Carbon Trading in BRICS Countries: Challenges and Recommendations

As one of the world’s largest emerging economies, BRICS countries are playing an increasingly important role in addressing the global issue of climate change. To achieve their emissions reduction targets, these nations are actively promoting the construction of carbon trading markets. However, they face multiple challenges and obstacles in this endeavor, including issues related to market norms, financial support, technical capacity, social participation, and development needs. This research investigates the problems and challenges faced by BRICS countries in terms of building carbon trading markets through literature reviews and case studies. To address these challenges, this research strengthening international cooperation and technical support, improving market norms and provide following recommendations: conducting regulatory measures, enhancing social participation and communication, and balancing the relationship between economic development and environmental protection requirements. Furthermore, it is crucial for these nations to continue to strengthen international cooperation and collaboration, working together to promote the construction of carbon trading markets, achieving their emissions reduction targets, and ensuring long-term sustainability and economic development

Protective effects of blocking PD-1 pathway on retinal ganglion cells in a mouse model of chronic ocular hypertension

PurposeIn this study, we aimed to investigate whether Programmed cell death 1 ligand 1/programmed cell death 1 ligand 2 (PD-L1/PD-L2) double knockout (dKO) has a protective effect on RGCs in a mouse model of chronic ocular hypertension (COHT).MethodsWe used superparamagnetic iron oxide to induce COHT in mice. Apoptosis of retinal ganglion cells (RGCs) and activation of microglia were evaluated using western blotting (WB) and immunofluorescence staining of the mouse retina. In addition, we also conducted transcriptome sequencing and further gene expression analyses using the gene ontology (GO) and Kyoto Encyclopedia of Genes (KEGG) database.ResultsIn the mouse model of COHT, PD-L1/PD-L2 prevented the apoptosis of RGCs to some extent. Blocking the programmed cell death 1 (PD-1) pathway also increased the number of anti-inflammatory M2-activated microglia and enhanced the phosphorylation of its related pathway signal transducer and activator of transcription (STAT)6. Sequencing results showed that this protective effect may have been achieved by regulating the NF−B, tumour necrosis factor (TNF), PI3K/Akt and toll-like receptor signaling pathway etc.ConclusionBlocking the PD-1 pathway has a protective effect on RGCs in the mouse model of COHT induced by superparamagnetic iron oxide

Acupuncture Enhances Effective Connectivity between Cerebellum and Primary Sensorimotor Cortex in Patients with Stable Recovery Stroke

Recent neuroimaging studies have demonstrated that stimulation of acupuncture at motor-implicated acupoints modulates activities of brain areas relevant to the processing of motor functions. This study aims to investigate acupuncture-induced changes in effective connectivity among motor areas in hemiparetic stroke patients by using the multivariate Granger causal analysis. A total of 9 stable recovery stroke patients and 8 healthy controls were recruited and underwent three runs of fMRI scan: passive finger movements and resting state before and after manual acupuncture stimuli. Stroke patients showed significantly attenuated effective connectivity between cortical and subcortical areas during passive motor task, which indicates inefficient information transmissions between cortical and subcortical motor-related regions. Acupuncture at motor-implicated acupoints showed specific modulations of motor-related network in stroke patients relative to healthy control subjects. This specific modulation enhanced bidirectionally effective connectivity between the cerebellum and primary sensorimotor cortex in stroke patients, which may compensate for the attenuated effective connectivity between cortical and subcortical areas during passive motor task and, consequently, contribute to improvement of movement coordination and motor learning in subacute stroke patients. Our results suggested that further efficacy studies of acupuncture in motor recovery can focus on the improvement of movement coordination and motor learning during motor rehabilitation

Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations

This paper reexamines the research on out-of-distribution (OOD) robustness in the field of NLP. We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. To address these issues, we propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we conduct a series of experiments on pre-trained language models for analysis and evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the relationship between in-distribution (ID) and OOD performance. We identify three typical types that unveil the inner learning mechanism, which could potentially facilitate the forecasting of OOD robustness, correlating with the advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and find that, despite exhibiting some effectiveness in specific cases, they do not offer significant improvement compared to vanilla fine-tuning. Further, we evaluate 5 LLMs with various adaptation paradigms and find that when sufficient ID data is available, fine-tuning domain-specific models outperform LLMs on ID examples significantly. However, in the case of OOD instances, prioritizing LLMs with in-context learning yields better results. We identify that both fine-tuned small models and LLMs face challenges in effectively addressing downstream tasks. The code is public at \url{https://github.com/lifan-yuan/OOD_NLP}.Comment: Accepted to NeurIPS 2023 Dataset and Benchmark Track. Code is available at \url{https://github.com/lifan-yuan/OOD_NLP

Acupuncture Modulates the Functional Connectivity of the Default Mode Network in Stroke Patients

Abundant evidence from previous fMRI studies on acupuncture has revealed significant modulatory effects at widespread brain regions. However, few reports on the modulation to the default mode network (DMN) of stroke patients have been investigated in the field of acupuncture. To study the modulatory effects of acupuncture on the DMN of stroke patients, eight right hemispheric infarction and stable ischemic stroke patients and ten healthy subjects were recruited to undergo resting state fMRI scanning before and after acupuncture stimulation. Functional connectivity analysis was applied with the bilateral posterior cingulate cortices chosen as the seed regions. The main finding demonstrated that the interregional interactions between the ACC and PCC especially enhanced after acupuncture at GB34 in stroke patients, compared with healthy controls. The results indicated that the possible mechanisms of the modulatory effects of acupuncture on the DMN of stroke patients could be interpreted in terms of cognitive ability and motor function recovery

Distributed Face Recognition Based on Load Balancing and Dynamic Prediction

With the dramatic expansion of large-scale videos, traditional centralized face recognition methods cannot meet the demands of time efficiency and expansibility, and thus distributed face recognition models were proposed. However, the number of tasks at the agent side is always dynamic, and unbalanced allocation will lead to time delay and a sharp increase of CPU utilization. To this end, a new distributed face recognition framework based on load balancing and dynamic prediction is proposed in this paper. The framework consists of a server and multiple agents. When performing face recognition, the server is used to recognize faces, and other operations are performed by the agents. Since the changes of the total number of videos and the number of pedestrians affect the task amount, we perform load balancing with an improved genetic algorithm. To ensure the accuracy of task allocation, we use extreme learning machine to predict the change of tasks. The server then performs task allocation based on the predicted results sent by the agents. The experimental results show that the proposed method can effectively solve the problem of unbalanced task allocation at the agent side, and meanwhile alleviate time delay and the sharp increase of CPU utilization

A Scene Recognition and Semantic Analysis Approach to Unhealthy Sitting Posture Detection during Screen-Reading

Behavior analysis through posture recognition is an essential research in robotic systems. Sitting with unhealthy sitting posture for a long time seriously harms human health and may even lead to lumbar disease, cervical disease and myopia. Automatic vision-based detection of unhealthy sitting posture, as an example of posture detection in robotic systems, has become a hot research topic. However, the existing methods only focus on extracting features of human themselves and lack understanding relevancies among objects in the scene, and henceforth fail to recognize some types of unhealthy sitting postures in complicated environments. To alleviate these problems, a scene recognition and semantic analysis approach to unhealthy sitting posture detection in screen-reading is proposed in this paper. The key skeletal points of human body are detected and tracked with a Microsoft Kinect sensor. Meanwhile, a deep learning method, Faster R-CNN, is used in the scene recognition of our method to accurately detect objects and extract relevant features. Then our method performs semantic analysis through Gaussian-Mixture behavioral clustering for scene understanding. The relevant features in the scene and the skeletal features extracted from human are fused into the semantic features to discriminate various types of sitting postures. Experimental results demonstrated that our method accurately and effectively detected various types of unhealthy sitting postures in screen-reading and avoided error detection in complicated environments. Compared with the existing methods, our proposed method detected more types of unhealthy sitting postures including those that the existing methods could not detect. Our method can be potentially applied and integrated as a medical assistance in robotic systems of health care and treatment

Research on the influencing factors of agricultural product purchase willingness in social e-commerce situation

From the perspective of social e-commerce, this paper discusses the influencing factors and mechanism of purchase intention of agricultural products. Based on the stimulus–organism–response model, this paper constructs the relationship model of agricultural product characteristics, social trust, social stickiness, social support, social interaction, consumer perceived value and consumer purchase intention; the related hypotheses are found, and the hypothesis and the structural model are verified by analysing the data. The results show that the characteristics of agricultural products, social stickiness, social support, social interaction and consumer perceived value are the key factors that influence the purchase intention of agricultural products. Perceived value is the intermediary mechanism of social stickiness, social support, social interaction and purchase intention