Search Results (867)

Objectives: Vancomycin is crucial for treating severe Gram-positive infections, but its narrow therapeutic index complicates dosing. Trough monitoring may inadequately reflect exposure, while AUC-guided dosing, although recommended, is often impractical. Alternative metrics such as the time in therapeutic range (TIR) and volatility index may reflect exposure dynamics. Augmented renal clearance (ARC) further challenges vancomycin therapy in Intensive Care Unit (ICU) settings. This study evaluated trough-based exposure metrics and their associations with ICU mortality and acute kidney injury (AKI). Methods: We retrospectively analyzed 109 ICU patients with sepsis receiving vancomycin. Exposure was assessed using mean trough concentrations, TIR (proportion of troughs within predefined ranges), and the volatility index, defined as the intra-individual standard deviation divided by the mean trough concentration (SD/mean). Outcomes were ICU mortality and AKI. Associations were evaluated using multivariable regression, bootstrap resampling, and restricted cubic splines. Results: TIR _>15 was independently associated with higher mortality (adjusted OR 3.88; p = 0.0326) and AKI stage II–III (adjusted OR 5.63; p = 0.0068). Higher mean troughs correlated with AKI stage II–III, whereas higher volatility showed an inverse association (adjusted OR 0.15; p = 0.0240). ARC (4.6%) occurred exclusively in younger patients and predicted subtherapeutic exposure (TIR _<10, p = 0.0485). Conclusions: Sustained troughs >15 mg/L were associated with mortality and nephrotoxicity, while the most favorable outcomes were descriptively observed at mean trough levels of approximately 8–12 mg/L, suggesting a possible narrow exposure range that requires prospective validation. These findings highlight the limitations of trough-based monitoring alone; the trough-derived metrics should be regarded as exploratory rather than validated decision-making tools. Full article

Mungbean is an important legume crop native to India. In this study, 500 indigenous mungbean accessions collected from diverse eco-geographical regions of India were evaluated for agronomic trait genetic variability and core collection development. The accessions were grown in an augmented design during 2019 and 2020, and data were recorded for seven quantitative and 13 qualitative traits. Analysis of variance (ANOVA), frequency distribution, and box-plot analyses revealed substantial phenotypic variation among the accessions. Traits including plant height (PHT), number of pods per plant (NPP), hundred-seed weight (HSW), and single-plant yield (SPY) exhibited high heritability coupled with high genetic advance, indicating the predominance of additive genetic effects. Principal component analysis showed that the first three principal components explained 70% of the total phenotypic variation. The Shannon–Weaver diversity index further indicated high levels of genetic diversity within the population. Based on quantitative traits, the accessions were grouped into six major clusters and 42 sub-clusters, with SPY, NPP, HSW, PHT, and days to 50% flowering (DFF) contributing substantially to genetic divergence. Correlation analysis suggested that direct selection for SPY and indirect selection through associated traits, including NPP, HSW, PHT, NSP, and pod length (POL), may enhance yield improvement. The germplasm collection also possessed desirable traits such as high yield potential, contrasting maturity groups, and plant types suitable for mechanical harvesting and bold-seeded type. A representative core set comprising 50 accessions was developed using the PowerCore program, providing valuable genetic resources for mungbean breeding and genetic improvement programs. Full article

In safety-critical domains such as aviation, autonomous driving, and defense, operators must process complex spatial and radar data under severe time pressure. Traditional two-dimensional interfaces often force a “head-down” posture, increasing cognitive workload and impairing situational awareness. Extended reality and multimodal interfaces—incorporating gesture, voice, and haptic feedback—offer a promising paradigm to mitigate these limitations by enabling natural, egocentric data visualization. This scoping review systematically maps the empirical evidence on immersive and multimodal interfaces designed for radar and spatial data visualization in critical operational environments. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines, a systematic search was conducted across five major databases for articles published between 2015 and 2025. Out of 538 unique records screened, 54 studies met the eligibility criteria and underwent structured data charting. The findings reveal a technological ecosystem heavily dominated by augmented reality and virtual reality, supplemented by non-extended reality multimodal baselines (n = 8) to evaluate sensory load distribution. While subjective metrics such as the NASA Task Load Index (n = 17, 31.4%) dominate current evaluation practices, there is a notable scarcity of objective real-time physiological biosensors (n = 7, 13%). Crucially, the synthesized data challenges uncritical technological optimism: while multimodal extended reality effectively mitigates visual bottlenecks, certain modalities like mid-air gestures frequently induce physical fatigue and a documented speed–accuracy trade-off. To fully realize the potential of immersive decision support systems, future research must prioritize standardized, ecologically valid evaluation frameworks and explore artificial intelligence-driven adaptive interfaces capable of dynamically modulating information density based on operator workload. Full article

In this study, the potential of extrusion for producing functional food from corn semolina enriched with an ultrafiltration (UF) retentate obtained from rose wastewater was investigated. Extrudates were produced using a single-screw laboratory extruder (Brabender 20DN), and their physical (expansion ratio, bulk density, and moisture content), functional (water absorption and solubility indices), and process characteristics (specific mechanical energy and mass flow rate) and phenolic content were investigated. The effect of the UF retentate’s amount (4 or 11%), the temperature in the third zone of the extruder (150 or 170 °C), and the working screw speed (180 or 220 min⁻¹) on the physical, functional and process characteristics was examined using a full factorial design. Increasing the retentate’s amount led to a decrease in the expansion, water solubility index, specific mechanical energy, and mass flow rate, as well as an increase in the bulk density, moisture content, and water absorption index of extrudates. The augmentation of temperature led to a decrease in the bulk density, water solubility index, specific mechanical energy, and mass flow rate and had no significant effect on the other characteristics. The increase in the screw speed resulted in extrudates with a lower water solubility index, water absorption index, and specific mechanical energy and a higher expansion and moisture content, while it had no significant effect on the density and mass flow rate. The UF retentate enhanced the total phenolic, phenolic acid, and flavonoid contents of the extrudates. Optimal conditions for producing high-quality extrudates were found at 5.02% UF retentate, 150 °C, and 207.8 min⁻¹. Full article

The rapid advancement of Generative Artificial Intelligence (Gen-AI) is fundamentally reshaping labor markets, creating an urgent need for higher education institutions to adapt their program capacities and curricula. This paper proposes a data-driven Robust Multi-Objective Planning (RMOP) framework to translate heterogeneous Gen-AI labor shocks into actionable, program-level decisions regarding enrollment scaling and curriculum design. Grounded in O*NET micro-task structures, we model occupational evolution as a dynamic system of substitution, augmentation, and insulation driven by logistic technology diffusion. Our simulations across STEM, trade, and arts occupations reveal sharply divergent trajectories: Information Security Engineers face a 62% total impact dominated by substitution, whereas Electricians retain over 80% insulation, and Musicians experience high exposure but low substitution. To bridge these macro-level forecasts with immediate institutional maneuvers, the framework couples an AI-adjusted Grey Model (GM(1,1)) demand model with a Program Effectiveness Index (PEI) to yield discrete enrollment policy levers (Expand, Contract, and Adjust). For curriculum optimization, we employ Ridge regression to rank employability-related curriculum drivers and NSGA-II to generate Pareto portfolios under competing institutional objectives, including employability, instructional cost, ethics, and environmental impact. Final implementable recommendations are selected through entropy-weighted TOPSIS, where student well-being and education equity are treated as supplementary decision criteria rather than direct prediction targets. In addition, an Automation Risk Score (ARS) and a K-means TC clustering module are used to illustrate potential transfer paths across broader institutional settings. Internal scenario checks show that the AI-adjusted GM(1,1) reduces average hold-out MAPE from 7.0% to 5.8% relative to the baseline GM(1,1), and that NSGA-II achieves slightly stronger Pareto coverage than MOPSO and MODE under the same curriculum-portfolio setting. These checks are interpreted as preliminary decision-support evidence rather than external predictive validation. Overall, RMOP is presented as a scenario-based decision-support framework that links Gen-AI occupational exposure, enrollment adjustment, and curriculum portfolio design. Full article

This study investigates how sustainable development indicators are shaped in the context of Kazakhstan. The focus is on the interrelationships between economic growth, dependence on the mining sector, and foreign direct investment. In addition, the analysis pays special attention to the impact of the principles of the “green” taxonomy and changes in the ESG direction on these processes. Using annual time-series data, the analysis employs the augmented Dickey–Fuller unit root test, Johansen cointegration methods, and long-run estimation methods, namely, fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS). The analysis showed that there is a long-term relationship between GDP, the level of mineral extraction, foreign direct investment and the SDG index. According to the results, economic growth and foreign investment contribute to improving sustainable development indicators, and this effect is statistically confirmed. Conversely, a significant share of the mining sector appears to be linked to an increase in the environmental burden associated with resource dependence, which has a negative impact in the long term. The absence of significant short-term causal relationships suggests that sustainable development indicators evolve through gradual structural and institutional changes rather than short-term fluctuations. These findings suggest that the sustainability of economic growth is influenced by its structural composition, with investment-led diversification and modernization enhancing playing a crucial role in achieving sustainable development goals, while the expansion of the mining sector may hinder this. The study highlights the need and importance of aligning economic policies with the principles of the “green taxonomy”, improving institutional frameworks, and promoting environmentally sustainable investments to support long-term sustainable development trajectories. Full article

The right ventricle (RV) is a primary determinant of outcomes in cardiac critical care. RV dysfunction independently predicts morbidity and mortality in conditions such as acute coronary syndromes, pulmonary embolism, and cardiogenic shock. This review synthesizes RV evaluation and management by integrating physiologic principles with bedside diagnostic and therapeutic strategies. The RV is exceptionally sensitive to acute afterload increases due to its adaptation to low-pressure pulmonary circulation. Evaluation utilizes a multimodal approach combining echocardiography, invasive hemodynamics, and specifically the pulmonary artery pulsatility index and central venous pressure/pulmonary capillary wedge pressure (CVP/PCWP) ratio and biomarkers. Management focuses on three pillars: individualized preload optimization, afterload reduction via selective pulmonary vasodilators, and contractility augmentation with inotropes. For refractory cases, mechanical circulatory support options like Impella RP, ProtekDuo, and VA-ECMO provide critical bridges to recovery or transplantation. Full article

Change detection (CD) refers to the analysis of changes in the utilization of land, buildings, and other targets in the same surface environment using relevant technologies and remote sensing images. Although deep learning-based change detection methods have achieved excellent results, they remain highly dependent on extensive labeled data. High-resolution remote sensing imagery typically encompasses an abundance of details and a greater quantity of pixels compared to low-resolution datasets. Therefore, data annotation costs are significantly higher. Currently, within the context of semi-supervised change detection (SSCD) driven by consistency learning, pseudo-labels are usually selected only by threshold screening, but this ignores the spatial relationships among pixels and does not fully utilize unlabeled data, thereby affecting the model’s performance. Consequently, we propose a semi-supervised high-resolution remote sensing image change detection method based on label expansion. First, a “one weak, two strong” (OW-TS) consistency regularization (CR) framework is introduced to constrain the overall consistency between the prediction results of weak and strong augmentations, as well as between the two strong augmentations. At the same time, the location interaction map (LIM) is introduced to utilize the global–local relationship between pixels and mine the consistency of pseudo-labels, thereby improving the model’s accuracy. Empirical findings indicate that when the model is trained utilizing 20% labeled data and 80% unlabeled data on the LEVIR-CD dataset, the IoU^c index reaches 83.38%. The model performs well in smoothing the boundary between changed and unchanged areas and is comparable in performance to some fully supervised methods. Full article

The development of new corn varieties is necessary to meet the corn demand. Using full diallel crosses is a method for developing high-yielding hybrid corn. This development requires systematic selection methods that incorporate various approaches in developing selection indices. This study aimed to develop a selection index concept for two full diallel cross populations and select potential hybrid crosses for preliminary yield evaluation. The study involved two populations of 100 corn seed genotypes from full diallel crosses (90 F₁ genotypes and 10 selfing elders) and five check varieties per population, planted using a Type II Augmented RCBD in eight blocks. Agronomic characteristics were analyzed using analysis of variance, heritability, factor analysis, and path analysis, with selection criteria aligned with heterotic potential, specific combining analysis, and heterobeltiosis. Analysis revealed significant genetic variation and moderate-to-high heritability for most traits. Correlation, factor, and path analyses identified cob diameter, number of rows per cob, and seeds per row as optimal selection criteria. Selection indices were developed by integrating standard heterosis, specific combining ability, and heterobeltiosis, with weights based on heritability and direct effects. Forty-four hybrid crosses showed potential for preliminary yield tests, with seven having the best final index compared to the reference variety. The p17 × p23 cross had the best potential for the final index. This study demonstrates the effectiveness of integrating multivariate analysis and selection indices in developing superior hybrid corn crosses. Further optimization is recommended through preliminary yield tests and molecular approaches. Full article

Decomposition of nonlinear, nonstationary multicomponent signals remains challenging for existing decomposition strategies, including frequency-based, data-driven, and subspace methods, which can suffer from mode mixing, leakage across components, and unreliable isolation of transients. Motivated by this gap, this study proposes Orthogonal Self-Similarity Decomposition (OSSD), which exploits a self-similarity structure in delay-embedded orbit geometry so that temporal organization, rather than spectrum alone, guides component construction. OSSD-Basic introduces three algorithmic novelties within a single pipeline: (1) an adaptive proxy-correlation band merging on the delay axis, (2) a dominant-component cascade that prevents energy-dominant carriers from masking weaker components, and (3) a double MGS + LS reprojection that collapses the inter-mode orthogonality index to numerical zero, regardless of merging and pruning operations. Synthetic experiments with known ground truth show that OSSD-Basic provides a parsimonious four-mode representation with exact inter-mode orthogonality (OI = 9.4 × 10⁻¹⁸), the highest reconstruction SNR among the evaluated baselines (27.14 dB), and the highest ground-truth diagonal correlation sum (3.038) among the tested methods, while using two fewer modes than EMD, VMD, and SSA. Daily streamflow forecasting on a U.S. Geological Survey discharge record further shows that augmenting OSSD-derived inputs with fractal descriptors and fractional-order differencing features yields progressive accuracy gains over the AR-ANN baseline, with R² improving from 0.855 to 0.915 at one-step-ahead and from 0.388 to 0.699 at four-step-ahead forecasting in the single-input setting, within a single-station case study on USGS 01554000. Overall, OSSD-Basic offers an interpretable multiscale decomposition with guaranteed inter-mode orthogonality and a structured feature pathway for oscillatory–transient mixtures. Full article

Mediterranean coastal groundwater-dependent ecosystems are among the most vulnerable environments to the combined effects of climate change and local anthropogenic pressures, yet long-term quantitative assessments disentangling these drivers remain limited. The 41-year hydro-ecological dynamics (1984–2025) of “Le Soglitelle”, a transitional man-made coastal GDE located in the Campania Plain (southern Italy), were reconstructed across three management regimes: illegal hunting via electric pumps augmentation of flooded areas (1984–2004), post-seizure transition (2005–2015), and fenced natural reserve sustained by artesian wells flow (2016–2025). A monthly multi-sensor time series of seven spectral indices was derived from cross-calibrated Landsat program Surface Reflectance products via Google Earth Engine. Spectral indices were then combined with climatic variables (precipitation, reference evapotranspiration, air temperature) and then integrated in a statistical framework including Mann–Kendall test, Pettitt test, and Principal Component Analysis. Significant breakpoints were identified for the water fraction (2007; mean decrease from 0.18 to 0.09) and the Normalized Difference Vegetation Index (2009; mean increase from 0.30 to 0.42), consistent with a hydrological regime shift following the interruption of anthropogenic pressures. The relationship between the water fraction and the Vegetation Soil Salinity Index was 2.7 times steeper in the last period than the first one, indicating that, for an equivalent flooded extent, osmotic stress on vegetation is substantially higher under the artesian flow alone, likely due to reduced dilution of saline inputs combined with the effect of ongoing climate change. PCA showed that PC1 reflected the transition from anthropogenic to more natural system conditions, whereas PC2 was associated with increasing ET₀, became more prominent during the last period of management, suggesting a shift toward stronger climate-driven control. Long-term satellite monitoring provides a quantitative baseline for designing targeted management interventions aimed at sustaining ecosystem functioning under ongoing Mediterranean warming. Full article

Generative AI, retrieval-augmented architectures, and multi-source automated analytical tools are now being deployed in increasingly exacting risk-analytic environments. Yet faster processing has not yielded commensurate reductions in false alarms, missed alarms, hallucinated outputs, or failures of responsibility attribution. Against that background, this study develops a biomimetic framework that integrates collective sensing with immune-inspired verification for analyzing complex risk information. Using an openly documented two-layer data architecture that combines authentic public-source samples with rule-generated, synthetic derivative samples, the study links biological-to-engineering mechanism translation, multi-objective optimization, National Institute of Standards and Technology (NIST)-aligned evaluation, and a governance-compatibility index within a single auditable design chain. The present evidence indicates that risk level continues to show a stable positive association with threat scores. At the same time, fabricated relations, despite their smaller aggregate volume, are more likely to accumulate in high-risk intervals. These patterns suggest that structural perturbations are more consequential than mere high-frequency noise in distorting judgment. More importantly, the study establishes the empirical and methodological conditions for a formal comparison of recognition quality, system resilience, and governance compatibility. Taken together, the paper offers a testable biomimetic mechanism model and a reproducible evaluative blueprint for auditable optimization in complex risk-information analysis. Full article

Component obsolescence and supply-chain disruptions increasingly force engineers to spend significant time manually searching and comparing PDF datasheets to identify compatible replacement parts. We propose an AI-powered datasheet assistant based on a Retrieval-Augmented Generation (RAG) pipeline that automatically processes datasheets to accelerate component identification and matching. The core contribution is a summary-driven retrieval mechanism: a Large Language Model (LLM) generates a structured semantic summary of an input datasheet, and the vector embedding of this summary is used to retrieve semantically similar components from a reference database. The system also supports natural language question answering and structured component comparison. Its architecture separates scalable text-only reference indexing from more expensive query-time summarization and reranking. Validation includes a controlled synthetic benchmark and a pilot-scale real-world evaluation on 18 publicly listed microcontroller datasheets grouped into six engineering families. The synthetic benchmark is used to assess pipeline behavior under controlled conditions, while the real-world evaluation measures performance on heterogeneous manufacturer datasheets. In the real-world evaluation, structured summaries generated with Claude Sonnet 4.5 combined with cross-encoder reranking achieved a 72.2% Family Retrieval Rate at $k=1$ (13/18; Wilson 95% CI: 49.1–87.5%). Additional experiments with local LLM summaries indicate that retrieval performance depends strongly on summary quality and model capability, with lightweight local summarizers producing lower first-candidate retrieval performance in this setup. The analysis further reports confidence intervals, no-summary baselines, chunking sensitivity, and an Image Reference Rate metric used as a lexical reference proxy rather than a direct measure of visual grounding. Full article

Background: Debridement, antibiotics, and implant retention (DAIR) is an established treatment for early periprosthetic joint infection (PJI) following hip arthroplasty; however, reported success rates remain highly variable, particularly in patients with significant comorbidities, fracture-related arthroplasty, or resistant microorganisms. Augmentation of standard DAIR with structured local antimicrobial strategies may improve infection control but remains insufficiently standardized and evaluated. Methods: This retrospective single-center case series evaluated outcomes of a standardized multimodal DAIR-based strategy, the Mursa protocol, in 16 consecutive patients treated for early hip PJI between 2022 and 2025. PJI was diagnosed according to European Bone and Joint Infection Society criteria. The treatment included radical surgical debridement and exchange of mobile components with sequential intraoperative antiseptic microdebridement using povidone–iodine and hypochlorous/hypochlorite solution, followed by postoperative drain-based local antimicrobial irrigation and systemic antibiotic therapy. Treatment success was defined as sustained infection eradication with implant retention, absence of clinical and radiological signs of infection, no requirement for long-term suppressive antibiotics, and no infection-related mortality at a minimum one-year follow-up. Results: The cohort was clinically complex, with a predominance of arthroplasty procedures performed for fracture-related indications (11/16), a high comorbidity burden (median Charlson Comorbidity Index 5), revision arthroplasty in four patients, and a high rate of resistant or polymicrobial infections. At final follow-up, 15 of 16 patients (93.8%) achieved treatment success. One patient required implant removal due to persistent polymicrobial infection. No irrigation-related complications, wound-healing problems, or clinically relevant systemic toxicity were observed. Conclusions: In this high-risk cohort, a structured multimodal DAIR protocol incorporating sequential antiseptic microdebridement and postoperative local antimicrobial irrigation was feasible, safe, and associated with encouraging infection control. However, these findings should be interpreted as hypothesis-generating, and further prospective comparative studies are required to validate the protocol. Full article

This work presents a trajectory indexing pipeline for accelerating Social Spatio-Temporal Keyword Pattern (S²KP) queries over Multiple-Aspect Trajectory (MAT) data. An S²KP query forms a sequence of spatial, temporal, textual, and social-rating constraints over trajectory episodes. The constraints are formulated in the form of regular expressions, thus offering high expressiveness and flexibility in query formulation. In this paper, we enhance spatial pruning by enhancing a well-established MAT index, the Episode-Based Multiple-Aspect Trajectory (EMT) Dual Index. The EMT Dual Index is augmented with curve-based keys (Hilbert, Z-order, and Gray-coded Z-order mappings), so that spatially related entities are projected into one-dimensional key ranges, enabling additional subtree pruning through interval overlap while preserving exact final matching semantics. The intervals are induced by the numbering of cells generated by a curve. Our experimental study on two representative MAT datasets (one synthetic and one real) demonstrates the effectiveness of our proposal. Full article