Search Results (39,670)

The textile industry generates significant volumes of waste, making the development of reliable methods to evaluate biodegradability a pressing need. While standardised protocols exist for plastics, no specific methodologies have been established for textiles, and the quantification of non-degraded residues is commonly based on mass loss: a measurement that is prone to recovery errors. This study investigated the biodegradation of cotton, polyester, and cotton/polyester blend fabrics in soil under thermophilic conditions using a combined methodological approach. Carbon mineralisation was quantified through a respirometric assay that was specifically adapted for textile substrates, while residual solid fractions were assessed in situ by X-ray microtomography (micro-CT), thus avoiding artefacts associated with sample recovery. Complementary analyses were performed using SEM and FTIR to characterise morphological and chemical changes. Results showed substantial biodegradation of cotton, negligible degradation of polyester, and intermediate behaviour for the cotton/polyester blend. Micro-CT enabled the visualisation of fibre fragmentation and the quantification of the residual. The integration of respirometric, imaging, and spectroscopic techniques provided a comprehensive assessment of textile biodegradability. This study highlights the potential of micro-CT as a non-destructive tool to improve the accuracy and robustness of textile biodegradability assessment by enabling direct quantification of the residual solid fraction that can support future LCA studies and the development of standardised protocols for textile biodegradability. Full article

This paper explores the ethical implications of an ongoing debate about evolution, animal suffering, and the goodness of God. Christopher Southgate describes a “fault-line” between those who believe the struggle, suffering, and destruction of the evolutionary process are aligned with God’s good purposes in creation and those who regard these evolutionary “disvalues” as contrary to God’s good purposes. Recent efforts at dialogue across the fault line have not resolved this basic disagreement, but have achieved notable consensus on eschatology: both sides share the hope of eschatological fulfilment for other-than-human creatures and an end to the suffering, struggle, and destruction of the present age. One under-explored aspect of this dialogue is its ethical significance; since evolutionary theodicies are theological evaluations of the natural world, they should inform our understanding of what we must do in response to its struggle and suffering. Having outlined the present state of the dialogue, I consider its implications for three particular ethical issues: (1) Eating meat. Southgate and Bethany Sollereder consider meat-eating in itself ethically unproblematic, for reasons not unconnected with their evolutionary theodicies. By contrast, I argue that the eschatological hope they, like me, affirm mandates Christians to refrain from avoidable violence toward our fellow-creatures. For many westerners, “avoidable violence” includes the killing of animals for food. (2) Ending extinction. Southgate has called for humans to be “co-redeemers,” sharing with God in the healing of the evolutionary process, including efforts to combat both anthropogenic and non-anthropogenic species extinction. Skeptical that humans are called to be co-redeemers, I agree that reducing anthropogenic species extinction is a proper act of repentance for the sin of ecological destruction, but am more wary of human attempts to prevent non-anthropogenic extinction. (3) Responding to pain. While I agree with Southgate and Sollereder that pain is usually biologically adaptive in this world, I refer to good scientific evidence for the existence of pain that is non-adaptive and detrimental to the flourishing of both humans and other animals. There is a prima facie ethical obligation to do what is in our power to relieve such pain. Full article

Out-of-distribution (OOD) detection is essential for ensuring the reliability of machine learning models deployed in safety-critical applications. Existing methods often rely solely on statistical properties of feature distributions while ignoring the geometric structure of learned representations. We propose TopoAD, a topology-aware OOD detection framework that leverages Euler Characteristic Curves (ECCs) extracted from intermediate convolutional activation maps and fuses them with standardized energy scores. Specifically, we employ a computationally efficient superlevel-set filtration with a local estimator to capture topological invariants, avoiding the high cost of persistent homology. Furthermore, we introduce task-adaptive aggregation strategies to effectively integrate multi-scale topological features based on the complexity of distribution shifts. We evaluate our method on CIFAR-10 against four diverse OOD benchmarks spanning far-OOD (Textures), near-OOD (SVHN), and semantic shift scenarios. Our results demonstrate that TopoAD-Gated achieves superior performance on far-OOD data with 89.98% AUROC on Textures, while the ultra-lightweight TopoAD-Linear provides an efficient alternative for near-OOD detection. Comprehensive ablation studies reveal that cross-layer gating effectively captures multi-scale topological shifts, while threshold-wise attention provides limited benefit and can degrade far-OOD performance. Our analysis demonstrates that topological features are particularly effective for detecting OOD samples with distinct structural characteristics, highlighting TopoAD’s potential as a sustainable solution for resource-constrained applications in texture analysis, medical imaging, and remote sensing. Full article

Incorporating waste engine oil bottoms (WEOBs) as rejuvenators into reclaimed asphalt pavement offers a sustainable solution to reduce the consumption of non-renewable resources. To explore the effect of WEOBs on aged asphalt, WEOB-rejuvenated asphalt (WEOB-asphalt) with different thermal-oxidative aging times was prepared. Subsequently, viscosity, double-edge-notched tension (DENT), temperature sweep, linear amplitude sweep (LAS), and Fourier transform infrared spectroscopy (FTIR) tests were conducted to investigate the performance of WEOB-asphalt. The results indicate that WEOB-asphalt shows acceptable thermal-oxidative aging ability within 180 min. The WEOB-asphalt experiences a small decrease in critical crack tip opening displacement within a 180 min aging time. Additionally, the temperature sensitivity of WEOB-asphalt is low, and the rutting factors at temperatures of 46 °C and 52 °C can significantly distinguish the thermal-oxidative aging performance of asphalt at different aging degrees. The fatigue life of WEOB-asphalt decreases compared to the original asphalt after 540 min of aging when the strain exceeds 0.04%. Furthermore, WEOB-asphalt displays increased carbonyl and sulfoxide groups, indicating poorer thermal-oxidative aging resistance than the original asphalt. Based on these results, it is suggested that WEOB-asphalt should be used in areas with mild climate conditions to avoid its rapid secondary aging. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Diffraction-based velocity analysis is a key data interpretation technique in geophysical exploration, typically relying on the geometric characteristics, energy distribution, or propagation paths of diffraction waves. The hyperbola-based method is a classical strategy in this category, which extracts depth-dependent velocity (or dielectric properties) by correlating the hyperbolic shape of diffraction events with subsurface parameters for characterizing subsurface structures and material compositions. In this study, we propose a layer-stripping velocity analysis method applicable to ground-penetrating radar (GPR) and lunar-penetrating radar (LPR) data, with two main innovations: (1) replacing traditional local optimization algorithms with an intuitive parallelism check scheme, eliminating the need for complex nonlinear iterations; (2) performing depth-progressive velocity scanning of radargram diffraction signals, where shallow-layer velocity analysis constrains deeper-layer calculations. This strategy avoids misinterpretations of deep geological objects’ burial depth, morphology, and physical properties caused by a single average velocity or independent deep-layer velocity assumptions. The workflow of the proposed method is first demonstrated using a synthetic rock-fragment layered model, then applied to derive the near-surface dielectric constant distribution (down to 27 m) at the Chang’e-4 landing site. The estimated values range from 2.55 to 6, with the depth-dependent profile revealing lunar regolith stratification and interlayer material property variations. Consistent with previously reported results for the Chang’e-4 region, our findings confirm the method’s applicability to LPR data, providing a new technical framework for high-resolution subsurface structure reconstruction. Full article

Habitat utilization is a critical determinant of animal survival and reproductive success. Clarifying species-specific habitat preferences provides essential insights into ecological requirements and forms the basis for sound conservation planning. The Temminck’s Tragopan (Tragopan temminckii), a medium-sized, sexually dimorphic pheasant endemic to montane forests of central and southern China, is classified as a nationally protected Class II species. Nevertheless, its fine-scale habitat selection during the breeding season remains inadequately documented. In 2024, we conducted a field investigation in the Laojunshan National Nature Reserve, Sichuan Province, to examine microhabitat use during this critical period. Our analysis revealed a significant preference for sites characterized by greater tree and bamboo height, higher canopy and bamboo cover, increased litter coverage, and taller shrub layers. In contrast, the species consistently avoided locations dominated by dense, tall herbaceous vegetation. Principal Component Analysis identified six principal components, collectively explaining 71.78% of the total environmental variance. The first component was primarily associated with bamboo structural attributes, the second with tree-layer structure, and the third with proximity to forest edges and streams. These findings indicate that effective conservation of this pheasant requires targeted forest management practices that preserve this specific suite of habitat characteristics, which are essential for ensuring reproductive success and long-term population viability. Full article

Path planning for multiple four-way shuttles in high-density warehousing is frequently hampered by efficiency-degrading conflicts, particularly head-on deadlocks. To address this challenge, this paper proposes a multi-agent reinforcement learning (MARL) framework based on Proximal Policy Optimization (PPO). The core of our approach is a novel Cooperative Avoidance Reward Mechanism (CARM), which employs a dual-component reward structure. This structure integrates a distance-guided reward to ensure efficient navigation towards targets and a cooperative avoidance reward that uses both immediate and delayed returns to incentivize implicit collaboration. This design effectively resolves conflicts and mitigates the policy instability often caused by traditional collision penalties. Experiments in a 20 × 20 grid simulation environment demonstrated that, compared to a rule-based A* and Conflict-Based Search (CBS) algorithms, the proposed method reduced the average travel distance and total time by 35.8% and 31.5%, respectively, while increasing system throughput by 49.7% and maintaining a task success rate of over 95%. Ablation studies further confirmed the critical role of CARM in achieving stable multi-agent collaboration. This work offers a scalable and efficient data-driven solution for real-time path planning in complex automated warehousing systems. Full article

Dual-LiDAR systems are widely deployed in autonomous driving, yet extrinsic calibration remains challenging in non-overlapping field-of-view (FoV) configurations where correspondence-based methods are unreliable. We propose an engineering-oriented 2.5D calibration framework that estimates horizontal extrinsics $(x,y,\mathrm{yaw})$ via motion-guided planar alignment and then refines them using Gaussian Process Implicit Surfaces (GPIS), which provide continuous and probabilistic surface constraints from spatially disjoint scans. This design avoids calibration targets and reduces dependence on strong scene assumptions, improving robustness under noise and weak structure. Extensive high-fidelity simulation experiments demonstrate centimeter-level lateral accuracy and sub-degree yaw error, consistently outperforming representative motion-based and BEV-based baselines under both clean and noisy settings. To further assess real-world applicability, we conduct a preliminary nuScenes case study by splitting LiDAR scans into front and rear subsets to emulate a non-overlapping dual-LiDAR setup, achieving improved yaw accuracy and competitive lateral precision. Overall, the proposed method serves as a practical refinement stage for non-overlapping dual-LiDAR calibration, with a favorable balance of accuracy, robustness, and engineering feasibility. Full article

Cable-driven robotic systems are widely used in applications requiring lightweight structures, large workspaces, and accurate force regulation. In such systems, the mechanical behavior of cable-driven actuators is strongly influenced by the elastic properties of the cable, transmission elements, and supporting structure, leading to an effective stiffness that varies with pretension, applied load, cable length, and operating conditions. These stiffness variations have a direct impact on force control performance but are often implicitly treated or assumed constant in control-oriented studies. This paper investigates the effects of operating-point-dependent (incremental) cable stiffness on actuator-level force control performance in cable-driven robotic systems. The analysis is conducted at the level of an individual cable-driven actuator to isolate local mechanical effects from global robot dynamics. Mechanical stiffness is characterized within a limited elastic domain through local linearization around stable operating points, avoiding the assumption of global linear behavior over the entire force range. Variations in effective stiffness induced by changes in pretension, load, and motion regime are analyzed through numerical simulations and experimental tests performed on a dedicated test bench. The results demonstrate that stiffness variations significantly affect force tracking accuracy, dynamic response, and disturbance sensitivity, even when controller structure and tuning parameters remain unchanged. Full article

Chinese local governments have long financed public services through land-sale revenues. The shift from selling undeveloped land to redeveloping existing urban areas has disrupted this traditional financing model, exposing a critical tension between the pursuit of immediate revenue and the assurance of long-term fiscal health. The continued dependence on land-based finance has led many local governments to overlook long-term public service obligations and the long-term operating deficits associated with intensive urban development. Thus, by examining the relationship between the development rights allocation and the sustainable fiscal capacity of the government, the study evaluates both short-term revenue generation and long-term expenditure commitments in urban redevelopment contexts. However, existing research has yet to provide actionable tools to reconcile this structural mismatch between short-term revenues and long-term liabilities. We employ a comprehensive analytical framework that integrates fiscal impact modeling with the optimization of development rights allocation. Based on this framework, we construct a quantitative, dual-period fiscal balance model using mathematical programming to analyze various combinations of land development rights supply strategies for achieving fiscal equilibrium. Our results identify multiple feasible supply combinations that can maintain fiscal balance while supporting sustainable urban development. The findings demonstrate that strategic development rights allocation functions as an effective tool for balancing short-term revenue needs with long-term obligations in local land finance systems. Our study contributes to establishing a sustainable land finance framework, particularly for jurisdictions lacking comprehensive land value capture mechanisms. The proposed approach offers an alternative to traditional land rights transfer models and provides guidance for avoiding long-term fiscal distress caused by excessive land transfer. The framework supports more sustainable urban redevelopment financing while maintaining fiscal responsibility across temporal horizons. Full article

(1) Background: Urban traffic micro-environments show strong spatial and temporal variability. Short and intensive campaigns remain a practical approach for understanding exposure patterns in complex environments, but they need clear and interpretable summaries that are not limited to simple site or time segmentation. (2) Methods: We carried out a multi-site campaign across five traffic-affected micro-environments, where measurements covered several pollutants, gases, and meteorological variables. A machine learning framework was introduced to learn interpretable operational regimes as recurring multivariate states using clustering with stability checks, and then we evaluated their added explanatory value and cross-site transfer using a strict site hold-out design to avoid information leakage. (3) Results: Five regimes were identified, representing combinations of emission intensity and ventilation strength. Incorporating regime information increased the explanatory power of simple NO₂ models and allowed the imputation of missing H₂S day using regime-aware random forest with an $R^2$ near 0.97. Regime labels remained identifiable using reduced sensor sets, while cross-site forecasting transferred well for NO₂ but was limited for PM, indicating stronger local effects for particles. (4) Conclusions: Operational-regime learning can transform short multivariate campaigns into practical and interpretable summaries of urban air pollution, while supporting data recovery and cautious model transfer. Full article

Interspecies communication is increasingly recognized as an affective–cognitive process co-created between humans and animals rather than a one-directional transmission of signals. This review integrates findings from ethology, neuroscience, welfare science, behavioral studies, and posthumanist ethics to examine how emotional expression, communicative intentionality, and relational engagement shape understanding across species. Research on primates, dogs, elephants, and marine mammals demonstrates that empathy, consolation, cooperative signaling, and multimodal perception rely on evolutionarily conserved mechanisms, including mirror systems, affective contagion, and oxytocin-mediated bonding. These biological insights intersect with ethical considerations concerning animal agency, methodological responsibility, and the interpretation of non-human communication. Emerging technological tools—bioacoustics, machine vision, and AI-assisted modeling—offer new opportunities to analyze complex vocal and behavioral patterns, yet they require careful contextualization to avoid anthropocentric misclassification. Synthesizing these perspectives, the review proposes a relational framework in which meaning arises through shared emotional engagement, embodied interaction, and ethically grounded interpretation. This approach highlights the importance of welfare-oriented, minimally invasive methodologies and supports a broader shift toward recognizing animals as communicative partners whose emotional lives contribute to scientific knowledge. This review primarily synthesizes empirical and theoretical research on primates and dogs, complemented by selected examples from elephants and marine mammals, which provide the most developed evidence base for the affective–cognitive and relational mechanisms discussed. Full article

It has become common to apply multiple drones to conduct plant-protection in large-scale farms, where dual-UAV synchronisation is representative. However, current studies are mainly dedicated to the spray quality of a single UAV, and it remains unclear whether synchronous operation affects spray effectiveness. This paper focuses on the spray efficacy and coupling effects of dual-UAV collaboration. Five-factor orthogonal four-level tests were conducted using the developed UAV collaboration system, and the results were compared with those of asynchronous and ideal linear superposition. It is indicated that (1) spray uniformity was impacted by the relative height between the UAVs and the flight speed of the UAVs (all the p-values < 0.02), whilst the deposition amount was affected by the relative horizontal spacing between the UAVs and the height of the left UAV relative to the forward flight direction (all the p-values < 0.04); (2) the proportion of high-quality spray in the coupling areas had a negative relation with the relative horizontal distance of the two UAVs, and the threshold of the effective coupling distance was 5 m; and (3) synchronous coupling should be avoided. If it is not, the left-side UAV (referring to the forward direction of flight) should be at a higher altitude (5 m or 6.5 m), be 0.5 m higher than the right and fly with a low or medium flight speed (3.5 m/s–4.5 m/s). The research can give a reference to the real spray operation by multiple UAVs. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly deployed across diverse domains. Many applications demand a high degree of automation, supported by reliable Conflict Detection and Resolution (CD&R) and Collision Avoidance (CA) systems. At the same time, public mistrust, safety and privacy concerns, the presence of uncooperative airspace users, and rising traffic density are increasing research interest toward decentralized concepts such as free flight, in which each actor is responsible for its own safe trajectory. This survey reviews CD&R and CA methods with a particular focus on decentralized automation. It analyzes qualitatively classical rule-based approaches and their limitations, then examines machine learning (ML)-based techniques that aim to improve adaptability in complex environments. Building on recent regulatory discussions, it further considers how requirements for trust, transparency, explainability, and interpretability evolve with the degree of human oversight and autonomy, addressing gaps left by prior surveys. Full article