Annular flow condensation is an efficient heat rejection process for thermal management, where the thermal and dynamic behaviors of the liquid film govern heat transfer. However, the temperature profile across the film, the associated interfacial behaviors, the local heat transfer characteristics, and their interrelationships remain experimentally unexplored. In this study, the novel knife-edge microsensor is integrated into a tube-in-tube condensation module to measure temperature profiles from the cooling wall to the vapor core across the liquid film during annular downflow condensation. PF-5060 condenses and flows downward in the annular channel outside a stainless-steel tube, while cooling water flows counter-currently inside the tube. Ultra-fine thermocouples instrumented evenly along the knife-edge sensor (10 thermocouples within 2 mm) provide detailed temperature measurements across the film at midstream and downstream locations. The liquid–vapor interface is determined from the saturation-temperature assumption, and local heat transfer characteristics are obtained from measure data along the condensation path. Experiments are conducted for PF-5060 and water mass velocities of 40.0–61.3 kg/m²s and 264.7–463.3 kg/m²s, respectively. Subcooling, superheating, and temperature fluctuations across the film are analyzed. Transient and steady-state temperature profiles across the film are assessed together with the corresponding interfacial and heat transfer characteristics, revealing their interrelationships. Film thickness variations under different operating conditions are also examined. The measured temperature profiles consistently remained smooth without abrupt change near the liquid–vapor interface, suggesting the absence of turbulence damping at the interface. Accordingly, a zero turbulence-damping factor is recommended for modeling of annular flow condensation heat transfer with large velocity difference across the interface.
Objective To examine how adults with epilepsy in Uganda experienced epilepsy self-management, stigma, and care following participation in a peer-led intervention (SMART-U) compared to enhanced treatment as usual (ETAU). Methods This qualitative study was embedded within an ongoing randomized controlled trial at two university neurology clinics. A purposive subsample from both study arms completed semi-structured interviews at 13 (n = 16) and 24 weeks post-intervention (n = 16). Interviews explored perceived benefits and challenges of care, self-management behaviors, stigma and recommendations for improvement. Transcripts were analyzed using thematic analysis. Results Four themes emerged: barriers to self-management, self-management behaviors and attitudes, participant perspectives on care, and facilitators of self-management. Participants in both groups described significant structural and social barriers, including stigma, limited support, and challenges accessing care. However, SMART-U participants more frequently reported increased confidence, engagement in proactive self-management (e.g., stress management, planning around seizures), improved communication with providers, and greater ability to cope with stigma. In contrast, ETAU participants more often characterized epilepsy care as provider-driven, with fewer changes in attitudes or behaviors. Over time, SMART-U participants demonstrated clearer shifts toward self-efficacy and a more reflective understanding of epilepsy. Conclusions Peer-led self-management support may enhance engagement, coping, and self-efficacy among people with epilepsy in resource-limited settings. Addressing stigma and strengthening social and healthcare support may be critical for improving outcomes in low-middle income countries.
Reliable fault detection and health monitoring of industrial robots are important for smart manufacturing in Industry 4.0. However, robotic platforms differ in their mechanical design, sensing characteristics, and operating conditions. These differences make it difficult for data-driven models to generalize to unseen robots and working conditions. To address this challenge, this paper proposes a cross-robot framework for open-set fault detection using only normal data from the target robot. The framework uses a condition-normalized residual adaptation mechanism in the latent space. First, a structured representation is learned from the source robot. The learned representation is then adapted to the target robot using only normal samples. Compactness-based constraints and statistical modeling of normal behavior are used to detect unknown faults as deviations from the normal representation. The framework is evaluated on two industrial robots under variations in load, speed, and motion. Two evaluation strategies are also considered: sequential normal-only adaptation to welding motion and direct open-set detection without further adaptation. The results show consistent fault detection across different robots and operating conditions. A comparative benchmarking analysis further demonstrates the effectiveness of the proposed approach and its ability to maintain reliable fault detection performance under different loading and motion conditions. This demonstrates the potential of the framework for practical industrial health monitoring.
The solar-driven photocatalytic reduction of COQ represents a vital frontier, offering substantial potential to concurrently alleviate the energy crisis and reduce environmental pollution. In this study, a core-shell BaTiO3/B-In2O3 (BTO/B-In2O3) heterojunction was constructed to combine photothermal effect of black indium oxide (B-In2O3) and polarization of tetragonal BTO. The BTO/B-In2O3 exhibited the excellent photocatalytic efficiency in reduction of CO2 to CO, with the yield of 146.56 and 188 mu mol center dot g-1 center dot h-1 under full-spectrum light with and without cooling water, being 6.07 and 7.79 times of that by BTO (24.14 mu mol center dot g-1 center dot h-1). Spin-polarized DFT is applied to further explore how CO2 reduction proceeds on BTO/B-In2O3, focusing on its electronic structure, electron flow, and underlying reaction mechanism. The heterojunction's photocatalytic CO2 reduction performance is enhanced by modulating its electronic structure and facilitating the reaction process. This study provides a theoretical foundation for the design of efficient ferroelectric-based photocatalysts.
Around 10% of global births are preterm (before 37 weeks of gestation), posing a significant challenge to maternal and neonatal health. Preterm infants face an increased risk of mortality and long-term health complications, impacting their survival and development across all life stages. Despite decades (~ 80 years) of research, effective methods to predict and prevent idiopathic or spontaneous preterm birth remain limited. Therefore, a deeper understanding of the pathophysiology of spontaneous preterm birth is warranted. This review explores some aspects of recent progress in unravelling the complex pathophysiology of both normal and preterm human birth. We present parturition as an inflammatory event, triggered by stressors affecting the uterine reproductive tissues (myometrium, decidua, and cervix), and involving multiple endocrine and paracrine pathways. These pathways, along with signals from fetal membrane senescence and the vaginal microbiome, contribute to labor induction. Proposed perspectives in parturition research include using mathematical modeling and machine learning (artificial intelligence) to map pregnancy trajectories and identify patient phenotypes associated with preterm birth risk. Additionally, incorporating preterm birth history into routine life course medical surveillance for affected individuals and their offspring is recommended. Finally, increased investment and prioritization from national funding bodies, along with greater support for international collaborations, are essential to identify the causes of preterm birth across multiple populations and develop new, effective treatments.
