Browse Articles

Impact of DC-DC converter interconnections on the performance of bidirectional EV chargers

A physical reservoir involving frequency virtual nodes for structural damage detection

Abstract This study proposes a physical reservoir computing approach for structural damage detection, in which the target structure itself serves as the physical reservoir. Physical reservoir computing is a computational framework that leverages the dynamics of physical systems as computational resources for processing time series data. The proposed physical reservoir in this study is a nonlinear feedback system, which involves frequency virtual nodes realized by dividing the measured response of the target structure into multiple frequency bands. The divided signals are then respectively passed through nonlinear activation functions, followed by being summed and fed back to the target structure. The structure with the frequency virtual nodes and nonlinear feedback acts as a high-dimensional physical reservoir even with a single pair of sensor and actuator, which enables damage classification by detecting the changes in its dynamical behavior. To validate the proposed approach, both numerical and experimental verifications on a thin plate and a thin-walled tube with a single pair of sensor and actuator were conducted. As a result, the proposed method achieved comparable damage classification accuracy to conventional neural networks, while significantly reducing the training and inference costs, demonstrating the potential of a novel framework of structural health monitoring based on the physical reservoir computing.

Bile reinfusion is associated with shifts in host-microbiota metabolic profiles following cholangiocarcinoma-associated microbiota transplantation

Abstract Cholangiocarcinoma (CCA) is a heterogeneous group of malignant tumours originating along the biliary tract. Previous studies have demonstrated that CCA is characterised by altered gut microbial composition and disrupted bile acid metabolism, both of which are critical determinants of host metabolic homeostasis. Although bile reinfusion (BR) has been proposed to improve surgical outcomes in CCA patients, its systemic metabolic effects and interaction with gut microbiota remain poorly understood. Here, we employed faecal microbiota transplantation (FMT) from CCA patients, with or without BR, into Wistar rats to investigate host-microbiota metabolic interactions using integrated 1 H NMR-based metabolomics and full-length 16S rRNA gene sequencing. Rats receiving CCA-derived microbiota displayed altered systemic metabolic phenotypes, characterised by lower levels of glucose, lactate, and succinate compared to normal microbiota recipients, whilst no significant differences in faecal metabolites were observed between these groups. Notably, BR was associated with shifts in gut microbial composition, marked by enrichment of Lactobacillaceae, altered intestinal fermentation metabolites (decreased short-chain fatty acids and increased succinate), and a convergence of peripheral plasma metabolite profiles towards those observed in healthy microbiota recipients. These findings reveal associations between bile reinfusion and shifts in microbial composition and systemic metabolic phenotypes, providing a basis for investigating microbiota-bile acid-host metabolic crosstalk and potential therapeutic implications for managing CCA-associated dysbiosis.

Bacterial contamination of mask inner surfaces during prolonged use by paramedics in prehospital emergency care

Abstract Paramedics are routinely exposed to respiratory secretions and aerosols in prehospital settings, necessitating prolonged or repeated mask use for infection prevention. However, evidence regarding time-dependent bacterial contamination of masks worn during extended prehospital duty remains limited. This within-subject repeated-measures study investigated bacterial contamination on the inner surface of KF94 masks worn by 24 Korean paramedics during routine duty. Mask samples were collected after 6, 12, and 24 h of wear. Bacterial contamination was quantified using colony-forming unit (CFU) counts, and bacterial species were identified via 16S rRNA gene sequencing. Differences across time points were analyzed using repeated-measures analysis of variance. Written informed consent was obtained from all participants, and institutional permission was secured from participating EMS agencies. Bacterial contamination increased significantly with prolonged mask-wearing duration (F(2,46) = 22.94, p  < 0.001, partial η²=0.50). Mean CFU counts increased from 134.64 ± 131.30 CFU/mL at 6 h to 595.25 ± 153.23 CFU/mL at 24 h, approximately a 4.4-fold increase. Gram-positive bacteria predominated, including species from the Bacillaceae, Staphylococcaceae, and Micrococcaceae families. Attitudes toward mask use were positive overall (mean 3.89/5), alongside moderate perceived discomfort during prolonged wear. Prolonged mask use among paramedics is associated with significant, time-dependent bacterial accumulation on the inner surface of masks. These findings provide preliminary evidence that may inform the development of occupation-specific, evidence-based guidelines on mask replacement and PPE management in prehospital emergency care.

Human milk small extracellular vesicles elicit changes in inflammatory response in infant human intestinal enteroids

Abstract Human milk (HM) is the key nutritional source for infants and the most effective preventative approach against serious gastrointestinal inflammatory diseases like necrotizing enterocolitis (NEC) in preterm infants. Numerous studies in animal models indicate HM-derived small extracellular vesicles (HMEV) reduce severity of inflammation; however, data on human-specific models are limited. Isolated HMEV were characterized from early-HM (colostrum and transitional), mature-HM, and a combined, pasteurized donor bank-like HM sample (pool-); pool-HMEV retains a profile aligned with HMEV from both early- and mature-HM samples. Exposure to pool-HMEV initiates unique gene signatures associated with reduction of inflammation including NFkB-driven TNFα signaling in human intestinal enteroids (HIEs) established from neonates with intestinal atresia or NEC. Prior exposure of HIEs to pool-HMEV lowers the magnitude of EGTA- and TNF-induced barrier disruption. This foundational research demonstrates HMEVs cause transcriptional and functional changes to the human intestinal epithelium and will support future studies on HMEV-based therapeutics.

Clinicopathological features, immunophenotyping, and immunohistochemical biomarkers associated with germline BRCA1/2 variants in breast cancer: a retrospective cohort of 265 patients

Characterization of the lysosomal arginine transporter SLC7A14

The impact of new-quality agricultural productivity on green total factor productivity in agriculture

Inducible rAAV producer cell lines yield vectors equivalent to transient transfection: a physicochemical and biological comparison

Abstract Recombinant adeno-associated virus (rAAV) is one of the most widely used viral vectors in gene therapy. Currently, rAAV is commonly produced by plasmid transient transfection (TT) into host cells; however, the requirement for transfection limits scalability in manufacturing. To overcome this limitation, transfection-free production methods using inducible producer cell lines (PCLs)–in which essential genes for rAAV production are integrated into the host genome–have been actively developed. Despite the promise of PCL technology, comprehensive evidence showing that PCL-derived rAAV particles are equivalent to those produced by the conventional TT method has been lacking. In this study, we established an inducible rAAV PCL using a newly designed single plasmid harboring all genes necessary for rAAV production. Through a single selection step, we successfully isolated a high-yield single-cell clone. Physicochemical analyses of the produced rAAV, including capsid protein composition, genome packaging length, and sedimentation coefficient, confirmed equivalence between PCL-derived rAAV and that produced by conventional transient transfection. Furthermore, biological evaluation demonstrated that PCL-derived and transfection-derived rAAV exhibited comparable infectivity and transduction profiles both in HeLa cells in vitro and in the mouse brain in vivo. These results demonstrate that PCL-derived rAAV particles are equivalent to those from the TT method, highlighting that our inducible PCL approach is a promising platform for scalable rAAV production.

An adaptive equivalent consumption minimization strategy with traffic preview for fuel cell hybrid electric vehicles

Mental health of healthcare workers in England during the first three years of the COVID-19 pandemic: The NHS CHECK study cohort

Background Maintaining healthcare workers’ (HCWs) mental health is vital to reduce staff absences and turnover, ultimately improving patient care. Most research focuses on clinical staff and single timepoints, overlooking non-clinical contributions. Aims To examine mental health variations among all staff types over three years and identify those most at risk of poor mental health outcomes. Methods Our prospective cohort study followed 22,092 HCWs from 17 English NHS Trusts. Online surveys assessed common mental disorders (CMDs), depression, anxiety, alcohol misuse, PTSD, moral injury, burnout, wellbeing, resilience, and post-traumatic growth at four timepoints from April 2020 to March 2023. Data were analysed cross-sectionally and weighted to represent Trust demographics. Results Approximately 50% of participants reported probable CMDs at all timepoints. The most consistent predictor of poor mental health was having met the baseline cut-off for that outcome. No consistent differences emerged between clinical and non-clinical staff. Younger, female, lower-paid staff, those feeling unsupported by colleagues/managers, and exposed to morally injurious events were most at risk of poor mental health outcomes. Conclusions All NHS staff types face persistent mental health struggles, with no significant improvement post-pandemic restrictions. Structural inequalities must be addressed long-term, alongside targeted, flexible support for staff in the short term.

Interpretable machine learning analysis of nonlinear error amplification under time pressure and positional ambiguity in elite blitz chess

Abstract Time pressure and positional ambiguity are two fundamental cognitive constraints that threaten human performance in sequential decision systems such as chess. However, the interactive and nonlinear nature of these factors has not yet been sufficiently quantified. In this study, 39,922 ply-level positions from blitz games of seven elite chess players on the Lichess platform were analysed using Stockfish 14.1 engine evaluation to examine how blunder probability varies across time pressure and positional ambiguity regimes. Cluster-robust logistic regression and histogram-based gradient boosting (HGB) models were applied comparatively and game phase included as a control variable. Permutation importance and SHAP values were used for explainability analyses. The findings reveal that blunder probability amplifies nonlinearly under the joint effect of low remaining time and high engine evaluation gap which is a pattern formally confirmed by restricted cubic spline regression ( $$\Delta \text {AIC} = 58.32$$ relative to a log-linear baseline). The proposed Amplification Index ( AmpInd ), defined as the exponentiated interaction coefficient between extreme time pressure and positional ambiguity, showed an additional error multiplier of approximately 5.1% at a 300 cp ambiguity level under the sub-10-second regime. This estimate remained stable across four model specifications including game phase control, sensitivity analysis, and mixed effects modeling. The HGB model achieved the highest discriminative performance (AUC $$= 0.806$$ ), and explainability analyses confirmed positional ambiguity and time pressure as the dominant determinants of model decisions. These results demonstrate that human errors are not random but concentrate under specific combinations of cognitive constraints. We offer a quantitative framework for context-sensitive error modeling and provide generalizable findings that can form the basis for developing adaptive decision support systems in human-centered AI research.

An analytical method to optimize residual stress in the ultrasonic rolling of titanium alloys Ti6Al4V

Ultrasonic rolling is an effective surface enhancement technique for titanium alloys because it introduces beneficial compressive residual stresses that improve fatigue resistance and service durability. In many aerospace and high-performance engineering applications, both the magnitude and penetration depth of the compressive residual stress field play critical roles in determining long-term structural reliability and resistance to crack initiation and propagation. However, the complex elastic–plastic deformation behavior induced by coupled static and ultrasonic loading makes accurate prediction and simultaneous optimization of these characteristics challenging. This study develops a unified analytical framework for simultaneously predicting and optimizing compressive residual stress magnitude and compressive layer depth during ultrasonic rolling of Ti6Al4V alloy. The proposed approach combines Hertzian contact mechanics, elastic–plastic deformation theory, and response surface methodology to systematically investigate the effects of static load, vibration amplitude, ultrasonic frequency, and rolling ball radius on residual stress evolution and hardened layer development. The analytical predictions were validated through comparison with experimentally reported residual stress profiles under different ultrasonic rolling conditions. The validation results demonstrate reasonable agreement between the analytical predictions and experimental observations in terms of residual stress distribution, peak compressive stress magnitude, and penetration depth. The parametric analysis indicates that static load and vibration amplitude exert dominant influences on residual stress evolution and compressive layer formation. The proposed framework provides a computationally efficient and physically interpretable methodology for analyzing and optimizing ultrasonic rolling parameters for Ti6Al4V alloy.

Organ masks applied in feature space improve weakly supervised scan-level CT classification

Cross-domain zero-shot semantic segmentation for unstructured environments via EVA-CLIP model, ensemble prompt engineering, and optimized text-image matching

Semantic segmentation provides essential scene understanding for unmanned ground vehicles to identify obstacles and plan paths in unstructured environments. Nevertheless, existing methodologies tailored for these settings typically necessitate linear probing or fine-tuning to accommodate novel scenarios, thereby suffering from a deficiency in zero-shot transferability. In response to this limitation, our study introduces a novel framework designed for robust zero-shot transfer in unstructured domains, capitalizing on the superior visual-linguistic alignment capabilities of the EVA-CLIP architecture. To augment segmentation precision, we initially utilize deep prompt tuning to adapt the visual feature extraction efficacy of the EVA-CLIP image encoder to unstructured terrain features. This strategy not only bolsters adaptability to irregular environments but also preserves the intrinsic zero-shot proficiency of the underlying model. Concurrently, we devise an ensemble prompt engineering scheme customized for unstructured settings to further elevate segmentation outcomes. Moreover, the framework optimizes the correspondence between text and images by integrating global and local representations from the respective encoders, thereby maximizing cross-modal alignment for superior segmentation. Empirical evaluations indicate that our methodology surpasses contemporary state-of-the-art techniques, yielding an increase in mIoU ranging from 1.2% to 43.9% on the Robot Unstructured Ground Driving (RUGD) benchmark. Furthermore, evaluations on the Rellis-3D dataset reveal that the model’s cross-domain zero-shot performance rivals that of supervised fine-tuning approaches, demonstrating robust generalization to previously unseen semantic classes.

Efficient dynamic cooperative deployment and task scheduling in multi-UAV-assisted MEC for dense dynamic environments

Correction: From displacement to hunger: How migration due to conflict affects food security in Yemen

Wear zone development characteristics and a discrete element method-based zonal quantification method for a ploughshare

Abstract To quantify the formation and spatial expansion of wear zones on a ploughshare under different tillage parameters, in this study, a soil‒ploughshare interaction model is established in EDEM 2024 using the discrete element method coupled with the Archard wear model. The effects of tillage speed, tillage depth, and penetration angle on ploughshare force, power consumption and high-wear area distribution are investigated. In accordance with the curved geometry of the ploughshare surface and the soil contact path, the working surface is divided into a cutting zone, a bearing zone, and a diversion zone. The overall and zonal proportions of the high-wear area are extracted, and the time-averaged high-wear area proportion, zonal expansion coefficient and wear area development index (WADI) are constructed. The results show that ploughshare wear is characterized by clear accumulation over time and spatial nonuniformity. The high-wear area first appears near the lower cutting edge and the front lower contact region and then expands towards the middle working surface along the sliding direction of the soil particles. When the tillage speed increases from 1.25 to 2.00 m/s, the overall high-wear area proportion increases from 6.00% to 26.14%, whereas that in the bearing zone increases from 2.55% to 33.19%. When the tillage depth increases from 125 to 200 mm, the overall high-wear area proportion increases from 15.23% to 45.12%, and that in the diversion zone increases from 0.30% to 14.46%, indicating that deep tillage promotes upwards wear expansion. When the penetration angle increases from 30° to 75°, the overall high-wear area proportion decreases from 26.14% to 3.03%, although the relative fluctuation of the load increases. Tillage speed mainly promotes wear expansion by increasing the particle sliding velocity and scouring frequency. Tillage depth increases the wear range by increasing the soil contact volume and contact area. Furthermore, the penetration angle suppresses the development of high-wear areas by changing the contact characteristics and soil flow path. The proposed zonal quantification method involves converting the spatial information in Archard wear contour maps into comparable indices, providing a basis for identifying critical wear zones, optimizing wear-resistant structures, and matching tillage parameters.

Phytochemical characterization and fungal screening of Sonneratia apetala fruit and products: Pectin and vitamin C extraction, amino acids and antioxidant activity

The increasing demand for sustainable bio-resources has spurred interest in underutilized species like Sonneratia apetala , a mangrove with traditionally consumed fruits. This study provides a comprehensive biochemical and microbiological profile of S. apetala fruit and its derived products (jam, jelly, and pickle), integrating nutritional valorization with safety assessment. Pectin was extracted via acid hydrolysis and ethanol precipitation, yielding 2% with high purity (99.9%), confirmed by FT-IR and NMR spectroscopy. Vitamin C was isolated with a 1% yield and 99.9% purity, verified by HPLC and NMR. Amino acid profiling revealed the raw fruit was rich in essential amino acids, notably histidine (26.6 mg/g). Processing significantly degraded most amino acids, though histidine showed remarkable stability (16.4–17.8 mg/g). Antioxidant analysis demonstrated exceptional activity; the ethyl acetate root extract exhibited a potent DPPH IC₅₀ of 0.74 µg/mL, surpassing many synthetic antioxidants, alongside high Total Phenolic and Flavonoid Contents (555.8 mg GAE/100g DW and 240.6 mg QE/100g DW, respectively). A strong correlation was observed between phenolic content and antioxidant capacity. Crucially, fungal screening showed no detectable growth (<10 CFU/mL) in all samples, indicating product safety and stability. In conclusion, S. apetala fruit is a promising source of high-quality pectin, vitamin C, and potent antioxidants, with processed products being microbiologically stable. This research positions S. apetala as a valuable, multi-purpose species for nutraceutical and food industrial applications, with recommendations for further investigation into specific root antioxidants and scaled-up pectin extraction.

Neural networks and behavioral frameworks to analyse the impact of media and individual awareness for controlling epidemics