Search Results (650)

The quality of machined marine propellers plays a critical role in underwater propulsion performance. Precision casting is the predominant manufacturing technique; however, deformation of wax models and rough blanks during manufacturing frequently cause deviations in the dimensions of final products and, thus, affect propellers’ performance and service life. Current inspection methods primarily involve using coordinate measuring machines and sampling. This approach is time-consuming, has high labor costs, and cannot monitor manufacturing quality in real-time. This study developed a real-time online automated measurement system containing a high-resolution CITIZEN displacement sensor, a four-degree-of-freedom measurement platform, and programmable logic controller-based motion control technology to enable rapid, automated measurement of blade deformation across the wax model, rough blank, and final product processing stages. The measurement data are transmitted in real time to a cloud database. Tests conducted on a standardized platform and real propeller blades confirmed that the system consistently achieved measurement accuracy to the second decimal place under the continual measurement mode. The system also demonstrated excellent repeatability and stability. Furthermore, the continuous measurement mode outperformed the single-point measurement mode. Overall, the developed system effectively reduces labor requirements, shortens measurement times, and enables real-time monitoring of process variation. These capabilities underscore its strong potential for application in the smart manufacturing and quality control of marine propellers. Full article

Digitalisation can positively impact the efficiency of real-world business processes, but may also introduce new cybersecurity challenges. One area that is particularly vulnerable to cyber-attacks is the business logic embedded in processes in which flaws may exist. This is especially the case when these processes are within web-based applications and services, which is increasingly becoming the norm for many organisations. Business logic vulnerabilities (BLVs) can emerge following the software development process, which may be difficult to detect by vulnerability detection tools. Through a systematic literature review and interviews with industry practitioners, this study identifies key BLV types and the challenges in detecting them. The paper proposes an eight-stage operational framework that leverages Artificial Intelligence (AI) for enhanced BLV detection and mitigation. The research findings contribute to the rapidly evolving theory and practice in this field of study, highlighting the current reliance on manual detection, the contextual nature of BLVs, and the need for a hybrid, multi-layered approach integrating human expertise with AI tools. The study concludes by emphasizing AI’s potential to transform cybersecurity from a reactive to a proactive defense against evolving vulnerabilities and threats. Full article

In recent years, near-field networks have become a vital part of smart grids, raising growing concerns about their security. Studying threat evolution mechanisms is key to building proactive defense systems, while early identification of threats enhances prediction and precision. Unlike traditional networks, threat sources in power near-field networks are highly dynamic, influenced by physical environments, workflows, and device states. Existing models, designed for general network architectures, struggle to address the deep cyber-physical integration, device heterogeneity, and dynamic services of smart grids, especially regarding physical-layer impacts, cross-system interactions, and proprietary protocols. To overcome these limitations, this paper proposes a threat evolution framework tailored to smart grid near-field networks. A novel semi-physical simulation method is introduced, combining traditional Control Flow Graphs (CFGs) for open components with real-device interaction to capture closed-source logic and private protocols. This enables integrated cyber-physical modeling of threat evolution. Experiments in realistic simulation scenarios validate the framework’s accuracy in mapping threat propagation, evolution patterns, and impact, supporting comprehensive threat analysis and simulation. Full article

Malawi’s Blantyre City faces escalating waste management challenges due to increased urbanization and inadequate waste collection services. This research utilized remote sensing (RS) and geographic information system (GIS) techniques to map potential illegal dump sites (PIDSs). MODIS and Sentinel-5P satellite imagery and GPS locations of dumpsites were used to extract environmental and spatial variables, including land surface temperature (LST), the enhanced vegetation index (EVI), Formaldehyde (HCHO), and distances from highways, rivers, and official dumps. An analytical hierarchical process (AHP) pairwise comparison matrix was used to assign weights for the six-factor variables. Further, fuzzy logic was applied, and weighted overlay analysis was used to generate the PIDS map. The results indicated that 10.27% of the study area has a “very high” probability of illegal dumping, while only 2% exhibited a “very low” probability. Validation with field data showed that the GIS and RS were effective, as about 89% of the illegal dumping sites were identified. Zonal statistics identified rivers as the most significant contributor to PIDS identification. The findings of this study underscore the significance of mapping PIDS in low-resource regions like Blantyre, Malawi, where inadequate waste management and illegal dumping are prevalent. Future studies should consider additional factors and account for seasonal variations. Full article

Fog computing technology grants computing and storage resources to nearby IoT devices, enabling a fast response and ensuring data locality. Thus, fog-enabled IoT environments provide real-time and convenient services to users in healthcare, agriculture, and road traffic monitoring. However, messages are exchanged on public channels, which can be targeted to various security attacks. Hence, secure authentication protocols are critical for reliable fog-enabled IoT services. In 2024, Harbi et al. proposed a remote user authentication protocol for fog-enabled IoT environments. They claimed that their protocol can resist various security attacks and ensure session key secrecy. Unfortunately, we have identified several vulnerabilities in their protocol, including to insider, denial of service (DoS), and stolen verifier attacks. We also prove that their protocol does not ensure user untraceability and that it has an authentication problem. To address the security problems of their protocol, we propose a security-enhanced blockchain-based secure authentication protocol for fog-enabled IoT environments. We demonstrate the security robustness of the proposed protocol via informal and formal analyses, including Burrows–Abadi–Needham (BAN) logic, the Real-or-Random (RoR) model, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation. Moreover, we compare the proposed protocol with related protocols to demonstrate the excellence of the proposed protocol in terms of efficiency and security. Finally, we conduct simulations using NS-3 to verify its real-world applicability. Full article

Local governments provide numerous services to their citizens. In doing so, they utilize two primary methods to deliver them: (1) producing them in-house with their own employees and equipment or (2) outsourcing them to external actors, which may take the form of other public agencies, for-profit firms, or non-profit organizations. In this entry, the authors review the logic of why local governments might choose one mechanism over another. The goal is to give readers a feel for the state of the academic literature in this regard. After reviewing basic concepts, such as the difference between the “provision” and “production” of services, the authors frame the discussion in terms of a variety of lenses used by scholars attempting to better understand the determinants of such decision-making. These include agency theory, transaction cost economics, and New Public Management. The authors also consider the role that management capacity plays in allowing cities to successfully deliver services to their constituents. Additionally, the authors offer a discussion regarding how local governments partner with non-profits in less formal ways than contracting to ensure their citizens have access to needed services. Finally, there is a review of the tradeoffs between efficiency and other values that should be accounted for when arranging service production. Full article

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living. Artificial intelligence (AI) and machine learning (ML) offer promising solutions for early fall prediction and continuous health monitoring. This paper introduces a novel cooperative AI model that forecasts the risk of future falls in the elderly based on behavioral and health abnormalities. Two AI models’ predictions are combined to produce accurate predictions: The ${AI}_1$ model is based on vital signs using Fuzzy Logic, and the ${AI}_2$ model is based on Activities of Daily Living (ADLs) using a Deep Belief Network (DBN). A meta-model then combines the outputs to generate a total fall risk prediction. The results show 85.71% sensitivity, 100% specificity, and 90.00% prediction accuracy when compared to the Morse Falls Scale (MFS). This emphasizes how deep learning-based cooperative systems can improve well-being for older adults living alone, facilitate more precise fall risk assessment, and improve preventive care. Full article

Applying Field Programmable Gate Array (FPGA) technology in cloud infrastructure and heterogeneous computations is of great interest today. FPGA as a Service assumes that the programmable logic device (PLD) is used as a remote (available over the Internet) service with an FPGA silicon chip on board. During the prototyping of FPGA-based projects within modern design flow, it is necessary to consider the processing delays caused by various factors, including the delay of data transfer between the kernel and host computer, limited clock frequency, and multiple parallel-running FPGA accelerator cards. To address these challenges, three techniques are proposed to reduce the required modification efforts and improve project performance. Based on the proposed models, the analytical evaluation of the functioning process of FPGA as a Service is performed to determine possibilities of improving productivity and reducing the response time. The practical experience of porting FPGA projects to new integrated environments is considered. The evaluation of the response time of FPGA as a Service using the queueing theory is proposed. It is shown that scaling and parallelization at the top level of project hierarchy, pipelining, and parameterization allow for the effective deployment of such FPGA systems for data centers and cloud infrastructures. The proposed techniques and models allow for an evaluation of the performance and response time of FPGA as a Service for formulating recommendations to improve technical characteristics. Full article

With the increasing demand for drones in diverse tasks, the Internet of Drones (IoD) has recently emerged as a significant technology in academia and industry. The IoD environment enables various services, such as traffic and environmental monitoring, disaster situation management, and military operations. However, IoD communication is vulnerable to security threats due to the exchange of sensitive information over insecure public channels. Moreover, public key-based cryptographic schemes are impractical for communication with resource-constrained drones due to their limited computational capability and resource capacity. Therefore, a secure and lightweight key agreement scheme must be developed while considering the characteristics of the IoD environment. In 2024, Alzahrani proposed a secure key agreement protocol for securing the IoD environment. However, Alzahrani’s protocol suffers from high computational overhead due to its reliance on elliptic curve cryptography and is vulnerable to drone and mobile user impersonation attacks and session key disclosure attacks by eavesdropping on public-channel messages. Therefore, this work proposes a lightweight and security-enhanced key agreement scheme for the IoD environment to address the limitations of Alzahrani’s protocol. The proposed protocol employs a physical unclonable function and simple cryptographic operations (XOR and hash functions) to achieve high security and efficiency. This work demonstrates the security of the proposed protocol using informal security analysis. This work also conducted formal security analysis using the Real-or-Random (RoR) model, Burrows–Abadi–Needham (BAN) logic, and Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation to verify the proposed protocol’s session key security, mutual authentication ability, and resistance to replay and MITM attacks, respectively. Furthermore, this work demonstrates that the proposed protocol offers better performance and security by comparing the computational and communication costs and security features with those of relevant protocols. Full article

In this study, a service quality (SQ) quantitative assessment method for retail industry operations was established to transform the perceptions and expectations of consumers into measurement framework requirements in the Indian market. The benefit of the recently created framework using interval-valued Pythagorean fuzzy to analyze the SQ is to handle imprecise human assessments, which is lacking in traditional SQ assessment methods. Therefore, we proposed a two-stage SQ assessment method by applying an extended novel Fuzzy methodology. We conducted a systematic literature review to identify the service quality and its products in the framework of the retail industry. In addition, we identified the gaps and proposed the measurement framework through consumer expectations and perceptions gaps. The present research findings confirmed that reliability and tangibility are important SQ dimensions reflecting the customer’s opinions. The findings help retail businesses make collective decisions on high-priority areas and effectively allocate resources to meet the needs of their customers. Full article

In a smart microgrid (SMG) system that deals with unpredictable loads and incorporates fluctuating solar and wind energy, it is crucial to have an efficient method for controlling frequency in order to balance the power between generation and load. In the last decade, cyberattacks have become a growing menace, and SMG systems are commonly targeted by such attacks. This study proposes a framework for the frequency management of an SMG system using an innovative combination of a smart controller (i.e., the Fuzzy Logic Controller (FLC)) with three conventional cascaded controllers, including Fractional-Order PI (FOPI), Tilt Integral Fractional Derivative (TID^μ), and Proportional Integral Derivative Acceleration (PIDA). The recently released Eel and Grouper Optimization (EGO) algorithm is used to fine-tune the parameters of the proposed controller. This algorithm was inspired by how eels and groupers work together and find food in marine ecosystems. The Integral Time Squared Error (ITSE) of the frequency fluctuation (ΔF) around the nominal value is used as an objective function for the optimization process. A diesel engine generator (DEG), renewable sources such as wind turbine generators (WTGs), solar photovoltaics (PVs), and storage components such as flywheel energy storage systems (FESSs) and battery energy storage systems (BESSs) are all included in the SMG system. Additionally, electric vehicles (EVs) are also installed. In the beginning, the supremacy of the adopted EGO over the Gradient-Based Optimizer (GBO) and the Smell Agent Optimizer (SAO) can be witnessed by taking into consideration the optimization process of the recommended regulator’s parameters, in addition to the optimum design of the membership functions of the fuzzy logic controller by each of these distinct algorithms. The subsequent phase showcases the superiority of the proposed EGO-based FFOPI-TID^μ-PIDA structure compared to EGO-based conventional structures like PID and EGO-based intelligent structures such as Fuzzy PID (FPID) and Fuzzy PD-(1 + PI) (FPD-(1 + PI)); this is across diverse symmetry operating conditions and in the presence of various cyberattacks that result in a denial of service (DoS) and signal transmission delays. Based on the simulation results from the MATLAB/Simulink R2024b environment, the presented control methodology improves the dynamics of the SMG system by about 99.6% when compared to the other three control methodologies. The fitness function dropped to 0.00069 for the FFOPI-TID^μ-PIDA controller, which is about 200 times lower than the other controllers that were compared. Full article

Incident prioritization is a critical task in enterprise environments, where textual descriptions of service disruptions often contain vague or ambiguous language. Traditional machine learning models, while effective in rigid classification, struggle to interpret the linguistic uncertainty inherent in natural language reports. This paper proposes a fuzzy logic-based framework for incident categorization and prioritization, integrating natural language processing (NLP) with a formal system of fuzzy inference. The framework transforms semantic embeddings from incident reports into fuzzy sets, allowing incident severity and urgency to be represented as degrees of membership in multiple categories. A mathematical model based on Mamdani-type inference and triangular membership functions is developed to capture and process imprecise inputs. The proposed system is evaluated on a real-world dataset comprising 10,000 incident descriptions from a mid-sized technology enterprise. A comparative evaluation is conducted against two baseline models: a fine-tuned BERT classifier and a traditional support vector machine (SVM). Results show that the fuzzy logic approach achieves a 7.4% improvement in F1-score over BERT (92.1% vs. 85.7%) and a 12.5% improvement over SVM (92.1% vs. 79.6%) for medium-severity incidents, where linguistic ambiguity is most prevalent. Qualitative analysis from domain experts confirmed that the fuzzy model provided more interpretable and context-aware classifications, improving operator trust and alignment with human judgment. These findings suggest that fuzzy modeling offers a mathematically sound and operationally effective solution for managing uncertainty in text-based incident management, contributing to the broader understanding of mathematical modeling in enterprise-scale social phenomena. Full article

Background: In an increasingly digitized supply chain landscape, small and medium-sized enterprises (SMEs) face mounting challenges in regard to delivering differentiated and responsive customer experiences. This study investigates the role of Industrial Internet of Things-enabled coopetition networks (IIoT-CNs) in enhancing the customer experience and value cocreation among SMEs. Grounded in Service-Dominant Logic, this research explores how interfirm collaboration and real-time data integration influence key performance indicators (KPIs), including perceived product quality, delivery timeliness, packaging standards, and product performance. Methods: An experimental design involving SMEs in Portugal’s ornamental stone sector contrasts traditional operations with digitally integrated coopetition practices. Results: While individual KPI improvements were not statistically significant, regression analysis revealed a significant positive relationship between IIoT-CN participation and the overall customer experience. The reduced variance in the performance metrics further suggests increased consistency and reliability across the network. Conclusions: These findings highlight IIoT-CNs as a promising model for SME digital transformation, contingent on trust, interoperability, and collaborative governance. This study contributes empirical evidence and practical insights for advancing customer-centric innovation in SME-dominated supply chains. Full article

China has effectively safeguarded biodiversity by building the world’s largest system of nature reserves, but the livelihoods of farmers near the reserves are often not guaranteed. This paper aimed to deeply explore the intrinsic relationship between the geographical location of households and their livelihood outcomes within seven nature reserves across three provinces in China. Innovatively, this study subdivided households’ livelihood outcomes into four patterns: high well-being with high dependency (H-H), high well-being with low dependency (H-L), low well-being with high dependency (L-H), and low well-being with low dependency (L-L), in order to comprehensively analyze the diversity of households’ livelihoods and further reveal the spatial logic and mechanisms underlying regional development imbalances. Methodologically, a combination of quantitative analysis and qualitative research was adopted. Representative villages in the protected area and outside the protected area were selected for semi-structured interviews with the village heads. Meanwhile, farmers were randomly selected in the villages for structured interviews and 1106 questionnaires were collected. Through variance analysis, the study first identified the unique advantages of H-H-pattern households in natural resource utilization. Subsequently, a multinomial logistic model was used to deeply analyze how geographical location (including whether a household was located within a protected area and the distance to markets) affected the transition mechanisms of the other three livelihood outcomes towards the H-H pattern. Based on this, marginal effect analysis was employed to further delineate the specific influence pathways of geographical factor changes on households’ livelihood outcome selection probabilities. The results showed that the geographical location of households’ residences had a significant impact on their livelihood outcomes. For households in the L-L and H-L patterns, proximity to markets could significantly increase the probability of their livelihood transitioning to the H-H pattern. Meanwhile, residing within protected areas significantly promoted the transition of L-L and H-L households to the H-H pattern but showed a certain inhibitory effect on L-H households. Marginal effects analysis further shows that both living in protected areas and reducing distance to markets increase the tendency of households to be highly dependent on natural resources for livelihood outcomes. Compensation policies should be designed according to local conditions, and subsidies for the development of ecotourism and other service industries should be increased for rural households in protected areas to ensure sustainable development rather than transfer payments. Full article

Mohandas Gandhi (1869–1948) and Aurobindo Ghose (1872–1950) hold distinct, yet overlapping, positions on the topic of war and peace, violence and nonviolence, and how evil ought to best be confronted. To some extent, the overlaps in their views can be seen as an effect of them basing their respective ideals on a shared foundation of Hindu teaching. More specifically, at least some portion of this overlap can potentially be seen as a function of the influence exerted upon both of these thinkers by the teachings of Swami Vivekananda, who was an inspiration to many modern Hindu thinkers, including both Gandhi and Ghose, as both figures attest. This paper will argue, apart from any historical influence he may or may not have had upon them, that Gandhi’s and Ghose’s views both, in different senses, comport well with the teaching of Swami Vivekananda. Specifically, the argument will be what could be called the utopian and realist orientations of Gandhi and Ghose, respectively, regarding the topic of violence, and we can find a logical reconciliation in Vivekananda’s philosophy of karma yoga: the path to liberation through service to the suffering beings of the world. Full article