Abstract:The flame-retardant gel polymer electrolytes present great potential for application in alkali metal batteries due to their high safety characteristics, such as good ability of dendrite inhibition, non-leakage, and non-flammability which can effectively address safety risks caused by thermal runaway or mechanical impact. This paper reviews the advances of flame-retardant gel polymer electrolytes in various alkali metal batteries, including lithium, sodium, aluminum, zinc, etc. The paper first discusses the flame-retardant mechanism of gel polymer electrolytes. Subsequently, the common preparation methods of flame-retardant gel polymer electrolytes are highlighted. Furthermore, the advancements of different-type flame-retardant gel polymer electrolytes in different alkali metal battery systems are symmetrically discussed. Finally, their current challenges and potential development directions are significantly proposed.

Abstract:This is an efficient fuel-saving product based on the resonance principle, which is an external energysaving device customized for logistics companies, transportation fleets, and individual vehicle owners. The product is designed to address the issues of improving engine thermal efficiency and reducing fuel losses. Based on the principles of wave and resonance, a pulse technology and self-vector resetting technology are proposed to improve the combustion efficiency. Mainstream brand vehicle tests have shown that the fuel efficiency of various brand vehicles has been effectively improved (6.15%～11.16%) in the testing range of 1500～3500 km, with the highest reaching 11.16% and the highest fuel saving per 100 kilometers being 3.29 liters. At the same time, theoretical analysis based on the design principle shows that the fuel-saving efficiency will gradually improve and eventually reach a stable optimum value after a certain adaptation period.

Abstract:On May 16, 2023, EU announced the formal publication of the regulation text for Carbon Border Adjustment Mechanism (CBAM) on the Official Journal of EU, and it officially came into force on the following day (May 17, 2023), which is the world’s first carbon tariff mechanism. EU is China’s second largest trading partner and export market, therefore the imposition of carbon tariffs by EU will inevitably have a significant impact on China’s export trade to EU and economic development. This article provides a detailed introduction to the development process, latest trends, specific content, and collection methods of EU CBAM. Based on this, and according to China’s customs import and export statistics, the impact of CBAM on China’s trade with EU is analyzed. At present, the most affected industries are steel and aluminum, but in the future, EU will reassess CBAM to determine whether to expand the scope of collection. China should actively adopt various coping strategies to safeguard the legitimate rights and interests of the foreign trade enterprises.

Abstract:Logic buit-in self-test (LBIST) is a technique in design for testability (DFT) that utilizes components of a circuit within a chip, board, or system to conduct self-testing of the digital logic circuit. LBIST plays a critical role in various applications, particularly those that are vital for life and mission, such as defense, aerospace, and autopilot systems. These applications necessitate the implementation of on-chip, on-board, or in-system selfchecks to enhance the overall system reliability and enable remote diagnostics. This article first presents the commonly used LBIST classifications and describes the classic and most successful LBIST architecture used in the industry, self-testing using MISR and parallel SRSG (STUMPS). Then, it summarizes the research teams and research progress at home and abroad. Then, it analyzes in detail the key technical points of LBIST such as the basic principle, timing control, deterministic self-test design, low-power design, X tolerance, and lists mainstream LBIST business tools, the software architecture and technical characteristics of LBIST are analyzed one by one. Finally, the problems that need to be further solved in current LBIST technology are discussed and prospects are made.

Abstract:The purpose of automatic judgment document summarization is to allow computers to automatically select, extract, and compress important information from legal texts so as to reduce workload of practitioners. Currently, most summarization algorithms based on pre-trained language models have limitations on the length of the input text, so they cannot effectively summarize long texts. In this thesis, an innovative extractive summarization algorithm is introduced, which uses a pre-trained language model to generate sentence vectors. Based on the Transformer encoder structure, the summarization task can be completed by fused information including sentence vectors, position and length of sentences. Experimental results showed that, the algorithm can effectively handle the task of summarizing long texts. In addition, the model was tested on the 2020 CAIL (challenge of AI in law) summarization dataset, and results showed that compared to the baseline model, the proposed model showed significant improvement in the ROUGE-1, ROUGE-2, and ROUGE-L metrics.

Abstract:In the research of face anti-spoofing (FAS), most related techniques are dependent to the RGB images or IR images, which lack sufficient biometric features and are vulnerable to ever-advancing presentation attacks. In this paper, a Transformer model based on combination of multiple facial regions is proposed to introduce multi-spectral technology into the task of facial live detection, aiming to obtain unique biological features of the real faces and increase the distinguishability from the fake faces. In the proposed model, multispectral images are utilized to broaden the spectral dimension for more reflection information, which can identify various materials. Besides, a spectral normalization method is preprocessed pixel by pixel to reduce the impacts of the environmental illumination variations and enhance the consistency of facial reflection features regionally. Then multiple core facial regions, like eyes, nose, mouth and cheeks, are selected as input of the deep learning model. Furthermore, a Transformer-based model is constructed to obtain both local regional features and inter association features of different facial regions, which are integrated into complete facial biometric features to achieve facial live detection. On the author’s self-built multi-spectral facial datasets, the results show that the proposed method achieved an accuracy of 95.72% for and a misclassification of 5.10% for live detection, which is superior to commonly used FAS models.

Abstract:Hydrogen can selectively remove cytotoxic reactive oxygen species. The presence of hydrogen in cancer cells can affect the balance of reactive oxygen species in cancer cells, leading to apoptosis of cancer cells. In addition, hydrogen has innate advantages over drugs since it is harmless to the human body and can highly penetrate cell membranes. However, due to the diffusion of hydrogen in the body without purpose, it is difficult to achieve good curative effect by oral hydrogen-rich water and injection hydrogen-rich saline. In this paper, the mechanism of hydrogen therapy is briefly introduced, where two kinds of main nanosystems for hydrogen delivery in hydrogen therapy are introduced by enumerating some current methods of nanomaterials-based hydrogen therapy. In the end, the future of nanomaterials in cancer hydrogen therapy is prospected.

Abstract:The coronavirus SARS-CoV-2 remains stable and highly infective in water for a long time, which makes the removal of virus from water an important approach to contain and block its spread. In this study, an antibody-engineered microrobot (AB-robot) was fabricated by using click chemistry to functionalize selfactuating Bacillus subtilis with neutralizing antibody P2C-1F11 against the SARS-CoV-2 S protein. The AB-robot can efficiently capture and remove SARS-CoV-2 from water by targeting SARS-CoV-2 S protein with P2C-1F11. The AB-robot displays rapid self-actuation capability in various aquatic media, including drinking water and tap water. And the virus clearance percentage of AB-robot in water was up to 95% against SARS-CoV-2 pseudovirus contaminant model. Moreover, scanning electron microscopy images show that a large number of virus particles were tethered to the surface of the AB-robot, further demonstrating the effective capture capability of the ABrobot. In summary, the significant contributions of both the P2C-1F11 for virus targeting and the fast microrobot motion for contacting the virus toward immediate capture and efficient removal of SARS-CoV-2 pseudovirus, which has potential application for preventing and blocking the spread of SARS-CoV-2 in water.

Abstract:The early stage of many ophthalmic diseases is usually accompanied by morphological and hemodynamic changes of microvessles. Thus, evaluation of the whole eye microvessels plays an important role in the comprehensive diagnosis of ophthalmic diseases. Due to the limited penetration depth of optical imaging techniques, especially in the case of low transmittance of the medium, it is difficult for existing optical imaging techniques to visualize the microvessels of the entire eye. For this purpose, this paper developed an ultrasound super-resolution imaging technique that can visualize the whole eye three-dimensional microvascular system with micron resolution. Specifically, a linear array with a central frequency of 10 MHz was used to rapidly acquire multi-frame sequences, and then a super-resolution image of a slice was obtained through microbubble signal extraction, localization, tracking and center point superposition. All the acquired slices were reconstructed into three-dimensional full-eye super-resolution microvascular images. In vivo experiment demonstrates that ultrasound localization microscopy (ULM) can visualize the microvascular system of the entire eye, which is important for early diagnosis of ophthalmic diseases.