Search Results (1,129)

The urban heat island (UHI) effect poses significant challenges to cities worldwide, particularly in regions like Thessaloniki, Greece, where rising temperatures exacerbate urban living conditions. This study investigates the effectiveness of sustainable urban planning strategies in mitigating the UHI effect by analyzing the spatial distribution of Land Surface Temperature (LST) during the summer heatwave of 2023. Utilizing LANDSAT 8–9 satellite imagery processed with QGIS, we calculated LST, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Built-up Index (NDBI). Additionally, urban structure data from OpenStreetMap (OSM) was integrated to assess the urban fabric. Our findings reveal significant spatial temperature variations, with densely built-up areas, such as the old town and industrial district, exhibiting higher LSTs compared to greener spaces. Based on these results, we propose targeted interventions, including the large-scale implementation of green roofs and the use of light-colored asphalts, which have shown potential for substantial LST reduction. This work underscores the importance of integrating these strategies into a standardized urban planning framework to enhance urban resilience, providing a model that can be applied to other European cities facing similar climate challenges. Full article

Accurate shoreline determination is essential for the study of coastal and inland water processes, hydrography, and the monitoring of aquatic and terrestrial environments. This study compares two modern remote sensing technologies: MLS conducted with a USV and photogrammetry using a UAV. The research was carried out on Lake Kłodno, characterised by a complex shoreline with vegetation and hydrotechnical structures. Both approaches satisfied the accuracy requirements of the IHO Special Order for shoreline extraction (≤5 m at the 95% confidence level). For the UAV-derived orthophoto, the error within which 95% of shoreline points were located (corresponding to 2.45·σ) was 0.05 m for the natural shoreline and 0.06 m for the variant including piers, both well below the IHO threshold. MLS achieved a 95% error of 1.16 m, which also complies with the Special Order criteria. UAV data enable clear interpretation of the land–water boundary, whereas MLS provides complete three-dimensional spatial information, independent of lighting conditions, and allows surveys of vegetated or inaccessible areas. The results demonstrate the complementarity of the two approaches: UAV is well suited to highly accurate shoreline mapping and the identification of hydrotechnical structures, while MLS is valuable for analysing the nearshore zone and for surveying vegetated or inaccessible areas. The findings confirm the value of integrating these approaches and highlight the need to extend research to other types of waterbodies, to consider seasonal variability, and to develop methods for the automatic extraction of shorelines. Full article

Accurate fig detection in complex environments is a significant challenge. Small targets, occlusion, and similar backgrounds are considered the main obstacles in intelligent harvesting. To address this, this study proposes Fig-YOLO, an improved YOLOv11n-based detection algorithm with multiple targeted architectural innovations. First, a Spatial–Frequency Selective Convolution (SFSConv) module is introduced into the backbone to replace conventional convolution, enabling joint modeling of spatial structures and frequency-domain texture features for more effective discrimination of figs from visually similar backgrounds. Second, an enhanced bi-branch attention mechanism (EBAM) is incorporated at the network’s terminal stage to strengthen the representation of key regions and improve robustness under severe occlusion. Third, a multi-branch dynamic sampling convolution (MFCV) module replaces the original C3k2 structure in the feature fusion stage, capturing figs of varying sizes through dynamic sampling and residual deep-feature fusion. Experimental results show that Fig-YOLO achieves precision, recall, and mAP@0.5 of 89.2%, 78.4%, and 87.3%, respectively, substantially outperforming the baseline YOLOv11n. Further evaluation confirms that the model maintains stable performance across varying fruit sizes, occlusion levels, lighting conditions, and data sources. Fig-YOLO’s innovations offer solid support for intelligent orchard monitoring and harvesting. Full article

Traditional hyperspectral image compression methods often struggle to achieve high compression ratios while maintaining satisfactory reconstructed image quality under low-bitrate conditions. With the progressive development of deep learning, it has demonstrated significant advantages in lossy image compression research. Compared to visible light images, hyperspectral images possess rich spectral information. When directly applying visible light image compression models to hyperspectral image compression, the spectral information of hyperspectral images is overlooked, making it difficult to achieve optimal compression performance. In this paper, we combine the characteristics of hyperspectral images by extracting spatial and spectral features and performing fusion-based encoding and decoding to achieve end-to-end lossy compression of hyperspectral images. The structures of the encoding end and the decoding end are in symmetry. Additionally, attention mechanism is incorporated to enhance reconstruction quality. The proposed model is compared with the latest hyperspectral image compression standard algorithms to validate its effectiveness. Experimental results show that, under the same image quality, the proposed method reduces the bpp (bits per pixel) by 4.67% compared to CCSDS123.0-B-2 on the Harvard hyperspectral dataset while also decreasing the spectral angle loss by 13.68%, achieving better performance. Full article

Shrinking cities are generally experiencing decreases in population, economic activity, and spatial expansion. However, whether this “low-growth” trajectory leads to an actual reduction in carbon emissions or is constrained by carbon lock-in effects and the complex interaction between urban shrinkage and carbon emissions remains unclear. To address this gap, this study examines 34 shrinking cities of the three northeastern provinces in China, utilizing nighttime light data to identify the spatial–temporal patterns of carbon emissions from a multidimensional perspective. Additionally, it explores the key drivers behind these emissions. Results show the following: (1) Spatiotemporally, carbon emissions are closely linked to shrinking cities, which also exhibit spatial–temporal heterogeneity. (2) There is a significant negative spatial correlation between carbon emissions and urban shrinkage degree (SD), with HL clusters (high–low clusters) and LH clusters (low–high clusters) being the main clustering types. (3) Through population, economic, and social driving factors, this paper identifies three synergistic effects shaping spatial–temporal carbon heterogeneity: passive reduction in economic scale (scale effect), volatility effect of structural transformation (structure effect), and spatial–institutional carbon lock-in (lock-in effect). The findings offer new insights into the low-carbon transition potential of shrinking cities and provide a basis for developing targeted policy frameworks to facilitate their sustainable transformation. Full article

Low-light image enhancement (LLIE) degradation arises from insufficient illumination, reflectance occlusion, and noise coupling, and it manifests in the frequency domain as suppressed amplitudes with relatively stable phases. To address the fact that pure spatial mappings struggle to balance brightness enhancement and detail fidelity, whereas pure frequency-domain processing lacks semantic modeling, we propose AFMNet—a dual-domain collaborative enhancement network guided by an information-theoretic frequency prior. This prior regularizes global illumination, while spatial branches restore local details. First, a Multi-Scale Amplitude Estimator (MSAE) adaptively generates fine-grained amplitude-modulation maps via multi-scale fusion, encouraging higher output entropy through adaptive spectral-energy redistribution. Next, a Dual-Branch Spectral–Spatial Attention (DBSSA) module—comprising a Frequency-Modulated Attention Block (FMAB) and a Scale-Variable Depth Attention Block (SVDAB)—is employed: FMAB injects the modulation map as a frequency-domain prior into the attention mechanism to conditionally modulate the amplitude of value features while keeping the phase unchanged, thereby helping to preserve structural information in the enhanced output; SVDAB uses multi-scale depthwise-separable convolutions with scale attention to produce adaptively enhanced spatial features. Finally, a Spectral-Gated Feed-Forward Network (SGFFN) applies learnable spectral filters to local features for band-wise selective enhancement. This collaborative design achieves a favorable balance between illumination correction and detail preservation, and AFMNet delivers state-of-the-art performance on multiple low-light enhancement benchmarks. Full article

The contemporary landscape is characterised by overlapping values and pressures, where ecosystem services and cultural spaces are used by diverse categories of users. In fragile contexts such as the Phlegraean Fields in Italy, the exponential growth of mass tourism has intensified the anthropogenic impacts, exacerbated by limited landscape awareness among local communities. Thus, walkability fosters direct exploration, while experiential transects provide a lens to read ecological, cultural, and perceptual layers of places. Together with digital storytelling, these approaches converge in a phygital approach that enriches physical experience without supplanting it. The study covered approximately 115 km of routes across five municipalities, combining road audits, an 11-item survey, participatory mapping, and ArcGIS StoryMaps. Results showed a structurally complex and functionally fragile mobility system: sidewalks are discontinuous, lighting insufficient, less than one quarter of the network is fully pedestrian, and cycling facilities are almost absent. At the same time, digital layers diversified routes and supported situated learning. By integrating geo-spatial analysis and phygital tools, the research demonstrates a replicable strategy to enhance the awareness and sustainable enjoyment of complex landscapes. The present research is part of the PNRR project Changes ‘PE5Changes_Spoke1-WP4-Historical Landscapes Traditions and Cultural Identities’. Full article

Spin angular momentum is a fundamental dynamical property of elementary particles and fields, playing a critical role in light–matter interactions. In optical studies, the optical spin angular momentum is closely linked to circular polarization. Research on the interaction between optical spin and matter or structures has led to numerous novel optical phenomena and applications, giving rise to the emerging field of spin optics. Historically, researchers primarily focused on longitudinal optical spin aligned parallel to the mean wavevector. In recent years, investigations into the spin–orbit coupling properties of confined fields—such as focused beams, guided waves, and evanescent waves—have revealed a new class of optical spin oriented perpendicular to the mean wavevector, referred to as optical transverse spin. In the optical near-field, such transverse spins arise from spatial variations in the momentum density of confined electromagnetic waves, where strong coupling between spin and orbital angular momenta leads to various topological spin structures and properties. Several reviews on optical transverse spin have been published in recent years, systematically introducing its fundamental concepts and the configurations that generate it. In this review, we detail recent advances in spin optics from three perspectives: theory, experimental techniques, and applications, with a particular emphasis on the fundamental physics of transverse spin and the resulting topological structures and characteristics. The conceptual and theoretical framework of spin optics is expected to significantly support further exploration of optical spin-based applications in fields such as optics imaging, topological photonics, metrology, and quantum technologies. Furthermore, these principles can be extended to general classical wave systems, including fluidic, acoustic, and gravitational waves. Full article

The principal challenge in the development of efficient porphyrin-based photosensitizers is the intrinsic aggregation-induced quenching effect, which significantly impairs the generation efficiency of singlet oxygen (¹O₂) in photodynamic therapy (PDT). This study addresses this limitation through a supramolecular approach grounded in host-guest chemistry. Partially methyl-substituted cucurbit[6]uril (HMeQ[6]) was selected as the macrocyclic host owing to its smaller portal size and larger outer diameter, features that facilitate both strong binding affinity and effective spatial isolation. A porphyrin derivative functionalized with two cationic arms (DPPY) was designed and synthesized as the guest molecule. The results derived from ¹H NMR titration and UV spectroscopy analyses demonstrate that, in aqueous solution, these components self-assemble via host-guest interactions to form a 2:1 stoichiometric supramolecular complex (DPPY@HMeQ[6]) with a binding constant of 2.11 × 10⁵ M⁻¹. TEM, AFM, and DLS analyses indicate that this complex further assembles into nanosheet structures with dimensions of approximately 100 nm. Spectroscopic analyses reveal that encapsulation by HMeQ[6] effectively inhibits π-π stacking aggregation of DPPY molecules, resulting in an approximate threefold increase in fluorescence intensity and an extension of fluorescence lifetime from 3.2 ns to 6.2 ns. Relative to free DPPY, the complex demonstrates a sixfold enhancement in ¹O₂ generation efficiency. Subsequently, 4T1 cells, derived from mouse triple-negative breast tumors, were selected as the experimental model. These cells exhibit high invasiveness and metastatic potential, thereby effectively recapitulating the pathological progression of human triple-negative breast cancer. In vitro cellular assays indicate efficient internalization of the complex by 4T1 cells, inducing a concentration-dependent increase in reactive oxygen species (ROS) and oxidative stress following light irradiation. The in vitro cytotoxicity of the supramolecular photosensitizer was assessed employing the CCK-8 assay and flow cytometry techniques. The half-maximal inhibitory concentration (IC₅₀) against cancer cells is 1.8 μM, with apoptosis rates reaching up to 65.3%, while exhibiting minimal dark toxicity. This study expands the potential applications of methyl-substituted cucurbiturils within functional supramolecular assemblies and proposes a viable approach for the development of efficient and activatable supramolecular photosensitizers. Full article

Wildfires in the Wildland–Urban Interface (WUI) pose escalating threats to socio-ecological systems, challenging regional resilience and sustainable recovery. Understanding the compound impacts of such fires requires an integrated, data-driven assessment of both ecological disturbance and social response. This study develops a multi-dimensional framework combining multisource remote sensing data (Landsat/Sentinel-2 NDVI and VIIRS nighttime light) with socio-structural indicators. A Composite Disturbance Index (ImpactIndex) was constructed to quantify ecological, population, and socioeconomic disruption across six fire clusters in the January 2025 Southern California wildfires. Mechanism analysis was conducted using Fixed-Effects OLS (M2) and Geographically Weighted Regression (GWR, M3) models. The ImpactIndex revealed that Eaton and Palisades experienced the most severe compound disturbances, while Border 2 showed purely ecological impacts. During-disaster CNLI signals were statistically decoupled from ecological disturbance (ΔNDVI) and dominated by site-specific effects (p < 0.001). GWR results (Adj. R² = 0.354) confirmed asymmetric spatial heterogeneity: high-density clusters (Palisades, Kenneth) exhibited a significant “Structural Burden” effect, whereas low-density areas showed weak, nonsignificant recovery trends. This “Index-to-Mechanism” framework redefines the interpretation of nighttime light in disaster contexts and provides a robust, spatially explicit tool for targeted WUI resilience planning and post-fire recovery management. Full article

(1) Background: Epigenetic mechanisms play a significant role in plant architecture. Histone deacetylases, as crucial epigenetic regulators, shape plant architecture by modifying chromatin structure and regulating gene expression. (2) Methods: This study combined bioinformatic identification of BpHST1 with its functional characterization in transgenic birch overexpressing 35S::BpHST1::FLAG, including phenotypic and cytological analyses. The putative direct targets of BpHST1 were further identified by integrating RNA-seq and ChIP-seq data. (3) Results: Phylogenetic analysis revealed that the HST1 orthologs from birch and peach form a distinct clade, consistent with their high degree of protein sequence conservation. BpHST1 exhibited light-inducible and leaf-preferential expression, with transcript levels elevated under light versus dark conditions, enriched in leaves relative to roots, and promoter activity confirming this spatial patterning. Overexpression of BpHST1 significantly suppressed plant height, cell length, cell width, and photosynthetic capacity. Integrated RNA-seq and ChIP-seq analysis suggested that BpLHCA2 possible functions as a direct downstream target of BpHST1, mediating plant growth and development. (4) Conclusions: Our findings delineated the role of BpHST1 in regulating plant architecture through comprehensive expression and functional analyses, and identified a candidate target gene. This study provided a novel insight into the molecular mechanisms governing plant architecture and offers potential strategies for future epigenetic breeding. Full article

A compact LED (light emission diode)-based illumination unit controlled by a microcontroller was developed for recombination-type silicon sensor structures. The system employs an 8 × 8 LED matrix that provides programmable spatial excitation patterns across a 2.2 × 2.2 mm sensor surface. Its operation is based on changes in the silicon surface recombination properties upon analyte interaction, producing photocurrent variations of 10–50 nA depending on the dipole moment. Compared with conventional laser-based systems, the proposed LED illumination significantly reduces cost, complexity, and power consumption while maintaining sufficient optical intensity for reliable photoresponse detection. The embedded controller enables precise timing, synchronization with the photocurrent acquisition unit, and flexible adaptation for various biological fluid analyses. This implementation demonstrates a scalable and cost-efficient alternative to stationary LBIC setups and supports integration into portable or IoT-compatible diagnostic systems. For comparative screening, the LED array was used instead of the focused laser beam typically employed in LBIC (laser beam-induced current) measurements. This paper substantially reduced the peak optical intensity at the sample surface, minimizing local thermal heating critical for enzyme-based or plasma samples sensitive to temperature fluctuations. Photocurrent mapping reveals charge-state modification of recombination centers at the SiOx/Si interface under optical excitation. Further optimization is expected for compact or simplified configurations, particularly those aimed at portable applications and automated physiological monitoring systems. Full article

With the growing focus on sustainable development, light rail transit (LRT) systems are increasingly viewed as key drivers of low-carbon mobility and spatial equity. However, as urban spatial structures become more stable, it remains unclear whether LRT systems can still enhance quality of life, property values and contribute to inclusive urban regeneration. This study explores Manchester, Sheffield, and Nottingham, three UK cities with recent LRT development experience, as case studies. Using LRT constructed or expanded between 1995 and 2019 as a quasi-natural experiment, a difference-in-differences (DID) model is applied to estimate the causal impact of LRT expansion on property prices. The results indicate that LRT construction can lead to a 4.44% to 8.29% increase in nearby property values, with a lagged effect observed after implementation. The impact is more pronounced in areas with well-developed bus networks and in lower-income areas. Further mechanism analysis suggests that the effect is indirectly driven by improved accessibility and enhanced convenience of access to local amenities. Full article

Body temperature serves as a crucial indicator of dairy cow health. Traditional rectal temperature (RT) measurement often induces stress responses in animals. Body temperature detection based on infrared thermography (IRT) offers non-invasive and timely advantages, contributing to welfare-oriented farming practices. However, automated detection and temperature extraction from critical cow regions are susceptible to complex illumination, black-and-white fur texture interference, and region of interest (ROI) deformation, resulting in low detection accuracy and poor robustness. To address this, this paper proposes the EDC-YOLO-World-DB framework to enhance detection and temperature extraction performance under complex illumination conditions. First, URetinex-Net and CLAHE methods are employed to enhance low light and overexposed images, respectively, improving structural information and boundary contour clarity. Subsequently, spatial relationship constraints between LU and AA are established using five-class text priors—lower udder (LU), around the anus (AA), rear udder, hind legs, and hind quarters—to strengthen the spatial localisation capability of the model for ROIs. Subsequently, a Dual Bidirectional Feature Pyramid Network architecture incorporating EfficientDynamicConv was introduced at the neck of the model to achieve dynamic weight allocation across modalities, levels, and scales. Task Alignment Metric, Gaussian soft-constrained centroid sampling, and combined IoU (C_IoU + G_IoU) loss were introduced to enhance sample matching quality and regression stability. Results demonstrate detection confidence improvements by 0.08 and 0.02 in low light and overexposed conditions, respectively; compared to two-text input, five-text input increases P, R, and mAP50 by 3.61%, 3.81%, and 1.67%, respectively; Comprehensive improvements yielded P = 88.65%, R = 85.77%, and mAP50 = 89.33%—further surpassing the baseline by 2.79%, 3.01%, and 1.92%, respectively. Temperature extraction experiments demonstrated significantly reduced errors for $T_{Max}$, $T_{Min}$, and $T_{avg}$. Specifically, for the mean error of LU, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 33.5%, and 4.27%, respectively; for AA, $T_{Max}$, $T_{Min}$, and $T_{avg}$ were reduced by 66.6%, 25.4%, and 11.3%, respectively. This study achieves robust detection of LU and AA alongside precise temperature extraction under complex lighting and deformation conditions, providing a viable solution for non-contact, low-interference dairy cow health monitoring. Full article

Regional economic diversity and unevenly allocated space-based resources have created unprecedented difficulties for collaborative and innovative supply chain construction. This paper sets up a tripartite evolutionary model of the government, upstream companies, and downstream companies to explore dynamic processes of regional supply chain collaborative innovation with bounded rationality. Through incorporation of hierarchical space organizations and policy incentive differentiation mechanisms, the model discerns actors’ behavioral evolution and strategic adjustment in a geographically divided structure. Adopting evolutionary game theory and numerical simulation, this paper includes crucial parameters like the conversion efficiency of return conversion, information-sharing coefficient, mutual trust coefficient, and fiscal subsidy coefficient for examining policy and spatial heterogeneity effects on information collaborative innovations. The results reveal that fiscal incentives are the primary driving factor for collaborative evolution across local supply chains. Adaptive profit-sharing and subsidy intensities both stimulate upstream innovation investments and downstream cooperation adoption efficiently, stimulating a shift out of inefficient equilibrium states towards sustainable high-cooperation states. Furthermore, the restructuring of space accelerates hierarchical differentiation—core region companies are able to act like initiators and leaders for collaborative innovations, while periphery companies encounter participatory barriers in terms of elevated coordination costs and incentive shortages. In light of this, it is therefore crucial to have a “core-driven, periphery-subsidized” policy system for eliminating spatial gaps, stimulating cross-regional information exchange, and building systemic robustness. These findings contribute to enhancing the overall efficiency, stability, and innovation capacity of regional supply chain systems. They also provide a theoretical basis for policy decision making and industrial upgrading across regions of varying scales and environments. Full article