Miracle-dz/UniFAR-FTUs huggingface dataset:
Perceived Pain Extent Is Not Associated with Physical, Psychological, or Psychophysical Outcomes in Women with Carpal Tunnel Syndrome.

Objective Our aims were 1) to investigate whether perceived pain extent, assessed from the pain drawing, relates to clinical, psychological, and psychophysical outcomes in women with carpal tunnel syndrome (CTS); 2) to assess differences in pain extent depending on the presence of median or extramedian symptoms; and 3) to investigate differences in pain extent according to severity (minimal, moderate, or severe) or laterality (unilateral or bilateral) of CTS. Methods One hundred forty (N = 140) women with CTS completed pain drawings, which were subsequently digitized, allowing pain extent to be calculated. Clinical features including pain intensity (numerical pain rating scale, 0-10) and disability (Boston Carpal Tunnel Questionnaire), psychological features including depression (Beck Depression Inventory), and psychophysical variables (pressure pain and thermal pain thresholds) were assessed. Spearman rho correlation coefficients were used to reveal the correlations between pain extent and other outcomes. Differences in pain extent according to severity (minimal, moderate, severe) or laterality (unilateral, bilateral) and the presence of extramedian symptoms were also evaluated. Results No significant associations were identified between pain extent and clinical, psychological, or psychophysical outcomes. Women with extramedian symptoms (88%) exhibited a larger (P < 0.001) pain extent (total: 24.2% ± 13.5%) than women with median symptoms (12%; total: 12.2% ± 6.9%). Pain extent was not significantly different depending on the severity or laterality of the symptoms. Conclusions Pain extent in the upper extremity was not associated with clinical, psychological, or psychophysical variables and was not related to the severity or laterality of the symptoms in women with CTS.
Spike-inspired Deep Neural Network Design Using Binary Weight

Recently, deep learning has achieved great results in many fields such as image classification. However, most general artificial neural networks with deep learning require graphics processing unit (GPUs) because of hard workload and it requires a large amount of power consumption. When we think about humans, we can do a lot of things without consuming a lot of power compared to computers or electric appliances. Human beings or organisms transmit and recognize information through signal transmission between neurons. This study aims to develop a novel deep neural network architecture which simulates the signaling system between biological neurons, unlike conventional neural networks. We propose a novel spike-inspired deep neural network structure with the spike-inspired block using binary weight motivated by spike’s on and off mechanism. We have also designed a modified DenseNet architecture consisting of spike-inspired blocks. Our proposed method was tested and validated with MNIST datasets. The obtained results show the potential of a spike-inspired deep neural network.
Vision-Based Apple Classification for Smart Manufacturing

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.
A New Multi-Input Multi-output DC-DC Bidirectional Converter with MPPT Control Applicable for PV

In this paper, a non-isolated multiport bidirectional dc-dc converter is proposed. Since the proposed converter is multiport, the reliability of the proposed converter in sensitive load applications is high. Due to the input current sharing among the paths, the current passes through the semiconductor components is divided with a specific ratio in the primary side of the converter, then, the related losses are decreased significantly. The conversion ratio of the proposed converter is better than the buck-boost converters with cascaded or series topologies. The input current ripple of the proposed converter is less than the conventional bidirectional converters. Moreover, the controlling scheme of the proposed converter is easy and the proposed converter has low input current ripple which makes it suitable for utilizing in photovoltaic systems. Therefore, the maximum photovoltaic module power through a MPPT (maximum power point tracking) controller in varying radiation conditions is extracted. In this study, the analytical analysis of the proposed dc-dc bidirectional converter for all operating modes is provided and the required equations are calculated. Finally, to demonstrate the validity of the analysis, the simulation results are extracted from PSCAD/EMTDC.
The Effect of Gamma and Beta Radiation on a UVTRON Flame Sensor: Assessment of the Impact on Implementation in a Mixed Radiation Field

Due to the short path length of alpha particles in air, a detector that can be used at a distance from any potential radiological contamination reduces the time and hazard that traditional alpha detection methods incur. This would reduce costs and protect personnel in nuclear power generation and decommissioning activities, where alpha detection is crucial to full characterisation and contamination detection. Stand-off alpha detection could potentially be achieved by the detection of alpha-induced radioluminescence, especially in the ultraviolet C (UVC) wavelength range (180–280 nm) where natural and artificial background lighting is less likely to interfere with detection. However, such a detector would also have to be effective in the field, potentially in the presence of other radiation sources that could mask the UVC signal. This work exposed a UVC sensor, the UVTRON (Hamamatsu, Japan) and associated electronics (driver circuit, microprocessor) to sources of beta and gamma radiation in order to assess its response to both of these types of radiation, as may be found in the field where a mixed radiation environment is likely. It has been found that the UVTRON is affected by both gamma and beta radiation of a magnitude that would mask any UVC signal being detected. 152Eu generated 0.01 pulses per second per Bq through beta and gamma interactions, compared to 210Po, which generates 4.72 × 10−8 cps per Bq from UVC radioluminescence, at 20 mm separation. This work showed that UVTRON itself is more susceptible to this radiation than the associated electronics. The results of this work have implications for the use of the UVTRON as a sensor in a stand-off detection system, highlighting the necessity for shielding from both potential gamma and beta radiation in any detector design.
Large Scale Myanmar to English Neural Machine Translation System

Myanmar language is one of the low resource languages. There are a few resources that include bilingual sentences. Therefore, there are some difficulties to collect or crawl the parallel sentences. Existing Myanmar Translation systems use rule-based as well as statistical-based approach with the small amount of parallel corpus. Therefore, the performance of Myanmar to English Machine Translation system is still low. In this paper, large scale parallel corpus is prepared and introduces the Myanmar to English Neural Machine Translation system. Nowadays, neural machine translation models became a popular research field and it reaches good results in some languages. In this work, we did the experiment on the word-level model and character-level model based on neural method for Myanmar to English translation. The evaluation results show that neural machine translation models lead to improve the performance of Myanmar to English translation.
WM380 (675–700 GHz) Bandpass Filters in Milled, Split-Block Construction

Filters play an invaluable role in RF analysis and communications hardware, blocking unwanted signals, limiting bandwidth, attenuating harmonic components, etc. In waveguide construction for mm-wave frequencies, where the powers are generally low, the iris-coupled-resonator bandpass filter has proven to be very practical and amenable to easy machining. At Virginia Diodes, much of our technology has been constructed using standard “split-block” techniques, in which high-precision computer numerical control milling machines are used to mill out the features from solid blocks of metal. Typical machining tolerances are on the order of +/−5–10 μm. Construction of such filters to ∼200 GHz has been routine but pushing the operation into the Terahertz regime requires special considerations. Described here is a WM380 (WR1.5) filter for the 500–750 GHz waveguide band having a passband of 675–700 GHz. The filter is proof of concept for a 640 GHz heterodyne polarimeter, designed as a part of a Small Business Innovative Research (SBIR) Grant to develop technology for NASA's planned Aerosol, Cloud, and Ecosystems mission. From the very beginning, filter design and optimizations were carried out assuming the structure would be milled, hence necessitating that all internal features accommodate the finite radii of available machine tools. Construction of this filter required careful attention to machining tolerances to be able to push the milling machines to their absolute limits of +/−2.5 μm or better. Measured results of a batch of filters are shown and compared to simulations to illustrate just how well these filters can be made. Furthermore, the filters can be tuned by simple mechanical means and data are presented to illustrate how easily the filters can be adjusted. Additional modifications to the filter topology to simplify machining, and allow other construction techniques to be utilized are also demonstrated. Ultimately it should be possible to push the split block technology to manufacture iris coupled resonator filters for use at frequencies of well over 1 THz.
Plant Temperature Sensors

Temperature is one of the most important environmental signals for plants. High and low temperatures have a variety of effects that affect plant growth and development profoundly. Further, temperature is an indication of seasonal change. Plants must survive under severe conditions in winter and prepare to resume growth and reach their reproductive stage in the following spring. Recent studies have focused on plant mechanisms responsible for sensing temperature and the molecular systems underlying plant reactions in response to this signal. In this review, we describe how plants sense ambient temperature to adapt to ambient-temperature changes.
Within-Canopy Experimental Leaf Warming Induces Photosynthetic Decline Instead of Acclimation in Two Northern Hardwood Species

Northern hardwood forests are experiencing higher temperatures and more extreme heat waves, potentially altering plant physiological processes. We implemented in-situ leaf-level warming along a vertical gradient within a mature forest canopy to investigate photosynthetic acclimation potential of two northern hardwood species, Acer saccharum and Tilia americana. After seven days of +3 °C warming, photosynthetic acclimation was assessed by measuring differences between heated and control photosynthetic rates (Aopt) at leaf optimum temperatures (Topt). We also measured the effects of warming and height on maximum rate of Rubisco carboxylation, stomatal conductance, transpiration, and leaf traits: leaf area, leaf mass per area, leaf nitrogen, and leaf water content. We found no evidence of photosynthetic acclimation for either species, but rather Aopt declined with warming overall. We found slight shifts in LMA and Narea, leaf traits associated with photosynthetic capacity, after one week of experimental warming. T. americana LMA and Narea was lower in the upper canopy heated leaves than in the control leaves, contributing a shift in Narea height distribution in the heated leaves. T. americana showed evidence of greater resiliency to warming, with greater thermoregulation, physiological plasticity, and evapotranspiration. As expected, Aopt of A. saccharum increased with height, but Aopt of T. americana was highest in the sub canopy, possibly due to constraints on leaf water balance and photosynthetic capacity in the upper canopy. Thus, models relying on canopy height or light environment may incorrectly estimate vertical variation of photosynthetic capacity. If these species are not able to acclimate to warmer temperatures, we could see alteration of plant carbon balance of these two key northern hardwood species.
Correlating Power Efficiency and Lifetime to Programming Strategies in RRAM-Based FPGAs

There is currently a surge of interest in RRAM-based FPGAs because of their lower area, power loss resilience and their suitability for near-threshold operation. However, materializing lower dynamic and static power dissipation turns out to be challenging, since this depends on the capability of the technology to infer a large separation between RRAM resistance states. While programming strategies have been developed to achieve this, their side effects in terms of programming power overhead and lifetime of RRAM cells are typically overlooked. The justification brought by previous work consists of the typically-low number of runtime reconfigurations of FPGA devices. This paper intends to pave the way for new usage models of RRAM-based FPGAs, featuring augmented flexibility and dynamicity, thus lending themselves to emerging energy-and workload-adaptive computing systems. The paper thus captures and quantifies the fundamental correlation between programming power of RRAM cells, operational static and dynamic power of mapped designs on FPGA, and device lifetime.
Experience: Understanding Long-Term Evolving Patterns of Shared Electric Vehicle Networks

Due to the ever-growing concerns on the air pollution and energy security, many cities have started to update their taxi fleets with electric ones. Although environmentally friendly, the rapid promotion of electric taxis raises problems to both taxi drivers and governments, e.g., prolonged waiting/charging time, unbalanced utilization of charging infrastructures and reduced taxi supply due to the long charging time. In this paper, we make the first effort to understand the long-term evolving patterns through a five-year study on one of the largest electric taxi networks in the world, i.e., the Shenzhen electric taxi network in China. In particular, we perform a comprehensive measurement investigation called ePat to explore the evolving mobility and charging patterns of electric vehicles. Our ePat is based on 4.8 TB taxi GPS data, 240 GB taxi transaction data, and metadata from 117 charging stations, during an evolving process from 427 electric taxis in 2013 to 13,178 in 2018. Moreover, ePat also explores the impacts of various contexts and benefits during the evolving process. Our ePat as a comprehensive investigation of the electric taxi network mobility and charging evolving has the potential to advance the understanding of the evolving patterns of electric taxi networks and pave the way for analyzing future shared autonomous vehicles.
Constrained Multi-view NMF with Graph Embedding for Face Clustering

Face clustering that aims to group faces from the same people is a key component in face tagging and attribute analysis. Nonnegative matrix factorization (NMF) has shown competitiveness for clustering, but lacks of discrimination in practical tasks. In this paper, we propose a constrained multi-view NMF method with graph embedding (GCMNMF) for face clustering. GCMNMF incorporates the graph constraint and label constraint into an unified framework. GCMNMF aims to seek latent discriminative representations for multiple views, and maintain the within-view geometric structure simultaneously. In addition, an iterative optimization algorithm based on multiplicative rules is developed to efficiently solve GCMNMF. Experimental results on two real-world datasets demonstrate the effectiveness of the proposed method on face clustering tasks.
Configuration-Based Fingerprinting of Mobile Device Using Incremental Clustering

Device fingerprinting has lately received great attention due to its effectiveness in fraud detection, secure authentication, and user tracking. Whereas fingerprinting performs well on labeled device data using classification methods, there are several researches concentrated on unlabeled mobile device data and existing methods often lack in precision and efficiency. To overcome this challenge, we focus on the use of the mobile device’s configuration-related characteristics as a mean to build a device fingerprint, which allows to distinctively and reliably characterize each device. In addition, an incremental clustering approach is proposed to classify unlabeled device data into clusters on the basis of their similarity. Moreover, we customize individual distance threshold for each user according to their device configurations’ modifying frequency, in order to construct a precise authentication mechanism between users and devices. The proposed clustering model and device fingerprinting system are evaluated on 8220 fingerprints from 815 different devices. The experimental results demonstrate the effectiveness and efficiency of our algorithms.
Keystroke Dynamics Based Authentication Using GFM

This paper presents a novel method for keystroke dynamics based authentication by utilising Generalised Fuzzy Model (GFM), which is a combination of Mamdani-Larsen and Takagi-Sugeno fuzzy models. We make use of Gaussian Mixture Model (GMM) and GFM for modelling using the individual keystroke measurement types and combinations of measurement types of the keystroke dynamics. To validate GFM on keystroke dynamics in the real-world situation, it is tested on CMU dataset and the performance of this model is superior to that of GMM.
Calculation of Optimal Switching Angles for a Multilevel Inverter using NR and GA-a comparison

Multilevel inverters have multiple industrial applications, as well as their use in renewable energy applications. Cascade Multilevel Inverters with balanced power supplies are the most widely used in research today. This paper presents the application of the Genetic Algorithm and Newton Raphson methods, to solve the system of non-linear equations to obtain the optimum switching angles to decrease the total harmonic distortion (THD) value. With the objective of obtaining a lower THD value, a better-quality output voltage waveform can be achieved in a single-phase, multilevel, cascading inverter. Selective Harmonic Elimination (SHE) is a technique that allows the selection of switching angles so that low order harmonics can be eliminated, thus achieving a significant reduction in the THD value. A comparison of the results obtained in this paper shows that the Genetic Algorithm has a better approach to optimizing harmonics in multilevel inverters compared to the Newton Raphson method.
Semi-Centralized Control of Distributed Generation in Smart Grids

This paper proposes a semi-centralized intelligent control approach for voltage regulation in distribution grids based on sensitivity calculations. The model checks the voltage magnitudes of each end of each lateral in the system one by one, then if any of these violates the allowed voltage magnitudes, each node in a single lateral sends its reactive power capability and sensitivity information to the sensor located at the beginning node of that lateral. This information is sorted at the sensor and required voltage is computed and assigned to the bids one by one. This paper tests this approach on a modified 33 Node Distribution Test system with several renewable energy sources: photovoltaics (PVs) and wind turbines (WTs), and presents the numerical results based on a 15 minute resolution load data, PV outputs and WT outputs.
An Agricultural Perspective on Flying Sensors: State of the Art, Challenges, and Future Directions

In recent years, unmanned aerial systems (UASs) equipped with suitable sensors have been widely acknowledged by the scientific community as smart tools that can enable farmers to monitor and respond to crop growth progress in real time. These smart flying sensors have already opened up a huge potential for stakeholders-such as governments, farmers, UAS manufacturers, sensor developers, policy makers, and climate experts-to work together to seek optimal solutions in the context of precision agriculture.
Semi-Supervised Learning Techniques for Automated Fault Detection and Diagnosis of HVAC Systems

This work demonstrates and evaluates semisupervised learning (SSL) techniques for heating, ventilation and air-conditioning (HVAC) data from a real building to automatically discover and identify faults. Real HVAC sensor data is unfortunately usually unstructured and unlabelled, thus, to ensure better performance of automated methods promoting machine-learning techniques requires raw data to be preprocessed, increasing the overall operational costs of the system employed and makes real time application difficult. Due to the data complexity and limited availability of labelled information, semi-supervised learning based robust automatic fault detection and diagnosis (AFDD) tool has been proposed here. Further, this method has been tested and compared for more than 50 thousand TUs. Established statistical performance metrics and paired t-test have been applied to validate the proposed work.
An averaging strategy to reduce variability in target-decoy estimates of false discovery rate

Decoy database search with target-decoy competition (TDC) provides an intuitive, easy-to-implement method for estimating the false discovery rate (FDR) associated with spectrum identifications from shotgun proteomics data. However, the procedure can yield different results for a fixed dataset analyzed with different decoy databases, and this decoy-induced variability is particularly problematic for smaller FDR thresholds, datasets or databases. In such cases, the nominal FDR might be 1% but the true proportion of false discoveries might be 10%. The averaged TDC protocol combats this problem by exploiting multiple independently shuffled decoy databases to provide an FDR estimate with reduced variability. We provide a tutorial introduction to aTDC, describe an improved variant of the protocol that offers increased statistical power, and discuss how to deploy aTDC in practice using the Crux software toolkit.