Analysis of surface and interior degradation of gas diffusion layer with accelerated stress tests for polymer electrolyte membrane fuel cell

The effects of surface and interior degradation of the gas diffusion layer (GDL) on the performance and durability of polymer electrolyte membrane fuel cells (PEMFCs) have been investigated using three freeze-thaw accelerated stress tests (ASTs). Three ASTs (ex-situ, in-situ, and new methods) are designed from freezing ?30 °C to thawing 80 °C by immersing, supplying, and bubbling, respectively. The ex-situ method is designed for surface degradation of the GDL. Change of surface morphology from hydrophobic to hydrophilic by surface degradation of GDL causes low capillary pressure which decreased PEMFC performance. The in-situ method is designed for the interior degradation of the GDL. A decrease in the ratio of the porosity to tortuosity by interior degradation of the GDL deteriorates PEMFC performance. Moreover, the new method showed combined effects for both surface and interior degradation of the GDL. It was identified that the main factor that deteriorated the fuel cell performance was the increase in mass transport resistance by interior degradation of GDL. In conclusion, this study aims to investigate the causes of degraded GDL on the PEMFC performance into the surface and interior degradation and provide the design guideline of high-durability GDL for the PEMFC.     

Facile synthesis of Ti3C2 MXene-modified Bi2.15WO6 nanosheets with enhanced reactivity for photocatalytic reduction of Cr(VI)

Through a facile hydrothermal method, we have successfully prepared Ti₃C₂/Bi_2.15WO₆ (TC/BWO) composite, and systematically investigated their reactivity for the photocatalytic reduction of Cr(VI) under visible light. X-ray diffraction and Raman analysis confirm the formation of heterostructure between Bi_2.15WO₆ and Ti₃C₂. The resultant 7TC/BWO composite exhibits enhanced photoactivity toward Cr(VI) reduction. After 120 min irradiation, the conversion of Cr(VI) reaches 92.5% with the quasi-first-order kinetic constant of k = 0.0145 min^?1, which is higher than that of pure BWO (30% and k = 0.0005 min^?1). The electrochemical and photoluminescent characterization confirm that the introduction of Ti₃C₂ is conducive to the separation of carriers, thus significantly improves the photocatalytic performance of TC/BWO. Furthermore, the radical capture experiments verify that the electrons are important for enhancing reduction of Cr(VI) to Cr(III). As a result, this research provides a comprehensive understanding of the reduction of Cr(VI) by TC/BWO composite under visible light.     

Public perceptions on artificial intelligence driven disaster management: Evidence from Sydney,Melbourne and Brisbane

In recent years, artificial intelligence (AI) is being increasingly utilised in disaster management activities. The public is engaged with AI in various ways in these activities. For instance, crowdsourcing applications developed for disaster management to handle the tasks of collecting data through social media platforms, and increasing disaster awareness through serious gaming applications. Nonetheless, there are limited empirical investigations and understanding on public perceptions concerning AI for disaster management. Bridging this knowledge gap is the justification for this paper. The methodological approach adopted involved: Initially, collecting data through an online survey from residents (n = 605) of three major Australian cities; Then, analysis of the data using statistical modelling. The analysis results revealed that: (a) Younger generations have a greater appreciation of opportunities created by AI-driven applications for disaster management; (b) People with tertiary education have a greater understanding of the benefits of AI in managing the pre- and post-disaster phases, and; (c) Public sector administrative and safety workers, who play a vital role in managing disasters, place a greater value on the contributions by AI in disaster management. The study advocates relevant authorities to consider public perceptions in their efforts in integrating AI in disaster management.     

毫米波三维异质异构集成技术的进展与应用

三维异质异构集成技术是实现电子信息系统向着微型化、高效能、高整合、低功耗及低成本方向发展的最重要方法,也是决定信息化平台中微电子和微纳系统领域未来发展的一项核心高技术。文章详细介绍了毫米波频段三维异质异构集成技术的优势、近年来的发展趋势以及面临的挑战。利用硅基MEMS 光敏复合薄膜多层布线工艺可实现异质芯片的低损耗互连,同时三维集成高性能封装滤波器、高辐射效率封装天线等无源元件,还能很好地处理布线间的电磁兼容和芯片间的屏蔽问题。最后介绍了一款新型毫米波三维异质异构集成雷达及其在远距离生命体征探测方面的应用。     

Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Prediction of mode I fracture toughness of rock using linear multiple regression and gene expression programming

Prediction of mode I fracture toughness (K_IC) of rock is of significant importance in rock engineering analyses. In this study, linear multiple regression (LMR) and gene expression programming (GEP) methods were used to provide a reliable relationship to determine mode I fracture toughness of rock. The presented model was developed based on 60 datasets taken from the previous literature. To predict fracture parameters, three mechanical parameters of rock mass including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), and elastic modulus (E) have been selected as the input parameters. A cluster of data was collected and divided into two random groups of training and testing datasets. Then, different statistical linear and artificial intelligence based nonlinear analyses were conducted on the training data to provide a reliable prediction model of K_IC. These two predictive methods were then evaluated based on the testing data. To evaluate the efficiency of the proposed models for predicting the mode I fracture toughness of rock, various statistical indices including coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were utilized herein. In the case of testing datasets, the values of R², RMSE, and MAE for the GEP model were 0.87, 0.188, and 0.156, respectively, while they were 0.74, 0.473, and 0.223, respectively, for the LMR model. The results indicated that the selected GEP model delivered superior performance with a higher R² value and lower errors.     

Adaptation to Endoplasmic Reticulum Stress Enhances Resistance of Oral Cancer Cells to Cisplatin by Up-Regulating Polymerase η and Increasing DNA Repair Efficiency

Endoplasmic reticulum (ER) stress response is an adaptive program to cope with cellular stress that disturbs the function and homeostasis of ER, which commonly occurs during cancer progression to late stage. Late-stage cancers, mostly requiring chemotherapy, often develop treatment resistance. Chemoresistance has been linked to ER stress response; however, most of the evidence has come from studies that correlate the expression of stress markers with poor prognosis or demonstrate proapoptosis by the knockdown of stress-responsive genes. Since ER stress in cancers usually persists and is essentially not induced by genetic manipulations, we used low doses of ER stress inducers at levels that allowed cell adaptation to occur in order to investigate the effect of stress response on chemoresistance. We found that prolonged tolerable ER stress promotes mesenchymal–epithelial transition, slows cell-cycle progression, and delays the S-phase exit. Consequently, cisplatin-induced apoptosis was significantly decreased in stress-adapted cells, implying their acquisition of cisplatin resistance. Molecularly, we found that proliferating cell nuclear antigen (PCNA) ubiquitination and the expression of polymerase η, the main polymerase responsible for translesion synthesis across cisplatin-DNA damage, were up-regulated in ER stress-adaptive cells, and their enhanced cisplatin resistance was abrogated by the knockout of polymerase η. We also found that a fraction of p53 in stress-adapted cells was translocated to the nucleus, and that these cells exhibited a significant decline in the level of cisplatin-DNA damage. Consistently, we showed that the nuclear p53 coincided with strong positivity of glucose-related protein 78 (GRP78) on immunostaining of clinical biopsies, and the cisplatin-based chemotherapy was less effective for patients with high levels of ER stress. Taken together, this study uncovers that adaptation to ER stress enhances DNA repair and damage tolerance, with which stressed cells gain resistance to chemotherapeutics.     

Extracellular Vesicles and Asthma—More Than Just a Co-Existence

Extracellular vesicles (EVs) are membranous structures, which are secreted by almost every cell type analyzed so far. In addition to their importance for cell-cell communication under physiological conditions, EVs are also released during pathogenesis and mechanistically contribute to this process. Here we summarize their functional relevance in asthma, one of the most common chronic non-communicable diseases. Asthma is a complex persistent inflammatory disorder of the airways characterized by reversible airflow obstruction and, from a long-term perspective, airway remodeling. Overall, mechanistic studies summarized here indicate the importance of different subtypes of EVs and their variable cargoes in the functioning of the pathways underlying asthma, and show some interesting potential for the development of future therapeutic interventions. Association studies in turn demonstrate a good diagnostic potential of EVs in asthma.     

WSN低功耗低时延路径式协同计算方法

针对云计算应用于无线传感器网络(Wireless Sensor Network,WSN)时延敏感型业务时存在的高传输时延问题,提出了一种WSN低功耗低时延路径式协同计算方法。该方法基于一种云雾网络架构开展研究,该架构利用汇聚节点组成雾计算层;在数据传输过程中基于雾计算层的计算能力分步骤完成任务计算,降低任务处理时延;由于汇聚节点计算能力较弱,时延降低将导致能耗增加,WSN工作寿命减短,为此提出能耗约束下的任务映射策略,并利用离散二进制粒子群优化(Binary Particle Swarm Optimization,BPSO)算法解决能耗约束下的时延优化问题。仿真结果表明,在相同的能耗约束下,对比其他算法,基于BPSO算法得出的映射方案能有效降低业务处理时延,满足时延敏感型业务的需求。     

基于阶段注意力机制的电力线提取算法

电力系统维护是电力系统稳定运行的重要保障，应用智能算法的无人机电力巡检则为电力系统维护提供便捷。电力线提取是自主电力巡检以及保障飞行器低空飞行安全的关键技术，结合深度学习理论进行电力线提取是电力巡检的重要突破点。本文将深度学习方法用于电力线提取任务，结合电力线图像特点嵌入改进的图像输入策略和注意力模块，提出一种基于阶段注意力机制的电力线提取模型（SA-Unet）。本文提出的SA-Unet模型编码阶段采用阶段输入融合策略（Stage input fusion strategy， SIFS），充分利用图像的多尺度信息减少空间位置信息丢失。解码阶段通过嵌入阶段注意力模块（Stage attention module，SAM）聚焦电力线特征，从大量信息中快速筛选出高价值信息。实验结果表明，该方法在复杂背景的多场景中具有良好的性能。