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

Regional Circulation Symbiosis: Study on the Sustainable Development of Regional Traditional Handicraft

Sustainability is a key development issue in the world today, and as countries strive to accelerate the implementation of the 2030 Agenda for Sustainable Development, culture is becoming a valuable resource in many areas of our lives. Many countries choose to invest in culture because of its capacity for inclusion, conversation and innovation, and as a powerful force for connecting the past to the future, while cultural heritage not only preserves historical memory, but is also an important vehicle for continuing local cultural traditions and sustaining national spirits. Among them, traditional handicrafts, as one of the important cultural heritages, have carried the economic and cultural aspirations of the people for thousands of years. However, in recent times, along with the historical process of machine production gradually replacing traditional handicraft production and commodity economy replacing natural economy, more and more traditional handicrafts are gradually weakening or even disappearing, which is a great loss to the demand of human cultural diversity. Nowadays, culture and creative industries are one of the fastest growing economic sectors in the world, culture plays a significant role in building a more cohesive, resilient and inclusive society, and the preservation and development of indigenous cultural resources has become an important way to revitalize the territory. Regional traditional handicrafts are not only an important cultural carrier but also an industrial resource with unlimited potential. Low energy consumption, intensive, decentralized and other industrial characteristics give it real significance as a production method in increasing employment, income generation and other economic aspects. For alleviating employment difficulties, providing economic growth channels, reconstructing cultural ecosystems, and maintaining social stability, regional traditional handicrafts have natural attribute advantages as a supplementary form of industrialized production. Therefore, this paper addresses three research questions: 1. How to inherit, educate, and develop local traditional handicraft? 2. How to revitalize/sustainable/diversify the economic development structure of the "local" area based on regional traditional handicraft? 3. How to preserve and diffuse the culture of regional traditional handicraft? By introducing the theory of "Regional Circular and Ecological Sphere", this study explores the ways in which different countries use regional resources for territorial revitalization. Taking China as an example, this study analyzes the internal ("professionalization/industrialization") and external ("scientific diffusion") changes of traditional handicrafts in different regions, and identifies the modern reconstruction strategies of their organizational types and operational systems. Taking " Ceramics" as the case of regional traditional handicraft, this study was conducted in Faenza, Italy and Yuzhou, China, to compare and contrast the traditional and innovative models of this industry in the framework of industrial reconstruction, so as to establish a universally applicable regional "Ceramics" symbiotic network with universal applicability to achieve circular, ecological, digital, sustainable territorial development and cultural revitalization. This study discusses the protection and inheritance of regional traditional handicrafts and industrial development from the perspectives of economy, culture and society, and provides a concrete implementation plan for regional prosperity, livelihood economy and cultural construction by applying the scientific theory of "symbiosis", so as to provide a practical way for territorial revitalization. Also, the purpose of this study is not only to protect cultural heritage, but also to intervene in the modern life through regional traditional handicraft, to provide new economic opportunities for the residents, to solve the social and economic problems, as well as to reconstruct the national culture in people's daily life, and to provide space and opportunities for modern design to intervene in the prosperity of the territory

Ultra-fast self-assembly and stabilization of reactive nanoparticles in reduced graphene oxide films.

Nanoparticles hosted in conductive matrices are ubiquitous in electrochemical energy storage, catalysis and energetic devices. However, agglomeration and surface oxidation remain as two major challenges towards their ultimate utility, especially for highly reactive materials. Here we report uniformly distributed nanoparticles with diameters around 10 nm can be self-assembled within a reduced graphene oxide matrix in 10 ms. Microsized particles in reduced graphene oxide are Joule heated to high temperature (∼1,700 K) and rapidly quenched to preserve the resultant nano-architecture. A possible formation mechanism is that microsized particles melt under high temperature, are separated by defects in reduced graphene oxide and self-assemble into nanoparticles on cooling. The ultra-fast manufacturing approach can be applied to a wide range of materials, including aluminium, silicon, tin and so on. One unique application of this technique is the stabilization of aluminium nanoparticles in reduced graphene oxide film, which we demonstrate to have excellent performance as a switchable energetic material

Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks

QTL mapping and transcriptomic analysis of fruit length in cucumber

A total of 151 recombinant inbred lines (RILs) were derived from the cross between ‘Cucumis sativus L. hardwickii’ (HW) and a cultivated Northern Chinese inbred line ‘XinTaiMiCi’ (XTMC). We used resequencing to construct the genetic map and analyze the genetic background of RIL population, and combined with the phenotypes of RIL population and the analysis of RNA-seq data, we located the major loci controlling the fruit length of cucumber and related analysis. A genetic map containing 600 bin markers was constructed via re-sequencing. Based on the phenotype data collected in two different seasons (spring 2021 and autumn 2022), the major quantitative trait loci (QTLs) controlling cucumber fruit length were located and their transcriptomic analysis carried out. The results revealed three QTLs (Fl2.1, Fl4.1, and Fl6.1) detected repeatedly in the two seasons, of which Fl4.1 was the dominant QTL. From the functional annotation of corresponding genes there, we discovered the gene Csa4G337340 encoding an auxin efflux carrier family protein. The expression of that gene was significantly lower in XTMC and the long-fruit RIL lines than in HW and the short-fruit RIL lines; hence, we speculated the gene could be negatively correlated with the fruit length of cucumber. Transcriptomic analysis showed that 259 differentially expressed genes (DEGs) were enriched in the plant hormone signal transduction pathway. In addition, among those DEGs, 509 transcription factors were detected, these distributed in several transcription factor gene families, such as bHLH, AP2/ErF -ERF, C2H2, and NAC. Therefore, we concluded that the major gene controlling the fruit length of cucumber is located in the interval of Fl4.1, whose gene Csa4G337340 may be involved in the negative regulation of fruit length. Further, genes related to plant hormone signal transduction and several transcription factors were also found involved in the regulation of cucumber fruit length. Our results provide a reference for the fine mapping of major genes and analyzing the mechanism of cucumber fruit length

Adsorbate-Induced Structural Evolution of Pd Catalyst for Selective Hydrogenation of Acetylene

ACKNOWLEDGMENT: This work was financially supported by National Natural Science Foundation of China (21908002), project funded by China Postdoctoral Science Foundation (2019M660416, 2020T130045) and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2004, XK1802-6). We would like to thank the UK catalysis Hub for help collecting the XAS.Peer reviewedPostprin

Analysis of water synergy benefits of coal de-capacity in China

The closure/withdrawal of mines, as one of the important measures to implement the State Council’s “Opinions on supporting the coal industries to resolve excess production capacity and achieve destructive development”, and is of great significance to the optimization of the regional energy structure, reduction of carbon emissions and environmental protection. In order to assess the synergistic benefits of water resources generated in the process of coal de-capacity and reveal the spatial and temporal evolution characteristics of closure/withdrawal mines, the number and capacity of closure/withdrawal mines during 2016—2022 were investigated with coal de-capacity as the policy background. Based on the analysis of the spatial and temporal distribution characteristics of the closure/withdrawal mines, the water resources synergy benefits brought about by coal de-capacity was quantified by combining the water resources-related mine water, water consumption and wastewater discharges coefficients in the coal mining and washing stages. The results show that a total of 4027 mines were closure/withdrawal from coal-related provinces in China during 2016—2022, with a de-capacity of 875 million t. The number of mines closure/withdrawal is concentrated in the upper reaches of the Yangtze River, and the areas with high density of coal de-capacity are concentrated in the "Ji" bays of the Yellow River. Meanwhile, the total of wasted water resources reduced by coal de-capacity is about 3 billion t, higher than the volume of China’s fourth largest freshwater Honghu lake, generating socio-economic benefits of about 46.165 billion yuan. The areas with positive benefits of water synergy are mainly located in the water shortage areas such as Southwest China, North China and the Yellow River basin. This research quantifies the synergistic benefits of water resources for coal de-capacity and discusses the future direction of mine water resources utilization, with a view to providing scientific basis and data support for sustainable development and “double carbon” target for coal de-capacity

Highly Selective and Stable Isolated Non-Noble Metal Atom Catalysts for Selective Hydrogenation of Acetylene

ACKNOWLEDGMENTS This work was financially supported by National Natural Science Foundation of China (21908002) and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2108).Peer reviewedPostprin

Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA

Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. To evaluate the VQA models' reasoning ability beyond shortcut learning, the VQA-CP v2 dataset introduces a distribution shift between the training and test set given a question type. In this way, the model cannot use the training set shortcut (from question type to answer) to perform well on the test set. However, VQA-CP v2 only considers one type of shortcut and thus still cannot guarantee that the model relies on the intended solution rather than a solution specific to this shortcut. To overcome this limitation, we propose a new dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. In addition, we overcome the three troubling practices in the use of VQA-CP v2, e.g., selecting models using OOD test sets, and further standardize OOD evaluation procedure. Our benchmark provides a more rigorous and comprehensive testbed for shortcut learning in VQA. We benchmark recent methods and find that methods specifically designed for particular shortcuts fail to simultaneously generalize to our varying OOD test sets. We also systematically study the varying shortcuts and provide several valuable findings, which may promote the exploration of shortcut learning in VQA.Comment: Fingdings of EMNLP-202

Think out Loud: Emotion Deducing Explanation in Dialogues

Humans convey emotions through daily dialogues, making emotion understanding a crucial step of affective intelligence. To understand emotions in dialogues, machines are asked to recognize the emotion for an utterance (Emotion Recognition in Dialogues, ERD); based on the emotion, then find causal utterances for the emotion (Emotion Cause Extraction in Dialogues, ECED). The setting of the two tasks requires first ERD and then ECED, ignoring the mutual complement between emotion and cause. To fix this, some new tasks are proposed to extract them simultaneously. Although the current research on these tasks has excellent achievements, simply identifying emotion-related factors by classification modeling lacks realizing the specific thinking process of causes stimulating the emotion in an explainable way. This thinking process especially reflected in the reasoning ability of Large Language Models (LLMs) is under-explored. To this end, we propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN). EDEN recognizes emotion and causes in an explicitly thinking way. That is, models need to generate an explanation text, which first summarizes the causes; analyzes the inner activities of the speakers triggered by the causes using common sense; then guesses the emotion accordingly. To support the study of EDEN, based on the existing resources in ECED, we construct two EDEN datasets by human effort. We further evaluate different models on EDEN and find that LLMs are more competent than conventional PLMs. Besides, EDEN can help LLMs achieve better recognition of emotions and causes, which explores a new research direction of explainable emotion understanding in dialogues