Search Results (1,138)

The use of ergogenic aids, particularly polyphenols such as 2S-hesperidin, has gained attention due to their potential to enhance exercise performance, although evidence in team sports and female athletes remains limited. This study aimed to investigate the acute effects of 2S-hesperidin (Cardiose^®) supplementation on physical performance and external and internal load during basketball-specific activity in female basketball players. A randomized, cross-over, single-blind design was employed in twelve competitive female players (age: 21.4 ± 2.2 years; Tier 3–4) who ingested 500 mg of 2S-hesperidin or placebo five hours before standardized testing and on-court practice. Physical performance was assessed through handgrip strength, countermovement jump (CMJ), drop jump (DJ), sprint, agility, and the 30–15 intermittent fitness test (30–15 IFT), while external and internal load variables were monitored during a standardized on-court practice designed to reflect the players’ typical training routine. In these exploratory analyses, differences favoring the 2S-hesperidin condition were observed for dominant handgrip strength, CMJ height, DJ height in the dominant leg, and end speed in 30–15 IFT , whereas no clear differences were observed in sprint, agility, or reactive strength index. External load variables showed no clear differences between conditions. Mean heart rate was lower in the supplementation condition, although this isolated finding should be interpreted cautiously in view of the small sample and the number of outcomes examined. Overall, the observed pattern suggests that acute 2S-hesperidin supplementation may be associated with selected neuromuscular and intermittent performance responses, without clear changes in external load. Given the small sample size and multiplicity of outcomes, these findings should be regarded as exploratory and hypothesis-generating rather than as confirmatory evidence of an ergogenic effect. Full article

Accurate stereo visual–inertial measurement is essential for mobile robots operating in Global Navigation Satellite System (GNSS)-denied and structurally complex environments. In stereo visual–inertial odometry (VIO), pose and trajectory outputs depend strongly on the point measurements delivered by the visual front end before sensor-fusion update. In sparse-texture but structurally regular scenes, tracked point features may exhibit poor persistence, uneven spatial distribution, and local tracking noise, even when informative line structures are present. Existing point–line VIO methods can improve positioning accuracy by introducing line landmarks or line residuals, but they usually modify the estimator state, measurement model, and Jacobian treatment. We present a scene-adaptive line-aware visual measurement conditioning method for stereo VIO front ends with point-measurement updates. The method uses 2-D image-line segments as lightweight structural priors and applies bounded normal-direction conditioning to reliable point measurements before a fixed-interface VIO back-end update. A sparse pruning safeguard removes only highly inconsistent long-lived tracks under strong structural support, while a scene-level confidence gate attenuates the intervention when line evidence is weak or unstable. The method is instantiated and evaluated in an S-MSCKF pipeline. On the reported EuRoC MAV sequences, it reduces the sequence-averaged absolute trajectory error (ATE) RMSE by approximately 13% relative to S-MSCKF, with 3–27% reductions on machine-hall sequences. On three real-world robot measurement sequences with an RTK-aided inertial reference, the mean Sim(2)-aligned planar position error decreases from 8.72 m to 7.31 m, and the mean yaw error decreases from $8.02°$ to $6.76°$; an additional scale-preserving SE(2) evaluation reveals sequence-dependent planar behavior and residual metric-scale sensitivity. Candidate-level stereo-consistency diagnostics show subpixel mean and 95th-percentile image-domain perturbations without systematic vertical-stereo bias, while the final reliability-weighted primary-view update is analytically bounded by approximately 0.221 pixels in the reported implementation. Runtime profiling reports an average front-end time of 33.34 ms on the tested CPU platform, close to the 33.3 ms frame period of the 30 Hz stereo input, although the $\mu +3\sigma$ runtime of 47.23 ms exceeds a strict frame-by-frame 30 Hz budget. These results suggest that line-aware front-end conditioning can improve visual measurement quality in structured stereo visual–inertial sensing without modifying the evaluated back-end interface. Full article

The COVID-19 pandemic placed prolonged, multi-year demands on the systems supporting frontline workers in healthcare, public safety, and education. In response, approximately 150 licensed behavioral-health professionals and credentialed educators, trained in Psychological First Aid (PFA), built and staffed the Maine FrontLine WarmLine (MFLWL) as unpaid Volunteers from 21 March 2020 through 31 May 2021 and began fielding calls in April 2020, contributing approximately 33,000 h of service. Although the MFLWL fielded 100 calls in aggregate, engagement persisted and expanded into Community Outreach, reaching approximately 250 frontline workers. To understand that paradox, this narrative case study analyzed two waves of open-ended Volunteer surveys (n = 22; n = 20) using consensus-based thematic analysis. Four themes emerged: a specific COVID-19 frontline focus; personal and professional Calling; an intentionally cultivated Culture of self-care and relational support; and stakeholder relationships marked by experienced systemic betrayal and moral injury. Low call volume, rather than eroding engagement, appears to have activated Volunteers’ sense of Calling, converting frustrated readiness into new capability and new avenues of service. From these findings, we derive Emerging Volunteer Engagement (EVE), a values-and-process framework situated within Disaster Ecology that positions Volunteers as principal drivers of disaster response rather than interchangeable resources, with implications for Volunteer organization, support, and liability policy in future disasters. Full article

Background: Reconstruction of craniofacial bone defects caused by tumor removal, infection, congenital anomalies, or trauma remains a major challenge. Recent advances in artificial intelligence (AI), digital imaging, and additive manufacturing have enabled personalized treatment strategies. This review discusses AI-driven workflows from cone-beam computed tomography (CBCT) image acquisition to implant fabrication using patient-specific bioceramics. Methods: Several electronic databases were searched, including Scopus, PubMed, ScienceDirect, and Web of Science. A peer-reviewed article published between 2015 and 2026 was considered for inclusion. This review provides an overview of AI applications in craniofacial tissue engineering, including CBCT image segmentation, three-dimensional reconstruction, virtual surgical planning, computer-aided design/computer-aided manufacturing (CAD/CAM), topology optimization, finite element analysis, and three-dimensional bioceramic scaffold printing. Results: AI improves accuracy, efficiency, and reproducibility of craniofacial reconstructions when used in conjunction with AI-assisted workflows. Through automated image segmentation and anatomical modeling, operator dependence can be reduced, and predictive algorithms can optimize biomechanical properties. CAD/CAM and 3D printing are used to manufacture bioceramic implants with controlled porosity, mechanical integrity, and enhanced osteoconductive properties. AI-driven predictive models have demonstrated potential for supporting material selection, manufacturing quality control, and treatment planning; however, their ability to reliably predict long-term postoperative outcomes requires further validation through prospective clinical studies. Data standardization, algorithm transparency, regulatory approval, clinical trial validation, and ethical considerations remain challenges. Conclusions: AI, CBCT imaging, computational modeling, and advanced bioceramic manufacturing are being combined to create an end-to-end digital workflow for personalized reconstruction. Clinical studies must be conducted prospectively to maximize the therapeutic potential of these technologies. Full article

After an anomaly is detected in an energy cyber-physical system, operators must decide whether to respond as if the process has physically failed or as if the measurements have lost integrity. These causes can require different actions, yet they may produce similar telemetry. We present a detection-gated triage framework that first applies a fixed canonical-variate detector and then uses nine physical-consistency features to distinguish physical process faults from measurement-integrity anomalies. Evaluations on the Tennessee Eastman Process and a wind-farm simulator yielded cause-attribution AUCs of 0.873 and 0.895 and end-to-end balanced accuracies of 0.805 and 0.792, respectively. Under the specified response-cost matrices, the policy reduced expected misattribution cost by 18.4% and 28.9% relative to blanket responses. Tests on real datasets show that ranking can transfer, but operating thresholds require plant-specific calibration. Exact matched sensor-fault/attack pairs give chance-level discrimination, demonstrating a fundamental boundary: measurement-only data cannot identify different causes that generate the same observations. The framework is therefore intended as an auditable triage aid, with ambiguous or out-of-distribution cases routed to review rather than treated as confirmed cyber attribution. Full article

This paper investigates the behavior of the Received Signal Strength Indicator (RSSI) in Z-Wave networks through a comparison between simulation-generated and experimentally measured data. A prototype star-topology Z-Wave network was implemented and RSSI values were measured at distances from 1 m to 5 m with a gradually increasing number of active end devices in a laboratory environment. In parallel, a software simulation environment was used to reproduce the same topology and generate RSSI values under different propagation assumptions. In order to compare the results obtained from real and simulated environments, the average deviation between the range of measured values for RSSI was calculated. The experimental results show that the RSSI values from the simulation product follow the trend of deteriorating values with increasing distance between the central and end devices. The calculated average deviation between the range of RSSI values in real and simulated environments shows that the simulated results are closest to the real ones at an exponential coefficient n ≈ 2, and the calculated deviation is from 2 dBm to 2.3 dBm. The results obtained support the evaluation of the simulation model and show its potential for studying signal behavior and aiding the design of reliable Z-Wave-based Internet of Things environments. Full article

Background: Systemic inflammation significantly impacts graft survival and clinical outcomes following living donor liver transplantation (LDLT). The systemic immune-inflammation index (SII), which integrates peripheral neutrophil, platelet, and lymphocyte counts, has shown prognostic value in various clinical settings, but its role in LDLT has not been thoroughly investigated. Methods: This retrospective cohort study included 378 consecutive adult patients with end-stage liver disease (ESLD) who underwent primary LDLT between March 2016 and February 2025. The SII was calculated as (neutrophil × platelet)/lymphocyte. Spearman correlation analysis was used to assess the relationships between the preoperative SII, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) and clinical outcomes, including the preoperative Model for End-Stage Liver Disease (MELD) score, duration of mechanical ventilation, and lengths of stay in the intensive care unit (ICU) and hospital. Receiver operating characteristic (ROC) curve analysis determined the optimal cut-off values for predicting 30-day mortality, and the areas under the curve (AUROCs) were compared using the DeLong test. Multivariable logistic regression was used to identify independent predictors of 30-day mortality. Results: Among 378 analyzable patients, the 30-day mortality rate was 7.9% (30/378). Both the SII and the NLR were significantly higher in non-survivors than in survivors (SII: median 368.8 vs. 169.1, p < 0.001; NLR: 6.0 vs. 2.4, p < 0.001), whereas the PLR did not differ significantly. On ROC analysis for 30-day mortality, the NLR and the SII showed comparable discrimination (NLR AUROC = 0.729, 95% CI: 0.63–0.82; SII AUROC = 0.705, 95% CI: 0.60–0.80; DeLong p = 0.43), both exceeding the PLR (AUROC = 0.541). The optimal SII cut-off was 275 × 10⁹ cells/L (sensitivity 70.0%; specificity 69.5%). Patients with an SII ≥ 275 × 10⁹ cells/L had significantly lower 30-day survival than those below the cut-off (83.5% vs. 96.4%; log-rank p < 0.001). On multivariable logistic regression adjusting for age and MELD score as continuous variables, an SII ≥ 275 × 10⁹ cells/L remained an independent predictor of 30-day mortality (aOR = 3.78; 95% CI: 1.60–8.93; p = 0.002); the MELD score was also independently predictive (aOR = 1.04 per point; 95% CI: 1.01–1.08; p = 0.018). The association persisted when the SII was modelled continuously (aOR = 1.60 per unit log SII; 95% CI: 1.08–2.38; p = 0.020). At the 275 cut-off, sensitivity for 30-day death was 70.0% and the positive predictive value 16.5%. Conclusions: The preoperative SII and NLR are simple, inexpensive, CBC-derived inflammatory indices significantly associated with 30-day mortality after LDLT for ESLD. An elevated preoperative SII independently predicts early post-transplant mortality and may aid perioperative risk stratification, although it does not outperform the NLR. Full article

In this paper, a pixel architecture that can be re-programmed to favor low noise, high speed, high dynamic range, or other characteristics desired in scientific imaging is proposed. Termed a programmable readout (PRO) pixel, this novel design combines a pinned photodiode, a deep-buried-channel CCD router, and multiple in-pixel amplifiers of different characteristics. By clocking the CCD router with different sequences, photogenerated charge can be steered to an appropriate readout amplifier given the application. The CCD router can be further programmed to store charge from multiple frames. The concept of such a pixel and its operation are described in detail and TCAD simulations aid in this discussion. To demonstrate this concept of programmability, a test chip is made with a CCD router and two in-pixel amplifiers: a floating diffusion amplifier (FDA) for fast readout and high-illumination imaging and a floating gate amplifier (FGA) for a low-noise Skipper-in-CMOS readout for low-light imaging. The test chip contains a 36 × 94 array of 20 µm pixels and contains 9 different pixel variants for experimental analysis. At the end of the paper, preliminary images captured from the different amplifiers are presented which demonstrate the ability to move charge to the selected readout while showcasing the different output characteristics of the two readout amplifiers. Furthermore, the output image from the Skipper-in-CMOS is compared across different numbers of non-destructive samples to demonstrate its noise-reduction capability. Full article

Accurate multi-object tracking and metric localization support traffic monitoring and cooperative intelligent transportation at complex intersections. This study presents a fixed-camera vision-map fusion framework that addresses two practical difficulties: axis-aligned boxes poorly represent turning vehicles, and unconstrained image-plane tracking can produce physically implausible trajectories. A map-aided frontend first generates candidate detections using improved You Only Look Once version 8 nano (YOLOv8n) horizontal bounding box (HBB) branch and an improved YOLOv8 oriented bounding box (OBB) branch. A high-definition (HD) map selector then retains the candidate geometry consistent with the straight-driving or turning region and converts it into a unified detection record. The selected reference point is projected to the ground plane through an offline-estimated homography, whereas the appearance feature bypasses the homography and is passed directly to the association stage. The tracking backend uses a 12-dimensional joint image/metric state, symmetric central-difference evaluations of the process and measurement functions, appearance-motion association, and a feasible-road projection derived from HD-map lane polygons. On the evaluated public sequences, the complete configuration achieved a multiple object tracking accuracy (MOTA) of 74.5%, an identification F1 score (IDF1) of 82.6%, 614 identity switches, and a throughput of 26.8 frames per second (FPS) on an RTX 4090 workstation. In a descriptive Vehicle-in-the-Loop case study involving one instrumented vehicle at one intersection, the overall localization mean absolute error (MAE) was 0.180 m, compared with 0.208 m for the baseline end-to-end configuration. These results indicate the feasibility of combining branch-specific vehicle geometry with map-constrained tracking; controlled same-detector comparisons, repeated multi-vehicle trials, and embedded-device latency and power profiling remain necessary for broader claims. Full article

College access programs are well documented to build procedural knowledge of admissions and financial aid, yet less is known about how they demystify the hidden curriculum of college-going for Latine students. To address this gap, we examine a four-day college access immersion program for Latine high school students in North Carolina. Guided by a relational engagement framework, we analyzed 205 open-ended survey responses from 96 Latine high school juniors and seniors, collected through the program’s anonymous daily evaluations, using a two-cycle iterative coding approach. Findings reveal five forms of learning through which participants described the program countering dominant narratives of exclusion: (1) reframing identity and culture as assets, (2) building confidence through intergroup exchange, (3) enacting agency for college access, (4) uncovering the hidden curriculum of financial well-being and college persistence, and (5) disrupting exclusionary assumptions about college access. The study extends relational engagement theory by demonstrating how culturally sustaining programmatic networks render implicit institutional knowledge visible and accessible. Implications for research and culturally responsive practice are discussed. Full article

The urgent need for novel glioblastoma (GBM) therapies has motivated the exploration of EGFR G-quadruplex (G4) targeting, which suppresses oncogene transcription independently of kinase activity. However, existing EGFR G4 ligands lack subtype selectivity. Through computer-aided design and systematic bioisosteric modifications of the 1H-imidazo[4,5-f][1,10]phenanthroline scaffold, we synthesized a series of derivatives. We evaluated their G4 interactions by molecular docking, UV-Vis, FRET, ITC, and CD. Compound 3, bearing a 6-bromopiperonyl group, exhibited nanomolar affinity for EGFR G4 (Kd = 102 nM) with ~146-fold selectivity over K-Ras G4, stabilizing it via end-stacking. It potently inhibited U87-MG glioblastoma cell proliferation (IC₅₀ = 0.49 μM), with a wide safety margin relative to normal HMC3 microglia (safety index = 14.18). Mechanistically, compound 3 triggered DNA damage, mitochondrial oxidative stress, and sequential multi-organelle damage to mitochondria, lysosomes, and the endoplasmic reticulum, while impairing cell migration and invasion. These findings establish compound 3 as a uniquely selective EGFR G4 stabilizer, offering a promising multi-organelle-damage strategy for glioblastoma therapy. Full article

The rapid growth of computing-intensive and latency-sensitive applications, including augmented reality, virtual reality, and self-driving systems, has increased the demand for low-latency and energy-efficient processing solutions. Mobile edge computing (MEC) has evolved as a transformative paradigm by relocating computation to the network edge, closer to end users. Unmanned aerial vehicles (UAVs) further strengthen MEC by offering flexible deployment, mobility, and reliable line-of-sight communication, making them suitable for temporary high-demand scenarios. Moreover, such latency-sensitive applications often generate numerous repetitive tasks and, thus, storing the results of these tasks can reduce both communication overhead and computational workload. However, jointly addressing the caching of task-results alongside offloading and resource allocation decisions in UAV-aided MEC networks remains a non-trivial challenge. In this study, an integrated task offloading and resource allocation with data caching (JORC) framework is proposed to address these challenges. The offloading and resource allocation problems are formulated as a Markov decision process and solved using the soft actor–critic reinforcement learning algorithm. In addition, dynamic and adaptive caching manages limited storage and reduces redundant computations by using a hybrid strategy that integrates the least-frequently used and least-recently used policies to reduce computational redundancy. Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches. Full article

Background/Objectives: Human papillomavirus (HPV) is now responsible for the majority of oropharyngeal squamous cell carcinomas (OPSCCs), an HPV-related cancer of direct relevance to the dental profession; oral-health providers may be well-positioned to perform routine oral examination, recognize suspicious signs, refer appropriately, and educate patients about HPV-related oropharyngeal cancer and HPV vaccination. This study assessed knowledge, attitudes and practices regarding sexually transmitted infections (STIs), HPV, HIV/AIDS and HPV-related oral and oropharyngeal cancer among secondary-school students in Apulia, Italy, with particular attention to the dental setting. Methods: A cross-sectional study was conducted by the University of Foggia among 1224 students from three secondary schools at the end of the 2025/2026 school year) using a self-administered 108-item questionnaire. A 63-item composite knowledge score was computed; “don’t know” answers were scored as incorrect and adequate knowledge defined a priori as ≥60% (Bloom’s cut-off). The minimum required sample (737) was derived using Cochran’s formula adjusted for a cluster-sampling design effect. Results: Overall knowledge was low (mean 28.5%, SD 18.2); only 5% reached the ≥60% threshold (Bloom’s cut-off). Only 14.8% of students were aware that HPV can cause oropharyngeal cancer, the lowest figure among all oncogenic outcomes assessed and significantly lower in boys (11.2%) than girls (17.3%; p = 0.003), despite boys carrying the higher OPSCC risk. Knowledge was independently associated with female sex, age and sexual experience. Despite poor knowledge, 61.2% of students would like to receive the HPV vaccine. Conclusions: The dental visit may represent an under-used opportunity to address this oral-cancer-prevention knowledge gap; the integration of oral-health professionals into school-based and clinical HPV-prevention strategies is a plausible public-health implication. Full article

Gut-associated lymph nodes (GALNs) constitute an important anatomical reservoir during early and chronic human immunodeficiency virus (HIV)/simian immunodeficiency virus (SIV) infection, but the fate of this compartment during terminal AIDS remains unclear. This study investigated viral persistence and lymph node injury in a rhesus macaque with long-term SIVmac239 infection and end-stage AIDS, characterized by profound CD4+ T cell depletion (30 cells/μL) and persistent viremia (1192 copies/mL). At necropsy, GALNs, including mesenteric, paracolic, and ileocecal lymph nodes, and non-gut-associated lymph nodes (NGALNs), including hepatic hilar, common iliac, and inguinal lymph nodes, were collected for viral RNA/DNA quantification, histopathology, immunofluorescence, and transcriptomics. SIV DNA was markedly lower in GALNs than in NGALNs, whereas SIV RNA was detectable only in NGALNs. GALNs exhibited extensive fibrotic remodeling, paracortical collapse, severe CD4+ T cell loss across B cell and paracortical regions, and pronounced CD8+ T cell accumulation within B cell zones. Transcriptomic profiling further revealed an exhaustion signature in GALNs, with reduced DNA replication and repair, impaired cell-cycle activity, and suppressed RNA processing, accompanied by activation of innate immune programs and attenuation of adaptive immune responses. These findings indicate that, during terminal AIDS, GALNs no longer serve as the predominant viral reservoir but instead undergo preferential and severe structural and functional exhaustion. This case highlights marked anatomical heterogeneity in lymph node vulnerability during advanced disease and supports a model in which collapse of the gut-associated immune barrier contributes to disease progression toward terminal AIDS. Full article

Implantable devices have undergone enormous development in the past several decades and more results are expected as there are still unanswered questions. In manufacturing, a relatively new technology called three-dimensional (3D) printing has become increasingly involved, from which hot-melt extrusion (HME) coupled with fused deposition modeling (FDM) is one of the most researched methods in producing implantable drug delivery systems (IDDS). The HME process is used to produce polymer filaments that are the carriers of the applied drugs, while FDM creates the implant itself from the filaments, based on the computer-aided designs. The availability of several polymers like polycaprolactone, polylactic acid, or thermoplastic polyurethane, etc., allows the incorporation of many active pharmaceutical ingredients while digital designs offer numerous variabilities in designs, formulations, and applications of 3D-printed IDDS. This allows more specific and detailed modifications in the end product which can forecast the possibility of personalized treatments and therapies. In this review, our research group gathered together different HME-FDM-printed IDDS to provide examples about the diverse applicability of 3D printing. These products, just like any other device and medicine in the medical field, must be characterized and evaluated properly. The methods and technology that are needed already exist and can be repeatedly used in the characterization of IDDSs; we also discuss these methods based on the available publications. In conclusion, every condition is given to make research and manufacture personalized 3D-printed IDDSs possible, however more research work and proof of safe usage are crucial to make these devices applicable in everyday medical treatments. Full article