Browse Articles

Research on measurement technology of optical fiber angle sensor based on MEA-BP

Abstract High-voltage disconnectors operate under strong electromagnetic radiation for long periods, making it prone to incomplete opening and closing. Current electronic sensors used to detect their status are susceptible to electromagnetic interference, leading to inaccurate measurement data. Therefore, based on the rotation characteristics of the moving contact during opening and closing, a method for measuring rotation angles in a straight line is proposed. A fiber optic angle sensor is designed according to the characteristics of reflective fiber optics to measure the rotation angle of disconnectors. The Mean Evolution Algorithm (MEA) is used to optimize the BP neural network model (MEA-BP) to compensate for the nonlinearity of the designed fiber optic angle sensor. Through experimental simulations, the nonlinearity compensation effect of the MEA-BP model is compared with that of the traditional BP neural network. The mean square error of nonlinearity compensation decreases from 0.0916 to 0.0211, and the fitting accuracy improves from 83.57% to 95.06%. The linear error decreases from 6.85% to 0.81%. An experimental platform using SBE27.5 kV/2000A disconnectors as the research object is established to test the accuracy of the fiber optic angle sensor. The experimental results show that the accuracy of the fiber optic angle sensor improves from 8.043% to 0.781% after nonlinearity compensation, indicating that this solution has higher accuracy and stronger feasibility.

Trajectory optimization for auxiliary power unit operation of an extender range electric vehicle

Mechanical performance and microstructural behavior of ultrasonically welded PVC-coated polyester fabric seams at subzero temperature

A multistage transfer learning framework for intelligent fault diagnosis of rotating machinery under variable operating conditions

Explaining and predicting consumer decision-making in E-commerce using an intelligent hybrid framework for impulsive and planned purchase behavior

Abstract Accurately distinguishing between impulsive and planned purchasing behavior remains a critical challenge in e-commerce analytics, given the multidimensional and dynamic nature of consumer data. Existing machine learning approaches often rely on conventional hyperparameter tuning strategies, which may limit model robustness and generalization. This study proposes a hybrid framework that integrates two bio-inspired metaheuristic algorithms—Walrus Optimization Algorithm (WaOA) and Weevil Damage Optimization Algorithm (WDOA)—with three gradient boosting classifiers (XGBoost, LightGBM, and CatBoost) to improve hyperparameter optimization and classification performance. Empirical evaluation indicates that the WaOA-optimized XGBoost model achieved strong predictive performance, reaching F1-scores of 0.9879 for impulsive purchases and 0.9798 for need-based purchases, with an overall testing accuracy of 97.3%. Although statistical tests suggest that performance differences among the evaluated models are not significant, the optimized configurations demonstrate consistently high predictive capability across evaluation metrics. Feature importance analysis identifies product rating, customer satisfaction, and loyalty program membership as key predictors of consumer decision type. The proposed framework demonstrates promising performance and interpretability, offering practical support for data-driven decision-making in digital retail environments.

DNMT3A drives mitochondrial dysfunction in intracerebral hemorrhage via PGC-1α promoter hypermethylation

Efficacy and safety of e.p.t.q. hyaluronic acid fillers in the context of aesthetic facial rejuvenation or beautification: a post-market clinical follow-up study

Rapid detection and quantification of glyphosate in water using a handheld portable biosensor

Abstract Glyphosate is the most extensively applied systemic herbicide worldwide, yet its safety remains under scrutiny, with ongoing investigations into potential carcinogenicity. Epidemiological studies associate chronic glyphosate exposure with elevated risks of non-Hodgkin lymphoma and possible endocrine disruption, emphasizing the need for sensitive detection methods. Here, we report a handheld enzymatic biosensor, GlyphoSense Chip, for direct, rapid detection of underivatized glyphosate in drinking water. The device integrates a photodiode-based CMOS chip with an engineered glyphosate N -acetyltransferase and a colorimetric reaction, achieving a sensitivity of 38 µV·mL µg −1 ·s −1 and quantification within one minute. Biosensor response was linear over 0.016–12.5 µg mL −1 ( R 2  = 0.993) with a detection limit of 0.028 µg mL −1 . Recovery analysis in fortified tap water yielded relative standard errors of 1.2–5.8%, and results were statistically indistinguishable from quantitative mass spectrometry ( p  > 0.05). This work establishes a robust, field-deployable platform for glyphosate monitoring in water resource safety applications.

Development and assessment of a reactive oxygen species generator independent of external stimuli utilizing metal–organic frameworks

Tree structural modeling from leaf-on point clouds for biomass carbon stock estimation

Emergence of multidrug-resistant and virulent Escherichia coli with APEC‑associated traits in broiler chickens from Ismailia, Egypt

Abstract Colibacillosis in poultry is a significant disease that leads to substantial economic losses and poses a potential zoonotic threat through the emergence of antimicrobial‑resistant strains. This study aimed to investigate the prevalence, antimicrobial resistance patterns, phylogenetic distribution, and virulence and resistance gene profiles of Escherichia coli isolates from broiler chickens on two private farms in Ismailia Governorate, Egypt. During April–July 2024, cloacal swabs were collected from diseased broiler chickens showing diarrhea and respiratory symptoms. Post-mortem examination revealed lesions consistent with colibacillosis, including Perihepatitis, Pericarditis, Enteritis, Splenomegaly, and Airsacculitis. E. coli was isolated and identified using selective culture media, biochemical assays, and PCR verification methods targeting the pho A gene. Antimicrobial susceptibility was assessed by the disc diffusion method against nine different antibiotic classes. Phylogenetic typing and PCR-based detection of virulence ( omp A, iss , iut A, iro N, eae A, kps MTII) and resistance genes ( bla TEM , bla CTX−M , tet A, aad A1, sul 1, qnr A, bla VIM−1 , bla IMP−1 ) were performed on the strains, and the data were statistically analyzed. Fifty-seven (28.5%) isolates were identified as E. coli , and both the hemolysis test and the Congo red binding test were positive for all isolates. Phylogenetic analysis showed that most isolates belonged to groups B2 (59.6%), B1 (21.1%), and D (19.3%). Notably, all isolates were resistant to ampicillin and tetracycline, while 93% of the isolates were resistant to cefuroxime and ceftriaxone. Overall, 100% were multidrug-resistant (MDR) strains, with a high MAR index (≥ 0.56). The most predominant resistance genes were bla TEM and tet A (100%), followed by bla CTX−M (91.2%) and aad A1 (85.9%). The main virulence determinants were omp A (100%), iss (78.9%), and iut A (64.9%). We observed correlations between phenotypic resistance, genotypic resistance, and the presence of virulence genes. The high burden of multidrug‑resistant isolates E.coli harbouring multiple virulence and extended‑spectrum β‑lactamase genes in Egyptian broilers underscores a serious threat to poultry production and public health. These findings highlight the urgent need for rational antimicrobial use, strengthened farm biosecurity, and continuous molecular surveillance within a One Health framework.

Investigating the test–retest reliability and behavioral correlates of pupil responses in the n-back task

Abstract Measurement of pupil size changes is a promising method to investigate information processing. Our study contributes to this stream of research (1) by investigating whether pupillary changes during performing a demanding working memory (n-back) task are associated with performance, and (2) by testing whether these pupil responses demonstrate temporal stability over a period of multiple weeks. We conducted two experiments using two different variants of an n-back task differing in task durations and found that both phasic and tonic pupil size measures are characterized by good test–retest reliability. Furthermore, phasic, task-evoked pupillary responses were positively correlated with task performance, whereas no evidence was found for a similar link in the case of tonic pupil size measures, possibly due to design characteristics. Importantly, the dissociation of validity and reliability regarding tonic pupil size measures suggests that pupil responses might be temporally stable also in such cases when the pupil response itself is not predictive of task performance. Our results imply that pupillometry can be an appropriate tool to assess individual differences in cognition or underlying neural functioning, but one must ensure that the between-sessions correlations among participants are caused by individual differences in information processing, and not by other methodological factors (e.g. participant-specific, systematic measurement noise).

Sustainable optimization of drilling performance in Al6061–B4C composites: process–property interactions for lightweight engineering applications

Abstract The demand for sustainable lightweight materials in automotive and aerospace sectors necessitates the development of metal matrix composites with optimized machinability and performance. In this study, Al6061–B 4 C composites containing 3, 6, and 9 wt% reinforcement were fabricated using stir casting and systematically evaluated for mechanical behavior and drilling performance. Contrary to conventional expectations, the incorporation of B 4 C resulted in a reduction in hardness, attributed to casting-induced porosity and particle agglomeration, highlighting critical processing–property limitations in composite fabrication. Drilling experiments were designed using a Taguchi L9 orthogonal array to investigate the influence of feed rate, spindle speed, and drill diameter on surface roughness. Statistical analysis revealed that feed rate is the dominant parameter governing surface quality across all compositions, contributing up to 52% of variation, while drill diameter becomes increasingly significant at higher reinforcement levels due to enhanced abrasive interactions. The 3 wt% B 4 C composite exhibited superior machinability under optimized conditions (1160 rpm spindle speed, 0.575 mm/rev feed rate, 6 mm drill diameter), achieving improved surface integrity with reduced tool–workpiece interaction effects. The study establishes critical process–property–machinability relationships and demonstrates that optimal reinforcement content must balance mechanical enhancement and manufacturability. These findings provide actionable insights for sustainable manufacturing of lightweight components, contributing to resource-efficient production strategies in advanced engineering applications.

Species-specific allometric models for estimating aboveground biomass and carbon stocks of plantation forests in northcentral Ethiopia

Dynamic morphological staging of Drosophila pupal development

Feasibility of automated AI-based contouring and stable radiomic feature assessment by HyperSight-CBCT Imaging for adaptive high-precision radiotherapy of prostate cancer

Abstract This study evaluated segmentation accuracy, efficiency, and radiomic feature stability for manual (MD), artificial intelligence-based (AI), and hybrid (MD + AI) contouring of pelvic organs on planning CT (pCT) and HyperSight cone-beam CT (hCBCT) for adaptive radiotherapy. Dice similarity and 95th percentile Hausdorff distance (HD95) quantified segmentation agreement, while radiomic feature stability was assessed using the concordance correlation coefficient (CCC). Agreement between segmentation approaches was highest for bladder and femora (median Dice 0.95–0.96; HD95 1.88–2.17 mm), intermediate for prostate and rectum (median Dice 0.92; HD95 2.22–2.62 mm), and lowest for seminal vesicles and penile bulb (median Dice 0.76–0.83; HD95 3.01–3.41 mm). AI and MD + AI reduced contouring times by about 90% and 60% compared to MD. Radiomic feature stability differed significantly between segmentation modes (all p adj ≤ 0.05). GLRLM features exhibited significantly higher stability than other features, whereas morphological features showed lower stability. Median radiomic feature stability was highest for bladder and femora, and intermediate for prostate and rectum. In conclusion, AI-based and hybrid contouring achieved high accuracy and substantial time savings, while texture- and intensity-based radiomic features showed robustness with AI segmentation. This study demonstrated feasibility of extracting distinct, reliable quantitative parameters based on AI-only contouring of pelvic structures.

A fuzzy logic model for taxi service assessment using passenger survey data from Ukraine and Poland

A high-gain symmetric-bipolar buck-boost AC-AC converter with step-changed output frequency and rectifier operation

Biological activity of Trachystemon orientalis extracts against Sitophilus oryzae and Oryzaephilus surinamensis

Abstract Stored product pests, such as Sitophilus oryzae and Oryzaephilus surinamensis , pose significant threats to global grain storage. Plant extracts rich in phenolics offer environmentally friendly alternatives to synthetic pesticides for managing these pests. This study is the first to evaluate the biological activity of shade-dried (GS) and oven-dried (ES) water extracts of Trachystemon orientalis against S. oryzae and O. surinamensis . Phytochemical analysis revealed that the GS extract exhibited higher total phenolic content and antioxidant capacity compared to the ES extract. Specifically, the GS extract contained higher concentrations of abscisic acid (3346.76 mg/10 6  g dried matter (dm)) and caffeic acid (1666.17 mg/10 6  g dm), whereas the ES extract was richer in o - and p -salicylic acid, p -coumaric acid, and rosmarinic acid. Orientation assays conducted at four concentrations (62.5–500 ppm) revealed time- and concentration-dependent changes in the distribution of adults between treated and control compartments. After 120 h, the presence of O. surinamensis at the GS 500 ppm site decreased to 44% (G = 36, p  ≤ 0.05), while S. oryzae presence dropped to 33% (G = 31.4, p  ≤ 0.05). Furthermore, higher concentrations of the GS extract significantly reduced F1 adult emergence for both pests ( p  < 0.05, Two-Way ANOVA). These findings suggest that T. orientalis extracts, particularly GS, represent promising environmentally friendly alternatives to chemical pesticides for the management of stored product pests. The efficacy of these extracts is closely associated with their higher phenolic content and specific bioactive compounds.

A two-stage deep learning model for risk identification in green supply chain finance