Leveraging Work-Related Stressors for Employee Innovation: The Moderating Role of Enterprise Social Networking Use

Enterprise social networking (ESN) techniques have been widely adopted by firms to provide a platform for public communication among employees. This study investigates how the relationships between stressors (i.e., challenge and hindrance stressors) and employee innovation are moderated by task-oriented and relationship-oriented ESN use. Since challenge-hindrance stressors and employee innovation are individual-level variables and task-oriented ESN use and relationship-oriented ESN use are team-level variables, we thus use hierarchical linear model to test this cross-level model. The results of a survey of 191 employees in 50 groups indicate that two ESN use types differentially moderate the relationship between stressors and employee innovation. Specifically, task-oriented ESN use positively moderates the effects of the two stressors on employee innovation, while relationship-oriented ESN use negatively moderates the relationship between the two stressors and employee innovation. In addition, we find that challenge stressors significantly improve employee innovation. Theoretical and practical implications are discussed

Biochar to improve soil fertility. A review

International audienceAbstractSoil mineral depletion is a major issue due mainly to soil erosion and nutrient leaching. The addition of biochar is a solution because biochar has been shown to improve soil fertility, to promote plant growth, to increase crop yield, and to reduce contaminations. We review here biochar potential to improve soil fertility. The main properties of biochar are the following: high surface area with many functional groups, high nutrient content, and slow-release fertilizer. We discuss the influence of feedstock, pyrolysis temperature, pH, application rates, and soil types. We review the mechanisms ruling the adsorption of nutrients by biochar

Racial discrimination and anti-discrimination : the impact of the COVID-19 pandemic on Chinese restaurants in North America

The COVID-19 pandemic has led to an increase in cases of racial discrimination against Asians, especially Chinese people. Despite an emerging stream of studies investigating various aspects of the COVID-19 pandemic, research on the behavioral consequences of racial discrimination during the pandemic remains scarce. In this work, we examined how racial discrimination stemming from the COVID-19 pandemic and subsequent anti-discrimination were manifested on online platforms. By conducting difference-in-differences analyses on two large-scale panel datasets from Yelp.com and SafeGraph, we explored the impact of COVID-19 on Chinese restaurants, relative to non-Chinese restaurants, at different phases of the COVID-19 pandemic. We found that the COVID-19 pandemic led to an immediate increase in racial discrimination, which was reflected in a significant drop in the customer patronage frequency of Chinese restaurants as compared to that of non-Chinese restaurants. Furthermore, analyses using multiple behavioral indicators generated by text mining and machine learning techniques consistently suggested that increased discrimination triggered anti-discrimination actions of customers on online platforms after the COVID-19 outbreak. This study contributes to the literature on racial discrimination by investigating a subtle but more factual form of racial discrimination evidenced by the customer patronage of Chinese restaurants, as well as user-generated content, by demonstrating that consumers can fight discrimination on online platforms

Wideband hybrid metamaterial absorber via compound design of multiple mechanisms

Broadband and high efficiency are the two core indexes of absorption research, which usually requires a balance between them. Therefore, how to take into account both and achieve broadband and efficient absorption is a hot topic in current research. In this paper, by the compound design of multiple mechanisms, a kind of wideband hybrid metamaterial absorber (HMA) is proposed. The overall structure consists of a layer of patterned resistive film and a layer of magnetic absorbing material (MAM) separated by the air. The resistive layer is designed as square ring type to regulate the local magnetic field, which results in significant magnetic field enhancement within the MAM layer, and this mechanism provides a prerequisite for wideband and high-efficiency absorption in the low frequency band. Furthermore, due to the electrical losses of the resistive film, another absorption band is additionally excited in the high frequency band. Thanks to the multiple mechanisms, the absorption efficiency above 90% in the 3.2–22.0 GHz frequency band can be realized, and the thickness of the overall structure is 7.0 mm that is 0.07 of the wavelengths at the lowest frequency point. To demonstrate this method, a prototype is designed, fabricated and measured. Both the simulation and experiment results verify the effectiveness of the proposed method. This work provides a new method to design wideband and high-efficiency electromagnetic absorption structures and may find potential applications in multi-functional planar or conformal structures

A Wireless Sensor Network Based Personnel Positioning Scheme in Coal Mines with Blind Areas

This paper proposes a novel personnel positioning scheme for a tunnel network with blind areas, which compared with most existing schemes offers both low-cost and high-precision. Based on the data models of tunnel networks, measurement networks and mobile miners, the global positioning method is divided into four steps: (1) calculate the real time personnel location in local areas using a location engine, and send it to the upper computer through the gateway; (2) correct any localization errors resulting from the underground tunnel environmental interference; (3) determine the global three-dimensional position by coordinate transformation; (4) estimate the personnel locations in the blind areas. A prototype system constructed to verify the positioning performance shows that the proposed positioning system has good reliability, scalability, and positioning performance. In particular, the static localization error of the positioning system is less than 2.4 m in the underground tunnel environment and the moving estimation error is below 4.5 m in the corridor environment. The system was operated continuously over three months without any failures

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A hybrid approach for partial discharge classification: combining traditional machine learning and deep neural network

Partial discharge (PD) is a critical issue in high-voltage equipment, and the accurate detection and classification of PDs are essential for preventing equipment failure. In recent years, various approaches have been proposed for PD classification, including traditional machine learning methods and deep learning techniques. Traditional machine learning algorithms, such as decision trees, support vector machines (SVM), and k-nearest neighbors (KNN), have been widely used for PD classification. However, these methods rely on manual feature extraction, which can be time-consuming and may not capture the complete range of PD characteristics. In contrast, deep learning techniques, including CNN and RNN, have shown promising results in PD classification by enabling the automatic extraction of relevant features from PD data. However, it requires a large amount of training data. This study proposes a novel approach for PD classification, combining traditional machine learning algorithms with deep neural networks to perform transfer learning. Firstly, manual feature extraction is conducted to extract PD features. Traditional machine learning clustering algorithms, such as K-means and affinity propagation clustering will be applied to these features to separate noises from PDs. Subsequently, the Partial Discharge Pattern Recognition and Diagnosis (PRPD) is plotted and fed into a CNN to classify each cluster. In order to apply it in real-life applications, minimizing the missing detection rate is considered the priority of the tunning process. The proposed method can effectively detect and classify PD which can aid in the development of effective PD diagnosis systems and contribute to the safe and reliable operation of high-voltage equipment.Bachelor of Engineering (Electrical and Electronic Engineering

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Since investors have diverse risk motives for green investments, this paper uses data envelopment analysis (DEA) and simulation to accurately evaluate the efficiency of green portfolios from the perspective of investors’ subjective risks and accordingly provide suitable investment selection strategies. On the one hand, the paper integrates investors’ risk preferences with efficiency evaluation models under the framework of behavioral finance, and then constructs a green portfolio efficiency evaluation model based on cumulative prospect theory on the basis of defining green portfolio efficiency. On the other hand, by bringing realistic Chinese stock data into the evaluation model and solving it with the help of large number iteration and DEA, the trends of frontier movements and selection options of green portfolios under the influence of different risk preferences are obtained and analyzed. The empirical simulation reveals that: (1) if investors’ risk aversion at return rises, it will not only reduce the expected prospective value of the green portfolio, but also shift down and flatten the frontier of the green portfolio; indicating that investors will tend to reduce their risk-tolerant attitude and prefer a conservative strategy under the same value condition. (2) If investors increase their risk-seeking in the case of losses, this will raise the expected prospect value of the green portfolio and lead to an inward and steeper green portfolio frontier; suggesting that, given equal value, investors prefer to increase their risk-taking capacity and use aggressive strategies in the hope of turning the profit around. (3) The efficiency results of green portfolios are very sensitive to changes in investors’ risk preferences, suggesting that investors need to select and match green portfolios with their own risk appetite levels. The above findings enrich and expand the risk types and evaluation models in previous green investment studies from the perspective of investors’ subjective risk

Efficiency Evaluation and Selection Strategies for Green Portfolios under Different Risk Appetites

Since investors have diverse risk motives for green investments, this paper uses data envelopment analysis (DEA) and simulation to accurately evaluate the efficiency of green portfolios from the perspective of investors’ subjective risks and accordingly provide suitable investment selection strategies. On the one hand, the paper integrates investors’ risk preferences with efficiency evaluation models under the framework of behavioral finance, and then constructs a green portfolio efficiency evaluation model based on cumulative prospect theory on the basis of defining green portfolio efficiency. On the other hand, by bringing realistic Chinese stock data into the evaluation model and solving it with the help of large number iteration and DEA, the trends of frontier movements and selection options of green portfolios under the influence of different risk preferences are obtained and analyzed. The empirical simulation reveals that: (1) if investors’ risk aversion at return rises, it will not only reduce the expected prospective value of the green portfolio, but also shift down and flatten the frontier of the green portfolio; indicating that investors will tend to reduce their risk-tolerant attitude and prefer a conservative strategy under the same value condition. (2) If investors increase their risk-seeking in the case of losses, this will raise the expected prospect value of the green portfolio and lead to an inward and steeper green portfolio frontier; suggesting that, given equal value, investors prefer to increase their risk-taking capacity and use aggressive strategies in the hope of turning the profit around. (3) The efficiency results of green portfolios are very sensitive to changes in investors’ risk preferences, suggesting that investors need to select and match green portfolios with their own risk appetite levels. The above findings enrich and expand the risk types and evaluation models in previous green investment studies from the perspective of investors’ subjective risk

Basic Study on Electrochemical Remediation of Heavy Metal Contaminated Water and Soil

Heavy metal pollution of soil is one of the major environmental problems at this stage, and soil pollution itself has the characteristics of accumulation and irreversibility, which is difficult to completely recover from the root. Therefore, the research work on soil remediation technology and practice has become the main work content. The research progress in recent years includes physical remediation, chemical remediation, bioremediation, agricultural remediation and other models. It is also imperative to study its technical characteristics and application scope