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Myocarditis is a rare cardiomyocyte inflammatory process, typically caused by viruses, with potentially devastating cardiac sequalae in both competitive athletes and in the general population. Investigation into myocarditis prevalence in the Coronavirus disease 2019 (COVID-19) era suggests that infection with Severe acute respiratory syndrome coronavirus (SARS-CoV-2) is an independent risk factor for myocarditis, which is confirmed mainly through cardiovascular magnetic resonance imaging. Recent studies indicated that athletes have a decreased risk of myocarditis after recent COVID-19 infection compared to the general population. However, given the unique nature of competitive athletics with their frequent participation in high-intensity exercise, athletes possess distinct factors of susceptibility for the development of myocarditis and its subsequent severe cardiac complications (e.g., sudden cardiac death, fulminant heart failure, etc.). Under this context, this review focuses on comparing myocarditis in athletes versus non-athletes, owing special attention to the distinct clinical presentations and outcomes of myocarditis caused by different viral pathogens such as cytomegalovirus, Epstein-Barr virus, human herpesvirus-6, human immunodeficiency virus, and Parvovirus B19, both before and after the COVID-19 pandemic, as compared with SARS-CoV-2. By illustrating distinct clinical presentations and outcomes of myocarditis in athletes versus non-athletes, we also highlight the critical importance of early detection, vigilant monitoring, and effective management of viral and non-viral myocarditis in athletes and the necessity for further optimization of the return-to-play guidelines for athletes in the COVID-19 era, in order to minimize the risks for the rare but devastating cardiac fatality.

Cardiac injury and sustained cardiovascular abnormalities in long-COVID syndrome, i.e. post-acute sequelae of coronavirus disease 2019 (COVID-19) have emerged as a debilitating health burden that has posed challenges for management of pre-existing cardiovascular conditions and other associated chronic comorbidities in the most vulnerable group of patients recovered from acute COVID-19. A clear and evidence-based guideline for treating cardiac issues of long-COVID syndrome is still lacking. In this review, we have summarized the common cardiac symptoms reported in the months after acute COVID-19 illness and further evaluated the possible pathogenic factors underlying the pathophysiology process of long-COVID. The mechanistic understanding of how Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) damages the heart and vasculatures is critical in developing targeted therapy and preventive measures for limiting the viral attacks. Despite the currently available therapeutic interventions, a considerable portion of patients recovered from severe COVID-19 have reported a reduced functional reserve due to deconditioning. Therefore, a rigorous and comprehensive cardiac rehabilitation program with individualized exercise protocols would be instrumental for the patients with long-COVID to regain the physical fitness levels comparable to their pre-illness baseline.

Abstract The verified text data of wheat varieties is an important component of wheat germplasm information. To automatically obtain a structured description of the phenotypic and genetic characteristics of wheat varieties, the aim at solve the issues of fuzzy entity boundaries and overlapping relationships in unstructured wheat variety approval data, WGIE-DCWF (joint extraction model of wheat germplasm information entity relationship based on deep character and word fusion) was proposed. The encoding layer of the model deeply fused word semantic information and character information using the Transformer encoder of BERT. This allowed for the cascading fusion of contextual semantic feature information to achieve rich character vector representation and improve the recognition ability of entity features. The triple extraction layer of the model established a cascading pointer network, extracted the head entity, extracted the tail entity according to the relationship category, and decoded the output triplet. This approach improved the model’s capability to extract overlapping relationships. The experimental results demonstrated that the WGIE-DCWF model performed exceptionally well on both the WGD (wheat germplasm dataset) and the public dataset DuIE. The WGIE-DCWF model not only achieved high performance on the evaluation datasets but also demonstrated good generalization. This provided valuable technical support for the construction of a wheat germplasm information knowledge base and is of great significance for wheat breeding, genetic research, cultivation management, and agricultural production.

Because of the high cost of manual surveys, the analysis of spatial change of forest structure at the regional scale faces a difficult challenge. Spaceborne LiDAR can provide global scale sampling and observation. Taking this opportunity, dense natural forest canopy cover (NFCC) observations obtained by combining spaceborne LiDAR data, plot survey, and machine learning algorithm were used as spatial attributes to analyze the spatial effects of NFCC. Specifically, based on ATL08 (Land and Vegetation Height) product generated from Ice, Cloud and land Elevation Satellite-2/Advanced Topographic Laser Altimeter System (ICESat-2/ATLAS) data and 80 measured plots, the NFCC values located at the LiDAR’s footprint locations were predicted by the ML model. Based on the predicted NFCC, the spatial effects of NFCC were analyzed by Moran’s I and semi-variogram. The results showed that (1) the Random Forest (RF) model had the strongest predicted performance among the built ML models (R2=0.75, RMSE=0.09); (2) the NFCC had a positive spatial correlation (Moran’s I = 0.36), that is, the CC of adjacent natural forest footprints had similar trends or values, belonged to the spatial agglomeration distribution; the spatial variation was described by the exponential model (C0 = 0.12×10-2, C = 0.77×10-2, A0 = 10200 m); (3) topographic factors had significant effects on NFCC, among which elevation was the largest, slope was the second, and aspect was the least; (4) the NFCC spatial distribution obtained by SGCS was in great agreement with the footprint NFCC (R2 = 0.59). The predictions generated from the RF model constructed using ATL08 data offer a dependable data source for the spatial effects analysis.

PurposeInsulin-like growth factor-1 (IGF-1) has a variety of neurotrophic effects, including neurogenesis, remyelination and synaptogenesis, and is an effective regulator of neuronal plasticity. Although multiple studies have investigated IGF-1 in depression-related disorders, few studies have focused on patients with a first episode of clearly diagnosed depression who had never used antidepressants before. Therefore, this study investigated first-episode and drug-naïve patients with depression to supplement the current evidence around IGF-1 levels in depressive disorders.Patients and methodsThis study consisted of two parts. In the first part, 60 patients with first-episode and drug-naïve depression and 60 controls matched for age, sex, and BMI were recruited from the outpatient department of the Fourth Hospital of Wuhu City, and the community. The case-control method was used to compare differences in serum IGF-1 levels between the two groups. In the second part, 13 case-control studies were screened through the database for meta-analysis to verify the reliability of the results.ResultsResults of the case-control study demonstrated that serum IGF-1 levels are significantly higher in patients with first-episode and drug-naïve depression compared to healthy controls (p

Fusarium head blight (FHB) is a destructive disease that affects wheat production. Detecting FHB accurately and rapidly is crucial for improving wheat yield. Traditional models are difficult to apply to mobile devices due to large parameters, high computation, and resource requirements. Therefore, this article proposes a lightweight detection method based on an improved YOLOv8s to facilitate the rapid deployment of the model on mobile terminals and improve the detection efficiency of wheat FHB. The proposed method introduced a C-FasterNet module, which replaced the C2f module in the backbone network. It helps reduce the number of parameters and the computational volume of the model. Additionally, the Conv in the backbone network is replaced with GhostConv, further reducing parameters and computation without significantly affecting detection accuracy. Thirdly, the introduction of the Focal CIoU loss function reduces the impact of sample imbalance on the detection results and accelerates the model convergence. Lastly, the large target detection head was removed from the model for lightweight. The experimental results show that the size of the improved model (YOLOv8s-CGF) is only 11.7 M, which accounts for 52.0% of the original model (YOLOv8s). The number of parameters is only 5.7 × 106 M, equivalent to 51.4% of the original model. The computational volume is only 21.1 GFLOPs, representing 74.3% of the original model. Moreover, the mean average precision (mAP@0.5) of the model is 99.492%, which is 0.003% higher than the original model, and the mAP@0.5:0.95 is 0.269% higher than the original model. Compared to other YOLO models, the improved lightweight model not only achieved the highest detection precision but also significantly reduced the number of parameters and model size. This provides a valuable reference for FHB detection in wheat ears and deployment on mobile terminals in field environments.

With the increasing demand for renewable energy worldwide, lithium-ion batteries are a major candidate for the energy shift due to their superior capabilities. However, the heat generated by these batteries during their operation can lead to serious safety issues and even fires and explosions if not managed effectively. Lithium-ion batteries also suffer from significant performance degradation at low temperatures, including reduced power output, a shorter cycle life, and reduced usable capacity. Deploying an effective battery thermal management system (BTMS) is crucial to address these obstacles and maintain stable battery operation within a safe temperature range. In this study, we review recent developments in the thermal management and heat transfer of Li-ion batteries to offer more effective, secure, and cost-effective solutions. We evaluate different technologies in BTMSs, such as air cooling, liquid cooling, phase change materials, heat pipes, external preheating, and internal preheating, discussing their advantages and disadvantages. Through comparative analyses of high-temperature cooling and low-temperature preheating, we highlight the research trends to inspire future researchers. According to the review of the literature, submerged liquid BTMS configurations show the greatest potential as a research focus to enhance thermal regulation in Li-ion batteries. In addition, there is considerable research potential in the innovation of air-based BTMSs, the optimization of liquid-based BTMSs, the coupling of heat pipes with PCMs, the integration of PCMs and liquid-cooled hybrid BTMSs, and the application of machine learning and topology optimization in BTMS design. The application of 3D printing in lithium-ion battery thermal management promises to enhance heat transfer efficiency and system adaptability through the design of innovative materials and structures, thereby improving the battery’s performance and safety.

Bamboo forests, as some of the integral components of forest ecosystems, have emerged as focal points in forestry research due to their rapid growth and substantial carbon sequestration capacities. In this paper, satellite-borne lidar data from GEDI and ICESat-2/ATLAS are utilized as the main information sources, with Landsat 9 and DEM data as covariates, combined with 51 pieces of ground-measured data. Using random forest regression (RFR), boosted regression tree (BRT), k-nearest neighbor (KNN), Cubist, extreme gradient boosting (XGBoost), and Stacking-ridge regression (RR) machine learning methods, an aboveground carbon (AGC) storage model was constructed at a regional scale. The model evaluation indices were the coefficient of determination (R2), root mean square error (RMSE), and overall estimation accuracy (P). The results showed that (1) The best-fit semivariogram models for cdem, fdem, fndvi, pdem, and andvi were Gaussian models, while those for h1b7, h2b7, h3b7, and h4b7 were spherical models; (2) According to Pearson correlation analysis, the AGC of Dendrocalamus giganteus showed an extremely significant correlation (p < 0.01) with cdem and pdem from GEDI, and also showed an extremely significant correlation with andvi, h1b7, h2b7, h3b7, and h4b7 from ICESat-2/ATLAS; moreover, AGC showed a significant correlation (0.01 < p < 0.05) with fdem and fndvi from GEDI; (3) The estimation accuracy of the GEDI model was superior to that of the ICESat-2/ATLAS model; additionally, the estimation accuracy of the Stacking-RR model, which integrates GEDI and ICESat-2/ATLAS (R2 = 0.92, RMSE = 5.73 Mg/ha, p = 86.19%), was better than that of any single model (XGBoost, RFR, BRT, KNN, Cubist); (4) Based on the Stacking-RR model, the estimated AGC of Dendrocalamus giganteus within the study area was 1.02 × 107 Mg. The average AGC was 43.61 Mg/ha, with a maximum value of 76.43 Mg/ha and a minimum value of 15.52 Mg/ha. This achievement can serve as a reference for estimating other bamboo species using GEDI and ICESat-2/ATLAS remote sensing technologies and provide decision support for the scientific operation and management of Dendrocalamus giganteus.

As an ecologically sensitive area with significant desertification problems, the Ring-Tarim Basin has a fragile ecological environment that is vulnerable to both natural and anthropogenic factors. Accurate long-term vegetation observations are ecologically, socially, and economically important for desertification control. In this study, based on the ground-measured data and the fractional vegetation cover (FVC) inversion dataset obtained by the image element dichotomy method, we used the methods of slope-trend analysis and multiple-regression residual analysis to analyze the spatial and temporal characteristics of the vegetation cover in the desertified area of the Ring-Tarim Basin. At the same time, we assessed the impacts of climate change and human activities on vegetation changes and the contribution of driving forces. The results showed that (1) The annual mean value of FVC in the growing season in the Ring-Tarim Basin generally showed a fluctuating and increasing trend during the period of 1993–2021; a decreasing trend during 1993–1999, with a change rate of −0.13 × 10−2a−1; and the fastest increasing trend during 2010–2021, with a change rate of 0.23 × 10−2a−1. (2) The effects of climate change and human activities on FVC changes in the growing season had great spatial heterogeneity. The areas where climate change and human activities had no significant effect on FVC changes in the growing season accounted for 86.25% and 77.91%, respectively, the areas where climate and human activities promoted FVC increase in the growing season accounted for 10.53% and 16.37%, respectively, and the areas where climate and human activities inhibited FVC increase in the growing season accounted for 3.22% and 5.72%, respectively. (3) About 76.9% of the FVC changes in the area around the Ring-Tarim Basin were caused by climate change and human activities. In addition to the eastern part of the study area, the vegetation cover of the oases in the west, north, and south generally showed an increasing trend, and the increasing area was proportional to the distribution density of the oasis cities. The trend of vegetation change in the area of the oasis and the fringes of the oasis was drastic. The contribution and inhibition of human activities to FVC, and the driving force of FVC change were greater than that of climate change. More than half of the area had an anthropogenic contribution of more than 60%, indicating that China’s ecological projects have had a significant effect on vegetation change in the extreme arid regions.

The utilization of liquid-cooled plates has been increasingly prevalent within the thermal management of batteries for new energy vehicles. Using Tesla valves as internal flow channels of liquid-cooled plates can improve heat dissipation characteristics. However, conventional Tesla valve flow channels frequently experience challenges such as inconsistencies in heat dissipations and unacceptably high levels of pressure loss. In light of this, this paper proposes a new type of Tesla valve with partitions, which is used as internal channel for liquid-cooled plate. Its purpose is to solve the shortcomings of existing flow channels. Under the working conditions of Reynolds number equal to 1000, the neural network prediction-NSGA-II multi-objective optimization method is used to optimize the channel structural parameters. The objective is to identify the optimal structural configuration that exhibits the greatest Nusselt number while simultaneously exhibiting the lowest Fanning friction factor. The variables to consider are the half of partition thickness H, partition length L, and the fillet radius R. The study result revealed that the optimal parameter combination consisted of H = 0.25 mm, R = 1.253 mm, L = 0.768 mm, which demonstrated the best performance. The Fanning friction factor of the optimized flow channel is substantially reduced compared to the reference channel, reducing by approximately 16.4%. However, the Nusselt number is not noticeably increased, increasing by only 0.9%. This indicates that the optimized structure can notably reduce the fluid’s friction resistance and pressure loss and slightly enhance the heat dissipation characteristics.

Hydrogel sweat cooling is one of the leading areas in the study of multiphase heat transfer. In this study, the principles, applications, current research status, and future trends of hydrogel sweat cooling technology are comprehensively reviewed. By combing through and analyzing the relevant literature, the research progress in hydrogel sweat cooling is presented from the application perspective, including its use in electronic devices, buildings, and clean-energy facilities. The principle of each application is illustrated, the research status is established, and pros and cons are proposed. To provide inspiration for future research, the development trend is set out. Our literature review indicates that research on advanced hydrogels is the most promising research direction, including studies on the effect of environmental and indoor factors on sweat cooling performance through numerical, experimental, and theoretical means. Challenges for future research mainly include conducting hydrogel numerical analysis which can be experimentally verified, developing advanced hydrogels in a green way, and achieving the precise regulation of hydrogel control through intelligent methods. Interdisciplinary integration might be promising as well due to the fact that it can reveal the hydrogel sweat cooling mechanism from a different perspective. This study aims to promote multiphase cooling technology in exploring the application of hydrogels in energy utilization criteria.

Abstract A low‐pollution single‐tube microgas turbine combustor using different gas fuels was studied. The combustion performance of two kinds of landfill gas and natural gas was tested in a single‐head combustor. The effects of fuel nozzle position, and the fuel distribution ratio between pilot and main combustion stage on pollutant emission were investigated under rated load condition. The flow field, temperature field, and pollutant generation characteristics with different fuel nozzle diameters and nozzle positions were analyzed by numerical simulation. The results show that: the combustor can form an obvious central recirculation zone, which is convenient for ignition and stable flame propagation; in the case of limited premixed length, the influence of fuel nozzle position on fuel–air mixing uniformity is less obvious than that of fuel nozzle diameter. The fuel–air mixing uniformity plays a decisive role in the temperature field and the generation of pollutants in the combustor. The combustor has good pollutant emission characteristics, and the NOx emission can be reduced to about 10 ppmv (15% O2 concentration) under the appropriate combination of fuel nozzle diameter and position, which provides a reference for the further optimization of low‐emission combustor.

The problem of desertification in the Tarim Basin, an area with a unique geography and climatic conditions, has received extensive research attention not only in China but also around the world. Between natural factors and human activities, the latter are considered the main cause of desertification, with the excessive use of land resources accelerating its risk. This study classified the degree of desertification into five types, no, light, moderate, severe, and extremely severe desertification, and focused on the spatio-temporal changes in LULC, desertification development, and their relationship in the Circum-Tarim Basin during the period of 1990–2020, and the results indicated the following. (1) Over the 30-year study period, farmland development was frequent in the basin. The total farmland area increased significantly by 1.40 × 104 km2, which resulted from the occupation of grassland (mainly low-covered and medium-covered grassland) and unused land (mainly saline–alkali land). (2) There was a general alleviation of the effects of desertification, but also local deterioration. The area of no-desertification land has significantly increased (an increase of 2.10 × 104 km2), and the degree of desertification has shifted significantly to adjacent lighter degrees, but the area of extremely severe desertification in certain regions has increased (an increase of 7.89 × 104 km2). (3) There was an inseparable relationship between LULC and desertification. Oasisization and desertification were two processes that interacted and were interrelated. There was an approximately 54.42% increase in no-desertification land area mainly occurring in the region where LULC types changed (Region II), although this area only accounted for 9.71% of the total area of the basin. There was an approximately 98.28% increase in the area of extremely severe desertification occurring where there were no changes in LULC types (Region I). Region II demonstrated the best effects of desertification prevention and control in the 30-year study period in the Circum-Tarim Basin. Land development and oasis expansion have led to concentrated water use, resulting in water scarcity in certain areas, which cannot support the needs of vegetation growth, thus aggravating the degradation. Hence, “adapting measures to local conditions, rational planning, zoning policies, precise prevention and control” will be the way forward for desertification control in the future in the Circum-Tarim Basin.

Using the SST k-ω turbulence model, a comprehensive numerical investigation was conducted to analyze the flow and heat transfer characteristics of steam within an isosceles trapezoidal cooling channel with pin-fin arrays modeled from the trailing edge of a F-class gas turbine high-temperature blade. The influence laws of various parameters, including the Reynolds number (Re, from 10,000 to 50,000), dimensionless pin-fin diameter (d/H, from 0.4 to 0.8), and dimensionless pin-fin spacing (S/H, from 1.6 to 2.4), on the flow and heat transfer performance of the isosceles trapezoidal cooling channel with pin-fin arrays were examined. Sensitivity analysis was employed to determine the relative significance of these influence parameters on the cooling performance of the isosceles trapezoidal channel with pin-fin arrays. Finally, the corresponding heat transfer and friction correlations within the investigated parameter range were developed. The research findings reveal that under different Reynolds numbers, as the dimensionless pin-fin diameter increases from 0.4 to 0.8, the friction factor within the isosceles trapezoidal cooling channel with pin-fin arrays increases by a factor of 3.25 to 3.41, while the overall average Nusselt number improves by 31.05% to 37.41%. Conversely, when the dimensionless pin-fin spacing increases from 1.6 to 2.4, the friction factor within the isosceles trapezoidal cooling channel with pin-fin arrays decreases by 67.38% to 69.18%, accompanied by a reduction in the overall average Nusselt number by 24.95% to 31.14%. When both the flow performance and heat transfer performance are taken into account, the importance of the influence parameters ranks as follows: Reynolds number, pin-fin diameter, and pin-fin spacing. It also suggests that smaller pin-fin diameters and larger pin-fin spacing should be selected in the design stage based on the variation laws of integrated thermal-hydraulic performance. The results may provide valuable references and insights for the design of steam cooling structures within high-temperature turbine blade trailing edge channels in advanced gas turbines.

Reinforcement soil slope with pile penetration is a new load bearing structure, which has a complex working mechanism, but few studies have been carried out. This paper aims to investigate the stability characteristics of this structure using model tests. The study investigates the lateral displacement and-pile bending moment caused by vertical loads and evaluates the influence of different factors, including the structure type (such as pile, cap of pile, and reinforcement material), number of reinforcing layers, spacing of reinforcement material, pile length, and slope rate on the load-carrying capacity of the pile penetration fill-reinforced load-bearing structure. The findings suggest that within a certain range, increasing the pile length and number of reinforcing layers, the limiting effect of the pile on the lateral displacement in the middle and at the bottom of the slope of the pile-penetrating reinforced structure is enhanced, which can reduce the extreme value of the bending moment and make the distribution of the bending moment of the pile more reasonable. The lateral limiting effect on the soil body can be maximized by appropriate reinforcement spacing. Within a certain range, the slope rate is reduced, which can reduce the extreme value of the bending moment, make the bending moment distribution of the pile more reasonable, and avoid the phenomenon of the localized force concentration of the pile.

The journal Int [...]

Semantic segmentation algorithms leveraging deep convolutional neural networks often encounter challenges due to their extensive parameters, high computational complexity, and slow execution. To address these issues, we introduce a semantic segmentation network model emphasizing the rapid generation of redundant features and multi-level spatial aggregation. This model applies cost-efficient linear transformations instead of standard convolution operations during feature map generation, effectively managing memory usage and reducing computational complexity. To enhance the feature maps’ representation ability post-linear transformation, a specifically designed dual-attention mechanism is implemented, enhancing the model’s capacity for semantic understanding of both local and global image information. Moreover, the model integrates sparse self-attention with multi-scale contextual strategies, effectively combining features across different scales and spatial extents. This approach optimizes computational efficiency and retains crucial information, enabling precise and quick image segmentation. To assess the model’s segmentation performance, we conducted experiments in Changge City, Henan Province, using datasets such as LoveDA, PASCAL VOC, LandCoverNet, and DroneDeploy. These experiments demonstrated the model’s outstanding performance on public remote sensing datasets, significantly reducing the parameter count and computational complexity while maintaining high accuracy in segmentation tasks. This advancement offers substantial technical benefits for applications in agriculture and forestry, including land cover classification and crop health monitoring, thereby underscoring the model’s potential to support these critical sectors effectively.

Gas turbine is one of the most efficiency conversion equipment of heat into power, which is widely used in aviation propulsion and power generation. With the gas inlet temperature of gas turbine skyrocketing recently, gas turbine can achieve higher efficiency but the super high temperature far exceeds the bearable temperature limits of blade material. This causes a great threaten in lifespan of turbine blades and making advanced and efficient cooling structures for blades essential. To enhance the cooling effectiveness of middle chord region of gas turbine blade, a novel droplet-shaped Kagome truss structure (DKTS) is proposed and investigated through experimental and numerical analysis. Subsequently, within jet impingement channel, DKTS is compared against circular pin-fins (CPF), droplet-shaped pin-fins (DPF), and circular Kagome truss structures (CKTS) for fluid flow and heat transfer performance. The experimental method is used to study effects on cooling characteristics between DKTS and CKTS under different operating conditions. The numerical method is used to study effects on cooling characteristics among CPF, DPF, CKTS and DKTS, and to study influences of investigated parameters on cooling performance of DKTS. Experimental results show that under equivalent windward areas, both heat transfer efficiency and flow performance of DKTS are better than those of CKTS. The results of numerical simulations indicate that at the same porosity across jet Reynolds numbers ranging 5000–50000, DKTS increases thermal-hydraulic performance by 11.1%–17.5% relative to CKTS. Moreover, DPF and DKTS reduce flow resistance and augment heat transfer capabilities as compared to CPF and CKTS. This shows that turbulators with droplet-shaped rods have better thermal-hydraulic performance. Finally, the effects of differing jet Reynolds numbers, diameter, tail angles, and inclination angles on fluid flow and heat transfer characteristics of DKTS are conducted. These results may provide a reference for further optimization researches or industrial applications of DKTS.

Lithium‐ion batteries (LIBs) have dominated the secondary batteries market in the past few decades. However, their widespread application is seriously hampered by the limited lithium resource and high cost. Recently, sodium‐ion batteries (SIBs) have generated significant attention because of their characteristics of abundant raw sources, low cost, and similar “rocking chair” mechanism with LIBs, which hold great application potential in large‐scale energy storage. Cathode materials with excellent electrochemical performance are in urgent demand for next‐generation SIBs. Herein, this review provides a comprehensive overview of the recent advances of the most promising SIBs cathode candidates, including layered oxides, polyanionic materials, and Prussian blue analogues. The currently existing issues that need to be addressed for these cathodes are pointed out, such as insufficient energy density, low electron conductivity, air sensitivity, and so on. This review also details the structural characteristics of these three cathode candidates. Moreover, the recent optimization strategies for improving the electrochemical performance are summarized, including element doping, morphology modification, structure architecture, and so on. Finally, the current research status and proposed future developmental directions of these three cathode materials are concluded. This review aims to provide practical guidance for the development of cathode materials for next‐generation SIBs.

This research presents a surrogate model and computational fluid dynamic analysis-based multi-objective optimization approach for microchannel heat sinks. The Non-dominated Sorting Genetic Algorithm is suggested to obtain the optimal solution set, and the Kriging model is employed to lower the simulation’s computational cost. The physical model consists of a coolant chamber, a mainstream chamber, and a solid board equipped with staggered Zigzag cooling channels. Five design variables are examined in relation to the geometric characteristics of the microchannel heat sinks: the length of inlet of the cooling channels, the width of the cooling channels, the length of the “zigzag”, the height of the cooling channels, and the periodic spanwise width. The optimal geometry is established by choosing the averaged cooling effectiveness and coolant mass flow rate which enters the mainstream chamber through the microchannel heat sinks as separate objectives. From the Pareto front, the optimal microchannel heat sinks structures are obtained. Three optimized results are studied, including the maximum cooling effectiveness, minimum coolant mass flow rate, and a compromise between the both objectives; a reference case using the median is compared as well. Numerical assessments corresponding to the four cases are performed, and the flow and cooling performance are compared. Furthermore, an analysis is conducted on the mechanisms that impact the ideal geometric parameters for cooling performance.

In arid areas, vegetation is the basis for maintaining the virtuous cycle of ecosystems, while water resources are the key factor restricting the survival of vegetation communities. The balance between water resources and vegetation is related to the sustainable development of ecological benefits and economic benefits in arid areas. Although research on the carrying potential of vegetation in arid areas has been emphasized, there is still a lack of spatial analysis of different vegetation types over large areas. Therefore, this study takes precipitation as the basic source of water resources, calculates the amount of available water resources in the basin from the available effective precipitation and available irrigation water, and it analyzes the spatial distribution of forest and grass vegetation types under the water resources constraint, combined with the ecological water demand of different vegetation types and based on the principle of “Water to determine the Vegetation”. The results showed that the ecological water demand of each vegetation type was as follows: Forest > Shrub Vegetation > Grassland Vegetation > Desert Vegetation. The range of comprehensive available water resources of vegetation was from 0 to 221.71 mm, which decreased with the altitude gradient. Then, the spatial distribution pattern of vegetation types constrained by the water resources in the Aksu River Basin showed a striped distribution from north to south, with shrub vegetation in the high-altitude mountainous area, grassland vegetation in the low-altitude area, and desert vegetation in the desert plain area, respectively, accounting for 29.05%, 10.74%, and 53.45% of the total basin. This research approach provides a scientific basis for the planning of forest and grass vegetation construction in arid regions.

This paper proposes a novel lamilloy cooling structure equipped with internal minichannels used in a combustor flame cylinder. The overall flow and heat transfer characteristics of three different internal minichannel configurations (parallel-flow, counter-flow, and staggered arrangements. PF, CF, and SF for short) are analyzed and compared. From the analysis of the heat transfer characteristic, it is found that the CF structure possesses the best cooling effect and temperature uniformity. The average cooling effectiveness (ηave) of the CF structure increased by 20.6% and 20.4%, and the average temperature gradient ((dT/dX)ave) decreased by 76.9% and 32.8%, compared with the SF structure and PF structure, respectively. From the analysis of flow characteristics, the coolant ejected from the minichannel one region of the SF structure completely blocks the direct contact between the mainstream and the lamilloy, while transferring more from the mainstream to the minichannel two region. Different from the SF structure, film cooling at the two outlet of the CF structure transfers most of the mainstream to the lower position, which further improves the cooling effect of the two internal minichannels.

In this study, we utilized dual-polarization weather radar and disdrometer data to investigate the kinematic and microphysical characteristics of an extreme heavy rainfall event that occurred on 20 July 2021, in Zhengzhou. The results are as follows: FY-2G satellite images showed that extremely heavy rainfall mainly occurred during the merging period of medium- and small-scale convective cloud clusters. The merging of these cloud clusters enhanced the rainfall intensity. The refined three-dimensional wind field, as retrieved by the multi-Doppler radar, revealed a prominent mesoscale vortex and convergence structure at the extreme rainfall stage. This led to echo stagnation, resulting in localized extreme heavy rainfall. We explored the formation mechanism of the notable ZDR arc feature of dual-polarization variables during this phase. It was revealed that during the record-breaking hourly rainfall event in Zhengzhou (20 July 2021, 16:00–17:00 Beijing Time), the warm rain process dominated. Effective collision–coalescence processes, producing a high concentration of medium- to large-sized raindrops, significantly contributed to heavy rainfall at the surface. From an observational perspective, it was revealed that raindrops exhibited significant collision interactions during their descent. Moreover, a conceptual model for the kinematic and microphysical characteristics of this extreme rainfall event was established, aiming to provide technical support for monitoring and early warning of similar extreme rainfall events.

With the rapid development of large-scale and intensive swine production, the emission of aerosols from swine farms has become a growing concern, attracting extensive attention. While aerosols are found in various environments, those from swine farms are distinguished from human habitats, such as residential, suburban, and urban areas. In order to gain a comprehensive understanding of aerosols from swine farms, this paper reviewed relevant studies conducted between 2000 and 2022. The main components, concentrations, and size distribution of the aerosols were systematically reviewed. The differences between aerosols from swine farms and human living and working environments were compared. Finally, the sources, influencing factors, and reduction technologies for aerosols from swine farms were thoroughly elucidated. The results demonstrated that the concentrations of aerosols inside swine farms varied considerably, and most exceeded safety thresholds. However, further exploration is needed to fully understand the difference in airborne microorganism community structure and particles with small sizes (

Abstract Multifunctional nanoaggregates are widely used in cancer phototheranostics. However, it is challenging to construct their multifunctionality with a single component, and deliver them rapidly and efficiently without complex modifications. Herein, a NIR‐absorbing small molecule named TBT‐2(TP‐DPA) is designed and certify its theranostic potentials. Then, their nanoaggregates, which are simply encapsulated by DSPE‐PEG, demonstrate a photothermal efficiency of 51% while keeping a high photoluminescence quantum yield in the NIR region. Moreover, the nanoaggregates can be excited and delivered by an 808 nm pulse laser to solid tumors within only 40 min. The delivery efficiency and theranostic efficacy are better than that of the traditional enhanced permeability and retention (EPR) effect (generally longer than 24 hours). This platform is first termed as the photoinduced thermoacoustic (PTA) process, and confirm its application requires both NIR‐responsive materials and pulse laser irradiation. This study not only inspires the design of multifunctional nanoaggregates, but also offers a feasible approach to their fast delivery. The platform reported here provides a promising prospect to boost the development of multifunctional theranostic drugs and maximize the efficacy of used medicines for their clinical applications.

In light of the significance of regulatory authorities and the rising demand for information disclosure, a vast amount of information on food safety news reports is readily accessible on the Internet. The extraction of such information for precise classification and provision of appropriate safety alerts based on their respective categories has emerged as a challenging problem for academic research. Given that most food safety-related events in news reports comprise lengthy text, the pre-trained language models currently employed for text analysis are generally limited in their capability to handle long documents. This paper proposes a long-text classification model utilising hierarchical Transformers. We categorise information in long documents into two distinct types: (1) multiple text chunks meeting the length constraint and (2) essential sentences within long documents, such as headings, paragraph start and end sentences, etc. Initially, our proposed model utilises the text chunks as input to the BERT model. Then, it concatenates the output of the BERT model with the important sentences from the document and use them as input to the Transformer model for feature transformation. Finally, we utilise a classifier for food safety news classification. We conducted several comparative experiments with various commonly used text classification models on a dataset constructed from publicly available information on food regulatory websites. Our proposed method outperforms existing methods, establishing itself as the leading approach in terms of performance.

Abstract Background Computer-aided drug design provides an effective method of identifying lead compounds. However, success rates are significantly bottlenecked by the lack of accurate and reliable scoring functions needed to evaluate binding affinities of protein–ligand complexes. Therefore, many scoring functions based on machine learning or deep learning have been developed to improve prediction accuracies in recent years. In this work, we proposed a novel featurization method, generating a new scoring function model based on 3D convolutional neural network. Results This work showed the results from testing four architectures and three featurization methods, and outlined the development of a novel deep 3D convolutional neural network scoring function model. This model simplified feature engineering, and in combination with Grad-CAM made the intermediate layers of the neural network more interpretable. This model was evaluated and compared with other scoring functions on multiple independent datasets. The Pearson correlation coefficients between the predicted binding affinities by our model and the experimental data achieved 0.7928, 0.7946, 0.6758, and 0.6474 on CASF-2016 dataset, CASF-2013 dataset, CSAR_HiQ_NRC_set, and Astex_diverse_set, respectively. Overall, our model performed accurately and stably enough in the scoring power to predict the binding affinity of a protein–ligand complex. Conclusions These results indicate our model is an excellent scoring function, and performs well in scoring power for accurately and stably predicting the protein–ligand affinity. Our model will contribute towards improving the success rate of virtual screening, thus will accelerate the development of potential drugs or novel biologically active lead compounds.