Search Results (765)

Critical Internet of Things (IoT) data in Fifth Generation Vehicular Ad Hoc Networks (5G VANETs) demands Ultra-Reliable Low-Latency Communication (URLLC) to support mission-critical vehicular applications such as autonomous driving and collision avoidance. Achieving the stringent Quality of Service (QoS) requirements for these applications remains a significant challenge. This paper proposes a novel framework integrating Software-Defined Networking (SDN) and Network Functions Virtualisation (NFV) as embedded functionalities in connected vehicles. A lightweight SDN Controller model, implemented via vehicle on-board computing resources, optimised QoS for communications between connected vehicles and the Next-Generation Node B (gNB), achieving a consistent packet delivery rate of 100%, compared to 81–96% for existing solutions leveraging SDN. Furthermore, a Software-Defined Wide-Area Network (SD-WAN) model deployed at the gNB enabled the efficient management of data, network, identity, and server access. Performance evaluations indicate that SDN and NFV are reliable and scalable technologies for virtualised and distributed 5G VANET infrastructures. Our SDN-based in-vehicle traffic classification model for dynamic resource allocation achieved 100% accuracy, outperforming existing Artificial Intelligence (AI)-based methods with 88–99% accuracy. In addition, a significant increase of 187% in flow rates over time highlights the framework’s decreasing latency, adaptability, and scalability in supporting URLLC class guarantees for critical vehicular services. Full article

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

Background: In this work, we examine determinants of staff departure rates in the NHS, a critical issue for workforce stability and continuity of care. High turnover, particularly among clinical staff, undermines service delivery and incurs substantial replacement costs. Methods: Here, we analyse a unique dataset derived from Electronic Staff Records at Ashford and St. Peter’s NHS Foundation Trust, using a machine learning approach to move beyond traditional survey-based methods, to assess propensity to leave. Results: In addition to established predictors such as salary and length of service, we identify drivers of increased risks of staff exits, including the distance between home and workplace and, especially for medical staff, cost centre vacancy rates. Conclusions: These findings highlight the multifactorial nature of staff retention and suggest the potential of local administrative data to improve workforce planning, for example, through hyperlocal recruitment strategies. Whilst further work will be required to assess the generalisability of our findings beyond a single Trust, our analysis offers insights for NHS managers seeking to stabilise staffing levels and reduce attrition through targeted interventions beyond pay and tenure. Full article

Automated parcel lockers (APLs) are transforming urban last-mile delivery by reducing failed distributions, decoupling delivery from recipient availability, optimizing carrier routes, reducing carbon foot-print and mitigating traffic congestion. The paper investigates the optimal design of APLs systems under stochastic demand and operational constraints, formulating the problem as a resource allocation optimization with service-level guarantees. We proposed a data-driven discrete-event simulation (DES) model implemented in ARENA to (i) determine optimal locker configurations that ensure customer satisfaction under stochastic parcel arrivals and dwell times, (ii) examine utilization patterns and spatial allocation to enhance system operational efficiency, and (iii) characterize inventory dynamics of undelivered parcels and evaluate system resilience. The results show that the configuration of locker types significantly influences the system’s ability to maintain high customers service levels. While flexibility in locker allocation helps manage excess demand in some configurations, it may also create resource competition among parcel types. The heterogeneity of locker utilization gradients underscores that optimal APLs configurations must balance locker units with their size-dependent functional interdependencies. The Dickey–Fuller GLS test further validates that postponed parcels exhibit stationary inventory dynamics, ensuring scalability for logistics operators. As a theoretical contribution, the paper demonstrates how DES combined with time-series econometrics can address APLs capacity planning in city logistics. For practitioners, the study provides a decision-support framework for locker sizing, emphasizing cost–service trade-offs. Full article

Air quality monitoring IoT is one of the approaches to achieving a sustainable future. However, the large area of IoT and the high number of monitoring microsites pose challenges for device maintenance to guarantee quality of service (QoS) in monitoring. This paper proposes a novel maintenance programming model for a large-area IoT containing 1500 monitoring microsites. In contrast to classic device maintenance, the addressed programming scenario considers the division of appropriate microsites into batches, the determination of the batch maintenance date, vehicle routing for the delivery of maintenance services, and a set of hard constraints such as QoS in air quality monitoring, the maximum number of labor working hours, and an upper limit on the total CO₂ emissions. Heuristics are proposed to generate the batches of microsites and the scheduled maintenance date for the batches. A genetic algorithm is designed to find the shortest routes by which to visit the batch microsites by a fleet of vehicles. Simulations are conducted based on government open data. The experimental results show that the maintenance and transportation costs yielded by the proposed model grow linearly with the number of microsites if the fleet size is also linearly related to the microsite number. The mean time between two consecutive cycles is around 17 days, which is generally sufficient for the preparation of the required maintenance materials and personnel. With the proposed method, the decision-maker can circumvent the difficulties in handling the hard constraints, and the allocation of maintenance resources, including budget, materials, and engineering personnel, is easier to manage. Full article

Background and aims: Rheumatic diseases are chronic, progressive conditions associated with severe pain, joint damage, disability, and even death. Healthcare interventions play a critical role in symptom management, patient education, and adherence to treatment plans. This study evaluates the role of healthcare interventions in the management of patients with rheumatic diseases, focusing on pain management, functional rehabilitation, patient education, and multidisciplinary collaboration. In addition, barriers to optimal care and potential solutions, including digital health technologies, are explored. Materials and methods: We conducted a narrative review of the scientific literature. Studies published between 2014 and 2025 were selected from PubMed, Scopus, Web of Science, Elsevier, Springer, Frontiers, and Wiley Online Library. Key areas of review included nurse-led pain management, education programs, and the impact of interdisciplinary care on patient outcomes. Results: Nursing interventions significantly improve pain control, treatment adherence, and self-management skills in patients with rheumatic diseases. Multidisciplinary approaches improve functional rehabilitation and increase quality of life in patients with rheumatic conditions. However, barriers such as insufficient health care resources, lack of patient awareness, and disparities in the availability of services hinder effective care delivery. Conclusions: A structured, multidisciplinary approach integrating healthcare interventions, digital health solutions, and patient-centered education is essential to optimize the management of rheumatic diseases. Future research should focus on improving access to non-pharmacological therapies and standardizing healthcare protocols for better patient outcomes. Full article

Due to the recent significant increase in the scale of both domestic and international cargo transportation, the transport sector is becoming an important factor in the country’s economic development. This implies the need to improve all links in the cargo transportation chain. A key role in it is played by logistics centers, which in their activities must meet both state (CO₂ emissions, reduction in road load, increase in transportation safety, etc.) and commercial (cargo transportation in the shortest time and at the lowest cost) requirements. The objective of the paper is freight transportation from China to European countries, reflecting issues of CO₂ emissions, reduction in road load, and increase in transportation safety. Transport operations from the manufacturer to the logistics center are especially important in this chain, since the efficiency of transportation largely depends on the decisions made by its employees. They select the appropriate types of transport (air, sea, rail, and road transport) and routes for a specific situation. In methodology, the analyzed problem can be presented as a dynamic multi-criteria decision model. It is assumed that the decision-maker—the manager responsible for planning transportation operations—is interested in achieving three basic goals: financial goal minimizing total delivery costs from factories to the logistics center, environmental goal minimizing the negative impact of supply chain operations on the environment, and high level of customer service goal minimizing delivery times from factories to the logistics center. The proposed methodology allows one to reduce the total carbon dioxide emission by 1.1 percent and the average duration of cargo transportation by 1.47 percent. On the other hand, the total cost of their delivery increases by 1.25 percent. By combining these, it is possible to create optimal transportation options, effectively use vehicles, reduce air pollution, and increase the quality of customer service. All this would significantly contribute to the country’s socio-economic development. It is proposed to solve this complex problem based on a dynamic multi-criteria model. In this paper, the problem of constructing a schedule of transport operations from factories to a logistics center is considered. The analyzed problem can be presented as a dynamic multi-criteria decision model. Linear programming and the AHP method were used to solve it. Full article

This study presents a reinforcement learning–based approach to optimize replenishment policies in the presence of uncertainty, with the objective of minimizing total costs, including inventory holding, shortage, and ordering costs. The focus is on single-level assembly systems, where both component delivery lead times and finished product demand are subject to randomness. The problem is formulated as a Markov decision process (MDP), in which an agent determines optimal order quantities for each component by accounting for stochastic lead times and demand variability. The Deep Q-Network (DQN) algorithm is adapted and employed to learn optimal replenishment policies over a fixed planning horizon. To enhance learning performance, we develop a tailored simulation environment that captures multi-component interactions, random lead times, and variable demand, along with a modular and realistic cost structure. The environment enables dynamic state transitions, lead time sampling, and flexible order reception modeling, providing a high-fidelity training ground for the agent. To further improve convergence and policy quality, we incorporate local search mechanisms and multiple action space discretizations per component. Simulation results show that the proposed method converges to stable ordering policies after approximately 100 episodes. The agent achieves an average service level of 96.93%, and stockout events are reduced by over 100% relative to early training phases. The system maintains component inventories within operationally feasible ranges, and cost components—holding, shortage, and ordering—are consistently minimized across 500 training episodes. These findings highlight the potential of deep reinforcement learning as a data-driven and adaptive approach to inventory management in complex and uncertain supply chains. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article

Audit quality is crucial, particularly for small and medium-sized enterprises (SMEs), due to their significant economic role. This study examined how spatial distance (physical separation) and social distance (perceived dissimilarity) between auditors and SME clients influence audit quality, focusing on technical quality (the tangible outputs of auditing) and process quality (the manner of service delivery). Using data from 449 SME executives across Thailand, the study investigated the mediating role of auditor independence within these relationships. The results from structural equation modeling revealed that spatial distance has no direct impact on audit quality but a negative effect on perceived auditor independence, which, in turn, diminishes audit quality indirectly. Conversely, social distance negatively impacts both technical and process quality directly and indirectly through auditor independence. The findings suggest that despite technological advancements facilitating remote auditing, maintaining some physical interaction remains vital for preserving client trust. Additionally, aligning auditor–client social similarities significantly enhances audit quality perceptions. This study provides practical implications for audit firms in managing client interactions effectively, particularly within SMEs. Full article

This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies. Full article

The conventional supply chain management has undergone major advancements following IoT-enabled revolution. The IoT-enabled drones in particular have ignited much recent attention for package delivery in logistics. The service delivery paradigm in logistics has seen a surge in drone-assisted package deliveries and tracking. There have been a lot of recent research proposals on various aspects of last-mile delivery systems for drones in particular. Although drones have largely changed the logistics landscape, there are many concerns regarding security and privacy posed to drones due to their open and vulnerable nature. The security and privacy of involved stakeholders needs to be preserved across the whole chain of Supply Chain Management (SCM) till delivery. Many earlier studies addressed this concern, however with efficiency limitations. We propose a Physical Uncloneable Function (PUF)-based secure authentication protocol (PSL-IoD) using symmetric key operations for reliable last-mile drone delivery in SCM. PSL-IoD ensures mutual authenticity, forward secrecy, and privacy for the stakeholders. Moreover, it is protected from machine learning attacks and drone-related physical capture threats due to embedded PUF installations along with secure design of the protocol. The PSL-IoD is formally analyzed through rigorous security assessments based on the Real-or-Random (RoR) model. The PSL-IoD supports 26.71% of enhanced security traits compared to other comparative studies. The performance evaluation metrics exhibit convincing findings in terms of efficient computation and communication along with enhanced security features, making it viable for practical implementations. Full article

Background: Emergency laparoscopic cholecystectomy (ELC) has emerged as a viable alternative to delayed elective surgery for acute gallstone disease, although its widespread adoption is hindered by cultural barriers. This study compares outcomes between elective and emergency laparoscopic cholecystectomy and evaluates the impact of implementing an ELC pathway on elective waiting times, patient outcomes, and overall service delivery. Methods: A prospective cohort study was conducted between December 2021 and December 2023, including all patients undergoing emergency or elective laparoscopic cholecystectomy. One-to-one propensity score matching, correlation statistics, and multivariate logistic regression were used to analyse outcomes. Results: Of 585 patients, 314 (53.4%) underwent emergency and 271 (46.3%) elective cholecystectomies. After matching, 474 patients were analysed (237 per group). The ELC pathway achieved an 81.4% first-presentation procedure rate, with 69.2% managed as day cases and 84.4% discharged the following day. Emergency cases had longer operative times (+9 min), higher rates of subtotal cholecystectomy (8.9% vs. 3.0%, p < 0.001), and more frequent postoperative ERCP (16.9% vs. 4.6%, p < 0.001). Other outcomes were comparable. Introduction of the ELC pathway significantly reduced elective waiting times from a median of nine to three months (R = −0.219, R² = 0.059, p < 0.001) and preoperative admissions (IQR 0–1, R = −0.223, R² = 0.050, p = 0.002). Conclusions: An ELC pathway is a safe and effective alternative to elective gallstone surgery, offering substantial benefits to patients and healthcare systems, while serving as a strategic, cost-conscious approach to reducing surgical waiting times and preoperative admissions. Its success hinges upon surgical expertise in acute decision making, skill in performing subtotal cholecystectomy, and access to institutional resources such as advanced imaging and ERCP services. Full article

Arid regions face multiple challenges such as population expansion, water scarcity, land degradation, and biodiversity reduction. Understanding temporal and spatial patterns of ecosystem service trade-offs and synergies is critical for sustainable development and effective ecosystem service management in arid regions under environmental stress. Taking the Tarim River Basin in China as an example, five ecosystem services (carbon sequestration, water yield, sediment delivery ratio, habitat quality, and food production) were studied at different scales in 1990, 2000, 2010, and 2020 in the inland arid region. Spearman correlation, geographical weighted regression, and self-organizing mapping were used to analyze the ecosystem service trade-offs and synergies. The results showed that the ecosystem services in the basin increased gradually; in particular, the water yield increased from 15.38 × 10⁹ m³ to 29.8 × 10 m³, and the food production increased from 11.03 × 10⁶ t to 29.26 × 10⁶ t. There was a significant positive correlation between carbon sequestration, water yield, and habitat quality, but a negative correlation between sediment delivery ratio and food production. The spatial distribution of trade-offs and synergies of ecosystem services varies in different years and on different scales. The area change in ecosystem service bundles at the pixel scale is relatively small, while the area change at the sub-basin scale is relatively large. This paper provides policy suggestions for the ecological management and sustainable development of the Tarim River Basin through the analysis of ecosystem service trade-offs and synergies. Full article

Background/Objectives: Operating rooms represent valuable and pivotal units of any hospital. Therefore, their management affects healthcare service delivery through rescheduling, staff shortage/overtime, cost inefficiency, and patient dissatisfaction, among others. To optimize scheduling, we aim to assess preoperative evaluation criteria that influence the prediction of surgery duration for gastric cancer (GC) patients. In GC, radical surgery with curative intent is the ideal treatment. Nevertheless, the intervention sometimes must be palliative if the patient’s status and tumor staging prove too advanced. Methods: A 6-year retrospective cohort study was performed in a tertiary care hospital, including all cases diagnosed with GC (ICD-10 code C16), confirmed through histopathology, and undergoing surgical treatment (N = 108). Results: The results of our study confirm male predominance (63.89%) among GC surgery candidates while bringing new perspectives on patient evaluation criteria and choice of surgical intervention (curative—Group 1, palliative—Group 2). Surgery duration, including anesthesiology (175.19 [95% CI (157.60–192.77)] min), shows a direct correlation with the number of lymph nodes dissected (Surgical duration [min] = 10.67 × No. of lymph nodes removed − 32.25). Interestingly, the aggressiveness of the tumor based on histological grade (highly differentiated being generally less aggressive than poorly differentiated) shows differential correlation with surgery duration among curative and palliative surgery candidates. Similarly, TNM staging indicates the need for a longer surgical duration (pTNM stage IIA, IIB, and IIIA) for curative interventions in patients with less advanced stages, as opposed to shorter surgery duration for palliative interventions (pTNM stage IIIC and IV). Conclusions: The study quantitatively presents the resources needed for the optimal surgical treatment of different groups of GC patients, as the disease coding systems in use regard the treatment of each pathology as “standard” in terms of patient management. The results obtained are anchored in the global perspectives of surgical outcomes and aim to improve the management of operating room scheduling, staff, and resources. Full article