Management Model Research Articles

A Blockchain-Based Model for Traceability and Quality Management of Raw Materials in Clothing Products

In this study, we employ blockchain-based traceability technology to construct a comprehensive model for raw material traceability and quality management in clothing products. Additionally, we investigate engineered water nanostructures (EWNS) for their antibacterial, hydrophilic, and anti-pilling effects on textiles. The EWNS can serve as an effective tool for monitoring quality, enabling precise tracking and verification of nanomaterials during clothing production. The research validated the traceability performance of the blockchain-based model for raw material traceability and quality management in clothing products, achieving a remarkable traceability accuracy of 99.92% and an exceptionally low traceability response time of merely 1.83 seconds. Moreover, after being treated with EWNS, antibacterial rates against E. coli and S. aureus in clothing antibacterial quality management reached 99.9% and 99.0%, respectively. In the hydrophilicity test, EWNS was applied to clothing for a duration of 1 to 7 hours, resulting in a significant reduction in the wetting time of clothing from 10.8 seconds to 1.2 seconds. In the anti-pilling tests, there was a notable enhancement in the effectiveness of anti-pilling properties after EWNS treatment. In summary, our model has achieved remarkable outcomes in material traceability and quality management, offering consumers and the clothing industry a reliable method to ensure product quality and traceability, while simultaneously improving performance aspects such as antibacterial efficacy, hydrophilicity, and resistance against pilling.

Energy management enhancement of a smart home supplied by renewable energy system

Solar energy is a reliable and eco-friendly solution for power outages in Karbala, Iraq. This study presents a smart grid technology model for energy management in electrical systems, optimizing power schemes and economic benefits through a unique spatial distribution approach in Iraq, with the primary objective of ensuring consistent base loads for smart homes while achieving other economic goals. The algorithm’s effectiveness was tested in three different scenarios. The energy was supplied by the national grid and battery bank-powered base loads. Meteorological data, including temperature and solar radiation, was gathered from a station in Karbala city for testing and evaluation. The study found that energy consumption decreased by 85% in April, with solar energy accounting for 37% of the total consumption. Smart homes saved 48% of energy, reducing reliance on the grid to 15%, as well as the reduction of energy consumption reached up to 47% and 60% in January and July, respectively, with solar energy estimated at 14% and 26% in those months.

Smart Patient Consent Management Model for Health Information Exchange Based on Blockchain Technology

Smart Patient Consent Management Model for Health Information Exchange Based on Blockchain Technology

Potensi Hutan Mangrove Sebagai Daya Tarik Wisata (Studi Kasus Pada Hutan Mangrove Idaman Kec. Tarowang, Kab. Jeneponto, Prov. Sulawesi Selatan)

The Research of the potential of mangrove forests as an educational tourism is a survey research that focuses on the richness of mangrove forest ecosystems and its potential as an economic driver for the community through educational tourism activities. This research was conducted in the Idaman mangrove forest, Tarowang sub-district, Jeneponto South Sulawesi province. Data collection techniques used were through questionnaires, interviews, and documentation. The results showed that the characteristics of the Idaman mangrove forest consist of various species of mangrove plants, birds, and other mangrove ecosystems that are very potential as a tourist attraction. The potential of the mangrove forest has not been optimized by the communities around tourism area so that it has not yet optimally contributed as an economic source. The constraints in developing of the Idaman mangrove forest as a tourist attraction are the absence of a community-based tourism development and management model on the attractiveness of the Idaman mangrove forest, the low creativity and capacity of the community, as well as limited tourism support facilities. The recommended model for developing mangrove forests is educational tourism based through nursery activities and planting of mangrove forests for conservation, utilization and diversification of mangrove forest products as culinary and handicraft products.

A multilevel model for organizational productivity management: an interpretive structural modeling approach

PurposeThis research highlights the ongoing concern about organizational productivity and the lack of focus on designing an optimal model. The authors aim to create a comprehensive model for managing organizational productivity, considering its impact on profitability, customer satisfaction, and employee morale. They use qualitative research methods, including Systematic Literature Review and Interpretive Structural Modeling (ISM).Design/methodology/approachIn this research using the qualitative research method of Systematic Literature Review, 57 variables affecting productivity were identified. These variables were placed in 16 layers by using the ISM method, which were classified analytically in four sections: INPUTS, OUTPUTS, OUTCOMES and IMPACTS. By determining the relationship between the sections, the research model was designed.FindingsThe potential model for organizational productivity management provides a comprehensive framework addressing critical factors like technology adoption, employee empowerment, organizational culture, and more. It identifies Linkage, Dependent, and independent variables. The lower layers consist of INPUTS such as Technological Tools, Organizational Values, and more. In the highest layer, impactful variables like Enhanced competitiveness, Improved decision-making, and Improved organizational culture are labeled as IMPACTS. Middle layer variables are categorized as OUTPUTS and OUTCOMES.Originality/valueIn this study, the concept of productivity management was redefined for the first time, and a multi-layered model for productivity management was creatively explicated using the structural equation modeling method.

Spatio-temporal variation in water quality due to the anthropogenic impact in Rudrasagar Lake, a Ramsar site in India.

Rudrasagar Lake, a vital habitat for diverse flora and fauna, supports over 2000 households to sustain their daily livelihoods. The current study attempts to examine the impact of human activities on spatio-temporal variation in the water quality of the study area. The study integrates extensive field surveys, sample processing, and statistical analysis to assess the recent status of wetland health. Latin Square Matrix (LSM) was employed to select the sampling sites while the Inverse Distance Weighting (IDW) interpolation technique was used for spatial variation mapping. Modified Weighted Arithmetic Water Quality Index (MWAWQI) and Comprehensive Pollution Index (CPI) were utilized for assessing seasonal variation water quality and pollution loads, respectively. The results showed that dissolved oxygen (DO) was strongly influenced by the tributaries, and recreational activities have substantially influenced the highest concentrations of biochemical oxygen demand (BOD), and total suspended solids (TSS). The central portion of the lake is particularly susceptible to pollution from extensive fishing and recreational activities while peripheral sites are strongly influenced by agricultural run-offs, seepages from brick industries, and municipal wastes characterized by high concentrations of pH, total hardness (TH), oxidation-reduction potential (ORP). The findings reveal remarkable spatio-temporal fluctuations and highlight the areas within the lake susceptible to anthropogenic activities. The study proposed a sustainable management model to ameliorate anthropogenic threats. Moreover, the study contributes to the scientific understanding of the challenges and ensures the long-term viability of wetland health as a vital ecological and socio-economic resource.

Nanoscale Spatiotemporal Dynamics of Microbial Adhesion: Unveiling Stepwise Transitions with Plasmonic Imaging.

Understanding bacterial adhesion at the nanoscale is crucial for elucidating biofilm formation, enhancing biosensor performance, and designing advanced biomaterials. However, the dynamics of the critical transition from reversible to irreversible adhesion has remained elusive due to analytical constraints. Here, we probed this adhesion transition, unveiling nanoscale, step-like bacterial approaches to substrates using a plasmonic imaging technique. This method reveals the discontinuous nature of adhesion, emphasizing the complex interplay between bacterial extracellular polymeric substances (EPS) and substrates. Our findings not only deepen our understanding of bacterial adhesion but also have significant implications for the development of theoretical models for biofilm management. By elucidating these nanoscale step-like adhesion processes, our work provides avenues for the application of nanotechnology in biosensing, biofilm control, and the creation of biomimetic materials.

Urban Flood Modeling for Sustainability Management: Role of Design Rainfall and Land Use

This study aimed to evaluate how different methods of determining design rainfall levels and land usage affect flood hydrographs in an urban catchment; specifically, the catchment in southern Poland. The data included daily precipitation records from 1981 to 2020 and land cover information from Corine Land Cover and Urban Atlas databases for 2006 and 2018. The analysis involved examining precipitation data, determining design rainfall levels, analyzing land usage databases, exploring the influence of design rainfall levels on hydrograph characteristics, and investigating the database’s impact on these characteristics. No discernible trend in precipitation was found. The highest design rainfall values followed the GEV distribution, while the lowest followed the Gumbel distribution. Both land usage databases indicated an increasing human influence from 2006 to 2018. This study conclusively showed that the method used for estimating design rainfall and the choice of the land usage database significantly affected hydrograph characteristics. Multivariate analyses are recommended for design rainfall assessments, while the Urban Atlas database is preferred for urban catchment land usage determinations due to its detailed information.

Effectiveness of the "Internet+" home nurse management model for elderly patients with chronic diseases.

Effectiveness of the "Internet+" home nurse management model for elderly patients with chronic diseases.

Real-time outage management in active distribution networks using reinforcement learning over graphs

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

A new simplified risk assessment model enhances postoperative prophylaxis of venous thromboembolism in Chinese adult patients with inguinal hernia (CHAT-3): A prospective, multicenter, randomized controlled trial.

Venous thromboembolism (VTE) significantly affects the prognosis of surgical patients with inguinal hernia. The complex Caprini score, commonly used for postoperative VTE risk assessment, poses practical challenges for surgeons in clinical settings. The CHAT-3 trial, a prospective, multicenter, randomized controlled trial, compared a simple three-factor model to assess VTE risk against routine practices in post-inguinal hernia surgery (IHS) patients. The patients were randomly assigned (1:1) to the intervention or control arm. The intervention group used the three-factor model to identify patients at moderate or high risk of VTE for subsequent prophylaxis according to clinical guidelines. Both groups were followed for four weeks, with randomization implemented using computer-generated sequences. The primary outcome measured was the rate of VTE prophylaxis. Secondary outcomes included time spent on VTE risk assessment (surgeon self-reported), postoperative D-dimer trends, perioperative VTE occurrence, bleeding events, and the net clinical benefit. Of the 1,109 participants, 508 in the experimental group and 601 in the control group completed follow-up. The three-factor model showed higher VTE prophylaxis rates in all patients (pharmacologic prophylaxis: 26.2% vs. 6.00%, P<0.001) and particularly in those at high risk (pharmacologic prophylaxis: 57.3% vs. 9.50%, P<0.001). The experimental group significantly reduced VTE risk assessment time compared to the Caprini score (1.39±0.55min vs. 5.73±1.35min, P<0.001). The experimental group had lower D-dimer levels (0.26±0.73mg/L vs. 0.35±0.55mg/L, P=0.028). In the experimental group, the patients did not experience an increased risk of VTE (0% vs. 1.66%, P=0.268) and bleeding (1.18% vs. 0.67%, P=0.558) compared to the controls. There was no significant difference in net clinical benefit, which combined VTE and bleeding events, between the experimental and control groups (1.18% vs. 0.83%, P=0.559). Applying the simple three-factor model in perioperative VTE management could quickly identify the patient with a high risk of VTE and improve the prophylaxis rate of perioperative VTE. XXX. ChiCTR2000033769.

Urban Roadside Flooding Analysis Using SWMM: A Case Study of a Road Section in Pokhara Metropolitan City, Nepal

Pokhara City, in Nepal, experiences heavy rainfall during four months of monsoon, causing stress on drainage infrastructures. This situation is exacerbated by poor drainage systems, ineffective urban planning, and rapid urbanization. This study aims to develop mitigation strategies for urban roadside flooding in the Barahi Chowk region in Pokhara City. The focus is to identify the causes and potential solutions for the recurring issue of urban roadside flooding. For this purpose, the study utilizes Storm Water Management Model (SWMM) computer modeling to analyze the hydraulic capacity of existing drainage systems during peak flows. The SWMM modeling reveals that the current drainage usage is between 80% and 100% during peak times, leading to flooding. Consequently, resizing or expanding various drainage sections using SWMM modeling is suggested. The study introduces Low Impact Development (LID) controls, such as rain gardens and permeable pavements, to manage surface runoff. Implementing rain gardens on just 3% of the impervious area in each sub-catchment showed an average 21.5% reduction in peak runoff and an average 6.68% reduction in the total runoff while implementing 5% in each sub-catchment, permeable pavements reduced peak runoff by 22-26% and total runoff by 8-11%. The research explores the impact of land use and land cover change and unplanned urban growth on roadside flooding, resulting in impermeable urban surfaces that disturb the natural drainage system and infiltration pattern. Thus, the outcomes of this study should be carefully considered by policymakers and stakeholders to address issues through sustainable urban planning and infrastructure development. Also, implementing measures like resizing drains and LID controls appears to be effective in controlling flooding during the study period and should be considered for future studies as well.

Managing Weak Signals In The Foreign Economic Activity of a Modern Enterprise: Preventing a Decrease in the Financial and Economic Security with a Strategic Approach to Solving the Problem

The main purpose of the article is to create a system for responding to weak signals in the foreign economic activity of a modern enterprise. The scientific task is to form models of response and management of weak signals in the foreign economic activity of a modern enterprise, in the context of preventing a decrease in the financial and economic security of the enterprise. The proposed approach is based on the use of methods of DFD and functional modeling. As a result, two key models were formed: the first is the main model of management decision-making in the system of responding to weak signals and the second is the management functions for responding to weak signals. Our research makes an innovative contribution by integrating DFD and functional modeling methodologies to develop comprehensive models that provide deeper and more effective understanding of managerial decision-making in the context of responding to weak signals in a dynamic foreign economic environment.

Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems.

МЕЖДУНАРОДНЫЙ ОПЫТ УПРАВЛЕНИЯ СИСТЕМОЙ ВЫСШЕГО ОБРАЗОВАНИЯ И ЕГО ЗНАЧИМОСТЬ ДЛЯ РОССИИ

This article is devoted to the analysis of modern trends in the management of the higher education system. It will examine the experience of foreign countries in managing the higher education system in the context of each individual country, as well as its significance for the Russian Federation. A comprehensive analysis of the international experience in the management of higher education systems was carried out. For the first time, a comparative analysis of management models in different countries has been carried out, which allows us to identify general trends and specific features of the management of the higher education system.

Review of the Adoption of TQM Models and the Achieved Sustainable Business Results in the MENA Telecommunication Industry

Purpose: Understanding the criticality of the telecommunication industry in the current time and appreciating the significance of adopting total quality management models in organizations to extend business sustainability. This paper provides a comparison between two telecommunication leaders in the MENA region: Zain &amp; Etisalat, to assess their adoption of TQM models and the impact on their organizational and market success. Methodology: this study uses a comparative case study. a mixed-methods methodology was applied, combining quantitative and qualitative data. Primary data collection methods included surveys given to employees at different levels to gauge the internal effects of TQM models on operational efficiency and employee satisfaction, as well as semi-structured interviews with key management personnel from both companies, aimed at gaining insights into the implementation and outcomes of TQM practices. Secondary data came from a number of sources, such as government publications that described TQM adoption and telecommunication trends, market analysis and financial performance data from industry databases like Statista, annual reports and official documentation from Zain and Etisalat, and relevant quality management certifications and standards (like ISO 9001:2015). Thematic analysis of interview transcripts to find recurring themes and implementation insights, content analysis of company reports and publications to evaluate the strategic alignment and integration of TQM models, and case study analysis to compare and contrast Zain and Etisalat's approaches to adopting and implementing TQM models were some of the qualitative data analysis techniques used. Findings: The study found that the adoption of TQM models like EFQM, Kaizen and Lean six sigma across the whole organization is more effective than localizing the application to one aspect as indicated by the comparison between Zain &amp; Etisalat. The comparison indicated that the application of TQM extended the sustainability and resilience of Etisalat and Zain in the face of business challenges by improving performance and streamlining processes to increase customer satisfaction. Unique Contribution to Theory, Practice and Policy: The study recommended that both Etisalat and Zain should consider incorporating sustainability practices into their TQM frameworks, given the growing focus on corporate social responsibility and sustainability. This could be illustrating a dedication to sustainable business practices by coordinating Kaizen and Lean Six Sigma initiatives with objectives related to social responsibility and the environment. To improve the synergy between Kaizen, EFQM, and Lean Six Sigma initiatives, both companies should foster cross-functional collaboration.

Short-duration prediction of urban storm-water levels using the residual-error ensemble correction technique

ABSTRACT Predicting water levels in urban storm-water sewer systems is a critical study that could provide vital information to help reduce the risk of flooding. This study proposed a new ensemble model based on the integration of a meta-learner model, residual-error corrections, and a multiple-output framework. To achieve the meta-learner model, three multiple-output data-driven-based (MOD) sewer flooding models employing support vector regression (SVR), k-nearest neighbor regression (KNR), and categorical gradient boosting regression (CGBR) techniques were constructed and applied to predict the short-duration evolution of water levels at seven storm-water gauging sites in Taipei city, Taiwan, considering 10-min datasets spanning nearly 6 years (2016–2021). The Bayesian optimization algorithm was utilized in the training phases for all the models to avoid overfitting or underfitting. Enhancing the analysis of feature importance was also conducted to explore model interpretability based on the SHapley Additive exPlanation (SHAP) algorithm. The outputs of storm-water management model (SWMM) were used as benchmark solutions. For the model validation phase, the proposed integrated model improved the lead-time-averaged Nash–Sutcliffe efficiency of single KNR, SVR, and CGBR models by 174.5, 42.4, and 69.4%, respectively, showing that the proposed accurate model could be useful for urban flood warning systems.

The implementation of an optimized neural network in a hybrid system for energy management

In the face of increasing global energy demand and volatile energy prices, many countries are searching for solutions to ensure their energy independence. One of the most popular solutions is to incorporate renewable energy sources in their energy systems. While there are many advantages to integrating renewable energy sources, it is important to note that their intermittent operation can present challenges. Energy storage and smart grid management systems are key solutions to overcome these challenges and ensure sustainable, reliable use of renewable energy sources. In this article, we present an intelligent electrical energy management system for hybrid energy systems. This management system is based on a multi-layer neural network that has undergone an architecture optimization phase to improve the accuracy of real-time energy management and simplify its implementation. The management model that was built demonstrated highly good performance across a range of test circumstances.

Fuzzy-decision tree modeling for H2S production management in an industrial-scale anaerobic digestion process

H2S is a byproduct in anaerobic digesters (ADs), causing significant challenges due to its negative impact on biogas quality and equipment corrosion. As a solution, previous researchers developed complex mechanistic models that are computationally expensive due to solving equations with many variables. These models were only tested within normal H2S ranges. This research introduces a novel hybrid fuzzy-decision tree (F-DT) model to predict H2S production from primary/secondary sludge ratio (P/S), dry solids (DS), and volatile solid (VS) reduction parameters in the Qom wastewater treatment plant in Iran. Notably, the H2S concentrations in this facility consistently surpass the acceptable thresholds reaching levels as high as 6500 ppm, in contrast to the designed accepted limit of 3000 ppm. The process involved collecting and pre-processing data, creating a decision tree classifier for rule generation, developing a fuzzy model suited for complex systems like AD, and optimizing it with a genetic algorithm. The analysis of the final model revealed that the P/S ratio significantly influences elevated H2S levels, indicating that maintaining a P/S ratio > 1.2 holds promise as an effective strategy. The model is both simple and interpretable, with decent accuracy in predicting H2S compared to previous models. This study not only introduces a useful method for optimizing industrial-scale ADs, but also aims to uncover the reasons behind high H2S levels.

Decision-making concordance: A comparative analysis between a multidisciplinary tumor board and a large language model (ChatGPT) in breast cancer management.

e13648 Background: Advances in breast cancer management have led to more tailored therapies, increasing the complexity of decision-making. Establishing multidisciplinary tumor boards (MTB) is crucial for optimizing patient care, challenging healthcare teams intellectually. The integration of expertise, deep understanding, and clinical judgment is crucial. Artificial intelligence (AI), particularly through rapid developments and Large Language Models (LLM) offers opportunities to revolutionize healthcare by processing complex ideas and integrating medical evidence into decisions. This study evaluates ChatGPT-4 concordance with a Breast MTB. Methods: This study included 124 patients discussed at our Tumor Board from March 2023 to January 2024, treatment decisions from ChatGPT-4 were compared with those made by the MTB. Results: The median age was 52 years (24-85), with 98% female and 51% premenopausal. In 36% of the patients, the diagnosis followed a screening, while 64% were diagnosed due to symptoms like a palpable nodule or skin changes. 33% of patients meet the NCCN criteria for a family history of cancer. The distribution of clinical stages at diagnosis was 0 (15%), I (35%), II (31%), III (10%), and IV (8%). Most cases were left-sided (52%), followed by right-sided (43%), and bilateral in 5%. The phenotype was Luminal A-like (35%), Luminal B-like HER2 positive (23%), Luminal B-like HER2 negative (15%), and triple negative (10%). The overall concordance between the LLM and MTB rate achieved was 80.65% in patient profiles, including ductal carcinoma in situ. Concordance rates for treatment option indications were high: surgery (96%), radiotherapy (98%), chemotherapy (97%) and endocrine therapy (100%). For additional evaluations (PET/CT or oncogeriatric assessment), the concordance rate was 99%, for indicating genomic signatures was 96%, and for referrals for genetic evaluation, it was 93%. The lowest concordance was observed in type of anti HER2 therapy at 68%. Overall management concordance for patients in stage IV was 50%. Conclusions: AI is seeking ways to integrate into clinical routines by identifying individualized and personalized therapy option for our patients. We explore the concordance and feasibility of employing the LLM ChatGPT-4 in executing one of the most complex tasks in patient care, achieving high rates of concordance. Currently, AI is not capable of replacing medical professionals, but the use of LLM such as ChatGPT as an assistance and support tool for clinicians is likely to expand and evolve. Its capabilities could make it a valuable support tool for therapeutic management in the near future. All authors confirm that this abstract was not thought of or written by ChatGPT.