Efficient Management Research Articles

The Role of AI in Data Engineering and Integration in Cloud Computing

This article presents a comprehensive analysis of the transformative role of Artificial Intelligence (AI) in revolutionizing data engineering and integration processes within cloud computing environments. The article examines the implementation of AI-driven solutions across multiple dimensions, including automated pipeline generation, intelligent schema matching, anomaly detection, and real-time data integration. Through a mixed-methods approach incorporating both quantitative and qualitative analyses, the article demonstrates significant improvements in data processing efficiency, with organizations achieving up to 67% reduction in processing time and 89% enhancement in accuracy. The article encompasses case studies from financial services, healthcare, and e-commerce sectors, providing concrete evidence of practical applications and scalability. Key findings reveal that AI-powered systems substantially outperform traditional approaches in cost efficiency, scalability, and data quality management, while simultaneously reducing operational overhead. The article also addresses implementation challenges, including legacy system integration and initial deployment complexities, offering strategic insights for organizations pursuing AI integration in their data engineering workflows. These article contribute to the broader understanding of how AI technologies can be effectively leveraged to address the growing challenges of data management in cloud computing environments, while providing a framework for future developments in this rapidly evolving field.

Management of Fusarium Wilt of Lettuce

Abstract In recent years, Fusarium wilt, caused by Fusarium oxysporum f. sp. lactucae, has become the most important lettuce disease throughout the world. Since the pathogen is seed-transmitted, the disease spreads through infected seeds, favoured by the global commerce. We describe the biology and epidemiology of the pathogen, as well as the differentiation of four physiological races, with their distribution in different geographical areas. The reaction of the lettuce types and varieties to the pathogen are also described, as well as the diagnostic tools available for an appropriate detection of the pathogen, including the races. Like other soil-borne diseases, Fusarium wilt of lettuce is difficult to manage with a single product and/or with a single approach. At present, minimizing crop losses due to Fusarium wilt on lettuce entails the deployment of all available existing disease control tools within an integrated management programme. The first preventative strategy is the use of healthy seeds. Stock seeds should be produced in the areas associated with the lowest disease risk and subjected to standardized health analysis. Furthermore, seeds should be treated with chemical/physical or biological treatments. Among chemical tools, soil disinfestation is becoming very difficult due to the loss of registered fumigants. A careful monitoring of the race situation in the field would be useful for efficient use of genetic means of control. The seed industry is investing in the search of new varieties resistant to Fusarium wilt. Looking forward, the development of commercially acceptable cultivars of desired types of lettuce with high levels of resistance to Fusarium wilt as well as refinement of existing or development of new cultural, chemical and biofungicide tools should lead to more efficient and effective disease management. Information © The Authors 2024

An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models

AbstractThe Engineering Procurement Construction (EPC) contract systems are widely employed in the construction industry. Among the prevalent issues in this sector, cash flow problems frequently lead to decreased productivity and efficiency. To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. Conventional construction models typically optimize only a single objective, such as time, cost, or delay, which may not effectively enhance overall performance. This study aims to develop a claim management system based on IMBOA and EPC principles to optimize multiple objectives, focusing on minimizing project costs and time delays while ensuring high-quality results. The basic methodology of this research involves integrating EPC and claim management principles with the IMBOA algorithm to create an efficient, high-quality system. This process starts with a comprehensive literature review on EPC, claims, MBOA, and related algorithms. Common disputes and claims in the construction industry are examined, and critical factors influencing these claims are identified. The Monarch Butterfly Optimization Algorithm (MBOA) and its improved version (IMBOA) are explored for their application in optimizing project performance. A case study in China's coal mining industry evaluates the effectiveness of the EPC approach, demonstrating that it minimizes time delays and costs. The IMBOA approach proposed in this study has the potential to mitigate 23% of risks and avoid 32% of risks associated with the action plan of China's coal mining industry. Furthermore, comparative analysis with other optimization models indicates that the developed IMBOA model yields superior results, reducing overall project time by 15% and cost by 18%.

What Drives the Top Management Team's Sentiment and Perception of Future Risk and Uncertainty? A Selling-Capability Based View

ABSTRACT Purpose This study postulates that a firm’s level of value appropriation capability (viz. selling capability) plays a pivotal role in influencing a firm’s top management team’s (TMT) sentiment and its perception of future risk and uncertainty. Further, the current study posits that the nexus between firm-level selling capability and the TMT’s sentiment and future risk perception is contingent upon the firm’s industry growth. Design/methodology/approach This research employs a textual analysis-based measure of TMT’s sentiment and perception of future risk and uncertainty and data envelopment analysis (DEA)-based measure of firm-level selling capability, using a sample of 243 USA-based firms during the period 2014 to 2020 and employing an endogeneity-robust econometric modeling technique. Findings The current study demonstrates that selling capability positively affects a firm’s TMT’s sentiment and negatively impacts the TMT’s perception of future risk and uncertainty. Furthermore, the association between selling capability and TMT’s sentiment and future risk perception are accentuated by industry growth. Research limitations/implications Future studies are warranted to unearth how the association between the focal variables changes depending on the effect of other firm-specific internal boundary conditions. Practical implications The results demonstrate sales and marketing managers should develop efficient sales organization and management techniques to bolster the overall firm-level selling capability. Managers should focus on enhancing their firm-level selling capability to maximize the positive impact on the TMT’s sentiment. Originality/value Although studies have shown the effect of managerial sentiment on firm behavior, there is no empirical evidence, to our knowledge, as to what drives TMT’s sentiment and perception of risk and uncertainty. The present treatise shows that a firm’s selling capability and industry growth affect the TMT’s sentiment and the perception of future risk and uncertainty.

Auxiliary rail vehicles – characteristics of the Polish rolling stock for special purpose works based on European Vehicle Number (EVN)

The obligatory National Vehicle Register (NVR) requires that each rail vehicle operating on railway lines be assigned a 12-digit European Vehicle Number (EVN). This enables, among others, efficient data management on rail vehicles. The article presents the coding method of EVN for auxiliary rail vehicles intended for special purpose works. The number of auxiliary rolling stock, average age and technical parameters were also analyzed, taking into account the vehicle subgroups. The analyses show, among other things, that in Poland in 2021, 3737 auxiliary rail vehicles were assigned EVN, of which 1285 vehicles had their own propulsion system. In general, for this type of vehicles, the average age was 38.1 years.

FinTech Adoption and Corporate Greenwashing: A Technology Affordance Perspective

AbstractAffordance theory suggests that technology offers certain opportunities or ‘affordances’ that can be exploited by users. In this context, we are exploring how FinTech adoption provides opportunities to address corporate greenwashing. Drawing on an affordance perspective, we assert that FinTech adoption effectively inhibits corporate greenwashing behaviour, primarily through facilitating green innovation and improving managerial efficiency. We also contend that the impact of FinTech adoption on reducing greenwashing behaviour is not uniform across all industries. It is more pronounced in heavy‐polluting industries, indicating that FinTech has a greater effect in encouraging accurate disclosure of environmental information in environmentally sensitive sectors. Conversely, its impact is weaker in high‐tech industries, which might already have strong environmental commitments. The findings contribute to the literature on sustainability, FinTech and governance.

Smart Fuel Monitoring for Agricultural Fleet Operations Using Tilt Sensors and Cloud Data Processing

This paper presents a novel Internet of Things (IoT)-based fuel monitoring and tracking system designed to address limitations of traditional fuel gauges and enhance fleet management efficiency. The system leverages an accelerometer and Arduino microcontroller to measure fuel level by detecting tilt angles within the tank. This data, along with real-time location from GPS, is transmitted to a cloud platform for processing and visualization. A mobile application, "Tracer," empowers users with insights into fuel level, location on a map, and distance traveled. Graphical fuel consumption patterns further aid in optimization strategies. This solution offers real-time data for improved fuel efficiency, proactive route planning, and overall fleet management. The proposed system is successfully implemented IOT based technology. This sys in Ada fruit IO cloud issued to provide information about vehicle location, fuel level in tank and distance travelled by the vehicle.

Digital transformation, human capital upgrading, and enterprise ESG performance: Evidence from Chinese listed enterprises

Research background: With the rapid development of digital technologies such as artificial intelligence (AI), big data, cloud computing, and blockchain, the deep integration of digital technology and traditional industries offers unprecedented opportunities and has become an important technological force driving the development of enterprise environmental, social, and governance (ESG) performance. Digital transformation increases the complementarity between the production and operation processes of enterprises and highly skilled labour, specifically manifested in the adaptive change of the human capital structure of enterprises in labour relations. Meanwhile, transforming enterprises’ human capital structure also affects ESG performance. Purpose of the article: This study investigates the effects of digital transformation on enterprise ESG performance and its underlying mechanisms of human capital upgrading. First, it combines cutting-edge literature to analyse the theoretical mechanism between digital transformation, human capital upgrading, and enterprise ESG performance, revealing the internal driving factors and implementation paths for improving enterprise ESG performance. Second, we use the data of 3928 listed enterprises in the Shanghai and Shenzhen A-share markets from 2009–2022 to analyse the impact of digital transformation on enterprise ESG performance from the perspective of human capital upgrading as well as the mechanism by which digital transformation affects enterprise ESG performance through human capital upgrading. Third, we provide policy recommendations for optimising the labour structure and enhancing enterprise ESG performance from the perspective of optimising human capital and digital technology applications. Methods: Based on data from 3928 listed enterprises in Chinese A-share markets from 2009-2022, we investigate the effects of digital transformation on enterprise ESG performance and the mechanisms of human capital upgrading. The panel mediation effect model tests the relationships between digital transformation, human capital upgrading, and enterprise ESG performance. Findings & value added: In theory, digital transformation is beneficial for optimising human capital structure, accelerating green technological innovation, and improving management efficiency, thus improving enterprise ESG performance. Mechanism testing shows that digital transformation enhances enterprise ESG performance through human capital upgrading. Heterogeneity analysis shows that the impact of digital transformation on ESG performance is more significant in enterprises that are capital- and technical-intensive, and located in the eastern and central regions. This study examines the microeconomic consequences of digital transformation, contributing to a deeper understanding of the theoretical logic inherent in enterprises’ optimisation of their labour structure. It also provides empirical evidence and decision support for developing industrial talent policies.

Blockchain Integration in Critical Systems Enhancing Transparency, Efficiency, and Real-Time Data Security in Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management

Blockchain technology has garnered significant attention for its potential to revolutionize critical systems by enhancing transparency, efficiency, and data security. This review examines the integration of blockchain in three essential domains: Agile Project Management, Decentralized Finance (DeFi), and Cold Chain Management. By leveraging decentralized ledgers and smart contracts, blockchain provides a robust framework for real-time tracking, data integrity, and automated compliance, addressing long-standing challenges across these sectors. In Agile Project Management, blockchain fosters seamless collaboration and transparent decision-making, minimizing bottlenecks and improving accountability. In DeFi, blockchain strengthens security for digital transactions and identity verification while offering financial autonomy and mitigating fraud risks. Within Cold Chain Management, blockchain ensures traceability, reduces data tampering risks, and enhances visibility throughout supply chain processes, safeguarding temperature-sensitive goods. This paper evaluates existing blockchain-based applications and frameworks, identifies current limitations, and discusses future opportunities for optimizing critical systems through blockchain. The findings highlight blockchain's transformative role in driving operational efficiency, security, and data transparency across diverse applications, providing a roadmap for industries to harness its full potential in critical environments.

Automated Gas Influx Handling Model and Mechanisms During Deep High-Temperature and High-Pressure Well Drilling

The exploration and development of oil and gas resources in deep formations is a key strategic priority for national energy production. However, manual methods for handling gas kicks suffer from low operating accuracy and inefficiency during high-temperature and high-pressure deep well drilling. To address the need for real-time bottomhole pressure prediction and control, an efficient gas–liquid–solid computing model was developed based on the gas slip model and cuttings settling velocity model. By integrating this model with an automatic choke adjustment system, an automatic gas kick attenuation model for deep well drilling was established. Results show that, compared to the driller’s and wait-and-weight methods, the automatic gas kick attenuation method significantly reduces peak choke pressure due to its larger frictional pressure drop and higher cuttings hydrostatic pressure. The automatic attenuation method not only leads to an average reduction of 28.42% in maximum choke/casing pressure but also accelerates gas removal, achieving gas kick attenuation ten times faster than the driller’s method and seven times faster than the wait-and-weight method. The study also investigates the influence of gas solubility, well depth, gas influx volume, formation permeability, and drilling fluid volumetric flow rate on gas kick attenuation characteristics. The findings provide a solid foundation for improving the efficiency of gas kick management in deep well drilling operations.

Patients Monitoring and Data Management System for Hospitals

This work concerns creating a monitoring system for a smart hospital using Raspberry Pi to measure vital signs. The readings are continually sent to central monitoring units outside the room instead of being beside the patients, to ensure less contacting between the medical staff and patients, also the cloud is used for those who leave the hospital, as the design can track on their medical cases. Data presentation and analysis were accomplished by the LabVIEW program. A Graphical User Interface (GUI) has been created by the Virtual-Instrument (VI) of this program that offer real-time access to monitor patients' measurements. If unhealthy states are detected, the design triggers alerts and sends SMS message to the doctor. furthermore, the clinicians can scan a QR code (which is assigned to each patient individually) to access its real-time measurements. The system also utilizes Electrocardiography (ECG) to detect abnormalities and identify specific heart diseases based on its extracted parameters to encourage patients to seek timely medical attention, while aiding doctors in making well-informed decisions. To evaluate the system's performance, it is tested in the hospital on many patients of different ages and diseases as well. According to the results, the accuracy measurement of SpO2 was about 98.39%, 97.7% for (heart rate) and 98.7% for body temperature. This shows that the system can offer many patients receiving health services from various facilities, and it ensures efficient data management, access control, real-time monitoring, and secure patient information aligning with healthcare standards.

Hospital Length-of-Stay Prediction Using Machine Learning Algorithms—A Literature Review

Predicting hospital length of stay is critical for efficient hospital management, enabling proactive resource allocation, the optimization of bed availability, and optimal patient care. This paper explores the potential of machine learning algorithms to revolutionize hospital length-of-stay predictions, contributing to healthcare efficiency and patient care. The main objective is to identify the most effective machine learning algorithm for building a predictive model capable of predicting hospital length of stay. The bibliographic search of the existing literature on machine learning algorithms applied to hospital length of stay predictions highlighted the most relevant papers within this area of research. The papers were analyzed in terms of model types and metrics that contributed to the considerable impact on healthcare decision making. We also discuss the challenges and limitations of machine learning algorithms for predicting length of stay, and the importance of data quality and ethical considerations.

Keterkaitan antara Akuntansi Syariah dan Zakat dalam Manajemen Keuangan

The main objective of this research is to analyse the extent of the application of sharia accounting in the management of zakat in zakat institutions. This research uses a qualitative approach with literature study as the main method. The qualitative approach was chosen because it allows to deeply explore abstract concepts such as Islamic accounting, zakat, and financial management, as well as their interconnections. Based on the findings of this study, it can be concluded that Islamic accounting has a crucial relationship and role in the management of zakat. The application of sharia principles such as al-amana, al-adl, and al-khas in zakat accounting not only ensures transparent and accountable fund management, but also increases public trust in zakat institutions. Good accounting practices such as segregation of funds, preparation of transparent financial statements, and independent audits are key in realising effective and efficient zakat management.

Reconfigurable Acceleration of Neural Networks: A Comprehensive Study of FPGA-based Systems

This paper explores the potential of Field-Programmable Gate Arrays (FPGAs) for accelerating both neural network inference and training. We present a comprehensive analysis of FPGA-based systems, encompassing architecture design, hardware implementation strategies, and performance evaluation. Our study highlights the advantages of FPGAs over traditional CPUs and GPUs for neural network workloads, including their inherent parallelism, reconfigurability, and ability to tailor hardware to specific network needs. We delve into various hardware implementation strategies, from direct mapping to dataflow architectures and specialized hardware blocks, examining their impact on performance. Furthermore, we benchmark FPGA-based systems against traditional platforms, evaluating inference speed, energy efficiency, and memory bandwidth. Finally, we explore emerging trends in FPGA-based neural network acceleration, such as specialized architectures, efficient memory management techniques, and hybrid CPU-FPGA systems. Our analysis underscores the significant potential of FPGAs for accelerating deep learning applications, particularly those requiring high performance, low latency, and energy efficiency.

A Hybrid Technique for Bidirectional Smart Charging of EVs Using BLDC Motor and Bidirectional Converter

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency.

Research on integration technology of architectural CAD and budget estimate

It is particularly important to integrate architectural CAD and budget integration technology in the field of modern construction project management. Faced with the increasing complexity of construction projects, the method of separating design and budget in the past is no longer suitable for the standards of efficient management. Starting from the basic concepts and core technologies of architectural CAD, this article studies the difficulties in data transmission and conversion, as well as the strong demand for integration technology throughout the entire building lifecycle. Analyzing the implementation methods of integrating architectural CAD and budget technology, including data docking, the application of BIM technology, and collaborative work modes, this article explains the positive role of this integration in enhancing the unity of design and budget, improving change response speed, and reducing costs, providing reference for the information development of the construction industry.

The impact of InsurTech on advancing sustainable specialty agricultural product insurance in China

The development of specialized agricultural product insurance is crucial for perfecting China’s agricultural insurance system and promoting rural revitalization. Currently, specialized agricultural product insurance in China faces numerous challenges in key areas such as rate determination, risk assessment, and loss adjustment, which have become bottlenecks restricting its sustainable development. This paper explores how insurance technology (InsurTech) can enhance the ecological system of specialized agricultural product insurance through technological innovation and overcome these industry pain points. It elaborates on how InsurTech can enhance farmers’ interactive experience and satisfaction, improve the management efficiency of insurance companies, strengthen government regulation, and promote the sustainable development of the insurance business. Moreover, this study deeply analyzes the practical barriers in implementing InsurTech and proposes corresponding strategies, including establishing innovative incentive mechanisms, improving relevant policies and regulations, building a multidimensional data platform to achieve data interoperability, and strengthening the construction of digital rural infrastructure to enhance farmers’ technological literacy, thereby advancing the sustainable development of InsurTech in specialized agricultural product insurance.

Enhancing employee performance appraisal through optimized association rule algorithms: a data mining approach.

The planning and development department of the China Yangtze river three gorges engineering development corporation aims to improve its intelligent performance management system in accordance with the company's vision. This paper presents a study on enhancing intelligent performance management in the Planning and Development Department of the China Yangtze River Three Gorges Engineering Development Corporation. By combining data mining techniques with association rule algorithms, a performance prediction model was established. The goal is to explore a performance management system aligned with the company's vision and the specific characteristics of construction enterprises. Experimental results show the effectiveness of the GABPNN model, achieving a prediction error of 5% or less in seven out of seventeen sample points, with a maximum relative error of 20.86%. The suggested performance management system presents a useful strategy for construction companies to improve their performance evaluation procedures, boosting overall management efficiency and aligning with their distinctive traits.

Analisis Efisiensi dan Efektivitas Pengelolaan Dana Desa pada Kantor Desa Kalebarembeng Kecamatan Bontonompo Kabupaten Gowa

This research aims to analyze the efficiency and effectiveness of budget management in Kalebarembeng village, Bontonompo District, Gowa Regency. The type of research used in this research is descriptive quantitative with the formula: Efficiency Ratio and Effectiveness Ratio. The data processed is primary data in the form of a report on the realization of the village income and expenditure budget using Kalebarembeng village government funding sources for the 2019-2023 period. The results of the research show that the level of efficiency in managing the Kalembarembeng village budget is still in the less efficient category, but the level of effectiveness is in the effective category. This situation means that the goals of the Kalebarembeng village government can be achieved but at very high costs.

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.