Small and medium-sized enterprises (SMEs) play a critical role in achieving environmental sustainability, particularly in developing economies where regulatory enforcement and resource constraints remain significant challenges. Drawing on Institutional Theory, this study examines how green leadership influences environmental performance in Ghanaian SMEs, with digital innovation as a mediating variable and environmental culture as a moderating variable. Institutional Theory provides the conceptual foundation for explaining how normative pressures embedded in leadership values and organizational culture, alongside mimetic pressures associated with digital innovation adoption, shape firms’ environmental outcomes. Using survey data collected from SMEs in Ghana and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results revealed that green leadership has a significant positive effect on both digital innovation and environmental performance. Digital innovation also significantly enhances environmental performance and partially mediates the relationship between green leadership and environmental performance. Notably, the findings demonstrated that environmental culture significantly moderates the relationship between digital innovation and environmental performance, with the effect stronger in organizations with a well-developed environmental culture. This indicates that internalized environmental values amplify the effectiveness of digital innovation initiatives. The study contributes to the sustainability and organizational literature by extending Institutional Theory to the SME context in a developing economy and by clarifying the conditional role of environmental culture in translating digital innovation into superior environmental performance. Practically, the findings suggest that SME leaders and policymakers should promote environmentally oriented leadership, invest in digital innovation, and cultivate strong environmental cultures to enhance sustainability outcomes.

Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.

The current state of the Moroccan mountains in general, and the Beni Iznassen Mountains in particular, is the result of a dynamic process that has accelerated in recent years due to rapid demographic growth and the associated pressure on mountain natural resources. This has led to significant degradation, varying in severity across different areas within the Beni Iznassen Mountain range. In the context of these imbalances between natural mountain resources and the daily needs of the local population, there has been an emergence of various challenges, such as poverty and marginalization, affecting the lives of the region’s residents and a noticeable decline in socioeconomic indicators. This situation has consequently driven migration towards regions that better meet the population’s needs. Therefore, it has become essential to pay attention to this natural area by restoring its residents’ livelihoods, breaking their isolation, and rationalizing the use of its land-based natural resources. This has made the region a focus of territorial development efforts by both the state and local stakeholders.

Immeasurable basic and applied information has been evolved on all important floricultural crops through large-scale worldwide research on interdisciplinary aspects. The quantum and quality of work done on Chrysanthemum, among all other ornamentals, are par excellence. Conscientious attempt has been made to collect the whole multidisciplinary experimental results achieved world over. Despite remarkable achievements in knowledge and technology, a major part of present experimental research on chrysanthemum is largely a routine repeat of work. Assessment of past and present work is now significant for preparing target-oriented future research resolutions. This will help to secure the favored results within a justifiable period.

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.