Management Model Research Articles

Safety and feasibility of enhanced recovery after surgery-based management model for ambulatory pediatric surgical procedures.

There is still some room for optimizing ambulatory pediatric surgical procedures, and the preoperative and postoperative management quality for pediatric patients needs to be improved. To discuss the safety and feasibility of the enhanced recovery after surgery (ERAS)-based management model for ambulatory pediatric surgical procedures. We selected 320 pediatric patients undergoing ambulatory surgery from June 2023 to January 2024 at The First People's Hospital of Liangshan Yi Autonomous Prefecture. Of these, 220 received ERAS-based management (research group) and 100 received routine management (control group). General information, postoperative ambulation activities, surgical outcomes (operation time, postoperative gastrointestinal ventilation time, and hospital stay), postoperative pain visual analogue scale, postoperative complications (incision infection, abdominal distension, fever, nausea, and vomiting), and family satisfaction were compared. The general information of the research group (sex, age, disease type, single parent, family history, etc.) was comparable to that of the control group (P > 0.05), but the rate of postoperative (2 h, 4 h, and 6 h after surgery) ambulation activities was statistically higher (P < 0.01), and operation time, postoperative gastrointestinal ventilation time, and hospital stay were markedly shorter (P < 0.05). The research group had lower visual analogue scale scores (P < 0.01) at 12 h and 24 h after surgery and a lower incidence of total postoperative complications than the control group (P = 0.001). The research group had higher family satisfaction than the control group (P = 0.007). The ERAS-based management model was safe and feasible in ambulatory pediatric surgical procedures and worthy of clinical promotion.

Applications of simulation modeling in mining project risk management: criteria, algorithm, evaluation

Project risk management in the mining industry is necessary to identify, analyze and reduce uncertainty. The engineering features of mining enterprises, by their nature, require improved risk management tools. This article proves the relevance of creating a simulation model of the production process to reduce uncertainty when making investment decisions. The purpose of the study is to develop an algorithm for deciding on the economic feasibility of creating a simulation experiment. At the same time, the features and patterns of the cases for which the simulation experiment was carried out were studied. Criteria for feasibility assessment of the model introduction based on a qualitative parameters became the central idea for algorithm. The relevance of the formulated algorithm was verified by creating a simulation model of a potassium salt deposit with subsequent optimization of the production process parameters. According to the results of the experiment, the damage from the occurrence of a risk situations was estimated as a decrease in conveyor productivity by 32.6%. The proposed methods made it possible to minimize this risk of stops in the conveyor network and assess the lack of income due to the risk occurrences.

Global Quality of Service (QoX) Management for Wireless Networks

In the fast-changing technological landscape, novel applications are emerging with the potential to reshape the world. These applications, while promising, impose stringent requirements in terms of quality of service (QoS). The advent of wireless networks like 5G, 6G and Wi-Fi 6 brings about resource management solutions to ensure these requirements while meeting the user expectations within the interconnected environment. Nevertheless, user behaviors are also evolving, highlighting the importance of satisfaction and quality of experience (QoE). Furthermore, changes in user behavior trigger shifts in business models, where the quality of business (QoBiz) takes on a pivotal role. This evolving ecosystem, encompassing QoS, QoE, and QoBiz, demands a comprehensive and adaptable approach that conventional QoS management frameworks fail to perform. This paper introduces an implementation methodology for a global QoS management model named QoXphere. The implementation methodology is grounded in machine learning techniques and addresses the multifaceted aspects of quality of service (QoX) and their interconnections within wireless networks. The objective is to facilitate dynamic resource management that not only elevates user satisfaction but also optimizes provider benefits. Real-world examples illustrate the methodology’s applicability in widely deployed networks, complemented by simulated scenarios of modern network environments that further validate the approach.

Evaluation of the efficacy of implementing Ecosystem-Based Management (EBM) strategies within fisheries across the middle and lower Yangtze River basin lakes in China

To mitigate the decline of fishery resources and the exacerbation of water pollution in Chinese lakes and to enhance the potential and sustainable use of lake fisheries, the Chinese government has actively implemented principles and models of ecosystem-based lake fishery management. This includes promoting ecological aquaculture techniques and the “retiring pens to restore lakes” policy. To assess the effects of ecosystem-based management on Chinese lake fisheries, data from 2007 to 2020 were collected for the Qiandao Lake, Tai Lake, Hongze Lake, Luoma Lake, and Gaobao Lake in the middle and lower reaches of the Yangtze River. The study employed Data Envelopment Analysis (DEA) to assess fishing efficiency through a static input-output efficiency model. Additionally, the Malmquist index and distance functions were used for a dynamic comparison of total factor productivity (TFP) across the five lake management models. The results indicate that trends in technological progress, scale efficiency, and TFP were consistent across the lakes from 2013 to 2020. Qiandao Lake, Tai Lake, and Hongze Lake showed a positive average annual growth rate in TFP, with Qiandao Lake having the highest average annual growth rate of 13.2%. In contrast, Gaobao and Luoma Lakes experienced a negative growth rate in TFP. Furthermore, Qiandao Lake exhibited significantly higher technical efficiency than the other four lakes, effectively achieving an efficient state, followed by Hongze Lake. The study demonstrates that technological progress and variations in scale efficiency are the primary factors driving the increase in TFP in fisheries. Promoting ecosystem-based “fishing for water conservation” aquaculture techniques and restricting aquaculture scale through the “retiring pens to restore lakes” policy contribute to enhancing TFP in ecological fisheries.

Overlap between priority conservation areas and natural assets appeals to a shared responsibility for global primate conservation

Expanding protected areas (PAs) coverage to match countries’ conservation responsibility is vital to stemming global biodiversity loss. Uneven biodiversity distribution and conservation responsibility among countries pose challenges to the implementation of conservation targets, and currently diminish conservation effectiveness. Using primates as a global model, we evaluated the conservation responsibility of countries to assess if their PA coverage adequately meets their conservation responsibility. We quantified spatial national responsibility for primate species conservation in each country from their range size of priority conservation areas (PCAs) and protected areas coverage on PCAs. We identified the high-responsibility countries for global primate conservation as countries that held > 1 % of global PCAs. We paid particular attention to the circumstances associated with PCAs in the least developed countries, especially the overlap between PCAs and the areas containing natural assets (natural assets areas). Seventeen countries were identified as high-responsibility countries that in total covered 84 % area of global PCAs. Current protected areas only covered less than 50 % of PCAs for fifteen of these high-responsibility countries. Among these fifteen countries, four countries (DR Congo, PA coverage: 19 %; Myanmar, 9 %; Madagascar, 29 %; Laos, 22 %) were the least developed countries. Most existing PCAs (95 %; SD 6 %) in these 17 countries are located within natural assets areas, which creates tension between primate conservation and local development. Global primate conservation is a shared responsibility among the international community. Developing countries have greater responsibility than most because of the overlapping of their PCAs with natural assets areas. For developing countries to fulfill their conservation responsibilities requires international conservation resources (funding support and cooperation). Protected areas expansion has to be matched with a realizable target for each country to avoid crippling conflicts between conservation and local development. Community-based management models, including other effective area-based conservation measures, may reduce potential conflict and improve conservation effectiveness.

Dynamic Optimization of a Supply Chain Operation Model with Multiple Products

Determination of the optimal operational policy for an automotive supply chain is explored under a centralized management approach using dynamic programming. A deterministic optimal control model is proposed to meet multi-product demand over a period while minimizing a cost performance index for a five-echelon network. The production-inventory levels are the state variables and the raw material acquisition rates are the control variables to be decided in the problem. The novelties include parts mixing operations, assembly requirements, and a push–pull chain operation strategy. The continuous model is solved using Iterative Dynamic Programming, an algorithm with successful applications in chemical engineering problems. Its implementation here is the first in supply chain (SC) management models. The results demonstrate that the proposal is suitable to represent the dynamic behavior of the SC and provides useful information to outline a cooperative decision-making process. Managerial insights are derived to improve the resilience and efficiency of the chain.

Evaluation of Indian Railway’s Supply Chain Parameters for Improved Sustainability Under Industry 4.0 Transformation: An Application of AHP and TOPSIS

Indian Railways (IR) is India’s major public transportation system and one of the world’s largest and busiest rail networks. The Indian government is in charge of owning and running it. This paper presents an alternative approach to measuring the overall operational performance of the Indian Railway supply chain. The operation of Indian railways from a supply chain perspective needs to be analyzed to improve customer service and inventory management.Industry 4.0 is used for the easy processing of individual transportation components efficiently and safely. The study is useful for the Indian Railways because it enables the classification of various issues related to government procurement. A supply chain model is proposed to assess service expectations and quality. Various issues (such as the integration system, financial behavior, and management perspective) related to supply chain management have been identified, and various factors affecting availability, railway service quality, and reliability have been analyzed for managing its supply chain. The Analytical Hierarchy Process (AHP) and TOPSIS have been used to examine inventory-related difficulties and make recommendations for the public procurement environment. The result of the AHP method showed that Model 1 (Integration System) has the highest Max_C_weight of 0.5348, followed by Model 2 (Financial Behavior, 0.381695) and Model 3 (Management Perspective, 0.399443). The consistency ratio (CR) for all three models was also less than 0.1. Further, the TOPSIS method was used to validate the accuracy of AHP results and found that the model is significant. This result will help the decision-makers at Indian Railways design an assessment and relationship management model that will help them make procurement the driving force behind their supply chain.As per COP27, Industry 4.0 will help to achieve net zero emissions. The net carbon emissions will be reduced to zero, which will result in a clean environment.To improve sustainability within the framework of the Industry 4.0 transition, the research aimed to evaluate the Indian Railway’s supply chain metrics. Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are methodologies used in this assessment.

Administrative management model and emergency services in Public Hospitals

Administrative management model and emergency services in Public Hospitals

Study on the effectiveness of a community hypertension management model based on home smart blood pressure monitoring using IoT technology

Study on the effectiveness of a community hypertension management model based on home smart blood pressure monitoring using IoT technology

A crisis awareness chain warning management model for patients with difficulties cooperating with magnetic resonance examinations.

This case-control study tested a crisis awareness-based chain warning management model for patients with difficulties cooperating with magnetic resonance imaging (MRI) examinations. All participants experienced difficulties cooperating with MRI examinations and underwent cranial magnetic resonance spectroscopy (MRS) and MRI at the same hospital in China. The control group (n = 1233) underwent examinations from January to June 2023 and received routine nursing care (pre-examination safety notification, instructions on cooperating during the examination, post-examination observation). A crisis awareness chain warning management model was implemented for the intervention group (n = 1352), who underwent examinations from July to December 2023. The groups were compared on average time for examination completion, quality of care and occurrence of complications. Data were collected using a self-devised data collection form. The average length of time to complete MRS and MRI was shorter for intervention group patients than for control group patients. The intervention group showed better pre-examination preparation, examination success rate, image quality attainment rate, and one-time examination success rate, and lower incidence of examination-related complications. This management model could increase MRI examination efficiency, improve quality of care, reduce complications and increase nurses' understanding of nursing continuity and crisis awareness.

Synthesis of activity-based costing and deep learning to support cost management: A case study in the automotive industry

Activity Based Costing (ABC) models for Cost Management and Big Data Analytics are deeply rooted in academia and practice to efficiently manage operational costs. Inspired by cost management and deep learning (DL) theories we develop a multilayer perceptron (MLP) using a nested particle swarm optimized (PSO-PSO) neural network algorithm for self-controlled architecture and weight optimization. This innovative approach enables to mimic ABC while increasing prediction accuracy. In a real-world case study, we use wheel manufacturing data provided by a large original equipment manufacturer (OEM) and follow a full factorial experimental design. Furthermore, we benchmark our novel DL approach with results derived from a traditional ABC analysis. We demonstrate that this intelligent cost estimation model can mimic ABC using few cost drivers contributing to efficient and transparent interorganizational cost management. Major quantitative findings within a conducted field experiment demonstrate the high forecast accuracy with low absolute Cost Percentage Error (CPE) deviation of the novel cost estimation approach in industry. The results extend the scope of PSO for the simultaneous optimization of the architecture and weights of neural networks. Additionally, we prove that the PSO-PSO algorithm is a suitable alternative to traditional optimization methods in DL.

Establishing a Comprehensive Nursing Clinic Using the Case Management Model.

To explore the practical effect of the case management model in a comprehensive nursing clinic. Based on the case management model, the authors constructed a comprehensive nursing clinic providing wound care, ostomy care, peripherally inserted central catheter care, drainage tube care, nursing consultations, and home care. They evaluated the practical effect of the comprehensive nursing clinic according to workload, economic benefits, and satisfaction of the medical staff and patients. Since the inception of the comprehensive nursing clinic, the number of visits has increased by 63.57%, and the satisfaction of patients and medical staff has also improved. This comprehensive nursing clinic based on the case management model meets the medical needs of patients, has improved the satisfaction of patients and the medical staff, and enhances the professional sense of value and comprehensive quality of specialized nurses.

OPTIMIZATION OF WASTE MANAGEMENT IN SUMBAWA DISTRICT USING A DYNAMIC SYSTEM MODEL

The main problem that occurs in society today apart from basic needs is the problem of waste. This waste problem certainly needs a solution in good, effective, sustainable and long-term management. The waste issue not only occur in big cities but also in developing areas. Sumbawa Regency as one of the regions that continues to develop and grow requires optimum and long-term planning of solid waste management and systems. In 2021, the waste handled in Sumbawa Regency will only be around 17.5% of the total waste. This shows that the waste management system in Sumbawa Regency is not optimal. Optimal waste management can be achieved by taking an academic approach through model development. One such model is the dynamic system model. This can describe the causal relationship between variables that affect solid waste problems and to provide an overview of the waste management system through simulations based on the factors that influence the processing model. Therefore, it is necessary to develop an optimization model for waste management specifically in Sumbawa Regency using a dynamic systems approach. The purpose of this study is to describe waste management in the form of an optimization model for waste management in Sumbawa Regency with a dynamic system. Optimization results show that the optimistic model yields a total of 127.01 tons/day, the moderate model yields a total of 98.23 tons/day and the pessimistic model produces 62.5 tons/day

Deep learning-based GLOF modelling for hazard assessment and risk management

ABSTRACT Glacial Lake Outburst Flood (GLOF) has become a crucial aspect as the increase in the meltdown of glaciers results in the breach of unstable debris dams. Hence, it is essential to understand the nature of the glacial lakes for proper planning and development of the region in the long term. In this paper, a deep learning network is developed for GLOF hazard and risk assessment. The Shepard Convolutional Neural Network Fused Deep Maxout Network (ShCNNFDMN) is developed by fusing the Shepard Convolutional Neural Networks (ShCNN) and the Deep Maxout Network (DMN) based on regression analysis. Here, various data and feature attributes, like geometric properties, location properties, lake-based properties, and global properties are determined from the glacial lake data. Afterthat, hazard assessment is carried out based on these parameters by the ShCNNFDMN. Then, risk assessment is performed based on the hazard levels and the feature attributes. The ShCNNFDMN is analyzed based on metrics, such as Hazard modelling error, Risk prediction error, Mean Average Error (MAE), and R-Squared are found to produce values of 0.462, 0.423, 0.358, and 0.288, respectively. The proposed method is useful in applications, like infrastructure planning, taking preventive and mitigative actions in downstream areas of glacier lakes.

Intuitive tracking: Blending competing approaches to exercise and eating.

Under the conditions of neo-liberal individual responsibilisation, self-tracking has become the predominant model of health management. More recently, though, intuition-based approaches to exercise and eating are also gaining traction. These two approaches are often located in opposition. While self-tracking uses datafication and calculability to structure health decisions, intuitive approaches encourage abandonment of rules and restrictions around exercise and food in favour of corporeal self-awareness and attunement to sensation. Although navigating these competing approaches is a common experience for all populations, the tensions between them are felt particularly acutely by people with complex health histories, such as eating disorders (EDs). In this article, we draw on mixed-methods longitudinal data, analysed using phenomenological analysis, to propose a novel framework - 'intuitive tracking'-which moves beyond understandings of self-tracking as the antithesis of intuitive engagement with exercise and health. Drawing on longitudinal interviews and photo elicitation with 19 women who are in recovery from EDs and using weightlifting as a tool to support their recovery, we demonstrate how attentiveness to bodily and emotional cues is successfully combined with an emphasis on monitoring health behaviours to support wellbeing. We conclude that theoretical understandings of self-tracking can and should make space for intuition-led decision-making.

Variability Modeling in Enterprise Architecture Management: Case Study and Survey on Existing Approaches

Managing and dealing with variability in business processes and the IT landscape is a common challenge in the everyday practice of most enterprises and organizations. Recent studies have observed that digital transformation, Internet-of-Things solutions and the introduction of artificial intelligence cause changes and challenges in enterprises that simultaneously require variability on several levels, for instance, business processes, data architecture, and services. Enterprise architecture models are considered a suitable way to visualize and manage dependencies between different levels of an enterprise. However, the management of variability in enterprise architectures has not received much attention in scientific research. This article aims to contribute to a better understanding of future investigation needs. Using a systematic literature analysis, the article structures the existing research work in the field and examines real-world challenges of variability based on a case study. We argue that there is a need for new constructs in enterprise architecture models that allow for expressing dependencies between variations on different enterprise architecture layers.

Patterns of self-medication and intention to seek pharmacist guidance among older adults during the COVID-19 pandemic in Macao: a cross-sectional study

IntroductionSelf-medication was remarkably popular during the COVID-19 pandemic. In older populations, the risk of self-medication is higher. Pharmacists are well positioned to provide public health education and disease prevention. This study aims to explore the self-medication patterns and intention to seek pharmacist guidance among older adults in Macao.MethodsA face-to-face cross-sectional survey was subsequently performed in March-April 2023 among older adults in Macao. The questionnaire was designed based on the Theory of Planned Behavior (TPB) framework. Multiple logistic regression was used to analyze predictors of self-medication behavior and multiple linear regression analysis to determine whether the TPB construct was the predictor of older adults’ intention to seek guidance from pharmacists.ResultsA total of 412 participants completed the questionnaire. The self-medication rate among older adults in Macao was 64.2%. The most commonly used types of medications were over-the-counter and traditional Chinese medicine, mainly from government anti-pandemic packages. The majority of individuals engaged in self-medication to treat COVID-19 symptoms or prevent COVID-19 infection. The prevalent reasons for self-medication were the perceived non-seriousness of the illness. 85 years old or older and university degree were significantly associated with self-medication behavior. Older adults had moderate intention to seek pharmacist guidance on medication use. The average scores (standard deviation) were 3.43 (1.10) for Attitude, 2.69 (0.99) for Subjective Norm, 3.56 (1.04) for Perceived Behavioral Control, and 3.07 (1.43) for Intention. Attitude, Subjective Norm, and Perceived Behavioral Control were all strong predictors of intention, which explained 53% of the variance in intention. In demographic factors, age was identified as a significant predictor of intentions.ConclusionsSelf-medication was widely practiced in Macao during the COVID-19 pandemic. To better control the risks associated with self-medication, the role of pharmacists is paramount. Enhancing the recognition and trust of pharmacists within society, modifying pharmacy management models, and strengthening pharmacists’ self-perception of their profession are all pivotal directions areas to further enhance their role.

On the Complementarity between CMMN and iStar in Complex Domain Modeling

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations.

A multi-objective lot sizing procurement model for multi-period cold chain management including supplier and carrier selection

The rapid expansion of the cold chain market is a key supply chain trend, but its high energy consumption conflicts with low-carbon goals. To address this, the paper proposes a multi-objective lot sizing procurement (LSP) model for managing the procurement of perishable products in the cold chain. This model, constrained by limited inventory and transportation capacity, aims to optimize multi-period procurement plans and order allocation and minimize total costs and carbon emissions. The proposed multi-objective LSP model adopts a posteriori mode, which contributes to enhancing the model's applicability, especially in countries where carbon tax and trading systems are not fully developed. To enhance decision makers’ decision efficiency and preserve the diversity of the original Pareto solutions as much as possible, the K-means++ algorithm is employed to prune the original Pareto solution set, providing decision-makers with three representative solutions (cost priority, balanced, and carbon priority solutions). In addition, the paper conducts sensitivity analysis, stability experiments, and compares the multi-objective LSP model with the benchmark model (relaxed carbon emission constraints). Experiments show that the multi-objective LSP model quickly and stably provides decision-makers with lot sizing purchasing plans for numerical examples of different scales and effectively controls the total carbon footprint of the entire cold chain at a low cost.

Dual layer energy management model for optimal operation of a community based microgrid considering electric vehicle penetration

This work develops a dual-layer energy management (DLEM) model for a microgrid (MG) consisting of a community, distributed energy resources (DERs), and a grid. It ensures the participation of all these energy entities of MG in the market and their interaction with each other. The first layer performs the scheduling operation of the community with the goal of minimizing its net-billing cost and sends the obtained schedule to the DER operator and grid. Further, the second layer formulates a power scheduling algorithm (PSA) to minimize the net-operating cost of DERs and takes into account the load demand requested by the community operator (COR). This PSA aims to achieve optimal operation of MG by considering solar PV power, requested demand, per unit grid energy prices, and state of charge of the battery energy storage system of the DER layer. Moreover, to study the impact of electric vehicles (EVs) load programs on DLEM, the advanced probabilistic EV load profile model is developed considering practical and uncertain events. The EV load is modelled for grid to vehicle mode, and a new mode of EV operation is introduced, i.e., vehicle to grid with EV demand response strategy (V2G_DRS) mode. The solar PV and load demand data are obtained from the MG setup installed and buildings present at the university campus. However, a scenario reduction technique is used to deal with the uncertainties of the obtained data. In order to evaluate the efficacy of the developed DLEM, its results are compared to previously reported energy management models. The results reveal that DLEM is superior to the existing models as it decreases the net-billing cost of COR by 13% and increases the profit of the DER operator by 17%. Further, it is found that for the highest EV penetration, i.e., 30 EVs, the V2G_DRS mode of EV operation reduces the total energy imported by COR by 11.39% and the net-billing cost of COR by 7.88%. Therefore, it can be concluded that the proposed model with the introduced V2G_DRS mode of EV makes the operation of all the entities of MG more economical and sustainable.