Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R2 of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance

Design and Optimization for a New Locomotive Power Battery Box

To solve the disadvantages of the low protection grade, high weight, and high cost of the existing locomotive power battery system, this study optimizes the existing scheme and introduces the design concept of two-stage protection. The purpose of the research is to improve the protection level of the battery pack to IP68, to optimize the sheet metal power battery box structure into a more lightweight frame structure, to simplify the cooling mode of the battery pack for natural air cooling, and to improve the battery protection level and maintain the heat exchange capability. In the course of the study, a design scheme with a two-stage protection function is proposed. The numerical model analyzes the self-load, transverse load, longitudinal load, mode, and fatigue, and optimizes the layout of the power tank cell. The optimized box model was physically tested and economically compared. The results show that: (1) The maximum load stress is 128.4 MPa, which is lower than 235 MPa, the ultimate stress of the box material, and the fatigue factor of the frame box structure is 3.75, which is higher than 1.0, and it is not prone to fatigue damage. (2) Under the low-temperature heating condition, the overall temperature rise of the battery pack is 4.3 °C, which is greater than 2.3 °C under the air conditioning heat dissipation scheme. Under the high-temperature charging condition, the overall temperature rise of the battery pack is 2.0 °C, and the temperature value is the same as the temperature rise under the air conditioning cooling scheme. Under the high-temperature discharge condition, the overall temperature rise of the battery pack is 3.0 °C, and the temperature value is greater than 2.1 °C under the air conditioning heat dissipation scheme. At the same time, the temperature rise under the three working conditions is less than the 15 °C stipulated in the JS175-201805 standard. The simulation results show that the natural airflow and two-stage protection structure can provide a good temperature environment for the power battery to work. (3) The optimized box prototype can effectively maintain the structural integrity of the battery cell in the box in extreme test cases, reducing the probability of battery fire caused by battery cell deformation. (4) The power battery adopts a two-stage protection design under the battery power level, which can simultaneously achieve battery protection and prevent thermal runaway, while reducing costs. The research results provide a new concept for the design of a locomotive power battery system. (5) The weight of the optimized scheme is 2020 kg, and the original scheme is 2470 kg; thus, the reduction in weight is 450 kg. Meanwhile, the volume of the optimized scheme is 1.49 m3, and the original scheme is 1.93 m3; thus, the reduction in volume is 0.44 m3

Design and Optimization for a New Locomotive Power Battery Box

To solve the disadvantages of the low protection grade, high weight, and high cost of the existing locomotive power battery system, this study optimizes the existing scheme and introduces the design concept of two-stage protection. The purpose of the research is to improve the protection level of the battery pack to IP68, to optimize the sheet metal power battery box structure into a more lightweight frame structure, to simplify the cooling mode of the battery pack for natural air cooling, and to improve the battery protection level and maintain the heat exchange capability. In the course of the study, a design scheme with a two-stage protection function is proposed. The numerical model analyzes the self-load, transverse load, longitudinal load, mode, and fatigue, and optimizes the layout of the power tank cell. The optimized box model was physically tested and economically compared. The results show that: (1) The maximum load stress is 128.4 MPa, which is lower than 235 MPa, the ultimate stress of the box material, and the fatigue factor of the frame box structure is 3.75, which is higher than 1.0, and it is not prone to fatigue damage. (2) Under the low-temperature heating condition, the overall temperature rise of the battery pack is 4.3 °C, which is greater than 2.3 °C under the air conditioning heat dissipation scheme. Under the high-temperature charging condition, the overall temperature rise of the battery pack is 2.0 °C, and the temperature value is the same as the temperature rise under the air conditioning cooling scheme. Under the high-temperature discharge condition, the overall temperature rise of the battery pack is 3.0 °C, and the temperature value is greater than 2.1 °C under the air conditioning heat dissipation scheme. At the same time, the temperature rise under the three working conditions is less than the 15 °C stipulated in the JS175-201805 standard. The simulation results show that the natural airflow and two-stage protection structure can provide a good temperature environment for the power battery to work. (3) The optimized box prototype can effectively maintain the structural integrity of the battery cell in the box in extreme test cases, reducing the probability of battery fire caused by battery cell deformation. (4) The power battery adopts a two-stage protection design under the battery power level, which can simultaneously achieve battery protection and prevent thermal runaway, while reducing costs. The research results provide a new concept for the design of a locomotive power battery system. (5) The weight of the optimized scheme is 2020 kg, and the original scheme is 2470 kg; thus, the reduction in weight is 450 kg. Meanwhile, the volume of the optimized scheme is 1.49 m3, and the original scheme is 1.93 m3; thus, the reduction in volume is 0.44 m3

Hazards and Improvement Measures of Microplastic Pollution: A Review

Microplastics is one category of plastics with relatively small diameter and is considered as the common ingredient of waste accumulation zone in oceans. However, since countless plastic products are emitted into oceans annually as waste all around the world, pollution caused by them is severe and the resulting problems have attracted attention globally, while current policies and cooperation around the globe for tackling microplastics pollution still need to be improved. To deal with microplatics-related problems in the ocean, our review first discussed the toxicity of microplastics based on previous research related to marine microplastics, which was caused by the plastics themselves and their leaching substances with impacts on marine creatures and human body along the food chain. After summarizing some measures that have been already performed, we suggested that the authority should take more actions to mitigate those problems resulted from microplastics, pay more attention on researching, and encourage citizens to offer their proposals. By finally analyzing the advantages and disadvantages of different handling methods, as well as physical, chemical, and biological treatment technologies on oceanic microplastic issues, our work provided experience on disposing microplastics waste under various actual situations with an example for more holistic waste treatment

Improving the Forecasting of Winter Wheat Yields in Northern China with Machine Learning–Dynamical Hybrid Subseasonal-to-Seasonal Ensemble Prediction

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield, using observational climate variables and satellite data. Meanwhile, some studies also illustrated the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully explored. Herein, we aimed to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and applied it to northern China at the S2S time scale. In this study, we adopted three mainstream machining learning algorithms (XGBoost, RF, and SVR) and the multiple linear regression (MLR) model, and three major datasets, including satellite data from MOD13C1, observational climate data from CRU, and S2S atmospheric prediction data from IAP CAS, used to predict winter wheat yield from 2005 to 2014, at the grid level. We found that, among the four models examined in this work, XGBoost reached the highest skill with the S2S prediction as inputs, scoring R2 of 0.85 and RMSE of 0.78 t/ha 3–4 months, leading the winter wheat harvest. Moreover, the results demonstrated that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. Our findings highlighted that the coupling of ML and S2S dynamical atmospheric prediction provided a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance

Evaluation of Dynamical Seasonal Prediction Skills for Tropical Cyclone Activity over the South China Sea in FGOALS-f2

Based on 35-year (1981–2015) ensemble (24 members) hindcasts of the IAP/LASG global seasonal prediction system named FGOALS-f2 V1.0 (FGOALS-f2), the tropical cyclone (TC) seasonal prediction skills over the South China Sea (SCS) during the TC peak season (July–November) are evaluated. Starting the prediction from June 20th, FGOALS-f2 can well capture the seasonal mean characteristics for both the genesis location and track of TCs over the SCS. For seasonal anomalous TC numbers, FGOALS-f2 underestimates the maximum and minimum of the TC number compared to the observation. The temporal correlation coefficients (TCCs) between FGOALS-f2 and the observation are 0.39 for the TC number and 0.51 for accumulated cyclone energy (ACE) over the SCS, respectively, which are both above the 95% significant level. Additionally, FGOALS-f2 has acceptable prediction skill for the seasonal mean number of TCs landing on three areas (coastal southeastern China, Indochina Peninsula, and Philippines) surrounding the SCS. The skillful prediction of SCS TCs could be ascribed to the well-predicted tropical anomaly of sea surface temperature (SSTA), TC and El Niño-Southern Oscillation (TC-ENSO) relations, and Genesis potential index (GPI)

Systematic Design of Trypsin Cleavage Site Mutated Exendin4-Cysteine 1, an Orally Bioavailable Glucagon-Like Peptide-1 Receptor Agonist

Exendin-4 is a strong therapeutic candidate for the treatment of metabolic syndrome. Related receptor agonist drugs have been on the market since 2005. However, technical limitations and the pain caused by subcutaneous injection have severely limited patient compliance. The goal of the study is to investigate a biologically active exendin-4 analog could be administered orally. Using intraperitoneal glucose tolerance tests, we discovered that exendin4-cysteine administered by oral gavage had a distinct hypoglycemic effect in C57BL/6J mice. Using Rosetta Design and Amber, we designed and screened a series of exendin4-cysteine analogs to identify those that retained biological activity while resisting trypsin digestion. Trypsin Cleavage Site Mutated Exendin4-cysteine 1 (TSME-1), an analog whose bioactivity was similar to exendin-4 and was almost completely resistant to trypsin, was screened out. In addition, TSME-1 significantly normalized the blood glucose levels and the availability of TSME-1 was significantly higher than that of exendin-4 and exendin4-cysteine. Collectively orally administered TSME-1, a trypsin-resistant exendin-4 analog obtained by the system, is a strong candidate for future treatments of type 2 diabetes

Novel 3.9 V Layered Na₃V₃(PO₄)₄ Cathode Material for Sodium Ion Batteries

A new compound Na₃V₃­(PO₄)₄ is successfully synthesized for sodium ion batteries using a sol–gel method. Composition analysis through ICP-OES confirms the stoichiometry of Na₃V₃(PO₄)₄. Structural analysis based on XRD reveals that the new material crystallizes in a monoclinic system with a <i>C</i>2/<i>c</i> space group. The new compound exhibits a layered structure containing 3D Na⁺ ion channels allowing excellent cycling and rate performance. Even at a high current rate of 3C (1C = 45 mA/g), it still delivers 82% of the theoretical capacity. Meanwhile, 92% of its capacity is retained after 100 electrochemical cycles. The voltage profiles of Na₃V₃­(PO₄)₄ show that it can reversibly uptake nearly one Na⁺ ion with a 3.9 V voltage plateau, which is the highest value among Na-containing V-based orthophosphates ever reported

Mechanism study on a plague outbreak driven by the construction of a large reservoir in southwest china (surveillance from 2000-2015)

<div><p>Background</p><p>Plague, a <i>Yersinia pestis</i> infection, is a fatal disease with tremendous transmission capacity. However, the mechanism of how the pathogen stays in a reservoir, circulates and then re-emerges is an enigma.</p><p>Methodology/Principal findings</p><p>We studied a plague outbreak caused by the construction of a large reservoir in southwest China followed 16-years’ surveillance.</p><p>Conclusions/Significance</p><p>The results show the prevalence of plague within the natural plague focus is closely related to the stability of local ecology. Before and during the decade of construction the reservoir on the Nanpan River, no confirmed plague has ever emerged. With the impoundment of reservoir and destruction of drowned farmland and vegetation, the infected rodent population previously dispersed was concentrated together in a flood-free area and turned a rest focus alive. Human plague broke out after the enzootic plague via the flea bite. With the construction completed and ecology gradually of human residential environment, animal population and type of vegetation settling down to a new balance, the natural plague foci returned to a rest period. With the rodent density decreased as some of them died, the flea density increased as the rodents lived near or in local farm houses where had more domestic animals, and human has a more concentrated population. In contrast, in the <i>Himalayan marmot</i> foci of the Qinghai-Tibet Plateau in the Qilian Mountains. There are few human inhabitants and the local ecology is relatively stable; plague is prevalence, showing no rest period. Thus the plague can be significantly affected by ecological shifts.</p></div