Search Results (371)

This study develops and applies an integrated methodology that combines deep learning-based computer vision and spatial statistics to automate the large-scale identification and analysis of morphological features in vernacular courtyard dwellings. Focusing on Liangshuaixiu dwellings in Wu’an, southern Hebei, we trained an HRNetV2 semantic segmentation model on high-resolution satellite imagery to identify and extract contours for 134,280 courtyard spaces. Core morphological parameters (area, orientation) were calculated and analyzed using GIS spatial statistics and the geographic detector model. The results show that (1) the computer vision pipeline achieved efficient recognition with satisfactory accuracy (~10% mean error); (2) spatial autocorrelation and hotspot analysis revealed distinct regional patterns, including a west–east increase in average courtyard area; and (3) geographic detector analysis demonstrated that courtyard morphology is shaped by complex interactions between natural and socio-economic factors. While average area and orientation were primarily governed by climate (air pressure, wind, temperature) and topography (elevation), diversity and internal variation were strongly influenced by nonlinear interactions, particularly between natural factors (e.g., wind–aspect) and between natural and human factors (e.g., population–climate). This work provides a scalable, data-driven framework for the quantitative spatial analysis of vernacular architectural heritage, advancing the understanding of building morphology as an outcome of coupled human–environment systems. Full article

This study provides the first characterization of the acoustic signals emitted by Lahille’s bottlenose dolphins (Tursiops gephyreus) in Argentine waters, establishing a baseline for the species’ southern distribution limit. Passive acoustic monitoring was conducted in the inner channels of the Bahía Blanca Estuary using a broadband hydrophone and F-POD detectors. We documented a diverse acoustic repertoire consisting of echolocation clicks, whistles, bray-calls, and chirps. Acoustic presence was predominantly diurnal and showed a marked synchronization with the tidal cycle, with peak activity occurring during the ebbing tide. Whistles had a mean center frequency of 8.1 kHz, showing spectral overlap with other regional populations in the Southwestern Atlantic. Echolocation clicks were broadband, with peak frequencies ranging from 18 to 127 kHz and a mode around 40 kHz. These results suggest that dolphin movement in the estuary’s internal channels is structured by hydrodynamics, likely reflecting foraging opportunities associated with tidal flow. Given the endangered status of this species, these data establish a technical framework for non-invasive monitoring and habitat-use assessments in complex estuarine environments. Full article

Space weather monitoring depends critically on passive sensor systems that detect and measure natural solar and geospace emissions without transmitting radio frequency energy. These include riometers, solar radio monitors, interplanetary scintillation detectors, GNSS-based ionospheric sensors, and broadband solar spectrographs that enable the provision of critical data required to forecast geomagnetic storms, protect critical infrastructures, and support aviation services, satellite operations, and defense services. However, with the increasing proliferation of radiocommunication technologies such as 5G/6G networks, dense HF/VHF/UHF deployments, and large constellations of low-Earth-orbit (LEO) satellites, the interference threat to these exceptionally sensitive receivers has grown. Most of these operate near the thermal noise floor and thus require strict protection criteria to ensure continuity of data. This review and perspective article provides a cross-disciplinary synthesis of scientific requirements, documented RFI case studies, and ongoing regulatory developments related to spectrum protection for passive space weather sensors. It systematically integrates perspectives on physical, technical, and regulatory aspects that are typically addressed separately in the literature. The article reviews the operating principles of major sensor classes and analyzes documented RFI cases affecting GNSS, riometers, CALLISTO, BINGO, and systems impacted by LEO satellite emissions, drawing from existing reports and regulatory submissions. Building on this evidence base, the work comparatively evaluates regulatory methods under consideration for WRC-27 shows that hybrid approaches combining primary allocations in core observation bands with secondary status and coordination procedures in adjacent bands offer the most viable path forward. This synthesis contextualizes and analyzes how technical protection criteria can be integrated with existing and evolving regulatory instruments to inform spectrum governance. The study concludes that without coordinated international spectrum management incorporating explicit protection thresholds and registration procedures, the long-term viability of space weather monitoring infrastructure faces significant risk in an increasingly congested radio frequency environment. Full article

New-type urbanization (NTU) is a key driver of high-quality development and progress toward the Sustainable Development Goals (SDGs) in China. While existing studies acknowledge the multidimensional nature of this process, they often measure it as a single composite aggregate. This approach masks the system’s local sensitivity to internal structural changes and obscures the spatially stratified heterogeneity of dominant drivers. To address this gap, this study constructs construct a comprehensive evaluation index system using panel data for 280 prefecture-level and above cities in China from 2001 to 2023. This study integrates the entropy-weighted TOPSIS method, a modified coupling coordination degree model (MCCD), geographically and temporally weighted regression (GTWR), and the optimal parameters geographical detector (OPGD). Using this framework, this study investigates the spatio-temporal characteristics of the coordinated high-quality development (CHQD) in NTU, systematically dissecting the spatial heterogeneity of local sensitivities and dominant drivers. The results indicate that the following: (1) CHQD exhibits a continuous upward trajectory characterized by significant regional convergence, with the center of gravity gradually shifting southwest. Structurally, green and social dimensions demonstrate the most rapid growth, progressively superseding spatial expansion as primary growth poles. (2) The structural decomposition reveals clear spatially stratified heterogeneity in local sensitivity. The coastal East faces “diminishing marginal utility” of traditional factor inputs, whereas the Central and Western regions continue to reap “structural dividends” from factor accumulation. (3) The dominant drivers shaping spatial heterogeneity have undergone a sequential evolution from an early “resource-space orientation” to a later “innovation-service orientation.” For instance, in the eastern region, the proportion of construction land (L2) had a single-factor explanatory power (q-statistic) of 0.791. However, its interactions with science and technology expenditure (E3) and other factors yielded q-statistics exceeding 0.820, indicating a marked synergistic effect. These findings support region-specific policy recommendations to promote CHQD and inform sustainable urbanization pathways in China. Full article

River basins are not merely geographical spaces but also cultural-historical ecosystems, where the spatial patterns of Intangible Cultural Heritage (ICH) profoundly reflect the long-term interaction between human and environment, as well as contemporary transformations. While international research on ICH has evolved from conceptual clarification to interdisciplinary theory-building, and spatial quantitative methods have been widely applied to cultural heritage analysis, the spatial patterns, multi-scale structures, and “natural-human” driving mechanisms of ICH in continental arid river basins—particularly in the Tarim River Basin (TRB, China’s largest inland river and a key corridor of the Silk Road)—remain underexplored. To address this gap, this study takes 313 ICH items in the TRB as the research object. It uses ArcGIS 10.8.1 to visualize their spatial distribution and employs an integrated methodology—including global Moran’s I, kernel density estimation (KDE), DBSCAN spatial clustering, and geographical detector (Geodetector)—to systematically reveal their spatial characteristics and influencing factors. The findings indicate that: (1) The distribution of ICH exhibits a multi-scale feature of “global randomness with local clustering”: spatial autocorrelation is not significant at the county level, while at the micro-geographical scale, a dendritic structure characterized by “one axis, three cores, denser in the north and sparser in the south” emerges, which is highly coupled with the river network. DBSCAN clustering further identifies a “mainstem axis–tributary node” cluster system and a relatively high proportion of peripheral “noise” heritage points. (2) Agglomeration patterns vary significantly across different ICH categories, with traditional craftsmanship showing high clustering, while traditional sports, entertainment, and acrobatics display highly fragmented distributions. (3) The study reveals and validates a ternary “Water–Tourism–Urbanization” driving framework that predominantly shapes the spatial heterogeneity of ICH: water resources constitute a fundamental ecological threshold, whereas tourism development and urbanization have emerged as more explanatory social driving forces, with widespread nonlinear enhancement interactions between natural and human factors. This research moves beyond the traditional view of river basins as static cultural “containers,” providing empirical evidence for their dynamic nature as “cultural-ecological co-evolutionary systems.” The proposed ternary framework not only offers a new perspective for understanding the spatial resilience of ICH in arid regions and the potential risks of “spectacularization” and “spatial polarization” amid rapid changes, but also provides a scientific basis for spatial governance, culture-tourism integration, and the formulation of conservation strategies for ICH at the basin scale. Full article

In this study, a decision-level detection framework is presented and evaluated; it integrates sensor data (e.g., temperature, humidity, gas readings) with machine learning (ML) models and computer vision-based smoke and fire detection systems, in an effort to increase overall robustness, as well as false-alarm reduction. To this end, sixteen (16) ML and deep learning (DL) models are employed on an Internet of Things (IoT) sensor dataset. Moreover, a range of YOLO (You Only Look Once) models, such as older versions (YOLOv5n, YOLOv8n), as well as newer versions (YOLOv10n, YOLOv11n, YOLOv12n), are employed on an image-label-based dataset. Model selection initially prioritizes lightweight architectures that are suitable for resource-constrained edge devices. Afterwards, the selected models are evaluated via well-known metrics, such as parameter count, F1-score/mean average precision (mAP) and real-time inference latency. In the same context, explainable AI (XAI) techniques, such as SHAP (SHapley Additive exPlanations) for ML models and LIME (Local Interpretable Model-agnostic Explanations) for the YOLO detectors, are integrated into the platform as well. According to the presented results, the Explainable Sensor Fusion (ESF) framework demonstrates a high level of internal consistency and logical reliability through its decision-level fusion paradigm in a controlled environment. Full article

Achieving reliable, grid-scale electricity generation from nuclear fusion, as envisioned by the DEMOnstration Fusion Power Plant (DEMO) and future commercial reactors, requires unprecedented plasma stability and long-term control. This operational goal is fundamentally challenged by, among others, the dynamic nature of the high temperature plasma and the need to monitor high-Z impurities, such as tungsten, which can severely compromise energy confinement, resulting in discharge disruption and damage to internal reactor walls. Real-time Soft X-ray (SXR) diagnostic systems are therefore an integral and critical component of fusion power plant infrastructure, providing essential temporal and spatial resolution data on these fast-evolving phenomena. To address the severe demands imposed by the extreme operating environment of future fusion reactors, such as DEMO (including intense neutron and gamma fluxes), this work details a current stage in the long-term development of an advanced and robust diagnostic system engineered specifically for technological preparation and future application in these high-fluence environments. This paper presents the third generation of the SXR measurement system, GEM3k, based on Gas Electron Multiplier (GEM) technology. This novel diagnostic utilizes a Field Programmable Gate Array (FPGA)-based architecture, specifically designed for the high-rate acquisition of energy- and spatially resolved plasma radiation distributions. The GEM3k design exploits the inherent radiation hardness of GEM detectors, positioning them as robust sensor units for monitoring plasma dynamics and impurity emissions in future fusion environments. The system readout comprises approximately 34,000 individual pixels mapped to nearly 3000 measurement channels in an XYUV coordinate configuration. This layout enables submillimeter spatial resolution simultaneously with a time resolution better than 10 ms. Addressing the engineering challenges of such a complex high-density readout, this work details the comprehensive design of the GEM3k system, focusing on its architecture, electronics, performance estimations, and data distribution strategies. By enabling precise tracking of impurities and fast plasma behavior, the GEM3k system contributes to the stable, high-gain operation required for future fusion reactors. This directly supports the development of sustainable fusion energy and its eventual integration into modern electricity grids. Furthermore, the planned enhancement to a real-time operating mode could pave a way for a next-generation system for direct integration into reactor control loops. Currently in the prototype phase with initial hardware tests completed, the GEM3k design leverages our extensive experience with diagnostics developed for the JET and WEST tokamaks. Full article

Background/Objectives: Gentamicin (GEN) is an aminoglycoside antibiotic used in veterinary medicine to treat infections caused mainly by Gram-negative bacteria. GEN is a mixture of pharmacologically active components, known as isoforms. The objective was to develop and validate a sensitive, accurate, and precise Ultra-Performance Liquid Chromatography with triple quadrupole mass detector (UPLC-MS/MS) method to quantify the different GEN isoforms in pig plasma and feces using streptomycin as an internal standard. Methods: Solid-phase extraction (SPE) was carried out. A high-strength silica (50 × 2.1 mm, 1.8 µm) column was used for chromatographic separation and a mobile phase of 0.26% HFBA in water (A) and acetonitrile (B) was delivered in a gradient with a flow rate of 0.5 mL/min. The column temperature was 40 °C and the sample injection volume was 30 µL. Results: The method showed good selectivity and specificity, with no interfering peaks. Calibration curves were linear in the range from 0.05 to 0.3 µg/mL for all isoforms in both matrices. Within- and between-run precision and accuracy were satisfactory for the lower limit of quantification (LLOQ), with coefficients of variation (CV) ≤ 13.4% and deviations ≤ 116.5% in plasma and CV ≤ 12.3% with deviations ≤ 101.7% in feces. No carry-over was observed, and analyte stability was confirmed under different storage conditions. Conclusions: The method development fulfilled all validation criteria established by the European Medicine Agency Guideline (EMA/CHMP/ICH/172948/2019). Moreover, the applicability of the method in clinical practice was demonstrated by the quantification of GEN in plasma and feces samples from pigs. Full article

Creeping landslides constitute the predominant form of long-term, slow-moving geohazards in high mountain gorge regions. Under the combined influence of gravity and external triggering factors, these landslides undergo persistent deformation, posing continuous threats to major transportation corridors, hydropower infrastructures, and nearby settlements. Li County is located within the active tectonic belt along the eastern margin of the Tibetan Plateau, characterized by highly variable topography, intensely fractured rock masses, and dense development of creeping landslides. The slip surfaces are typically deeply buried and concealed. Consequently, conventional drilling and profile-based investigations, limited by high costs, sparse sampling points, and poor spatial continuity, are insufficient for identifying the deep-seated structures of such landslides. To address this challenge, this study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to obtain ascending and descending deformation rate fields for 2022–2024, revealing pronounced spatial heterogeneity and persistent activity across three types of landslides. Based on the principle of mass conservation, the sliding-surface depths of eight typical landslides were inverted, revealing pronounced heterogeneity. The maximum sliding-surface depths range from 32 to 98 m and show strong agreement with borehole and profile data (R² > 0.92; RMSE ±4.96–±16.56 m), confirming the reliability of the inversion method. The GeoDetector model was used to quantitatively evaluate the dominant factors controlling landslide depth. Elevation was identified as the primary control factor, while slope aspect exhibited significant influence in several landslides. All factor combinations showed either “bi-factor enhancement” or “nonlinear enhancement”, indicating that slip-surface depth is governed by synergistic interactions among multiple factors. Boxplot-based statistical analyses further revealed three typical patterns of slip-surface variation with elevation and slope, based on which the landslides were classified into rotational, push-type translational, and traction-type translational categories. By integrating statistical patterns with mechanical models, the study achieves a transition from “form” to “state”, enabling inference of the internal mechanical conditions and evolutionary stages from the observed surface morphology. The results of this study provide an effective technical approach for deep structural detection, identification of controlling factors, and stability evaluation of creeping landslides in high mountain gorge environments. Full article

A vertical Ni/β-Ga₂O₃ Schottky barrier diode was fabricated on an unintentionally doped bulk (−201)-oriented β-Ga₂O₃ single crystal and investigated with a focus on the underlying photoresponse mechanisms. The device exhibits well-defined rectifying behavior, characterized by a Schottky barrier height of 1.63 eV, an ideality factor of 1.39, and a high rectification ratio of ~9.7 × 10⁶ arb. un. at an applied bias of ±2 V. The structures demonstrate pronounced sensitivity to deep-ultraviolet radiation (λ ≤ 280 nm), with maximum responsivity observed at 255 nm, consistent with the wide bandgap of β-Ga₂O₃. Under 254 nm illumination at a power density of 620 μW/cm², the device operates in a self-powered mode, generating an open-circuit voltage of 50 mV and a short-circuit current of 47 pA, confirming efficient separation of photogenerated carriers by the built-in electric field of the Schottky junction. The responsivity and detectivity of the structures increase from 0.18 to 3.87 A/W and from 9.8 × 10⁸ to 4.3 × 10¹¹ Hz^0.5cmW⁻¹, respectively, as the reverse bias rises from 0 to −45 V. The detectors exhibit high-speed performance, with rise and decay times not exceeding 29 ms and 59 ms, respectively, at an applied voltage of 10 V. The studied structures demonstrate internal gain, with the external quantum efficiency reaching 1.8 × 10³%. Full article

Exploring green organic solvents is a global demand. Most of the currently used solvents pose some concerns regarding environmental sustainability and occupational health risks. In this work, propylene glycol was employed for the first time as a green solvent for mobile phase preparation in the reversed phase chromatographic separation of a mixture of two antioxidants, glutathione and ascorbic acid. The slight viscosity of propylene glycol was manipulated by using water as a co-fluidizing agent to facilitate pumping. Method optimization was performed using factorial design experimental Expert 13^® Software (Minneapolis, MN, USA) to achieve the maximum resolution and the minimum run time. The reported method was properly validated according to the International Conference on Harmonization criteria at the linearity range of 1–500 µg/mL, with acceptable accuracy and precision for both drugs. The method was effectively applied for the quantification of both drugs in their commercial pharmaceutical formulation. The proposed method was assessed for environmental and operator safety by means of global tools like AGREE and MoGAPI and has proved high degrees of greenness. Propylene glycol has several benign properties, such as low volatility, less toxicity, compatibility with UV detectors and very low flammability, that will soon assemble it as a promising alternative for the conventionally used solvents. Full article

In the Republic of Korea, fire outbreaks caused by electrical devices are one of the most frequent accidents, causing severe damage to human lives and infrastructure. The metropolitan police, The National Institute of Scientific Investigation, and the National Fire Research Institute conduct fire root-cause inspections to determine whether these fires are external or internal infrastructure fires. However, obtaining results is a complex process. In addition, the situation has been hampered by the lack of sufficient digital forensics and relevant programs. Apart from electrical devices, multi-sockets are among the main fire instigators. In this study, we aim to verify the feasibility of utilizing YOLO-based deep-learning object detection models for fire-cause inspection systems for multi-sockets. Particularly, we have created a novel image dataset of multi-socket fire causes with 3300 images categorized into the three classes of socket, both burnt-in and burnt-out. This data was used to train various models, including YOLOv4-csp, YOLOv5n, YOLOR-csp, YOLOv6, and YOLOv7-Tiny. In addition, we have proposed an improved YOLOv5n-SE by adding a squeeze-and-excitation network (SE) into the backbone of the conventional YOLOv5 network and deploying it into a two-stage detector framework with a first stage of socket detection and a second stage of burnt-in/burnt-out classification. From the experiment, the performance of these models was evaluated, revealing that our work outperforms other models, with an accuracy of 91.3% mAP@0.5. Also, the improved YOLOv5-SE model was deployed in a web browser application. Full article

Intangible cultural heritage (ICH) is deeply embedded in everyday social life, yet its officially recognized spatial distribution reflects both the independent influences of cultural traditions, development trajectories, and governance practices, and the complex interactions among them. Focusing on 494 national-level ICH items across ten categories in Jiangsu(J), Zhejiang(Z), and Shanghai(H), this study adopts a social-geographical perspective to examine both the spatio-temporal evolution and the driving mechanisms of ICH recognition in one of China’s most developed regions. After rigorous verification of point-based ICH locations, we combine kernel density estimation and the average nearest neighbor index to trace changes across five batches of national designation, and then employ the univariate and interaction detectors of the Geodetector model to assess the effects of 28 natural, socioeconomic, and cultural-institutional variables. The results show, first, that ICH exhibits significant clustering along river corridors and historical cultural belts, with a persistent high-density core in the Shanghai–southern Jiangsu–northern Zhejiang zone and a clear shift over time from highly concentrated to more dispersed and territorially balanced recognition. Second, human-environment factors—especially factors such as urban and rural income and consumption; residents’ education and cultural expenditures; and public education and cultural facilities—have far greater explanatory power than natural conditions, while different ICH categories embed distinctively in urban and rural socio-economic contexts. Third, bivariate interactions reveal that natural and macroeconomic “background” variables are strongly amplified when combined with demographic and cultural factors, whereas interactions among strong human variables show bivariate enhancement with diminishing marginal returns. In summary, these findings enrich international debates on the geography of ICH by clarifying how recognition processes align with regional development and social equity agendas, and they provide a quantitative basis for category-sensitive, place-based strategies that coordinate income policies, public cultural services, and the joint safeguarding of tangible and intangible heritage in both urban renewal and rural revitalization planning. Full article

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

Systematically assessing the supply–demand disparities of urban–rural ecosystem services (ES) is a key pathway to optimizing resource allocation, promoting urban–rural integration and advancing regional sustainable development. Taking Zhengzhou City as a case study, this research evaluates and compares urban–rural differences across four dimensions: potential supply, actual supply, real human needs (RHN), and effective supply. Furthermore, focusing on actual supply, the study integrates a geographical detector and Bayesian belief network to identify key driving factors, delineate optimal optimization zones, and propose differentiated management strategies. The results show that: (1) Urban RHN accounts for 69.70% of the total in Zhengzhou, with a spatial pattern of “higher in the east and core, lower in the west and periphery”, and the internal heterogeneity is significantly greater than that of rural areas. (2) Potential supply is “higher in rural areas and in the west”, whereas actual supply is concentrated in central urban districts, reflecting a net service flow from rural to urban areas. (3) High-level effective supply areas cover 37.28% of urban regions, about 18 percentage points higher than rural regions. Rural deficits are primarily caused by low conversion efficiency of supply rather than insufficient potential. (4) Optimal urban optimization zones are mainly distributed in peripheral urban streets, while rural zones are concentrated in eastern townships. Through multidimensional supply–demand comparison and spatial optimization, this study provides a scientific basis for the coordinated enhancement of urban–rural ES, differentiated governance and regional sustainable development. Full article