Search Results (723)

Objectives: This study aimed to evaluate auditory cortical maturation in pediatric cochlear implant (CI) users using speech-evoked cortical auditory evoked potentials (CAEPs) and to compare P1 latency responses with age-matched normal-hearing (NH) peers. Secondary objectives included examining the relationship between P1 latency, age, and duration of implant use to assess experience-dependent cortical plasticity. Materials and Methods: Seventy children were enrolled, including 40 prelingually deaf CI users and 30 NH controls matched for age and sex. CAEPs were recorded using the HEARLab system with three speech tokens representing low (/m/), mid (/g/), and high (/t/) frequencies, presented at 55 dB SPL in a free-field setup. The P1 component was identified as the first positive deflection between 50 and 150 ms after stimulus onset. Group comparisons were performed using Student’s t-test, and correlations between P1 latency, age, and implant-use duration were analyzed using the Pearson correlation test (p < 0.05). Results: Mean P1 latencies were significantly longer in CI users than in NH peers for the /m/ and /t/ stimuli (p = 0.036 and p = 0.045, respectively), while no significant difference was found for /g/ (p = 0.542). In NH children, P1 latency negatively correlated with age (r = −0.44, p < 0.05), indicating maturation-related shortening. Among CI users, longer implant-use duration was associated with shorter P1 latencies across all speech tokens (/m/: r = −0.37; /g/: r = −0.49; /t/: r = −0.43; p < 0.05 for all). Conclusions: Speech-evoked CAEPs provide a sensitive and objective measure of auditory cortical development in children with cochlear implants. P1 latency reflects both chronological and hearing-age-related maturation, supporting its clinical use as a biomarker for cortical plasticity and rehabilitation progress in pediatric CI care. Full article

Objectives: This clinical commentary presents a four-pillar rehabilitation framework implemented in the elite football setting of OFI Crete FC and designed to facilitate the return of football players to training and competitive play. The framework is structured around five core components: (a) effective load management during training and matches, (b) individualized rehabilitation programs and injury prevention strategies integrated within the recovery phase, (c) a novel on-field rehabilitation framework, and (d) an extended secondary prevention plan. Methods: This comprehensive approach was implemented over a three-year period with the OFI Crete FC football team and involved 87 elite professional players between the ages of 17 and 35. Throughout this time, 180 injuries were documented, ranging from mild to severe injuries. Results: The outcome illustrated that only 40% of these injuries led to players missing official matches, while the recurrence or follow-up injury rate was limited to just 10%. Over the course of the three years, a steady 60% decline in injury rates was observed. Conclusions: These findings emphasize the crucial importance of training load management, the integration of injury prevention strategies throughout the rehabilitation process, and the early initiation of on-field rehabilitation. Within the clinical setting of OFI Crete FC, the implementation of this integrated rehabilitation framework was associated with favorable observations in injury incidence, player absence days, and return-to-play timelines, which may reflect that the approach has potential benefits while remaining observational in nature. Full article

The integration of eye-tracking into consumer-grade virtual reality (VR) headsets presents a transformative opportunity for assessing user mental states within simulated, immersive environments. However, the validity of this built-in technology must be established against gold-standard real-world eye-tracking systems. This study employs a novel paradigm using a physically moving object to evaluate the accuracy of dynamic smooth pursuit, a key oculomotor function in mental state assessment. We rigorously validated the performance of the HTC Vive Pro Eye’s integrated eye-tracker against the Tobii Pro Glasses 3 using a high-precision OptiTrack motion capture system as ground-truth for object position. Eight participants completed both 2D and 3D gaze-tracking tasks. In the 2D condition, they tracked a dot on a screen, while in the 3D condition, they tracked a physically moving object. The real-world object trajectories captured by OptiTrack were replicated within a VR environment. Gaze data from both the VR headset and the Tobii glasses were recorded simultaneously and compared to the OptiTrack baseline using Dynamic Time Warping (DTW) to quantify accuracy. Results revealed a task-dependent performance. In the 2D task, the Tobii glasses demonstrated significantly lower DTW distances, indicating superior accuracy. Conversely, in the 3D task, the VR headset significantly outperformed the glasses, showing a closer match to the real object trajectory. This suggests that while traditional eye-trackers excel in constrained 2D contexts, integrated VR eye-tracking is more accurate for naturalistic 3D gaze pursuit. We conclude that VR headset eye-tracking is not only a reliable but also a cost-effective tool for research, particularly offering enhanced performance for studies conducted within immersive 3D simulations. Full article

To address the low-speed starting noise in a small electric vehicle, this study proposes and validates a systematic diagnostic and optimization methodology. A novel objective testing method, based on energy tracking and matching, is first employed for precise noise source localization. Combined with electromagnetic force wave analysis, this method identifies the coupling between a 24th-order motor excitation and a powertrain structural mode as the root cause. Subsequently, a low-cost, integrated optimization scheme is presented, which synergistically combines three strategies: motor control refinement, powertrain natural frequency tuning, and mount isolation enhancement. Experimental validation demonstrates that this multi-domain approach reduces the sound pressure level at the driver’s ear by 4–6 dB(A), effectively eliminating the abnormal audible noise during starting and significantly improving the in-cabin sound quality. This paper offers a cost-effective engineering framework for resolving low-speed, low-frequency noise problems in electric vehicles. Full article

Cow efficiency is vitally important to beef sustainability, and computer simulation models may be useful tools to identify characteristics of the most efficient cow genotypes for a given production environment. The objective of this analysis was to determine whether the Beef Cattle Systems Model could replicate empirical research demonstrating a genotype–nutritional environment interaction for efficiency of feed conversion to calves weaned. Combinations of cow genotypes for lactation potential (8, 10, and 12 kg/d at peak milk) and growth potential (450, 505, and 650 kg mature weight) were simulated across four dry matter intake levels (58, 76, 93, and 111 g/kg BW^0.75). At lower dry matter intakes, cows had lesser body condition scores and weight and longer postpartum intervals, but dry matter intake had minimal influence on pregnancy percentage or calf-weaning weight. These trends match empirical research except for pregnancy percentage, where decreasing dry matter intake had a dramatic effect on pregnancy percentage in high-milking, high-growth-potential genotypes. Efficiency of feed conversion was greatest at low dry matter intake for the model simulation with no evidence of a genotype–dry matter intake interaction, which is in contrast to empirical research demonstrating a genotype–dry matter intake interaction. In conclusion, standard nutrition equations do not replicate the genotype–nutritional environment interaction observed in empirical research studies. Full article

The proliferation of Internet of Things (IoT) devices has created unprecedented security challenges characterized by resource constraints, heterogeneous network architectures, and severe class imbalance in attack detection datasets. This paper presents a comprehensive benchmark evaluation of five Generative Adversarial Network (GAN) architectures for energy-aware intrusion detection: Standard GAN, Progressive GAN (PGAN), Conditional GAN (cGAN), Graph-based GAN (GraphGAN), and Wasserstein GAN with Gradient Penalty (WGAN-GP). Our evaluation framework introduces novel energy-normalized performance metrics, including Accuracy-per-Joule (APJ) and F1-per-Joule (F1PJ), that enable principled architecture selection for energy-constrained deployments. We propose an optimized WGAN-GP architecture incorporating diversity loss, feature matching, and noise injection mechanisms specifically designed for classification-oriented data augmentation. Experimental results on a stratified subset of the BoT-IoT dataset (approximately 1.83 million records) demonstrate that our optimized WGAN-GP achieves state-of-the-art performance, with 99.99% classification accuracy, a 0.99 macro-F1 score, and superior generation quality (MSE 0.01). While traditional classifiers augmented with SMOTE (i.e., Logistic Regression and CNN1D-TCN) also achieve 99.99% accuracy, they suffer from poor minority class detection (77.78–80.00%); our WGAN-GP improves minority class detection to 100.00% on the reported test split (45 of 45 attack instances correctly identified). Furthermore, WGAN-GP provides substantial efficiency advantages under our energy-normalized metrics, achieving superior accuracy-per-joule performance compared to Standard GAN. Also, a cross-dataset validation across five benchmarks (BoT-IoT, CICIoT2023, ToN-IoT, UNSW-NB15, CIC-IDS2017) was implemented using 250 pooled test attacks to confirm generalizability, with WGAN-GP achieving 98.40% minority class accuracy (246/250 attacks detected) compared to 76.80% for Classical + SMOTE methods, a statistically significant 21.60 percentage point improvement ($p<0.0001$). Finally, our analysis reveals that incorporating diversity-promoting mechanisms in GAN training simultaneously achieves best generation quality AND best classification performance, demonstrating that these objectives are complementary rather than competing. Full article

Background/Objectives: This study evaluated the clinical image quality of three-dimensional synthetic MRI (3D SI) compared with conventional MRI (cMRI), focusing on tissue contrast, anatomical detail, and motion sensitivity. Methods: Patients with nonspecific neurological symptoms were included. Both cMRI and 3D SI were acquired on single-vendor 1.5 T and 3 T scanners with slice thicknesses of 1.0–1.7 mm. Two experienced neuroradiologists and one fellow independently evaluated matched scans using a 0–100 scale. Assessed parameters included signal-to-noise ratio (SNR), gray–white matter contrast, artifacts, motion robustness, and confidence in detecting perivascular spaces, white matter lesions, and subtle pathology. Interrater agreement was measured using Krippendorff’s alpha and ICC2. Multiple linear regression analyzed associations between image quality ratings and imaging method. Results: Images of 31 patients were analyzed. Three-dimensional SI demonstrated sufficient-to-good overall image quality and high robustness to motion. Cortical-surface-to-cerebrospinal-fluid contrast on FLAIR was rated lower for 3D SI than for cMRI. False-positive lesion detection occurred more frequently on 3D SI FLAIR, particularly among experienced readers. cMRI achieved significantly higher T1-weighted SNR than 3D SI (8.76 points, p < 0.001). Experienced readers consistently rated SNR and tissue contrast higher than the fellow. Vascular signal range was broader on 3D SI, reducing sensitivity to vascular abnormalities. Conclusions: Three-dimensional synthetic MRI provides clinically usable image quality and fulfills its primary diagnostic purpose, offering advantages in acquisition efficiency and robustness to motion. Nevertheless, limitations in cortical contrast, vascular signal characterization, and reader-dependent interpretive variability constrain its reliability for subtle or detail-critical findings. Full article

Background and Objectives: Accurate breast tumor segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is crucial for treatment planning, therapy monitoring, and quantitative studies of breast cancer response. However, deep learning models often have worse performance when applied to new hospitals because scanner hardware, acquisition protocols, and patient populations differ from those in the training data. This study investigates how such center-related domain shift affects automated breast DCE-MRI tumor segmentation on the multi-center MAMA-MIA dataset. Methods: We trained a standard 3D U-Net for primary tumor segmentation under two evaluation settings. First, we constructed a random patient-wise split that mixes cases from the three main MAMA-MIA center groups (ISPY2, DUKE, NACT) and used this as an in-distribution reference. Second, we designed a balanced leave-one-center-out cross-validation (LoCoCV) protocol in which each center is held out in turn, while training, validation, and test sets are matched in size across folds. Performance was assessed using the Dice similarity coefficient, 95th percentile Hausdorff distance (HD95), sensitivity, specificity, and related overlap measures. Results: On the mixed-center random split, the best three-channel model achieved a mean Dice of about 0.68 and a mean HD95 of about 19.7 mm on the held-out test set, indicating good volumetric overlap and boundary accuracy when training and test distributions match. Under balanced LoCoCV, the one-channel model reached a mean Dice of about 0.45 and a mean HD95 of about 41 mm on unseen centers, with similar averages for the three-channel variant. Compared with the random split baseline, Dice and sensitivity decreased, while HD95 nearly doubled, showing that boundary errors become larger and segmentations less reliable when the model is applied to new centers. Conclusions: A model that performs well on mixed-center random splits can still suffer a substantial loss of accuracy on completely unseen institutions. The balanced LoCoCV design makes this out-of-distribution penalty visible by separating center-related effects from sample size effects. These findings highlight the need for robust multi-center training strategies and explicit cross-center validation before deploying breast DCE-MRI segmentation models in clinical practice. Full article

Multi-object tracking (MOT) in videos captured by Unmanned Aerial Vehicles (UAVs) is critically challenged by significant camera ego-motion, frequent occlusions, and complex object interactions. To address the limitations of conventional trackers that depend on static, rule-based association strategies, this paper introduces STC-SORT, a novel tracking framework whose core is a two-level reasoning architecture for data association. First, a Spatio-Temporal Consistency Graph Network (STC-GN) models inter-object relationships via graph attention to learn adaptive weights for fusing motion, appearance, and geometric cues. Second, these dynamic weights are integrated into a 4D association cost volume, enabling globally optimal matching across a temporal window. When integrated with an enhanced AEE-YOLO detector, STC-SORT achieves significant and statistically robust improvements on major UAV tracking benchmarks. It elevates MOTA by 13.0% on UAVDT and 6.5% on VisDrone, while boosting IDF1 by 9.7% and 9.9%, respectively. The framework also maintains real-time inference speed (75.5 FPS) and demonstrates substantial reductions in identity switches. These results validate STC-SORT as having strong potential for robust multi-object tracking in challenging UAV scenarios. Full article

Objectives: Polycystic ovary syndrome (PCOS) is a heterogeneous disorder with diverse pathogenetic mechanisms and clinical manifestations. Phenotype D PCOS is characterized by oligomenorrhoea and polycystic ovaries without hyperandrogenism. Altered adipokine profiles may contribute to reproductive and metabolic disturbances. Chemerin is an adipokine involved in inflammatory and metabolic processes. It remains unclear whether altered chemerin levels in PCOS reflect metabolic dysfunction alone or are directly associated with hyperandrogenism. The aim of this study was to compare serum chemerin levels in women with normoandrogenic PCOS and a control group. Methods: This cross-sectional preliminary study included 49 women with phenotype D PCOS and 40 healthy, age- and body mass index (BMI)-matched controls. Anthropometric, biochemical, hormonal parameters, and serum chemerin concentrations were assessed. Results: Serum chemerin concentrations did not differ significantly between the groups. In the PCOS group, the 95% confidence interval ranged from 198.61 to 234.37, while in the controls, it ranged from 187.13 to 216.21. In women with PCOS, chemerin showed significant positive correlations with weight, BMI, waist and hip circumference, total adipose tissue, and both gynoid and android fat content. Positive correlations were also observed with highly sensitive C-reactive protein (hs-CRP), insulin, glucose, triglycerides, and Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), and a negative correlation was found with high-density lipoprotein (HDL) cholesterol. Chemerin was weakly negatively correlated with sex hormone binding globulin (SHBG) and positively correlated with the free androgen index (FAI). In the control group, chemerin correlated positively with CRP, insulin, triglycerides, total and gynoid adipose tissue, and negatively correlated with HDL cholesterol and SHBG. Conclusions Although chemerin levels did not differ from controls, chemerin was associated with metabolic and inflammatory markers in both groups. These findings should be considered preliminary due to the limited sample size. Chemerin may reflect metabolic and inflammatory status rather than hyperandrogenism in normoandrogenic PCOS. Full article

We present an adaptive governance engine for blockchain-enabled Vehicular Ad Hoc Networks (VANETs) that regulates the latency–energy–coherence trade-off under rapid topology changes. The core contribution is an Ideal Information Cycle (an operational abstraction of information injection/validation) and a modular VANET Engine implemented as a real-time control loop in NS-3.35. At runtime, the Engine monitors normalized Shannon entropies—informational entropy S over active transactions and spatial entropy $H_{\mathrm{spatial}}$ over occupancy bins (both on $[0,1]$)—and adapts the consensus mode (latency-feasible PoW versus signature/quorum-based modes such as PoS/FBA) together with rigor parameters via calibrated policy maps. Governance is formulated as a constrained operational objective that trades per-block resource expenditure (radio + cryptography) against a Quality-of-Information (QoI) proxy derived from delay/error tiers, while maintaining timeliness and ledger-coherence pressure. Cryptographic cost is traced through counted operations, $E_{\mathrm{crypto}}=e_h{n}_{\mathrm{hash}}+e_{\mathrm{sig}}{n}_{\mathrm{sig}}$, and coherence is tracked using the LCP-normalized definition $D_{\mathrm{ledger}}\left(t\right)$ computed from the longest common prefix (LCP) length across nodes. We evaluate the framework under urban/highway mobility, scheduled partitions, and bounded adversarial stressors (Sybil identities and Byzantine proposers), using 600 s runs with 30 matched random seeds per configuration and 95% bias-corrected and accelerated (BCa) bootstrap confidence intervals. In high-disorder regimes ($S\gtrsim 0.8$), the Engine reduces total per-block energy (radio + cryptography) by more than 90% relative to a fixed-parameter PoW baseline tuned to the same agreement latency target. A consensus-first triggering policy further lowers agreement latency and improves throughput compared with broadcast-first baselines. In the emphasized urban setting under high mobility ($v=30$ m/s), the Engine keeps agreement/commit latency in the sub-100 ms range while maintaining finality typically within sub-150 ms ranges, bounds orphaning (≤10%), and reduces average ledger divergence below $0.07$ at high spatial disorder. The main evaluation is limited to $N\le 100$ vehicles under full PHY/MAC fidelity. PoW targets are intentionally latency-feasible and are not intended to provide cryptocurrency-grade majority-hash security; operational security assumptions and mode transition safeguards are discussed in the manuscript. Full article

Background/Objectives: A common challenge in both forensic and bioarchaeological research is commingling, the intermixing of skeletal material originating from multiple individuals or contexts. To tackle that problem past reassociation methods primarily relied on visual assessment or metric comparisons. However, recent advances in geometric morphometrics show strong potential for improving the sorting of commingled remains. This study applies a three-dimensional (3D) geometric morphometric method to evaluate its effectiveness in reassociating adjoining thoracic vertebrae. Methods: Two vertebral pairs, T4–T5 and T5–T6, from 65 and 73 individuals, respectively, were analyzed. These pairs were chosen due to limited anatomical variability, while they were also the most consistently preserved pairs. All specimens were scanned using a structured-light 3D scanner, and the dataset was derived from three Greek skeletal collections representing different geo-chronological contexts. Fourteen anatomical landmarks were placed on the superior rim and articular facets of the lower vertebra and mirrored onto the lower rim and facets of the adjoining upper vertebra. To remove the size effects the landmark coordinates were converted to Procrustes coordinates, while examining morphological similarity was quantified using Euclidean distances. For each pair, the vertebrae with the smallest Euclidean distances were considered the most probable true anatomical matches. Results: The correct T4–T5 match fell within the three smallest distances in 66.2% of cases, while for the T5–T6 pair, correct matches were found between the first three possible matches in a percentage of 43.8%. These findings indicate that the method can eliminate roughly 50–70% of incorrect matches and therefore narrow the plausible pairings. Conclusions: Future research incorporating more pairs and an expanded landmark dataset may result in greater accuracy for reassociation with 3D geometric morphometrics. Full article

Background/Objectives: Type 1 gastric neuroendocrine tumors (gNET) arise in the setting of autoimmune chronic atrophic gastritis and secondary hypergastrinemia. Vitamin D deficiency (VDD) has been associated with bone impairment and adverse outcomes in patients with neuroendocrine tumor (NET); however, data specifically addressing gNET remain limited. This study aimed to evaluate vitamin D status, supplementation requirements, and bone involvement in patients with type 1 gNET compared with those with entero-pancreatic NET (EP-NET). Methods: This retrospective study included patients with type 1 gNET followed at a tertiary referral center between 2010 and 2025 and an age- and sex-matched EP-NET cohort. VDD prevalence, time and dose required for normalization, supplementation formulations, bone status, and dietary habits were analyzed. Results: Twenty-six patients were included (thirteen gNET and thirteen EP-NET). VDD was significantly more prevalent in the gNET group compared with the EP-NET group (92.3% vs. 46.2%, p = 0.03, OR: 14). gNET required significantly higher daily cholecalciferol doses (3198.9 ± 1629 vs. 1580 ± 1121 IU/day, p = 0.008) and more frequently required multiple supplementation formulations (38.5% vs. 0%, p = 0.04). Multivariable linear regression analysis restricted to VDD patients confirmed that gNET was independently associated with higher daily cholecalciferol dose requirements (p = 0.037). Bone impairment, defined as osteoporosis or osteopenia, was significantly more common in the gNET group (61.5% vs. 15.4%, p = 0.04, OR: 8.8). Dietary adherence did not differ between groups. Conclusions: Type 1 gNET show a higher burden of VDD, increased vitamin D supplementation requirements, and a higher prevalence of bone impairment compared with EP-NET, irrespective of dietary habits. These findings suggest disease-specific mechanisms and support the need for tailored management in these patients. Full article

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

Global warming and increasing heat events necessitate long-term assessments of passive design strategies to ensure thermal resilience under future climatic conditions. Although machine-learning-based Surrogate Models (SMs) offer timely approximation of building performance compared to conventional simulation-based approaches, the lack of uncertainty quantification raises concerns about the reliability of their design optimization outcomes. This study aims to develop a robust surrogate-assisted optimization framework, based on a probabilistic Bayesian Neural Network (BNN) model and supported by an uncertainty-aware objective function. The framework is applied to an affordable housing case study in Surakarta, Indonesia, evaluating its generalizability under current and future climatic scenarios for 2050, 2070, and 2090. Thermal resilience is assessed through overheating hours exceeding acceptability limits in Southeast Asian context, using a parametric workflow implemented in Ladybug-tools and Grasshopper 3D. Compared to simulated test data, the BNN model demonstrates reliable predictive accuracy and probabilistic inference (R² = 0.99, MAE = 0.52%, CRPS = 0.38%). Furthermore, validation against re-evaluated optimal solutions shows low error ranges (RMSE = 0.43%, MAE = 0.33%), outperforming the deterministic SM optimization approach—using Artificial Neural Networks—by a factor of five. Overall, the uncertainty-aware framework provides a feasible, overconfidence-resistant, and reliable surrogate-assisted optimization method, identifying optimal solutions closely matching those from simulation-based optimization while reducing computational time by 96%. Full article