Search Results (5,354)

This study introduces a real-time optimization framework for eco-driving of heavy-duty vehicles over long-distance routes. A longitudinal dynamic model incorporating powertrain performance and fuel consumption is formulated, and the eco-driving scenario is expressed as a quadratic programming (QP) problem. To improve computational efficiency, a novel variable-step spatial segmentation method is introduced, which ensures a balance between modeling accuracy and computational cost. Simulations involving mixed-terrain scenarios verify the effectiveness of the proposed approach. The results show that the QP-based method achieves fuel savings comparable to those offered by dynamic programming while significantly reducing computation time to sub-second levels; thus, the proposed strategy offers real-time applicability. These findings demonstrate the feasibility of global optimal velocity profile generation in practical eco-driving scenarios. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Silent speech recognition (SSR) enables communication without vocalization by interpreting biosignals such as electromyography (EMG) and electroencephalography (EEG). Most existing SSR systems rely on high-density, non-wearable sensors and focus primarily on isolated word recognition, limiting their practical usability. This study presents a wearable SSR system capable of accurate sentence-level recognition using single-channel EMG and EEG sensors with real-time wireless transmission. A moving window-based few-shot learning model, implemented with a Siamese neural network, segments and classifies words from continuous biosignals without requiring pauses or manual segmentation between word signals. A novel sensor fusion model integrates both EMG and EEG modalities, enhancing classification accuracy. To further improve sentence-level recognition, a statistical language model (LM) is applied as post-processing to correct syntactic and lexical errors. The system was evaluated on a dataset of four military command sentences containing ten unique words, achieving 95.25% sentence-level recognition accuracy. These results demonstrate the feasibility of sentence-level SSR using wearable sensors through a window-based few-shot learning model, sensor fusion, and ML applied to limited simultaneous EMG and EEG signals. Full article

Background/Objective: Sleep is a vital determinant of human health, where both its quantity and quality directly impact physical and mental well-being. Thermoregulation plays a pivotal role in sleep quality, as the body’s ability to regulate temperature varies across different sleep stages. This study examines the effects of a novel real-time temperature adjustment (RTA) mattress, which dynamically modulates temperature to align with sleep stage transitions, compared to constant temperature control (CTC). Through polysomnographic (PSG) assessments, this study evaluates how adaptive thermal regulation influences sleep architecture, aiming to identify its potential for optimizing restorative sleep. Methods: A prospective longitudinal cohort study involving 25 participants (13 males and 12 females; mean age: 39.7 years) evaluated sleep quality across three conditions: natural sleep (Control), CTC (33 °C constant mattress temperature), and RTA (temperature dynamically adjusted: 30 °C during REM sleep; 33 °C during non-REM sleep). Each participant completed three polysomnography (PSG) sessions. Sleep metrics, including total sleep time (TST), sleep efficiency, wake after sleep onset (WASO), and sleep stage percentages, were assessed. Repeated-measures ANOVA and post hoc analyses were performed. Results: RTA significantly improved sleep quality metrics compared to Control and CTC. TST increased from 356.2 min (Control) to 383.2 min (RTA, p = 0.030), with sleep efficiency rising from 82.8% to 87.3% (p = 0.030). WASO decreased from 58.2 min (Control) and 64.6 min (CTC) to 49.0 min (RTA, p = 0.067). REM latency was notably reduced under RTA (110.4 min) compared to Control (141.8 min, p = 0.002). The REM sleep percentage increased under RTA (20.8%, p = 0.006), with significant subgroup-specific enhancements in males (p = 0.010). Females showed significant increases in deep sleep percentage under RTA (12.3%, p = 0.011). Conclusions: Adaptive thermal regulation enhances sleep quality by aligning mattress temperature with physiological needs during different sleep stages. These findings highlight the potential of RTA as a non-invasive intervention to optimize restorative sleep and promote overall well-being. Further research could explore long-term health benefits and broader applications. Full article

Background/Objectives: Aging and metabolic disorders are associated with a decline in muscle function, referred to as age-related sarcopenia. The underlying mechanisms of sarcopenia include cellular senescence, imbalanced protein homeostasis, accumulation of oxidative and inflammatory stressors, and mitochondrial dysfunction. Probiotic supplementation improves the gut microbiome and enhances muscle function via the gut–muscle axis. However, details of molecular mechanisms and the development of an appropriate treatment are under active investigation. Methods: We have examined the effects of Lactiplantibacillus plantarum LM1001, a probiotic that reportedly improves the digestibility of branched-chain amino acids in myocyte cultures, but exactly how it contributes to muscle structure and function remains unclear. Results: We show that aged mice (male C57BL6/J) fed a high-fat diet (HFD) exhibit weak muscle strength, as reflected by a reduction in grip strength. LM1001 supplementation increases muscle strength and restores myofibril size, which has been altered by HFD in aged mice. Expression of myogenic proteins is increased, while protein markers for muscle atrophy are downregulated by LM1001 treatment via the IGF-1/Akt/FoxO3a pathway. LM1001 improves gut microbiota that are altered in aged HFD-fed mice, by increasing their abundance in beneficial bacteria, and efficiently maintains the epithelial lining integrity of the large intestine. Conclusions: We conclude that LM1001 supplementation serves a beneficial role in patients suffering from sarcopenia and metabolic disorders, improving their muscle function, gut microbiota, and intestinal integrity. Full article

Advanced composite materials and manufacturing technologies are critical to sustain human presence in space. Mechanical testing and analysis are needed to elucidate the effect of processing parameters on composites’ material properties. In this study, test specimens are 3D printed via a fused-filament fabrication (FFF) approach from a basalt moon dust-polylactic acid (BMD-PLA) composite filament and from pure PLA filament. Compression and tensile testing were conducted to determine the yield strength, ultimate strength, and Young’s modulus of specimens fabricated under several processing conditions. The maximum compressive yield strength for the BMD-reinforced samples is 27.68 MPa with print parameters of 100% infill, one shell, and 90° print orientation. The maximum compressive yield strength for the PLA samples is 63.05 MPa with print parameters of 100% infill, three shells, and 0° print orientation. The composite samples exhibit an increase in strength when layer lines are aligned with loading axis, whereas the PLA samples decreased in strength. This indicates a fundamental difference in how the composite behaves in comparison to the pure matrix material. In tension, test specimens have unpredictable failure modes and often broke outside the gauge length. A portion of the tension test data is included to help guide future work. Full article

This study aims to explore the utility of artificial intelligence (AI) in classifying dementia severity based on gait analysis data and to examine how machine learning (ML) can address the limitations of conventional statistical approaches. The study included 34 individuals with mild cognitive impairment (MCI), 25 with mild dementia, 26 with moderate dementia, and 54 healthy controls. A support vector machine (SVM) classifier was employed to categorize dementia severity using gait parameters. As complexity and high dimensionality of gait data increase, traditional statistical methods may struggle to capture subtle patterns and interactions among variables. In contrast, ML techniques, including dimensionality reduction methods such as principal component analysis (PCA) and gradient-based feature selection, can effectively identify key gait features relevant to dementia severity classification. This study shows that ML can complement traditional statistical analyses by efficiently handling high-dimensional data and uncovering meaningful patterns that may be overlooked by conventional methods. Our findings highlight the promise of AI-based tools in advancing our understanding of gait characteristics in dementia and supporting the development of more accurate diagnostic models for complex or large datasets. Full article

Background/Objectives: The associations between plasma homocysteine and pre-stroke antithrombotic medication and the effects these have on clinical outcomes of patients undergoing ischemic stroke remains unclear. This study aimed to evaluate the combined effect of plasma homocysteine levels and the use of pre-stroke antithrombotic medication on the clinical outcomes of patients experiencing first-ever and recurrent ischemic strokes. Methods: Anonymized data from consecutive patients who experienced ischemic stroke and had their plasma homocysteine levels evaluated were retrospectively analyzed. Pre-stroke antithrombotic medication status, clinical variables potentially influencing homocysteine concentrations, and stroke recurrence data were collected. Clinical outcomes were assessed using the modified Rankin Scale 3 months after stroke onset. The association between hyperhomocysteinemia and clinical outcomes was evaluated using logistic regression models. Results: Hyperhomocysteinemia was significantly associated with unfavorable clinical outcomes (adjusted odds ratio [aOR], 1.32; 95% confidence interval, 1.04–1.69) in the 2767 patients who were analyzed. The absence of pre-stroke antithrombotic medication use was associated with unfavorable outcomes (aOR range, 1.29–1.56), specifically in patients with first-ever stroke (aOR range, 1.45–1.64) but not in patients with recurrent strokes (aOR range, 0.70–1.04). Conclusions: Hyperhomocysteinemia and non-use of pre-stroke antithrombotic medication were significantly related to unfavorable outcomes in patients experiencing their first-ever stroke. These findings might provide prognostic insights into stroke management and patient stratification. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Background/Objectives: Headache disorders, including migraine and tension-type headache (TTH), are among the most prevalent and disabling neurological conditions globally. This study aimed to evaluate temporal changes, demographic disparities, and socio-geographic variation in the burden of headache disorders across European countries. Methods: We analyzed data from the Global Burden of Disease Study 2021, covering 45 European countries grouped into Western, Central, and Eastern regions. We examined age-standardized prevalence, incidence, and disability-adjusted life year (DALY) rates for headache disorders between 1990 and 2021. Analyses were stratified by sex, age group, region, and country-level socio-demographic index (SDI). All estimates were reported with 95 percent uncertainty intervals where relevant. Spearman correlation was used to assess associations between disease burden and SDI. Results: Between 1990 and 2021, the number of individuals with headache disorders in Europe rose from 345.0 to 370.6 million, although age-standardized prevalence remained stable. The burden of migraine slightly increased, with age-standardized DALY rates rising from 648.35 to 657.27 per 100,000 population. Conversely, TTH showed a minor decline in both prevalence and DALY rates. Women and individuals aged 30 to 44 years consistently exhibited the highest burden, particularly for migraine. Higher SDI scores were positively correlated with DALY rates for migraine (rho = 0.392, p = 0.008) but negatively correlated for TTH (rho = −0.466, p = 0.001). Conclusions: Headache disorders continue to pose a major and largely unmitigated health burden across Europe. Regionally targeted strategies are essential to reduce disability and improve outcomes across diverse European populations. Full article

As lunar exploration missions advance, the need for safe and sustainable in situ energy systems has become increasingly critical. This study investigates the thermoelectric performance of Bi₂Te₃-based thermoelectric materials under the natural temperature variations on the lunar surface, aiming to illustrate the potential of thermoelectric generation technology in power supply for a crewed moon base. A numerical approach was employed to assess the energy conversion behavior and optimize the geometric design of a thermoelectric module couple consisting of a P-leg and N-leg. The results indicate that Bi₂Te₃-based modules exhibit promising functionality under cryogenic conditions, highlighting their potential as an in situ power source during the long lunar night. Furthermore, geometric optimization was shown to significantly enhance the overall thermoelectric performance. The present study illustrates that TEG technology offers a viable pathway toward reliable energy generation in extreme lunar environments, supporting future mission sustainability. Full article

TGR5 has emerged as a promising therapeutic target for obesity and metabolic disorders due to its regulatory roles in energy expenditure, glucose homeostasis, thermogenesis, and gut hormone secretion. This review summarizes the structural mechanisms of TGR5 activation, focusing on orthosteric and allosteric ligand interactions, toggle switch dynamics, and G protein coupling based on cryo-EM and docking-based models. A wide range of bioactive natural compounds including oleanolic acid, curcumin, betulinic acid, ursolic acid, quinovic acid, obacunone, nomilin, and 5β-scymnol are examined for their ability to modulate TGR5 signaling and elicit favorable metabolic effects. Molecular docking simulations using CB-Dock2 and PDB ID 7BW0 revealed key interactions within the orthosteric pocket, supporting their mechanistic potential as TGR5 agonists. Emerging strategies in TGR5-directed drug development are also discussed, including gut-restricted agonism to minimize gallbladder-related side effects, biased and allosteric modulation to fine-tune signaling specificity, and AI-guided optimization of natural product scaffolds. These integrated insights provide a structural and pharmacological framework for the rational design of safe and effective TGR5-targeted therapeutics. Full article

Functional bowel disorders (FBDs) are chronic gastrointestinal conditions characterized by recurrent symptoms associated with gut microbiota dysbiosis. Although accumulating evidence suggests that probiotics can improve symptoms in patients with FBD, the underlying mechanisms remain to be fully elucidated. In this randomized, double-blind, placebo-controlled clinical trial, 38 adults meeting the Rome IV diagnostic criteria of functional constipation (FC) and functional diarrhea (FD) received either a multi-strain probiotic complex or placebo for 8 weeks. Clinical outcomes were evaluated using the Irritable Bowel Syndrome Severity Scoring System (IBS-SSS), bowel habits questionnaire, and IBS Quality of Life (IBS-QoL) instrument. Fecal samples were collected at baseline and at week 8 for gut microbiota profiling via 16S rRNA gene sequencing and metabolomic analysis using gas chromatography–mass spectrometry. Probiotic supplementation significantly reduced the severity of abdominal bloating and its interference with quality of life, and improved the body image domain of the IBS-QoL. Beta diversity analysis showed significant temporal shifts in the probiotic group, while 16S rRNA sequencing revealed an increased relative abundance of Faecalibacterium prausnitzii and Blautia stercoris. Fecal metabolomic analysis further indicated elevated levels of metabolites implicated in the gut–brain axis. Multi-strain probiotic supplementation alleviated gastrointestinal symptoms and improved aspects of psychosocial well-being in adults with FBDs, potentially through modulation of the human gut microbiome. Full article

This study investigates the multifractal characteristics and nonlinear cross-correlations between two major carbon credit indices—S&P Global Carbon Index and EEX Global Carbon Index—and key global financial assets: the Euro/US Dollar exchange rate, Dow Jones Industrial Average, gold, Western Texas Intermediate, and Bitcoin. Using daily data from August 2020 to June 2025, we apply the Asymmetric Multifractal Detrended Cross-Correlation Analysis framework to examine the strength, asymmetry, and persistence of interdependencies across varying fluctuation magnitudes. Our findings reveal consistent multifractality in all asset pairs, with stronger multifractal spectra observed in those linked to Bitcoin and Western Texas Intermediate Crude Oil price. The analysis of generalized Hurst exponents indicates higher persistence for small fluctuations and antipersistent behavior for large fluctuations, particularly in pairs involving the S&P Global Carbon Index. We also detect significant asymmetry in the cross-correlations, especially under bearish trends in Bitcoin and Western Texas Intermediate. Surrogate data tests confirm that multifractality largely stems from fat-tailed distributions and temporal correlations, with genuine multifractality identified in the S&P Global Carbon Index–Dow Jones Industrial average pair. These results highlight the complex and nonlinear dynamics governing carbon markets, offering critical insights for investors, policymakers, and regulators navigating the intersection of environmental and financial systems. Full article

Flammulina velutipes (enoki mushroom) is a functional edible mushroom rich in antioxidants, polysaccharides, mycosterols, fiber, and minerals. Accumulating evidence highlights its therapeutic potential across diverse pathological contexts, including boosting cognitive function. However, its role in neuromodulation has not been systematically explored. This study examined the effects of methanolic and ethanolic extracts of F. velutipes on primary hippocampal neurons. Neurons were treated with different extract concentrations, followed by assessments of cell viability, cytoarchitecture, neuritogenesis, maturation, and neuroprotection under oxidative stress. The extracts were further characterized by GC-MS to identify bioactive metabolites, and molecular docking combined with MM-GBSA binding energy analysis was employed to predict potential modulators. Our results demonstrated that the methanolic extract significantly enhanced neurite outgrowth, improved neuronal cytoarchitecture, and promoted survival under oxidative stress, whereas the ethanolic extract produced moderate effects. Mechanistic studies indicated that these neuroprotective and neurodevelopmental benefits were mediated through activation of the NTRK receptors, as validated by both in vitro assays and molecular docking studies. Collectively, these findings suggest that F. velutipes extracts, particularly methanolic fractions, may serve as promising neuromodulatory agents for promoting neuronal development and protecting neurons from oxidative stress. Full article