Search Results (4,602)

Geographic Named Entity Recognition (Geo-NER) is a crucial task for extracting geography-related entities from unstructured text, and it plays an essential role in geographic information extraction and spatial semantic understanding. Traditional approaches typically treat Geo-NER as a sequence labeling problem, where each token is assigned a single label. However, this formulation struggles to handle nested entities effectively. To overcome this limitation, we propose Geo-MRC, an improved model based on a Machine Reading Comprehension (MRC) framework that reformulates Geo-NER as a question-answering task. The model identifies entities by predicting their start positions, end positions, and lengths, enabling precise detection of overlapping and nested entities. Specifically, it constructs a unified input sequence by concatenating a type-specific question (e.g., “What are the location names in the text?”) with the context. This sequence is encoded using BERT, followed by feature extraction and fusion through Gated Recurrent Units (GRU) and multi-scale 1D convolutions, which improve the model’s sensitivity to both multi-level semantics and local contextual information. Finally, a feed-forward neural network (FFN) predicts whether each token corresponds to the start or end of an entity and estimates the span length, allowing for dynamic inference of entity boundaries. Experimental results on multiple public datasets demonstrate that Geo-MRC consistently outperforms strong baselines, with particularly significant gains on datasets containing nested entities. Full article

To enhance the thermo-mechanical coupling performance of heavy-duty bearings with smart sensing capability in electric shovel applications, this study proposes a multi-objective optimization methodology for sensor-embedded bearing rings incorporating smart sensor-embedded grooves. Driven by multi-physics coupling analysis and experimental validation, a coupled thermal–mechanical model integrating frictional heat generation, heat transfer, and stress response was established. Parametric finite element simulations were conducted, with varying groove depths and axial positions. A comprehensive performance index combining three metrics—maximum temperature, equivalent stress, and principal strain—was formulated to evaluate design efficacy. Experimental tests on thermal and strain responses were employed to validate the simulation model confirming its predictive ability. Among the 21 parameter combinations, the configuration featuring an 8 mm groove depth located 20 mm from the large end face exhibited relatively optimal synergy across thermal dissipation, structural strength, and strain sensitivity. The proposed framework provides a certain theoretical and practical guidance for the design and optimization of the sensor-embedded groove structure in intelligent heavy-duty bearings. Full article

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

Introduction: Robotic splenectomy has emerged as a promising alternative to laparoscopic surgery, offering potential advantages in precision, ergonomics, and individualized surgical planning. In the context of personalized medicine, robotic technology may enable tailoring of surgical strategies to patient-specific anatomy, spleen size, and comorbid hematologic conditions. However, its clinical superiority remains uncertain due to limited and heterogeneous evidence. Methods: We performed a systematic review and meta-analysis following PRISMA guidelines, utilizing PubMed, CINAHL, Web of Science, and EMBASE databases to locate studies on robotic splenectomies in children. This review was prospectively registered in PROSPERO (CRD420251104285). Risk of bias was assessed using the ROBINS-I tool for non-randomized studies. Random-effects models were fitted using restricted maximum likelihood (REML), and confidence intervals were adjusted using either Knapp–Hartung (HKSJ) or modified Knapp–Hartung (mKH) methods when appropriate. 95% prediction intervals were calculated, and the certainty of evidence for each outcome was assessed using the GRADE approach. Results: This review included 272 pediatric patients from 16 studies conducted between 2003 and 2025, of which five were included in the meta-analysis. No statistically significant differences were observed between robotic and laparoscopic splenectomy for operative time, intraoperative blood loss, conversion to open surgery, blood transfusions, or complications. However, the direction of effect estimates consistently favored the robotic approach. A statistically significant reduction in hospitalization days (−0.93 days; 95% CI: −1.61 to −0.24; p = 0.01) was found, though this became marginally significant after HKSJ adjustment (p = 0.06). Intraoperative blood loss showed significance in the primary model (−63.88 mL; 95% CI: −120.38 to −7.38; p = 0.03), but not after mKH correction (p = 0.16). Heterogeneity was substantial-to-extreme for several outcomes and was only partially accounted for by leave-one-out sensitivity analyses. All findings were rated as very low certainty according to the GRADE framework. Conclusions: Robotic-assisted splenectomy in pediatric patients has been reported as technically feasible and performed safely in selected cases. However, the small number of studies, their retrospective design, substantial methodological heterogeneity, and the resulting very low certainty of the evidence according to GRADE preclude any firm conclusions about its comparative safety or efficacy versus laparoscopy. Well-designed prospective studies are needed to clarify its clinical benefits. Full article

With the recent increase in the threat posed by unmanned aerial vehicles (UAVs) operating in environments where conventional detection systems such as radar, optical, or acoustic detection are impractical, attention is paid to methods for detecting low-flying UAVs with small radar cross-section (RCS). The most commonly used detection methods are radar detection, which is susceptible to electromagnetic (EM) interference, and optical detection, which is susceptible to weather conditions and line-of-sight. This research aims to demonstrate the possibility of using passive optical fiber Bragg grating (FBG) as a sensitive element array for low-flying UAV detection and localization. The principle is as follows: an optical signal that propagates through an optical fiber can be modulated due to the FBG reaction on the air pressure caused by a low-flying (even hovering) UAV. As a result, a small target—the DJI Avata drone can be detected and tracked via intensity surge determination. In this paper, the experimental setup of the proposed FBG-based UAV detection system, measurement results, as well as methods for analyzing UAV-caused downwash are presented. High-speed data reading and processing were achieved for low-flying drones with the possible presence of EM clutter. The proposed system has shown the ability to, on average, detect an overpassing UAV’s flight height around 85 percent and the location around 87 percent of the time. The key advantage of the proposed approach is the comparatively straightforward implementation and the ability to detect low-flying targets in the presence of EM clutter. Full article

Landscape character assessment (LCA) is a systematic approach used to classify, describe, and analyze the physical and cultural attributes that define the landscape. The traditional approaches to LCA are fundamentally subjective and descriptive, relying on human evaluations of aesthetic value, and they often show inconsistencies in results when assessed by different observers for the same landscape. This research aims to establish a spatial and quantitative methodology through GIS for evaluating the landscape character of King Khalid University (KKU)’s campus in the Southern Province of Saudi Arabia, which is considered crucial for designing a sustainable and context-sensitive landscape. To identify the feasible developed areas and their sustainable characteristics, three key landscape variables were measured and spatially expressed, subsequently averaged to categorize landscape character. The variables include land use and land cover, which were obtained from Sentinel 2 remote sensing data through supervised classification, as well as landforms and hydrological settings derived from a digital elevation model (DEM) utilizing GIS functionalities. The findings revealed three distinct landscape characters, each characterized by quantifiable landscape attributes. The landscapes exhibiting the most significant character encompass approximately 20% (1074 ha) of the study area, whereas those with the least significance account for 6.5% (342 ha). The remaining 73.5% (3884 ha) is classified as landscapes with an average significance character. The results provide a solid scientific basis for choosing locations in the campus’s study area that promote environmentally friendly and sustainable landscape development. This method improves objectivity in LCA and offers a reproducible framework for implementation in arid and semi-arid areas. Full article

Machine learning-based digital soil mapping often struggles with spatial heterogeneity and long-range dependencies. To address these limitations, this study proposes Multi-Attention Convolutional Neural Networks (MACNN). This deep learning algorithm integrates multiple attention mechanisms to improve mapping accuracy. First, environmental covariates are determined from the soil-landscape model. These are then fed as structured input to the Convolutional Neural Network. Next, by incorporating Transformer self-attention and multi-head attention mechanisms, this study effectively models the long-range dependencies between soil types and features. Concurrently, the Convolutional Block Attention Module (CBAM) is introduced. CBAM features both channel and spatial dual attention, enabling adaptive weighting of crucial feature channels and spatial locations. This significantly enhances the algorithm’s sensitivity to discriminative information. To validate its effectiveness, the proposed MACNN algorithm was used for soil type mapping in Heilongjiang Province. Compared to Random Forest, Decision Tree, and One-Dimensional Convolutional Neural Network algorithms, MACNN demonstrated superior classification performance. It achieved an overall classification accuracy of 81.27%. An ablation study was conducted to investigate the importance of individual modules within the proposed algorithm. The findings indicate that progressively integrating Transformer and CBAM modules into the 1D-CNN baseline significantly enhances algorithm performance through synergistic gains. Therefore, this integrated algorithm offers a feasible solution to improve digital soil mapping accuracy, providing significant reference value for future research and applications. Full article

Optimizing the siting and servicing of urban facilities is a core operations research problem that must reconcile heterogeneous demand, spatial constraints, and network-realistic travel. We present GD-ARISE, a GIS-integrated and data analytics pipeline that maintains a pedestrian–road network metric from demand inference through siting to routing. The workflow has three modules: (i) GIS integration that unifies spatial layers on one network and distance metric; (ii) data analytics that builds multi-criteria suitability via the Analytic Hierarchy Process (AHP) and maps scores to adaptive service radii; (iii) optimal location-and-routing that selects nonoverlapping sites with a transparent greedy rule (SCASS) and computes depot-to-depot routes via simulated annealing on the same metric. A case study in Seoul’s Gangnam District yields a high-coverage portfolio and feasible collection routes. We add a theoretical framework that casts SCASS as a conflict-graph problem, document the AHP elicitation with consistency checks, and report robustness analyses including sensitivity to AHP weights and to radius bounds. Results indicate that core hotspots remain stable to weighting, whereas mid-range corridors shift as criteria priorities or spatial parameters change. Full article

Urban mobility in Bangkok is constrained by congestion, modal fragmentation, and gaps in First and Last Mile (FLM) access. This study develops a GIS-based framework that combines maximal-coverage location allocation with post-optimization accessibility diagnostics to inform intermodal hub siting. The network model compares one-, three-, and five-hub configurations using a 20 min coverage standard, and we conduct sensitivity tests at 15 and 25 min to assess robustness. Cumulative isochrones and qualitative overlays on BTS, MRT, SRT, Airport Rail Link, and principal water routes are used to interpret spatial balance, peripheral reach, and multimodal alignment. In the one-hub scenario, the model selects Pathum Wan as the optimal central node. Transitioning to a small multi-hub network improves geographic balance and reduces reliance on the urban core. The three-hub arrangement strengthens north–south accessibility but leaves the west bank comparatively underserved. The five-hub configuration is the most spatially balanced and network-consistent option, bridging the west bank and reinforcing rail interchange corridors while aligning proposed hubs with existing high-capacity lines and waterway anchors. Methodologically, the contribution is a transparent workflow that pairs coverage-based optimization with isochrone interpretation; substantively, the findings support decentralized, polycentric hub development as a practical pathway to enhance FLM connectivity within Bangkok’s current network structure. Key limitations include reliance on resident population weights that exclude floating or temporary populations, use of typical network conditions for travel times, a finite pre-screened candidate set, and the absence of explicit route choice and land-use intensity in the present phase. Full article

Variable renewable energy sources and cross-zonal trades stress transmission grids, pushing them toward thermal limits. This systematic review, reported in accordance with PRISMA 2020, examines how demand-side response (DSR) can provide relief at the transmission scale. We screened peer-reviewed literature and operator documentation, from 2010 to 2025, indexed in Web of Science, Scopus, and IEEE Xplore; organized remedial actions across supply, network, and demand/storage levers; and categorized operational attributes (time to effect, spatial targeting, activation lead times, telemetry, and measurement and verification). Few reviewed sources explicitly link DSR to transmission congestion relief, highlighting the gap between its mature use in frequency and adequacy services and its still-limited, location-specific application on the grid. We identify feasibility conditions, including assets downstream of the binding interface, minute-scale activation, and feeder-grade baselines with rebound accounting. This implies the following design requirements: TSO–DSO eligibility registries and conflict resolution, portfolio mapping to power-flow sensitivities, and co-optimization with redispatch, HVDC, topology control, and storage within a security-constrained optimal-power-flow framework. No full-text risk-of-bias assessment or meta-analysis was undertaken; the review used English-only title/abstract screening. Registration: none. Funding: none. Full article

We present a photoacoustic spectroscopy (PAS)-based method using a laser Doppler vibrometer (LDV) for real-time detection of laser-induced damage (LID) in optical components. By measuring audible frequency surface vibrations, the method enables remote, non-contact, and sensitive detection. Experiments with various dielectric optics (slide glass and single-layer coatings) and pulse durations (7 ns and 360 ps) of an Nd:YAG laser (wavelength of 1064 nm) showed detection accuracy comparable to microscopy. Vibration spectra correlated with natural modes calculated by finite element modeling, and vibrations according to the detecting location were observed. The method remained effective under typical mounting conditions, demonstrating its practical applicability. This PAS-LDV approach offers a promising tool for in situ monitoring of LID in high-power laser systems. Full article

Nano- and microplastics (NMPs) in waterways reflect the impact of anthropogenic activities. This study examined spatial variations in the presence and types of NMPs in Galveston Bay (Texas, USA) surface waters and eastern oysters (Crassostrea virginica). The results reveal most MPs carried by surface waters are fibers > films > fragments. Up to 200 MPs were present in individual oysters [=1.88 (± 0.22 SE) per g wet weight]. Oyster health, based on condition index, varied spatially, but was not correlated with MP load. Based on attenuated total reflectance—Fourier-transform infrared spectroscopy, polyamide and polypropylene were frequently found in waters in the upper bay while ethylene propylene and polyethylene terephthalate were more common in the lower parts of the bay. Pyrolysis–gas chromatography–mass spectrometry revealed a very large range in concentrations of NMPs, from 28 to 10,925 µg ∑NMP/g wet weight (or 172 to 67,783 µg ∑NMP/g dry weight) in oysters. This chemical analysis revealed four main types of plastics present in oysters regardless of location: polypropylene, nylon 66, polyethylene and styrene butadiene rubber. Based on this finding, the average daily intake of NMPs estimated for adult humans is 0.85 ± 0.45 mg NMPs/Kg of body weight/day or a yearly intake of 310 ± 164 mg NMPs/Kg of body weight/year. These findings reveal higher body burdens of plastics in oysters are revealed by the chemical analysis relative to the traditional approach; this is not unexpected given the higher sensitivity and selectivity of mass spectrometry and inclusion of the nanoplastic particle range (i.e., <1 mm) in the sample preparation and analysis. Full article

Yellow rust (YR), caused by Puccinia striiformis f. sp. tritici (Pst), is a global disease infecting wheat that seriously affects the yield and the quality of grains. Wheat breeding line C855 is immune to the mixed Pst isolates CYR32 + CYR33 + CYR34 under field conditions. To identify the Yr-loci carried by C855, in this study, an F₂ population derived from the crossing of C855 with Yannong 999, a YR-sensitive cultivar, was established, and the infection type (IT) of each F₂ individual was estimated. The correlation analysis results show that YR resistance was significantly positively correlated with grain weight and grain size. Using a 120K single-nucleotide polymorphism (SNP) array, the F₂ population was genotyped, and a high-density genetic map covering 21 wheat chromosomes and consisting of 5362 SNP markers was built. Then, five Yr-QTLs on chromosomes 1B, 2A, 2B, and 2D were identified. Of these, the QTL on chromosome 2A, temporarily named QYr.sxau-2A.1, is a major-effect QTL explaining 15.62% of the phenotypic variance. One PCR-based marker SSR2A-14 for QYr.sxau-2A.1 was developed, and the C855 allele of SSR2A-14 corresponded to the stronger Yr resistance. QYr.sxau-2A.1, located in the 228.02~241.58 Mbp physical interval, is different from all the known Yr loci on chromosomes 2A. Within the interval, there are 30 annotated genes, including a nucleotide-binding site and a leucine-rich repeat (NBS-LRR)-encoding gene with the linkage marker NRM2A-16 of QYr.sxau-2A.1. Our results reveal a novel major-effect QYr.sxau-2A.1, which provided resistance to YR and is a molecular marker for wheat breeding. Full article

Groundwater springs are an important source of drinking water in Lithuania; however, they are highly sensitive to microbial and chemical contamination. The aim of this study was to assess microbial and chemical contamination in springs from different regions of Lithuania. Five springs were analyzed: Kučgaliai, Smardonė, Salomėja and Žalsvoji (Biržai and Pasvalys districts, Northern Lithuania) and Svilė (Kelmė district, Central Lithuania). Water samples were collected four times a year—during spring, summer, autumn, and winter—and analyzed according to international standards. Chemical parameters included pH, nitrites, nitrates, chlorides, sulfates, and permanganate index, while microbiological analysis targeted Escherichia coli, Enterococcus spp., and coliform bacteria. The results revealed substantial differences between karst and groundwater-fed springs. Karst springs were more vulnerable to fluctuations in contamination, with Smardonė exhibiting extremely high sulfate concentrations and significant microbial loads. In contrast, Kučgaliai, although located in a karst region, was covered and protected, and its water fully complied with hygiene standards. Groundwater-fed springs showed less variability but were still affected by surface sources. The highest microbial contamination was recorded in autumn and winter, coinciding with increased rainfall and reduced dilution capacity. Full article

Convolutional neural networks (CNNs) have strong noise resistance, and this study utilizes this property to weaken the impact of noise on structural damage identification data. After structural damage occurs, the modal parameters at the unit level are particularly sensitive to changes in damage and can therefore be used as important characteristic indicators for identifying damage. This article establishes a finite element model of steel truss and introduces damage at different positions and degrees. The free vibration process of the structure is simulated by the finite element method (FEM), and the first-order modal characteristic parameters, including modal strain energy and modal strain, are extracted for each damage situation. Subsequently, these modal parameters and the corresponding damage information are input as training samples into the CNN model for automatic identification of structural damage. The results show that the constructed CNN model can accurately identify the location and degree of structural damage, with a damage localization accuracy of 100% and a relative error of only 6.6% for damage degree identification. Among various characteristic indicators, modal strain energy difference exhibits better sensitivity and stability. Compared with traditional backpropagation (BP) neural networks, the CNN shows improved detection accuracy, by about 35%, and computation time is only 2.4% of BP networks. In addition, the CNN maintains good recognition performance in low order modes, which is of great significance for easily obtainable measurement data in practical engineering. In summary, the CNN method shows superior performance in damage localization, damage degree recognition, and noise resistance and has high engineering application value. Full article