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Landslide hazards affect the security of human life and property. Mapping the spatial distribution of landslide hazard risk is critical for decision-makers to implement disaster prevention measures. This study aimed to predict and zone landslide hazard risk, using Guixi County in eastern Jiangxi, China, as an example. An integrated dataset composed of 21 geo-information layers, including lithology, rainfall, altitude, slope, distances to faults, roads and rivers, and thickness of the weathering crust, was used to achieve the aim. Non-digital layers were digitized and assigned weights based on their landslide propensity. Landslide locations and non-risk zones (flat areas) were both vectorized as polygons and randomly divided into two groups to create a training set (70%) and a validation set (30%). Using this training set, the Random Forests (RF) algorithm, which is known for its accurate prediction, was applied to the integrated dataset for risk modeling. The results were assessed against the validation set. Overall accuracy of 91.23% and Kappa Coefficient of 0.82 were obtained. The calculated probability for each pixel was consequently graded into different zones for risk mapping. Hence, we conclude that landslide risk zoning using the RF algorithm can serve as a pertinent reference for local government in their disaster prevention and early warning measures.

CaOFeS is a semiconducting oxysulfide with polar layered triangular structure. Here a comprehensive theoretical study has been performed to reveal its physical properties, including magnetism, electronic structure, phase transition, magnetodielectric effect, as well as optical absorption. Our calculations confirm the Ising-like G-type antiferromagnetic ground state driven by the next-nearest neighbor exchanges, which breaks the trigonal symmetry and is responsible for the magnetodielectric effect driven by exchange striction. In addition, a large coefficient of visible light absorption is predicted, which leads to promising photovoltaic effect with the maximum light-to-electricity energy conversion efficiency up to 24.2%.

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Chicken reticuloendotheliosis virus (REV) causes the atrophy of immune organs and immuno-suppression. The pathogenic mechanisms of REV are poorly understood. The aim of this study was to use RNA sequencing to analyse the effect of REV on immunity and cell proliferation in chicken lymphocytes from peripheral blood in vitro. Overall, 2977 differentially expressed genes (DEGs) were examined from cells between infected with REV or no; 56 DEGs related to cell proliferation and 130 DEGs related to immunity were identified. MTT, Q-PCR, and FCM indicated that REV reduced the number of lymphocytes by inhibiting the transition of S/G1 phase through FOXO and p53 pathways. Similarly, REV infection would destroy the immune defense of lymphocytes through MAPK-AP1 via Toll-like receptor-, NOD-like receptor-, and salmonella infection pathways to reduce the secretion of IL8 and IL18. In addition, the reduction of lymphocytes also might be responsible for the lower levels of IL8 and IL18, and the rescue of lymphocytes would been activated still through FOXO and p53 pathways. Moreover, the immune response for REV in lymphocytes would activate by up-regulating the expression of NOD1, MYD88, and AP1 through Toll-like receptor-/NOD-like receptor/salmonella-MAPK-AP1 pathways. These results indicate that REV could affect lymphocytes from peripheral blood by inhibit the cell proliferation and the immune system. It also was revealed that NOD1, MYD88, and AP1 were the key genes to activate the immune response through Toll-like receptor-/NOD-like receptor/salmonella-MAPK-AP1 pathways. These findings establish the groundwork and provide new clues for deciphering the molecular mechanism underlying REV infection in chickens.

Nowadays, buy-online-and-pick-up-in-store (BOPS) is a popular sales project to promote product sales. Implementing BOPS in the dual-channel low-carbon supply chain (DLSC) can not only improve low-carbon manufacturers’ profit but also reduce energy consumption in it. This paper focuses on how to design the contract which can ensure the implementation of BOPS in the DLSC consisting of one manufacturer and one retailer considering consumers’ low-carbon preference. Based on the analysis of game theory, two kinds of BOPS contract (MW contract with the dominant manufacturer making decision on wholesale price and RW contract with the dominant retailer making decision on wholesale price) with fixed compensation are designed and compared to obtain the better contract which is more effective on the implementation of BOPS. The findings show that MW contract is better than RW contract for the DLSC to implement BOPS. When consumers’ low-carbon preference and BOPS preference and the anti-cross-price elasticity are high enough, the DLSC can implement BOPS under the MW contract because it has Pareto efficiency on the profit of the original DLSC. We further find the sales price is decreasing in consumers’ low-carbon preference and anti-cross-price elasticity, while the wholesale price is increasing in consumers’ low-carbon preference. Finally, the results are verified by numerical examples.

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Reliable prediction of landslide occurrence is important for hazard risk reduction and prevention. Taking Guixi in northeast Jiangxi as an example, this research aimed to conduct such a landslide risk assessment using a multiple logistic regression (MLR) algorithm. Field-investigated landslides and non-landslide sites were converted into polygons. We randomly generated 50,000 sampling points to intersect these polygons and the intersected points were divided into two parts, a training set (TS) and a validation set (VT) in a ratio of 7 to 3. Thirteen geo-environmental factors, including elevation, slope, and distance from roads were employed as hazard-causative factors, which were intersected by the TS to create the random point (RP)-based dataset. The next step was to compute the certainty factor (CF) of each factor to constitute a CF-based dataset. MLR was applied to the two datasets for landslide risk modeling. The probability of landslides was then calculated in each pixel, and risk maps were produced. The overall accuracy of these two models versus VS was 91.5% and 90.4% with a Kappa coefficient of 0.814 and 0.782, respectively. The RP-based MLR modeling achieved more reliable predictions and its risk map seems more plausible for providing technical support for implementing disaster prevention measures in Guixi.