Efficient Management Research Articles

Solid Waste Collection and Disposal Practices in Bekwarra Local Government Area: Environmental Impacts and Community Awareness

Background: Waste poses a threat to public health and the environment if it is not stored, collected and disposed of properly. This study investigates the different solid waste collection and disposal practices and its effect on the environment in Bekwarra Local Government of Cross River State. Methods: This study utilized a cross sectional descriptive approach where a self-developed questionnaire and an observation checklist to gather relevant information on solid waste disposal practices were used to collect data. A total of 400 female adults were interviewed in this study. Results: A total of 400 participants completed and returned the questionnaires giving a response rate of 99.2%. Results from the study showed that majority of the respondents produced vegetable waste (15%) while a few of the respondents produced food waste (3%). The study also showed that the use of open containers by the indigenes was the most common method practice with 61.8% while the lest method practice was the use of sack bags (2.5%). It was observed that majority of the respondents 364 (91%) had knowledge of solid waste disposal while 36 (9.6%) had no knowledge of solid waste disposal. Results from the observation checklist showed that only 163 (41%) of respondents had storage containers for waste compared to 235 (59%) without storage containers. Conclusions: It is investigated during the research that due to rapid growth in population, increments in solid waste generation rate, management deficiencies, lack of legislative implementation and funding, the solid waste management systems in Bekwarra are not working effectively. Thus, an inefficient municipal solid waste management system may create serious negative environmental impacts like infectious diseases, land and water pollution, obstruction of drains and loss of biodiversity.

Smart Health Records: Integrating AI for Efficient Data Management

The rapid evolution of healthcare demands innovative solutions for managing the vast quantities of data generated daily. This paper examines the concept of Smart Health Records (SHRs) and their integration with Artificial Intelligence (AI) to improve healthcare delivery. SHRs represent an advanced iteration of electronic health records, equipped with AI-powered tools to ensure real-time data sharing, predictive analytics, and automated administrative processes. AI’s capabilities, including natural language processing, machine learning, and predictive modeling, streamline data management and enhance clinical decision-making. However, the transition to SHRs is not without challenges, such as data privacy, algorithm accuracy, and resistance to technological adoption. This paper discusses best practices for implementing AI-driven SHRs and examines future trends, emphasizing their potential to revolutionize healthcare by fostering patient-centered care, improving efficiency, and enabling personalized medicine. Keywords: Smart Health Records, Artificial Intelligence, Electronic Health Records, Healthcare Data Management, Predictive Analytics

Modeling Mobile Applications for Proximity-Based Promotion Delivery to Shopping Centers Using Petri Nets

This article presents the design and implementation of an API that delivers real-time promotional notifications to mobile devices based on their proximity to shopping centers, calculated using the Haversine formula. Developed in Laravel, the API determines whether a mobile device is within a 600 m radius of any registered shopping center, such as Soriana, GranD, and HEB, and sends the relevant promotional information. The system uses Petri nets to model asynchronous behavior, enabling efficient concurrency management between the mobile application and the API. This structure ensures optimized message delivery, preventing communication collisions and delays. The mobile application, developed in Kotlin, integrates geolocation services to capture and update the user’s location in real time. The results indicate an improvement in response time and proximity detection accuracy, highlighting the effectiveness of the Petri net model for systems requiring concurrent interaction. The combination of Laravel, Kotlin, and formal modeling with Petri nets proves to be an effective and scalable solution for proximity-based mobile applications.

Digital Libraries: Challenges and Problems

ABSTRACT: Digital libraries have revolutionized the way information is accessed, managed, and shared. However, despite their potential to provide unprecedented access to vast amounts of digital content, they face numerous challenges that hinder their widespread adoption and effectiveness. This paper explores the key challenges associated with the development and maintenance of digital libraries. One of the primary concerns is the issue of digital preservation, as the rapid pace of technological change risks making digital content obsolete or inaccessible over time. Another significant challenge is ensuring the accessibility and usability of digital libraries for diverse user groups, including individuals with disabilities and those lacking technological proficiency. Furthermore, the integration of various content formats, metadata standards, and systems often creates interoperability issues, complicating the efficient retrieval and management of digital resources. Privacy and copyright concerns also present hurdles, particularly in ensuring that content is accessible while adhering to legal and ethical standards. Finally, the digital divide remains a persistent issue, as unequal access to technology prevents certain populations from benefiting from digital library resources. This paper discusses these challenges in detail and suggests potential strategies for overcoming them, emphasizing the need for collaboration among libraries, technology developers, and policymakers to address these complex issues.

Establishment of an indirect ELISA method for detecting bovine coronavirus antibodies based on N protein

Bovine Coronavirus (BCoV) is a significant pathogen responsible for neonatal calf diarrhea, winter dysentery in adult cattle, and bovine respiratory diseases. Infection with the virus can result in hemorrhagic diarrhea, decreased milk production, and potentially fatal outcomes in cattle, leading to considerable economic repercussions for the cattle industry. Efficient management of BCoV relies on swift and precise detection techniques. CHO cells were utilized to express a secreted recombinant nucleocapsid protein (N), whereby rabbit polyclonal antibodies (pAb) were generated through immunization. An indirect enzyme-linked immunosorbent assay (iELISA) based on N protein was established for the detection of BCoV antibodies. Reaction conditions were optimized using a checkerboard approach, with the optimal antigen concentration at 1.25 μg/mL and the optimal antibody dilution at 1:200, the cutoff value distinguishing negative and positive serum samples was 0.986. The sensitivity test indicated that this rabbit pAb had a maximum dilution of 218 within the assay range, did not cross-react with BHV-1, BVDV, BRV, and BRSV positive serum samples, and shown great specificity. The developed iELISA method and commercial kit were used to test 58 bovine serum samples, and the concordance rate was 94.83%. In summary, we have developed a cost-efficient and precise iELISA method based on N protein that serves as a useful diagnostic tool for BCoV in clinical samples and epidemiological research.

Recurrent patellar dislocation: treatments and challenges

Recurrent Patellar Dislocation (RPD) is a common knee sports injury, mainly affecting pediatric and adolescent populations, posing a significant challenge in orthopedic clinical practice. Although a variety of treatments have been reported, and many of them have shown good initial results, there is a lack of long-term follow-up results. Each treatment method has its own unique characteristics and limitations, and there is no standardized and unified treatment plan. This article provides a comprehensive review of current treatments for RPD. We believe that regardless of the surgical method used, patellar dislocation should not occur at 0°–90° of postoperative knee flexion and extension, and the range of motion should not be limited. Our ultimate goal is to restore patellar stability and improve lower limb alignment, thereby restoring knee function as much as possible. In addition, future treatment options for RPD are also discussed. In the future, there should be more in-depth research on the risk factors and pathogenesis that lead to recurrent patellar dislocation, as well as more randomized controlled trials focusing on different treatment methods. A comprehensive understanding of these is crucial for implementing preventive measures and developing targeted treatment strategies. The goal of this narrative review is to offer clinicians a deeper understanding of RPD treatment, enhance clinical decision-making skills, and encourage personalized and efficient management of RPD treatment.

Nanoparticle-Assisted Phonon Transport Modulation in Si/Ge Heterostructures Using Neuroevolution Potential Machine Learning Models

Abstract Reducing thermal boundary resistance (TBR) is critical for enhancing thermal management efficiency and optimizing the performance of electronic and thermoelectric devices. In this study, we employed non-equilibrium molecular dynamics (NEMD) simulations using Neuroevolution Potential (NEP) machine learning models to investigate how embedding nanoparticles in Si/Ge heterostructures impacts TBR. Our results show a significant reduction in TBR, attributed to enhanced phonon density of states matching via resonance, promoting more efficient elastic phonon transport across the interface. However, this approach also leads to a substantial increase in bulk thermal resistance, highlighting a trade-off where overall heat dissipation is compromised. To address this, we propose an alternative strategy of positioning a nanoparticle directly at the interface to modulate interfacial modes, improving phonon transport efficiency without adversely affecting bulk thermal properties. NEMD simulations validate this approach, showing comparable TBR reduction while mitigating the bulk thermal resistance increase observed with the resonance-based embedding method. This study offers valuable insights into resolving interfacial heat dissipation challenges, providing a balanced strategy for optimizing thermal transport efficiency in nanoscale material systems.

Assessing potential reductions of agricultural GHG in countries with different land productivities: Long-term integrated efficiency in DEA hybrid meta-frontier model.

Globally, the agricultural sector is responsible for the emission of ca. 9.3 gigatons of CO2 equivalent annually. A realistic efficiency model orientation, considering agricultural policy objectives in a given region of the world, is a crucial premise for finding the optimal path for achieving global targets of emission reduction. The main objective of this article is to assess agricultural greenhouse gases (GHG) potential reductions in countries with different farming productivity accounting for food security and economic performance of agricultural sector. The analysis focuses on non-radial slack that, theoretically, may be easily reduced by the improvement in available resource management. The DEA-based hybrid super-efficiency meta frontier model with undesirable output was employed, using the efficiency approach integrating three sustainability dimensions. The dataset consisted of data from 99 countries (2005-2018) divided into clusters. Several potential model orientations were tested and discussed with regard to agricultural policy objectives. It was found that by reducing slacks, agricultural emissions can be decreased by 0.74 Gt of CO2eq per year. Hence, removing only management inefficiencies would help achieve up to 80% of the global reduction targets in agriculture without a substantial technological change. However, the efficiency change component turned out to be mainly negative over the period studied; thus, a specific focus on agricultural policy is needed in terms of supporting farmers with a more rational use of their resources.

IMPLEMENTASI METODE RAPID APPLICATION DEVELOPMENT DAN PENGUJIAN KATALON PADA APLIKASI LAPORAN BARANG RUSAK

The management of State-Owned Assets (BMN) within the Secretariat General of the National Resilience Council faces challenges in recording and reporting damaged assets. The current manual process leads to delays, data inaccuracies, and hinders effective asset maintenance. This study aims to design and develop the Labarus (Laporan Barang Rusak) application as a digital solution to support faster and more accurate recording and monitoring of damaged BMN assets. The Rapid Application Development (RAD) method was used to accelerate the application development process through an iterative approach that allows for prototyping and immediate validation by users. Application testing was conducted using Katalon to ensure functional compliance with requirements and compatibility across various devices. The outcome of this research is a web-based application that enables users to report damaged goods online, track the status of repairs, and automatically generate damage and repair reports. This application reduce recording errors, speed up the repair process, and support the ongoing digital transformation in Setjen Wantannas. This research contributes by developing a web-based application called LABARUS (Laporan Barang Rusak) to replace the slow and error-prone manual processes, thereby enhancing the efficiency and accuracy of asset management in the Setjen Wantannas.

Optimizing Fruit Production: A Review of Sensor-based Approaches in Orchard Management

Fruit crops produce higher yields per hectare than other horticultural crops. Fruits are also a rich source of vitamins, minerals, and antioxidants. It is necessary to monitor and maintain the orchard properly to increase both production and quality of fruits. However, maintaining large orchards is quite difficult. The application of different sensors in fruit crops helps in precise orchard management and, therefore, helps in sustainable production in the long run. Remote sensing tools are useful in gathering data on factors necessary and act as a constraint for increasing the yield potential of fruit crops. An increase in the cost incurred in maintaining the orchards resulted in a low benefit cost ratio in the production of fruits. The application of sensors for the continuous monitoring of orchard facilitates in increased water use efficiency and precise orchard management strategies. This article reviews the use of sensors in various aspects related to fruits crops management.

The Data Artifacts Glossary: a community-based repository for bias on health datasets

BackgroundThe deployment of Artificial Intelligence (AI) in healthcare has the potential to transform patient care through improved diagnostics, personalized treatment plans, and more efficient resource management. However, the effectiveness and fairness of AI are critically dependent on the data it learns from. Biased datasets can lead to AI outputs that perpetuate disparities, particularly affecting social minorities and marginalized groups.ObjectiveThis paper introduces the “Data Artifacts Glossary”, a dynamic, open-source framework designed to systematically document and update potential biases in healthcare datasets. The aim is to provide a comprehensive tool that enhances the transparency and accuracy of AI applications in healthcare and contributes to understanding and addressing health inequities.MethodsUtilizing a methodology inspired by the Delphi method, a diverse team of experts conducted iterative rounds of discussions and literature reviews. The team synthesized insights to develop a comprehensive list of bias categories and designed the glossary’s structure. The Data Artifacts Glossary was piloted using the MIMIC-IV dataset to validate its utility and structure.ResultsThe Data Artifacts Glossary adopts a collaborative approach modeled on successful open-source projects like Linux and Python. Hosted on GitHub, it utilizes robust version control and collaborative features, allowing stakeholders from diverse backgrounds to contribute. Through a rigorous peer review process managed by community members, the glossary ensures the continual refinement and accuracy of its contents. The implementation of the Data Artifacts Glossary with the MIMIC-IV dataset illustrates its utility. It categorizes biases, and facilitates their identification and understanding.ConclusionThe Data Artifacts Glossary serves as a vital resource for enhancing the integrity of AI applications in healthcare by providing a mechanism to recognize and mitigate dataset biases before they impact AI outputs. It not only aids in avoiding bias in model development but also contributes to understanding and addressing the root causes of health disparities.

Optimizing Battery Maintenance and Reliability in Ground Control Station for Tethered High-Altitude Platforms

Abstract This paper presents an integrated system for ensuring uninterrupted power supply to tethered high-altitude platform systems (HAPS) by strategically managing the repair and replenishment of batteries in a k-out-of-n:G, COLD system. We assume that the batteries are identical and their lifetimes are independent of each other and exponentially distributed. The batteries deteriorate independently due to failures and await repair. The repair facility is activated when the number of working batteries decreases to L (L<n), and a replenishment order for n−k+1 batteries is placed when the number of operational batteries falls to N (N<L). We derive an explicit solution for the system-state probability and analyze key performance measures. Furthermore, we employ the particle swarm optimization (PSO) algorithm to determine the optimal cost for the proposed optimization problem and use the Morris method for sensitivity analysis. The results provide insights into efficient battery management strategies for HAPS, ensuring reliable power supply while minimizing costs.

IMPLEMENTASI BAHASA PEMPROGRAMAN PHP DAN MYSQL DALAM PERANCANGAN APLIKASI TICKET CENTER RESERVATION PO. NPM

The rapid development of information technology today significantly impacts various sectors, including business, to compete more effectively. One of the important applications of technology is the development of a web-based information system for managing bus departure schedules and ticket reservations. This research aims to design and implement a web-based ticket reservation system for PO. NPM in Bukittinggi City, in order to improve efficiency and address data redundancy issues that occurred in the previous manual system. The system design method used is the Waterfall method. This system is built using PHP as the server-side programming language and MySQL as the database to store ticket reservation data and bus departure schedules. The result of this research is a web-based ticket reservation system that allows passengers to book tickets online without having to visit the ticket counter. The implementation of the web-based information system at PO. NPM can improve passenger comfort, operational efficiency, and better data management. This system also contributes to improving service quality and reducing the potential for errors in data processing.

Enhancing Patient Management Effectiveness: The Roles of Transformational Leadership, Safety Culture, Employee Satisfaction, and Service Digitalization in the Digital Healthcare Era

The effectiveness of patient management in hospitals is influenced by transformational leadership, patient safety culture, and employee satisfaction. In the digital era, technology plays a crucial role in improving managerial efficiency. This study examines the relationship between these factors and patient management effectiveness, with service digitalization as a moderating variable. This study aims to analyze the influence of transformational leadership, patient safety culture, and employee satisfaction on patient management effectiveness while exploring the role of service digitalization as a moderator. An explanatory approach with a quantitative research design was used. Data were collected through a Likert-scale questionnaire from 207 employees of Dian Husada Hospital Mojokerto and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings show that employee satisfaction positively affects patient management effectiveness, while patient safety culture has a negative influence. Transformational leadership positively impacts employee satisfaction but does not directly affect patient management effectiveness. Meanwhile, service digitalization does not show a significant moderating effect. These results suggest that hospitals should focus on improving employee satisfaction and managing safety culture effectively. Strengthening transformational leadership is essential to creating a supportive work environment, while optimizing service digitalization can enhance employee performance. This study fills a gap in the literature on factors influencing patient management effectiveness and the role of digitalization in hospital settings. However, its cross-sectional design and limited hospital sample restrict generalizability. Future studies should use longitudinal approaches with broader samples for better external validity.

Demand Forecasting and Allocation Optimization of Green Power Grid Supply Chain Based on Machine Learning Algorithm: A Study Based on the Whole-Process Data of Power Grid Materials

The efficient management of the green power grid supply chain is of great significance in addressing global energy transformation and achieving sustainable development goals. However, traditional methods struggle to effectively cope with the complexity and dynamics of demand forecasting and the multi-objective optimization problems in material allocation. In response to this challenge, this paper proposes a machine-learning-based demand forecasting and allocation optimization method, aiming to improve the management efficiency of the supply chain and reduce environmental impacts. First, based on the whole-process data of power grid materials, a multi-model fusion strategy is adopted for demand forecasting. By combining machine learning models such as long short-term memory (LSTM), extreme gradient boosting (XGBoost), and random forest, the prediction accuracy and the generalization ability of the model are significantly improved. Moreover, a “distributed collaborative optimization algorithm” is proposed. By decomposing the power grid regions, this paper optimizes transportation routes and inventory management, and comprehensively reduces transportation, inventory, and environmental protection costs while taking into account the real-time requirements in a complex supply chain environment. Finally, an empirical analysis is carried out in combination with the optimized allocation plan, verifying the practical effectiveness of the proposed method. The results indicate that the optimized scheme significantly outperforms the traditional method in terms of total cost, transportation efficiency, and carbon emissions. Specifically, the optimized scheme achieves a 13% reduction in transportation costs, a 10% decrease in inventory costs, and a 25% cut in environmental protection expenses. Additionally, it decreases transportation-related carbon emissions by approximately 250 tons. The demand forecasting and allocation optimization method based on machine learning has obvious economic and environmental advantages in the green power grid material supply chain. By effectively integrating various algorithms, this paper enhances the accuracy and stability of material management while substantially reducing operating costs and carbon emissions. This is in line with the sustainable goals of green power grid development. The paper provides an optimized framework with practical value for managing the green supply chain in the power grid industry.

A new principal in a low-performing school and school members’ reactions: an organizational socialization perspective

PurposeImproving the management efficiency of new principals in low-performing schools has important practical value and theoretical significance. Based on organizational socialization theory, this study adopts a single case study method to analyze in-depth the organizational socialization process of a new principal after taking over a low-performing school.Design/methodology/approachThrough three years of longitudinal data analysis in a Chinese school, the study establishes four stages that the new principal experienced after taking office. Then, it analyzes the interaction between the new principal and school members (vice-principals, department heads and teachers) in each stage.FindingsThree main determinants can be perceived to affect the effectiveness of the new principal: the former principal’s imprint, school members’ internal contagion and school members’ demand contradiction. The study further shows that the new principal needed to pay attention to school members’ attitudes toward himself and the former principal as well as focus on building relationships with management.Originality/valueThis study expanded the analysis from teachers to school members (including vice-principals and department heads) to comprehensively analyze the organizational socialization of a new principal.

Hyperledger Fabric for the (digitalized) lifecycle of construction products: Applied review on fastening technology

AbstractBlockchain technology is a digital decentralized data ledger recording transactions in an encrypted format. Its implementation can potentially hold significant advantages for the built environment, particularly in manufacturing and building product usage aligned with Building Information Modeling (BIM). This paradigm shift toward decentralized transactions can foster security, reliability, and accountability. Hyperledger Fabric (HLF), an enterprise‐grade distributed ledger, offers a modular, scalable, and confidential digital framework. This article introduces HLF‐based workflows to address inefficiencies in BIM and fastener product lifecycle management, such as fragmented data handling and limited process automation. Leveraging chaincodes linked to BIM models, HLF simplifies, enhances transparency, and automates construction product lifecycle processes. Contract data and execution details are managed through a blockchain stored in a common data environment (CDE) and linked to chaincodes. The article presents the conceptualization and implementation of automated workflows, emphasizing efficiency and transparency. While showcasing successful deployments, it also highlights areas for future improvement and development. The proposed framework represents a pioneering step toward a decentralized cooperative environment in the construction industry, aligning with the transformative potential of blockchain technology.

Maslahah sebagai Landasan Transformasi Undang-Undang Wakaf di Indonesia: Analisis Filosofis terhadap Kesejahteraan Sosial

This research aims to analyze the application of the maslahah principle in the transformation of waqf law in Indonesia and its impact on social welfare. the importance of effective and sustainable waqf management to improve the quality of life of the community. The research method used is a qualitative approach with normative analysis, which examines the regulations and practices of waqf management in Indonesia. The research results show that the maslahah principle is applied in the transformation of waqf law in Indonesia through integration with various policies and regulations governing the management of waqf assets. Law Number 41 of 2004 concerning Waqf has integrated the principle of maslahah by determining that the management of waqf assets must be carried out in the public interest and can provide maximum benefits for the community. The impact of the maslahah philosophy on social welfare in the context of waqf is very significant, where the application of this principle has increased the effectiveness and efficiency of waqf asset management, so that it can be used for various social programs such as education, health and social infrastructure. Real examples such as productive waqf management, istibdal of waqf assets, and the formation of the Indonesian Waqf Board (BWI) show how the principle of maslahah can directly improve community welfare.

Optimizing Irrigation and Nitrogen Application for Greenhouse Tomato Using the DSSAT–CROPGRO–Tomato Model

The aim of this study was to optimize water-saving and high-efficiency irrigation and nitrogen application scheduling for greenhouse tomato cultivation in North China. Using experimental data on water and nitrogen inputs, the DSSAT-GLUE parameter adjustment tool was employed to calibrate the genetic parameters of the DSSAT–CROPGRO–Tomato model. Simulations were conducted to assess greenhouse tomato growth, water use, and yield under varying water and nitrogen conditions. After calibration, the model showed average relative errors of 3.19% for the phenological stages, 3.33% for plant height, and 4.52% for yield dry weight, meeting accuracy standards. The results from the calibrated model indicated that increasing irrigation or nitrogen levels initially enhanced yield but led to diminishing returns beyond optimal ranges. The maximum tomato yield and water–nitrogen use efficiency were achieved with irrigation quotas between 320 and 340 mm and nitrogen applications between 360 and 400 kg·ha−1. These findings provide a guideline for efficient water and nitrogen management for greenhouse tomatoes under drip irrigation conditions.

Environmental Influences on Seed Germination and Seedling Emergence in Four Echinochloa Taxa

Echinochloa species are troublesome weeds that cause serious problems in paddy fields. Due to the fact that the genus Echinochloa comprises numerous species and subspecies, research on its seed germination and emergence ecology is still insufficient. In this study, the influence of varying temperatures; light, osmotic, and saline conditions; and depth of seed burial on the germination of Echinochloa seeds and the emergence of seedlings was determined through laboratory and pot tests: E. crus-galli var. crus-galli, E. crus-galli var. mitis, E. crus-galli var. praticola, and E. caudata. Seed germination of the constant temperatures in the four Echinochloa taxa was examined between 15 and 40 °C, and optimum germination occurred over the following temperature ranges: 25‒35 °C for E. crus-galli var. crus-galli; 15‒25 °C for E. crus-galli var. mitis; 15‒40 °C for E. crus-galli var. praticola; and 15‒35 °C for E. caudata. Fluctuating temperatures were conducive to seed germination in all four Echinochloa taxa. Except for E. crus-galli var. crus-galli and E. crus-galli var. mitis exposed to very acidic conditions (pH = 4), germination of seedlings from the four taxa of Echinochloa was not evidently affected by pH or light. Seed germination of the four Echinochloa taxa decreased as water stress decreased (<−0.2 MPa); however, it occurred across a wide spectrum of salt concentrations (0–320 mM NaCl). The seeds that were placed 0–0.5 cm below the surface had the highest rate of seedling emergence, which declined gradually as burial depth increased. This result demonstrates that deep tillage is an efficient management method for decreasing the seedling emergence of various weed species. This study’s findings will enhance our comprehension of the conditions necessary for the germination and emergence of Echinochloa seeds, as well as furnish information that may assist in managing its growth.