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"Yuan, Liang"
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التخفيف من حدة الفقر في الصين المعاصرة
استنادا إلى نظرة عامة على أوضاع الفقر، يقدم هذا الكتاب مسار التخفيف من حدة الفقر والتنمية في الصين، ويشرح نموذج التنمية والتخفي من حدة الفقر بخصائص صينية والتمسك بمباديء (سيطرة الحكومة ومشاركة المجتمع والاعتماد على الذات والتنمية الموجهة والتنمية الشاملة) كما يقدم الكتاب تلخيصا شاملا لإنجازات الصين العظيمة وخبراتها الهامة وإسهاماتها الرئيسية في قضية التخفيف من حدة الفقر في العالم، ويعرض بإيجاز نظرات وممارسات التخفيف المستهدف من الفقر في العصر الجديد من أجل توفير مراجع لكسب المعركة ضد الفقر في الصين وقضية التخفيف من حدة الفقر في العالم.
Book
Controllable photomechanical bending of metal-organic rotaxane crystals facilitated by regioselective confined-space photodimerization
Molecular machines based on mechanically-interlocked molecules (MIMs) such as (pseudo) rotaxanes or catenates are known for their molecular-level dynamics, but promoting macro-mechanical response of these molecular machines or related materials is still challenging. Herein, by employing macrocyclic cucurbit[8]uril (CB[8])-based pseudorotaxane with a pair of styrene-derived photoactive guest molecules as linking structs of uranyl node, we describe a metal-organic rotaxane compound, U-CB[8]-MPyVB, that is capable of delivering controllable macroscopic mechanical responses. Under light irradiation, the ladder-shape structural unit of metal-organic rotaxane chain in U-CB[8]-MPyVB undergoes a regioselective solid-state [2 + 2] photodimerization, and facilitates a photo-triggered single-crystal-to-single-crystal (SCSC) transformation, which even induces macroscopic photomechanical bending of individual rod-like bulk crystals. The fabrication of rotaxane-based crystalline materials with both photoresponsive microscopic and macroscopic dynamic behaviors in solid state can be promising photoactuator devices, and will have implications in emerging fields such as optomechanical microdevices and smart microrobotics.
The preparation of materials that display macro-mechanical responses to external stimuli is challenging. Here, the authors synthesize metal-organic rotaxane frameworks that contain photoactive axles as linkers; light irradiation triggers photodimerization of the ligands, which leads to macroscopic photomechanical bending of individual bulk crystals.
Journal Article
Node Classification of Imbalanced Data Using Ensemble Graph Neural Networks
In real-world scenarios, many datasets suffer from class imbalance. For example, on online review platforms, the proportion of fake and genuine comments is often highly skewed. Although existing graph neural network (GNN) models have achieved notable progress in classification tasks, their performance tends to rely on relatively balanced data distributions. To tackle this challenge, we propose an ensemble graph neural network framework designed for imbalanced node classification. Specifically, we employ spectral-based graph convolutional neural networks as base classifiers and train multiple models in parallel. We then adopt a bagging ensemble strategy to integrate the predictions of these classifiers and determine the final classification results through majority voting. Furthermore, we extend this approach to fake review detection tasks. Extensive experiments conducted on imbalanced node classification datasets (Cora and BlogCatalog), as well as fake review detection (YelpChi), demonstrate that our method consistently outperforms state-of-the-art baselines, achieving significant gains in accuracy, AUC, and Macro-F1. Notably, on the Cora dataset, our model improves accuracy and Macro-F1 by 3.4% and 2.3%, respectively, while on the BlogCatalog dataset, it achieves improvements of 2.5%, 1.8%, and 0.5% in accuracy, AUC, and Macro-F1, respectively.
Journal Article
DDI-SSL: Drug–Drug Interaction Prediction Based on Substructure Signature Learning
Drug–drug interactions (DDIs) are entities composed of different chemical substructures (functional groups). In existing methods that predict drug–drug interactions based on the usage of substructures, each node is perceived as the epicenter of a sub-pattern, and adjacent nodes eventually become centers of similar substructures, resulting in redundancy. Furthermore, the significant differences in structure and properties among compounds can lead to unrelated pairings, making it difficult to integrate information. This heterogeneity negatively affects the prediction results. In response to these challenges, we propose a drug–drug interaction prediction method based on substructure signature learning (DDI-SSL). This method extracts useful information from local subgraphs surrounding drugs and effectively utilizes substructures to assist in predicting drug side effects. Additionally, a deep clustering algorithm is used to aggregate similar substructures, allowing any individual subgraph to be reconstructed using this set of global signatures. Furthermore, we developed a layer-independent collaborative attention mechanism to model the mutual influence between drugs, generating signal strength scores for each class of drugs to mitigate noise caused by heterogeneity. Finally, we evaluated DDI-SSL on a comprehensive dataset and demonstrated improved performance in DDI prediction compared to state-of-the-art methods.
Journal Article
Ultra-strong long-chain polyamide elastomers with programmable supramolecular interactions and oriented crystalline microstructures
Polyamides are one of the most important polymers. Long-chain aliphatic polyamides could bridge the gap between traditional polyamides and polyethylenes. Here we report an approach to preparing sustainable ultra-strong elastomers from biomass-derived long-chain polyamides by thiol-ene addition copolymerization with diamide diene monomers. The pendant polar hydroxyl and non-polar butyrate groups between amides allow controlled programming of supramolecular hydrogen bonding and facile tuning of crystallization of polymer chains. The presence of thioether groups on the main chain can further induce metal–ligand coordination (cuprous-thioether). Unidirectional step-cycle tensile deformation has been applied to these polyamides and significantly enhances tensile strength to over 210 MPa while maintaining elasticity. Uniaxial deformation leads to a rearrangement and alignment of crystalline microstructures, which is responsible for the mechanical enhancement. These chromophore-free polyamides are observed with strong luminescence ascribed to the effect of aggregation-induced emission (AIE), originating from the formation of amide clusters with restricted molecular motions.
Long-chain polyamides could bridge the gap between traditional polyamides and polyethylenes. Here the authors show the preparation of diamide diene monomers derived from natural resources coupled by thiol-ene addition copolymerization to form long-chain amide-containing polymers for the synthesis of ultra-strong elastomers.
Journal Article
Further elaborations on topology optimization via sequential integer programming and Canonical relaxation algorithm and 128-line MATLAB code
This paper provides further elaborations on discrete variable topology optimization via sequential integer programming and Canonical relaxation algorithm. Firstly, discrete variable topology optimization problem for minimum compliance subject to a material volume constraint is formulated and approximated by a sequence of discrete variable sub-programming with the discrete variable sensitivity. The differences between continuous variable sensitivity and discrete variable sensitivity are discussed. Secondly, the Canonical relaxation algorithm designed to solve this sub-programming is presented with a discussion on the move limit strategy. Based on the discussion above, a compact 128-line MATLAB code to implement the new method is included in Appendix
1
. As shown by numerical experiments, the 128-line code can maintain black-white solutions during the optimization process. The code can be treated as the foundation for other problems with multiple constraints.
Journal Article
Impulse oscillometry for detection of small airway dysfunction in subjects with chronic respiratory symptoms and preserved pulmonary function
Background
Subjects with chronic respiratory symptoms and preserved pulmonary function (PPF) may have small airway dysfunction (SAD). As the most common means to detect SAD, spirometry needs good cooperation and its reliability is controversial. Impulse oscillometry (IOS) may complete the deficiency of spirometry and have higher sensitivity. We aimed to explore the diagnostic value of IOS to detect SAD in symptomatic subjects with PPF.
Methods
The evaluation of symptoms, spirometry and IOS results in 209 subjects with chronic respiratory symptoms and PPF were assessed. ROC curves of IOS to detect SAD were analyzed.
Results
209 subjects with chronic respiratory symptoms and PPF were included. Subjects who reported sputum had higher R5–R20 and Fres than those who didn’t. Subjects with dyspnea had higher R5, R5–R20 and AX than those without. CAT and mMRC scores correlated better with IOS parameters than with spirometry. R5, R5–R20, AX and Fres in subjects with SAD (n = 42) significantly increased compared to those without. Cutoff values for IOS parameters to detect SAD were 0.30 kPa/L s for R5, 0.015 kPa/L s for R5–R20, 0.30 kPa/L for AX and 11.23 Hz for Fres. Fres has the largest AUC (0.665, P = 0.001) among these parameters. Compared with spirometry, prevalence of SAD was higher when measured with IOS. R5 could detect the most SAD subjects with a prevalence of 60.77% and a sensitivity of 81% (AUC = 0.659, P = 0.002).
Conclusion
IOS is more sensitive to detect SAD than spirometry in subjects with chronic respiratory symptoms and PPF, and it correlates better with symptoms. IOS could be an additional method for SAD detection in the early stage of diseases.
Journal Article
Quantifying particle-to-particle heterogeneity in aerosol hygroscopicity
The particle-to-particle heterogeneity in aerosol hygroscopicity is crucial for understanding aerosol climatic and environmental effects. The hygroscopic parameter κ, widely applied to describe the hygroscopicity for aerosols both in models and observations, is a probability distribution highly related to aerosol heterogeneity due to the complex sources and aging processes. However, the heterogeneity in aerosol hygroscopicity is not adequately represented in observations and model simulations, leading to challenges in accurately estimating aerosol climatic and environmental effects. Here, we propose an algorithm for quantifying the particle-to-particle heterogeneity in aerosol hygroscopicity, based on information-theoretic entropy measures, by using the data that come only from the in situ measurement of the hygroscopicity tandem differential mobility analyzer (H-TDMA). Aerosols in this algorithm are assumed to be simple binary systems consisting of the less hygroscopic and more hygroscopic components, which are commonly used in H-TDMA measurement. Three indices, including the average per-particle species diversity Dα, the bulk population species diversity Dγ, and their affine ratio χ are calculated from the probability distribution of κ to describe aerosol heterogeneity. This algorithm can efficiently characterize the evolution of aerosol heterogeneity with time in the real atmosphere. Our results show that the heterogeneity varies much with aerosol particle size, and large discrepancies exist in the width and peak value of particle number size distribution (PNSD) with varied heterogeneity after hygroscopic growth, especially for conditions with high relative humidity. This reveals a vital role of the heterogeneity in ambient PNSD and significant uncertainties in calculating the climate-relevant properties if the population-averaged hygroscopicity is applied by neglecting its heterogeneity. This work points the way toward a better understanding of the role of hygroscopicity in evaluating aerosol climatic and environmental impacts.
Journal Article
A systematic review and meta-analysis of long term physical and mental sequelae of COVID-19 pandemic: call for research priority and action
The long-term physical and mental sequelae of COVID-19 are a growing public health concern, yet there is considerable uncertainty about their prevalence, persistence and predictors. We conducted a comprehensive, up-to-date meta-analysis of survivors’ health consequences and sequelae for COVID-19. PubMed, Embase and the Cochrane Library were searched through Sep 30th, 2021. Observational studies that reported the prevalence of sequelae of COVID-19 were included. Two reviewers independently undertook the data extraction and quality assessment. Of the 36,625 records identified, a total of 151 studies were included involving 1,285,407 participants from thirty-two countries. At least one sequelae symptom occurred in 50.1% (95% CI 45.4-54.8) of COVID-19 survivors for up to 12 months after infection. The most common investigation findings included abnormalities on lung CT (56.9%, 95% CI 46.2–67.3) and abnormal pulmonary function tests (45.6%, 95% CI 36.3–55.0), followed by generalized symptoms, such as fatigue (28.7%, 95% CI 21.0–37.0), psychiatric symptoms (19.7%, 95% CI 16.1–23.6) mainly depression (18.3%, 95% CI 13.3–23.8) and PTSD (17.9%, 95% CI 11.6–25.3), and neurological symptoms (18.7%, 95% CI 16.2–21.4), such as cognitive deficits (19.7%, 95% CI 8.8–33.4) and memory impairment (17.5%, 95% CI 8.1–29.6). Subgroup analysis showed that participants with a higher risk of long-term sequelae were older, mostly male, living in a high-income country, with more severe status at acute infection. Individuals with severe infection suffered more from PTSD, sleep disturbance, cognitive deficits, concentration impairment, and gustatory dysfunction. Survivors with mild infection had high burden of anxiety and memory impairment after recovery. Our findings suggest that after recovery from acute COVID-19, half of survivors still have a high burden of either physical or mental sequelae up to at least 12 months. It is important to provide urgent and appropriate prevention and intervention management to preclude persistent or emerging long-term sequelae and to promote the physical and psychiatric wellbeing of COVID-19 survivors.
Journal Article