Search Results (616)

Background: Smart-city road and intersection management increasingly aims to smooth bus operations and reduce stop-and-go driving, but cities often lack auditable indicators linking routine fleet data with comparable energy and environmental KPIs. Methods: This study develops a Monitoring–Reporting–Verification (MRV) workflow for daily bus records from a 2024 Polish metropolitan fleet (diesel, compressed natural gas (CNG), hybrid, and battery-electric buses). Records were quality checked, harmonized to MJ/km, aggregated to bus-month observations, and analyzed using a linear mixed-effects model with propulsion technology, season, and activity level as fixed effects and vehicle-level random intercepts. Environmental impacts were then calculated under well-to-wheel (WTW) boundaries using Environmental Footprint 3.1 (EF 3.1) impact categories, Poland’s 2024 electricity mix, and illustrative electricity-mix scenarios through 2050. Results: Relative to diesel, BEV and HEV were associated with lower adjusted energy intensity (ratios 0.272 and 0.681, respectively), whereas the CNG–diesel contrast was directionally higher but statistically inconclusive under the available CNG sample. BEV energy intensity more than doubled in winter in descriptive terms, and vehicle-specific heterogeneity remained high (ICC ≈ 0.61). The BEV climate profile improved under electricity decarbonization, while some EF categories showed mix-dependent trade-offs. The 3–10% traffic-management variants are interpreted as screening assumptions rather than measured ITS effects. Conclusions: Routine bus records can support auditable MRV and preliminary screening of fleet and corridor interventions, but causal traffic-management evaluation requires route-level trajectory, congestion, and before–after data. Full article

Background: In zoster-associated pain (ZAP), earlier epidural steroid injection (ESI) has been associated with better outcomes, but optimal timing remains unclear, and prior studies have largely relied on pain reduction alone. Methods: In this multicenter retrospective cohort, 215 patients with ZAP who completed a three-session ESI course were classified into early (<30 days) and delayed (≥30–≤180 days) groups. The primary endpoint was complete remission at 12 weeks (≥50% visual analog scale [VAS] reduction, VAS ≤ 2, and sensory normalization); successful response (≥50% VAS reduction) served as the secondary endpoint. An ordered three-category framework and an exploratory generalized Youden index threshold analysis were applied. Results: Complete remission occurred in 82.1% versus 39.0% and successful response in 91.7% versus 67.8%. Each additional day of delay was associated with lower odds of complete remission (adjusted odds ratio [aOR], 0.957; p < 0.001) and higher odds of a worse outcome category (aOR, 1.030; p < 0.001). Exploratory candidate boundaries were 22 and 42 days. Conclusions: Earlier ESI initiation was associated with a higher likelihood of complete remission incorporating pain reduction, low residual pain intensity, and sensory normalization. These findings highlight the clinical relevance of treatment timing and recovery assessment beyond pain reduction alone in ZAP. Full article

Underground concrete structures are exposed to a multi-ion groundwater and seepage–leakage coupling environment for a long time, and it is difficult to observe visually, which makes it difficult to accurately characterize important boundary conditions and defect states, resulting in significant time-varying and spatially differing characteristics of the concrete deterioration process. Therefore, its durability assessment and life prediction are significantly different from those of above-ground structures. Aiming at the complex prediction problem of limited service information of underground concrete, this paper summarizes and combs the evolution process of underground concrete life prediction methods, and puts forward the evolution process of five generation prediction frameworks: from a deterministic mechanism model (Gen-1) to a multi-physical field coupling model (Gen-2), a probabilistic reliability framework (Gen-3), a data-driven and physical information fusion method (Gen-4) and then to a digital twin framework for online update and system integration (Gen-5). Differently from the traditional review by model category, this paper reveals the internal logic of life prediction from single life point values to time-varying risk assessment from the perspective of the transformation of prediction targets and problem structures. Based on the comparison of typical underground service environments, it is further shown that the key constraints of prediction ability are usually derived from insufficient observability and limited parameter identifiability, as well as model structure errors introduced by deterioration mechanism switching and local defects, rather than physical model complexity. On this basis, this paper proposes the selection idea of life prediction methods for different underground scenes, emphasizing measurable characterization, hierarchical verification and hierarchical calculation as the core, and effectively connecting the mechanism model, uncertainty analysis, data update and operation and maintenance decisions. In this paper, the life prediction of underground concrete is redefined as a dynamic evaluation process embedded in the whole life management of infrastructure, which provides a theoretical framework and research direction for the construction of a reliable and deployable life prediction system of underground concrete. Full article

Ongoing processes appear to be both open-ended and, in an important sense, complete. In the context of the Growing Block Theory of time, this combination generates a tension: if a process is genuinely ongoing, it seems incomplete; yet if it is complete, it appears closed and no longer directed at a non-existent future. This paper argues that this tension is only apparent. Building on Stout’s conception of occurrent continuants and on the distinction between temporal existence and temporal location central to Growing Block accounts, I examine two hybrid views according to which a process, considered as ongoing, and processes, considered as having gone on, fall under different categories of persistence. I argue that both versions of the hybrid view ultimately fail to account for the relation between dynamic existence and temporal location in a growing universe. As an alternative, I propose understanding ongoing processes as temporally expanding wholes with open boundaries. In this view, an ongoing process is always complete, though not completed, because its boundary at the edge of becoming is dynamically open rather than a genuine temporal part. This account preserves the motivations behind hybrid views while avoiding their ontological costs. Full article

This study investigates how different rural landscape types shape visual attention and physiological responses, with the aim of informing more targeted rural landscape renewal. Four typical rural landscape types in the suburbs of Hefei, China, were examined: Flat Farmland (FF), Hilly Forest (HF), Developed Plain (DP), and Water-network Lowland (WNL). All four study villages are project villages in the suburban area of Hefei where rural revitalization is currently being advanced. This study therefore treats them as empirical cases within the context of rural revitalization in China, using them to examine perceptual differences among rural landscape types and their implications for rural landscape renewal. A two-stage research design was adopted to balance field realism and laboratory control. In the first stage, 40 representative scene images were selected by combining field video records with fluctuations in on-site skin conductance response (SCR). In the second stage, laboratory experiments were conducted while participants viewed the selected images, during which eye-tracking, skin conductance, and heart rate data were recorded simultaneously. These measures were used to characterize visual attention allocation and autonomic physiological responses across different rural landscape types, rather than to directly measure landscape preference. For Area of Interest (AOI) analysis, each image was coded into six landscape element categories: vegetation, buildings, roads, sky, vernacular buildings, and water bodies. The results revealed significant typological differences in overall visual search patterns and autonomic responses. Gaze hotspots were concentrated on identifiable targets and boundary regions in the foreground and midground, whereas the sky attracted relatively limited attention. FF primarily emphasized vernacular buildings and farmland boundaries, HF emphasized settlement interfaces and spatial transition nodes, DP emphasized road junctions and facilities along routes, and WNL emphasized water bodies and water–land interface zones. These findings suggest that a two-stage multimodal design can provide supporting evidence for understanding type-specific perceptual responses and can support more targeted strategies for rural landscape renewal. Full article

The high concentration of CO generated in confined spaces following a gas explosion constitutes the primary lethal factor, and its rapid elimination represents a critical technical bottleneck in emergency rescue operations. This study systematically investigates the confined thermodynamic characteristics of CO catalytic oxidation over hopcalite across a wide temperature range of 15–65 °C. Based on the ideal gas assumption and constant-volume boundary conditions, the thermodynamic processes were classified into two categories: constant-volume variable-temperature and constant-temperature constant-volume. The influence of temperature on enthalpy change, heat release, entropy change, and the chemical equilibrium constant was quantitatively examined. The results demonstrate that the total enthalpy change and heat release remained negative throughout the entire temperature range, exhibiting a trend of “initial increase, subsequent decrease, followed by a slight rise”, with the maximum exothermic value observed at 25 °C. The total entropy change was persistently negative across the full temperature range; the positive offset contribution of the physical entropy change induced by temperature elevation was negligible, resulting in a consistently high absolute value of the total entropy change. The logarithm of the standard equilibrium constant decreased linearly with increasing temperature yet remained as high as 180.48 at 65 °C, indicating that the reaction maintains an extremely strong thermodynamic spontaneity and a nearly complete conversion limit under all tested conditions. Full article

Tropical lignocellulosic residues are increasingly relevant feedstocks for lignin-containing nanocellulose composites, but their performance cannot be predicted from botanical origin or bulk lignin percentage alone. This review defines the interface as the geometrical boundary between phases and the interphase as the finite, compositionally graded region in which lignin distribution, nanocellulose morphology, adsorbed water, and the surrounding matrix jointly govern stress transfer and mass transport. Using an evidence-weighted framework, the literature is organized into the following categories: residual-lignin nanofibrils, redeposited-lignin systems, lignin nanoparticle assemblies, compatibilized thermoplastic hybrids, and all-lignocellulosic sheets. Representative quantitative observations show that controlled residual lignin can the increase water contact angle from approximately 35 degrees to 78 degrees and reduce oxygen permeability by up to 200-fold in nanopapers, while selected PLA/LCNF systems show tensile-strength and modulus increases of 37% and 61%, respectively; however, high or poorly distributed lignin can suppress fibrillation, lower viscosity, weaken gel networks, and reduce reproducibility. The most defensible near-term product windows are packaging layers, grease/oil barrier papers, coatings, paper-like multilayers, and selected porous media. Thermoplastic matrices remain process-sensitive, and biomedical, additive-manufacturing, nano-reactor, and energy-material claims require stronger validation of the extractables, rheology, humidity history, TEA/LCA metrics, and end-of-life behavior. This review, therefore, provides a critical, application-backward roadmap for tropical biorefineries in which interfacial function, wet handling, drying energy, and process integration are assessed together rather than treated as independent variables. The abbreviations used in the abstract are defined as follows: CNFs, cellulose nanofibrils; CNC, cellulose nanocrystals; LCNF, lignin-containing cellulose nanofibrils; LCNCs, lignin-containing cellulose nanocrystals; PLA, poly(lactic acid); PHB, polyhydroxybutyrate; PHAs, polyhydroxyalkanoates; PVA, poly(vinyl alcohol); DESs, deep eutectic solvents; TEA, techno-economic analysis; LCA, life-cycle assessment; ML, machine learning. Full article

Monitoring marine organisms plays a vital role in biodiversity conservation, marine environmental management, and fisheries resource management. However, the underwater environment is often low-light and turbid, leading to indistinct target boundaries. Moreover, the wide variety of marine organisms—with significant differences in color, scale, texture, and morphology—can easily result in missed detections. To address these challenges, this paper proposes a multi-class marine organism detection method using multi-scale attention-enhanced You Only Look Once 11 nano (YOLO11n). The method incorporates the Convolutional Block Attention Module (CBAM) into the YOLO11n network, enabling the model to better focus on key feature regions while effectively suppressing background noise interference in complex marine environments. In addition, the model is trained using the Complete Intersection over Union (CIoU) loss function, which enhances bounding box regression accuracy, especially in handling targets of varying scales. The effectiveness of the proposed method is validated on the publicly available BrackishMOT dataset. The proposed model achieves an overall mAP@0.5 of 0.481, computed as the average AP across six organism categories. Category-wise results indicate stronger performance on visually distinguishable targets, such as Jellyfish, Starfish, and Small fish, with AP values of 0.808, 0.678, and 0.677, respectively. In contrast, performance remains limited for rare or visually ambiguous categories. These results suggest that the proposed method is effective for multi-class marine organism detection, particularly when discriminative visual features are present. Full article

Semantic segmentation of very-high-resolution remote sensing imagery benefits from combining RGB appearance with Digital Surface Model (DSM) height information, especially in urban scenes where spectrally similar objects often differ in elevation. On the ISPRS Vaihingen and Potsdam benchmarks, BATFNet achieves mIoU scores of 84.06% and 85.31%, respectively, outperforming representative RGB–DSM fusion baselines on most land-cover categories. BATFNet is a supervised boundary-aware Transformer fusion network that uses DSM-derived edge priors to guide bidirectional cross-modal attention and decoder refinement. With a dual-branch ResNet-50 backbone for modality-specific feature extraction, the proposed framework effectively integrates RGB and DSM information while recovering fine spatial details. These results show that exploiting DSM-derived structural cues improves boundary delineation and reduces confusion among spectrally similar urban classes. Full article

The algorithmic overproduction of images that increasingly characterizes our media environment reveals a radically new relationship with visual representation. What emerges is not merely an inability to distinguish an iconographic model from its real referent, nor simply the dissolution of the boundary between true and false, reality and simulacrum (Baudrillard), but rather a growing indifference toward such distinctions. The incapacity to discern between “real” and “fabricated” images no longer appears problematic; the very categories of true and false tend to collapse into one another, resulting in the abandonment of interpretative engagement. After analyzing this phenomenon, the article proposes a pedagogical framework designed to respond to such a condition. It argues that neither image decoding (visual literacy) nor creative production alone are sufficient. What is required is a form of training in imagination, enacted through an ethics of the gaze and a digital archeology; through practices of digital estrangement; as well as through exercises in embodiment and sensory re-anchoring. The ultimate goal is to develop tools that shift the relationship with images from consumptive to interrogative—fostering what we call Exit Literacy: the capacity not only to read the world, but to desire to exit its passively offered version, reclaiming an active role in critique and meaning-making. Full article

Accurate parcel-level crop field information is essential for precision agriculture, field management, and crop monitoring based on Unmanned Aerial Vehicle (UAV) imagery. However, it remains difficult to achieve both reliable crop-type recognition and fine boundary delineation from UAV RGB imagery. Although deep learning-based semantic segmentation models can effectively identify crop types, they often produce coarse or incomplete boundaries. The Segment Anything Model (SAM) can produce high-quality boundaries, but it depends on manual prompts and lacks semantic recognition ability, which limits its use in large-scale automatic mapping. To address this issue, this study proposes a parcel-level crop field identification framework based on Semantic–SAM fusion, enabling automatic semantic recognition and fine boundary extraction without manual prompts. Based on UAV RGB remote sensing imagery, this study developed a two-stage Semantic–SAM framework. Semantic segmentation models, including DeepLabv3+, U-Net, HRNet, and PSPNet, were first used to generate initial results. Then, bounding boxes or internal high-confidence points were extracted from the initial field regions as prompts for SAM to refine the segmentation. The final results preserved crop category information while producing finer boundaries. To evaluate the framework, this study compared four semantic segmentation models and their Semantic–SAM versions on the same-region test set, and further tested their spatial generalization ability on the different-region test set. The results showed that the Semantic–SAM framework provided more consistent gains in boundary quality, with regional recognition accuracy improving in several models and test scenarios. On the same-region test set, the PSPNet-based framework showed clear improvement, with mean Intersection over Union (mIoU) increasing from 78.99% to 83.13% under point-box prompts. The U-Net-based framework achieved the best mIoU of 87.09% with box prompts. On the different-region test set, the DeepLabv3+-based framework showed the largest gain in spatial generalization, with mIoU increasing from 67.22% to 73.45% under point-box prompts. Overall, the PSPNet-based fusion framework showed a better balance in accuracy, boundary quality, and robustness under different-region conditions. These results demonstrate that Semantic–SAM fusion supports automatic multi-class crop field mapping and boundary refinement from UAV RGB imagery without manual prompts or SAM fine-tuning, providing a practical approach for parcel-level crop monitoring and precision agriculture applications. Full article

This review develops a configurational account of the relationship between religion and transnationalism by addressing a specific analytical limitation in the existing literature: its tendency to oscillate between substantializing religious traditions as already constituted entities that move across borders and segmenting transnational religion into disconnected domains such as networks, migrant communities, diasporic identities, institutions, political mobilization, digital mediation, social support, or pilgrimage. While these approaches have generated substantial empirical insight, they leave undertheorized the relational formation through which religious authority, practice, identity, material circulation, symbolic boundary-making, institutional organization, and mediated presence are assembled and made socially effective across multiple scales. To clarify this problem, the review reconstructs scholarship on religion and transnationalism through five major thematic domains: transnational religious networks, religious identity in transnational contexts, religion as a catalyst of transnationalism, the embedding of religion in transnational social practices, and distinctive forms of transnational religion. This reconstruction shows that transnational religious phenomena are inadequately understood as the spatial extension of pre-given traditions, as residual expressions of ethnicity or migration, or as discrete networks, movements, institutions, or diasporic communities. They are better grasped as historically contingent and relationally ordered formations whose temporary coherence is produced through the interaction of actors, authorities, practices, discourses, infrastructures, legal-regulatory environments, memories, obligations, and material flows. Building on the concept of social configuration, the review therefore proposes transnational religious configurations as a more precise unit of analysis for studying how the religious and the transnational are mutually constituted rather than externally connected. It defines such configurations as historically specific formations in which religious categories, institutions, practices, authorities, material resources, symbolic boundaries, and cross-border conditions of possibility are articulated across local, national, transnational, and global scales. The review operationalizes this approach through three analytical levels—conditions of possibility, construction and characteristics, and social realities and consequences—and illustrates its explanatory purchase by examining a new phenomenon within the contemporary transnational revival of Shi‘i Islam. Full article

Semantic segmentation of high-resolution remote sensing images constitutes an important foundation for urban mapping and land-cover interpretation. However, objects in remote sensing scenes usually exhibit large-scale variations, significant intra-class differences, and complex background interference. Due to these factors, existing methods for complex high-resolution scenes still suffer from insufficient global semantic modeling, boundary blurring, and small-object omission. To address the above challenges, this paper proposes a Global Category-Center Prior-Guided Spatial-Frequency Collaborative Network (GC2F-Net). Specifically, ResNet-50 is adopted as the encoder, and a Global Category-Center Module is utilized to generate a global category-center prior based on deep features, which is then combined with a Fourier Global Enhancement Module to enhance deep features in the frequency domain. During the decoding stage, a Local Category-Aware Frequency Attention Module is employed to progressively refine feature representations under the guidance of the global category-center prior, thereby achieving collaborative improvement in global semantic consistency and local detail recovery. Experimental results demonstrate that GC2F-Net achieves robust and competitive segmentation performance on multiple public remote sensing semantic segmentation datasets. The proposed method provides an effective spatial-frequency collaborative modeling paradigm for the semantic segmentation of high-resolution remote sensing images. Full article

Securing IoT networks presents fundamental challenges rooted in hardware constraints: firmware is often non-upgradeable and every security boundary is fixed at manufacture. Machine learning-based intrusion detection offers a scalable response, yet nearly all published systems assume clean training data and clean inference conditions. Production IoT environments satisfy neither assumption. Sensors degrade, packets drop, and adversaries deliberately corrupt telemetry streams to evade detection. The framework described here is built around that reality. The proposed framework is distinguished from prior work by four design decisions. First, three encoding branches, a residual DNN, a 1D-CNN, and a BiLSTM, are run in parallel and are fused by concatenation, each capturing structural patterns in tabular traffic data that the others miss. Second, a dual-view consistency loss trains the model under simultaneous feature masking and Gaussian noise, penalizing prediction divergence between two independently corrupted views of the same sample. Third, we introduce entropy-weighted attention: rather than fixed learned weights, per-feature importance is adjusted dynamically from information entropy measured across training batches, giving higher-entropy features stronger influence because they carry more discriminative variation. Fourth, branch-dropout regularization randomly silences entire branches during training, forcing each to develop independently useful representations instead of co-adapting. Class imbalance is handled through severity-aware loss weighting which scales contributions by the operational cost of missing each attack category, not purely by inverse frequency. On UNSW-NB15, the full model achieves 99.99% accuracy, 100% precision, 99.97% recall, and a false-negative rate of 2.65 × 10⁻⁴—the lowest across all compared architectures. Full article

Mining areas in high-altitude cold and arid mountains exhibit heterogeneous land-cover types, large spatial extent, and fragmented boundaries, which makes large-area monitoring difficult with manual interpretation. This study proposes CS-DeepLabV3+, an enhanced semantic segmentation framework built upon DeepLabV3+ for 1-m optical imagery in the Kunlun Mountains. A contextual modeling block is inserted between the encoder output and the atrous spatial pyramid pooling module to strengthen long-range dependency modeling under complex backgrounds. In the decoder, a channel attention block is applied to fused features to suppress redundant responses and improve separability among confusing categories. Experiments on a self-built dataset (Kunlun-Set) demonstrate improved boundary delineation and region consistency for typical mining-related classes (e.g., tailings ponds, stockpiles, and industrial yards). CS-DeepLabV3+ achieved an 81.83% mean intersection-over-union on the test set, outperforming the DeepLabV3+ baseline by 3.52 percentage points. Ablation studies verify that contextual modeling and channel recalibration provide complementary gains. Full article