Search Results (17,044)

Wetlands, frequently termed the “kidneys of the Earth,” represent one of the most vital global ecosystems. Despite their limited spatial extent, plateau wetlands function as unique ecological units that play a pivotal role in the global carbon cycle, water resource regulation, and biodiversity conservation, while exhibiting acute sensitivity to climate change. Advances in remote sensing technology—characterized by macro-scale cover-age, temporal efficiency, and non-invasive operations—have established it as a corner-stone for the dynamic monitoring and analysis of these environments. This study presents a bibliometric synthesis of 2138 publications (1982–2024) retrieved from the Web of Science Core Collection. We systematically evaluated publication trajectories, international collaborative networks, disciplinary shifts, core journals, and the spatiotemporal evolution of research hotspots. Our findings reveal an exponential growth in scholarly output alongside a marked diversification of research fields. Geographically, research is predominantly clustered around the Tibetan Plateau, flanked by the Alps and the Himalayas, with sparse representation in other regions. Future endeavors should prioritize underrepresented low-latitude and remote regions through strengthened international synergy and the integration of emerging technologies, such as UAVs and hyperspectral sensors. Full article

System logs generated in IoT-based and sensor-driven cloud environments encode execution traces and complex relationships among services, functions, and data stores. In many IoT deployments, telemetry is pre-processed at the edge and then integrated into backend services (e.g., application servers and databases) for analytics and operations. During this integration, service executions record relational dependencies (e.g., function-to-data-store interactions) as operational logs (or aggregated statistics), which constitute key evidence for operating sensor-driven services. We therefore evaluate TLV using publicly reproducible backend execution logs as a representative backend model and discuss the generality and limitations of this choice. However, most existing retrieval-augmented generation (RAG) approaches remain document-centric, representing logs as flat textual chunks that fail to preserve execution flow and entity relationships, which are critical for diagnosing complex service execution pipelines in sensor-driven cloud backends. In this study, we propose Trace-LogVector (TLV), a relational log representation that transforms system logs into trace-level retrieval units while explicitly preserving execution order and entity interactions. TLV is constructed based on the Chunk as Relational Data (CARD) design principle, which represents execution flows using entity-centric multi-chunk structures rather than single aggregated text chunks. To evaluate the impact of relational log representation, we conduct controlled experiments comparing single-chunk and CARD-based multi-chunk TLV under identical embedding and retrieval settings. Retrieval performance is quantitatively assessed using Hit@5 and Mean Reciprocal Rank at 5 (MRR@5). Experimental results show that the proposed multi-chunk TLV achieves a Hit@5 of 1.000 and an MRR@5 of 0.900, consistently outperforming the single-chunk baseline across all evaluation queries. These findings demonstrate that preserving execution contexts and entity relationships as relational retrieval units is a key factor in improving RAG-based system log analysis for monitoring and diagnosing large-scale sensor networks and cloud systems. Full article

Traffic sign recognition is critical for intelligent transportation systems and autonomous driving. Conventional convolutional neural networks (CNNs) typically utilize the ReLU activation function for its computational efficiency; however, alternative activation functions can improve computing effectiveness capacity in recognition tasks. In this study, we propose a CNNs model enhanced with the Gaussian Error Linear Unit (GELU) activation function. We evaluate its performance on benchmark datasets and compare it against both ReLU and Leaky ReLU baseline. Experimental results show that the proposed GELU-activated CNNs achieves a recognition accuracy of 99.75% and provides small but consistent improvements over ReLU and Leaky ReLU models, particularly under challenging conditions such as occlusion and low lighting. These findings highlight GELU’s potential to enhance the robustness and reliability of traffic sign recognition in Electric Vehicles for autonomous driving applications. Full article

Wearable systems based on inertial measurement units (IMUs) have attracted considerable interest in recent years in the field of gait analysis. However, most gait studies using such devices have been conducted in laboratory rather than clinical settings. This study evaluated a commercially available IMU-based insole system in two cohorts: a clinical group (59 ± 18, years) recruited from podiatry clinics and a non-clinical group (28 ± 7, years) recruited from a university with no reported complaints. Participants wore the IMU-based device and performed treadmill walking (clinical group) and overground walking (non-clinical group). Spatiotemporal parameters were compared between groups using statistical analyses included the Shapiro–Wilk test, Mann–Whitney test, and Welch’s t-tests for non-bilateral data, and a two-factor linear mixed-effects model estimated by restricted maximum likelihood (REML) for bilateral spatiotemporal parameters to evaluate group, foot-side, and interaction effects. Ten of the twenty-two spatiotemporal parameters showed significant group differences, with statistical significance observed in at least one foot for parameters measured bilaterally. The observed differences may reflect a combination of clinical characteristics, age-related effects, and walking environment influences. Findings are discussed in relation to potential biomechanical mechanisms, factors influencing results and the clinical utility of IMU systems. Future research should investigate specific foot conditions under standardized walking conditions with age-matched cohorts. Full article

Gas analyzers in post-combustion CO₂ capture plants are accurate but slow and sequential, yielding sparse, non-synchronous concentration records across absorber stages. We address this missing-data problem by reconstructing continuous CO₂ profiles with Moving Horizon Estimation (MHE) constrained by a mechanistic absorber model and available measurements; these MHE reconstructions are used as supervisory labels to train an end-to-end Stacked Denoising Autoencoder–Gated Recurrent Unit (SDAE-GRU) model. At run time, we deploy a hybrid soft sensor using the SDAE-GRU together with the mechanistic model and fuse their open-loop predictions via covariance-weighted blending with Gaspari-Cohn localization. We validate this approach on a pilot-scale MEA absorber using data from seven pilot runs conducted at distinct operating conditions, using datasets 1–5 for training/tuning and 6–7 for blind validation. On the blind validation runs, the hybrid estimator achieves a MAPE of 3.79% for stage-wise CO₂ predictions (averaged over all stages and time samples), outperforming both constituents evaluated standalone: 7.86% for the GRU-only soft sensor and 6.79% for the mechanistic model. Because MHE is used only offline to generate labels and to estimate model-error covariances, the deployed estimator is lightweight and suitable for online monitoring. Full article

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

Background/Objectives: Intraoperative neuromonitoring (IONM) has improved safety in thyroid and parathyroid surgery, yet intermittent IONM (I-IONM) may miss traction injuries developing between stimulations. We evaluated the feasibility and clinical utility of a trend-based intermittent monitoring mode (NIM Vital NerveTrend®) that records closely spaced stimulations and plots amplitude and latency over time. Methods: We conducted a prospective observational study at a high-volume endocrine surgery unit (January–September 2025). Forty-four consecutive patients undergoing thyroidectomy and/or parathyroidectomy with NerveTrend® were enrolled. Electromyography (EMG) responses were categorized as Green (amplitude > 50% of baseline and latency < 110%), Yellow (amplitude < 50% or latency > 110%), Red (amplitude < 50% and latency > 110%), and Loss of Signal (LOS: amplitude <100 µV). Primary outcomes included LOS prevalence and the association between stimulation frequency and the appearance of Yellow trends. Ethical approval: AVEN protocol 486/2024/OSS/AOUPR; informed consent obtained. Results: Of 71 nerves at risk (NAR), 55 had a valid baseline and were analyzed; LOS occurred in 3/55 NAR (5.5%). The mean number of stimulations per NAR was 4.5 (range 1–9). Cases with both Green and Yellow points had a significantly higher mean number of stimulations than cases with only Green points (5.1 vs. 3.8; Student’s t-test p = 0.0059). One Red measurement occurred in a case that progressed to LOS. Conclusions: NerveTrend® provided near real-time functional feedback while maintaining the simplicity of I-IONM. Increased stimulation frequency was associated with early Yellow trend alerts, potentially signaling traction stress and enabling timely surgical adjustments. Larger multicenter studies and protocol standardization are warranted. Full article

The revitalization of vitality in historic cultural districts can enhance a city’s cultural attractiveness and promote the upgrading of the urban cultural industry and sustainable development. Revealing the threshold and synergistic effects of different districts’ scene elements on district vitality helps to identify the distribution patterns of district vitality and provides a basis for managerial decision-making. This study first uses a geographic information system (ArcGIS) to overlay Baidu heatmaps with the street-network distribution in order to depict the spatiotemporal heterogeneity of district vitality and to compute vitality values by partitions at the district scale. Subsequently, based on an explanatory framework that integrates the physical space and subjective cognition, multi-source data such as street-view panoramas and points of interest (POIs) are quantified to obtain scene-element values for each unit area. Then, the scene-element values and vitality values are integrated into a consolidated database. Additionally, the LightGBM model and the SHAP method are employed to evaluate each element’s marginal contribution and relative importance to district vitality, thereby screening out the key scene elements. Finally, by means of SHAP dependence plots and interaction-effect analysis, the threshold intervals of the key elements and their synergistic relationships are identified, revealing the nonlinear threshold effects and synergies by which scene elements influence spatial vitality. The results show that during rest days, district vitality exhibits stronger diffusion, and the synergistic effect between Leisure-Facility Attractiveness and Street-Network Accessibility is the most prominent in enhancing vitality. High Exhibition-Facility Attractiveness is difficult to sustain crowds on its own; only when Leisure-Facility Attractiveness is likewise high does its effectiveness increase significantly. When Transport Accessibility is within the 0.20–0.40 interval, the positive effect of Leisure-Facility Attractiveness is significantly amplified. An excessive Traditional–Modern Facility Mix readily leads to homogenization of districts; therefore, when introducing modern business formats, local cultural characteristics must be retained. Overall, the generation of district vitality relies more on the synergy between material factors and subjective cognition than on improvements to any single element. The findings of this study provide suggestions for the planning of scene elements and the enhancement of vitality in historic cultural districts. Full article

Background/Objectives: This study aimed to investigate the prognostic significance of the triglyceride–glucose index (TGI), triglyceride-to-high-density lipoprotein cholesterol (TG/HDL-C) ratio, and inflammatory biomarkers in predicting short-term mortality among intensive care unit (ICU) patients with sepsis. Additionally, this study evaluated whether combining these indices with conventional clinical scores improves prognostic accuracy. Methods: This retrospective cohort study included 600 adult ICU patients diagnosed with sepsis according to Sepsis-3 criteria between January 2020 and April 2025. Clinical, biochemical, and hematological data were collected within the first 24 h of ICU admission. Metabolic indices (TGI, TG/HDL-C) and inflammatory markers (neutrophil-to-lymphocyte ratio [NLR], systemic immune-inflammation index [SII], and pan-immune-inflammation value [PIV]) were analyzed. The primary outcome was 28-day mortality. Receiver operating characteristic (ROC) analyses, Kaplan–Meier survival curves, and a multivariable logistic regression model were applied to determine prognostic performance. Results: Non-survivors exhibited significantly higher levels of TGI, TG/HDL-C, NLR, SII, and PIV compared to survivors (all p < 0.001). In ROC analysis, TGI (AUC = 0.75, 95% CI: 0.71–0.79), TG/HDL-C (AUC = 0.72, 95% CI: 0.68–0.76), and PIV (AUC = 0.78, 95% CI: 0.74–0.82) demonstrated good discriminative power for predicting 28-day mortality. Multivariate logistic regression identified TGI > 8.95 (OR = 1.44, 95% CI: 1.19–1.74, p < 0.001), TG/HDL-C > 3.95 (OR = 1.31, 95% CI: 1.08–1.59, p = 0.005), and PIV > 260 (OR = 1.49, 95% CI: 1.22–1.82, p < 0.001) as independent predictors of mortality. Integrating TGI and PIV with the SOFA score improved prognostic performance (ΔAUC = +0.04). Conclusions: Both TGI and TG/HDL-C are independent predictors of short-term mortality in septic ICU patients, reflecting the contribution of metabolic dysregulation to disease severity. The PIV demonstrated comparable predictive ability to conventional severity scores. Combining metabolic and inflammatory biomarkers with established clinical indices may enhance early risk stratification and guide personalized management strategies in sepsis. Full article

Background/Objectives: Acute heart failure (AHF) is a leading cause of hospitalization and mortality among very old patients, yet this group is underrepresented in prognostic studies. Carbohydrate antigen 125 (CA125) has emerged as a potential biomarker of congestion and inflammation, but its value in patients aged 80 years and over remains unclear. We aimed to evaluate the prognostic value of plasma CA125 measured at admission for 12-month all-cause mortality and the composite outcome of mortality or heart failure (HF) readmission in very elderly patients hospitalized for AHF. Methods: We conducted a prospective observational study of patients aged ≥80 years admitted to an acute geriatric unit for AHF. CA125 and NT-proBNP were measured within 24 h of admission. Outcomes were assessed at 12 months. Survival analyses were performed using Kaplan–Meier curves, Cox regression models, and restricted cubic splines. Results: A total of 210 patients (mean age 89.8 ± 5.3 years; 75.3% females; 88.1% frail) were recruited. During the one-year follow-up, 70 deaths (37.2%) and 68 HF hospital readmissions (36.1%) were recorded. Patients in the highest CA125 tertile had an increased cumulative mortality risk (log-rank p = 0.061). A CA125 value ≥ 100 U/mL independently predicted both mortality (HR 1.88, 95% CI 1.15–3.09; p = 0.012) and the composite endpoint (HR 1.54, 95% CI 1.04–2.29; p = 0.031). Measures of functional dependence and frailty demonstrated greater discriminative ability than biomarkers. Conclusions: In very elderly patients hospitalized for AHF, elevated CA125 at admission independently predicted 12-month mortality and HF readmission. CA125 provides complementary prognostic information to geriatric assessment and may support risk stratification in this vulnerable population. Full article

Slope instability can cause severe disasters, making stability prediction essential. Machine learning has become a key tool for this purpose, as it avoids complex mechanical calculations and efficiently handles high-dimensional data. Currently, the data used in machine learning primarily originate from real-world cases. However, such cases are inherently limited in quantity and often fail to comprehensively represent all potential slope conditions. To address these limitations, this study proposes a method for constructing numerical simulation databases. Based on this, we develop a model establishment method for rapid evaluation of slope stability integrating numerical simulation with engineering cases. This study uses six characteristic parameters to assess slope stability, including unit weight γ, cohesion c, internal friction angle φ, slope angle α, slope height H, and pore pressure ratio r_u. Through extensive literature mining, we established a database of 684 engineering cases. Based on statistical analysis of input parameters, a numerical simulation scheme was designed. Batch calculations were performed using MATLAB to determine simulation results. The engineering case database was then partitioned into training and testing sets for model development and validation. Subsequently, the numerical simulation database was incorporated into the training set for retesting. Results demonstrate that when considering all predictive indicators, the prediction accuracy of the GRNN-based model improved from 85% to 88.3%, while the PNN-based model showed an increase from 69% to 88.3%. This study offers new insights for optimizing numerical simulation design and enhancing machine learning performance in slope stability prediction. Full article

Background: Gastric cancer (GC) remains a global health challenge, with high mortality rates often linked to late-stage diagnosis. Novel, non-invasive biomarkers are urgently needed to improve the detection and prognosis of this malignant pathology. This study aimed to evaluate the diagnostic and prognostic utility of serum Cluster of Differentiation 276 (CD276) and Dickkopf Related Protein 3 (DKK3) in patients with GC. Methods: In this case–control study, serum levels of CD276 and DKK3 were quantified in 40 GC patients and 40 age-matched healthy controls. The diagnostic performance of each marker and their combination was assessed using Receiver Operating Characteristic (ROC) curve analysis. Correlations between biomarker levels and clinicopathological features were evaluated using Spearman’s correlation. The Kaplan–Meier method and the Cox Proportional Hazards Regression Model were used to assess survival. Results: Serum CD276 levels were found to be significantly elevated in GC patients compared to healthy controls (median 60.06 vs. 18.71 units, p < 0.001). Conversely, serum DKK3 levels were significantly suppressed in the GC group (median 92.47 vs. 121.02 units, p < 0.001). In ROC analysis, CD276 demonstrated excellent diagnostic accuracy as a standalone biomarker (AUC: 0.836). DKK3 showed independent diagnostic value (AUC: 0.792), but adding DKK3 to CD276 did not provide statistically significant incremental benefit (DeLong’s p = 0.443). Survival analysis was underpowered due to limited events and short follow-up duration. Conclusions: In patients with predominantly locally advanced gastric cancer, CD276 can be a primary diagnostic marker, and the addition of DKK3 does not demonstrate a statistically significant improvement but may provide complementary information. Performance in early-stage disease requires validation in future studies. The opposing dysregulation of these markers, reflecting immune checkpoint activation (CD276) and tumor suppressor loss (DKK3), provides a robust and synergistic noninvasive signature. To assess the prognostic value of these two markers, studies involving a larger number of patients and a longer follow-up period are needed. Full article

The growing integration of renewable energy sources in grid-connected microgrids (MG) has made it increasingly challenging to attain the most cost-effective and emission-efficient power dispatch in the face of uncertainty. This study addresses the scheduling problem of MG under utility-induced demand side load participation level for residential areas. Our research overcomes the constraints of conventional techniques by utilizing quantum-inspired particle swarm optimization (QPSO) to improve the operational efficiency and resilience of MG’s. In this study, a three-stage stochastic framework is proposed to address the optimal energy scheduling of MGs while taking economic and emission aspects into account. Using real-time meteorological data, five Cases were investigated and simulated using MATLAB/Simulink. Without the involvement of load participation, MG’s producing units in first Case, had carbon emissions of 797.110 kg and an operating cost of 267.10 €. Similar to this, the impact of demand side on the MG was evaluated in the remaining Cases. According to the simulation results, the fifth Case, which has optimal DGs scheduling, is the suggested way to improve MGs efficiency and provide a dependable power supply with low operating costs, emission reduction, and convergence features. This study not only demonstrates the practicality of QPSO algorithms but also paves the way for more resilient, efficient, and sustainable energy systems. Full article

The South African Government prioritises Small, Micro-, and Medium Enterprises (SMMEs) as catalysts for employment creation, in alignment with Sustainable Development Goal 8 (SDG 8), Decent Work and Economic Growth, which advocates for sustained, inclusive, and sustainable economic growth. However, the extent to which agro-processing SMMEs translate this policy ambition into measurable socio-economic gains remains contested due to persistent structural, financial, and operational constraints. This study develops a comprehensive, data-driven business support framework tailored to agro-processing SMMEs in the Free State province of South Africa. Employing a mixed-methods approach, survey data from 88 agro-processing SMMEs were analysed across 18 business performance dimensions. Average agreement scores and performance gaps were utilised to diagnose strengths and vulnerabilities within the sector. While overall performance was relatively strong (average agreement score: 86.7%), a critical weakness emerged in operational cost management (76.1%), revealing a 14.2% gap relative to the highest-performing dimension, equipment selection (90.3%). Based on these empirical insights, the study proposes a three-tiered business support architecture: (i) maintaining and leveraging high-performing dimensions (≥85% agreement), (ii) targeted enhancement for moderate-performing areas (80–84.9%), and (iii) crisis intervention for critical weaknesses (<80%). The framework integrates cross-cutting support services, including financing, regulatory guidance, and technology access, delivered through a phased implementation strategy comprising crisis intervention, system establishment, and optimisation and scaling. A multi-channel delivery mechanism, combining a hub-and-spoke model, mobile support units, and a digital platform, ensures provincial accessibility. By translating performance diagnostics into differentiated policy action, the framework promotes efficient resource allocation, supports both high-potential and vulnerable agro-processing SMMEs, and embeds a robust monitoring and evaluation system to track key performance indicators. The study contributes to the SMME development literature by demonstrating how structured, tiered, and context-specific support models can strengthen resilience, competitiveness, and sustainable agro-industrial growth in developing-country settings. Full article

Objective: This study evaluates the pre–post changes in dental knowledge, attitudes, skills, and behaviors following a community health worker (CHW)-led intervention. Methods: Adult caregivers from migrant worker families living near the United States–Mexico border participated in the five-week, in-person, CHW-led intervention program. The two-hour once/week interactive sessions were held in Spanish and included oral health education, skill-building, and goal-setting. Participants completed pre- and post-surveys about dental knowledge, attitudes, skills, and oral hygiene behaviors (N = 117). Participants self-reported frequency of brushing and flossing in the prior week, which was dichotomized to reflect meeting the American Dental Association (ADA) guidelines of brushing twice/day and flossing once/day. Mean group comparisons and paired t-tests were conducted to assess pre- and post-intervention differences. Intervention feedback was also evaluated. Results: Pre-intervention, most adults met hygiene guidelines, and in the overall sample, there were no significant differences post-intervention. However, there were meaningful behavior change differences observed among subgroups not meeting ADA guidelines at baseline. Among the 32% of adults who did not meet ADA brushing guidelines and the 61% that did not meet ADA flossing guidelines at baseline, there were significant improvements post-intervention and increased weekly frequency for brushing (p < 0.001) and flossing (p < 0.001). Pre-intervention, 30% reported not being taught to properly brush or floss; post-intervention, only 3% reported not being taught this skill (p < 0.001). Knowledge (p < 0.001) and some attitudes, including self-efficacy (p < 0.001), significantly increased post-intervention. Program feedback from participants and CHWs was positive, and 81% of participants shared materials. Conclusions: After the CHW-led intervention, there were increases in the adults’ self-reported dental knowledge, some attitudes, and hygiene skills. Toothbrushing and flossing frequency increased post-intervention among the subgroups of adults that were not already meeting ADA guidelines at baseline. Full article