Government environmental information disclosure and corporate carbon performance

Environmental problem is the key to the healthy development of China’s eco-economy, and the environmental responsibility of micro-enterprises under the vision of “Dual Carbon” has attracted more attention. Under the effect of formal environmental regulation, firms will improve their environmental performance by improving technology and resource utilization. As an informal environmental system, can government environmental information disclosure (GEID) guide firms to actively carry out green innovation, ultimately improve the carbon emission problem of firms, have a positive impact on the carbon performance of enterprises, and provide strong support to protect ecological environment? To address this question, this study used the Pollution Information Transparency Index (PITI) to measure GEID, and empirically tested the impact of GEID on corporate carbon performance using a sample of listed companies involved in China’s mining and manufacturing industries from 2013 to 2018. The study found that the higher the degree of GEID, the better was the corporate carbon performance. However, the improved public participation weakened the effect of GEID on corporate carbon performance. GEID reduced the carbon emission intensity of firms and improved their carbon performance via green innovation. Further research indicated that the enhanced GEID in state-owned enterprises significantly improved carbon performance of firms. This study provides empirical evidence for GEID to improve corporate carbon performance, and also proposes a policy strategy for the government to guide firms to undertake green innovation and promote firms to improve efficient carbon use

Muscle-Strengthening Activities and Sociodemographic Correlates among Adults: Findings from Samples in Mainland China

A growing body of research has investigated the level of participation in muscle-strengthening (MS) activities and their correlates among Western populations; however, scarce attention has been paid to this issue among Chinese adults. This study aimed to describe the level of MS activities and to explore the relationships between sociodemographic correlates and level of MS activities in a large sample of Chinese adults. For this study, 3073 adults were recruited from 13 cities in Hubei Province. A self-reported questionnaire was utilized to collect data on MS activities and sociodemographic information among participants in this study. According to World Health Organization physical activity guidelines, MS activities should be undertaken at least two days per week. Multivariate logistic regression was used to explore the sociodemographic correlates of MS activities. The statistical significance level was set up as p < 0.05. The prevalence of MS activities among participants was 28.5%. MS activities among the total samples were associated with sex (adjusted odds ratios (aORs) for male = 1.98, 95% confidence intervals (95% CI): 1.67–2.34) and family composition (aOR for multiple children = 1.35, 95%CI: 1.12–1.64). Among males, normal weight status (aOR = 1.39, 95%CI: 1.08–1.78) and multiple children (aOR = 1.58, 95% CI: 1.21–2.05) were associated with MS activities. There was no association of sociodemographic factors with MS activities among females. Our results suggest that approximately 70% of adults in Hubei Province do not engage in recommended MS activities. These activities were associated with sex and family composition, which differed between sexes. This study provides sex-specific information on MS activity interventions. Future studies should use improved designs to explore more sociodemographic (e.g., health status, marital status and smoking status) and other dimensional correlates of MS activities among Chinese adults, to provide an evidence base for improved health behavior interventions

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm

A dataset of the quality of soybean harvested by mechanization for deep-learning-based monitoring and analysis

Deep learning and machine vision technology are widely applied to detect the quality of mechanized soybean harvesting. A clean dataset is the foundation for constructing an online detection learning model for the quality of mechanized harvested soybeans. In pursuit of this objective, we established an image dataset for mechanized harvesting of soybeans. The photos were taken on October 9, 2018, at a soybean experimental field of Liangfeng Grain and Cotton Planting Professional Cooperative in Guanyi District, Liangshan, Shandong, China. The dataset contains 40 soybean images of different qualities. By scaling, rotating, flipping, filtering, and adding noise to enhance the data, we expanded the dataset to 800 frames. The dataset consists of three folders, which store images, label maps, and record files for partitioning the dataset into training, validation, and testing sets. In the initial stages, the author devised an online detection model for soybean crushing rate and impurity rate based on machine vision, and research outcomes affirm the efficacy of this dataset. The dataset can help researchers construct a quality prediction model for mechanized harvested soybeans using deep learning techniques

Achieving Incentive, Security, and Scalable Privacy Protection in Mobile Crowdsensing Services

Mobile crowdsensing as a novel service schema of the Internet of Things (IoT) provides an innovative way to implement ubiquitous social sensing. How to establish an effective mechanism to improve the participation of sensing users and the authenticity of sensing data, protect the users’ data privacy, and prevent malicious users from providing false data are among the urgent problems in mobile crowdsensing services in IoT. These issues raise a gargantuan challenge hindering the further development of mobile crowdsensing. In order to tackle the above issues, in this paper, we propose a reliable hybrid incentive mechanism for enhancing crowdsensing participations by encouraging and stimulating sensing users with both reputation and service returns in mobile crowdsensing tasks. Moreover, we propose a privacy preserving data aggregation scheme, where the mediator and/or sensing users may not be fully trusted. In this scheme, differential privacy mechanism is utilized through allowing different sensing users to add noise data, then employing homomorphic encryption for protecting the sensing data, and finally uploading ciphertext to the mediator, who is able to obtain the collection of ciphertext of the sensing data without actual decryption. Even in the case of partial sensing data leakage, differential privacy mechanism can still ensure the security of the sensing user’s privacy. Finally, we introduce a novel secure multiparty auction mechanism based on the auction game theory and secure multiparty computation, which effectively solves the problem of prisoners’ dilemma incurred in the sensing data transaction between the service provider and mediator. Security analysis and performance evaluation demonstrate that the proposed scheme is secure and efficient

Preparation of Low Volatile Organic Compounds Silver Paste Containing Ternary Conductive Fillers and Optimization of Their Performances

Conductive silver paste is a key material in the fields of printed circuits and printed electronic devices. However, the preparation of conductive silver paste with low-cost and volatile organic compounds (VOCs) is still a challenge. In this work, conductive silver pastes with excellent comprehensive performances were developed by using water-borne polyurethane (WPU) as the bonding phase and using the ternary mixture of Ag microflakes (Ag MFs), Ag nanowires (Ag NWs), and Ag nanoparticles (Ag NPs) as the conductive phase. WPU endowed conductive silver pastes with the adhesion along with releasing a few VOCs during the curing. Results showed that a small amount of Ag NPs or Ag NWs dramatically enhanced the electrical conductivity of silver paste paint film filled only with Ag MFs. The electrical resistivity for optimal ternary mixture conductive silver paste was 0.2 × 10−3 Ω∙cm, and the conductive phase was composed of 20.0 wt% Ag MFs, 7.5 wt% Ag NWs, and 2.5 wt% Ag NPs. Meanwhile, the adhesive strength and hardness of silver paste paint film were effectively improved by increasing the curing temperature. The optimal overall performance of the conductive silver pastes was achieved at the curing temperature of 160 °C. Therefore, this work can provide a new route for preparing conductive silver pastes with high performances