Management Quality Research Articles

Administrative Strategies Affecting Organizational Sustainability of State Enterprises in Bangkok Metropolitan Areas, Thailand

The purpose of this research was to study the effect of administrative strategies on organizational sustainability of state enterprises in Bangkok Metropolitan Areas. The samples used in this study were 375 employees working in Thai state enterprises in Bangkok Metropolitan Areas. The research questionnaire was used as an instrument to collect data. Statistics used in this study consisted of percentage, mean, standard deviation, Correlation and Structural Equation Modeling (SEM). The research results showed that administrative strategies had an effect on organizational sustainability of state enterprise in Bangkok Metropolitan Areas with Chi-square Probability equivalent to 0.073, which was less than 0.05; CMIN/DF equivalent to 1.207, which was less than 3; GFI equivalent to 0.961, which was more 0.90; and RMSEA equivalent to 0.024, which was less than 0.06. The results of statistical analysis indicated that the four statistical tests passed the evaluation criteria at good fit; it can be concluded that administrative strategies had an effect on organizational sustainability of state enterprises in Bangkok Metropolitan Areas, which can be presented as strategic management ((β = 0.89, p = .00), learning organization (β = .75, p = .00) and total quality management (β = .83, p = .00) respectively.

Research on the mechanisms of 2D road runoff pollution migration and the influence of pipeline overflow onto roads

In this paper, a novel numerical model capable of high-resolution, accurate simulation of the accumulation, wash-off, and migration of nonpoint source (NPS) pollutants on roads is proposed, effectively addressing the challenge of limited pipe network data for high-density urban building communities. This approach is based on a 1D-2D hydrodynamic and water quality dynamic bidirectional coupling model: GAST-SWMM. The calculation accuracy of the GAST two-dimensional road NPS wash-off model is validated via comparison with experimental data. The obtained Nash-Sutcliffe efficiency (NSE) is greater than 0.8. Moreover, the model was used to simulate the NPSs in a densely populated urban region of Xi'an, China, lacking building community pipeline data. The NPS pollutant transport and fate under the influence of both road runoff and the building community hydrodynamic water quality during rainfall events with a specific return period were examined. The proposed model can effectively and accurately replicate the accumulation and removal of NPS pollutants on a two-dimensional road and their dynamic interaction with the drainage network. With increasing rainfall return period, the peak time of the surface contaminant total load is postponed. The maximum surface pollutant load durations during rainfall events with 2-, 10-, and 50-year return periods are 60, 75, and 80 min, respectively. During the peak surface pollutant load time, the overflow pollutant fraction can exceed 85% for a 50-year rainfall return period. The simulation method presented in this paper accurately captures the spatial and temporal variations in NPS pollutants in densely populated urban areas, even when pipe network data for building communities are lacking. This method offers valuable technical assistance for urban environmental management and water quality protection.

Waste management and water quality evaluation prediction in urban environments through advanced robust hybrid machine learning algorithms

Water quality management is a crucial aspect of environmental protection, requiring the monitoring and regulation of effluent discharges into surface water bodies. This research introduces a novel approach to predicting Water Quality Evaluation (WQE) through a unique hybrid model, ABC-DWKNN-ICA, which integrates the Distance-weighted K-Nearest Neighbors (DWKNN) algorithm with the Artificial Bee Colony (ABC), Firefly Algorithm (FA), Imperialist Competitive Algorithm (ICA), and Gravitational Search Algorithm (GSA). Utilizing a comprehensive dataset of 1106 data points from Telangana, India, spanning 2018–2020, the study examines a range of water quality parameters, including Ground Water Level (GWL), Potential of Hydrogen (PH), Electrical Conductivity (EC), and others. The ABC-DWKNN-ICA model demonstrates exceptional performance in terms of Recall, Precision, Accuracy, and F1 Score for WQE prediction, distinguishing itself with enhanced feature selection, improved classification accuracy, robustness to noise and outliers, reduced dimensionality, and scalability to large datasets. This hybrid model represents a significant advancement over existing approaches, including traditional Hybrid Machine Learning (HML) algorithms such as ABC-DWKNN, FA-DWKNN, ICA-DWKNN, and GSA-DWKNN. By focusing on the capabilities of ABC-DWKNN-ICA rather than comparing all HML algorithms, the research highlights its superior effectiveness in water quality prediction, with performance metrics of 0.83 for Recall, 0.86 for Precision, 0.91 for Accuracy, and 0.86 for F1 Score. This study thus fills a critical research gap by demonstrating the model's value in environmental data analysis and offering promising prospects for more effective management of water resources. Additionally, feature selection, dimensionality reduction, enhanced accuracy, noise handling, and imbalanced dataset management are key advantages of the proposed model.

Historical Development and Anhancement of Managerial Quality: A Comprehensive Analysis

This study investigates the historical development and enhancement of managerial quality, focusing on its role in economic growth and organizational success. Despite existing research on management, a clear and standardized definition of managerial quality remains absent, presenting a knowledge gap in the field. The research employs a comparative analysis of management theories and practical methodologies to define and improve management quality. Findings highlight the key factors influencing managerial effectiveness, including decision-making efficiency and personnel qualifications. The results suggest that improving management quality directly enhances organizational performance and competitiveness. These findings have significant implications for developing effective management systems in both national and international markets.

Drug Management and Service Quality in Outpatient Pharmacies at the Haji Padjonga Daeng Ngalle Regional General Public Hospital in Takalar Regency, Indonesia

Drug Management and Service Quality in Outpatient Pharmacies at the Haji Padjonga Daeng Ngalle Regional General Public Hospital in Takalar Regency, Indonesia

Effects of perceived stress on college students’ sleep quality: a moderated chain mediation model

BackgroundPerceived Stress has been shown as a key contributor to sleep quality, but the underlying mechanism between perceived stress and sleep quality remains unknown. This study aimed to investigate the impact of perceived stress on sleep quality of college students and the chain mediating roles of presence of meaning in life (PML) and depression, as well as the moderating role of search for meaning in life (SML).MethodsParticipants were 8178 college students (4599 boys and 3579 girls; Mage = 19.10 years, SD = 1.08) who completed self-report questionnaire, including the Perceived Stress Scale (PSS), the Pittsburgh Sleep Quality Index (PSQI), the Meaning in Life Questionnaire (MLQ), and the Patient Health Questionnaire-9 (PHQ-9).ResultsThe results showed that higher perceived stress was directly related to poorer sleep quality. This negative impact on sleep quality was mediated through the chained roles of PML and depression. Additionally, the study found that SML moderates the influence of perceived stress, PML and depression on sleep quality. Specifically, for individuals actively search for meaning, the adverse effects of perceived stress and depression on sleep quality are diminished. Concurrently, the positive influence of PML on sleep quality is enhanced.ConclusionThis study revealed that the PML and depression mediate the effect of perceived stress on sleep quality, with SML playing a significant protective role. These results emphasize the necessity of integrating strategies to enhance PML and SML into interventions designed to improve emotion management and sleep quality among college students.

The National Cancer Institute Clinical Trials Planning Meeting to Address Gaps in Observational and Intervention Trials for Cancer-Related Cognitive Impairment.

Cancer-related cognitive impairment (CRCI) is a broad term encompassing subtle cognitive problems to more severe impairment. CRCI severity is influenced by host, disease, and treatment factors and affects patients prior to, during, and following cancer treatment. The National Cancer Institute (NCI) Symptom Management and Health-Related Quality of Life Steering Committee (SxQoL SC) convened a Clinical Trial Planning Meeting (CTPM) to review the state of the science on CRCI and to develop both Phase II/III intervention trials aimed at improving cognitive function in cancer survivors with non-central nervous system (CNS) disease and longitudinal studies to understand the trajectory of cognitive impairment and contributing factors. Participants included experts in the field of CRCI, members of the SxQOL SC, patient advocates, representatives from all seven NCI Community Oncology Research Program (NCORP) Research Bases, and the NCI. Presentations focused on the following topics: measurement, lessons learned from pediatric and geriatric oncology, biomarker and mechanism endpoints, longitudinal study designs, and pharmacologic and behavioral intervention trials. Panel discussions provided guidance on priority cognitive assessments, considerations for remote assessments, inclusion of relevant biomarkers, and strategies for ensuring broad inclusion criteria. Three CTPM working groups (longitudinal studies and pharmacologic and behavioral intervention trials) convened for one year to discuss and report on top priorities and to design studies. The CTPM experts concluded sufficient data exist to advance Phase II/Phase III trials utilizing selected pharmacologic and behavioral interventions for the treatment of CRCI in the non-CNS setting with recommendations included herein.

Latent Profile Analysis of Pressure Injury Knowledge Levels Among Nursing Staff in Tertiary General Hospitals.

Aims/Background The prevalence of pressure injuries (PIs) is a widely used clinical indicator of patient safety and quality of care. Nurses' understanding of pressure injury (PI) can significantly impact the treatment outcomes for patients. This study, based on latent profile analysis (LPA), reveals the characteristics associated with PI knowledge levels among clinical nurses in district and county tertiary medical institutions. We aim to help nursing managers formulate training plans accurately so that clinical nurses can provide high-level skin care services for patients. Method In June 2023, 1482 nurse staff from 4 tertiary general hospitals at the district and county level in Chengdu were chosen as research subjects using the convenience sampling method. Responses to the general information questionnaire, the Chinese Version of Pressure Ulcer Knowledge Assessment Tool (C-PUKAT), and the Chinese Version of Attitude towards Pressure ulcer Prevention (C-APuP) were used to compare the population's characteristics based on LPA. Results Three latent profiles of nurses' PI knowledge were identified: weak foundation type (46.3%), strengthening foundation type (42.7%), and special improvement type (11.0%). Subjects' departments, administrative positions, highest degrees and PI prevention attitude scores, as well as whether they have participated in the training, all differed significantly between latent profile groups (p < 0.05). Conclusion The PI knowledge level of nursing staff at the district and county tertiary general hospitals requires urgent improvement. Nursing managers should prioritize the management level and quality of PI training among clinical nursing staff. Precise training programs can be developed based on different categories of nursing staff to enhance their PI knowledge, thereby effectively improving the quality of healthcare for inpatients.

Hierarchy and cause–effect relationship of barriers to circular business models in food systems: a Fuzzy-DEMATEL analysis in the Berlin-Brandenburg area in Germany

ABSTRACT Food systems face significant environmental challenges, necessitating a shift towards sustainability and resource efficiency. Implementing circular economy principles and adopting new business models can significantly contribute to more sustainable food systems. These approaches address major issues in the linear economic model, such as excessive food loss and waste, resource depletion, and market volatility. However, due to various barriers, adopting new circular food systems confronts sluggish progress. This study identifies the hierarchical structure and cause-and-effect relationships of these barriers using the example of selected food systems in Berlin-Brandenburg, Germany. Employing the Fuzzy-DEMATEL analysis approach, this research identifies four primary causal barriers and four effect barriers classified across market development, finance, technological availability, knowledge management, as well as institutional domains. Furthermore, the study ranks these barriers based on their significance. It reveals that the top barriers are poor product design and waste management infrastructure, insufficient quantity and quality of material flow, and a lack of collaboration in the supply chain. In addition, weak policies, along with their inadequate evaluation and monitoring, act as the most influential causal barriers. These findings hold significant implications for practitioners and policymakers striving to promote the transition to circular food systems through innovative business models. Practitioners are advised to prioritize collaborations with different stakeholders to ensure proper waste management, by-product valorization, and resource efficiency. Concurrently, policymakers should enact supportive policies and effective governance structures based on the needs of the businesses and its stakeholders.

Evaluation of the PM2.5 concentrations in South America: Climatological patterns and trend analysis

This study assessed the concentration of particulate matter with an aerodynamic diameter of 2.5 (PM2.5) over South America (SA) based on climatological patterns and trend analysis. This study used monthly PM2.5 data from the Copernicus Atmosphere Monitoring Service (CAMS) at the European Centre for Medium-range Weather Forecasts from 2003 to 2022. The bilinear interpolation method was applied to the data resampling from 0.75° × 0.75°–0.25° × 0.25° spatial resolution, and subsequently, the climatological analysis was performed on seasonal and annual scales of PM2.5 concentrations for the 20-year series. The assessment comprised three main stages: 1) descriptive statistics, 2) seasonal and annual climatology patterns, and 3) trend analysis (Mann-Kendall - MK and Pettitt tests). Results indicated that the largest annual PM2.5 concentrations were found in the Amazon region and northern Paraguay due the El-Niño South Oscillation events (2003–2005, 2007, 2010, 2020, and 2022). The annual average PM2.5 concentrations exceeded the WHO recommended limit (5 μg m−3), ranging from 15.57 μg m−3 (1999) and 22.74 μg m−3 (2007 and 2010). The highest PM2.5 concentrations were observed in the Amazon and Chile (Santiago), reaching 160.4 μg m−3 annually and 177.8 μg m−3 seasonally (spring). Elevated seasonal PM2.5 concentrations were attributed to increased deforestation and wildfires associated with meteorological systems such as the Bolivian high and subtropical jets during dry periods (winter and spring). The MK test revealed a significant reduction in PM2.5 concentrations between 2003 and 2022, observed in Argentina (north), Brazil (arc of reforestation), Bolivia (south), and Chile (north). Reduction values varied between 0.5 and 10 μg m−3.year−1 annually and during winter, attributed to no-tillage practices in regions cultivating commodities. The Pettitt test also identified significant structural changes occurring in 2006–2008 and 2010–2011, aligning with areas highlighted in the MK test. This study provides valuable insights for air quality and environmental monitoring managers to mitigate PM2.5 pollution in SA.

Unravelling Migraine Stigma: A Comprehensive Review of Its Impact and Strategies for Change.

Migraine-related stigma is a pervasive issue impacting nearly half of chronic migraine patients, with significant consequences for their quality of life, disability and mental health. Despite its profound effects, migraine stigma remains under-recognised in both clinical practice and research. This narrative review explores the three primary types of stigmas affecting migraine patients: public, structural and internalised. Public stigma involves negative societal attitudes and stereotypes that trivialise the condition. Structural stigma is reflected in policies that restrict access to necessary care and resources. Internalised stigma occurs when patients absorb these negative views, leading to self-blame and diminished self-worth. Addressing these different types of stigmas is crucial for improving the understanding, diagnosis and treatment of migraine. Educational efforts, advocacy and policy reform are essential strategies in this context. A deep understanding of stigma is vital for developing effective interventions that enhance clinical management and patient quality of life. Ultimately, reducing stigma can lead to better health outcomes and a more comprehensive approach to migraine care.

Explainable AI in Manufacturing and Industrial Cyber–Physical Systems: A Survey

This survey explores applications of explainable artificial intelligence in manufacturing and industrial cyber–physical systems. As technological advancements continue to integrate artificial intelligence into critical infrastructure and industrial processes, the necessity for clear and understandable intelligent models becomes crucial. Explainable artificial intelligence techniques play a pivotal role in enhancing the trustworthiness and reliability of intelligent systems applied to industrial systems, ensuring human operators can comprehend and validate the decisions made by these intelligent systems. This review paper begins by highlighting the imperative need for explainable artificial intelligence, and, subsequently, classifies explainable artificial intelligence techniques systematically. The paper then investigates diverse explainable artificial-intelligence-related works within a wide range of industrial applications, such as predictive maintenance, cyber-security, fault detection and diagnosis, process control, product development, inventory management, and product quality. The study contributes to a comprehensive understanding of the diverse strategies and methodologies employed in integrating explainable artificial intelligence within industrial contexts.

Point and interval forecasting approach for short-term urban subway passenger flow based on residual term decomposition and fuzzy information granulation

Accurate forecasting information of short-term subway passenger flow is an important scientific reference for daily operations and urban management. The rapid time-varying nature of subway passenger flow caused by various factors that affect travel behavior poses a huge challenge to accurate forecasting. The complexity and uncertainty of data mainly focus on residual terms that deeply reflect the fluctuations. Reasonably mining the residual terms has become the key to achieving accurate forecasting. Therefore, this paper proposes a sophisticated decomposition strategy to extract and analyze the useful information hidden in the residual terms. Firstly, the potential trend, seasonal and residual terms from the original data are extracted by seasonal-trend decomposition procedure based on loess. Secondly, the residual term is decomposed into a series of subcomponents with different frequency features by symplectic geometric mode decomposition. Thirdly, these subcomponents are classified into three clusters based on fuzzy C-means clustering (FCM), and then the corresponding forecasting models are matched to the three obtained clusters and trend and seasonal terms. Finally, based on a decomposition-ensemble framework and information granulation for high-frequency components, we have established point and interval forecasting approach, respectively. Three experiments on real data sets in Beijing, Guangzhou and Shenzhen are conducted to verify the performance of the proposed approach, and the experimental results show that our approach is superior to all benchmark models and contributes to improving operational management and service quality.

Air Pollution and Corporate Innovation: Does Top Management Quality Mediate and Government Talent Policy Moderate?

Air pollution not only poses significant threats to the physical and mental well-being of individuals, but it also has the potential to trigger a regional brain drain, thus inhibiting corporate innovation performance. This study explores the impact of air pollution on corporate innovation from the perspective of top management quality. We find that lower air quality significantly reduces the quality of corporate top management, thereby reducing their innovation output. However, local government talent attention alleviates the negative impact of air pollution on corporate innovation. Further analysis reveals that the local government’s environmental attention aggravates the negative effects of air pollution on corporate innovation. Finally, executive compensation alleviates the negative impact of air pollution on corporate innovation.

Industry-Wide Application of Value Investing: A Comprehensive Analysis of Technology, Healthcare, Energy, and Catering Sectors

Value investing has been proved to be a valid means to dive into deeper market, in order to find the intrinsic value. It is grounded in a long-term perspective, focusing on the fundamental aspects of a business, such as its financial health, management quality, competitive position, and growth prospects. On the basis of above information, this paper is going to talk about the practical investing behaviors in different industries, including the technology, healthcare, energy and catering industries, surpassing the dimension of theory. With the improvement of every industry, innovations and risks have been taken into consideration, directing investors to take promising or conservative methods. By applying the principles of value investing across different industries, investors can seek to build portfolios that are not only diversified but also grounded in the fundamental strengths of the businesses they invest in. In each of these industries, value investors must conduct thorough due diligence, analyzing financial statements, market positions, competitive advantages, and growth potential.

Extended Half-life Antibodies: A Narrative Review of a New Approach in the Management of Atopic Dermatitis.

Atopic dermatitis (AD) is a chronic, inflammatory skin disease characterized by intense pruritus and eczematous lesions, significantly impacting physical health and quality of life. The pathogenesis of AD involves genetic predisposition, immune dysregulation, and environmental factors, with a defective skin barrier playing a crucial role. Treatment options for AD include both topical and systemic therapies, with advanced treatments like Janus kinase inhibitors and biologics offering significant improvements but facing limitations in safety and dosing frequency. Extended half-life antibodies represent a promising advancement for the management of immune-mediated inflammatory diseases, including AD. These antibodies, engineered for prolonged circulation and reduced dosing frequency, target key cytokines and immune pathways known to be involved in the pathogenesis of AD, offering potential for less frequent administration while maintaining efficacy. Currently, two such agents are in phase2 trials. APG777, targeting interleukin-13 (IL-13), and IMG-007, targeting OX40 receptor, have shown promising preclinical and early clinical results. They demonstrated prolonged half-lives and the potential for less frequent dosage regimen, along with significant improvements in AD symptoms. These therapies could enhance patient adherence and reduce healthcare burdens by decreasing injection frequencies and clinic visits. As research continues, extended half-life antibodies could significantly improve AD management and patient quality of life. Further studies will determine the long-term safety and efficacy of extended half-life antibodies, with ongoing innovations in antibody engineering likely to broaden their applications and benefits.

Examining The Dual Impact of Comprehensive Psychological Intervention and Ideological and Political Education on College Students' Health Behaviour

Background: College students face unique challenges including lifestyle transitions and increased academic demands, highlighting the need for interventions that address both behavioral and psychological health. Recent insights into ideological education and mental health suggest they significantly influence students' health-related choices. Objective: This study aimed to evaluate the effects of an Integrated Physical Education (IPE) curriculum coupled with a psychosocial intervention on the health-related behaviors of college students. Methods: We employed a pre-test/post-test design to assess changes in self-reported health behaviors, physical activity levels, stress management, and sleep quality following the intervention. Results: The intervention led to noticeable improvements across several areas. Participants experienced reduced stress levels and enhanced sleep quality, attributed to the intervention's focus on relaxation techniques, potentially improving overall energy levels. Additionally, there was a positive shift in physical activity patterns, with an increase in moderate-intensity exercises and a decrease in lower-intensity activities. However, the study also identified barriers to sustaining these healthy behaviors, such as time constraints and elevated stress during examinations, which hindered consistent participation in fitness regimes and healthy eating. Lack of social support also emerged as a challenge in maintaining these lifestyle changes. Conclusions: The findings suggest that integrating comprehensive psychological therapies with political and ideological education can effectively promote positive changes in health-related behaviors among college students. The study recommends incorporating practical advice on integrating healthy routines into daily schedules to address time management challenges, thereby enhancing the sustainability of health-promoting behaviors. Recommendations: Future interventions should focus on strategies for overcoming time barriers and enhancing social support to facilitate long-term adherence to healthy behaviors among college students.

Exploring the Relationship between Health Habits, Procrastination, Technology Use, and Well-being among Chinese Students

Objectives: The study aims at showing the complex relationship between technology usage, procrastination, mental health, academic stress, sleeping quality, and well-being among the students in China. Through application of standardized survey questionnaire, primary data were gathered among diversified students. Methods: The study, accomplished through partial least squares structural equation modeling (PLSSEM), showed that mental health and procrastination had a significant direct effect of well-being, which highlighted a role of two factors in shaping the student psychological and emotional condition. Results: Among other factors, sleep quality was found to be a mediating link in the relationships between the student's mental health, procrastination, and sense of well-being, which points out the essence of good sleep attitude in the process of the student's well-being's promotion. Conclusion: This study has a number of both practical and theoretical implications as well. It adds to the existing body of knowledge on the relevant variables, and provides significant insights to the policymakers, Chinese institutes' management, government, and other stakeholders. Moreover, the findings add to the understanding of what affected the wellness of students, and their practical consequence means developing specific programs i. e., mental health management, procrastination and sleep quality improvement for this population.

Financial management, efficiency, and care quality: A systematic review in the context of Health 4.0.

This article investigates the intersection between innovation, Health 4.0, and financial management in the healthcare industry, emphasizing the importance of operational efficiency and quality of care. The study aims to analyze how financial management processes in healthcare relate to Health 4.0 and enhance care quality. It begins with a thorough theoretical grounding, proposing a framework that connects Health 4.0 with financial management practices. A systematic review of the literature was conducted, identifying trends, challenges, and opportunities in the financial management of Health 4.0. The results highlight selected articles on responsible innovation, Health 4.0 technologies, investments in health, hospital efficiency, performance forecasting, and high-cost patient management. These articles were clustered into "Data Analysis and Machine Learning in Healthcare" and "Health Management and Sustainability," providing a categorized understanding of the topics. The study reveals that Health 4.0 offers significant opportunities for process efficiency and cost reduction without compromising service quality. It highlights strategic advantages in addressing contemporary healthcare challenges by optimizing processes, improving financial projections, and incorporating advanced technologies efficiently. The successful implementation of Health 4.0 can lead to substantial improvements in service quality, adding value to patients and driving local economic development. This article offers valuable insights for healthcare professionals and managers, emphasizing the transformative potential of Health 4.0 and outlining strategies for its effective implementation. The clustering of articles provides a clearer understanding of current research in Health 4.0, contributing significantly to the field and guiding future research directions.

Development of an Integrated Information System for Lecturer Performance Management

Lecturer performance evaluation continues to have issues that impede university goals. The purpose of this research is to develop an information system for evaluating lecturer performance (IS-LPA). The application was designed for three user groups: administrators, lecturers, and assessors. The application development utilizes the System Development Life Cycle (SDLC) model. The results of the feasibility test demonstrated an average score of 8.46, and the effectiveness test yielded an average achievement of 85.29%. This application has been shown to be feasible and effective for assessing lecturer performance in order to improve university management and service quality.