The political economy of food pricing policy in China

The overall goal of this paper is to analyse the political economy of food price policies in China during the global food crisis. The results show that given China's unique economic and political context and the nature of its agricultural markets, the government's reaction to the crisis was swift and decisive. Responses, which considered the interests of the relevant stakeholders, included both short-term counter-measures that covered a wide range of domestic and border policies as well as long-term policy changes on biofuels and agricultural investment. This, in conjunction with the country's political system, meant that the decision-making process encountered no problems and that the impacts of policy responses by the government achieved the envisaged objectives

Farmer Participation, the Dairy Industry, and the Rise of Dairy Production in China

With rapid income growth, dairy production and consumption in China have increased significantly. This emergence of the dairy sector will provide opportunities for farmers to participate in a high-value, potentially more lucrative enterprise. The overall goal of this paper is to analyze the major determinants of farmers’ participation in dairy production. Our main question is whether or not the pace of the emergence of the dairy processing industry has affected the ability of farmers to participate in dairy production and whether or not it has limited the expansion of their herd size. Based on household, village and processor surveys conducted in the Greater Beijing region, our analysis shows that the location of dairy processing firms is one of the key factors that determines the participation of farmers in dairy production. Although other factors affect participation and herd size—for example, access to roads and the ability to get a job off the farm (which affects the opportunity cost of household members)—access to dairy processors is shown to be the major factor that has encouraged the growth of dairy production over the past decade. The results also show that poor, less educated farmers with relatively less access to land are not excluded from the rapid expansion of the Greater Beijing dairy market

Genome-wide identification of rubber tree (Hevea brasiliensis Muell. Arg.) aquaporin genes and their response to ethephon stimulation in the laticifer, a rubber-producing tissue

Expression profiles of the 51 HbAQP genes in the laticifer of rubber tree clone RRIM928. (PDF 36 kb

Attention-Block Deep Learning Based Features Fusion in Wearable Social Sensor for Mental Wellbeing Evaluations

With the progressive increase of stress, anxiety and depression in working and living environment, mental health assessment becomes an important social interaction research topic. Generally, clinicians evaluate the psychology of participants through an effective psychological evaluation and questionnaires. However, these methods suffer from subjectivity and memory effects. In this paper, a new multi- sensing wearable device has been developed and applied in self-designed psychological tests. Speech under different emotions as well as behavior signals are captured and analyzed. The mental state of the participants is objectively assessed through a group of psychological questionnaires. In particular, we propose an attention-based block deep learning architecture within the device for multi-feature classification and fusion analysis. This enables the deep learning architecture to autonomously train to obtain the optimum fusion weights of different domain features. The proposed attention-based architecture has led to improving performance compared with direct connecting fusion method. Experimental studies have been carried out in order to verify the effectiveness and robustness of the proposed architecture. The obtained results have shown that the wearable multi-sensing devices equipped with the attention-based block deep learning architecture can effectively classify mental state with better performance

Aqua­bis(2-chloro­acetato-κO)(1,10-phenanthroline-κ2 N,N′)copper(II)

In the title complex, [Cu(C2H2ClO2)2(C12H8N2)(H2O)], the CuII ion is five-coordinated by two N atoms [Cu—N = 2.005 (2) and 2.029 (2) Å] from the 1,10-phenanthroline ligand, two O atoms [Cu—O = 1.943 (2)–1.966 (2) Å] from two 2-chloro­acetate ligands and one water mol­ecule [Cu—O = 2.253 (2) Å] in a distorted square-pyramidal geometry. The crystal structure exhibits inter­molecular O—H⋯O hydrogen bonds, short Cl⋯Cl contacts [3.334 (1) Å] and π–π inter­actions [centroid–centroid distance 3.621 (11) Å]

LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models

Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. However, LLMs trained with SFT sometimes make simple mistakes and result in hallucinations on reasoning tasks such as question-answering. Without external feedback, it is difficult for SFT to learn a good mapping between the question and the desired answer, especially with a small dataset. This paper introduces an alternative to SFT called Natural Language Feedback for Finetuning LLMs (LaFFi). LaFFi has LLMs directly predict the feedback they will receive from an annotator. We find that requiring such reflection can significantly improve the accuracy in in-domain question-answering tasks, providing a promising direction for the application of natural language feedback in the realm of SFT LLMs. Additional ablation studies show that the portion of human-annotated data in the annotated datasets affects the fine-tuning performance.Comment: Paper accepted in Human-Centric Representation Learning workshop at AAAI 2024 (https://hcrl-workshop.github.io/2024/

A Lightweight Spatial and Temporal Multi-Feature Fusion Network for Defect Detection

This article proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets. The demo code of the proposed method can be linked soon: http://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.ht

Conditionally Immortalized Mouse Embryonic Fibroblasts Retain Proliferative Activity without Compromising Multipotent Differentiation Potential

Mesenchymal stem cells (MSCs) are multipotent cells which reside in many tissues and can give rise to multiple lineages including bone, cartilage and adipose. Although MSCs have attracted significant attention for basic and translational research, primary MSCs have limited life span in culture which hampers MSCs' broader applications. Here, we investigate if mouse mesenchymal progenitors can be conditionally immortalized with SV40 large T antigen and maintain long-term cell proliferation without compromising their multipotency. Using the system which expresses SV40 large T antigen flanked with Cre/loxP sites, we demonstrate that mouse embryonic fibroblasts (MEFs) can be efficiently immortalized by SV40 large T antigen. The conditionally immortalized MEFs (iMEFs) exhibit an enhanced proliferative activity and maintain long-term cell proliferation, which can be reversed by Cre recombinase. The iMEFs express most MSC markers and retain multipotency as they can differentiate into osteogenic, chondrogenic and adipogenic lineages under appropriate differentiation conditions in vitro and in vivo. The removal of SV40 large T reduces the differentiation potential of iMEFs possibly due to the decreased progenitor expansion. Furthermore, the iMEFs are apparently not tumorigenic when they are subcutaneously injected into athymic nude mice. Thus, the conditionally immortalized iMEFs not only maintain long-term cell proliferation but also retain the ability to differentiate into multiple lineages. Our results suggest that the reversible immortalization strategy using SV40 large T antigen may be an efficient and safe approach to establishing long-term cell culture of primary mesenchymal progenitors for basic and translational research, as well as for potential clinical applications