Search Results (2,786)

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

This study examines the Environmental, Social, and Governance (ESG) factors to be used in guiding suppliers and creating responsible supply chains. By adopting an integrated mixed-method approach (Delphi in conjunction with the survey method), this research determines the ESG factors prioritized by companies in relation to the assessment, qualification, and tendering of suppliers. We discover that the adaptation of ESG factors follows an unorganized and heterogenous process, resulting in complex adoption. Finally, this study searches for an evolutionary set of trajectories that suppliers can undertake to evolve according to their ESG maturity and their importance in the supply chains, revealing the operational plans necessary for suppliers to become Champions. 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

In view of the bottleneck problems existing in the 3D ultrasonic testing of aircraft composite laminated structures—including heavy reliance on manual operation, resulting in low detection efficiency, and the inability of traditional robotic arms to adapt to the testing of complex curved surfaces due to their dependence on predefined fixed trajectories—this paper proposes an automated 3D ultrasonic testing method based on 3D vision guidance for robotic arms. Firstly, the proposed Yolo-Mask model is adopted to realize the visual recognition and segmentation of composite component regions, after which the segmentation results are mapped to the depth map and further converted into the surface point cloud of the material. Secondly, on the basis of point cloud preprocessing and trajectory point extraction, the automatic planning of the robotic arm’s scanning trajectory is achieved, which drives the robotic arm to perform precise motion and to synchronously collect spatial pose and ultrasonic testing data. Finally, 3D reconstruction is completed via a fusion algorithm, and 3D images of the material’s internal structures are generated. Experimental verification shows that the proposed method achieves a Segm-mAP of 97.4%, a detection speed of 11.7 fps, and a 3D imaging error of less than 0.1 mm, thereby realizing fully automated detection throughout the entire process. This research provides an effective solution for the non-destructive testing of aircraft composite structures. 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

This article addresses the challenges of regulation and trajectory tracking in a nonlinear, multivariable drone quadrotor system using a discrete-time Generalized Proportional Integral (GPI) controller, which is the discrete-time version of its continuous-time counterpart. The discrete-time formulation offers several advantages, including simplified trajectory planning by eliminating time derivatives, reduced computational demands, and lower complexity in nominal feed-forward input functions. The proposed GPI controller ensures asymptotic exponential stability for both attitude and position, enabling effective trajectory tracking. Its effectiveness has been validated through numerical simulations, which demonstrate excellent stabilization and tracking performance even in the presence of atmospheric disturbances and measurement noise. Full article

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called ${\theta }^{\ast }$-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle ${\theta }^{\ast }$ global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. 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

As typical underactuated systems, overhead cranes are widely utilized in heavy-load transportation. However, their strong nonlinear coupling and underactuated characteristics complicate precise positioning and payload swing suppression. Furthermore, model uncertainties and external disturbances in practical environments increase control complexity and degrade system performance. To address these issues, this paper develops a trajectory tracking control scheme based on time delay estimation (TDE). Specifically, some transformations are made for the dynamic model and the TDE mechanism is used to estimate unknown nonlinear dynamics and external disturbances. Then, a sliding mode trajectory tracking controller, along with the TDE mechanism, is proposed for the trajectory tracking control and uncertainties estimation of the overhead crane system. Rigorous mathematical analysis is provided to demonstrate the asymptotic stability of the closed-loop system. Finally, simulation results verify the effectiveness of the proposed method in comparison with the existing control methods. Full article

Unmanned Aerial Vehicle (UAV) trajectory planning in complex three-dimensional environments with threats remains a challenging optimization problem requiring efficient algorithms and threat detection capabilities. This study proposes the Conservative Enhanced Dwarf Mongoose Optimization Algorithm (CEDMOA), which introduces four key innovations to the original DMOA: hybrid population initialization, adaptive vocalization parameters, elite-guided learning strategy, and intelligent restart mechanisms. This work proposed the integration of CEDMOA with a novel vision-based threat detection system using YOLO object detection technology, enabling the identification and incorporation of threats into the optimization process. CEDMOA was comprehensively evaluated on the CEC2022 benchmark test suite, demonstrating superior performance compared to other state-of-the-art algorithms in solution quality and convergence stability. The results show the approach successfully generates an optimal collision-free flight trajectory in complex environments in UAV trajectory planning with both static and dynamic threats. Combining metaheuristic optimization with computer vision technology provides a robust framework for autonomous navigation that adapts to changing threat conditions. Experimental results validate the effectiveness of both the enhanced algorithm and the vision-based threat integration approach for practical UAV operations. 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

Dispersed archaeological landscapes are often rich in heritage value but weakly integrated into regional tourism systems. This creates difficulties in visitor orientation, interpretive continuity, and conservation-sensitive tourism planning. In response to this problem, this study examines the Adana–Kozan–Anavarza axis in southern Türkiye and proposes a spatial corridor framework for organising tourism development within a dispersed archaeological landscape. The research integrates spatial accessibility assessment, service-capacity evaluation, field observation, and sequential route design in order to establish a hierarchical gateway–transition–anchor configuration. Anavarza, one of the largest archaeological complexes of Cilicia, represents a monumental urban heritage site and a biocultural landscape situated within a Mediterranean ecological zone historically associated with Pedanius Dioscorides. Although current visitor volumes remain moderate, official statistics indicate a substantial increase in annual entries between 2022 and 2024, reflecting rising destination visibility. This emerging growth trajectory underscores the need for proactive spatial governance mechanisms prior to the onset of congestion and environmental degradation pressures. The findings suggest that Adana can function as a metropolitan gateway, Kozan as an intermediate staging node, and Anavarza as the archaeological anchor within a realistic multi-day visitor sequence. In this configuration, visitor functions are distributed across multiple nodes, while the ecological and archaeological sensitivity of the anchor landscape is more cautiously managed through spatial sequencing. Rather than proposing a predictive model, the study develops and assesses a context-responsive spatial planning framework grounded in accessibility, infrastructural feasibility, and conservation-sensitive visitor distribution. Beyond the local case, the study offers a transferable hierarchical staging logic for corridor-based heritage planning. Full article

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy–Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin’s minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton–Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity. Full article