Past distribution of epiphyllous liverworts in China: The usability of historical data

Epiphyllous liverworts form a special group of bryophytes that primarily grow on the leaves of understory vascular plants in tropical and subtropical evergreen broadleaf forests. Being sensitive to moisture and temperature changes, epiphyllous liverworts are often considered to be good indicators of climate change and forest degradation. However, they are a poorly collected and taxonomically complicated group, with an only partly identified distribution pattern. In this study, we built four models based on 24 environmental variables at four different spatial resolutions (i.e., 1 km, 5 km, 10 km, and 15 km) to predict the past distribution of epiphyllous liverworts in China, using Maxent model and 63 historical location records (i.e., presence‐only data). Both area under the curve of the receiver operating characteristic (AUC) and true skill statistic (TSS) methods are used to assess the model performance. Results showed that the model with the predictors at a 15‐km resolution achieved the highest predictive accuracy (AUC=0.946; TSS=0.880), although there was no statistically significant difference between the four models (p > 0.05). The most significant environmental variables included aridity, annual precipitation, precipitation of wettest month, precipitation of wettest quarter, and precipitation of warmest quarter, annual mean NDVI, and minimum NDVI. The predicted suitable areas for epiphyllous liverworts were mainly located in the south of Yangtze River and seldom exceed 35°N, which were consistent with the museum and herbarium records, as well as the historical records in scientific literatures. Our study further demonstrated the value of historical data to ecological and evolutionary studies

A New Feature Points Reconstruction Method in Spacecraft Vision Navigation

The important applications of monocular vision navigation in aerospace are spacecraft ground calibration tests and spacecraft relative navigation. Regardless of the attitude calibration for ground turntable or the relative navigation between two spacecraft, it usually requires four noncollinear feature points to achieve attitude estimation. In this paper, a vision navigation system based on the least feature points is designed to deal with fault or unidentifiable feature points. An iterative algorithm based on the feature point reconstruction is proposed for the system. Simulation results show that the attitude calculation of the designed vision navigation system could converge quickly, which improves the robustness of the vision navigation of spacecraft

Safety-Specific Leadership, Goal Orientation, and Near-Miss Recognition: The Cross-Level Moderating Effects of Safety Climate

Near-miss recognition is an increasingly important area of research in safety management. Drawing on the self-determination theory, we ask whether and how safety-specific transformational leadership and safety-specific active transactional leadership promote near-miss recognition. We also explore the boundary condition by focusing on the moderating role of safety climate. We analyzed time-lagged data from 370 participants, and found that safety-specific transformational leadership enhances employees’ near-miss recognition (by enhancing their learning goal orientation), and that safety-specific active transactional leadership also positively influences employees’ near-miss recognition (by stimulating their performance goal orientation). In addition, we show that safety climate strengthens the relationship between safety-specific transactional leadership and employees’ performance goal orientation, but does not affect the relationship between safety-specific transformational leadership and employees’ learning goal orientation. We discuss the implications and limitations of the research

Thioredoxin-1 maintains mechanistic target of rapamycin (mTOR) function during oxidative stress in cardiomyocytes

Thioredoxin 1 (Trx1) is a 12-kDa oxidoreductase that catalyzes thiol-disulfide exchange reactions to reduce proteins with disulfide bonds. As such, Trx1 helps protect the heart against stresses, such as ischemia and pressure overload. Mechanistic target of rapamycin (mTOR) is a serine/threonine kinase that regulates cell growth, metabolism, and survival. We have shown previously that mTOR activity is increased in response to myocardial ischemia-reperfusion injury. However, whether Trx1 interacts with mTOR to preserve heart function remains unknown. Using a substrate-trapping mutant of Trx1 (Trx1C35S), we show here that mTOR is a direct interacting partner of Trx1 in the heart. In response to H2O2 treatment in cardiomyocytes, mTOR exhibited a high molecular weight shift in non-reducing SDS-PAGE in a 2-mercaptoethanol-sensitive manner, suggesting that mTOR is oxidized and forms disulfide bonds with itself or other proteins. The mTOR oxidation was accompanied by reduced phosphorylation of endogenous substrates, such as S6 kinase (S6K) and 4E-binding protein 1 (4E-BP1) in cardiomyocytes. Immune complex kinase assays disclosed that H2O2 treatment diminished mTOR kinase activity, indicating that mTOR is inhibited by oxidation. Of note, Trx1 overexpression attenuated both H2O2-mediated mTOR oxidation and inhibition, whereas Trx1 knockdown increased mTOR oxidation and inhibition. Moreover, Trx1 normalized H2O2-induced down-regulation of metabolic genes and stimulation of cell death, and an mTOR inhibitor abolished Trx1-mediated rescue of gene expression. H2O2-induced oxidation and inhibition of mTOR were attenuated when Cys-1483 of mTOR was mutated to phenylalanine. These results suggest that Trx1 protects cardiomyocytes against stress by reducing mTOR at Cys-1483, thereby preserving the activity of mTOR and inhibiting cell death

Electroplating lithium transition metal oxides.

Materials synthesis often provides opportunities for innovation. We demonstrate a general low-temperature (260°C) molten salt electrodeposition approach to directly electroplate the important lithium-ion (Li-ion) battery cathode materials LiCoO2, LiMn2O4, and Al-doped LiCoO2. The crystallinities and electrochemical capacities of the electroplated oxides are comparable to those of the powders synthesized at much higher temperatures (700° to 1000°C). This new growth method significantly broadens the scope of battery form factors and functionalities, enabling a variety of highly desirable battery properties, including high energy, high power, and unprecedented electrode flexibility

An Analysis of the Multi-Criteria Decision-Making Problem for Distributed Energy Systems

Choosing a distributed energy system (DES) is a multi-criteria decision-making problem. Decision-makers should not only consider the cost of the system, but also consider the energy efficiency and environmental protection of the system. In order to help decision-makers choose the best DES, this paper designs seven different DESs based on specific examples, using five criteria: investment cost, operation cost, primary energy consumption, primary energy utilization, and yearly CO2 emission. Additionally, three methods of super-efficiency Data Envelopment Analysis (DEA), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Complex Proportional Assessment (COPRAS) are used to evaluate the system priority and analyze the sensitivity under different decision-making scenarios. The results show that when decision-makers only consider cost factors, traditional systems are the best choice. However, renewable energy systems are the best choice when decision-makers consider energy efficiency and environmental protection rather than cost. Among them, the photovoltaic storage system is the best system in many decision-making scenarios, because of its comprehensive advantages in cost, energy efficiency, and environmental benefit. Simultaneously, the system’s prioritization of different decision-making methods is different. In this paper, according to the Spearman correlation index test, the results achieved from TOPSIS and COPRAS are relevant and feasible

Analysis of Influencing Factors and Trend Forecast of Carbon Emission from Energy Consumption in China Based on Expanded STIRPAT Model

With the convening of the annual global climate conference, the issue of global climate change has gradually become the focus of attention of the international community. As the largest carbon emitter in the world, China is facing a serious situation of carbon emission reduction. This paper uses the IPCC (The Intergovernmental Panel on Climate Change) method to calculate the carbon emissions of energy consumption in China from 1996 to 2016, and uses it as a dependent variable to analyze the influencing factors. In this paper, five factors, total population, per capita GDP (Gross Domestic Product), urbanization level, primary energy consumption structure, technology level, and industrial structure are selected as the influencing factors of carbon emissions. Based on the expanded STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, the influencing degree of different factors on carbon emissions of energy consumption is analyzed. The results show that the order of impact on carbon emissions from high to low is total population, per capita GDP, technology level, industrial structure, primary energy consumption structure, and urbanization level. On the basis of the above research, the carbon emissions of China′s energy consumption in the future are predicted under eight different scenarios. The results show that, when the population and economy keep a low growth rate, while improving the technology level can effectively control carbon emissions from energy consumption, China′s carbon emissions from energy consumption will reach 302.82 million tons in 2020