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LiCoO2 coated with graphene and PSS:PEDOT（GP-LCO）is synthesized by a facile and scalable method. The structure and morphology of LiCoO2（LCO）and GP-LCO have been investigated by X-ray diffraction（XRD）and scanning electron microscopy（SEM）. Transmission electron microscopy（TEM）further demonstrate the existence of graphene and PSS:PEDOT. The electrochemical properties of the composites are investigated by cyclic voltammetry and AC impedance measurements, which show that the conductive film of graphene（1%）and PSS:PEDOT（2%）on the surface significantly decreases the charge transfer resistance and improves the cycle stability of LiCoO2. Galvanostatic charge-discharge test reveals that with only 2% Super P as conductive additive, the GP-LCO delivers a capacity of 173.9 mAh/g at 0.1 C and 118.0 mAh/g at 10 C, which are higher than the pristine LCO in the potential range of 3.0-4.5 V（vs. Li/Li+）. Moreover, it is clearly noticed that the GP-LCO exhibits a capacity retention of 76.6% after 100 cycles at 0.1C, while the LCO only presents a capacity retention of 41.1%.

Abstract. Mountain pine beetle (MPB) outbreaks in the western United States result in widespread tree mortality, transforming forest structure within watersheds. While there is evidence that these changes can alter the timing and quantity of streamflow, there is substantial variation in both the magnitude and direction of hydrologic responses, and the climatic and environmental mechanisms driving this variation are not well understood. Herein, we coupled an eco-hydrologic model (RHESSys) with a beetle effects model and applied it to a semiarid watershed, Trail Creek, in the Bigwood River basin in central Idaho, USA, to examine how varying degrees of beetle-caused tree mortality influence water yield. Simulation results show that water yield during the first 15 years after beetle outbreak is controlled by interactions between interannual climate variability, the extent of vegetation mortality, and long-term aridity. During wet years, water yield after a beetle outbreak increased with greater tree mortality; this was driven by mortality-caused decreases in evapotranspiration. During dry years, water yield decreased at low-to-medium mortality but increased at high mortality. The mortality threshold for the direction of change was location specific. The change in water yield also varied spatially along aridity gradients during dry years. In wetter areas of the Trail Creek basin, post-outbreak water yield decreased at low mortality (driven by an increase in ground evaporation) and increased when vegetation mortality was greater than 40 % (driven by a decrease in canopy evaporation and transpiration). In contrast, in more water-limited areas, water yield typically decreased after beetle outbreaks, regardless of mortality level (although the driving mechanisms varied). Our findings highlight the complexity and variability of hydrologic responses and suggest that long-term (i.e., multi-decadal mean) aridity can be a useful indicator for the direction of water yield changes after a disturbance.

The safety and efficacy of the lipid nanoparticle (LNP) delivery system are crucial for the successful development of messenger RNA vaccines. We designed and synthesized a series of ketal ester lipids (KELs), featuring a biodegradable ketal moiety in the linker and ester segments in the tail. Through iterative optimization of the head and tail groups of KELs, we tuned the pKa and molecular shapes, and identified (4S)‐KEL12 as a safe and efficient ionizable lipid for mRNA delivery. (4S)‐KEL12 LNP showed significantly higher delivery efficacy and lower toxicity than the DLin‐MC3‐DMA LNP. In comparison to SM‐102 LNP, (4S)‐KEL12 LNP exhibited better spleen tropism, reduced liver tropism, and hepatotoxicity. Additionally, (4S)‐KEL12 demonstrated good biodegradability following intramuscular or intravenous injection. Notably, (4S)‐KEL12 LNP encapsulated with a therapeutic mRNA cancer vaccine elicited robust cellular immune responses leading to substantial tumor regression along with prolonged survival in tumor‐bearing mice. Our results suggest that (4S)‐KEL12 LNP holds great promise for mRNA vaccine delivery. The comprehensive analysis of the structure‐activity relationship, toxicity, biodegradability, distribution, expression, efficacy, and stereochemistry of these LNPs will greatly contribute to the rational design and discovery of novel lipid‐based delivery systems. [ABSTRACT FROM AUTHOR]

Electrocatalysts are extensively employed to suppress the shuttling effect in lithium-sulfur (Li-S) batteries. However, it remains challenging to probe the sulfur redox reactions and mechanism at the electrocatalyst/LiPS interface after the active sites are covered by the solid discharge products Li2S/Li2S2. Here, we demonstrate the intrinsic autocatalytic activity of the Li2S (100) plane towards lithium polysulfides on single-atom nickel (SANi) electrocatalysts. Guided by theoretical models and experimental data, it is concluded that LiPS dissociates into Li2S2 and short-chain LiPS on the Li2S (100) plane. Subsequently, Li2S2 undergoes further lithiation to Li2S on the Li2S (100) surface, generating a new Li2S (100) layer, thus enabling the autocatalytic formation of a new Li2S (100) surface. Benefiting from the autocatalytic growth of Li2S, the concentration of LiPS in the electrolyte remains at a lower level, enabling Li-S batteries under high loading and low electrolyte conditions to exhibit superior electrochemical performance. This study reveals the autocatalytic growth of Li2S crystals at the solid-liquid interface in lithium-sulfur batteries enabling good electrochemical performance under high loading and low electrolyte conditions. [ABSTRACT FROM AUTHOR]

Upper and lower limb rehabilitation training is essential for restoring patients' physical movement ability and enhancing muscle strength and coordination. However, traditional rehabilitation training methods have limitations, such as high costs, low patient participation, and lack of real-time feedback. The purpose of this study is to design and implement a rehabilitation training evaluation system based on virtual reality to improve the quality of patients' rehabilitation training. This paper proposes an upper and lower limb rehabilitation training evaluation system based on virtual reality technology, aiming to solve the problems existing in traditional rehabilitation training. The system provides patients with an immersive and interactive rehabilitation training environment through virtual reality technology, aiming to improve patients' participation and rehabilitation effects. This study used Kinect 2.0 and Leap Motion sensors to capture patients' motion data and transmit them to virtual training scenes. The system designed multiple virtual scenes specifically for different upper and lower limbs, with a focus on hand function training. Through these scenes, patients can perform various movement training, and the system will provide real-time feedback based on the accuracy of the patient's movements. The experimental results show that patients using the system show higher participation and better rehabilitation training effects. Compared with patients receiving traditional rehabilitation training, patients using the virtual reality system have significantly improved movement accuracy and training participation. The virtual reality rehabilitation training evaluation system developed in this study improves the quality of patients' rehabilitation and provides personalized treatment information to medical personnel through data collection and analysis, promoting the systematization and personalization of rehabilitation training. This system is innovative and has broad application potential in the field of rehabilitation medicine. [ABSTRACT FROM AUTHOR]

【Objective】 Soil temperature affects crop growth and biogeochemical processes in terrestrial systems, and understanding its variation is hence important to evaluate the impact of global warming on agricultural production and soil gas emissions. The aim of this paper is to analyze the change in soil temperature over the past 50 years at Wudaogou Hydrological Station in Anhui province, China. 【Method】 The analysis was based on data measured from 1971 to 2020. Monthly average temperature at different soil depths from 0 to 320 cm, as well as their relationship with ambient air temperature was analyzed, using linear trend estimation, M-K test and Morlet wavelet method. 【Result】 ①The temperature at different soil layers has changed. Except in the period from June to September, soil temperature in other months has been in increase, with the largest increase seen in March and the least increase in October. Temperature in the depths of 0~160 cm has been increasing, with the increasing rate decreasing along the soil depth. In contrast, the temperature at the depth of 320cm has been dropping. ②The wavelet analysis showed two dominant periods in the monthly-average temperature: 3~6, 8~12 a, and a further period of 15~25 a. ③The M-K mutation test showed that the temperature at depths from 0 to 320 cm had undergone abrupt changes in 2012, 2017, 2016, 2019 and 1972. ④The temperature at different soil depths was correlated with ambient air temperature differently, with the temperature in the depths of 0~80 cm being more sensitive to air temperature. No correlation was found between the air temperature and soil temperature at the depth of 320 cm. 【Conclusion】 The monthly average temperature at different soil layers has been in increase over the past centuries, likely due to global warming, though the increasing rate and its correlation with air temperature varied with soil depth.

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

In structural health monitoring, because the number of sensors used is far lower than the number of degrees of freedom of the structure being monitored, the optimization problem of the location and number of sensors in the structures is becoming more and more prominent. However, spatial grid structures are complex and diverse, and their dynamic characteristics are complex. It is difficult to accurately measure their vibration information. Therefore, an appropriate optimization method must be used to determine the optimal positioning of sensor placement. Aiming at the problem that spatial grid structures have many degrees of freedom and the fact that it is difficult to obtain complete vibration information, this paper analyzed the typical EI method, MKE method, and EI-MKE method in the arrangement of the measuring points, and it was verified that the EI method was more suitable for the vibration detection of spatial grid structures through the example of a plane truss and spatial grid structures. Measuring points under the assumption of structural damage were explored, and it was proposed that there might have been a stable number of measuring points that could cover the possible vibration mode changes in the structures. At the same time, combined with the three-level improved Guyan recursive technique, in order to obtain better complete modal parameters, the influence of the number of measuring points on the complete vibration mode information was studied. It was concluded that MACd was better than MACn as the quantitative target. [ABSTRACT FROM AUTHOR]

As managers of agricultural and natural resources are confronted with uncertainties in global change impacts, the complexities associated with the interconnected cycling of nitrogen, carbon, and water present daunting management challenges. Existing models provide detailed information on specific sub-systems (e.g., land, air, water, and economics). An increasing awareness of the unintended consequences of management decisions resulting from interconnectedness of these sub-systems, however, necessitates coupled regional earth system models (EaSMs). Decision makers’ needs and priorities can be integrated into the model design and development processes to enhance decision-making relevance and “usability” of EaSMs. BioEarth is a research initiative currently under development with a focus on the U.S. Pacific Northwest region that explores the coupling of multiple stand-alone EaSMs to generate usable information for resource decision-making. Direct engagement between model developers and non-academic stakeholders involved in resource and environmental management decisions throughout the model development process is a critical component of this effort. BioEarth utilizes a bottom-up approach for its land surface model that preserves fine spatial-scale sensitivities and lateral hydrologic connectivity, which makes it unique among many regional EaSMs. This paper describes the BioEarth initiative and highlights opportunities and challenges associated with coupling multiple stand-alone models to generate usable information for agricultural and natural resource decision-making.

The Three Gorges Project is the largest hydraulic hub project in the world, and its hydrological management has altered the hydrological environment of the reservoir area, affecting the carbon emission and absorption of the reservoir water. In this study, representative hydrological stations in the Three Gorges Reservoir area were selected as research sites to monitor the CO2 and CH4 fluxes of the reservoir water and nine environmental factors during the drainage and impoundment periods in 2022. The study aimed to explore the mechanisms of hydrological management and environmental factors on greenhouse gas emissions. The results showed that the mean CO2 fluxes of the reservoir water during the drainage and impoundment periods were (103.82 ± 284.86) mmol·m−2·d−1 and (134.39 ± 62.41) mmol·m−2·d−1, respectively, while the mean CH4 fluxes were (1.013 ± 0.58) mmol·m−2·d−1 and (0.571 ± 0.70) mmol·m−2·d−1, respectively, indicating an overall "carbon source" characteristic. Through the evaluation of the characteristic importance of environmental factors, it was found that the main controlling factors of CO2 flux during the drainage period were total phosphorus (TP) and chlorophyll a (Chl_a), while total nitrogen (TN) was the main controlling factor during the impoundment period. Dissolved organic carbon (DOC) was the main controlling factor of CH4 flux during the different periods. Based on these findings, a "source-sink" mechanism of CO2 and CH4 in the Three Gorges Reservoir water under reservoir regulation was proposed. This study is of great significance for revealing the impact of reservoir construction on global ecosystem carbon cycling and providing scientific support for formulating "emission reduction and carbon sequestration" plans and achieving "dual carbon" goals. [ABSTRACT FROM AUTHOR]

Given their intricate nature and inherent ambiguity, sarcastic texts often mask deeper emotions, making it challenging to discern the genuine feelings behind the words. The proposal of the sarcasm detection task is to assist us with more accurately understanding the true intention of the speaker. Advanced methods, such as deep learning and neural networks, are widely used in the field of sarcasm detection. However, most research mainly focuses on sarcastic texts in English, as other languages lack corpora and annotated datasets. To address the challenge of low-resource languages in sarcasm detection tasks, a zero-shot cross-lingual transfer learning method is proposed in this paper. The proposed approach is based on prompt learning and aims to assist the model with understanding downstream tasks through prompts. Specifically, the model uses prompt templates to construct training data into cloze-style questions and then trains them using a pre-trained cross-lingual language model. Combining data augmentation and contrastive learning can further improve the capacity of the model for cross-lingual transfer learning. To evaluate the performance of the proposed model, we utilize a publicly accessible sarcasm dataset in English as training data in a zero-shot cross-lingual setting. When tested with Chinese as the target language for transfer, our model achieves F1-scores of 72.14% and 76.7% on two test datasets, outperforming the strong baselines by significant margins. [ABSTRACT FROM AUTHOR]

This study introduces a novel sensitivity analysis approach to assess the resilience and susceptibility of hydrologic systems to the stresses of climate change, moving away from conventional top-down methodologies. By exploring the hydrological sensitivity of the upper Kızılırmak River basin using the Variable Infiltration Capacity (VIC) hydrologic model, we employed a sensitivity-based approach as an alternative to the traditional Global Climate Model (GCM)-based methods, providing more insightful information for water managers. Considering the consistent projections of increasing temperature over this region in GCMs, the hydrologic system was perturbed to examine gradients of a more challenging climate characterized by warming and drying conditions. The sensitivity of streamflow, snow water equivalent, and evapotranspiration to temperature (T) and precipitation (P) variations under each perturbation or "reference" climate was quantified. Results indicate that streamflow responds to T negatively under all warming scenarios. As the reference climates become drier, streamflow sensitivity to P increases, indicating that meteorological drought impacts on water availability could be exacerbated. These results suggest that there will be heightened difficulty in managing water resources in the region if it undergoes both warming and drying due to the following setbacks: (1) water availability will shift away from the summer season of peak water demand due to the warming effects on the snowpack, (2) annual water availability will likely decrease due to a combination of warming and lower precipitation, and (3) streamflow sensitivity to hydroclimatic variability will increase, meaning that there will be more extreme impacts to water availability. Water managers will need to plan for a larger set of extreme conditions. [ABSTRACT FROM AUTHOR]