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University students often encounter academic challenges and setbacks, which can impact their beliefs about their capabilities, leading to a phenomenon known as learned helplessness. This study explores the antecedents of learned helplessness through the interaction between teacher support, self-efficacy, and cultural dimensions such as power distance, uncertainty avoidance, and masculinity.

Drawing from constructivism, the authors empirically examined a conceptual model utilizing structural equation modeling (SEM). A total of 268 university students completed a self-survey on learned helplessness via a Google Form.

The study shows that LH is influenced by personal attributes, perceived support, and cultural context. Higher self-efficacy is linked to lower LH scores, while teacher support indirectly affects LH through self-efficacy. Power distance and uncertainty avoidance also directly impact LH. Additionally, cultural influences indirectly affect LH through perceived support and self-efficacy. However, the hypothesized connections between masculinity and LH were not supported.

The study advances LH literature by exploring it among university students using a multidimensional, constructivist approach, providing valuable insights for educational authorities. We illuminate how individual, social, and cultural factors influence LH, underscoring the serial mediation between self-efficacy and perceived teacher support. Existing literature frequently overlooks the intricate interplay among cultural dimensions and other factors, limiting the formulation of comprehensive solutions. Our research breaks new ground in quantitative LH examination within education, delving into Hofstede’s cultural dimensions and suggesting strategies to mitigate uncertainty and power distance for student well-being and success.

The primary purpose of this research is to identify and compare the multifractal behavior of different sectors during these crises and analyze their implications on market efficiency.

We used multifractal detrended fluctuation analysis (MF-DFA) to analyze stock returns from various sectors of the Moscow Stock Exchange (MOEX) in between two significant periods. The COVID-19 pandemic (January 1, 2020, to December 31, 2021) and the Russia–Ukraine conflict (RUC) (January 1, 2022, to June 30, 2023). This method witnesses multifractality in financial time series data and tests the persistency and efficiency levels of each sector to provide meaningful insights.

Results showcased persistent multifractal behavior across all sectors in between the COVID-19 pandemic and the RUC, spotting heightened arbitrage opportunities in the MOEX. The pandemic reported a greater speculative behavior, with the telecommunication and oil and gas sectors exhibiting reduced efficiency, recommending abnormal return potential. In contrast, financials and metals and mining sectors displayed increased efficiency, witnessing strong economic performance. Findings may enhance understanding of market dynamics during crises and provide strategic insights for the MOEX’s investors.

Understanding the multifractal properties and efficiency of different sectors during crisis periods is of paramount importance for investors and policymakers. The identified arbitrage opportunities and efficiency variations can aid investors in optimizing their investment strategies during such critical market conditions. Policymakers can also leverage these insights to implement measures that bolster economic stability and development during crisis periods.

This research contributes to the existing body of knowledge by providing a comprehensive analysis of multifractal properties and efficiency in the context of the MOEX during two major crises. The application of MF-DFA to sectoral stock returns during these events adds originality to the study. The findings offer valuable implications for practitioners, researchers and policymakers seeking to navigate financial markets during turbulent times and enhance overall market resilience.

Research internships are integral components of undergraduate programs that nurture students' skill sets. However, the current trend of these internships adopting a direct apprenticeship model, often termed “cookbook” laboratory sessions, falls short of cultivating 21st-century skills. Therefore, this study proposes an innovative Outcomes-directed Research Internship Model (ODRIM) designed to enhance research competencies.

This study introduces a pioneering research internship model by integrating the course-based undergraduate research experience (CURE) framework with principles rooted in outcomes-based education (OBE). To evaluate its effectiveness and compare it with traditional research internship models (TRIMs), a two-group pretest-posttest design was utilized. Statistical analysis was performed on data collected from 66 participants to assess their research competencies.

The results revealed a significant positive difference in utilizing the tailored innovative ODRIM track (pre-mean = 17.525, post-mean = 19.875; p = 0.009) compared to the TRIM track (pre-mean = 19.769, post-mean = 19.615; p = 1.000) in enhancing overall student research competency. Specifically, this innovative internship model notably enhanced students’ abilities in conducting research, showcasing technical skills, collaborating within teams, and disseminating research outcomes, encompassing the composition of research articles and reports.

This tailored approach to the “Research Internship” encourages a harmonious blend of hands-on, inquiry-based learning and clear, measurable learning goals. It not only cultivates an immersive and thorough research experience to improve students’ research competencies, but also guarantees a concentrated alignment with predetermined research learning objectives.

This study aims to report the effectiveness of family based treatment and pharmacotherapy on an adolescent boy with anorexia nervosa and intellectual disability.

The authors reported the case of a 16-year-old boy with AN and ID and referred him to a family therapist and psychiatrist in Tehran.

The patient experienced fatigue, weakness, dissatisfaction, suicidal thoughts and self-harm signs over the past one year. He also had behaviors such as abstinence from eating and voluntary vomiting. He was diagnosed with AN and MDD.

The results suggest that these interventions can be effective but should be used with special consideration. Combining family therapy and pharmacotherapy might offer a chance to alleviate anorexia symptoms in people with ID.

The purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.

A literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.

The performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.

This study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.