Rigorous Training Research Articles

Tomato quality assessment and enhancement through Fuzzy Logic: A ripe perspective on precision agriculture

This study presents a meticulous exploration of tomato quality assessment and enhancement employing Fuzzy Logic, focusing on beefsteak, Roma, and cherry varieties. The research incorporates a multivariate approach encompassing shape, size, texture, and color analysis. Utilizing a controlled imaging system, 165 tomatoes were captured at various ripening stages to ensure uniformity in lighting, camera position, and background color. Image processing and computer vision techniques, including segmentation and feature extraction, were applied to comprehensively evaluate the tomatoes. Quality assessment involved considerations of three stages of skin coloration, defect quantity, and skin texture calculated using the statistical method of entropy. The decision-making process and quality evaluation were facilitated by a Mamdani-type Fuzzy Inference System, leveraging numerical values of external characteristics such as dimension, calyx shape, ripeness stage, and defect regions. The fuzzy logic-based system employed 22 fuzzy rules and three different membership functions (trapezoidal, Gaussian, and triangular), enabling a nuanced mapping of transitions between classes within the input variables. Furthermore, the system underwent rigorous training and validation using 80% and 20% respectively of the captured images. Through the system, we were able to identify and segment areas of tomatoes exhibiting poor conditions. This approach extends beyond conventional methods of estimating fruit ripeness, which typically rely on color assessment or destructive firmness evaluations.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer.

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Deprogramming Implicit Bias: The Case for Public Interest Technology

Abstract New technologies have fundamentally transformed the systems that govern modern life, from criminal justice to health care, housing, and beyond. Algorithmic advancements promise greater efficiency and purported objectivity, but they risk perpetuating dangerous biases. In response, the field of public interest technology has emerged to offer an interdisciplinary, human-centered, and equity-focused approach to technological innovation. This essay argues for the widespread adoption of public interest technology principles, including thinking critically about how and when technological solutions are deployed, adopting rigorous training to educate technologists on ethical and social context, and prioritizing the knowledge and experiences of communities facing the disproportionate harms or uneven benefits of technology. Tools being designed and deployed today will shape our collective future, and collaboration between philanthropy, government, storytellers, activists, and private-sector technologists is essential in ensuring that these new systems are as just as they are innovative.

Gut Microbiota Modulation

This review explores the transformative potential of gut microbiota modulation in sports medicine, where the convergence of advanced technology, rigorous training, and injury management aims to mitigate sports-related injuries. Athletes in high-intensity sports demand exceptional physical prowess, adhering to grueling training regimens for peak fitness levels. However, the pursuit of athletic excellence often leads to exhaustive physical stress and sport-specific injuries. Personalized sports medicine has emerged as an adaptive discipline, utilizing diverse technologies and methods to deliver customized medical care and enhance performance. The gut microbiota, a complex microbial community in the gastrointestinal system, significantly influences energy production, digestion, and immune defense. Similar to personalized sports medicine, the gut microbiota of every individual is unique, shaped by genetics, diet, lifestyle, and environment. Recent research underscores the profound impact of gut microbiota on athletic performance, recovery, and overall health. This review consolidates current knowledge regarding gut microbiota modulation in sports medicine, revealing its potential to revolutionize athlete care and performance enhancement. The uniqueness of our review lies in considering the interplay between gut health and personalized sports medicine, a relatively unexplored area. The study highlights the prospect of tailoring nutritional and training regimens based on the unique gut microbiota profile of every individual, marking a new era in sports medicine. Addressing a significant literature gap, this review emphasizes the importance of gut microbiota modulation in personalized sports medicine—a paradigm shift poised to enhance athletic performance, expedite recovery, and reduce injury risk.

Strategic HRM in the logistics and shipping sector: Challenges and opportunities

Strategic Human Resource Management (HRM) plays a critical role in the success and sustainability of organizations, particularly in dynamic and competitive industries such as logistics and shipping. This review explores the challenges and opportunities inherent in implementing Strategic HRM practices within the logistics and shipping sector. In recent years, the logistics and shipping industry has witnessed significant transformations due to globalization, technological advancements, and evolving customer demands. Consequently, HRM strategies must adapt to these changes to effectively manage human capital and drive organizational performance. One of the primary challenges faced by HR professionals in the logistics and shipping sector is the recruitment and retention of skilled talent. The industry's high demand for specialized skills, coupled with a shortage of qualified professionals, intensifies competition among companies. Moreover, the transient nature of employment in this sector, characterized by seasonal fluctuations and contract-based arrangements, complicates talent management efforts. Strategic HRM approaches must address these challenges by implementing robust recruitment strategies, offering competitive compensation packages, and providing opportunities for continuous learning and career development. Additionally, the logistics and shipping industry operates in a highly regulated environment with stringent safety and compliance standards. HRM practices must ensure that employees are adequately trained and compliant with industry regulations to mitigate risks and maintain operational efficiency. This necessitates the implementation of rigorous training programs, safety protocols, and performance management systems to uphold organizational standards and ensure employee well-being. Despite these challenges, Strategic HRM in the logistics and shipping sector presents significant opportunities for innovation and growth. Advancements in technology, such as automation and data analytics, enable HR professionals to streamline processes, enhance workforce productivity, and make data-driven decisions. Moreover, strategic partnerships with educational institutions and vocational training centers can facilitate the development of a skilled workforce tailored to the industry's evolving needs. Strategic HRM in the logistics and shipping sector is vital for addressing challenges, optimizing workforce performance, and capitalizing on opportunities for sustainable growth. By adopting a strategic approach to human resource management, organizations can navigate the complexities of the industry landscape and achieve competitive advantage in the global market.

Strategic HRM in the logistics and shipping sector: Challenges and opportunities

Strategic Human Resource Management (HRM) plays a critical role in the success and sustainability of organizations, particularly in dynamic and competitive industries such as logistics and shipping. This review explores the challenges and opportunities inherent in implementing Strategic HRM practices within the logistics and shipping sector. In recent years, the logistics and shipping industry has witnessed significant transformations due to globalization, technological advancements, and evolving customer demands. Consequently, HRM strategies must adapt to these changes to effectively manage human capital and drive organizational performance. One of the primary challenges faced by HR professionals in the logistics and shipping sector is the recruitment and retention of skilled talent. The industry's high demand for specialized skills, coupled with a shortage of qualified professionals, intensifies competition among companies. Moreover, the transient nature of employment in this sector, characterized by seasonal fluctuations and contract-based arrangements, complicates talent management efforts. Strategic HRM approaches must address these challenges by implementing robust recruitment strategies, offering competitive compensation packages, and providing opportunities for continuous learning and career development. Additionally, the logistics and shipping industry operates in a highly regulated environment with stringent safety and compliance standards. HRM practices must ensure that employees are adequately trained and compliant with industry regulations to mitigate risks and maintain operational efficiency. This necessitates the implementation of rigorous training programs, safety protocols, and performance management systems to uphold organizational standards and ensure employee well-being. Despite these challenges, Strategic HRM in the logistics and shipping sector presents significant opportunities for innovation and growth. Advancements in technology, such as automation and data analytics, enable HR professionals to streamline processes, enhance workforce productivity, and make data-driven decisions. Moreover, strategic partnerships with educational institutions and vocational training centers can facilitate the development of a skilled workforce tailored to the industry's evolving needs. Strategic HRM in the logistics and shipping sector is vital for addressing challenges, optimizing workforce performance, and capitalizing on opportunities for sustainable growth. By adopting a strategic approach to human resource management, organizations can navigate the complexities of the industry landscape and achieve competitive advantage in the global market.

The prediction and analysis of heart disease using XGBoost algorithm

Heart diseases remain a global health concern, with their intricate aetiology and multifactorial risk factors making early diagnosis challenging. Recognizing the pressing need for accurate prediction tools, this research ventured into harnessing the power of machine learning, notably the Xtreme Gradient Boosting (XGBoost) algorithm, to fill this gap. The main object is to devise a robust predictive framework capable of early and accurate identification of heart disease. Specifically, our methodology unfolded systematically, beginning with data preprocessing, then delving into incisive feature selection, rigorous model training, and finally, thorough evaluation. This study is meticulously conducted on the heart.csv dataset, a comprehensive repository of cardiovascular data points. The experimental outcomes were nothing short of revelatory. Not only did the XGBoost model manifest superior performance metrics, but its precision also outpaced several contemporary models referenced in existing literature. Ultimately, our findings underscore the profound potential of the XGBoost algorithm in heart disease predictions. Beyond academic intrigue, this research holds tangible implications for healthcare practitioners, potentially offering a novel tool for early interventions and patient management.

Plants Disease Classification and Recommendation System using Deep Learning

The agriculture sector is the backbone of the global economy, and the health of plants and crops is of paramount importance to ensure food security and sustainable agriculture. Plant diseases pose a significant threat to crop yield and quality, making early detection and effective management crucial. This project presents a comprehensive solution for plant/crop disease identification and management, leveraging the power of deep learning. The primary objective of this project is to develop a robust system capable of accurately classifying plant and crop diseases from images, followed by recommending suitable treatments or interventions. The project begins by creating a diverse and extensive dateset of plant images affected by various diseases, covering a wide range of plant species. The deep learning model employed in this project is a Convolutional Neural Network (CNN), which has proven to be highly effective in image classification tasks. The model undergoes rigorous training on the dataset, learning to recognize unique disease patterns and symptoms in plants. In addition to disease classification, the system is designed to recommend appropriate interventions, such as treatment options and preventive measures, based on the identified disease

Fake News Identification in Urdu Tweets Using Machine Learning Models

There is an increasing number of people who generate and distribute content online, especially via social media platforms, which is primarily responsible for the proliferation of fake information. Fake information can cause controversy and distort people's perspectives, so it needs to be addressed immediately. The goal of this work is to detect false information in Urdu tweets, a difficult task given the language's large user population and particular grammatical difficulties. We offer an all-inclusive machine learning system that reliably classifies tweets in Urdu as legitimate or false. The methodology we use consists of several key steps: preprocessing, which includes normalizing, tokenizing, removing stop words, and stemming to prepare the data for analysis; data collection, which involves compiling and annotating a sizable dataset of Urdu tweets; and feature extraction, which makes use of technique TF-IDF to extract the semantic and syntactic nuances of the language. We investigate various machine learning models, including RNNs and CNNs, and more sophisticated neural networks like SVM, Random Forest, Logistic Regression, Naive Bayes, and Decision Tree to find the most efficient method for resolving this classification problem. The models are put through a rigorous training and assessment process using measures including the F1 score, accuracy, precision, and recall. Furthermore, a thorough examination of their confusion matrices is done. Our study's findings suggest that deep learning models hold much promise for resolving the problem of inaccurate information in Urdu. This opens the door for additional research and the creation of real-time algorithms for spotting false information. The subject of information integrity in Urdu language content is improved by this work, which also sheds light on the applicability of machine learning techniques in many linguistic contexts. Using SVM, Random Forest, Logistic Regression, Naive Bayes, and Decision Tree we achieved accuracies of 74%,91%, 76%, 78%, and 67% respectively. Meanwhile, CNN and RNN are the classifiers with the highest accuracy levels at 91% and 99% respectively. The results demonstrate that the CNN Model achieved 99% highest accuracy in detecting fake news from Urdu Tweets.

Down Syndrome-Basque Alzheimer Initiative (DS-BAI): Clinic-Biological Cohort.

Down syndrome (DS) is the most common genetically determined intellectual disability. In recent decades, it has experienced an exponential increase in life expectancy, leading to a rise in age-related diseases, including Alzheimer's disease (AD). Specific health plans for the comprehensive care of the DS community are an unmet need, which is crucial for the early and accurate diagnosis of main medical comorbidities. We present the protocol of a newly created clinical and research cohort and its feasibility in real life. The Down Syndrome-Basque Alzheimer Initiative (DS-BAI) is a population-based, inclusive, multidisciplinary initiative for the clinical-assistance and clinical-biological research approach to aging in DS led by the CITA-Alzheimer Foundation (Donostia, Basque Country). It aims to achieve the following: (1) provide comprehensive care for adults with DS, (2) optimize access to rigorous and quality training for socio-family and healthcare references, and (3) create a valuable multimodal clinical-biological research platform. During the first year, 114 adults with DS joined the initiative, with 36% of them showing symptoms indicative of AD. Furthermore, adherence to training programs for healthcare professionals and families has been high, and the willingness to collaborate in basic and translational research has been encouraging. Specific health plans for DS and conducting clinical and translational research on the challenges of aging, including AD, are necessary and feasible.

Sleep, Diet Quality, and Lipid Profile Assessment in NCAA Division I Acrobatics and Tumbling Student-Athletes: A Cross-Sectional Study

Objective Acrobatics and Tumbling (A&T), an emerging National Collegiate Athletics Association (NCAA) sport, involves athletes with rigorous training backgrounds, usually extending from youth through early adulthood. This study examines the sleep health, diet quality, and lipid profile of A&T athletes clustered by their performance position. Methods Forty-two A&T athletes, clustered as tops (n = 19; age = 19.6 ± 1.0 years; body mass index [BMI] = 22.3 ± 1.7 kg/m2) and bases (n = 23; age = 19.6 ± 1.3 years; BMI = 25.7 ± 2.5 kg/m2), completed preseason sleep and diet quality (Rapid Eating Assessment for Participants–Shortened [REAP-S]) surveys. Fasting blood samples were collected for lipid analysis. Body composition was assessed via dual-energy X-ray absorptiometry. Results Most athletes (71.4%; base n = 14, top n = 16) reported insufficient sleep (≤7 hours) and “good” sleep quality (90.4%, n = 38; base n = 18, top n = 20). Average REAP-S score was 29.24 ± 3.74. Approximately 31% (n = 13) displayed at least one undesirable lipid concentration according to medical guidelines for normal levels (total cholesterol [TC] < 200 mg/dL, triglycerides [TG] < 150 mg/dL, high-density lipoprotein cholesterol [HDL-C] > 40 mg/dL, low-density lipoprotein cholesterol [LDL-C] < 130 mg/dL). Approximately 20% exhibited elevated TC (top n = 4, base n = 4), 12.5% had elevated TG (base n = 5), 2.5% showed low HDL-C (base n = 1), and 10% presented elevated LDL-C (top n = 2, base n = 2). Conclusions Most athletes experienced suboptimal sleep (≤7 hours/night) and 31% displayed at least one undesirable lipid concentration (elevated TC, TG, or LDL-C or reduced HDL-C). Tailoring interventions with sports dietitians is recommended, focused on increasing monounsaturated and polyunsaturated fat intake while reducing saturated fat consumption. These interventions could mitigate cardiovascular risks, improve recovery, and possibly enhance athletic performance.

Outpatient follow-up of tumour diseases through video-based value-oriented behavioural activation (ViVA): study protocol for a randomised controlled trial

BackgroundIn Germany, approximately half a million people are diagnosed with cancer annually; this can be traumatic and lead to depression, anxiety, and adjustment disorders necessitating psycho-oncological intervention. Value-oriented behavioural activation, adopted from depression psychotherapy, aims to provide structured support to help patients adjust their personal values, goals, and activities within the context of their changed life situation. This trial aims to evaluate the effectiveness of video-based value-oriented behavioural activation against German S3-Guideline-compliant aftercare for cancer patients dealing with psychological distress.MethodsThis trial will use covariate-adaptive randomisation according to gender and type of tumour disease to assign participants to one of two study arms (value-oriented behavioural activation consisting of 12 manualised follow-up sessions delivered via video consultation vs. S3-Guideline-compliant aftercare comprising three supportive talks). Psychological strain, psychosocial distress, quality of life, work-related outcomes, fear of cancer recurrence, goal adjustment, satisfaction with the consultant-participant relationship, and rumination will be measured at baseline, twice during treatment, posttreatment, and at the 6-month follow-up. The target sample of 146 tumour patients experiencing high psychosocial distress will be recruited at the Rehazentrum Oberharz, Germany.DiscussionThis trial aims to test the effectiveness of value-oriented behavioural activation in aftercare for tumour patients, focusing on its capacity to reduce distress and the potential for long-term effects evaluated through a 6-month follow-up. The study’s possible challenges include enrolling a sufficient sample and ensuring adherence to treatment, mitigated through in-person recruitment and rigorous training of staff. If successful, the results will be of high public health relevance, especially for psychotherapeutic care in rural areas and among patients with limited mobility considering the video-based approach of the trial.Trial registrationThis study was registered at the German Clinical Trials Register: DRKS00031900 on Sep 19, 2023.

Factors associated with substance use among preclinical medical students in Turkey: a cross-sectional study.

Medical students experience high levels of stress due to their rigorous training, which can negatively affect their mental health. This study aimed to investigate substance use habits of medical students at Istanbul University-Cerrahpaşa and the association on their mental health and demographic factors. This cross-sectional survey study was conducted in March-April 2022 among preclinical medical students (years 1-3 of a 6-year program). A confidential, anonymous online survey consisting of four sections on sociodemographic and educational characteristics, nicotine use and dependence [Fagerström Test for Nicotine Dependence (FTND)], alcohol use [Alcohol Use Disorders Identification Test (AUDIT)], mental health status [12-item General Health Questionnaire (GHQ-12)], was distributed to 1131 students via WhatsApp and Telegram text messages. Mann-Whitney U and Kruskal Wallis tests compared variables' distribution in the questionnaire categories. Spearman's correlation assessed associations between scales. Significance was p < 0.05. The study included 190 medical students. A total of 26.3% of the participants were smokers, with 8.4% showing moderate to high levels of nicotine dependence. An estimated 45.8% and 8.4%reported low-risk consumption and risky usage of alcohol, respectively. There were statistically significant associations between substance use and demographic factors such as sex, GPA, and religious belief. The study found a statistically significant correlation between FTND scores and GHQ-12 scores, and, between FTND scores and AUDIT scores. The findings of this study will inform the development of interventions to improve the mental health and academic performance of medical students at Istanbul University-Cerrahpaşa. Furthermore, it will raise awareness about the importance of addressing substance use among medical students in Turkey.

Up-to-date understanding of overtraining syndrome and overlap with related disorders

Abstract: Introduction: The rigorous training regimens of elite athletes are, by nature, emotionally, physically, and mentally demanding. These regimens can result in marked improvements in athletic performance when they incorporate adequate intervals of rest and recovery. Continued training in the absence of adequate recovery progresses to a constellation of symptoms that affect medical and mental health, which is referred to as overtraining syndrome (OTS). Method: A literature review was conducted using PubMed, a database of reference on medical research maintained by the US national Library of Medicine and the National Institute of Health. Search terms included “overtraining syndrome”, “Unexplained Underperformance” (UUPS), “Burnout” in athletes. Results: The collection of the published work provides insight into the multidimensional composition and complexity of OTS in athletes. OTS progresses along a continuum without adequate recovery. Conclusion: Decreased athletic performance in the setting of increased training intensity without appropriate recovery, often accompanied by changes in mood, motivation, and general well-being are the distinctive features of OTS. OTS is preceded by stages of underperformance that are reversable and preventable, highlighting the importance of recognizing, managing, and preventing overtraining in athletes in order to prevent long-term negative outcomes is a maladaptive response to an imbalance between exercise and recovery.

Abstract WP29: Early Adopter of Intravenous Tenecteplase: Two Year Clinical Experience in a Non-Academic Suburban Comprehensive Stroke Center for Acute Ischemic Stroke Treatment

Background: The most recent American Heart Association/American Stroke Association acute ischemic stroke guidelines recognize tenecteplase (TNK) as an alternative to alteplase (ALT) for acute ischemic stroke (AIS) treatment. TNK has a longer half-life, and is given as a single intravenous bolus compared to the complex and cumbersome ALT administration. A switch to TNK was implemented at our Comprehensive Stroke center (CSC) after a rigorous training of nurses and providers on May 1, 2021. We sought to evaluate the comparative performance of two thrombolytics over the period of last two years of ALT and first two years on TNK use. Methods: This was a retrospective review of prospectively collected database of consecutive AIS patients treated with ALT between May 2019 to April 2021 and those who received TNK from May 2021 to April 2023 at our CSC. Data samples were analyzed with respect to baseline patient characteristics, stroke severity, Door to Needle (DTN) time, 90 day Modified Rankin Scores (MRS), complications, and cost savings. Statistical analyzes were used for between group comparisons (SPSS). Results: A total of 115 patients were treated with ALT and 134 - with TNK. There were no significant differences in baseline characteristics or stroke severity between the groups (mean NIHSS of 10 for both). The mean DTN was 53.0 minutes for ALT and 41.5 minutes for TNK (p&lt;0.001). There was a strong trend towards improved 90-day clinical outcomes for TNK-treated patients (mRS 2.5 vs 3.1, p=0.06). No significant differences in rates of symptomatic Intracerebral Hemorrhage (SICH) were observed between two groups (2.6% ALT vs 2.9% TNK). Estimated cost savings of TNK use over two year period were $75,102. Conclusion: Our real-world data suggests that intravenous TNK for AIS appears to be safe and may result in improved clinical outcomes and substantial cost savings compared to ALT.

The Using of the Metaverse in the Field of Education and Training in Police Colleges

Objectives: The study addresses the possibility of using Metaverse technology in the fields of education and training in police colleges. The Metaverse has been highlighted as the future of education and training. Methods: The study adopted a dual approach that entailed reviewing 13 studies and conducting a survey to collect qualitative data on metaverse use. Results: The results and analysis of the study showed that there is a high desire to use the metaverse, and that the current education and training programs in police institutions need to be developed to keep pace with the current technological reality. The metaverse can also be applied in police institutions in a way that enhances police education, sports training, military training, professional applications, and management. Data and records. Conclusions: The study recommends adopting the metaverse in police institutions, conducting a comprehensive assessment of metaverse risks, and rigorous training for teachers in police institutions.

One Psychology Profession, Many Standards: A Narrative Review of Training, Licensing, and Practice Standards and Their Implications for International Mobility.

This narrative review paper contrasts the professional prospects of psychologists in Japan and the U.S., discussing how divergent training, licensing, and practice standards appear to influence psychologists' profession in each country. Licensed psychologists in the U.S. practice with significant autonomy, which can be seen as a reflection of rigorous training requirements. In contrast, certified public psychologists in Japan complete a shorter-duration training regimen yet encounter more restrictive professional standards and greater financial challenges. These varying standards often create barriers to professional practice that impact psychologists on a global scale. Limited international mobility restricts opportunities for psychologists to learn abroad, exchange knowledge, and deliver culturally sensitive care to diverse populations, despite the need for such services among foreign individuals or immigrants in both countries. Furthermore, these disparities impede broader collaborative efforts to address global mental health challenges. Aligning training and licensing standards globally could enhance psychologists' international mobility, ensure consistent quality of care, and foster global collaboration. This alignment could improve access to culturally sensitive psychological services and help bridge the mental health care gap worldwide. This review emphasizes the necessity of further cross-cultural comparisons to understand the impact of training and licensing standards on clinical practice quality and accessibility. By presenting this comparative analysis, the study aims to inspire similar efforts, promoting global licensing reciprocity and the integration of professional psychology in an increasingly interconnected world.

이스포츠 선수의 권리 강화를 위한 법제도적 방안 연구

The e-sports industry is attracting worldwide attention as a new industry. This global popularity is due in part to the emergence and role of star players, an important component of the e-sports industry. In particular, to become an e-sport player, they undergo rigorous training since childhood, and their career as an e-sports player is also shorter than that of other sports. In other words, they start at a young age and are retiring in their late 20s. Accordingly, our country is preparing a standard contract as one of the ways to guarantee the rights of e-sports players to prevent unfair situations that may arise during contracts between players and clubs. However, there are limitations in the content and use of the standard contract, raising questions about whether the legislative purpose is being achieved. Additionally, there are no regulations regarding support for retired players, so players who retire at an early age are having difficulty planning their future. Accordingly, in this article, measures to protect and strengthen the rights of e-sports players for the healthy and sustainable development of the e-sports industry include updating the standard contract, support for retired players, and forming a player council.

DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings

Energy prediction in buildings plays a crucial role in effective energy management. Precise predictions are essential for achieving optimal energy consumption and distribution within the grid. This paper introduces a Long Short-Term Memory (LSTM) model designed to forecast building energy consumption using historical energy data, occupancy patterns, and weather conditions. The LSTM model provides accurate short, medium, and long-term energy predictions for residential and commercial buildings compared to existing prediction models. We compare our LSTM model with established prediction methods, including linear regression, decision trees, and random forest. Encouragingly, the proposed LSTM model emerges as the superior performer across all metrics. It demonstrates exceptional prediction accuracy, boasting the highest R2 score of 0.97 and the most favorable mean absolute error (MAE) of 0.007. An additional advantage of our developed model is its capacity to achieve efficient energy consumption forecasts even when trained on a limited dataset. We address concerns about overfitting (variance) and underfitting (bias) through rigorous training and evaluation on real-world data. In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.

Optimizing Clinical Implementation of Hypofractionation: Comprehensive Evidence Synthesis and Practical Guidelines for Low- and Middle-Income Settings

The global cancer burden, especially in low- and middle-income countries (LMICs), worsens existing disparities, amplified by the rising costs of advanced treatments. The shortage of radiation therapy (RT) services is a significant issue in LMICs. Extended conventional treatment regimens pose significant challenges, especially in resource-limited settings. Hypofractionated radiotherapy (HRT) and ultra-hypofractionated/stereotactic body radiation therapy (SBRT) offer promising alternatives by shortening treatment durations. This approach optimizes the utilization of radiotherapy machines, making them more effective in meeting the growing demand for cancer care. Adopting HRT/SBRT holds significant potential, especially in LMICs. This review provides the latest clinical evidence and guideline recommendations for the application of HRT/SBRT in the treatment of breast, prostate, and lung cancers. It emphasizes the critical importance of rigorous training, technology, stringent quality assurance, and safety protocols to ensure precise and secure treatments. Additionally, it addresses practical considerations for implementing these treatments in LMICs, highlighting the need for comprehensive support and collaboration to enhance patient access to advanced cancer care.