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People generally spend most of their time indoors; thus, household air pollution (HAP), especially indoor air pollution, is of paramount importance. In this study, real‐time particulate matter (PM2.5) monitors were used to collect high temporal resolution concentrations of fine PM2.5 in different microenvironments from 104 urban and rural households in Guizhou Province to investigate the urban–rural difference in HAP and the contribution of cooking activity to HAP. Results showed that PM2.5 concentrations among different rural microenvironments varied largely, ranging from 26.10 ± 33.27 (mean ± standard deviation) in the bedroom to 69.20 ± 72.50 μg/m3 in the kitchen while varying slightly across various urban microenvironments, ranging from 32.30 ± 17.5 in the living room to 34.65 ± 16.37 μg/m3 in the kitchen. Cooking activity can rapidly increase PM2.5 concentrations in the kitchen, whose peaks were at least five times higher than baseline levels. The contribution of cooking to the kitchen PM2.5 was 18.43 ± 12.17% and 5.39 ± 4.15% in rural and urban homes, respectively. Biomass burning affected indoor PM2.5 concentrations significantly, where rural households using biomass had the highest PM2.5 levels than households burning other fuels. This study identified the urban–rural disparities in household PM2.5 pollution and the contributions of cooking activities to HAP, illustrating the importance of cooking activities to indoor air pollution, which can assist in controlling indoor air pollution. [ABSTRACT FROM AUTHOR]

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. [ABSTRACT FROM AUTHOR]

The Resin Transfer Moulding process receives great attention from both academia and industry, owing to its superior manufacturing rate and product quality. Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process simulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus, the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a comprehensive dataset consisting of mould filling patterns of the Resin Transfer Moulding process at different injection locations for a composite dashboard panel case study is presented. The problem description and significance of the dataset are outlined. The distribution of this comprehensive dataset aims to lower the barriers to entry for researching machine learning approaches in composite moulding applications, while concurrently providing a standardised baseline for evaluating newly developed algorithms and models in future research works. [ABSTRACT FROM AUTHOR]

The smoking ban is not working in some entertainment venues in China. PM2.5 pollution induced by cigarette smoking in these places is still unknown. In this study, we took mahjong clubs as target places, monitored the real-time PM2.5 concentration, and recorded on-site inspection information to investigate the PM2.5 pollution characteristics induced by cigarette smoking. In occupied and smoking rooms, the geomean and median values of PM2.5 concentration were 276 and 347 μg/m3. The number of smoking persons in room is the dominant factor to enhance PM2.5 levels. For each additional person smoking in the room, PM2.5 increased by 32% (95% CI: 31%, 33%). Regarding adjacent nonsmoking rooms, PM2.5 increased due to the air penetration from smoking rooms and the increase trend usually had a lag time of 20 minutes. The geomean values of PM2.5 concentrations were 109 μg/m3 in the nonsmoking and occupied rooms and 148 μg/m3 in the vacant rooms. To close the door of the room is the most effective measure to alleviate the PM2.5 penetration. The geomean values of 24-hour and annual average excess exposure concentration if spending 5 hours/day and 2 days/week in mahjong club were 32.1 and 9.2 μg/m3, respectively; both exceeded the World Health Organization's air quality guidelines, 15 and 5 μg/m3, respectively. Our results revealed severe PM2.5 pollution and exposure risk by cigarette smoking in entertainment venues, as well as the penetration effect on adjacent nonsmoking areas. The smoking bans should be strictly enforced in entertainment venues, and the setting of smoking areas in indoor public places should be scientific to avoid diffusion into nonsmoking areas. [ABSTRACT FROM AUTHOR]

Aiming at solving the convergence rate problem in the latter stage of circuit evolution design, adaptive HereBoy algorithm together with the population evolution idea of GA is adapted to study the impact of adaptive-adjust factor on the evolution of convergence rate. One circuit model which is based on the similarities between combinatorial circuit and neural network is proposed, on which the matrix encoding scheme of combinatorial circuit is discussed. Besides of this, extrint evolution is adapted to evaluate the circuits fitness value, and adaptive-adjust factor in GA is introduced to improve the population diversity and convergence rate. The implementation of 2-bit binary multiplier circuits evolution shows that the average evolution generations and the evolution time all reduce obviously than conventional HereBoy algorithm, and that the average increase rate raises with the increase of evolution generation in the latter stage of circuit evolution. [ABSTRACT FROM AUTHOR]

Graphene and its derivatives have been a burning issue in the last 10 years. Although many reviews described its application in electrochemical detection, few were focused on food detection. Herein, we reviewed the recent progress in applying graphene and composite materials in food detection during the past 10 years. We pay attention to food coloring materials, pesticides, antibiotics, heavy metal ion residues, and other common hazards. The advantages of graphene composites in electrochemical detection are described in detail. The differences between electrochemical detection involving graphene and traditional inherent food detection are analyzed and compared in depth. The results proved that electrochemical food detection based on graphene composites is more beneficial. The current defects and deficiencies in graphene composite modified electrode development are discussed, and the application prospects and direction of graphene in future food detection are forecasted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Pei, Ren, Huang, Chen, Jin, Shang, Wang, Liu, Liang and Abd El-Aty.)

During the 2019 novel coronavirus (COVID-19) pandemic, many countries took strong lockdown policy to reduce disease spreading, resulting in mitigating the ambient air pollution due to less traffic and industrial emissions. However, limited studies focused on the household air pollution especially in rural area, the potential risk induced by indoor air pollution exposure was unknown during this period. This field study continuously measured real-time PM 2.5 levels in kitchen, living room, and outdoor in the normal days (Period-1) and the days of COVID-19 lockdown overlapping the Chinese Spring Festival (Period-2) in rural homes in China. The average daily PM 2.5 concentrations increased by 17.4 and 5.1 μg/m 3 in kitchen and living room during Period-2, respectively, which may be due to more fuel consumption for cooking and heating caused by larger family sizes than those during the normal days. The ambient PM 2.5 concentration in rural areas in Period-2 decreased by 6.7 μg/m 3 compared to the Period-1, less than the drop in urban areas (26.8 μg/m 3 ). An increase of mass fraction of very fine particles in ambient air was observed during lockdown overlapping annual festival days, which could be explained by the residential solid fuel burning. Due to higher indoor air pollution level and longer time spent in indoor environments, daily personal exposure to PM 2.5 was 134 ± 40 μg/m 3 in Period-2, which was significantly higher than that during in Period-1 (126 ± 27 μg/m 3 , p < 0.05). The increase of personal PM 2.5 exposure during Period-2 could potentially have negative impact on human health, indicating further investigations should be performed to estimate the health impact of global COVID-19 lockdown on community, especially in rural homes using solid fuels as the routine fuels., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

TEAD4 is a member of transcriptional enhancer factor (TEF) family of transcription factors and plays a pivotal role in regulating embryonic development and muscle regeneration. Known previously, dysfunction of TEAD4 in mouse myoblasts impairs myotube development. However, the effects of TEAD4 on multipotency of muscle-derived stem cells (MDSCs) have not been clearly understood. Recently, bovine MDSCs (bMDSCs) were successfully isolated from adult bovine muscle. Our derived bMDSCs could differentiate into mesodermal cells, including myotubes, adipocytes, and osteoid cells. Our results also revealed that bMDSCs had the capacity to develop into ectodermal and endodermal lineages including neuron-like cells and insulin-secreting cells. After TEAD4 knock-down (TEAD4-KD), bMDSCs still kept the original capacity to differentiate into neuron-like cells and insulin-secreting cells, as shown by acquisition of both neuronal and pancreatic markers normally expressed in differentiated cells. However, up-regulation of CAV3 and βMHC failed during myogenesis of bMDSCs with TEAD4-KD, although TEAD4-KD in bMDSCs did not affect osteoid cells and myotube formation. More interestingly, adipogenic differentiation of TEAD4-KD bMDSCs was significantly suppressed. During adipogenic differentiation, TEAD4-KD systematically impaired upregulation of TEAD1, TEAD2, and TEAD3, as well as the activation of C/EBP2, ADD1, and PPARγ as the key transcription factors for adipogenic differentiation. Finally, TEAD4-KD led to the failure of adipogenesis from bMDSCs. Together, our results support that TEAD4 is essential during adipogenic differentiation of bMDSCs. It has little effect on myogenesis of bMDSCs, and does not affect ostegenesis, neurogenesis, or pancreatic differentiation of bMDSCs. Our findings will be helpful for future study on the roles of the TEAD family during differentiation of MDSCs, and for controlling MDSC differentiation for stem cell applications., Competing Interests: None.