Search Results (51,437)

Femicide is one of the most extreme manifestations of gender-based violence and remains a persistent challenge across Latin America. This study examines the association between socioeconomic and contextual factors and femicide rates across 12 Latin American countries during 2014–2024 using an unbalanced panel dataset. The dependent variable is the femicide rate per 100,000 women, while the explanatory variables include the Gini index, GDP per capita, female labor force participation, and intentional homicide rates. Owing to heteroskedasticity, serial correlation, and contemporaneous dependence across panels, the model was estimated using Panel-Corrected Standard Errors (PCSE) with a common AR(1) disturbance structure. The Gini index was negatively and statistically significantly associated with femicide rates (β = −0.057, p = 0.004), whereas intentional homicide rates showed a positive and statistically significant association (β = 0.059, p < 0.001). GDP per capita retained a negative but non-significant coefficient (β = −0.371, p = 0.076), while female labor force participation was also negative and non-significant (β = −0.008, p = 0.556). These findings indicate that the associations between femicide and socioeconomic conditions are sensitive to model specification, while income inequality and generalized lethal violence remain statistically significant correlates. The results underscore the multidimensional nature of femicide and the need for cautious interpretation of aggregate cross-national associations. Full article

Background/Objectives: Intraocular lens (IOL) constants are conventionally optimized globally across an entire calibration dataset, assuming that systematic prediction error is independent of axial length (AL). This assumption is known to fail in short and long eyes. We evaluated whether AL-segmented constant optimization reduces refractive prediction error, whether the benefit depends on formula family, and whether changepoints discovered in one population transfer to a disjoint one. Methods: In a pooled 6451-eye training dataset (6 IOL models), the corrected Akaike Information Criterion selected the optimal number (0–4) and positions of AL changepoints for six formulas (classic three-constant Haigis, new-Haigis H1/H2, SRK/T, Hoffer Q, Holladay 1), enforcing ≥40 eyes and ≥2.0 mm per segment. Locked changepoints were applied to two disjoint single-lens test subgroups (Vivinex, n = 887; SA60AT, n = 821; three-center retrospective cohort) and independently re-discovered within each. Root-mean-square prediction error (RMSE) reduction was assessed by bootstrap confidence interval, Diebold–Mariano test, and Wilcoxon signed-rank test. Results: Segmentation reduced training RMSE for all six formulas, particularly for Hoffer Q (−3.9%) and Holladay 1 (−2.8%; confidence intervals excluding zero, Diebold–Mariano p < 0.0001). With locked changepoints, both formulas again showed the largest test-dataset improvements (3.0–6.3%; confidence intervals excluding zero in all four lens–workflow combinations), followed by SRK/T (0.9–1.3%, up to 2.5% with independently discovered changepoints). Haigis-family formulas showed smaller reductions (0.2–1.6%) with confidence intervals including zero in several combinations. Conclusions: AL-segmented optimization benefits Hoffer Q and Holladay 1 most robustly—formulas lacking a direct anterior chamber depth predictor—with a smaller benefit for SRK/T and an inconsistent benefit for Haigis-family formulas. Changepoints from a pooled training population transfer usefully, though not optimally, to individual lens models. Full article

Allelopathy is an ecological process in which released chemicals modify the performance of neighbouring organisms, whereas phytotoxicity describes an inhibitory response to a substance under a defined assay and, by itself, does not demonstrate field-level allelopathy. This critical narrative review combines a structured re-screening of the literature with targeted updating through 21 August 2026. Searches used combinations of allelopath*, allelochemical*, phytotox*, bioherbicid*, hormesis, biostimulant*, rhizosphere, microbiome, formulation, resistance, and regulation; peer-reviewed studies were complemented by authoritative regulatory and resistance databases. Evidence was appraised according to experimental context and mechanistic strength, explicitly distinguishing field or whole-plant validation from controlled bioassays, experimentally supported molecular targets from physiological or omics associations, and computational predictions. The synthesis covers chemical diversity, botanical and microbial sources, agro-industrial by-products, mechanisms of phytotoxicity and low-dose responses, rhizosphere interactions, and formulation strategies. Important corrections emerge from this evidence hierarchy: strigolactones are carotenoid-derived apocarotenoid hormones/signals rather than sesquiterpenes; multi-target activity does not preclude resistance evolution; cyanobacterial responses cannot be directly extrapolated to weeds or crops; and hormetic stimulation under controlled conditions is not equivalent to reliable field biostimulant performance. The principal translational gaps are insufficient dose–response standardisation, limited crop-selectivity and field validation, variable biomass chemistry, incomplete carrier and non-target safety data, and regulatory uncertainty for multifunctional products. Allelochemicals therefore represent promising components of integrated crop protection, but their agronomic value depends on rigorous evidence, formulation-specific validation, and context-dependent deployment rather than on laboratory phytotoxicity alone. Full article

Augmented reality (AR) enhances the real world scene by overlaying virtual information onto it. Vision-based 3D tracking and registration is the key technology for ensuring the fusion of virtual and real content in monocular AR systems. Existing mainstream visual tracking and registration methods are susceptible to illumination variations, motion blur, target occlusion, and dynamic background interference in complex scenarios. They also suffer from low computational efficiency, cumulative pose errors, and insufficient stability, making them difficult to deploy on low power edge devices such as embedded systems and mobile terminals. To address these issues, this paper proposes a lightweight monocular AR 3D tracking and registration method that integrates ORB-FREAK features, mismatching outlier filtering, background weighted mean shift, and template-based relocalization. The method does not rely on depth sensors or neural network inference, enabling efficient and accurate lightweight pose estimation. Specifically, we first combine the ORB (Oriented FAST and Rotated BRIEF) descriptor with the FREAK (Fast Retina Keypoint) algorithm for feature detection and initial matching. Hamming distance is used for coarse filtering of mismatched point pairs, and an ascending sort combined with an iterative sequential sampling strategy is applied to solve the optimal homography matrix, significantly improving the accuracy and efficiency of matrix estimation. Then, distance constraints among feature points are imposed on the target registration region to optimize the selection, and camera pose is computed based on the matching between 2D feature points and their corresponding 3D spatial coordinates, eliminating the error accumulation problem of conventional algorithms. Real-time feature matching is further used to correct the optical flow tracking sequence and camera pose, ensuring the continuity of the AR tracking process. Finally, a background weighted mean shift algorithm is introduced to narrow the feature detection range and suppress background interference, complemented by a template-matching relocalization module and a dynamic model update strategy, which effectively enhance the robustness of continuous tracking and registration under complex conditions. Experimental results demonstrate that, in extreme scenarios such as low light conditions, high speed motion, and occlusion, the proposed method achieves AR 3D tracking and registration success rates of 86.7%, 82.3%, and 78.5%, respectively. It exhibits superior performance in pose estimation accuracy and anti-interference capability in complex environments, with significantly reduced computational overhead. Moreover, it can achieve robust and continuous AR 3D tracking and registration on low power edge devices, effectively adapting to demanding AR application scenarios and providing reliable technical support for lightweight AR applications. Full article

The use of remote sensing data in hydrological applications has increased, especially in regions with limited ground-based observations. Satellite precipitation products (SPPs) provide extensive temporal and spatial coverage but may contain biases that can affect their performance in hydrological simulations. This study evaluates the performance of four near-real-time SPPs for daily streamflow modeling in the Tulijá River Basin (TRB), Mexico: Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS), PERSIANN-Dynamic Infrared Rain Rate near real-time (PDIR-Now), and the Early Run and Late Run products of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). The SPPs were first compared with meteorological station precipitation data and subsequently bias-corrected using the Linear Scaling (LS) method. The CEQUEAU hydrological model simulated streamflow using three precipitation datasets: meteorological stations, original SPPs, and bias-corrected SPPs. For simulations using observed precipitation, the model was calibrated for 1991–2014 and validated for 1968–1990; for SPP-based simulations, calibration and validation were performed for 2003–2011 and 2012–2014, respectively. Model performance was assessed using the Nash–Sutcliffe efficiency (NSE), percent bias (PBIAS), and coefficient of determination (R²). The results show that CEQUEAU performance varies by precipitation dataset. Simulations using observed precipitation yielded NSE values close to 0.70 during both calibration and validation, whereas the original SPPs yielded NSE values below 0.18, including negative values. After bias correction, IMERG-Early and IMERG-Late yielded NSE values of approximately 0.55 during both periods. These findings highlight the importance of analyzing the performance of near-real-time SPPs in hydrological applications, especially in tropical regions with complex topography. Full article

Comparisons of point–line visual–inertial systems can be distorted when trajectories cover different time intervals or when metric estimates receive scale correction. This study asks whether the recorded performance of an EDLines and line-flow system persists under identical temporal support and metric-preserving alignment. The evaluated EDLines (Edge Drawing Lines) visual–inertial navigation system (EDL-VINS) combines conditioned line extraction, pyramidal line-flow association, and Plücker-line factors within a point–line–inertial sliding window. To answer this question, we audit the retained trajectories across all eleven EuRoC sequences, resample the three systems onto a common time grid, and evaluate them without scale correction. The robustness of the resulting comparison is then tested under alternative alignment and timestamp support; gap-aware interpolation; and shorter-horizon, rotational, and archived-output variation analyses. EDL-VINS retains the lowest aggregate translational and rotational trajectory error, although sequence-specific exceptions and short-horizon results show that the ordering is not universal. These findings provide an auditable characterization of the evaluated configuration and show why temporal support, alignment, and data provenance must be explicit in visual–inertial comparisons. Full article

Background: In children with medical complexity (CMC), respiratory involvement represents one of the main causes of morbidity. Early recognition of nocturnal hypoventilation or sleep-disordered breathing (SDB) may allow timely intervention and reduce perioperative respiratory complications. This study aimed to evaluate the role of sleep studies in guiding clinical decision-making and perioperative respiratory management in CMC patients undergoing spinal arthrodesis for scoliosis correction. Methods: We retrospectively analysed clinical data from 357 patients (196 females) who underwent scoliosis surgery between 20 November 2012 and 11 June 2024. All patients received a standardized preoperative respiratory assessment at the Pulmonology Unit of IRCCS Burlo Garofolo. Results: Patients were classified into seven diagnostic groups, including neurological, neuromuscular, cerebral palsy, malformative, and other complex conditions. Median age at surgery was 12 years (range 2–22). Sleep studies were performed in 119 patients and identified abnormalities suggestive of sleep-disordered breathing or nocturnal hypoventilation, including elevated apnea–hypopnea index, oxygen desaturation, or abnormal transcutaneous carbon dioxide levels. Before surgery, 20 patients were receiving non-invasive ventilation nIV), initiated based on sleep study findings or pulmonary function impairment, while two patients were supported with continuous positive airway pressure (CPAP). Median Paediatric Intensive Care Unit (PICU) stay was 4.0 days (IQR 3.0–5.5 days; range 1–23), with no statistically significant differences across diagnostic categories (p = 0.12). Postoperatively, respiratory support was frequently required, including invasive mechanical ventilation, NIV, or CPAP, but the duration of invasive ventilation was 15.5 h (range 1–36). Only one patient required tracheostomy, and no perioperative deaths occurred. Conclusions: In CMC undergoing spinal arthrodesis, preoperative sleep studies significantly influence the clinical decision-making process and support perioperative respiratory assessment by identifying occult respiratory vulnerability and informing tailored ventilatory strategies. While associated with structured management pathways, the prospective impact of sleep-guided interventions on postoperative clinical outcomes remains to be confirmed through controlled studies. Full article

Assessing visual fatigue is crucial in eye-controlled interaction. Traditional methods are either overly subjective or rely on highly invasive, costly equipment and complex procedures that require expert supervision. This study proposes a machine-learning-based approach for visual fatigue assessment. Data collection employs non-intrusive, easily monitored eye-tracking to capture ocular eye movement data and task performance data, while subjective questionnaires label fatigue states. For feature selection, participant-level Wilcoxon signed-rank tests with Benjamini–Hochberg FDR correction were used to identify fatigue-related indicators, and a redundancy-removal step based on Spearman correlation yielded a final set of six non-redundant features. For the assessment method, we introduced a gradient boosting decision tree–logistic regression (GBDT-LR) model whose hyperparameters are optimized via Bayesian optimization. All models were evaluated under a unified 5-fold stratified cross-validation framework with within-fold standardization and nested hyperparameter tuning. Results indicate that this model can effectively predict the state of visual fatigue. Compared with the performance of five other models—gradient boosting decision tree (GBDT), logistic regression (LR), support vector machine (SVM), random forest (RF), and RF-SVM—the proposed GBDT-LR model achieved an assessment accuracy of 89.79%, demonstrating strong predictive performance. This study provides an effective method for predicting visual fatigue in eye-controlled interaction, laying a research foundation for optimizing the user experience of eye-controlled interaction and promoting the sustainable development of eye-control technology. Full article

Groundwater discharge contributes to stream baseflow, but coupling strength varies among catchments and transferable quantification methods are limited. We present GWBASE, an open-source Python algorithm that pairs U.S. Geological Survey (USGS) wells and gages within hydrographic catchments and ranks coupling at each gage by linear regression and mutual information (MI, capturing nonlinear and lagged dependence) on monthly $\Delta$WTE–$\Delta Q$ records from baseflow-dominated months. We apply it to the Great Salt Lake Basin (Utah; ∼$\mathrm{93,000}{\mathrm{km}}^2$ with 8752 USGS wells, 1906–2025). GWBASE ranked four terminal-gage catchments using a seasonally corrected, within-well regression as the primary estimate; three of the four show statistically significant groundwater–baseflow coupling. The ranking depends on the metric: absolute magnitude (cfs per foot) is dominated by the large Bear River catchment, whereas size-normalized sensitivity and coupling tightness identify the smaller Little Cottonwood Creek. The basin-scale aggregate (∼4 cfs per foot of basin-averaged decline) is dominated by Bear River and, once catchment-level uncertainty is propagated, is not distinguishable from zero. Because national groundwater records are predominantly intermittent, GWBASE resolves seasonal-to-interannual storage coupling rather than event-scale exchange. It is best used for screening and ranking catchment-scale coupling rather than yielding a single basin-scale coefficient. Full article

GRACE and GRACE-FO satellite gravimetry provide unique observations of terrestrial water storage (TWS), but their coarse effective resolution and intermittent temporal gaps limit water-resource applications at subregional and basin scales. This study presents a framework to temporally reconstruct and spatially downscale GRACE TWS anomalies for the transboundary Bug River Basin (Poland–Ukraine–Belarus), a region where in situ monitoring is limited and further disrupted by the 2022 war in Ukraine. First, missing monthly GRACE TWS anomalies (2002–2024) are imputed using a Random Forest model driven only by lagged GRACE values (1–3 months) and seasonal timing, thereby avoiding potential information leakage. Second, the continuous GRACE signal is downscaled to 0.1° using an independent set of hydroclimatic predictors with lagged and rolling features, together with elevation, land type and lithology. Model performance is evaluated under strict spatiotemporal holdouts and cross-validation. The key methodological advance is a bias-aware, block-wise mass-conserving correction that reconciles downscaled fields with the original GRACE water mass at coarse resolution. After downscaling to 0.1°, systematic residual biases between aggregated high-resolution estimates and GRACE observations are quantified monthly and redistributed within spatial blocks using river-runoff-based weights. This procedure enforces exact mass closure while preserving physically meaningful sub-grid variability. Full article

Switching-aware spectrum selection requires balancing interference avoidance against retuning costs. This paper evaluates a hybrid neural–polynomial deep Q-network (HNP-DQN) in an eight-channel, measurement-driven 5 GHz testbed with exogenous sweeping and random interference. The architecture combines learned latent features with an element-wise second-order expansion, layer normalization (LayerNorm), and a dueling Double Deep Q-Network (DDQN) backbone. Files are separated before window construction, and the evaluation includes a pre-inspected pilot and two outcome-uninspected distance and power transfers. The comparison includes strong deterministic rules and a capacity-matched multilayer perceptron (MLP) trained with the same DDQN procedure. All methods are evaluated on common trajectories with paired inference across 10 independently trained seeds and Holm correction. Under deterministic sweeping, the schedule-aware rule was given the declared initial phase and one-channel-per-step direction and used an internal step counter to track the deterministic progression. These schedule variables and the corresponding step index are absent from HNP-DQN’s 48-dimensional observation. The rule matched the trajectory-wise dynamic-programming upper bound and outperformed HNP-DQN in all three settings (differences calculated as HNP-DQN minus the rule: $-1.91$, $-3.51$, and $-1.97$; all adjusted $p=0.023$). This information-asymmetric operational comparison shows the advantage attainable when the declared sweep specification and its progression are directly exploited; however, it does not provide a matched-information architectural ranking. By contrast, under random jamming, all comparisons between HNP-DQN and the threshold rule were inconclusive after correction, as were all six capacity-matched comparisons between HNP-DQN and the MLP. Of the 24 exploratory ablation comparisons, one favored the $\gamma =0$ variant in the random pilot, whereas the other 23 were inconclusive. Accordingly, this paper provides an information-aware, approximately parameter-matched reference for evaluating when explicit knowledge, observation-based control, or additional learning complexity is justified within the declared measurement-replay scope. Full article

Facial occlusion removes expression-relevant evidence and remains a major source of error in camera-based affective sensing. Existing approaches to occlusion-robust facial expression recognition often rely on specialized attention, reconstruction, semantic, or geometric pipelines, whereas aggressive training using synthetic occlusions may impair discrimination on clean images or overfit to synthetic corruption patterns. We address this tension between cleanness and robustness through clean-anchored hard occlusion fine-tuning (CA-HOFT). HardMix samples structured and random occlusion modes according to a facial region-weighted distribution. An explicit classification branch for the non-HardMix source view preserves ground-truth supervision, while a fixed reference teacher initialized from the preceding mixed-occlusion stage supplies a stationary distribution for both paired student views. These signals jointly train a single classifier while retaining a single-backbone inference pathway. Across five independent training runs, the ResNet-18 student achieves 90.08% accuracy on the Real-World Affective Faces Database (RAF-DB) and 87.38% accuracy with 83.34% macro-F1 on Occlusion-RAF-DB, with corresponding sample standard deviations of 0.21, 0.12, and 0.32 percentage points. The retained model has 11.18 million parameters and requires 1.814 giga multiply–accumulate operations (GMACs). Controlled comparisons and ablations indicate that it has the most favorable observed cleanness–robustness trade-off among the tested epoch-matched alternatives; however, fixed-checkpoint comparisons on Occlusion-RAF-DB are not significant after Holm correction. AffectNet-8 and evaluations using natural occlusion provide supporting evidence, while broader cross-domain validation remains future work. Full article

Background/Objectives: Fungal skin infections are commonly assessed using clinical examination and conventional histopathology, including hematoxylin-eosin (HE), periodic acid–Schiff (PAS), and Grocott methenamine silver (GMS) staining. However, these methods provide limited spatially resolved biochemical information. This proof-of-concept study investigated whether Fourier transform infrared (FTIR) chemical imaging can identify biochemical patterns associated with histologically defined fungal infection-associated tissue compartments in human skin. Methods: Archived formalin-fixed, paraffin-embedded skin samples with histological evidence of fungal infection were investigated. The study group comprised 19 patients, of whom 12 fulfilled the histological and technical eligibility criteria for quantitative FTIR analysis. These 12 independent biological cases yielded 46 histologically defined regions of interest (ROIs), comprising 16 fungal infection-associated ROIs, 14 keratosis/keratinised tissue ROIs, and 16 vital epidermis ROIs. ROI assignment was guided by corresponding HE-, PAS-, and GMS-stained sections. Results: Fungal infection-associated tissue compartments showed partially distinct spectral characteristics compared with vital epidermis and keratinised tissue. The most prominent exploratory differences occurred within the 900–1300 cm⁻¹ fingerprint region. Case-level statistical analysis showed significant differences between fungal infection-associated tissue and vital epidermis at approximately 1185 and 1240 cm⁻¹ after false discovery rate correction, whereas substantial overlap with keratinised tissue remained. Case-level PCA retained tissue-associated spectral structure after biological aggregation, although fungal infection-associated and keratinised tissue showed partial overlap. Unsupervised clustering further demonstrated spatially coherent spectral compartments corresponding to histologically identifiable tissue structures. Conclusions: FTIR chemical imaging may complement conventional histopathology by providing label-free, spatially resolved biochemical information on fungal infection-associated tissue compartments. Because fungal elements are embedded within surrounding keratinised and epithelial tissue, the observed spectral characteristics should be interpreted as exploratory infection-associated tissue signatures rather than fungal-specific diagnostic biomarkers. Larger independent studies with case-wise validation are required before diagnostic application can be considered. Full article

Gene therapy and genome editing increasingly depend on the safe, effective, and cell-selective delivery of nucleic acids and protein–nucleic acid complexes. Although viral vectors remain important for applications requiring durable gene expression, non-viral vectors offer advantages in cargo capacity, modularity, transient expression, potential repeat dosing, and avoidance of vector–genome integration. Lipid nanoparticles (LNPs), polymeric nanoparticles, inorganic nanomaterials, extracellular vesicles (EVs), and biomimetic hybrid systems have consequently become central platforms for delivery of siRNA, mRNA, plasmid DNA, antisense oligonucleotides, and CRISPR-based genome editors. Among these, ionizable LNPs are currently the most clinically mature non-viral technology, supported by the clinical success of siRNA therapeutics and mRNA vaccines, as well as the emergence of in vivo CRISPR therapies. Nevertheless, efficient endosomal escape, cell-type-selective targeting, extrahepatic delivery, and repeat-dose tolerability remain substantial barriers. Polymeric vectors provide broad chemical tunability, allowing adjustment of charge density, degradability, stimulus responsiveness, intracellular trafficking, and cargo release. However, toxicity and batch-to-batch reproducibility remain key concerns. EVs provide a biologically derived alternative with favorable membrane interfaces and potential advantages for protein and ribonucleoprotein delivery, but their clinical translation is constrained by heterogeneity, loading efficiency, product characterization, and scalable manufacturing. This review summarizes recent advances in non-viral gene-delivery platforms, compares their strengths and limitations, and discusses future directions in cell-selective delivery, endosomal escape, transient delivery of genome-editing machinery, engineered EVs, hybrid vectors, and manufacturing-oriented development. The field is transitioning from organ-level delivery toward delivery of the correct payload to the correct cell type at a clinically relevant exposure and safety margin. Full article

The American Association of State Highway and Transportation Officials (AASHTO) Load and Resistance Factor Design (LRFD) live-load distribution-factor (DF) equations were calibrated on straight bridges, while their use for horizontally curved I-girder bridges is bounded by the L_as/R < 0.06 rad criterion in Article 4.6.1.2.4b of the AASHTO LRFD Bridge Design Specifications, 10th ed. (2024). This study quantifies their accuracy beyond that limit using the finite element method (FEM) in 35 three-dimensional CSiBridge models subjected to numerical consistency checks: three composite plate-girder arrangements (4–6 girders, 9.0 m deck) at central angles of 0–15°, with near-limit, span-transfer, sensitivity, and out-of-range extensions to 25°, under the AASHTO LRFD vehicular design live-load model (HL-93) and the Iraqi Class 100 wheeled military vehicle (HB115; 1150 kN). At the limit, curvature amplification is only 1.8–2.6%. Beyond it, the exterior-moment equations become unconservative almost immediately; FEM demand exceeds AASHTO by 21–29% at 15°, whereas the interior-shear equations remain conservative. A two-part correction factor (CF) of the form CF = R₀[1 + (a + a₁S/L)(L/R)] is proposed (R² ≈ 0.97) and predicts the withheld out-of-range cases within 3.3%. Within the tested envelope, exterior-girder amplification depends primarily on L/R; for HB115, its rate is about half that of HL-93. Direct CSiBridge reconstruction of two published 1/10-scale laboratory specimens shows good agreement in global deflection and moderate agreement in strain-based transverse distribution. Because full-scale measurements for the exact 38 m reference configuration were unavailable, this evidence is treated as external experimental benchmarking of the modeling methodology rather than complete validation of the full parametric matrix. Full article