Physical detection of misbehavior in relay systems with unreliable channel state information

We study the detection 1 of misbehavior in a Gaussian relay system, where the source transmits information to the destination with the assistance of an amplify-and-forward relay node subject to unreliable channel state information (CSI). The relay node may be potentially malicious and corrupt the network by forwarding garbled information. In this situation, misleading feedback may take place, since reliable CSI is unavailable at the source and/or the destination. By classifying the action of the relay as detectable or undetectable, we propose a novel approach that is capable of coping with any malicious attack detected and continuing to work effectively in the presence of unreliable CSI. We demonstrate that the detectable class of attacks can be successfully detected with a high probability. Meanwhile, the undetectable class of attacks does not affect the performance improvements that are achievable by cooperative diversity, even though such an attack may fool the proposed detection approach. We also extend the method to deal with the case in which there is no direct link between the source and the destination. The effectiveness of the proposed approach has been validated by numerical results

An open unified deep graph learning framework for discovering drug leads

Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and molecule optimization. Current efforts are disparate, involving the establishment of models for each stage, followed by multi-stage multi-model integration. However, this is non-ideal, as clumsy integration of incompatible models increases research overheads, and may even reduce success rates in drug discovery. Facilitating compatibilities requires establishing inherent model consistencies across lead discovery stages. Towards that effect, we propose an open deep graph learning (DGL) based pipeline: generative adversarial feature subspace enhancement (GAFSE), which first unifies the modeling of these stages into one learning framework. GAFSE also offers standardized modular design and streamlined interfaces for future expansions and community support. GAFSE combines adversarial/generative learning, graph attention network, graph reconstruction network, and optimizes the classification/regression loss, adversarial/generative loss, and reconstruction loss simultaneously. Convergence analysis theoretically guarantees model generalization performance. Exhaustive benchmarking demonstrates that the GAFSE pipeline achieves excellent performance across almost all lead discovery stages, while also providing valuable model interpretability. Hence, we believe this tool will enhance the efficiency and productivity of drug discovery researchers.Comment: This article is used as the preliminary studies for the application of Lee Kuan Yew Postdoctoral Fellowship (LKYPDF) 2023 in Singapore. All rights reserve

Antenatal depression is associated with perceived stress, family relations, educational and professional status among women in South of China: a multicenter cross-sectional survey

BackgroundAntenatal depression is a commonly seen mental health concern for women. This study introduced a multicenter cross-sectional survey with a large sample to provide new insights into pregnant women’s depression, its socio-demographic and obstetric characteristics correlates, and its perceived stress among Chinese pregnant women.MethodsThis study conducted an observational survey according to the STROBE checklist. The multicenter cross-sectional survey was performed from August 2020 to January 2021 by distributing paper questionnaires among pregnant women from five tertiary hospitals in South China. The questionnaire included socio-demographic and obstetrics information, the Edinburgh Postnatal Depression Scale, and the 10-item Perceived Stress Scale. For the analyses, the Chi-square test and Multivariate logistic regression were utilized.ResultsAmong 2014 pregnant women in their second/third trimester, the prevalence of antenatal depression was 36.3%. 34.4% of pregnant women reported AD in their second trimester of pregnancy, and 36.9% suffered from AD in third trimester of pregnancy. A multivariate logistic regression model indicated that unemployed women, lower levels of education, poor marital relationships, poor parents-in-law relationships, concerns about contracting COVID-19, and higher perceived stress could aggravate antenatal depression among participants (p<0.05).ConclusionThere is a high proportion of antenatal depression among pregnant women in South China, so integrating depression screening into antenatal care services is worthwhile. Maternal and child health care providers need to evaluate pregnancy-related risk factors (perceived stress), socio-demographic factors (educational and professional status), and interpersonal risk factors (marital relations and relationship with Parents-in-law). In future research, the study also emphasized the importance of providing action and practical support to reduce the experience of antenatal depression among disadvantaged sub-groups of pregnant women

AMS measurement of 53Mn and its initial application at CIAE

The determination of cosmogenic 53Mn in terrestrial archives has important applications, such as burial ages, exposure age and erosion rates. Accelerator mass spectrometry (AMS) is the most sensitive technique to detect minute amounts of 53Mn. 53Mn measurements were developed at the China Institute of Atomic nergy (CIAE) using the DE-Q3D equipped AMS system. This approach was recently optimized with the goal to reach the sensitivity required for AMS measurements of 53Mn in deep-sea ferromanganese crust (DSFC) samples. Based on these improvements of sample preparation, current beam transmission and so on, 53Mn in two samples of DSFC was measured by AMS. The ratios of 53Mn/Mn corresponding to an age of 3.77 ± 0.42 and 13.73 ± 2.74 Ma by 129I dating method are (5.01 ± 2.15) 10 13 and (1.90 ± 0.96) 10 13. The ratios are close to the experimental reference values, deduced from the previous research. The experimental progress, performances and results are presented in this contribution.This work was mainly supported by the National Natural Science Foundations of China (NSFC), under Grant No. 11075221, and a partly supported by the National Natural Science Foundation of China under Grant Nos. 10705054, 41073044 and 11265005

Peer Effects and Voluntary Green Building Certification

Empirical evidence is provided to show that peer effects have statistically significant and positive impacts on the diffusion of green building certificates. Application and approval records of green certificates by commercial buildings in NY and AZ are used. The challenge of self-selection is addressed by the usage of fixed effects and the challenge of reflection is addressed by the time lag delay between a building’s application and its approval. Empirical results show that an additional approved LEED certificate within a zip code will increase the probability of a commercial building in the same zip code to apply for a LEED certificate by 3–4 percentage points; an additional approved Energy Star certificate within a zip code will increase the probability of a commercial building in the same zip code to apply for an Energy Star certificate by 1–2 percentage points

Solid-State Dye-Sensitized Solar Cells with Conjugated Polymers as Hole-Transporting Materials

DSCs are a promising alternative to conventional silicon-based solar cells owing to their low cost and relatively high efficiency. However, the utilization of a liquid electrolyte containing the iodine/iodide redox couple in traditional DSCs brings practical problems for their long-term application, which leads to rapid development of SDSCs based on inorganic p-type semiconductors or organic HTMs. In this review, we summarize the current research on SDSCs using conjugated polymer as HTM. Special attention is paid to understand the effects of polymer HTM structure and deposition process on SDSC performance. The limiting factors for SDSC energy conversion efficiency are discussed and strategies to improve device performance are proposed