Search Results (123,730)

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: Cefiderocol (FDC) presents challenges in antimicrobial susceptibility testing (AST). The reference standard is the broth microdilution (BMD) method with iron-depleted cation-adjusted Mueller-Hinton broth (ID-CAMHB). Still, it is cumbersome for routine clinical laboratory use, while variable accuracy has been reported with available commercial systems. Variability in interpretive criteria and areas of technical uncertainty (ATUs) further complicate assessments. Methods: This review and expert opinion presents: (1) an overview of non-susceptibility to FDC and then delves into the performance of current FDC AST methods for Enterobacterales, Pseudomonas aeruginosa, and Acinetobacter baumannii complex; (2) a practical decision framework to guide clinical microbiologists in making informed choices. Results and Conclusions: For Enterobacterales, including carbapenem-resistant Enterobacterales (CRE), and Pseudomonas aeruginosa, we propose disk diffusion (DD) as a preliminary screening tool to classify isolates as susceptible (S) or resistant (R). Confirmatory testing using the UMIC^® FDC system or the ID-CAMHB BMD method is recommended for R isolates. In cases of discrepancy, repeating the test with ID-CAMHB BMD is advised. Additionally, isolates falling within the ATU during DD testing should be retested using the UMIC^® system or ID-CAMHB BMD. For A. baumannii complex, since EUCAST breakpoints have not been defined yet, we propose a stepwise framework based on the first DD result: isolates with inhibition zones <17 mm are considered non-susceptible and should be confirmed with standard BMD. Those between 17 and 22 mm require retesting with a commercial BMD method, with further confirmation recommended if S isolates with zones ≥23 mm may be considered S without additional testing. Full article

This review comprehensively examines the application of deep learning technologies across the entire soybean production chain, encompassing areas such as disease and pest identification, weed detection, crop phenotype recognition, yield prediction, and intelligent operations. By systematically analyzing mainstream deep learning models, optimization strategies (e.g., model lightweighting, transfer learning), and sensor data fusion techniques, the review identifies their roles and performances in complex agricultural environments. It also highlights key challenges including data quality limitations, difficulties in real-world deployment, and the lack of standardized evaluation benchmarks. In response, promising directions such as reinforcement learning, self-supervised learning, interpretable AI, and multi-source data fusion are proposed. Specifically for soybean automation, future advancements are expected in areas such as high-precision disease and weed localization, real-time decision-making for variable-rate spraying and harvesting, and the integration of deep learning with robotics and edge computing to enable autonomous field operations. This review provides valuable insights and future prospects for promoting intelligent, efficient, and sustainable development in soybean production through deep learning. 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

Immunotherapy has revolutionized cancer treatments but still faces challenges, particularly with response rates plateauing around 20–40%. This is primarily due to the immunosuppressive nature of the tumor microenvironment (TME) and the lack of required antigen availability. This emphasizes finding agents that can improve these response rates, and curcumin has emerged as a promising natural compound with the potential to reengineer the TME by establishing its anti-inflammatory, antioxidant, pro-apoptotic, and anti-angiogenic effects. This review synthesizes the mechanisms by which curcumin affects major oncogenic pathways to synergize with immunotherapies, including immune checkpoint inhibitors, adoptive cell therapies, and cancer vaccinations. Finally, we discuss future directions, current clinical trials, and bioavailability issues with utilizing curcumin clinically. Full article

Plant-derived extracellular vesicles (PDEVs) are nanoscale, phospholipid bilayer-enclosed vesicles secreted by living cells through cytokinesis under physiological and pathological conditions. Owing to their high biocompatibility and stability, PDEVs have attracted considerable interest in regenerative medicine applications. They can exhibit the capacity to enhance cellular proliferation, migration, and multi-lineage differentiation through immunomodulation, anti-inflammation effects, antioxidative protection, and tissue regeneration mechanisms. Given their abundant availability, favorable safety profile, and low immunogenicity risks, PDEVs have been successfully employed in therapeutic interventions for skeletal muscle disorders, cardiovascular diseases, neurodegenerative conditions, and tissue regeneration applications. This review mainly provides a comprehensive overview of PDEVs, systematically examining their biological properties, standardized isolation and characterization methodologies, preservation techniques, and current applications in regenerative medicine. Furthermore, we critically discuss future research directions and clinical translation potential, aiming to facilitate the advancement of PDEV-based therapeutic strategies. Full article

This paper presents a detailed investigation into the isometric properties of osculating and rectifying curves on smooth immersed surfaces in ${\mathbb{E}}^3$. We examine the geometric interactions between these curves, specifically when the osculating curve is associated with one surface and the rectifying curve with another. The main objective of this study is to identify the conditions under which these curves exhibit isometric behavior, preserving their intrinsic geometric properties along their respective Frenet frames. Our findings demonstrate that these curves retain isometric characteristics along the tangent, normal, and binormal directions, offering new insights into their structural invariance. This research makes a significant contribution to the broader field of differential geometry, with potential applications in surface theory. Full article

Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis remain incurable. Current therapeutic strategies primarily focus on slowing disease progression, alleviating symptoms, and improving patients’ quality of life, including the management of comorbid conditions. Over the past few decades, the incidence of diagnosed neurodegenerative disorders has risen significantly. As the number of affected individuals continues to grow, so does the urgent need for effective treatments that can halt or mitigate the progression of these diseases. Among the most promising therapeutic resources are bioactive compounds derived from fungi. The high quality of proteins, polysaccharides, unsaturated fatty acids, triterpenoids, sterols, and secondary metabolites found in fungi have attracted growing interest from researchers across multiple disciplines. One intensively studied direction involves the use of naturally occurring fungi-derived nutraceuticals in the treatment of various diseases, including neurodegenerative conditions. This article provides an overview of recent findings on fungal compounds—such as phenolic compounds, carbohydrates, peptides and proteins, and lipids—that may have potential applications in the treatment of neurodegenerative diseases and the alleviation of their symptoms. Full article

Objective: To compare the effect of different bone augmentation procedures, namely, autogenous bone blocks (ABBs) versus guided bone regeneration (GBR), on patient-reported outcomes (PROMs). Methods: This systematic review was conducted according to the PRISMA guidelines. A MEDLINE, Embase, and Web of Science search was conducted by two independent reviewers in combination with a free-hand search in relevant journals until June 2025. Outcomes were PROMs to enhance our understanding of the evolution of these procedures. Results: The electronic search yielded 6291 articles. After title screening, 67 articles were further analyzed for abstract review, which resulted in 14 articles eligible for full-text reading. Six articles were finally included based on the exclusion and inclusion criteria with a total of 295 patients. The overall study quality was low, since only two RCTs could be included. One study demonstrated a high risk of bias. Different PROMs were examined and compared such as pain, edema, neurosensory disturbance, Patient-Reported Predominant Symptom, OHIP-14, postoperative analgesic usage, willingness to repeat, and likelihood to recommend. Meta-analysis was not achievable due to a lack of direct comparisons and heterogeneity in terms of PROMs. Evaluation points varied between pretreatment and up to nearly 10-years of follow-up. Conclusions: Despite significant heterogeneity and reporting, this systematic review concluded that ABB and GBR are well-tolerated procedures. Trends such as transient postoperative pain and swelling with a minor occurring of neurosensory disturbances were reported in a few studies. Overall, a good perception of postoperative recovery was reported for both treatment modalities. Good quality of life was noted related to GBR procedures. Patient-reported outcomes were only analyzed for patients who completed the entire follow-up period. This may introduce bias, as patients who dropped out and were more likely to experience complications were not represented, potentially resulting in a more favorable portrayal of the outcomes. Further well-conducted prospective studies with a long follow-up are needed for an evidence-based evaluation and comparison of PROMs for these procedures. Full article

Industrial equipment fault diagnosis faces dual challenges: significant data distribution discrepancies caused by diverse operating conditions impair generalization capabilities, while underutilized spatio-temporal information from multi-source data hinders feature extraction. To address this, we propose a spatio-temporal collaborative perception-driven feature graph construction and topology mining methodology for variable-condition diagnosis. First, leveraging the operational condition invariance and cross-condition consistency of fault features, we construct fault feature graphs using single-source data and similarity clustering, validating topological similarity and representational consistency under varying conditions. Second, we reveal spatio-temporal correlations within multi-source feature topologies. By embedding multi-source spatio-temporal information into fault feature graphs via spatio-temporal collaborative perception, we establish high-dimensional spatio-temporal feature topology graphs based on spectral similarity, extending generalized feature representations into the spatio-temporal domain. Finally, we develop a graph residual convolutional network to mine topological information from multi-source spatio-temporal features under complex operating conditions. Experiments on variable/multi-condition datasets demonstrate the following: feature graphs seamlessly integrate multi-source information with operational variations; the methodology precisely captures spatio-temporal delays induced by vibrational direction/path discrepancies; and the proposed model maintains both high diagnostic accuracy and strong generalization capacity under complex operating conditions, delivering a highly reliable framework for rotating machinery fault diagnosis. Full article

This study proposes a deep learning-based method to improve overlay alignment precision in laser direct writing systems. Alignment errors arise from multiple sources in nanoscale processes, including optical aberrations, mechanical drift, and fiducial mark imperfections. A significant portion of the residual alignment error stems from the interpretation of mark coordinates by the vision system and algorithms. Here, we developed a convolutional neural network (CNN) model to predict the coordinates calculation error of 66,000 sets of computer-generated defective crosshair marks (simulating real fiducial mark imperfections). We compared 14 neural network architectures (8 CNN variants and 6 feedforward neural network (FNN) configurations) and found a well-performing, simple CNN structure achieving a mean squared error (MSE) of 0.0011 on the training sets and 0.0016 on the validation sets, demonstrating 90% error reduction compared to the FNN structure. Experimental results on test datasets showed the CNN’s capability to maintain prediction errors below 100 nm in both X/Y coordinates, significantly outperforming traditional FNN approaches. The proposed method’s success stems from the CNN’s inherent advantages in local feature extraction and translation invariance, combined with a simplified network architecture that prevents overfitting while maintaining computational efficiency. This breakthrough establishes a new paradigm for precision enhancement in micro–nano optical device fabrication. Full article

This review article compiled previous reports in the fabrication of hydroquinone (HQ) electrochemical sensors using differently modified electrodes. The electrode materials, which are also called electrocatalysts, play a crucial role in electrochemical detection of biomolecules and toxic substances. Metal oxides, MXenes, carbon-based materials such as reduced graphene oxide (rGO), carbon nanotubes (CNTs), layered double hydroxides (LDH), metal sulfides, and hybrid composites were extensively utilized in the fabrication of HQ sensors. The electrochemical performance, including limit of detection, linearity, sensitivity, selectivity, stability, reproducibility, repeatability, and recovery for real-time sensing of the HQ sensors have been discussed. The limitations, challenges, and future directions are also discussed in the conclusion section. It is believed that the present review article may benefit researchers who are involved in the development of HQ sensors and catalyst preparation for electrochemical sensing of other toxic substances. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

Background: Chronic Kidney Disease (CKD) is a major global health issue, with diabetes being its primary cause and cardiovascular disease contributing significantly to patient mortality. Recently, two classes of medications—sodium–glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs)—have shown promise in protecting both kidney and heart health beyond their effects on blood sugar control. Methods: We conducted a narrative review summarizing the findings of different clinical trials and mechanistic studies evaluating the effect of SGLT2i and GLP-1 RAs on kidney function, cardiovascular outcomes, and overall disease progression in patients with CKD and DKD. Results: SGLT2i significantly mitigate kidney injury by restoring tubuloglomerular feedback, reducing intraglomerular hypertension, and attenuating inflammation, fibrosis, and oxidative stress. GLP-1 RAs complement these effects by enhancing endothelial function, promoting weight and blood pressure control, and exerting direct anti-inflammatory and anti-fibrotic actions on renal tissues. Landmark trials—CREDENCE, DAPA-CKD, and EMPA-KIDNEY—demonstrate that SGLT2i reduce the risk of kidney failure and renal or cardiovascular death by 25–40% in both diabetic and non-diabetic CKD populations. Likewise, trials such as LEADER, SUSTAIN, and AWARD-7 confirm that GLP-1 RAs slow renal function decline and improve cardiovascular outcomes. Early evidence suggests that using both drugs together may offer even greater benefits through multiple mechanisms. Conclusions: SGLT2i and GLP-1 RAs have redefined the therapeutic landscape of CKD by offering organ-protective benefits that extend beyond glycemic control. Whether used individually or in combination, these agents represent a paradigm shift toward integrated cardiorenal-metabolic care. A deeper understanding of their mechanisms and clinical utility in both diabetic and non-diabetic populations can inform evidence-based strategies to slow disease progression, reduce cardiovascular risk, and improve long-term patient outcomes in CKD. Full article

Background/Objectives: Romania remains a tuberculosis (TB) hotspot in the European Union, yet host-derived factors of poor outcomes are poorly characterised. We quantified circulating pro-inflammatory cytokines and examined their interplay with behavioural risk factors, the nutritional status, and the clinical course in adults hospitalised with pulmonary TB. We analysed 80 adults with microbiologically confirmed pulmonary TB and 40 respiratory symptom controls; four TB patients (5%) died during hospitalisation, all within 10 days of admission. Methods: A retrospective analytical case–control study was conducted at the Constanța regional TB referral centre (October 2020—October 2023). Patients with smear- or culture-confirmed TB were frequency-matched by sex, 10-year age band, and BMI class to culture-negative respiratory controls at a 2:1 ratio. The patients’ serum interferon-γ (IFN-γ), interleukin-1α (IL-1α), interleukin-1β (IL-1β), and tumour-necrosis-factor-α (TNF-α) were quantified within 24 h of admission; the neutrophil/lymphocyte ratio (NLR) was extracted from full blood counts. Independent predictors of in-hospital mortality were identified by multivariable logistic regression; factors associated with the length of stay (LOS) were modelled with quasi-Poisson regression. Results: The median TNF-α (24.1 pg mL⁻¹ vs. 16.2 pg mL⁻¹; p = 0.009) and IL-1β (5.34 pg mL⁻¹ vs. 3.67 pg mL⁻¹; p = 0.008) were significantly higher in the TB cases than in controls. TNF-α was strongly correlated with IL-1β (ρ = 0.80; p < 0.001), while NLR showed weak concordance with multiplex cytokine patterns. Among the patients with TB, four early deaths (5%) exhibited a tripling of TNF-α (71.4 pg mL⁻¹) and a doubling of NLR (7.8) compared with the survivors. Each 10 pg mL⁻¹ rise in TNF-α independently increased the odds of in-hospital death by 1.8-fold (95% CI 1.1–3.0; p = 0.02). The LOS (median 29 days) was unrelated to the smoking, alcohol, or comorbidity load, but varied across BMI strata: underweight, 27 days; normal weight, 30 days; overweight, 23 days (Kruskal–Wallis p = 0.03). In a multivariable analysis, under-nutrition (BMI < 18.5 kg m⁻²) prolonged the LOS by 19% (IRR 1.19; 95% CI 1.05–1.34; p = 0.004) independently of the disease severity. Conclusions: A hyper-TNF-α/IL-1β systemic signature correlates with early mortality in Romanian pulmonary TB, while under-nutrition is the dominant modifiable determinant of prolonged hospitalisation. Admission algorithms that pair rapid TNF-α testing with systematic nutritional assessment could enable targeted host-directed therapy trials and optimise bed utilisation in high-burden settings. Full article