Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Power Transfer Using Resonant Coupling For Wireless Capacitive Load

There are several wireless technologies that have been built to move energy to a very wide array. Power transfer may be performed at a lower frequency than the described ones. The adjustments not only supported power transmission inside the high unit, but also included certain control mechanisms. Those works did not recognize the overall angular frequency output. This paper’s outcome, which cares about machine performance, gives the method which is higher than those tests. This research looks at a capacitive load circuit inside a wireless network. Available angular frequencies below 12 kilorad/s may be provided to the wireless network for the capacitive load. Selection depends on how much power the charge absorbs and how much the performance of the device is required. The approximation outcome at angular frequency ω = 3003.5 rad/s is power input Sin = 0.3328 mVA, and power output Sout = 0.3178 mVA. Even experiencing small amount of power loss, this frequency barely has enough capacity to pass to the load. Then, it adds the next ω = 2572.27 rad / s. This frequency generates Sin = 8.9684 mVA, and Sout = 8.0924 mVA. It can be admitted that both current input and output are growing larger than the first one. Another ω = 8305.34 rad/s is the last frequency setting. Sin ánd Sout will be 40.7964 mVA and 31.0623 mVA. This system is still considerable when a high demand for power is required for the wireless load and the system efficiency is acceptable.
Facial expression recognition using convolutional neural network with weighted loss function

Facial expression is an important content in human communication as it is participating in nearly half of the human interaction. The recognition of facial expression has been applied in security, criminal research, entertainment applications, and other human-computer-interaction fields. Facial expression recognition is being extensively studied in recent years and have reached satisfactory results. However, the type of images of expressions in the commonly used datasets are unbalanced and cause the recognition of one to two facial expressions are harder than others, which affects the accuracy of the whole, Therefore, we create a loss function aim to decrease the effect of this unbalance. With the combination of the state-of-art convolutional neural network and our loss function, the accuracy on the FER-2013 dataset has raised about 2%.
A Concrete Dam Deformation Prediction Method Based on LSTM With Attention Mechanism

Dams are the main water retaining structures in the hydraulic engineering field. Safe operations of dams are important foundations to ensure the hydraulic functionalities of these engineering structures. Deformation, as the most intuitive feature of the dams’ operation behaviors, can comprehensively reflect the dam structural states. In this case, the analysis of the dam prototype deformation data and the establishment of a real-time prediction model become frontier research contents in the field of dam safety monitoring. Considering the multi-nonlinear relationships between dam deformation and relative influential factors as well as the time lag effect of these influential factors, this article adopts long-short-term memory (LSTM) network algorithm in deep learning to deal with the long-term dependence existing in dam deformation and explore the deformation law. The method proposed in this work can effectively avoid the gradient disappearance and gradient explosion problems by using the recurrent neural network (RNN). In addition, this work adopts the Attention mechanism to screen the information that has significant influence on deformation, combining the Adam optimization algorithm that has high calculation efficiency and low memory requirement to improves the learning accuracy and speed of the LSTM. The model overfitting is avoided by applying the Dropout mechanism. The effectiveness of this proposed model in studing the long time series deformation prediction of concrete dams is confirmed by case studies, whose MSE (mean square error) and other 4 error indexes can be reduced.
Trend Analysis of Passenger Evacuation Modeling Technology in Urban Rail Transit

Urban rail transit is an important part of urban public transport, which has the advantages of large passenger traffic volume, punctuality and efficiency, but it also has the disadvantage of weak resistance to some disasters. In the event of accidents, how to effectively evacuate a large number of passengers has become the hot issue among the researchers in recent years. Based on the literature of passenger evacuation model in city rail transit at home and abroad, On this basis, combined with the safety characteristics of urban rail transit in China, we the development trend of passenger emergency evacuation modeling technology.
LSTM Classification of sEMG Signals For Individual Finger Movements Using Low Cost Wearable Sensor

The electrical activity of the muscles that control finger movements can be extracted during the performance of these movements and using machine learning techniques, the myoelectric signals can be decoded and classified according to the movement that generated the specific signal. The focus of this paper is to classify sEMG signal using easily accessible cheap hardware to capture the signal. Furthermore, to employ neural networks to classify the signal using established methodology i.e. feature extraction, with the highest possible accuracy. To classify these sEMG signals, an LSTM network has been developed and was able to classify 12 individual finger movements with accuracies reaching 90%.
Imitating Agents in A Complex Environment by Generative Adversarial Imitation Learning

The generative adversarial imitation learning (GAIL) shows the ability to find reward functions to explain expert players’ behaviors in some low-dimensional environments using hand-crafted features as inputs. In this research, we aim to extend GAIL to complex environments and using raw images as inputs. We propose to (1) use convolutional neural networks to deal with image inputs, (2) adopt a structure called global-local discriminator to GAIL, and (3) represent trajectories as state-state pairs instead of state-action pairs. Our approach successfully imitates given players in Super Mario Bros. To our knowledge, the results are the first to have successful imitations in complex environments based on image inputs.
Mechanisms of the intracellular localization of the SUMO-activating enzyme Aos1/Uba2

Dynamic posttranslational modification with ubiquitin related proteins of the SUMO (small ubiquitin-related modifier) family is an important cellular mechanism to alter the activity, abundance or localization of proteins. Reversible covalent attachment of SUMO to target proteins requires the catalytic activities of an E1-activating enzyme, an E2-conjugating enzyme, one of several E3 ligases as well as specific isopeptidases. Consistant with the existence of nuclear and cytoplasmic SUMO substrates, the components of the enzymatic machinery are also found in both compartments. This raises the interesting question how these pools are generated. The presented work aimed to identify the mechanisms underlying the intracellular localization of the SUMO E1 complex. Since both subunits, Aos1 and Uba2, are predominantly localized in the nucleus, I initially determined the generation of the nuclear pool. I demonstrated that nuclear import can occur in two ways: Aos1 and Uba2 can be imported independently by distinct nuclear localization signals (NLSs), and the assembled complex can be imported by the NLS of Uba2. In both cases the import is mediated by the receptor importin beta and the adaptor protein importin alpha. Functional studies concerning the generation of the minor cytoplasmic pool of Aos1/Uba2 indicated that the proteins are not actively exported from the nucleus into the cytoplasm. Since the nuclear and cytoplasmic pools of E1 are not subjected to frequent exchange and the cytoplasmic fraction of E1 is very small I reasoned that the SUMO activating acitvity in the cytoplasm would be constantly low. This raised the question whether the E1 s intracellular localization is at all of general importance for SUMOylation in different cellular compartments. With yeast UBA2 shuffle strains, in which the endogenous UBA2 gene is deleted and cells are kept alive by a removable exogenous copy, I studied the ability of cytoplasmic E1 to substitute for endogenous E1. Surprisingly, deletion of endogenous E1 can be rescued both by exogenous cytoplasmic or nuclear E1. Yeast strains with either cytoplasmic or nuclear E1 exhibit similar modification patterns, indicating that the localization of E1 might not determine compartment specific SUMOylation. These findings suggest that thioester charged Ubc9 may shuttle to and allow efficient SUMOylation both in the nuclear and cytoplasmic compartment.
e-SALPAKAN: Development of a Rule-Based Arbiter for Games of the Generals’ Pieces Ranking Detection using RFID

This study aims to developed an automated Game of the Generals "GG" pieces rank detection to eliminate the presence of an arbiter. The system can detect each GG pieces and addressed the issues of being biased by the arbiter in determining who wins the game. Upon series of experiments conducted by the proponents, the system shows 100% accuracy of detection of pieces ranking. The designed system provides improved performance on deciding whose piece wins in faceoff of both team and to display the status of every piece tapped on the device and also display whose piece win in every game. The design of the device with RFID technologies leads to the collection of data about which piece has been ready and tapped and which piece has turned next, the portion of which succeeds in their encounter with the remaining pieces left. The use of Rule-based algorithm has been introduced for decision making of an arbiter and resulted to detect and decides whose player piece that have been tapped win, loss or draw.
Design of Compact Dual-Band RF Rectifiers for Wireless Power Transfer and Energy Harvesting

In this paper, we propose a compact dual-band impedance matching network (DBIMN) for radio frequency (RF) rectifiers. The DBIMN is achieved with a single-stage T-type network, with only three segments of the transmission line. We investigate the closed-form design equations, as a design guideline of the DBIMN. In addition, we propose the design methodology of the rectifier using the DBIMN. For validation, we design two dual-band rectifiers (0.915 and 2.45 GHz), for different input power levels. The rectifier with high input power level, is designed for a wireless power transmission (WPT) system. With a 12 dBm input power, the measured power conversion efficiencies (PCEs) are 81.7 and 73.1% at the working frequencies. The PCEs becomes 69.2 and 64.1% (at −1 dBm input power) for the low-power input rectifier that can be used in an RF energy harvesting (RF-EH) system. The simulated and measured results match each other well. Compared with previous designs, the proposed designs have advantages in compactness and high efficiency.
An Intelligent Storytelling System for Narrative Conflict Generation and Resolution

Conflict is essential for the development of complex plots or more in-depth character design. Therefore, many literary works have employed a conflict-centered narrative structure. However, it is challenging to incorporate conflicts in a planning-based story generation because a planning algorithm constructs a plan that avoids conflicts, which can occur when one action negates the precondition of the other character’s action. To address this issue, this paper presents an intelligent storytelling system that can automatically generate conflicts and their resolutions using the interleaved causal relationships between different characters in the story. We describe an example to show that the proposed algorithm can successfully generate a story that contains inter-personal conflicts and is presented in the Virtual Environment. We conclude with a discussion and future study.
A Magnetostrictive Tactile Sensing Unit and the Integration of Sensor Array for Intelligent Manipulator

A magnetostrictive tactile sensing unit and the integrated sensor array have been developed, based on the sensing process of human skin and the inverse magnetostrictive effect. The working mechanism of the magnetostrictive tactile sensing unit has theoretically been studied by using electromagnetism theory and cantilever beam theory, and the relationship between the applied force and output voltage has been determined. The measurement of sensing performance about the static and dynamic force has been carried out, and it is found that the sensitivity of a sensing unit is 127 mV/N, resolution is 0.07 N, and response time is 24 ms. The sensing unit possesses a high reliability with the test of 5 000 cycles. The optimum distance between adjacent sensing units of the sensor array is determined by COMSOL multiphysics software when the sensing unit is integrated into a sensor array. By integrating four magnetostrictive tactile sensing units into a 2 $\times 2$ sensor array, it could monitor and map the force distribution applied on the manipulator in the grip. The tactile sensor array shows great potential within the application of intelligent manipulator.
Evolution of Millimeter-Wave Antenna Solutions and Designs to Cellular Phones

The article presents practical, innovative, and inspiring perspectives on the major requirements for millimeter-Wave (mm-Wave) antennas in cellular phones, the main challenges to the mm-Wave antennas in cellular phones, the corresponding solutions to the challenges, the factors to trigger the solution evolution, the underlying philosophy of the strategical driving force for the solution generation, and the underlying philosophy of the tactical driving force for the design variants to better contribute to the retrospect on the reported developments and the prospect on the promising advances of mm-Wave antennas in the cellular phones.
Simvastatin Induces In Vitro Mineralization Effects of Primary Human Odontoblast-Like Cells

Simvastatin (SV) is an often prescribed statin reducing the LDL-concentration in circulating blood. The aim of this study was to evaluate the pleiotropic effects of SV to primary human odontoblast-like cells. Twenty four wisdom teeth of different subjects were extracted and the pulp tissue was removed and minced under sterile conditions. After mincing, the requested cells were passaged according to established protocols. Osteoblastic marker (ALP conversion), viability and mineralization were determined at days 14, 17 and 21 after simvastatin exposition (0.01 µM, 0.1 µM, 1.0 µM, 2.0 µM). The sample size per group was 24 cultures with three replicates per culture for ALP-conversion and mineralization and 6 replicates for viability. A Kruskal–Wallis test was used for statistical analysis. After adding SV, viability was significantly (p < 0.01) decreased in a time- and dose-dependent manner, whereas after 21 days, mineralization was significant (p < 0.01). ALP-conversion in groups with SV concentrations of 1 and 2 µM SV was significantly (p < 0.01) increased. Pleiotropic effects regarding mineralization in higher SV concentrations were possibly induced via alternative mineralization pathways as almost equal elevations of ALP conversion were not evident in the control and experimental groups.
Effect of awareness of being monitored on wearing time and wearing of orthopaedic footwear.

OBJECTIVE To investigate the effect of awareness of being monitored on wearing time and adherence to wearing orthopaedic footwear. Quantitative assessment of wearing time was made using direct measurement with temperature sensors during the first 3 months after provision of footwear. DESIGN Randomized controlled trial. INTERVENTION Awareness of the purpose of the study: measuring wearing time of their orthopaedic footwear. METHODS All 55 participants had a temperature sensor built into the medial arch of the left insole of their orthopaedic footwear. Participants were assigned randomly to either an "awareness group" (n = 25, mean age 67 years) and knew they were being monitored for wearing time, or a "no awareness group" (n = 30, mean age 65 years) and only knew their shoe temperature was being measured. Differences were assessed with a linear mixed model. RESULTS Mean (standard deviation; SD) wearing time in the intervention group was 7.32 h/day (SD 4.2), and 6.11 h/day (SD 4.1) in the control group (p = 0.017). A significant interaction effect was found for awareness and pathology group (p = 0.036). The difference was especially large (7.0 (SD 4.7) vs 2.4 (SD 2.2) h/day) in the subgroup of people with diabetes. CONCLUSION Awareness of being monitored increases wearing time and wearing of orthopaedic footwear.
Misuse of the Michaelis–Menten rate law for protein interaction networks and its remedy

For over a century, the Michaelis–Menten (MM) rate law has been used to describe the rates of enzyme-catalyzed reactions and gene expression. Despite the ubiquity of the MM rate law, it accurately captures the dynamics of underlying biochemical reactions only so long as it is applied under the right condition, namely, that the substrate is in large excess over the enzyme-substrate complex. Unfortunately, in circumstances where its validity condition is not satisfied, especially so in protein interaction networks, the MM rate law has frequently been misused. In this review, we illustrate how inappropriate use of the MM rate law distorts the dynamics of the system, provides mistaken estimates of parameter values, and makes false predictions of dynamical features such as ultrasensitivity, bistability, and oscillations. We describe how these problems can be resolved with a slightly modified form of the MM rate law, based on the total quasi-steady state approximation (tQSSA). Furthermore, we show that the tQSSA can be used for accurate stochastic simulations at a lower computational cost than using the full set of mass-action rate laws. This review describes how to use quasi-steady state approximations in the right context, to prevent drawing erroneous conclusions from in silico simulations.
A generalized machine‐learning aided method for targeted identification of industrial enzymes from metagenome: A xylanase temperature dependence case study

Growing industrial utilization of enzymes and the increasing availability of metagenomic data highlight the demand for effective methods of targeted identification and verification of novel enzymes from various environmental microbiota. Xylanases are a class of enzymes with numerous industrial applications and are involved in the degradation of xylose, a component of lignocellulose. The optimum temperature of enzymes is an essential factor to be considered when choosing appropriate biocatalysts for a particular purpose. Therefore, in silico prediction of this attribute is a significant cost and time‐effective step in the effort to characterize novel enzymes. The objective of this study was to develop a computational method to predict the thermal dependence of xylanases. This tool was then implemented for targeted screening of putative xylanases with specific thermal dependencies from metagenomic data and resulted in the identification of three novel xylanases from sheep and cow rumen microbiota. Here we present thermal activity prediction for xylanase, a new sequence‐based machine learning method that has been trained using a selected combination of various protein features. This random forest classifier discriminates non‐thermophilic, thermophilic, and hyper‐thermophilic xylanases. The model's performance was evaluated through multiple iterations of sixfold cross‐validations as well as holdout tests, and it is freely accessible as a web‐service at arimees.com.
Overview of Time Synchronization for IoT Deployments: Clock Discipline Algorithms and Protocols

Internet of Things (IoT) is expected to change the everyday life of its users by enabling data exchanges among pervasive things through the Internet. Such a broad aim, however, puts prohibitive constraints on applications demanding time-synchronized operation for the chronological ordering of information or synchronous execution of some tasks, since in general the networks are formed by entities of widely varying resources. On one hand, the existing contemporary solutions for time synchronization, such as Network Time Protocol, do not easily tailor to resource-constrained devices, and on the other, the available solutions for constrained systems do not extend well to heterogeneous deployments. In this article, the time synchronization problems for IoT deployments for applications requiring a coherent notion of time are studied. Detailed derivations of the clock model and various clock relation models are provided. The clock synchronization methods are also presented for different models, and their expected performance are derived and illustrated. A survey of time synchronization protocols is provided to aid the IoT practitioners to select appropriate components for a deployment. The clock discipline algorithms are presented in a tutorial format, while the time synchronization methods are summarized as a survey. Therefore, this paper is a holistic overview of the available time synchronization methods for IoT deployments.
All-Cause Mortality and Cardiovascular Death between Statins and Omega-3 Supplementation: A Meta-Analysis and Network Meta-Analysis from 55 Randomized Controlled Trials

Statins and omega-3 supplementation have shown potential benefits in preventing cardiovascular disease (CVD), but their comparative effects on mortality outcomes, in addition to primary and secondary prevention and mixed population, have not been investigated. This study aimed to examine the effect of statins and omega-3 supplementation and indirectly compare the effects of statin use and omega-3 fatty acids on all-cause mortality and CVD death. We included randomized controlled trials (RCTs) from meta-analyses published until December 2019. Pooled relative risks (RRs) and 95% confidence intervals (CIs) were calculated to indirectly compare the effect of statin use versus omega-3 supplementation in a frequentist network meta-analysis. In total, 55 RCTs were included in the final analysis. Compared with placebo, statins were significantly associated with a decreased the risk of all-cause mortality (RR = 0.90, 95% CI = 0.86–0.94) and CVD death (RR = 0.86, 95% CI = 0.80–0.92), while omega-3 supplementation showed a borderline effect on all-cause mortality (RR = 0.97, 95% CI = 0.94–1.01) but were significantly associated with a reduced risk of CVD death (RR = 0.92, 95% CI = 0.87–0.98) in the meta-analysis. The network meta-analysis found that all-cause mortality was significantly different between statin use and omega-3 supplementation for overall population (RR = 0.91, 95% CI = 0.85–0.98), but borderline for primary prevention and mixed population and nonsignificant for secondary prevention. Furthermore, there were borderline differences between statin use and omega-3 supplementation in CVD death in the total population (RR = 0.92, 95% CI = 0.82–1.04) and primary prevention (RR = 0.85, 95% CI = 0.68–1.05), but nonsignificant differences in secondary prevention (RR = 0.97, 95% CI = 0.66–1.43) and mixed population (RR = 0.92, 95% CI = 0.75–1.14). To summarize, statin use might be associated with a lower risk of all-cause mortality than omega-3 supplementation. Future direct comparisons between statin use and omega-3 supplementation are required to confirm the findings.