Fabrication of Sound Absorption Gypsum/Hempcrete Composite with Robust Antistatic Electricity by Taguchi Optimization Method

Hempcrete is a sustainable bio-composite consisting of the woody component of hemp stalk, and lime blended with water. Hempcrete has been considered as an eco-friendly alternative to traditional construction building materials. This study has fabricated hempcrete composite consists of gypsum and hemp shives for sound absorption, and graphite was incorporated to improve the antistatic electricity. Three controlled factors: the length of hemp, the content of hemp, and graphite with four levels for each factor were chosen. Taguchi method based on orthogonal array L16 was employed to conduct the experiments. According to the results of Taguchi’s orthogonal array L16, analysis of variance (ANOVA) was used to evaluate the effects of various factors. The determination of optimal production parameters of gypsum/hempcrete composite was also investigated. According to the analysis, it has indicated that hemp content and graphite were the two most effective parameters on sound absorption, where hemp content exhibited a significant effect at 99% confidence level, and the confidence level of graphite is 95%. The antistatic electricity of gypsum/hempcrete composite is highly related to the incorporation of graphite with 99% confidence level

Two-stage optimization strategy of multi-objective Volt/Var coordination in electric distribution network considering renewable uncertainties

Renewable energy is connected to the distribution network in the form of distributed generation, and its output is random and fluctuating. The high penetration of renewable energy will weaken the security and economy of the electric distribution network. In view of the uncertainty of renewable power output, Monte Carlo simulation method and simultaneous backward reduction method are used to scenario generation and reduction of wind power and photovoltaic, so as to represent their randomness. On this basis, a two-stage optimization strategy for Volt/Var coordination in distribution network is proposed. In the first stage, a network reconfiguration model with the goal of minimizing the power loss of the system is established, and the particle swarm optimization algorithm is used to solve it. In the second stage, a Volt/Var optimization model with the goal of minimizing the node voltage offset and the system power loss is established, and the multi-objective model is solved by combining the network topology obtained in the first stage with the reactive power adjustment device and voltage regulating equipment such as switching capacitor, static var compensator and on-line tap changer. The distribution network’s Volt/Var is optimized using the non-dominated sorted genetic algorithm-II. The optimization results of the first stage are regarded as the conditions of the second stage. Lastly, the IEEE 33-node extension system is used to validate the effectiveness of the suggested strategy

Transformer fault classification for diagnosis based on DGA and deep belief network

Power transformer plays a very important role in power system, its long-term operation will cause various kinds of faults. Accurate identification and timely elimination of transformer faults are the basis of safe operation of power grid. As one of the most commonly used fault diagnosis methods, dissolved gas analysis (DGA) technology is used to identify fault types through dissolved gas in transformer oil, and its reliability has been proved. In order to analyze these gases and diagnose transformer fault types with the results, many methods have been developed, such as Key Gas Method, Method of Duval, IEC 60599 Method, Method of Dornenburg and Method of Rogers, etc. In some cases, the accuracy of these traditional methods is reduced and cannot be applied for diagnosis, since they have fixed input features and is not flexible for input combination. In order to achieve the propose of solving this defect, this paper introduces a deep belief network-based DGA method to diagnose the faults and states of power transformers with customized input features. For this work, six fault classifications were considered based on the nine characteristics extracted from the gases precipitated from the insulating oil of power transformers. The deep belief network was tested using oil samples collected from power transformers. Experiments have shown that the performance of the network has obtained relatively good accuracy results

Cashiers and bisphenols: Occupational exposure and health implications in south China

Bisphenols are ubiquitous chemicals used in various industries, raising concerns due to their potential to disrupt endocrine systems, particularly among occupational populations. This study concurrently assessed bisphenol A (BPA) and its 12 analogues in 325 urine samples from cashiers and non-cashiers residing in South China. Results revealed that BPA was the most prevalent bisphenol in urine, subsequent to bisphenol S (BPS), bisphenol F, bisphenol AF (BPAF) and bisphenol E (BPE), with detection frequencies at 60−99%. BPA exhibited the highest median concentration of 1.16 ng/mL. Urine samples from cashiers showed relatively high levels of BPA, BPS and BPE, highlighting potential occupational exposure implications. Variations in urinary bisphenol concentrations across gender and age groups were explored. Significant correlations were identified between urinary BPA and BPE, as well as BPS and BPAF, indicative of shared exposure sources and pathways. Cashiers had 1.50–13.4 times higher exposure than non-cashiers to these bisphenols. The median exposure to five bisphenols exceeded the established tolerable daily intake for BPA, set at 0.2 ng/kg bw/day, by 1.10–180 times. This study underscores the urgent need for assessing the potential health implications of bisphenol exposure, especially for high-exposure groups like cashiers, and suggests actions to reduce these risks

HBV preS Mutations Promote Hepatocarcinogenesis by Inducing Endoplasmic Reticulum Stress and Upregulating Inflammatory Signaling

This study aimed to elucidate the effects and underlying mechanisms of hepatitis B virus (HBV) preS mutations on hepatocarcinogenesis. The effect of the preS mutations on hepatocellular carcinoma (HCC) occurrence was evaluated using a prospective cohort study with 2114 HBV-infected patients, of whom 612 received antiviral treatments. The oncogenic functions of HBV preS mutations were investigated using cancer cell lines and Sleeping Beauty (SB) mouse models. RNA-sequencing and microarray were applied to identify key molecules involved in the mutant-induced carcinogenesis. Combo mutations G2950A/G2951A/A2962G/C2964A and C3116T/T31C significantly increased HCC risk in patients without antiviral treatment, whereas the preS2 deletion significantly increased HCC risk in patients with antiviral treatment. In SB mice, the preS1/preS2/S mutants induced a higher rate of tumor and higher serum levels of inflammatory cytokines than did wild-type counterpart. The preS1/preS2/S mutants induced altered gene expression profiles in the inflammation- and metabolism-related pathways, activated pathways of endoplasmic reticulum (ER) stress, affected the response to hypoxia, and upregulated the protein level of STAT3. Inhibiting the STAT3 pathway attenuated the effects of the preS1/preS2/S mutants on cell proliferation. G2950A/G2951A/A2962G/C2964A, C3116T/T31C, and preS2 deletion promote hepatocarcinogenesis via inducing ER stress, metabolism alteration, and STAT3 pathways, which might be translated into HCC prophylaxis