Miracle-dz/UniFAR-FTUs huggingface dataset:
Interdisciplinary Exploration of the Impact of Job Stress on Teachers' Lives

Work stress is an increasingly frequent and growing phenomenon in today's society, where speed and impatience to constantly face new challenges and challenges in professional, family and social life prevail. In the world of work, responsibilities, constant transformations, making difficult decisions and the demands of technological development can be sources of stress for education professionals, which is why learning to identify work stressors, identifying the signs of stress, managing to prevent them and developing the right strategies for their management is becoming an increasingly important priority for both teachers and educational institutions due to the costs they entail. This article contextualizes the Cuban scenario where our teachers work, identifies the main causes of stress at work, the conflicts, the physical and emotional symptoms it brings with it and highlights the importance of self-care to maintain the quality of life of education workers and make the educational institution a healthy space. The research is based on the dialectical-materialistic approach, theoretical, empirical and mathematical statistical methods were used in the different stages of the study, and the bibliographic file technique was used for the critical study of the documents.
Using machine learning to detect sarcopenia from electronic health records

Introduction Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR. Methods Adults undergoing musculoskeletal testing (December 2017–March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia. Results Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51–71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00–71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28–91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41–93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants. Conclusions Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations.
Antibiotic and Heavy Metal Co-Resistant Strain Isolated from Enrichment Culture of Marine Sediments, with Potential for Environmental Bioremediation Applications

Antibiotics and heavy metals have caused serious contamination of the environment and even resulted in public health concerns. It has therefore become even more urgent to adopt a sustainable approach to combating these polluted environments. In this paper, we investigated the microbial community of marine sediment samples after 255 days of enrichment culture under Cu (II) and lincomycin stress and ZC255 was the most resistant strain obtained. The 16S rRNA gene sequence confirmed that it belonged to the genus Rossellomorea. Strain ZC255 was resistant to 12 kinds of antibiotics, and had a superior tolerance to Cu (II), Pb (II), Ni (II), Zn (II), Cr (III), and Cd (II). Moreover, it exhibits strong bioremoval ability of Cu and lincomycin. The removal efficiency of Cu (II) and lincomycin can achieve 651 mg/g biomass and 32.5 mg/g biomass, respectively. Strain ZC255 was a promising isolate for pollution bioremediation applications.
Decision making mechanism in resource based theory: A literature review, synthesis, and future research

Abstract Many companies have encountered vagueness, uncertainty, complexity, and ambiguity in formulating strategies during the COVID-19 pandemic. Therefore, they urgently require a decision-making mechanism to determine the resources/capabilities to achieve a competitive advantage in external environmental change. This research aims to discover publications in RBT, particularly the decision-making mechanism. The review highlighted the novelty of the decision-making mechanism of dynamic capability in RBT that the researchers had never previously reviewed. This review research enabled a systematic literature review (SLR) method embedded with bibliometric and systematic mapping study (SMS) analysis. The results provided 27 final publications, highlighting three keywords (dynamic capabilities, decision making, and enterprise resource management) and Teece and colleagues’ paper as the center of reference. The publications revealed that the decision-making mechanism covered a complex context, big data analysis and multi-criteria decision-making, dynamic, updated characteristics mechanism, and subject fields. Moreover, the publications were generally conducted in common industries, and several decision-support tools were found in the RBT. In summary, publications still need to provide more information to present the decision-making mechanism in enterprise resource management. Therefore, the proposed theoretical framework and conceptual decision-making model should be developed to follow a dynamic environment.
Reduce Phase Unbalance with Cross-phase of PV and EV Chargers, using Convex Optimization on Quadratic Constraint in Distribution Network

In the near future, Photovoltaic (PV) network and Electric Vehicle Charging station (EVC) will be deployed in Ho Chi Minh City (HCMC), the use of Cross-phase characteristic will help to reduce the influence of these distributed sources and will improve the imbalance. phase of the current low voltage distribution network. The optimization aims to reduce the loss caused by phase unbalance. Convex optimization model is considered to solve the optimization problem with quadratic constraint and voltage balance equation system (VUF) and phase constraints. Algorithms run according to the above model including OPF, Cross-phase and using unbalanced 3-phase IEEE 33 bus and IEEE 192 bus systems. The results show that using the Cross-phase characteristic significantly reduces phase imbalance.
The Role of Cloud Computing on Accounting Information System Quality:A Study in Hotel Industry

The hotel industry is a significant sector for Indonesian economic growth. Nowadays, cloud computing is bringing much potential for hotels to increase their competitive advantage, specifically in facilitating Information Systems (IS). However, we found still limited study that investigates cloud-based IS in accounting functions. Therefore, this study aims to analyze 1) the impact of the human aspect in determining cloud performance, 2) the impact of cloud performance on the relative advantage and compatibility of cloud-based Accounting Information Systems (AIS) in the hotel industry, and 3) the impact of its relative advantage and compatibility on cloud-based AIS quality. Using purposive sampling, the data was collected from accounting and/or information manager hotels in North Sumatra, Indonesia. The collected data were analyzed using variance-based Structural Equation Modeling (SEM). We found that human, cloud performance, and technology adaptability are the critical antecedents of cloud-based AIS quality in the hotel industry. Hotel leaders should consider user computer-self efficacy and cloud-AIS alignment design while developing and integrating cloud-based AIS into their general system.
Unravelling fluvial versus glacial legacy controls on boulder‐bed river geomorphology for semi‐alluvial rivers in Fennoscandia

River management is founded on predictable self‐organisation between river form and catchment controls in alluvial rivers. However, a substantial proportion of rivers are not fully alluvial. In previously glaciated landscapes, boulder‐rich glacial till influences river channel form and process. Increasing interest in nature‐ and process‐based river restoration requires knowledge of pre‐disturbance natural processes, which does not exist for semi‐ and non‐alluvial rivers in Fennoscandia. We aimed to determine the role of Pleistocene glaciation and subsequent deglaciation versus Holocene fluvial processes in controlling channel form of boulder‐bed rivers in Fennoscandia. We quantified morphological characteristics of northern Swedish boulder‐bed rivers, in which channel morphology was minimally impacted by humans, and used the degree of alluvial signatures to infer fluvial and legacy glacial controls. We conducted surveys of reach‐scale channel geometry, boulder and wood distributions and catchment characteristics for 20 reference reaches (drainage area: 11–114 km2). Reaches ranged in slope from 1% to 8% and were extremely diverse in channel geometry. Rivers showed little self‐organisation at the reach scale; no association exists between channel width and channel slope or bed sediment size. Boulders were rarely clustered into bedforms (e.g., step‐pools) typical of boulder‐bed mountain rivers. Drainage area was positively correlated with channel capacity but not channel width, slope or sediment size. Channel boulder density was best predicted by surveys of terrestrial boulders. Consequently, channel geometry, boulder size and the distribution of boulders were primarily controlled by legacy glacial conditioning rather than current fluvial processes, with some alluvial adjustment of smaller particles within the boulder template. Therefore, restoration of semi‐alluvial rivers should take into account local sediment and geomorphic conditions rather than use management principles built for fully alluvial rivers.
Slotted Patch Excited ME Dipole Antenna for Broadband Millimeter Wave and Satellite Applications

A broadband slotted patch antenna excited Magne-to- Electric dipole (ME-dipole) antenna is proposed in this paper for millimeter wave and satellite communication. The ME-dipole is placed on a coupled-feed cavity-backed slotted patch antenna. The slotted patch antenna acts as both a radiator and a ground for the ME-dipole. By combining the ME-dipole and the slotted patch antenna, a -10dB impedance bandwidth covering over 22.86-44.35 GHz (63.9%) is realized. The pattern of the antenna element remains stable throughout this bandwidth. Based on this antenna element, a 4x4 single linearly polarized (LP) array is designed and tested. Measured results show that a -10 dB impedance bandwidth of 51.2%, and a peak gain of 20.4 dBi with a 3dB gain bandwidth over 40.6% are realized.
The Utility of Renal Mass Biopsy in Large Renal Masses

Objectives The role of needle core renal biopsy in large renal masses, defined as lesions larger than 4 cm, is debatable, as larger renal masses are associated with malignant histology. We aim to review the safety and impact of renal biopsy on the management of large renal masses. Methods A retrospective, single-center review of all renal biopsies performed between January 2011 and December 2020 at Royal Perth Hospital was conducted. Indications for biopsy, complications and final management plans were correlated to assess the value of biopsies in large renal masses. Results In total, 126 biopsies were performed. Indeterminate imaging findings and comorbidities were the main indications for biopsies. We identified 116 (92.1%) diagnostic biopsies and 10 (8.0%) non-diagnostic biopsies due to insufficient samples or inflammatory tissue. Of the diagnostic biopsies, 99 (78.6%) were malignant and 17 (13.5%) were benign. Unnecessary extirpative surgery was avoided in 17 patients. Histology included renal cell carcinoma (96%) and other malignancies such as urothelial carcinoma (3%) and non-Hodgkin’s lymphoma (1%). Benign biopsies identified histology including angiomyolipoma (35.3%) and oncocytoma (52.5%). The median follow-up time was 68 months (range 19–132 months). Conclusion Renal biopsies in large renal masses may aid in preventing unnecessary surgery, especially in situations where imaging findings are equivocal or in patients with many comorbidities.
Role of Alpha-Fetoprotein (AFP) in Diagnosing Childhood Cancers and Genetic-Related Chronic Diseases

Simple Summary The article presents the role of alpha-fetoprotein in the diagnosis and monitoring of treatment for selected genetic diseases and early childhood cancers. The authors draw attention to diagnostic pitfalls related to physiological AFP production in the first year of life, inconsistencies in laboratory tests, and result interpretation. Abstract Alpha-fetoprotein (AFP) is a protein commonly found during fetal development, but its role extends beyond birth. Throughout the first year of life, AFP levels can remain high, which can potentially mask various conditions from the neurological, metabolic, hematological, endocrine, and early childhood cancer groups. Although AFP reference values and clinical utility have been established in adults, evaluating AFP levels in children during the diagnostic process, treatment, and post-treatment surveillance is still associated with numerous diagnostic pitfalls. These challenges arise from the presence of physiologically elevated AFP levels, inconsistent data obtained from different laboratory tests, and the limited population of children with oncologic diseases that have been studied. To address these issues, it is essential to establish updated reference ranges for AFP in this specific age group. A population-based study involving a statistically representative group of patients could serve as a valuable solution for this purpose.
A Data Enhancement Algorithm for DDoS Attacks Using IoT

With the rapid development of the Internet of Things (IoT), the frequency of attackers using botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) and other cyber attacks on the internet has significantly increased. In the actual attack process, the small percentage of attack packets in IoT leads to low accuracy of intrusion detection. Based on this problem, the paper proposes an oversampling algorithm, KG-SMOTE, based on Gaussian distribution and K-means clustering, which inserts synthetic samples through Gaussian probability distribution, extends the clustering nodes in minority class samples in the same proportion, increases the density of minority class samples, and improves the amount of minority class sample data in order to provide data support for IoT-based DDoS attack detection. Experiments show that the balanced dataset generated by this method effectively improves the intrusion detection accuracy in each category and effectively solves the data imbalance problem.
Graphene Network with Ni and Al Nanoparticles as the Composite Precursor: Atomistic Simulation

In this work, the method of fabrication of the composites based on a graphene network and metal nanoparticles (Ni and Al) by deformation-heat treatment is considered by molecular dynamics simulation. A graphene network filled with Al or Ni nanoparticles are considered to study the fabrication of the composites and their mechanical properties. The diameter of Ni and Al nanoparticle is 6.2 and 7.5 Å, while in the final composite state, the ratio of metal and carbon atoms in the graphene/Al and graphene/Ni systems are 8 and 7.7 at. %, respectively. It is shown that hydrostatic compression is an effective way to obtain graphene/metal composites. In the compressed structure (composite state) no pores are found since compression is conducted to the maximum possible density. It is found that the graphene/Ni composite has better mechanical properties in comparison with the graphene/Al composite. The enhanced mechanical properties of the graphene/Ni composite are explained by the formation of a graphene network of high strength with metal nanoparticles uniformly distributed over the graphene pores. It has been established, that in the graphene/Al composite, both during hydrostatic and uniaxial tension, due to its low cohesion energy with graphene, metal nanoparticles begin to coagulate, which is more energetically favorable. The larger Al nanoparticles that appear in the structure are the weak places of the composite, where the fracture of the composite occurs most easily.
Market-segment specialization and long-term growth in a tourism-based economy.

espanolEste trabajo aborda el problema del crecimiento a largo plazo en una economia turistica. En particular, analiza el papel de la especializacion en un segmento de mercado turistico en la fase de declive de la industria. Para ello se construyo en modelo de crecimiento endogeno donde los diferentes tipos de capital se representan por medio de los posibles valores de la elasticidad-precio de la demanda. Se resolvio el problema de optimizacion social a traves de la aplicacion de la teotria de control optimo. Los resultados muestran que tasas de consumo no decrecientes en la economia son posibles si la oferta turistica se orienta a segmentos de mercado mas selectos que el puro turismo de masas. EnglishThis paper addresses the problem of long term growth in a tourism-based economy. Specifically, it analyses the role of the market-segment specialization in the decline phase of the industry. To do this, an endogenous growth model is built where the different types of tourism are represented by the different values of the price-elasticity of demand. The social optimization problem is solved analytically by using optimal control theory. The results show that non-decreasing consumption rates in the economy are possible by reorienting the supply to more selected market segments than pure mass tourism.
Plug and Power: Fingerprinting USB Powered Peripherals via Power Side-channel

The literature and the news regularly report cases of exploiting Universal Serial Bus (USB) devices as attack tools for malware injections and private data exfiltration. To protect against such attacks, security researchers proposed different solutions to verify the identity of a USB device via side-channel information (e.g., timing or electromagnetic emission). However, such solutions often make strong assumptions on the measurement (e.g., electromagnetic interference-free area around the device), on a device’s state (e.g., only at the boot or during specific actions), or are limited to one particular type of USB device (e.g., flash drive or input devices). In this paper, we present PowerID, a novel method to fingerprint USB peripherals based on their power consumption. PowerID analyzes the power traces from a peripheral to infer its identity and properties. We evaluate the effectiveness of our method on an extensive power trace dataset collected from 82 USB peripherals, including 35 models and 8 types. Our experimental results show that PowerID accurately recognizes a peripheral type, model, activity, and identity.
Artificial Intelligence Berbasis QSPR Dalam Kajian Inhibitor Korosi

Baja termasuk material yang memiliki ketahanan rendah terhadap serangan korosi Ketika berada pada lingkungan korosif. Inhibitor organik mampu menghambat korosi dengan efisiensi inhibisi yang tinggi. Tinjauan komparatif penting bagi pengembangan metode evaluasi kinerja inhibitor disajikan dalam karya ini. Kami mereview perkembangan artificial intelligence berbasis mesin learning dengan model QSPR dalam kajian penghambatan korosi. Makalah ini menjelaskan bagaimana metode pembelajaran mesin berbasis data dapat menghasilkan model yang menghubungkan sifat-aktivitas molekuler dengan penghambatan korosi oleh inhibitor berbasis bahan alam (green inhibitor). Teknik ini dapat digunakan untuk memprediksi kinerja senyawa yang belum disintesis atau diuji. Keberhasilan model ini memberikan paradigma untuk penemuan senyawa baru yang cepat, penghambat korosi yang efektif untuk berbagai logam dan paduan.
Transcriptomic Profiling of Subcutaneous Backfat in Castrated and Intact Alentejano Pigs Finished Outdoors with Commercial and Fiber-Rich Diets

In this work, we studied the backfat transcriptome of surgically castrated (C), intact (I) and intact fed an experimental diet (IE) outdoor-reared male Alentejano (AL) pigs. The experimental diet was a high-fiber diet with locally produced legumes and by-products associated with a boar taint reduction effect. At slaughter (~160 kg), backfat samples were collected for total RNA sequencing. Intact pigs presented leaner carcasses, more total collagen, and more unsaturated intramuscular fat content than C animals. A total of 2726 differentially expressed genes (DEGs, |log2 FC|> 0.58, q < 0.05) were identified between C and I with overexpressed genes related to muscular activity (MYH1, ACTA1) or collagen metabolism (COL1A1, COL1A2) in I pigs. Between C and IE, 1639 DEGs of genes involved in lipidic metabolism (LEP, ME1, FABP4, ELOVL6) were overexpressed in C. Finally, only 28 DEGs were determined between I and IE. Clustering results indicated a drastic influence of the testis in the transcriptome of subcutaneous fat of AL pigs, while the diet had a marginal effect. Diet can reduce stress by increasing satiety in animals, and could have induced an increase of skatole degradation due to the higher expression of the CYP2A19 gene in the IE group.
Achieving Certified Robustness for Brain-Inspired Low-Dimensional Computing Classifiers

Brain-inspired hyperdimensional computing (HDC) in machine learning applications has been achieving great success in terms of energy efficiency and low latency. The proposal of low-dimensional computing (LDC) classification model not only improves the inference accuracy of existing HDC-based models but also gets rid of the ultra-high dimension in them. However, the security part of LDC model to adversarial perturbations has not been touched. In this paper, we adopt the bounding technique, interval bound propagation (IBP), to train a LDC classification model that is provably robust against $L_{\infty}$ norm-bounded adversarial attacks. Specifically, we propagate the $L_{\infty}$ norm-bounded bounding box around the original input through layers of LDC model using interval arithmetic. After propagation, the worst case prediction logits can be computed based on the upper bound and the lower bound of the output bounding box. By minimizing the loss between the worst case prediction and the true label, the predicted label could be kept invariant over all possible adversarial perturbations within $L_{\infty}$ norm-bounded ball. We evaluate the algorithm on both MNIST and fashion MNIST datasets. The experiment results corroborate that our trained models with IBP exhibit robustness against strong projected gradient descent (PGD) attacks and memory errors.