Search Results (616)

The precise identification of terminal area arrival procedures is crucial for airspace planning, traffic management, and safety analysis. Traditional methods are limited in automatically detecting specific procedural maneuvers from large amounts of trajectory data. This paper proposes a methodology with knowledge-based filtering to automatically identify three common air traffic control arrival procedures, namely Point Merge System, Vector for Space, and Trombone, from historical trajectory data. After clustering the landing trajectories in the terminal area, we identify the predominant flight patterns. Then, a knowledge-based filtering algorithm, designed based on knowledge of the procedure and geometry criteria, is employed to precisely extract trajectories with different procedure patterns. Experimental results demonstrate that this method effectively identifies the distinct procedural trajectories. An in-depth analysis of the extracted trajectories reveals significant characteristics and differences in their spatial distribution, trajectory structure, and operational efficiency. This work provides data-driven decision support for evaluating terminal area operational performance and arrival procedures. Full article

Within the context of regional energy governance, land use has emerged as a critical regulatory interface for managing energy demand. Clarifying the land-use–energy nexus is a technical prerequisite for evidence-based and spatially explicit energy planning. This study develops a digital modeling framework that integrates machine learning (Random Forest, achieving R² = 0.95/0.91 for training/testing) and spatial simulation (Patch-generating Land Use Simulation model, with 82.5% accuracy for industrial land) to quantify land-use-driven energy dynamics in Shaanxi Province, China (2005–2030). Key findings reveal: (1) socioeconomic factors dominate land-use expansion, with service industries (14.8–22.4%) and infrastructure (13.5–18.9%) acting as primary drivers, leading to a projected 94.2% growth in urban built-up areas and a tripling of total energy consumption; (2) structural transitions indicate a declining industrial energy share (from 68% to 54%) and reduced coal dependency (from 78% to 62%), though with significant regional disparities; (3) spatial analysis identifies critical energy path-dependency risks in Xi’an City and Yulin City, which are projected to account for 70% of provincial consumption by 2030. These results demonstrate that land-use structure constitutes a direct physical interface linking regional development with energy demand trajectories. The findings underscore the necessity of transitioning from generalized energy policies toward data-driven, land-use-based energy constraints, providing a digital evidentiary base for more precise and stable regional energy governance. Full article

Large-scale aerospace components (e.g., wings, fuselage sections, wing boxes, and rocket segments) feature large dimensions, low stiffness, complex interfaces, and strict assembly tolerances. Traditional rigid tooling and manual alignment struggle to meet the demands of high precision, efficiency, and flexibility in modern aerospace manufacturing. This paper presents a structured literature review on the automated assembly of large-scale aerospace components, summarizing advances in three core domains: pose adjustment and positioning mechanisms, digital measurement technologies, and trajectory planning and control. Particular emphasis is placed on two cross-cutting themes: measurement uncertainty analysis and flexible assembly, which are critical for high-quality docking. The review classifies pose adjustment mechanisms into four categories (NC positioners, parallel kinematic machines, industrial robots, and novel mechanisms) and digital measurement into five branches (vision metrology, large-scale metrology, measurement field construction, uncertainty analysis, and auxiliary techniques). It also outlines five trajectory planning and control routes, covering traditional methods, multi-sensor fusion, digital twins, flexible assembly, and emerging intelligent approaches. The analysis reveals that current research suffers from fragmentation among mechanism design, metrology, and control, with insufficient integration of uncertainty propagation and flexible deformation modeling. Future systems will rely on heterogeneous equipment collaboration, uncertainty-aware closed-loop control, high-fidelity flexible modeling, and digital twin-driven decision-making. This review provides a unified framework and a technical reference for developing reliable, flexible, and scalable automated assembly systems for next-generation aerospace structures. Full article

At present, commonly used vegetable pot seedling planters can be divided into two categories: one has a complex structure and high manufacturing cost, and the other has a simple structure but poor planting quality. In order to solve this problem, an open-hinge four-bar-mechanism planting manipulator is designed, which has many advantages, such as a simple structure, strong force transfer performance, and the ability to achieve complex trajectory curves. The physical characteristics of pot seedlings are measured; this provides a basis for the structural and dimensional design of the planter and the shape design of the duckbill. According to the analysis of the planting process, the design requirements of the planting mechanism are formulated. The motion path of the mechanism and the motion of each pair are planned and designed; a planetary gear train is used to restrain the rotating pair consisting of connecting rod 1 and connecting rod 2; a cam high pair mechanism is used to restrain the rotating pair consisting of connecting rod 2 and connecting rod 3; and a cam linkage mechanism is used to control the opening and closing action of the duckbill. Finally, a single-degree-of-freedom fully mechanical planting mechanism is designed. The experimental results show that the trajectory of the initial soil entry point of the planting mechanism is consistent with the design requirements and theoretical simulation results. In the transplanting experiment, the rate of qualified planting erectness was 94.79%, among which the rate of excellent planting erectness was 92.45%, and the mechanism has high reliability. The design of this mechanism offers a fully automatic pot seedling planting method, which can provide a reference for research on the full automation of transplanting equipment. Full article

Urban demographic dynamics—including migration, aging, fertility change, and population redistribution—are central to sustainable urban development, urban resilience, and long-term well-being. In many small and transition economies, rapid urbanization combined with sustained emigration and population aging poses significant challenges for urban planning, labor markets, housing systems, and public services. The purpose of the paper is to evaluate urban sustainability-related demographic risks by a composite index and assess long-term demographic dynamics with different trajectories of migration flows and fertility. Since migration flows are more intense among urban population, depopulation is very high in peripheral rural areas, and urbanization is about 64% in Armenia, the results of the research will inform national and urban policy makers to reshape policy frameworks to enhance long-term urban resilience. This study develops a demographic threat index (DTI) to assess demographic risks relevant to urban sustainability in Armenia over the period 2000–2023. The index integrates 20 indicators grouped into three pillars—population change, population health, and socio-economic vulnerability—with indicator weights derived using principal component analysis (PCA). The results reveal a persistent increase in demographic risks, marked by accelerated population aging, declining youth cohorts, and rising socio-economic vulnerability, particularly in urban contexts. A decomposition of population change demonstrates that net migration has been the dominant driver of demographic dynamics, outweighing the combined effects of fertility and mortality. Scenario-based population projections further indicate that even optimistic increases in fertility are insufficient to stabilize population trajectories without sustained positive migration. By linking demographic security to urbanization, migration, and socio-economic vulnerability, the study highlights the importance of integrated urban and demographic policy frameworks. The proposed index offers a replicable tool for evaluating demographic risks in countries facing similar urban and demographic transitions and provides evidence-based insights for urban planning, migration management, and sustainable city strategies. Full article

Digital Twin (DT) systems combining physics-based simulation with hardware execution are critical for Industry 4.0 manufacturing, yet proprietary software solutions remain expensive and platform-dependent. This work addresses three technical challenges: maintaining geometric and kinematic fidelity across CAD-to-simulation conversion pipelines, synchronizing dual physics engines (Unity and ROS middleware) under hardware latency constraints, and optimizing motion planning while preserving trajectory quality and interactive responsiveness. We developed an integrated framework for a 7-Degree-of-Freedom manipulator using CAD modeling, URDF/SRDF semantic representation, and bidirectional Unity-ROS (Robot Operating System) communication via WebSocket connectors. Motion planning uses RRTConnect from OMPL with collision-aware optimization through the Flexible Collision Library. Validation across 12 manipulation trials demonstrated positional synchronization accuracy of ±2.0 degrees, motion planning performance of 0.064 ± 0.020 s. Latency analysis reveals that hardware execution is the dominant system bottleneck, significantly exceeding network communication delays. The system achieves performance metrics comparable to proprietary industrial solutions. This work establishes a replicable, cost-effective Industry 4.0 framework, demonstrating that modern game engine technology combined with open-source robotics middleware can deliver DT systems matching proprietary solutions. The architecture and validated implementation enable adaptation to alternative robotic platforms and support broader adoption of simulation-validated automation in manufacturing contexts. Full article

Electricity demand forecasting is essential for optimizing energy management and planning in microgrids and institutional contexts. The purpose of this article is to demonstrate how flexibility characterization can serve as a structural foundation for prediction, providing a contextualized framework that surpasses the limitations of traditional approaches. Representative trajectories (A–D), derived from entropy and variability metrics, were consolidated from historical user data and used as the basis for modeling. Two complementary approaches were implemented: ARIMA models, which capture endogenous dynamics, and ARX models, which extend this capacity by incorporating exogenous cyclical variables (hour, day of the week, month) and lagged predictors. A systematic grid search was conducted to identify optimal parameter configurations, followed by validation through rolling forecasts with a 24-h horizon, relevant for operators of microgrids, institutional managers, and energy planners. Performance was evaluated using MAE, RMSE, MAPE, and SMAPE, ensuring comparability across trajectories. Results show that ARIMA consistently achieved lower error rates in stable trajectories (A and C), with SMAPE values around 2.0%, while ARX provided substantial improvements in irregular ones (B and C), reducing SMAPE from 3.7–5.9% to approximately 2.2–2.6%. In highly irregular profiles (D), all models converged to similar accuracy (SMAPE ≈ 9.0%). When applied to individual users, predictive errors varied more widely depending on trajectory assignment: stable users showed SMAPE values around 3–4%, while irregular users exhibited much higher errors, exceeding 18–21%. Unlike conventional methods that treat characterization and prediction as separate processes, this study integrates both into a unified framework, enabling forecasts to capture stability, cyclicity, and adaptability. The methodology was further applied to individual users by assigning them to representative trajectories and adjusting predictions through baseline scaling. Overall, the findings demonstrate that embedding forecasts within characterized trajectories transforms prediction into a contextualized analysis of flexibility, enabling accurate short-term forecasts and supporting practical applications in energy planning, demand management, and economic dispatch. The framework has been designed to support electricity demand forecasting across multiple contexts, from microgrids and institutional systems to larger territorial and national scales. Through contextual calibration, the methodology ensures adaptability and broader relevance for energy forecasting and demand-side management. Full article

Background: Advance care planning is a strategy to define goals and preferences for future care and treatment aligned to patient values. The IMplementing Pediatric Advance Care Planning Toolkit (IMPACT) provides a holistic, family-oriented approach to involve families of children with life-limiting conditions and their clinicians in ACP, starting early in disease trajectories. This study explores how children with life-limiting conditions, and their parents and clinicians experience ACP conversations based on IMPACT. Methods: A multicenter, qualitative interview study using inductive thematic analysis was conducted. A total of 27 cases of children with life-limiting conditions were included in the study from February 2019 to December 2019. Interviews with 18 clinicians, 24 mothers, 8 fathers and 3 children were conducted. Results: Clinicians and families of children with life-limiting conditions valued to be involved in ACP conversations based on IMPACT. Although it confronted both parents and clinicians with the impact of caring for a child with a life-limiting condition, sharing the family’s narrative resulted in a stronger relation between families and clinicians. This relation was experienced as a good foundation to share values and preferences for future care and treatment. However, a shared understanding of goals of future care, and treatment based on the conversation was experienced to a limited extent. Conclusions: ACP conversations based on IMPACT facilitated family-centered conversations, and were valued by families of children with life-limiting conditions and their clinicians. The meaning of the family’s narrative in relation to goals and preferences for future care and treatment needs ongoing conversations and coaching on the job of clinicians initiating those conversations. Full article

Habitat quality is critical for spatial planning strategies and ecological conservation initiative, evaluating the health of the natural environment that supports human survival. However, current approaches pay insufficient attention to revealing the evolution and spatial heterogeneity of the habitat quality simultaneously. In this study, a comprehensive and practical framework was therefore developed for mechanistic habitat quality analysis, which incorporates an adaptable evolutionary model alongside multiple spatial statistical methods. Ningxia, located in Northwest China, was selected as a case study area due to its fragile ecosystem. The proposed framework was then applied to characterize the evolutionary process and spatial heterogeneity of habitat quality in Ningxia. Key factors driving spatial heterogeneity were also found at the same time. From 2000 to 2024, habitat quality in Ningxia is characterized by good habitat and shows significant improvement, following a progressive trajectory. The proportion of poor habitat has been significantly reduced from 29.26% to 24.63%, while that of excellent habitat has been increased from 1.68% to 2.33% over the past two decades. Variation in habitat quality is more pronounced in northern and southern regions, while remaining relatively stable in the central Yellow River ecological corridor. Both natural and socioeconomic factors have an impact on the habitat change in this region, such as the Normalized Difference Vegetation Index (NDVI), Net Primary Productivity (NPP), and Gross Domestic Product (GDP). Vegetation factors play vital roles in spatial variation in habitat quality, while the influences of socioeconomic factors are relatively small. The spatial heterogeneity is driven by nonlinear synergistic effects among numerous factors. This paper developed a feasible framework to retrieve the evolution and spatial heterogeneity pattern of habitat quality, which provides a robust methodology for further habitat assessment at the ecologically fragile regions worldwide. Full article

The transformation of post-mining landscapes represents a critical challenge for structurally affected coal regions undergoing decarbonisation. This study examines land-use transformation in a former brown coal mining area in the north-west of the Czech Republic, focusing on the interplay between social legitimacy, territorial governance, and development trajectories. The research aims to assess (i) the level of public awareness of the transformation process, (ii) the alignment between residents’ and key local actors’ preferences regarding future land-use trajectories, and (iii) the acceptance of renewable energy as part of the area’s future development. The empirical analysis combines a CAWI survey of residents with structured CATI interviews conducted with local stakeholders. The findings reveal strong support for environmental and landscape restoration, alongside conditionally positive but more ambivalent attitudes towards renewable energy development. While ecological renewal is widely perceived as desirable, the long-term sustainability of the transformation process depends on social legitimacy, institutional trust, and the degree of alignment between strategic planning and local preferences. The results highlight that successful post-mining land-use transformation requires not only environmental and economic planning but also systematic engagement with social acceptance and territorially embedded governance. Full article

Electricity systems increasingly operate under deep uncertainty driven by geopolitical risk, volatile fuel markets, trade fragmentation, security threats, and technological change. Under such conditions, cost-optimal planning based on assumed trajectories may lead to fragile outcomes, particularly for small and geopolitically exposed systems such as Israel’s. This paper assesses the resilience of alternative electricity transition strategies for Israel using a qualitative robustness framework inspired by Decision Making under Deep Uncertainty and scenario-based energy security analysis. Six policy-relevant strategies are evaluated across structurally distinct stress scenarios. Resilience is assessed along three dimensions: security of supply, dependency exposure, and economic vulnerability, using anchored qualitative scoring and dominance rules. The results indicate that gas-centric strategies exhibit limited robustness, while strategies combining solar deployment with adaptive gas management, smart grids, microgrids, and domestic clean-technology capabilities achieve higher resilience across a wide range of futures. The paper contributes a structured qualitative approach to resilience assessment and offers policy-relevant insights for electricity transitions under deep uncertainty. Full article

With the rapid development in data mining and learning analytics, integrating predictive analytics into educational data has become increasingly critical for supporting students’ learning trajectories. In many schooling systems, the academic tracks (such as Liberal Arts or Science) and the performance of junior high school students can substantially shape their subsequent university pathways and career planning. Despite the long-term impact of these decisions, academic track selections and the evaluation of students’ potential are often made without systematic and evidence-based guidance. Predictive computer applications can assist, but the training of accurate models and the selection of adequate features remain key challenges. This paper details our process of engineering such an application comprising two tasks based on 1357 real-world junior high school academic performance records. The first task applies a classification approach to predict students’ academic track orientation, while the second task employs a multi-output regression model to forecast students’ future academic performance in senior high school. Our approach shows that the stacking ensemble model achieved a classification accuracy of 85.76%, whereas the Bi-LSTM model with multi-head attention attained an overall R² exceeding 82% in performance forecasting; both models demonstrated strong and reliable predictive capability. Moreover, the proposed approach provides inherent interpretability by decomposing predictions at the subject level. Feature importance analysis reveals how different academic subjects contribute variably to both academic track decisions and future academic performance, offering actionable insights for academic counselling and future study planning. By bridging predictive modelling with students’ educational and career planning needs, this study advances the practical application of educational data mining and provides support for evidence-based academic guidance and future career choices in real-world contexts. Full article

Extreme heat poses growing health risks in high-density cities, yet static assessments often fail to capture dynamic pedestrian exposure. This study quantifies the supply and demand disparity between urban shade provision and actual pedestrian demand in Fuzhou, China, during a specific extreme heat event. Integrating high-resolution mobile signaling data with dynamic urban shade simulations, we classified the road network into risk quadrants and analyzed behavioral drivers using XGBoost and SHAP algorithms. Results show a pronounced disparity: high-risk zones carry the highest pedestrian flows (a mean daily volume of 28.6 pedestrian trajectories per segment) but exhibit minimal shade coverage (3.14%), while comfort zones provide 5.5 times greater shading coverage for comparable activity levels. In contrast, surplus zones exhibit substantial shading capacity but limited pedestrian use, indicating inefficient spatial allocation of cooling resources. Further analysis shows that pedestrian accumulation in high-risk zones is primarily driven by functional necessity, whereas pedestrian flows in comfort zones are more sensitive to thermal conditions. These findings reveal structurally embedded thermal exposure risk and support a shift from static metrics toward dynamic urban planning to protect vulnerable pedestrian flows. Full article

The perception that hormonal contraception causes weight gain is a general belief that frequently hinders the initiation and continuation of effective family planning. This narrative review analyses data from Cochrane systematic reviews and recent pharmacogenomic studies to separate patient perception from metabolic reality. Analysis of high-quality data, including Cochrane systematic reviews, indicates that the association between Combined Hormonal Contraceptives (CHCs)—including oral pills, the transdermal patch, and the vaginal ring—and weight gain is not supported by consistent high-quality evidence. Placebo-controlled trials demonstrate that these methods are weight-neutral on average. Perceived weight increases in CHC users are likely mediated in part by fluid retention linked to the estrogenic stimulation of the Renin–Angiotensin–Aldosterone System (RAAS), rather than adipose tissue accumulation. Conversely, Depot Medroxyprogesterone Acetate (DMPA) represents a verified clinical risk for weight gain, showing a demonstrated clinical association with significant fat mass accumulation. Hypothesized biological mechanisms for this increase include hypothalamic appetite stimulation and glucocorticoid-like activity. The etonogestrel implant occupies a complex middle ground. While population-level data suggests weight neutrality, recent exploratory pharmacogenomic research has identified a specific variant in the Estrogen Receptor 1 (ESR1) gene. For the minority of women carrying this variant, the implant may trigger clinically significant weight gain, suggesting a biological basis for their subjective experience despite statistical evidence. Ultimately, the persistence of the weight gain concern is fueled by the nocebo effect and the misattribution of natural age-related weight trajectories to contraceptive use. Full article

Background/Objective: Older patients with end-stage renal disease who receive dialysis often discontinue treatment before the end of their lives. However, the trajectory of family caregiving in this specific context remains under-researched. This study explored the experiences of family members caring for older patients with end-stage kidney disease (ESKD), from the introduction of dialysis to end-of-life care. Methods: This qualitative descriptive study included three family members caring for older patients with end-stage renal disease who were undergoing dialysis in Japan. Data were collected through semi-structured, one-on-one interviews and analyzed using inductive qualitative content analysis within a qualitative descriptive design. Results: The results identified seven categories regarding the family’s experience from dialysis initiation to end-of-life care: Key findings, particularly regarding the terminal phase, included ‘shock of dialysis treatment discontinuation’, ‘last moments shared with the patient’, ‘nostalgic memories of the patient over time, and ‘reflections on end-of-life care for the patient.’ Families described a process wherein the sudden need for proxy decision-making, often without prior discussion, was linked to feelings of regret. Conclusions: The findings describe the continuous experiences of family caregivers in the Japanese context. These exploratory insights suggest that the absence of early Advance Care Planning may contribute to caregiver distress during the withdrawal phase. The results highlight the need for culturally sensitive renal supportive care that fosters communication and understanding of patients’ wishes to mitigate the ethical burdens on families. Full article