Management Technology Research Articles

Predicting sensitivity to glucose in metal sulfides: A structural and surface characterization study

The increasing prevalence of diabetes is leveraging the development of improved management technologies, including non-enzymatic glucose sensors that offer enhanced stability and longer operation times. However, the lack of reliable methods to predict materials' electrocatalytic activity remains a significant barrier to their advancement. Herein, we obtained transition metal sulfides (CuS, Ag2S, FeS2, and α-NiS) with similar shapes and sizes using wet chemical methods. Detailed structural and surface characterization revealed the presence of metals in mixed oxidation states and the formation of disulfides and polysulfides on the surface of air-exposed materials. The optical measurements supported by theoretical calculations indicated band gaps of 1.74, 0.98, and 1.14 eV in CuS, FeS2, and Ag2S, respectively. Nickel sulfide exhibited an intraband transition at 1.42 eV. The electrocatalytic activity toward glucose was investigated using cyclic voltammetry and chronamperometry. The lowest oxidation potential of 615 mV showed α-NiS, whereas the highest – Ag2S (860 mV). The sensitivity determined in chronoamperometry measurements increased in the order of α-NiS>CuS>Ag2S>FeS2. Furthermore, selectivity studies revealed that FeS2 responded only to strong reductants, while α-NiS to all examined electroactive species. In the last step, a clear relationship was identified between the d-band center position (dbc) in metal sulfides and their electrochemical performance. The dbc relative to the Fermi level is optimal in CuS and NiS (around −2 eV) for forming bonds with glucose and intermediates, while in Ag2S and FeS2, it lays too far or too close for effective electrooxidation.

Internet of Things-enhanced blockchain technology: an overview of advancements and prospects

Through a systematic review of the literature, this study provides a comprehensive summary of the latest findings on the acceptance and application of blockchain technology in supply chain management. It was founded on a comprehensive analysis of thirty studies published in prestigious journals. The need for secure, decentralized AI systems to thwart potential cyberattacks is growing as AI is increasingly used in cybersecurity. The ideal choice for enhancing the security and privacy of AI systems is blockchain technology (BT), due to its immutable and decentralized data storage properties. This comprehensive literature review focuses on the integration of decentralized IT with cybersecurity. It provides a comprehensive BT and IoT cybersecurity taxonomy to get you started. This work contributes to a more sophisticated understanding of the integration between BT and IoT by demonstrating compelling results that highlight the benefits of these connections for cybersecurity. These findings are coupled with the potential cybersecurity advantages and difficulties of IoT. It also addresses the beneficial uses of blockchain-enabled IoT to solve cybersecurity problems. Finally, this paper addresses future directions for blockchain-based cybersecurity research across a range of sectors, including industry, agriculture, IoT, and health.

A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

A Perspective of Solid-State Fermentation As Emergent Technology for Organic Waste Management in the Framework of Circular Bioeconomy

Analysis of the Role of Judicial Institutions in Sharia Economic Law Enforcement in Indonesia

This study aims to analyze the concrete role of judicial institutions in the enforcement of sharia economic law in Indonesia. The main focus is to understand how the Religious Courts, District Courts, and Supreme Courts handle sharia economic cases and identify the challenges faced in the process. The research method used is a qualitative approach with a case study design. Data were obtained through in-depth interviews, observations, literature studies, and documentation analysis. The results of the study show that the Religious Court has a central role in the application of sharia principles in banking, insurance, and sharia capital market cases. Collaboration between Religious Courts and District Courts is important in handling complex cases involving aspects of national law and sharia. However, several challenges such as the lack of special training for judges, limited resources, and unclear regulations are still the main obstacles in the enforcement of sharia economic law. This research makes a theoretical contribution by enriching the literature on sharia economic law and practicality by providing recommendations to increase the effectiveness of sharia economic law enforcement. The main recommendations include increasing training and capacity building of judges, harmonization of regulations, and the use of information technology in case management. Thus, this research is expected to support the creation of a fairer and more effective legal system in the enforcement of sharia economic law in Indonesia.

Functional Bio-Based Polymeric Hydrogels for Wastewater Treatment: From Remediation to Sensing Applications.

In recent years, many researchers have focused on designing hydrogels with specific functional groups that exhibit high affinity for various contaminants, such as heavy metals, organic pollutants, pathogens, or nutrients, or environmental parameters. Novel approaches, including cross-linking strategies and the use of nanomaterials, have been employed to enhance the structural integrity and performance of the desired hydrogels. The evolution of these hydrogels is further highlighted, with an emphasis on fine-tuning features, including water absorption capacity, environmental pollutant/factor sensing and selectivity, and recyclability. Furthermore, this review investigates the emerging topic of stimuli-responsive smart hydrogels, underscoring their potential in both sorption and detection of water pollutants. By critically assessing a wide range of studies, this review not only synthesizes existing knowledge, but also identifies advantages and limitations, and describes future research directions in the field of chemically engineered hydrogels for water purification and monitoring with a low environmental impact as an important resource for chemists and multidisciplinary researchers, leading to improvements in sustainable water management technology.

Fishing Vessel Type Recognition Based on Semantic Feature Vector

Identifying fishing vessel types with artificial intelligence has become a key technology in marine resource management. However, classical feature modeling lacks the ability to express time series features, and the feature extraction is insufficient. Hence, this work focuses on the identification of trawlers, gillnetters, and purse seiners based on semantic feature vectors. First, we extract trajectories from massive and complex historical Vessel Monitoring System data that contain a large amount of dirty data and then extract the semantic features of fishing vessel trajectories. Finally, we input the semantic feature vectors into the LightGBM classification model for classification of fishing vessel types. In this experiment, the F1 measure of our proposed method on the East China Sea fishing vessel dataset reached 96.25, which was 6.82% higher than that of the classical feature-modeling method based on fishing vessel trajectories. Experiments show that this method is accurate and effective for the classification of fishing vessels.

Enhancing transparency and security in blood donation management through blockchain technology

In the context of the increasing importance of digitalization and cybersecurity, exploring the application of blockchain and smart contract technologies in blood donation management aims to enhance data transparency and privacy protection, addressing challenges in current healthcare systems. This paper explores the transformative potential of blockchain and smart contract technologies in digitalizing the blood donation process. By leveraging the Ethereum platform and employing a Browser/Server (B/S) architecture integrated with Solidity programming and web development practices, this paper proposes a novel framework designed to enhance the transparency, digitalization, and shared accessibility of blood donation data. The implementation of this system within a healthcare context promises to streamline the donation process, ensuring the integrity and confidentiality of donor data, thereby fostering trust among all stakeholders involved. The findings indicate that the application of blockchain technology not only facilitates a more efficient and secure management of blood donation records but also sets a precedent for future healthcare innovations.

Digital transformation for safer circular lithium-ion battery supply chains: a blockchain ecosystem-data perspective

Lithium-ion battery (LIB) circular supply chains (CSCs) present unique safety challenges among operation processes. Blockchain technology can be a promising solution for addressing these challenges, by enabling effective tracking and verification of safety-related information throughout the supply chain. However, how blockchain can mitigate safety issues from a supply chain perspective is poorly understood. This study proposes a theory-supported framework through intervention-based research (IBR) to provide guidance for LIB CSC safety management. The conceptual framework and theoretical propositions set the foundation for research on a comprehensive blockchain ecosystem specifically designed for CSC safety management. The framework includes the design of a blockchain architecture with capabilities tailored to address safety concerns, the involvement of multiple stakeholders, and the development of a safety measurement matrix. This study suggests research and practical directions which lay the groundwork for leveraging blockchain technology to improve safety management in LIB CSC. This research contributes to sustainable supply chains by proposing a conceptual framework for mitigating safety concerns in LIB CSC, paving the way for effective blockchain implementation. In addition, this study contributes to advancing the theoretical design understanding and application of blockchain technology in CSC safety management.

KUALITAS SISTEM INFORMASI MANAJEMEN DAN EFEKTIVITAS PENGAMBILAN KEPUTUSAN PADA PERUSAHAAN KONSTRUKSI DI JAWA BARAT DARI PERSPEKTIF: TEKNOLOGI INFORMASI, MANAJEMEN PENGETAHUAN DAN PENGENDALIAN INTERNAL

Construction companies in carrying out their activities require management information systems and technology in decision-making. The purpose of this research is to know and analyze the impact of information technology, knowledge management, and internal control on the quality of management information systems, which implies the effectiveness of decision-making for construction companies in West Java, both simultaneously and partially. This study employs a quantitative, cross-sectional research method. The research population is 1160 construction companies in West Java, with a sample of 150 construction companies in West Java consisting of 3 observation units, namely project managers, site engineers, and site managers. The research findings reveal that information technology not only supports but also significantly enhances the quality of management information systems. Furthermore, knowledge management directly influences the improvement of a good quality management information system, which means that knowledge management has a direct and significant positive impact on the quality management information system. Information technology, knowledge management, and internal control can enhance effective decision-making through quality management information systems.

Constraints Associated with the Adoption of Sustainable Cassava Weed Management Technology for Cassava Systems in Nigeria

The study investigated the constraints associated with the adoption of sustainable cassava weeds management technology for cassava systems (SCWMTCS) in Nigeria. Multistage was utilized in selecting 384 respondents for the study. Descriptive and inferential statistics were used to analyse the data collected. Results revealed that respondents have varied perceptions of the severity of constraints encountered in their process of adoption of SCWMTCS in the study area. Constraints comprising of frequent gluts in cassava market frustrates farmers, lack of processing equipment and poor value chain system for cassava products and frequent encroachments or destructive grazing of cassava farms by nomadic herders is very frustrating were rated high severity in the study area. Also, 17 constraints were rated to be moderately severe in the study area out of the 26 listed constraints encountered by cassava farmers.

Unveiling the Potential: Remote Monitoring and Telemedicine in Shaping the Future of Heart Failure Management.

Heart failure (HF) remains a significant burden on global healthcare systems, necessitating innovative approaches for its management. This manuscript critically evaluates the role of remote monitoring and telemedicine in revolutionizing HF care delivery. Drawing upon a synthesis of current literature and clinical practices, it delineates the pivotal benefits, challenges, and personalized strategies associated with these technologies in HF management. The analysis highlights the potential of remote monitoring and telemedicine in facilitating timely interventions, enhancing patient engagement, and optimizing treatment adherence, thereby ameliorating clinical outcomes. However, technical intricacies, regulatory frameworks, and socioeconomic factors pose formidable hurdles to widespread adoption. The manuscript emphasizes the imperative of tailored interventions, leveraging advancements in artificial intelligence and machine learning, to address individual patient needs effectively. Looking forward, sustained innovation, interdisciplinary collaboration, and strategic investment are advocated to realize the transformative potential of remote monitoring and telemedicine in HF management, thereby advancing patient-centric care paradigms and optimizing healthcare resource allocation.

Leveraging technology for improved contract management in the energy sector

Effective contract management is critical for the energy sector, where complex agreements and regulatory requirements demand precision and oversight. Leveraging technology for improved contract management can transform how energy companies manage their contracts, enhancing efficiency, compliance, and strategic alignment. This paper explores the impact of technological advancements on contract management processes in the energy sector, emphasizing digital solutions and automation. The energy sector deals with multifaceted contracts involving various stakeholders, including suppliers, contractors, regulatory bodies, and customers. Traditional contract management methods, often characterized by manual processes and paper-based documentation, are prone to errors, delays, and inefficiencies. Technology, particularly contract lifecycle management (CLM) software, offers comprehensive solutions to these challenges by digitizing and automating contract management processes. CLM software facilitates the entire contract lifecycle, from drafting and negotiation to execution and renewal. These platforms provide centralized repositories for all contract documents, ensuring easy access and retrieval. Advanced features such as automated alerts and notifications for key dates and obligations help companies stay compliant with contractual and regulatory requirements, reducing the risk of penalties and legal disputes. Moreover, artificial intelligence (AI) and machine learning (ML) capabilities integrated into CLM solutions enable intelligent contract analysis and risk assessment. AI-driven tools can extract critical data from contracts, identify potential risks, and suggest mitigative actions. This predictive insight enhances decision-making, allowing energy companies to proactively address issues before they escalate. Blockchain technology also holds significant potential for contract management in the energy sector. Smart contracts, enabled by blockchain, offer a secure and transparent way to automate contractual obligations. These self-executing contracts reduce the need for intermediaries and enhance trust among parties, ensuring that terms are met efficiently and without dispute. In addition to these technologies, cloud-based platforms offer scalability and flexibility, allowing energy companies to manage contracts remotely and collaboratively. This is particularly beneficial in an industry where projects span multiple locations and jurisdictions. In conclusion, leveraging technology for contract management in the energy sector results in streamlined processes, improved compliance, and enhanced strategic alignment. By adopting digital solutions and automation, energy companies can mitigate risks, reduce costs, and drive operational efficiency, ultimately contributing to their sustainability and competitiveness in a rapidly evolving market. Keywords: Leveraging, Technology, Energy Sector, Contract Management, Improved.

Enhancing sustainability in electric mobility: Exploring blockchain applications for secure EV charging and energy management

The global shift towards reducing carbon emissions has spurred a growing interest in electric vehicles (EVs) over traditional fossil fuel-based internal combustion engines. Concurrently, blockchain technology has emerged as a transformative solution across various sectors, characterized by its decentralized database and distributed ledger. Its notable attributes, including transparency, immutability, traceability, security, auditability, and authenticity, have led major companies to recognize its potential integration into the EV sector. As a result, there has been a steady increase in case studies and research initiatives exploring blockchain applications in EVs. This article provides a detailed overview of the use of blockchain in the EV domain, focusing on the challenges and applications in four key areas: EV charging infrastructure, energy trading, supply chain management, and energy management. Initially, the basic concepts of blockchain technology are briefly explained, followed by its applications in different fields. The study presents the opportunities and crucial requirements for implementing blockchain technology for secure energy trading, charging, energy management, and supply chain operations. While blockchain can improve security and efficiency in the EV domain, it remains vulnerable to various attacks. This article explores these attacks and other potential challenges in integrating blockchain technology in the EV sector. The main findings of this rEView provide insights into the importance of blockchain in the EV sector and underscore the need to overcome existing challenges for the widespread adoption of this technology in the near future.

Influencing heat transfer and drag in magneto-hydrodynamic non-Newtonian fluids with polymer additives: A novel theoretical approach

This article examines the impact of polymers on boundary layer flow and heat transfer in non-Newtonian fluids over a magnetized stretching surface. Using the Oldroyd-B model, Reiner-Philippoff fluid stress deformation, and Maxwell’s equations, the study explores polymer behavior in a magnetic field. Similarity transformations are applied to derive the governing equations, which are solved numerically. The effects of polymers and magnetic fields on drag and heat transfer are illustrated graphically. The analysis highlights that introducing polymers increases skin drag and the Nusselt number while reducing the magnetic flux coefficient. Higher polymer concentrations enhance drag and heat transfer. The magnetic field significantly improves heat transfer and reduces skin drag. The research offers valuable insights into the optimal use of polymer additives to modify heat transfer and drag in non-Newtonian fluid flow, contributing to advancements in fluid dynamics and thermal management technologies.

Eksplorasi Potensi Penggunaan Blockchain Dalam Optimalisasi Manajemen Pelabuhan di Indonesia: Tinjauan Literatur

This research explores the potential use of Blockchain technology to optimize port management in Indonesia, which is a crucial element in the global supply chain. The research method employed is a literature review, with analysis and synthesis of information to understand Blockchain's contribution to improving port management. The identified potential uses of Blockchain include real-time logistics tracking and monitoring, data validation and authentication, increased transparency and trust, and port process efficiency. The study also highlights the use of smart technology and smart contracts in port management, as well as challenges in adopting Blockchain technology. A comprehensive conceptual framework is proposed for the application of Blockchain in Indonesia's maritime industry, including a feasibility analysis framework to assess implementation readiness. The results of this literature review are expected to provide deep insights and serve as a foundation for further research and assist stakeholders in making decisions related to the application of Blockchain technology in port management.

A Critical Review of Emerging Technologies for Flash Flood Prediction: Examining Artificial Intelligence, Machine Learning, Internet of Things, Cloud Computing, and Robotics Techniques

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a prospective survey.

Radiologists encounter significant challenges when segmenting and determining brain tumors in patients because this information assists in treatment planning. The utilization of artificial intelligence (AI), especially deep learning (DL), has emerged as a useful tool in healthcare, aiding radiologists in their diagnostic processes. This empowers radiologists to understand the biology of tumors better and provide personalized care to patients with brain tumors. The segmentation of brain tumors using multi-modal magnetic resonance imaging (MRI) images has received considerable attention. In this survey, we first discuss multi-modal and available magnetic resonance imaging modalities and their properties. Subsequently, we discuss the most recent DL-based models for brain tumor segmentation using multi-modal MRI. We divide this section into three parts based on the architecture: the first is for models that use the backbone of convolutional neural networks (CNN), the second is for vision transformer-based models, and the third is for hybrid models that use both convolutional neural networks and transformer in the architecture. In addition, in-depth statistical analysis is performed of the recent publication, frequently used datasets, and evaluation metrics for segmentation tasks. Finally, open research challenges are identified and suggested promising future directions for brain tumor segmentation to improve diagnostic accuracy and treatment outcomes for patients with brain tumors. This aligns with public health goals to use health technologies for better healthcare delivery and population health management.

Sustainable waste management solutions: Optimization of hybrid and gasification waste-to-energy plants

This study investigates an urban waste management model to integrate different energy generation technologies and waste processing routes in a megastructure with thermal, biological, and recyclable superstructures. The model was simulated for three metropolitan regions in Brazil, with waste transportation between existing landfills to improve energy generation efficiency and financial income. The gasification appears as a promising alternative to the originally modeled hybrid incineration configuration. Results suggest that the overall financial performance of plants benefits when the utilization of the thermal superstructure is restricted in smaller plants, with waste mass flow of less than 6 kg/s. Gasification is preferred for bigger plants, with paybacks of up to 11 years, net present value of 2 billion dollars, and an internal rate of return of 20.32 % in the most likely scenario without subsidies. The study shows significant increase in attractiveness by the utilization of waste-collecting taxes, for which a payback of 6 years was reached, demonstrating it as a financially interesting. The internal rate of return results confirms the economic feasibility of the business for scenarios with waste-collection taxes. Integrating gasification technology in municipal waste management into the megastructure model emerges as a promising approach for sustainable waste management and energy generation.

Ionic-liquid-based technologies for waste management

Ionic-liquid-based technologies for waste management

Achieving higher eco-efficiency for three staple food crops with ecosystem services based on regional heterogeneity in China

Farmland ecosystems can provide both economic products and ecosystem services that are important in maintaining food security and ecological safety. However, the ecological functions of farmland ecosystems have not been adequately evaluated in China, especially from the perspective of ecological efficiency for three staple food crops (wheat, maize and rice). Moreover, the contribution of ecosystem services value (ESV) to agricultural eco-efficiency (AEE), and the factors that influence AEE for the three staple food crops in different regions are still unclear. In this study, data envelopment analysis (DEA) was applied to calculate the AEE using group-frontier and meta-frontier methods based on regional heterogeneity. We measured AEE for wheat, maize and rice production with and without ESV and compared the gap between ecological inefficiency resulting from management inefficiency and technology gap inefficiency. The research results showed that, first, the involvement of ESV could effectively promote AEE. In particular, water conservation, soil conservation and the nutrient cycle contributed to the promotion of eco-efficiency. The mean ΔAEE (the difference in AEE with or without ESV) of wheat, maize and rice increased by 15 %, 10 % and 13 % with group-frontier and by 20 %, 10 % and 11 % with meta-frontier, respectively. Second, the improvement of AEE was higher in ecologically sensitive areas than that in other areas, especially for wheat and rice. Third, although the involvement of ESV reduced ecological inefficiency, specifically for management inefficiency, its contribution differed significantly across regions. We provide suggestions on improving AEE and reducing inter-regional differences in China. In brief, these findings suggest that, except for grain yields, ecosystem services need to be recognized in the production of three staple food crops, especially in ecologically sensitive areas. This is helpful for crop planning and planting division in terms of sustainable development.