Search Results (7,478)

Urban Air Mobility (UAM) introduces new safety challenges as small unmanned aircrafts begin to operate at high density in complex urban environments. Traditional air traffic management (ATM) systems developed for manned aviation are unable to accommodate the autonomy, mission diversity, and dynamic obstacle conditions typical of low-altitude operations. This review examines recent research on mid-air collision risk and airspace safety modeling for UAM and identifies key challenges in adapting existing safety concepts to small-scale and autonomous flight. The study compares international management frameworks of the United States, Europe, and China. Then analyzes representative airspace structures such as Free, Layered, Zoned, and Pipeline configurations. It further reviews deterministic and probabilistic separation models, geometric and optimization-based avoidance strategies, and structured airspace approaches such as the virtual-tube concept for coordinated swarm navigation. The findings highlight the lack of integrated models that couple human, energy, and communication factors into quantitative risk assessment. The paper concludes by outlining future research needs in uncertainty modeling, digital-twin simulation, and interoperability to support safe and scalable UAM development. Full article

Continuous healthcare monitoring systems generate non-stationary physiological data streams, where evolving statistical properties and patterns often invalidate static models and fixed user classifications. To address this challenge, we propose drift-aware adaptive architecture that integrates concept drift detection into a distributed edge–cloud data analytics pipeline. In the proposed design, a concept drift is elevated from a maintenance signal to the primary mechanism governing user-state adaptation, model evolution, and inference consistency. Within the proposed system, the edge tier performs low-latency inference and preliminary drift screening under strict resource constraints, while the cloud tier executes advanced drift detection and validation, orchestrates user reclassification and model retraining, and manages model evolution. A feedback loop synchronizes edge and cloud operations, ensuring that detected drift triggers appropriate system transitions, either reassigning a user to an updated state category or initiating targeted model updates. This architecture reduces reliance on static group assignments, improves personalization, and preserves model fidelity under evolving physiological conditions. We analyze the drift types most relevant to healthcare data streams, evaluate the suitability of lightweight and cloud-grade drift detectors, and define the system requirements for stability, responsiveness, and clinical safety. Evaluation across 21 concurrent users demonstrates that drift-aware adaptation reduced prediction MAE by 40.6% relative to periodic retraining, with an end-to-end adaptation latency of 66 ± 37 s. Hierarchical cloud validation reduced the false-positive retraining rate from 88.9% (edge-only triggering) to 27.3%, while maintaining uninterrupted inference throughout all adaptation events. Full article

This paper examines the philosophy and practice of interfaith dialogue (IFD) developed by Daisaku Ikeda (1928–2023), a prominent religious leader and peace philosopher. It explores how his dialogical approach can contribute to the United Nations’ Sustainable Development Goals (SDGs) and pathways to global peace. Ikeda’s dialogue is not confined to doctrinal debate or temporary reconciliation among faith communities. Rather, it is framed as a transformative process in which participants from diverse religious and civilizational traditions rebuild relationships through mutual respect and understanding, thereby contributing to personal transformation and broader societal change. Focusing on Ikeda’s core concepts—humanism, the dignity of life, and human revolution—this study first clarifies the philosophical foundations of his interfaith dialogue rooted in Nichiren Buddhism and a life-affirming worldview. It then examines major dialogues with global thinkers and leaders (e.g., Arnold J. Toynbee, Linus Pauling, Mikhail Gorbachev, and Johan Galtung) and selected institutional practices associated with Soka Gakkai International (SGI), the Institute of Oriental Philosophy (IOP), and the Ikeda Center for Peace, Learning, and Dialogue. These cases illustrate how Ikeda’s IFD functions as praxis for civilizational understanding, social cohesion, conflict transformation, and solidarity for the public good. The paper further analyzes the linkages between Ikeda’s IFD and SDG 16 (Peace, Justice and Strong Institutions), SDG 17 (Partnerships for the Goals), SDG 4 (Quality Education—especially Target 4.7 on Global Citizenship Education), and SDG 10 (Reduced Inequalities). It argues that IFD can operate as both a normative and practical resource for mitigating religious conflict, strengthening inclusion, enhancing global citizenship education and education for sustainable development (ESD), and fostering multistakeholder partnerships. The paper also reflects on the challenges of translating an approach grounded in a particular religious tradition into broader SDG governance contexts. Full article

Frank Herbert’s Dune (1965) establishes a universe defined by the “Butlerian Jihad”, a historical crusade that banned artificial intelligence and created a vacuum filled by religious engineering. This paper argues that in this post-cybernetic setting, religion functions as a sociological operating system designed for political control rather than a metaphysical connection to the divine. The study analyzes the Missionaria Protectiva to demonstrate how the Bene Gesserit order creates belief systems by co-opting and re-engineering Islamic theology. It suggests that the order’s manual of superstitions serves as a library of cultural scripts that primes the indigenous population to accept a manufactured Messiah, specifically the Mahdi. Consequently, the protagonist Paul Atreides is reinterpreted not as a traditional “White Savior” or authentic religious prophet but as a “hacker” who utilizes these pre-planted Islamic codes to access and manipulate the social infrastructure of Arrakis. His prescience functions as a form of biological predictive analytics that traps him in a deterministic loop of his own calculation. Ultimately, this reading suggests that Dune offers a critique of “techno-theology” by showing how the instrumentalization of the Mahdi figure transforms the concept of Jihad from a spiritual struggle into an unstoppable, automated algorithm of violence. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article

Underground mining environments are complex and hazardous operations where emergencies continue to happen. Underground mine emergencies require rapid, high-stakes decision-making under conditions of uncertainty, stress, and limited visibility. Conventional mine emergency training largely relies on instruction-based approaches which provide insufficient exposure to the cognitive and behavioral demands of real underground emergency situations. There has been an identified need to train miners for knowledge, skills, abilities, and other characteristics (KSAOs). This study proposes an Adaptive Immersive Training Framework (AITF), a cognitively grounded architecture that integrates cognitive task analysis (CTA), KSAOs, and situational awareness assessment for miner self-escape training and readiness. The AITF aligns NIOSH-identified self-escape competencies with immersive training scenarios designed to assess and develop cognitive readiness and decision-making. CTA of historical mine accidents is introduced as a foundational design method for translating accident investigation findings into simulation scenarios and performance metrics. A CTA of 2006 Darby Mine No. 1 explosion is presented as a proof of concept. The proposed framework supports individualized assessment, iterative scenario refinement, and data-driven feedback. The AITF advances miner training toward cognitive preparedness during mine emergencies and provides a foundation for future training systems that leverage digital tools, digital twins, and artificial intelligence for the mines of the future. Full article

This article examines the politics of visuality in Israel through the case study of Alami House, a Palestinian home in the village of Hiribya that became the nucleus of Kibbutz Ziqim in 1949 and was later transformed into a heritage site near the Gaza border. Drawing on theories of visual culture, affect, and heritage, the study traces the shifting visual and ideological functions of the site—from its early use as a kibbutz “watchtower,” through its renovation and rebranding as a heritage museum and wine bar, to its symbolic role during and after the Gaza War. It argues that the Israeli gaze toward the Palestinian—manifested in both the spatial design and the performative experience of the site—embodies a dual operation of seeing and unseeing, whereby the Palestinian is simultaneously acknowledged and erased. The essay introduces the concept of disciplined visuality to describe this politically orchestrated management of what may be seen, remembered, or forgotten. By analyzing Alami House as a microcosm of Israeli heritage-making, the article reveals how visuality functions as a tool of power, shaping both the material and conceptual landscape of the Israeli–Palestinian conflict. Full article

In this paper, a new type of soft group called the soft symmetric difference group (SSD-group) is introduced and systematically developed. This structure is constructed by integrating soft set theory with group theory through the symmetric difference operation and set inclusion. Fundamental concepts such as characteristic soft symmetric difference groups, soft symmetric difference subgroups, normal soft symmetric difference subgroups, soft normalizers, and soft cosets are defined, and their essential algebraic properties are investigated. Several characterizations of soft normality are also established through these concepts. Various axiomatic results are obtained, providing necessary and sufficient conditions for a soft set to form an SSD-group. Furthermore, soft quotient (factor) groups of SSD-groups are introduced and their structural properties are examined in detail. The relationship between SSD-group theory and classical group theory is also established through several corresponding concepts. Illustrative examples are provided to demonstrate the applicability and internal consistency of the proposed framework. Overall, the results obtained in this study extend existing soft group structures and contribute to the development of algebraic theory within the context of soft sets, while also providing a foundation for further generalizations to other algebraic frameworks such as semigroups, rings, and fields. Full article

Sustainability can be broadly understood as the capacity of human societies to operate within ecological limits while maintaining long-term social and economic stability. Within global policy frameworks, it has evolved from a normative ideal to a structured and measurable paradigm of governance. This article outlines the institutional and political evolution of sustainability, tracing how international agreements—from the 1972 Stockholm Conference to Agenda 2030 and the Paris Agreement—have transformed environmental concerns into quantifiable commitments. The modern concept of sustainability emphasises integration across sectors and scales, linking environmental protection with development, equity, and resilience. Understanding this trajectory is essential for interpreting current global governance mechanisms and for promoting coherent, data-driven approaches to sustainable development. Full article

Chemical coagulation is a highly important step of the conventional treatment processes, determination of the optimum coagulant dose to meet the demand of particulate materials and natural organic matters (NOMs) in raw water is crucial for good drinking water quality. WTC-Coag is a universal non-site-specific coagulant prediction model using three raw water quality parameters, UV₂₅₄, colour, and turbidity, as model inputs. The empirical model can determine the dose for maximum dissolved organic carbon (DOC) removal to achieve the conditions of enhanced coagulation; it also features an operator-selectable input—% setpoint (as % DOC removal)—to establish a dose for the desirable treated water quality. This hybrid modelling and control approach in practice is extremely useful for operators to be able to optimise the process by balancing between water quality and use of resources (chemical and sludge disposal costs) for sustainable operation. This paper discusses the practicality of this hybrid modelling approach via a long-term evaluation by comparing the plant dose against predicted dose using five years historical operations and water quality data. The assessment covered raw water quality change against treatment performance, predictability, usability and operator behaviour in response to the dose change situation. During the study period, five “black water” events were captured, and the performance of the predictability due to operational changes and operator’s response in these extreme events have been analysed. The comparison between the predicted enhanced dose and the plant dose indicated enhanced coagulation would not be always required. Furthermore, the selection of 50% setpoint from the targeted dose option matched well with the plant dose during which the lower-dose situation would be sufficient, with 90% of the predicted doses within ±10 mg/L of the plant dose and 95% of the predicted doses within ±15 mg/L of the plant dose during the normal period. The use of a correction factor to compensate for the particulate demand due to powdered activated carbon (PAC) dose during “black water” events has shown to be effective. The 50% setpoint matches with the plant alum dose over the entire period after accounting for the PAC dose, with 70% of the predicted doses within ±10 mg/L and 80% within ±15 mg/L of the plant dose. All the coagulation-related prediction functions have been evaluated and confirmed their non-site-specific nature. This study is unique in terms of using real operations data for an extended period to evaluate this novel hybrid modelling concept towards the sustainability goal. Full article

The aging of oil-paper insulated transformer components significantly impacts their service life. Accurate health assessment is crucial for predicting failure rates and residual life, which is vital for ensuring operational safety. This paper employs the bathtub curve concept and Weibull distribution to fit collected oil-paper insulated transformer failure rate data, obtaining the failure rate curve. Considering operational environment and load factors, a health index model is established for residual life prediction. By optimizing the weight and bias parameters of the backpropagation (BP) neural network using an adaptive chaotic sequence strategy, a multi-parameter correlated transformer life prediction model is constructed. A cross-validation mechanism is introduced to enhance the model’s generalization ability. Experimental results from training and testing demonstrate that the proposed method achieves higher prediction accuracy, with average errors of 5.36% for annual failure rate and 3.32% for residual life, confirming its effectiveness and applicability in transformer life prediction. Full article

The increasing deployment of collaborative and industrial robots in manufacturing systems places high demands on equipment reliability, availability, and maintenance efficiency. Robotic workcells, in which multiple automated subsystems operate in tightly coordinated cycles, are particularly sensitive to unplanned downtime, as failures of individual components can disrupt the entire production process. Traditional time-based preventive maintenance is often insufficient under such conditions, as it does not adequately reflect actual operating loads or component degradation. This paper proposes a structured framework for the design of an integrated maintenance concept for a multi-robot packaging workcell. The framework systematically combines component identification, criticality assessment, and the selection of appropriate maintenance strategies, including preventive, predictive, corrective, proactive, and reactive approaches. Preventive maintenance is complemented by condition-based monitoring and trend analysis of selected diagnostic parameters, enabling predictive decision-making for critical components. The proposed methodology further integrates maintenance planning and performance evaluation through a computerized maintenance management system (CMMS), supporting the coordination of maintenance activities and the assessment of key performance indicators. The novelty of the proposed framework lies primarily in the dynamic allocation of maintenance strategies based on semi-quantified component criticality and in the structured integration of predictive diagnostic information with CMMS-supported maintenance planning. Unlike traditional RCM-based or single-strategy maintenance approaches, the framework enables coordinated preventive, predictive, corrective, proactive, and reactive actions within a unified decision-making architecture, supporting proactive continuous improvement of maintenance performance through a closed-loop feedback mechanism that updates component criticality based on real-time operational data. The framework is demonstrated on a robotic workcell comprising a collaborative robot, an industrial robot, pneumatic subsystems, and a centralized control architecture. The results suggest that the integrated approach may provide a coherent basis for reducing reactive maintenance actions, improving system availability, and supporting data-driven maintenance planning. As a conceptual framework with partial (pilot) practical implementation within the context of this paper, the proposed approach establishes a foundation for future broader implementation, experimental validation and the integration of advanced diagnostic and prognostic methods, mainly in the context of multi-Robot workcell and production process maintenance. Full article

Urban areas require context-specific bioenergy solutions to advance toward circular and sustainable energy systems. In Bogotá, urban pruning and grass-cutting residues constitute a relatively stable biomass stream; however, the absence of district-scale valorization infrastructure leads to their direct disposal in landfill. This study develops and applies a GIS-based planning methodology to support the territorial design of a small-scale anaerobic digestion plant using urban green waste. In this study, “small-scale” is understood as an early-stage urban facility concept compatible with the available pruning stream of approximately 1200–1300 t/month of valorizable biomass, corresponding only to an order-of-magnitude energy range of a few hundred kW_e/kW_t, rather than to a final engineering design. The approach integrates official geospatial data with logistical, environmental, and institutional criteria to characterize biomass availability and evaluate location alternatives under real urban constraints. A continuous location model based on the Weber problem is first applied to estimate a theoretical lower bound of spatial effort, using public schools weighted by enrollment as a proxy for sensitive urban demand. Subsequently, a GIS-assisted Analytic Hierarchy Process (AHP) is implemented to incorporate environmental exclusions, territorial compatibility, and the operational structure of exclusive waste service areas. Results show that the optimal geometric location diverges from the territorially feasible alternative once environmental restrictions and biomass supply coherence are explicitly considered. The findings highlight that urban bioenergy infrastructure planning is governed less by pure spatial efficiency than by the integration of supply, demand, and institutional constraints. The proposed methodology provides a reproducible decision-support tool for urban bioenergy planning and contributes to sustainable waste management, circular economy strategies, and local energy resilience in cities of the Global South. Full article

Although the transition from the Waterfall model to Agile practices has accelerated software delivery, it has often weakened documentation practices, contributing to persistent information asymmetry between Product Managers and Developers. This study introduces an LLM-based reverse engineering framework designed to assist product management workflows by analyzing source code and generating enriched development tickets. The proposed Interactive Product Management Assistant leverages the long-context capabilities of Gemini 1.5 Pro together with a context-caching mechanism to analyze large codebases, identify ambiguities in product requests, highlight potential edge cases, detect possible cascading dependencies (“domino effects”), and generate code pointers that guide developers to relevant implementation areas. The framework was evaluated through case studies on several open-source projects, including WordPress, ERPNext, Ghost, and Odoo. The results suggest that the system can support requirement clarification, improve visibility of potential implementation impacts, and reduce exploratory effort during code analysis. In addition, the implemented preprocessing and caching mechanisms reduce analysis costs and improve operational efficiency during iterative interactions. Rather than providing a large-scale quantitative before-and-after comparison, this paper presents a qualitative case study and a proof-of-concept implementation to demonstrate the feasibility of the proposed approach. Overall, the findings demonstrate the feasibility of using LLM-assisted reverse engineering to support requirements analysis and product–developer collaboration, highlighting the potential of AI-based tools to complement traditional requirements engineering practices in complex software projects. Full article