the positive development of psychosocial outcomes for children and youth with sickle cell disease (SCD). This study examined how sense of mastery and attitude towards illness outcomes changed over 6 months for 100 campers aged 8–16 years with SCD. The outcomes were measured twice before and twice after camp. Latent growth curve modeling was used to analyze data. Results showed no changes in the outcomes for this study population. Implications for future research designs, populations, and outcomes are discussed, as are implications for communications about camp, and policy and practice.
Troubleshooting Tool for VxLAN Bridging

Modern day data centers are geographically separated to improve service availability and performance. Tunneling technologies help stretch the layer 2 domain between remote data centers to ensure data consistency and allow movement of virtualized workloads between locations without any downtime. Amongst the available tunneling protocols, VxLAN is widely used as it is an open standard and relays unmodified layer 2 traffic over existing layer 3 domain. VxLAN communication between two hosts that are in the same subnet is referred to as VxLAN Bridging. Misconfigurations or inconsistent designs in VxLAN are rather difficult and time taking to troubleshoot considering the scale and complexity of the protocol. Hence, we came up with a tool that can perform certain redundant checks quickly, significantly narrow down the fault domain as well as perform a design check on the overall network by having central connectivity through the VTEPs. The paper attempts to identify a logical approach at troubleshooting VxLAN bridging scenarios and put it together as an easy-to-use tool to simplify the task of a network engineer. The tool was tested with devices from Arista Networks and has been successful in reducing the time taken to troubleshoot most commonly occurring issues with VxLAN Bridging where OSPF is used as the underlay routing protocol.
Antecedents to Thai pre-service teacher eco-friendly behavior: A confirmatory factor analysis

The research aimed to analyze the eco-friendly behavior (EFB) of Thai undergraduate pre-service teachers (PSTs). Multi-stage random sampling was used to select 211 individuals majoring in industrial education from four Thai universities. The second-order confirmatory factor analysis used LISREL 9.10 and SPSS for Windows 21 was used for the descriptive statistics. Furthermore, five elements were perceived to influence PST EFB positively. This included the product’s eco-friendly price (EFP), eco-friendly safety (EFS), eco-friendly image (EFI), eco-friendly environment (EFE), and finally eco-friendly quality (EFQ). Moreover, it was determined that a product’s EFI was most important to the PSTs, followed by its EFQ, EFS, EFE, and EFP. Moreover, of the 20 items the PSTs were asked about, they believed that non-plastic bag use for EFB was judged most important. However, at the other end of EFB was a product’s price importance.
Strain maps at the atomic scale below Ge pyramids and domes on a Si substrate

In this letter, the strain field below uncapped Ge islands of a different shape on a Si(001) substrate is estimated by molecular dynamics simulations at a realistic scale. Comparison to the Fourier transform maps of transmission electron micrographs, recently reported in literature, shows a very good agreement. We point out that the complex deformation in silicon, just below the edges of the Ge islands, is far from being uniaxial. The stress distribution generated by such a strain determines the range of interdot repulsion.
Intelligent Proactive Fault Tolerance at the Edge through Resource Usage Prediction

The proliferation of demanding applications and edge computing establishes the need for efficient management of the underlying computing infrastructures, urging the providers to rethink their operational methods. In this paper, we propose an Intelligent Proactive Fault Tolerance (IPFT) method that leverages the edge resource usage predictions through Recurrent Neural Networks (RNNs). More specifically, we focus on the process faults, which are related with the inability of the infrastructure to provide Quality of Service (QoS) in acceptable ranges due to the lack of processing power. In order to tackle this challenge we propose a composite deep learning architecture that predicts the resource usage metrics of the edge nodes and triggers proactive node replications and task migration. Taking also into consideration that the edge computing infrastructure is also highly dynamic and heterogeneous, we propose an innovative Hybrid Bayesian Evolution Strategy (HBES) algorithm for automated adaptation of the resource usage models. The proposed resource usage prediction mechanism has been experimentally evaluated and compared with other state of the art methods with significant improvements in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Additionally, the IPFT mechanism that leverages the resource usage predictions has been evaluated in an extensive simulation in CloudSim Plus and the results show significant improvement compared to the reactive fault tolerance method in terms of reliability and maintainability.
Downregulation of the Protein C Signaling System Is Associated with COVID-19 Hypercoagulability—A Single-Cell Transcriptomics Analysis

Because of the interface between coagulation and the immune response, it is expected that COVID-19-associated coagulopathy occurs via activated protein C signaling. The objective was to explore putative changes in the expression of the protein C signaling network in the liver, peripheral blood mononuclear cells, and nasal epithelium of patients with COVID-19. Single-cell RNA-sequencing data from patients with COVID-19 and healthy subjects were obtained from the COVID-19 Cell Atlas database. A functional protein–protein interaction network was constructed for the protein C gene. Patients with COVID-19 showed downregulation of protein C and components of the downstream protein C signaling cascade. The percentage of hepatocytes expressing protein C was lower. Part of the liver cell clusters expressing protein C presented increased expression of ACE2. In PBMC, there was increased ACE2, inflammatory, and pro-coagulation transcripts. In the nasal epithelium, PROC, ACE2, and PROS1 were expressed by the ciliated cell cluster, revealing co-expression of ACE-2 with transcripts encoding proteins belonging to the coagulation and immune system interface. Finally, there was upregulation of coagulation factor 3 transcript in the liver and PBMC. Protein C could play a mechanistic role in the hypercoagulability syndrome affecting patients with severe COVID-19.
Improving Solution Accuracy and Convergence for Stochastic Physics Parameterizations with Colored Noise

Stochastic parameterizations are used in numerical weather prediction and climate modeling to help capture the uncertainty in the simulations and improve their statistical properties. Convergence issues can arise when time integration methods originally developed for deterministic differential equations are applied naively to stochastic problems. In previous studies, it has been demonstrated that a correction term, known in stochastic analysis as the Itô correction, can help improve solution accuracy for various deterministic numerical schemes and ensure convergence to the physically relevant solution without substantial computational overhead. The usual formulation of the Itô correction is valid only when the stochasticity is represented by white noise. In this study, a generalized formulation of the Itô correction is derived for noises of any color. The formulation is applied to a test problem described by an advection–diffusion equation forced with a spectrum of fast processes. We present numerical results for cases with both constant and spatially varying advection velocities to show that, for the same time step sizes, the introduction of the generalized Itô correction helps to substantially reduce time integration error and significantly improve the convergence rate of the numerical solutions when the forcing term in the governing equation is rough (fast varying); alternatively, for the same target accuracy, the generalized Itô correction allows for the use of significantly longer time steps and, hence, helps to reduce the computational cost of the numerical simulation.
Efficient Association of Low and High RF Power Rectifiers for Powering Ultra-Low Power Devices

Nowadays, the efficiency of Radio Frequency (RF) energy harvesting circuits is continuously increasing and, at the same time, the energy consumption of connected devices is drastically decreasing. Despite that, storing and delivering such kind of harvested energy to the device in an appropriate manner is still a challenge. This paper presents the design of a harvester able to efficiently collect energy from both low and high power levels of the RF field. The objective is to correctly power supply an ultra-low power consumption device requiring a regulated voltage. The starting point is the characterization of a commercial Power Management Integrated Circuit which operates with ultra-low power levels. Then, a RF harvester circuit, consisting in the association of a low and a high level power rectifier, is presented. By the means of a circulator, the RF power is absorbed by the low power rectifier or reflected to the high power rectifier. The rectifiers have their outputs associated in series. At a frequency of 868 MHz, the efficiencies of the association of rectifiers are 43 % and 76 % and were obtained for input powers of −20 dBm and 0 dBm respectively.