Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Multi-layer Power Enterprise Data Management Architecture Based on Big Data

In view of the characteristics of current electric power data, such as massive, high dimensional and multi-source heterogeneous, to meet the development needs of electric power enterprises, a multi-layer power enterprise data management architecture based on big data is proposed in the paper on the basis of summarizing the concept, development status, major difficulties and challenges in the field of electric power data control. Firstly, a general mathematical model of data management and control architecture is established with reference to the characteristics of electric power data, and the key technologies of its data processing are described algorithmically. Then, after analyzing and referring to the idea of big data platform architecture construction, a multi-layer system architecture for data management and control of electric power enterprises is further proposed. The architecture is divided into three layers: infrastructure virtualization layer, cloud computing support platform layer and power data application layer, which truly realizes the integration of physical facilities, data resources and business applications in one while taking into account security. Finally, the possible future research directions in this field are summarized and prospected.
Transcriptome Reveals the Molecular Mechanism of the ScALDH21 Gene from the Desert Moss Syntrichia caninervis Conferring Resistance to Salt Stress in Cotton

The desert moss Syntrichia caninervis has proven to be an excellent plant material for mining resistance genes. The aldehyde dehydrogenase 21 (ScALDH21) gene from S. caninervis has been shown to confer tolerance to salt and drought, but it is unclear how the transgene ScALDH21 regulates tolerance to abiotic stresses in cotton. In the present work, we studied the physiological and transcriptome analyses of non-transgenic (NT) and transgenic ScALDH21 cotton (L96) at 0 day, 2 days, and 5 days after salt stress. Through intergroup comparisons and a weighted correlation network analysis (WGCNA), we found that there were significant differences between NT and L96 cotton in the plant hormone, Ca2+, and mitogen-activated protein kinase (MAPK) signaling pathways as well as for photosynthesis and carbohydrate metabolism. Overexpression of ScALDH21 significantly increased the expression of stress-related genes in L96 compared to NT cotton under both normal growth and salt stress conditions. These data suggest that the ScALDH21 transgene can scavenge more reactive oxygen species (ROS) in vivo relative to NT cotton and improve cotton resistance to salt stress by increasing the expression of stress-responsive genes, responding quickly to stress stimuli, enhancing photosynthesis and improving carbohydrate metabolism. Therefore, ScALDH21 is a promising candidate gene to improve resistance to salt stress, and the application of this gene in cotton provides new insights into molecular plant breeding.
Beam Tracking Scheme Based On Particle Filter Under Device Theoretical Trajectory Information

Due to the frequency channel changes between mobile device and base station, beam tracking in a high-dynamic environment is still a challenging task for high accuracy with low probability of beam mismatch. Recently, beam tracking techniques based on the particle filter algorithm have been proposed to track nonlinear targets. However, the particle filter needed the device state transition model, which in high-dynamic environments is difficult to perfectly fit the actual device state. This study focused on tracking mobile devices in the scenario of device theoretical trajectory information (DTTI), by giving a corresponding compensation and utilizing the difference in signal strength and phase between devices in actual and theoretical positions of the tracking beam. In this way, the add motion compensation particle filter (AMC-PF) does not demand perfect information about the device state transition equation. Through simulations and analysis, the AMC-PF beam tracking scheme in a high-dynamic fast tracking environment has lower average tracking error than the normal sequence importance resampling particle filter (SIR-PF) beam tracking scheme in DTTI high dynamic environment.
Environmental delegation versus sales delegation: a game-theoretic analysis

Recently, in their 2019 paper, Poyago-Theotoky and Yong consider a managerial Cournot duopoly with pollution externalities and emission taxes and propose an explicit environmental incentive in a managerial compensation contract. The authors compare several exogenous equilibria emerging in the symmetric sub-games in which the owner offers either the environmental delegation contract or the standard sales delegation contract: abatement and social welfare (resp. emission taxes) under environmental delegation are higher (resp. lower) than under sales delegation. The present work extends their model using a game-theoretic approach to analyse the asymmetric sub-games, in which only one firm adopts the environmental contract, and adds the contract decision stage. Results show that the environmental contract never emerges as the unique sub-game perfect Nash equilibrium of this non-cooperative managerial decision game. Indeed, if the green R&D technology is efficient, the sales contract emerges as the unique Pareto-inefficient Nash equilibrium. Otherwise, if the green R&D technology is inefficient, multiple Nash equilibria in pure strategies exist (coordination game). Our findings offer direct policy implications.
Underpinning Learning Theories of Medical Educational Games: A Scoping Review

Background Gamification is the process of game thinking and game mechanics to attract learners and solve problems. It is a unique growing phenomenon in education and training programs. Educational games, by application of game design and game elements in learning environments, motivate students to learn and improve the teaching and learning process. Herein, this scoping review presents an overview of the theoretical underpinnings of gamification that is crucial in understanding the theoretical pillar of successful educational games. Methods This scoping review follows Arksey and O’Malley’s stages of scoping review. In this review, the gamification in medical education articles that implicitly or explicitly presented underpinning learning theories of gamification in medical education was retrieved. So, keywords such as gamification, learning theories, higher education, and medical education were searched in Scopus, PubMed, WEB OF SCIENCE, EMBASE, ERIC, and Cochrane Library from 1998 to March 2019. Results The search indicated 5416 articles which were narrowed down by title and abstract relatedness. 464 articles entered the second phase of the study and after reviewing their full text, finally, 10 articles which were explicitly and implicitly reported the underpinning learning theories remained. Conclusion Gamification is a strategy using game design techniques for non-game experiences for more effective learning and provides a more attractive environment for teaching and learning. Designing gamification based on learning theories (behavioral, cognitive, and constructivist), makes them more efficient, and the application of learning theories in designing gamification is recommended.
Urinary complement profile in IgA nephropathy and its correlation with the clinical and pathological characteristics

Background and objectives The activated complement profile in IgA nephropathy (IgAN) is still unclear. Our study investigated the profile of urinary complements in IgAN patients and its correlations with clinical and pathological characteristics. Methods Urinary protein abundance was detected by liquid chromatography-tandem mass spectrometry (LC–MS/MS) in 50 IgAN, 50 membranous nephropathy (MN), and 68 healthy controls (HC). Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to identify differentially expressed proteins in IgAN patients. The differentially expressed complement proteins were screened in IgAN patients, and their correlations with laboratory or pathological parameters were analyzed. Thereafter, 7 complement components were validated by enzyme-linked immunosorbent assay (ELISA) in the urine samples of 45 IgAN patients. Results There were 786 differentially expressed proteins between IgAN and HC. KEGG analysis showed that differentially expressed urinary proteins in IgAN were enriched with complement. Of these, 67% of urinary complement protein abundance was associated with the estimated glomerular filtration rate. The urinary complement-related protein collectin12 (colec12), complement H factor (CFH), complement H factor-related protein 2 (CFHR2), and complement B factor (CFB) were positively correlated with serum creatinine; colec12, CFHR2, CFB, and C8g were positively correlated with glomerulosclerosis; CFH, CFHR2, C8g, and C9 were positively correlated with tubular atrophy/interstitial fibrosis. Conclusion Abnormally increased components of complement pathways significantly correlate with reduced renal function, proteinuria, and renal histological damage in IgAN. It could provide a potential biomarker panel for monitoring IgAN and provide clues for therapeutic choice targeting complement system of IgAN patients.
Esophageal Food Impaction and Foreign Object Ingestion in Gastrointestinal Tract: A Review of Clinical and Endoscopic Management

Foreign bodies in the digestive tract represent a frequent pathology for the emergency service. Foreign bodies are represented by impacted food bolus or voluntarily and involuntarily swallowed objects. Involuntary swallowing of objects occurs most frequently in children, especially between six months and three years of age. Food impaction is mostly encountered among adults with different pathologies. For the removal of swallowed foreign bodies, digestive endoscopy is the gold standard method, being successful in over 95% of cases without significant complications. For the endoscopic management of foreign bodies, it is crucial to be aware of the indications, devices, techniques and patient preparation in order to achieve successful and safe removal from the digestive tract. Recommendations in this article are based on a review of the literature and extensive personal experience.
A Critical Note on the Guide to the Expression of Uncertainty in Measurement (GUM)

In 1809 Carl Friedrich Gauss' masterpiece “Theoria Motus Corporum Coelestium in sectionibus conicis solem ambientium” on the motion of celestial bodies was published. This book contains also a first version of the famous error propagation formula, which is still the basis of modern metrology and of any relevant national and international standard. Gauss' ideas are reflected in the GUM, the “Guide to the Expression of Uncertainty in Measurement”, published in 1993 by ISO, which is a key document for national measurement institutes and industrial calibration laboratories for evaluating uncertainty in the output of a measurement system.
Cattaneo-Christov double diffusion based heat transport analysis for nanofluid flows induced by a moving plate

Abstract Hybrid nanofluids have gained much attention due to their better stability, enhanced thermal conductivity, and physical strength. This manuscript investigates nanofluid flow, induced due to an infinite plate, which is moving (at a constant velocity) toward/receding from normal stagnation point flow. The surface of the plate is suspended and immersed with water as a base fluid and nanoparticles, namely Copper and Alumina. The flow is governed by Reynolds number (Re), which is proportional to the constant velocity of the moving plate and is an exact reduction of the Navier–Stokes equations. Moreover, Hiemenz’s planar and Homann’s axisymmetric flows normal to the stagnation point are considered. Heat transport analysis is carried out by using Cattaneo–Christov theory with Ohmic heating and heat source/sink effects. The governing equations are solved by bvp4c in MATLAB. The behavior of skin friction, flow, and energy distribution is perceived by variation of pertinent parameters. The numerical and asymptotic solutions are computed for the wall shear stress parameter. It is seen that the numerical solution matches its asymptotic behaviors over an intermediate range of small and large valued Reynolds number. The asymptotic values (small-Re) of wall stress reduces for plate advancing toward the stagnation-point flow and receding from the stagnation-point flow for Hiemenz and Homann flow. The increment in magnetic parameter reduced the fluid flow by generating a resistive force against it; however, the energy of the system is enhanced because of it. The augmentation of nanofluid volume fraction initiates the random motion among the nanoparticles which raises the temperature field. Thermal relaxation time parameter causes the particles to require supplementary time for the conduction of heat toward the adjacent particles, which declines the thermal transport. In general, the thermal transport is enhanced for the hybrid nanofluids rather than mono nanofluids.
Efficient polarimetric BRDF transformations

In order to characterize a target, the basic information that is of interest is spectral, polarization and distance. Imaging spectropolarimetry is a powerful tool for obtaining the polarization state of a scene and to discriminate manmade objects in a cluttered background. With respect to polarization, often the measurements are limited to the first three components of the Stokes vector, excluding circular polarization. The scene is therefore characterized in four directions of linear polarization, I0, I90, I45 and I135. An efficient polarimetric BRDF model defined in a local coordinate system has recently been published. The model will now be extended to a global coordinate system for linear polarized radiation. This includes the first three elements of the Stokes vector. We will provide examples for surface of intrinsically different scattering materials, bulk scattering materials and clear coated surfaces.
Long-Term Prestress Loss Calculation Considering the Interaction of Concrete Shrinkage, Concrete Creep, and Stress Relaxation

In order to accurately calculate the long-term prestress losses of prestressed tendons, a time-varying model of long-term prestress loss considering the interaction between concrete shrinkage, creep, and the stress relaxation of prestressed tendons was constructed. Then, a method for calculating the long-term prestress losses of concrete structures was developed. A long-term prestress loss test of a prestressed concrete T-beam in a long-term field test environment was carried out. The measured values of long-term prestress losses are compared with the calculated results of JTG 3362-2018, AASHTO LRFD-2007, and the time-varying law model. The results show that the long-term effective tension of the T-beam decreases gradually with the increase in the load holding time. At the beginning of loading, the tensile force changes rapidly and then gradually slows down. The later the tensile age or the higher the initial loading stress level, the smaller the long-term prestress losses of the prestressed tendons. The long-term prestress loss values calculated by JTG 3362-2018, AASHTO LRFD-2007, and the time-varying law model increase with the increase in the load holding time. In the early stage of loading, the rate of change slows down and tends to be stable. The calculated results of JTG 3362-2018 and AASHTO LRFD-2007 are significantly different from the measured values. However, the calculated results of the time-varying law model are in good agreement with the measured values. The average coefficients of variation of the long-term prestress loss calculated by JTG 3362-2018, AASHTO LRFD-2007, and the time-varying law model are 17%, 10%, and 5%, respectively. The time-varying law model of the long-term prestress losses of prestressed tendons is accurate, and the long-term prestress loss of prestressed reinforcement can be predicted effectively.
A Simple Equivalent Circuit Model for Metasurface Based Reflection-Type Polarization Converter

A simple equivalent circuit model (ECM) is proposed to characterize reflection-type polarization converters comprising anisotropic metasurfaces. The impedance matrix of the metasurface in the xy coordinate system is obtained by multiplying the impedance matrix in the diagonal axis with a rotated matrix. The diagonal impedance matrix can be calculated by a simple LC series-parallel circuit. Some typical polarization converters with different metasurface shapes are demonstrated to illustrate the effectiveness of the proposed method.
Approaches for Plant Leaf Classification: A Review

Classification of leaves play a vital role in agriculture and Ayurveda. Majority of the research in this field depends upon plant morphology. Among the different parts of the plant, leaves are easy to locate, abundant and occur throughout their lifetime. Plant disease detection, weed management and plant deficiency detection are a few research areas that focus on leaf classification. The size, color and shape may vary depending on geographical conditions and seasonal changes. Hence, classification of leaves is a tedious task. Different approaches have been derived in the last few years. In this paper, various state-of-the-art techniques and their challenges are studied in detail. Comparisons of different approaches in terms of accuracy have also been discussed.
A Malware Detection Approach Based on Deep Learning and Memory Forensics

As cyber attacks grow more complex and sophisticated, new types of malware become more dangerous and challenging to detect. In particular, fileless malware injects malicious code into the physical memory directly without leaving attack traces on disk files. This type of attack is well concealed, and it is difficult to find the malicious code in the static files. For malicious processes in memory, signature-based detection methods are becoming increasingly ineffective. Facing these challenges, this paper proposes a malware detection approach based on convolutional neural network and memory forensics. As the malware has many symmetric features, the saved training model can detect malicious code with symmetric features. The method includes collecting executable static malicious and benign samples, running the collected samples in a sandbox, and building a dataset of portable executables in memory through memory forensics. When a process is running, not all the program content is loaded into memory, so binary fragments are utilized for malware analysis instead of the entire portable executable (PE) files. PE file fragments are selected with different lengths and locations. We conducted several experiments on the produced dataset to test our model. The PE file with 4096 bytes of header fragment has the highest accuracy. We achieved a prediction accuracy of up to 97.48%. Moreover, an example of fileless attack is illustrated at the end of the paper. The results show that the proposed method can detect malicious codes effectively, especially the fileless attack. Its accuracy is better than that of common machine learning methods.
MDTL: A Novel and Model-Agnostic Transfer Learning Strategy for Cross-Subject Motor Imagery BCI

In recent years, deep neural network-based transfer learning (TL) has shown outstanding performance in EEG-based motor imagery (MI) brain-computer interface (BCI). However, due to the long preparation for pre-trained models and the arbitrariness of source domain selection, using deep transfer learning on different datasets and models is still challenging. In this paper, we proposed a multi-direction transfer learning (MDTL) strategy for cross-subject MI EEG-based BCI. This strategy utilizes data from multi-source domains to the target domain as well as from one multi-source domain to another multi-source domain. This strategy is model-independent so that it can be quickly deployed on existing models. Three generic deep learning models for MI classification (DeepConvNet, ShallowConvNet, and EEGNet) and two public motor imagery datasets (BCIC IV dataset 2a and Lee2019) are used in this study to verify the proposed strategy. For the four-classes dataset BCIC IV dataset 2a, the proposed MDTL achieves 80.86%, 81.95%, and 75.00% mean prediction accuracy using the three models, which outperforms those without MDTL by 5.79%, 6.64%, and 11.42%. For the binary-classes dataset Lee2019, MDTL achieves 88.2% mean accuracy using the model DeepConvNet. It outperforms the accuracy without MDTL by 23.48%. The achieved 81.95% and 88.2% are also better than the existing deep transfer learning strategy. Besides, the training time of MDTL is reduced by 93.94%. MDTL is an easy-to-deploy, scalable and reliable transfer learning strategy for existing deep learning models, which significantly improves model performance and reduces preparation time without changing model architecture.
Islanding Operation and Seamless Resynchronization in DFIG–SPV System Equipped With Functional Enhancements Through ATLLAD Control Framework

Doubly fed induction generator (DFIG) and solar photovoltaic (SPV) array-equipped hybrid wind-solar generation system is increasingly acknowledged as a viable techno-economic solution for remote-area electrification. Despite the progress made in this frontier, two major hurdles still persist. The first hurdle relates to the power inadequacy for local loads and the mode transitional features in DFIG–SPV system, during grid outages, which are rather frequent in remote areas. Another issue in DFIG–SPV system at remote areas is its functional vulnerability at the irregular grid conditions like unbalance and distorted voltages. Taking these aspects into consideration, this article implements a new arc-tangent least logarithmic absolute difference (ATLLAD) based control framework for DFIG–SPV system. The operational scheme ensures disruption-free load power at islanding scenarios, whereas the control strategy enables seamless transitions without inducing overcurrents or voltage spikes during mode switching. To deal with DFIG-side functional vulnerability at irregular grid conditions, ATLLAD-based control uses an innovative approach to impart an additional degree of freedom for the simultaneous suppression of DFIG torque oscillations and reduction in DFIG stator current disturbances. Meanwhile, the enhancement at the grid side involves the injection of improved quality grid currents despite the irregularities in the grid voltages and the load currents. Test results from an experimental rig of the DFIG–SPV system illustrate the satisfactory performance of the ATLLAD control approach.
Information Retrieval from Facebook for Social Network Analysis

Complex Event Processing (CEP) is becoming increasingly important for effectively modeling and early identification of potentially dangerous events in many application domains, such as video surveillance, business process management, and social network analysis. Consequently, there is a growing need to automatically extract information from social networks that could be useful for further and deeper analysis. As a result, in this paper we describe an application that can automatically detect users’ interactions with Facebook, which has been used by many social network analysis applications and can serve as a guideline for researchers in this field.