Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessment of the Possibility of Using 5G to Build Telemedicine Networks in Various Environment

The paper discusses the main requirements that arise when using 5G networks in telemedicine. First, in the paper investigate the network bandwidth requirements, as the transfer of large amounts of medical data, such as video consultations, medical images, and data from mounting devices, requires a fast and reliable connection. As a potential solution to this problem, proposed integration into the telemedicine network of segments based on 5G technology, which provide the necessary parameters to improve the efficiency and quality of telemedicine services. The main idea is to assess the capabilities of 5G technology from the point of view of solving the problem of creating a scalable telemonitoring platform. As part of this task, it is necessary to determine the possibilities of the fifth-generation technology for the organization of telemonitoring at the maximum possible distance while simultaneously ensuring a guaranteed data transfer speed, considering the simultaneous connection of a large number of devices and sensors. Capability analysis was performed for different environments (signal propagation models) and for different raw data.
A Novel Role for DOC2B in Ameliorating Palmitate-Induced Glucose Uptake Dysfunction in Skeletal Muscle Cells via a Mechanism Involving β-AR Agonism and Cofilin

Diet-related lipotoxic stress is a significant driver of skeletal muscle insulin resistance (IR) and type 2 diabetes (T2D) onset. β2-adrenergic receptor (β-AR) agonism promotes insulin sensitivity in vivo under lipotoxic stress conditions. Here, we established an in vitro paradigm of lipotoxic stress using palmitate (Palm) in rat skeletal muscle cells to determine if β-AR agonism could cooperate with double C-2-like domain beta (DOC2B) enrichment to promote skeletal muscle insulin sensitivity under Palm-stress conditions. Previously, human T2D skeletal muscles were shown to be deficient for DOC2B, and DOC2B enrichment resisted IR in vivo. Our Palm-stress paradigm induced IR and β-AR resistance, reduced DOC2B protein levels, triggered cytoskeletal cofilin phosphorylation, and reduced GLUT4 translocation to the plasma membrane (PM). By enhancing DOC2B levels in rat skeletal muscle, we showed that the deleterious effects of palmitate exposure upon cofilin, insulin, and β-AR-stimulated GLUT4 trafficking to the PM and glucose uptake were preventable. In conclusion, we revealed a useful in vitro paradigm of Palm-induced stress to test for factors that can prevent/reverse skeletal muscle dysfunctions related to obesity/pre-T2D. Discerning strategies to enrich DOC2B and promote β-AR agonism can resist skeletal muscle IR and halt progression to T2D.
Description of three new species of Callyntrura (Japonphysa) (Collembola, Entomobryidae) from China with the aid of DNA barcoding

﻿Abstract Callyntrura(s.l.) Börner, 1906 is the largest genus of the subfamily Salininae and contains 11 subgenera and 98 species from all over the world (mainly Asia), with eight species recorded from China. In the present paper, three new species of Callyntrura(s.l.) are described from China: C. (Japonphysa) xinjianensissp. nov.; C. (J.) tongguensissp. nov. and C. (J.) raoisp. nov. Their differences in colour pattern, chaetotaxy and other characters are slight, however distances of COI mtDNA support their validation as three new distinct species. A key to the Chinese Callyntrura(s.l.) is provided.
Competition between H4PteGlu and H2PtePAS Confers para-Aminosalicylic Acid Resistance in Mycobacterium tuberculosis

Tuberculosis remains a serious challenge to human health worldwide. para-Aminosalicylic acid (PAS) is an important anti-tuberculosis drug, which requires sequential activation by Mycobacterium tuberculosis (M. tuberculosis) dihydropteroate synthase and dihydrofolate synthase (DHFS, FolC). Previous studies showed that loss of function mutations of a thymidylate synthase coding gene thyA caused PAS resistance in M. tuberculosis, but the mechanism is unclear. Here we showed that deleting thyA in M. tuberculosis resulted in increased content of tetrahydrofolate (H4PteGlu) in bacterial cells as they rely on the other thymidylate synthase ThyX to synthesize thymidylate, which produces H4PteGlu during the process. Subsequently, data of in vitro enzymatic activity experiments showed that H4PteGlu hinders PAS activation by competing with hydroxy dihydropteroate (H2PtePAS) for FolC catalysis. Meanwhile, over-expressing folC in ΔthyA strain and a PAS resistant clinical isolate with known thyA mutation partially restored PAS sensitivity, which relieved the competition between H4PteGlu and H2PtePAS. Thus, loss of function mutations in thyA led to increased H4PteGlu content in bacterial cells, which competed with H2PtePAS for catalysis by FolC and hence hindered the activation of PAS, leading to decreased production of hydroxyl dihydrofolate (H2PtePAS-Glu) and finally caused PAS resistance. On the other hand, functional deficiency of thyA in M. tuberculosis pushes the bacterium switch to an unidentified dihydrofolate reductase for H4PteGlu biosynthesis, which might also contribute to the PAS resistance phenotype. Our study revealed how thyA mutations confer PAS resistance in M. tuberculosis and provided new insights into studies on the folate metabolism of the bacterium.
Detection of Defects in Soybean Seeds by Extracting Deep Features with SqueezeNet

A vital part of ensuring the quality of soybean products is detecting defects. The current study presents a five-category classification of soybean seed defects: broken, immature, intact, skin-damaged, and spotted soybean seeds. Our goal is to improve the overall quality of soybean products through accurately identifying and categorizing defects. Computer vision techniques and machine learning algorithms are combined comprehensively to achieve this goal. To begin with, soybean seed images are analyzed using the SqueezeNet model, a deep-learning architecture known for its efficiency in image analysis. Features extracted from soybeans indicate the types of defects they present and their key visual characteristics. Then, we applied three widely used machine learning algorithms, including Artificial Neural Network (ANN), Logistic Regression (LR), and Random Forest (RF), to classify soybean seed images. A labeled dataset of soybean seed images with known defects is used to train and fine-tune each algorithm. An appropriate evaluation metric is used for assessing each algorithm. It provides valuable insights into the application of machine learning techniques in improving product quality by improving the detection and classification of soybean seed defects.
APPLICATION OF THE EMOTIONAL INDEX IN THE STUDY OF THE IMPACT OF TELEVISION ADVERTISING ON THE RECIPIENT

The purpose of the article was to present cognitive neuroscience techniques (GSR and HR) for the study of emotions and to verify whether there is a correlation with the corresponding stages of models of advertising influence on the viewer. The study was based on the AIDA model. The results of the analyzed advertisement showed that it is possible to assess with a high degree of accuracy whether the advertisement was properly designed (for the adopted model). By analyzing the emotional index EI determined from the GSR and HR studies, it is possible to make appropriate adjustments at the stage of advertising implementation. Referring directly to the analyzed advertisement, it should be noted that positive and negative emotions can appear in different situations, depending on gender. The same scenes can be perceived positively by women and negatively by men, and vice versa. Such insights should be taken into account during design and preliminary testing even before broadcasting.
ScholarWorks@UARK ScholarWorks@UARK

Several recent studies have shown that mindfulness-based practices have been effective in treating sleep problems. However, these studies have primarily looked at multi-component interventions that take place over several weeks or months. No studies have evaluated the efficacy of short-term mindfulness-based practices on sleep the same night. This study aims to do just that. The sample consisted of 10 students attending the University of Arkansas. All participants slept in a sleep laboratory on two consecutive weekday nights. During one night, they completed a mindfulness-based exercise. The other night, they completed a similar control task (counterbalanced). Polysomnography-based sleep data were collected on both nights to assess sleep quality (i.e., sleep efficiency and sleep onset latency). Saliva samples were collected 30 and 0 minutes before bedtime and 0, 15, and 30 minutes post-awakening to assess endocrine stress responses. There was no statistically significant difference in the mean sleep efficiency or mean sleep onset latency between the control night and mindfulness night. There were no significant mean differences in cortisol before bedtime. There were, however, significant group differences in cortisol post-awakening (i.e., lower cortisol at awakening and greater cortisol awakening response following the mindfulness condition). While the results from the current study do not support that an acute mindfulness exercise will have meaningful improvements in sleep the next night, there may be a relation between mindfulness meditation and cortisol stress levels the subsequent day.
Modelling and Optimizing CSP-PV Hybrid Systems Using the Hybrid Optimization and Performance Platform (HOPP)

The hybridization of concentrating solar power (CSP) with thermal energy storage (TES), photovoltaics (PV), and electrochemical battery energy storage systems (BESS) has the potential to provide continuous, high-capacity-factor energy production at a lower cost than a PV-BESS or CSP with TES alone. Because of the system complexity of CSP technology, it is challenging to evaluate the technological and financial performance of a CSP-PV hybrid system without detailed modeling of annual operations. To address this challenge, we have developed a modeling framework for evaluating the performance and financial viability of CSP systems hybridized with PV and BESS technologies. This modeling effort incorporates CSP tower and trough configurations into an existing modeling tool recently developed by NREL, the Hybrid Optimization and Performance Platform (HOPP). This paper provides a brief overview of our methodology, as well as an example case study. CSP with TES hybridized with PV provides the best benefit-to-cost ratio compared to the other simulated technology combinations. However, for the conditions considered, this configuration only increases the benefit-to-cost ratio by about 1% compared to the CSP with TES configuration. The PV-BESS system provides the lowest benefit-to-cost ratio compared to the other configurations explored because of the relatively low capacity credit received by the system.
Drone Based Smart Agriculture Monitoring System for Chickpea Crop

As per Indian economic survey, agriculture employs over 50% of the Indian work force and contributes around 17-18% of the GDP. Plant diseases affect the quality and quantity of agricultural products which affect the food safety. However, automation of plant disease identification is a possible solution to increase the crop yield. In the present paper, a Convolutional Neural Network (CNN) based model has been proposed in order to monitor the health of chickpea crop. Drone has been used to capture the images of chickpea crop. Further, leaf properties using image data is analyzed that would help to detect disease. Furthermore, image augmentation i.e. rotation of image, zooming, horizontal flip and brightness enhancement have been used to increase the accuracy of the proposed system. CNN based model uses 80% of dataset to train the classifier, while 20% dataset is used to test the model. Finally, performance parameters of the classifier for the crop health monitoring have been compared.
Predicting Depression on Twitter with Word Embedding by Pretrained Language Model

Depression is a common mental illness. In the field of Natural Language Processing, the research of how to detect and analyze users' sentiment from social media has been a hot topic. This research aims to achieve a prediction on whether a user tends to depression by the content of his or her tweets. We selected several different pretrained word embedding methods and classification models and tried to apply them for analyzing the sentiments contained in the Twitter dataset. The results were compared and evaluated to find the most efficient method to predict the depressive tendencies of users on Twitter. Our experiments show that embedding vectors from pretrained BERT models have a definite advantage in the prediction task.
Influences on Off-Highway Vehicle (OHV) Recreation Use Patterns within a Complex Trail System in Southwest Idaho

viii LIST OF TABLES . xiv LIST OF FIGURES . xv CHAPTER ONE: GROUP CHARACTERISTICS INFLUENCE DISTRIBUTION PATTERNS OF OFF-HIGHWAY VEHICLE RECREATION WITHIN A COMPLEX TRAIL SYSTEM IN SOUTHWEST IDAHO . 1 Abstract . 1. 1 Background . 2 Introduction . 4 OHV Management . 4 Recreationist Characteristics and Behavior . 6 Tracking Human Distributions and Visitor-employed GPS Surveys . 8 Objectives . 9 Methods. 10 Study Area . 10 Data Collection . 11 Cleaning and Operationalization of Spatial and Temporal Data . 12 Group Characteristics. 13
Computerized Framework for Yoga Pose Estimation Using Deep Learning Algorithm

This research paper explores yoga design prediction using deep learning. A new method for automatic detection and classification of yoga poses with images or videos is presented, which includes the development of a neural network (CNN)-based deep learning model. The model uses spatial relationships and local patterns in yoga poses using convolutional techniques for feature extraction. In addition, layers are used to capture the moment of addiction in video-based yoga design projections. creates a large database of recorded images and videos of yoga poses for training and test models. This database contains many yoga practitioners of various skill levels. The main points and tags of the site are recorded in the data to monitor the learning of the deep learning model. To start the model, adaptive learning is performed using weights previously learned from a large dataset. This adaptive learning improves the performance of the model by using the information learned from the dataset. Experiments were conducted to evaluate the effectiveness and accuracy of the proposed model. The results demonstrate the deep learning model's ability to evaluate correct yoga poses from photos and videos. The performance model shows that it can provide real feedback, monitor progress, and help users practice yoga correctly. The results of this study lead to predictive yoga using the power of deep learning. The proposed model improved the general yoga practitioner, paving the way for personal yoga practice, virtual training, and non-invasive physical therapy.
Fault Diagnosis of Full-Bridge Power Converter for SRMs Based on Modified Current Detection

This article proposes a fault diagnosis method for power transistors of full-bridge power converter (FBPC) in switched reluctance motors (SRMs) based on a modified current detection strategy. First, the circuit status is analyzed for fault operation. A novel current measurement scheme based on cross-bridge current is then provided to obtain the enough current information, and two current variables are given and dealt with based on sensor currents. On the basis of driving signal and the corresponding currents, a characteristic variable is proposed to highlight the fault type and locate faulty power transistor. Different fault situations are simulated on a three-phase SRM and followed by the experiments. A good agreement between the analysis and the measured results validates the proposed fault diagnosis method.
Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence

Introduction Bipolar disorder (BD) is a chronically progressive mental condition, associated with a reduced quality of life and greater disability. Patient admissions are preventable events with a considerable impact on global functioning and social adjustment. While machine learning (ML) approaches have proven prediction ability in other diseases, little is known about their utility to predict patient admissions in this pathology. Aim To develop prediction models for hospital admission/readmission within 5 years of diagnosis in patients with BD using ML techniques. Methods The study utilized data from patients diagnosed with BD in a major healthcare organization in Colombia. Candidate predictors were selected from Electronic Health Records (EHRs) and included sociodemographic and clinical variables. ML algorithms, including Decision Trees, Random Forests, Logistic Regressions, and Support Vector Machines, were used to predict patient admission or readmission. Survival models, including a penalized Cox Model and Random Survival Forest, were used to predict time to admission and first readmission. Model performance was evaluated using accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC) and concordance index. Results The admission dataset included 2,726 BD patients, with 354 admissions, while the readmission dataset included 352 patients, with almost half being readmitted. The best-performing model for predicting admission was the Random Forest, with an accuracy score of 0.951 and an AUC of 0.98. The variables with the greatest predictive power in the Recursive Feature Elimination (RFE) importance analysis were the number of psychiatric emergency visits, the number of outpatient follow-up appointments and age. Survival models showed similar results, with the Random Survival Forest performing best, achieving an AUC of 0.95. However, the prediction models for patient readmission had poorer performance, with the Random Forest model being again the best performer but with an AUC below 0.70. Conclusion ML models, particularly the Random Forest model, outperformed traditional statistical techniques for admission prediction. However, readmission prediction models had poorer performance. This study demonstrates the potential of ML techniques in improving prediction accuracy for BD patient admissions.
Leaf-age and petiole biomass play significant roles in leaf scaling theory

Foliage leaves are essential for plant survival and growth, and how plants allocate biomass to their leaves reveals their economic and ecological strategies. Prior studies have shown that leaf-age significantly influences leaf biomass allocation patterns. However, unravelling the effects of ontogeny on partitioning biomass remains a challenge because it is confounded by the effects of environmental factors. Here, we aim to elucidate whether leaf-age affects the allocation to the lamina and petiole by examining leaves of known age growing in the same general environmental context. We sampled 2698 Photinia serratifolia leaves developing in the same environment from April to November 2021, representing eight leaf-ages (n > 300 for each leaf-age). Petiole and lamina biomass, and lamina area were measured to evaluate the scaling relationships using reduced major axis regression protocols. The bootstrap percentile method was used to determine the differences in scaling exponents among the different leaf-ages. ANOVA with Tukey’s HSD was used to compare the ratios of petiole and lamina biomass to lamina area across the leaf-ages. Correlation tests were used to determine if exponents, intercepts, and ratios differed significantly across the different leaf-ages. The data indicated that (i) the ratio of petiole and lamina biomass to lamina area and the scaling exponent of lamina biomass versus lamina area correlate positively with leaf-age, and (ii) the scaling exponent of petiole biomass versus lamina area correlates negatively with leaf-age. Leaf maturation process involves an inverse proportional allocation between lamina and petiole biomass for expanding photosynthetic area. This phenomenon underscores the effect of leaf-age on biomass allocation and the importance of adopting an ontogenetic perspective when entertaining plant scaling theories and unravelling the principles governing shifts in biomass allocation throughout the leaf lifespan.
Meltdose Tacrolimus Population Pharmacokinetics and Limited Sampling Strategy Evaluation in Elderly Kidney Transplant Recipients

Background: Meltdose tacrolimus (Envarsus®) has been marketed as a formulation achieving a more consistent tacrolimus exposure. Due to the narrow therapeutic window of tacrolimus, dose individualization is essential. Relaxation of the upper age limits for kidney transplantations has resulted in larger numbers of elderly patients receiving tacrolimus. However, due to the physiological changes caused by aging, the tacrolimus pharmacokinetics (PK) might be altered. The primary aim was to develop a population PK model in elderly kidney transplant recipients. Secondary aims were the development and evaluation of a limited sampling strategy (LSS) for AUC estimation. Methods: A total of 34 kidney transplant recipients aged ≥65 years, starting on meltdose tacrolimus directly after transplantation, were included. An eight-point whole blood AUC0–24h and an abbreviated dried blood spot (DBS) AUC0–24h were obtained. The PK data were analyzed using nonlinear mixed effect modeling methods. Results: The PK data were best described using a two-compartment model, including three transit compartments and a mixture model for oral absorption. The best three-sample LSS was T = 0, 2, 6 h. The best four-sample LSSs were T = 0, 2, 6, 8 h and T = 0, 1, 6, 8 h. Conclusions: The developed population PK model adequately described the tacrolimus PK data in a population of elderly kidney transplant recipients. In addition, the developed population PK model and LSS showed an adequate estimation of tacrolimus exposure, and may therefore be used to aid in tacrolimus dose individualization.
Medical IoT Platform with its Applications in Total Course of Disease and Health Management

Medical data collected from different healthcare scenarios and a variety of IoT platforms is inevitably fragmented, partial and incomplete. In an effort to mitigate the problem, a single medical IoT platform was utilized to cover both total course of disease and health management. And a set of technique standards were applied for easier connections of diverse medical IoT devices. In addition, the key techniques, architecture and applications of the presented platform across total course of disease and health management were introduced in this paper. So far the presented platform has been commercially employing and showed its potential to health data fusion, analysis and decision making. Furthermore, comprehensive coverage of health data across scenarios and devices is highly beneficial to next-generation of accurate health services, course analysis and medical predictions.
ETHNOBOTANICAL SURVEY IN KOT MANZARAY BABA VALLEY MALAKAND AGENCY, PAKISTAN

57 East longitude. The total area of Malakand agency is 43200 acres. Malakand agency lies in the semi-arid climatic region with maximum precipitation in winter in the western parts, whereas in the eastern part precipitation occur in the summer. The highest peak in the study area is Hazarnau at an altitude of 2727 m. From time immemorial man used plants to alleviate their suffering from diseases. About 35,000-25, 0000 plants have been used for medicinal purposes (Walter & Hamelton, 1993). Pakistan has rich history on the folk use of plants. Afridi (1986) listed 67 medicinal plants from Khyber agency. Haq & Hussain (1983) reported local medicinal and other traditional uses of plants of Mansehra. Medicinal plants of Rawalpindi (Arshad & Akram, 1999), Harboi (Durrani & Hussain, 2003), Kurram (Gillani