Search Results (38,151)

Traditional Instrument Landing Systems (ILSs) suffer from inherent limitations, including limited signal coverage and high maintenance costs. To address these issues, we propose a novel approach procedure design method based on the BeiDou Satellite-Based Augmentation System (BDSBAS), which is particularly suitable for airports with complex terrain conditions or no ground-based navigation infrastructure. In this method, a geometric constraint model of the approach trajectory is constructed in a local Cartesian coordinate system, and obstacle clearance performance is evaluated based on the Obstacle Assessment Surface (OAS) theory. Global Navigation Satellite System (GNSS) observations and BDSBAS augmentation data are processed collaboratively to calculate aircraft position solutions, as well as the corresponding horizontal and vertical protection levels, enabling comprehensive evaluation of the navigation system’s accuracy, integrity, continuity, and availability. The feasibility and performance of the proposed method are verified through practical airport deployment and dynamic flight tests. Experimental results show that the 95th-percentile horizontal and vertical position errors reach 1.31 m and 4.17 m, respectively, and all valid observation epochs fully comply with the protection level and alert limit specifications. Compared with the conventional ILS-based scheme, the proposed method reduces the total OAS area and the missed-approach Z-surface area by 44.25% and 67.90%, respectively. The findings demonstrate that BDSBAS can effectively support approach procedure design and guarantee high-precision navigation performance, while significantly reducing the reliance on airport-specific ground navigation infrastructure. Full article

Research Highlights: This study quantifies aboveground biomass for Oreopanax oroyanus and Escallonia resinosa in an Andean relict forest and examines their conservation relevance related to the scale of emissions voluntarily reported by small corporate emitters. Background and Objectives: Andean relict forests face severe fragmentation and degradation. This research evaluates carbon stocks in the Bosque de Zárate Reserved Zone (Peru) and explores how these findings may inform climate mitigation and conservation initiatives by examining their potential alignment with emissions voluntarily reported by Peruvian firms participating in a carbon disclosure system. Materials and Methods: A total of 27 plots were evaluated between 3034 and 3200 m a.s.l., tree height and diameter (DBH ≥ 10 cm) were measured for key species, and biomass was estimated using a pantropical allometric equation. Landsat imagery (1985–2025) was analyzed to assess long-term vegetation conditions, while Dynamic World land cover and Sentinel-1 radar (2018–2025) were used to assess forest cover and canopy structure changes. Voluntarily reported emissions of Peruvian firms participating in the “Carbon Footprint Peru” system (2012–2024) were analyzed to contextualize the forest results in the potential corporate interest in climate mitigation in Peru. Results: Total aboveground carbon stock for the altitudinal belt in the study area was 919.4 Mg C (18.6 Mg C ha⁻¹), equivalent to 3374.2 Mg CO₂, with Escallonia resinosa accounting for approximately 71% of the estimated stock. Multi-decadal satellite observations indicated persistent forest cover within the evaluated belt, while analysis of voluntarily reported corporate emissions identified numerous service-sector firms with annual emissions below 100 Mg CO₂ eq, providing context for the potential scale of future conservation-financing initiatives. Conclusions: Relict forests offer relevant localized carbon storage linked to other ecosystem services. Providing field-based carbon data may support the development of locally relevant community-led initiatives meaningful to climate-financing initiatives. However, the existing carbon stock does not by itself represent a source of carbon credits, and carbon capture-specific studies would need to be implemented to fully assess the mitigation capacity of these ecosystems. Full article

This study investigated the effectiveness and accuracy of the analytic hierarchy process (AHP) and frequency ratio (FR) models, which integrated remote sensing (RS) and geographic information systems (GIS), for delineating and validating groundwater potential zones (GWPZs) in Northern Mozambique. For both methods, six key factors influencing groundwater potential were selected from which thematic maps were generated for each. In the AHP method, the influencing factors (IFs) were subjected to a hierarchy of criteria and sub-criteria. Then, using a pairwise comparison matrix based on professional assessment and literature review, relative weights were assigned to each factor. Analysis of the AHP ultimately resulted in the integration of the various thematic layers using the weighted sum tool in ArcGIS 10.8 to produce a composite GWPZ map. In the FR method, 1026 borehole locations for the area were randomly divided into two sets: 718 boreholes (70%) were used as a training dataset, and the remaining 308 boreholes (30%) were kept as a testing dataset for validation purposes. From the training dataset, the ratio of the probability of a groundwater event (wells) occurring in a class of influencing factors to the overall probability of that event happening in the study area was calculated as the FR for that class, representing the weight assigned to each factor. The overall FR was also computed using the weighted sum tool to produce a GWPZ map. The map produced from each model delineated the area into five zones: “very low”, “low”, “moderate”, “high” and “very high”. The testing dataset was then used to validate the GWPZ maps using the field data overlay technique. The respective validation results revealed the AHP model outperformed the FR model in terms of accuracy for delineating GWPZs. Full article

Given the widespread application of carbon fiber reinforced polymer (CFRP) structures in aerospace and automotive manufacturing, the critical role of structural health monitoring (SHM) is increasingly evident. Traditional damage quantification methods exhibit poor transferability and reusability in different structural areas, which are difficult to adapt to the complex and diverse damage characteristics of CFRP structures. To address this challenge, a multi-scale adaptive cross-area damage quantification method based on transfer learning (MSACADQ-TL) is proposed in this paper. Firstly, a multi-scale feature extraction framework is presented, which decomposes signals from various paths into multiple frequency domain sub-signals and analyzes the dynamic trends of these signals across different signal scales, providing richer damage feature information. Moreover, the difference between the source domain and the target domain is minimized to enhance transferability of features. Furthermore, this paper applies a damage quantification based on the Bidirectional Gated Recurrent Unit (BiGRU) model to achieve cross-area damage quantification. To validate the proposed method’s effectiveness, this paper designed three different cross-area damage transfer scenarios. Experimental results demonstrate that the MSACADQ-TL model outperforms other comparison methods in damage quantification tasks, which is effective for quantification damage in CFRP structures. Full article

Regional climate models (RCMs) remain affected by limited spatial resolution and systematic biases relative to observations, posing a major challenge for the provision of actionable climate information at the local scale. Bridging the gap between coarse, biased model outputs and site-specific climate needs is therefore both urgent and an active area of research. Here, we present a diagnosis of near-surface air temperature biases, based on the vertical structure of the atmospheric column, in an ensemble of EURO-CORDEX simulations over a complex Alpine region. We show how the conventional and commonly applied temperature bias correction, aimed at removing the discrepancy between the model representation of orography and the actual terrain elevation, still leaves biases of a magnitude comparable to or even greater than the applied correction. After the orographic correction, residual biases originate from both free-atmosphere temperature biases and low-level biases. Each contribution to the TAS bias is quantified through an inter-model seasonal analysis. This study highlights the limitations of RCMs in representing complex orography and boundary-layer conditions in Alpine regions. It also provides interpretative keys for a better understanding of the underlying site-specific physical causes of TAS biases and cautions against the potential pitfalls of a straightforward application of bias-correction methods. Full article

Background: In September 2024, South Korea launched a nationwide Tertiary Hospital Restructuring Project to refocus tertiary hospitals on severe, emergency, and rare disease care, amid a tertiary care system long burdened by patient concentration and acutely strained by the mass resignation of trainee doctors. In the early phase of implementation, understanding how the decision-makers who translate policy into institutional change perceive the project is essential. Methods: We employed a qualitative design using reflexive thematic analysis. Semi-structured, in-depth interviews were conducted with directors from five tertiary hospitals—four in the capital area and one in a non-capital area—selected through purposive sampling. Interviews were conducted between 30 October and 7 November 2025, transcribed verbatim, and analyzed following the six-phase approach of Braun and Clarke. Results: Four themes were constructed. First, directors described the project as catalyzing a cultural shift toward a high-severity-centered model, though contested at the departmental level. Second, a severity paradox emerged, in which treating sicker patients worsened profitability while large restructuring costs strained sustainability. Third, referral networks expanded but were constrained by the absence of an integrated information technology (IT) system across the referral network. Fourth, a specialist- and physician assistant (PA)-centered workforce model stabilized care yet remained institutionally unsettled. Participants recommended refined severity criteria, complexity-based reimbursement, a legal framework for PAs, an integrated IT referral system, and network-based training. Conclusions: As perceived by the participating directors, the project reshaped organizational culture and clinical case-mix, but its long-term viability depends on financial, infrastructural, and workforce supports that have not kept pace with structural change. Sustaining the reform will likely require a shift from competition toward a collaborative, region-centered network. Full article

Fast Reroute (FRR) after link failures is essential for carrier-grade Software-Defined Networking (SDN), yet hybrid deployments remain dominated by slow legacy routing convergence. This paper presents two port-state-driven FRR mechanisms for homogeneous and hybrid SDN networks. First, Port-State-Aware Fast Reroute (PSA-FRR) uses OpenFlow port-status events to trigger proactive, rule-based protection in the data plane. Second, Port-State-Aware Neural Fast Reroute (PSAR-FRR) formulates hybrid FRR as a controller-local multi-class classification problem and predicts the backup egress port from a port-centric state representation, enabling microsecond-scale decision latency. We evaluate the methods on the Abilene wide-area network (WAN) topology using Mininet with Open vSwitch (OVS) and a Ryu controller (homogeneous case) and Graphical Network Simulator-3 (GNS3) with Cisco IOS routers (hybrid baseline). In homogeneous SDN emulation, PSA-FRR restores connectivity within 30–100 ms under the evaluated configurations. In the hybrid baseline, conventional routing protocols converge in 13.8–256.1 s (Enhanced Interior Gateway Routing Protocol (EIGRP), Intermediate System to Intermediate System (IS-IS), Open Shortest Path First (OSPF), Border Gateway Protocol (BGP), and Routing Information Protocol (RIP)), confirming that control-plane recovery cannot meet a 50 ms target. Using the collected dataset, PSAR-FRR reduces controller decision time from 6.753 $\mathsf{\mu }$s (PSA-FRR rule evaluation) to 0.214 $\mathsf{\mu }$s (deep neural network (DNN) inference), a 31.5× speedup. These results show that port-state awareness combined with learned, controller-local policies can substantially reduce the decision-to-action latency of FRR, providing a practical path toward low-latency failure recovery in SDN migration scenarios. Full article

This study addresses the need for reliable field-based tools to assess soil structural quality in semi-arid rangelands, where existing visual methods such as the Visual Evaluation of Soil Structure (VESS) and its modified version for grasslands (GrassVESS) may be limited by site conditions. The objective was to develop and evaluate a modified visual soil structure assessment method (RangelandVESS) adapted to livestock grazed rangelands in northern Mexico. Soil sampling was conducted in grazed and exclusion zones and soil structure was assessed using VESS, GrassVESS, and RangelandVESS. Thereafter, the scores of each method were compared with soil health indicators and above-ground biomass. Results showed distinctions in soil structural conditions between exclusion and overgrazed areas. RangelandVESS seems to demonstrate greater sensitivity to degradation, particularly in sandy soils, where it identified poorer structural quality compared to the other methods. Significant correlations were obtained between RangelandVESS scores and bulk density, infiltration, and biomass supporting its robustness. In contrast, no significant differences among methods scores were observed in clayey–loamy soils. However, RangelandVESS may provide a more reliable assessment of degradation. Overall, the findings suggest that RangelandVESS is a suitable tool for assessing soil structure in semi-arid rangelands and offers improved capacity to detect grazing induced degradation. Full article

Mangroves provide critical ecosystem services, including coastal protection, biodiversity conservation, carbon sequestration, and environmental purification. Detailed mangrove mapping is therefore essential for effective conservation, restoration, and management. With the proliferation of remotely sensed imagery across diverse spatial resolutions, comparative studies on mapping efficiency are vital for optimizing long-term, large-scale monitoring strategies. This study utilized multi-source satellite imagery, which includes Landsat-8 (15 m and 30 m), Sentinel-2 (10 m), ZiYuan-3 (ZY-3), and GaoFen-1 (GF-1) (2 m), to map mangroves in the Beibu Gulf by integrating spectral, texture, and topographic features. The optimal feature combination was determined experimentally. We comprehensively compared the identification accuracy, identification results, and area estimates derived from three distinct methods: pixel-based Random Forest (RF), object-oriented RF, and a U-Net + ResNet-34 deep learning model. Key findings include the following: (1) Feature importance varied by resolution; terrain features significantly improved accuracy for medium-resolution images (e.g., Landsat-8), increasing the F1-score by 1.91%, while texture features were critical for high-resolution images (e.g., ZY-3 and GF-1), improving the F1-score by 2.99%. (2) Deep learning achieved the highest accuracy using Sentinel-2 combined with terrain features, yielding an F1-score of 96.45%. (3) Higher-resolution imagery enabled the detection of smaller mangrove patches; deep learning produced the fewest internal gaps, whereas pixel-based RF suffered from severe “salt-and-pepper” noise. (4) While object-oriented RF using Sentinel-2 imagery produced results most consistent with the validation set (recall = 94.38%), notable area discrepancies (9.7%) persisted, primarily due to tidal dynamics. We conclude that deep learning models, particularly when integrating Sentinel-2 data with terrain features, provide superior accuracy for mangrove mapping. Nevertheless, tidal dynamics continue to pose a significant challenge for computer-aided identification approaches. Full article

Objective: To characterize age- and sex-related differences in maxillary strut (MS) morphometry, shape, and pneumatization in children using computed tomography (CT). Methods: Cranial CT examinations from 180 children aged 1–18 years were retrospectively evaluated. After one participant with a rudimentary unilateral MS was excluded, the primary bilateral complete-case analyses included 179 children and 358 MSs (179 right and 179 left). The anteroposterior diameter (MS-AP), area (MS-A), and mediolateral diameter (MS-ML) were measured bilaterally. Each side was classified as trapezoidal or hourglass-shaped, yielding bilateral trapezoidal, bilateral hourglass-shaped, or asymmetric participant-level patterns. Results: All morphometric parameters were higher in males than in females (MS-AP and MS-ML, p < 0.001; MS-A, p = 0.008), with no significant right–left differences. All parameters differed across chronological age strata and age groups (all p < 0.001). Age-by-sex interactions were significant for MS-AP (p < 0.001) and MS-A (p = 0.001), but not for MS-ML (p = 0.257). Bilateral trapezoidal, bilateral hourglass-shaped, and asymmetric patterns occurred in 40.8%, 30.2%, and 29.1% of participants, respectively. Shape pattern was associated with sex (p = 0.003), but not with age group (p = 0.359), and the right–left shape distributions did not differ significantly (p = 0.070). Pneumatization was unilateral in 4 out of 179 participants (2.2%; exact 95% confidence interval: 0.6–5.6%). Conclusions: Pediatric MS dimensions vary with age and sex, while shape shows a sex-related distribution and pneumatization is uncommon. These CT-based findings provide baseline anatomical data on pediatric MS development and may inform future radiological and surgical research. Full article

Global water scarcity is mainly due to climate change, population growth, excessive use of water resources and inadequate water management. Alternative sources such as reusing greywater are promising in urban environments. In this context, phytoremediation, the direct use of plants to remove soil and water pollution, could be an encouraging alternative nature-based solution to traditional reuse methods. While constructed wetlands are the most common method for greywater phytoremediation, their limited feasibility in cities makes green roofs and walls a practical urban alternative. This review examines studies conducted over the last 20 years. The combination of greywater treatment through phytoremediation with urban greenery would help close the water cycle as well as make cities more sustainable. However, for this solution to be viable, several challenges must be addressed, including limited roof or wall surface area compared to traditional systems, the precise volume of water to be managed, and the selection of suitable substrates and plants essential for effective phytoremediation. This work reviews the main mechanisms and factors that drive the phytoremediation process in constructed wetlands, and based on this knowledge, delves into the challenges of applying this process to the urban context through green roofs and facades on buildings. Full article

When multi-cylinder diesel engines operate under variable speed and load conditions, the distribution of normal vibration responses drifts with the operating conditions. This makes it difficult for diagnostic models to distinguish normal operating-condition fluctuations from genuine fault deviations, leading to false alarms or missed detections. Meanwhile, fault samples are usually limited in practical applications. To address these problems, this study proposes an anomaly detection method based on multi-feature relational modeling and conditional-memory augmentation. The method performs the cycle-wise alignment of multi-point vibration signals according to the firing phase of each cylinder. It integrates local waveform morphology, impact energy, and energy-centroid information in the non-uniform angular domain to construct a raw–relative dual relational representation. It further uses speed conditions to modulate latent features and employs a sparse normal memory to constrain reconstruction sources, enabling the model to learn normal relational patterns under different operating conditions using only normal samples. Tests involving misfire, intake-valve clearance anomaly, and exhaust-valve clearance anomaly were conducted on a TBD234V12 diesel-engine test bench. The proposed method achieved an accuracy, true positive rate (TPR), F1-score, and area under the receiver operating characteristic curve (AUROC) of 97.44%, 98.98%, 98.30%, and 98.88%, respectively, with a false-positive rate (FPR) of 7.21% under the sample-level alarm definition. The results show that the method reduces the interference of operating-condition-induced normal-pattern drift with anomaly determination and improves the accuracy of fault warning within the range of the operating conditions covered in this study. Full article

Addressing the issue of ecological vulnerability and against the backdrop of urban expansion in arid regions, this study constructs a composite urban resilience capacity index (CURCI) for the urban agglomeration on the northern slopes of the Tianshan Mountains and examines its space-time evolution together with the relative contribution of key indicators. The evaluation system was constructed from economic, social, ecological, and infrastructure dimensions. The CRITIC-entropy combined weighting method, TFPW-MK method, standard deviation ellipse, and XGBoost-SHAP model were used to analyze urban resilience capacity from 2005 to 2022. Results show that, temporally, CURCI showed a sustained overall increase. Urumqi City and Karamay City grew relatively fast as dual core cities, while peripheral and county-level cities improved gradually. Spatially, resilience was higher in the north and west and lower in the south and east. High-value areas expanded, low-value areas gradually disappeared, and the resilience centroid point remained near the boundary between Urumqi City and Changji City, with a northwest–southeast orientation. In terms of factor contributions, economic factors contributed most, followed by social and ecological factors. Based on contribution differences, cities were classified into industrial-advantage, ecological-advantage, natural-constraint, and population-pressure types. These findings provide a reference for resilience assessment and differentiated governance in arid-region urban agglomerations. Full article

Background and Objectives: Sepsis caused by Gram-negative bloodstream infections is associated with substantial mortality in intensive care units (ICUs). Admission measurements reflect initial disease severity but may not adequately capture subsequent clinical evolution or treatment response. This study evaluated whether a model based on 72 h reassessment demonstrated greater discrimination than a baseline model for mortality occurring between the 72 h landmark and day 28. Materials and Methods: This retrospective, single-center cohort study included adult ICU patients with Sepsis-3-defined sepsis and confirmed Gram-negative bloodstream infection admitted between January 2018 and November 2023. The primary analysis was conducted as a 72 h landmark cohort analysis with a fixed-horizon binary outcome and was restricted to patients who were alive and under observation at 72 h and had complete clinical and laboratory data. Separate multivariable logistic regression models were constructed using baseline and 72 h variables. Model discrimination was assessed using the area under the receiver operating characteristic curve (AUC), and the AUCs were compared using DeLong’s test for correlated ROC curves. Internal validation of the final 72 h model was performed using 1000 bootstrap resamples. Results: A total of 422 patients were included, of whom 134 (31.8%) died within 28 days. Baseline septic shock (adjusted odds ratio [OR] = 3.994; 95% confidence interval [CI]: 2.296–6.949; p < 0.001), higher 72 h SOFA score (adjusted OR = 1.603 per point; 95% CI: 1.424–1.805; p < 0.001), higher 72 h C-reactive protein level (adjusted OR = 1.005 per mg/L; 95% CI: 1.001–1.008; p = 0.005), and higher 72 h lactate level (adjusted OR = 1.189 per mmol/L; 95% CI: 1.007–1.403; p = 0.041) were independently associated with mortality occurring between the 72 h landmark and day 28. The 72 h model demonstrated greater discrimination than the baseline model (AUC: 0.867 vs. 0.799; AUC difference = 0.068; 95% CI: 0.027–0.109; p = 0.0012). The optimism-corrected AUC after bootstrap validation was 0.861. Conclusions: Among patients with Gram-negative sepsis who were alive at the 72 h landmark, a model incorporating baseline septic shock, 72 h SOFA score, CRP, and lactate demonstrated greater discrimination than the baseline model for subsequent mortality through day 28. These findings support the clinical relevance of 72 h reassessment for risk stratification, although external validation is required before routine clinical implementation. Full article

Background/Objectives: The objective of this study is to evaluate anatomical factors associated with operative time and audiological outcomes in endoscopic stapes surgery, with particular focus on quantitative temporal bone CT measurements. Methods: A retrospective review was conducted of 40 patients (51 ears) who underwent exclusive endoscopic stapes surgery between 1 April 2014 and 31 March 2023 by a single surgeon. Preoperative and postoperative audiometric data, operative time, and high-resolution temporal bone CT measurements—including scutum angle and external auditory canal (EAC) cross-sectional area—were analyzed. Correlation analysis was performed to assess associations with operative time and air–bone gap (ABG) closure. Receiver operating characteristic (ROC) analysis was conducted as an exploratory assessment to identify anatomical thresholds associated with prolonged operative time. Additional analyses were performed to evaluate the potential influence of surgical period on operative time. Results: Significant improvements were observed in postoperative air conduction, bone conduction, and ABG (all p < 0.0001). Mean ABG closure was 21.4 dB, and 74.5% of ears achieved postoperative ABG ≤ 10 dB. Operative time demonstrated a strong positive correlation with scutum angle (r = 0.8968, p < 0.0001) and a moderate negative correlation with EAC area (r = −0.5759, p < 0.0001). No significant correlation was identified between CT measurements and audiological outcomes. Exploratory ROC analysis suggested that an EAC area < 0.1922 cm² and scutum angle > 71.4° were associated with longer-than-median operative times. No significant difference in operative time was observed among the three predefined surgical periods, and linear regression analysis demonstrated no significant association between surgical period and operative time. Conclusions: Endoscopic stapes surgery provides favorable audiological outcomes. While preoperative CT-based anatomical parameters were not associated with hearing results, narrower EAC dimensions and larger scutum angles were associated with increased operative time under the endoscopic approach. These findings may facilitate preoperative surgical planning by identifying patients who are likely to present greater technical difficulty during endoscopic stapes surgery. However, the proposed CT thresholds should be regarded as exploratory findings and require prospective external validation before routine clinical implementation. Full article