Search Results (2,227)

Influenza A(H1N1)pdm09 remains one of the predominant seasonal influenza viruses worldwide and continues to evolve under the combined effects of host immunity, vaccination, and changing epidemiological conditions. However, the long-term impact of the COVID-19 pandemic on its evolutionary dynamics remains poorly understood. We investigated the genetic variability and phylodynamic evolution of the hemagglutinin (HA) and neuraminidase (NA) genes of Italian A(H1N1)pdm09 viruses collected between 2009 and 2026. Time-calibrated phylodynamic analyses, Bayesian Skyline Plots (BSPs), Lineages Through Time (LTT) graphs, principal component analysis (PCA), and codon-based selection analyses were used to characterize long-term evolutionary patterns. Both HA and NA followed continuous evolutionary trajectories, with a marked post-2020 genetic shift associated with the emergence of 6B.1A.5a.2-related clades. HA showed greater evolutionary variability than NA, whereas selection analyses identified only one positively selected site in HA and none in NA, consistent with reduced adaptive diversification in recent strains. Phylodynamic analyses revealed a marked decline in effective population size and lineage accumulation during the COVID-19 pandemic, followed by renewed expansion after 2022. Overall, Italian A(H1N1)pdm09 viruses exhibited reduced genetic diversity, limited evidence of positive selection, and coordinated genomic restructuring following the pandemic. These findings provide new insights into the long-term evolutionary dynamics of A(H1N1)pdm09 in Italy and reinforce the importance of sustained genomic surveillance for anticipating evolutionary changes and informing evidence-based public health strategies. Full article

We study a conditional finite-dimensional operator construction based on two imported Recognition-Science inputs: a continuous comparison cost satisfying a multiplicative coherence axiom and equilibrium calibration, and a three-coordinate, eight-tick ledger schedule. The first input uniquely determines the reciprocal cost $J(x)=\frac{1}{2}(x+x^{-1})-1$. A cyclic Gray traversal of the configuration graph $Q_3$ fixes the cyclic ordering of an assumed eight-sample register with shift P. The additional half-cycle antiperiodicity criterion selects the odd Fourier sector $\mathcal{V}=\ker (P^4+I)$; unitarity alone does not select this sector because P is unitary on the full register. On the realification of $\mathcal{V}$, $P^2$ defines an additional complex structure, and, after assigning an independent beat duration, the principal logarithm defines one self-adjoint stroboscopic interpolation, unique only after imposing the principal-zone convention. We distinguish the exact reciprocal action from a local adapter hypothesis needed to relate its mismatch coordinates to Hilbert-space defect coordinates. For the regular shift, the rational cyclotomic decomposition identifies $\mathcal{V}$ as the minimal faithful ${\mathsf{\Phi }}_8$-block. Under the stated relative normalization of the local bridge, leading cost–defect agreement is equivalent to an isometric adapter tangent, while a positive self-adjoint single-filter commit that commutes with the beat has at most three non-negative mode factors on the minimal register; defect descent restricts them to $[0,1]$. The relaxed commit is a contraction and, in a normalized quantum interpretation, represents a trace-nonincreasing accepted branch whose complementary trace weight is recorded by a measurement outcome or environment; the in-sector component nevertheless evolves exactly and unitarily. For coupled ququart cores, adjoining the standard conjugate Weyl shift to the clock inherited from P spans the full finite-dimensional operator algebra. This proves algebraic representability of arbitrary finite-dimensional Hermitian dynamics, not a physical rule selecting their coefficients. Full article

When a lithium-ion cell’s series resistance is underestimated, the pre-update terminal-voltage innovation contains the first-order term $e_k\approx -I_k{R}_{0,k}\delta b$. This term breaks conditional sign symmetry and creates an odd response under current reversal. We test that mechanism before using it as an activation rule. The operational null is a near-zero conditional innovation centre with weak innovation–current coupling; declared falsifiers are comparable coupling under the nominal model, the wrong correlation sign under positive resistance error, failure of charge/discharge polarity reversal, or negative-control activation approaching ohmic-mismatch activation. A persistence-confirmed gate combines normalised-innovation-squared exceedances, innovation–current compatibility, a positive local resistance correction, and five consecutive qualifying windows. Sixty settings were ranked on 10 calibration seeds and frozen before disjoint holdouts. From 1.0× to 2.0× R₀, the sign-imbalance index increased from 0.0040 to 0.0786, and |$corr\left(e,I\right)$| increased from 0.0658 to 0.7777. The 30-seed static holdout produced 0/30 nominal activations, 27/30 detections at 1.5×, and 30/30 detections at 2.0–3.0×. A disjoint linear-drift audit yielded 0/30 pre-ramp activations and 30/30 detections at a median 1.71× multiplier, reducing late-drift SOC RMSE from 3.129% to 0.895%. A signed-current audit confirmed the predicted polarity reversal. An estimator-unseen audit gave 30/30 ohmic detections but retained 2/30 current-linked non-ohmic and 1/30 current-bias triggers, so the rule is not a unique fault classifier. NASA and LG records remain diagnostics of fixed versus always-on adaptation; they do not validate the gate or independent absolute SOC. The contribution is a falsifiable residual-symmetry mechanism with an explicit evidence boundary, rather than a post hoc symmetry label or hardware claim. Full article

The frequency-dependent impedance of electrical connectors, switchgear loops, and related contact structures can indicate degradation at electrical contact interfaces. Conventional contact measurements with a vector network analyzer require dedicated adapters and impedance matching, and the object under test usually has to be removed from the operating system. To simplify field measurements and avoid direct electrical contact, this study employs an established two-probe inductive-coupling/ABCD de-embedding framework in conjunction with a purpose-designed broadband current probe and calibration fixture. Commercial injection and receiving probes were first evaluated to identify practical limitations associated with probe resonance, magnetic-path air gaps, cable clamping, and probe-to-probe coupling. A parametric CST study on ferrite material, winding turns, and core geometry was then used to select a 3W800 NiZn ferrite core with six winding turns and dimensions of 5 mm inner diameter, 10 mm outer diameter, and 5 mm height. The prototype exhibited relatively flat S₁₂/S₂₁ responses from 1 to 400 MHz, with S₂₁ changing from approximately −13.5 dB at 1 MHz to −17.5 dB at 400 MHz and without a pronounced resonance. Quantitative impedance accuracy was validated only over 1–100 MHz: validation against an impedance analyzer using a 108 nH inductor and an 82 pF capacitor yielded mean relative magnitude errors of 4.00% and 4.5%, respectively. Field measurements on a 10 kV switchgear circuit were additionally conducted over 1–30 MHz to demonstrate non-invasive broadband impedance acquisition under practical contact conditions. Accordingly, the 1–400 MHz range in this work refers to probe-transfer characterization rather than experimentally validated impedance accuracy over the full band. Full article

Fall detection faces challenges of visual occlusions and scale variations in complex multi-person scenarios. To address these issues, this paper proposes MTC-DETR, an end-to-end multi-task collaborative detection framework. After a feature extraction backbone, MTC-DETR builds a Dynamic Scale-Routing and Task-Tuning Encoder to adapt to scale variations, which integrates Deformable Large-Kernel Attention for intra-scale long-range feature enhancement and Dynamic Cross-Scale Fusion and Task-Aware Dispatch for adaptive multi-scale aggregation and task-specific feature calibration. Then, a Topology-Guided Dual-Branch Decoder is designed for object detection and keypoint detection. The Topology-Guided Local–Global Synergistic Attention reconstructs occluded keypoints via multi-hop graph convolutions, and the cross-branch pose prior guides Deformable Cross-Attention sampling in the object detection branch to enhance the localization robustness. Finally, a Homoscedastic Uncertainty Dynamic Joint Loss is introduced to resolve gradient conflicts during multi-task optimization. Experimental results show that MTC-DETR achieves mAP@0.5 scores of 92.7%, 88.9%, and 90.4% on the CAUCAFall, DiverseFall10500, and MT-Fall datasets, respectively. The proposed framework outperforms 14 state-of-the-art methods, proving its robustness and potential for real-world fall detection applications. The model was deployed on the Leju Kuavo 5 Robot. Experimental results from the deployment demonstrate that the model achieves an inference speed of 44.2 FPS (INT8) on an edge computing platform, meeting the real-time requirements for fall detection. Full article

Marine fishery system resilience supports ecological and productive functions under interacting environmental and socioeconomic pressures. We assessed resilience and forecastability using 117 region–year observations from nine Chinese coastal provincial-level regions during 2011–2023. A fixed-reference Resistance–Adaptability–Recovery index combined equal and entropy weights calibrated on 2011–2018 data. We evaluated autocorrelation-adjusted trends, measurement sensitivity, regional score distributions, conditional associations using geographically and temporally weighted regression (GTWR), and rolling-origin forecast performance. The mean index increased from 0.307 in 2011 to 0.426 in 2023, and eight regions retained significant monotonic increases after false-discovery-rate correction. Alternative weighting, calibration, indicator-deletion, and aggregation specifications preserved the positive temporal direction while producing moderate rank variation. GTWR yielded the lowest in-sample AICc among the three regression specifications, supporting a spatiotemporally varying representation of conditional associations. Under rolling-origin testing, particle swarm optimization-tuned support vector regression produced an RMSE of 0.0318, whereas persistence achieved the lowest RMSE (0.0246) and lower region-level RMSE in seven of nine regions. Accordingly, the 2024–2030 results were reported as conditional scenarios. Eight regional specification ranges crossed zero; Guangdong remained positive across the nine specified trend-window and weighting combinations. The framework improves temporal comparability and separates historical assessment, spatial association analysis, and forecast evaluation. Full article

Autonomous Vehicles (AVs) have been widely recognized as a promising solution to urban commuting congestion. However, quantitative evidence based on city-scale simulations of complete road networks in megacities remains limited. This study uses the complete urban road network of Beijing to investigate the influence of autonomous driving on commuting efficiency. Eleven autonomous vehicle penetration scenarios ranging from 0% to 100% at 10% intervals are established within the Simulation of Urban MObility (SUMO) microscopic traffic simulation platform. Human-driven vehicles are modeled using the Krauss car-following model, whereas autonomous vehicles are represented by the Cooperative Adaptive Cruise Control (CACC) model. The vehicle behavioral parameters are literature-based, adopted from published studies and open test data rather than calibrated against empirical Beijing traffic data, while the road network and commuting demand are constructed from Beijing-specific OpenStreetMap and mobile-signaling data. The simulation results reveal three major findings. First, autonomous driving exhibits a gradual efficiency transition over an approximate penetration range of 30% to 50% (identified qualitatively from the simulation trend rather than by a formal statistical change-point estimate). Below this threshold, behavioral heterogeneity between autonomous and human-driven vehicles intensifies traffic flow instability, whereas above it, the cooperative control capability of CACC becomes dominant and substantially improves overall network performance. Second, under full autonomous vehicle penetration, the city-wide average commuting speed increases from 7.20 m/s to 8.27 m/s, representing a 15% gain in the trip-weighted mean commuting speed (distinct from the 16% gain in the flow-weighted network speed reported in the Results), while the mean in-network simulated travel time per completed trip decreases from 561 s to 270 s. This travel-time value is an operational in-network measure and is not directly comparable to a full perceived door-to-door commute. Third, the efficiency benefits of autonomous driving display significant spatial heterogeneity. Speed improvements reach 16% to 20% on expressways and radial commuting corridors but remain between 4% and 8% on urban arterial roads. These findings indicate that the potential efficiency gains associated with autonomous driving, estimated here under fixed commuting demand and therefore as an upper bound, are constrained by the spatial characteristics of the road network. The results provide quantitative evidence supporting priority deployment of autonomous vehicles on expressways and major commuting corridors in megacities. Full article

Site-specific weed management can reduce herbicide use and improve the sustainability of crop production, but its adoption remains constrained by the complexity and costs of many sensing technologies. Integrating weed detection algorithms into optical sensing systems already used for precision agriculture may represent a practical strategy to overcome these limitations. This study aimed to adapt the SWIM Weed Detection (SWIM-WD) module, originally developed for UAV imagery, to images acquired by the Augmenta Field Analyzer, a tractor-mounted optical sensor. The method was evaluated using images acquired in a commercial maize field. Calibration images acquired under weed-free conditions were used to derive crop structural parameters, whereas validation images containing weeds were used to evaluate two approaches, the Simple Method (SM) and an Improved Method (IM), the latter incorporating image-specific maize row detection. Both approaches provided Weed Green Cover (WGC) estimates showing high agreement with reference values (CCC = 0.87–0.92; R² = 0.82–0.89). The findings provide preliminary evidence that SWIM-WD can be adapted to a tractor-mounted sensing platform, supporting precision agriculture systems with weed detection capabilities without dedicated hardware. Further validation across fields, crop stages, and environmental conditions is required to assess the robustness and general applicability of the method. Full article

Continuous monitoring of water quality is essential for supporting environmental management and enabling timely decision-making in wastewater treatment facilities. Although numerous Internet of Things (IoT) solutions have been proposed for environmental monitoring, many rely on proprietary cloud platforms or commercial gateways that limit flexibility, scalability, and integration with customized applications. This paper presents the design, implementation, and field validation of a modular IoT architecture for real-time water quality monitoring based on distributed sensor nodes, long-range LoRa communication, and a self-hosted web platform. The proposed architecture integrates sensor nodes equipped with calibrated pH, dissolved oxygen, and turbidity sensors, a hybrid LoRa/Wi-Fi Main Controller implementing a custom master–slave communication protocol, and a Python-based back-end with a PostgreSQL database for data acquisition, storage, visualization, and historical analysis. The complete system was deployed and experimentally validated in a real coupled constructed wetland located at the Universidad del Atlántico, Colombia, where three monitoring stations continuously acquired and transmitted water quality measurements over a one-month evaluation period. During the experimental deployment, the system generated more than 4.5 million measurement records (297 MB) while recording average RSSI values between −55.7 and −58.1 dBm (standard deviation: 1.6–2.2 dB). The developed web platform successfully supported real-time visualization and historical analysis of all acquired measurements. These results demonstrate the feasibility of the proposed architecture as a practical, scalable, and modular solution for continuous environmental monitoring that can be readily adapted to other distributed water quality monitoring applications. Full article

To mitigate the impact of inaccurate forecasts of electric vehicle charging load and photovoltaic power generation on the operation of PV–storage–charging systems, a risk-aware adaptive conformal rolling-horizon scheduling method is proposed. First, a net-load point forecast is constructed from the forecasts of electric vehicle charging load and photovoltaic generation, and a one-sided conformal upper bound is derived using historical forecast residuals. Subsequently, the conformal calibration parameter is updated online based on the miscoverage outcomes of completed forecasts, enabling the net-load upper bound to adapt to recent changes in forecast errors. Finally, the dynamic upper bound is incorporated into model predictive control to achieve rolling-horizon optimization of battery charging and discharging as well as grid power purchase and sale. The results show that, compared with the fixed conformal method, the proposed method improves the event-period coverage rate and reduces the number of event-period miscoverage instances. Compared with the point-forecast method, it reduces low-SOC exposure and enhances the system’s capability to cope with net-load forecast errors at a relatively low cost. Full article

Hyperlocal meteorological sensing is essential for accurate solar photovoltaic forecasting, yet professional-grade meteorological stations require investments easily exceeding $1000 USD per node, making distributed deployments economically inaccessible. This work presents a modular internet of things (IoT) device based on the ESP32 microcontroller integrating temperature, humidity, luminosity, and solar panel voltage sensing in an IP68-rated enclosure at a total hardware cost of about $65 USD when components are sourced in Germany. The enclosure-mounted temperature sensor is subject to a daytime radiative-heating bias and is not a calibrated ambient-air measurement. A hybrid architecture decouples external model training, performed on a conventional computer using the software Python and the open-source library TensorFlow, from autonomous on-device inference: every 15 min, the embedded feedforward network produces a single one-step-ahead (15 min) prediction of solar panel voltage from the most recent 96 real sensor readings; the resulting sequence of 96 such predictions, logged and assembled over a full day, forms the diurnal forecast profile reported below. This is executed via a three-layer feedforward network with 3011 parameters (11.8 KB). The network is trained offline on site-collected data and deployed on the microcontroller as static weight matrices without cloud connectivity. An on-device incremental gradient descent mechanism enables model adaptation after deployment without external retraining. The system was evaluated through two field deployments: a short period of hardware and firmware validation in Ulm, Germany, and a 115-day deployment in Zapopan, Mexico, comprising 84 days of training and 31 days of autonomous operation with zero missing records (no 15 min interval failed to log a reading); a real-time-clock fault affecting the final three validation days is addressed separately below and excluded from the reported metrics. Over a clean 28-day daytime window, the embedded model attained a coefficient of determination of 0.9165 and a mean absolute error of 0.2975 V (4.65% of the operational range), outperforming a climatology baseline (skill score 0.64) while not surpassing a 24 h persistence baseline. A frozen-weight ablation confirms that the on-device update mechanism yields a small but statistically significant accuracy gain ($p=0.001$), showing that autonomous incremental learning is implementable on low-cost hardware and produces a measurable effect, without cloud connectivity. Full article

Aerospace ground facilities operating in harsh "three-high" marine environments face rapid corrosion that threatens structural safety. Automated visual inspection is fundamentally hindered by two intertwined challenges: extreme scarcity of annotated real samples, and the intrinsic complexity of corrosion targets themselves—micro-pitting and fine cracks occupy only a handful of pixels with signals easily overwhelmed by complex backgrounds, while mature corrosion regions exhibit extreme geometric irregularities that defeat conventional detection and regression methods. To tackle these challenges, this paper proposes CorrSense, a YOLOv11-based framework with three synergistic innovations. First, we establish a dual-path data ecosystem combining real acquisition with diffusion-driven augmentation that synthesizes visually realistic and structurally consistent corrosion samples to expand training distribution. Second, we develop a saliency-based feature enhancement strategy using parameter-free SimAM attention in shallow layers, which amplifies micro-corrosion responses while suppressing background activations with zero parameter overhead. Third, we formulate a dual-pronged geometric calibration strategy: a customized BiFPN with learnable weighted fusion for dynamic cross-scale feature orchestration, coupled with a morphology-adaptive CIoU loss that modulates aspect ratio constraints according to target shape. Extensive experiments demonstrate that CorrSense consistently outperforms state-of-the-art detectors including YOLOv8, YOLOv9, and RT-DETR on challenging samples, particularly on irregularly shaped corrosion and micro-pitting targets where competing methods typically struggle, validating its effectiveness as a promising solution for intelligent corrosion inspection in coastal launch sites. Full article

Commercial laser-processing systems are typically designed for a specific manufacturing process, limiting their adaptability in research and prototyping environments. This study presents the staged evolution of a laboratory-scale prototype originally conceived for metal powder bed fusion into a reconfigurable laser-processing platform. Successive mechanical, optical, electrical, and control developments are consolidated, including the integration and calibration of a 200 W fibre laser, a galvanometric scanning system, motorised powder feed and build platforms, a recoater mechanism, and a combined LabVIEW and weldMARK control architecture. The capabilities of the resulting platform were assessed through optical commissioning by laser marking and two experimental case studies involving single-layer fusion of AISI 316L powder and laser transmission welding of dissimilar thermoplastics. The marking trials provided qualitative confirmation of beam delivery, focal adjustment, and programmed path reproduction. In contrast, the powder experiments produced continuous fused regions, demonstrating controlled laser–powder interaction without constituting full multilayer powder-bed-fusion validation. Thermoplastic welding generated mechanically resistant joints, with failure occurring cohesively within the foam substrate rather than at the welded interface. These results demonstrate the potential of the developed system as a reconfigurable research platform for different laser-processing operations. Nevertheless, fully automated multilayer powder bed fusion still requires improvements in platform levelling, machine-zero integration, process synchronisation, and atmosphere monitoring. Full article

Digital salary advance and earned wage access platforms require intelligent monitoring mechanisms capable of identifying behavioral anomalies, estimating operational risk, and preserving auditable transaction evidence. Existing financial anomaly detection approaches commonly rely on isolated predictive models, static anomaly thresholds, or rule-based monitoring, providing limited support for behavior-aware enterprise monitoring. This study proposes a behavioral drift-aware adaptive anomaly fusion framework for explainable risk monitoring in salary advance FinTech systems. The framework introduces the Behavioral Risk Deviation Index (BRDI), which integrates temporal irregularity, behavioral entropy, transaction velocity, burst activity, and historical behavioral drift into a unified behavioral state representation. BRDI adaptively regulates the fusion of Isolation Forest-based statistical anomaly scores and reconstruction-based nonlinear anomaly scores according to each employee’s behavioral profile. The framework further combines calibrated ensemble risk scoring, SHAP-based explainability, and blockchain-assisted audit verification using SHA-256 hashing and Merkle-root validation. Experimental evaluation on 52,846 anonymized salary advance transactions demonstrates that the framework supports behavior-aware anomaly detection, interpretable risk prioritization, and trustworthy audit traceability in enterprise financial environments. The proposed approach contributes a unified behavioral state representation, a deterministic adaptive anomaly fusion mechanism, and an integrated architecture for trustworthy AI-assisted salary advance monitoring. Full article

Background/Objectives: Preoperative risk stratification in renal cell carcinoma (RCC) remains challenging for tumor aggressiveness and for adherent perinephric fat (APF) relevant to surgical planning. Although intratumoral radiomics are established, the peritumoral and perirenal fat microenvironment is biologically active and may encode imaging biomarkers. This systematic review and meta-analysis evaluated CT-based peritumoral and perirenal fat radiomics for preoperative grading, staging, and APF prediction and examined whether fat-derived features add incremental value beyond intratumoral models. Methods: Following PRISMA 2020 and a registered protocol (PROSPERO CRD420251150155), databases were searched through March 2026, with documented verification searches through August 2026. Eligible studies extracted CT radiomics from peritumoral or perirenal fat in adults with RCC and reported discrimination metrics. Random-effects meta-analyses (restricted maximum likelihood with Hartung–Knapp intervals) were performed; incremental value was estimated from nested within-study AUC differences under predefined rules; and methodological quality was assessed with PROBAST, RQS, METRICS, and TRIPOD, and certainty with an adapted GRADE framework. Results: Twenty-five retrospective studies (10,761 patients) were included. Fat radiomics were most consistent for APF prediction (pooled AUC 0.848, 95% CI 0.738–0.917; I² = 0%). Pooled AUCs were 0.772 (0.599–0.884; I² = 93%; 95% prediction interval 0.298–0.964) for grade and 0.813 (0.681–0.899; I² = 72%) for stage. Combined fat-plus-tumor models were numerically higher than tumor-only models in 12 of 15 studies (exploratory p = 0.022), but the formally estimable nested increment (stage family, five studies) was small (delta-AUC +0.017; governing modified Hartung–Knapp 95% CI −0.008 to +0.042) and statistically compatible with no true difference; fat-only models showed no evidence of differing from tumor-only models. PROBAST rated 72% of studies at high risk of bias, and 88% originated from China. Certainty ranged from VERY LOW (grade prediction; grade-family fat-versus-tumor comparison) to LOW (all remaining outcomes). Conclusions: CT-based peritumoral and perirenal fat radiomics show the most consistent signal for APF prediction, while the measurable increment from adding fat features to tumor models is small and of unestablished clinical utility. Standardized fat segmentation, calibration and decision-curve reporting and prospective multicenter validation are required before clinical translation. Full article