Spatiotemporal variations and risk characteristics of potential non-point source pollution driven by LUCC in the Loess Plateau Region, China

With increasing human activities, regional substrate conditions have undergone significant changes. These changes have resulted in temporal and spatial variations of non-point source pollution sources, which has a significant impact on the quality of the regional soil, surface water, and groundwater environments. This study focused on the human-disturbed Loess Plateau region and used an enhanced potential non-point-source pollution index (PNPI) model to explore the dynamic changes of regional potential non-point-source pollution (PNP) and the associated risk due to land use and land cover change (LUCC) over the past 31 years. The Loess Plateau region is mainly composed of cultivated land, grassland and forest, which together account for 93.5% of the watershed area. From 1990 to 2020, extensive soil and water conservation measures were implemented throughout the Loess Plateau region, resulting in a significant reduction in the non-point source pollution risk. Using the quantile classification method, the study area’s PNP risk values were categorized into five distinct levels. The results revealed a polarization phenomenon of PNP risk in the region, with an increase in non-point source pollution risk in the human-influenced areas and a rapid expansion of the very high-risk area. However, the non-point source pollution risk in the upstream water source area of the watershed reduced over the study period. In recent years, the rapid urbanization of the Loess Plateau region has been the primary reason for the rapid expansion of the very high PNP risk area throughout the watershed. This study highlights the significant impact of LUCC on the dynamic changes in PNP risk within the Loess Plateau region, providing crucial insights into future conservation and urban planning policies aimed at enhancing the ecological health and environmental quality of the region

Genome-wide association analysis identifies 30 new susceptibility loci for schizophrenia

We conducted a genome-wide association study (GWAS) with replication in 36,180 Chinese individuals and performed further transancestry meta-analyses with data from the Psychiatry Genomics Consortium (PGC2). Approximately 95% of the genome-wide significant (GWS) index alleles (or their proxies) from the PGC2 study were overrepresented in Chinese schizophrenia cases, including ∼50% that achieved nominal significance and ∼75% that continued to be GWS in the transancestry analysis. The Chinese-only analysis identified seven GWS loci; three of these also were GWS in the transancestry analyses, which identified 109 GWS loci, thus yielding a total of 113 GWS loci (30 novel) in at least one of these analyses. We observed improvements in the fine-mapping resolution at many susceptibility loci. Our results provide several lines of evidence supporting candidate genes at many loci and highlight some pathways for further research. Together, our findings provide novel insight into the genetic architecture and biological etiology of schizophrenia

Reasoning Method Based on Intervals with Symmetric Truncated Normal Density

Error parameters are inevitable in systems. In formal verification, previous reasoning methods seldom considered the probability information of errors. In this article, errors are described as symmetric truncated normal intervals consisting of the intervals and symmetric truncated normal probability density. Furthermore, we also rigorously prove lemmas and a theorem to partially simplify the calculation process of truncated normal intervals and independently verify the formulas of variance and expectation of symmetric truncated interval given by some scholars. The mathematical derivation process or verification codes are provided for most of the key formulas in this article. Hence, we propose a new reasoning method that combines the probability information of errors with the previous statistical reasoning methods. Finally, an engineering example of the reasoning verification of train acceleration is provided. After simulating the large-scale cases, it is shown that the simulation results are consistent with the theoretical reasoning results. This method needs more calculation, while it is more effective in detecting non-error’s fault factors than other error reasoning methods

Reasoning Method Based on Intervals with Symmetric Truncated Normal Density

Error parameters are inevitable in systems. In formal verification, previous reasoning methods seldom considered the probability information of errors. In this article, errors are described as symmetric truncated normal intervals consisting of the intervals and symmetric truncated normal probability density. Furthermore, we also rigorously prove lemmas and a theorem to partially simplify the calculation process of truncated normal intervals and independently verify the formulas of variance and expectation of symmetric truncated interval given by some scholars. The mathematical derivation process or verification codes are provided for most of the key formulas in this article. Hence, we propose a new reasoning method that combines the probability information of errors with the previous statistical reasoning methods. Finally, an engineering example of the reasoning verification of train acceleration is provided. After simulating the large-scale cases, it is shown that the simulation results are consistent with the theoretical reasoning results. This method needs more calculation, while it is more effective in detecting non-error’s fault factors than other error reasoning methods

Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method

Construction of Life-Cycle Simulation Framework of Chronic Diseases and Their Comorbidities Based on Population Cohort

Life-cycle population follow-up data collection is time-consuming and often takes decades. General cohort data studies collect short-to-medium-term data from populations of different age groups. The purpose of constructing a life-cycle simulation method is to find an efficient and reliable way to achieve the way to characterize life-cycle disease metastasis from these short-to-medium-term data. In this paper, we have presented our effort at construction of a full lifetime population cohort simulation framework. The design aim is to generate a comprehensive understanding of the disease transition for full lifetime when we only have short-or-medium term population cohort data. We have conducted several groups of experiments to show the effectiveness of our method

Fatigue-free artificial ionic skin toughened by self-healable elastic nanomesh

Robust ionic sensing materials that are both fatigue-resistant and self-healable like human skin are essential for soft electronics and robotics with extended service life. However, most existing self-healable artificial ionic skins produced on the basis of network reconfiguration suffer from a low fatigue threshold due to the easy fracture of low-energy amorphous polymer chains with susceptible crack propagation. Here we engineer a fatigue-free yet fully healable hybrid ionic skin toughened by a high-energy, self-healable elastic nanomesh, resembling the repairable nanofibrous interwoven structure of human skin. Such a design affords a superhigh fatigue threshold of 2950 J m−2 while maintaining skin-like compliance, stretchability, and strain-adaptive stiffening response. Moreover, nanofiber tension-induced moisture breathing of ionic matrix leads to a record-high strain-sensing gauge factor of 66.8, far exceeding previous intrinsically stretchable ionic conductors. This concept creates opportunities for designing durable ion-conducting materials that replicate the unparalleled combinatory properties of natural skins more precisel

Examining the linkage between economic policy uncertainty, coal price, and carbon pricing in China: Evidence from pilot carbon markets

Economic policies affect companies’ production decisions. And the energy consumption volume is an intuitive reflection of the enterprise’s production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study

Interval Number-Based Safety Reasoning Method for Verification of Decentralized Power Systems in High-Speed Trains

Decentralized power systems are commonly used in high-speed trains. However, many parameters in decentralized power systems are uncertain and inevitably have errors. We present a reasoning method based on the interval numbers for decentralized power systems in high-speed trains. Uncertain parameters and their unavoidable errors are quantitatively described by interval numbers. We also define generalized linear equations with interval numbers (LAIs), which can be used to describe the movement of the train. Furthermore, it is proven that the zero sets of LAIs are convex. Therefore, the inside of the fault-tolerance area can be formed by their vertexes and edges and represented by linear inequalities. Consequently, we can judge whether the system is working properly by verifying that the current system state is in the fault-tolerance area. Finally, a fault-tolerance area is obtained, which can be determined by linear equations with an interval number, and we test the correctness of the fault-tolerance area through large-scale random test cases