Search Results (280)

Accurately extracting deviation features in frequency response curves, which reflect winding deformation states, and selecting appropriate machine learning algorithms are critical for achieving a precise quantitative diagnosis of winding deformation based on frequency response analysis (FRA). To address the existing challenges in transformer winding fault diagnosis, including the absence of a systematic feature evaluation framework for frequency response data and the limited recognition accuracy of machine learning models, a novel hybrid feature selection and diagnostic framework was developed. First, a high-dimensional feature pool comprising 25 numerical indices was extracted from experimental FRA curves. To eliminate feature redundancy and arbitrary selection, a hybrid mechanism integrating maximum-relevance, minimum-redundancy (mRMR) with random forest (RF) was developed to dynamically construct task-specific optimal feature subsets. Furthermore, an inverse-distance-weighted K-nearest neighbors (IKNN) model was introduced to enhance diagnostic sensitivity by accounting for feature-space distance variations. Experimental results obtained from a laboratory winding model demonstrate that the proposed mRMR-RF-IKNN model significantly outperforms traditional and optimized benchmarks across multiple macro-evaluation metrics. This study provides a systematic, intelligent screening mechanism that ensures high-precision identification of both the types and severity of faults in power transformers. Full article

Objectives: To characterize the prenatal phenotypic spectrum, genetic findings, and pregnancy outcomes of fetal renal agenesis (RA), and to clarify the complementary roles of chromosomal microarray analysis (CMA) and whole exome sequencing (WES) in phenotype-stratified prenatal evaluation. Methods: This retrospective study included 203 RA fetuses between March 2017 and November 2025. All cases underwent genome-wide copy number variant (CNV) analysis, and selected cases underwent WES. Detection rates were compared across subgroups by laterality, isolated vs. non-isolated phenotype, fetal sex, and presence of extrarenal anomalies. Pregnancy outcomes and postnatal imaging follow-up were collected when available. A systematic literature review of prenatal genetic testing in RA fetuses was performed. Results: Among 203 fetuses, unilateral RA accounted for 92.6% of cases, and 65.0% were isolated. Chromosomal abnormalities were identified in 15 fetuses (7.4%), including aneuploidies and pathogenic or likely pathogenic (P/LP) CNVs. WES identified P/LP single nucleotide variants in 8 of 127 cases (6.3%), increasing to 8.7% when variants with potential clinical relevance were included. Diagnostic yield of WES was significantly higher in bilateral RA, non-isolated cases, and fetuses with extrarenal anomalies. Postnatal follow-up confirmed RA in most liveborn cases, although additional phenotypes emerged in some children. Literature synthesis identified recurrent CNVs at 16p11.2 and 22q11.21 and frequent involvement of FRAS1, FREM2, GFRA1, and GREB1L. Conclusions: RA shows marked phenotypic and genetic heterogeneity. CMA remains a first-tier prenatal test, while WES provides substantial incremental yield in bilateral, non-isolated, or extrarenal-associated RA. Integrated, phenotype-driven testing with longitudinal follow-up supports improved prognostication and genetic counseling. Full article

Background/Objectives: Epilepsy is characterized by unpredictable seizures and drug resistance, along with rising antimicrobial resistance (AMR), highlighting the urgent need for innovative dual-action therapies. This study aimed to design, develop, and evaluate novel pyrazolone derivatives for a dual antimicrobial and antiepileptic potential. Methods: Novel pyrazolone derivatives were designed, synthesized (using 2,4-dinitrophenylhydrazine/semicarbazide condensation with ethyl acetoacetate), and evaluated through molecular docking against antimicrobial (4URM, 3FYV, 3FRA) and neuronal targets (4COF, 5TP9, 5L1F). The in vitro antimicrobial activity was assessed against Gram-positive (S. aureus) and in vitro Gram-negative (E. coli, P. aeruginosa) strains via agar cup plate assays, while in vivo antiepileptic efficacy was tested in a PTZ-induced seizure model in Swiss albino mice. Results: Compound IIa showed potent dual activity, inhibiting E. coli (9 mm zone at 80 μg/mL) and S. aureus (9.5 mm at 80 μg/mL), alongside a significantly delayed seizure onset in the PTZ-induced mouse model (100% survival rate, 45 sec delayed seizure onset, p < 0.001). Compounds Ia and Id showed selective activity against E. coli (6 mm at 80 μg/mL) and P. aeruginosa (7 mm at 80 μg/mL), respectively. Docking studies revealed that compound IIa has a superior binding affinity (−7.57 kcal/mol for 3FYV) compared to standards, driven by hydrogen bonds (SER X: 49) and hydrophobic interactions (LEU X: 20). Conclusions: This study presents a novel approach by proposing a rationally designed pyrazolone scaffold exhibiting both antimicrobial and antiepileptic activity, which integrates in silico modeling with experimental validation. Compound IIa emerged with preliminary dual biological activities, exhibiting strong antibacterial activity, a superior binding affinity toward both bacterial and neuronal targets, and notable seizure prevention in vivo. These findings show the potential of multifunctional pyrazolone derivatives as a new treatment strategy for addressing drug-resistant infections linked to epilepsy and support further optimization toward clinical development. Full article

The spaceborne full-polarimetric (FP) synthetic aperture radar (SAR) is an advanced sensor for high-resolution Earth observation. However, FP data acquired by such a system are prone to distortions induced by ionospheric Faraday rotation (FR). From the perspective of exploiting these distortions, this enables the estimation of the ionospheric FR angle (FRA), and consequently the total electron content, across most global regions (including the extensive ocean areas) using spaceborne FP SAR measurements. The accuracy of FRA estimation, however, is highly sensitive to noise interference. This study addresses denoising in FRA retrieval based on the Bickel–Bates estimator, with a specific focus on noise reduction methods built upon the adaptive Goldstein filter (AGF) that was originally designed for radar interferometric processing. For the first time, three signal-to-noise ratio (SNR)-based AGFs suitable for FRA estimation are investigated. A key feature of these filters is that their SNRs are all defined using the amplitude of the Bickel–Bates estimator signal rather than the FRA estimates themselves. Accordingly, these AGFs are applied to the estimator signal instead of the estimated FRAs. Two of the three AGFs are developed by adopting the mathematical forms of SNRs and filter parameters consistent with the existing SNR-based AGFs for interferogram. The third AGF is newly proposed by utilizing more general mathematical forms of SNR and filter parameter that differ from the first two. Specifically, its SNR definition aligns with that widely used in image processing, and its filter parameter is derived as a function of the defined SNR plus an additionally introduced adjustable factor. The three SNR-based AGFs tailored for FRA estimation are tested and evaluated against existing AGF variants and classical image denoising methods using three sets of FP SAR Datasets acquired by the L-band ALOS PALSAR sensor, encompassing an ocean-only scene, a plain land–ocean combined scene, and a more complex land–ocean combined scene. Experimental results demonstrate that all three filters can effectively mitigate noise, with the newly proposed AGF achieving the best performance among all denoising methods included in the comparison. Full article

The quality of high-density lipoproteins (HDLs) is characterized by lipid and protein composition, oxidation and glycation extent, and particle size, while the quantity of HDL-C is just the cholesterol amount in HDL. The inverse association between HDL-C and cardiovascular disease (CVD) and hypertension has been well established; however, the U-shaped mortality risk observed from HDL-C underscores that HDL quality and function are equally important. The present cross-sectional study assessed the correlations of serum lipid and glucose profiles, and low-density lipoprotein (LDL) and HDL characteristics, with blood pressure (BP) distribution in ordinary middle-aged Korean participants (n = 50; mean age 47.0 ± 11.7 years; males: n = 25, 49.2.0 ± 11.7 years; females: n = 25, 44.8 ± 11.5 years), with particular focus on HDL quality and its antioxidant capacity. This study observed that serum elevated triglyceride (TG) and glucose levels were directly proportional to elevated systolic BP (SBP) and diastolic BP (DBP), whereas serum total cholesterol (TC), LDL-C, and HDL-C were not correlated with BP. However, HDL-C/TC (%) was negatively associated with SBP (p = 0.036), while TG/HDL-C and glucose/HDL-C ratios were positively associated with both SBP and DBP, suggesting that TG and glucose proportions relative to HDL-C are probable predictors of hypertension. Elevations of TG, oxidation, and glycation in LDL were positively associated with elevations of BP, whereas LDL particle size was negatively correlated with BP. Similarly, elevations of TG and glycation in HDL₂ and HDL₃ were positively correlated with elevations of BP, while the particle size of HDL₂ was negatively correlated with BP. The heightened HDL₂-associated paraoxonase (PON) activity and ferric ion reduction ability (FRA) negatively correlated with LDL oxidation and particle size, whereas elevated HDL₃-associated PON and FRA activities were inversely related to LDL glycation. An enhanced glycation in HDL₂ was negatively correlated with HDL₂-associated PON activity and FRA, while an increase in HDL₂ particle size was only dependent on the associated PON activity but not on FRA. In conclusion, observational outcomes demonstrated that improved HDL quality and functionality (characterized by large particle size, reduced glycation, and higher FRA and PON activities) were inversely correlated with LDL oxidation, glycation, particle shrinkage, and the risk of hypertension. Full article

Transformers are essential power transformation equipment in power systems. Winding deformation is one of the main forms of transformer winding faults, which may cause performance degradation or even overall damage to the equipment. As the commonly used methods for diagnosing winding deformation, frequency response analysis (FRA) has problems such as the reliance on expert experience, insufficient universality for windings of different voltage levels and connection methods, etc. If the equivalent network parameters of the windings are identified based on the frequency response curve, the universality and effectiveness can be fundamentally guaranteed. This paper presents a comprehensive review and classification of domestic and international methods for parameter identification of transformer winding equivalent network based on FRA. It elaborates on the principles of parameter identification, as well as the correlation mechanism between frequency response curves and the equivalent network model of transformer windings. In addition, an evaluation is conducted on the principles, strengths, and key challenges of different algorithmic of parameter identification. Drawing upon existing research cases, practical recommendations are provided for the application of different algorithms. Finally, the challenges currently facing research in transformer winding parameter identification are analyzed, and potential future development trends are discussed. Full article

Background: Emotion recognition using electroencephalography (EEG) offers a non-invasive means of measuring brain responses to affective stimuli. However, since EEG signals can vary significantly between subjects, developing a deep learning model capable of accurately predicting emotions is challenging. Methods: To address that challenge, this study proposes a deep learning approach that fuses EEG features with demographic information, specifically age, sex, and nationality, using an attention-based mechanism that learns to weigh each modality during classification. The method was evaluated using three benchmark datasets: SEED, SEED-FRA, and SEED-GER, which include EEG recordings of 31 subjects of different demographic backgrounds. Results: We compared a baseline model trained solely on the EEG-derived features against an extended model that fused the subjects’ EEG and demographic information. Including demographic information improved the performance, achieving 80.2%, 80.5%, and 88.8% for negative, neutral, and positive classes. The attention weights also revealed different contributions of EEG and demographic inputs, suggesting that the model learns to adapt based on subjects’ demographic information. Conclusions: These findings support integrating demographic data to enhance the performance and fairness of subject-independent EEG-based emotion recognition models. Full article

Impedance Spectroscopy (IS) is widely used to analyze the dynamic behavior and degradation of electrochemical systems such as batteries. IS has also been successfully applied to study the performance and degradation mechanisms of photovoltaic (PV) devices. Traditionally, IS is performed with Frequency Response Analyzers (FRA), which apply small-signal perturbations and measure the impedance response of the system. However, those instruments are costly and not suitable for in situ diagnostics. This work proposes a methodology to perform IS measurements on PV systems using a power converter, thereby eliminating the need for external specialized equipment. The proposed approach includes a theoretical analysis of the converter dynamics to derive an expression for the duty cycle amplitude, which is required to maintain a constant perturbation magnitude across a range of frequencies. The methodology is experimentally validated using a synchronous Boost converter connected to a PV panel and controlled by a Texas Instruments F28379D digital signal processor (DSP), which injects the perturbation signal in the converter’s duty cycle. Moreover, the voltage and current measurements are performed with an oscilloscope. The results demonstrate that the proposed converter-based IS method accurately reproduces the impedance spectra obtained with a commercial FRA, confirming its feasibility as a low-cost, flexible, and scalable solution for PV impedance characterization and diagnostics. Full article

Idiopathic nephrotic syndrome (INS) is the most prevalent glomerular illness in children. Even while immunologic processes are well-established, oxidative stress is becoming more widely acknowledged as a significant factor in the etiopathogenesis of illness. Assessing its activity and treatment response may be made easier with the use of trustworthy, non-invasive indicators to track redox balance. We report on the oxidative stress levels of a 10.7-year-old boy with INS with five clinical time points in one year. The FRAS5 analyzer was used to calculate the oxidative stress index (OSI), plasma antioxidant capacity (PAT) and derivatives of reactive oxygen metabolites (d-ROMs) as biomarkers. A 4-tier oxidative state classification scheme based on d-ROM and PAT thresholds was used to interpret the values. The patient had low antioxidant defense, moderate oxidative and increased OSI at relapses, a positive transition to reduced oxidative burden and enhanced defense during remission. The order of events showed a dynamic redox response associated with glucocorticoid (GC) medication and disease activity. The potential value of d-ROM, PAT, and OSI as dynamic biomarkers for tracking disease activity, response to treatment and residual oxidative burden in pediatric INS is supported by this case. To confirm their function in more comprehensive clinical decision-making, more research is required. Full article

Background/Objectives: Radiolabeled fibroblast activation protein inhibitors (FAPIs) are emerging as promising imaging agents assessing fibrotic diseases. This study evaluates [⁶⁸Ga]Ga-DATA^5m.SA.FAPi for imaging pulmonary fibrosis in two mouse models, bleomycin-induced (BLM) and a transgenic (fra-2^tg) model, both displaying characteristics of human pulmonary fibrotic diseases. Methods: In the BLM model, C57BL/6 mice were treated with bleomycin or isotonic sodium chloride (controls) for 4, 5, and 6 weeks, followed by [⁶⁸Ga]Ga-DATA^5m.SA.FAPi PET/CT scans. Fra-2^tg mice and wildtype (WT) littermates underwent at 7, 11, and 18/19 weeks of age a PET/CT scan. The selected timepoints correspond to early, middle, and late disease stages for each model. Imaging was complemented by ex vivo quantification, histological, and immunohistochemical (IHC) analyses. Results: In BLM mice, pulmonary [⁶⁸Ga]Ga-DATA^5m.SA.FAPi uptake showed a trend toward increase as early as 5 weeks of treatment compared with the controls, which was confirmed by ex vivo analysis (BLM: 3.31 ± 0.29%ID/g, n = 5; control: 1.61 ± 0.29%ID/g, n = 4; p = 0.0035). In fra-2^tg mice, no significant differences could be detected. IHC revealed elevated pulmonary FAP expression specifically at early (BLM) and mild (fra-2^tg) disease stages, whereas for BLM, tracer uptake was more pronounced at later stages. Conclusions: Our findings complement and extend observations from previous studies and support the potential of FAPI tracers as molecular imaging agents for pulmonary fibrosis. Full article

Railroad-highway grade crossings (RHGCs) are critical points of conflict between roadway and rail systems, contributing to over 2000 crashes and 250 fatalities annually in the United States. This study applied machine learning methods (ML) techniques to model and predict crash frequency at RHGCs, using a comprehensive dataset from the Federal Railroad Administration (FRA) and Tennessee Department of Transportation (TDOT). The dataset included 807 validated crossings, incorporating roadway geometry, traffic volumes, rail characteristics, and control features. Five ML models—Random Forest, XGBoost, PSO-Elastic Net, Transformer-CNN, and Autoencoder-MLP—were developed and compared to a traditional Negative Binomial (NB) regression model. Results showed that ML models significantly outperformed the NB model in predictive accuracy, with the Transformer-CNN achieving the lowest Mean Squared Error (21.4) and Mean Absolute Error (3.2). Feature importance analysis using SHAP values consistently identified Annual Average Daily Traffic (AADT), Truck Traffic Percentage, and Number of Lanes as the most influential predictors, findings that were underrepresented or statistically insignificant in the NB model. Notably, the NB model failed to detect the nonlinear relationships and interaction effects that ML algorithms captured effectively. While only three variables were statistically significant in the NB model, ML models revealed a broader spectrum of critical crash determinants, offering deeper interpretability and higher sensitivity. These findings emphasize the superiority of machine learning approaches in modeling RHGC safety and highlight their potential to support data-driven interventions and policy decisions for reducing crash risks at grade crossings. Full article

This study compared the muscle quality of largemouth bass (Micropterus salmoides) reared in a simplified indoor factory-scale recirculating aquaculture system (F-RAS) with those reared in a traditional pond (TP). Juveniles from the same cohort (with a mean initial body mass of approximately 16 g) were stocked into the two systems and reared for ten months. The F-RAS is a high-density indoor system utilising octagonal concrete tanks with an effective water volume of 100 m³ and a stocking density of 130 fish m⁻³. The TP is a low-density system, where fish are reared in earthen ponds with a total area of 4000 m² at a density of 1.7 fish m⁻³. At the end of the experiment, 20 fish per group were randomly sampled for morphological analysis, while subsets of 6 fish per group were used for texture analysis, 3 fish per group for water-holding capacity, 3 fish per group for proximate composition, and 9 fish per group (pooled into 3 biological replicates) for amino acid, fatty acid and volatile compound analyses. The results showed that the F-RAS group exhibited superior texture, with significantly higher chewiness, springiness and muscle fibre density. Nutritionally, the F-RAS group had significantly greater crude protein, EPA, DHA and total n-3 fatty acid contents. Although glutamate and leucine levels were lower in the F-RAS group, cysteine and histidine levels were higher. Analysis of volatile compounds indicated improved flavour in the F-RAS group, with a marked reduction in off-flavour compounds such as 1-octen-3-ol and hexanal. Overall, largemouth bass produced in F-RAS showed better muscle quality than those from the TP in terms of texture, nutritional value and flavour. This study provides a reference for future research on the regulation of muscle quality in largemouth bass using a simplified F-RAS. Full article

This research deals with a multi-analytical approach to characterise three polychrome wooden crucifixes attributed to Fra’ Umile da Petralia or, in one case, close to his style. The analysed wooden sculptures belong to (1) the Sanctuary of Saint Umile from Bisignano (Cosenza, Italy); (2) the Santissimo Crocifisso Church at Cutro (Crotone, Italy); (3) the Saint Salvatore Church at Gangi (Palermo, Italy). Fra’ Umile (Giovanni Francesco Pintorno) was born in Petralia Soprana (Palermo, Sicily) between 1600 and 1601, died on 9 February 1639, and belonged to the Order of Friars Minor. Systematic research of the materials and constructive techniques of wooden sculptures is still not very wide, although diagnostic analyses could represent a useful tool for art historians and restorers for these typologies of works of art. The wooden sculptures were subjected to both non-invasive and micro-invasive investigations. Digital direct X-ray radiography, SEM-EDX, and X-ray fluorescence analyses have been carried out, revealing for the three analysed sculptures a similar construction technique and similar pictorial materials. The provided diagnostic evidence supports the coincident chronology and the same attribution to Fra’ Umile da Petralia or his workshop, confirming the proposals based on the stylistic comparative analyses and archival historical documents. The results of this first multi-analytical investigation, documenting the artistic technique and construction system, represent a starting point for future systematic study on the artistic production of this prolific artist and sculptor never studied from a technical–scientific point of view to date. Full article

Cuban sugarcane wax alcohol (policosanol) is a blend of eight characteristic aliphatic alcohols extracted from the Cuban sugarcane and widely recognized for its multifunctional applications and therapeutic properties. In the present study, the potency of policosanol (POL) was assessed for its ability to prevent metabolic stress and associated disorders posed by a high-cholesterol (HC) and high-galactose (HG) diet in zebrafish (Danio rerio). Adult zebrafish (n = 56/group) were fed either with an HC+HG diet (containing 4%, w/w cholesterol and 30%, w/w galactose), or an HC+HG amalgamated diet with POL (final 0.1% w/w or 0.5% w/w). Zebrafish in the specified groups were sacrificed post-30 weeks of feeding, and blood and organs (liver, brain, and eyes) were processed for biochemical, histological, and immunohistochemical (IHC) analysis. After 30 weeks of feeding, the highest mortality (12.5%) was noticed in the HC+HG supplement group, which was reduced to 4.5% with co-supplementation of POL (0.1% and 0.5%). In a dose-dependent manner, POL significantly reversed HC+HG elevated levels of total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), glucose, and malondialdehyde (MDA), while substantially augmenting plasma high-density lipoprotein cholesterol (HDL-C), sulfhydryl content, ferric ion reduction ability (FRA), and paraoxonase (PON) activity. In addition, POL mitigated HC+HG-induced hepatomegaly, inflammation, and fatty liver changes. Consistently, POL minimizes ROS generation and cellular senescence in the brain and substantially improves HC+HG-induced cognitive changes (cessation of swimming ability and motion), with a marked ~5 times higher swimming distance. Notably, POL mitigated the HC+HG-induced corneal opacity and attenuated oxidative stress, apoptosis, 4-hydroxynonenal (4-HNE) accumulation, and myelin sheath degeneration in the retina. The findings underscore the therapeutic potential of policosanol in attenuating oxidative stress, metabolic changes, and various organ damage caused by prolonged exposure to the HC+HG diet. Full article

The paper presents a comprehensive analysis of cement-based composites incorporating both fine recycled concrete aggregates (fRAs) and recycled concrete powder (RCP), which were used for 3D concrete printing. The study evaluates properties ranging from fresh-state behaviour to hardened properties, durability, and microstructural characteristics. In the final stage, a life cycle assessment (LCA) was conducted. A study found that it is feasible to print a composite containing up to 100% fRA as a replacement for natural river sand. Notably, an increase in fRA content enhances the buildability of the mix, as confirmed by green strength tests. However, the open time of mixes containing fRA and RCP was shortened. Incorporation of RCP and fRA led to a decrease in shrinkage within the first 24 h of hydration. Mechanical studies reported a significant reduction in compressive strength (up to 55%) when RCP and fRA were introduced to the mix. Despite the reduction in mechanical properties in specific configurations, all mixes—including 100% fRA and 10% RCP—exhibited compressive strengths above 30 MPa, demonstrating their potential suitability for use in the construction industry. The durability properties of mixes modified with fRA show that there is a statistically significant reduction in flexural strength after 25 and 50 freeze–thaw cycles. In terms of compressive strength, cast specimens did not exhibit any notable reduction in mechanical performance after freezing and thawing cycles. The LCA results demonstrate the high potential for using fRA and RCP derived from concrete waste in the additive manufacturing industry. Full article