Miracle-dz/UniFAR-FTUs huggingface dataset:
End-to-End Performance Evaluation of R-SV / R-GOOSE Messages for Wide Area Protection and Control Applications

Synchrophasor measurements play an important role in smart grid wide area protection. Recently, IEC 61850-90-5 has defined transmission of IEEE C37.118. 1 synchrophasors over IEC 61850 messages. Routable versions of SV / GOOSE messages mapped to network and transport layers called R-SV / R-GOOSE have been proposed. This paper presents the performance evaluation of End-to-End (ETE) delays in R-SV / R-GOOSE messages exchanged for different wide area protection and control applications. The computational delays at publisher and subscriber are calculated using the C-based open-source framework for generating R-SV and R-GOOSE. The communication network delays are calculated by performing extensive communication network simulations using network simulation tools. Finally, the End-to-End (ETE) delay performance considering both computational and communication delays of R-SV and R-GOOSE messages for realizing different wide area control and protection applications (WAPC) are presented.
Adaptive Sliding Mode Control for Converters in Smart Grid System with Disturbances

Different types of converters have been serving as indispensable components in energy conversion process of smart grid, playing important roles in energy saving and efficiency promotion. In this paper, an observer based composite adaptive discrete-time sliding mode controller (ADSMC) is proposed for buck converter systems with mismatch disturbances. To eliminate the effects of multiple disturbances, a time-delay disturbance observer is first applied to have accurate estimations and subsequently compensated by designing an modified sliding surface. Further more, to alleviate the control input chattering and ensure shorter arrival time, a new adaptive discrete-time sliding mode reaching law is designed and applied. Religious stability and convergence analysis of the closed-loop system are presented. Finally, the effectiveness of ADSMC is verified by simulation results. As compared to the conventional discrete-time sliding mode controller, the proposed ADSMC can obtain higher voltage tracking accuracy and stronger disturbance rejection ability.
Power Generation System using Solar and Wind - A Hybrid Approach

Today energy is the fundamental motivation for financial improvement. Yet, because of the gradual pace of ecological concern, sustainable power gives a huge interest. This elective power source is persistently accomplishing more noteworthy fame because of persistent decrease in fossil powers. It is the energy comes from sun, wind, downpour and so on Among the non-customary, sustainable power sources, sun powered energy manages the cost of incredible potential for change into electric power and wind turbine based power as well. Expanding power yield from a sun based framework is alluring to increment productivity. To amplify power yield, requirements to keep the boards adjusted with the sun. This paper manages the power age utilizing sun based power and wind power. The proposed framework guarantees the advancement of the change of sun-based energy into power by appropriately arranging the board as per the place of the sun as well as wind turbine.
A Review: Disease Detection in Wheat Plant using Conventional and Machine Learning Algorithms

Decade by decade entire globe has been experiencing rapid population growth that leads to many problems in food management and security. Wheat is a crucial crop that is cultivated in the world because wheat provides 20% of calories and protein for human beings. Due to its heavy contribution to human beings and cattle nutrition, there is a rapid growth in production in the past few decades. The wheat production was 106.41 million metric tons from India in 2021-2022. As per our survey, the total wheat production in Uttar Pradesh is 35 million metric tons approximately. As the cultivation is high, there is a high chance of diseases. As per nature, wheat cultivation is affected by many conditions, which reduces production from time to time. In the year 2021-2022, the total production of wheat in entire India is 106.41 million metric tons out of this 30% (31 million metric tons), which is a severe threat to food bonds. In such cases, the early detection of diseases which affects the plant most in the growth stage by applying various crop disease detection techniques is necessary. In this study, an overall analysis of the different methods used for disease detection in plants using artificial intelligence based on machine learning and deep learning techniques has been analyzed.
Prediction of Stock Price Fluctuations Based on Artificial Intelligence System

Stock price fluctuations prediction is the demonstration of attempting to decide the future worth of an organization stock or other monetary instrument exchanged on a monetary trade. The effective prediction of a stock’s future price will expand financial backer’s benefits. This paper attempts to resolve the issue of stock market prediction utilizing artificial intelligence (simulated intelligence) systems. The stock market prediction can be demonstrated in light of two chief examinations called specialized and key. The essential target of this work is to foster a Brain organization in light of LSTM to foresee stock market developments utilizing tweets.
A theory for negative resistance and memory effects in thin insulating films and its application to Au-ZnS-Au devices

Negative resistance and memory effects occurring in formed thin insulating films are described and new experimental results for zinc sulphide thin film devices are presented. Contrary to previous work the voltage for maximum current is shown not to be a constant. The switching time of the devices is interpreted in terms of an activation energy. Previous theories are shown to be unsatisfactory and a theory based on the thermal rupture of conducting filaments and applicable to all such formed devices is presented.
Resilience Assessment Framework For Distribution Systems Performance Under Extreme Conditions

The devastating impact of extreme weather-related events is increasingly evident on power grids, especially on distribution grids. The severity of their potential impact calls for 1) developing a suitable resilience assessment framework to capture the system performance and 2) assessing relevant mitigative strategies to lessen the impact of such events. This paper proposes a framework to identify grid vulnerabilities using the energy-at-risk concept to select, disconnect and isolate grid portions due to a resilience event. The proposed framework mainly consists of two steps; i) processing the utility's available infrastructure, i.e., a network model, possible switching combinations, and outage information for those combinations in a graph-based database and ii) implementing a novel optimal switching algorithm leveraging database and grid simulated metrics. These switching actions are generated to implement load curtailment in a rolling manner during anticipated grid scarcity conditions. In this study, a test case is created using two taxonomy feeders. It is simulated against an extreme temperature event, e.g., a long, relatively cold, and prolonged freeze peak, thereby creating stress on the grid. It is demonstrated that the proposed framework allows utilities to predict the energy-at-risk during such resilience events and design suitable outage management strategies.
Hybrid Electric Vehicle Charging Station Design - A Case Study

Thanks to the advent of electric vehicles (EV), their influx into the automobile market is expected to create additional pressure in the existing utility grid. The greatest challenge at the moment is the installation of an adequate number of charging stations in a shorter span of time. This work proposes a novel design of a hybrid micro-grid for Electric Vehicle charging station (EVCS) in a community based living space. The proposed work is investigated for the campus of National Institute of Technology, Trichy, India. This work aims to utilize the solar based renewable energy as the additional source of power generation besides the main utility grid. The hybrid micro-grid provides an environment friendly and cost effective way for the users to charge their electric vehicles. The proposed work develops an algorithm that allots charging slots and provides cost-savings for the users integrating a user interface for a user-friendly experience. A comprehensive business model is also developed that considers hardware installation costs, future economic variations, and potential expansion possibilities to assess the feasibility of implementation of the proposed work.
Platform for Radiological Emergency Agent-Based Integrated Simulation Model

This paper introduced the current status of the developed evacuation model called PRISM. It aims to conduct sensitivity or optimization research in planning radiological emergency strategies, which utilizes an agent-based model reinforced by infrastructure elements. The evacuation process is dependent on the source term diffusion, dose assessment model, path-finding, and large-scale agent behavior affected by the infrastructure elements.
Review of Deep Learning for Physiological Signal-based Healthcare Applications

Deep Learning has been considered as the successful approach for generated as well as discriminative task which is recently proved with medical analysis of two-dimensional images and it is highly potential even though the physiological data are in the form of signals of single dimension the beneficially should be analyzed using the novel approach of various healthcare applications based on the physiological signals using deep learning. The studies include recent scientific researchers done on deep learning based on the physiological signal data that includes the electrocardiogram (ECG), electromyogram (EMG), electrooculogram and electroencephalogram. The main goal of the research is the study of deep learning for the physiological signal-based model for various healthcare applications and development of physiological signal-based model for stress detection in working people and wearable physiological system. It helps to compare, categorize and comprehend the parameters off deep learning approaches for the analysis of physiological signals. Dataset resources, deep learning model, type of input data and healthcare applications are compared. In the perspective of signal data analyzing the physiology for healthcare applications and data modality as well as the concept of system CNN architecture based on deep learning and using data set.
Are We Really “Crossing The Boundary”? Assessing A Novel Integrated Math/Science Course

Cynthia Finelli, University of Michigan Cynthia J. Finelli, Ph.D., is Director of the Center for Research and Learning North and associate research scientist in the College of Engineering at the University of Michigan. In addition, she actively pursues research in engineering education and assists other faculty in their scholarly projects. She also is past Chair of the Educational Research and Methods Division of American Society of Engineering Education and guest co-editor for a special issue of the International Journal of Engineering Education on applications of engineering education research.
Complementary sliding control of non-linear systems

In this paper, a novel continuous complementary sliding control was proposed to improve the tracking performance given the available control bandwidth and the extent of parameter uncertainty. With this control law, we have shown that the guaranteed ultimate bound of the tracking error was reduced by half, as compared with the conventional continuous sliding control. The error transient response during the reaching phase has also been remarkably improved by the proposed control. To illustrate its superiority, we presented a composite complementary sliding control scheme for a cascade uncertain non-linear system, which includes the benchmark non-linear problem, TORA. Simulation results on TORA showed good responses to any initial conditions and to single-frequency sinusoidal disturbances.
An Analytical Solution for a Redundant Manipulator with Seven Degrees of Freedom

In this paper, an analytical solution for the inverse kinematics of a redundant manipulator with seven degrees of freedom and an offset rotation axis is presented. We provide a model for the redundant manipulator and introduce its self-motion. Considering geometric symmetry, sixteen sets of manipulator joint angles are obtained at once. Avoidance of singularities and obstacles is illustrated by simulation results.
Quantitative Evaluation of Blood Damage in a Centrifugal VAD by Computational Fluid Dynamics

This study explores a quantitative evaluation of blood damage that occurs in a continuous flow left ventricular assist device (LVAD) due to fluid stress. Computational fluid dynamics (CFD) analysis is used to track the shear stress history of 388 particle streaklines. The accumulation of shear and exposure time is integrated along the streaklines to evaluate the levels of blood trauma. This analysis, which includes viscous and turbulent stresses, provides a statistical estimate of possible damage to cells flowing through the pump. Since experimental data for hemolysis levels in our LVAD are not available, in vitro normalized index of hemolysis values for clinically available ventricular assist devices were compared to our damage indices. This approach allowed for an order of magnitude comparison between our estimations and experimentally measured hemolysis levels, which resulted in a reasonable correlation. This work ultimately demonstrates that CFD is a convenient and effective approach to analyze the Lagrangian behavior of blood in a heart assist device
Coarsening a knowledge structure

This paper describes an assessment procedure which does not deal explicitly with the whole knowledge structure, but rather operates on a faithful summary of the structure. The summary is achieved through the notion of coarsening of a structure, where the knowledge states are grouped into equivalence classes. We first define a stochastic assessment procedure running on the coarsening. Then we specify a set of possible properties for the equivalence relation. In particular, we define a notion of convexity for a knowledge structure. The results are justified by a simulation study.
Measures of Maximal Tactile Pressures during a Sustained Grasp Task Using a TactArray Device Have Satisfactory Reliability and Concurrent Validity in People with Stroke

Sensor-based devices can record pressure or force over time during grasping and therefore offer a more comprehensive approach to quantifying grip strength during sustained contractions. The objectives of this study were to investigate the reliability and concurrent validity of measures of maximal tactile pressures and forces during a sustained grasp task using a TactArray device in people with stroke. Participants with stroke (n = 11) performed three trials of sustained maximal grasp over 8 s. Both hands were tested in within- and between-day sessions, with and without vision. Measures of maximal tactile pressures and forces were measured for the complete (8 s) grasp duration and plateau phase (5 s). Tactile measures are reported using the highest value among three trials, the mean of two trials, and the mean of three trials. Reliability was determined using changes in mean, coefficients of variation, and intraclass correlation coefficients (ICCs). Pearson correlation coefficients were used to evaluate concurrent validity. This study found that measures of reliability assessed by changes in means were good, coefficients of variation were good to acceptable, and ICCs were very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s in the affected hand with and without vision for within-day sessions and without vision for between-day sessions. In the less affected hand, changes in mean were very good, coefficients of variations were acceptable, and ICCs were good to very good for maximal tactile pressures using the average pressure of the mean of three trials over 8 s and 5 s, respectively, in between-day sessions with and without vision. Maximal tactile pressures had moderate correlations with grip strength. The TactArray device demonstrates satisfactory reliability and concurrent validity for measures of maximal tactile pressures in people with stroke.
Silicon nanowires grown on iron-patterned silicon substrates

This letter reports the synthesis of silicon nanowires on iron-patterned silicon substrates in a controlled fashion using a method involving thermal evaporation of pure silicon powder. The positions of these silicon nanowires were controlled by depositing iron in desired areas on the substrates. Transmission electron microscopy, high-resolution transmission electron microscopy, and scanning electron microscopy images indicate that the products are straight crystalline silicon nanowires with diameters of 10–60 nm. The formation mechanism of the nanowires is discussed.
Convergence rate of the expected spectral functions of symmetric random matrices is equal to o (n-1/2)

Let us begin with a short review of investigations of the theory of random matrices. At first, random matrices were studied by many researchers in multidimensional statistical analysis (MSA), but these matrices had fixed dimensions. The necessity for studying of the spectrum of random matrices of increasing dimensions arose during the 1950s in nuclear physics, because the distributions of the eigenvalues of random matrices of high order have cumbersome forms and therefore, it was worthwhile to derive limit theorems for normalized spectral functions of random matrices. Wigner [45-47], Bronk [8], Lifshits [34], Mehta [36-37], Pastur [38], Arharov [1] and others tried to explain some empirical results in nuclear physics, disordered condenser systems and in MSA with the help of eigenvalues of random matrices of increasing dimension. Due to their works many results were discovered in the area of random matrices with increasing dimensions.
Association between Bullying Victimization and Aggression in Lebanese Adolescents: The Indirect Effect of Repetitive Negative Thinking—A Path Analysis Approach and Scales Validation

(1) Background: The purpose of the present study was to validate the Perseverative Thinking Questionnaire (PTQ) and the Buss–Perry Aggression Questionnaire-Short Form (BPAQ-SF) and test whether repetitive negative thinking plays an indirect role in the relationship between bullying victimization and aggression among Lebanese adolescents. (2) Methods: This cross-sectional study was conducted between January and May 2022 and included 379 Lebanese adolescent students (64.9% females, mean age 16.07 years). (3) Results: The three-factor solution of the PTQ and the four-factor solution of the BPAQ-SF showed excellent model fit. PTQ mediated the association between bullying victimization and physical aggression, verbal aggression, hostility, and anger. (4) Conclusions: This study expands on previous research by showing that repetitive negative thinking, an impactful socio-cognitive factor for students’ mental health, has a mediating (indirect) effect on the cross-sectional relationship between bullying victimization and aggression. This suggests that interventions aiming to prevent aggressive behaviors among adolescent students may be more effective if focused on repetitive negative thinking.
Evaluation and Monitoring of the Natural Toxin Ptaquiloside in Bracken Fern, Meat, and Dairy Products

Ptaquiloside, a naturally occurring cancer-causing substance in bracken fern, has been detected in the meat and milk of cows fed a diet containing bracken fern. A rapid and sensitive method for the quantitative analysis of ptaquiloside in bracken fern, meat, and dairy products was developed using the QuEChERS method and liquid chromatography–tandem mass spectrometry. The method was validated according to the Association of Official Analytical Chemists guidelines and met the criteria. A single matrix-matched calibration method with bracken fern has been proposed, which is a novel strategy that uses one calibration for multiple matrices. The calibration curve ranged from 0.1 to 50 µg/kg and showed good linearity (r2 > 0.99). The limits of detection and quantification were 0.03 and 0.09 µg/kg, respectively. The intraday and interday accuracies were 83.5–98.5%, and the precision was <9.0%. This method was used for the monitoring and exposure assessment of ptaquiloside in all routes of exposure. A total of 0.1 µg/kg of ptaquiloside was detected in free-range beef, and the daily dietary exposure of South Koreans to ptaquiloside was estimated at up to 3.0 × 10−5 µg/kg b.w. /day. The significance of this study is to evaluate commercially available products in which ptaquiloside may be present, to monitor consumer safety.
The Effect of Deep and Slow Breathing on Retention and Cognitive Function in the Elderly Population

The purpose of this study was to apply deep and slow breathing to the elderly, who can be classified as potential dementia patients, to confirm changes in the cognitive functions of learning and memory. Forty-five elderly subjects were randomly and evenly divided into a rest group (RG), a before group (BG), and an after group (AG). Measurements of their cognitive abilities were obtained before testing (PT), 30 min after learning (STT), and 24 h after learning (LTT). After PT measurements were obtained from all three groups, the RG and AG conducted new cognitive skills learning, while the BG performed deep and slow breathing (DSB) for 30 min before learning new cognitive skills. After all the three groups underwent 30 min of learning, the STT was performed. Subsequently, the AG performed DSB for 30 min. Finally, 24 h after learning, the LTT was conducted for all three groups. Changes were compared and analyzed by measuring the retention of new cognitive skills and attention, working memory, and spatial perception of cognitive functions. A two-way repeated measure analysis of variance measured the effect of the application of DSB in the three groups. These results demonstrated a significant interaction of time and time*group in all measurements of retention and attention, working memory, and spatial perception. This study confirms the benefit of DSB as part of a dementia prevention training protocol.
Skin Microbiota: Setting up a Protocol to Evaluate a Correlation between the Microbial Flora and Skin Parameters

The concept of skin microbiota is not really clear and more accurate approaches are necessary to explain how microbial flora can influence skin biophysical parameters in healthy individuals and in pathology patients with non-infectious skin disease. The aim of this work is to provide a suitable, fast and reproducible protocol to correlate skin parameters with the composition of skin microbiota. For this purpose, the work was split into two main phases. The first phase was focused on the selection of volunteers by the administration of a specific questionnaire. The skin microbiota was then collected from the forehead of selected volunteers as a test area and from the shoulder as control area. On the same skin area, the biophysical parameters, such as trans-epidermal water loss (TEWL), sebum level (SL), porphyrin intensity, keratin content and stratum corneum water content were taken. All parameters were taken at t0 and after 15 days without changes in the volunteers’ lifestyle. A strong correlation was found between forehead and shoulder area for porphyrin intensity, pH and TEWL parameters, and between Cutibacterium acnes and some biophysical parameters both in the forehead and the shoulder area. The procedural setup in this work represents the starting point for evaluating problematic skins and the efficacy of cosmetic products or treatment against skin dysbiosis.