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Lead (Pb), as one of the main pollution elements, has resulted in large-scale soil pollution around the world. Even if phytoremediation can solve this problem, the selection of restoration potential plants has always been a scientific problem. As a multifunctional repair plant, Neyraudia reynaudiana can rehabilitate both polluted soils and slopes. N. reynaudiana has been widely used in terrain restoration in southern China before. This study was the first to study the growth and Pb absorption and enrichment capacity of N. reynaudiana in Xuzhou, north of the Yangtze River. In this study, N. reynaudiana was planted in soils with different lead concentrations, and the change of lead content in roots, shoots, and soils, as well as the redox enzyme, was tested and analyzed during each growth stage. The results showed that the roots could absorb Pb and transfer 79.45% to the shoots at most. With the growth of the plant, the ability to accumulate and transfer gradually increased. Moreover, when the soil Pb concentration was above 800 mg kg−1, the ability to accumulate by N. reynaudiana was significantly restrained. Furthermore, superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), and peroxidase (POD) first acted on the redox response in the initial phase, while increasing the pollutant concentration or the growth of N. reynaudiana in the later stage, and the glutathione reductase (GR) redox system continued to feed back on the lead stress. This study proved that N. reynaudiana is a kind remediation plant for lead pollution soil and could repair soil with a lead pollution concentration lower than 800 mg kg−1. The results provide a theoretical reference for clarifying the action mechanism and threshold value of N. reynaudiana in rehabilitating soil lead pollution and provide practical guidance for the planting proportion of N. reynaudiana. [ABSTRACT FROM AUTHOR]

C48H46Cu2N4Na2O14S2, triclinic, P 1 ‾ (no. 2), a = 11.2259(11) Å, b = 11.6492(11) Å, c = 11.7594(12) Å, α = 11.2259(11)°, β = 11.6492(11)°, γ = 11.7594(12)°, V = 1248.5(2) Å3, Z = 1, Rgt(F) = 0.0554, wRref(F2) = 0.1605, T = 173 K. [ABSTRACT FROM AUTHOR]

• MSI was used for the 1st time to predict the DF content of Chinese cabbage leaves. • RF model was obtained by comparing with BP neural network, RBF, and MLR models. • The RF model is suitable for predicting DF content throughout the plant growth period. Multispectral imaging, combined with stoichiometric values, was used to construct a prediction model to measure changes in dietary fiber (DF) content in Chinese cabbage leaves across different growth periods. Based on all the spectral bands (365–970 nm) and characteristic spectral bands (430, 880, 590, 490, 690 nm), eight quantitative prediction models were established using four machine learning algorithms, namely random forest (RF), backpropagation neural network, radial basis function, and multiple linear regression. Finally, a quantitative prediction model of RF learning algorithm is constructed based on all spectral bands, which has good prediction accuracy and model robustness, prediction performance with R2 of 0.9023, root mean square error (RMSE) of 2.7182 g/100 g, residual predictive deviation (RPD) of 3.1220 > 3.0. In summary, this model efficiently detects changes in DF content across different growth periods of Chinese cabbage, which offers technical support for vegetable sorting and grading in the field. [ABSTRACT FROM AUTHOR]

This study presents an adaptive neural network finite-time recursive sliding mode control method to address the moving path following (MPF) problem when designing an automatic carrier landing system for unmanned aerial vehicle (UAV). To improve the automaticity and safety of landing on a moving carrier for a UAV, a non-singular three-dimensional MPF method that considers only the vertical and lateral errors relative to the moving sliding path is proposed to establish moving path guidance. Moreover, a tracking model for quaternion-based attitude errors was developed for attitude control. Then, an adaptive neural network observer was adopted to approximate the disturbance from dynamic uncertainty and air-wake turbulence, and an anti-saturation system was used to address actuator saturation for the UAV. Based on the Lyapunov global stability theorem, a controller based on finite-time sliding mode back-stepping can be developed, in which virtual control laws and virtual regulation laws are adopted to improve the dynamic maneuverability and convergence performance of the controller. Finally, numerical comparison simulations were performed to verify the effectiveness and superiority of the proposed method in terms of tracking accuracy and convergence. [ABSTRACT FROM AUTHOR]

Composite phase change materials (CPCM) containing photo absorbers are regarded as promising conversion and thermal energy storage materials for solar energy. Understanding the impact of irradiation intensity on the photo-thermal conversion process is of crucial significance for improving the photo-thermal conversion efficiency (PTCE) of CPCM. However, previous studies have overlooked the evolution of temperature patterns and the solid-liquid phase interface in CPCM, posing challenges in revealing photo-thermal conversion characteristics. In response to these challenges, the present study introduces an improved test method that integrates monitoring plane and infrared thermography. Paraffin/graphite nanoparticles CPCM are used as conversion and thermal storage material. The results show that the dominated heat transfer mode in different stages is different, and the natural convection within the liquid CPCM has enhanced the melting process. When the irradiation intensity is increased from 450 W/m2 to 1250 W/m2, the average temperature and average charging rate increase by 33.75% and 197.27%, respectively. At an irradiation intensity of 1050 W/m2, the CPCM achieves the highest overall PTCE of 42.40%, which is an 8.85% increase in contrast to the irradiation intensity of 450 W/m2. The results provide guidance for improving the PTCE of CPCM in the application of direct absorption solar collectors. [ABSTRACT FROM AUTHOR]

In northern China, promoting low-carbon heating technologies is pivotal in improving air quality and reducing carbon emissions. Solar-assisted air source heat pump systems have attracted extensive attention for the advantages of high energy efficiency and low carbon emissions. However, the existing reviews on solar-assisted air source heat pump systems mostly focus on technique development. This study comprehensively investigates the challenges and opportunities faced by solar-assisted air source heat pump systems in the combined aspects of technology, economy, and policy in China. Technical obstacles under diverse climate conditions, inefficient thermal energy storage, long payback periods, and a lack of subsidy policies pose significant challenges to solar-assisted air source heat pump systems. The main concerns of the solar-assisted air source heat pump system for the user, government, and manufacturer are costs, the financial burden of subsidy, and investment risk, respectively. Advances in new technologies, energy storage materials, defrosting, and optimization methods are promising to improve the performance of solar-assisted air source heat pump systems. Favourable renewable energy policies, energy performance contracting mode, and integrated energy systems give solar-assisted air source heat pump systems a bright future in China. Based on these identified factors, a SWOT (strengths, weaknesses, opportunities, and threats) analysis is conducted to propose strategies for the advancement of solar-assisted air source heat pump systems. This work is expected to give valuable insights to researchers, users, manufacturers, and policy makers for the promotion of solar-assisted air source heat pump systems towards low-carbon space heating. [Display omitted] • Identifying challenges and opportunities of solar-assisted air source heat pump. • Critical review of the combined aspects of technology, economy, and policy. • Investigating the concerns of the user, government, and manufacturer. • SWOT analysis and promotion strategies for solar-assisted air source heat pump. [ABSTRACT FROM AUTHOR]

• Prepare a material with superior solar absorption and thermal storage capability. • Propose a direct absorption/storage solar collector with less heat transfer process. • Boost photo-thermal conversion by light transmission and thermal conduction. • Light transmission enhancing method increases conversion efficiency by up to 8.29%. • Thermal conduction enhancing method boosts conversion efficiency by up to 9.57%. Direct absorption/storage solar collectors (DASSC) using composite phase change materials (CPCM) have attracted great attention in the solar energy field for the advantages of less heat exchange processes and high heat storage density. However, the low thermal conductivity of the CPCM has greatly restricted the thermal performance of the DASSC. This study aims to improve the photo-thermal conversion efficiency (PTCE) of DASSC using light transmission and thermal conduction enhancing methods within a new developed CPCM. Firstly, paraffin/expanded graphite CPCM is developed for direct solar absorption, conversion and storage. Secondly, both light transmission and thermal conduction enhancing methods have been investigated to improve the charging rate and PTCE of the solar collector. The paraffin/expanded graphite CPCM exhibits high absorbance in the visible light region with higher radiation energy, which is 3.03 times that of pure paraffin. At low irradiation intensity (400 W/m2), thermal conduction enhancing method performs better than light transmission enhancing method, in which the charging rate are increased by 15.64 % and 13.24 % compared to the non-enhanced collector, respectively. However, with irradiation intensity increase, such as 1000 W/m2, the light transmission enhancing method has a slightly better effect than the thermal conduction enhancing method, which increase the PTCE by 8.29 % and 8.25 %, respectively. This research introduces a new view to think about the method for improving the efficiency of DASSC. [ABSTRACT FROM AUTHOR]

Interactions between carbon (C), nitrogen (N), and phosphorus (P), the vital indicators of ecological restoration, play an important role in signaling the health of ecosystems. Rapidly and accurately mapping foliar C, N, and P is essential for interpreting community structure, nutrient limitation, and primary production during ecosystem recovery. However, research on how to rapidly map C, N, and P in restored areas with mixed plant communities is limited. This study employed laser imaging, detection, and ranging (LiDAR) and hyperspectral data to extract spectral, textural, and height features of vegetation as well as vegetation indices and structural parameters. Causal band, multiple linear regression, and random forest models were developed and tested in a restored area in northern China. Important parameters were identified including (1), for C, red-edge bands, canopy height, and vegetation structure; for N, textural features, height percentile of 40–95%, and vegetation structure; for P, spectral features, height percentile of 80%, and 1 m foliage height diversity. (2) R2 was used to compare the accuracy of the three models as follows: R2 values for C were 0.07, 0.42, and 0.56, for N they were 0.20, 0.48, and 0.53, and for P they were 0.32, 0.39, and 0.44; the random forest model demonstrated the highest accuracy. (3) The accuracy of the concentration estimates could be ranked as C > N > P. (4) The inclusion of LiDAR features significantly improved the accuracy of the C concentration estimation, with increases of 22.20% and 47.30% in the multiple linear regression and random forest models, respectively, although the inclusion of LiDAR features did not notably enhance the accuracy of the N and P concentration estimates. Therefore, LiDAR and hyperspectral data can be used to effectively map C, N, and P concentrations in a mixed plant community in a restored area, revealing their heterogeneity in terms of species and spatial distribution. Future efforts should involve the use of hyperspectral data with additional bands and a more detailed classification of plant communities. The application of this information will be useful for analyzing C, N, and P limitations, and for planning for the maintenance of restored plant communities. [ABSTRACT FROM AUTHOR]

In this work, a novel nitrogenous Fe-TPPE metal-organic-framework (MOF) with excellent visible light catalytic activity was successfully prepared, and a magnetic composite (Fe 3 O 4 @Fe-TPPE) with Z-type heterojunction structure was designed to synergistically treat organic pollutants and Cr(Ⅵ). The materials were carefully characterized by powder X-ray diffractometry, scanning electron microscope, Fourier transform infrared analysis, UV–visible diffuse reflectance spectra, X-ray photoelectron spectroscopy, thermogravimetric analysis, etc. The characterizations of photoelectric properties showed that the novel Fe-TPPE MOF exhibited strong response to visible light and Fe 3 O 4 @Fe-TPPE had even stronger response. Furthermore, the adsorption and photocatalytic degradation capabilities of Fe-TPPE and Fe 3 O 4 @Fe-TPPE, and the versatility as photocatalysts for aqueous pollutants were assessed through removal studies of the model pollutants tetracycline, rhodamine, methyl blue and Cr (VI). Additionally, the removal mechanism of the Fe 3 O 4 @Fe-TPPE for the above pollutants was explored. The high removal efficiency, fast magnetic separation, great recycling stability and minimal leak during application all demonstrate the good potential of Fe 3 O 4 @Fe-TPPE for practical and sustainable treatment of aqueous pollutants. [Display omitted] • A novel Fe-TPPE MOF is prepared using polynitrogen conjugated molecules as ligands. • A novel Fe 3 O 4 @Fe-TPPE composite with slice-ball intercalation structure is prepared. • Fe 3 O 4 @Fe-TPPE has excellent visible light catalytic performance and stability. • Fe 3 O 4 @Fe-TPPE has good reusability, applicability and environmental friendliness. [ABSTRACT FROM AUTHOR]

Background and Objective: Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.The dataset was collected by SportVU from 632 games during the 2015–2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models’ performance.The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search.This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays.Methods: Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.The dataset was collected by SportVU from 632 games during the 2015–2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models’ performance.The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search.This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays.Results: Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.The dataset was collected by SportVU from 632 games during the 2015–2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models’ performance.The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search.This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays.Conclusion: Understanding and recognizing basketball offensive set plays, which involve intricate interactions between players, have always been regarded as challenging tasks for untrained humans, not to mention machines. In this study, our objective is to propose an artificial intelligence model that can automatically recognize offensive plays using a novel self-supervised learning approach.The dataset was collected by SportVU from 632 games during the 2015–2016 season of the National Basketball Association (NBA), with a total of 90,524 possessions. A multi-agent motion prediction pretraining model was built on the basis of axial-attention transformer and trained with different masking strategies: motion prediction (MP), motion reconstruction (MR), and MP + MR joint strategy. A downstream play-level classification task and similarity search were used to evaluate the models’ performance.The results showed that the MP + MR joint masking strategy maximized the ability of the model compared with individual masking strategies. For the classification task, the joint strategy achieved a top-1 accuracy of 81.5% and top-3 accuracy of 97.5%. In the similarity search evaluation, the joint strategy attained a top-5 accuracy of 76% and top-10 accuracy of 59%. Additionally, with the same MP + MR joint masking strategy, our HoopTransformer model outperformed the two baseline models in the classification task and similarity search.This study presents a self-supervised learning model and demonstrates the effectiveness and potential of the model in accurately comprehending and capturing player movements and complex interactions during offensive plays. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explanatory scientific mapping of Perovskite solar cell technology (PSCT) is presented. • Prominent authors and organizations shaping the landscape of PSCT are presented. • By using keywords and thematic analysis, trending topics and the evolution of research are explored. • Perspectives of the primary issues and recommendations for PSCT are proposed. Extensive literature on Perovskite solar cell technology (PSCT) has become widespread as technology enables more innovations in the field. Despite efficient platforms like Web of Science, Scopus, Google Scholar, and others, challenges persist for new researchers in search of synchronized key information in the PSCT field. Inefficiencies arise from scattered data, hindering the research cycle for the new researchers. Moreover, policy-making and planning by the stakeholders (Government, Academia, and Industry) in research and innovation rely on data-driven approaches, emphasizing the need for identifying key information to mitigate technological risks in the field of PSCT. This research gap is addressed by the effective extraction of key knowledge from the fragmented literature of PSCT. To achieve this, a comprehensive scientific mapping is conducted via bibliometric analysis, encompassing a collection of 11,092 scientific publications. All relevant data that can bridge the mentioned research gap has been meticulously extracted and analyzed from the mentioned literature which involves the trend from inception, main contributors (countries, researchers, and production sources) to PSCT, detailed keywords, and thematic analysis. The key results reveal that People's republic of China, the USA, and India significantly contribute to PSCT research, leading in publications and citations. "ACS Applied Materials and Interfaces" followed by "Journal of Materials A," "Solar RRL," and "Advanced Energy Materials." Keyword analysis identifies four clusters: "stability," "efficiency," "optical properties," and environmental concerns. The trends show the perspective of the PSCT sub-areas, like stability for commercialization, innovative methods for performance improvement, and considerations for lead-free options. [ABSTRACT FROM AUTHOR]

• Factors affecting spatial heterogeneity of WSA, MWD and K factor are clarified. • Soil property, terrain and land use contribute to 47%, 41% and 9% of total variation. • Influencing network and paths of factors driving WSA, MWD and K are confirmed. • Terrain signally affects WSA, MWD and K by altering soil nature and land use pattern. Soil aggregate stability and erodibility reflect soil resistance to erosion. Although the factors influencing soil aggregate stability and erodibility have been extensively studied, the driving effects of these factors and their interactions remain limited. 184 sampling sites were selected in topsoil (0–20 cm) of a small watershed (1.42 km2) in a Mollisol watershed to measure soil bulk density (BD), soil porosity (SP), soil moisture content (MC), soil organic carbon (SOC), total nitrogen (TN), >0.25 mm water stable aggregates content (WSA > 0.25), mean weight diameter (MWD) and K factor. Pearson correlation analysis, semi-variance function, redundancy analysis (RDA), and structural equation model (SEM) were used to quantify the impact of environmental variables (individual and interaction) on the spatial variations of WSA > 0.25 , MWD, and K factor. The findings indicated that higher values of WSA > 0.25 and MWD are observed in the central and western watershed, while the K values tend to be lower in areas with high WSA > 0.25 and MWD values within the watershed. The Exponential model optimally described WSA > 0.25 , MWD, and K factor with C 0 /(C + C 0) indicating moderate spatial dependence for MWD (39.79 %) and K factor (42.86 %), while strong spatial autocorrelation for WSA > 0.25 (7.23 %). Soil properties (moisture content, silt content, and bulk density), topography (elevation, SPI, and slope), and land use contributed 46.6 %, 41.4 %, and 9.1 % of the spatial variation in WSA > 0.25 , MWD, and K factor, respectively. SEM revealed that silt content, SOC, and water condition played a fundamental role in controlling the spatial variability of WSA > 0.25 , MWD, and K factor. Topography exerted both direct or indirect effects by coupling land use or soil properties spatially. Land use had direct or indirect effects on WSA > 0.25 and K factor through regulating MC, but it primarily influences MWD indirectly through impacting MC. These results could clarify the roles and influencing paths of factors controlling the spatial heterogeneity of WSA > 0.25 , MWD, and K factor, contributing to optimizing land management strategies. [ABSTRACT FROM AUTHOR]

Epidemiological evidence shows that diabetic patients are susceptible to high temperature weather, and brown adipose tissue (BAT) activity is closely related to type 2 diabetes (T2DM). Activation of BAT under cold stress helps improve T2DM. However, the impact of high temperature on the activity of BAT is still unclear. The study aimed to investigate the impact of heat stress on glucose and lipid metabolism in T2DM mice by influencing BAT activity. High-fat feeding and injecting streptozotocin (STZ) induced model of T2DM mice. All mice were randomly divided into three groups: a normal(N) group, a diabetes (DM) group and a heat stress diabetes (DMHS) group. The DMHS group received heat stress intervention for 3 days. Fasting blood glucose, fasting serum insulin and blood lipids were measured in all three groups. The activity of BAT was assessed by using quantitative real-time PCR (qRT-PCR), electron microscopy, and PET CT. Furthermore, the UHPLC-Q-TOF MS technique was employed to perform metabolomics analysis of BAT on both DM group and DMHS group. The results of this study indicated that heat stress aggravated the dysregulation of glucose and lipid metabolism, exacerbated mitochondrial dysfunction in BAT and reduced the activity of BAT in T2DM mice. This may be related to the abnormal accumulation of branched-chain amino acids (BCAAs) in the mitochondria of BAT. • Heat stress aggravates the glucolipid metabolic disorders in T2DM mice. • Heat stress reduces BAT activity and aggravates mitochondrial dysfunction. • Heat stress may lead to abnormal accumulation of BCAAs in BAT mitochondria. [ABSTRACT FROM AUTHOR]