Miracle-dz/UniFAR-FTUs huggingface dataset:
Exercise makes better mind: a data mining study on effect of physical activity on academic achievement of college students

The effect of physical activity (PA) on academic achievement has long been a hot research issue in physical education, but few studies have been conducted using machine learning methods for analyzing activity behavior. In this paper, we collected the data on both physical activity and academic performance from 2,219 undergraduate students (Mean = 19 years) over a continuous period of 12 weeks within one academic semester. Based on students’ behavioral indicators transformed from a running APP interface and the average academic course scores, two models were constructed and processed by CHAID decision tree for regression analysis and significance detection. It was found that first, to attain higher academic performance, it is imperative for students to not only exhibit exceptional activity regularity, but also sustain a reduced average step frequency; second, the students completing running exercise with an average frequency of 1 time/week and the duration of 16–25 min excelled over approximately 88 percentage of other students on academic performance; third, the processing validity and reliability of physical observation data in complex systems can be improved by utilizing decision tree as a leveraging machine learning tool and statistical method. These findings provide insights for educational practitioners and policymakers who will seek to enhance college students’ academic performance through physical education programs, combined with data mining methods.
Adhesion and Stab-resistant Properties of FDM-printed Polymer/Textile Composites

Stab-resistant clothing has been used for centuries by soldiers. Today, it is also used by policemen and other people in dangerous jobs or situations. While chain-mail or metal inserts in protective vests are heavy and uncomfortable, lightweight and bendable alternatives are currently the subject of investigation. Special textile fabrics offer a certain level of stab-resistance that can be improved by different coatings. In this study, we investigated composites of different flexible 3D printing materials, used for the fused deposition modelling (FDM) technique, on woven fabrics. Besides the adhesion between both parts of these composites, the quasi-static stab-resistant properties were investigated and compared with those of pure textile fabrics and 3D prints, respectively.
Real-Time Implementation of a Novel Design Approach for Sub-GHz Long-Range Antenna for Smart Internet of Things Communication

This research article designs and develops a planar small-size antenna design for smart Internet of Things (IoT) communication with long-range technology (LoRa). The proposed system is best suited for transceiver systems in this automation and sensing era. In the proposed antenna, the ground, the radiating element, and the stub feed are designed on the same side of the substrate, keeping in mind that it can print the LoRa module. The design consists of a meandered monopole, a dipole structure as a ground, and a stub feed. A different design approach is employed to get an optimized result. The antenna is made up of a rectangular feed stub to which a connecting wire is attached. The overall dimension of the antenna is 55 m × 55 m × 1.6 mm. To verify the proposed design, an antenna was fabricated and measured, which covers the LoRa frequency band at 868 MHz, providing a sufficient bandwidth of 10 MHz and a gain of more than 0.5 dB in the operating band. A designed antenna is implemented for sensor data communication with the LoRa module device and device interface Arduino platform. The antenna is connected as a transmitter and receiver one by one to verify its performance with machine-to-machine communication using the LoRa module. The size, bandwidth, and radiation efficiency of this antenna are better than the antennas in the literature. The designed antenna is successfully implemented with LoRa connectivity and communicates the data up to 8 km in line-of-sight communication, more than 1 km in urban environments, and approximately 250 m of connectivity in building areas.
Peculiarities of the Formation of Students’ Business Communication Skills within the Distance Learning

The article deals with the problems of the formation of students’ business communications skills at higher educational institutions. The authors ground the hypothesis that the implementation of nonstandard ways and forms of teaching foreign language provides the increasing of effectiveness of formation of students’ business communicative skills. The goal of research work deals with the theoretical justification and presentation of nonstandard interactive means and forms of educational activity oriented on the formation of business communication skills (BCS) of students within distance learning. Due to the applied qualitative and quantitative methods of research (conducted surveys, experimental research work, data analysis, etc.), it was proved the necessity of implementation of the interactive technologies into the educational process. The authors found out that modern society requires the new generation of specialists who must be educated, professional in their fields, and able to compete within the rapidly changing paradigms of the business systems. For the further implementation by teachers, they are given samples of nonstandard forms of educational activity, certain interactive technologies, instructions, and advice on ways of increasing the effectiveness of BCS. There are described prospects of the further research activity, which will be devoted to the problem of strengthening of BCS.
PRMT1 accelerates cell proliferation, migration, and tumor growth by upregulating ZEB1/H4R3me2as in thyroid carcinoma

Thyroid carcinoma (TC) represents the most prevalent malignancy of the endocrine system. Protein arginine methyltransferase 1 (PRMT1) is a critical member of the protein arginine methyltransferase family in mammals and is involved in multiple biological processes. This study aimed to investigate the function of PRMT1 in TC. In the present study, human TC cell lines (8505C, CAL62, and BCPAP) and a normal human thyroid cell line Nthy-ori 3-1 were employed. Small interfering RNA targeting PRMT1 was used to knock down PRMT1 expression in 8505C cells, and PRMT1 overexpression plasmids were transfected into BCPAP cells. Cell viability was assessed using a CCK-8 and colony formation assays. Apoptosis was measured using flow cytometry and TUNEL assays. Cell migration was assessed using wound healing and Transwell assays. Reverse transcription-quantitative PCR was used to determine the mRNA expression levels of PRMT1. Western blotting was used to detect the protein expression levels of PRMT1, E-cadherin, vimentin, H4R3me2as, and zinc-finger E homeobox-binding 1 (ZEB1). Notably, PRMT1 expression was elevated in TC (P<0.01). PRMT1 knockdown inhibited TC cell proliferation and migration and concurrently enhanced migration. Furthermore, PRMT1 knockdown suppressed tumor growth and metastasis in a mouse model of TC. PRMT1 downregulation increased E-cadherin expression and decreased the expression of vimentin, H4R3me2as, and ZEB1 in the TC cells and the mouse model of TC. Conversely, PRMT1 overexpression had the opposite effect on TC malignant characteristics (P<0.05). These findings suggest that PRMT1 knockdown inhibited TC malignancy by downregulating H4R3me2as/ZEB1, thereby highlighting novel therapeutic targets and diagnostic markers for the management of TC.
Association between IGF-1 gene polymorphism and milk production traits in Polish Red-and-White cattle

The aim of this study was to analyze polymorphism C– 512T in the IGF-1 gene in relation to milk production traits in Polish Red-and-White cattle. Traits analyzed were: milk yield, content of fat, protein, lactose, dry matter and somatic cell count. Polymorphism analysis showed presence of 2 alleles in the investigated herd with frequency C – 0.51, T – 0.49. Association analysis showed that cows with CC and CT genotypes are characterized by higher milk yield in the second lactation comparing to those with TT genotype. Moreover, CC genotype was favorable for somatic cell count.
Portrayal of Bullying in Selected Picture Books: A Content Analysis

This article discusses findings and implications from a research study using content analysis to investigate one question: How is bullying portrayed in selected picture books? The question is important because bullying has been, and continues to be, a pervasive problem in and out of school. This research study is needed because it investigates portrayals of the bully, bullied, and bystanders, unlike previous research that focused only on the bully. This article provides a review of research on bullying, highlighting international and national research that used content analysis to analyze picture books on bullying. It discusses content analysis as the research methodology, and describes data sources, data categories, data collection methods, and data analysis procedures. It ends by identifying major findings, discussing implications of findings, and describing limitations of the study. Keywords: bullying, portrayal, picture books, content analysis
The Chains of Ferroptosis Interact in the Whole Progression of Atherosclerosis

Abstract Atherosclerosis (AS), a category of cardiovascular disease (CVD) that can cause other more severe disabilities, increasingly jeopardizes human health. Owing to its imperceptible and chronic symptoms, it is hard to determine the pathogenesis and precise therapeutics for AS. A novel type of programmed cell death called ferroptosis was discovered in recent years that is distinctively different from other traditional cell death pathways in morphological and biochemical aspects. Characterized by iron overload, redox disequilibrium, and accumulation of lipid hydroperoxides (L-OOH), ferroptosis influences endothelial cells, vascular smooth muscle cells (VSMCs), and macrophages, as well as inflammation, partaking in the pathology of many cardiovascular diseases such as atherosclerosis, stroke, ischemia-reperfusion injury, and heart failure. The mechanisms behind ferroptosis are so sophisticated and interwoven that many molecules involved in this procedure are unknown. This review systematically depicts the initiation and modulation of ferroptosis and summarizes the contribution of ferroptosis to AS, which may open a feasible approach for target treatment in the alleviation of AS progression.
Fault Diagnosis for Industrial Robots Based on Informer

Timely and accurate fault diagnosis plays an important role in the reliability and safety of modern industrial robot systems, such as directed energy deposition additive manufacturing (DED-AM) systems. Aiming at the fact that few fault samples can be collected under actual working conditions, an end-to-end deep learning fault diagnosis approach for industrial robot systems based on Informer is proposed. This model solves the problem of poor fault diagnosis and generalization ability caused by limited fault samples under actual working conditions. Based on self-attention distillation and probabilistic sparse self-attention mechanism, the model focuses on hidden knowledge related to faults, thereby improving fault feature utilization. Furthermore, a deep one-dimensional convolutional network is deployed to extract features for classification. An experimental study based on the real-world industrial robot operational dataset was implemented. The results indicate that Informer shows merits in the fault diagnosis for industrial robot compared with the existing intelligent fault diagnosis algorithms.
Sporothrix brasiliensis infecting cats in northeastern Brazil: New emerging areas in Paraíba state

ABSTRACT: Sporotrichosis is a subcutaneous mycosis caused by fungus of the Sporothrix complex, and in Brazil the main species reported is Sporothrix brasiliensis, of which the diseased cat is the transmitter. Although, its occurrence has increased in the state of Paraíba, Brazil, since 2016, data on the disease in this state are limited. Therefore, this research aimed to identify molecularly isolates of Sporothrix spp. from domestic cats from cities in Paraíba, and in this way to expand the understanding of the disease in the state. Thirty-nine samples were analyzed, obtained from skin lesions of domestic felines, from the following cities in Paraíba: João Pessoa, Pilões, Patos, Areia, Bananeiras and Guarabira. Cytological analysis was performed to screen the samples, followed by fungal culture, and the molecular characterization of the isolates was performed, using the species-specific Polymerase Chain Reaction (PCR) or partial sequencing of the calmodulin gene. All isolates were identified as S. brasiliensis. The sequencing showed 100% similarity to the S. brasiliensis CBS 120339 strain. In view of this, it is concluded that in the study areas the species involved in cases of feline sporotrichosis is S. brasiliensis, its presence in Paraíba demonstrated the spread of the agent in regions distant from the epicenters in Brazil, alerting to the possible occurrence of zoonotic outbreaks similar to those found in the South and Southeast regions of the country. In addition, it highlights the emerging role of felines in the transmission of sporotrichosis in new endemic areas of Brazil.
Shallow Water Equations

14 This paper presents a new discretization of the rotating shallow water equations and a 15 set of decisions, ranging from a simplification of the continuous equations down to the 16 implementation level, yielding a code that is fast and accurate. Accuracy is reached by 17 using WENO reconstructions on the mass flux and on the nonlinear Coriolis term. The 18 results show that the build-in mixing and dissipation, provided by the discretization, al19 low a very good material conservation of potential vorticity and a minimal energy dis20 sipation. Numerical experiments are presented to assess the accuracy, which include a 21 resolution convergence, a sensitivity on the the free-slip vs. no-slip boundary conditions, 22 a study on the separation of waves from vortical motions. Speed is achieved by a series 23 of choices rather than a single recipe. The main choice is to discretize the covariant form 24 written in index coordinates. This form, rooted in the discrete differential geometry, re25 moves most of the grid scale terms from the equations, and keep them only where they 26 should be. The model objects appearing in resulting continuous equations have a nat27 ural correspondence with the grid cell features. The other choices are guided by the max28 imization of the arithmetic intensity. Finally this paper also proves that a pure Python 29 implementation is not only possible but also very fast, thanks to the possibility of hav30 ing compiled Python. As a result, the code performs 2 TFlop per second using thousand 31 cores. 32 Plain Language Summary 33 Using a simplified model of the ocean and atmosphere dynamics, this paper presents 34 a set of numerical and programming decisions that yields a code that is both fast and 35 accurate. The accuracy is assessed in terms of capacity of the code to maintain dynamic 36 structures over long periods of time while avoiding the emergence of numerical noise in 37 the solution. Accuracy is achieved by using a very accurate discretization on two deci38 sive terms of the model equations. Speed is achieved by a series of choices ranging from 39 a simplification of the continuous equations down to the implementation level. This pa40 per also proves that a pure Python code is a viable alternative to perform simulations 41 on high performance computing centers with as much as 2 TFlop per second using thou42 sand cores. 43 –2– ESSOAr | https://doi.org/10.1002/essoar.10507325. 1 | CC_BY_4. 0 | First posted online: Thu, 17 Jun 2021 08:56:22 | This content has not been peer reviewed. manuscript submitted to Journal of Advances in Modeling Earth Systems (JAMES)
Sporadic Renal Hemangioblastoma: A Case Report of a Rare Entity

Hemangioblastoma, also known as capillary hemangioblastoma, is a rare benign mesenchymal tumor commonly found in the central nervous system (CNS). It can also manifest in various organs, including the kidney. Renal hemangioblastoma (RH) is often associated with Von Hippel‐Lindau (VHL) disease, but sporadic occurrences are observed infrequently. While RH is usually asymptomatic, it can also cause abdominal pain and hematuria. In this study, we present a case of an elderly patient without history of VHL but complaining of abdominal pain for three days. Serological evaluations were unremarkable, and a CT scan identified a 2.4 cm mixed solid-cystic mass lesion on the left kidney's superior aspect. The patient subsequently underwent a biopsy followed by lesion ablation. Microscopic analysis revealed sheets of eosinophilic cells with ovoid nuclei, showing focal rhabdoid and spindle cell features, with an intricate capillary network. Focal nuclear atypia without necrosis or mitosis was noted. Immunohistochemistry (IHC) demonstrated positive staining for inhibin, S100, PAX8, and vimentin, along with patchy positivity for CD10 and RCC. Negative staining was observed for cytokeratin AE1/AE3, CK7, EMA, CK8/18, desmin, and HMB-45. The overall morphological characteristics and distinct IHC markers were consistent with RH. Although its pathogenesis remains unclear because of its rarity, distinguishing RH from renal cell carcinoma is crucial. IHC markers facilitate differentiation among lesions. The preferred treatment involves ablation or partial nephrectomy. Further assessment for possible VHL syndrome is essential, considering the distinct management approaches for sporadic and VHL-linked RH.
Multi-Channel Time-Frequency Domain Deep CNN Approach for Machinery Fault Recognition Using Multi-Sensor Time-Series

In the industry, machinery failure causes catastrophic accidents and destructive damage to the machines. It causes the machinery to stop and reduces production, causing financial losses to the industry. As a result, identifying machine faults at an early stage is critical. With the rapid advancement in artificial intelligence-based methods, developing automated systems that can diagnose machinery faults is necessary and challenging. This paper proposes a multi-channel time-frequency domain deep convolutional neural network (CNN)-based approach for machinery fault diagnosis using multivariate time-series data from multisensors (tachometer, microphone, underhang bearing accelerometer, and overhand bearing accelerometer). The wavelet synchro-squeezed transform (WSST) based technique is used to evaluate the time-frequency images from the multivariate time-series data. The time-frequency images are fed into the multi-channel deep CNN model for automated fault detection. The proposed multi-channel deep CNN model is multi-headed, considering the time-frequency domain information of each channel time-series data for automated fault detection. The proposed model’s performance is compared to benchmark models regarding testing accuracy, total parameters, and model size. Experiments have shown that the proposed model outperforms benchmark models regarding classification accuracy. The proposed multi-channel CNN model has obtained the accuracy and F1-score values of 99.48% and 99% for fault classification using time-frequency images of multi-sensor data. Finally, the proposed model’s performance is measured regarding inference time when deployed on edge computing devices such as the Raspberry Pi and the Nvidia Jetson AGX Xavier.
Effect of Activation of the 5-HT 4 Receptor System on the Calcium Activity of Neuron-Glial Networks in vitro

Calcium signaling plays a crucial role in memory formation and learning processes, both of which are adversely affected in the pathogenesis of depressive disorders. The serotonin system is involved in the regulation of synapse formation and activity. The current study examined the impact of serotonin system activation on calcium activity within neuron-glial networks in primary cultures of hippocampal cells. Our findings demonstrate that stimulation of 5-HT4R receptors leads to an elevation in the frequency of calcium events within the cells. However, it concurrently reduces the level of synchronization among these events.
Supply Chain Forecasting in a Fast-Moving Global Economy: Review, Limits and Future Directions

The supply chain demand forecasting field has evolved to meet customer demand, preventing lost sales opportunities, and reducing maintenance costs. However, the large number of supply chain components (producers, vendors, ware-houses, etc.) made it impossible to assess the quality of one forecasting method on all these components. In this paper, a comprehensive literature review has been done to identify the main challenges of the supply chain forecasting. This was followed by classifying these challenges based on the type of problem presented in each paper. Furthermore, experiments have been conducted to evaluate forecast quality of the widely-used artificial intelligence models. To model the effects of recent events, such as the COVID-19 pandemic and geopolitical conflicts, various data profiles with similar patterns to those events have been used. The most used neural network architecture for forecasting has been tested against a benchmark AI approach, revealing the limitations of current forecasting techniques on less stable demands. Subsequently, several research directions have been proposed.
Coastal Traders’ Adaptation: Farming as Secondary Livelihood Amidst COVID-19

Amid the economic disruptions from the COVID-19 pandemic, coastal communities in Bantul Regency, particularly Baru Beach and Goa Cemara Beach, experienced vulnerabilities and potential poverty, challenging the SDGs’ first priority of eradicating poverty. This study aimed to evaluate community characteristics and assess livelihood diversification strategies during the pandemic. Using purposive sampling in the selected regions, descriptive analysis revealed that the majority of business individuals were of productive age, held high school education, had over ten years of trading experience, and mainly worked in commerce, with farming as a secondary occupation. Despite the pandemic’s economic challenges, these communities displayed resilience through diversification into commerce, agriculture, and service sectors, with a notable shift from offline to online activities. This diversification not only buffered against immediate economic shocks but also suggested avenues for sustainable livelihoods, aligning with the broader ambitions of environmental sustainability and ecological balance in the context of the Agricultural Environment, Ecology, and Resources.