Browse Articles

Comparative performance of one-stage and two-stage deep learning models for instance segmentation of overhanging dental restorations on bitewing radiographs

Climate-modulated upwelling drives phytoplankton variability and biomass connectivity in the Humboldt Archipelago coastal system

Abstract Marine productivity driven by phytoplankton biomass (chlorophyll-a, Chl) sustains biodiversity, fisheries, and ecosystem services in the Humboldt Archipelago (29°S), an arid coastal upwelling region within the Humboldt Current System. The archipelago is characterized by strong bathymetric gradients and a submarine canyon that generate complex circulation patterns, potentially affecting phytoplankton aggregation. Although upwelling occurs year-round, its efficiency varies seasonally and interannually under the influence of regional atmospheric forcing and large-scale climate modes, including the El Niño–Southern Oscillation (ENSO) and the Pacific Meridional Mode (PMM). We evaluated how local hydrographic structure, upwelling dynamics, and climate variability interact to regulate Chl variability and biomass connectivity within the Coquimbo–Humboldt upwelling system. To address this, CTD-fluorescence profiles collected at a fixed site during 21 near-monthly surveys between November 2022 and December 2024 were combined with satellite observations and atmospheric data. Chlorophyll variability was analyzed using hierarchical Generalized Additive Models, of which the baseline physical model explained 80% of Chl deviance, while inclusion of the PMM increased it to ~ 84% and substantially improved model performance. Seasonal Chl variability was primarily associated with mixed-layer depth shoaling and intermediate Ekman transport, indicating strong control by short-term physical forcing. In contrast, the PMM-Ekman transport interaction revealed that upwelling efficiency depended on the large-scale climatic background. Satellite observations further suggested the episodic export of phytoplankton biomass from the Coquimbo Bay system into the archipelago. These results demonstrate that climate modes modulate local upwelling efficiency, shaping phytoplankton dynamics in one of the most biodiverse coastal regions of the Southeastern Pacific.

Spatio-temporal clustering characteristics of hepatitis B in China: an 18-year study

Decade-long warming accelerates antibiotic resistance in grassland soils

Digital technology adoption and clean energy transition among rural households in China

Adaptive fuzzy-reinforcement framework for real-time task scheduling and resource optimization in medical edge computing

Abstract This paper proposes a hybrid fuzzy-reinforcement learning framework for real-time task scheduling and resource optimization in medical edge computing. The proposed framework introduces a direct mathematical coupling between fuzzy inference and reinforcement learning rather than a simple hybrid combination. By integrating fuzzy logic to evaluate task urgency, bandwidth congestion, and battery constraints with a reinforcement learning agent, the framework dynamically refines offloading strategies. Simulation results in Internet of Medical Things (IoMT) environments demonstrate the framework’s superiority over existing benchmarks, achieving up to 42% lower average latency, 31% greater energy efficiency, and up to 20% improvement over Dynamic Priority-Based Task Scheduling and Adaptive Resource Allocation (DPTARA) under the evaluated benchmark settings in the completion rate of critical tasks. Operating with a linear time complexity of $$O\left(N\cdot M\right),$$ the proposed system guarantees scalable and robust performance for delay-sensitive healthcare applications. Experimental results across four benchmark IIoT healthcare datasets demonstrate the effectiveness of the proposed framework. Specifically, the model achieves average accuracy improvements of 2.8–4.6% over state-of-the-art baselines, while reducing false negative rates by up to 35.4%. In addition, the proposed fuzzy–reinforcement learning scheduler decreases average task latency by 23.7%, improves resource utilization by 18.9%, and enhances system adaptability under dynamic workload conditions. These results confirm the framework’s robustness, scalability, and suitability for real-time medical edge computing environments.

Study on abrasive water jet machining of compacted graphite iron on Taguchi method

Abstract In this study, the influence of abrasive flow rate (AFR), stand-off distance (SOD), and traverse speed (TS) on surface roughness (SR), kerf width (KW), and kerf taper angle (KTA) during the abrasive water jet machining (AWJM) of 10 mm thick compacted graphite iron (CGI) was investigated. A full-factorial Taguchi L27 orthogonal array was employed to optimize the response parameters in terms of the SR, KW, and KTA. Analysis of variance (ANOVA) was performed to evaluate the statistical significance and contribution ratios of each parameter. The results revealed that AFR was the most dominant factor affecting SR and KTA, whereas TS had the greatest influence on KW. In contrast, SOD had only a minor impact on all the response variables. The optimized machining conditions for CGI were determined as 325 g/min AFR, 1.5 mm SOD, and 20 mm/min TS for the minimum SR. The findings highlight that the AWJM process can effectively machine CGI with high precision and minimal thermal damage, offering a viable alternative to conventional methods for difficult-to-machine materials.

Music–taste interactions enhance gustatory and sensorimotor brain activity

Prevalence of non-communicable diseases among people living with HIV at HIV Clinic of Kigali University Teaching hospital (CHUK), a cross-sectional study

The impact of large language models on collaborative learning processes and outcomes in higher education for English translation

Hidden diversity and chemical variation within Parmelia saxatilis group (Lecanorales, lichenized Ascomycota) and description of a new species P. tobolewskiana

Multi-scenario simulation of land use change and its effects on ecosystem service value: a case study of the three provinces in the middle reaches of the Yangtze River, China

Development and validation of the Life after Stroke questionnaire as a patient-reported outcome measure for stroke survivors living at home

Science sleuths uncover more than 100 suspicious images in Thermo Fisher antibody catalogue

Risk factors associated with gestational diabetes mellitus in Ghana: a prospective cohort study

Synergistic effects of alumina and graphite reinforcement on microstructural evolution and tribological performance of friction stir processed copper based composites

Integration of stepped care for perinatal mood and anxiety disorders among women attending maternal and child health clinics in Kenya: Protocol for a cluster randomized controlled trial

Background Perinatal mood and anxiety disorders (PMAD) cause substantial morbidity and mortality globally. Kenya, like many low- and middle-income countries, experiences severe shortage of specialized mental healthcare workers and poor coverage of screening and management for PMAD. Provision of screening and treatment by lay and non-specialist providers in the context of routine maternal child health services may enhance access to mental health services and improve outcomes. Methods and analysis In a hybrid type II cluster randomized clinical trial in 20 facilities in Western Kenya, we will evaluate the clinical, service delivery, and implementation outcomes of a stepped-care model integrating: 1) screening of all pregnant women for depression and anxiety symptoms, 2) Problem Management Plus (PM+) counseling delivered by trained lay providers, and 3) telepsychiatry for women with severe symptoms, suicidality, or inadequate response to PM + . The intervention package also includes a bundle of implementation strategies such as audit and feedback, process mapping, health educational health talks, and procurement of antidepressants for all sites. In addition, intervention sites will receive provider training, structured supervision and financial compensation for providers. Facilities will be randomized 1:1 to intervention and enhanced standard of care (basic screening and referral to specialist care), using restricted randomization. We will enroll a total of 2,970 women. Women will be eligible if they are pregnant and ≥20 weeks’ gestation, attending antenatal care at the facility, ≥ 14 years old, and screen positive for PMAD symptoms (Patient Health Questionnaire [PHQ]-2 ≥ 3 and/or Generalized Anxiety Disorder-[GAD]-2 ≥ 3). Assessments will be conducted at enrollment (pregnancy), 6 weeks, 14 weeks, and 6 months postpartum among participants in both study arms to align with routine well-baby visits. Primary outcomes are PMAD symptoms (PHQ-9 and GAD-7 score) change at 14 weeks postpartum using an intent-to-treat analysis. Secondary outcomes include quality of life at 14 weeks postpartum, PM+ mechanism of action, and adverse pregnancy outcomes at 6 weeks postpartum. Using mixed methods, we will evaluate acceptability and fidelity of the intervention and compare service delivery and other implementation outcomes (penetration, efficiency, equity, cost) across arms and alongside multilevel drivers of implementation success. Ethics and dissemination The study protocol was approved by Kenyatta National Hospital/University of Nairobi (P425/04/2023) and the University of Washington (STUDY00017933). All participants will provide written informed consent. Findings will be published in peer-reviewed journals and international conferences. Trial registration Trial registration number NCT06456307 .

Experimental and numerical simulation assessment of fracture evolution and tensile strength recovery in Grout-reinforced fractured surrounding rocks

Dressing-induced hemodynamic instability in patients with heart failure: Implications for nursing care

Background Heart failure (HF) restricts activities of daily living, impacting prognosis and quality of life. Dressing requires sustained upper-limb movements, postural transitions, and fine-motor tasks that may impose cardiovascular and autonomic demands, potentially informing on physiological tolerance during daily activities. Objective This observational study compared hemodynamic, autonomic, subjective, and upper-limb sensor count (ULSC) responses during dressing across HF clinical courses to characterize recovery dynamics during daily activities and identify nursing support requirements. Methods We compared healthy controls (HCs; n = 15), de novo HF (NO-HF; n = 12), and recurrent HF (R-HF; n = 12) groups. Heart rate (HR), systolic blood pressure (SBP), peripheral oxygen saturation, HR variability, Borg scale score, ULSC, dressing duration, and HR recovery (HRR) were measured at rest, during dressing, and after 20 min of recovery. Linear mixed-effects models tested group, time, and interaction. Pairwise comparisons were performed via Bonferroni adjustment. Results HR increased during and immediately post-dressing across groups, with NO-HF exhibiting higher HR than R-HF. HRR varied among groups (HC vs. NO-HF: P = 0.108; HC vs. R-HF: P < 0.0001; NO-HF vs. R-HF: P = 0.026). SBP displayed a group effect (P = 0.034). Peak HR responses tended to occur immediately post-dressing in HC and NO-HF, whereas R-HF descriptively showed a delayed peak around 5 min. No significant group × time interaction was observed. ULSC showed no group differences after dressing time and body size adjustments. Patients with HF had higher Borg scores than did HCs (NO-HF, P < 0.001; R-HF, P = 0.028). Conclusions Despite being low-intensity, dressing was associated with measurable physiological and subjective HF responses. R-HF trended toward slower HR recovery; NO-HF demonstrated consistently higher HR levels and perceived exertion. Monitoring recovery responses and supporting pacing and symptom self-monitoring during daily activities may be important in HF nursing care.

Structure of the mouse cytoplasmic lattice