Search Results (18,562)

Lubricant condition analysis is a valuable diagnostic tool for assessing engine performance and ensuring the reliable operation of diesel engines. While traditional diagnostic techniques—such as Fourier transform infrared spectroscopy (FTIR)—are constrained by slow response times, high costs, and the need for specialized personnel. In contrast, dielectric spectroscopy, impedance analysis, and soft computing offer real-time, non-destructive, and cost-effective alternatives. This review examines recent advances in integrating these techniques to predict lubricant properties, evaluate wear conditions, and optimize maintenance scheduling. In particular, dielectric and impedance spectroscopies offer insights into electrical properties linked to oil degradation, such as changes in viscosity and the presence of wear particles. When combined with soft computing algorithms, these methods enhance data analysis, reduce reliance on expert interpretation, and improve predictive accuracy. The review also addresses challenges—including complex data interpretation, limited sample sizes, and the necessity for robust models to manage variability in real-world operations. Future research directions emphasize miniaturization, expanding the range of detectable contaminants, and incorporating multi-modal artificial intelligence to further bolster system robustness. Collectively, these innovations signal a shift from reactive to predictive maintenance strategies, with the potential to reduce costs, minimize downtime, and enhance overall engine reliability. This comprehensive review provides valuable insights for researchers, engineers, and maintenance professionals dedicated to advancing diesel engine lubricant monitoring. Full article

Background/Objectives: Glioblastoma (GBM) is an aggressive brain tumor known for its profound heterogeneity and treatment resistance. Dysregulated complement signaling and epigenetic alterations have been implicated in GBM progression. This study identifies kynureninase (KYNU), a key enzyme in the kynurenine pathway, as a novel regulator of complement components and investigates its interaction with histone deacetylase 6 (HDAC6) in the context of therapeutic targeting. Methods: KYNU expression, and its association with complement signaling in GBM, were analyzed using publicly available datasets (TCGA, GTEx, HPA). Pathway enrichment was performed via LinkedOmics. In vitro studies in GBM cell lines (U87, U251, T98G) assessed the effects of KYNU silencing and treatment with an HDAC6 inhibitor (tubastatin) and a BET inhibitor (apabetalone) on gene expression and cell viability. Results: Bioinformatic analyses revealed significant overexpression of KYNU in GBM tissues compared to normal brain tissue. KYNU expression was positively associated with genes involved in complement and coagulation cascades. In vitro experiments demonstrated that KYNU silencing reduced the expression of C3, C3AR1, and C5AR1 and suppressed GBM cell viability. Treatment with tubastatin, while reducing viability, paradoxically upregulated complement genes, suggesting potential limitations in therapeutic efficacy. However, this effect was mitigated by KYNU knockdown. Combined treatment with apabetalone and tubastatin effectively suppressed KYNU expression and enhanced cytotoxicity, particularly in cells with high complement expression. Conclusions: Our findings establish the KYNU–HDAC6–complement axis as a critical regulatory pathway in GBM. Targeting KYNU-mediated complement activation through combined epigenetic approaches—such as HDAC6 and BET inhibition—represents a promising strategy to overcome complement-driven resistance in GBM therapy. Full article

Background: Botulinum toxin type A (BoNT/A) is widely used in both clinical and aesthetic settings to induce temporary neuromuscular paralysis by inhibiting acetylcholine release. Although generally regarded as safe and effective, complications such as iatrogenic ptosis or facial asymmetry may occur and persist for several weeks or even months, with no standardized method currently available to accelerate recovery. Objective: This article explores the hypothesis that photobiomodulation (PBM)—a non-invasive modality recognized for its neuroregenerative potential—may facilitate the reversal of BoNT/A-induced neuromuscular blockade. Discussion: PBM enhances mitochondrial activity by stimulating cytochrome c oxidase in nerve and muscle tissues, thereby increasing ATP production and modulating intracellular signaling pathways associated with neuroplasticity, cell survival, and synaptogenesis. Preclinical studies have demonstrated that PBM can upregulate neurotrophic factors (e.g., BDNF, NGF), enhance SNAP-25 expression, and promote structural remodeling of neurons in both young and aged brains. These mechanisms are biologically consistent with the regenerative processes required for recovery from BoNT/A-induced effects. While controlled clinical trials for this specific application are currently lacking, anecdotal clinical reports suggest that PBM may accelerate functional recovery in cases of BoNT/A-related complications. Conclusions: Although this approach has not yet been tested in clinical trials, we propose that photobiomodulation may hypothetically serve as a supportive strategy to promote neuromuscular recovery in patients experiencing adverse effects from BoNT/A. This hypothesis is grounded in robust preclinical evidence but requires validation through translational and clinical research. Full article

Breast cancer detection through non-invasive and accurate techniques remains a critical challenge in medical diagnostics. This study introduces a deep learning-based framework that leverages a microwave radar system equipped with an arc-shaped array of six antennas to estimate key tumor parameters, including position, size, and depth. This research begins with the evolutionary design of an ultra-wideband octagram ring patch antenna optimized for enhanced tumor detection sensitivity in directional near-field coupling scenarios. The antenna is fabricated and experimentally evaluated, with its performance validated through S-parameter measurements, far-field radiation characterization, and efficiency analysis to ensure effective signal propagation and interaction with breast tissue. Specific Absorption Rate (SAR) distributions within breast tissues are comprehensively assessed, and power adjustment strategies are implemented to comply with electromagnetic exposure safety limits. The dataset for the deep learning model comprises simulated self and mutual S-parameters capturing tumor-induced variations over a broad frequency spectrum. A core innovation of this work is the development of the Attention-Based Feature Separation (ABFS) model, which dynamically identifies optimal frequency sub-bands and disentangles discriminative features tailored to each tumor parameter. A multi-branch neural network processes these features to achieve precise tumor localization and size estimation. Compared to conventional attention mechanisms, the proposed ABFS architecture demonstrates superior prediction accuracy and interpretability. The proposed approach achieves high estimation accuracy and computational efficiency in simulation studies, underscoring the promise of integrating deep learning with conformal microwave imaging for safe, effective, and non-invasive breast cancer detection. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder primarily characterized by memory loss, cognitive decline, and structural brain atrophy. Substantial sex differences have been observed in its incidence, clinical trajectory, and response to treatment. Women are disproportionately affected, exhibiting faster progression and more severe cognitive impairment. Exercise has emerged as a promising non-pharmacological intervention to mitigate AD-related decline, yet growing evidence reveals that its benefits vary by sex. This review synthesizes current findings from human and animal studies, focusing on how exercise impacts AD differently in males and females. In women, exercise is more strongly associated with improvements in cognitive function, neurotrophic support, and emotional regulation. In men, benefits tend to involve structural preservation and oxidative adaptations. Underlying mechanisms include differential hormonal profiles, inflammatory responses, and neuroplastic signaling pathways. These findings underscore the need to consider sex as a biological variable in AD research. Developing sex-specific exercise strategies may enhance therapeutic outcomes and support more individualized approaches in AD prevention and care. Full article

This study aimed to explore the effects of dietary supplementation with soy isoflavones (SI) in the later stages of pregnancy on the antioxidant capacity of sows and intestinal health of newborn piglets. Forty sows with similar body weights and parity (average of 1–2 parity) were randomly divided into two groups (n = 20): the control group and SI group (dose: 100 mg/kg of feed). Feeding was started on day 85 of gestation and continued until farrowing. SI supplementation significantly increased the antioxidant levels in the serum of the sows and newborn piglets, placental tissue, and the intestinal tract of the piglets. This observation was indicated by a decreased activity of the oxidative stress marker malondialdehyde (MDA); increased activity of antioxidant enzymes such as superoxide dismutase, glutathione peroxidase, and catalase; and enhanced total antioxidant capacity. The organ indices of the intestine and liver and the villus height/crypt depth of the jejunum of newborn piglets significantly increased. SI supplementation activated the Nrf2 signaling pathway in the jejunum of neonatal piglets and the expression of placental antioxidant proteins, and it downregulated the expression of the Bax and Caspase 3 apoptotic proteins in the placenta and neonatal piglets. Intestinal and placental barrier integrity was strengthened. For example, ZO-1, Occludin, and Claudin 1 exhibited elevated expression. In conclusion, dietary supplementation with SI enhanced the antioxidant capacity of sows and piglets and improved the health of the placenta and intestinal tract of newborn piglets. Full article

Background: The metabolic dysfunction-associated steatotic liver disease (MASLD) represents an escalating global health concern, with effective treatments still lacking. Given its complex pathogenesis, multi-targeted strategies are highly desirable. Methods: This study reports the isolation of four flavone C-glycosides (FCGs) from Dianthus superbus L. and explores their potential in treating MASLD. The bioactivity and underlying mechanisms of FCGs were systematically evaluated by integrating network pharmacology, molecular docking, and zebrafish model validation. Results: Network pharmacology analysis revealed that FCGs may modulate multiple MASLD-related pathways, including lipid metabolism, insulin signaling, inflammation, and apoptosis. Molecular docking further confirmed strong binding affinities between FCGs and key protein targets involved in these pathways. In the zebrafish model of MASLD induced by egg yolk powder, FCGs administration markedly attenuated obesity, hepatic lipid accumulation, and liver tissue damage. Furthermore, FCGs improved lipid metabolism and restored locomotor function. Molecular analyses confirmed that FCGs upregulated PPARγ expression to promote lipid metabolism, restored insulin signaling by enhancing INSR, PI3K, and AKT expression, and suppressed inflammation by downregulating TNF, IL-6 and NF-κB. Additionally, FCGs inhibited hepatocyte apoptosis by elevating the BCL-2/BAX ratio. Conclusions: These findings highlight the multi-pathway regulatory effects of FCGs in MASLD, underscoring its potential as a novel therapeutic candidate for further preclinical development. Full article

Background/Objectives: Sweet potato is a tropical and subtropical crop and its growth and yield are susceptible to low-temperature stress. However, the molecular mechanisms underlying the low temperature stress of sweetpotato are unknown. Methods: In this work, combined transcriptome and metabolism analysis was employed to investigate the low-temperature responses of two sweet potato cultivars, namely, the low-temperature-resistant cultivar “X33” and the low-temperature-sensitive cultivar “W7”. Results: The differentially expressed metabolites (DEMs) of X33 at different time stages clustered in five profiles, while they clustered in four profiles of W7 with significant differences. Differentially expressed genes (DEGs) in X33 and W7 at different time points clustered in five profiles. More DEGs exhibited continuous or persistent positive responses to low-temperature stress in X33 than in W7. There were 1918 continuously upregulated genes and 6410 persistent upregulated genes in X33, whereas 1781 and 5804 were found in W7, respectively. Core genes involved in Ca²⁺ signaling, MAPK cascades, the reactive oxygen species (ROS) signaling pathway, and transcription factor families (including bHLH, NAC, and WRKY) may play significant roles in response to low temperature in sweet potato. Thirty-one common differentially expressed metabolites (DEMs) were identified in the two cultivars in response to low temperature. The KEGG analysis of these common DEMs mainly belonged to isoquinoline alkaloid biosynthesis, phosphonate and phosphinate metabolism, flavonoid biosynthesis, cysteine and methionine metabolism, glycine, serine, and threonine metabolism, ABC transporters, and glycerophospholipid metabolism. Five DEMs with identified Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were selected for correlation analysis. KEGG enrichment analysis showed that the carbohydrate metabolism, phenylpropanoid metabolism, and glutathione metabolism pathways were significantly enriched and played vital roles in low-temperature resistance in sweet potato. Conclusions: These findings contribute to a deeper understanding of the molecular mechanisms underlying plant cold tolerance and offer targets for molecular breeding efforts to enhance low-temperature resistance. Full article

Accurate assessment of the state of health (SOH) of lithium-ion batteries (LIBs) is fundamental to ensuring safe operation. However, due to the complex electrochemical processes during battery operation and the limited availability of training data, accurate estimation of the state of health remains challenging. To address this, this paper proposes a prediction framework based on dual-view voltage signal features and an improved Long Short-Term Memory (LSTM) neural network. By relying solely on readily obtainable voltage signals, the data requirement is greatly reduced; dual-view features, comprising kinetic and aggregated aspects, are extracted based on the underlying reaction mechanisms. To fully leverage the extracted feature information, Scaled Dot-Product Attention (SDPA) is employed to dynamically score all hidden states of the long short-term memory network, adaptively capturing key temporal information. The experimental results based on the NASA PCoE battery dataset indicate that, under various operating conditions, the proposed method achieves an average absolute error below 0.51% and a root mean square error not exceeding 0.58% in state-of-health estimation, demonstrating high predictive accuracy. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

Objectives: Light intensity is a critical environmental factor regulating plant growth, development, and stress adaptation. However, the physiological and molecular mechanisms underlying light responses in Aconitum kusnezoffii, a valuable alpine medicinal plant, remain poorly understood. This study aimed to elucidate the adaptive strategies of A. kusnezoffii under different light intensities through integrated physiological and transcriptomic analyses. Methods: Two-year-old A. kusnezoffii plants were exposed to three controlled light regimes (790, 620, and 450 lx). Leaf anatomical traits were assessed via histological sectioning and microscopic imaging. Antioxidant enzyme activities (CAT, POD, and SOD), membrane lipid peroxidation (MDA content), osmoregulatory substances, and carbon metabolites were quantified using standard biochemical assays. Transcriptomic profiling was conducted using Illumina RNA-seq, with differentially expressed genes (DEGs) identified through DESeq2 and functionally annotated via GO and KEGG enrichment analyses. Results: Moderate light (620 lx) promoted optimal leaf structure by enhancing palisade tissue development and epidermal thickening, while reducing membrane lipid peroxidation. Antioxidant defense capacity was elevated through higher CAT, POD, and SOD activities, alongside increased accumulation of soluble proteins, sugars, and starch. Transcriptomic analysis revealed DEGs enriched in photosynthesis, monoterpenoid biosynthesis, hormone signaling, and glutathione metabolism pathways. Key positive regulators (PHY and HY5) were upregulated, whereas negative regulators (COP1 and PIFs) were suppressed, collectively facilitating chloroplast development and photomorphogenesis. Trend analysis indicated a “down–up” gene expression pattern, with early suppression of stress-responsive genes followed by activation of photosynthetic and metabolic processes. Conclusions: A. kusnezoffii employs a coordinated, multi-level adaptation strategy under moderate light (620 lx), integrating leaf structural optimization, enhanced antioxidant defense, and dynamic transcriptomic reprogramming to maintain energy balance, redox homeostasis, and photomorphogenic flexibility. These findings provide a theoretical foundation for optimizing artificial cultivation and light management of alpine medicinal plants. Full article

In space gravitational wave detection, various physical effects in the spacecraft, such as self-gravity, electricity, and magnetism, will introduce undesirable parasitic stiffness. The coupling noise between stiffness and the motion states of the test mass critically affects the performance of scientific detection, making accurate stiffness identification crucial. In response to the question, this paper proposes a method to optimize the test mass motion state for enhancing stiffness identification performance. First, the dynamics of the test mass are studied and a recursive least squares algorithm is applied for the implementation of on-orbit stiffness identification. Then, the motion state of the test mass is parametrically characterized by multi-frequency sinusoidal signals as the variable to be optimized, with the optimization objectives and constraints of stiffness identification defined based on convergence time, convergence accuracy, and engineering requirements. To tackle the dual-objective, computationally expensive nature of the problem, a multigranularity surrogate-assisted evolutionary algorithm with individual progressive constraints (MGSAEA-IPC) is proposed. A fuzzy radial basis function neural network PID (FRBF-PID) controller is also designed to address complex control needs under varying motion states. Numerical simulations demonstrate that the convergence time after optimization is less than 2 min, and the convergence accuracy is less than 1.5 × 10⁻¹⁰ s⁻². This study can provide ideas and design references for subsequent related identification and control missions. Full article

This paper proposes a multi-hop peer-to-peer (P2P) video streaming architecture designed to support dynamic, conversation-aware communication. The primary contribution is a decentralized system built on WebRTC that eliminates reliance on a central media server by employing super node aggregation. In this architecture, video streams from multiple peer nodes are dynamically routed through a group of super nodes, enabling real-time reconfiguration of the network topology in response to conversational changes. To support this dynamic behavior, the system leverages WebRTC data channels for control signaling and overlay restructuring, allowing efficient dissemination of topology updates and coordination messages among peers. A key focus of this study is the rapid and efficient reallocation of network resources immediately following conversational events, ensuring that the streaming overlay remains aligned with ongoing interaction patterns. While the automatic detection of such events is beyond the scope of this work, we assume that external triggers are available to initiate topology updates. To validate the effectiveness of the proposed system, we construct a simulation environment using Docker containers and evaluate its streaming performance under dynamic network conditions. The results demonstrate the system’s applicability to adaptive, naturalistic communication scenarios. Finally, we discuss future directions, including the seamless integration of external trigger sources and enhanced support for flexible, context-sensitive interaction frameworks. Full article

Background: Short-chain fatty acids (SCFAs), microbial metabolites also known as postbiotics, are essential for maintaining gut health. However, their antiproliferative effects on gastric cancer cells and potential interactions with conventional therapies remain underexplored. This study aimed to investigate the effects of three SCFA salts—magnesium acetate (A), sodium propionate (P), and sodium butyrate (B)—individually and in combination (APB), as well as in combination with dexamethasone (Dex), on AGS gastric adenocarcinoma cells. Methods: AGS cells were treated with PB, AP, AB, APB, Dex, and APB+Dex. Cell viability was assessed to determine antiproliferative effects, and the IC₅₀ of APB was calculated. Flow cytometry was used to evaluate apoptosis and necrosis. Reactive oxygen species (ROS) levels were measured to assess oxidative stress. Proteomic analysis via LC-MS was performed to identify differential protein expression and related pathways impacted by the treatments. Results: SCFA salts showed significant antiproliferative effects on AGS cells, with APB exhibiting a combined IC₅₀ of 568.33 μg/mL. The APB+Dex combination demonstrated strong synergy (combination index = 0.76) and significantly enhanced growth inhibition. Both APB and APB+Dex induced substantial apoptosis (p < 0.0001) with minimal necrosis. APB alone significantly increased ROS levels (p < 0.0001), while Dex moderated this effect in the combination group APB+Dex (p < 0.0001). Notably, the APB+Dex treatment synergistically targeted multiple tumour-promoting mechanisms, including the impairment of redox homeostasis through SLC7A11 suppression, and inhibition of the haemostasis, platelet activation network and NF-κB signalling pathway via downregulation of NFKB1 (−1.34), exemplified by increased expression of SERPINE1 (1.99) within the “Response to elevated platelet cytosolic Ca²⁺” pathway. Conclusions: These findings showed a multifaceted anticancer mechanism by APB+Dex that may collectively impair cell proliferation, survival signalling, immune modulation, and tumour microenvironment support in gastric cancer. Full article

Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments. Full article