Search Results (855)

In dynamic and information-rich contexts, systems must be capable of making instantaneous, context-aware decisions. Such scenarios require optimization methods that are both fast and flexible. This paper introduces an innovative hardware-based intelligent optimization framework, deployed on FPGAs, designed to support autonomous decisions in real-time systems. In contrast to conventional methods based on a single chaotic map, our scheme brings together six separate chaotic generators in simultaneous operation, orchestrated by an adaptive voting system based on past results. The system, in conjunction with the Secretary Bird Optimization Algorithm (SBOA), constantly adjusts its optimization approach according to the changing profile of the objective function. This delivers first-rate, timely solutions with improved convergence, resistance to local minima, and a high degree of adaptability to a variety of decision-making contexts. Simulations carried out on reference standards and engineering problems have demonstrated the scalability, responsiveness, and efficiency of the proposed model. These characteristics make it particularly suitable for use in embedded intelligence applications in sectors such as intelligent production, robotics, and IoT-based infrastructures. The suggested solution was tested using post-synthesis simulations on Vivado 2022.2 and experimented on three concrete engineering challenges: welded beam design, pressure equipment design, and tension/compression spring refinement. In each situation, the adaptive selection process dynamically determined the most suitable chaotic map, such as the logistics map for the Welded Beam Design Problem (WBDP) and the Tent map for the Pressure Vessel Design Problem (PVDP). This led to ideal results that exceed both conventional static methods and recent references in the literature. The post-synthesis results on the Nexys 4 DDR (Artix-7 XC7A100T, Digilent Inc., Pullman, WA, USA) show that the initial Q16.16 implementation exceeded the device resources (128% LUTs and 100% DSPs), whereas the optimized Q4.8 representation achieved feasible deployment with 80% LUT utilization, 72% DSP usage, and 3% FF occupancy. This adjustment reduced resource consumption by more than 25% while maintaining sufficient computational accuracy. Full article

As cities confront rising populations and mounting environmental pressures, waste is rapidly transforming from a logistical liability into a strategic economic resource. In this article, we investigate the evolving nexus between waste and urban economic systems by analyzing over 2000 scientific publications sourced from Web of Science and Scopus. Using advanced semantic embedding and network analysis, we identify seven major research communities at the intersection of digital innovation, circular economy, and smart urban infrastructure. Through PageRank-based influence mapping, we highlight key contributions that shape each thematic cluster—ranging from AI-powered waste classification to blockchain-enabled traceability and IoT-driven logistics. Our results reveal a dynamic and interdisciplinary research landscape where waste valorisation is not only a sustainability imperative but also a driver of urban economic renewal. This study offers both a conceptual map and a methodological framework for understanding how cities can embed intelligence, efficiency, and circularity into waste systems as part of a broader transition to regenerative, data-informed urban economies. Full article

The rapid growth of the gig economy and digital platforms is challenging traditional gender roles, particularly in developing countries where structural inequalities continue to shape labor and household dynamics. Despite growing global interest in gender equity and digital inclusion, limited research has examined how gig work, digital access, and women’s income contributions interact to influence household gender dynamics within culturally conservative contexts. This study aimed to investigate the multidimensional impacts of women’s participation in gig work on time use redistribution, intra-household decision making, gender ideology, and role reversal within households in Pakistan. Using a cross-sectional survey design, data were collected from a representative sample of married couples engaged in the gig economy across urban and peri-urban areas of Pakistan. A quantitative analysis was conducted employing a combination of an analysis of variance, ordinal logistic regression, hierarchical multiple regression, and structural equation modeling to evaluate the direct and indirect relationships between constructs. The findings revealed that women’s gig work participation significantly predicted enhanced digital access, greater income contributions, and increased intra-household decision-making power. These, in turn, contributed to a measurable shift in gender ideology toward equality norms and a partial reversal of traditional gender roles, particularly in household labor division. The study concludes that the intersection of economic participation and digital empowerment serves as a catalyst for progressive gender restructuring within households. Policy implications include the need for gender-responsive labor policies, investment in digital infrastructure, and targeted interventions to support empowering women in non-traditional work roles. Full article

In the rapidly evolving global business landscape, servitization and digitalization have emerged as key strategies for enhancing firm competitiveness and performance. This study examines their impact, along with the moderating role of government support, in the Indonesian shipping industry. Drawing on the resource-based view (RBV), servitization and digitalization are conceptualized as internal drivers of performance, while Resource Dependence Theory (RDT) positions government support as an external factor that reduces environmental uncertainty and strengthens these relationships. Using data from 345 shipping companies, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that both servitization and digitalization positively affect competitiveness and performance. Furthermore, government support significantly enhances these effects by providing resources such as infrastructure and financial incentives, facilitating the adoption of digital strategies and service-based models. Beyond firm outcomes, these transformations align with broader sustainability objectives by improving resource efficiency, reducing waste and delays, and potentially lowering the environmental footprint of logistics activities. This study advances theoretical understanding by demonstrating the central role of external resources—particularly government support—in enabling successful digital and service transformations. For policymakers, the findings emphasize the need for targeted incentives and infrastructure to accelerate industry-specific innovation and sustainability goals. For practitioners, they highlight the importance of aligning strategic initiatives with government policies to maximize the benefits of servitization and digitalization. Full article

Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article

This benchmarking study situates Jordan’s trade indicators relative to comparators (Egypt, Lebanon, Saudi Arabia, and the United Arab Emirates) with descriptive analysis. Using indicators for port competitiveness, geopolitical stability, logistics infrastructure, and trade facilitation within a Modified Input–Process–Output framework, based on secondary data from conventional international indicators (“Fund for Peace Fragile States Index,” “Institute for Economics & Peace Global Peace Index,” “OECD Trade Facilitation Indicators,” “UN Comtrade Trade Volume Records, 2022–2023,” “UN Conference on Trade and Development Port Performance Scorecard,” and “World Bank Logistics Performance Index”). The outcomes of this analysis demonstrate that Jordan’s strengths in terms of institutional quality and geopolitical stability are countermanded by relatively poor digital technology adoption and governance of ports, and homogeneity in exports. Using M-IPO model and SWOT analysis, it was identified that specific actions are needed to improve Jordan’s trade performance, especially as a hub for regional logistics, including investment and facilitation of digital system adoption, commensurate infrastructure, and flexibility in governance. Full article

Green ammonia has emerged as a promising alternative fuel for maritime decarbonization, owing to its carbon-free combustion, favorable volumetric energy density, and well-established logistics infrastructure compared to other alternatives. However, critical gaps persist in the development of an integrated fuel supply framework, which hinders the large-scale adoption of ammonia-fueled vessels. Therefore, this paper proposes an onshore wind-powered green ammonia plant located along the Gaolan–Yangpu feeder route. The plant comprises PEM electrolysis, nitrogen separation, Haber–Bosch synthesis, and storage facilities. An optimal plant configuration is subsequently derived through hourly simulations based on wind power generation and a priority-based capacity expansion algorithm. Key findings indicate that a stable ammonia supply—synchronized with monsoon wind patterns and capable of fueling vessels with 10 MW propulsion systems consuming around 680 tons per fortnight—requires a 72 MW onshore wind farm, a 63 MW PEM electrolyzer, 3.6 MW of synthesis facility, and 3205 tons of storage. This configuration yields a levelized cost of ammonia (LCOA) of approximately USD 700/ton, with wind turbines and electrolyzers (including replacement costs) accounting for over 70% of the total cost. Sensitivity analysis further shows that wind turbine and electrolyzer prices are the primary factors affecting ammonia costs. Although variations in operational parameters may significantly alter final configuration, they cause only minor (±1%) fluctuations in the levelized cost without significantly altering its overall trend. Full article

This study examines the challenges and opportunities of managing electronic waste (e-waste) in the Galapagos Islands, a globally significant yet vulnerable subnational insular jurisdiction (SNIJ). Drawing on theories of Circular Economy (CE) and Political Industrial Ecology (PIE), the research investigates the status of e-waste in the archipelago, the barriers to implementing CE practices, and the institutional dynamics shaping material flows. Using a mixed-methods approach—including archival analysis, participant observation, and semi-structured interviews with key informants from government, private, and nonprofit sectors—the findings presented here demonstrate that e-waste management is hindered by limited capital, infrastructure, public awareness, and fragmented governance. While some high-capital institutions can export e-waste to mainland Ecuador, most residents and low-capital entities lack viable disposal options, leading to accumulation and improper disposal. The PIE analysis yielded findings that highlight how institutional power and financial capacity dictate the sustainability of e-waste pathways, with CE loops remaining largely incomplete. Despite national policy support for CE, implementation in Galapagos remains aspirational without targeted financial and logistical support. This case contributes to broader discussions on waste governance in island settings and underscores the need for integrated, equity-focused strategies to address e-waste in small island developing states (SIDS) and SNIJs globally. Full article

Switch-point heating systems are essential for railway reliability and safety in winter, but present logistical and economic challenges in remote regions. This study presents a novel application of a hydrogen-enabled microgrid as an off-grid energy solution for powering a switch-point heating system at a rural Austrian railway station, offering an alternative to conventional grid-based electricity with a specific focus on enhancing the share of renewable energy sources. The proposed system integrates photovoltaics (PV), optional wind energy, and hydrogen storage to address the seasonal mismatch between a high energy supply in the summer and peak winter demand. Three energy supply scenarios are analysed and compared based on local conditions, technical simplicity, and economic viability. Energy flow modelling based on site-specific climate and operational data is used to determine hydrogen production rates, storage capacity requirements and system sizing. A comprehensive cost analysis of all major subsystems is conducted to assess economic viability. The study demonstrates that hydrogen is a highly effective solution for seasonal energy storage, with a PV-only configuration emerging as the most suitable option under current site conditions. Thus, it offers a replicable framework for decarbonising critical stationary railway infrastructure. Full article

Maritime ship transportation is not only the core infrastructure of the global logistics system but also is closely related to national security and sustainable development. However, the human factor remains the primary source of risk leading to maritime accidents during ship navigation. In recent years, multi-source data has been recognized as an important means to improve the efficiency of ship operations and navigation safety. In this paper, the major research methods and technical pathways of maritime multi-source data in recent years have been systematically reviewed, and a comprehensive technical framework from data acquisition and preprocessing to practical application has been constructed. Focusing on the data layer, application layer, and system layer, this paper comprehensively analyzes the key technologies of maritime navigation based on multi-source data. At the same time, this paper also highlights the advantages and cutting-edge methods of multi-source data in typical application scenarios—such as track extraction, target recognition, behavior detection, path planning, and collision avoidance—and analyzes their performance and adaptation strategies in different usage contexts. Through the combination of theory and engineering practice, this paper looks forward to the future development of ship intelligence and water transportation systems, providing a theoretical basis and technical support for the construction of intelligent shipping systems. Full article

The safe and sustainable management of healthcare waste (HCW) is essential for minimizing environmental impacts and protecting public health, particularly in developing countries with limited logistical infrastructure. Despite the growing adoption of routing optimization in HCW logistics, few studies integrate waste generator segmentation with algorithmic planning. This study proposes an optimization approach based on the Periodic Vehicle Routing Problem (PVRP), incorporating a segmentation of waste generators by volume. Two solution methods, the Clarke and Wright (CW) heuristic and Particle Swarm Optimization (PSO), are applied and compared through a real-world case study in Paraná, Brazil. Results show that PSO significantly outperforms CW in reducing travel distance and CO₂ emissions. For small generators, PSO achieves reductions of up to 41% in distance and 41.37% in emissions, compared to CW’s 35.42%. For large generators, PSO was reduced by 22% and 21.81%, respectively. The proposed method demonstrates the potential for scalable, data-efficient waste management strategies. This research contributes to sustainable urban logistics by bridging segmentation and routing optimization in resource-constrained settings, offering actionable insights for policymakers and planners. Full article

The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure. Full article

Rapid access to defibrillators, blood products, and time-critical medicines can improve survival, yet urban congestion and fragmented infrastructure delay deliveries. We present and evaluate an end-to-end framework for beyond-visual-line-of-sight (BVLOS) UAV logistics in Essex (UK), integrating (I) strategic depot placement, (II) a hybrid obstacle-aware route planner, and (III) a time-window-aware (TWA) Mixed-Integer Linear Programming (MILP) scheduler coupled to a battery/temperature feasibility model. Four global planners—Ant Colony Optimisation (ACO), Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Rapidly Exploring Random Tree* (RRT*)—are paired with lightweight local refiners, Simulated Annealing (SA) and Adaptive Large-Neighbourhood Search (ALNS). Benchmarks over 12 destinations used real Civil Aviation Authority no-fly zones and energy constraints. RRT*-based hybrids delivered the shortest mean paths: RRT* + SA and RRT* + ALNS tied for the best average length, while RRT* + SA also achieved the co-lowest runtime at $v=60\mathrm{km}{\mathrm{h}}^{-1}$. The TWA-MILP reached proven optimality in 0.11 s, showing that a minimum of seven UAVs are required to satisfy all 20–30 min delivery windows in a single wave; a rolling demand of one request every 15 min can be sustained with three UAVs if each sortie (including service/recharge) completes within 45 min. To validate against a state-of-the-art operations-research baseline, we also implemented a Vehicle Routing Problem with Time Windows (VRPTW) in Google OR-Tools, confirming that our hybrid planners generate competitive or shorter NFZ-aware routes in complex corridors. Digital-twin validation in AirborneSIM confirmed CAP 722-compliant, flyable trajectories under wind and sensor noise. By hybridising a fast, probabilistically complete sampler (RRT*) with a sub-second refiner (SA/ALNS) and embedding energy-aware scheduling, the framework offers an actionable blueprint for emergency medical UAV networks. Full article

This study reviews the role of Child-Friendly Green Spaces (CFGS) in supporting children’s psychological, physical, and educational recovery following natural disasters. The main research question guiding this review is the following: how do CFGS contribute to holistic child well-being and resilience in disaster-affected contexts, and what barriers and strategies influence their effective integration into recovery frameworks? Employing a rigorous literature review methodology, we synthesized interdisciplinary evidence from environmental psychology, urban planning, public health, and education, encompassing studies published between 2000 and 2024. Findings demonstrate that CFGS significantly reduce trauma-related symptoms such as anxiety, depression, and post-traumatic stress, promotes physical health through active play, and foster educational engagement by improving concentration, attendance, and informal learning opportunities. Furthermore, CFGS contribute directly to multiple Sustainable Development Goals, particularly SDG 3 (Good Health and Well-being), SDG 4 (Quality Education), and SDG 11 (Sustainable Cities and Communities). Despite these advantages, CFGS are often overlooked in formal disaster recovery planning due to prioritization of immediate relief, financial and logistical challenges, and socio-cultural factors. To address these challenges, this study proposes a participatory, culturally sensitive framework for CFGS implementation, which integrates inclusive design, multi-sector collaboration, and ongoing monitoring and evaluation. Grounded in theoretical perspectives such as the Biophilia Hypothesis, Bronfenbrenner’s Ecological Systems Theory, and restorative environments, CFGS are reframed as critical infrastructures for children’s holistic recovery and resilience. The findings underscore the urgent need to embed CFGS within disaster recovery and urban planning policies to promote child-centered, sustainable community development. Full article

Every year, more and more general and other types of cargo are transported by containers, and many ports, including small and medium-sized ones, are trying to join the container transportation processes. Port connectivity with container shipping is associated with easier and faster cargo processing and reduced environmental impact by optimizing ship arrivals and processing in small and medium-sized ports. Small and medium-sized ports are often limited by port infrastructure, especially suitable quays; therefore, it is very important to correctly assess the capabilities of such ports so that ships do not have to wait for entry and so that quays and other port infrastructure are optimally used. The research is relevant because small and medium-sized ports are increasingly involved in the activities of logistics chains and are becoming very important for the development of individual regions. The wider use of small and medium-sized ports in logistics chains is a new and original research direction. Optimal assessment of port or terminal and berth utilization is possible using the principles of probability theory. The article develops and presents a probabilistic method for assessment of port and terminal and ship mooring at their berths, using possible and actual time periods, based on the principles of transport process organization and linked to the capabilities of the port infrastructure and terminal superstructure. The conditional probability method was used to assess port and terminal capacity, as well as a method for assessing ship maneuverability under limited conditions. The developed probabilistic method for assessing port terminals and ship berthing at port quays can be used in any port or terminal, taking into account local conditions. Combined theoretical research and experimental results of the optimal use of small and medium-sized ports ensure sufficient research quality. Full article