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Adoption and heterogeneous impacts of hired mechanization services among smallholder farmers in Kenya, Zambia, and Zimbabwe
Assessing the validity of self-reported weight, height, and body mass index among hispanic breast cancer survivors
Development of a hybrid RSM–Fermatean fuzzy WASPAS framework for multi-objective drilling optimization of WAAM nickel–stainless steel bimetallics
Abstract This study develops an integrated RSM–Fermatean fuzzy WASPAS framework for multi-response drilling optimization of wire arc additively manufactured ERNiCrMo-3/316LSi nickel–stainless steel bimetallic structures. WAAM bimetallics often contain bead waviness, geometric deviation, residual stress, thermal distortion, and microstructural heterogeneity, which make drilling performance difficult to optimize through a single response. The effects of cutting speed, feed rate, tool point angle, and coolant condition were evaluated through twelve drilling trials. Four responses were considered: surface roughness, material removal rate, hole dimensional accuracy, and energy consumption. The measured results showed clear variation across the tested conditions. Surface roughness ranged from 0.666 to 2.166 μm, material removal rate from 10 to 75 mm³/min, hole dimensional accuracy from 90 to 99%, and energy consumption from 120 to 300 W. Response Surface Methodology was used to model factor effects and interaction behavior, while Fermatean fuzzy logic was applied to reduce the influence of response uncertainty in ranking. WASPAS combined the weighted criteria into a single ranking index to identify the best compromise among the tested alternatives. The selected condition was 50 m/min cutting speed, 0.1 mm/rev feed rate, 135° tool point angle, and oil-based cooling, producing Ra = 1.1667 μm, MRR = 50 mm³/min, HDA = 96%, and EC = 220 W. This condition offered a balanced trade-off among surface quality, productivity, dimensional accuracy, and energy demand. The observed performance is associated with reduced chip load, stable tool engagement, improved chip evacuation, lower friction, and controlled heat generation at the tool–workpiece interface. Statistical validation and sensitivity analysis showed that the ranking remained stable within the tested experimental range. The proposed framework provides a reproducible decision-support method for drilling the investigated WAAM bimetallic system and requires validation before use in other WAAM material combinations.
Metastable multimeric G-quadruplex 2′FY-RNA aptamers that selectively bind pyoverdines
Adipose-derived mesenchymal stem cell-conditioned medium and proliferation of intestinal stem cells in mesenteric ischemia and reperfusion
A fog-aware lightweight vision transformer architecture for real-time coastal landscape recognition on edge devices
Abstract Intelligent landscape recognition sits at the heart of smart coastal tourism, yet pushing vision Transformers onto resource-constrained edge hardware remains awkward—the self-attention mechanism scales quadratically with token count, and marine atmospheric haze quietly erodes recognition accuracy long before it is noticed in the laboratory. We propose a hybrid lightweight architecture that swaps standard self-attention in the early stages for depthwise separable convolution token mixers, switches to kernel-based linear attention in the deeper stages, and follows an adaptive channel reduction policy that trims feature dimensions where redundancy is empirically highest. A Fog-Aware Feature Calibration module, motivated by the Koschmieder atmospheric scattering model, is embedded between the convolutional and attention stages so that learned dehazing happens inside the network rather than as a separate preprocessing pass. Training proceeds through a three-phase pipeline that interleaves progressive structured pruning with temperature-scheduled knowledge distillation from a Swin-Base teacher; the composite objective shrinks the model to 4.8 M parameters and 0.91 GFLOPs without abrupt accuracy collapse. On a newly collected 17,565-image dataset spanning eight coastal scene categories across six shoreline regions of Zhejiang Province, our model reaches 92.6% ± 0.4% Top-1 accuracy (mean ± std over three independent runs), the best result among all baselines below 6 M parameters, including the recently released MobileViT-v2, FastViT, TinyViT, EfficientViT and RepViT. On an NVIDIA Jetson Orin Nano with TensorRT INT8 optimisation the system delivers 18.3 ms latency (54.6 FPS) within a 7.4 W average power envelope, and a 200 m visibility heavy-fog subset that mixes synthetic and real captures shows an 85.7% accuracy retention rate—6.8 percentage points above the strongest baseline. Cross-dataset zero-shot tests on Fujian, Hainan and the Places365 coastal subset, together with deployment benchmarks on Jetson Nano 4 GB and Coral Edge TPU, suggest the design transfers reasonably—though not effortlessly—beyond the original collection sites.
Experimental and probabilistic reliability of fiber-reinforced concrete (FRC) under drop-weight impact
Risk factors for acquisition of SARS-CoV-2 Omicron variant among a prospective cohort of French Healthcare Workers: impact of hybrid immunity
Stacked graph attention network hypertuned by Heteroscedastic and Evolutionary Bayesian Optimization for fault identification on nuclear power plants robust to sensor drift
Abstract Fault identification in Nuclear Power Plants (NPPs) is critical for ensuring operational safety, reliability, and efficiency. Traditional diagnostic methods often rely on physical models and expert systems, which may struggle to capture the complex dynamics of transient events. To overcome these limitations, this paper proposes an optimized stacked Graph Attention Network (GAT) for fault detection in NPPs by modeling the complex interdependencies among system components as graphs. Transient operational data are transformed into graph representations, where nodes correspond to system variables, and edges capture physical relationships. The architecture of the proposed model is optimized using a Heteroscedastic and Evolutionary Bayesian Optimization (HEPO), ensuring the use of the best configuration. The proposed GAT-based model, hypertuned by HEPO, is trained to recognize patterns associated with both normal and faulty transient conditions, including sensor anomalies and actuator failures. Based on synthetic data generated from the Personal Computer Transient Analyzer (PCTRAN), the proposed model achieved results above 0.96 for accuracy, precision, recall, and F1-score in a statistical analysis.
Spontaneous formation of highly concentrated phenylacetylcarbinol via in situ phase separation in deep eutectic solvents using Candida magnoliae whole cells
Energy-barrier-mediated particle and cell transport switching and sorting in a magnetophoretic microfluidic platform under a rotating magnetic field
Clients’ perspectives on switching from old stable ART regimens to new dolutegravir-based regimen at joint clinical research centre Uganda
Genomic investigation reveals local persistence and regional circulation of dengue virus associated with recent outbreaks in Kenya
Abstract Dengue is an arbovirus disease caused by dengue virus. The virus has recently caused a global surge in cases, with Africa reporting over 250 million and 900 deaths since 2023. This study was conducted during the 2023 dengue outbreak in Mombasa, with the aim of understanding outbreak cases in the context of earlier cases and recent global epidemics. During the 2023 outbreak, 56 of 406 samples tested positive for DENV: including DENV-1, DENV-2, and DENV-3. The study generated 38 whole genome sequences, 24 from 2023 to 14 from cases identified between 2020 and 2025. Phylogenetic analysis showed DENV-2 exhibited patterns suggesting long-term local circulation, while DENV-1 and DENV-3 were more widespread regionally, and are linked to recent outbreaks in Eastern and Western Africa. These findings highlight the need for integrated strategies to curb dengue in Kenya, such as measures to disrupt local transmission as well as importation of new strains.
Lung ultrasound score for predicting surfactant therapy in preterm neonates with respiratory distress syndrome a systematic review and meta analysis
Abstract Respiratory distress syndrome (RDS) is the common cause of respiratory morbidity in preterm neonates due to the primary surfactant deficiency. Early surfactant delivery improves outcomes, whereas oxygen-based criteria may not quickly recognize the surfactant requirement. Lung ultrasound (LUS), a radiation-free bedside tool, help in guiding surfactant therapy using scoring systems. Therefore, this systematic review and meta-analysis evaluated the diagnostic accuracy of LUS score in predicting surfactant requirement in preterm neonates (< 37 weeks’ gestational age [GA]) with RDS. Eighteen studies including 997 neonates were analyzed using a bivariate random-effects model. The pooled sensitivity and specificity of overall studies were 0.85 (95% CI: 0.83–0.87) and 0.80 (95% CI: 0.77–0.82). A diagnostic odds ratio (DOR) was 32.12 (95% CI: 18.99–54.33), indicating strong overall discriminatory ability. The area under sROC curve was 0.682. Subgroup analysis ≤ 6 had higher sensitivity but lower specificity which is more useful for early screening, cutoff between 7 and 8 had balanced diagnostic value and ≥ 9 cutoff had higher specificity which showed stronger confirmation for surfactant requirement. Overall, LUS can support timely clinical decision making and early surfactant administration in the neonatal intensive care unit (NICU) when used alongside clinical assessment.
Age-related differences in processing unconventional text formats
Abstract Typographic formats influence reading efficiency; however, knowledge remains limited regarding how these effects change across the lifespan, especially for orthographic distortions in digital environments. This study examines how conventional formats (lowercase and uppercase) and unconventional formats (mixed-case and LEET) affect reading times and the integration of meaning while reading five-word phrases. Three hundred and three adults (18–84 years) read short sentences (five words) presented in the four formats, while reading times and memory accuracy were recorded. The results showed a graded cost pattern: conventional formats yielded the fastest reading times, mixed-case imposed moderate costs, and LEET produced the greatest slowdown and a slight reduction in accuracy. Moreover, a significant interaction between format and age was observed: although reading slowed with age in all formats, this effect was especially pronounced for LEET. These findings suggest that extreme orthographic distortions increase perceptual and pre-lexical demands, revealing limits in reading adaptation associated with aging.
Effects of different land use types on grassland soil and vegetation in the agropastoral transition zone of northern China
Assessing significance of seed coat micromorphology in taxonomy of tribe Spiranthinae (Orchidaceae)
Comparing emotion regulation, perceived discrimination, perceived social support and anxiety in North African immigrant and native French populations
Abstract Discrimination is a major psychosocial stressor associated with adverse mental health outcomes among ethnic minorities. However, the mechanisms linking discrimination to anxiety remain poorly understood, particularly among North African populations in France. Drawing on Hatzenbuehler’s minority stress mediation framework, this study examined whether emotion regulation and perceived social support explain the relationship between perceived discrimination and anxiety. A total of 370 adults living in France (198 North African immigrants and 172 native French participants) completed measures of anxiety (GAD-7), perceived discrimination (InDI-M), emotion regulation (CERQ-short), and perceived social support (MSPSS). Group comparisons and mediation analyses were conducted using non-parametric tests and bootstrapping. North African participants reported higher levels of perceived discrimination than native French participants, but differences were found for anxiety or maladaptive emotion regulation. Mediation analyses showed that only among North African participants, perceived discrimination was associated with higher anxiety, partially through maladaptive emotion regulation. These findings suggest that discrimination may be associated with anxiety through emotion regulation processes rather than through differences in overall anxiety levels between groups, highlighting the importance of examining culturally and socially embedded psychological mechanisms linking discrimination to emotional well-being.