Analyzing the effects of physical activity levels on aggressive behavior in college students using a chain-mediated model

This study aims to examine the mediating role of self-efficacy (SE) and self-control (SC) in the relationship between physical activity (PA) and aggressive behaviors (AB) among college students. It provides a basis for the prevention and control of AB among college students. This study employed a survey research methodology, including the PA Level Scale, the General Self-efficacy Scale, the Self-control Scale, and the Chinese Aggressive Behaviors Scale on 950 college students. The chain mediating effect test and Bootstrap analysis were applied. The results were as follows: (1) There was a main effect of PA on SE, SC, and AB as well as all sub-indicators (physical aggression, verbal aggression, anger, hostility, self-directed aggression), i.e., PA had a direct effect on the control of all three; (2) PA level was significantly negatively correlated with AB and significantly positively correlated with SE and SC. That is, the higher the level of PA, the better the SE and SC, and the lower the probability of AB; (3) The three pathways had mediating effects: PA → SE → AB, PA → SC → AB, PA → SE → SC → AB, with effect sizes of 8.78%, 28.63%, and 19.08%, respectively. It is concluded that regular PA is a potent method for decreasing aggressive behavior and psychological issues in university students while additionally promoting self-efficacy and self-control. Increasing the intensity of PA may enhance the effectiveness of these chain benefits

Urban−rural gradients reveal joint control of elevated CO₂ and temperature on extended photosynthetic seasons

Photosynthetic phenology has large effects on the land-atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO₂) concentrations, which reflect expected future environmental conditions. Using more than 880 urban-rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO₂, we investigated the combined impacts of elevated CO₂ and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO₂ and temperature, whereas the delayed end of season was mainly attributed to CO₂ enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change

Warmer spring alleviated the impacts of 2018 European summer heatwave and drought on vegetation photosynthesis

Future projections of climate extremes are expected to become more frequent. Parts of Europe experienced an extensive heatwave and drought during 2018. However, its impacts on terrestrial carbon cycle remain elusive. Here we investigated the vegetation responses to the heatwave and drought during 2018 based on satellite solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance (NIRv) data, which were used to estimate gross primary productivity (GPP). Results showed that there were no significant (p= 0.60) reductions in GPP across most of Europe during April-August of 2018. The higher temperatures in spring enhanced vegetation GPP, largely alleviated the negative impacts of heatwave and drought on vegetation photosynthesis during the subsequent summer, which resulted in evident compensation effects. Concurrently, warmer spring also had lagged effects by diminishing soil moisture, accompanied by scarce precipitation, leading to water stress on plant growth during summer. This observation-based study highlights the need for more considerations of seasonal compensation and lagged effects on the interactions between climate extreme events and biosphere.Peer reviewe

Global diversity and biogeography of potential phytopathogenic fungi in a changing world

Phytopathogenic fungi threaten global food security but the ecological drivers of their global diversity and biogeography remain unknown. Here, we construct and analyse a global atlas of potential phytopathogenic fungi from 20,312 samples across all continents and major oceanic island regions, eleven land cover types, and twelve habitat types. We show a peak in the diversity of phytopathogenic fungi in mid-latitude regions, in contrast to the latitudinal diversity gradients observed in aboveground organisms. Our study identifies climate as an important driver of the global distribution of phytopathogenic fungi, and our models suggest that their diversity and invasion potential will increase globally by 2100. Importantly, phytopathogen diversity will increase largely in forest (37.27-79.12%) and cropland (34.93-82.51%) ecosystems, and this becomes more pronounced under fossil-fuelled industry dependent future scenarios. Thus, we recommend improved biomonitoring in forests and croplands, and optimised sustainable development approaches to reduce potential threats from phytopathogenic fungi

A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data

On-board the Landsat-8 satellite, the Thermal Infrared Sensor (TIRS), which has two adjacent thermal channels centered roughly at 10.9 and 12.0 μm, has a great benefit for the land surface temperature (LST) retrieval. The single-channel algorithm (SC) and split-window algorithm (SW) have been applied to retrieve the LST from TIRS data, which need the land surface emissivity (LSE) as prior knowledge. Due to the big challenge of determining the LSE, this study develops a temperature and emissivity separation algorithm which can simultaneously retrieve the LST and LSE. Based on the laboratory emissivity spectrum data, the minimum-maximum emissivity difference module (MMD module) for TIRS data is developed. Then, an emissivity log difference method (ELD method) is developed to maintain the emissivity spectrum shape in the iterative process, which is based on the modified Wien’s approximation. Simulation results show that the root-mean-square-errors (RMSEs) are below 0.7 K for the LST and below 0.015 for the LSE. Based on the SURFRAD ground measurements, further evaluation demonstrates that the average absolute error of the LST is about 1.7 K, which indicated that the algorithm is capable of retrieving the LST and LSE simultaneously from TIRS data with fairly good results

Improved Coal Feeding Control System of Thermal Power Plant Based on PLC

Based on PLC, the improved coal feeding system of thermal power plant is developed, which solves the problems of poor timeliness and inaccurate quantity of coal feeder. In the process of belt transportation, a dynamic coal feeding rate is established. By controlling the coal feeding rate and then controlling the running speed of the belt, a certain amount of coal is supplied to the thermal power plant, so as to provide more accurate coal, improve the corresponding timeliness, and have better economic benefits

A Temperature and Emissivity Separation Algorithm for Landsat-8 Thermal Infrared Sensor Data

On-board the Landsat-8 satellite, the Thermal Infrared Sensor (TIRS), which has two adjacent thermal channels centered roughly at 10.9 and 12.0 μm, has a great benefit for the land surface temperature (LST) retrieval. The single-channel algorithm (SC) and split-window algorithm (SW) have been applied to retrieve the LST from TIRS data, which need the land surface emissivity (LSE) as prior knowledge. Due to the big challenge of determining the LSE, this study develops a temperature and emissivity separation algorithm which can simultaneously retrieve the LST and LSE. Based on the laboratory emissivity spectrum data, the minimum-maximum emissivity difference module (MMD module) for TIRS data is developed. Then, an emissivity log difference method (ELD method) is developed to maintain the emissivity spectrum shape in the iterative process, which is based on the modified Wien’s approximation. Simulation results show that the root-mean-square-errors (RMSEs) are below 0.7 K for the LST and below 0.015 for the LSE. Based on the SURFRAD ground measurements, further evaluation demonstrates that the average absolute error of the LST is about 1.7 K, which indicated that the algorithm is capable of retrieving the LST and LSE simultaneously from TIRS data with fairly good results

A Mechanistic Model for Estimating Rice Photosynthetic Capacity and Stomatal Conductance from Sun-Induced Chlorophyll Fluorescence

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (Vcmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal Vcmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, Vcmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate Vcmax and gs with high accuracy (R2 > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of Vcmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change

A methodology for cable damage identification based on wave decomposition

© 2018 Elsevier Ltd Vibration-based damage identification has been widely studied in the field of structural health monitoring (SHM) for several decades. It is well known, however, that low-order modal parameters, being among the most frequently used, are not sensitive to local damage. A suitable methodology is therefore needed to extract such damage features from the dynamic response of structures. In the present work, local bending behavior of cables is studied for damage identification. First, the dynamic response of a cable is decomposed into evanescent wave and propagating wave components. It is proven that the contribution of the evanescent wave is spatially concentrated, and is sensitive to local damage. A signal transform is proposed next, which allows the estimation of the wave components from the measured cable response. The reflection coefficient of the evanescent wave (REW), which can be calculated from the estimated wave coefficients, depends only on the characteristics of the local discontinuity, and proves to be a robust indicator for local damage. The feasibility of the proposed methodology is studied by means of a simulated experiment, considering a cable model with two locally damaged parts. The results show that the intensity of REW is significantly higher near the damage locations, allowing damage localization. From the estimated REW near the damage locations, the damage levels can be estimated, showing the potential of this methodology for damage assessment of cable structures.status: publishe