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• A total of 23 factors covering almost all factors frequently used in GPM. • LightGBM-C model achieved the highest model performance for GPM. • Arid regions are influenced significantly by climate factors PRE, EVA. • Future research on GPM should also consider DD, EV, NDVI, and GWD. Groundwater potential mapping (GPM) provides the valuable information on groundwater volume that can be withdrawn from the aquifer without affecting the environmental conditions. In this study, GPM was innovatively explored by including three typical climate factors - precipitation (PRE), evaporation (EVA), and ground surface temperature (GST) - and by taking into consideration a total of 23 conditional groundwater factors during the model construction. Three ensemble learning models are used for the modeling process: random forest (RF), XGBoost, and LightGBM. The study was conducted in the southern regions of Yinchuan Plain, which is known for its temperate continental climate with low precipitation and high evaporation. It was found that all six adopted models (i.e., RF-C, XGBoost-C, LightGBM-C, RF, XGBoost, LightGBM) made reasonable predictions of groundwater potential, with areas with high and very high potential concentrated in the southwest region, where the Yellow River enters the study area. The LightGBM-C model performs the best (OA: 0.769, F1 score: 0.667, AUC: 0.921), while the RF model performs the worst (OA: 0.654, F1 score: 0.4, AUC: 0.757). According to the LightGBM-C model, there are more productive wells in areas with high groundwater potential (22 wells, 0.0152 per km2), while fewer non-productive wells are found (3 wells, 0.0021 per km2). The performance of LightGBM-C and RF-C models has been notably enhanced by climate factors (AUC + 0.073, OA + 0.025, F1 score + 0.052 for LightGBM, and AUC + 0.073, OA + 0.051, F1 score + 0.149 for RF). The further cumulative importance results indicate that the three ensemble models which considered climate factors demonstrated a substantial sensitivity to PRE (27.78%), EVA (28.14%), and GST (23.34%). In arid and semi-arid regions, PRE and EVA are highly recommended factors for GPM, while DD (35.17%), EV (35.22%), NDVI (28.06%) and GWD (25.77%) are also confirmed to be important when climate factors are not taken into account. [ABSTRACT FROM AUTHOR]

The continuous updating and iteration of electronic devices brings about a significant accumulation of heat. It is urgent to enhance the thermal properties of polymer‐based composites to alleviate the heat dissipation problem in the microelectronics industry. In this paper, the hot drawing process was introduced to increase the crystallinity of ultra‐high molecular weight polyethylene (UHMWPE) fiber and improve its thermal conductivity. The ultra‐high molecular weight polyethylene fiber/epoxy resin (UHMWPE fiber/Epoxy) composites with high thermal conductivity were prepared by compositing UHMWPE fiber with epoxy resin under vacuum conditions. Due to the good crystallinity of the UHMWPE fiber (86%), an efficient thermal conduction path is provided for phonons in the axial direction along the fibers. The results show that the thermal conductivity of the composites reaches a maximum of 3.51 W/mK (in‐plane), which is 17.47 times higher than that of the epoxy resin (0.19 W/mK). The thermography pictures indicate that the composites have better heat transfer capacity with an increase in the draw ratio (λ). In addition, the composites also have the characteristics of low density and low electrical conductivity. These researches will provide a new idea for the preparation of all‐polymer composites with high thermal properties. Highlights: A hot drawing process was introduced to increase the crystallinity of UHMWPE fiber for improving thermal conductivity.High crystallinity facilitates the reduction of phonon scattering and the transmission of thermal energy by phonons.The obtained polymer composites exhibit an ultra‐high in‐plane thermal conductivity. [ABSTRACT FROM AUTHOR]

A new slow wave structure (SWS), named E-plane loaded sine waveguide (EPLSW) SWS is proposed for the development of G-band traveling wave tubes (TWTs). The structure, derived from the sine waveguide (SW) SWS, features staggered rectangular gratings on the two E-planes, intersecting with the sine-shaped gratings. The natural electron beam tunnel lies between these gratings. The EPLSW-SWS offers improved characteristics compared to the conventional SW-SWS. High-frequency simulation studies reveal that the passband of the EPLSW-SWS extends by more than 33 GHz over the SW-SWS under identical dispersion conditions. Additionally, the interaction impedance of EPLSW-SWS surpasses that of the SW-SWS in the frequency range of 195–255 GHz. Further analysis through particle-in-cell (PIC) results presents that when operating at 19.5 kV and 145 mA for the sheet beam current, it attains a maximum saturated power of 132.7W and a 3 dB bandwidth exceeding 50 GHz. [ABSTRACT FROM AUTHOR]

Organic phase change materials (PCMs) have received increasing attention for their applications in the fields such as solar energy storage and thermal management. Low thermal conductivity is a major key issue that hinders their practical applications. It has become a research hotspot to investigate the three-dimensional (3D) network method to improve the thermal performance of organic PCMs compared with the traditional blending method. Thermally conductive fillers form a 3D network in the resin matrix, which can enrich the thermal conductive pathway and effectively enhance the thermal conductivity. This paper mainly summarizes the research progress, preparation methods and influencing factors of composite PCMs, focusing on the methods of constructing 3D thermal conductivity network. It is hoped to provide reference or suggestion for the preparation of novel PCMs. [ABSTRACT FROM AUTHOR]

Lightweight carbon fiber/carbon composites (CFC) are widely used in solar silicon furnace, semiconductor long crystal furnace and other high-temperature equipment due to its superior low thermal conductivity, low expansion coefficient, high purity, good mechanical properties, and high temperature resistance. In this paper, the lightweight chopped CFC was prepared by the method of liquid phase dispersion of chopped carbon fibers-vacuum filtration-curing molding-high temperature carbonization and graphitization. The thermal conductivity of lightweight chopped CFC with different densities (0.12, 0.16 and 0.18 g/cm3) was studied, and the thermal conductivity of lightweight chopped CFC at high temperature was also studied. The results show that the thermal conductivity (1000 °C) of the chopped CFC gradually increases with the increasing density from 0.179 W/(mK) (0.12 g/cm3) to 0.190 W/(mK) (0.16 g/cm3), 0.282 W/(mK) (0.18 g/cm3), indicating that with the increasing density, the thermal conduction path of the carbon fiber increases, the thermal conductivity of the CFC material increases, and then the thermal insulation performance decreases. In addition, with the increasing temperature, the phonon vibration increases, the thermal conductivity of the CFC material also increases, and then the thermal insulation performance decreases. [ABSTRACT FROM AUTHOR]

This study makes a significant contribution to the field of groundwater potential mapping (GWPM) by exploring the application of ensemble learning models (ELMs), specifically boosting ensemble models (BEMs), which have not been fully utilized in GWPM. By employing six ELMs (random forest, AdaBoost, XGBoost, CatBoost, GBDT and LightGBM), along with Tree-structured Parzen Estimator in Luoning County, China, this study identifies key indicators (topographic position index, distance to rivers and topographic wetness index) and demonstrates the superior model performance of XGBoost compared to other ELMs. Additionally, correlation analysis confirms the accuracy of XGBoost in predicting relationships between important indicators and groundwater potentials. Finally, the findings provide valuable insights for sustainable groundwater management strategies in Luoning County and emphasize the need for further exploration of ELMs, development of comprehensive performance evaluation and indicator systems, reduction of the inconsistencies between indicators and predication results and practical research to support future sustainable groundwater management. [ABSTRACT FROM AUTHOR]

Groundwater nitrate contamination has emerged as a pressing global concern. Given its potential for long-term impacts on aquifers, protective measures should primarily focus on prevention. Drawing on the theory of groundwater vulnerability (GV), the original DRASTIC model and parameters related to human activities are employed as inputs and integrated with the LightGBM regression algorithm to facilitate nitrate index (NI) prediction tasks. The SHAP analysis is conducted to effectively examine the contribution of parameters to the NI prediction and interpret the issue of parameter interactions. In addition, to mitigate the limitations of the intrinsic GV model, a composite nitrate index (CNI) is developed by linearly combining the DRASTIC index with the NI. The framework presented in this study provides adaptive strategies for managing groundwater resources over different time periods. A representative region for arid and semiarid climates, the Yinchuan region, is studied using the framework. As compared to 2012, the intrinsic GV index has changed spatially in 2022. Human activities have increased the influence of the nitrate concentration as shown by the Pearson correlation coefficient of −0.082 between the DRASTIC index and nitrate concentration. A significant increase in pollution levels was predicted by NI, ranging from −0.116 to 0.968. According to SHAP analysis, the significant increase in NI levels in 2022 was mainly due to high-value industrial and agricultural production. In 2022, 12.02% of the areas had an increase of at least 0.549 in the CNI. 42.1% of the areas were classified as moderate or high CNI levels. The farm was identified as a high-contributing source to nitrate pollution. The small-scale agricultural and livestock activities in non-urban areas also contribute to groundwater pollution. Dynamic groundwater management strategies need to be implemented in high-growth and high-level CNI areas. [Display omitted] • DRASTIC model and anthropogenic parameters were combined to predict nitrate index. • The innovative composite nitrate SGV framework were applied to Yinchuan regions. • SHAP analysis results support an increase in nitrate loading caused by human activity. • Dynamic groundwater management strategies were designed for different levels of CNI. [ABSTRACT FROM AUTHOR]