Current Satisfaction With Government for Poverty Alleviation of the Wulingshan Region in China

From the perspective of the rural poverty regarding government’s poverty-alleviation, this paper builds the evaluation index system of government’s poverty alleviation satisfaction by Analytic Hierarchy Process (AHP) and then conducts a research into government’s poverty alleviation satisfaction in Wulingshan Region by Fuzzy Comprehensive Evaluation (FCE) method. The result shows that satisfaction rate of poor people to government is comparatively high between fairly and general satisfaction, while their satisfaction of education and health is higher than that of culture, economy and grassroots construction respectively. The paper analyses the consequence and puts forward policy implications on how to develop the Wulingshan Region

The Research on the Path to Eradicate Poverty for NGOs in a New Period

This paper starts with advantages of participating in poverty alleviation of the NGOs, indicate the advantages the NGOs possess are the supplements to the government-led poverty alleviation so as to illustrate that it is necessary to participate in poverty alleviation for the NGOs. On the basis of discussing the main actions that NGOs take, the paper examines the obstacle of the participation and analyses the reasons, lastly designs a path to promote the NGOs to participate in the pro-poor activities, aiming to popularize the pattern to lift the poverty-alleviation efficiency, promote the impoverished to shake off poverty and become better off, and achieve the goal of building a well-off society in an all-round way lastly

Understanding thermal induced escape mechanism of optically levitated sphere in vacuum

The escape phenomenon, mainly caused by thermal effects, is known as an obstacle to the further practical application of optical levitation system in vacuum. Irregular photophoresis induced by thermal effects can act as an amplifier of Brownian motion. Studies on this topic provide interpretation for particle escaping phenomenon during the pressure decreasing process, as well as valuable insights into the micro- and nanoscale thermal effects in optical trap in vacuum. In this paper, we derive and test a dynamic model for the motion of an optically levitated particle in a non-equilibrium state and demonstrate the escaping mechanism of heated particles. The result of theoretical investigations is consistent with experimental escape at 0.1mbar. This work reveals and provides a theoretical basis for the stable operation of laser levitated oscillator in high vacuum and pave the way for the practicability of ultra-sensitive sensing devices

The global landscape and research trend of phase separation in cancer: a bibliometric analysis and visualization

BackgroundCancer as a deathly disease with high prevalence has impelled researchers to investigate its causative mechanisms in the search for effective therapeutics. Recently, the concept of phase separation has been introduced to biological science and extended to cancer research, which helps reveal various pathogenic processes that have not been identified before. As a process of soluble biomolecules condensed into solid-like and membraneless structures, phase separation is associated with multiple oncogenic processes. However, there are no bibliometric characteristics for these results. To provide future trends and identify new frontiers in this field, a bibliometric analysis was conducted in this study.MethodsThe Web of Science Core Collection (WoSCC) was used to search for literature on phase separation in cancer from 1/1/2009 to 31/12/2022. After screening the literature, statistical analysis and visualization were carried out by the VOSviewer software (version 1.6.18) and Citespace software (Version 6.1.R6).ResultsA total of 264 publications, covering 413 organizations and 32 countries, were published in 137 journals, with an increasing trend in publication and citation numbers per year. The USA and China were the two countries with the largest number of publications, and the University of Chinese Academy of Sciences was the most active institution based on the number of articles and cooperations. Molecular Cell was the most frequent publisher with high citations and H-index. The most productive authors were Fox AH, De Oliveira GAP, and Tompa P. Overlay, whilst few authors had a strong collaboration with each other. The combined analysis of concurrent and burst keywords revealed that the future research hotspots of phase separation in cancer were related to tumor microenvironments, immunotherapy, prognosis, p53, and cell death.ConclusionPhase separation-related cancer research remained in the hot streak period and exhibited a promising outlook. Although inter-agency collaboration existed, cooperation among research groups was rare, and no author dominated this field at the current stage. Investigating the interfaced effects between phase separation and tumor microenvironments on carcinoma behaviors, and constructing relevant prognoses and therapeutics such as immune infiltration-based prognosis and immunotherapy might be the next research trend in the study of phase separation and cancer

Analysis on the Correlation Degree between the Driver’s Reaction Ability and Physiological Parameters

In this paper, the correlation degree between driver’s reaction time and the physiological signal is analyzed. For this purpose, a large number of road experiments are performed using the biopac and the reaction time test systems to collect data. First, the electroencephalograph (EEG) signal is processed by using the fast Fourier and the inverse Fourier transforms. Then, the power spectrum densities (PSD) of α, β, δ, and EEG wave are calculated by Welch procedure. The average power of the power spectrum of α, β, and θ is calculated by the biopac software and two ratio formulas, (α+θ)/β and α/β, are selected to be the impact factors. After that the heart rate and the standard deviation of RR interval are calculated from the electrocardiograph (ECG) signal. Lastly, the correlation degree between the eight impact factors and the reaction time are analyzed based on the grey correlation analysis. The results demonstrate that α/β has the greatest correlation to the reaction time except EEG-PSD. Furthermore, two mathematical models for the reaction time-driving time and the α/β-driving time are developed based on the Gaussian function. These mathematical models are then finally used to establish the functional relation of α/β-the reaction time

Continuous Spiking Graph Neural Networks

Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ordinary differential equations (ODE). However, the implementation of CGNNs requires significant computational power, making them challenging to deploy on battery-powered devices. Inspired by recent spiking neural networks (SNNs), which emulate a biological inference process and provide an energy-efficient neural architecture, we incorporate the SNNs with CGNNs in a unified framework, named Continuous Spiking Graph Neural Networks (COS-GNN). We employ SNNs for graph node representation at each time step, which are further integrated into the ODE process along with time. To enhance information preservation and mitigate information loss in SNNs, we introduce the high-order structure of COS-GNN, which utilizes the second-order ODE for spiking representation and continuous propagation. Moreover, we provide the theoretical proof that COS-GNN effectively mitigates the issues of exploding and vanishing gradients, enabling us to capture long-range dependencies between nodes. Experimental results on graph-based learning tasks demonstrate the effectiveness of the proposed COS-GNN over competitive baselines

Development and validation of diagnostic and activity-assessing models for relapsing polychondritis based on laboratory parameters

BackgroundRelapsing polychondritis (RP) as a rare autoimmune disease is characterized by recurrent inflammation of the organs containing cartilage. Currently, no biomarkers have been integrated into clinical practice. This study aimed to construct and evaluate models based on laboratory parameters to aid in RP diagnosis, assess activity assessment, and explore associations with the pathological process.MethodsRP patients and healthy controls (HCs) were recruited at the Peking Union Medical College Hospital from July 2017 to July 2023. Clinical data including Relapsing Polychondritis Disease Activity Index (RPDAI) score and laboratory tests were collected. Differences in laboratory data between RP patients and HCs and active and inactive patients were analyzed.ResultsThe discovery cohort (cohort 1) consisted of 78 RP patients and 94 HCs. A model based on monocyte counts and neutrophil to lymphocyte ratio (NLR) could effectively distinguish RP patients from HCs with an AUC of 0.845. Active RP patients exhibited increased erythrocyte sedimentation rate, complement 3, platelet to lymphocyte ratio (PLR), NLR, and C-reactive protein to albumin ratio (CAR) compared with stable patients, which were also positively correlated with RPDAI. Notably, CAR emerged as an independent risk factor of disease activity (OR = 4.422) and could identify active patients with an AUC of 0.758. To confirm the reliability and stability of the aforementioned models, a replication cohort (cohort 2) was enrolled, including 79 RP patients and 94 HCs. The monocyte-combined NLR and CAR showed a sensitivity of 0.886 and 0.577 and a specificity of 0.830 and 0.833 in RP diagnosis and activity prediction, respectively. Furthermore, lower natural killer cell levels in RP patients and higher B-cell levels in active patients may contribute to elucidating the pathological mechanisms of disease occurrence and exacerbation.ConclusionsThe utilization of laboratory parameters provides cost-effective and valuable markers that can assist in RP diagnosis, identify disease activity, and elucidate pathogenic mechanisms

Detect Depression from Social Networks with Sentiment Knowledge Sharing

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing attention, with a significant portion of individuals resorting to social networks as an outlet for expressing emotions. Using deep learning techniques to discern potential signs of depression from social network messages facilitates the early identification of mental health conditions. Current efforts in detecting depression through social networks typically rely solely on analyzing the textual content, overlooking other potential information. In this work, we conduct a thorough investigation that unveils a strong correlation between depression and negative emotional states. The integration of such associations as external knowledge can provide valuable insights for detecting depression. Accordingly, we propose a multi-task training framework, DeSK, which utilizes shared sentiment knowledge to enhance the efficacy of depression detection. Experiments conducted on both Chinese and English datasets demonstrate the cross-lingual effectiveness of DeSK