Search Results (3,084)

The railway architectural heritage in Jilin Province, as a significant component of Northeast China’s modern railway network, demonstrates how construction techniques, cultural integration, and social transformation have evolved throughout different historical periods. In this study, we conducted a systematic survey of 474 railway heritage buildings along the province’s main line. In order to quantitatively classify the spatiotemporal distribution characteristics of the heritage sites, we used five key Geographic Information System (GIS) methods—kernel density estimation, nearest neighbour index, spatial autocorrelation, standard deviational ellipses, and mean centre analysis—along with information entropy, relative richness, and the Bray–Curtis dissimilarity index. We continued our binary logistic regression using four prerequisite parameters—location, structure, architecture, and function—which contribute to the prerequisite, fundamental, and driving factors of architectural heritage. We concluded that local culture shapes geopolitics, population migration triggers economic conservation, and design trends carry ideology. These three factors intertwine to influence architecture and spatial patterns. Compared with previous studies, this research fills the gap concerning the architectural characteristics of towns at various lower-and mid-level stations, as well as the construction activities during the affiliated land period. This study provides a systematic framework for analysing railway heritage corridors and supports their sustainable conservation and reuse. Full article

The Yangtze River fishing ban policy is a central measure in China’s watershed governance, and the adaptability of its policy tools and collaborative mechanisms directly influences the sustainability and effectiveness of basin management. This study systematically examines the evolution of policy themes, the characteristics of policy tool combinations, and their alignment with intergovernmental collaborative governance needs, drawing on 120 central government policy texts issued between 2002 and 2024. Using frequency analysis and policy tool coding, the findings reveal that (1) policy themes have shifted from fishery resource control to comprehensive ecological protection and, more recently, to integrated watershed management, thereby driving progressively higher demands for intergovernmental collaboration. (2) The policy tool structure has long been dominated by environmental tools, supplemented by supply-side tools, while demand-side tools remain underdeveloped. Imbalances persist, such as excessive emphasis on resource inputs over capacity building in supply-side tools, rigid constraints with limited flexibility in environmental tools, and a reliance on publicity while underutilizing market incentives in demand-side tools. (3) Tool combinations have adapted to changing collaboration needs, evolving from rigid constraints and fiscal subsidies to institutional frameworks and cross-regional cooperation, ultimately forming a governance model characterized by systemic guarantees and diversified collaboration. Based on these findings, this study recommends strengthening long-term governance mechanisms, improving cross-regional collaborative structures, authorizing local governments to design context-specific implementation details, enhancing fishermen’s livelihood security and social development, expanding public participation and oversight, and exploring market mechanisms for realizing ecological product value. These measures aim to advance collaborative governance in the Yangtze River Basin and foster a balanced integration of ecological protection and social development. Full article

Higher technical education plays a strategic role in equipping the workforce to navigate rapid technological advancements and evolving labor market demands. Within the Rome Technopole framework, Spoke 4 targets ITS Academies, promoting the development of flexible, modular programs that integrate advanced technical skills with ethical, legal, and societal perspectives. This study reports on a pilot training initiative on Artificial Intelligence (AI) co-designed by the Istituto Superiore di Sanità (ISS), aimed at exploring the ethical, practical, and educational relevance of AI in higher technical education. The module was developed and tested through a multi-stakeholder collaboration involving educators, institutional actors, and learners. A four-phase approach was adopted: (1) initial stakeholder consultation to identify needs and content directions, (2) collaborative design of the training module, (3) online delivery and engagement using a CAWI-based focus group, and (4) mixed-method evaluation, combining quantitative assessments and open-ended qualitative feedback. This design facilitated asynchronous participation and encouraged critical reflection on the real-world implications of AI. Through the four-phase approach, the pilot module was developed, delivered, and assessed with 37 participants. Quantitative analysis revealed high ratings for clarity, relevance, and perceived utility in terms of employability. Qualitative feedback highlighted the interdisciplinary design, the integration of ethical reasoning, and the module’s broad applicability across sectors—particularly Healthcare and Industry. Participants suggested including more real-world case studies and collaborative learning activities to enhance engagement. The findings support the feasibility and added value of embedding ethically informed, interdisciplinary AI education in professional technical training pathways. Developed within the Rome Technopole ecosystem, the pilot module offers a promising approach to fostering critical digital literacy and preparing learners for responsible engagement with emerging technologies. Full article

Military kill chains are the sequence of events, tasks, or functions that must occur to successfully accomplish a mission. As the Department of Defense moves towards Combined Joint All-Domain Command and Control, which will require the coordination of multiple networked assets with the ability to share data and information, kill chains must evolve into kill webs with multiple paths to achieve a successful mission outcome. Mathematical frameworks for kill webs provide the basis for addressing the complexity of this system-of-systems analysis. A mathematical framework for kill chains and kill webs would provide a military decision maker a structure for assessing several key aspects to mission planning including the probability of success, alternative chains, and parts of the chain that are likely to fail. However, to the best of our knowledge, a generalized and flexible mathematical formulation for kill chains in military operations does not exist. This study proposes four probabilistic models for kill chains that can later be adapted to kill webs. For each of the proposed models, events in the kill chain are modeled as Bernoulli random variables. This extensible modeling scaffold allows flexibility in constructing the probability of success for each event and is compatible with Monte Carlo simulations and hierarchical Bayesian formulations. The probabilistic models can be used to calculate the probability of a successful kill chain and to perform uncertainty quantification. The models are demonstrated on the Find–Fix–Track–Target–Engage–Assess kill chain. In addition to the mathematical framework, the MIMIK (Mission Illustration and Modeling Interface for Kill webs) software package has been developed and publicly released to support the design and analysis of kill webs. Full article

Objective: Medical Visual Question Answering (Med-VQA), a key technology that integrates computer vision and natural language processing to assist in clinical diagnosis, possesses significant potential for enhancing diagnostic efficiency and accuracy. However, its development is constrained by two major bottlenecks: weak few-shot generalization capability stemming from the scarcity of high-quality annotated data and the problem of catastrophic forgetting when continually learning new knowledge. Existing research has largely addressed these two challenges in isolation, lacking a unified framework. Methods: To bridge this gap, this paper proposes a novel Evolvable Clinical-Semantic Alignment (ECSA) framework, designed to synergistically solve these two challenges within a single architecture. ECSA is built upon powerful pre-trained vision (BiomedCLIP) and language (Flan-T5) models, with two innovative modules at its core. First, we design a Clinical-Semantic Disambiguation Module (CSDM), which employs a novel debiased hard negative mining strategy for contrastive learning. This enables the precise discrimination of “hard negatives” that are visually similar but clinically distinct, thereby significantly enhancing the model’s representation ability in few-shot and long-tail scenarios. Second, we introduce a Prompt-based Knowledge Consolidation Module (PKC), which acts as a rehearsal-free non-parametric knowledge store. It consolidates historical knowledge by dynamically accumulating and retrieving task-specific “soft prompts,” thus effectively circumventing catastrophic forgetting without relying on past data. Results: Extensive experimental results on four public benchmark datasets, VQA-RAD, SLAKE, PathVQA, and VQA-Med-2019, demonstrate ECSA’s state-of-the-art or highly competitive performance. Specifically, ECSA achieves excellent overall accuracies of 80.15% on VQA-RAD and 85.10% on SLAKE, while also showing strong generalization with 64.57% on PathVQA and 82.23% on VQA-Med-2019. More critically, in continual learning scenarios, the framework achieves a low forgetting rate of just 13.50%, showcasing its significant advantages in knowledge retention. Conclusions: These findings validate the framework’s substantial potential for building robust and evolvable clinical decision support systems. Full article

This paper investigates the complex interplay between European and pre-Hispanic urban traditions in shaping colonial urbanism across the Americas, with particular emphasis on the transformation of the City of Mexico atop the remnants of the ancient city of Mexico-Tenochtitlan. It contends that the development of the viceregal capital was not merely a straightforward transplantation of the Castilian urban model, but rather a process profoundly influenced—and in many respects enabled—by the sophisticated spatial organisation of the Mexica metropolis. The research examines how the foundational urban layout of Mexico-Tenochtitlan informed the design of the colonial city, highlighting both continuities and divergences between indigenous and Castilian urban frameworks, and analysing the fusion of these traditions in the formation of a novel urban entity. Employing a historical-analytical methodology, this article combines documentary research, comparative analysis of urban configurations from both cultures, and case studies of early colonial settlements. The findings suggest that the City of Mexico evolved into a paradigm of hybrid urbanism, wherein European planning doctrines were adapted and interwoven with enduring indigenous spatial logics and symbolic systems—a synthesis that not only characterised the viceregal capital but also established a precedent for urban development throughout Spanish America. Full article

To address the current lack of specialized equipment for offshore wind platform installation and the unresolved challenges in deploying large offshore converter stations, this paper proposes a novel T–U-shaped barge for large offshore wind structures. First, a hydrodynamic model of the T–U-shaped barge is constructed and analyzed in ANSYS-AQWA. The influence of resonance occurring in the gap at the U-shaped stern on the frequency-domain model of the T–U-shaped barge is investigated. Subsequently, two installation configurations are examined: loading at the bow and loading at the stern of the T–U-shaped barge. This study comprehensively considers key components of the float-over installation system, including leg mating units (LMUs), deck support units (DSUs), fenders, and mooring cables. The results show that, for both installation schemes, the dynamic load distribution on each LMU evolves as the load-transfer stage progresses, and the sensitivity to wave period varies across different load-transfer stages, even under the same operating condition. This study evaluates the performance of the proposed T–U-shaped barge in the float-over installation of large offshore converter stations, demonstrating that its distinctive configuration endows it with strong functionality and provides valuable references for optimizing offshore wind-structure installation methods, as well as for the design and manufacturing of installation equipment. Full article

Addressing earthquake risk remains a significant global challenge, requiring rapid assessment of evacuation shelters for effective disaster response. Existing frameworks, such as FEMA’s Hazus, Copernicus EMS, and UNOSAT, offer valuable insights but are typically regional, static, and event-focused, lacking mechanisms for continuous shelter-level updates. This study introduces the Shelter Hazard Impact and Lifeline Outage Estimation (SHILOE) framework. SHILOE is a stepwise estimation approach integrating multisensor datasets for time-scaled assessments of shelter functionality and operability. These datasets include seismic intensity, liquefaction probability, tsunami inundation, IoT-derived power outage data, communication network disruptions, and social media. Application to the 2024 Noto Peninsula earthquake showed that ≥93.6% of designated and activated shelters were impacted by at least one hazard, with all experiencing at least one lifeline outage. The framework delivers estimates through three phases: immediate (within tens of minutes, e.g., simulation-based hazard models and lifeline data), intermediate (days, e.g., observation-based datasets), and refinement (ongoing, e.g., Social Networking Service and detailed field surveys). By progressively incorporating new data across these phases, SHILOE generates dynamic, facility-level insights that capture evolving hazard exposure and lifeline status. These outputs provide actionable information for emergency managers to prioritize resources, reinforce shelters, and sustain critical services, thereby advancing disaster resilience. Full article

The detection and characterization of exoplanets are central topics in astronomy, and high-contrast imaging techniques such nulling interferometry, coronagraphs, and extreme adaptive optics (ExAO) are key tools for the direct detection of exoplanets. This review synthesizes the pivotal role of these techniques in astronomical research and critically analyzes their role as key drivers of progress in the field. Nulling interferometry suppresses stellar light through the phase control of multiple telescopes, thereby enhancing the detection of faint planetary signals. This technology has evolved from the initial Bracewell concept to the LIFE (Large Interferometer For Exoplanets) technique, which will achieve a contrast ratio of 10⁻⁷ in the mid-infrared wavelength range in the future. Coronagraphs block starlight to create a “dark region” for direct observation of exoplanets. By leveraging innovative mask designs, theoretical contrast ratios of up to 4 × 10⁻⁹ can be achieved. ExAO systems achieve precise wavefront correction to optimize the high-contrast imaging performance and mitigate atmospheric disturbances. By leveraging wavefront sensing, thousand-element deformable mirrors, and real-time control algorithms, these systems suppress the turbulence correction residuals to 80 nm RMS, enabling ground-based telescopes to achieve a Strehl ratio exceeding 0.9. This work provides a comprehensive analysis of the underlying principles, prevailing challenges, and future application prospects of these technologies in astronomy. Full article

Drive-through establishments are becoming increasingly prominent in urban transport systems; however, their impacts on traffic generation, spatial form, and sustainability remain insufficiently understood. Conventional trip generation manuals often rely on static predictors, such as gross floor area, which can misrepresent demand in high-turnover, convenience-driven contexts and fail to capture operational, behavioral, and environmental effects. This knowledge gap underscores the need for an integrated framework that supports both effective planning and congestion mitigation, particularly in cities experiencing rapid motorization and shifting mobility behaviors. This study investigated the evolving dynamics in trip generation associated with drive-through services and their influence on urban development patterns. A mixed-methods approach was employed, combining a systematic literature review, meta-analysis of queue data, cross-comparison of trip generation rates from international and Asian datasets, and case-based scenario modeling. The results revealed that drive-throughs intensify high-frequency, impulse-driven vehicle trips, thereby causing congestion, reducing pedestrian accessibility, and reinforcing auto-centric land use configurations, while also enhancing consumer convenience and commercial efficiency. This study contributes to the literature by synthesizing inconsistencies in regional datasets; introducing a systems-based framework that integrates structural, behavioral, and environmental determinants with road network topology; and outlining policy applications that align trip generation with zoning, design standards, and sustainable infrastructure planning. Full article

Semiconductor wafer manufacturing is one of the most complex and data-intensive industrial processes, comprising 500–1000 tightly interdependent steps, each requiring nanometer-level precision. As device nodes approach 3 nm and beyond, even minor deviations in parameters such as oxide thickness or critical dimensions can lead to catastrophic yield loss, challenging traditional physics-based control methods. In response, the industry has increasingly adopted regression analysis and predictive modeling as essential analytical frameworks. Classical regression, long used to support design of experiments (DOE), process optimization, and yield analysis, has evolved to enable multivariate modeling, virtual metrology, and fault detection. Predictive modeling extends these capabilities through machine learning and AI, leveraging massive sensor and metrology data streams for real-time process monitoring, yield forecasting, and predictive maintenance. These data-driven tools are now tightly integrated into advanced process control (APC), digital twins, and automated decision-making systems, transforming fabs into agile, intelligent manufacturing environments. This review synthesizes foundational and emerging methods, industry applications, and case studies, emphasizing their role in advancing Industry 4.0 initiatives. Future directions include hybrid physics–ML models, explainable AI, and autonomous manufacturing. Together, regression and predictive modeling provide semiconductor fabs with a robust ecosystem for optimizing performance, minimizing costs, and accelerating innovation in an increasingly competitive, high-stakes industry. Full article

The rapid emergence of antibiotic-resistant bacteria requires solutions that extend beyond conventional antibiotics. Bacteriophages (phages) provide targeted antibacterial action but face two key limitations: (1) their narrow natural host ranges and (2) the rapid emergence of evolved bacterial resistance. This review focuses specifically on evolved resistance and highlights two complementary strategies to overcome it by using phage-adaptive evolution and manipulating bacterial fitness trade-offs. Adaptive evolution accelerates phage/bacteria coevolution under host-mediated and environmental selective pressures such as receptor variability, bacterial resistance mutations, and nutrient limitations, resulting in phages with broader host targeting within resistant populations and enhanced lytic activity. Simultaneously, bacterial resistance to phages often leads to fitness costs, including restored antibiotic susceptibility or reduced virulence. These strategies support the rational design of phage/antibiotic combinations that suppress resistance and enhance therapeutic efficacy. In this review, we clarify the distinction between intrinsic host range limitations and evolved resistance, focusing on how adaptive strategies can specifically counter the latter. We discuss the underlying mechanisms, practical applications, and significance of this approach in clinical, agricultural, and environmental areas. Full article

Background: Stapes prostheses represent one of the earliest and most widely applied “biomedical actuators” designed to restore hearing in patients with otosclerosis. Unlike conventional actuators, which convert energy into motion, stapes prostheses function as passive or smart micro-actuators, transmitting and modulating acoustic energy through the ossicular chain. Objective: This paper provides a comprehensive analysis of stapes prostheses from an engineering and biomedical perspective, emphasizing design principles, materials science, and recent innovations in smart actuators based on shape-memory alloys combined with surgical applicability. Methods: A narrative review of the evolution of stapes prostheses was consolidated by institutional surgical experience. Comparative evaluation focused on materials (Teflon, Fluoroplastic, Titanium, Nitinol) and design solutions (manual crimping, clip-on, heat-activated prostheses). Special attention was given to endoscopic stapes surgery, which highlights the ergonomic and functional requirements of new device designs. Results: Traditional fluoroplastic and titanium pistons provide reliable sound conduction but require manual crimping, with a higher risk of incus necrosis and displacement. Innovative prostheses, particularly those manufactured from nitinol, act as self-crimping actuators activated by heat, improving coupling precision and reducing surgical trauma. Emerging designs, including bucket-handle and malleus pistons, expand applicability to complex or revision cases. Advances in additive manufacturing and middle ear cement fixation offer opportunities for customized, patient-specific actuators. Conclusions: Stapes prostheses have evolved from simple passive pistons to innovative biomedical actuators exploiting shape-memory and biocompatible materials. Future developments in stapes prosthesis design are closely linked to 3D printing technologies. These developments have the potential to enhance acoustic performance, durability, and patient outcomes, thereby bridging the gap between otologic surgery and biomedical engineering. Full article

This paper presents a comparative study on the transdisciplinary design of resilient urban subcenters, examining the interplay between human-led and artificial intelligence (AI)-generated design approaches. By employing holistic design methods, we prepare and present revitalization projects for two areas of urban space. Our goal was to create a resilient urban subcenter that contributes to the development of a resident. The first revitalized site reflects the multicultural past of the city. The second project addresses the need to revitalize a subcenter reserved for residents. In the non-AI approach, holistic design is implemented across various universities, fields, and academic disciplines—the humanities, social sciences, engineering, and the arts. Transdisciplinary teams of sociologists, engineers, interior designers, architects, urban geographers, and acousticians transcend workshop limitations as well as cognitive boundaries, promoting the creation of new, unconventional knowledge. The AI-integrated approach employs artificial intelligence in a dual capacity: both as a generator of alternative design visions and as an analytical tool for assessing technological readiness. The findings contribute to the evolving discourse on sustainable urban development and the transformative potential of technology in transdisciplinary design practices. Full article

At present, the Beetle Antennae Search (BAS) algorithm has achieved remarkable success in image segmentation. However, when dealing with some complex image segmentation problems, particularly in the context of instance segmentation, which aims to identify and delineate each distinct object of interest, even within the same semantic class, there are problems such as poor optimization performance, slow convergence speed, and low stability. Therefore, to address the challenges of instance segmentation, an improved image segmentation model is proposed, and a novel BAS algorithm called the Crossover and Mutation Beetle Antennae Search (CMBAS) algorithm is designed to optimize it. The core of our approach treats instance segmentation as a sophisticated clustering problem, where each cluster center corresponds to a unique object instance. Firstly, an improved intra-class distance based on fuzzy membership weighting is designed to enhance the compactness of individual instances. Secondly, to quantify the genetic potential of individuals through their fitness performance, CMBAS uses an adaptive crossover rate mechanism based on fitness ranking and establishes a ranking-driven crossover probability allocation model. Thirdly, to guide individuals to evolve towards excellence, CMBAS uses a strategy for individual mutation of longicorn beetle antennae based on DE/current-to-best/1. Furthermore, the symmetry-aware adaptive crossover and mutation operations enhance the balance between exploration and exploitation, leading to more robust and consistent instance-level segmentation results. Experimental results on five typical benchmark functions demonstrate that CMBAS achieves superior accuracy and stability compared to the BAGWO, BAS, GWO, PSO, GA, Jaya, and FA algorithms. In image segmentation applications, CMBAS exhibits exceptional instance segmentation performance, including an enhanced ability to distinguish between adjacent or overlapping objects of the same class, resulting in smoother and more continuous instance boundaries, clearer segmented targets, and excellent convergence performance. Full article