Search Results (427)

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article

This paper presents the design, manufacturing, instrumentation and validation by tests (ground and icing wind tunnel) of a full-scale Hybrid Laminar Flow Control (HLFC) leading-edge demonstrator based on Airbus A330 outer wing plan-form. The Ground-Based Demonstrator (GBD) was developed to reproduce a full-scale, realistic wing section integrating into the leading-edge three key systems: micro-perforated skin for the hybrid laminar flow control suction system (HLFC), the hot-air Wing Ice Protection System (WIPS) and a folding “bull nose” Krueger high-lift device. The demonstrator combines a superplastic-formed and diffusion-bonded (SPF/DB) perforated titanium skin mounted on aluminum ribs jointed with a carbon-fiber-reinforced polymer (CFRP) wing box. Titanium internal ducts were designed to ensure uniform hot-air distribution and structural compatibility with composite components. Manufacturing employed advanced aeronautical processes and precision assembly under INCAS coordination. Ground tests were performed using a dedicated hot-air and vacuum rig delivering up to 200 °C and 1.6 bar, thermocouples and pressure sensors. The results confirmed uniform heating (±2 °C deviation) and stable operation of the WIPS without structural distortion. Relevant tests were performed in the CIRA Icing Wind Tunnel facility, verifying the anti-ice protection system and Krueger device. The successful design, fabrication, testing and validation of this multifunctional leading edge—featuring integrated HLFC, WIPS and Krueger systems—demonstrates the readiness of the concept for subsequent aerodynamic testing. Full article

Background: Traumatic brain injury (TBI) is a leading cause of disability but one of the least recognized health problems in the world, affecting up to 69 million people annually. The associated lifelong disability in survivors, the loss of economic productivity, and being a risk factor for dementia consume 0.5% of global economic activity. Yet TBI is still largely invisible in national surveillance systems and not well represented in chronic disease frameworks. Consequently, governments are not equipped to provide proportional financing of acute care and long-term care of survivors, nor to build health care systems and resources for improving outcomes of TBI through policy frameworks targeting prevention, treatment, and equitable access. Objective: This commentary aims to provide a comprehensive picture of the global effort to formally recognize TBI as a notifiable and chronic condition, including the justifications for recognition, the formation of an international coalition of stakeholders, and the strategic plan for resolution at WHA79 of the World Health Assembly, one of the first concerted multinational efforts that occurred as a side event during the 78th World Health Assembly (WHA78) in May 2025. Methods: This commentary integrates information from epidemiological studies, global registries, and testimonies from people with lived experience of TBI. We analyze these data to develop policy needs and corresponding initiatives to address key needs. These include coordinated efforts to advocate change, such as technical briefings, consultations with stakeholders, and storytelling led by survivors, all of which informed and formed a part of the WHA78 side event. Our efforts have garnered wide, multi-sector support. Results: The WHA78 side event showed that ministries of health, neurosurgical, neurological, and rehabilitation societies, academic researchers, WHO representatives, and survivors all unprecedentedly support the recognition of the importance of TBI, facilitating national policies for its prevention and treatment via standardized surveillance. More than 30 non-governmental groups officially supported the campaign. A sponsoring member state made a public commitment to co-sponsor a WHA resolution, which set the stage for ongoing diplomatic progress and engagement across regions. Conclusion: To improve global brain health equity, access to long-term care, and the resilience of health systems, it is important to recognize TBI as a notifiable and chronic condition. A dedicated WHA resolution would make TBI a part of global health governance, making sure that it is counted, tracked, and dealt with as quickly and comprehensively as possible. It is both a technical necessity and a moral duty to help survivors and families and fight for justice in global health systems. Full article

The tool-point frequency response function (FRF) of a spindle–tool system plays a crucial role in predicting machining stability. Among the factors influencing the FRF, the interface characteristics between the spindle and the tool holder are particularly significant, especially when different holder designs are used. This study focused on identifying these interface characteristics for two common tool holder types—BT and BBT—to improve FRF prediction accuracy. The receptance coupling substructure analysis (RCSA) method was employed in conjunction with finite element modeling (FEM) to characterize the spindle–tool holder interfaces without needing extensive experimental tapping tests. Finite element models were developed to generate receptance components for various tool holder–tool assemblies, enabling efficient and accurate coupling within the RCSA framework. The identified interface parameters were applied to predict the tool-point FRFs of the cutter clamped in a BT tool holder with different overhang lengths. The predicted and measured tool compliances differed by 3–6.4%, demonstrating high agreement and reliability. The proposed methodology provides a powerful tool for predictive modeling of dynamic behavior in spindle–tool systems under varying tooling conditions, enhancing process planning and evaluation of the cutting stability in high-precision machining. Full article

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic. Full article

This paper presents a two-layer consistency control framework for the collaborative assembly of multiple aircraft in complex environments, comprising a low-level control layer and a high-level guidance layer. The control layer develops a robust anti-interference law by integrating an extended state observer (ESO) with Backstepping for attitude control and employing constrained Backstepping for velocity regulation. The guidance layer ensures safe and coordinated assembly. A time-varying communication topology is adopted to guarantee collision-free maneuvers. An assembly trajectory is generated for each aircraft based on a position allocation strategy and the Dubins path planning method. To achieve time-coordinated arrival, a speed consensus protocol is designed, guiding the aircraft into a sparse formation. Subsequently, consensus-based control laws for both attitude and velocity are implemented to transition into a tight formation. The effectiveness of the proposed framework is validated through aircraft six-degree-of-freedom (6-DoF) simulations, which confirm that it significantly improves the safety and robustness of the multi-aircraft assembly process. Full article

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article

In the field of earthquake-resistant design, there is an increasing emphasis on evaluating buildings as integrated systems rather than as assemblies of independent components. Hybrid wall systems based on structural steel and reinforced concrete offer a promising alternative to existing approaches by combining the stiffness and toughness of concrete with the ductility and flexibility of steel, which enhances resilience and seismic performance. The objective of this scientific study is to obtain preliminary analytical estimates of the earthquake response of a prototype hybrid steel RC coupled wall building that is being developed as part of a joint research program between the National Center for Research on Earthquake Engineering (NCREE) and New Zealand’s Centre for Earthquake Resilience (QuakeCoRE). Nonlinear response history analyses were carried out on the prototype building, using scaled ground motions and nonlinear hinge properties assigned to the primary lateral force resisting elements to replicate the expected inelastic behavior of the hybrid system. The results were used to evaluate story drift demands, deformation patterns, coupling beam behavior, and buckling restrained brace behavior, providing a system-level perspective on the expected earthquake performance of the proposed hybrid wall system. To deepen the current experimental understanding of the seismic behavior of the proposed hybrid structural system, a large-scale shaking table test is planned at NCREE as the next stage of this collaborative research. Full article

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed. Full article

Automated precision drilling is essential for aircraft skin manufacturing, yet current robotic systems face dual challenges: chatter-induced inaccuracies in hole quality and limited access to confined spaces such as air inlets. To overcome these limitations, this paper develops a compact drilling robot for drilling large-curvature skins of aircraft air inlets. Targeting the precision drilling requirements for complex-curvature aircraft air inlets, we present the robot’s overall design scheme, detailing each module’s composition to ensure precision drilling. In-depth analysis of the robot’s large-curvature adaptability precisely calculates the wheel assembly dimensions. To ensure high-precision drilling bit entry into guide mechanisms, a flexible drilling spindle mechanism is designed, with calculated and verified elastic ranges. An integrated intelligent control system is developed, combining vision recognition, real-time pose adjustment, and automated drilling workflow planning. Finally, traversability and drilling capabilities are validated using a simplified air inlet model. Test results confirm successful traversal on R200 mm curvature skins and automated drilling of Carbon Fiber-Reinforced Polymer (CFRP)/7075 aluminum stacks with a diameter of Φ4–Φ6 mm, achieving dimensional errors of less than 0.05 mm and normal direction errors of less than 0.65°. Full article

This article examines whether, and under what conditions, there is room for new mosques in Belgian cities by analyzing how media controversies around mosque projects are assembled. We study a corpus of press articles (2014–2024) using a two-step approach: First, keyword mapping identifies dominant discursive patterns across six themes (mobility, legality, size and visibility, social cohesion and integration, security and extremism, financing). Second, argument coding links lexical signals to public modes of judgment through actor–network theory (ANT) and controversy registers. Applied to five case studies across Flanders, Wallonia, and the Brussels-Capital Region, this framework offers comparative depth. The results show that identity and security controversies frequently outweigh strict urban planning controversies; neutral planning criteria (e.g., traffic congestion, permit compliance) are often recoded as symbolic markers of alterity. Regional contrasts provide nuance to this pattern: in Flanders, politicization through security/identity is salient; in Wallonia, debates emphasize size, form, and spatial integration; in Brussels-Capital, technico-legal compliance intertwines with aesthetic visibility. Media operate as boundary objects that hierarchize registers and amplify controversies. We conclude that mosques are treated less as ordinary urban infrastructure than as contested symbols of belonging and visibility. Moving toward negotiated pluralism requires institutional mechanisms that ensure transparency, equal treatment, local anchoring, and symbolic requalification. Full article

We presented a biomimetic approach to designing robot-to-human handover of objects in a collaborative assembly task. We developed a human–robot hybrid cell where a human and a robot collaborated with each other to perform the assembly operations of a product in a flexible manufacturing setup. Firstly, we investigated human psychology and biomechanics (kinetics and kinematics) for human-to-robot handover of an object in the human–robot collaborative set-up in three separate experimental conditions: (i) human possessed high trust in the robot, (ii) human possessed moderate trust in the robot, and (iii) human possessed low trust in the robot. The results showed that human psychology was significantly impacted by human trust in the robot, which also impacted the biomechanics of human-to-robot handover, i.e., human hand movement slowed down, the angle between human hand and robot arm increased (formed a braced handover configuration), and human grip forces increased if human trust in the robot decreased, and vice versa. Secondly, being inspired by those empirical results related to human psychology and biomechanics, we proposed a novel robot-to-human object handover mechanism (strategy). According to the novel handover mechanism, the robot varied its handover configurations and motions through kinematic redundancy with the aim of reducing potential impulse forces on the human body through the object during the handover when robot trust in the human was low. We implemented the proposed robot-to-human handover mechanism in the human–robot collaborative assembly task in the hybrid cell. The experimental evaluation results showed significant improvements in human–robot interaction (HRI) in terms of transparency, naturalness, engagement, cooperation, cognitive workload, and human trust in the robot, and in overall performance in terms of handover safety, handover success rate, and assembly efficiency. The results can help design and develop human–robot handover mechanisms for human–robot collaborative tasks in various applications such as industrial manufacturing and manipulation, medical surgery, warehouse, transport, logistics, construction, machine shops, goods delivery, etc. Full article

Driven by Industry 5.0, efficient obstacle avoidance of robotic arms in dynamic environments is a key bottleneck for human–robot collaboration in smart manufacturing. Traditional path planning methods such as Rapidly-exploring Random Tree and artificial potential field work stably in static settings but exhibit flaws including path oscillation and poor real-time performance under dynamic obstacles. Deep reinforcement learning adapts to environmental changes but is limited by low sample efficiency and high computational costs, failing industrial demands. This study proposes a collaborative framework integrating improved Rapidly-exploring Random Tree Star and Deep reinforcement learning. It uses Rapidly-exploring Random Tree Star to guide Deep reinforcement learning’s strategy exploration, reducing invalid sampling by 62%, and leverages Deep reinforcement learning’s global optimization to enhance dynamic obstacle prediction. The framework achieves a task success rate of 93.8%, surpassing traditional Rapidly-exploring Random Tree Star by 21.5%, with an average path length of 1.97 m and system energy consumption of 12.6 kWh. Experiments demonstrate superior performance in extreme dynamic scenarios, including a 94.7% success rate in multi-robot collaboration. Industrial cases confirm improvements in automobile manufacturing assembly cycle time to 8.4 s per task, yield rate to 98.7%, and reductions in energy consumption by 34% and human intervention by 85.6%, providing a reliable dynamic obstacle avoidance solution for Industry 5.0 applications. Full article

Ex-ante appraisal of agricultural policy needs a transparent way to trace how sectoral interventions translate into production. We study the Polish CAP case and ask how much selected actions matter for livestock sectors. We assembled intervention-level budgets from the CAP Strategic Plan for Poland (2023–2027) and sectoral final output for milk, pigs, beef and poultry from Statistics Poland/Eurostat. We built matrices that map actions to sectors, normalized transfers by sectoral output, and separated dedicated from spillover effects. We report two cross-sections (2024, 2028) and a robustness test that perturbs I 1–I 2 allocation shares by ±10% under fixed envelopes. Horizontal income support dominates. In 2024, the cumulative effect of all analyzed actions equaled 16.68% of final output in milk, 14.43% in beef, 5.15% in pigs and 4.29% in poultry; by 2028, these values ease to 15.07%, 12.93%, 3.84% and 4.15%. Coupled payments to cows and young cattle add contributions in milk and beef. The ±10% reweighting of I 1–I 2 keeps the sector ranking unchanged; level changes are moderate (about 0.4–1.2 percentage points). A compact matrix approach provides a replicable map from interventions to sectors and highlights the preponderance of horizontal income support. The pattern—strongest relative support in milk and beef—appears robust to plausible allocation uncertainty. The main limitation is the use of final output as a revenue proxy; extending the matrix to all CAP actions and adding price–quantity feedback would be a natural next step. Policy implication: modest rebalancing of I 1–I 2 shares will not overturn sectoral exposure, but adjustments targeted at beef move levels the most. Full article

Urban modeling is being reshaped by advances in artificial intelligence (AI) and data-rich sensing. This review assembles an integrated evidence base connecting spatial dynamic modeling (SDM), planning support systems (PSSs), urban analytics, and governance concerns. We analyze 1290 publications (2000–2025) using a reproducible pipeline that combines structured literature retrieval with retrieval-augmented generation (RAG) for semantic screening and evidence extraction. Bibliometric mapping and a rigorous coding framework structure the synthesis. The results reveal three linked trajectories. First, SDM has progressed from rule-based simulation toward learned spatial representations using deep and multimodal learning. Second, PSS has evolved from static analytical tools to interactive and participatory environments that embed AI for scenario exploration and stakeholder engagement. Third, governance themes such as transparency, fairness, and accountability have gained importance but remain unevenly implemented in modeling workflows. Building on these findings, we advance AI-aligned SDM, which integrates explainability, uncertainty reporting, documentation, and participation into model design to strengthen institutional accountability and evidence-based planning. A forward research agenda emphasizes methodological fusion between simulation and learning, institutional design for continuous model stewardship, and epistemic pluralism connecting local knowledge with AI to advance equitable and transparent urban governance. Full article