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There is a recent growing interest to find a lasting intervention to rural poverty (RP) in developing countries based on farmer entrepreneurship and innovation. The purpose of this paper, therefore, is to examine the relation between entrepreneurship and RP alleviation in two resource-constrained provinces of China. This paper assesses the influence of three capabilities of farm entrepreneurs – educational, economic and socio-cultural – on farmer entrepreneurship growth and how these, in turn, impact alleviation of RP.

Household survey data comprising 363 respondents were taken from four deprived communities in two provinces of China. The paper employed structural equation modeling (SEM), using AMOS 21.0 alongside SPSS 20.0 to test the relations between the constructs.

The results show that a statistically significant and positive relation exists between entrepreneurship and RP alleviation in China. The findings of the study further reveal that qualitative growth of entrepreneurship has a stronger positive influence on RP alleviation than on quantitative growth, and socio-cultural capabilities of respondents significantly and positively affect entrepreneurial growth of farmers, rather than education and economic capabilities.

The use of data from four communities in two provinces tends to limit the ability to generalize the findings of the study. Furthermore, the survey did not collect information on non-farm entrepreneurs, making it impossible to compare the findings from farm entrepreneurs with non-farm entrepreneurs.

The findings have practical implications for policy makers in rural China toward addressing targeted RP. This paper, therefore, suggests that entrepreneurship should be pursued vigorously among farmers in rural areas of China to help solve poverty. The paper also presents a useful lesson for various stakeholders in poverty alleviation programs in other developing countries.

This paper contributes to the academic literature on the entrepreneurship–RP alleviation nexus by combining the theory of capability and SEM in the analysis of an emerging economy such as China.

The purpose of this research is to examine the drivers of sustainable supplier selection (SSS) and investigate the extent to which it is associated with a buyer's financial performance within an emerging economy context.

The data were collected from 235 supply chain and procurement professionals in Thailand. The structural relationship was tested using partial least squares based structural equation modeling (PLS-SEM) and PROCESS tool.

Based on the empirical findings, firms that pursue sustainability initiatives during supplier selection process enjoy better financial performance than their competitors. The analysis suggests six hypothetical paths explain SSS. Suppliers' human rights and safety focus are the most powerful determinants of SSS. Significantly, positive support was found for the SSS and buyers' financial performance relationship. Finally, there is a significant moderating effect of resource investment on sustainability efforts.

Data for the study were collected from a single industry, so the findings are indicative but not representative of all supply chains. Due to this limitation, the findings cannot be generalized across other countries and industries. This study is a starting point in understanding the role of SSS in creating a sustainable supply chain. Future research may develop a comprehensive understanding of the nature and magnitude of the impact of SSS on sustainable supply chains.

This paper contributes toward an understanding of the determinants of SSS and its consequences for sustainable supply chains.

本研究旨在探究選擇可持續供應商的驅動因素，及探討在新興經濟的環境下選擇可持續供應商與買方的財務表現之間有多大的關係。

數據取自235名在泰國的供應鏈及採購專業人士。研究人員使用基於偏最小二乘為基礎的結構方程模型 (PLS-SEM) 及流程 (PROCESS) 工具 測試了結構的關係。

基於實證結果，在選擇供應商的程序中、奉行持續性措施的公司的財務表現會較其對手更好。分析顯示有六個假設性的途徑可闡釋可持續供應商的選擇。供應商的人權及以安全為焦點是選擇可持續供應商的最有力的決定因素。明顯地、選擇可持續供應商及買方財務表現之間的關係已找到支持的證據。最後，資源的投資對可持續性方面的努力產生重大的調節效果。

由於用來研究的數據取自單一行業，故研究結果是指示性的、而非代表所有供應鏈。因為這個局限，所以研究結果不能概括其它國家或行業。唯本研究就了解選擇可持續供應商在創造一個可持續的供應鏈上所扮演的角色方面踏出了第一步。未來的研究或會就選擇可持續供應商對可持續供應鏈予以何種影響及多大的影響提供更全面的解說。

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.