Search Results (22,777)

This manuscript presents a transferable and reproducible methodology for quantitative 3D tree-structure mensuration and transparent, rule-based decision support for tree risk management. The workflow integrates (i) Structure-from-Motion/Multi-View Stereo (SfM–MVS) reconstruction from multi-view imagery, (ii) independent referencing to ensure metric scaling and a consistent local frame, and (iii) point cloud analytics to derive branch-level geometric descriptors (e.g., base diameter, length, inclination, slenderness, and projected reach). A clear rule-based layer operationalizes Tree Risk Assessment Qualification (TRAQ)-style risk components and As Low As Reasonably Practicable (ALARP) principles to map geometry and exposure into auditable management recommendations (e.g., monitoring intervals, pruning/weight reduction, supplemental support, and exclusion-zone planning). To provide a real-data example, the demonstration uses the public Fuji-SfM apple orchard dataset, including three neighboring trees with partially overlapping crowns for tree instance extraction and subsequent TRAQ/ALARP scenarios on an outer tree. The proposed decision layer is intentionally based on external geometry and exposure; internal decay indicators and species-specific mechanical properties (e.g., Modulus of Elasticity (MOE), Modulus of Rupture (MOR)) are outside this demonstration and should be incorporated via complementary diagnostics in operational deployments. Full article

In response to the pressing issues of unclear adhesion mechanisms during the soil-removal process in peanut harvesting, poor soil fragmentation quality, and difficulties in separating the pods from the soil. Based on TRIZ theory, this study has innovatively designed a separation device that relies on external forces, such as kneading and squeezing. A mechanical model of soil fragmentation and separation was developed. The key factors affecting the device’s operational performance were identified. Through theoretical analysis and discrete element simulation, this study elucidates the working principle by which the device crushes and separates soil particles using kneading and squeezing forces. Through analysis of one-factor and orthogonal experiments, the optimal operating parameter combination for the device was determined to be: a drum installation clearance of 104.7 mm, a rotational speed difference of 75.2 rpm, and a pattern roughness of Grade III (reticulated). The system’s performance metrics are a soil removal rate of 96.59% and a pod damage rate of 2.48%. Field tests have confirmed that the deviation from simulation results is minimal. The device’s performance meets the requirements of actual production. Full article

The complex flow behavior of the metal around the stirring tool during welding directly determines the microstructural evolution, defect formation, and mechanical properties of the welded joint, and thus becomes the core physical process affecting welding quality and process stability. In this study, to characterize the three-dimensional material flow behavior of AZ31 magnesium (Mg) alloy during friction stir welding (FSW), conventional metallographic sectioning was adopted as the primary observation method, and copper foil was used as the marker material. The flow trajectories of the materials after welding were investigated via three configurations of the marker material. The results indicate that three typical characteristic zones exist along the vertical direction, which are the shoulder-affected zone (SAZ), the pin-affected zone (PAZ), and the swirl zone from top to bottom. Specifically, the material in the SAZ is dominated by laminar flow; the PAZ exhibits complex mixed-flow characteristics; while the swirl zone shows an obvious rotational flow pattern. Based on the principles of material mechanics and fluid mechanics, a force-flow coupled “simple flow model around a rotating cylinder” was proposed, which defines three flow modes corresponding to the different characteristic zones within the weld. Full article

The school garden has a long-standing pedagogical tradition linked to active, experiential, and community-based education, represented by authors such as Montessori, Freinet, and Dewey. Currently, its role has been consolidated as a relevant educational resource used to address the challenges of sustainability education, pedagogical innovation, and student holistic development. This research takes a qualitative approach based on a multiple case study conducted in four educational centers in Spain and Portugal. Semi-structured interviews, documentary analysis, and reflective memoranda were used. Content analysis was performed using a deductive–inductive coding approach in ATLAS.ti software v. 25th, combining literature-derived categories with those emerging from the data, following a thematic analysis (TA) approach. The results suggest that school gardens promote meaningful learning, the development of transversal competencies, improved school climate, and community involvement. Pedagogical, social, and emotional benefits were identified, as well as high levels of satisfaction among all participants. However, obstacles were found to persist, mainly related to a lack of time and teacher coordination. The study confirms that the school garden serves as a pedagogical resource with a high transformative potential. Its effectiveness depends on intentional curricular integration, teacher commitment, and the engagement of the educational community, aligning with the principles of an active, sustainable, and contextualized pedagogy. Full article

Background/Objectives: This study examined the concurrent validity and test–retest reliability of the novel virtual reality-based Stroop test (VRST), developed based on the principles of embodied cognition. The VRST simulates a clothing-sorting task to assess inhibitory control using cognitive and behavioral (kinematic) measures. Methods: A total of 224 cognitively healthy older adults (mean age = 71.51 years) completed the VRST and a traditional Stroop test in randomized order. The VRST implemented a fixed-difficulty design consisting of 30 incongruent trials, where participants were required to sort virtual objects by their semantic category while ignoring conflicting color cues. The task duration ranged from approximately 1 to 3 min. The VRST assessed task completion time, error count, 3D movement distance, and hesitation latency. Test–retest reliability was examined after two weeks. Concurrent validity was analyzed via Pearson correlation coefficients with traditional Stroop metrics. Test–retest reliability was assessed using intraclass correlation coefficients (ICCs). Results: VRST performance metrics showed significant correlations with traditional Stroop completion time: task completion time (r = 0.821; p < 0.001), movement distance (r = 0.801; p < 0.001), and hesitation latency (r = 0.784; p < 0.001), indicating good concurrent validity. No significant correlations were observed for error counts. Test–retest analysis showed high reliability for completion time (ICC > 0.9; p < 0.001), movement distance (ICC > 0.9; p < 0.001), and hesitation latency (ICC > 0.9; p < 0.001), but not for error count. These findings suggest that the VRST provides reliable and ecologically grounded behavioral indicators of inhibitory control. Conclusions: This preliminary study supports the VRST as a valid and reliable measure of inhibitory control in healthy older adults. By combining kinematic data with realistic task contexts, the VRST extends executive function assessment beyond traditional methods. Although limited to non-clinical populations, the findings suggest its utility for detecting subtle variations in executive functioning during healthy aging, warranting further investigation across broader cognitive profiles. Full article

The last years have seen the increasing development of innovative railway pantographs based on smart materials and equipped with monitoring features based on wireless sensor nodes. In this scenario, one of the most important challenges is the power supply of pantograph sensors. Energy harvesting systems have been proposed for powering monitoring sensors in a variety of applications, including railway pantographs. These systems convert ambient energy sources into electrical energy. The use of energy harvesting systems coupled with storage devices, such as rechargeable batteries or supercapacitors, can be a very promising solution for making the sensors self-powered, thus avoiding the drawbacks associated with supplying from the main grid or disposable batteries. In this paper, the operating principles of the main technologies used for energy harvesting in railway pantographs are described in detail, together with some examples of laboratory prototypes and commercial devices. The proposed analysis focuses on the perspectives and challenges of various energy harvesting technologies and can help select the most suitable technology for the development of innovative sensorized pantographs. Full article

Current diagnostic assessments of lung disease rely primarily on medical imaging and global pulmonary function tests, which either provide static structural information or collapse complex regional behavior into global indices. As a result, important information about regional mechanical heterogeneity and early pathological changes may remain inaccessible. In this work, we introduce a conceptual diagnostic framework for the lung based on mechanical system identification and evaluate its feasibility using simulation-based analysis. Rather than directly imaging internal lung structure, the lung–thorax system is treated as an identifiable viscoelastic dynamical system whose internal mechanical properties can be inferred from its response to controlled external excitation. A multi-degree-of-freedom mechanical representation of the lung was developed to capture the dominant low-frequency behavior of the chest wall and major lung regions. Sensitivity and Fisher-information analysis confirmed the structural identifiability of regional stiffness parameters (FIM eigenvalues λ₁ = 1.75 × 10⁻⁹ and λ₂ = 8.91 × 10⁻¹⁰). Inverse fitting experiments accurately recovered simulated stiffness perturbations (e.g., k₀₁ = 240 → 239.5; k₀₂ = 154 → 159.5) from noisy frequency response data, while classification experiments achieved the complete separation of simulated pathological configurations in an idealized synthetic scenario, supporting theoretical discriminability rather than clinical performance claims. These findings demonstrate the theoretical feasibility of a diagnostic paradigm in which regional lung mechanical alterations can in principle be identified through mechanical system identification rather than direct imaging, thereby suggesting a complementary approach for a non-invasive assessment of regional lung mechanics from externally measured responses. By quantifying regional stiffness and mechanical heterogeneity, this framework may also support the personalization and monitoring of pulmonary rehabilitation strategies in chronic respiratory disease. Full article

Ensuring uninterrupted access to essential medicines in public healthcare systems is a persistent challenge with clinical, economic, and environmental implications. Oncology services are particularly vulnerable to stockouts, which compromise therapeutic continuity and increase reliance on urgent procurement with high carbon and waste footprints. This study proposes a risk-informed, data-driven framework for pharmaceutical inventory governance in a high-complexity public oncology hospital in Chile, aligning with sustainability goals and green supply chain principles. Using operational data from 2023–2024, we integrate descriptive analytics, ABC–XYZ segmentation, and a continuous-review (s, Q) policy extended through a Logistic Risk Index (LRI) that consolidates demand variability, supply performance, and clinical-economic criticality. Empirical analysis reveals strong expenditure concentration in AX/AY segments and significant misalignment between institutional and analytically derived parameters. A Monte Carlo simulation N = 1000 runs per scenario) compares baseline, adjusted, and fully risk-informed policies under stochastic demand and lead-time conditions. Results show that the risk-informed configuration reduces stockout exposure by up to 46%, improves fill rates (93.1% → 96.4%), and shortens replenishment delays, while maintaining total logistic cost stability. Critically, urgent orders decrease from 27.4 to 14.8 per year, avoiding an estimated 630 kg CO₂ emissions and 25 kg of packaging waste annually. These findings demonstrate that resilience, efficiency, and sustainability are not competing objectives but can be jointly achieved through integrated analytics and governance. The proposed approach offers a scalable blueprint for public health systems seeking to transition from reactive inventory management toward anticipatory, transparent, and sustainability-oriented decision-making, contributing to SDG 3 (health and well-being) and SDG 12 (responsible consumption and production). Full article

Acoustic microactuation technology has emerged as an effective approach for fabrication of micro- and nanoscale objects, enabling precise processing and shaping control of microscale materials by efficiently transmitting ultrasonic vibration energy and focusing energy locally. In this work, the proposed platform is regarded as an acoustically driven micromachine, in which ultrasonic excitation acts as the primary microactuation mechanism. Micrometer-scale copper wires are widely used in microelectronics and precision manufacturing. However, their small dimensions and low rigidity make fixation and forming particularly challenging. To achieve controllable forming of fine copper wires, this study introduces an ultrasonic vibration energy-focusing principle and investigates an ultrasonic post-processing method tailored for such materials, with the aim of enhancing processing stability and forming accuracy. An ultrasonic processing experimental platform for copper wires was established, and multiple micro-tool designs—including glass fiber, 304 stainless steel wire with support, and elastic hard 304 stainless steel—were evaluated. Single-point and continuous processing experiments were conducted by varying micro-tool materials and support configurations, and the influence of feed speed on processing width and depth was systematically analyzed. The results indicate that a hard 304 stainless steel micro-tool supported by a hard plastic ring provides the best overall performance. Feed speed has a significant effect on processing depth, with a maximum average depth of approximately 0.95 μm achieved at a feed speed of 1 mm/min. These findings demonstrate the feasibility of ultrasonic processing for the effective forming of fine copper wires and confirm that appropriate micro-tool design and feed speed are critical for achieving stable and reliable processing results. The proposed system employs an ultrasonically actuated micro-tool to perform post-processing on micrometer-scale copper wires. The ultrasonic vibration serves as a microactuation mechanism that enhances local deformation and material response during micro-machining. Full article

The digital transformation of oil and gas pipeline networks has generated substantial volumes of unstructured maintenance documentation from communication systems, creating an urgent need for automated summarization to improve operational efficiency. However, domain-specific text summarization for pipeline communication maintenance remains challenging due to scarce labeled data and the high computational cost of fine-tuning large pretrained models. Parameter-efficient fine-tuning alleviates this issue, but its effectiveness strongly depends on appropriate hyperparameter selection. This paper proposes a unified framework that combines weight-decomposed low-rank adaptation with an enhanced Artificial Bee Colony algorithm for automated hyperparameter optimization. The enhanced algorithm addresses two specific limitations of the standard Artificial Bee Colony algorithm: uninformed random initialization that ignores promising regions, and premature abandonment of stagnated solutions that discards partially useful search directions. These two components represent principled design choices, each targeting a distinct bottleneck in applying swarm intelligence search to high-dimensional mixed-type hyperparameter spaces. The method introduces a hybrid initialization strategy to exploit prior knowledge and a stagnation-guided local search mechanism to refine stagnated solutions instead of discarding them, achieving a better balance between exploration and exploitation. Experimental results on a public Chinese summarization benchmark and an industrial oil and gas pipeline communication maintenance corpus show that the proposed approach consistently outperforms full fine-tuning, manually tuned parameter-efficient methods, and several evolutionary optimization baselines in terms of ROUGE metrics. The automated search introduces modest additional computational overhead compared to manual tuning while eliminating expert-dependent hyperparameter configuration and achieving consistent performance gains across both datasets. Overall, the proposed framework provides an efficient and robust solution for adapting large language models to specialized summarization tasks in the context of pipeline communication system maintenance. Full article

The rapid increase of electric mobility is challenging the deployment design and operation of electric vehicle charging infrastructure in a scalable, sustainable, operationally reliable, and regulation-compliant manner. Although advances in both digitization and artificial intelligence in recent years have made smarter charging solutions possible, today’s approaches tend to concentrate on individual technical parts without considering holistic views. This paper introduces an AI-driven integrated management model for sustainable EV charging infrastructures, composed of four interconnected layers, namely, Eco-Design, Digital Tools, Risk Management, and Governance. In particular, each layer focuses on specific aspects of functionality, including environmentally friendly design decisions, digital monitoring capabilities, proactive risk reduction, and strategic coordination. Compared with existing approaches that address isolated technical or operational aspects, the proposed model provides an integrated, multi-layer architecture that unifies eco-design, digital intelligence, risk management and governance, offering a more holistic and scalable foundation for sustainable EV charging infrastructures. It represents the conceptual output of a structured integration of existing technologies, design principles and governance needs. Considering that fragmented, solution-specific advances are reduced by including interdependencies between layers, the model allows us to better integrate technical operations, resilience mechanisms and sustainability goals. The model is theoretical and offers a scalable point of reference for researchers, as well as infrastructure operators and politicians. Full article

Flotation circuits typically incorporate grinding stages, yet mathematical models for these processes often operate on different principles, leading to misalignment in circuit design. Building on a previously established grinding model for flotation performance, this research introduces significant advances to develop a more comprehensive and industrially relevant framework. The primary innovation is the integration of mechanical entrainment and gangue recovery into the kinetic model, distinguishing between species captured by true flotation and those carried to the surface despite being non-hydrophobic. We developed a robust set of grinding-mill equations based on first-order kinetics to describe the mass-fraction transformation of both true-flotation and entrainment species. To ensure practical applicability, a systematic experimental and modeling methodology for parameter adjustment is introduced, providing a clear sequence for identifying breakage rate constants and flotation kinetic parameters. The proposed strategy was validated using two distinct case studies: an expanded analysis of a copper sulfide ore (ore A) and a new case involving significant gangue entrainment (ore B). The results demonstrate that the model accurately predicts species kinetics, providing a high-fidelity, cost-effective tool to optimize mineral recovery and prevent economic losses from overgrinding in industrial processing plants. 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

The smart city concept has become a dominant framework for contemporary urban governance, largely driven by advances in digital technologies and data-driven decision-making. However, the prevailing technocratic orientation of smart city development risks marginalising the sociopolitical dimensions of urban governance, particularly citizen and stakeholder participation. Although smart governance frameworks increasingly recognise participation as a normative principle, limited empirical attention has been paid to the participation motivators that drive engagement among different urban stakeholder groups. This study addresses this gap by analysing the key motivators influencing stakeholder participation in urban development within a smart city context. Building on established behavioural and participation theories, the article develops an Urban Participation Motivator Model comprising four core motivators: social pressure, emotional trigger, rational motivation, and reward for participation. The model is empirically tested using quantitative survey data from 620 respondents representing four stakeholder groups in Riga, Latvia: municipal residents, municipal employees, municipal politicians, and real estate developers. Data are analysed using descriptive statistics and non-parametric methods, including the Kruskal–Wallis test. The results reveal statistically significant differences in the perceived importance of participation motivators across stakeholder groups. Emotional triggers and social pressure emerge as the most influential motivators overall, while rational motivation is particularly salient for professional stakeholders. Reward for participation plays a weaker but differentiated role, being most relevant for municipal employees. These findings highlight the need for differentiated motivator-sensitive urban communication and participation strategies to enhance inclusiveness, democratic legitimacy, and long-term engagement in smart city development. Full article

Welding is a critical joining process in civil and transportation infrastructure, enabling the fabrication of complex steel structural systems used in bridges, buildings, and other essential infrastructures. Despite strict adherence to established welding codes and standards, such as AWS D1.1 and AASHTO/AWS D1.5, welding flaws and service-induced defects can occur in welded components. Cause of defects and their structural impact, along with detection, sizing, and localization of these anomalies and flaws, are crucial for adequate maintenance, repair, or replacement planning without compromising the functionality of in-service components. Among available NDT techniques, ultrasonic testing (UT) remains one of the most widely adopted methods of weld inspection due to its depth of penetration, sensitivity to internal defects, and suitability for field deployment. Recent advancements in ultrasonic technologies, particularly Phased Array Ultrasonic Testing (PAUT), along with its emerging approaches such as Full Matrix Capture (FMC) and the Total Focusing Method (TFM), have significantly enhanced inspection accuracy, repeatability, and interpretability. These techniques enable flexile beam steering, multi-angle interrogation, and improved imaging of complex geometries. This paper presents a comprehensive review of PAUT for the inspection of welded steel infrastructure adhering to the recommendations and requirements of the relevant codes and standards, synthesizing the current literature on PAUT principles, wave modes, probe configurations, and data acquisition strategies. Emphasis is placed on the practical implementation of PAUT in civil infrastructure inspection, its advantages over conventional NDT methods, and its potential to support informed decisions related to quality acceptance, repair, and long-term maintenance planning. This paper concludes by identifying current challenges and future research directions for advanced ultrasonic inspection of welded steel structures. Full article