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This paper proposes an algorithm for the conversion of raster data to hexagonal DGGSs in the GPU by redevising the encoding and decoding mechanisms. The researchers first designed a data structure based on rhombic tiles to convert the hexagonal DGGS to a texture format acceptable for GPUs, thus avoiding the irregularity of the hexagonal DGGS. Then, the encoding and decoding methods of the tile data based on space-filling curves were designed, respectively, so as to reduce the amount of data transmission from the CPU to the GPU. Finally, the researchers improved the algorithmic efficiency through thread design. To validate the above design, raster integration experiments were conducted based on the global Aster 30 m digital elevation dataDEM, and the experimental results showed that the raster integration accuracy of this algorithms was around 1 m, while its efficiency could be improved to more than 600 times that of the algorithm for integrating the raster data to the hexagonal DGGS data, executed in the CPU. Therefore, the researchers believe that this study will provide a feasible method for the efficient and stable integration of massive raster data based on a hexagonal grid, which may well support the organization of massive raster data in the field of GIS.

Ionogels have emerged as promising candidates for low‐grade thermal energy harvesting due to their leak‐free electrolytes, exceptional flexibility, thermal stability, and high thermopower. While substantial progress in the thermoelectric performance of p‐type ionogels, research on n‐type ionic materials lags behind. Striking a harmonious balance between high mechanical performance and thermoelectric properties remains a formidable challenge. This work presents an advanced n‐type ionogel system integrating polyethylene glycol diacrylate (PEGDA), hydroxyethyl methacrylate (HEMA), 1‐allyl‐3‐methylimidazolium chloride ([AMIM]Cl), and bacterial cellulose (BC) through a rational design strategy. The synergistic combination of photo‐polymerization and hydrogen‐bonding networks effectively immobilizes imidazolium cations while enabling rapid chloride ion transport, creating a pronounced cation‐anion mobility disparity that yields a substantial negative ionic Seebeck coefficient of −7.16 mV K⁻¹. Furthermore, BC's abundant hydroxyl groups establish multivalent hydrogen bonds within the ternary polymer matrix, endowing the composite with exceptional mechanical properties—notably a tensile strength of 3.2 MPa and toughness of 4.1 MJ m⁻3. Moreover, the ionogel exhibits sensitive responses to stimuli such as pressure, strain, and temperature. The thermoelectric modules fabricated can harness body heat to illuminate a bulb, showcasing great potential for low‐grade energy harvesting and ultra‐sensitive sensing. An n‐type thermoelectric ion gel with excellent thermoelectric, mechanical, and sensing properties is fabricated via photopolymerization, leveraging an imidazole cation anchoring strategy to enhance thermoelectric performance and multiple hydrogen bonds from bacterial cellulose to improve mechanical strength.

With the increasing demand for dissolving pulp, large quantities of hemicelluloses were generated and abandoned. These hemicelluloses are very promising biomass resources for preparing carbon spheres. However, the pore structures of the carbon spheres obtained from biomass are usually poor, which extensively limits their utilization. Herein, the carbon microspheres derived from hemicelluloses were prepared using hydrothermal carbonization and further activated with different activators (KOH, K2CO3, Na2CO3, and ZnCl2) to improve their electrochemical performance as supercapacitors. After activation, the specific surface areas of these carbon spheres were improved significantly, which were in the order of ZnCl2 > K2CO3 > KOH > Na2CO3. The carbon spheres with high surface area of 2025 m2/g and remarkable pore volume of 1.07 cm3/g were achieved, as the carbon spheres were activated by ZnCl2. The supercapacitor electrode fabricated from the ZnCl2-activated carbon spheres demonstrated high specific capacitance of 218 F/g at 0.2 A/g in 6 M KOH in a three-electrode system. A symmetric supercapacitor was assembled in 2 M Li2SO4 electrolyte, and the carbon spheres activated by ZnCl2 showed excellent electrochemical performance with high specific capacitance (137 F/g at 0.5 A/g), energy densities (15.4 Wh/kg), and good cyclic stability (95% capacitance retention over 2000 cycles).

Aims Aboveground plant litter inputs are important sources of soil carbon (C). We aimed to establish how experimentally altered litter inputs affect soil C to 1-m depth across different ecosystems, and over different timeframes. Methods We performed a meta-analysis of 237 studies across 248 sites worldwide to assess the influence of treatment magnitude, treatment duration, initial soil C content, and climate on the response of soil C to altered aboveground litter inputs. Results Overall, soil C content was lower under litter removal, but higher under litter addition compared to controls. The effects of litter manipulation were apparent throughout the soil profile and were related to treatment magnitude. Soil C content declined markedly with increasing duration of litter removal, whereas the positive effect of litter addition attenuated over time. Cropland management practices (bare fallow or additional straw incorporation) had similar effects on soil C to litter removal and addition treatments. Conclusions Our study reveals rapid and consistent changes in soil C content with altered litter inputs and provides important insights into plant residue management to enhance soil C sequestration. We highlight the need for long-term experiments, with a greater focus on the processes underpinning soil C storage in different ecosystems.

Plant diversity has a strong impact on a plethora of ecosystem functions and services, especially ecosystem carbon (C) storage. However, the potential context-dependency of biodiversity effects across ecosystem types, environmental conditions and carbon pools remains largely unknown. In this study, we performed a meta-analysis by collecting data from 95 biodiversity-ecosystem functioning (BEF) studies across 60 sites to explore the effects of plant diversity on different C pools, including aboveground and belowground plant biomass, soil microbial biomass C and soil C content across different ecosystem types. The results showed that ecosystem C storage was significantly enhanced by plant diversity, with stronger effects on aboveground biomass than on soil C content. Moreover, the response magnitudes of ecosystem C storage increased with the level of species richness and experimental duration across all ecosystems. The effects of plant diversity were more pronounced in grasslands than in forests. Furthermore, the effects of plant diversity on belowground plant biomass increased with aridity index in grasslands and forests, suggesting that climate change might modulate biodiversity effects, which are stronger under wetter conditions but weaker under more arid conditions. Taken together, these results provide novel insights into the important role of plant diversity in ecosystem C storage across critical C pools, ecosystem types and environmental contexts.

Flood forecasting using traditional physical hydrology models requires consideration of multiple complex physical processes including the spatio-temporal distribution of rainfall, the spatial heterogeneity of watershed sub-surface characteristics, and runoff generation and routing behaviours. Data-driven models offer novel solutions to these challenges, though they are hindered by difficulties in hyperparameter selection and a decline in prediction stability as the lead time extends. This study introduces a hybrid model, the RS-LSTM-Transformer, which combines Random Search (RS), Long Short-Term Memory networks (LSTM), and the Transformer architecture. Applied to the typical Jingle watershed in the middle reaches of the Yellow River, this model utilises rainfall and runoff data from basin sites to simulate flood processes, and its outcomes are compared against those from RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models. It was evaluated against RS-LSTM, RS-Transformer, RS-BP, and RS-MLP models using the Nash–Sutcliffe Efficiency Coefficient (NSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Bias percentage as metrics. At a 1-h lead time during calibration and validation, the RS-LSTM-Transformer model achieved NSE, RMSE, MAE, and Bias values of 0.970, 14.001m 3 /s, 5.304m 3 /s, 0.501% and 0.953, 14.124m 3 /s, 6.365m 3 /s, 0.523%, respectively. These results demonstrate the model's superior simulation capabilities and robustness, providing more accurate peak flow forecasts as the lead time increases. The study highlights the RS-LSTM-Transformer model's potential in flood forecasting and the advantages of integrating various data-driven approaches for innovative modelling.

The types of carbides and their effects on the fatigue failure mechanism in carburized CSS-42L steel were systematically studied in the present investigation. The results indicate that the main carbides in carburized CSS-42L steel are Cr-rich M23C6 carbides and Mo-rich M6C carbides. M23C6 carbides precipitate along grain boundaries and interconnect, forming network carbides. Rolling contact fatigue (RCF) tests reveal that fatigue cracks in CSS-42L steel can initiate both at the contact surface and within the subsurface. During RCF, the spalling of large-sized, networked M23C6 carbides creates micro-spalling pits on the contact surface, inducing local stress concentration that triggers the initiation of surface cracks. The surface cracks initially propagate perpendicularly to the contact surface and then shift to propagate parallelly to the contact surface, ultimately causing large-scale spalling of the surface layer. Subsurface cracks initiate at a position approximately 100 μm below the contact surface, with their propagation direction roughly parallel to the contact surface. Meanwhile, the development of subsurface cracks can connect with surface cracks, leading to the expansion of surface micro-pitting. Network carbides facilitate the propagation of secondary cracks, leading to the formation of grid-distributed crack networks.

To address the challenge of instability control in the collaborative bearing structure of stope roof and barrier pillars in short-wall interval filling mining, a combined approach of theoretical analysis, numerical simulation, and field application was adopted. A mechanical model of the roof–pillars collaborative bearing structure was established, and the analytical solutions of roof bending stress were derived. The effects of mining mechanics and structural parameters on the stability of the roof–pillars structure were further analyzed, and the optimal branch roadway width, number of barrier pillars, and elastic modulus of the backfilling body were determined. The results were validated through numerical simulation and field testing. The results indicate that: ① The analytical solution of the roof deflection equation shows significant variation in the maximum bending stress distribution along the branch roadway roof, with the peak stress occurring at the branch roadway roof, which is identified as the most vulnerable area for roof instability. ② Orthogonal tests and variance analysis reveal that the elastic modulus of barrier pillars exerts the most significant influence on roof stability (variance 120.3), followed by branch roadway width (56.5) and the interval number of barrier pillars (19.8). The optimized parameter combination includes a branch roadway width of 5 m, two barrier pillars (10 m in width), and a backfilling body with an elastic modulus of 1.2 GPa. Under these conditions, the mean maximum roof bending stress is 14.8 MPa, with a range of 22.4 MPa, achieving both mechanical equilibrium and economic efficiency. ③ Numerical simulations show that during three mining cycles, the stress distribution of barrier pillars gradually transitions from a “double-peak” to a “single-peak” pattern. The maximum vertical stress increases from 15 MPa to 24.65 MPa; however, the barrier pillars remain stable, the development of the surrounding rock plastic zone is controllable, and the roof deformation (163.5 mm) is within the safety threshold. ④ In industrial applications, field measurements at the 2-106 filling working face show that the maximum deformation of the roof and floor is 54 mm, and roof separation is less than 24 mm, thereby validating the effectiveness of the optimized parameters. This study reveals the collaborative bearing mechanism of the roof–pillars system under the condition of two unfilled branch roadways, proposes optimization criteria for structural parameters, and provides theoretical support and engineering reference for the safe application of short-wall interval filling mining technology.

The geophysical exploration of mines mainly involves advanced detection to delineate areas with abnormal water content for the prevention and drainage of hydrogeological disasters in coal mines. In this study, transient electromagnetic method was used for the detection of coal mine working faces. After the coal mine working face is connected, “geophysical exploration and drilling” work must be carried out before backfilling. After the transient electromagnetic data of geophysical exploration is collected, the basic result map is formed through data processing. Combined with professional mapping processing software Voxler, the software performs 3D visualization processing, which can display detection results more intuitively and stereoscopically, making it easy to analyze and interpret anomalies in space, and achieving good application results.

Solid‐state batteries represent the future of energy storage technology, offering improved safety and energy density. Garnet‐type Li7La3Zr2O12 (LLZO) solid‐state electrolytes‐based solid‐state lithium batteries (SSLBs) stand out for their appealing material properties and chemical stability. Yet, their successful deployment depends on conquering interfacial challenges. This review article primarily focuses on the advancement of interfacial engineering for LLZO‐based SSLBs. We commence with a concise introduction to solid‐state electrolytes and a discussion of the challenges tied to interfacial properties in LLZO‐based SSLBs. We deeply explore the correlations between structure and properties and the design principles vital for achieving an ideal electrode/electrolyte interface. Subsequently, we delve into the latest advancements and strategies dedicated to overcoming these challenges, with designated sections on cathode and anode interface design. In the end, we share our insights into the advancements and opportunities for interface design in realizing the full potential of LLZO‐based SSLBs, ultimately contributing to the development of safe and high‐performance energy storage solutions. This review article delves into the challenges encountered by garnet‐based solid‐state lithium batteries (SSLBs) and the latest developments in interfacial engineering specific to these batteries. It aims to provide a comprehensive understanding of the design principles and the potential future of interfacial engineering in enhancing the performance of garnet‐based SSLBs.