Strong anisotropy of Sc2X2Se2 (X = Cl, Br, I) monolayers contributes to high thermoelectric performance.

As a novel type of anisotropic two-dimensional material, extensive attention has been paid to the thermoelectric (TE) properties of FeOCl-type monolayers, such as Al2X2Se2 (X = Cl, Br, I), Sc2I2S2, and Ir2Cl2O2. Recently, theoretical works based on first-principles calculations have been powerful driving forces in field of TE research. In this work, we perform an investigation into the TE properties of Sc2X2Se2 (X = Cl, Br, I) monolayers based on density functional theory (DFT). A study on the stability, including AIMD simulation and phonon calculation, shows the stable structure of Sc2Cl2Se2, Sc2Br2Se2, and Sc2I2Se2 monolayers. Additionally, the electronic and thermal transport properties of Sc2X2Se2 monolayers are anisotropic along the x and y directions. Moreover, the combination of excellent Seebeck coefficient and ultralow lattice thermal conductivity contributes to outstanding ZT values, and the ZT values follow the order: Sc2I2Se2 > Sc2Br2Se2 > Sc2Cl2Se2. At 300 K, we obtained maximum ZT of 0.34, 0.77, and 1.97 for Sc2Cl2Se2, Sc2Br2Se2, and Sc2I2Se2, respectively, by n-type doping in the x direction. These results demonstrate that monolayer Sc2X2Se2 (X = Cl, Br, I) materials are promising thermoelectric materials, Sc2I2Se2 has more desirable properties along the x direction, and n-type doping can significantly enhance the ZT values. Our work lays a foundation for exploring the TE transport properties of FeOCl-type monolayers.

FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation.

As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning. This problem causes clients to forget about global knowledge during their local training phase and then tends to slow convergence and degrades accuracy. In this work, we propose a method named FedRAD, which is based on relational knowledge distillation that further enhances the mining of high-quality global knowledge by local models from a higher-dimensional perspective during their local training phase to better retain global knowledge and avoid forgetting. At the same time, we devise an entropy-wise adaptive weights module (EWAW) to better regulate the proportion of loss in single-sample knowledge distillation versus relational knowledge distillation so that students can weigh losses based on predicted entropy and learn global knowledge more effectively. A series of experiments on CIFAR10 and CIFAR100 show that FedRAD has better performance in terms of convergence speed and classification accuracy compared to other advanced FL methods.

Environment and chronic disease in rural areas of Heilongjiang, China (ECDRAHC).

PURPOSE: Environmental factors such as long-term exposure to cold can increase the risk of chronic diseases. However, few studies have focused on the impact of environmental factors and lifestyle changes on chronic diseases. To fully explore the association between exposure to environmental factors and the prevalent risk of various chronic diseases, we conducted a large cohort study (Environment and Chronic Disease in Rural Areas of Heilongjiang, China (ECDRAHC)). The ECDRAHC collected detailed questionnaire data covering 10 sections, physical measurements and blood and urine samples. In this study, we describe the design and implementation of the cohort study and present the findings for the first 10 000 participants. PARTICIPANTS: The ECDRAHC study was carried out in rural areas where the annual average temperature is 2.9°C, and aimed to recruit 40 000 participants who are long-term residents aged 35-74 years. The participants will be followed up every 5 years. Currently, ECDRAHC has reached 26.7% (n=10 694) of the targeted population. FINDINGS TO DATE: A total of 10 694 adults aged 35-74 years were recruited, including 61.7% women. The prevalence of current smokers was 46.8% in men and 35.4% in women. The mean blood pressure was 140.2/89.9 mm Hg and 135.7/85.0 mm Hg in men and women, respectively. The mean body mass index was 24.74 kg/m2 in men and 24.65 kg/m2 in women, with >7.3% being obese (>30 kg/m2). The main non-communicable diseases found in phase 1 were hypertension, diabetes, hypertriglyceridaemia and metabolic syndrome, with a higher prevalence of 51.0%, 21.6%, 46.8% and 42.6%, respectively. FUTURE PLANS: We plan to complete the follow-up for the first phase of the ECDRAHC in 2024. The second and third phase of the cohort will be carried out steadily, as planned. This cohort will be used to investigate the relationship between environmental factors, lifestyle, and genetic and common chronic diseases.

Switchable In Situ SO2 Capture and CF3 Migration of Enol Triflates with Peroxyl Compounds under Iron Catalysis.

Switchable in situ SO2 capture and CF3 migration of enol triflates with peroxyl compounds under iron catalysis are presented. By regulating the structure of peroxides, a variety of keto-functionalized dialkyl sulfones and α-trifluoromethyl ketones were selectively synthesized in good yields under mild conditions.

Evaluation of Risk Factors for Chronic Obstructive Pulmonary Disease in the Middle-Aged and Elderly Rural Population of Northeast China Using Logistic Regression and Principal Component Analysis.

Purpose: To investigate the environmental, immune, and inflammatory factors associated with chronic obstructive pulmonary disease (COPD) in middle-aged and older Chinese individuals. Patients and Methods: A community-based case-control study was conducted among 471 patients with COPD and 485 controls. The information on COPD of the participants was collected through face-to-face interviews, and serum samples were measured at the laboratory. The main risk factors for COPD were analyzed using principal component analysis (PCA) and logistic regression. Results: Nine hundred and fifty-six respondents were included in the analysis. The results of the PCA-logistic regression analysis showed significant differences in the environmental factors, medical history, and serum C-reactive protein (CRP) levels between patients and controls. COPD was markedly more usual in those with smoking index >200 (OR, 1.42; 95% CI, 1.28-1.57); exposure to outdoor straw burning (OR, 1.64; 95% CI, 1.47-1.83); use of coal, wood, and straw indoors (OR, 2.31; 95% CI, 1.92-2.78); history of respiratory disease and coronary heart disease (OR, 3.58; 95% CI, 3.12-4.10), congestive heart failure (OR, 1.23; 95% CI, 1.09-1.38), and cerebrovascular disease (OR, 1.15; 95% CI,1.02-1.31); and higher serum level of CRP (OR, 1.20; 95% CI, 1.11-1.30). Compared to the logistic regression analysis, PCA logistic regression analysis identified more important risk factors for COPD. Conclusion: PCA-logistic regression analysis was first utilized to explore the influencing factors among rural residents in Northeast China Environmental aged 40 years and above, it was found that environmental factors, medical history, and serum CRP levels mainly affected the prevalence of COPD.