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INTRODUCTION: Image recognition technology has immense potential to be applied in industrial energy systems for energy conservation. However, the low recognition accuracy and generalization ability under actual operation conditions limit its commercial application. OBJECTIVES: To improve the recognition accuracy and generalization ability, a novel image recognition method integrating deep learning and domain knowledge was applied to assist energy saving and emission reduction for industrial energy systems. METHODS: As a typical industrial scenario, the defrosting control in the refrigeration system was selected as the specific optimization object. By combining deep learning algorithm with domain knowledge, a residual-based convolutional neural network model (RCNN) was proposed specifically for frosty state recognition, which features the residual input and average pooling output. Based on the real-time recognition of frosty levels, a defrosting control optimization method was proposed to initiate and terminate the defrosting operation on demand. RESULTS: By combining the advanced image recognition technique with specific energy domain knowledge, the proposed RCNN enables both high recognition accuracy and strong generalization ability. The recognition accuracy of RCNN reached 95.06% for the trained objects and 93.67% for non-trained objects while that of only 75.86% for the conventional CNN. By adopting the presented system optimization method assisted by RCNN, the defrosting frequency, accumulated time and energy consumption were 53.8%, 57.02% and 34.5% less than the original control method. Furthermore, the environmental and cost analysis illustrated that the annual reduction in CO(2) emissions is 2145.21 to 3412.84 kg and the payback time was less than 2.5 years which was far below the service life. CONCLUSION: The technical feasibility and significant energy-saving benefits of deep learning-based image recognition method were demonstrated through the field experiment. Our study shows the great application potential of image recognition technology and promotes carbon neutrality in industrial energy systems.

The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of state of nuclear matter (nEOS) with heavy ion collisions at a few hundreds MeV/u beam energies. Based on Geant 4 packages, the fast simulations of the detector responses to the collision events generated using transport model are conducted. The overall performance of CEE, including spatial resolution of hits, momentum resolution of tracks and particle identification ability has been investigated. Various observables proposed to probe the nEOS, such as the production of light clusters, $\rm t/^3He$ yield ratio, the radial flow, $\pi^{-}/\pi^{+}$ yield ratio and the neutral kaon yields, have been reconstructed. The feasibility of studying nEOS beyond the saturation density via the aforementioned observables to be measured with CEE has been demonstrated., Comment: 16 pages, 25 figures

In remote-sensing image processing tasks, images with higher resolution always result in better performance on downstream tasks, such as scene classification and object segmentation. However, objects in remote-sensing images often have low resolution and complex textures due to the imaging environment. Therefore, effectively reconstructing high-resolution remote-sensing images remains challenging. To address this concern, we investigate embedding context information and object priors from remote-sensing images into current deep learning super-resolution models. Hence, this paper proposes a novel remote-sensing image super-resolution method called Context-Guided Constrained Network (CGC-Net). In CGC-Net, we first design a simple but effective method to generate inverse distance maps from the remote-sensing image segmentation maps as prior information. Combined with prior information, we propose a Global Context-Constrained Layer (GCCL) to extract high-quality features with global context constraints. Furthermore, we introduce a Guided Local Feature Enhancement Block (GLFE) to enhance the local texture context via a learnable guided filter. Additionally, we design a High-Frequency Consistency Loss (HFC Loss) to ensure gradient consistency between the reconstructed image (HR) and the original high-quality image (HQ). Unlike existing remote-sensing image super-resolution methods, the proposed CGC-Net achieves superior visual results and reports new state-of-the-art (SOTA) performance on three popular remote-sensing image datasets, demonstrating its effectiveness in remote-sensing image super-resolution (RSI-SR) tasks.

Tunable lens technology inspired by the human eye has opened a new paradigm of smart optical devices for a variety of applications due to unique characteristics such as lightweight, low cost, and facile fabrication over conventional lens assemblies. The fast-growing demands for tunable optical lenses in consumer electronics, medical diagnostics, and optical communications require the lens to have a large focal length modulation range and high compactness. Herein, for the first time, an all-solid tunable soft lens driven by highly transparent dielectric elastomer actuators (DEAs) based on poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) and waterborne polyurethane (PEDOT:PSS/WPU) transparent electrodes is developed. The deformation of the tunable soft lens is achieved by the actuation of DEAs, mimicking the change of the surface profile of the human eye to achieve remarkable focal length variations. Upon electrical activation, this tunable soft lens can vary its original focal length by 209%, which is one of the highest among current tunable soft lenses and far beyond that of the human eye. This study demonstrates that transparent DEAs are capable of achieving focus-variation functions, and potentially useful in artificial robotic vision, visual prostheses, and adjustable glasses, which will induce significant effects on the future development of tunable optics.

According to Beck's cognitive model of depression, depression has an effect on schema, which leads to biased cognition. Depressive states and depression symptoms are considered to occur on a continuum, with only qualitative differences between them. However, it remains unclear whether there are significant differences in information processing among healthy individuals. In this study, we investigated the event- related potentials (ERPs) and event-related spectrum perturbation (ERSP) of healthy individuals with various depressive states during emotional stimulation. We found that participants in a high depressive state had a lower P300 amplitude. Moreover, we identified significant differences in fast and slow neural responses in the frontal and parietal lobes, which might indicate a cognitive bias in healthy individuals.

According to Beck's cognitive model of depression, Major Depressive Disorder (MDD) has an effect on schema which leads to distorted perceptions. Depressive states and MDD are considered to occur on a continuum, and there are not only qualitative but also quantitative differences between them. However, it remains unclear whether there are significant differences in information processing among healthy individuals. In this study, we investigate the Event Related Potentials (ERPs) and behavior of healthy subjects with various depressive states during emotional stimuli. We have found that subjects with high depressive states have slower reaction time and lower P300 amplitude. Besides, we have found latency between frontal and parietal lobe, which might support the top-down visual mechanism.

Significant developments have been made in the past few decades in lanthanide (Ln) ions doped fluorosilicate glass-ceramics (Flusi-GCs). As a novel generation of luminescence materials with a wide range of applications, Flusi-GCs as a single host combine the advantages of glass and ceramics/crystals as well as fluorides and silicates. In this review, the chemical design principles and experimental procedures of Flusi-GCs are summarized in detail. Flusi-GCs are categorized as those containing heavy metal fluoride PbxCd1–xF2, rare earth (RE) fluoride RF3 (R = Y, La, Gd), alkaline earth fluoride MF2 (M = Ca, Sr, Ba), fluoride xMF2–yRF3 (R = Y, La–Lu), mAF-nRF3 (A = Li, Na, K), KTF3 (T = Zn, Mn) and K2SiF6 nanocrystals (NCs). Theoretical breakthroughs mainly by molecular dynamic (MD) simulation have been recapitulated as efficient routes for composition-design, nano-crystallization-prediction, and performance-optimization of Flusi-GCs containing target fluoride NCs. Essential research progresses pertaining photonic applications have been made in random lasers, communication amplifiers, optical fibers, and spectral converters, white light-emitting-diodes (WLEDs), and thermal sensors. In the end, we propose three prospective future research directions for Flusi-GCs.

To solve a series of problems, such as damage, increased impurities and obstacles caused by medium corrosion, and erosion in the long-term use of the pipeline, a multi-body distributed adaptive pipeline plugging and repairing robot with good driving performance and strong reliability is proposed by using the modular design method. By establishing the obstacle-crossing model of the robot, the traction equation and obstacle-crossing equation of the driving wheel and supporting wheel are studied. It can be seen from the equation that reducing the deflection angle of the driving wheel or reducing the speed of the motor spindle can improve the obstacle-crossing ability. The driving unit model is established in ADAMS software, and its running speed, driving force, and obstacle-crossing height are simulated and analysed. The model prototype is established and verified with experiments. When the deflection angle of the robot driving wheel is set to 20° to 35°, it meets the design requirements. When the deflection angle of the driving wheel is set to 20°, the robot has large traction, good running stability and high obstacle-crossing height. The maximum obstacle-crossing height is 6 mm. At this time, the robot reaches the best running state. The design of an adaptive leakage plugging robot for buried pipelines provides an important reference for the research and development of pipeline emergency prevention and control equipment.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the world's first satellite-borne photon-counting laser altimeter with unprecedented detection performance. Noise removal is an important process applied to raw data and determines the quality of the end product. Assuming that the sparse spatial distribution of noise photons makes them more easily isolated than signal photons, we propose a noise-removal algorithm without input parameters based on quadtree isolation. MATLAS was used to evaluate the performance of our algorithm. We compare our algorithm to the improved density-based spatial clustering of applications with noise (DBSCAN) algorithm. Experimental results show that our algorithm accurately extracts signal photons from raw data and is superior to the improved DBSCAN in accuracy and time efficiency. This novel algorithm makes it possible to efficiently remove noise from photon-counting light detection and ranging (LiDAR) data.

We investigate the development of the directed, $v_1$, and elliptic flow, $v_2$, in heavy ion collisions in mid-central Au+Au reactions at $E_\mathrm{lab}=1.23 A$ GeV. We demonstrate that the elliptic flow of hot and dense matter is initially positive ($v_2>0$) due to the early pressure gradient. This positive $v_2$ transfers its momentum to the spectators, which leads to the creation of the directed flow $v_1$. In turn, the spectator shadowing of the in-plane expansion leads to a preferred decoupling of hadrons in the out-of-plane direction and results in a negative $v_2$ for the observable final state hadrons. We propose a measurement of $v_1-v_2$ flow correlations and of the elliptic flow of dileptons as methods to pin down this evolution pattern. The elliptic flow of the dileptons allows then to determine the early-state EoS more precisely, because it avoids the strong modifications of the momentum distribution due to shadowing seen in the protons. This opens the unique opportunity for the HADES and CBM collaborations to measure the Equation-of-State directly at 2-3 times nuclear saturation density., 8 pages, 5 figures

State-of-the-art 3D object detectors are usually trained on large-scale datasets with high-quality 3D annotations. However, such 3D annotations are often expensive and time-consuming, which may not be practical for real applications. A natural remedy is to adopt semi-supervised learning (SSL) by leveraging a limited amount of labeled samples and abundant unlabeled samples. Current pseudolabeling-based SSL object detection methods mainly adopt a teacher-student framework, with a single fixed threshold strategy to generate supervision signals, which inevitably brings confused supervision when guiding the student network training. Besides, the data augmentation of the point cloud in the typical teacher-student framework is too weak, and only contains basic down sampling and flip-and-shift (i.e., rotate and scaling), which hinders the effective learning of feature information. Hence, we address these issues by introducing a novel approach of Hierarchical Supervision and Shuffle Data Augmentation (HSSDA), which is a simple yet effective teacher-student framework. The teacher network generates more reasonable supervision for the student network by designing a dynamic dual-threshold strategy. Besides, the shuffle data augmentation strategy is designed to strengthen the feature representation ability of the student network. Extensive experiments show that HSSDA consistently outperforms the recent state-of-the-art methods on different datasets. The code will be released at https://github.com/azhuantou/HSSDA., Comment: Accepted by CVPR2023

Let $n$ be a positive integer and $q$ a prime power. We prove that a refined version of Brou\'{e}'s abelian defect group conjecture holds for unipotent $\ell$-blocks of ${\rm GL}_n(q)$, where $\ell\nmid q$. We also give a sufficient condition on general $\ell$-blocks of ${\rm GL}_n(q)$ to satisfy the refined abelian defect group conjecture., Comment: version 2, minor typo corrections

Utilizing the first reduced Steenrod $3$-th power operation and higher order Bocksteins, we obtain the homotopy decomposition of the suspension of an $(n-1)$-connected $(2n+2)$ dimensional Poincar\'{e} duality complex after localization away from $2$ in the range $2\leq n\leq 5$., Comment: 17 pages

Speech pre-training has shown great success in learning useful and general latent representations from large-scale unlabeled data. Based on a well-designed self-supervised learning pattern, pre-trained models can be used to serve lots of downstream speech tasks such as automatic speech recognition. In order to take full advantage of the labed data in low resource task, we present an improved pre-training method by introducing a supervision-enhanced acoustic unit (SEAU) pattern to intensify the expression of comtext information and ruduce the training cost. Encoder representations extracted from the SEAU pattern are used to generate more representative target units for HuBERT pre-training process. The proposed method, named SeHuBERT, achieves a relative word error rate reductions of 10.5% and 4.9% comared with the standard HuBERT on Turkmen speech recognition task with 500 hours and 100 hours fine-tuning data respectively. Extended to more languages and more data, SeHuBERT can aslo achieve a relative word error rate reductions of approximately 10% at half of the training cost compared with HuBERT., Submitted to ICASSP 2023

Multilingual end-to-end models have shown great improvement over monolingual systems. With the development of pre-training methods on speech, self-supervised multilingual speech representation learning like XLSR has shown success in improving the performance of multilingual automatic speech recognition (ASR). However, similar to the supervised learning, multilingual pre-training may also suffer from language interference and further affect the application of multilingual system. In this paper, we introduce several techniques for improving self-supervised multilingual pre-training by leveraging auxiliary language information, including the language adversarial training, language embedding and language adaptive training during the pre-training stage. We conduct experiments on a multilingual ASR task consisting of 16 languages. Our experimental results demonstrate 14.3% relative gain over the standard XLSR model, and 19.8% relative gain over the no pre-training multilingual model., Subimitted to ICASSP 2023

Through synthesizing novel difluoro-monomer (1,6-N,N’-bis(4-fuorobenzamide) trimethylhexane (BFBT)) and adjusting the structure of bisphenol monomer (hydroquinone (HQ) or 4,4’-biphenol (BH)), two kinds of novel semi-aromatic polyamides (HQ-BBT and BH-BBT) containing ether bond and pendant methyl were synthesized in this work. The inherent viscosities of these obtained polymers were in the range of 0.921–1.151 dLg−1. The polyamides HQ-BBT and BH-BBT exhibited high glass transition temperatures (Tg) of 144–160 °C and weight loss temperature (T5%) of 401–406 °C. The resultant polyamides HQ-BBT and BH-BBT showed good melting flowability with complex viscosities of 1931–252 Pa s and 3829–376 Pa s, respectively, and they can be hot-pressed into thin films, which showed tensile strengths of 64.4–64.6 MPa and storage modulus over 1.28 GPa at about 150 °C, indicating their good thermal and mechanical properties. Interestingly, we found that the aggregation structures of these two kinds of polymers were quite different with the addition of different bisphenol. HQ-BBT with HQ as monomer was amorphous and can be dissolved in polar solvents such as NMP, DMF and so on, while BH-BBT with BH as monomer was found to be crystalline polymer with moderate melting point of 245 °C and much higher glass transition temperature of 160 °C. These moderate Tm and Tg were very beneficial for the melting processing of the high-performance polymers.

Polyaniline (PANI)-based thermoelectric (TE) materials have attracted considerable attention for flexible electronic energy harvesting devices because of properties such as solution processibility,...

A series of neutron absorbing materials with good neutron absorbing capacity, high strength and good thermal property were designed and prepared in this work. First of all, polyphenylene sulfide containing different mole content of salicylic acid (SAPPS) in the main chain was synthesized by nucleophilic substitution polymerization under high pressure. Then the composites with different content of nano Gd2O3 and modified PPS were prepared by melt blending method. The testing results indicated that the copolymers SAPPS were synthesized successfully, there was an interface interaction between nano Gd2O3 and the matrix without the need for surfactants or coupling agents. Additionally, the content of nano Gd2O3 had no obvious influence on the thermal property of the composites. While following with the increase of the content of nano Gd2O3, the tensile strength of the composites increased firstly and then decreased, when the content of nano Gd2O3 was 10 wt%, the tensile strength of 10%Gd2O3/5%SAPPS reached the maximum value of 74.9 MPa. The results of neutron shielding testing indicated that the content of nano-particles had a large effect on the neutron shielding rate of composites. The neutron shielding rate of 50%Gd2O3/5%SAPPS composite was up to 83%. All of these results indicated that the Gd2O3/5%SAPPS had potential to be applied to the high-temperature resistance and thermal shielding materials in nuclear and aerospace applications.

Given a closed, smooth, connected, orientable $4$-manifold $M$, whose integral homology groups can have $2$-torsion, we determine the homotopy decomposition of the double suspension $\Sigma^2M$ as wedge sums of some elementary $\mathbf{A}_3^3$-complexes, which are $2$-connected finite complexes of dimension at most $6$. Furthermore, we utilize the Postnikov square (or equivalently Pontryagin square) to find sufficient conditions for the homotopy decompositions of $\Sigma^2M$ to desuspend to that of $\Sigma M$.

Team situation awareness (TSA) is one of the key factors that affect team decision-making and implementation. TSA is influenced by performance shaping factors (PSFs) in digital nuclear power plants (NPPs). In order to identify the influencing relationships and extent between TSA and PSFs, a model is needed to describe and correlate them. Firstly, based on TSA cognitive process, the main PSFs influencing the level of TSA are identified, including team knowledge and experience level, information display quality, attention and attitude, and team stress level. Then a conceptual model of TSA influencing factors is proposed to explore the path relationships and influencing mechanism between TSA and PSFs, and a structural equation model (SEM) that relates TSA to its PSFs is developed subsequently. Finally, by analyzing human factor events and small deviation reports, data from 178 samples were obtained and substituted into the structural equation model to analyze and identify the relationships between the PSFs. The results show that the preceding PSFs have significant effects on TSA. Based on path coefficients, positive effects were: team knowledge and experience level (0.504), information display quality (0.370), attention and attitude (0.249). Negative effect was: team stress level (−0.384). The results of this study can provide a theoretical basis for the prevention and control of TSA errors, and a qualitative analysis model for the quantitative evaluation of TSA reliability.

In order to solve the inefficient use of multi-source heterogeneous data information cross fusion and the low accuracy of prediction of landslide displacement, the current research proposed a new prediction model combining variable selection, sparrow search algorithm, and deep extreme learning machine. A cement mine in Fengxiang, Shaanxi Province, was studied as a case. The study first identified the variables related to landslide displacement of rock slope, and removed redundant variables by using Pearson correlation and gray correlation analysis. To avoid the impacts of random input weights and random thresholds in the DELM model, the SSA algorithm is used to optimize the model’s parameters, which can generate the optimal parameter combinations. The results showed an enhanced generalization ability of the model by removal of redundant variables by Pearson correlation and gray correlation analysis, and higher accuracy in the prediction of landside displacement of rock slope by SSA-DELM compared to other traditional machine learning algorithms. The current study is significant in the literature on rock slope disaster analysis.

For the combinatorial optimization displacement algorithms based on heuristic search or swarm intelligence, it is a difficult problem to maintain the spatial relationship and distribution characteristics of map features. This article proposes an optimal algorithm to buildings displacement based on immune genetic algorithm (IGA) with the constraint of safety zones. In the study, the displacement problem of buildings is defined as a multi-objective optimization problem, and then the immune genetic algorithm is used to search the optimal solution. In order to keep the spatial relationship and globe spatial distribution characteristics of buildings as far as possible and avoid topology errors, Voronoi diagram and buffer areas are used to construct the displacement safety zone of each building to limit the displacement range of buildings; meanwhile, the strategy to shift the building group as a whole is used to keep local building patterns. Finally, the effectiveness of the improved algorithm is verified by taking the displacement of buildings in a block of beijing as an example. The results indicate that the algorithm can not only solve the proximity conflicts, but also keep the spatial relationship and spatial distribution characteristics of map objects.

The perennial ornamental peanut Arachis glabrata represents one of the most adaptable wild Arachis species. This study used PacBio combined with BGISEQ-500 RNA-seq technology to study the transcriptome and gene expression dynamics of A. glabrata. Of the total 109,747 unique transcripts obtained, >90,566 transcripts showed significant homology to known proteins and contained the complete coding sequence (CDS). RNA-seq revealed that 1229, 1039, 1671, 3923, 1521 and 1799 transcripts expressed specifically in the root, stem, leaf, flower, peg and pod, respectively. We also identified thousands of differentially expressed transcripts in response to drought, salt, cold and leaf spot disease. Furthermore, we identified 30 polyphenol oxidase encoding genes associated with the quality of forage, making A. glabrata suitable as a forage crop. Our findings presented the first transcriptome study of A. glabrata which will facilitate genetic and genomics studies and lays the groundwork for a deeper understanding of the A. glabrata genome.

The current study is designed to investigate whether the entrained oscillations could affect performance and infor- mation processing in short-term working memory and an arithme- tic task. According to some previous studies, it is known that the flicker frequencies close to 10.2 Hz increase senior people’s mem- orization of the words that subjects learned before. The current study is neither just studying the effects of entrained oscillation of alpha peak waves, nor focusing a broader range at 0, 6, 11.5, or 18Hz. In the experiment, besides a memory task, subjects also need to do the arithmetic task. Current results indicate that the reaction time of the experimental group is faster than the control group. Entrained oscillations may help people become more sensitive and cognitive.

Marine symbiotic fungi represent an intriguing source of discovery of novel secondary metabolites with various biological activities. Sea anemones are benthic marine invertebrates, however, the cultivable symbiotic fungi residing in the sea anemones are paid few attentions compared to those derived from their cnidarian counterparts. Here we show the identification of antimicrobial secondary metabolites from the sea anemone-derived symbiotic fungi. Out of five isolated fungal strains, only the strain SMA01 showed strong antimicrobial activities, which was assigned into the genus Emericella based on the morphological characteristics and the ITS sequencing. Media swift from liquid fermentation to solid rice medium presented little influence on its antibacterial activity. A chemical investigation of the ethyl acetate extract of the Emericella sp. SMA01 led to discovery of the primary antibiotic metabolite phenazine-1-carboxylic acid. The IC50 values of the phenazine-1-carboxylic acid against Phytophthora capsici, Gibberella zeae, and Verticillium dahliae were determined to be 23.26–53.89 µg/mL. To the best of our knowledge, this was the first report of Emericella sp. in sea anemones. The current study may benefit understanding of the defensive chemical interactions between the symbiotic fungi and their host sea anemones.

Ribosomes are required for plant growth and development, and ribosome biogenesis-deficient mutants generally display auxin-related phenotypes. Although the relationship between ribosome dysfunction and auxin is known, many aspects of this subject remain to be understood. We previously reported that MIDASIN 1 (MDN1) is an essential pre-60S ribosome biogenesis factor (RBF) in Arabidopsis. In this study, we further characterized the aberrant auxin-related phenotypes of mdn1-1, a weak mutant allele of MDN1. Auxin response is disturbed in both shoots and roots of mdn1-1, as indicated by the DR5:GUS reporter. By combining transcriptome profiling analysis and reporter gene detection, we found that expression of genes involved in auxin biosynthesis, transport, and signaling is changed in mdn1-1. Furthermore, MDN1 deficiency affects the post-transcriptional regulation and protein distribution of PIN-FORMED 2 (PIN2, an auxin efflux facilitator) in mdn1-1 roots. These results indicate that MDN1 is required for maintaining the auxin system. More interestingly, MDN1 is an auxin-responsive gene, and its promoter can be targeted by multiple AUXIN RESPONSE FACTORs (ARFs), including ARF7 and ARF19, in vitro. Indeed, in arf7 arf19, the auxin sensitivity of MDN1 expression is significantly reduced. Together, our results reveal a coordination mechanism between auxin and MDN1-dependent ribosome biogenesis for regulating plant development.

Previous studies have suggested that the plastid translation elongation factor, elongation factor thermo unstable (EF-Tu), encoded by RAB GTPASE HOMOLOG 8D (RAB8D) is essential for plant growth. Here, through analyzing the root phenotypes of two knock-down alleles of RAB8D (rab8d-1 and rab8d-2), we further revealed a vital role for RAB8D in primary root development through the maintenance of both the stem cell niche (SCN) and the meristem. Our results showed that RAB8D deficiency affects the root auxin response and SCN maintenance signaling. RAB8D interacts with GENOMES UNCOUPLED 1 (GUN1) in vivo. Further analysis revealed that GUN1 is over-accumulated and is required for both stem cell death and maintenance of root architecture in rab8d Arabidopsis mutants. The ATAXIA-TELANGIECTASIA-MUTATED (ATM)-SUPPRESSOR OF GAMMA RESPONSE 1 pathway is involved in the regulation of root meristem size through upregulating SIAMESE-RELATED 5 expression in the rab8d-2 allele. Moreover, ETHYLENE RESPONSE FACTOR 115 is highly expressed in rab8d-2, which plays a role in further quiescent center division. Our observations not only characterized the role of RAB8D in root development, but also uncovered functions of GUN1 and ATM in response to plastid EF-Tu deficiency.

Kidney damages caused by cadmium are considered to be one of the most dangerous consequences for the human body. This study aimed to investigate the protective effects of fucoxanthin supplementation on mice models subjected to cadmium-induced kidney damage. The mice treated with cadmium chloride (CdCl2) were observed to have significantly reduced the cross-section area of glomeruli. Cadmium exposure has also caused the damage of the structural integrity of mitochondria and increased blood urea nitrogen (BUN), kidney injury molecule 1 (KIM1), and neutrophil gelatinase associated lipocalin (NGAL) levels. Peroxidase (POD), superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) levels in cadmium-exposed mice were markedly declined. Caspase3, caspase8, and caspase9 gene expressions in association with apoptosis were dramatically elevated in renal tissues. The CdCl2 treated mice were orally administered with 50 mg/kg Shenfukang, 10 mg/kg, 25 mg/kg, and 50 mg/kg fucoxanthin for 14 days. The results revealed that high doses of fucoxanthin administration significantly decreased BUN, KIM1, NGAL levels, increasing POD, SOD, CAT, and ascorbate APX levels. Fucoxanthin administration also promoted recovery of the renal functions, micro-structural organization, and ultra-structural organization in the renal cells. In summary, the ameliorative effects of fucoxanthin supplementation against cadmium-induced kidney damage were mediated via inhibiting oxidative stress and apoptosis, promoting the recovery of structural integrity of mitochondria.

The next generation Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2)/Advanced Topographic Laser Altimeter System (ATLAS) was launched in 2018 to provide large amounts of spaceborne laser altimetry data. The joint block adjustment with laser altimetry data and satellite remote sensing images can promote limited stereoscopic positioning accuracy without ground control points (GCPs). However, there are two problems in the joint block adjustment, that is, the reliable ATLAS laser altimetry points (LAPs) selection and the discrepancies of image-object points. To solve the above problems, a surface point cloud extraction algorithm based on wavelet reconstruction was proposed. Then, the LAPs selection constraint model was established, so the terrain influence was resolved. Finally, a block joint adjustment method has been proposed with remote sensing images and the reliable LAPs considering error of their plane coordinates, which could achieve the accurate corresponding image points of the reliable LAPs, remarkably decrease the discrepancies of image-object points. Fourteen pairs of ATLAS data, seven ZY3-02 stereo images and eleven Mapping Satellite-1 stereo images in Zhengzhou, China were collected to validate the method performance. Experiment results have shown that the consistency of the image-object points and the positioning accuracy of stereoscopic images without GCPs have been improved. Compared with the free net block adjustment, the height, east and north positioning accuracy of ZY3-02 and Mapping Satellite-1 stereo images have increased by 61%, 56%, 60% and 56%, 38%, 37%, respectively.