Cathode Materials for Lithium Sulfur Batteries: Design, Synthesis, and Electrochemical Performance

With the rapid development of electronic devices, portable electronics, and electric vehicles, the energy density and cycle life of LIBs are insufficient for the demands. Based on the reaction mechanisms, lithium-sulfur (Li-S) batteries have a high specific capacity of 1672 mAh/g, with a theoretical energy density up to 2600 Wh/Kg. However, the sulfur cannot serve as cathode individually because of its isolation nature and soluble compounds, which necessitates a second component as a conducting matrix and sulfur host. Thus, sulfur cathodes have diversified through microstructure designing with various materials, including inorganic compounds, polymers, carbon materials, and their hybrids, which should be satisfied several essential requirements, such as high stable incorporation with sulfur, high electrical conductivity of electrode materials, and loose framework to suffer the volume expansion of cathode during charge-discharge process. These investigations may provide the effective routes to prepare different new cathode materials with unique structures and morphologies for Li-S batteries, which improve cycling stability, coulombic efficiency, and rate capacity of the electrode at higher current density

Application of Artificial Fish Swarm Algorithm in Radial Basis Function Neural Network

Neural network is one of the branches with the most active research, development and application in computational intelligence and machine study. Radial basis function neural network (RBFNN) has achieved some success in more than one application field, especially in pattern recognition and functional approximation. Due to its simple structure, fast training speed and excellent generalization ability, it has been widely used. Artificial fish swarm algorithm (AFSA) is a new swarm intelligent optimization algorithm derived from the study on the preying behavior of fish swarm. This algorithm is not sensitive to the initial value and the parameter selection, but strong in robustness and simple and easy to realize and it also has parallel processing capability and global searching ability. This paper mainly researches the weight and threshold of AFSA in optimizing RBFNN. The simulation experiment proves that AFSA-RBFNN is significantly advantageous in global optimization capability and that it has outstanding global optimization ability and stability

Spatio-Temporal Patterns of Water Table and Vegetation Status of a Deserted Area

Understanding groundwater-vegetation interactions is crucial for sustaining fragile environments of desert areas such as the Horqin Sandy Land (HSL) in northern China. This study examined spatio-temporal variations in the water table and the associated vegetation status of a 9.71 km2 area that contains meadowland, sandy dunes, and intermediate transitional zones. The depth of the water table and hydrometeorologic parameters were monitored and Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were utilized to assess the vegetation cover. Spatio-temporal variations over the six-year study period were examined and descriptive groundwater-vegetation associations developed by overlaying a water table depth map onto a vegetation index map derived from MODIS. The results indicate that the water table depends on the local topography, localized geological settings, and human activities such as reclamation, with fluctuations occurring at annual and monthly scales as a function of precipitation and potential evapotranspiration. Locations where the water table is closer to the surface tend to have more dense and productive vegetation. The water table depth is more closely associated with vegetative density in meadowlands than in transitional zones, and only poorly associated with vegetation in sandy dunes

Upscaling Stem to Community-Level Transpiration for Two Sand-Fixing Plants: Salix Gordejevii and Caragana Microphylla

The information on transpiration is vital for sustaining fragile ecosystem in arid/semiarid environment, including the Horqin Sandy Land (HSL) located in northeast China. However, such information is scarce in existing literature. The objectives of this study were to: (1) measure sap flow of selected individual stems of two sand-fixing plants, namely Salix gordejevii and Caragana microphylla, in HSL; and (2) upscale the measured stem-level sap flow for estimating the community-level transpiration. The measurements were done from 1 May to 30 September 2015 (i.e., during the growing season). The upscaling function was developed to have one dependent variable, namely sap flow rate, and two independent variables, namely stem cross-sectional area of Salix gordejevii and leaf area of Caragana microphylla. The results indicated that during the growing season, the total actual transpiration of the Salix gordejevii and Caragana microphylla communities was found to be 287 31 and 197 24 mm, respectively, implying that the Salix gordejevii community might consume 1.5 times more water than the Caragana microphylla community. For this same growing season, based on the Penman-Monteith equation, the total actual evapotranspiration for these two communities was estimated to be 323 and 229 mm, respectively. The daily transpiration from the upscaling function was well correlated with the daily evapotranspiration by the Penman-Monteith equation (coefficient of determination R2 0.67), indicating the applicability of this upscaling function, a useful tool for managing and restoring sand-fixing vegetations. 2017 by the authors

Heat stress affects tassel development and reduces the kernel number of summer maize

Maize grain yield is drastically reduced by heat stress (HTS) during anthesis and early grain filling. However, the mechanism of HTS in reproductive organs and kernel numbers remains poorly understood. From 2018 to 2020, two maize varieties (ND372, heat tolerant; and XY335, heat sensitive) and two temperature regimens (HTS, heat stress; and CK, natural control) were evaluated, resulting in four treatments (372CK, 372HTS, 335CK, and 335HTS). HTS was applied from the nine-leaf stage (V9) to the anthesis stage. Various morphological traits and physiological activities of the tassels, anthers, and pollen from the two varieties were evaluated to determine their correlation with kernel count. The results showed that HTS reduced the number of florets, tassel volume, and tassel length, but increased the number of tassel branches. HTS accelerates tassel degradation and reduces pollen weight, quantity, and viability. Deformation and reduction in length and volume due to HTS were observed in both the Nongda 372 (ND372) and Xianyu 335 (XY335) varieties, with the average reductions being 22.9% and 35.2%, respectively. The morphology of the anthers changed more conspicuously in XY335 maize. The number of kernels per spike was reduced in the HTS group compared with the CK group, with the ND372 and XY335 varieties showing reductions of 47.3% and 59.3%, respectively. The main factors underlying the decrease in yield caused by HTS were reductions in pollen quantity and weight, tassel rachis, and branch length. HTS had a greater effect on the anther shape, pollen viability, and phenotype of XY335 than on those of ND372. HTS had a greater impact on anther morphology, pollen viability, and the phenotype of XY335 but had no influence on the appearance or dissemination of pollen from tassel

JourneyDB: A Benchmark for Generative Image Understanding

While recent advancements in vision-language models have had a transformative impact on multi-modal comprehension, the extent to which these models possess the ability to comprehend generated images remains uncertain. Synthetic images, in comparison to real data, encompass a higher level of diversity in terms of both content and style, thereby presenting significant challenges for the models to fully grasp. In light of this challenge, we introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images within the context of multi-modal visual understanding. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images, each paired with the corresponding text prompts that were employed in their creation. Furthermore, we additionally introduce an external subset with results of another 22 text-to-image generative models, which makes JourneyDB a comprehensive benchmark for evaluating the comprehension of generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension in relation to both content and style interpretation. These benchmarks encompass prompt inversion, style retrieval, image captioning, and visual question answering. Lastly, we evaluate the performance of state-of-the-art multi-modal models when applied to the JourneyDB dataset, providing a comprehensive analysis of their strengths and limitations in comprehending generated content. We anticipate that the proposed dataset and benchmarks will facilitate further research in the field of generative content understanding. The dataset is publicly available at https://journeydb.github.io.Comment: Accepted to the Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023

A CsI hodoscope on CSHINE for Bremsstrahlung {\gamma}-rays in Heavy Ion Reactions

Bremsstrahlung $\gamma$ production in heavy ion reactions at Fermi energies carries important physical information including the nuclear symmetry energy at supra-saturation densities. In order to detect the high energy Bremsstrahlung $\gamma$ rays, a hodoscope consisting of 15 CsI(Tl) crystal read out by photo multiplier tubes has been built, tested and operated in experiment. The resolution, efficiency and linear response of the units to $\gamma$ rays have been studied using radioactive source and $({\rm p},\gamma)$ reactions. The inherent energy resolution of $1.6\%+2\%/E_{\gamma}^{1/2}$ is obtained. Reconstruction method has been established through Geant 4 simulations, reproducing the experimental results where comparison can be made. Using the reconstruction method developed, the whole efficiency of the hodoscope is about $2.6\times 10^{-4}$ against the $4\pi$ emissions at the target position, exhibiting insignificant dependence on the energy of incident $\gamma$ rays above 20 MeV. The hodoscope is operated in the experiment of $^{86}$Kr + $^{124}$Sn at 25 MeV/u, and a full $\gamma$ energy spectrum up to 80 MeV has been obtained.Comment: 9 pages, 19 figure

Deep Learning for Microfluidic-Assisted <i>Caenorhabditis elegans</i> Multi-Parameter Identification Using YOLOv7

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union ([email protected]) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an [email protected] of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms

Probing high-momentum component in nucleon momentum distribution by neutron-proton bremsstrahlung {\gamma}-rays in heavy ion reactions

The high momentum tail (HMT) of nucleons, as a signature of the short-range correlations in nuclei, has been investigated by the high-energy bremsstrahlung $\gamma$ rays produced in $^{86}$Kr + $^{124}$Sn at 25 MeV/u. The energetic photons are measured by a CsI(Tl) hodoscope mounted on the spectrometer CSHINE. The energy spectrum above 30 MeV can be reproduced by the IBUU model calculations incorporating the photon production channel from $np$ process in which the HMTs of nucleons is considered. A non-zero HMT ratio of about $15\%$ is favored by the data. The effect of the capture channel $np \to d\gamma$ is demonstrated