Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological mechanisms of English academic stress and academic burnout: the mediating role of rumination and moderating effect of neuroticism

Introduction Academic stress is a significant and prevalent phenomenon among college students. According to the Demands-Resources Model, when individuals are unable to cope with stress that exceeds their capacity, burnout may occur. Although English courses hold a significant position in university education, there has been limited research on the mechanisms linking English academic stress to English academic burnout. Methods This study recruited 1,130 undergraduate students taking English courses. Participants completed online questionnaires assessing English academic stress, rumination, English academic burnout, and neuroticism traits. A moderated mediation model was constructed to examine the relationship among these variables. Results The results indicate that (1) Rumination serves as a mediator in the relationship between English academic stress and burnout; (2) neuroticism significantly moderates the pathway between English academic stress and rumination. Specifically, students with high neuroticism tendencies are more prone to developing rumination when faced with high levels of English academic stress. Conclusion These findings offer valuable insights into the psychological mechanisms underlying the association between English learning stress and academic burnout. They emphasize the importance of addressing rumination as a mediator and considering individuals’ levels of neuroticism in interventions aimed at preventing and alleviating academic burnout among university students.
An Emotion Type Informed Multi-Task Model for Emotion Cause Pair Extraction

Emotion-Cause Pair Extraction (ECPE) aims to jointly extract emotion clauses and the corresponding cause clauses from a document, which is important for user evaluation or public opinion analysis. Existing research addresses the ECPE task through a two-step or an end-to-end approach. Although previous work shows promising performances, they suffer from two limitations: 1) they fail to take full advantage of emotion type information, which has advantages for modelling the dependencies between emotion and cause clauses from a semantic perspective; 2) they ignored the interaction between local and global information, which is important for ECPE. To address these issues, we propose an ECPE Pair Generator (ECPE-PG), with a Clause-Encoder layer, a Pre-Output layer and an Information Interaction-based Pair Generation (IIPG) Module embedded. This model first encodes clauses into vector representations through the Clause-Encoder layer and then preforms emotion clause extraction (EE), cause clause extraction (CE) and emotion type extraction (ETE), respectively, through the Pre-Output layer, on the basis of which the IIPG module analyzes the complex emotional logic of relationships between clauses and estimates the candidate pairs based on the interaction of global and local information. It should be noted that emotion type information is regarded as a crucial indication in the IIPG module to assist the identification of emotion-cause pairs. Experimental results show that our method outperforms the state-of-the-art methods on benchmark datasets.
Distributed energy‐efficient channel assignment in cognitive mesh network for IoT systems

Internet of Things (IoT) has attracted a lot of attention to solve the real‐world problem to improve quality of life. Cognitive radio mesh networks (CRMNs) can provide connectivity to a large number of IoT nodes and have the potential to self‐organize and reconfigure to make efficient use of spectrum resources. The growing concern regarding energy consumption in communication infrastructure and low power IoT nodes has led researchers to regard minimizing of energy consumption. In this paper, we propose a distributed energy‐efficient channel assignment in CRMNs for the IoT systems. We formulate an optimization problem for channel assignment and sharing in CRMNs for IoT systems with an objective to maximize energy‐efficiency of the network. We consider constraints on both intra and inter‐link interference. We then propose two distributed approaches to solve the channel assignment problem: (1) greedy approach and (2) game theoretic approach while considering the scalability and self‐organization requirements of the CRMNs for the IoT systems. We then present extensive simulation results to demonstrate the performance of proposed schemes.
The Design, Modeling and Experimental Investigation of a Micro-G Microoptoelectromechanical Accelerometer with an Optical Tunneling Measuring Transducer

This treatise studies a microoptoelectromechanical accelerometer (MOEMA) with an optical measuring transducer built according to the optical tunneling principle (evanescent coupling). The work discusses the design of the accelerometer’s microelectromechanical sensing element (MSE) and states the requirements for the design to achieve a sensitivity threshold of 1 µg m/s2 at a calculated eigenvalue of the MSE. The studies cover the selection of the dimensions, mass, eigenfrequency and corresponding stiffness of the spring suspension, gravity-induced cross-displacements. The authors propose and experimentally test an optical transducer positioning system represented by a capacitive actuator. This approach allows avoiding the restrictions in the fabrication of the transducer conditioned by the extremely high aspect ratio of deep silicon etching (more than 100). The designed MOEMA is tested on three manufactured prototypes. The experiments show that the sensitivity threshold of the accelerometers is 2 µg. For the dynamic range from minus 0.01 g to plus 0.01 g, the average nonlinearity of the accelerometers’ characteristics ranges from 0.7% to 1.62%. For the maximum dynamic range from minus 0.015 g to plus 0.05 g, the nonlinearity ranges from 2.34% to 2.9%, having the maximum deviation at the edges of the regions. The power gain of the three prototypes of accelerometers varies from 12.321 mW/g to 26.472 mW/g. The results provide broad prospects for the application of the proposed solutions in integrated inertial devices.
What Is Hidden in Patients with Unknown Nephropathy? Genetic Screening Could Be the Missing Link in Kidney Transplantation Diagnosis and Management

Between 15–20% of patients with end stage renal disease (ESRD) do not know the cause of the primary kidney disease and can develop complications after kidney transplantation. We performed a genetic screening in 300 patients with kidney transplantation, or undiagnosed primary renal disease, in order to identify the primary disease cause and discriminate between overlapping phenotypes. We used a custom-made panel for next-generation sequencing (Agilent technology, Santa Clara, CA, USA), including genes associated with Fabry disease, podocytopaties, complement-mediated nephropathies and Alport syndrome-related diseases. We detected candidate diagnostic variants in genes associated with nephrotic syndrome and Focal Segmental Glomerulosclerosis (FSGS) in 29 out of 300 patients, solving about 10% of the probands. We also identified the same genetic cause of the disease (PAX2: c.1266dupC) in three family members with different clinical diagnoses. Interestingly we also found one female patient carrying a novel missense variant, c.1259C>A (p.Thr420Lys), in the GLA gene not previously associated with Fabry disease, which is in silico defined as a likely pathogenic and destabilizing, and associated with a mild alteration in GLA enzymatic activity. The identification of the specific genetic background may provide an opportunity to evaluate the risk of recurrence of the primary disease, especially among patient candidates living with a donor kidney transplant.
Marine Predator Algorithm-Based Optimal PI Controllers for LVRT Capability Enhancement of Grid-Connected PV Systems

Photovoltaic (PV) systems are becoming essential to our energy landscape as renewable energy sources become more widely integrated into power networks. Preserving grid stability, especially during voltage sags, is one of the significant difficulties confronting the implementation of these technologies. This attribute is referred to as low-voltage ride-through (LVRT). To overcome this issue, adopting a Proportional-Integral (PI) controller, a control system standard, is proving to be an efficient solution. This paper provides a unique algorithm-based approach of the Marine Predator Algorithm (MPA) for optimized tuning of the used PI controller, mainly focusing on inverter control, to improve the LVRT of the grid, leading to improvements in the overshoot, undershoot, settling time, and steady-state response of the system. The fitness function is optimized using the MPA to determine the settings of the PI controller. This process helps to optimally design the controllers optimally, thus improving the inverter control and performance and enhancing the system’s LVRT capability. The methodology is tested in case of a 3L-G fault. To test its validity, the proposed approach is compared with rival standard optimization-based PI controllers, namely Grey Wolf Optimization and Particle Swarm Optimization. The comparison shows that the used algorithm provides better results with a higher convergence rate with overshoot ranging from 14% to 40% less in the case of DC-Link Voltage and active power and also settling times in the case of MPA being less than PSO and GWO by 0.76 to 0.95 s.
THE RELATIONSHIP BETWEEN POSTURAL AND NUTRITIONAL STATUS IN PRESCHOOL CHILDREN

The aim of the research is to determine the postural status, nutritional status, and their interrelationship, as well as to identify differences in relation to gender dimorphism. The research methods used in this study are observational and descriptive with a cross-sectional design. Data were collected from a sample of 177 participants from the preschool institutions in the city of Bijeljina. The study included 92 female partici-pants and 85 male participants, aged six years (6±0.5). The postural status was asse-ssed using the Napoleon Wolanski method and nutritional status using standardized calibrated instruments that served to calculate the BMI. To determine the relationship between nutritional and postural status, the Spearman correlation coefficient was used, while the chi-square test was applied to determine differences in postural status with regard to gender dimorphism. The study found no significant relationship between nutritional and postural status in preschoolage children, regardless of gender dimorphism. However, it revealed a high prevalence of postural disorders and overweight among the children. Participating in appropriate and organized physical activity during preschool and leisure time is crucial for children's health, requiring consistent observation and evaluation of relevant factors.
Potential involvement of neutrophils on exercise effects in breast cancer malignancy

[Purpose] This review aimed to comprehensively explore and elucidate multifaceted neutrophils in breast cancer, particularly in the context of physical activity. Neutrophils play a critical role in the tumor microenvironment and systemic immune response, despite their short half-life and terminal differentiation. Through a thorough review of research related to changes in immunity in breast cancer during exercise, this review aims to provide comprehensive insights into immunological changes, especially focusing on neutrophils. Recognizing that much of the existing research has predominantly focused on T cells and nature killer (NK) cells, our review seeks to shift the spotlight toward understanding how exercise affects neutrophils, a less-explored but critical immune response component in breast cancer. [Methods] This study involved an extensive review of the literature (from 2000 to 2023) using the PubMed, Science Direct, and Google Scholar databases. The keywords chosen for the searches were “immune cells and exercise,” “exercise and breast cancer,” “tumor microenvironment and neutrophils,” and “neutrophils and exercise and breast cancers. ” [Results] Neutrophils in the tumor microenvironment can exhibit distinct phenotypes and functions. These differences have yielded conflicting results regarding tumor progression. Exercise plays a positive role in breast cancer and alters the immune system. Physical activity can quantitatively and functionally regulate neutrophils under various conditions such as metabolic disruption or senescence. [Conclusion] This short communication outlines exercise-induced neutrophil diversification and its role in breast cancer progression, both within and systemically within the tumor microenvironment. Exercise may provide benefits through the potential neutrophil involvement in breast cancer.
A calibration technique for the stereo camera system with the laser illumination

Applications of computer vision techniques assume an image acquisition from one or another sensing system. This system should be calibrated before the usage to obtain proper results. In this paper a calibration technique for the stereo camera system with the laser illumination is proposed. Modern approaches to the calibration of the different sensing systems are indicated. The characteristics of the specific system required a calibration are described. The main calibration tasks and subtasks for the given system and also the main stages of the proposed technique are highlighted. The need to rotate the laser illumination relative to the axis between the cameras through 8 degrees is proven. An approach to the calibration of the illumination laser beam directions is developed. The accounting of the parameters which can be obtained as an issue of the complex calibration of the stereo camera system with the laser illumination makes it possible to improve the results of the analyzed system utilization for mobile robots.
Combinatorial Analysis of Deep Learning and Machine Learning Video Captioning Studies: A Systematic Literature Review

Recent improvements formulated in the area of video captioning have brought rapid revolutions in its methods and the performance of its models. Machine learning and deep learning techniques are both employed in this regard. However, there is a lack of tracing the latest studies and their remarkable results. Although several studies have been proposed employing the ML and DL algorithms in different other areas, there is no systematic review utilizing the video captioning task. This study aims to examine, evaluate, and synthesize the primary studies into a thorough Systematic Literature Review (SLR) that provides a general overview of the methods used for video captioning. We performed the SLR to determine the research problems under which machine learning models were preferred over the deep learning models and vice versa. We collected a total of 1,656 studies retrieved from four electronic databases; Scopus, WoS, IEEE Xplore, and ACM, based on our search string from which 162 published studies passed the selection criteria related to one primary and two secondary research questions after a systematic process. Moreover, insufficient data collection and inefficient comparison of results are common issues identified during the review process. We conclude that the 2D/3D CNN for video feature extraction and LSTM for caption generation, METEOR and BLEU performance evaluation tools, and MSVD dataset are most frequently employed for video captioning. Our study is the pioneer in comparing the implementation of ML and DL algorithms employing the video captioning area. Thus, our study will accelerate the critical assessment of the state-of-the-art in other research fields of video analysis and human-computer interaction.
Leucine Zipper Downregulated in Cancer-1 Interacts with Clathrin Adaptors to Control Epidermal Growth Factor Receptor (EGFR) Internalization and Gefitinib Response in EGFR-Mutated Non-Small Cell Lung Cancer

The epidermal growth factor receptor (EGFR) is a common driver of non-small cell lung cancer (NSCLC). Clathrin-mediated internalization (CMI) sustains EGFR signaling. AXL is associated with resistance to EGFR-tyrosine kinase inhibitors (TKIs) in EGFR-mutated (EGFRM) NSCLC. We investigated the effects of Leucine zipper downregulated in cancer-1 (LDOC1) on EGFR CMI and NSCLC treatment. Coimmunoprecipitation, double immunofluorescence staining, confocal microscopy analysis, cell surface labelling assays, and immunohistochemistry studies were conducted. We revealed that LDOC1 interacts with clathrin adaptors through binding motifs. LDOC1 depletion promotes internalization and plasma membrane recycling of EGFR in EGFRM NSCLC PC9 and HCC827 cells. Membranous and cytoplasmic EGFR decreased and increased, respectively, in LDOC1 (−) NSCLC tumors. LDOC1 depletion enhanced and sustained activation of EGFR, AXL, and HER2 and enhanced activation of HER3 in PC9 and HCC827 cells. Sensitivity to first-generation EGFR-TKIs (gefitinib and erlotinib) was significantly reduced in LDOC1-depleted PC9 and HCC827 cells. Moreover, LDOC1 downregulation was significantly associated (p < 0.001) with poor overall survival in patients with EGFRM NSCLC receiving gefitinib (n = 100). In conclusion, LDOC1 may regulate the efficacy of first-generation EGFR-TKIs by participating in the CMI of EGFR. Accordingly, LDOC1 may function as a prognostic biomarker for EGFRM NSCLC.
Unveiling the Incidence and Graft Survival Rate in Kidney Transplant Recipients With De Novo Thrombotic Microangiopathy: A Systematic Review and Meta-Analysis

De novo thrombotic microangiopathy (TMA) is a rare and challenging condition in kidney transplant recipients, with limited research on its incidence and impact on graft survival. This study conducted a systematic review and meta-analysis of 28 cohorts/single-arm studies and 46 case series/reports from database inception to June 2022. In meta-analysis, among 14,410 kidney allograft recipients, de novo TMA occurred in 3.20% [95% confidence interval (CI): 1.93–4.77], with systemic and renal-limited TMA rates of 1.38% (95% CI: 06.5–2.39) and 2.80% (95% CI: 1.27–4.91), respectively. The overall graft loss rate of de novo TMA was 33.79% (95% CI: 26.14–41.88) in meta-analysis. This study provides valuable insights into the incidence and graft outcomes of de novo TMA in kidney transplant recipients.
Liposomal Glutathione Augments Immune Defenses against Respiratory Syncytial Virus in Neonatal Mice Exposed in Utero to Ethanol

We previously reported that maternal alcohol use increased the risk of sepsis in premature and term newborns. In the neonatal mouse, fetal ethanol (ETOH) exposure depleted the antioxidant glutathione (GSH), which promoted alveolar macrophage (AM) immunosuppression and respiratory syncytial virus (RSV) infections. In this study, we explored if oral liposomal GSH (LGSH) would attenuate oxidant stress and RSV infections in the ETOH-exposed mouse pups. C57BL/6 female mice were pair-fed a liquid diet with 25% of calories from ethanol or maltose–dextrin. Postnatal day 10 pups were randomized to intranasal saline, LGSH, and RSV. After 48 h, we assessed oxidant stress, AM immunosuppression, pulmonary RSV burden, and acute lung injury. Fetal ETOH exposure increased oxidant stress threefold, lung RSV burden twofold and acute lung injury threefold. AMs were immunosuppressed with decreased RSV clearance. However, LGSH treatments of the ETOH group normalized oxidant stress, AM immune phenotype, the RSV burden, and acute lung injury. These studies suggest that the oxidant stress caused by fetal ETOH exposure impaired AM clearance of infectious agents, thereby increasing the viral infection and acute lung injury. LGSH treatments reversed the oxidative stress and restored AM immune functions, which decreased the RSV infection and subsequent acute lung injury.
A large vocabulary online handwriting recognition system for Turkish

Handwriting recognition in general and online handwriting recognition in particular has been an active research area for several decades. Most of the research have been focused on English and recently on other scripts like Arabic and Chinese. There is a lack of research on recognition in Turkish text and this work primarily fills that gap with a state-of-the-art recognizer for the first time. It contains design and implementation details of a complete recognition system for recognition of Turkish isolated words. Based on the Hidden Markov Models, the system comprises pre-processing, feature extraction, optical modeling and language modeling modules. It considers the recognition of unconstrained handwriting with a limited vocabulary size first and then evolves to a large vocabulary system. Turkish script has many similarities with other Latin scripts, like English, which makes it possible to adapt strategies that work for them. However, there are some other issues which are particular to Turkish that should be taken into consideration separately. Two of the challenging issues in recognition of Turkish text are determined as delayed strokes which introduce an extra source of variation in the sequence order of the handwritten input and high Out-of-Vocabulary (OOV) rate of Turkish when words are used as vocabulary units in the decoding process. This work examines the problems and alternative solutions at depth and proposes suitable solutions for Turkish script particularly. In delayed stroke handling, first a clear definition of the delayed strokes is developed and then using that definition some alternative handling methods are evaluated extensively on the UNIPEN and Turkish datasets. The best results are obtained by removing all delayed strokes, with up to 2.13% and 2.03% points recognition accuracy increases, over the respective baselines of English and Turkish. The overall system performances are assessed as 86.1% with a 1,000-word lexicon and 83.0% with a 3,500-word lexicon on the UNIPEN dataset and 91.7% on the Turkish dataset. Alternative decoding vocabularies are designed with grammatical sub-lexical units in order to solve the problem of high OOV rate. Additionally, statistical bi-gram and tri-gram language models are applied during the decoding process. The best performance, 67.9% is obtained by the large stem-ending vocabulary that is expanded with a bi-gram model on the Turkish dataset. This result is superior to the accuracy of the word-based vocabulary (63.8%) with the same coverage of 95% on the BOUN Web Corpus.