Background: Multiple sclerosis is often a longitudinal disease continuum with an initial relapsing-remitting phase (RRMS) and later secondary progression (SPMS). Most currently approved therapies are not sufficiently effective in SPMS. Early detection of SPMS conversion is therefore critical for therapy selection. Important decision-making tools may include testing of partial cognitive performance and magnetic resonance imaging (MRI). Aim of the work: To demonstrate the importance of cognitive testing and MRI for the prediction and detection of SPMS conversion. Elaboration of strategies for follow-up and therapy management in practice, especially in outpatient care. Material and methods: Review based on an unsystematic literature search. Results: Standardized cognitive testing can be helpful for early SPMS diagnosis and facilitate progression assessment. Annual use of sensitive screening tests such as Symbol Digit Modalities Test (SDMT) and Brief Visual Memory Test-Revised (BVMT-R) or the Brief International Cognitive Assessment for MS (BICAMS) test battery is recommended. Persistent inflammatory activity on MRI in the first three years of disease and the presence of cortical lesions are predictive of SPMS conversion. Standardized MRI monitoring for features of progressive MS can support clinically and neurocognitively based suspicion of SPMS. Discussion: Interdisciplinary care of MS patients by clinically skilled neurologists, supported by neuropsychological testing and MRI, has a high value for SPMS prediction and diagnosis. The latter allows early conversion to appropriate therapies, as SPMS requires different interventions than RRMS. After drug switching, clinical, neuropsychological, and imaging vigilance allows stringent monitoring for neuroinflammatory and degenerative activity as well as treatment complications.

In today’s rapidly evolving organizational landscape, understanding the dynamics of employee incentives is crucial for fostering high performance. This research delves into the intricate interplay between moral and financial incentives and their repercussions on employee performance within the dynamic context of healthcare organizations. Drawing upon a comprehensive analysis of 226 respondents from three healthcare organizations in Klang Valley, Peninsular Malaysia, the study employs a quantitative approach to explore the relationships between independent variables (career growth, recognition, decision-making, salary, bonus, promotion) and the dependent variable of employee performance. The research unveils that moral incentives, including career growth, recognition, and decision-making, significantly impact employee performance. Professionals motivated by opportunities for growth, acknowledgment, and participation in decision-making demonstrate heightened engagement and commitment. In the financial realm, competitive salaries, performance-based bonuses, and transparent promotion pathways are identified as crucial factors influencing employee performance. The study advocates a holistic approach, emphasizing the synergistic integration of both moral and financial incentives. Healthcare organizations are encouraged to tailor their incentive structures to create a supportive and rewarding workplace, addressing the multifaceted needs and motivations of healthcare professionals. The implications extend beyond academia, offering practical guidance for organizations seeking to optimize workforce dynamics, foster job satisfaction, and ensure the sustainability of healthcare organizations.

The construction of researcher profiles is crucial for modern research management and talent assessment. Given the decentralized nature of researcher information and evaluation challenges, we propose a profile system for Chinese researchers based on unsupervised machine learning and algorithms. This system builds comprehensive profiles based on researchers’ basic and behavior information dimensions. It employs Selenium and Web Crawler for real-time data retrieval from academic platforms, utilizes TF-IDF and BERT for expertise recognition, DTM for academic dynamics, and K-means clustering for profiling. The experimental results demonstrate that these methods are capable of more accurately mining the academic expertise of researchers and performing domain clustering scoring, thereby providing a scientific basis for the selection and academic evaluation of research talents. This interactive analysis system aims to provide an intuitive platform for profile construction and analysis.

Yunnan is rich in cultural heritage, with its primitive pottery techniques coexisting with modern pottery techniques, and is known as the “Museum of Ceramic History”. Due to regional and socio-economic development factors, some folk pottery and craftsmen have faded out of sight or only circulated in a few small areas and specific environments. The study analyzes and summarizes the characteristics of Yunnan folk pottery and industry and evaluates the Yunnan folk pottery value based on the conditional valuation method. The study takes the folk pottery of the Bai nationality in Dali, Yunnan as an example and obtains the evaluation results of the purchasing motivation value of the pottery through a questionnaire survey. 45.26% of people pay for their existence value, 26.03% pay for their choice value, and 28.71% pay for their legacy value. Based on the evaluation results, the study proposes targeted activation paths for Yunnan folk pottery, including innovative development combined with new technologies, highlighting the functional characteristics of pottery, and brand building. This study will help Yunnan folk pottery find more suitable ways of protection and inheritance in the rapid development of materials and technology. This study can help inheritors gain the possibility of sustainable development and provide reference value for the activation path of other traditional folk.

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.

This study examined the correlations between highly entangled variables such as leadership, work environment, effective communication, reward fairness, and physical facilities for faculty members. The data was gathered from faculty members of educational institutions in Pakistan using a survey questionnaire, and the sampling method was purposive sampling. For this study, data was obtained from a varied group of education professionals from several places in Pakistan, each with a distinct degree of education and experience. This study demonstrates how independent factors affect faculty performance and can have a further impact on organizational productivity. The findings indicated that good organizational behavior had a considerable favorable influence on faculty performance. The paper reviews significant literature on the proposed factors and makes recommendations for further research.