Browse Articles

Novelty stressor induces differential brain oxidative stress and antioxidant profiles between proactive and reactive stress coping styles

Edge-intelligent safelink-V2X: A low-latency cooperative framework for real-time vulnerable road user protection

The protection of Vulnerable Road Users (VRUs) remains a major challenge in modern transportation safety, as onboard line-of-sight and adverse weather conditions limit conventional onboard sensors. Existing systems that rely solely on vehicle-based sensing or on isolated communication struggle to provide timely, accurate alerts in dynamic urban environments. To address these shortcomings, this paper introduces SafeLink-V2X, a comprehensive Vehicle-to-Everything Cooperative Warning Framework designed to enhance safety for pedestrians, cyclists, and scooter riders. SafeLink-V2X employs Cellular Vehicle-to-Everything (C-V2X) and Dedicated Short-Range Communications (DSRC) protocols to enable direct data exchange of location, velocity, and heading between connected vehicles, smart infrastructure, and VRUs via smartphones or wearable tags. By applying sensor fusion and machine learning–based conflict prediction, the system identifies potential collision points and issues real-time, context-aware warnings through vehicle HMIs and VRU devices, promoting immediate evasive action. Evaluation on urban intersection simulations (detailed in Section 5) demonstrates that SafeLink-V2X reduces simulated collision probability by up to 91.4%, increases situational awareness measures by 44%, and lowers end-to-end alert latency by 30% compared to baseline onboard-only and communication-only systems under the same conditions.

Influencing factors of functional exercise adherence in stroke survivors: a cross-sectional study based on structural equation modeling

A late fusion multi-task learning for respiratory waveform and rate estimation from photoplethysmography

Continuous respiratory monitoring enables early detection of physiological deterioration, yet conventional capnography remains impractical for prolonged use. Photoplethysmography (PPG) offers a non-invasive alternative that encodes respiratory information through baseline wander (respiratory-induced intensity variation; RIIV), amplitude modulation (respiratory-induced amplitude variation; RIAV), and frequency modulation (respiratory-induced frequency variation; RIFV) of the pulsatile waveform. Existing PPG-based deep learning approaches, whether operating on the raw signal or on these physiological modulations, are limited to single-task architectures that estimate either respiratory rate or reconstruct the respiratory waveform in isolation, without jointly addressing both outputs. We propose a late fusion multi-task framework in which dedicated encoder branches independently process each modulation before fusion, and dual decoders simultaneously reconstruct the respiratory waveform and estimate the respiratory rate. The framework was evaluated on the CapnoBase ( n  = 42) and BIDMC ( n  = 52) benchmarks across multiple training strategies. For respiratory-rate estimation, the best transfer-learning configurations achieved a mean absolute error (MAE) of 2.27 bpm on CapnoBase and 1.33 bpm on BIDMC. For waveform reconstruction, the corresponding MAE values were 19.00% and 20.90%, with moderate correlations ( r  = 0.662 and r  = 0.591, respectively). Sequential transfer learning consistently outperformed all other strategies, whereas pooled training degraded both outputs, demonstrating that capnography-derived and impedance-derived waveforms are not interchangeable training targets. These findings establish that short-window PPG can simultaneously support respiratory-rate estimation and waveform reconstruction, when reference signal compatibility is explicitly addressed in multi-task training.

Controlled blasting technology for high in-situ stress and biased pressure tunnels: research and application

The impact of transparency and imitation over complex networks in strategic classification

Classification algorithms are widely used in critical domains such as healthcare, bank loans, credit and fraud detection. These systems should be transparent, yet it remains unclear how individuals will use explanations to adjust their own features. Individuals often access multiple sources of information, from insights provided by institutions to experiences shared among peers. Based on the information received, individuals may decide to strategically adapt to obtain a favourable outcome, honestly improving or attempting to game the system. This paper studies the impact of transparency and social information on strategic classification. We assume that agents adapt based on best response and behavioural imitation along the edges of social networks. We observe that increasingly opaque decision rules can negatively impact the utility of institutions, especially in dense social networks. The number of False Positives is reduced in networks with a lower average degree, when users imitate the average behaviour, as opposed to the most extreme behaviours. When imitating the most extreme behaviour among their connections, users change their features to a large extent in networks with a higher average degree (i.e., higher density). This applies to both honest improving and gaming, with more pronounced impacts in the case of the latter, creating an additional source of risk for institutions. Our model and results reveal that behavioural imitation patterns and social network effects influence the downstream effects of algorithmic transparency.

A robust binary secretary bird optimization method for high-dimensional data classification

Abstract Feature selection is a key step in machine learning–based decision systems, especially in medical and biomedical applications, where datasets often contain a large number of features that can negatively affect both accuracy and interpretability. In this study, we introduce the binary secretary bird optimization algorithm (B-SBOA), a binary version of the secretary bird optimization algorithm specifically developed for feature selection tasks. The proposed approach translates the hunting and escape behaviors of the secretary bird into effective binary search strategies, allowing a well-balanced trade−off between exploration and exploitation. B-SBOA was tested on twenty-five benchmark datasets from the UCI repository and compared with nine well-known binary metaheuristic algorithms, including PSO, GWO, MPA, HBO, SMA, SFOA, DOA, SCA, and MSO. The experimental results show that B-SBOA consistently delivers performance that is either superior to or competitive with existing methods across F-score, precision, recall, and other standard metrics. B-SBOA was evaluated on 25 benchmark datasets from the UCI repository and compared with nine well-known binary metaheuristic algorithms. The results show that B-SBOA achieves superior performance, with average improvements reaching 3–8% in F-score and 2–6% in precision and recall compared to competing methods. In several high-dimensional datasets such as Arrhythmia and Hillvalley, the proposed method achieved the highest classification accuracy while reducing the number of selected features.

Identification of a HOMA-IR cut-off point for cardiometabolic risk and modifiable risk factors in peruvian adolescents

Background Although HOMA-IR is widely used to assess insulin resistance, reported cut-off values vary substantially across population, particularly during adolescence. The aim of this study was to determine the distribution of HOMA-IR values. identify a HOMA-IR cut-off associated with metabolic syndrome (MS), and assess modifiable risk factors of IR in a longitudinal cohort of Peruvian adolescents. Methods We performed a secondary data analysis from a longitudinal adolescent’s study. A sample of 371 adolescents (14.5 ± 0.1 years old) from low- medium socioeconomic status. ROC curve analysis was used to identify the specific cut-off point to classify IR using the sensitivity and specificity values in comparison with the MS. Multiple logistic regression analysis including diet, physical activity and body composition from adolescence, excess weight during infancy and family history of non-chronic disease was included to identify risk factors (FHCD) associated with IR. Results The HOMA-IR was 3.29 (SD 1.71) with no differences by sex. We identified 3.9 for HOMA-IR as the cut-off point with sensitivity (72.4%) and specificity (75.4%) for predicting MS. IR was present in 28.6% (95% CI 24.2;33.4%); 84% had at least one cardiometabolic risk factor and low HDL and abdominal obesity were the most prevalent (62 and 35%, respectively). Adolescents with higher fat mass index (OR 16.03, 95% CI 6.79 to 37.86), and those physically inactive (OR 2.08 95% CI 1.06 to 4.07) were more likely to have IR. No association was found with diet, excess weight at infancy and FHCD. Conclusions A cut-offs point of 3.9 for HOMA-IR allows to identify adolescents with high metabolic risk. Strategies to promote lower FMI and improve the physical activity levels could reduce the risk of IR in adolescents.

Mentally simulated motor actions are resistant to acute stress in police officers

Modified control method of a motion compensated gangway

With the growing demand for offshore operations such as wind farm maintenance and maritime transportation, motion-compensated gangways are widely employed on vessels to counteract ship motions in roll, pitch, and heave. However, the persistent and unpredictable ship motion disturbance, combined with nonlinearities in hydraulic system makes motion-compensated gangways more complex dynamic characteristics, which brings huge challenges for the controller design. To address the aforementioned problems, this paper proposes an improved cascade control strategy. Specifically, a dynamic model of the motion-compensated gangway, accounting for the ship motion disturbance, is established using Kane’s method, and the coupling between the gangway’s end-effector and ship motions is investigated. A multi-degree-of-freedom velocity compensation strategy is then introduced into the improved cascade control approach. Finally, simulations are included to validate the effectiveness of the proposed control strategy.

Charge energy-dependent interlock and failure behavior of CFRP/Al E-SPR joints

Use of the “STANDARD G6PDTM” quantitative point-of-care test in neonates and infants

Severe neonatal hyperbilirubinaemia represents a considerable cause of mortality and long term-morbidity in neonates born in low resource settings. Early identification of risk factors, such as glucose-6-phosphate dehydrogenase (G6PD) status, has the potential to prevent severe hyperbilirubinaemia and improve the clinical outcomes. The primary aim of the study was to assess equivalency of cord blood and neonatal capillary blood for diagnosis of G6PD deficiency using the quantitative point-of-care “STANDARD G6PD TM ” test (SD Biosensor, Korea). The secondary aim was to analyse changes in G6PD activity in the first 4 months of life. A total of 75 neonates born in Shoklo Malaria Research Unit (SMRU) clinics were selected based on their G6PD status assessed through routine cord blood screening using the “STANDARD G6PD TM ” test. Using activity thresholds established before in this setting, 25 G6PD deficient, 25 G6PD intermediate and 25 G6PD normal neonates were identified and re-tested using capillary blood collected within 24 hours of life and at day 7 by both “STANDARD G6PD TM ” test and gold standard spectrophotometric assay. They were also followed-up at 1 and 4 months of age to study haematologic and G6PD activity changes over time. The results showed that the “STANDARD G6PD TM ” can be used reliably up to one week of life for testing neonates using the same thresholds established in cord blood. Agreement of G6PD activity measured by the point-of-care test as compared to the gold standard spectrophotometry remained excellent at all sampling time-points. Nevertheless, G6PD activity assessed longitudinally in the same participants decreased over time, both at 1 month of age and at 4 months of age, and interpretation of results in female infants with intermediate activity might require different thresholds. The study demonstrated that the “STANDARD G6PD TM ” can effectively support clinical care in neonates and infants in populations with prevalent G6PD deficiency at the primary care level and especially in low-resource settings.

Intestinal FXR deficiency uncouples steatosis protection from liver inflammation and fibrosis in MASH-diet fed mice

Abstract The Farnesoid X Receptor (FXR), a nuclear bile acid (BA) receptor highly expressed in the liver and intestine, is a potential pharmacological target for Metabolic dysfunction-Associated SteatoHepatitis (MASH). While intestinal FXR inhibition reduces high-fat diet (HFD)-induced hepatic steatosis, its role in MASH progression remains unclear. This study investigates the impact of intestinal FXR-deficiency on MASH development in a diet-induced murine model. Intestinal FXR-deficient ( int FXR KO) and control mice were fed a high-fat, sucrose, and cholesterol-enriched diet (HFSC) for 24 weeks. Intestinal immune phenotyping, microarray, 16 S rRNA sequencing, bile acid quantification and liver assessments (histology, biochemistry and single-cell RNA sequencing (scRNA-seq)) were performed. int FXR KO mice were protected against HFSC diet-induced obesity and hepatic steatosis but exhibited altered expression of intestinal barrier-associated genes, with increased cytotoxic CD8 + T-lymphocytes. Microbiota composition and bile acid profiles were altered, including reduced Lachnospiraceae species correlating negatively with liver hyocholic acid levels. Despite a protection against hepatic steatosis, liver inflammation and fibrosis were unchanged in int FXR KO mice. Transcriptomic and Immune cell scRNA-seq analysis revealed alteration in immune-related pathways with an increased neutrophil proportion and higher cDC1:cDC2 and CD4:CD8 T cell ratios. Thus, intestinal FXR-deficiency limits steatosis but promote a distinct hepatic immune-inflammatory response and does not prevent progression to MASH.

Rheological properties and compressive properties of alkali-activated slag-fly ash geopolymer fluid solidified soil

To study the influence of the content of cementitious materials, water-solid ratio, fiber content and NaOH content on the rheological properties and compressive properties of geopolymer fluid solidified soil, and to reveal the influence mechanism of different factors on geopolymer fluid solidified soil (GFSS). Geopolymers prepared from slag and fly ash were mixed into aeolian soil as cementing materials, and the rheological parameters of GFSS were tested by MCR rheometer, and its compressive strength was tested after curing for 28 days, and its micro-morphology was obtained by SEM. When the content of cementing material is increased from 8% to 16%, the fluidity of GFSS decreased by 62.01 mm and the compressive strength increases by 121.52%. When the water-solid ratio increased from 0.26 to 0.34, the fluidity increased by 81.79%, and the compressive strength first increased and then decreased, reaching the peak when the water-solid ratio was 0.30. With the increase of fiber content, the fluidity will decrease, and the compressive strength will also increase first and then decrease. When the fiber content is 5‰, the fiber will form a stable three-dimensional network structure, and the compressive strength will reach 1.19MPa. With the increase of NaOH content, the fluidity of slurry decreases, the system structure becomes denser and the mechanical properties are significantly improved. When the proportion of NaOH is 6%, cementing material is 12%, water-solid ratio is 0.30, and fiber content is 5‰, the comprehensive performance of GFSS is good, which can meet the application requirements of secondary excavation and backfill engineering. The research results are of great significance for improving the design standard of solidified soil backfill engineering, increasing the service life of solidified soil backfill engineering and reducing the disaster of pavement collapse in seasonal frozen soil areas.

Dual-functional acetogenin nanofibers: bridging biomedical activity with brain-inspired neuromorphic devices

Degree of hypertension and subclinical coronary atherosclerosis in asymptomatic individuals without cardiovascular disease

Objective This study sought to evaluate the association between degree of hypertension and subclinical coronary atherosclerosis. Design and method We analyzed 7,332 asymptomatic individuals (mean age 52.8 ± 7.8 years; 4,680 [63.8%] men) without cardiovascular disease who voluntarily underwent coronary computed tomography angiography (CCTA) as part of a general health examination. Hypertension classification was adapted from the American College of Cardiology/American Heart Association 2025 guidelines. The degree of coronary artery disease (CAD) was evaluated using CCTA and classified as normal coronary arteries, non-obstructive CAD (diameter stenosis <50%), and obstructive CAD (diameter stenosis ≥50%). Results The participants were classified into 4 groups: normal (systolic blood pressure [SBP] <120 mmHg and diastolic blood pressure [DBP] <80 mmHg; n = 2,500), elevated (SBP 120–129 mmHg and DBP < 80 mmHg; n = 969), stage 1 hypertension (SBP 130–139 mmHg or DBP 80–89 mmHg; n = 2,841), and stage 2 hypertension (SBP ≥ 140 mmHg or DBP ≥ 90 mmHg; n = 1,022). After adjusting for cardiovascular risk factors, the stage 1 hypertension group was significantly associated with non-obstructive CAD (adjusted odds ratio [aOR] 1.335; 95% confidence interval [CI], 1.156–1.541). Furthermore, the stage 2 hypertension group had a significant association with both non-obstructive CAD (aOR, 1.483; 95% CI, 1.234–1.784) and obstructive CAD (aOR, 1.696; 95% CI, 1.194–2.409). Conclusions In this large cross-sectional study, the degree of hypertension was associated with an increased risk and severity of subclinical coronary atherosclerosis. These findings highlight the potential importance of early recognition of blood pressure elevation and cardiovascular risk stratification in asymptomatic individuals.

Future changes in climate suitability, yields, and calorie optimization of four global staple food crops

Editorial Note: Provincial division of economic zones based on the improved urban gravity model: A case study of Hunan Province, China

Clustering matrix-object data by correlational structure as proxy causal signals

Abstract Matrix-object clustering addresses samples with multiple records per object. Many existing methods overlook within-object dependencies, which reduces interpretability and mixes heterogeneous regimes. We propose a clustering approach that uses intra-object correlational structure as a proxy for causal signals to separate regimes prior to any formal causal discovery. Each object is transformed into a rank-based correlation representation, enabling standard distance-based clustering while preserving interpretability. On synthetic and real-world datasets, the method yields stable, interpretable clusters that reduce regime mixing. We emphasize the boundary that correlation does not imply causation; correlational patterns are used only as proxy signals under the stated assumptions.

Impact of socioeconomic, demographic, maternal, infant and healthcare factors on early initiation of breastfeeding in Bangladesh: Evidence from Bangladesh demographic health survey (BDHS) 2022 data

Background Introducing an infant to the breast within an hour of birth is known as early initiation of breastfeeding (EIBF). Early initiation of breastfeeding (EIBF) was measured using BDHS 2022 data and defined as initiation of breastfeeding within one hour of birth, based on maternal self-report of the time elapsed between delivery and first breastfeeding. Using maternal self-reported timing of first breastfeeding from the Bangladesh Demographic and Health Survey (BDHS) 2022, this study examined factors associated with EIBF among mothers in Bangladesh. Methods Data from 4,758 women who were fertile and had given birth were included in the study. To investigate the prevalence of EIBF and its association with different factors (socioeconomic, demographic, maternal, infant, and healthcare-related), descriptive analysis, and bivariate analysis using Pearson chi-square tests were carried out. Significant EIBF factors were found using binary logistic regression analysis. Results Skin-to-skin contact within one hour of birth was significantly associated with higher odds of EIBF (OR = 1.62; 95% CI: 1.39–1.89). Cesarean delivery was associated with substantially lower odds of EIBF (OR = 0.40; 95% CI: 0.33–0.49). Compared with home deliveries, births in government facilities (OR = 0.58; 95% CI: 0.47–0.71) and private/NGO facilities (OR = 0.53; 95% CI: 0.42–0.66) were associated with reduced likelihood of EIBF. Significant regional variation in EIBF was observed across administrative divisions. Maternal age, education, and household wealth index were not consistently associated with EIBF after adjustment. Based on the univariate analysis, the prevalence of EIBF among mothers in Bangladesh is 63.3%. The 95% Confidence Interval for this prevalence is (61.9% – 64.7%). Conclusion The significance of skin-to-skin contact for EIBF in Bangladesh is demonstrated in this study. The results indicate the necessity of focused initiatives to support EIBF, especially in medical settings and after cesarean deliveries. To understand the geographical differences in EIBF rates and create strategies to deal with them, more investigation is required. These discoveries can influence practice and policy to raise EIBF rates among Bangladesh’s varied demographics, improving the health of mothers and their offspring in the process.