Management Model Research Articles

Revisiting a Faculty Career Management Life Cycle Model: Anticipating and Navigating Career Transitions in Academic Medicine.

Career management models are valuable tools for faculty pursuing a career in academic medicine. These models help faculty transition through various stages of their careers, including commonly pursued academic advancements from assistant professor to full professor, as well as less common transitions like moving from full-time to part-time status, taking sabbaticals, going on medical leave, or assuming executive leadership roles. The success of faculty members across these stages is influenced by both environmental factors and individual-level characteristics. Recognizing career stages and transitions, as well as the impact of personal and environmental factors on career growth, is crucial. The proposed Faculty Career Self-Management Model (FCSM) provides a visual and descriptive framework to guide individual faculty and the professionals who support them in understanding, preparing for, and navigating career stages and professional transitions to build and sustain meaningful careers in academic medicine. The FCSM serves as a framework to explore, develop, and share best practices in supporting faculty vitality across the career lifespan.

Transforming Patient Outcomes: Cutting-Edge Applications of AI and ML in Predictive Healthcare

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has emerged as a transformative force, offering unprecedented potential to enhance patient outcomes. This paper explores the cutting-edge applications of AI and ML in predictive healthcare, focusing on how these technologies are revolutionizing early detection, personalized treatment, and patient management. Through advanced algorithms and data-driven insights, AI and ML are enabling healthcare providers to predict disease progression, optimize therapeutic interventions, and improve patient monitoring in real-time. Key applications discussed include predictive modeling for chronic disease management, AI-powered diagnostic tools, and the use of machine learning in precision medicine. The paper also addresses the challenges associated with these technologies, including data privacy concerns, algorithmic bias, and the need for robust validation. By examining current case studies and future trends, this work aims to highlight the transformative impact of AI and ML on patient care, ultimately driving a shift toward more proactive, tailored, and efficient healthcare systems.

Understanding the determinants of consumer grocery stockpiling behavior

AbstractGrocery stockpiling is a common behavioral response to the emergence of disasters or heightened uncertainty. Nonetheless, the phenomenon and methods for mitigating it are not well understood. Using a model of household shopping and inventory management, we conceptualize stockpiling as a result of an increase in the fixed cost of making grocery shopping trips, or the opportunity cost of time associated with shopping. In a laboratory experiment, we find that stockpiling increases (decreases) by 78 and 41% (22%) with an increase in fixed costs and price reductions (imposition of purchase limits), respectively. We also find that stockpiling leads to fewer (more) grocery trips by 33 and 22% (36%) under the same three conditions, respectively. Our experiment and subsequent cluster analysis suggest that loss aversion suppresses stockpiling. Our experiment shows that imposing purchase limits, a common retail response to stock‐outs, can trigger stockpiling during shopping trips without purchase limits. Although we do not claim external validity, our study suggests that store managers and policymakers should be careful about solutions during a stockpiling event, such that they do not exacerbate stockpiling, which may disproportionately affect vulnerable groups and disrupt supply chains.

Demand for Information for Wildland Fire Management

Significant resources have been devoted to increasing the supply of data and information products for wildland fire management. There has been comparatively less emphasis on understanding the demand for these products. There are large differences in the number of information sources that fire managers use in decision making. We developed a value-of-information model for wildland fire managers to formulate hypotheses about what factors drive these differences. Data from a comprehensive internet survey targeting a well-defined population of the Southwest wildland fire managers are used to test these hypotheses. Results are generally consistent with hypotheses generated from the value-of-information model. Multiple regression results suggest information use increases with the number of decisions that managers make and is greater during fire season than before. Information use is affected by a manager’s level of education, age, experience, job type, the agencies they work for, and the multi-agency dispatch centers where they work. Agency and dispatch center effects explain more of the variation in information use than differences in the respondents’ personal characteristics. To better understand fire manager demand for information, future research could explore in more detail what specific attributes of agencies and dispatch centers affect use of information for wildland fire management.

Comprehensive Pain Management in Total Joint Arthroplasty: A Review of Contemporary Approaches

Background: Total hip and knee arthroplasties are among the most effective and widely performed procedures in modern medicine, providing substantial benefits to patients with end-stage osteoarthritis. These surgeries have transformed the treatment of degenerative joint disease, significantly enhancing functionality and quality of life for patients. Despite considerable advancements in surgical techniques and postoperative care, managing postoperative pain remains a major challenge, impacting both clinical recovery and patient satisfaction. The persistence of postoperative pain as a barrier to recovery underscores the need for improved pain management strategies. Methods: A comprehensive narrative review of the literature was conducted, focusing on the physiological mechanisms underlying surgical pain, the role of anesthesia techniques, and the development of multimodal pain management approaches used in total joint arthroplasty. This review emphasizes the components of modern multimodal strategies, which combine multiple pharmacologic and non-pharmacologic methods to address the various mechanisms of postoperative pain. Results: Current pain management strategies employ a dynamic, multimodal approach that covers the perioperative period. These strategies aim to optimize pain control while minimizing side effects. They incorporate a range of methods, including nerve blocks, non-opioid analgesics, opioids, and non-pharmacologic techniques such as physical therapy. However, evidence regarding the efficacy and optimal combinations of these interventions varies widely across studies. Conclusions: This variation has led to inconsistent pain management practices across institutions. To standardize and improve care, this paper presents the authors’ institutional pain management model, offering a potential framework for broader application and adaptation in the field of joint arthroplasty.

Governance and Leadership in Advancing Entrepreneurial Universities: Exploring Awareness and Change Dynamics in South Africa

This research explores the role of awareness, as framed by the ADKAR (Awareness, Desire, Knowledge, Ability, and Reinforcement) change management model, in driving the transformation of South African higher education institutions (HEIs) into Entrepreneurial Universities (EnU). The study focuses on the critical question of whether raising awareness of entrepreneurship among academics can catalyse transformative changes within universities, fostering an entrepreneurial mindset and practices across the institution. In the context of South Africa’s socio-economic challenges, such as high unemployment and inequality, entrepreneurial universities are seen as key drivers of innovation and economic development. The research findings underscore that awareness, the foundational step of the ADKAR model, is essential for initiating the entrepreneurial shift within HEIs. Academics who are aware of the importance of entrepreneurship are more likely to engage in activities that promote innovation, student entrepreneurship, and industry collaboration. However, the study also highlights that awareness alone is insufficient for a full institutional transformation. Leadership commitment and active involvement are critical in moving beyond awareness to implement sustainable entrepreneurial initiatives. Without strong leadership that aligns institutional governance structures and policies with entrepreneurial objectives, the entrepreneurial agenda may struggle to gain traction. The research concludes that while raising awareness among academics is a necessary first step, leadership at universities must also be equipped with the skills and understanding to champion entrepreneurship at an institutional level. Comprehensive leadership training, continuous evaluation of entrepreneurial initiatives, and fostering cross-departmental and external collaborations are essential for sustaining momentum towards the full realisation of Entrepreneurial Universities in South Africa. This study contributes to the broader discourse on entrepreneurship in higher education by providing insights into the specific challenges and opportunities facing South African universities as they navigate the path towards becoming entrepreneurial institutions.

Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs

Complex microscopic simulation models of strategic Air Traffic Management (ATM) performance assessment and decision-making are hindered by several factors. One of the most important is the existence of hidden parameters—such as aircraft take-off weight (TOW) and the selected cost index (CI)—which, if known, would allow for more effective performance modeling methodologies for assessing Key Performance Indicators (KPIs) at various levels of abstraction/detail, e.g., system-wide, or at the level of individual flights. This research proposes a data-driven methodology for the estimation of flights’ hidden parameters combining mechanistic and advanced Artificial Intelligence/Machine Learning (AI/ML) models. Aiming at microsimulation models, our goal is to study the effect of these estimations on the prediction of flights’ KPIs. In so doing, we propose a novel methodology according to which data-driven methods are trained given optimal trajectories (produced by mechanistic models) corresponding to known hidden parameter values, with the aim of predicting hidden parameters’ values of unseen trajectories. The results show that estimations of hidden parameters support the accurate prediction of KPIs regarding the efficiency of flights: fuel consumption, gate-to-gate time and distance flown.

Progressing a river health assessment framework to tropical waters

Diverse anthropogenic activities have threatened the valuable tropical biodiversity and freshwater reserves. Despite that, there is little concern in the Brazilian legislation about the ecological integrity of aquatic ecosystems. We proposed a framework based on two joint indices, allowing the creation of a management tool. The first is the Tropical Water Healthy Index (TWHI), a river index that integrates pressures on aquatic ecosystems, their conditions, and societal/governmental response. The second is the Suitability of the Class of Use (SCU), which measures whether rivers’ conditions are in accordance with their classes of use proposed for the legal Brazilian water framework. Our results showed that most sites had good river health conditions, with the worst conditions occurring in areas with high urban occupations followed by agricultural areas. The second index indicated that few streams were poorly or incompatible with their classes of use, suggesting their classifications were based on current conditions but not future intentions. The force of these joint analyses was shown in the Melchior River stretch, which was classified as compatible with SCU, but the TWHI was critical. This is good to show that we simultaneously have a problem with the legislation and to indicate something that needs to be done quickly for managers. Therefore, we built a management model that can assess the real situation of aquatic ecosystems and define efficient management strategies, producing alerts for the decision-maker.

The Innovation of Dormitory Management Model for Post-00s College Students

The Innovation of Dormitory Management Model for Post-00s College Students

TALENT MANAGEMENT STRATEGY FOR SUCCESSION PLANNING AT PT K INDONESIA

Objective: PT K, a leading company in the FMCG industry in Indonesia, faces significant risks due to its over-reliance on key leaders with specialized knowledge and skills, heightening organizational risk if these leaders depart unexpectedly. This dependency jeopardizes operational continuity due to inadequate internal regeneration and a low succession ratio over the past two years. Addressing these critical issues, this study aims to review and refine the talent management strategies of PT K in line with business goals and needs related to leader succession planning. Theoretical Framework: In this topic, the main concept and theoriy that underpin the research is integrated talent management model developed by Silzer and Dowell (2010). Method: The study carried out was applied research. The study utilizes primary data from interviews with the President Director, Head of Division Human Capital & General Affairs, and Head of People Development & Learning Center of PT K, alongside secondary data from internal documents. Interviews were conducted to find out business strategy direction, as well as PT K's talent management policies and needs. Apart from that, it was also to find out current talent management practices and the need for improvement. Meanwhile, the documents studied are documents about Long-Term Development Program at PT K for period of 2020 – 2025, which is used to determine PT K's HR management directions and policies. Results and Discussion: Based on the results of interviews and document studies, it was obtained: (1) talent management directions and policies according to business targets and strategies, as well as (2) findings of gaps in the four main activities of talent management practices at PT K. Based on these two things, a map, targets and actions to improve PT K's talent management strategy for the 2024-2026 period were determined. Research Implications: The study's implications suggest that addressing these gaps will enhance leadership continuity and organizational resilience, offering a model applicable to similar companies in the FMCG sector and providing valuable insights for both academic research and professional practice. Originality/Value: This is the first study related to talent management to be carried out empirically at PT K, which is one of the leaders in the FMCG industry in Indonesia. It is hoped that research findings and results will provide valuable input regarding current practices that will enrich the body of knowledge related to talent and HR management.

Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring and management of lake water quality. It critically evaluates the performance of various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, and Hyperion, in assessing key water quality parameters including chlorophyll-a (Chl-a), turbidity, and colored dissolved organic matter (CDOM). This review highlights the specific advantages of each satellite platform, considering factors like spatial and temporal resolution, spectral coverage, and the suitability of these platforms for different lake sizes and characteristics. In addition to remote sensing platforms, this paper explores the application of a wide range of machine learning models, from traditional linear and tree-based methods to more advanced deep learning techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). These models are analyzed for their ability to handle the complexities inherent in remote sensing data, including high dimensionality, non-linear relationships, and the integration of multispectral and hyperspectral data. This review also discusses the effectiveness of these models in predicting various water quality parameters, offering insights into the most appropriate model–satellite combinations for different monitoring scenarios. Moreover, this paper identifies and discusses the key challenges associated with data quality, model interpretability, and integrating remote sensing imagery with machine learning models. It emphasizes the need for advancements in data fusion techniques, improved model generalizability, and the developing robust frameworks for integrating multi-source data. This review concludes by offering targeted recommendations for future research, highlighting the potential of interdisciplinary collaborations to enhance the application of these technologies in sustainable lake water quality management.

SOCIAL AND PSYCHOLOGICAL ASPECTS OF FORMING AN EFFECTIVE INNOVATIVE TEAM

This article highlights issues related to the social and psychological foundations of the formation of a team capable of working effectively and responding to modern challenges of the market environment. It was determined that such bases are the individual motives of the members of the labor team, namely their personal beliefs regarding the expediency of performing professional tasks. It is established that they are formed at the early stages of the socialization of the individual under the influence of education, upbringing, norms of social morality and are corrected during his subsequent working life. It has been found that motives can be more stable than incentives, which can cause an undesirable final result of the enterprise's activity. With this in mind, the task of institutional management should be the creation of an effective motivational system aimed at strengthening the role of incentives in the structure of employee motivation for professional development, professional development, creativity, creativity and initiative. The article provides recommendations on directions for adjusting the national management system in order to create an effective innovative team. In particular, to improve the work of the team and the final efficiency of the enterprise, it is recommended to create an effective system of labor incentives through the introduction of elements of foreign management models. The proposed elements make it possible to create an effective model of personnel motivation management, as they are aimed at strengthening the personal motives of employees to protect their own professional interests and dignity, use the opportunity for self-development and self-realization, involvement in the process of enterprise management and decision-making, evaluation of one's own labor contribution and recognition of the obtained results, adjustment constructive business relations in the team. It was determined that these innovations should become the basis for further improvement of the motivation of employees in the domestic management system, since each branch and field of activity has its own characteristics, taking into account the possibility of their further adjustment.

QOL-20. NATIONAL BRAIN TUMOR SOCIETY BRAIN TUMOR QUALITY OF LIFE RESEARCH AGENDA

Abstract BACKGROUND QoL is a critical aspect of health care outcomes, particularly in the context of brain tumor treatment and survivorship. There are evidence gaps in optimal strategies for improving QoL among brain tumor patients and care partners. Understanding the multifaceted dimensions of QoL and identifying key areas for brain tumor QoL research can significantly enhance patient care and support. Engaging the brain tumor community in defining QoL outcomes and determining research priorities can help orient research resources to community needs and perspectives. OBJECTIVE To summarize the process and outcomes of an NBTS-led collaborative effort to co-design a comprehensive research agenda on QoL for brain tumor patients and care partners, as outlined in the “Brain Tumor QoL Research Agenda.” METHODS The agenda was developed through a collaborative effort involving experts in neuro-oncology, patients, care partners, patient advocacy groups, and healthcare professionals over a two-year period. A review of existing literature, patient experiences, and expert opinions informed the identification of key research priorities. A multistakeholder advisory group held monthly meetings where brain tumor QoL was defined. In 2023, we conducted two “Research Roundtables” for adults and pediatric/AYA. The World Cafe method captured data and a survey consensus process to validate findings. RESULTS The QoL research agenda delineates a multifaceted framework encompassing domains crucial for understanding and improving QoL in brain tumor patients. Key focus areas include symptom management, psychosocial support, care burden, treatment decision-making, survivorship issues, and health care delivery models. An RFP was released by NBTS with input from stakeholder advisors in April 2024. CONCLUSION The Brain Tumor QoL Research Agenda created a roadmap for advancing research efforts aimed at enhancing the QoL of individuals affected by brain tumors. By addressing the identified research priorities, healthcare stakeholders can work towards implementing effective interventions and policies to optimize patient outcomes and well-being. This program was funded through a Patient-Centered Outcomes Research Institute® (PCORI®) Eugene Washington PCORI Engagement Award (EACB-23261).

Evaluate the Differential Effectiveness of the Case Management and Primary Nursing Models in the Implementation of Discharge Planning.

This study aims to compare the effectiveness of the case management model and the primary nursing care model by focusing on hospital stay length, readmission, follow-up resource utilisation and survival. To improve patient continuity of care, a discharge planning team has been established at the hospital to implement the service concept of long-term medical care. The team works with a multidisciplinary medical team to provide case management, which ensures high-quality patient care. Retrospective case-control study. This study collected data from the medical record information system database and the Ministry of Health and Welfare's care service management information system to explore medical and follow-up care utilisation of patients discharged between 2017 and 2018. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, with the checklist used to ensure transparent and complete reporting of the methodology and results. To ensure comparability between the case management model group and the primary nursing model group, propensity score matching was used. After matching, 660 individuals from the case management model group and 2876 individuals from the primary nursing model group were selected for further analysis. In this study, patients were primarily involved in the research as participants, providing data through their medical records for analysis. However, no direct involvement from the public or patients was used in the planning or design stages of the research. The study found that factors such as activities of daily living (ADL) score ≤ 60 points, catheterisation, poor chronic disease control and inadequate primary caregiver capacity can lead to longer hospitalisations. Males and patients with catheters have a higher risk of readmission within 30 days, and men, those aged ≥ 75 years, those with ADL score ≤ 60, catheterisation, pressure sores or unclean wounds, financial problems, insufficient primary caregiver capacity and those readmitted within 14 days after discharge had significantly increased mortality after discharge. Although the case management model group had higher hospitalisation days, they had lower readmission rates and higher survival rates than the primary nursing model group. Early consultation with the case manager for discharge planning can help patients to continue to receive care and utilise relevant resources after returning home. Identifying and addressing patient-specific factors can significantly improve patient outcomes by reducing hospitalizations and readmissions, and lowering mortality rates. The case management model is more effective than the primary nursing care model in reducing readmission rates and increasing survival rates. Early consultation with case managers for discharge planning is crucial to ensuring patient care continuity and resource utilization.

FITTING DYNAMICALLY CONSISTENT FORWARD RATE CURVES: ALGORITHM AND COMPARISON

This paper introduces a new methodology for estimating dynamically consistent forward rate curves, which are essential for obtaining arbitrage-free valuation and risk management models. Using U.S. Treasury data from January 2013 to June 2023, we fit dynamically consistent forward rates curves and test the goodness of fit between theoretical and observed prices. We compare the traditional static forward rate curve parameterizations (including Nelson–Siegel, Svensson, and cubic splines) with our new dynamically consistent methodology. The dynamically consistent forward rate curve estimates generally outperform the traditional static methods in reproducing market prices.

Microgrid energy optimization management considering consumer segmentation optimization and dynamic time intervals

ABSTRACT To prioritize critical loads and enhance microgrid energy management efficiency, this study introduces a method that combines consumer segmentation optimization and dynamic time intervals. A penalty-based approach is used to minimize unmet load demands, prioritizing energy for critical loads. A joint classification model for multi-type consumers is developed using a convolutional neural network autoencoder and hierarchical clustering. Dynamic time intervals are applied to reduce iterations and improve memory and processor efficiency in solving the energy management model. Simulation tests on a microgrid with distributed generation, battery storage, and consumption units confirm the method’s effectiveness. Results show that configuring dynamic time intervals and segmenting loads by priority yield solutions similar to fixed interval methods. Finally, the energy optimization management effects of the proposed method were compared with those of two latest methods. The comparison results demonstrate that if a microgrid underwent four different disconnection scenarios from the main distribution network, the proposed method saves 23.15%, 23.08%, 23.79%, and 34.61% time to achieve energy optimization management compared with that of the first latest method, and 24.20%, 23.87%, 25.11%, and 36.18% time than that of the second latest method.

Blockchain-Based Intelligent Inventory Management for Hospital Supply Chain (HSC) with Deep Learning Techniques

A medication shortage may have far-reaching consequences, making it an important inventory management concern for healthcare organizations. Intelligent demand forecasting is essential to make cold chain businesses more competitive and save transportation and storage costs. Hence, this paper proposes a blockchain-based intelligent inventory management model (BC-IIMM) with deep learning techniques for hospitals to prevent running out of supplies, run their systems sustainably, and optimize their profits in the hospital supply chain (HSC).Preventing the contamination of pharmaceuticals requires a pharmaceutical cold chain monitoring system that uses IoT devices, blockchain, and cloud computing to track items across their entire supply chain reliably. One other perk is that both doctors and patients may benefit from drug recommendations made by a deep belief network (DBN) model. Further, to construct deep learning (DL), the proposed cold chain monitoring system produces large-scale, high-quality data. Holding cold chain items would become less expensive and cold chain inventory management decisions would be easier with it.Next, they provide a numerical result proving the suggested method works better than the baselines regarding refilling cost and shortage rate.

Evaluation of Learning Management Standards to Improve Higher Education Quality Standards Through Internal Quality Audits

Independent evaluation is required through an internal quality control system through internal quality audit activities. This study evaluated the achievement of learning management standards, inhibiting factors, and recommendations for improving education quality standards at Palembang Polytechnic. This type of qualitative research uses observation methods, structured interviews, and literature studies. The research instrument is the author directly involved in the research. The informants in this study were determined using a purposive technique: three research informants. The study’s results obtained data on implementing internal quality audits on learning management standards that have not been implemented. An effective internal quality management model is needed as a platform or application that supports collecting, storing, and managing data and information on website-based internal quality management system services to support fast, precise, and efficient internal quality management system integration.

A Study on the Water Management Knowledge of Traditional Villages from the Perspective of Stormwater Resilience—A Case Study of Changqi Ancient Village in Guangdong, China

With the advancement of resilience concepts, enhancing resilience capacity has become an effective approach to addressing rainwater and flooding issues. Most rural planning and construction efforts adopt urban planning models from economically developed regions, often leading to surface hardening, which subsequently causes drainage difficulties and severe surface water accumulation during the rainy season. In contrast, traditional Lingnan villages, exemplified by Guangdong’s Changqi Ancient Village, continue to function normally in flood-prone areas, suggesting that their water management knowledge merits investigation. Previous research on rainwater management in traditional Chinese villages has predominantly been qualitative, lacking scientific data support. This study employs an eco-social resilience perspective, combining field surveys and interviews with villagers, and utilizes the SWMM (Storm Water Management Model) software to conduct both qualitative and quantitative analyses of Changqi Ancient Village. The findings reveal the following: (1) The SWMM effectively quantifies rainwater and flood management in traditional villages. (2) From an ecological resilience perspective, the village’s geographical location is crucial. The topography, along with a rainwater regulation system comprising rivers, ponds, ditches, and permeable pavements, significantly influences the village’s drainage performance. (3) From a social resilience perspective, community participation is vital to the long-term stable development of traditional villages. This includes post-disaster collective fundraising by villagers for the restoration of rainwater and flood management facilities, the formulation of village regulations, and the construction and restoration of spiritual sites. (4) From an eco-social resilience perspective, the eco-social resilience system exhibits adaptive cyclical characteristics, where the geographical environment and the local economy significantly shape the ecological spatial patterns of Changqi, while positive interaction between nature and human society ensures the system’s dynamic equilibrium.

Research on the Current Situation and Sustainable Development Measures for Urban–Rural Water Supply Integration in Yunnan Province, China

Urban–Rural Water supply integration is one of the effective ways to address rural drinking water safety issues. With the rapid economic development and urbanization, the gap between urban and rural water supply has become increasingly evident, and rural areas face severe challenges such as insufficient water sources and substandard water quality. Yunnan Province, due to its unique topography and natural environment, encounters numerous difficulties in promoting the construction and development of urban–rural water supply integration, and there is a severe lack of research specifically focused on sustainable development measures for urban–rural water supply integration in Yunnan Province. In light of this, this paper first reviews the current research status on urban–rural water supply integration both domestically and internationally, pointing out that existing studies mainly focus on optimizing urban water supply systems, addressing rural drinking water safety issues, and exploring water supply management models. It then analyzes the challenges faced by Yunnan Province in advancing urban–rural water supply integration, including limitations in engineering construction, inadequate operation and maintenance, complex and variable water source conditions, and insufficient public awareness of water conservation. In response to these issues, a series of sustainable development measures are proposed, including revitalizing existing assets, enhancing construction planning and quality, promoting water pricing reforms, upgrading the technical system for drinking water safety assurance, and advancing the information technology construction of urban–rural water supply integration, with the aim of providing references and insights for the sustainable development of urban–rural water supply integration in Yunnan Province and other regions.