Search Results (11,388)

Bearing fault diagnosis encounters limitations including insufficient accuracy, elevated model complexity, and demanding hyperparameter optimization. This research introduces a diagnostic framework combining variational mode decomposition (VMD) and fast Fourier transform (FFT) for extracting comprehensive temporal–spectral characteristics from vibration data. The methodology employs a hybrid deep learning architecture integrating convolutional neural networks (CNNs) with Transformers, where CNNs identify local features while Transformers capture extended dependencies. Meta-learning-enhanced Bayesian optimization and HyperBand (Meta-BOHB) is utilized for efficient hyperparameter selection. Evaluation on the Case Western Reserve University (CWRU) dataset using 5-fold cross-validation demonstrates a mean classification accuracy of 99.91% with exceptional stability (±0.08%). Comparative analysis reveals superior performance regarding precision, convergence rate, and loss metrics compared to existing approaches. Cross-dataset validation using Mechanical Fault Prevention Technology (MFPT) and Paderborn University (PU) datasets confirms robust generalization capabilities, achieving 100% and 98.75% accuracy within 5 and 7 iterations, respectively. Ablation studies validate the contribution of each component. Results demonstrate consistent performance across diverse experimental conditions, indicating significant potential for enhancing reliability and reducing operational costs in industrial fault diagnosis applications. The proposed method effectively addresses key challenges in bearing fault detection through advanced signal processing and optimized deep learning techniques. Full article

Background: Dexmedetomidine (Dex) is a well-known a2-adrenoceptor agonist with sedative, anxiolytic, sympatholytic, and analgesic effects that has been used principally as adjuvant sedation in the ICU. The enhanced clinical experience of Dex’s use and its physiological effects encourage its application beyond the initial indications. Aim: The purpose of this review is to summarize the current knowledge of Dex’s recently expanded applications in critically ill intensive care unit (ICU) adult patients. Methods: It is a narrative review that critically examines studies published since 2015 and referring to Dex’s use in ICU patients. Results: Despite the preliminary applications and the weak existing recommendation, the unique arousable sedation, in combination with mild opioid-spare analgesic effects, has been confirmed to effectively improve ICU outcomes. Moreover, the anxiolytic and sympatholytic actions have proved to sufficiently enhance sleep quality and prevent and treat ICU delirium and post-ICU syndrome, especially among elderly patients. Recently, increasing evidence advocates for promising neuro-, renal-, and cardio-protective and anti-inflammatory effects of Dex, which are attributed to autophagy and apoptosis inhibition and sympatholytic and ischemia/reperfusion (I/R) injury-protective effects. Conclusions: Beyond sedation, Dex seems to present promising neuroprotective, anti-inflammatory, and immunomodulating effects. Full article

Introduction: Antiphospholipid syndrome (APS) is an autoimmune prothrombotic disorder associated with both venous and arterial thrombosis, most notably ischemic stroke. Patients face a high risk of recurrence, and yet optimal strategies for secondary prevention remain uncertain. Methods: We conducted a narrative review of the literature on secondary prevention of ischemic stroke in APS. We performed a comprehensive literature search of PubMed for English-language articles on secondary stroke prevention in APS. Studies were included if they were original human research (e.g., randomized trials, cohort, or case–control studies) or relevant reviews addressing APS-related stroke prevention. Results: Vitamin K antagonists (VKAs) remain the standard of care for high-risk patients with arterial events. Several randomized controlled trials demonstrated higher recurrence rates, particularly of stroke, among APS patients treated with direct oral anticoagulants (DOACs). The optimal target INR remains debated; pooled analyses suggest no clear advantage of high-intensity anticoagulation (INR 3–4) over standard-intensity (INR 2–3), but individualized adjustment is warranted in select cases. In patients with recurrence despite adequate anticoagulation, adding an antiplatelet agent may be beneficial, although supporting evidence is limited. Adjunctive statin therapy shows promise in reducing endothelial dysfunction and prothrombotic markers, with observational data suggesting a possible protective effect, although randomized evidence is lacking. In addition, patent foramen ovale (PFO) closure has been proposed in selected APS patients with paradoxical embolisms, particularly when combined with anticoagulation. Non-pharmacological strategies, including structured lifestyle modification and rigorous vascular risk-factor management, are strongly recommended, as traditional cardiovascular risk factors synergistically increase recurrence risk. Conclusions: Secondary prevention of ischemic stroke in APS requires an individualized approach. VKAs remain first-line, with consideration of antiplatelet add-on, statins, lifestyle interventions, and PFO closure in appropriate settings. Future well-designed clinical trials are needed to refine INR targets, validate combination strategies, and clarify the role of adjunctive therapies in this complex patient population. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Traditional cotton field plastic film residue monitoring relies on manual sampling, with low efficiency and limited accuracy; therefore, large-scale nondestructive monitoring is difficult to achieve. A UAV-based prediction method for shallow plastic film residue pollution in cotton fields that uses RDT-Net and machine learning is proposed in this study. This study focuses on the weight of residual plastic film in shallow layers of cotton fields and UAV-captured surface film images, establishing a technical pathway for drone image segmentation and weight prediction. First, the images of residual plastic film in cotton fields captured by the UAV are processed via the RDT-Net semantic segmentation model. A comparative analysis of multiple classic semantic segmentation models reveals that RDT-Net achieves optimal performance. The local feature extraction process in ResNet50 is combined with the global context modeling advantages of the Transformer and the Dice-CE Loss function for precise residue segmentation. The mPa, F1 score, and mIoU of RDT-Net reached 95.88%, 88.33%, and 86.48%, respectively. Second, a correlation analysis was conducted between the coverage rate of superficial residual membranes and the weight of superficial residual membranes across 300 sample sets. The results revealed a significant positive correlation, with R² = 0.79635 and PCC = 0.89239. Last, multiple machine learning prediction models were constructed on the basis of plastic film coverage. The ridge regression model achieved optimal performance, with a prediction R² of 0.853 and an RMSE of 0.1009, increasing accuracy in both the segmentation stage and prediction stage. Compared with traditional manual sampling, this method substantially reduces the monitoring time per cotton field, significantly decreases monitoring costs, and prevents soil structure disruption. These findings address shortcomings in existing monitoring methods for assessing surface plastic film content, providing an effective technical solution for large-scale, high-precision, nondestructive monitoring of plastic film pollution on farmland surfaces and in the plow layer. It also offers data support for the precise management of plastic film pollution in cotton fields. Full article

Background and Objectives: Low-grade triple-negative breast carcinomas (LG-TNBCs) represent a rare subset of breast cancers that deviate from the aggressive clinical course typically associated with triple-negative tumors. This narrative review aims to consolidate current knowledge on LG-TNBCs, highlighting their diagnostic features, molecular characteristics, and clinical implications to guide appropriate patient management and prevent overtreatment. Materials and Methods: We conducted a comprehensive narrative review using PubMed/MEDLINE, Embase, and Scopus databases up to September 2025. Search terms included combinations of “triple-negative breast carcinoma”, “low-grade”, “adenoid cystic carcinoma”, “secretory carcinoma”, “acinic cell carcinoma”, “tall cell carcinoma with reversed polarity”, “low-grade adenosquamous carcinoma”, and “fibromatosis-like metaplastic carcinoma.” Studies reporting clinicopathologic, immunohistochemical, or molecular data were included. Results: LG-TNBCs include seven distinct entities: adenoid cystic carcinoma, secretory carcinoma, acinic cell carcinoma, tall cell carcinoma with reversed polarity, low-grade adenosquamous carcinoma, fibromatosis-like metaplastic carcinoma, and mucoepidermoid carcinoma. These neoplasms are characterized by distinct morphologic patterns, specific immunohistochemical profiles, and recurrent molecular alterations such as ETV6-NTRK3 fusions and MYB rearrangements. Despite their triple-negative immunoprofile, they demonstrate indolent clinical behavior with excellent prognosis and low metastatic potential, although local recurrence is reported in variants exhibiting infiltrative, locally aggressive behavior. Conclusions: Recognition of LG-TNBCs is essential to prevent overtreatment and guide personalized patient management. Molecular characterization provides diagnostic confirmation and therapeutic opportunities, particularly for NTRK-fusion-positive tumors treatable with targeted inhibitors, highlighting the importance of precision medicine in rare breast tumors. Full article

Natural shampoos are increasingly designed to provide multifunctional benefits beyond cleansing, including hair conditioning, scalp protection, and reduced irritation potential. POLEVAN^®, a proprietary levan-based polysaccharide produced enzymatically from sugar, offers a combination of oligo- and polysaccharide fractions with potential cosmetic applications. This study evaluated POLEVAN^® in shampoo formulations for three targeted effects: improving hair glossiness, enhancing scalp moisturization, and boosting foam while enabling reduced surfactant levels. Glossiness was assessed ex vivo using damaged hair tresses. Moisturization was assessed in a randomized clinical trial, comparing the test formulation with hyaluronic acid (HA), employing corneometer readings and Trans Epidermal Water Loss (TEWL) measurements. The study was subject-blinded, and all outcomes were determined solely through quantitative, device-based measurements, minimizing observer bias. Foaming performance was tested using the Shaking Cylinder Method. Shampoos containing 2% POLEVAN^® significantly increased hair glossiness by 24% (p = 0.0375) versus a non-significant increase without POLEVAN^®. Moisturization studies showed no significant difference between POLEVAN^® and HA in maintaining hydration or preventing TEWL over 4 weeks. Foam analysis demonstrated that addition of POLEVAN^® allowed up to 50% reduction in surfactant content without compromising foam generation or stability. These results highlight POLEVAN^® as a multifunctional natural ingredient capable of improving sensory and performance attributes of shampoos while supporting gentler formulations. Full article

Background/Objectives: The incidence of early-onset colorectal cancer (EOCRC) is rising worldwide, although its biological and clinical features remain incompletely understood. Emerging evidence implicates gut microbial dysbiosis as a key driver of EOCRC pathogenesis, acting through complex interactions with host genetics, mucosal immunity, and early-life exposures. This review synthesizes current evidence on EOCRC-specific microbial signatures, delineates host–microbiome interactions, and evaluates how these insights may inform precision prevention, early detection, and therapeutic strategies. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science up to August 2025, using combinations of “early-onset colorectal cancer,” “gut microbiome,” “dysbiosis,” and “host–microbiome interactions.” Both clinical and preclinical studies were included. Extracted data encompassed microbial composition, mechanistic insights, host-related factors, and microbiome-targeted interventions. Evidence was synthesized narratively to highlight consistent patterns, methodological limitations, and translational implications. Results: EOCRC is consistently associated with enrichment of pro-inflammatory and genotoxic taxa (e.g., Fusobacterium nucleatum, colibactin-producing Escherichia coli, enterotoxigenic Bacteroides fragilis) and depletion of short-chain fatty acid–producing commensals. Multi-omics analyses reveal distinct host–microbiome signatures influenced by germline predisposition, mucosal immunity, sex, and early-life exposures. However, substantial methodological heterogeneity persists. Collectively, these data point to candidate microbial biomarkers for early detection and support the rationale for microbiome-targeted preventive and adjunctive therapeutic approaches. Conclusions: EOCRC harbors unique microbial and host–environmental features that distinguish it from late-onset disease. Integrating host determinants with microbiome signatures provides a framework for precision prevention and tailored therapeutic strategies. Future priorities include harmonizing methodologies, validating microbial biomarkers in asymptomatic young adults, and rigorously testing microbiome-targeted interventions in clinical trials. Full article

In this work, we present a gettering technique for multicrystalline silicon (mc-Si) by combining a nanowire structure with thermal treatment under nitrogen in an infrared lamp furnace. The silicon nanowires were elaborated using the Silver Nanoparticles Chemical Etching (Ag-NPsCE) technique. The optimal conditions for achieving effective gettering were determined based on the minority carrier lifetime (τ_eff) measurements. The results show τ_eff as a function of the gettering temperature and etching time, both before and after the removal of Ag nanoparticles using HNO₃. In both cases, the surface was identically treated with a 10% HF dip immediately prior to the carrier lifetime measurements. The highest τ_eff value, prior to Ag removal, was obtained after an etching duration of 3 min and was 6 µs at an excess carrier density Δn = 1 × 10¹⁴ cm⁻³. Moreover, τ_eff improves after silver removal. Therefore, removing Ag atoms using an aqueous HNO₃ solution is necessary to prevent this issue. Following Ag nanoparticle removal, τ_eff further increases, reaching 19 µs at a gettering temperature of 850 °C. Similarly, the electrical conductivity (ρ) and carrier mobility (μ) improve significantly after gettering, where the resistivity increases from 5.5 Ω·cm for the reference mc-Si to 1.9 Ω·cm, and the mobility rises from 122 cm²·V⁻¹·s⁻¹ to 253 cm²·V⁻¹·s⁻¹ after nanowire-based gettering at 850 °C. Overall, this method provides a scalable, practical, and cost-effective route to optimize mc-Si for high-performance photovoltaic applications. Full article

To ensure the reliable operation of power converters and prevent catastrophic failures, this paper proposes a novel online fault diagnosis strategy for a four-level converter. The core of this strategy is an optimized multi-kernel extreme learning machine model. Specifically, the model extracts multi-scale features from three-phase output currents by combining Gaussian and polynomial kernels and employs particle swarm optimization to determine the optimal kernel fusion scheme. Experimental validation was performed on an online diagnosis platform for a four-level converter. The results show that the proposed method achieves a high diagnostic accuracy of 99.35% for open-circuit faults. Compared to conventional methods, this strategy significantly enhances diagnostic speed and accuracy through its optimized multi-kernel mechanism. Full article

Long-term excessive intake of alcohol can cause serious damage to the liver, and the study of natural active ingredients with hepatoprotective effects is of great significance for the prevention and treatment of alcoholic liver injury. This study explored the ameliorative effects of high F-value oligopeptides (HFOPs) from Chlamys farreri, Pueraria lobata extract (PL), and their complex (HFOPs + PL) on alcoholic liver injury. Results showed HFOPs + PL significantly reduced alanine aminotransferase (ALT) and aspartate aminotransferase (AST) leakage, increased superoxide dismutase (SOD) and glutathione (GSH) activities, and decreased the production of malondialdehyde (MDA) and inflammatory factors in alcohol-induced HepG2 cells. In mice, it prolonged intoxication time, shortened detoxification time, enhanced hepatic ADH and aldehyde dehydrogenase (ALDH) activities, reduced serum AST and ALT levels, and improved antioxidant capacity. Its effects were better than PL alone and comparable to HFOPs alone. HFOPs and PL alleviate alcoholic liver injury by enhancing ethanol metabolism, reducing oxidative stress, and suppressing inflammation, providing theoretical support for their combined use in alcohol detoxification and liver protection. Full article

With the rapid development of smart manufacturing and the low-altitude economy, drone technology—as a vital component of next-generation intelligent equipment—has garnered significant attention from researchers. Path planning, one of the core challenges in drone technology advancement, directly impacts the efficiency and safety of drone mission execution. However, most existing drone path planning algorithms suffer from issues such as requiring extensive interactive information or being prone to getting stuck in local optima. This study introduces a multi-strategy enhanced Pelican Optimization Algorithm (MIPOA) tailored for UAV path planning. To improve the quality of the initial population, a hybrid initialization approach combining low-discrepancy sequences with heuristic refinement is developed. The low-discrepancy component promotes a more uniform distribution across the search space, while the heuristic mechanism enhances the fitness of selected individuals and reduces redundant exploration. Furthermore, a subgroup mean-guided updating strategy is designed to accelerate convergence toward the global optimum. To maintain exploration ability, a random reinitialization boundary mechanism is incorporated, effectively preventing premature convergence. To validate the algorithm’s performance, MIPOA is compared with eleven benchmark metaheuristics on the CEC2017 test suite, and statistical analyses confirm its superior optimization capability. Finally, MIPOA is applied to 3D UAV path planning under four threat scenarios in a realistic environment, demonstrating robust adaptability and achieving successful mission completion. Full article

In eutrophic shallow lakes, dissolved oxygen (DO) exhibits significant temporal variations, regulated by the combined effects of photosynthesis and water temperature (WT). High-frequency monitoring enables a detailed capture of DO diel cycles, providing a more comprehensive understanding of the dynamic changes within lake ecosystems. This study involved high-frequency (10 min intervals) in situ monitoring of DO over a three-year period (2020–2022) in the littoral zone of Taihu Lake, China. Random forest regression analysis identified WT, photosynthetically active radiation (PAR), and relative humidity (RH) as the three most influential variables governing DO dynamics. The relative importance of these factors varied seasonally (0.117–0.392), with PAR dominating in summer (0.383), whereas WT had the highest importance in other seasons (0.312–0.392). Cusum analysis further revealed that the DO-WT relationship changed from a dome-shaped pattern in spring, autumn, and winter to a bowl-shaped pattern in summer, indicating that thermal stratification intensified oxygen gradients. In addition, the majority of DO recovery occurred in the late afternoon during summer, suggesting that severe oxygen consumption delayed the daytime accumulation of DO. Our findings emphasize the critical roles of photosynthesis, respiration, and abiotic factors in shaping DO dynamics. This research enhances our understanding of DO fluctuations in eutrophic shallow lakes and provides valuable insights for ecosystem management, supporting the development of effective strategies to prevent and mitigate hypoxia. Full article

Gastrointestinal disorders, such as colorectal cancer (CRC), pose a substantial health burden worldwide, showing increased incidence rates across different age groups. Detecting and removing polyps promptly, recognized as CRC precursors, are crucial for prevention. While traditional colonoscopy works well, it is vulnerable to specialist errors. This study suggests an AI-based diminutive sessile polyp localization assistant utilizing the YOLO-V8 family. Comprehensive evaluations were conducted using a diverse dataset that was assembled from various available datasets to support our investigation. The final dataset contains images obtained using two imaging methods: white light endoscopy (WLE) and narrow-band imaging (NBI). The research demonstrated remarkable results, boasting a precision of 96.4%, recall of 93.89%, and F1-score of 94.46%. This success can be credited to a meticulously balanced combination of hyperparameters and the specific attributes of the comprehensive dataset designed for the colorectal polyp localization in colonoscopy images. Also, it was proved that the dataset selection was acceptable by analyzing the polyp sizes and their coordinates using a special matrix. This study brings forth significant insights for augmenting the detection of diminutive sessile colorectal polyps, thereby advancing technology-driven colorectal cancer diagnosis in offline scenarios. This is particularly beneficial for gastroenterologists analyzing endoscopy capsule images to detect gastrointestinal polyps. Full article

Preeclampsia is a multisystem disorder that develops during pregnancy and is associated with severe complications for both the pregnant woman and her infant. It remains a leading cause of maternal and perinatal mortality and morbidity. Although it affects only 2–8% of pregnancies, over 70,000 women and 500,000 children die from it each year. The exact etiology of preeclampsia is unclear; it is often referred to as a disease of theories and hypotheses. This paper reviews the most significant hypotheses and studies that aim to explain the etiology of preeclampsia. This may help identify new research paths and concepts that could bring us closer to understanding the exact etiology of preeclampsia. The complexity of pathogenetic relationships and mechanisms, heterogeneous clinical presentations, and the development of underlying changes early in pregnancy when patients are clinically asymptomatic and appear healthy are among the main reasons for difficulty identifying the exact causes of preeclampsia. Furthermore, preeclampsia is specific to human pregnancy; there is no ideal animal study model whose results could be fully extrapolated to humans. A more holistic approach that combines all the information, hypotheses, and pathogenetic relationships may offer hope for understanding why preeclampsia occurs and how to prevent and treat it effectively. A better understanding of the precise etiology of the condition holds promise for developing new options for the early diagnosis, effective prevention, and modern causal treatment of preeclampsia. This would reduce the risk of severe complications in affected patients and could have enormous implications for clinical practice. Full article