Search Results (2,135)

Built heritage is increasingly affected by climate-driven processes, yet its capacity to inform broader understandings of urban environmental change remains insufficiently explored. Here, we synthesize the recent literature (2020–2024) on the application of the Historic Urban Landscape (HUL) approach to the integrated management of cultural heritage under climate risk, reframing the historic built environment as a multi-scale diagnostic medium for climate–urban interactions. We analyze the steps and tools employed to support decision-making across territorial planning, risk assessment, and heritage governance in the papers selected from Web of Science, Science Direct, and Scopus databases. Results show that the approach is a flexible analytical framework that allows the integration of heterogeneous data, multi-criteria evaluations, and diverse stakeholder perspectives across spatial and temporal scales. Information modeling tools are shown to play a central role in structuring territorial knowledge, identifying patterns of vulnerability, and supporting comparative analyses across urban contexts. Nonetheless, significant challenges persist, including limited quantification of climate-induced degradation mechanisms, uncertainties in linking vulnerability assessments to predictive models, structural constraints on participatory implementation, and a tendency to apply the approach as a checklist due to inadequate understanding of its holistic dimensions. Overall, the HUL approach emerges as a scalable and transferable framework for embedding cultural heritage within climate research, advancing the conceptual integration of built heritage into resilience science and sustainability-oriented urban systems. Full article

Against the backdrop of uneven educational development and structural constraints in rural Mainland China, teacher agency is critical for driving professional growth and instructional improvement. Rural educators face distinct challenges—limited resources, isolated work contexts, and systemic pressures—that shape their capacity to enact change. While scholarship has documented the roles of contextual resources and individual beliefs in shaping teacher agency, less is known about the mediating mechanisms linking job resources and self-efficacy to agency within China’s rural educational landscape. This study examines how perceived job resources (teaching resources, administrative support, colleague support, parental support) and teaching self-efficacy collectively shape rural teachers’ agency, to inform policy and practice for strengthening their professional capacity. Drawing on a quantitative survey of 625 rural teachers, we employ a two-stage analytical approach: first, descriptive statistics, t-tests, ANOVA, and Pearson correlations to map baseline variable relationships; second, Hayes’ PROCESS macro (Model 4) with bootstrapping to test the mediating role of teaching self-efficacy between job resources and teacher agency. Findings reveal the following: (1) Rural teachers report moderate agency (M = 3.53/5), indicating room for growth; (2) All four job resource dimensions significantly and positively predict agency (β = 0.099–0.163); (3) Teaching self-efficacy is a robust predictor of agency (β = 0.785–0.822, p < 0.001) after controlling for resources; (4) Self-efficacy partially mediates the links between each job resource and agency, with indirect effects ranging from 0.269 (teaching resources) to 0.451 (colleague support), highlighting its central role in translating contextual resources into agentic action. We conclude that fostering rural teacher agency requires a holistic approach addressing both external job resources and internal self-efficacy. Policymakers and administrators should prioritize investments in teaching resources, collaborative support structures, and professional development to build educators’ confidence and competence. Limitations include self-report bias, cross-sectional design constraints on causal inference, and limited generalizability. Future research should use longitudinal designs and broader samples to deepen understandings of agency in structurally constrained educational settings. Full article

Tactile working memory limits the amount of information that can be processed through touch, with important implications for the design of haptic communication systems. Although visual and auditory working memory have been extensively investigated, tactile working memory, particularly for spatial and spatiotemporal sequences, remains less well understood. The present study examined tactile working memory capacity in two psychophysical experiments. Participants reproduced sequential vibrotactile stimuli delivered to the forearm via a 3 × 3 array of voice-coil actuators by entering responses through keypresses. Both experiments employed an adaptive 3-up/1-down staircase procedure, in which sequence length was adjusted according to response accuracy, and thresholds were estimated from reversal points. In Experiment 1 (Ordered Recall), participants reproduced both the spatial locations and the temporal order of stimulation, yielding a memory capacity threshold of approximately four items. In Experiment 2 (Unordered Recall), participants recalled only the set of stimulated locations without regard to order, resulting in a higher threshold of approximately five items. These results demonstrate that incorporating temporal sequencing demands into spatial recall substantially increases cognitive load and reduces effective tactile memory capacity. The findings clarify fundamental limits of tactile working memory and provide practical guidance for the development of haptic interfaces, wearable feedback systems, and sensory substitution technologies that must balance information complexity with human cognitive constraints. Full article

The global transition towards sustainable food systems has intensified the search for alternative protein sources that can meet human nutritional demands with reduced environmental impacts. Although microalgae are rich in protein, their applications in food remain limited due to thick cell walls and intense green color. The aim of this study is to modify Chlorella vulgaris by high-pressure homogenization (HPH) and decolorization to improve its processability for extrusion-based 3D printing. Microalgal biomass was pretreated by HPH at different pressures (10,000, 15,000, 20,000 psi) for one to three passes, followed by pigment removal using ethanol of different concentrations (70, 85, 100%). Microscopic imaging shows that HPH effectively disrupted microalgal cell walls and caused cell disintegration, resulting in increased foaming stability (22–28%) but lower solubility (up to 24%), with other functional properties largely preserved. Ethanol treatments markedly decolored microalgae and increased their water-holding capacity (10–45%) and solubility (6–11%). The formulation of HPH-treated decolorized microalgae with soy protein isolate and xanthan gum increased the viscosity (66–179%) and elasticity (78–235%) of printing inks. The resulting 3D prints show higher hardness (47–128%), springiness (up to 155%) and chewiness (47–408%). The information obtained from this study provides guidance for modifying the functional and rheological properties of microalgae and contributes to advancing the formulation and manufacturing of microalgae-based foods. Full article

The dynamism of adventure tourism necessitates the precise identification of areas with suitable natural, infrastructural, and service capacities for hosting activities. The aim of this study is to assess the multi-scenario spatial suitability for the sustainable development of adventure tourism camps using a Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach. The datasets used included topographic, climatic, environmental, accessibility, natural and cultural attraction, and service infrastructure indicators. The relevant criteria were first standardized, and their weights were determined using the Analytic Hierarchy Process (AHP). Subsequently, the layers were integrated through a Weighted Linear Combination (WLC) model. Four scenarios were designed for sensitivity analysis: the first scenario with balanced weight distribution (S_bal), the second prioritizing accessibility (S_acc), the third focusing on natural attractions (S_att), and the fourth emphasizing services (S_serv). The results indicated that approximately 21% and 9% of Chaharmahal and Bakhtiari province have high and very high potential for adventure activities, respectively, which were selected as initial options for the multi-scenario analysis. In the balanced (S_bal) scenario, 31% and 13% of the area of these options fell into high and very high suitability classes, respectively. The Service-Based Scenario (S_serv) increased the share of high and very high suitability areas to 34% and 19%, while Accessibility-Based Scenario (S_acc) reduced these classes to 27% and 10%. In the Attraction-Based Scenario (S_att), the areas in the high and very high suitability classes were 30% and 12%, respectively. The findings demonstrate that altering the priority of components can significantly change the spatial pattern of suitability, and sustainable planning of adventure tourism activities should be conducted based on management objectives and regional capacities. The proposed framework is generalizable to other regions and can serve as a basis for decision-making in balanced development, optimal infrastructure allocation, and sustainable management of adventure tourism. Full article

We developed a real-time alert and data management framework that integrates programmable logic controllers, RS-485 industrial communication, Structured Query Language Server, Message Queuing Telemetry Transport (MQTT), and the nodemation (n8n) automation platform, using a filling machine production line as a case study. The system collects and analyzes the operational status and production line data of the filling machine in real time, storing all information in a database for preservation. Through MQTT, the data is sent to n8n for automated processing. When equipment anomalies occur or data exceed predefined thresholds, the system automatically notifies maintenance personnel via communication software APIs. Additionally, users can query daily production capacity or related data using n8n’s AI functions. This architecture offers low cost, rapid deployment, cross-platform integration, and high flexibility. It not only improves anomaly handling efficiency but also preserves complete historical records, supporting trend analysis, report generation, and decision optimization, thereby assisting the filling production line in achieving long-term stable and intelligent management. Full article

Six 1,4-disubstituted pyrazoles linked to a benzenesulfonamide and a benzodioxane unit have been synthesized through a copper(I)-catalyzed formal [3+2] cycloaddition (32CA) reaction of alkynes with 3-arylsydnones. The Cu-catalyzed sydnone–alkyne cycloaddition (CuSAC) procedure has been optimized to promote the formation of the pyrazole ring and to deliver in three steps the six target compounds 5a–f, fully characterized by ¹H/¹³C-NMR and mass spectrometry (EIMS). Ten solvent conditions were evaluated. The reaction proceeded most efficiently in the presence of copper(II) sulfate pentahydrate in aqueous t-butanol in the presence sodium acetate, to reach a yield of 96%. The mechanism of the Cu(I)-catalyzed reaction has been studied within the Molecular Electron Density Theory (MEDT). This rection is a domino process that consists in a Cu(I)-catalyzed formal [3+2] cycloaddition followed of an extrusion of CO₂ yielding the final pyrazole. The capacity of heterocyclic compounds 5a–f to interact with human cyclophilin A (Cyp A), which is a host cofactor for hepatitis C virus (HCV) and human immunodeficiency virus 1 (HIV-1), and with the HIV-1 protein gp120-CD4 was evaluated using molecular docking. Compounds 5a,b,d,f showed a satisfactory protein binding capacity. The physicochemical and metabolic properties of the compounds were also evaluated in silico. These predictions provide important information to guide future design in this series of potential antiviral agents. Full article

Pig behavior statistics can reflect their health status. Conventional approaches depend on manual observation to derive behavioral information from video recordings, a process that demands substantial time and human effort. To overcome these limitations in indoor intensive farming environments, this study introduces an effective approach for recognizing pig behaviors, employing an enhanced YOLOv8n architecture. The approach utilizes advanced object detection algorithms to automatically identify pig behaviors, including stand, lie, eat, fight, and tail-bite, from overhead video footage of the enclosure. First, images of daily pig behaviors are collected using cameras to build a pig behavior dataset. To boost detection accuracy, the SE attention mechanism is embedded within the feature extraction backbone of the YOLOv8n network to enhance its representational capacity, strengthening the model’s capacity to grasp overarching contextual information and improve the expressiveness of extracted features. The GIoU loss function is employed during training to reduce computational cost and accelerate model convergence. Moreover, integrating Ghost convolution into the backbone significantly reduces both computational complexity and the total number of parameters. The experimental findings reveal that the optimized YOLOv8n model contains just 1.71 million parameters, marking a 42.93% reduction relative to the baseline model. Its floating-point operations total 5.0 billion, indicating a 38.27% decrease, while the mean average precision (mAP@50) reaches 96.8%, surpassing the original by 2.6 percentage points. Compared with other widely used YOLO-based object detection frameworks, the proposed approach achieves notably higher accuracy while requiring significantly lower computational resources and model complexity. Full article

Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas, raising important sustainability concerns related to community safety, infrastructure continuity, social equity, and long-term planning capacity. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and capable of representing co-evolving behavioral and network processes under compound hazard conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. To support sustainable and equitable local planning, the framework prioritizes spatially resolved outputs, including neighborhood clearance time, isolation probability, accessibility loss, and shelter demand imbalance. By emphasizing modularity, configurability, and policy-relevant metrics, this review connects methodological advances in evacuation modeling to the broader sustainability goals of resilient infrastructure systems, inclusive disaster risk reduction, and locally informed emergency planning. Full article

Polyunsaturated fatty acids (PUFAs), especially the long-chain omega-3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are essential nutrients for aquatic organisms and play key roles in growth, reproduction, neural development, and immune function. In freshwater ecosystems and aquaculture systems, the availability of these lipids depends on complex interactions within aquatic food webs, where PUFAs are produced by primary producers and transferred to higher trophic levels. This review summarizes current knowledge on the biosynthesis, regulation, and trophic transfer of PUFAs in freshwater aquaculture food webs, with particular emphasis on interactions among microalgae, zooplankton, and fish larvae. The main biochemical pathways and regulatory mechanisms responsible for PUFA synthesis in microalgae are described, together with the environmental factors that influence their production. The role of zooplankton at an intermediate trophic level is discussed, highlighting their ability to retain, modify, and transfer dietary fatty acids to higher consumers. Finally, the capacity of freshwater fish larvae to synthesize and regulate long-chain PUFAs through key metabolic enzymes is examined, along with the influence of diet and environmental conditions on these processes. By integrating information from molecular, biochemical, physiological, and ecological studies, this review provides an overview of the mechanisms underlying PUFA production and trophic transfer in freshwater aquaculture food webs. Full article

As zero-energy buildings (ZEBs) become increasingly mandatory, photovoltaic (PV) systems play a key role in increasing on-site energy generation. For staged ZEB certification based on the energy self-sufficiency ratio (ESSR), it is essential to determine the required power generation and to design PV systems with appropriate installation area and location. This study proposes a systematic design decision-support process for PV system planning that links required energy generation to panel installation strategies. The process enables the determination of a feasible installation area and location of PV panels and was implemented as a design-support program. The proposed process was applied to an apartment building under construction with a ZEB certification grade 5. Compared to the existing design, the optimal design reduced the required PV system capacity by 1.7% while increasing the predicted power generation by approximately 2.8%. The reported improvement in energy generation represents a relative comparison between design alternatives evaluated under identical modeling assumptions and therefore remains valid for comparative design decision-making. Field measurements conducted at a residential building with installed PV systems showed that the predicted power generation is consistent with measured trends, supporting comparative design evaluation and feasibility screening in early-stage PV planning. The developed design process provides a practical framework for early-stage PV system planning, supporting informed design decisions to meet target energy self-sufficiency requirements in ZEBs. Full article

Hyperspectral imaging provides rich spatial–spectral information but generates huge data volumes, posing significant challenges for storage, transmission, and real-time processing in remote sensing applications. In this study, we propose SpecResNet, a 3D autoencoder-based model for hyperspectral image compression. This framework introduces hybrid residual blocks for preserving representational power and a spectral calibration (SC) block to enhance spectral fidelity. It also uses Squeeze-and-Excitation (SE) blocks for adaptive feature recalibration. Our model obtains different compression operating points by varying model capacity, with bitrate emerging implicitly from the learned latent representations. Experiments on several benchmark datasets show that SpecResNet surpasses the performance of existing frameworks on most datasets in terms of PSNR, MS-SSIM, and SAM, demonstrating its strong potential. Our results suggest that SpecResNet offers a promising trade-off for efficient hyperspectral image compression, with potential for further refinement in complex scenes. Full article

Physically unclonable functions (PUFs) are critical security primitives used in authentication and cryptographic key generation. Among these, structural color-based PUFs offer distinct advantages, including fade resistance and the ability to conceal multi-dimensional information. However, current fabrication methods rely heavily on wet processes and laser ablation. Consequently, there is a significant need for flexible PUF labels capable of being produced through a facile and dry process. Here, we present stress-relief modulated photonic crystal PUF labels designed for multi-level dynamic encryption. We achieve random patterning of nanograting-based photonic crystals by leveraging curved pinning edge-induced interruptions and the uncontrolled bulking of the polymeric elastomer due to the uneven adhesion force from the tape. Using artificial intelligence-based deep learning algorithms, we authenticate the labels by extracting structural color, brightness, and saturation, which are determined by the grating periodicity, depth, and orderliness of each pixel. Furthermore, we integrated these photonic crystal patterns with dynamically modulated optical erasure to extend encryption capacity from the spatial to the temporal dimension. We anticipate this approach will enable advanced wearable anti-counterfeiting labels and multi-level digital encryption systems. Full article

Accurate irrigated cropland mapping is critical for agricultural water management and food security. Existing image-based irrigation mapping workflows primarily rely on vegetation indices and synthetic aperture radar (SAR) backscatter features, which have limited capacity to characterize the temporal evolution of irrigation processes and crop growth conditions. The AlphaEarth Foundation (AEF) model developed by Google DeepMind provides compact embeddings with temporal semantic information learned via self-supervision, yet their utility for irrigation mapping has not been systematically assessed. In this study, a comprehensive assessment of AEF embeddings for irrigated cropland mapping was performed in terms of feature separability, classification performance, and spatiotemporal transferability. Experiments were conducted in two representative irrigated regions: the Guanzhong Plain in China and Kansas in the USA. Class separability of the 64 embedding dimensions was quantified using the Jeffries–Matusita (JM) distance. Then, the AEF embeddings were compared with the Sentinel feature set (Sentinel-2 bands, normalized difference vegetation index(NDVI), enhanced vegetation index(EVI), normalized difference water index(NDWI) and Sentinel-1 vertical transmit vertical receive(VV), vertical transmit horizontal receive(VH)) using K-means clustering and supervised classifiers, including Decision Tree (DT), Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multi-layer Perceptron (MLP). Finally, transfer experiments across 2022 and 2024 in the Guanzhong Plain and Kansas were conducted to examine cross-year and cross-region performance. The results showed that AEF embeddings consistently provide stronger class separability in both study areas, with a maximum JM distance of 1.58 (A29). Using AEF embeddings, RF achieved overall accuracies (OA) of 0.95 in the Guanzhong Plain and 0.93 in Kansas, outperforming models based on Sentinel-1/2 bands and indices. Notably, unsupervised K-means clustering on AEF embeddings yielded OA > 0.85, indicating high intrinsic separability between irrigated and rainfed croplands. Transfer experiments further demonstrate stable temporal transfer (cross-year OA > 0.87), whereas cross-region transfer is constrained by differences in irrigation regimes, crop phenology and management practices, resulting in limited spatial generalization (OA~0.3). Overall, this study demonstrates the potential of high-information-density representations from geospatial foundation models for irrigated cropland mapping and provides methodological and technical insights to support transfer learning and operational mapping over large areas. Full article

The deep image prior (DIP) suggests that it is possible to train a randomly initialized network with a suitable architecture to solve inverse imaging problems by simply optimizing its parameters to reconstruct a single degraded image. However, the prior knowledge exploited by vanilla DIP relies on basic local convolutions, which inevitably limits the performance of inverse imaging tasks to the generative capacity of the model. Furthermore, image information is often not only related to neighboring pixels but also dependent on global color features and spatial distribution. Simple local convolutions used in inverse imaging cannot capture precise fine-grained details. Moreover, DIP is an unsupervised process but requires iterations to learn inverse imaging, consuming computational power and limiting the adaptation of global attention. To solve these problems, this article explores an efficient global prior module—a tri-directional multi-head self-attention mechanism—aiming to learn pixel-wise correlations along three directions: horizontal, vertical, and channel-wise. Our observations found that global learning can effectively enhance the detail information of edge pixels, making images more vivid and textures clearer. In addition, tri-directional multi-head self-attention can efficiently replace the global perception ability of pixel-level self-attention. Finally, we demonstrate that global learning can effectively improve the imaging effect of inverse imaging problems and enhance the information of texture edge pixels. Moreover, tri-directional multi-head self-attention can effectively alleviate the computation redundancy of pixel-level self-attention, thus achieving efficient and high-quality inverse imaging tasks. The principle of this method lies in global feature capture and efficient attention modeling, striking a balance between detail fidelity and computational practicality. Full article