Search Results (5,114)

This study develops a deterministic two-sex compartmental model to investigate the population-level effects of continuous sterile-male releases on Anopheles mosquitoes. The model distinguishes aquatic stages, unmated females, fertile-mated females, sterile-mated females, wild males, and released sterile males, while incorporating density-dependent aquatic recruitment, sex-specific adult emergence, natural mortality, and mating competition between wild and sterile males. We establish the nonnegativity and boundedness of solutions and characterise the wild-mosquito-free and nontrivial wild-mosquito equilibria. The wild-mosquito-free equilibrium is locally asymptotically stable because viable reproduction requires the joint presence of unmated females and wild males, generating a mate-finding Allee effect near extinction. Accordingly, population persistence is characterised by an explicit release-dependent threshold rather than a conventional infectious-disease reproduction number. When the intrinsic demographic condition $\mathcal{Q}>1$ holds, two nontrivial wild-mosquito equilibria may coexist below the critical release rate. Numerical eigenvalue calculations at sampled baseline release levels identify the lower branch as unstable and the upper branch as locally asymptotically stable. A center-manifold/Lyapunov–Schmidt argument establishes a nondegenerate saddle–node bifurcation of the full six-dimensional system at the critical continuous release rate. Numerical simulations are consistent with bistability under subcritical releases and show suppression of the selected established wild population under the sustained supercritical scenarios examined here; these observations are not asserted as a global convergence theorem. For the illustrative baseline parameter set, the critical release rate is approximately 1850 sterile males per day. One-at-a-time and Latin-hypercube/partial rank correlation analyses identify recruitment, maturation, adult survival, sterile-male mortality, and mating competitiveness as major determinants of the release requirement and post-intervention mosquito abundance. Although the numerical threshold is not an operational release recommendation, the framework provides an analytically transparent basis for evaluating continuous sterile-insect interventions and identifying the parameters requiring reliable field estimation. Full article

Wind turbine SCADA anomaly detectors are commonly calibrated using data from the same asset or development period, whereas deployment requires transfer across turbines with different operating distributions. This mismatch can produce optimistic thresholds, excessive false alarms, and misleading generalization estimates. This study proposes UC-RCF, an uncertainty-calibrated residual conformal framework integrating nonlinear multi-output normal behavior modeling, embargoed blocked cross-fitting, uncertainty-normalized residuals, operating support assessment, channel-wise conformal evidence, reflected cumulative criticality, and cross-asset alarm calibration. Evaluation followed a nested leave-one-turbine-out, asset-disjoint protocol on CARE v6, comprising 95 monitoring cases from 36 turbines across three wind farms, including 45 anomalous and 50 normal cases. UC-RCF achieved a pooled outer-fold CARE score of 0.5903, fault coverage of 0.3920, weighted earliness of 0.2423, event-level reliability of 0.5760, and normal-case accuracy of 0.8706. It detected 25 anomalous cases and generated alarms in 18 normal cases. A farm-stratified paired turbine-cluster bootstrap estimated a CARE improvement of 0.0349 over the initial full configuration, with a 95% percentile interval of 0.0055–0.0671 and a bootstrap probability of improvement of 0.991. These estimates remain exploratory because the effective resampling units comprise only 36 physical turbines from three wind farms. Mahalanobis monitoring achieved a higher CARE score of 0.6012, detecting 21 anomalous cases while generating alarms in nine normal cases. UC-RCF therefore provided broader fault coverage and four additional anomalous-case detections but incurred a higher false-alarm burden, demonstrating a sensitivity–reliability trade-off rather than universal detector dominance. Ablation analysis identified farm-normalized residual magnitude as the strongest case-level discriminator, with a receiver operating characteristic area under the curve of 0.854. Heteroscedastic uncertainty scaling and operating support adjustment did not consistently improve CARE or raw discrimination; the auxiliary framework components instead provide mechanisms for uncertainty characterization, score comparability, temporal persistence, and calibration auditing. At the CARE-optimal nominal case-level false-alarm budget of $\alpha =0.30$, the empirical normal-case false-alarm rate was 0.36. Because temporal dependence and cross-asset distribution shift can violate exchangeability, $\alpha$ is interpreted as an operational calibration target rather than a theoretically guaranteed case-level error bound. Overall, UC-RCF provides an interpretable and auditable framework for investigating residual evidence, operating support shift, temporal persistence, and calibration reliability under cross-asset deployment. Full article

Functional nucleic acids can connect molecular recognition with chemical signal generation, but their diagnostic value depends on the performance of the complete sample-to-answer pathway. This critical narrative review examines representative studies published through 31 July 2026, with emphasis on recognition, amplification, material interfaces, sample preparation, readout and clinical interpretation under realistic conditions. Hybridization and ligation probes, aptamers, DNAzymes, DNA nanostructures and CRISPR-associated systems are compared by specificity, kinetics, leakage and matrix compatibility. Rolling circle amplification, hybridization chain reaction, catalytic hairpin assembly and enzymatic isothermal amplification are evaluated as reaction networks whose products must remain accessible to the selected interface. Functional materials are classified by their actual analytical role, including transduction, signal amplification, capture/enrichment, spatial organization and reagent storage. Evidence is distinguished between mechanistic studies, spiked matrices, clinical specimens, manufactured-format reproducibility and demonstrated clinical utility. We further integrate sample-to-answer workflow, assay time, complexity, regulatory considerations and clinically relevant decision thresholds. Across the literature, reliable performance depends on selective recognition before high-gain reactions, compatibility between amplification products and interfaces, explicit controls for inhibition and leakage, and validation across independent lots and representative clinical populations. These principles define a path from analytical proof of concept to reproducible and clinically interpretable diagnostic testing. Full article

The Sequential Organ Failure Assessment (SOFA) score grades dysfunction in six components in people, using thresholds fixed against human reference ranges and a human case mix. Whether those numbers transfer to cats has not been examined. Of 52 cats assessed for eligibility, 23 met predefined systemic inflammatory response syndrome criteria, 22 of them non-septic, and an adapted score was calculated at admission from non-invasive measurements and one venous sample. A complete score was obtained in every cat. Under the applied bilirubin thresholds, the hepatic sub-score exceeded zero in 21 of 23 cats and, with the renal component, accounted for 63.0% of the mean total score; removing that component lowered the median total from 6.0 to 3.0 and left three cats scoring zero. Under stated physiological assumptions, translating the original PaO₂/FiO₂ thresholds through the feline oxyhaemoglobin dissociation curve placed the first respiratory threshold near 91%, rather than the 95% applied here, and removed the respiratory point from six cats. Five of the 23 cats did not survive to discharge, too few for any outcome analysis, and none was attempted. The framework was measurable in this setting, but its thresholds describe admission abnormalities rather than organ dysfunction and require feline-specific calibration. Full article

Supersonic ejectors offer a promising solution for extending low-pressure well life and increasing total power output in geothermal plants by entraining low-pressure fluid using a high-pressure primary flow. While steam supersonic ejectors are widely used in industrial applications such as refrigeration, their deployment in geothermal power generation remains largely unexplored, leaving a critical gap in field-validated numerical models for utility-scale two-phase systems. To address this, this study presents a 3D Computational Fluid Dynamics (CFD) framework validated against industrial-scale field tests conducted at the Theistareykir Geothermal Power Plant in Iceland (connecting wells ThG-11 and ThG-15). Four RANS turbulence models (Standard $k-\epsilon$, RNG $k-\epsilon$, Realizable $k-\epsilon$, and $k-\omega$) were evaluated in ANSYS Fluent using a homogeneous Eulerian wet-steam formulation. The Realizable $k-\epsilon$ model demonstrated superior accuracy, achieving the lowest absolute error (6.4%) against field data. While non-equilibrium thermodynamic relaxation caused a systematic 8.9–12.6% overprediction in primary motive flow, secondary entrainment predictions closely tracked physical performance, with entrainment ratio errors reaching 0.00% under stable operation. Crucially, the model identifies operational boundaries: while field data places the physical backflow limit at an inlet pressure ratio of $2.2$ ($9.9\mathrm{bar}$ difference), numerical divergence near zero-entrainment establishes a conservative modelling threshold at $2.6$. By defining these physical and numerical limits while quantifying nozzle-sizing safety margins, this work provides a verified benchmark for scaling up CFD models for full-capacity geothermal ejector networks. Full article

In data-sparse Central Asian watersheds, machine learning and Geographic Information Systems (GIS) are increasingly applied to hydrological hazard classification. This article compares Random Forest (RF), XGBoost, and Long Short-Term Memory (LSTM) models for daily elevated-discharge classification in the Koksu River basin, Zhetysu Region, Kazakhstan (1614 km², semi-arid, snowmelt- and glacier-influenced climate; 2005–2023), using verified discharge, precipitation, and temperature records from four monitoring stations. The elevated-discharge threshold (80th percentile, ~90.5 m³/s) was computed using only the training period (2005–2017) to avoid temporal data leakage into validation and test periods. Class imbalance, addressed via class weighting, was moderate under this threshold. All models were benchmarked against a naive persistence baseline. Contrary to expectation, the persistence baseline achieved the highest Critical Success Index (CSI = 0.821, 95% block-bootstrap CI [0.725, 0.891]), narrowly ahead of XGBoost (CSI = 0.813, [0.703, 0.897]), Random Forest (CSI = 0.809, [0.691, 0.897]), and LSTM (CSI = 0.763, [0.630, 0.873]); the confidence intervals overlap substantially, indicating no statistically distinguishable advantage of any trained model over simple persistence in this basin. Feature-importance and ablation analyses confirmed that lagged discharge, not precipitation, drove nearly all predictive skill: a discharge-lags-only model performed as well as or better than the complete pipeline, while a meteorology-and-seasonality-only model performed markedly worse (CSI ≈ 0.57–0.59). As a preliminary, exploratory illustration rather than a core result, Gumbel-derived return-period discharges (Q10, Q25, and Q100) were separately translated into illustrative, uncalibrated, DEM-proximity-based inundation extents, a candidate direction for future work rather than an engineering-grade flood-mapping contribution of this study. These results indicate that, in this basin and period, machine learning classifiers provide no clearly demonstrated advantage over simple discharge persistence, underscoring the necessity of routine persistence benchmarking before adopting machine learning approaches for early warning in Central Asian watersheds facing increasing flood risk under climate change. Full article

Intensive care units generate dense physiological, laboratory, imaging, microbiological and treatment data, yet much of the clinical reasoning that drives decisions is recorded only in free text. Over the past decade, clinical sentiment analysis has repeatedly shown that the affective tone of nursing and medical notes is associated with mortality. The incremental discrimination over established severity scores has nevertheless been small, of the order of 0.01 in the area under the receiver operating characteristic curve in the largest published intensive care cohort, and general-purpose sentiment tools transfer poorly to critical care text. We argue that this plateau reflects a misspecified prediction target rather than a limitation of natural language processing. Mortality at 28 days is not the question the clinician asks at the bedside. We propose that the clinically useful quantity is the narrative–physiology discordance score: the signed difference, expressed on a common calibrated scale, between the short-horizon deterioration risk implied by the documented assessment and the risk implied by time-aligned multimodal data. We specify this quantity formally, fix index time and forecast horizon, and set out the leakage, copy-forward and provenance controls without which retrospective performance is uninterpretable. We then treat alert burden as a design constraint rather than a post hoc observation, deriving the operating threshold from an explicit and context-dependent alert budget, and we outline a staged evaluation pathway aligned with TRIPOD + AI and DECIDE-AI in which discordant cases are adjudicated by clinicians. Until that adjudication has been performed, discordance is a record-level inconsistency that prompts reassessment, not evidence of missed deterioration. Clinical sentiment analysis becomes useful when it stops predicting death and starts flagging disagreement. Full article

The sustainable provision of regulating services—such as climate regulation, water purification, and soil conservation—is fundamental to both ecological integrity and human well-being, yet the market mechanisms required to sustain their value remain critically underdeveloped. To explore the sustainable process of realizing the value of regulating services, this study introduces three stakeholders—environmental protection enterprises, the government, and residents—and constructs an evolutionary game model to analyze the behavioral decisions of each stakeholder and the key factors influencing system equilibrium. To validate the theoretical model, we further employed MATLAB R2023a to conduct simulation analyses using a real-world case study and data from Yunnan Province, examining the impact of various variables on the outcomes of the evolutionary game. The following results were obtained: (1) Multiple equilibrium solutions exist within the evolutionary game system, indicating that there is no single path to realizing the value of regulating services. Among these, full government support is key to realizing the value of regulating services. (2) A critical parameter exists in the multi-party game for realizing the value of regulating services, and this parameter exhibits a significant threshold effect; once the threshold is exceeded, the behavioral decisions of the participating actors will change. (3) The long-term sustainable realization of the value of regulating services requires multi-stakeholder collaboration. The contributions of this study lie in expanding the scope of research on the realization of ecosystem service value, strengthening the proactivity of participating stakeholders within the framework of sustainable value realization, overcoming the limitations of traditional static equilibrium analysis, and revealing the evolutionary conditions for the sustainable realization of the value of regulating services. Full article

Rhenium (Re) is a scarce technology-critical transition metal used in high-temperature superalloys, catalysts and other advanced materials, yet its occurrence in terrestrial biota and potential relevance to environmental exposure remains poorly characterised. This study investigated total Re in wild mushroom fruiting bodies collected from selected green spaces in Leicester and Leicestershire, United Kingdom, using inductively coupled plasma mass spectrometry (ICP–MS) and censoring-aware statistical analysis. The analytical dataset comprised 157 records. After exclusion of four unmatched tissue-only records, 153 analytical portions were reconstructed into 121 fruiting-body-level biological units, avoiding pseudoreplication from separately analysed cap and stem tissues. The analysis incorporated procedural digestion blanks, sample-specific dry-weight analytical limits and interval censoring. Under the primary analytical scenario, 73/121 units (60.3%) were non-detects, 21 (17.4%) were detected below the operational lower limit of quantification (LLOQ), seven (5.8%) were partially quantified and 20 (16.5%) were fully quantified. Overall, 48/121 units (39.7%) contained at least one Re signal above the operational detection threshold, whereas 27/121 (22.3%) contained at least one quantitatively valid component. Fully quantified concentrations ranged from 3.91 to 163.70 ng g⁻¹ dry weight. The complete dataset comprised 73 left-censored, 28 interval-censored and 20 exact observations. An interval-censored lognormal model estimated a population median of 1.055 ng g⁻¹ dry weight (95% bootstrap confidence interval: 0.672–1.571), with all 1000 bootstrap refits converging successfully. Classification was identical for all 59 units directly comparable under the primary and stricter same-batch procedural-background definitions. These findings extend the limited evidence for Re occurrence in wild fungal fruiting bodies and should be interpreted as environmental-occurrence data rather than as evidence of either adverse risk or safety. Because total elemental Re does not resolve chemical form, gastrointestinal bioaccessibility or internal dose, the measured concentrations do not support quantitative estimates of dietary exposure or human-health risk. Full article

South Korea identifies only about 2.2% of K-12 students as eligible for special education, compared with approximately 15% of students receiving services under the Individuals with Disabilities Education Act (IDEA) in the United States and approximately 27% of Australian students receiving educational adjustments for disability. Existing explanations have largely attributed this disparity to Korea’s narrow statutory disability categories. However, this paper argues that disability definitions alone cannot adequately explain Korea’s exceptionally low rate of special education eligibility. Instead, it examines how the relationship between disability registration and special-education eligibility may affect access to special education, even though the two are determined through distinct institutional processes. In Korea, registered disability status carries consequences across multiple policy domains, including eligibility for welfare benefits, employer compliance with the mandatory employment quota, and access to supernumerary university admission tracks. Using comparative institutional and legal analysis supported by official national statistics, this paper advances the administrative certification model as a new institutional framework that complements existing medical, social, and human rights models of disability. South Korea is examined as a critical case and compared with the United States, Germany, Finland, England, Japan, Canada, and Australia to examine how differences in certification arrangements relate to access to special education. Because national indicators differ in age range, identification criteria, and reporting conventions, the comparison is descriptive rather than a causal test. The analysis suggests that certification-centered eligibility may contribute to the educational exclusion of medically fragile, neurodivergent, and twice-exceptional students. This paper’s theoretical contribution is to identify administrative certification as an institutional mechanism that may shape educational access beyond statutory disability categories. Its practical contribution is to identify greater separation between educational eligibility and disability certification as a potential reform option and to propose an accommodation-based tier of support, informed by Section 504 of the U.S. Rehabilitation Act, for students whose educational needs warrant support but who do not meet the certification threshold. Full article

Dams are critical infrastructures for water supply, flood mitigation, hydropower generation, and sustainable water resources management. However, operational maneuvers and extreme hydrometeorological events may require controlled reservoir releases, which can increase flood risk in downstream communities. In the context of climate change and increasing hydrometeorological variability, sustainable reservoir operation requires an integrated assessment of water availability, flood hazard, and downstream vulnerability. This study investigates the potential flood risk associated with controlled water releases from the Colibița Dam in Romania and their implications for sustainable water resources management and adaptation to climate change. Hydrometeorological data, topographic measurements, dam characteristics, and precipitation data for the period 2001–2024 were considered. A one-dimensional hydraulic model developed in MIKE 11 was used to simulate water releases through the bottom and secondary bottom outlets under low-flow and increased-flow conditions. Three operational scenarios were evaluated: (i) opening each outlet individually at 50% capacity, (ii) opening each outlet individually at 100% capacity, and (iii) simultaneously opening both outlets at 100% capacity. The results indicate that water releases under low-flow conditions generally pose a limited flood risk to downstream areas. The highest flood risk occurs during periods of elevated river discharge when both outlets are operated simultaneously at full capacity, causing the water level at the Bistrița Bârgăului hydrometric station to exceed the danger threshold by 21 cm. At the Bistrița station, the water level exceeds the attention level by 87 cm while remaining 13 cm below the flood level. The findings demonstrate that integrating hydrometeorological forecasts with hydraulic modeling improves adaptive reservoir operation, reduces downstream flood risk, and strengthens resilience to climate-related hazards, thereby supporting Sustainable Development Goal (SDG) 13.1. Full article

The South Asian monsoon sustains nearly one-quarter of the global population, yet the combined effects of water supply and atmospheric evaporative demand on regional aridity remain poorly integrated in long-term assessments. This study characterizes spatial and temporal aridity dynamics across the South Asian Monsoon region from 1901 to 2024 using the UNEP Aridity Index, offering a comprehensive view of hydroclimatic stress beyond precipitation alone. We integrate gridded climate observations with sea surface temperature records through Empirical Orthogonal Function decomposition and an interpretable machine learning framework, comparing linear regression against Random Forest and Hybrid models trained on historical data and validated independently. Our analysis reveals pronounced warming, spatially heterogeneous drying concentrated in northwestern regions, and a robust ENSO-aridity teleconnection modulated by the Indian Ocean Dipole. Machine learning models demonstrate superior skill in capturing nonlinear, threshold-dependent responses, yet their performance varies substantially across aridity zones, with linear approaches failing entirely in humid regions while hybrid frameworks excel in drylands. Critically, after removing long-term trends, interannual predictability persists most strongly in hyper-arid and semi-arid zones, where ENSO and IOD signals remain detectable, but declines elsewhere. SHapley Additive exPlanations identify Niño3.4 as the dominant oceanic predictor, with extreme El Niño events disproportionately intensifying aridity. These findings demonstrate that tropical ocean signals alone explain only modest year-to-year variability in the regional mean, with predictability concentrated in specific dryland zones. This work provides a diagnostic foundation for drought early-warning systems while emphasizing the need to incorporate local terrestrial processes and long-term trends for operational forecasting in one of the world’s most climate-vulnerable regions. Full article

This article proposes a new integrated analog implementation of the Pan–Tompkins algorithm. This algorithm, generally implemented in the digital domain at the cost of higher power consumption, is one of the most efficient for QRS detection in electrocardiographic signals. This circuit has been designed for use on small mammals like rats for the study of pulmonary arterial hypertension. The specificities of the architecture proposed in this article are a sub-threshold-operated architecture to perform the amplification, noise filtering, and differentiation of the signal in a unique block to limit the power consumption; an analog squarer; and an analog monostable reshaping-circuit followed by a digital counter clocked by an ultra-low power relaxation oscillator. Compared with the state of the art, this circuit, designed with the X-FAB xh018 180 nm CMOS technology, presents one of the lowest power consumptions currently reported, at 28.5 nW, 76% of which is consumed in the current, voltage, and time references. Post-layout simulation results show a reliable 98.57% heart beat detection accuracy using the MIT-BIH arrhythmia database and 99.85% accuracy for healthy sinus rhythm electrocardiograms. The robustness of the circuit is also investigated, and mitigating solutions are proposed for each critical block. Full article

The increasing integration of decentralised photovoltaic (PV) systems and electric vehicles into low-voltage (LV) distribution networks introduces significant variability in net-load patterns, exemplified by the “duck curve” (daytime overgeneration) and sharp peak demands. Addressing these challenges is critical to maintaining network reliability and minimising infrastructure costs. This study presents a comparative analysis of mixed integer linear programming (MILP) and heuristic control strategies within a model predictive control (MPC) framework for load levelling using a battery energy storage system (BESS), evaluated under perfect and LightGBM-based net-load forecast scenarios. These forecasts serve as input to the MPC framework, which optimally schedules MPC charging and discharging actions of MPC over a 24 h horizon to minimise the net-load deviations from predefined thresholds. Under perfect forecasts, MILP achieves superior load levelling performance through predictive optimisation, while the heuristic provides a robust, low-complexity baseline. With LightGBM forecasts, the performance gap between MILP and heuristic approaches widens due to forecast uncertainties, underscoring the critical role of robust control strategies in handling imperfect net-load predictions for BESS operation. Full article

Achieving robust misalignment tolerance is a critical challenge in electric vehicle (EV) wireless power transfer (WPT) systems. To overcome this challenge, this paper proposes an optimised Double-D (DD) transmitter and a series-connected Double-D and Quadrature (DDQ) receiver architecture. By integrating a quadrature (Q) coil, the system effectively compensates for magnetic flux drops during lateral x-axis shifts, ensuring highly robust operation and uniform efficiency across the entire SAE J2954 spatial tolerance zone. Designed for WPT1 power class and Z1 air gap specifications, the system’s safety and performance were evaluated through the ANSYS 2017 (Electromagnetic Suite 18.0) Maxwell 3D finite element method (FEM) and physical prototyping. A 110-piece ferrite core structure successfully limits electromagnetic exposure to the 27 µT safety threshold. Validated via experimental measurements, the system achieves a measured peak efficiency of 96.61% under perfect alignment. Furthermore, to evaluate the structural limits of the topology, extreme misalignment stress tests were conducted. The results demonstrate that the system maintains over 93% measured efficiency across a broad lateral offset range up to 50 cm. This exceptional tolerance highlights the magnetic stability of the DDQ structure and its strong potential for future dynamic charging applications. The strong correlation between numerical models and experimental data confirms a highly reliable and electromagnetically compatible solution for EV charging. Full article