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Workplace training produces 2 types of human capital: general and firm-specific. Based on specific human capital theories pioneered by Gary Becker, this article empirically examines the impact of on-the-job training and off-the-job training on wage levels in urban China. It is found that off-the-job training in previous firms significantly raises wages at a worker's current firm, whereas previous on-the-job training has no effect on current wages. The results are consistent with theory and U.S. experience. The results are robust to various specifications and procedures correcting for potential error clustering and selection bias problems. It is suggested that advocating firm and government cosponsored general training programs targeting disadvantaged urban populations is a desirable and feasible strategy in an attempt to relieve the increasing unemployment pressure in China.

Hyperspectral images (HSI) contain powerful spectral characterization capabilities and are widely used especially for classification applications. However, the rich spectrum contained in HSI also increases the difficulty of extracting useful information, which makes the feature extraction method significant as it enables effective expression and utilization of the spectrum. Traditional HSI feature extraction methods design spectral features manually, which is likely to be limited by the complex spectral information within HSI. Recently, data-driven methods, especially the use of convolutional neural networks (CNNs), have shown great improvements in performance when processing image data owing to their powerful automatic feature learning and extraction abilities and are also widely used for HSI feature extraction and classification. The CNN extracts features based on the convolution operation. Nevertheless, the local perception of the convolution operation makes CNN focus on the local spectral features (LSF) and weakens the description of features between long-distance spectral ranges, which will be referred to as global spectral features (GSF) in this study. LSF and GSF describe the spectral features from two different perspectives and are both essential for determining the spectrum. Thus, in this study, a local-global spectral feature (LGSF) extraction and optimization method is proposed to jointly consider the LSF and GSF for HSI classification. To increase the relationship between spectra and the possibility to obtain features with more forms, we first transformed the 1D spectral vector into a 2D spectral image. Based on the spectral image, the local spectral feature extraction module (LSFEM) and the global spectral feature extraction module (GSFEM) are proposed to automatically extract the LGSF. The loss function for spectral feature optimization is proposed to optimize the LGSF and obtain improved class separability inspired by contrastive learning. We further enhanced the LGSF by introducing spatial relation and designed a CNN constructed using dilated convolution for classification. The proposed method was evaluated on four widely used HSI datasets, and the results highlighted its comprehensive utilization of spectral information as well as its effectiveness in HSI classification. [ABSTRACT FROM AUTHOR]

As a promising pharmacophore, sulfondiimines have drawn increasing attention in recent years, but their uptake in medicinal chemistry is jeopardized by the scarcity of related transformations. Herein, we report a facile and mild N -alkylation protocol of N H-diphenyl sulfondiimines with alkyl halides to prepare a myriad of N -alkylated diphenyl sulfondiimines. Owing to air atmosphere, room temperature, as well as mild reaction conditions, this protocol has exhibited great potential in organic synthesis and medicinal chemistry by the late-stage functionalization of natural products. [ABSTRACT FROM AUTHOR]

Many companies have paid attention to environmental preservation in recent years. Firms label their goods or services ecologically responsible, becoming a quickly growing trend. This article tries to establish the link between green elements and competitive advantage tactics to progress sustainable manufacturing. It aims to discover the relationship between green innovation, green market orientation, green entrepreneurial orientation, technology readiness, and product quality to attain sustainable business success. Employing simple random sampling, surveys were distributed via email. The present study is a quantitative analysis, and the analytical tool utilized is AMOS and smart PLS program applications. The empirical outcomes find that green factors help companies improve the quality of products and sustainable success. Also, technology readiness positively influences the quality of products and sustainable success in business. In addition, the impact of green entrepreneurial orientation on the quality of products and sustainable success is positive and significant. The findings illustrated that green market orientation is an essential factor in the quality of products and sustainable success in business. Another point is that green innovation positively affects the quality of products and sustainable success. Finally, the quality of products has significantly affected sustainable success in business. [ABSTRACT FROM AUTHOR]

The Belt and Road Initiative puts higher requirements for the logistics industry. As one of the most energy-consuming industries, logistics is a high-carbon emission industry. Its impact on the environment cannot be ignored. In this context, how to respond to the "Belt and Road" under the concept of sustainable development, to promote the logistics industry to achieve "low consumption, low emissions, high efficiency" of regional sustainability, has become the most important development of China's logistics industry. Therefore, based on previous research, this paper establishes an input–output index system and uses the SBM-DEA model and Malmquist index model to analyze the efficiency of low-carbon logistics in 17 provinces from 2006 to 2020, explore the overall level of the logistics and the factors affecting efficiency, and compare the efficiency in different periods and regions. Then, through the Tobit regression model, the four main factors affecting the efficiency of the logistics industry are analyzed. The results show that: (1) The highest value of low-carbon logistics efficiency of each province is 1.0000, and the lowest value is only 0.0944. The difference in logistics efficiency values among provinces is large, so there is great room for improvement and development potential. (2) From 2006 to 2020, the low-carbon logistics efficiency of the regions showed an overall upward trend, and the MI index values of each province reached or approached the DEA effective state. Among them, technological progress has a promoting effect, while scale and pure technical efficiency have hindered the efficiency growth. (3) Economic growth and industry structure have a positive effect, while energy consumption and government expenditure are negatively correlated with efficiency. [ABSTRACT FROM AUTHOR]

Sound intelligibility (SdI) is investigated in the context of modelling aural comfort in the design of metro commercial spaces. The term describes an individual's clear aural perception of their surrounding environment. The present paper describes the key objective variables used to evaluate and quantify SdI for inclusion in models predicting aural comfort. In-situ and laboratory questionnaire surveys were conducted to measure SdI in various spatial forms of metro commercial spaces. The significantly influencing factors were studied using Pearson correlation and linearity analysis. The results show that acoustic parameters such as sound levels, sound source, and reverberation time (RT) are not key factors in the evaluation of SdI, but the variables of spatial form and sound absorptions are. Based on the findings of analyzing the key influencing factors, prediction models for quantifying SdI were developed. The results show that there is no holistic approach to quantifying SdI for all kinds of metro commercial space, due to the distinct spatial form of each type of the spaces. • Sound Intelligibility (SdI), a factor for modelling aural comfort is introduced. • A prediction model for Sdl of metro spaces is developed as an example. • Spatial forms, crowd, acoustic, and absorption properties are useful parameters for SdI. • Moving forward, a holistic approach to quantify SdI across spaces will be beneficial. [ABSTRACT FROM AUTHOR]