Overtime work, job autonomy, and employees’ subjective well-being: Evidence from China

IntroductionChinese workers suffer more from overtime than in many countries. Excessive working hours can crowd out personal time and cause work-family imbalance, affecting workers’ subjective well-being. Meanwhile, self-determination theory suggests that higher job autonomy may improve the subjective well-being of employees.MethodsData came from the 2018 China Labor-force Dynamics Survey (CLDS 2018). The analysis sample consisted of 4,007 respondents. Their mean age was 40.71 (SD = 11.68), and 52.8% were males. This study adopted four measures of subjective well-being: happiness, life satisfaction, health status, and depression. Confirmation factor analysis was employed to extract the job autonomy factor. Multiple linear regression methods were applied to examine the relationship between overtime, job autonomy, and subjective well-being.ResultsOvertime hours showed weak association with lower happiness (β = −0.002, p < 0.01), life satisfaction (β = −0.002, p < 0.01), and health status (β = −0.002, p < 0.001). Job autonomy was positively related to happiness (β = 0.093, p < 0.01), life satisfaction (β = 0.083, p < 0.01). There was a significant negative correlation between involuntary overtime and subjective well-being. Involuntary overtime might decrease the level of happiness (β = −0.187, p < 0.001), life satisfaction (β = −0.221, p < 0.001), and health status (β = −0.129, p < 0.05) and increase the depressive symptoms (β = 1.157, p < 0.05).ConclusionWhile overtime had a minimal negative effect on individual subjective well-being, involuntary overtime significantly enlarged it. Improving individual’s job autonomy is beneficial for individual subjective well-being

Qwen-VL: A Frontier Large Vision-Language Model with Versatile Abilities

We introduce the Qwen-VL series, a set of large-scale vision-language models designed to perceive and understand both text and images. Comprising Qwen-VL and Qwen-VL-Chat, these models exhibit remarkable performance in tasks like image captioning, question answering, visual localization, and flexible interaction. The evaluation covers a wide range of tasks including zero-shot captioning, visual or document visual question answering, and grounding. We demonstrate the Qwen-VL outperforms existing Large Vision Language Models (LVLMs). We present their architecture, training, capabilities, and performance, highlighting their contributions to advancing multimodal artificial intelligence. Code, demo and models are available at https://github.com/QwenLM/Qwen-VL.Comment: Code, demo and models are available at https://github.com/QwenLM/Qwen-V

TouchStone: Evaluating Vision-Language Models by Language Models

Large vision-language models (LVLMs) have recently witnessed rapid advancements, exhibiting a remarkable capacity for perceiving, understanding, and processing visual information by connecting visual receptor with large language models (LLMs). However, current assessments mainly focus on recognizing and reasoning abilities, lacking direct evaluation of conversational skills and neglecting visual storytelling abilities. In this paper, we propose an evaluation method that uses strong LLMs as judges to comprehensively evaluate the various abilities of LVLMs. Firstly, we construct a comprehensive visual dialogue dataset TouchStone, consisting of open-world images and questions, covering five major categories of abilities and 27 subtasks. This dataset not only covers fundamental recognition and comprehension but also extends to literary creation. Secondly, by integrating detailed image annotations we effectively transform the multimodal input content into a form understandable by LLMs. This enables us to employ advanced LLMs for directly evaluating the quality of the multimodal dialogue without requiring human intervention. Through validation, we demonstrate that powerful LVLMs, such as GPT-4, can effectively score dialogue quality by leveraging their textual capabilities alone, aligning with human preferences. We hope our work can serve as a touchstone for LVLMs' evaluation and pave the way for building stronger LVLMs. The evaluation code is available at https://github.com/OFA-Sys/TouchStone.Comment: https://github.com/OFA-Sys/TouchSton

Cultivation Practice of Chinese Medicinal Herbs

An innovative cultivation technique for Chinese medicinal herbs had been practiced in China, which led a new road for medicinal herbs production without input of chemical fertilizer and chemical pesticides. The organic practice was based on the principle of biodiversity for pest control. An example of Panax notoginseng (Burk.) F. H. Chen was chosen for explaining cultivation technology under forest. The key technologies for P. notoginseng cultivation under forest include forest land selection, land tillage, seedling breeding and transplanting, and on-farm organic management. These technologies can standardize herbs production in large-scale under forest, and the quality and safety of P. notoginseng can be effectively improved without applying chemical pesticides and chemical fertilizer in the production process

Crop Diversity for Yield Increase

Traditional farming practices suggest that cultivation of a mixture of crop species in the same field through temporal and spatial management may be advantageous in boosting yields and preventing disease, but evidence from large-scale field testing is limited. Increasing crop diversity through intercropping addresses the problem of increasing land utilization and crop productivity. In collaboration with farmers and extension personnel, we tested intercropping of tobacco, maize, sugarcane, potato, wheat and broad bean – either by relay cropping or by mixing crop species based on differences in their heights, and practiced these patterns on 15,302 hectares in ten counties in Yunnan Province, China. The results of observation plots within these areas showed that some combinations increased crop yields for the same season between 33.2 and 84.7% and reached a land equivalent ratio (LER) of between 1.31 and 1.84. This approach can be easily applied in developing countries, which is crucial in face of dwindling arable land and increasing food demand

Exogenous leucine alleviates heat stress and improves saponin synthesis in Panax notoginseng by improving antioxidant capacity and maintaining metabolic homeostasis

Panax notoginseng saponins (PNSs) are used as industrial raw materials to produce many drugs to treat cardio-cerebrovascular diseases. However, it is a heat-sensitive plant, and its large-scale artificial cultivation is impeded by high temperature stress, leading to decreases in productivity and PNSs yield. Here, we examined exogenous foliar leucine to alleviate heat stress and explored the underlying mechanism using metabolomics. The results indicated that 3 and 5 mM exogenous foliar leucine significantly alleviated heat stress in one-year- and two-year-old P. notoginseng in pots and field trials. Exogenous foliar leucine enhanced the antioxidant capacity by increasing the activities of antioxidant enzymes (POD, SOD) and the contents of antioxidant metabolites (amino acids). Moreover, exogenous foliar leucine enhanced carbohydrate metabolism, including sugars (sucrose, maltose) and TCA cycle metabolites (citric acid, aconitic acid, succinic acid and fumaric acid), in P. notoginseng leaves, stems, and fibrous roots to improve the energy supply of plants and further alleviate heat stress. Field experiments further verified that exogenous foliar leucine increased the productivity and PNSs accumulation in P. notoginseng. These results suggest that leucine application is beneficial for improving the growth and quality of P. notoginseng under heat stress. It is therefore possible to develop plant growth regulators based on leucine to improve the heat resistance of P. notoginseng and other crops