Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Feature Extraction EMG For Classification of Finger Movement

Many studies using EMG have been carried out, one example is the detection of hand movements with EMG. In general, research on hand movement detection uses MyoArmband as a module for recording EMG data. One of the obstacles to using the MyoArmband is that it's quite expensive and there are quite a lot of channels up to eight channels. Therefore, this study presents a comparison of the classification of five finger movements using KNN, Decision Tree, and Random Forest using only 3 EMG channels in the forearm muscles. In addition, this study also compares and combines the use of four feature extraction with Time-Domain such as RMS, VAR, MAV, and IEMG and compares the effect of using data transformation using Standardscaller on finger movement classification performance. The result of this study is that the addition of features can increase the accuracy value obtained. The best combination in this model is the RMS + MAAV + VAR feature with an accuracy of 95.15%, a precision of 95.15%, a sensitivity of 95.2%, and a specificity of 98.8%. In addition, the use of data transformation does not really affect the RF model.
Automating the Identification of Feedback Quality Criteria and the CanMEDS Roles in Written Feedback Comments Using Natural Language Processing

Introduction: Manually analysing the quality of large amounts of written feedback comments is time-consuming and demands extensive resources and human effort. Therefore, this study aimed to explore whether a state-of-the-art large language model (LLM) could be fine-tuned to identify the presence of four literature-derived feedback quality criteria (performance, judgment, elaboration and improvement) and the seven CanMEDS roles (Medical Expert, Communicator, Collaborator, Leader, Health Advocate, Scholar and Professional) in written feedback comments. Methods: A set of 2,349 labelled feedback comments of five healthcare educational programs in Flanders (Belgium) (specialistic medicine, general practice, midwifery, speech therapy and occupational therapy) was split into 12,452 sentences to create two datasets for the machine learning analysis. The Dutch BERT models BERTje and RobBERT were used to train four multiclass-multilabel classification models: two to identify the four feedback quality criteria and two to identify the seven CanMEDS roles. Results: The classification models trained with BERTje and RobBERT to predict the presence of the four feedback quality criteria attained macro average F1-scores of 0.73 and 0.76, respectively. The F1-score of the model predicting the presence of the CanMEDS roles trained with BERTje was 0.71 and 0.72 with RobBERT. Discussion: The results showed that a state-of-the-art LLM is able to identify the presence of the four feedback quality criteria and the CanMEDS roles in written feedback comments. This implies that the quality analysis of written feedback comments can be automated using an LLM, leading to savings of time and resources.
Optimal Rates for Agnostic Distributed Learning

The existing optimal rates for distributed kernel ridge regression (DKRR) often rely on a strict assumption, assuming that the true concept belongs to the hypothesis space. However, agnostic distributed learning is more common in practice, where the target regression may lie outside the kernel space. In this paper, we refine the excess risk bounds for DKRR and demonstrate that DKRR still achieve capacity-dependent optimal rates in the agnostic setting. Our theoretical findings indicate that the condition on the number of partitions not only influences computational efficiency but also impacts the range of situations where optimal rates are applicable. To relax the strict condition on the number of partitions, we first derive a sharper estimate for the difference between empirical and expected covariance operators. We then leverage additional unlabeled examples to reduce the label-independent error terms, further extending the optimal rates to more situations in the agnostic setting. In addition to the generalization error bounds in expectation, we also present refined excess risk bounds in high probability, where the optimal rates can also pertain to the agnostic setting. Finally, through both theoretical and empirical comparisons with related work, we demonstrate that our findings provide higher statistical applicability and computational advantages.
Effect of Garlic Straw with Silage Corn Stalks on Hu Sheep Rumen Fermentation and Microbial Community In Vitro

Garlic, an important economic crop, provides nutrient-rich straw. When appropriately balanced with silage corn stalks, it is a high-quality forage resource. However, studies on the impact of garlic straw with silage corn stalks on Hu sheep’s digestive metabolism and rumen microbiota are scarce. In this study, different addition ratios of garlic straw and silage corn stalks were utilized for in vitro experiments. We designed six experimental groups (CON, G0, G20, G40, G60, G80, and G100) based on varying ratios of garlic straw to silage corn stalks. Rumen microbiota was analyzed through 16S rRNA sequencing. Nutrient composition analysis indicated that garlic straw’s relative feeding value (RFV) closely resembled that of silage corn stalks. After 24 h of fermentation, dry matter digestibility and in vitro gas production significantly increased, reaching peak values at a 60% addition ratio. Furthermore, volatile fatty acids (VFAs) such as acetic, propionic, and butyric acid exhibited elevated contents, with the highest yields observed at 60% inclusion. At the genus level, Prevotella, Rikenellaceae RC9 gut group, and Succiniclasticum were identified as the dominant bacterial groups. The gas production test showed a significant decrease in the G80 group compared to others. Microbial analysis revealed a higher abundance of Prevotella in G80 compared to G20, offering valuable insights for reducing greenhouse gas emissions from ruminant animals. Finally, this study predicted the impact of garlic straw with silage corn stalks’ addition on Hu sheep’s metabolic pathways and biological functions of the rumen microbiota. This research highlights the potential for effectively utilizing garlic straw as a feed resource for Hu sheep and proposes a rational proportion for combining garlic straw with silage corn stalks.
Review of Piezoelectric Properties and Power Output of PVDF and Copolymer-Based Piezoelectric Nanogenerators

The highest energy conversion efficiencies are typically shown by lead-containing piezoelectric materials, but the harmful environmental impacts of lead and its toxicity limit future use. At the bulk scale, lead-based piezoelectric materials have significantly higher piezoelectric properties when compared to lead-free piezoelectric materials. However, at the nanoscale, the piezoelectric properties of lead-free piezoelectric material can be significantly larger than the bulk scale. The piezoelectric properties of Poly(vinylidene fluoride) (PVDF) and Poly(vinylidene fluoride-co-trifluoroethylene) (PVDF-TrFE) lead-free piezoelectric nanomaterials are reviewed and their suitability for use in piezoelectric nanogenerators (PENGs) is determined. The impact of different PVDF/PVDF-TrFE composite structures on power output is explained. Strategies to improve the power output are given. Overall, this review finds that PVDF/PVDF-TrFE can have significantly increased piezoelectric properties at the nanoscale. However, these values are still lower than lead-free ceramics at the nanoscale. If the sole goal in developing a lead-free PENG is to maximize output power, lead-free ceramics at the nanoscale should be considered. However, lead-free ceramics are brittle, and thus encapsulation of lead-free ceramics in PVDF is a way to increase the flexibility of these PENGs. PVDF/PVDF-TrFE offers the advantage of being nontoxic and biocompatible, which is useful for many applications.
Internet of Thing (IoT) and Data Analytics with Challenges and Future Applications

The Internet of Things (IoT) has emerged as a transformative technology, enabling seamless connectivity and communication between smart devices and the digital world. IoT's integration with data analytics has opened up a myriad of possibilities for extracting valuable insights from the vast volumes of data generated by connected devices. This research paper explores the convergence of IoT and data analytics, shedding light on the challenges encountered during their integration and envisioning future applications that can revolutionize various domains. The paper begins with a comprehensive review of the IoT ecosystem and its significance in shaping the modern era of interconnected devices. It highlights the diverse range of IoT applications across industries, including smart cities, healthcare, agriculture, manufacturing, transportation, and more. Moreover, it delves into the underlying technologies and communication protocols that enable IoT devices to gather, transmit, and process data efficiently. As data generated by IoT devices continues to escalate exponentially, effective data analytics becomes paramount to extract meaningful insights and support informed decision-making. The research delves into various data analytics techniques, such as machine learning, artificial intelligence, and big data analytics, illustrating their relevance in harnessing the potential of IoT-generated data. However, amidst the promises, several challenges impede the seamless amalgamation of IoT and data analytics. Issues concerning data privacy, security, interoperability, scalability, and data quality are thoroughly examined. The research emphasizes the need for robust frameworks and standards to address these challenges and ensure the secure and efficient functioning of IoT ecosystems. In the final section, this paper envisions the future applications of IoT and data analytics. It envisions intelligent and autonomous systems that adapt to dynamic environments, leading to advancements in predictive maintenance, personalized healthcare, optimized resource management, and enhanced user experiences. Furthermore, the potential societal impacts, including ethical considerations, are also explored.
Fractal Characteristic of Regional Economic Relation Intensity and It's Application in Western China

Fractal method is introduced into the study of regional economy based on the analysis of human-environment complex system in western China. The spatial fractal characteristics are determined by the results from the economic relation intensity model. The economic relation intensities amongst different cities and counties in the Aikesu area are calculated and spatial distributions are analyzed, compared with the fundamental corresponding characteristics of self-affine fractal, thus providing guidance for the regional planning.
Graphitic Carbon Nitride/MOFs Hybrid Composite as Highly Selective and Sensitive Electrodes for Calcium Ion Detection

The metal–organic framework (MOF) is a class of materials that exhibits a notable capacity for electron transfer. This unique framework design offers potential applications in various fields, including catalysis, gas storage, and sensing. Herein, we focused on a specific type of MOF called Ti-MOF. To enhance its properties and functionality, the composite material was prepared by incorporating graphitic carbon nitride (g-C3N4) into the Ti-MOF structure. This composite, known as g-C3N4@Ti-MOF, was selected as the active material for ion detection, specifically targeting calcium ions (Ca2+). To gain a comprehensive understanding of the structural and chemical properties of the g-C3N4@Ti-MOF composite, several analytical techniques were employed to characterize the prepared g-C3N4@Ti-MOF composite, including X-ray diffraction (XRD), SEM-EDX, and FT-IR. For comparison, different pastes were prepared by mixing Ti-MOF or g-C3N4@Ti-MOF, graphite, and o-NPOE as a plasticizer. The divalent Nernstian responses of the two best electrodes, I and II, were 28.15 ± 0.47 and 29.80 ± 0.66 mV decade−1, respectively, with concentration ranges of 1 µM–1 mM and 0.1 µM–1 mM with a content 1.0 mg Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE and 0.5 mg g-C3N4@Ti-MOF: 250 mg graphite: 0.1 mL o-NPOE, respectively. The electrodes showed high sensitivity and selectivity for Ca2+ ions over different species. The suggested electrodes have been successfully employed for Ca2+ ion measurement in various real samples with excellent precision (RSD = 0.74–1.30%) and accuracy (recovery = 98.5–100.2%), and they exhibited good agreement with the HPLC.
Statistical significance maps based on machine learning. On the use of data-dependent upper bounds

The analysis of neuroimaging data has become a standard in the clinical practice and is frequently used to corroborate the diagnosis and monitor the evolution of neurological disorders. Over the last decades, the analysis of these data has evolved from visual inspection to computer analysis, with machine learning based approaches being particularly common in recent years. These models are particularly useful for classifying data but their ability to extract regions of interest from the comparison of two populations has not yet been adequately explored.In this paper, we demonstrate a tool to build significance maps from medical brain imaging. These maps contain the regions of interest for the comparison of two given groups and are built using the accuracy rate of a statistical classifier trained to separate these groups. In contrast to previous works, the significance level of each region is calculated from corrected accuracy rates, which are computed by considering the upper bound for the classification error (worst case). This makes the presented approach very conservative and reinforces the importance of the reported regions. In addition, we propose a methodology to calculate data-adapted upper bounds based on the Bayesian theory.
Medical Supplies Export Controls and Bans During the Early COVID-19 Pandemic in Southeast Asian Countries Compliance with the WTO

The global markets for crucial COVID-19 products (medical supplies) are highly concentrated. Most of developing countries are highly dependent on imports for these products. However, majority of countries implemented the export controls and bans on medical supplies due to the limitation of resources they have. This research examines the Southeast Asian policy on medical supplies export controls and bans in response to the COVID-19 Pandemic and its justification for export restrictions based on Article XI paragraph (2), Article XX, and Article XXI GATT 1994. The research methods are based on qualitative methods and normative juridical research methods. This research uses secondary data from journals, books, official documents, and websites related to the Southeast Asian Government policy on export. The results show that the justification for the export ban and restriction on medical equipment can be exempted by referring to Article XX regarding general exceptions and regarding security exceptions in Article XXI (b) (iii). Most of the Southeast Asian countries tried to follow the provisions of GATT 1994 and its reflection to be a good international citizen by compliance the international law.
Pharmaceutical Agents as Potential Drivers in the Development of Early-Onset Colorectal Cancer: Case-Control Study

Background: The incidence of early-onset colorectal cancer (EOCRC) rose abruptly in the mid 1990s, is continuing to increase, and has now been noted in many countries. By 2030, 25% of American patients diagnosed with rectal cancer will be 49 years or younger. The large majority of EOCRC cases are not found in patients with germline cancer susceptibility mutations (eg, Lynch syndrome) or inflammatory bowel disease. Thus, environmental or lifestyle factors are suspected drivers. Obesity, sedentary lifestyle, diabetes mellitus, smoking, alcohol, or antibiotics affecting the gut
Sense of coherence and nutritional maturity versus the feeling of stress among young adults

Objective: The aim of this study was to establish the interdependencies between the sense of coherence, nutritional maturity and the sense of stress in a group of young adults, considering their diet. Ultimately, research efforts were aimed at testing the predictive power of the study variables against the feeling of stress. Method: The study was conducted in a group of 100 people in the 20–33 age range that were following either a vegetarian and meat diet. The Antonovsky’s Sense of Coherence Questionnaire, the Nutritional Maturity Questionnaire (Potocka & Najder, 2016) and the Sense of Stress Questionnaire (Plopa & Makarowski, 2010) were applied. Results: The results indicate a significantly lower intensity of emotional tension, intrapsychic, external and general stress, as well as significantly higher indicators of the sense of coherence and nutritional maturity in the group of vegetarians. Moreover, nutritional maturity was found to be negatively correlated to the sense of stress, while the sense of coherence was noted to be positively correlated to the nutritional maturity. In the end, it was demonstrated that the sense of coherence played an important role in predicting stress as experienced by young adults. Conclusion: The research provided important knowledge on the relationship between attitudes towards nutrition and experiencing stress in a group of young adults.
High-Precision Corrosion Detection via SH1 Guided Wave Based on Full Waveform Inversion

Corrosion detection for industrial settings is crucial for safe and efficient operations. Due to its high imaging resolution, the guided–wave full–waveform inversion tomography technique has significant potential for corrosion detection of plate metals. Limited by the long wavelengths of A0 and S0 mode waves, this method exhibits inadequate detection resolution for the earlier shallow and small corrosion defects. Based on the relatively short wavelength characteristics of the SH1 mode wave, we propose a high–precision corrosion detection method via SH1 guided wave using the full waveform inversion algorithms. By conducting finite element simulations of ultrasonic–guided waves on aluminum plates with varying corrosion defects, a comparison was made to assess the detection precision across A0, S0, and SH1 modes. The comparison results showed that, whether for regular or irregular defects, the SH1 mode wave always exhibited higher imaging accuracy than the A0 and S0 mode waves for shallow and small–sized defects. The corresponding experiments were conducted on an aluminum plate with simple or complex defects. The results of the experiments reconfirmed that the full waveform inversion method using SH1 guided wave can effectively reconstruct the shape and size of small and shallow corrosion defects within aluminum plates.
A longitudinal study of the association between social capital and mortality in community-dwelling elderly Brazilians

The aim of this study was to verify whether social capital is a predictor of all-cause mortality in community-dwelling elderly Brazilians. Participation included 935 surviving elderly from the elderly cohort of the Bambui Project in 2004, who were followed until 2011. The outcome was all-cause mortality and the exposure of interest was social capital, measured in its two components, cognitive (social cohesion and social support) and structural (social participation and neighborhood satisfaction). Sociodemographic variables, health conditions, and smoking were included in the analysis for adjustment purposes. Data analysis was based on the Cox proportional hazards model, providing hazard ratios (HR) and 95% confidence intervals (95%CI). The social participation dimension of social capital’s structural component was the only dimension independently associated with mortality: elderly Brazilians that did not participate in social groups or associations showed a two-fold higher risk of death (HR = 2.28; 95%CI: 1.49-3.49) compared to their peers. The study’s results reveal the need to extend interventions beyond the specific field of health in order to promote longevity, focusing on environmental and social characteristics. Social Capital; Mortality; Aged; Social Participation; Cohort Studies ARTIGO ARTICLE This article is published in Open Access under the Creative Commons Attribution license, which allows use, distribution, and reproduction in any medium, without restrictions, as long as the original work is correctly cited.
Pengaruh Faktor Ekspektasi Kinerja, Nilai Harga, Motivasi Hedonis dan Sosial terhadap Minat Masyarakat Kabupaten Buleleng dalam Penggunaan Mobile Banking

This study aims to determine the effect of performance expectations, price values, hedonic and social motivations on people's interest in using mobile banking. The method in this research is associative quantitative method with primary data. The sample used was 346 people of Buleleng Regency aged 20-24 who were selected through purposive sampling technique. Determination of the number of samples is done with the tables of Isaac and Michael. The research data were processed and analyzed by multiple linear regression analysis. The results of the study stated that each variable of performance expectation, price and social value had a positive and significant effect on people's interest in using mobile banking. Meanwhile, the hedonic motivation variable has no influence on people's interest in using mobile banking. All independent variables in this study were explained by the theory of UTAUT 2 (Unified Theory of Acceptance and Use of Technology).
Utilizing Blended Learning to Mitigate the Challenges Brought by Natural Disasters in South African Schools

This study investigated the potential benefits and challenges associated with the implementation of blended learning as a strategy for curriculum recovery and enhancement of student performance in Grade 12 science and mathematics education. Blended learning, which combines traditional face-to-face teaching methods with online learning, was recognized for its capacity to address educational difficulties. The research employed a theoretical framework integrating the Technology Acceptance Model and Sociocultural Constructivism theory to examine the viewpoints of teachers regarding technology adoption and the impact of personal and social interactions on learning outcomes. A qualitative interpretive case study design was employed, and interviews were carried out with four science and mathematics teachers. The findings revealed that teachers acknowledged the advantages of blended learning, including its potential to overcome barriers to learning and augment student engagement. However, external challenges about technology infrastructure, limitations of online platforms, and the necessity for professional development were identified. Teachers displayed varying levels of internal challenges, including familiarity and confidence in implementing blended learning. The study underscored the importance of continuous support, infrastructure development, and pedagogical training to effectively integrate blended learning into science and mathematics classrooms. Therefore, by addressing these challenges and capitalizing on the benefits of blended learning, the potential for improving curriculum recovery and enhancing student performance in the face of disruptions caused by natural disasters and other educational obstacles was emphasized.