Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve of Neonatal Seizure Detection based on EEG Signal using K-mean clustering

Seizures are one of the most frequent medical emergencies. They are identified by visual electroencephalography (EEG) results assessment, and neuroscientific professionals are responsible for their care. This method takes a long time, and the results are inconsistent. A system for identifying epileptic seizures from EEG data that is collected from healthy individuals and epileptic patients is presented in this research to solve this issue. This system is based on a study of EEG data utilizing the Discrete Wavelet Transform (DWT) and both linear and nonlinear classifiers, Neonatal human EEG recordings are included in the dataset used in proposed model. A 19-channel EEG equipment was used to record the brainwave activity of 79 term newborns referred to the NICU at the Helsinki University Hospital. Five patients with structural seizure and non-seizure annotations were chosen for the experimental studies from these databases. Three experts independently identified the presence of seizures and non-seizures in the EEGs using visual interpretation, while statistical features were retrieved using the K-mean algorithm and the Least-Squares Support Vector Machine (LS-SVM) was utilized to identify seizures. To improve the model’s performance, (statistical and Hjorth and zero crossing) features were extracted, K-means’ feature selection using LS-SVM, and obtaining high accuracy 99.58% by P3-O1.
Pathway for Water Transport through Breathable Nanocomposite Membranes of PEBAX with Ionic Liquid [C12C1im]Cl

Water transport through membranes is an attractive topic among the research dedicated to dehydration processes, microenvironment regulation, or more simply, recovery of freshwater. Herein, an atomistic computer simulation is proposed to provide new insights about a water vapor transport mechanism through PEBAX membranes filled with ionic liquid (IL) [C12C1im]Cl. Starting from experimental evidence that indicates an effective increase in water permeation as the IL is added to the polymer matrix (e.g., up to 85·10−3 (g·m)/(m2·day) at 318.15 K for PEBAX@2533 membranes loaded with 70% of IL), molecular dynamics simulations are proposed to explore the key role of IL in water transport inside membranes. The polar region composed of anions and cationic head groups of the IL is demonstrated to serve as the pathway for water transport through the membrane. Water molecules always stay near the pathway, which becomes wider and thus has a larger water-accessible area with increasing IL concentration. Hence, the diffusion coefficients of water molecules and ions increase as the IL concentration increases. The simulation provides useful indications about a microscopic mechanism that regulates the transport of water vapor through a kind of PEBAX/IL membrane, resulting in full agreement with the experimental evidence.
Design of a 100 kW Axial Flux Permanent Magnet Direct Drive Machine for a Hybrid Electric Vehicle

The commercialization and development of electric and hybrid electric vehicles (HEVs) are one of the main steps in transportation electrification. The design of the optimum electric machines as the fundamental part of the propulsion system of electric vehicles (EVs) and HEVs is of great importance. The optimization of the electric machine in large search space is not practical. Therefore, it is crucial to provide an initial design to limit the optimization variables and reduce the search space. This paper aims to provide a step-by-step guideline for the design of an axial flux permanent magnet synchronous machine (AFPMSM) that operates as a direct drive machine in an HEV. The performance of the designed machine is validated using 3-D finite element analysis results. The designed AFPMSM performance parameters are reported over a wide range of operating points within the torque speed envelope.
The Optimization of RBFNN Gearshift Controller Parameters for Electric Vehicles Using PILCO Reinforcement Learning

In order to improve the efficiency of gearshift controller parameter optimization and obtain a good gearshift controller control effect, this article proposes an optimization method for electric vehicle transmission gearshift controller, and selects dual clutch transmission as the research object that establishes an 11-degree-of-freedom gearshift dynamics model and a feedforward-feedback gearshift control model. The feedforward control is chosen from the target trajectory given by the Legendre pseudo-spectral approach, and the feedback controller is a Gaussian kernel radial basis function neural network controller. The feedback controller performs parameter optimization by the Probabilistic Inference for Learning Control (PILCO) reinforcement learning algorithm to obtain a control strategy that matches the actual gearshift conditions. By comparing how well the main/secondary moving disk can follow the target trajectory during various optimization iterations, it is verified that the algorithm requires only a few experiments to complete the optimization, and the optimized Radial Basis Function Neural Network (RBFNN) control has a better control effect by comparing the results of different iterations. Applying the learned controller to various slope and load circumstances yields data that demonstrate that all can have an obvious optimization effect with good robustness. Additionally, the reinforcement learning technique suggested in this research can be used for various gearshift controller parameter optimization to assist engineers and technicians in increasing the effectiveness of their Research and Development.
Improving Subset Linear Discriminant Analysis Algorithm Using Overlapping Clustering

In recent years, there have been many proposals to improve the performance of traditional linear discriminant analysis (LDA). One of these is subset-improving linear discriminant analysis (S-LDA), which is based on clustering the whole set of classes into subsets and performing the LDA locally on these subsets. However, this algorithm suffers from an improper mapping of the classes to the corresponding subset during the testing procedure due to the inevitable discrepancy between the images used for training and those for testing. This discrepancy is caused either by spatial distortion or noise. The wrong mapping is severe when the number of data samples is small which is a common scenario in biometric datasets. In this paper, overlapping clustering is proposed for class clustering, to overcome the aforementioned problem. The proposed algorithm outperforms the S-LDA by 35.4% in mapping accuracy when using the PolyU palmprint database, 36.96%, resp. 33.92% when using left resp. Right palm images from the IIT Delhi Touchless Palmprint Database.
The Influence of Human Resource Practices on Internal Customer Satisfaction and Organizational Effectiveness

It is generally believed that the impact of Human Resource Practices on internal customer satisfaction can create comparative advantage for the organizational performance. The main objective of this study was to find out the impact of Human Resource Practices on internal customer satisfaction and organizational effectiveness. The impact of human resource practices on the overall performance of organizations has been a leading subject of research and the results have been encouraging, indicative of positive relationship between Human Resource practices and organizational effectiveness. Data was collected through personally administered questionnaire-based survey from 290 banking personnel of Pakistan. Structural equation modeling was used to examine the anticipated model. The results showed that some Human Resource Practices appear to be linked to internal customer satisfaction and organizational effectiveness. The implications for practitioners were to modify and emphasize certain human resource practices, and to emphasize the role of internal customers for organizational effectiveness enhancement. These findings revealed the importance of internal customers in enhancing employee morale, organizational commitment, employee productivity, turnover rate and the organization’s ability to attract talent.
Evaluation of Agronomic Efficiency with Regional Source of Natural Potassium in the Brazilian Midwest

Brazil plays an important role in global food production, but faces challenges due to its dependence on imported fertilizers. To reduce this vulnerability of the agricultural sector, the use of natural sources such as agrominerals, also known as rock dust, is gaining ground. The objective of this study was to characterize and evaluate a new source of natural potassium, extracted from a deposit located in the Brazilian Midwest, through geological characterization and agronomic evaluation through yield tests, soil and foliar potassium content. The tests were conducted in a greenhouse with seven treatments, two soil types and four replications, with millet, soybeans and beans in succession to evaluate the residual effect of the product. The agromineral was classified as a nepheline syenite saprolite with an average K2O content of 11.6%. The effects of the agromineral were promising, especially in the medium term. In the case of beans, at the standard dose of 60 K2O, yields in clay soils were 3.6 Mg ha-1 higher than in the 60 KCl treatment, which obtained only 2.3 Mg ha-1, probably due to absorption or leaching losses, since the effect evaluated was residual in nature. For all the crops evaluated, the agromineral showed an increase in potassium levels in both the soil and the leaves compared to the control group, indicating that it is a potential alternative to gradually reduce the use of traditional chemical fertilizers. Field trials are recommended to validate these benefits, taking into account more realistic environmental variations.
Early Career Materials Researcher Prospective

This article presents an overview of the current status and future prospects of integrated nonlinear photonics in the long-wave infrared (LWIR) spectrum, spanning 6 to 14 μm. This range is well-suited for applications such as chemical identification, environmental monitoring, surveillance, search and rescue, and night vision. Nevertheless, the advancement of a mature, low-loss chip-level platform for the LWIR remains in its infancy. We examine the materials growth techniques, and fabrication methods associated with integrated nonlinear photonics in the LWIR, highlighting promising platforms like chalcogenide glass, single-crystalline diamond, Ge/SiGe, and III–V compounds. Furthermore, we explore loss mechanisms, dispersion engineering, nonlinear generation of broadband supercontinuum and frequency combs, and device performance, encompassing photodetectors and modulators. Lastly, we propose a roadmap for the future development of integrated nonlinear photonics in the LWIR.
Exogenous glutathione maintains the postharvest quality of mango fruit by modulating the ascorbate-glutathione cycle

Background Mango fruit is prone to decay after harvest and premature senescence, which significantly lowers its quality and commercial value. Methods The mango fruit (Mangifera indica L.cv. Guixiang) was treated with 0 (control), 2, 5, and 8 mM of reduced glutathione (GSH) after harvest. The fruit was stored at 25 ± 1 °C for 12 days to observe the changes in the antioxidant capacity and postharvest quality. Results Compared with the control, the 5 mM GSH treatment significantly decreased the weight loss by 44.0% and 24.4%, total soluble solids content by 25.1% and 4.5%, and soluble sugar content by 19.0% and 27.0%. Conversely, the 5 mM GSH treatment increased the firmness by 25.9% and 30.7% on days 4 and 8, respectively, and the titratable acidity content by 115.1% on day 8. Additionally, the 5 mM GSH treatment decreased the malondialdehyde and hydrogen peroxide contents and improved the antioxidant capacity of mango fruit by increasing the superoxide dismutase and peroxidase activities and upregulating the expression of the encoding genes. Meanwhile, the higher levels of monodehydroascorbate reductase, dehydroascorbate reductase, and glutathione reductase enzyme activities and gene expressions accelerated the AsA-GSH cycle, thereby increasing the accumulation of AsA and GSH and maintaining the redox balance. Conclusions Overall, the experimental results suggest that 5 mM GSH maintains high antioxidant capacity and postharvest quality of mangoes and can use as an effective preservation technique for postharvest mangoes.
Adaptive Event-Triggered Output Feedback Fuzzy Control for Nonlinear Networked Systems With Packet Dropouts and Actuator Failure

This paper studies the problem of adaptive event-triggered dynamic output feedback fuzzy control for nonlinear networked control systems. Two crucial factors, packet dropouts and actuator failure, are taken into consideration simultaneously. Takagi–Sugeno fuzzy model is introduced to describe considered systems. The Bernoulli random distribution process is employed to depict the phenomenon of data missing. The actuator failure model is adopted to depict actuator failure. An innovative adaptive event-triggered strategy is built to save computational resource. In the light of Lyapunov stability theory, a fuzzy dynamic output feedback controller is designed to guarantee the stochastic stability and $\mathcal {H}_{\infty }$ performance for considered systems. Finally, simulation results are provided to demonstrate the usefulness of the proposed control strategy.
Climate change and agriculture

1. Overview 2 1.1. Context of National Communications 2 1.2. Effects of current climate variability 2 1.3. Drivers of agricultural response to climate change 2 1.4. Previous studies 5 2. Adaptation 6 2.1. Stakeholder choices for agricultural adaptation 6 2.2. Adaptation of vulnerable areas 8 3. Methods and tools 9 3.1. General considerations 9 3.2. Agroclimatic indices and GIS 10 3.3. Statistical models and yield functions 11 3.4. Process-based crop models 11 3.5. Calibration and validation of the crop models 14 3.6. Economic tools 15 3.7. Combining climate change scenarios with agricultural tools and models 16 4. Limitations and sources of uncertainty 17 4.1. Climate change scenarios 17 4.2. Climate variability 17 4.3. Stakeholders adaptation 17 4.4. Agricultural models 17 4.5. Effects of CO2 on crops 18 4.6. Issues of scale 18 4.7. Socio-economic projections 18 5. Integrated Assessments 18 6. Information on Datasets 18 9. References 20
Evaluating Fluid Based Transient Calorimetric Method for Measurement of the Ferrite Core Losses

An evaluation of fluid based transient calorimetric method for measurement of ferrite core losses is presented in this paper. The calorimetric type of measurement of the ferrite core losses is considered to be an attractive solution to overcome the limits of the electrical measurements as it is largely independent of the excitation of the magnetics and the operating frequency. First, the performance of the adiabatic calorimeter with variable stirring speed is evaluated. Further on, the influence of specific heat capacity of different ferrite materials on measurement results was investigated by DC current injection method applied on different ferrite core samples. Finally, the proposed method for measuring ferrite loss is tested experimentally. Experimental results were presented in comparison to the results acquired from analytical calculation.
Staying at home in the COVID-19 period: Effects on well-being and physical activity in women living with overweight or obesity

Background: Between March and May 2020, the COVID-19 pandemic led governments to take specific lockdown measures. Investigations have defined obesity as a risk factor for disease severity, but none has addressed the effects of lockdown on psychological well-being and physical activity in this population. The objective of the study was to analyze the evolution (before vs. during lockdown) of well-being and physical activity among French adult women living with overweight or obesity. Methods: 250 women, divided into 5 Body Mass Index (BMI) categories (healthy weight, overweight, obesity I, II or III), provided online information concerning their self-perceived psychological well-being and level of physical activity before and after one month (± 1 week) of lockdown. Results: This study shows that BMI is a factor that is associated with well-being and physical activity. Compared to women with a healthy weight who significantly increased their level of physical activity, those living with overweight or obesity perceived a significant decrease in psychological well-being, positive affect, and physical exercise. Conclusions: This study highlights the importance of taking into account the well-being and active behavior of women living with overweight or obesity during any lockdown period that could occur in future pandemics.
AdhMMP8 Vector Administration in Muscle: An Alternate Strategy to Regress Hepatic Fibrosis

The development of several vaccines against the SARS-CoV2 virus and their application in millions of people have shown efficacy and safety in the transfer of genes to muscle turning this tissue into a protein-producing factory. Established advanced liver fibrosis, is characterized by replacement of hepatic parenchyma by tissue scar, mostly collagen type I, with increased profibrogenic and proinflammatory molecules gene expression. Matrix metalloproteinase 8 (MMP-8) is an interstitial collagen-degrading proenzyme acting preferentially on collagen type I when activated. This study was carried out to elucidate the effect of an intramuscularly delivered adenoviral vector containing proMMP-8 gene cDNA (AdhMMP8) in male Wistar rats with experimental advanced liver fibrosis induced by thioacetamide. Therapeutic effects were monitored after 1, 2, or 3 weeks of a single dose (3 × 1011 vp/kg) of AdhMMP8. Circulating and liver concentration of MMP-8 protein remained constant; hepatic fibrosis decreased up to 48%; proinflammatory and profibrogenic genes expression diminished: TNF-α 2.28-fold, IL-1 1.95-fold, Col 1A1 4-fold, TGF-β1 3-fold and CTGF 2-fold; and antifibrogenic genes expression raised, MMP-9 2.8-fold and MMP-1 10-fold. Our data proposes that the administration of AdhMMP8 in muscle is safe and effective in achieving liver fibrosis regression at a comparable extent as when the adenoviral vector is delivered systemically to reach the liver, using a minimally invasive procedure.
Interactive complex ontology matching with local and global similarity deviations

Ontology serves as a central technique in the semantic web to elucidate domain knowledge. The challenge of dealing with the heterogeneity introduced by diverse domain ontologies necessitates ontology matching, a process designed to identify semantically interconnected entities within these ontologies. This task is inherently complex due to the broad, diverse entities and the rich semantics inherent in vocabularies. To tackle this challenge, we bring forth a new interactive ontology matching method with local and global similarity deviations (IOM-LGSD) for ontology matching, which consists of three novel components. First, a local and global similarity deviation (LGSD) metrics are presented to measure the consistency of similarity measures (SMs) and single out the less consistent SMs for user validation. Second, we present a genetic algorithm (GA) based SM selector to evolve the SM subsets. Lastly, a problem-specific induced ordered weighting aggregating (IOWA) operator based SM aggregator is proposed to assess the quality of selected SMs. The experiment evaluates IOM-LGSD with the ontology alignment evaluation initiative (OAEI) Benchmark and three real-world sensor ontologies. The evaluation underscores the effectiveness of IOM-LGSD in efficiently identifying high-quality ontology alignments, which consistently outperforms comparative methods in terms of effectiveness and efficiency.
A rapid literature review on the health-related outcomes of long-term person-centered care models in adults with chronic illness

Introduction The strong association between age and the increasing prevalence of chronic diseases, makes it imperative to promote self-care throughout life. Systematic knowledge on the health findings of person-centered care models may contribute to designing effective healthcare strategies to promote empowerment for self-care in long-term care. Objective To assess the association between the implementation of person-centered care models that promote self-care training in long-term care and health-related outcomes, among adults with chronic illness. Methods A rapid review of the literature was performed following the Cochrane rapid review methodology. The electronic databases CINAHL, MedicLatina, MEDLINE, and Psychology and Behavioral Sciences Collection were searched for randomized experimental studies, published between 2017 and 2022, that implemented interventions based on person-centered models to promote self-care in adults aged ≥18 years with chronic diseases and needing long-term health care. Verification of the eligibility of the articles and the extraction of data were performed by two independent investigators. Quantitative data on the health-related variables assessed were collected and, through narrative synthesis, health outcomes were grouped into individual, institutional and societal levels. Results Eight studies, mostly conducted in European countries, were included. All satisfied more than 60% of the methodological quality score. A large variability among studies was found regarding the number of participants, the data collection period and duration of the intervention, the samples selected and the care model implemented. A high number of health-related outcomes (n = 17) were analyzed in the studies, using 52 different instruments. The main health-related outcomes were multidimensional, with implications at the individual, institutional and societal levels. The promotion of overall health and wellbeing (n = 4), the implementation of patient-centered care models (n = 1), the positive and more frequent interactions with health professionals (2), the decrease on staff psychosocial distress (n = 1), and the absence of added costs (n = 1), while improving family caregivers’ skills (n = 1) were the main health-related outcomes described. Conclusion There is a need to develop robust experimental studies focused on the views and experiences of all stakeholders and conducted in different countries and cultures. Short-, medium- and long-term health outcomes should be measured using internationally accepted and validated scales for chronic patients.
Stability Analysis of Triple Active Bridge Converter with Hybrid Loads and Different Control Strategies

This article investigates the stability of triple active bridge (TAB) converter with different types of loads. In this paper, port2 of TAB converter adopts voltage control to supply a constant power load (CPL), and port3 uses voltage control to supply a resistor or uses current control to supply a battery. Then, an extra element theorem-based (EET) modelling method based on the small signal model of the TAB converter is developed to derive the output impedance of different cases. The AC sweeping is implemented to validate the effectiveness of derivation. Furthermore, the impedance-based analysis method is applied to analyze the system stability. The analysis result shows that the instability issue caused by CPL at port2 can be suppressed by reducing the resistance load value at port3 or output current of port3. Simulation and experimental results are given to validate the stability analysis.