Search Results (346)

This study investigates the generation and suppression of the whistling noise caused by flow through the narrow gap of a vehicle’s side mirror, an aerodynamic phenomenon often reported as a source of discomfort to passengers. The research employs a simultaneous approach, combining wind tunnel experiments to determine the geometries and wind conditions at a flow speed of 22 m/s contributing to whistle generation at between 7 kHz and 8 kHz with numerical simulations utilizing compressible Large Eddy Simulation (LES) techniques for an in-depth investigation of the underlying aerodynamics. The Simplified Side-mirror Model (SSM) is developed, enabling precise wind visualization, and facilitating the identification of fundamental aerodynamic sound sources via vortex sound theory. The analysis reveals that the whistling sound is intricately linked to edge tone phenomena, driven by vortex shedding and flow instabilities at the angled shape in a narrow gap. Building on these insights, the study introduces the Suppressed Whistle Model (SWM), a configuration including shapes resembling a vortex generator that successfully mitigates the whistling by disrupting the identified flow structures causing the whistling sound. The suggested design is validated through wind visualization, comparing the numerical flow structures with the experimental ones. The experimental whistling sound pressure level of SWM decreases by about 20 dB compared to SSM, and a similar trend can be confirmed in the numerical results. Full article

This work presents the simulation and design of 3C-SiC power MOSFETs, focusing on critical parameters including avalanche impact ionization, breakdown voltage, bulk and channel mobilities, and the trade-off between on-resistance and breakdown voltage. The device design is carried out by evaluating the blocking voltage of scaled structures as a function of the blocking layer’s doping concentration. To mitigate edge-effect breakdown at the p-well/n-drift interface, a step-profile doping strategy is employed. Multiple transistor layouts with varying pitches are developed using a commercially available device simulator. Results are benchmarked against a one-dimensional analytical model, validating the on-state resistance, current–voltage behavior, and overall accuracy of the simulation approach. For the selected material properties, simulations predict that a 600 V 3C-SiC MOSFET achieves an on-state resistance of 0.8 mΩ·cm², corresponding to a 7 μm drift layer with a doping concentration of 1 × 10¹⁶ cm⁻³. Full article

Astrocytes and microglia constitute nearly half of all central nervous system cells and are indispensable for its proper function. Both exhibit striking morphological and functional heterogeneity, adopting either neuroprotective (A2, M2) or proinflammatory (A1, M1) phenotypes in response to cytokines, pathogen-associated molecular patterns (PAMPs)/damage-associated molecular patterns (DAMPs), toll-like receptor 4 (TLR4) activation, and NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome signaling. Crucially, many of these phenotypic transitions arise during the earliest stages of neurodegeneration, when glial dysfunction precedes overt neuronal loss and may act as a primary driver of disease onset. This review critically examines glial-centered hypotheses of neurodegeneration, with emphasis on their roles in early disease phases: (i) microglial polarization from an M2 neuroprotective state to an M1 proinflammatory state; (ii) NLRP3 inflammasome assembly via P2X purinergic receptor 7 (P2X7R)-mediated K⁺ efflux; (iii) a self-amplifying astrocyte–microglia–neuron inflammatory feedback loop; (iv) impaired microglial phagocytosis and extracellular-vesicle–mediated propagation of β-amyloid (Aβ) and tau; (v) astrocytic scar formation driven by aquaporin-4 (AQP4), matrix metalloproteinase-9 (MMP-9), glial fibrillary acidic protein (GFAP)/vimentin, connexins, and janus kinase/signal transducer and activator of transcription 3 (JAK/STAT3) signaling; (vi) cellular reprogramming of astrocytes and NG2 glia into functional neurons; and (vii) mitochondrial dysfunction in glia, including Dynamin-related protein 1/Mitochondrial fission protein 1 (Drp1/Fis1) fission imbalance and dysregulation of the sirtuin 1/peroxisome proliferator-activated receptor gamma coactivator 1-alpha (Sirt1/PGC-1α) axis. Promising therapeutic strategies target pattern-recognition receptors (TLR4, NLRP3/caspase-1), cytokine modulators (interleukin-4 (IL-4), interleukin-10 (IL-10)), signaling cascades (JAK2–STAT, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), phosphoinositide 3-kinase–protein kinase B (PI3K–AKT), adenosine monophosphate-activated protein kinase (AMPK)), microglial receptors (triggering receptor expressed on myeloid cells 2 (TREM2)/spleen tyrosine kinase (SYK)/ DNAX-activating protein 10 (DAP10), siglec-3 (CD33), chemokine C-X3-C motif ligand 1/ CX3C motif chemokine receptor 1 (CX3CL1/CX3CR1), Cluster of Differentiation 200/ Cluster of Differentiation 200 receptor 1 (CD200/CD200R), P2X7R), and mitochondrial biogenesis pathways, with a focus on normalizing glial phenotypes rather than simply suppressing pathology. Interventions that restore neuroglial homeostasis at the earliest stages of disease may hold the greatest potential to delay or prevent progression. Given the complexity of glial phenotypes and molecular isoform diversity, a comprehensive, multitargeted approach is essential for mitigating Alzheimer’s disease and related neurodegenerative disorders. This review not only synthesizes pathogenesis but also highlights therapeutic opportunities, offering what we believe to be the first concise overview of the principal hypotheses implicating glial cells in neurodegeneration. Rather than focusing on isolated mechanisms, our goal is a holistic perspective—integrating diverse glial processes to enable comparison across interconnected pathological conditions. Full article

Aim: This ex vivo study aimed to evaluate the effect of low-energy 445 nm diode laser irradiation on permanent fluoride retention in human enamel. Materials and Methods: Eighty caries-free extracted permanent human teeth were used to prepare 480 enamel discs (2 × 2 mm). Baseline fluoride content in untreated enamel specimens (control group E) was measured using particle-induced gamma-ray emission (PIGE). All specimens then received a topical application of acidulated phosphate fluoride for 5 min, followed by rinsing with double-distilled water for 1 min. Fluoride quantification was subsequently repeated. Specimens were randomly allocated into two groups: fluoridated only (EF; n = 240) and fluoridated plus laser-treated (EFL; n = 240). Each group was further subdivided based on storage conditions: either in air or in double-distilled water at 36 °C for 7 days. Laser irradiation was performed using a 445 nm diode laser in continuous-wave mode at 350 mW for 30 s, with a beam diameter of 10 mm, an energy density of 13.375 J/cm², and a power density of 0.445 W/cm². Results: At baseline, mean fluoride content across all specimens was 702.23 ± 201 ppm. Immediately after fluoridation, fluoride levels increased to 11,059 ± 386 ppm in the EF group and 10,842 ± 234 ppm in the EFL group, with no significant difference between groups. After 7 days of storage in air, fluoride retention decreased to 5714 ± 1162 ppm in EF and 5973 ± 861 ppm in EFL, again without significant difference. However, after 7 days of immersion in double-distilled water, the EF group exhibited complete loss of acquired fluoride, with values falling below baseline (337 ± 150 ppm). In contrast, the EFL group retained a substantial portion of the fluoride acquired during fluoridation (total 1533 ± 163 ppm), indicating that laser irradiation significantly prevented fluoride loss (p < 0.001). Conclusions: Low-energy 445 nm diode laser irradiation of fluoridated enamel significantly enhances fluoride retention under aqueous conditions simulating osmotic processes. Laser treatment preserved a substantial portion of fluoride acquired during fluoridation, whereas fluoridated but unlased enamel lost nearly all fluoride, with levels dropping below baseline. This approach may offer clinical benefits for improving enamel fluoride enrichment, thereby increasing resistance to acid challenge and reducing caries risk. Full article

Reliable forecasts of dam releases are essential to anticipate downstream hydrological responses and to improve the operation of fluvial early warning systems. This study integrates an explicit release prediction module into a digital forecasting framework using the Lindoso–Touvedo hydropower cascade in northern Portugal as a case study. A data-driven approach couples short-term electricity price forecasts, obtained with a gated recurrent unit (GRU) neural network, with dam release forecasts generated by a Random Forest model and an LSTM model. The models (GRU and LSTM) were trained and validated on hourly data from November 2024 to April 2025 using a rolling 80/20 split. The GRU achieved R² = 0.93 and RMSE = 3.7 EUR/MWh for price prediction, while the resulting performance metrics confirm the high short-term skill of the LSTM model, with MAE = 4.23 m³ s⁻¹, RMSE = 9.96 m³ s⁻¹, and R² = 0.98. The surrogate Random Forest model reached R² = 0.91 and RMSE = 47 m³/s for 1 h discharge forecasts. Comparison tests confirmed the statistical advantage of the AI approach over empirical rules. Integrating the release forecasts into the Delft FEWS environment demonstrated the potential for real-time coupling between energy market information and hydrological forecasting. By improving forecast reliability and linking hydrological and energy domains, the framework supports safer communities, more efficient hydropower operation, and balanced river basin management, advancing the environmental, social, and economic pillars of sustainability and contributing to SDGs 7, 11, and 13. Full article

Taraxacum mongolicum Hand.-Mazz (TMHM), a primary source of dandelion, is a globally recognized edible and medicinal plant with significant potential in food, medicine, daily chemical products, and animal husbandry. Although hypoglycemic effects have been reported in other Taraxacum species, the specific hypoglycemic constituents and mechanisms of TMHM are not well understood. The absence of comprehensive multi-target screening methodologies has hindered the elucidation of TMHM’s dual inhibitory effects on α-amylase and α-glucosidase, as well as its associated molecular mechanisms. In this study, a multi-target screening strategy was developed to concurrently evaluate α-amylase and α-glucosidase inhibition, integrating multi-target affinity ultrafiltration coupled with ultra-performance liquid chromatography-tandem mass spectrometry (MTAUF-UPLC-MS/MS), molecular docking, and molecular dynamics (MD) simulations. Using this approach, 13 dual-target inhibitors were identified from TMHM. Moreover, at least 5 of these compounds exhibited anti-diabetic activities comparable to the positive control drug acarbose, suggesting that they are principal bioactive constituents responsible for its hypoglycemic effects. Subsequent investigation of the antioxidant capacities of 7 out of the 13 bioactive compounds revealed that most exhibited more potent antioxidant activities than vitamin C (Vc). Based on these findings, molecular docking and MD simulations further validated that quercetin (8) and kaempferol (15), which demonstrated significant hypoglycemic and antioxidant activities, exhibited particularly strong affinities and stable interactions with α-amylase and α-glucosidase, respectively. In conclusion, these findings underscored the considerable potential of TMHM as a natural source of multifunctional bioactive compounds for nutraceutical, functional, and pharmaceutical applications. This study provided a critical foundation for elucidating the mechanisms underlying TMHM’s anti-diabetic effects and its therapeutic potential in mitigating diabetes-related complications, thereby facilitating future development and utilization. Full article

This study proposes a lightweight autoencoder-based detection framework for the efficient detection of multi-class malicious traffic within a private 5G network slicing environment. Conventional deep learning-based detection approaches encounter difficulties in real-time processing and edge environment applications because of their significant computational complexity and resource demands. To address this issue, this study balances traffic data using slice-label-based hierarchical sampling and performs domain-specific feature grouping to reflect semantic similarity. Independent autoencoders are trained for each group, and the latent vectors from the encoder outputs are combined to be used as input for an SVM-based multi-class classifier. This structure reflects traffic differences between slices while also improving computational efficiency. Four sets of experiments were constructed to verify the model’s performance and evaluate its structural performance, resource usage efficiency, classifier generalization performance, and whether it met SLA constraints from various perspectives. As a result, the proposed Multi-AE model achieved an accuracy of 0.93, a balanced accuracy of 0.93, and an ECE of 0.03, demonstrating high stability and detection reliability. Regarding resource utilization efficiency, GPU utilization was under 7%, and the average memory usage was approximately 5.7 GB, demonstrating resource efficiency. In SLA verification, inference latency below 10 ms and a throughput of 564 samples/s were achieved based on URLLC. This study is significant in that it experimentally demonstrated a detection structure that achieves a balance of accuracy, lightweight design, and real-time performance in a 5G slicing environment. Full article

Background: The synthesis and biological investigation of pyrrolidine (L-gulo) iminosugars bearing an organic boron pharmacophore in ortho and meta positions of an N-benzyl group is reported. This paper completes the structure–activity relationship data for this novel family of boron-bearing iminosugars. These can establish reversible intramolecular interactions via dative bonding from nucleophilic amino acid side chains to the empty p-orbital of the boron atom. Methods: Inhibitory activities against two panels of glycosidases and cancer cell lines were investigated to ascertain structure–activity relationship profiles for these novel iminosugar drug leads. Results: These iminosugars display selective, moderate-to-weak inhibitions (IC₅₀s = 116–617 μM) of β-D-galactosidase (bovine liver), and indications of inhibition of β-D-glucosidases (almond, bovine liver) (IC₅₀s = 633 and 710 μM) and α-D-glucosidases (rice, yeast, rat intestinal maltase) (IC₅₀s = 106–784 μM). The boronic acid group emerges as a useful pharmacophore for management of lysosomal storage disorders via the chaperone-mediated therapy approach. The cancer assays revealed that the A2780 ovarian carcinoma cell line is selectively inhibited by all compounds screened and the MIA-Pa-Ca2 pancreatic carcinoma cell line is selectively inhibited by most compounds. Growth inhibition and GI₅₀ values were most potent for the meta 7 side-product. Conclusions: Beyond the cancer cell line inhibition and dose-response capabilities, the real therapeutic potential of these borylated drugs lies in their switch on/switch off activation under boron neutron capture therapy (BNCT) radiotherapeutic conditions, thus providing an important area of application for borylated monosaccharides. Full article

Urban air pollution in Kazakhstan poses persistent risks; this study synthesizes measured concentrations, source evidence, and policy responses to inform mitigation in cold, inversion-prone cities. We compile national monitoring (Kazhydromet), community PM_2.5 sensors, emissions inventories and recent CEMS provisions, and appraise modeling approaches (Gaussian screening, Eulerian CTMs, and data-driven forecasting). Seasonal descriptive comparisons are performed for Astana using 56,944 observations (2023–2024), partitioned into heating and non-heating periods, and published receptor apportionment is integrated. Across major cities, annual PM_2.5 generally exceeds WHO guidelines and winter stagnation drives episodes. In Astana, the heating season means rose relative to non-heating equivalents—PM_2.5 12.3 vs. 10.6 μg m⁻³ (+16%) and SO₂ 21.9 vs. 14.8 μg m⁻³ (+23%)—while NO was unchanged; higher means but lower medians indicate episodic winter peaks. Receptor analyses attribute large shares of PM_2.5 to traffic (spark-ignition engines 30% and diesel 7%) and coal-related contributions including secondary nitrate (15%), consistent with power/heat and vehicle dominance. Evidence supports prioritizing clean heating (coal-to-gas and efficiency), transport emission controls, and dense monitoring to enable accountability within Kazakhstan’s Environmental Code and decarbonization strategy. A tiered modeling workflow can quantify intervention impacts and deweather trends; the near-term focus should be on reducing winter exposures. Full article

The Vehicular Internet of Things (V-IoT) has emerged as a cornerstone of next-generation intelligent transportation systems (ITSs), enabling applications ranging from safety-critical collision avoidance and cooperative awareness to infotainment and fleet management. These heterogeneous services impose stringent quality-of-service (QoS) demands for latency, reliability, and fairness while competing for limited and dynamically varying spectrum resources. Conventional schedulers, such as round-robin or static priority queues, lack adaptability, whereas deep reinforcement learning (DRL) solutions, though powerful, remain computationally intensive and unsuitable for real-time roadside unit (RSU) deployment. This paper proposes a lightweight and interpretable reinforcement learning (RL)-based spectrum management framework for Vehicular Internet of Things (V-IoT) networks. Two enhanced Q-Learning variants are introduced: a Value-Prioritized Action Double Q-Learning with Constraints (VPADQ-C) algorithm that enforces reliability and blocking constraints through a Constrained Markov Decision Process (CMDP) with online primal–dual optimization, and a contextual Q-Learning with Upper Confidence Bound (Q-UCB) method that integrates uncertainty-aware exploration and a Success-Rate Prior (SRP) to accelerate convergence. A Risk-Aware Heuristic baseline is also designed as a transparent, low-complexity benchmark to illustrate the interpretability–performance trade-off between rule-based and learning-driven approaches. A comprehensive simulation framework incorporating heterogeneous traffic classes, physical-layer fading, and energy-consumption dynamics is developed to evaluate throughput, delay, blocking probability, fairness, and energy efficiency. The results demonstrate that the proposed methods consistently outperform conventional Q-Learning and Double Q-Learning methods. VPADQ-C achieves the highest energy efficiency (≈$8.425\times {10}^7$ bits/J) and reduces interruption probability by over $60\%$, while Q-UCB achieves the fastest convergence (within ≈190 episodes), lowest blocking probability (≈0.0135), and lowest mean delay (≈0.351 ms). Both schemes maintain fairness near $0.364$, preserve throughput around 28 Mbps, and exhibit sublinear training-time scaling with $\mathcal{O}\left(1\right)$ per-update complexity and $\mathcal{O}\left(N^2\right)$ overall runtime growth. Scalability analysis confirms that the proposed frameworks sustain URLLC-grade latency (<0.2 ms) and reliability under dense vehicular loads, validating their suitability for real-time, large-scale V-IoT deployments. Full article

On 7 January 2025, a M_S 6.8 earthquake struck Dingri County, southern Tibet, within the extensional regime of the central Himalaya–southern Tibetan Plateau. Using ascending and descending Sentinel-1A SAR data, we applied a two-pass Differential InSAR (D-InSAR) approach with SRTM DEM data to retrieve high-precision coseismic deformation fields. We observed significant LOS deformation, revealing peak displacements of −1.06 m and +0.76 m, with deformation concentrated along the Denmo Co graben and clear offsets along its western boundary fault. Nonlinear inversion using the Okada elastic dislocation model and a quadtree down-sampled dataset yields a rupture plane 28.42 km long and 12.81 km wide, striking 183.51°, dipping 55.41°, and raking −71.95°, consistent with a predominantly normal-faulting mechanism with a minor left-lateral component. Distributed-slip inversion reveals that peak slip (4.79 m) was concentrated in the upper ~10 km of the fault, with the main asperity located in the central fault segment. The seismic moment is estimated to be 4.24 × 10¹⁹ Nm, which corresponds to a magnitude of M_W 7.05. Coulomb failure stress (ΔCFS) calculations indicate stress increases (>0.01 MPa) at the northern and southern rupture terminations (5–10 km depth) and the flanks at 15–20 km depth, suggesting elevated seismic potential in these regions. This integrated InSAR–modeling–stress analysis provides new constraints on the source parameters, slip distribution, and tectonic implications of the 2025 Dingri earthquake, offering important insights for regional seismic hazard assessment. Full article

Background: Various thiolactones are known as biologically active compounds, capable of stimulating the development of several human diseases and quorum sensing of Gram–positive bacteria. The enzymatic hydrolysis of thiolactones represents a promising approach to preventing their action. Methods: Thirteen enzymes, including various lactonases and serine hydrolases were studied in this work using several substrates including the homocysteine thiolactone (HTL), and its derivatives the N–acetylhomocysteine thiolactone (C₂–HTL) and the isobutyryl–homocystein thiolactone (i–but–HTL). The potential interactions of the ligands with the surface of enzymes molecules were predicted in silico using computational modeling and checked in wet experiments in vitro. Results: Based on the data obtained several enzymes were selected with localization of the thiolactones near their active sites, indicating the possibility of effective catalysis. The lactonase (AiiA), metallo-β-lactamase (NDM-1) and the organophosphate hydrolase with hexahistidine tag (His₆–OPH) were among them. Determination of catalytic characteristics of enzymes in the hydrolytic reactions with the HTL and the C₂–HTL revealed the maximal value of catalytic efficiency constant for the NDM-1 in the hydrolysis of the HTL (826 M⁻¹ s⁻¹). The maximal activity in the hydrolysis of C₂–HTL was established for AiiA (137 M⁻¹ s⁻¹). The polyaspartic (PLD₅₀) and the polyglutamic (PLE₅₀) acids were used to obtain polyelectrolyte complexes with enzymes. The further combination of these complexes with the clotrimazole and polymyxin B possessing antimicrobial properties resulted in notable improvement of their action in relation to Staphylococcus cells. Conclusions: It was revealed that the antimicrobial activity of the polymyxin B is enhanced by 9–10 times against bacteria and yeast when combined with the His₆–OPH polyelectrolyte complexes. The antimicrobial activity of clotrimazole was increased by ~7 times against Candida tropicalis cells in the case of the AiiA/PLE₅₀/Clotrimazole combination. These results make the obtained biology attractive and promising for their further advancement to practical application. Full article

Background and Clinical Significance: developmental language disorder (DLD) and specific learning disorder (SLD) may coexist, resulting in significantly broader impairments to oral and written language skills. Understanding the neurobiological basis of these deficits is crucial, and electrophysiological assessment of the auditory system offers an objective approach not influenced by behavioral factors. The present study describes the audiological electrophysiology of an adolescent diagnosed with both DLD and SLD. Case Presentation: R.B., a 15-year-old adolescent with a history of SLD and DLD, presented with persistent deficits in oral language (syntax) and written (decoding) skills after 7 months of intensive therapy. Basic audiological tests confirmed hearing within normal limits. An electrophysiological battery, including the click-brainstem auditory evoked potential (c-ABR), medium latency auditory evoked potential (MLAEP), long-latency auditory evoked potential (P300), and frequency following response (FFR), was performed to investigate information processing in the auditory trajectory. The c-ABR confirmed the integrity of the auditory pathway up to the brainstem. MLAEP revealed a differential ear effect, with significant asymmetry in the Na-Pa interamplitude, pointing to a dysfunction in subcortical processing. The P300 showed a prolonged latency in the left ear (437 ms), and there was no response in the right. The FFRs under ideal and impaired listening conditions demonstrated impaired perception of speech and revealed that the neurophysiological responses did not correspond to the eliciting stimulus. Conclusions: The present case study showed that electrophysiological testing of the auditory system provided objective and quantitative evidence for a neurobiological basis of the language deficits of an adolescent with DLD and SLD. The work demonstrated that when comorbidities are present, a multidisciplinary investigation of both the linguistic and auditory systems can be helpful. Full article

Fiducial marker implantation for tumor localization in radiotherapy is effective but invasive and carries complication risks. To address this, we propose a marker-less tumor tracking framework to explore the feasibility of a cross-patient deep learning model, aiming to eliminate the need for per-patient retraining. A novel degradation model generates realistic simulated data from digitally reconstructed radiographs (DRRs) to train a Restormer network, which transforms clinical fluoroscopic images into clean, DRR-like images. Subsequently, a DUCK-Net model, trained on DRRs, performs tumor segmentation. We conducted a feasibility study using a clinical dataset from 7 lung cancer patients, comprising 100 distinct treatment fields. The framework achieved an average processing time of 179.8 ms per image and demonstrated high accuracy: the median 3D Euclidean tumor center tracking error was $1.53$ mm, with directional errors of $0.98\pm 0.70$ mm (LR), $1.09\pm 0.74$ mm (SI), and $1.34\pm 0.94$ mm (AP). These promising results validate our approach as a proof-of-concept for a cross-patient marker-less tumor tracking solution, though further large-scale validation is required to confirm broad clinical applicability. Full article

Enterprise document management faces a significant challenge: traditional clustering methods focus solely on content similarity while ignoring organizational context, such as priority, workflow status, and temporal relevance. This paper introduces FLACON (Flag-Aware Context-sensitive Clustering), an information-theoretic approach that captures multi-dimensional document context through a six-dimensional flag system encompassing Type, Domain, Priority, Status, Relationship, and Temporal dimensions. FLACON formalizes document clustering as an entropy minimization problem, where the objective is to group documents with similar contextual characteristics. The approach combines a composite distance function—integrating semantic content, contextual flags, and temporal factors—with adaptive hierarchical clustering and efficient incremental updates. This design addresses key limitations of existing solutions, including context-aware systems that lack domain-specific intelligence and LLM-based methods that require prohibitive computational resources. Evaluation across nine dataset variations demonstrates notable improvements over traditional methods, including a 7.8-fold improvement in clustering quality (Silhouette Score: 0.311 vs. 0.040) and performance comparable to GPT-4 (89% of quality) while being ~7× faster (60 s vs. 420 s for 10 K documents). FLACON achieves $O(m log n)$ complexity for incremental updates affecting $m$ documents and provides deterministic behavior, which is suitable for compliance requirements. Consistent performance across business emails, technical discussions, and financial news confirms the practical viability of this approach for large-scale enterprise document organization. Full article