Search Results (2,084)

Public schools often operate with shared devices, unstable connectivity, and limited support for digital tools, which can make feature-heavy platforms difficult to adopt and sustain. This study reports the first formal design iteration and formative evaluation of VLEPIC, a school-centred virtual learning environment (VLE) developed to support secondary English as a Foreign Language in a low-resource Ecuadorian public school. Using a design-based research approach with a convergent mixed-methods design, one Grade 10 cohort (n = 42; two intact classes) used VLEPIC for one month as a complement to regular lessons. Data were collected through questionnaires on perceived usability and motivation, platform usage logs, and open-ended feedback from students and the teacher; results were analysed descriptively and thematically and then integrated to inform design decisions. Students reported high perceived usability and strong motivational responses in attention, relevance, and satisfaction, while confidence was more heterogeneous. Usage logs indicated recurrent but uneven engagement, with distinct low-, medium-, and high-activity profiles. Qualitative feedback highlighted enjoyment and clarity alongside issues with progress tracking between missions, navigation on mobile devices, and task submission reliability. The main contribution is a set of empirically grounded, context-sensitive design principles linking concrete interface and task-design decisions to perceived usability, motivation, and real-world usage patterns in constrained school settings. Full article

Background/Objectives: Children in paediatric palliative care often face school attendance barriers due to complex health needs. This study describes post-training perceptions of a training program by a pediatric hospice team to prepare school care assistants to safely include children with complex conditions, focusing on procedural skills, knowledge of the child, and family partnership. Methods: Care assistants who completed a structured course at the Paediatric Palliative Care Centre, University Hospital of Padua (2023–2024), were surveyed immediately after training. The program combined classroom instruction with hands-on simulation using high-fidelity mannequins and standard devices, including suction, pulse oximetry, ventilation, enteral feeding, and tracheostomy care. It also covered modules on urgent and emergency management, as well as family communication. An anonymous online questionnaire gathered socio-demographic data, prior training, clinical tasks performed, self-efficacy levels, and open-ended feedback. Quantitative results were analyzed descriptively, while qualitative comments were subjected to thematic analysis. Results: Of 130 invited assistants, 105 participated (81%). Participants reported strong perceived confidence: 85% selected the upper end of the 5-point scale (“very” or “extremely”) for routine-management ability, and 60% selected these same response options for emergency-management ability. In the most severe events recalled, 60.5% of incidents were resolved autonomously, 7.6% involved contacting emergency services, and 3.8% involved community or hospice nurses. Seventy-five percent judged the course comprehensive; thematic analysis of 102 comments identified satisfaction, requests for regular refreshers, stronger practical components, and requests for targeted topics. Conclusions: Immediately after the session, participants tended to select the upper end of the self-assurance item for both routine and emergency tasks. Combining core emergency procedures with personalized, child-specific modules and family-partnership training may support safety, trust, and inclusion. Regular refreshers and skills checks are advised. Full article

Rubber materials undergo continuous wear in high-pressure seal applications. To address the risk of adhesive wear and consequent leakage of rubber seals operating under reciprocating sliding in high-pressure hydrogen storage and refueling systems, this study employed high-pressure hydrogen tribology testing. Ball-on-disk reciprocating tests were conducted using a 316L stainless-steel ball against silica-filled nitrile butadiene rubber (NBR), and the friction response and wear-morphology evolution were compared under ambient air, 1 MPa hydrogen (H₂), 50 MPa H₂, 50 MPa nitrogen (N₂), and grease-coated conditions. Under dry sliding, the coefficient of friction (COF) of NBR in air and hydrogen ranged from 1.34 to 1.44, whereas it decreased markedly to 0.942 in 50 MPa N₂. The wear volume under the four dry conditions was concentrated in the range of ~0.292–0.320 mm³. After grease coating, the steady-state COF in air and at 50 MPa H₂ dropped to 0.099 and 0.105, respectively, and the wear features changed from ridge-like wear patterns/tear pits to regular, smooth indentations with slight running marks. The results demonstrate that a lubricating film can effectively separate direct metal–rubber contact and suppress stick–slip, enabling a low-friction, low-wear, and highly stable interface in high-pressure hydrogen, and providing a practical engineering route for reliable operation of rubber seals in hydrogen service. Full article

Neurodegenerative diseases are distinguished by synaptic dysfunction and chronic neuroinflammation, which accelerate neuronal loss and impair network resilience. Synaptic plasticity, that is, the ability to adapt to changes, is progressively lost. This ability is part of hormesis, an adaptive biphasic response, nowadays acknowledged as a promising tool in chronic degenerative diseases, since it offers a framework for personalized interventions. Growing evidence supports exercise as a powerful approach for managing neurodegenerative disorders, due to its capacity to enhance neuroplasticity through the direct targeting of the biomolecular processes involved. Indeed, regular exercise can drive many molecular mediators and signals toward neuroplasticity improvement, potentially slowing neurodegeneration. This narrative review focuses on exercise as a promising therapeutic approach in neurodegenerative diseases, based on its ability to shape synaptic plasticity at the molecular level. Some biomediators involved in synaptic plasticity function/dysfunction and neuroinflammation/neurodegeneration are addressed as therapeutic targets of exercise, and different exercise regimens are discussed as specific therapeutic interventions to contain the burden of some neurodegenerative conditions. Some clinical trials including exercise in the treatment of neurodegenerative diseases are summarized. Since no definitive disease-modifying cure exists for these illnesses, exercise’s ability to shape synaptic plasticity emerges as a highly attractive therapeutic approach. Full article

This study proposes a cross-disciplinary computational framework to advance the sustainable design of free-form grid roofs in hot climates, integrating architectural geometry with building thermal performance to enhance climate adaptability. Numerical analyses systematically explore the impact of thermal objectives, initial configurations, shape control strategies, and boundary constraints. The optimization results demonstrate that targeting indoor temperature under extreme heat yields saddle-shaped, self-shading morphologies, which achieve a measurable improvement in thermal comfort by reducing indoor temperatures by approximately 2 °C. A key practical finding is that symmetric-point control outperforms full-point control. While full-point control may generate forms with complex central depressions that complicate drainage, symmetric-point control consistently yields morphologies that are inherently more regular, symmetric, and constructible. This results in a superior balance among thermal performance, practical design attributes (e.g., drainage feasibility and construction simplicity), and geometric coherence—a combination that aligns closely with real-world engineering requirements. Furthermore, directional boundary constraints are proven to be effective tools for regulating passive shading performance. The proposed framework provides engineers and designers with a systematic and automated method for the climate-responsive and low-carbon design of free-form architectural morphologies, contributing to the development of more sustainable and resilient building infrastructure. Full article

Environmental changes and sensor aging can cause sensor drift in sensor array responses (i.e., a shift in the measured signal/feature distribution over time), which in turn degrades gas classification performance in real-world deployments of electronic-nose systems. Previous studies using the UCI Gas Sensor Array Drift Dataset as a benchmark reported promising drift compensation results but often lacked robust statistical validation and may overcompensate for drift by suppressing class-discriminative variance. To address these limitations and rigorously evaluate improvements in sensor-drift compensation, we designed two domain adaptation tasks based on the UCI electronic-nose dataset: (1) using the first batch to predict remaining batches, simulating a controlled laboratory setting, and (2) using Batches 1 through $n-1$ to predict Batch n, simulating continuous training data updates for online training. Then, we systematically tested three methods—our semi-supervised knowledge distillation method (KD) for sensor-drift compensation; a previously benchmarked method, Domain-Regularized Component Analysis (DRCA); and a hybrid method, KD–DRCA—across 30 random test-set partitions on the UCI dataset. We showed that semi-supervised KD consistently outperformed both DRCA and KD–DRCA, achieving up to 18% and 15% relative improvements in accuracy and F1-score, respectively, over the baseline, proving KD’s superior effectiveness in electronic-nose drift compensation. This work provides a rigorous statistical validation of KD for electronic-nose drift compensation under long-term temporal drift, with repeated randomized evaluation and significance testing, and demonstrates consistent improvements over DRCA on the UCI drift benchmark. Full article

Radon is one of the leading causes of lung cancer worldwide. Following the implementation of the European Council Directive 2013/59/EURATOM, regular measurements of radon concentrations in workplaces have been carried out in European countries for approximately ten years. This provides a basis for assessing the exposure of workers and the general population to radon, as well as for determining the need to implement measures aimed at reducing this exposure. In addition to commonly used methods that focus on eliminating radon sources or minimizing its ingress into buildings, there are also temporary measures available, such as using air purifiers to improve indoor air quality. Although they are not recommended as a standalone or definitive solution, they can be useful as an interim measure—until appropriate actions to reduce indoor radon concentrations are implemented. In this study, five commercially available air purifiers were tested under controlled laboratory conditions to assess their impact on radon and its decay products. The results show that none of the tested devices significantly reduced gaseous radon concentrations. However, the air purifiers were highly effective in removing radon progeny, achieving a 95–99% reduction in potential alpha energy concentration (PAEC) and reducing the equilibrium factor from 48 to 76% to 0–2%. From a sustainability perspective, these findings are relevant for public health protection, responsible consumer decision-making, and evidence-based indoor air quality management. By distinguishing between ineffective radon gas removal and effective reduction of dose-relevant decay products, this study supports sustainable risk mitigation strategies and helps prevent the misuse of energy- and resource-intensive technologies for purposes they cannot fulfill. Full article

Background/Objectives: The vestibular labyrinth is classically viewed as a sensor of low-frequency head motion—linear acceleration for the otoliths and angular velocity/acceleration for the semicircular canals. However, there is now substantial evidence that air-conducted sound (ACS) can also activate vestibular receptors and afferents in mammals and other vertebrates. This sound sensitivity underlies sound-evoked vestibular-evoked myogenic potentials (VEMPs), sound-induced eye movements, and several clinical phenomena in third-window pathologies. The cellular and biophysical mechanisms by which a pressure wave in the cochlear fluids is transformed into a vestibular neural signal remain incompletely integrated into a single framework. This study aimed to provide a narrative synthesis of how ACS activates the vestibular labyrinth, with emphasis on (1) the anatomical and biophysical specializations of the maculae and cristae, (2) the dual-channel organization of vestibular hair cells and afferents, and (3) the encoding of fast, jerk-rich acoustic transients by irregular, striolar/central afferents. Methods: We integrate experimental evidence from single-unit recordings in animals, in vitro hair cell and calyx physiology, anatomical studies of macular structure, and human clinical data on sound-evoked VEMPs and sound-induced eye movements. Key concepts from vestibular cellular neurophysiology and from the physics of sinusoidal motion (displacement, velocity, acceleration, jerk) are combined into a unified interpretative scheme. Results: ACS transmitted through the middle ear generates pressure waves in the perilymph and endolymph not only in the cochlea but also in vestibular compartments. These waves produce local fluid particle motions and pressure gradients that can deflect hair bundles in selected regions of the otolith maculae and canal cristae. Irregular afferents innervating type I hair cells in the striola (maculae) and central zones (cristae) exhibit phase locking to ACS up to at least 1–2 kHz, with much lower thresholds than regular afferents. Cellular and synaptic specializations—transducer adaptation, low-voltage-activated K⁺ conductances (K_LV), fast quantal and non-quantal transmission, and afferent spike-generator properties—implement effective high-pass filtering and phase lead, making these pathways particularly sensitive to rapid changes in acceleration, i.e., mechanical jerk, rather than to slowly varying displacement or acceleration. Clinically, short-rise-time ACS stimuli (clicks and brief tone bursts) elicit robust cervical and ocular VEMPs with clear thresholds and input–output relationships, reflecting the recruitment of these jerk-sensitive utricular and saccular pathways. Sound-induced eye movements and nystagmus in third-window syndromes similarly reflect abnormally enhanced access of ACS-generated pressure waves to canal and otolith receptors. Conclusions: The vestibular labyrinth does not merely “tolerate” air-conducted sound as a spill-over from cochlear mechanics; it contains a dedicated high-frequency, transient-sensitive channel—dominated by type I hair cells and irregular afferents—that is well suited to encoding jerk-rich acoustic events. We propose that ACS-evoked vestibular responses, including VEMPs, are best interpreted within a dual-channel framework in which (1) regular, extrastriolar/peripheral pathways encode sustained head motion and low-frequency acceleration, while (2) irregular, striolar/central pathways encode fast, sound-driven transients distinguished by high jerk, steep onset, and precise spike timing. Full article

The deployment of Advanced Driver-Assistance Systems (ADAS) in economically constrained markets frequently relies on hardware architectures that lack dedicated graphics processing units. Within such environments, the integration of deep neural networks faces significant hurdles, primarily stemming from strict limitations on energy consumption, the absolute necessity for deterministic real-time response, and the rigorous demands of safety certification protocols. Meanwhile, traditional geometry-based lane detection pipelines continue to exhibit limited robustness under adverse illumination conditions, including intense backlighting, low-contrast nighttime scenes, and heavy rainfall. Motivated by these constraints, this work re-examines geometry-based lane perception from a sensor-level viewpoint and introduces a Binary Line Segment Filter (BLSF) that leverages the inherent structural regularity of lane markings in bird’s-eye-view (BEV) imagery within a computationally lightweight framework. The proposed BLSF is integrated into a complete pipeline consisting of inverse perspective mapping, median local thresholding, line-segment detection, and a simplified Hough-style sliding-window fitting scheme combined with RANSAC. Experiments on a self-collected dataset of 297 challenging frames show that the inclusion of BLSF significantly improves robustness over an ablated baseline while sustaining real-time performance on a 2 GHz ARM CPU-only platform. Additional evaluations on the Dazzling Light and Night subsets of the CULane and LLAMAS benchmarks further confirm consistent gains of approximately 6–7% in F₁-score, together with corresponding improvements in IoU. These results demonstrate that interpretable, geometry-driven lane feature extraction remains a practical and complementary alternative to lightweight learning-based approaches for cost- and safety-critical ADAS applications. Full article

Foodborne pathogens remain a major global concern, demanding rapid, accessible, and determination technologies. Conventional methods, such as culture assays and polymerase chain reaction, offer high accuracy but are time-consuming for on-site testing. This study presents a portable, smartphone-assisted dual-mode biosensor that combines colorimetric and photothermal speckle imaging for improved sensitivity in lateral flow assays (LFAs). The prototype device, built using low-cost components ($500), uses a Raspberry Pi for illumination control, image acquisition, and machine learning-based signal analysis. Colorimetric features were derived from normalized RGB intensities, while photothermal responses were obtained from speckle fluctuation metrics during periodic plasmonic heating. Multivariate linear regression, with and without LASSO regularization, was used to predict Salmonella concentrations. The comparison revealed that regularization did not significantly improve predictive accuracy indicating that the unregularized linear model is sufficient and that the extracted features are robust without complex penalization. The fused model achieved the best performance (R² = 0.91) and consistently predicted concentrations down to a limit of detection (LOD) of 10⁴ CFU/mL, which is one order of magnitude improvement of visual and benchtop measurements from previous work. Blind testing confirmed robustness but also revealed difficulty distinguishing between negative and 10³ CFU/mL samples. This work demonstrates a low-cost, field-deployable biosensing platform capable of quantitative pathogen detection, establishing a foundation for the future deployment of smartphone-assisted, machine learning-enabled diagnostic tools for broader monitoring applications. Full article

Women of reproductive age, especially those with polycystic ovarian syndrome (PCOS), often use glucagon-like peptide-1 receptor agonists (GLP-1RAs) to improve their metabolic functions. A growing body of evidence suggests that GLP-1R signaling may directly affect ovarian physiology, influencing granulosa cell proliferation, survival pathways, and steroidogenic production, in addition to its systemic metabolic effects. Nonetheless, there is a limited comprehension of the molecular mechanisms that regulate these activities and their correlation with menstrual function, reproductive potential, and assisted reproduction. This comprehensive review focuses on ovarian biology, granulosa cell signaling networks, steroidogenesis, and translational fertility outcomes, integrating clinical, in vivo, and in vitro information to elucidate the effects of GLP-1 receptor agonists on reproductive health. We conducted a thorough search of PubMed, Scopus, and Web of Science for randomized trials, prospective studies, animal models, and cellular experiments evaluating the effects of GLP-1RA on reproductive or ovarian outcomes, in accordance with PRISMA criteria. The retrieved data included metabolic changes, androgen levels, monthly regularity, ovarian structure, granulosa cell growth and death, FOXO1 signaling, FSH-cAMP-BMP pathway activity, and fertility or IVF results. Clinical trials shown that GLP-1 receptor agonists improve menstrual regularity, decrease body weight and central adiposity, increase sex hormone-binding globulin levels, and lower free testosterone in overweight and obese women with PCOS. Liraglutide, when combined with metformin, significantly improved IVF pregnancy rates, whereas exenatide increased natural conception rates. Mechanistic studies demonstrate that GLP-1R activation affects FOXO1 phosphorylation, hence promoting granulosa cell proliferation and anti-apoptotic processes. Incretin signaling altered steroidogenesis by reducing the levels of StAR, P450scc, and 3β-HSD, so inhibiting FSH-induced progesterone synthesis, while simultaneously enhancing BMP-Smad signaling. Animal studies demonstrated both beneficial (enhanced follicular growth, anti-apoptotic effects) and detrimental results (oxidative stress, granulosa cell death, uterine inflammation), indicating a context- and dose-dependent response. GLP-1 receptor agonists influence female reproductive biology by altering overall physiological processes and specifically impacting the ovaries via FOXO1 regulation, steroidogenic enzyme expression, and BMP-mediated FSH signaling. Preliminary clinical data indicate improved reproductive function in PCOS, as seen by increased pregnancy rates in both natural and IVF cycles; nevertheless, animal studies reveal a potential risk of ovarian and endometrial damage. These results highlight the need for controlled human research to clarify reproductive safety, molecular pathways, and optimum therapy timing, particularly in non-PCOS patients and IVF settings. Full article

Background and Clinical Significance: Cutaneous Rosai-Dorfman disease (CRDD) is a rare, benign histiocytic proliferative disorder, accounting for approximately 3% of all Rosai-Dorfman disease (RDD) cases. Currently, the diagnosis of CRDD relies on invasive pathological examination due to the absence of reliable non-invasive alternatives. This case series evaluates the potential utility of high-frequency ultrasound (HFUS) as an adjunctive diagnostic tool for CRDD. Case Presentation: We present three CRDD cases, correlating HFUS features with histopathology. All cases showed hypoechoic lesions with varying infiltration depths and morphologies, though no specific diagnostic features were identified. HFUS clearly delineated involvement of the dermal and subcutaneous layers, assessed morphological characteristics like contour regularity and border definition, and evaluated vascularity. This information is crucial for clinical decision-making. HFUS also demonstrated value in therapeutic follow-up. In Case 1, it objectively showed a reduction in lesion size and decreased internal vascularity, providing clear evidence of treatment response. Conclusions: Although HFUS cannot independently diagnose CRDD and histopathology remains the gold standard, it serves as a valuable complementary tool. HFUS allows evaluation of deeper tissue structures, infiltration depth, and vascularity. As a non-invasive modality, it is useful for treatment monitoring, therapy guidance, and prognosis assessment. Integrating HFUS into the CRDD workflow enables more comprehensive and precise management. Full article

Background: Inadequate water intake is prevalent among Chinese college students, a group at a critical stage for establishing lifelong health habits. However, nationwide data on their knowledge, attitudes, and practices (KAP) regarding water intake remain scarce. This study aims to describe regional variations in water-related KAP among undergraduates across seven major geographical regions of China, providing evidence for developing targeted health promotion strategies. Methods: A cross-sectional survey employed multistage stratified convenience sampling to recruit undergraduate students (N = 3161) from one university in each of China’s seven regions. Participants completed a KAP questionnaire. Data analysis utilized chi-square tests with Bonferroni correction, reporting effect sizes and confidence intervals. Results: A total of 3161 valid responses were obtained (response rate: 98.3%). Students in South China demonstrated the lowest awareness of regular water intake (52.0%) but the highest awareness of daily recommended water intake (32.9%). South China and Northeast China exhibited weaker recognition of water’s importance (65.6% and 94.0%, respectively) and the lowest prevalence of “thirst-driven” drinking behavior (21.7% and 32.4%, respectively). Conclusions: The knowledge, attitude, and practice (KAP) status regarding water consumption among Chinese university students is concerning and exhibits significant regional disparities. Key issues include knowledge gaps, disconnect between attitudes and behaviors, and deeply ingrained unscientific drinking habits. Analysis based on KAP theory indicates that future health promotion strategies must move beyond mere knowledge dissemination and adopt region-specific, multilevel comprehensive interventions. Full article

In citrus cultivation practice, regular monitoring of leaf leaf relative water content (RWC) can effectively guide water management, thereby improving fruit quality and yield. When applying hyperspectral technology to citrus leaf moisture monitoring, the precise quantification of RWC still needs to address issues such as data noise and algorithm adaptability. The noise interference and spectral aliasing in RWC sensitive bands lead to a decrease in the accuracy of moisture inversion in hyperspectral data, and the combined sensitive bands of chlorophyll (LCC) in citrus leaves can affect its estimation accuracy. In order to explore the optimal prediction model for RWC of citrus leaves and accurately control irrigation to improve citrus quality and yield, this study is based on 401–2400 nm spectral data and extracts noise robust features through continuous wavelet transform (CWT) multi-scale decomposition. A high-precision estimation model for citrus leaf RWC is established, and the potential of CWT in RWC quantitative inversion is systematically evaluated. This study is based on the multi-scale analysis characteristics of CWT to probe the time–frequency characteristic patterns associated with RWC and LCC in citrus leaf spectra. Pearson correlation analysis is used to evaluate the effectiveness of features at different decomposition scales, and the successive projections algorithm (SPA) is further used to eliminate band collinearity and extract the optimal sensitive band combination. Finally, based on the selected RWC and LCC-sensitive bands, a high-precision predictive model for citrus leaf RWC was established using partial least squares regression (PLSR). The results revealed that (1) CWT preprocessing markedly boosts the estimation accuracy of RWC and LCC relative to the original spectrum (max improvements: 6% and 3%), proving it enhances spectral sensitivity to these two indices in citrus leaves. (2) Combining CWT and SPA, the resulting predictive model showed higher inversion accuracy than the original spectra. (3) Integrating RWC Scale7 and LCC Scale5-2224/2308 features, the CWT-SPA fusion model showed optimal predictive performance (R² = 0.756, RMSE = 0.0214), confirming the value of multi-scale feature joint modeling. Overall, CWT-SPA coupled with LCC spectral traits can boost the spectral response signal of citrus leaf RWC, enhancing its prediction capability and stability. Full article

In this paper, we propose an alternative kernel estimator for the regression operator of scalar response variable S given a functional random variable T that takes values in a semi-metric space. The new estimator is constructed through the minimization of the least absolute relative error (LARE). The latter is characterized by its ability to provide a more balanced and scale-invariant measure of prediction accuracy compared to traditional standard absolute or squared error criterion. The LARE is an appropriate tool for reducing the influence of extremely large or small response values, enhancing robustness against heteroscedasticity or/and outliers. This feature makes the LARE suitable for functional or high-dimensional data, where variations in scale response are common. The high feasibility and strong performance of the proposed estimator are supported theoretically by establishing its stochastic consistency. The latter is derived with precision of the convergence rate under mild regularity conditions. The ease implementation and the stability of the estimator are justified by simulation studies and an empirical application to near-infrared (NIR) spectrometry data. Of course, to explore the functional architecture of this data, we employ random matrix theory (RMT), which is a principal analytical tool of econophysics. Full article