Efficient Management Research Articles

Do dual holders enhance or hinder managerial efficiency?

ABSTRACT This study examines the influence of dual ownership on managerial efficiency using panel data from a dataset of U.S. firms spanning 1996 to 2017. Our findings indicate that dual ownership negatively affects managerial efficiency, demonstrating diminishing productivity in resource utilization when dual ownership is present or increases. Cross-sectional analyses suggest that the adverse effect of dual holders on managerial efficiency is more pronounced in dual-held firms with weaker corporate governance and greater financial constraints. Additional analyses, including propensity score matching, change specifications, Lewbel’s instrumental variable approach, and alternative measures of managerial efficiency, corroborate our findings.

Entropy optimization in a radiative and chemically reactive EMHD flow of a nanofluid coexisting Ohmic dissipation and multiple slips

Purpose This research focuses on the controlling irreversibilities in a radiative, chemically reactive electromagnetohydrodynamics (EMHD) flow of a nanofluid toward a stagnation point. Key considerations include the presence of Ohmic dissipation, linear thermal radiation, second-order chemical reaction with the multiple slips. With these factors, this study aims to provide insights for practical applications where thermal management and energy efficiency are paramount. Design/methodology/approach Lie group transformation is used to revert the leading partial differential equations into nonlinear ODE form. Hence, the solutions are attained analytically through differential transformation method-Padé and numerically using the Runge–Kutta–Fehlberg method with shooting procedure, to ensure the precise and reliable determination of the solution. This dual approach highlights the robustness and versatility of the methods. Findings The system’s entropy generation is enhanced by incrementing the magnetic field parameter (M), while the electric field (E) and velocity slip parameters (ξ) control its growth. Mass transportation irreversibility and the Bejan number (Be) are significantly increased by the chemical reaction rate (Cr). In addition, there is a boost in the rate of heat transportation by 3.66% while 0.05⩽ξ⩽0.2; meanwhile for 0.2⩽ξ⩽1.1, the rate of mass transportation gets enhanced by 12.87%. Originality/value This paper presents a novel approach to analyzing the entropy optimization in a radiative, chemically reactive EMHD nanofluid flow near a stagnation point. Moreover, this research represents a significant advancement in the application of analytical techniques, complemented by numerical approaches to study boundary layer equations.

Multi-Timescale Energy Consumption Management in Smart Buildings Using Hybrid Deep Artificial Neural Networks

Demand side management is a critical issue in the energy sector. Recent events such as the global energy crisis, costs, the necessity to reduce greenhouse emissions, and extreme weather conditions have increased the need for energy efficiency. Thus, accurately predicting energy consumption is one of the key steps in addressing inefficiency in energy consumption and its optimization. In this regard, accurate predictions on a daily, hourly, and minute-by-minute basis would not only minimize wastage but would also help to save costs. In this article, we propose intelligent models using ensembles of convolutional neural network (CNN), long-short-term memory (LSTM), bi-directional LSTM and gated recurrent units (GRUs) neural network models for daily, hourly, and minute-by-minute predictions of energy consumptions in smart buildings. The proposed models outperform state-of-the-art deep neural network models for predicting minute-by-minute energy consumption, with a mean square error of 0.109. The evaluated hybrid models also capture more latent trends in the data than traditional single models. The results highlight the potential of using hybrid deep learning models for improved energy efficiency management in smart buildings.

Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem.

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

Optimizing Water Productivity through the Conjunctive Use of Borewell and Canal Water for Enhancing Sheep Productivity in Arid Climate of Western Rajasthan, India

In the arid regions of Rajasthan, water scarcity poses a significant challenge to both agriculture and livestock productivity. Sustainable practices in such climates require effective management of limited water resources. This study focuses on optimizing water productivity through the conjunctive use of canal water and borewell water to address the problem of water scarcity while maximizing output per unit of water. The experiment involved eighteen healthy lambs of aged 2-3 months which were managed intensively and divided into three groups of six lambs each based on a randomized block design. All groups were fed Pennisetum glaucum (pearl millet) and Medicago sativa (lucerne) ad-libitum along with concentrates adjusted according to their body weights. The groups were offered three types of drinking water: canal water (G1), a combination of canal and borewell water (G2) and borewell water (G3) in a clean bucket twice a day. No significant effect on total feed intake and body weight of the lambs was found based on the drinking water source. However, G1 and G2 lambs, which were offered canal water, consumed significantly (p<0.05) less water (74.4 ± 1.84 liters) compared to the lambs in G3 (81.6 ± 1.91 liters). The virtual water requirement per kilogram of weight gain, calculated from both direct water intake and indirect intake through feed was lowest in G1 lambs (5771.34 liters) followed by G2 lambs (6129.00 liters) and G3 lambs (6312.00 liters) per kilogram of weight gain, respectively. These results indicate that water productivity was highest with canal water (G1) and lowest with borewell water (G3) per kilogram of weight gain. This study underscores the importance of efficient water management in maximizing productivity and resource use efficiency in water-limited arid regions.

MPPT Solar Controller: A Theoretical Design and Prototype Development

This paper discusses the theoretical design and ongoing prototype development of a Maximum Power Point Tracking (MPPT) solar controller system aimed at efficient energy harvesting and smart load management. The system is designed to optimize energy extraction from a solar panel using MPPT algorithms, with smart load management for real-time power distribution. The controller incorporates cloud-based monitoring, AI optimization, and a user-friendly interface. The system is expected to deliver robust, efficient, and safe operation under varying environmental conditions

Developing remote patient monitoring infrastructure using commercially available cloud platforms

Wearable sensor devices for continuous patient monitoring produce a large volume of data, necessitating scalable infrastructures for efficient data processing, management and security, especially concerning Patient Health Information (PHI). Adherence to the Health Insurance Portability and Accountability Act (HIPAA), a legislation that mandates developers and healthcare providers to uphold a set of standards for safeguarding patients’ health information and privacy, further complicates the development of remote patient monitoring within healthcare ecosystems. This paper presents an Internet of Things (IoT) architecture designed for the healthcare sector, utilizing commercial cloud platforms like Microsoft Azure and Amazon Web Services (AWS) to develop HIPAA-compliant health monitoring systems. By leveraging cloud functionalities such as scalability, security, and load balancing, the architecture simplifies the creation of infrastructures adhering to HIPAA standards. The study includes a cost analysis of Azure and AWS infrastructures and evaluates data processing speeds and database query latencies, offering insights into their performance for healthcare applications.

Adaptive Traffic Signal Control Method Based on Offline Reinforcement Learning

The acceleration of urbanization has led to increasingly severe traffic congestion, creating an urgent need for effective traffic signal control strategies to improve road efficiency. This paper proposes an adaptive traffic signal control method based on offline reinforcement learning (Offline RL) to address the limitations of traditional fixed-time signal control methods. By monitoring key parameters such as real-time traffic flow and queue length, the proposed method dynamically adjusts signal phases and durations in response to rapidly changing traffic conditions. At the core of this research is the design of a model named SD3-Light, which leverages advanced offline reinforcement learning to predict the optimal signal phase sequences and their durations based on real-time intersection state features. Additionally, this paper constructs a comprehensive offline dataset, which enables the model to be trained without relying on real-time traffic data, thereby reducing costs and improving the model’s generalization ability. Experiments conducted on real-world traffic datasets demonstrate the effectiveness of the proposed method in reducing the average travel time. Comparisons with several existing methods highlight the clear advantages of our approach in enhancing traffic management efficiency.

Effectiveness of Internal Control of Financial Reporting (PIPK) on Inventory Management at BPS Pasuruan Regency

Inventory is one of the important and liquid assets in an organization, including at BPS Pasuruan Regency. Effectively managed inventory can maintain smooth operations, minimize the risk of deviations, and help the organization achieve its goals. The flow of inventory management includes the process of receiving, storing, issuing, and controlling. Internal financial reporting control (PIPK) is the key to ensuring that inventory management has been running effectively and accountably. This study aims to analyze the effectiveness of the implementation of PIPK on inventory management at BPS Pasuruan Regency. The study was conducted using a qualitative approach with a case study method. Data were collected through interviews with inventory managers, direct observation, and documentation. The results of the study indicate that PIPK at BPS Pasuruan Regency has been implemented quite well. The implementation of PIPK has had a positive impact on inventory management at BPS Pasuruan Regency, such as increasing accountability, efficiency, and effectiveness in inventory management.

A Deep Learning Dependent Controller for Advanced Ultracapacitor SoC Concept to Increase Battery Life Span of Electric Vehicles

ABSTRACTOn a global scale, significant progress is being made in the field of battery technology for Electric Vehicle (EV) applications, driven by the need to combat carbon emissions and mitigate the effects of global warming. Accurately determining critical parameters, making sure battery storage system diagnosis, and functioning are correct are critical to the feasibility of EVs. However, insufficient supervision and safety measures for these storage systems may lead to serious problems like a thermal runaway, overcharging, overheating, cell imbalances, and fire hazards. To tackle these challenges, the presence of an efficient battery management system becomes paramount. By facilitating accurate monitoring, managing heat dissipation, regulating charging‐discharging procedures, guaranteeing battery safety, and offering protection measures, this system is essential to maximizing battery performance. The key intention of this innovative approach is to improve the longevity of EV batteries during extended periods of operation. By assessing vehicle velocity, remaining battery energy, and State of Charge (SoC), the proposed method effectively manages SoC in both the battery and ultracapacitor. This control is accomplished through a two‐stage convolutional neural network‐based system known as the Charge Sustain‐CNN Controller and the Charge Deplete‐CNN Controller. These controllers are fine‐tuned using the Fractional Latrans‐Hunt optimization (FLHO) algorithm to optimize the performance. The evaluation criteria encompass the battery and ultracapacitor's energy efficiency, as well as vehicle velocity. This novel approach significantly improves the energy storage system in EVs, leading to enhanced energy efficiency and prolonged battery life. Ultimately, experimental results validate the practicality and effectiveness of this developed method. Specifically, the proposed approach attained the Battery's SoC of 72.47%, 91.99%, and 82.88% for the different drive cycles including the FTP75, J1015, and UDDS, respectively.

Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark

The current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R2), Root Mean Squared Error (RMSE), and Concordance Index (C-index). The Random Forest model achieved the highest prediction accuracy among all machine learning models, followed by Linear Regression and the Decision Trees. The scatter plot for Linear Regression demonstrates good predictive accuracy for mid-range values. However, it shows significant deviations at the extremes, indicating that the model struggles to capture the full range of variability in the data. The bar chart of coefficients pinpoints the variables with the greatest impact on the predictions, providing suggestions for potential areas that can be improved and providing model interpretability. Future work could incorporate more predictive statistics models focusing on improving the models for extreme values by assessing non-linear models, feature engineering methods, and expanding research into less influential variables. The results greatly impact several sections, including aquaculture management, policy-making, and operational strategies, providing valuable insights for stakeholders and decision-makers. Apache Spark was used for data processing and machine learning model implementation; Apache Cassandra was also used for data storage, ensuring efficient large dataset management and SQL tools for structured data handling; Oracle VM VirtualBox for cross-platform virtualization; and Spark Connector was also used.

ANFIS Fuzzy convolutional neural network model for leaf disease detection

Leaf disease detection is critical in agriculture, as it directly impacts crop health, yield, and quality. Early and accurate detection of leaf diseases can prevent the spread of infections, reduce the need for chemical treatments, and minimize crop losses. This not only ensures food security but also supports sustainable farming practices. Effective leaf disease detection systems empower farmers with the knowledge to take timely actions, leading to healthier crops and more efficient resource management. In an era of increasing global food demand and environmental challenges, advanced leaf disease detection technologies are indispensable for modern agriculture. This study presents an innovative approach for detecting pepper bell leaf disease using an ANFIS Fuzzy convolutional neural network (CNN) integrated with local binary pattern (LBP) features. Experiments involve using the models without LBP, as well as, with LBP features. For both sets of experiments, the proposed ANFIS CNN model performs superbly. It shows an accuracy score of 0.8478 without using LBP features while its precision, recall, and F1 scores are 0.8959, 0.9045, and 0.8953, respectively. Incorporating LBP features, the proposed model achieved exceptional performance, with accuracy, precision, recall, and an F1 score of higher than 99%. Comprehensive comparisons with state-of-the-art techniques further highlight the superiority of the proposed method. Additionally, cross-validation was applied to ensure the robustness and reliability of the results. This approach demonstrates a significant advancement in agricultural disease detection, promising enhanced accuracy and efficiency in real-world applications.

Predicting temporomandibular disorders in adults using interpretable machine learning methods: a model development and validation study

IntroductionTemporomandibular disorders (TMD) have a high prevalence and complex etiology. The purpose of this study was to apply a machine learning (ML) approach to identify risk factors for the occurrence of TMD in adults and to develop and validate an interpretable predictive model for the risk of TMD in adults.MethodsA total of 949 adults who underwent oral examinations were enrolled in our study. 5 different ML algorithms were used for model development and comparison, and feature selection was performed by feature importance ranking and feature decreasing methods. Several evaluation indexes, including the area under the receiver-operating-characteristic curve (AUC), were used to compare the predictive performance. The precision-recall curve (PR), calibration curve, and decision curve analysis (DCA) further assessed the accuracy and clinical utility of the model.ResultsThe performance of the random forest (RF) model was the best among the 5 ML models. An interpretable RF model was developed with 7 features (gender, malocclusion, unilateral chewing, chewing hard substances, grinding teeth, clenching teeth, and anxiety). The AUCs of the final model on the training set, internal validation set, and external test set were 0.892, 0.854, and 0.857, respectively. Calibration and DCA curves showed high accuracy and clinical applicability of the model.DiscussionAn efficient and interpretable TMD risk prediction model for adults was successfully developed using the ML method. The model not only has good predictive performance, but also enhances the clinical application value of the model through the SHAP method. This model can provide clinicians with a practical and efficient TMD risk assessment tool that can help them better predict and assess TMD risk in adults, supporting more efficient disease management and targeted medical interventions.

Optimizing anemia management using artificial intelligence for patients undergoing hemodialysis

Patients with end-stage kidney disease (ESKD) frequently experience anemia, and maintaining hemoglobin (Hb) levels within a targeted range using erythropoiesis-stimulating agents (ESAs) is challenging. This study introduces a gated recurrent unit-attention-based module (GAM) for efficient anemia management among patients undergoing chronic dialysis and proposes a novel alert system for anticipating the need for red blood cell transfusions. Data on demographic characteristics, dialysis metrics, drug administration, laboratory tests, and transfusion history were retrospectively collected from patients undergoing hemodialysis at Kangwon National University Hospital between 2017 and 2022. After preprocessing, a final dataset of 252 patients was used for model training. Our model functions in two major phases: (1) Hb level prediction and ESA dose recommendation and (2) transfusion alert framework. The GAM model outperformed traditional machine learning algorithms, including linear regression, XGBoost, and multilayer perceptron, in predicting Hb levels (R-squared value = 0.60). The model also demonstrated a recommendation accuracy of 0.78 compared to that of clinical experts, indicating a high degree of concordance with the ESA dosing recommendations. Additionally, the model exhibited considerably high accuracy (0.99) for transfusion alarms. Thus, the GAM model holds promise for improving anemia management in patients with ESKD by optimizing ESA dosages and providing timely transfusion alerts.

“I’d just love to hear what the community has to say”: Exploring the potential of community-driven vaccine messaging amongst ethnic minority communities

ABSTRACT Community-based communication interventions improve health outcomes and foster positive behavioral changes across diverse populations. However, their potential to support vaccine readiness remains underexplored. This paper addresses this gap by investigating the opinions and preferences of ethnic minority community members who attended a health information session regarding community-delivered vaccine messaging. The study explored the perceptions and willingness to engage in community-led communication efforts about vaccination, focusing on COVID-19 booster vaccines. In-depth interviews were conducted between November and December 2023 with individuals from ethnic minority communities in Central Eastern Sydney who attended an information session about COVID-19 boosters. Four key themes emerged from speaking to this specific group, highlighting diverse communication preferences and intentions. Participants emphasized obtaining vaccine-related knowledge from various sources, particularly through interpersonal discussions. Trusted voices like general practitioners (GPs) and targeted media campaigns were deemed to be crucial. Recommendations included leveraging community champions and establishing a central body for efficient vaccination campaign management in multicultural communities. Personal engagement through community discussions was stressed, with inclusive spaces recommended in various settings. Effective strategies include transparent communication, credible health professional endorsement, and addressing concerns with balanced responses. The findings accentuate the potential effectiveness of grassroots, community-centric initiatives in promoting informed vaccine messaging, countering misinformation, and engaging communities in meaningful health dialogs. Further research on tailored communication and strategic partnerships could enhance this initiative and promote sustained health literacy within ethnic minority communities.

Qualitative evaluation and corrective planning for centrifugal pump maintenance at Soekarno-Hatta airport

This study focuses on enhancing the maintenance processes of centrifugal pumps at Soekarno-Hatta Airport’s Water Treatment Unit in Indonesia, crucial for meeting the clean water needs of the airport, which served around 19.8 million passengers in 2022. Using a qualitative methodology, the research involved focus group discussions with the unit’s operators, technicians, and engineers to pinpoint maintenance challenges and devise solutions. Key findings reveal issues such as insufficient routine maintenance, unplanned repairs, and inadequate staffing, leading to operational disruptions and pump failures. The study highlights the role of Total Productive Maintenance (TPM) in reducing machine breakdowns and improving efficiency. It emphasizes the critical role of centrifugal pumps in the airport’s water supply system. The research proposes several corrective measures, adhering to the 5W + 1H framework, including regular lubrication, bearing replacements, hiring more staff, and advanced training on PLC systems. These actions aim to rectify immediate maintenance problems and establish a foundation for the long-term effectiveness of the pump systems. Conclusively, the study underscores the need for a comprehensive maintenance strategy that aligns with standard operating procedures and preventive maintenance. This approach is essential for boosting the operational performance and reliability of the Water Treatment Unit. It has broader implications for similar infrastructure facilities, underscoring the importance of efficient maintenance management.

Raw Material Management Strategy in The Production Process of Tos-Tos Tortilla Chips (Case Study at PT Dua Kelinci)

PT Dua Kelinci is a company based in Pati that operates in the food industry. The purpose of this study is to analyze the raw material management strategy in the production process within PT Dua Kelinci’s Tos-tos Tortilla Chips division. The focus of this study is the role of the production supervisor in managing raw materials and ensuring efficient processes. This study employed a qualitative descriptive method, utilizing primary data collected through interviews. The data was then analyzed through data reduction, data presentation, and conclusion drawing. The findings of the study indicate that PT Dua Kelinci implements its raw material management strategy by (1) following a production process in accordance with work guidelines that outline monthly food orders, (2) recording input and output materials for accurate tracking, and (3) sourcing materials based on the quantity and variety of food orders, ensuring timely procurement of required flavors. This approach ensures a smooth production process, helps maintain product quality, and fulfills customer demands. Efficient management of raw materials not only reduces the likelihood of production delays but also ensures operational stability and cost control. The study concludes that strategic raw material management is essential for sustaining production efficiency and product quality at PT Dua Kelinci.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

PRINCÍPIO DA EFICIÊNCIA NA ADMINISTRAÇÃO PÚBLICA: ANÁLISE CRÍTICA DO PRINCÍPIO DA EFICIÊNCIA NA GESTÃO PÚBLICA

The main objective of this work is to carry out a critical analysis of the implications and challenges related to the application of the principle of efficiency in Brazilian public management. The tensions between server administration, the search for results, transparency and resource management will be highlighted, using a theoretical approach and emphasizing the applicability of Law. Furthermore, the role of public servants will be highlighted as fundamental to achieving administrative efficiency. The training and engagement of these professionals are essential for public policies to be effective and meet the needs of society. The research will focus on reviewing the literature and analyzing relevant documents to investigate efficiency in public administration in Brazil, contributing to the formulation of more effective and equitable public policies. Adopting an exploratory and applied approach, the research will include bibliographic and documentary analyses, with the aim of clarifying the applicability of the principle of efficiency in public management and its impact on the various sectors of Public Administration, as provided for in the Federal Constitution.