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Communication Strategy Education with a Cultural Perspective through a Co-Culture Communication Approach
No full textThis study presents a Systematic Literature Review (SLR) on communication strategy education with a cultural perspective through the co-culture communication approach. The review aims to synthesize theoretical foundations, pedagogical models, and empirical findings to better understand how cultural perspectives inform communication strategy education. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework guided the search and selection process. Articles published between 2010 and 2025 were retrieved from Scopus, Web of Science, and Google Scholar using keywords related to communication strategy, co-culture communication, and intercultural competence. Through screening of 274 articles and the use of inclusion and exclusion criteria, the analysis included 42 peer-reviewed studies. Thematic synthesis identified four key findings: (1) the addition of a cultural perspective improves education for communication strategy through the development of critical cultural awareness; (2) co-culture pedagogies for communication focus on inclusivity and the voice of the marginal; (3) intercultural competence is a significant product of education; and (4) digital innovations facilitate the wider applicability of co-culture perspectives within higher education. This systematic review acknowledges the deficiencies within empirical evidence, especially within the non-Western world, and calls for longitudinal studies and investigations into digital pedagogies. The results contribute insightful information for teachers, policymakers, and researchers who aim to engage culture perspectives within the education of communication strategy for the achievement of equity, inclusivity, and intercultural competence
English as a Commodity: Linguistic Capitalism in Bangladesh’s Private Tutoring and Coaching Centers
No full textThis paper explores the commodification of English in the case of the Bangladesh-based private tutoring centers, where English is being sold as a means of socioeconomic mobility and career success. Through qualitative interviews with coaching center owners, teachers, and parents, the study addresses how English as an asset is published and how tutoring supports the social disparities. Results indicate that English proficiency is regarded as a symbolic and economic capital that opens opportunities to some individuals and denies others. The paper has emphasized the effect of linguistic capitalism on education and provided perspectives on minimizing inequalities in language education
Enhancing Bipolar Disorder Detection Using Heterogeneous Ensemble Machine Learning Techniques
No full textThis paper introduces a novel Heterogeneous Ensemble Machine Learning (HEML) approach
designed to detect bipolar disorder, a significant healthcare challenge that demands precise and
prompt diagnosis for effective treatment. The HEML method integrates multiple machines
learning models, incorporating various physiological, behavioral, and contextual data from
patients. By using a comprehensive feature selection technique, relevant features are extracted
from each data source and utilized to train individual classifiers for detecting mental disorders.
The classifiers include Adaboost, Decision Tree, K-nearest neighbors, Multilayer Perceptron,
Random Forest, Relevance Vector Machine, and XGB, with Logistic Regression serving as the
meta-model. This ensemble of classifiers enhances overall performance by capturing a wider range
of characteristics related to mental disorders. The research evaluates the HEML method across
three bipolar disorder datasets: Dataset1 (a multimodal dataset), Dataset2 (a sensor-based dataset),
and Dataset3 (a real-time dataset). The HEML approach surpasses traditional methods, achieving
superior accuracy rates of 95.21% with Dataset 1, 99.28% with Dataset 2, and 99% with Dataset
3. It outperforms individual models in detecting bipolar disorder, delivering the best Precision,
Recall, F1 score, and Kappa Score. This comparative analysis advances the field of mental health
diagnosis by leveraging the strengths of ensemble machine learning to improve accuracy and
reliability in detection methods
Integrating Pixel-Based Algorithms for Area Measurement in Brain Tumor Classification
No full textDiagnosing brain cancers in medicine necessitates an examination utilizing magnetic resonance imaging (MRI). picture processing techniques in the medical domain are integral to computed tomography detection in MRI due to their excellent picture fidelity and little radiation exposure. Nonetheless, there remain deficiencies in the interpretation, analysis, and imaging of brain tumors in detection. This study seeks to identify brain tumors to ascertain their size and extent by a pixel-based methodology. The dataset utilized originates from Cipto Mangunkusumo Hospital in Jakarta and comprises T1 contrast and BMP sequences. The research procedure will employ many methodologies, including active contours, Otsu's method, and a combination of techniques, with comparisons utilizing the MRI MicroDicom viewer. The image testing phase utilizing Matlab and Python with thirteen image datasets. The findings from this study, which involved segmentation and extraction techniques to quantify the area of brain tumors using a pixel-based approach, indicate that the combined method outperforms alternative methods by achieving superior accuracy of 99%. Other methods fail to attain this level of accuracy, and the combined method also demonstrates optimal error differentiation in template matching
A Review of Syngas-Fueled Free Piston Linear Engine Generators: Efficient Fuel Opportunities
No full textAs global energy consumption continues to rise and conventional fuel sources become scarcer, the need for efficient and sustainable technologies is becoming more urgent. Free piston linear engine generators (FPLGs) are emerging as a viable alternative to conventional engines, offering benefits such as lower friction losses and enhanced efficiency. This paper examines the potential of syngas, an alternative fuel produced through the gasification of biomass, coal, and waste, for powering FPLGs. Syngas stands out due to its broad flammability range, lower emissions, and high hydrogen content, all of which could improve the performance and environmental impact of FPLGs. The review synthesizes data from over 50 peer-reviewed studies published between 2000 and 2024, addressing both the benefits and challenges of using syngas as a fuel. Notable findings include syngas’s high combustion efficiency, reduced emissions compared to conventional fuels, and its suitability for both compression ignition and spark ignition engines. The review further explores syngas’s application in FPLGs, highlighting its effects on combustion efficiency and the environmental advantages of its use. This work provides a thorough overview of how syngas can enhance FPLG efficiency while contributing to cleaner energy solutions
Comparative Review of AI Applications in Urban Transport: Insights from China’s City Brain and Singapore’s LTA Smart Mobility
No full textIn recent years, cities around the world have increasingly turned to artificial intelligence (AI) as a means to address pressing challenges in urban mobility, traffic congestion, and emergency response management. Recent literature shows that AI-driven transportation systems have yielded notable improvements in traffic efficiency, commuter satisfaction, and the pursuit of sustainable mobility in both developed and developing contexts. Among the most prominent examples are China’s City Brain, developed by Alibaba Cloud, and Singapore’s Smart Mobility 2030 strategy, led by the Land Transport Authority (LTA). This review fills a gap in cross-national comparative studies by examining the technical architectures and operational outcomes of these systems and analyzing how governance structures, policy frameworks, and socio-cultural contexts shape their deployment. Drawing on peer-reviewed literature, policy documents, and official reports, the study proposes a multi-dimensional analytical framework for evaluating AI applications in urban transport, offering practical insights and policy implications
Small and Medium Enterprises & Artificial Intelligence: A Systematic Literature Review
No full textThe growth of the world is incredibly reliant on small and medium-sized enterprises (SMEs) that promote innovation. SMEs are also challenged by a shortage of resources, stiff competition and intricate market environments. To a greater extent, they are surmounting these hurdles by using Artificial Intelligence (AI) and Machine Learning (ML) technologies to streamline the work of laborers, make better decisions and enrich customer experience. The research on how AI and ML integrate in SMEs and their effects on business performance will be explored. The body of the present study relied on a systematic literature review methodology designed to estimate the advantages, constraints, and use of AI and ML in 4 major business areas: supply chain management, marketing, finance, and customer service, and ethical and social implications. The moral and social impacts of all the above can be mentioned regarding how AI and ML contribute to SMEs' competitiveness and sustainability. With the help of successful case studies and the results of the literature research, this paper reveals how AI and ML promote innovation and sustainable growth. Despite the numerous advantages, barriers including technological ignorance, costly implementation, and privacy and protection of information are challenges impeding the SMEs. To eliminate such problems, the paper gives great attention to cooperation with the providers of technology, personnel education, and attention to data security. The study is part of the literature about AI and ML implementation within the SME sector since it specifies the key strategies to succeed in any competitive environmen
Numerical Methods: Comparative Analysis of Different Methods for Non-Linear Equations
No full textSolving nonlinear equations analytically becomes increasingly complex as functions grow in difficulty or when multiple nonlinear components are involved. This study aims to address that challenge by applying and comparing two well-established numerical methods—the Bisection Method and the False Position Method—in approximating the real roots of nonlinear equations. These iterative techniques are evaluated based on their accuracy, convergence rate, and computational efficiency. Specifically, the study investigates the number of iterations required, the magnitude of relative errors, and the number of significant digits in the final approximations. The results show that while both methods are capable of reaching the desired tolerance, the False Position Method converges faster and yields a higher accuracy score. The findings contribute to the practical selection of numerical methods by providing a comparative analysis that guides users in choosing the most appropriate technique based on the nature of the nonlinear function
A Comprehensive Review on the Role of Warm-Up and Cool-Down in Reducing Delayed Onset Muscle Soreness in Athletes
No full textDelayed Onset Muscle Soreness (DOMS) in athletes results from intense or novel exercise, with associated pain, stiffness, and impaired function 24–72 hours after activity. Traditional warm-up and cool-down exercises have been employed to reduce these effects, but their additive effect is not yet well understood. To provide a comprehensive review of literature from 2017-2025 on the efficacy of warm-up and cool-down strategies in reducing the intensity of DOMS in athletes. An extensive literature search on PubMed, Google Scholar, and manual reference searching was conducted. Randomized controlled trials, systematic reviews, and meta-analyses were included for consideration. PEDro, CASP, and JBI tools were employed to critically evaluate the studies. Twelve studies meeting the inclusion criteria were included. Warm-ups enhanced muscle preparatory state, improved circulation, and reduced risk of injury. Cool-down interventions, including stretching, foam rolling, and cold-water immersion, were associated with reduced muscle soreness and faster recovery. Combination of warm-up and cool-down protocols resulted in greater effects than when delivered separately. Adjuvant modalities such as massage, vibration, and shock wave therapy supported recovery in specific populations. Variability in methodology, small samples, and athlete population heterogeneity limited generalizability of the results. The incorporation of organized warm-up and cool-down routines is essential in DOMS management and athlete recovery. Future research must create standardized, sport-focused protocols, increase methodological quality, and study long-term effect
AI and Digitalization in Manufacturing: Impacts on Enterprise Operational Performance
No full textThis study examines the impact of artificial intelligence (AI) and digitalization on the operational performance of manufacturing enterprises, with a focus on the Chinese automotive tire industry. Using a mixed-methods approach, we analyzed financial reports, conducted executive interviews, and applied structural equation modeling to data from three leading enterprises from 2022 to 2024. The results show that enterprises with higher levels of AI and digitalization—such as Enterprise A with a 22.3% annual growth in digital investment—achieved significant improvements in operational performance. Specifically, each unit increase in digitalization level correlated with an 8% rise in Return on Assets (ROA). Enhanced digitalization also positively affected production and R&D performance, reducing R&D cycles by 22.8% annually and cutting per-project costs by 27.1%. These findings underscore the critical role of AI and digitalization in driving innovation and operational efficiency in manufacturing