Determinants of the competitive advantage of dairy supply chains: Evidence from the Chinese dairy industry

In this study, we use an evidence-based approach to examine the factors that determine the competitive advantage of dairy supply chains using evidence from the Chinese dairy industry. We focus on the quality assurance of dairy products, which is considered one of the fundamental influential factors. We investigate interrelationships among the identified determinants, which include dairy production behavior, dairy cow culture model, government regulations, corporate social responsibility, and quality assurance, and examine how these determinants influence the competitive advantage of dairy supply chains. We employ the structural equation modeling approach in which grouped observable variables that represent the identified determinants are extrapolated from primary data collected through a questionnaire survey. Our key findings show that by mediating the effects of dairy production behavior and the dairy cow culture model, government regulation and corporate social responsibility significantly affect the quality assurance of dairy products. In turn, dairy production behavior and the dairy cow culture model significantly affect the competitive advantage of the dairy supply chain via the fully mediated effects of the quality assurance of dairy products. Specifically, the dairy cow culture model helps ensure the safety and quality of milk supply, allowing core dairy firms to control product quality throughout the dairy supply chain. Our empirical study shows that the identified determinants interact to assure the quality of dairy products and enhance the competitive advantage of the dairy supply chain in China

Exploration on the Training Mode of Computer Professionals Based on the Concept of “New Engineering”

With the national industrial upgrading and technological innovation in recent years, the construction industry is leading in the direction of informatization, industrialization, intelligence and international integration, which puts forward new requirements for the current traditional mode of computer talent training. The innovation of talent training mode, the improvement of education and teaching, the improvement of education resources and so on have become the urgent problems of new engineering computer professional talent training Absolutely. This paper analyzes the current situation of "new engineering" talent demand and training, points out the shortcomings of the current computer talent training in the teaching concept, teaching mode, teachers and so on, and explores the new engineering computer talent training mode. And take the practice of Henan University School of civil engineering and architecture in the new engineering personnel training as an example, hope to have a certain reference significance for the new engineering computer professional personnel training. Keywords: new engineering; computer; interdisciplinary training; subject integration DOI: 10.7176/JEP/12-8-03 Publication date:March 31st 202

GPT Understands, Too

While GPTs with traditional fine-tuning fail to achieve strong results on natural language understanding (NLU), we show that GPTs can be better than or comparable to similar-sized BERTs on NLU tasks with a novel method P-tuning -- which employs trainable continuous prompt embeddings. On the knowledge probing (LAMA) benchmark, the best GPT recovers 64\% (P@1) of world knowledge without any additional text provided during test time, which substantially improves the previous best by 20+ percentage points. On the SuperGlue benchmark, GPTs achieve comparable and sometimes better performance to similar-sized BERTs in supervised learning. Importantly, we find that P-tuning also improves BERTs' performance in both few-shot and supervised settings while largely reducing the need for prompt engineering. Consequently, P-tuning outperforms the state-of-the-art approaches on the few-shot SuperGlue benchmark

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later

Purple-bluish tongue is associated with platelet counts, and the recurrence of epithelial ovarian cancer

AbstractObjectiveTo evaluate the relationship between purple-bluish tongue and platelet counts, and further to examine their associations with the recurrence of epithelial ovarian cancer.MethodsA total of 82 epithelial ovarian cancer patients were enrolled in this study. Cluster analysis was used for grouping patients’ Prgb (Red-R; Green-G; Blue-B; Average percentage of RGB, Prgb) values. Receiver operating characteristic (ROC) curve was performed for detecting the diagnostic standard of purple-bluish tongue. χ2 test was used to assess the relationship between purple-bluish tongue and platelet counts, and the recurrence of epithelial ovarian cancer. The perioperative (preoperative) platelet level was examinedwith tongue image and disease recurrence.ResultsTongue images were classified into two groups basing on Prgb values of images by cluster analysis. The numbers of cases in cluster “1” (normal color tongue) was 16 and cluster “2” (purple-bluish tongue) was 66. Two groups of Prgb values, classified by cluster analysis, were significantly correlated with vision-based tongue color recognition (Kappa = 0.852, P < 0.001). ROC curve showed that the ratio of Pb to Pr had the highest diagnostic value. The sensitivity and the specificity of the ratio of Pb to Pr were 95.3% and 88.9% respectively and the optimal cut-off point was 0.71. Purple-bluish tongue was significantly correlated with increased platelet counts (P < 0.001). Both the increased platelet counts (P = 0.01) and purple-bluish tongue were associated with recurrence of epithelial ovarian cancer (P < 0.001).ConclusionThe ratio of Pb to Pr greater than 0.71 could serve as an indicator for purple-bluish tongue diagnosing used in symptom pattern identification in Traditional Chinese Medicine. Purple-bluish tongue, associated with increased platelet counts, was also closely correlated with the recurrence of epithelial ovarian cancer

Comprehensive characterization of a time-course transcriptional response induced by autotoxins in Panax ginseng using RNA-Seq

The lists for all the differentially expressed genes. (XLS 1362 kb

Materials characterization of innovative composite materials for solar-driven thermochemical heat storage (THS) suitable for building application

Thermochemical Heat Storage (THS) systems have recently attracted a lot of attention in research and development. One of the main parameters that influence the performance of a THS system is the thermochemical materials. This paper aims to investigate thermochemical materials which are suitable for both short-term and long-term building heat storage application driven by solar energy for an open system. Innovative composite materials using MgCl2-MgSO4, CaCl2-LiCl and MgSO4-CaCl2salts mixtures impregnated into vermiculite, and potassium formate (KCOOH) impregnated into silica gel will be presented in this study. Initial screening and characterization results of the composite THS materials based on the energy density using differential scanning calorimetry analysis, mass loss against temperature using thermogravimetric analysis, and moisture vapor adsorption isotherms testing are discussed. The characterization analysis suggest that the vermiculite with salts mixtures are promising candidates for thermochemical heat storage (THS) systems compared to composite materials with individual salts. Meanwhile the potential of KCOOH-silica gel as THS materials may be further investigated in the future. The performance of the materials may be further optimized in the future by changing the concentration ratio of the mixed salts