Buru Regency is the primary hub for producing eucalyptus oil, a highly valued commodity in the region. The oil extracted from the eucalyptus epidemic plant possesses antiseptic, antibacterial, and antifungal characteristics. Amidst the Covid-19 pandemic, numerous industries require it as a fundamental component of pharmaceuticals. An essential factor in the eucalyptus oil production process is the presence of eucalyptus leaves. Therefore, leaf-sorting workers, including women, are required to ensure this availability. However, in reality, the daily lives of eucalyptus leaf massagers are characterized by challenging economic conditions and a socio-economic environment that lags behind workers in other sectors. This study aims to examine and investigate the roles and work patterns of employed women and the strategies they employ to ensure the consistent flow of household income. The research employed a qualitative methodology with a phenomenological approach. A total of 24 informants were purposefully selected based on their involvement in achieving the research objectives. The results indicate that the COVID-19 pandemic has altered the circumstances of women who collect leaves and rely heavily on eucalyptus trees as a natural resource. Physical adaptation strategies are the preferred methods used to fulfill household requirements. Implementing physical adaptations does not deter women leaf-sorters from continuing their work. Their commitment to meeting their basic needs significantly motivates them to persist in their role as leaf sorters. The income of women engaged in sorting eucalyptus leaves in their households during the COVID-19 pandemic.

Accurate drug-drug interaction (DDI) prediction is essential to prevent adverse effects, especially with the increased use of multiple medications during the COVID-19 pandemic. Traditional machine learning methods often miss the complex relationships necessary for effective DDI prediction. This study introduces a deep learning-based classification framework to assess adverse effects from interactions between Fluvoxamine and Curcumin. Our model integrates a wide range of drug-related data (e.g., molecular structures, targets, side effects) and synthesizes them into high-level features through a specialized deep neural network (DNN). This approach significantly outperforms traditional classifiers in accuracy, precision, recall, and F1-score. Additionally, our framework enables real-time DDI monitoring, which is particularly valuable in COVID-19 patient care. The model’s success in accurately predicting adverse effects demonstrates the potential of deep learning to enhance drug safety and support personalized medicine, paving the way for safer, data-driven treatment strategies.

This study replicates and extends Corbett and Kirsch (2001) and Vastag (2004) using a new data set to investigate the drivers of ISO 14000 certification diffusions using decision tree analysis. The findings indicate that at the national level, ISO 14000 certification diffusions are influenced by factors other than ISO 9000 certification diffusions, such as the number of environmental treaties signed and ratified, industrial activities as a percentage of GDP, and GDP per capita, thus provides a range of managerial insights and enhances scholarly understanding of sustainability beyond the influence of ISO 9000. Future studies might extend the countries included in this study to see if the results are the same. Future research may include other factors like a country’s Environmental, Social, and Governance (ESG) indicators to better understand its commitment to sustainability, including environmental sustainability. The country’s culture may influence customers, investors, and other stakeholders’ knowledge and desire for sustainable practices and inspire firms to obtain ISO 14000 certifications. Since larger firms may seek ISO 14000 certification, future studies may evaluate the influence of the number of large firms in various countries as drivers of ISO certification diffusions.

This study will explore the direct and indirect impacts of collaborative governance innovation on organizational value creation in higher vocational education in China in the context of the digital era. This paper employs a mixed research methodology to construct and validate a model of the relationship between collaborative governance, digital competence, value chain restructuring, and value creation. This study first adopted an exploratory sequential design. In the qualitative interviews, 15 experts from education, business, and other related fields were used as respondents to explore accurate variable factors and determine the value of the research framework. The quantitative research used structural equation analysis to analyze 979 valid online questionnaires. Finally, the rationality of the research results was verified through case studies.  The findings are clear: collaborative governance significantly positively impacts value creation, indirectly affecting organizational value creation through value chain restructuring. Furthermore, digital capabilities significantly contribute to the value chain restructuring process. This paper provides a theoretical basis and practical guidance for higher vocational education organizations to improve their governance and innovation capabilities.

In the wake of the COVID-19 pandemic, the prevalence of online education in primary education has exhibited an upward trajectory. Relative to traditional learning environments, online instruction has evolved into a pivotal pedagogical modality for contemporary students. Thus, to comprehensively comprehend the repercussions of environmental changes on students’ psychological well-being in the backdrop of prolonged online education, this study employs an innovative methodology. Founded upon three elemental feature sequences—images, acoustics, and text extracted from online learning data—the model ingeniously amalgamates these facets. The fusion methodology aims to synergistically harness information from diverse perceptual channels to capture the students’ psychological states more comprehensively and accurately. To discern emotional features, the model leverages support vector machines (SVM), exhibiting commendable proficiency in handling emotional information. Moreover, to enhance the efficacy of psychological well-being prediction, this study incorporates an attention mechanism into the traditional Convolutional Neural Network (CNN) architecture. By innovatively introducing this attention mechanism in CNN, the study observes a significant improvement in accuracy in identifying six psychological features, demonstrating the effectiveness of attention mechanisms in deep learning models. Finally, beyond model performance validation, this study delves into a profound analysis of the impact of environmental changes on students’ psychological well-being. This analysis furnishes valuable insights for formulating pertinent instructional strategies in the protracted context of online education, aiding educational institutions in better addressing the challenges posed to students’ psychological well-being in novel learning environments.

This study aims to explore the feasibility of using virtual reality technology to educate students with learning difficulties in the Asir region. To achieve the study aims, the researcher employed a descriptive design and deployed a quantitative technique, depending on the questionnaire as the main instrument for data collection. The research was carried out on a cohort of 240 educators hailing from the Asir region who were enlisted through a process of random sampling. The results of this study show that factors like infrastructure, human resources, administrative regulation, and student population have an impact on the use of virtual reality technology. The results suggest that there are no statistically significant differences in the development of using virtual reality technology among teachers of students with learning disabilities in the Asir region when taking into account factors such as experience and level of qualification.