Search Results (1,081)

Ground-based radar monitoring of low Earth orbit (LEO) satellites is constrained by terrain occlusion and short visibility windows. Deploying UAVs as mobile platforms can extend coverage, but beam alignment, obstacle avoidance, and kinematic limits have not been jointly addressed. This paper proposes a UAV path planning framework that treats the ground station–UAV–satellite (G-U-S) collinear relationship as a planning prior. Ideal UAV positions are first computed from satellite visible arcs. In obstacle-free airspace, these positions directly define the flight path. When no-fly zones intersect the ideal trajectory, a geometry-guided RRT* planner generates collision-free detours by favoring sampling near the beam axis. The beam safety corridor is then embedded with kinematic limits as a conservative hard constraint within a Minimum Snap Bézier QP model, whose convex hull property bounds the trajectory within the coverage region. Simulations over a representative visible arc of the target satellite (approximately 3.3 min) achieve 100% beam coverage in the obstacle-free scenario and 95.2% with three no-fly zones placed along the ideal trajectory. Visible arc duration varies with satellite orbital trajectory; the framework is applicable to any arc whose geometry satisfies the kinematic constraints of the UAV platform. An ablation experiment confirms that the geometry-guided sampling strategy is essential: replacing it with uniform random sampling reduces coverage by 9.5 percentage points and triggers a diagnostic fallback mechanism. Edge case stress testing further identifies the conditions under which beam containment and obstacle avoidance come into tension. The proposed framework provides a simulation-validated trajectory planning approach for UAV-assisted LEO satellite monitoring in complex airspace. Full article

Purpose: We aimed to compare the volume of the lateral geniculate nucleus (LGN) in patients with different stages of retinitis pigmentosa (RP) with regard to age, sex and symmetry of the LGN. Methods: The investigated cohort included 13 patients with moderate (median Snellen visual acuity 0.75) and 18 patients with advanced (median Snellen visual acuity 0.06) RP-related visual field loss. The volumes of the left and right LGNs were manually measured using ITK-SNAP software after an examination of the brain with a 7 Tesla MRI. A linear mixed statistical model was used to assess LGN volume regarding age and gender of moderate and advanced RP patients and symmetry of both LGNs. Results: The mixed-effects linear model did not reveal a significant effect of disease group on LGN volume after adjusting for age and sex (F(1.27) = 0.01, p = 0.91). A significant effect of the LGN side was demonstrated, with the volume of the right LGN being significantly greater than that of the left (F(1.29) = 29.45, p < 0.001) in both disease groups, left–right. The interaction between disease group and LGN side was not statistically significant (F(1.29) = 0.45, p = 0.51). There is a tendency for LGN to decrease with age (F(1.27) = 3.84, p = 0.060), and there is no gender predilection (F(1.27) = 0.11, p = 0.74) in RP patients. There was correlation found between left LGN volume and visual acuity (ρ = 0.64) and central retinal thickness (ρ = 0.71) in the moderate group). Conclusions: No significant differences in LGN volume were found between patients with moderate and advanced RP. Furthermore, the volume of the right LGN was larger than the volume of the left LGN in RP patients, and this asymmetry is not gender-dependent. Correlation was found between the left LGN volume and visual acuity and central retinal thickness in moderate group. Our findings may have clinical implications for future RP management. Full article

Background: Dysregulation of the microbiota–gut–brain axis has been implicated in attention-deficit/hyperactivity disorder (ADHD), but randomized controlled trial evidence for probiotic supplementation remains inconsistent. Objective: This systematic review and meta-analysis evaluated the effects of probiotic supplementation on ADHD-related clinical outcomes and emotional–behavioral outcomes in children and adolescents. Methods: PubMed, Embase, Cochrane CENTRAL, Web of Science, PsycINFO, and EBSCO were searched from database inception to October 2025. Randomized controlled trials including participants under 18 years of age with DSM-defined ADHD were eligible. Pooled effects were calculated using standardized mean differences (SMDs) or mean differences (MDs) with 95% confidence intervals (CIs) under random-effects models. Results: Nine RCTs involving 482 participants were included, with intervention durations ranging from 8 to 12 weeks. In the broad exploratory synthesis of ADHD-related clinical outcomes, probiotic supplementation did not show a clear benefit (SMD = −0.25, 95% CI: −0.57 to 0.07; p = 0.131). Because this analysis combined conceptually heterogeneous instruments, greater interpretive weight was placed on domain-specific analyses. No clear effects were observed for more specific core symptom-related measures, including SNAP-IV inattention scores and CPT-derived attention-related performance measures. In domain-specific exploratory analyses, CPRS total scores showed a small reduction following probiotic supplementation (SMD = −0.33, 95% CI: −0.65 to −0.01; p = 0.041), whereas CBCL total scores did not show a statistically significant effect under the random-effects model (MD = −1.93, 95% CI: −6.30 to 2.44; p = 0.386). The SNAP-IV hyperactivity finding was statistically significant but was largely driven by a single study and should be interpreted cautiously. Conclusions: Current 8–12-week RCT evidence does not demonstrate a clear benefit of probiotic supplementation for ADHD core symptom-related measures. However, exploratory findings suggest that probiotic supplementation may have potential for improving broad parent-reported emotional–behavioral outcomes, particularly CPRS-assessed behavioral symptoms. This potential benefit should be interpreted cautiously because it was based on a small number of heterogeneous trials and was not consistently supported by CBCL outcomes. Larger, rigorously designed trials are needed to confirm these broad parent-reported behavioral and emotional findings and clarify their clinical relevance. Full article

Path planning for cargo unmanned aerial vehicles (UAVs) in continuous indoor–outdoor heterogeneous environments poses a critical challenge: promoting payload stability under sharp turns and abrupt altitude variations while maintaining navigational efficiency. To address this issue, this paper proposes a hybrid A*–APF path planning framework that embeds trajectory smoothness optimization directly into the planning process rather than treating it as a post-processing step. An improved A* algorithm is developed by incorporating a trajectory smoothness term into its cost function to penalize sharp turns during global path generation. The resulting path is further refined using an enhanced artificial potential field (APF) method with virtual target points and multi-field force synthesis to mitigate local minima. In addition, the Ramer–Douglas–Peucker algorithm is employed to remove redundant waypoints, and a trajectory generation module based on B-spline interpolation and minimum snap optimization is introduced to produce smooth and dynamically feasible trajectories. Numerical simulation results demonstrate that, in indoor warehouse environments, the proposed method reduces the average turning angle by 88.4% (to 23.1°) compared with the standard A* algorithm while maintaining a comparable path length of 135.11 m. In large-scale outdoor urban scenarios, it achieves a path smoothness of 0.0124 with an average turning angle of 40.0°, substantially outperforming the Genetic Algorithm (104.6°) and Particle Swarm Optimization (83.5°) on turning angle while delivering competitive computation times of 0.52–1.51 s. An ablation study confirms that the improved A* and enhanced APF components each contribute independently to turning angle reduction and local minima avoidance, respectively, and that their integration yields the optimal balance across all metrics. These results indicate the proposed framework’s effectiveness for UAV-based last-mile delivery in scenarios requiring seamless indoor–outdoor transitions under payload stability constraints. Full article

Accurate fleet electrification assessment requires microscopic traffic simulation grounded in real-world demand, physics-based vehicle models, and routing that respects the lane-connection topology of urban networks. We present ISTVEL (Istanbul Simulation Tool for Vehicle Electrification), an open-source framework that ingests hourly Istanbul Metropolitan Municipality (IMM) loop-detector data, snaps detectors to OpenStreetMap edges, synthesises SUMO demand via a connection-graph Breadth-First Search (BFS) algorithm eliminating teleportation artifacts, and post-processes tripinfo.xml output to compute per-trip energy, use-phase CO₂, and energy operating cost (ECO₁₀₀), correctly distinguishing gross battery draw, regenerative recovery, and net grid consumption. Applied to the Kadıköy district of Istanbul ($3.2{\mathrm{km}}^2$, 08:00–09:00, January 2025, $\approx 2950$ vehicles), ISTVEL demonstrates that a full battery-electric vehicle (BEV) fleet reduces use-phase (operational) CO₂ by 80.1% and energy operating cost by 66.5% versus the internal-combustion-engine vehicle (ICEV) baseline at current Turkish grid intensity ($\gamma =0.45\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$). However, these figures reflect use-phase emissions only (tailpipe combustion for ICEV; upstream grid emissions $\gamma \times E_{\mathrm{net}}$ for BEV) and exclude vehicle manufacturing, battery production, and upstream fuel extraction. Opportunistic in-transit dynamic wireless power transfer (DWPT) charging at 0.5 km spacing reduces post-trip battery replenishment demand by a further 67.1%, shifting grid supply from post-trip charging to in-transit delivery; total system electricity demand (including DWPT supply) is 895.7 kWh, marginally above the plain-BEV baseline of 848.1 kWh due to charging losses at ${\eta }_{\mathrm{cs}}=0.95$. Framework transferability is further demonstrated on the Fatih district under an identical protocol. Full article

Background: Food insecurity and water insecurity are increasingly recognized as interconnected social determinants of health; however, their co-occurrence remains underexplored in U.S. populations. SNAP households with children experience a high prevalence of both food and water insecurity. This study estimated the prevalence of food insecurity and water insecurity and examined their co-occurrence among SNAP households with children in the Southern U.S. Methods: A cross-sectional, web-based survey was conducted among 683 SNAP participants residing in households with children. Food insecurity was assessed using the 10-item USDA Adult Food Security Survey Module, and water insecurity was measured using the Household Water Insecurity Experiences (HWISE) scale. Descriptive statistics estimated prevalence, and regression analyses assessed associations. Results: Food insecurity (75.1%) and water insecurity (53.9%) were highly prevalent among surveyed SNAP households with children. Nearly half of households (48.6%) experienced both conditions, while 19.6% were secure in both. Food insecurity alone was reported by 26.5% of households, and water insecurity alone by 5.3%. Higher food insecurity scores were associated with increased odds of water insecurity (AOR = 1.33, 95% CI: 1.26–1.40, p < 0.001). Conclusions: Food insecurity and water insecurity frequently co-occur among SNAP households with children. Integrated public health strategies addressing both food and water access are needed to reduce disparities and improve household well-being. Full article

Eosinophils are highly granulated white blood and tissue cells that play complex roles in the immune system including host protection against helminthic parasites, viruses, fungi, and bacteria. These bone marrow-derived cells cause tissue damage in a range of diseases and disorders, particularly in allergy, asthma, and chronic rhinosinusitis with nasal polyps. Eosinophils are recruited to tissues in response to chemotactic signals, and during inflammation, they release a plethora of mediators, including immunoregulatory cytokines, through multiple pathways involving degranulation, respiratory burst, lipid mediator release, exosome release, and extracellular trap formation. Degranulation from eosinophils has been implicated as a major effector mechanism in airway diseases, particularly late phase asthma responses and in nasal polyps from patients with chronic rhinosinusitis. In degranulation responses, eosinophils release numerous granule proteins by classical exocytosis, compound exocytosis, piecemeal degranulation, and cytolysis, which refers to cell lysis through membrane rupture and cell destruction. Cytolysis can lead to suicidal extracellular trap formation, which is a regulated form of cell death involving the release of extracellular DNA traps and granule proteins. Granule release from eosinophils is dependent on activation of specific and tightly regulated intracellular signaling pathways, including Rac and Rab guanosine triphosphatases, soluble NSF attachment protein (SNAP) receptors (SNAREs), Cdk5 kinase, and actin dynamics. These observations have shown selective and nonredundant roles for signaling in degranulation responses. In this review, we explore findings from the literature on the mechanisms controlling granule-derived mediator release from eosinophils. Full article

Accurate R-peak detection in electrocardiogram (ECG) signals is fundamental for cardiovascular analysis. However, most existing methods address differences in sampling frequency (f_s) through signal resampling or transfer learning, which may alter the temporal definition of annotated events. In this study, we propose a f_s consistent framework for ECG R-peak detection that avoids both resampling and retraining. The proposed method is based on low-sampling morphological learning combined with physiological temporal constraints (PTC). A lightweight classifier based on Extreme Gradient Boosting (XGB) was trained on 128-Hz ECG data from the MIT-BIH Normal Sinus Rhythm Database to learn local morphological structures, and feature extraction is defined in milliseconds with time-normalized derivatives to ensure consistency across f_s. The trained model is directly applied to higher-f_s datasets (360 Hz, 500 Hz, and 1000 Hz) without modification. Final peak locations are determined through deterministic processing, including PTC and local snap processing. Experimental results demonstrated that the proposed method achieved stable detection performance across multiple sampling frequencies. When evaluated in a sample-wise manner, the proposed method achieved mean F1-scores of 0.885 on MIT-BIH Arrhythmia Database (360 Hz), 0.848 on Lobachevsky University Electrocardiography Database (LUDB, 500 Hz, sinus rhythm), 0.837 on LUDB (500 Hz, arrhythmia), and 0.953 on PTB Diagnostic ECG Database (1000 Hz), without any resampling or retraining. The integration of probabilistic candidate detection and deterministic temporal alignment enables consistent peak localization under cross-frequency conditions. These findings demonstrate that augmenting machine learning with deterministic decision mechanisms provides a principled framework for f_s-consistent ECG peak detection. Full article

This study focuses on the development of satellite-image-based models for estimating Chl-a and TSS concentrations in Lake Palic. Using Sentinel-2 images, we ensured high spatial resolution. Image processing was conducted using the SNAP (Sentinel Application Platform version 10.0.0, developed by the European Space Agency (ESA), Frascati, Italy) software with the C2RCC algorithm. A series of environmental indices was calculated and correlated with measured Chl-a and TSS values to support the development of a reliable predictive model. Two modeling approaches were applied: simple linear regression based on selected spectral indices and measured Chl-a and TSS concentrations, and machine learning methods to improve predictive performance and capture potentially nonlinear relationships. The models were evaluated using standard metrics and verified against field measurements. The overall process was implemented at two locations within the lake, the middle and the outflow. Spatial variability in optical conditions prevented the development of a single unified model. While Chl-a concentrations could be estimated with reasonable accuracy, TSS retrieval remained limited by optical complexity and spatial variability in water quality conditions. This suggests that the lake exhibits spatially variable optical regimes, in which different optically active constituents dominate the spectral response, thereby directly influencing model performance. Full article

Neurodegeneration and oxidative stress are central drivers of immune-mediated neuropathies. Capsaicin, the active ingredient in chili pepper and a direct agonist of the transient receptor potential vanilloid (TRPV1) channel, is used clinically to treat neuropathic pain. We previously demonstrated immunomodulatory and antioxidative effects of capsaicin in experimental autoimmune neuritis in vivo and Schwann cells (SC) in vitro. However, the molecular mechanisms underlying the maintenance of axonal integrity in dorsal root ganglion (DRG) and SC homeostasis remain unclear. In this study, we described the effects of capsaicin on DRG and SC in vitro under both naïve and S-Nitroso-N-acetyl-DL-penicillamine (SNAP)-induced oxidative stress conditions. Capsaicin induced an upregulation of the antioxidative cascade involving Nrf2, Ho-1, and Nqo1 in naïve DRG neurons and restored axonal growth under preventive and therapeutic settings. Preventive treatment enhanced catalase expression, whereas treatment increased regeneration-associated Gap43 and Atf3. Inhibition of TRPV1 with capsazepine partly attenuated the protective effect of axonal outgrowth, indicating TRPV1-mediated neuroprotection. In SC, capsaicin increased mitochondrial ATP production and spare respiratory capacity, inducing a transient Nrf2-dependent antioxidant response. Capsaicin suppressed expression of myelination markers under basal conditions but promoted expression of myelination- and repair-associated markers under oxidative stress. The findings support capsaicin as a regulator of neuronal and Schwann cell oxidative stress adaptation. Full article

This paper presents a non-learning-based, seed-dependent, semi-automatic pedestrian candidate generation pipeline for LiDAR point clouds. The proposed method is designed to support 3D annotation workflows by reducing irrelevant candidate clusters while improving the reliability of pedestrian candidate selection under distance-dependent point sparsity. The pipeline integrates distance-aware DBSCAN clustering, Single Template Matching (STM), and Centralized Point Augmentation (CPA). First, LiDAR points within the camera field of view are preprocessed, and pedestrian candidate clusters are generated using DBSCAN parameters configured according to distance intervals. Ground-snapping-based bounding-box refinement and height-based filtering are then applied to improve geometric consistency and reduce non-pedestrian candidates. In the second stage, STM compares PCA-aligned projected silhouettes of candidate clusters with a seed pedestrian template to suppress false positives. To address silhouette instability caused by sparse mid-range pedestrian points, CPA adds centroid-contracted points in the projection-relevant plane before template matching. Experiments on pedestrian-containing frames from the KITTI dataset show that STM improves precision from 27.6% to 60.5% and increases the F1-score from 36.8% to 51.4% compared with the initial DBSCAN-based candidate generation stage. The final CPA configuration improves recall from 44.7% to 46.7% and the overall F1-score from 51.4% to 52.1%, while revealing a precision–recall trade-off. Supplementary IoU analysis shows that the final DBSCAN–STM–CPA configuration maintains meaningful spatial overlap with pedestrian ground-truth boxes, achieving 88.9% at 3D IoU ≥ 0.10 and 81.6% at BEV IoU ≥ 0.25. Runtime analysis further shows that height-based filtering reduces the average per-frame processing time from 151.5 ms to 125.1 ms, while the final CPA configuration introduces only a small overhead, resulting in 126.2 ms per frame. These results demonstrate that the proposed DBSCAN–STM–CPA pipeline can provide reliable pedestrian candidates for semi-automatic 3D labeling without requiring class-specific detector training. Full article

The valorization of agri-food by-products represents a key strategy for improving sustainability and promoting circular economy principles in food systems. Pumpkin seed press cake is a protein-rich by-product with potential application in bakery products. The aim of this study was to evaluate the feasibility of using defatted pumpkin seed press cake flour (PPSF) as a major ingredient in cookie formulations and to optimize its incorporation in order to maximize nutritional quality and sensory acceptability. Chemical characterization showed that PPSF has a superior nutritional profile compared to wheat flour, containing 55.75% protein, 8.78% minerals, and 6.15% total dietary fiber, along with significantly higher levels of total phenolics, total carotenoids, and β-carotene (0.26 mg/100 g). Formulation optimization using response surface methodology (RSM) enabled a high inclusion level of 69.61% PPSF, with 41.32% sugar and a baking time of 9 min and 29 s. The developed predictive models for diameter, thickness, overall acceptability, and bending stiffness were highly significant (p < 0.05) with a non-significant lack of fit (p > 0.05), confirming their statistical reliability for exploring the design space. The optimized C-PPSF (defatted pumpkin seed press cake flour) cookies showed a significant nutritional improvement, with protein content increasing from 13.05% to 30.17% and antioxidant capacity (DPPH) rising from 2.90% to 7.10%. While the enriched cookies had a darker color (L* 51.98) and reduced snapping force (39.7 N) due to gluten dilution, they maintained stable geometric parameters and achieved higher sensory scores for aroma, taste, and overall acceptability compared to the control. The main finding of this study is that PPSF can replace a substantial proportion of wheat flour in cookies while maintaining consumer acceptability and significantly improving nutritional quality. The optimized formulation with approximately 70% PPSF shows that this by-product has the potential to serve as a major ingredient in bakery products rather than only as a nutritional supplement. These results confirm that PPSF is a powerful functional ingredient that supports zero-waste manufacturing and provides a foundation for its broader use in bakery formulations within circular economy approaches. Future research should focus on shelf-life stability, bioaccessibility of bioactive compounds, volatile aroma profiling (e.g., GC–MS analysis), and industrial-scale validation of PPSF-based formulations. Full article

Background/Objectives: Paranasal sinus morphometry on computed tomography (CT) is of interest for forensic sex estimation, but many published predictive models rely on in-sample formulas without cross-validation, external testing, or release of model parameters. We aimed to characterize sex differences, pneumatization patterns, asymmetry, and age relationships of the paranasal sinuses in a Turkish adult population, and to develop, cross-validate, and transparently report a predictive model for sex estimation, explicitly benchmarked against the single best morphometric feature. Methods: In this single-center, STROBE-compliant retrospective cross-sectional study, maxillary, frontal, and sphenoid sinus volumes were measured by semi-automated active-contour segmentation in ITK-SNAP on CT scans of 499 adults (282 male, 217 female; 18–65 years). Between-sex differences were tested with the Mann–Whitney U test with Bonferroni correction; effect sizes used Cliff’s delta and the probability of superiority. L1-regularized logistic regression, random forest, and gradient boosting were trained with 10-fold stratified cross-validation and a held-out 20% test set, and compared with a univariate frontal-volume benchmark. Results: All three sinus volumes were larger in males (all Bonferroni-adjusted p < 0.001), with the largest effect among the individual sinuses for the frontal sinus (Cliff’s delta = 0.53; probability of male superiority = 0.77). The best classifier was L1-regularized logistic regression (10-fold cross-validated AUC 0.79 ± 0.07; held-out test AUC 0.80; accuracy 70%). Because the area under the ROC curve of a single continuous marker equals its probability of superiority, frontal volume alone reached an AUC of approximately 0.77; the multivariable model therefore added little beyond this single feature. Age could not be reliably estimated (test mean absolute error ≈ 10.8 years; R² ≈ 0). Conclusions: Paranasal sinus volumes show robust sex dimorphism, concentrated in the frontal sinus, but provide only moderate sex discrimination—appropriate as one corroborating input in a forensic identification workflow rather than a stand-alone determinant. Age cannot be reliably estimated from sinus morphometry in this cohort. Full model coefficients are reported to permit independent replication. Full article

Background and Objectives: The neurocognitive sequelae of COVID-19 have attracted attention as part of post-COVID condition (PCC), yet standardized tools for screening and quantifying PCC-related cognitive impairment remain scarce. The present study aimed to develop and validate the Screening for Neurocognitive Abilities Post-COVID (SNAP-COVID), a self-report questionnaire designed to capture current symptom burden and perceived changes in cognitive functioning relative to pre-COVID status in a Greek-speaking sample. Materials and Methods: Data collection occurred in three phases between August 2024 and February 2025. Dataset A (N = 27) was used for test–retest reliability. Dataset B (N = 300) was used for exploratory factor analysis (EFA), reliability testing, and convergent validity analyses with the Brain Fog Scale (BFS). Dataset C (N = 317) was used for independent validation through confirmatory factor analysis (CFA). Results: Initial EFA of the 30-item SNAP-COVID scale suggested a four-factor model, yet further item refinement yielded a robust three-factor, 24-item solution: (1) General Cognitive Functions (17 items, α = 0.948), (2) Sensory Hypersensitivity (4 items, α = 0.829), and (3) Language and Communication (3 items, α = 0.950). The total scale demonstrated excellent internal consistency (α = 0.95). Convergent validity was evident by significant correlations between SNAP impact scores and BFS scores (r = −0.442, p < 0.001). CFA confirmed the three-factor structure with acceptable fit indices (χ²(249) = 677.29, p < 0.001; CFI = 0.882; TLI = 0.869; RMSEA = 0.074; SRMR = 0.032). Conclusions: The SNAP-COVID scale is a reliable and valid instrument. Its multidimensional structure captures global and domain-specific difficulties, addressing a critical gap in post-infectious cognitive assessment. Full article

Mechanical traps are widely used for urban rat surveillance and management, including the brown rat, Rattus norvegicus (Berkenhout). However, relatively few studies have examined the effects of environment, placement, and trap type on trapping outcomes in residential buildings. In this study, we deployed 50 pairs of snap traps and live cage traps for five nights (500 trap nights) in concrete-covered basements in Brooklyn and dirt-covered crawlspaces in Queens, New York City, USA. Capture success differed by site, with Brooklyn yielding higher overall success (38%) than Queens (13%) (p < 0.001). Capture success in Brooklyn was lower in compactor rooms than in other areas (vertical infrastructure, open-floor travel corridors, and burrow entrances). In Queens, success was similar at different placement areas. The two trap types had similar overall capture success (p = 0.090). Cage traps produced more female-biased samples (90% females), while snap traps yielded more balanced sex ratios (53% females) (p < 0.001). In addition, median body mass distributions also differed, with snap traps capturing heavier rats than cage traps at both sites. These findings demonstrate that both placement and trap design are important considerations for urban rat surveillance and urban rat management. Full article