Search Results (16)

Lithium (Li) metal is one of the most promising anode materials for next-generation, high-energy, Li-based batteries due to its exceptionally high specific capacity and low reduction potential. Nonetheless, intrinsic challenges such as detrimental interfacial reactions, significant volume expansion, and dendritic growth present considerable obstacles to its practical application. This review comprehensively summarizes various recent strategies for the modification and protection of metallic lithium anodes, offering insight into the latest advancements in electrode enhancement, electrolyte innovation, and interfacial design, as well as theoretical simulations related to the above. One notable trend is the optimization of electrolytes to suppress dendrite formation and enhance the stability of the electrode–electrolyte interface. This has been achieved through the development of new electrolytes with higher ionic conductivity and better compatibility with Li metal. Furthermore, significant progress has been made in the design and synthesis of novel Li metal composite anodes. These composite anodes, incorporating various additives such as polymers, ceramic particles, and carbon nanotubes, exhibit improved cycling stability and safety compared to pure Li metal. Research has used simulation computing, machine learning, and other methods to achieve electrochemical mechanics modeling and multi-field simulation in order to analyze and predict non-uniform lithium deposition processes and control factors. In-depth investigations into the electrochemical reactions, interfacial chemistry, and physical properties of these electrodes have provided valuable insights into their design and optimization. It systematically encapsulates the state-of-the-art developments in anode protection and delineates prospective trajectories for the technology’s industrial evolution. This review aims to provide a detailed overview of the latest strategies for enhancing metallic lithium anodes in lithium-ion batteries, addressing the primary challenges and suggesting future directions for industrial advancement. Full article

Metabolism-associated fatty liver disease (MAFLD), a growing health problem worldwide, is one of the major risks for the development of cirrhosis and liver cancer. Oral administration of nobiletin (NOB), a natural citrus flavonoid, modulates the gut microbes and their metabolites in mice. In the present study, we established a mouse model of MAFLD by subjecting mice to a high-fat diet (HFD) for 12 weeks. Throughout this timeframe, NOB was administered to investigate its potential benefits on gut microbial balance and bile acid (BA) metabolism using various techniques, including 16S rRNA sequencing, targeted metabolomics of BA, and biological assays. NOB effectively slowed the progression of MAFLD by reducing serum lipid levels, blood glucose levels, LPS levels, and hepatic IL-1β and TNF-α levels. Furthermore, NOB reinstated diversity within the gut microbial community, increasing the population of bacteria that produce bile salt hydrolase (BSH) to enhance BA excretion. By exploring further, we found NOB downregulated hepatic expression of the farnesoid X receptor (FXR) and its associated small heterodimer partner (SHP), and it increased the expression of downstream enzymes, including cholesterol 7α-hydroxylase (CYP7A1) and cytochrome P450 27A1 (CYP27A1). This acceleration in cholesterol conversion within the liver contributes to mitigating MAFLD. The present findings underscore the significant role of NOB in regulating gut microbial balance and BA metabolism, revealing that long-term intake of NOB plays beneficial roles in the prevention or intervention of MAFLD. Full article

In light of the depletion of conventional energy sources, it is imperative to conduct research and development on sustainable alternative energy sources. Currently, electrochemical energy storage and conversion technologies such as fuel cells and metal-air batteries rely heavily on precious metal catalysts like Pt/C and IrO₂, which hinders their sustainable commercial development. Therefore, researchers have devoted significant attention to non-precious metal-based catalysts that exhibit high efficiency, low cost, and environmental friendliness. Among them, perovskite oxides possess low-cost and abundant reserves, as well as flexible oxidation valence states and a multi-defect surface. Due to their advantageous structural characteristics and easily adjustable physicochemical properties, extensive research has been conducted on perovskite-based oxides. However, these materials also exhibit drawbacks such as poor intrinsic activity, limited specific surface area, and relatively low apparent catalytic activity compared to precious metal catalysts. To address these limitations, current research is focused on enhancing the physicochemical properties of perovskite-based oxides. The catalytic activity and stability of perovskite-based oxides in Oxygen Reduction Reaction/Oxygen Evolution Reaction (ORR/OER) can be enhanced using crystallographic structure tuning, cationic regulation, anionic regulation, and nano-processing. Furthermore, extensive research has been conducted on the composite processing of perovskite oxides with other materials, which has demonstrated enhanced catalytic performance. Based on these different ORR/OER modification strategies, the future challenges of perovskite-based bifunctional oxygen electrocatalysts are discussed alongside their development prospects. Full article

Infectious bursal disease (IBD) is an acute, highly contagious, immunosuppressive, and fatal infectious disease of young chickens caused by infectious bursal disease virus (IBDV). Since 2017, a new trend has been discovered in the IBDV epidemic, with very virulent IBDV (vvIBDV) and novel variant IBDV (nVarIBDV) becoming the two current dominant strains in East Asia including China. In this study, we compared the biological characteristics of the vvIBDV (HLJ0504 strain), nVarIBDV (SHG19 strain), and attenuated IBDV (attIBDV, Gt strain) using specific-pathogen-free (SPF) chicken infection model. The results showed that vvIBDV distributed in multiple tissues, replicated the fastest in lymphoid organs such as bursa of Fabricius, induced significant viremia and virus excretion, and is the most pathogenic virus with a mortality of more than 80%. The nVarIBDV had a weaker replication capability and did not kill the chickens but caused severe damage to the central immune organ bursa of Fabricius and B lymphocytes and induced significant viremia and virus excretion. The attIBDV strain was found not to be pathogenic. Further studies preliminarily suggested that the expression level of inflammatory factors triggered by HLJ0504 was the highest, followed by the SHG19 group. This study is the first to systematically compare the pathogenic characteristics of three IBDVs closely related to poultry industry from the perspectives of clinical signs, micro-pathology, virus replication, and distribution. It is of great importance to obtain an extensive knowledge of epidemiology, pathogenicity, and comprehensive prevention, and control of various IBDV strains. Full article

As blockchain technology continues to advance, smart contracts, a core component, have increasingly garnered widespread attention. Nevertheless, security concerns associated with smart contracts have become more prominent. Although machine-learning techniques have demonstrated potential in the field of smart-contract security detection, there is still a lack of comprehensive review studies. To address this research gap, this paper innovatively presents a comprehensive investigation of smart-contract vulnerability detection based on machine learning. First, we elucidate common types of smart-contract vulnerabilities and the background of formalized vulnerability detection tools. Subsequently, we conduct an in-depth study and analysis of machine-learning techniques. Next, we collect, screen, and comparatively analyze existing machine-learning-based smart-contract vulnerability detection tools. Finally, we summarize the findings and offer feasible insights into this domain. Full article

Dunaliella salina is currently one of the most commercially valuable microalgae species in the world. In reponse to selenite, D. salina is a microalgae with a high selenium content, thereby increasing its value, which is crucial for increasing its economic value as a nutrional supplement. However, the effects of selenite on D. salina are still unclear, and its molecular mechanism of the response to selenite stress is also elusive. Here, in order to study the effects of selenite on D. salina and the corresponding regulatory mechanism, we characterized the physiological phenotypes of D. salina under different selenite concentrations and carried out a quantitative proteomic study. The results showed that the effective concentration for 50% growth inhibition (EC₅₀) of the algae was 192.7 mg/L after 11 days of cultivation. When selenite concentration was lower than 100 mg/L, selenite did not hinder the growth of D. salina in the early stage, but shortened the cell growth cycle, although cell growth was significantly inhibited when the concentration of selenium was higher than 250 mg/L. Bioaccumulation experiments showed that the content of intracellular selenium in D. salina cells reached the highest level under the treatment with 50 mg/L selenite, and the contents of total selenium and organic selenium in D. salina cells were 499.77 μg/g and 303.01 μg/g (dry weight), respectively. Proteomic analysis revealed that a series of proteins related to stress responses, amino acid metabolism and energy production pathways were profoundly altered by the selenite treatment. Glutathione peroxidase (GPX7), a selenium-containing protein, was identified in the group given the selenium treatment. Moreover, proteins involved in photoreactions and oxidative phosphorylation were significantly upregulated, indicating that D. salina effectively balanced the energy demand and energy production under selenite stress. This study provides novel insights into the responses to selenite of D. salina, a microalgae candidate as a biological carrier of selenium and would be helpful for the development of industrial strains rich in selenium. Full article

Accurate segmentation of lung nodules from pulmonary computed tomography (CT) slices plays a vital role in the analysis and diagnosis of lung cancer. Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in the automatic segmentation of lung nodules. However, they are still challenged by the large diversity of segmentation targets, and the small inter-class variances between the nodule and its surrounding tissues. To tackle this issue, we propose a features complementary network according to the process of clinical diagnosis, which made full use of the complementarity and facilitation among lung nodule location information, global coarse area, and edge information. Specifically, we first consider the importance of global features of nodules in segmentation and propose a cross-scale weighted high-level feature decoder module. Then, we develop a low-level feature decoder module for edge feature refinement. Finally, we construct a complementary module to make information complement and promote each other. Furthermore, we weight pixels located at the nodule edge on the loss function and add an edge supervision to the deep supervision, both of which emphasize the importance of edges in segmentation. The experimental results demonstrate that our model achieves robust pulmonary nodule segmentation and more accurate edge segmentation. Full article

The phase equilibrium and phase transformation of the Al–Si–Y ternary system were investigated in 80 annealed alloys using an electron probe microanalysis (EPMA), X-ray diffractometry (XRD) and differential scanning calorimetry (DSC). The phase equilibrium at 773 K was determined, and the phase distribution and solid solubility of the Al–Si–Y isothermal section at 773 K were obtained. A total of 23 three-phase zones and 4 two-phase zones were obtained, and 2 new ternary compounds, AlSi₄Y₅ and Al₂Si₃Y₅, were identified from the non-aluminum-rich corner. Additionally, the phase transition temperatures of representative alloys were determined by the DSC method, and then the phase transition temperatures were processed to obtain the experimental points of vertical sections. In the Al–Si–Y alloy system, the phase diagrams of the vertical sections with X(Al) = 90 at.%, 80 at.%, 70 at.% and 60 at.% at the aluminum-rich corner were calculated, and then the experimental points were inserted into the vertical section phase diagrams. The results of the vertical sectional experiments obtained from the validation experiments are in good agreement with the vertical sectional data obtained from the calculations, indicating that the validated thermodynamic description is useful for the microstructure design of the aluminum-rich corner of the Al–Si–Y ternary alloy. Full article

The isothermal section of the La–Co–Ni ternary system at 723 K has been constructed in this work by using X-ray diffraction (XRD), scanning electron microscopy, and energy dispersion spectroscopy techniques (SEM-EDS). The experimental results show no existence of ternary compounds at 723 K. The isothermal section consists of 16 two-phase regions and 8 three-phase regions. La₃Co and La₃Ni, La₂Co₃ and La₂Ni₃, La₂Co₇ and La₂Ni₇, and LaCo₅ and LaNi₅ form a continuous solid solution. The ternary solid solubility of Ni in LaCo₁₃ phase and La₂Co_1.7 phase was determined to be 15.61 at.% and 9.61 at.%, respectively. The solid solubility of Co in the LaNi₃, La₇Ni₃, and LaNi phases was 18.07 at.%, 5.62 at.%, and 8.49 at.%, respectively. The present experimental results are important for the design of La(Fe,Si)₁₃-based magnetic refrigeration materials. Full article

Stocking density presents a key factor affecting livestock and poultry production on a large scale as well as animal welfare. However, the current manual counting method used in the hemp duck breeding industry is inefficient, costly in labor, less accurate, and prone to double counting and omission. In this regard, this paper uses deep learning algorithms to achieve real-time monitoring of the number of dense hemp duck flocks and to promote the development of the intelligent farming industry. We constructed a new large-scale hemp duck object detection image dataset, which contains 1500 hemp duck object detection full-body frame labeling and head-only frame labeling. In addition, this paper proposes an improved attention mechanism YOLOv7 algorithm, CBAM-YOLOv7, adding three CBAM modules to the backbone network of YOLOv7 to improve the network’s ability to extract features and introducing SE-YOLOv7 and ECA-YOLOv7 for comparison experiments. The experimental results show that CBAM-YOLOv7 had higher precision, and the recall, mAP@0.5, and mAP@0.5:0.95 were slightly improved. The evaluation index value of CBAM-YOLOv7 improved more than those of SE-YOLOv7 and ECA-YOLOv7. In addition, we also conducted a comparison test between the two labeling methods and found that the head-only labeling method led to the loss of a high volume of feature information, and the full-body frame labeling method demonstrated a better detection effect. The results of the algorithm performance evaluation show that the intelligent hemp duck counting method proposed in this paper is feasible and can promote the development of smart reliable automated duck counting. Full article

At present, fish farming still uses manual identification methods. With the rapid development of deep learning, the application of computer vision in agriculture and farming to achieve agricultural intelligence has become a current research hotspot. We explored the use of facial recognition in fish. We collected and produced a fish identification dataset with 3412 images and a fish object detection dataset with 2320 images. A rotating box is proposed to detect fish, which avoids the problem where the traditional object detection produces a large number of redundant regions and affects the recognition accuracy. A self-SE module and a fish face recognition network (FFRNet) are proposed to implement the fish face identification task. The experiments proved that our model has an accuracy rate of over 90% and an FPS of 200. Full article

Although long polar codes with successive cancellation decoding can asymptotically achieve channel capacity, the performance of short blocklength polar codes is far from optimal. Recently, Arıkan proposed employing a convolutional pre-transformation before the polarization network, called polarization-adjusted convolutional (PAC) codes. In this paper, we focus on improving the performance of short PAC codes concatenated with a cyclic redundancy check (CRC) outer code, CRC-PAC codes, since error detection capability is essential in practical applications, such as the polar coding scheme for the control channel. We propose an enhanced adaptive belief propagation (ABP) decoding algorithm with the assistance of CRC bits for PAC codes. We also derive joint parity-check matrices of CRC-PAC codes suitable for iterative BP decoding. The proposed CRC-aided ABP (CA-ABP) decoding can effectively improve error performance when partial CRC bits are used in the decoding. Meanwhile, the error detection ability can still be guaranteed by the remaining CRC bits and adaptive decoding parameters. Moreover, compared with the conventional CRC-aided list (CA-List) decoding, our proposed scheme can significantly reduce computational complexity, to achieve a better trade-off between the performance and complexity for short PAC codes. Full article

The sex ratio is an important factor affecting the economic benefits of duck groups in the process of hemp duck breeding. However, the current manual counting method is inefficient, and the results are not always accurate. On the one hand, ducks are in constant motion, and on the other hand, the manual counting method relies on manpower; thus, it is difficult to avoid repeated and missed counts. In response to these problems, there is an urgent need for an efficient and accurate way of calculating the sex ratio of ducks to promote the farming industry. Detecting the sex ratio of ducks requires accurate counting of male ducks and female ducks. We established the world’s first manually marked sex classification dataset for hemp ducks, including 1663 images of duck groups; 17,090 images of whole, individual duck bodies; and 15,797 images of individual duck heads, which were manually captured and had sex information markers. Additionally, we used multiple deep neural network models for the target detection and sex classification of ducks. The average accuracy reached 98.68%, and with the combination of Yolov5 and VovNet_27slim, we achieved 99.29% accuracy, 98.60% F1 score, and 269.68 fps. The evaluation of the algorithm’s performance indicates that the automation method proposed in this paper is feasible for the sex classification of ducks in the farm environment, and is thus a feasible tool for sex ratio estimation. Full article

A video-based method to quantify animal posture movement is a powerful way to analyze animal behavior. Both humans and fish can judge the physiological state through the skeleton framework. However, it is challenging for farmers to judge the breeding state in the complex underwater environment. Therefore, images can be transmitted by the underwater camera and monitored by a computer vision model. However, it lacks datasets in artificial intelligence and is unable to train deep neural networks. The main contributions of this paper include: (1) the world’s first fish posture database is established. 10 key points of each fish are manually marked. The fish flock images were taken in the experimental tank and 1000 single fish images were separated from the fish flock. (2) A two-stage attitude estimation model is used to detect fish key points. The evaluation of the algorithm performance indicates the precision of detection reaches 90.61%, F1-score reaches 90%, and Fps also reaches 23.26. We made a preliminary exploration on the pose estimation of fish and provided a feasible idea for fish pose estimation. Full article

Power capacitors suffer multiple impulse voltages during their lifetime. With the multiple impulse voltage aging, the internal insulation, oil-film dielectric may deteriorate and even fail in the early stage, which is called accumulative effect. Hence, the time-domain dielectric response of oil-film dielectric with multiple impulse voltage aging is studied in this paper. At first, the procedure of the preparation of the tested samples were introduced. Secondly, an aging platform, impulse voltage generator was built to test the accumulative effect of capacitor under multiple impulse voltage. Then, a device was used to test the time-domain dielectric response (polarization depolarization current, PDC) of oil-film dielectric in different aging states. And finally, according to the PDC data, extended Debye model and characteristic parameters were obtained by matrix pencil algorithm identification. The results indicated that with the increase of impulse voltage times, the time-domain dielectric response of oil-film dielectric changed accordingly. The polarization current curve moved up gradually, the insulation resistance decreased when subjected to the repeated impulses. In frequency domain, the frequency spectrum of tan δ changed along with the impulse accumulation aging, especially at low frequency. At last, combined with the aging mechanism of oil-film dielectric under multiple impulse voltage, the test results were discussed. Full article