Search Results (15,904)

The growing need to mitigate climate change has accelerated the development of Carbon Capture, Utilization, and Storage (CCUS) technologies, where the safe transport of supercritical CO${}_2$ (sCO${}_2$) through pipelines is a key challenge. The flow behavior in such systems is strongly influenced by phase-change processes under transient conditions such as decompression and heat transfer and is further complicated by the presence of impurities (e.g., N${}_2$, CH${}_4$, and Ar). These impurities modify thermodynamic properties and phase boundaries, thereby affecting the overall flow dynamics. In this study, the influence of impurities on leakage, mass flow rate, and decompression wave propagation in sCO${}_2$ pipelines is investigated using computational fluid dynamics (CFD) simulations. A real-fluid model (RFM) implemented in the CONVERGE CFD solver is employed, with a tabulation-based approach to accurately capture thermodynamic and transport properties across multiphase regimes. The simulations were validated against available experimental data and performed for varying impurity concentrations to assess their impact on key flow variables, including pressure, temperature, and wave speed. Although simplifying assumptions were used, the results are in fairly good agreement with experimental observations and provide a better understanding of the phase behavior induced by impurities during transient decompression. Additionally, the effects of outlet geometry, pipeline configuration, and the choice of equation of state are examined, highlighting their influence on the predicted flow response. The validity of the RFM modeling framework is further demonstrated by simulations of a large-scale pipeline configuration representative of industrial conditions, which will serve as a benchmark for future improvements. Full article

Tourism has become a structural driver of land-system transformation, influencing urban restructuring, rural land consumption, coastal development, and housing dynamics. Although tourism sustainability has received growing scholarly attention, less systematic evidence exists on how land governance and spatial planning frameworks mediate tourism-related land-use change. This study presents a systematic review of peer-reviewed journal articles published between 2000 and 2025 examining the relationship between spatial planning, land-use regulation, and tourism development. Following PRISMA guidelines, a structured search strategy and multi-stage screening process were applied using predefined inclusion and quality criteria, resulting in a final dataset of 58 studies. The findings indicate that tourism-driven land transformation is shaped by interconnected governance layers, including statutory planning instruments, institutional coordination mechanisms, and land administration infrastructures. However, these dimensions are rarely analyzed within an integrated framework. By synthesizing tourism planning and land administration scholarship through a land governance perspective, this review clarifies how regulatory tools and administrative systems interact in shaping spatial outcomes across scales. The study offers a structured basis for future comparative research and for more coherent policy responses to tourism-related land governance challenge. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article

Redundant robot manipulators are widely used in constrained operations and tasks in complex environments. However, when multiple task constraints and inequality constraints coexist, motion planning becomes significantly more difficult. In high-dimensional configuration spaces, conventional planners are prone to local minima and may generate trajectories that are difficult to execute in real time. To address these issues, this paper proposes a hierarchical, redundancy-driven real-time replanning framework. First, we perform Cartesian sampling on the task-constraint manifold to reduce the search dimension and generate multiple candidate joint configurations for each Cartesian sample via a redundancy mapping. During connection, manipulability and executability margin are used as evaluation metrics, so that redundant degrees of freedom are explicitly exploited in tree expansion and configuration selection. Second, at the local execution layer, we employ a null-space manipulability optimization strategy to continuously improve dexterity while keeping the primary task unchanged and combine it with a priority-based hard inequality constraint filtering mechanism to project the nominal motion onto the feasible set under joint limits, velocity bounds, and safety-distance constraints in real time. Unlike existing approaches that treat global planning and local control as loosely coupled modules, the proposed framework unifies redundancy reconfiguration, feasibility maintenance, and topological replanning within a single closed-loop structure, thereby reinterpreting local minima as event-triggered topology-switching conditions. To handle the mismatch between dynamic environments and real-time perception, we further introduce a feasibility-margin monitoring mechanism that triggers event-based replanning based on changes in manipulability, constraint scaling, and safety distance, enabling fast topology-level switching and escape from local minima. Simulation and experimental results show that the proposed method effectively restores manipulability through redundancy-driven configuration adjustment and achieves a higher success rate of local recovery under dynamic obstacle intrusion. In forced replanning scenarios, the framework further demonstrates faster environmental response and lower replanning overhead while maintaining better task-constraint stability compared with existing approaches. Full article

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices. Full article

The Historic Urban Landscape (HUL) approach provides a vital and extensive framework for heritage conservation. However, local practices often struggle to spatially translate qualitative assessments into quantitative controls at the urban block level, the most effective basic scale for administrative implementation, thereby limiting effective responses to the Management of Change. By integrating HUL with the theory of Dynamic Integrity, this study constructs a multi-dimensional evaluation index system and proposes a HUL evaluation method based on Character-Based Zoning. Taking the 125 urban block units of the historic urban area of Jingdezhen as a case study, this research integrates historical mapping, GIS spatial analysis, and Co-occurrence Network Analysis to reveal the internal structural logic of the heritage system. The study finds that the HUL of Jingdezhen is a multi-nodal dynamic system driven by four core elements: ritual beliefs, administrative management, production activities, and commercial guilds. Critically, modern visual intrusions severely impact the core heritage components within this system, specifically the Dubang and ritual culture. Based on the three dimensions of Heritage Richness, Landscape Sensitivity and Value Centrality, the study systematically identifies a total of 11 types of urban block units within the plots that characterize distinct historic landscape features and transformation patterns. This research not only deepens the localized application of HUL theory but also provides a scientific basis and methodological support for the Management of Change and periodic assessment in dynamic heritage environments. Full article

Rapid urbanisation and climate change are intensifying urban heat stress, posing significant challenges for climate-responsive urban planning. Digital and data-driven approaches, including GIS, remote sensing, microclimate simulation, and artificial intelligence (AI), have advanced urban climate analysis; however, their capacity to support human-centred planning remains insufficiently synthesised. This review analyses 78 peer-reviewed studies (2015–2025) to evaluate how digital methods address urban microclimate and outdoor thermal comfort. The reviewed studies are classified into four methodological groups: spatial data analytics, simulation-based models, parametric and optimisation workflows, and AI-driven or hybrid approaches. The results show that the majority of studies rely on proxy indicators, such as land surface temperature and sky view factor, while physiologically based comfort indices (e.g., PET and UTCI) are applied in a limited proportion of studies and remain largely confined to microscale simulations. A persistent scale mismatch is identified between large-scale analytics and pedestrian-level thermal experience, alongside geographic and climatic biases, particularly in hot-arid regions. Unlike previous reviews, this study integrates digital methodologies, urban microclimate processes, and human-centred thermal comfort within a unified framework. The findings provide actionable insights for planners and designers by supporting the integration of thermal comfort into multi-scale, climate-responsive decision-making. Full article

This study presents an integrated approach for producing antioxidant-rich polar fractions from Inula salicina L. via pressurised ethanol/water extraction (PLE-EtOH/H₂O), optimised by coupling a central composite design and response surface methodology (CCD-RSM) with Aspen Plus^® simulation. The effects of PLE temperature, extraction time, and EtOH/H₂O ratio for yield, total phenolic (TPC) and flavonoid (TFC) content, and Trolox equivalent antioxidant capacity (TEAC) measured in ABTS^•+-scavenging, cupric ion reducing antioxidant (CUPRAC) and oxygen radical absorbance (ORAC) assays were assessed via a multi-response optimisation approach. Optimal conditions were set at 82 °C, 27 min, and 60% EtOH (v/v), yielding ~29 g extract per 100 g plant material, characterised by high TPC (227 mg GAE/g), TFC (34 mg QE/g), and TEAC values in the CUPRAC (1473 mg TE/g), ABTS (869 mg TE/g), and ORAC assays (1165 mg TE/g). The TPC and TEAC values of the post-extraction residue were >92% lower than those of unextracted I. salicina, confirming efficient recovery of the major portion of antioxidant-active constituents by PLE-EtOH/H₂O. The high in vitro radical scavenging capacity, reducing power, and photoprotective potential (sun protection factor ~50 at 0.5 mg/mL) of the I. salicina extract are consistent with its phenolic-rich composition, with chlorogenic acid (~97 mg/g extract) and its derivatives being the major constituents. The validated Aspen Plus^® model closely aligned with the CCD-RSM predictions, supporting process scale-up and energy feasibility and demonstrating an industry-relevant, green-solvent PLE process for producing higher value-added I. salicina fractions with potential applications in the food, pharmaceutical, nutraceutical, and cosmetic sectors. Full article

Artificial intelligence (AI) is increasingly used to support tuberculosis (TB) screening and diagnosis, particularly through computer-aided detection (CAD) applied to chest radiography (CXR). However, the programmatic value of AI depends not only on diagnostic accuracy but also on implementation context, threshold calibration, and integration into diagnostic pathways. We conducted a narrative, state-of-the-art review of AI applications across the TB diagnosis pathway. Evidence was synthesized from World Health Organization policy documents, independent validation initiatives, and peer-reviewed studies published between 2010 and 2026, with a structured selection process aligned with PRISMA principles. CAD for CXR is the most mature AI application and is recommended by WHO for TB screening and triage among individuals aged ≥15 years in specific contexts. Across studies, CAD-CXR demonstrates sensitivity comparable to human readers, although performance varies by product, population, and imaging conditions, necessitating local threshold calibration. Evidence from implementation studies suggests improvements in screening efficiency and potential cost-effectiveness in high-burden settings. Other AI modalities, including computed tomography (CT)-based imaging analysis, point-of-care ultrasound interpretation, cough or stethoscope sound analysis, clinical risk models, and genomic resistance prediction show promising but heterogeneous results, with most requiring further independent validation and prospective evaluation. AI has the potential to strengthen TB screening and diagnostic pathways, but its impact depends on integration into health systems and evaluated using patient- and program-level outcomes rather than accuracy alone. A differentiated approach is needed, with responsible scale-up of policy-endorsed tools alongside rigorous evaluation of emerging technologies to support effective and equitable TB care. Full article

Urban floods have become a major systemic risk to sustainable urban development under climate change and increasingly frequent extreme hydro-meteorological events. Yet evidence on the long-term evolution of urban flood resilience (UFR) and its structural constraints at the provincial scale remains limited. This study develops a PSR-based framework to assess UFR and diagnose its dominant obstacles using data for 21 prefecture-level cities in Sichuan Province from 2011 to 2023, including meteorological, geomorphological, socioeconomic, infrastructure, environmental, and public service indicators. A combined AHP–EWM is used to integrate subjective and objective information, TOPSIS is applied to derive a composite UFR index and subsystem scores, and an obstacle degree model is employed to identify key constraints and their temporal evolution. Results show that: (1) UFR in Sichuan Province fluctuated but increased overall during 2011–2023, reaching its highest level in 2023; (2) resilience improvement was driven mainly by the response subsystem, while the pressure subsystem showed the greatest interannual variability; and (3) the annual top five obstacles were highly persistent and insufficient response capacity was the dominant long-term constraint on resilience enhancement. These findings underscore that improving the adequacy, institutional robustness, and operational stability of response systems is central to enhancing UFR. This study provides empirical support for the assessment of provincial-scale resilience and policy-oriented flood risk governance. Full article

In recent years, typhoon activity over the South China Sea (SCS) has intensified, and interactions between typhoons and mesoscale eddies profoundly regulate the regional oceanic environment and air–sea energy exchange. To systematically investigate the position- and polarity-dependent eddy responses to typhoon forcing, we developed a typhoon–eddy spatial matching algorithm and analyzed the global mesoscale eddy dataset (2006–2020) combined with China Meteorological Administration (CMA) best-track typhoon records. Composite and correlation analyses were employed to examine variations in the eddy surface available potential energy (SAPE) and sea-surface temperature (SST) within a 7-day window before and after typhoon passage, with the typhoon power dissipation index (PDI) used to quantify storm intensity. Composite results reveal distinct dual-asymmetric responses: (1) Energetically, eddies on the left side of typhoon tracks exhibit overall weakening, with anticyclonic eddies (ACEs) showing more pronounced energy decay; in contrast, right-side eddies undergo significant intensification, and cyclonic eddies (CEs) display stronger enhancement than ACEs. (2) Thermally, all eddy types experience net cooling after typhoon passage, with right-side eddies showing stronger SST reductions than left-side ones, and CEs exhibiting more intense cooling than ACEs. Time-scale correlation analyses further demonstrate that the eddy energy change rate (EECR) of left-side CEs, right-side CEs, and right-side ACEs is positively correlated with PDI, whereas left-side ACEs show no significant correlation. For the SST change rate (SSTCR), all types of eddy events exhibit significant negative correlations with PDI, with weaker correlations for CEs and stronger correlations for ACEs. This study demonstrates that the track-relative position of tropical cyclones and the polarity of pre-existing mesoscale eddies exert a systematic control on the observed eddy responses to tropical cyclone forcing in the SCS. These results provide observational constraints on the asymmetric oceanic responses induced by tropical cyclones and offer insights into the interpretation of typhoon–ocean interaction diagnostics in marginal seas. Full article

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, and simplistic feature fusion strategies. In response to the above challenges, this paper proposed the Multi-Scale Frequency-Enhanced YOLO (MSFE-YOLO) algorithm that integrates multi-scale frequency domain enhancement with defect-aware attention mechanisms. First, a Multi-Scale Frequency-Enhanced Convolution (MSFC) module was constructed, which extracted multi-scale spatial features in parallel through depth-adaptive dilated convolutions, explicitly modeled high-frequency edge information using the Laplacian operator, and achieved adaptive fusion of multi-branch features via learnable weights. Second, a Cross-Stage Partial with Multi-Scale Defect-Aware Attention (C2MSDA) module was designed, integrating Sobel operator-based edge perception, multi-scale spatial attention, and adaptive channel attention to collaboratively enhance features across spatial, channel, and edge domains through a gated fusion strategy. Finally, an Adaptive Feature Fusion Enhancement (AFFE) module was proposed to achieve adaptive aggregation of multi-level features through a data-driven weight generation network and cross-scale feature interaction mechanism. Experimental results on the NEU-DET and GC10-DET datasets demonstrated that MSFE-YOLO achieved the mAP@0.5 of 79.8% and 66.7%, respectively, which were 1.7% and 2.1% higher than the benchmark model YOLOv11s respectively, while maintaining an inference speed of 89.3 FPS, which satisfied the real-time detection requirements in industrial scenarios. Full article

Understanding how users perceive and attend to the anthropomorphic appearance and empathic responses of artificial intelligence medical conversational agents (AIMCAs) can help reveal the key judgment cues underlying trust formation and use decisions, while also informing interface and dialog design. To this end, this study employs a 3 (appearance anthropomorphism: high, medium, low) × 2 (empathic response: present, absent) within-subject eye-tracking experiment, combined with subjective scales and brief post-task open-ended feedback. During a static prototype viewing task based on hypothetical consultation scenarios, we concurrently recorded trust, behavioral intention, and visual measures for key areas of interest (AOIs; appearance area, conversational content area, and overall interface area). Eye-tracking measures were normalized by AOI coverage proportion to improve cross-AOI comparability. The results show that both anthropomorphic appearance and empathic response significantly increased users’ trust in AIMCAs and their behavioral intention. An interaction between these two types of social cues was also observed, suggesting that when visual embodiment and linguistic style are aligned at the social level, users are more likely to form favorable overall judgments. At the level of visual processing, however, no interaction effect was found, and the eye-tracking measures showed only partial main effects, indicating that subjective synergy does not necessarily correspond to synergistic changes in attentional allocation. Overall, anthropomorphic appearance and empathic response exerted consistent facilitating effects on outcome variables, but displayed different patterns of attentional allocation and information prioritization at the visual level. Accordingly, AIMCA design should emphasize consistency between appearance cues and conversational strategies, optimize users’ initial judgments and interface comprehension, and use intention through verifiable information organization and clear boundary cues. Full article

Deep soft-rock tunnels are prone to large deformations and structural damage. This study used the Guanyinping Tunnel as a prototype and conducted 1/50-scale progressive loading model tests under three support configurations: rock-bolt-free, equal-length rock bolts, and mixed long–short rock bolts. Rock stress, radial rock displacement (u), and rock bolt axial force (F_N) at the vault, arch shoulders, sidewalls, and wall feet were monitored to reveal reinforcement mechanisms and mechanical response. The results indicated that stress evolution in the bolt-free case exhibited significant spatial heterogeneity. The vault experienced horizontal stress concentration, while the arch shoulder underwent vertical stress concentration. u underwent a three-stage nonlinear progression: elastic linear growth, plastic linear growth, and plastic-accelerated growth. Displacement at the vault was markedly larger than that at other locations. Equal-length rock bolts substantially improved the rock mass stability by delaying stress concentration and fracture propagation. This reinforcement raised the elastic response threshold to 96 kPa and substantially reduced u. F_N at the vault and shoulder followed linear growth, accelerated growth, and then gradual decline, whereas F_N at the sidewalls and wall feet exhibited a steady linear trend. Combined long and short rock bolts produced a multi-level anchoring effect. Short bolts induced a shallow arching action, while long bolts provided deep suspension. This synergy raised the elastic response threshold to a maximum of 120 kPa and moderated the stress reduction process. Deep residual stresses increased to 74.3–88.4% of peak values. The displacement gradient between shallow and deep rock masses was significantly reduced. The coordinated deformation capacity within the anchoring zone was markedly enhanced. F_N distribution exhibited spatial differentiation: short bolts carried the load initially, followed by the activation of long bolts. Both anchoring schemes increased residual stress and mitigated rock mass deformation. The deformation control effect was stronger in shallow rock mass than in deep rock mass. Improvements at the vault and arch shoulders exceeded those at the sidewalls and wall feet. The mixed short–long bolt configuration was superior because it maximized the self-bearing capacity of the deep rock mass. The findings provide experimental data and theoretical guidance for the design and optimization of rock-bolt support in deep soft-rock tunnels. Full article

Distance learners in higher education are often assumed to face limited peer interaction, potentially weakening their sense of belonging. This study examines peer relationships and belonging among students in distance and blended university programs, with attention to the role of virtual reality (VR) within digitally mediated learning environments. Immersive VR teaching is included in the curriculum for distance learning students in the studied programs. Using a mixed-methods design, survey data and open-ended responses were collected from 17 students in Information Studies and Information Service Design. An adapted Classroom Community Scale was supplemented with items addressing the perceived contribution of different communication technologies. Contrary to expectations, fully distance learners did not report weaker agreement with statements reflecting belonging than blended students; on several items, they expressed stronger agreement, particularly regarding perceived peer support and learning opportunities. Results indicate that conventional 2D communication tools, particularly chats and video calls, are central to sustaining peer relationships. VR was not perceived as essential but described by some students as an added value supporting shared experience and group cohesion. Overall, belonging emerges as a socio-technical achievement shaped by communication practices rather than physical proximity. Full article