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Being more and more widely used for a variety of water treatments, chitosan and activated carbons are playing an increasingly significant part nowadays. Activated carbons purify water through the pore structure and adsorb ions or other particles. Chitosan also adsorb a large amount of metallic ions and purifies water through the reactions by the functional groups. This paper discusses the different features of the two substances and then gives a comparison between the two types of adsorbents by comparing their characteristics, conditions and applications. Specifically, the suitable temperatures, the specific modifications and solubility are discussed, together with other factors. The difference in their physical and chemical properties plays an important role in the comparison. For physical properties, the activated carbons have strong mechanical strength and are soluble in many types of solvents. By contrast, chitosan is generally soluble in an acidic solution. There are also some differences in the adsorption abilities and ways to purify solutions. Next, chitosan is more easily dissolved in solution with low PH and at room temperature. However, the activated carbons require lower PH and lower temperature to be dissolved. Then, activated carbons are more likely to cause secondary pollution due to the impurities in the activated carbons. The two substances require different modifications to increase the rate of adsorption. As a result, the firms should consider the features of the two types of adsorbents and choose the better one. They should also understand the suitable conditions for each adsorbent.

Information is a critical factor shaping firms’ strategic decisions. In the era of digital governance, governments can improve information transparency through public data openness, yet how this influences corporate sustainability practices remains underexplored. Using data on Chinese A-share listed firms from 2007 to 2023, this study employs a staggered difference-in-differences approach to examine the impact of Government Open Data Platforms on corporate environmental, social, and governance (ESG) performance. The results show that the establishment of Government Open Data Platforms significantly improves firms’ ESG performance. Mechanism analysis reveals that the effect operates through two channels: first, by reducing firms’ perceived uncertainty, thereby enhancing their internal motivation to engage in long-term ESG investments; second, by increasing external attention, which strengthens stakeholder oversight and reputational incentives. Further heterogeneity analyses show that such positive impact is stronger in cities with higher-quality data platforms, among non-heavily polluting firms, and for state-owned enterprises. Overall, these results highlight that government-led data openness can lower information frictions, serving as an effective soft governance tool to promote corporate sustainability.

In situ vaccination is a promising strategy to convert the immunosuppressive tumor microenvironment into an immunostimulatory one with limited systemic exposure and side effect. However, sustained clinical benefits require long-term and multidimensional immune activation including innate and adaptive immunity. Here, we develop a probiotic food-grade Lactococcus lactis -based in situ vaccination (FOLactis) expressing a fusion protein of Fms-like tyrosine kinase 3 ligand and co-stimulator OX40 ligand. Intratumoural delivery of FOLactis contributes to local retention and sustained release of therapeutics to thoroughly modulate key components of the antitumour immune response, such as activation of natural killer cells, cytotoxic T lymphocytes, and conventional-type-1-dendritic cells in the tumors and tumor-draining lymph nodes. In addition, intratumoural administration of FOLactis induces a more robust tumor antigen-specific immune response and superior systemic antitumour efficacy in multiple poorly immune cell-infiltrated and anti-PD1-resistant tumors. Specific depletion of different immune cells reveals that CD8 + T and natural killer cells are crucial to the in situ vaccine-elicited tumor regression. Our results confirm that FOLactis displays an enhanced antitumour immunity and successfully converts the ‘cold’ tumors to ‘hot’ tumors. The probiotic Lactococcus lactis has been used for the delivery of therapeutic molecules. Here the authors engineer Lactococcus lactis to express a fusion protein of Flt3L and OX40 ligand, eliciting anti-tumor immune response in preclinical cancer models.

The Signal is an end-to-end encrypted communication protocol composed of a double ratchet (DR) protocol and an extended triple Diffie-Hellman (X3DH) protocol. Its complex ratchet structure and the characteristics of protocol composition make it challenging to realize formal analysis. A formal analysis method based on logic of events theory (LoET) is proposed to conduct a security analysis of the Signal protocol. The method includes inference rules with key relation and key chain as the core to realize the formal analysis of ratchet structure, and the inference relation between sub-protocols is established by putting forward the composition theorem. The proposed method achieves a formal analysis of Signal, revealing that it does not satisfy a strong authentication property during the X3DH phase. The results show that the LoET-based method can be effectively applied in the formal analysis of Signal protocols, thus promoting the application and development of these protocols with ratchet structure and composition properties.

Assembling large, heavy space segments presents a significant challenge in aerospace engine production. Rigid collisions often occur during the docking process, impacting the precision and quality of engine assembly. Traditional manual docking depends on workers’ experience to prevent collisions, but it is labor-intensive and low in productivity, making it impractical. Parallel robots, known for their high precision and heavy load capacity, are widely used in precision assembly under heavy load conditions. Therefore, automated docking methods using parallel robots capable of avoiding rigid collisions have emerged as an excellent solution to these issues. This paper presents a framework for easy implementation in practical production. The Stewart parallel robot facilitates automatic docking of heavy aerospace components without rigid collisions. Fractional-order variable damping admittance control is proposed, allowing the robot to dynamically adjust the assembly trajectory based on real-time interaction forces, thus preventing rigid collisions during docking. Additionally, adaptive robust sliding mode control is developed, enhancing the robot’s tracking accuracy for desired poses and making it suitable for high-precision assembly.

Various physical information can be leaked while the encryption algorithm is running in the device. Side-channel analysis exploits these leakages to recover keys. Due to the sensitivity of deep learning to the data features, the efficiency and accuracy of side channel analysis are effectively improved with the application of deep learning algorithms. However, a considerable part of existing reserches are based on traditional neural networks. The effectiveness of key recovery is improved by increasing the size of the network. However, the computational complexity of the algorithm increases accordingly. Problems such as overfitting, low training efficiency, and low feature extraction ability also occur. In this paper, we construct an improved lightweight convolutional neural network based on the feature fusion network. The new network and the traditional neural networks are respectively applied to the side-channel analysis for comparative experiments. The results show that the new network has faster convergence, better robustness and higher accuracy. No overfitting has occurred. A heatmap visualization method was introduced for analysis. The new network has higher heat value and more concentration in the key interval. Side-channel analysis based on feature fusion network has better performance, compared with the ones based on traditional neural networks.

Bilayer rhombohedral-stacked transition-metal dichalcogenides (3R-TMDCs) combining high carrier mobility, good electrostatic control, and exotic switchable polarization are emerging as promising semiconducting channels for beyond-silicon electronics. However, despite great efforts, the growth of wafer-scale bilayer 3R-TMDCs single crystals remains difficult due to challenges in the synergistic control of phase structure and grain orientation. Here we design a hole-doping-assisted strategy to synthesize a series of two-inch bilayer 3R-TMDCs single crystals on c-plane sapphire. The introduction of hole dopants (e.g. Hf, V, Nb, Ta) not only increases the interlayer coupling to break the formation energy degeneracy of bilayer 3R-stacked and hexagonal-stacked TMDCs, but also promotes the parallel steps formation on sapphire surfaces to induce the unidirectionally aligned bilayer grain nucleation. The fabricated ferroelectric semiconductor field-effect transistors based on bilayer Hf-MoS 2 demonstrate high endurance (more than 10 5 cycles) and long retention time (exceeding one year) due to the restriction of interlayer charge defect migration/aggregation caused by sliding ferroelectricity. This work proposes a promising strategy for synthesizing wafer-scale ferroelectric semiconductor single crystals, which could promote the further exploration of logic-in-memory chips. 2D rhombohedral-stacked transition metal dichalcogenides (3R-TMDs) combine high carrier mobility and ferroelectricity, but their large-scale synthesis remains challenging. Here, the authors report a hole-doping-assisted strategy to synthesize various wafer-scale bilayer 3R-TMD single crystals, showing the realization of high-performance ferroelectric transistors.

Partial neural distinguishers limit the available ciphertext bit combinations in differential neural cryptanalysis. When the training data size and the number of bits are not appropriately selected, label collisions can occur, which adversely affects key recovery efficiency. This paper conducts an analysis to investigate the correlation between the number of bits and the data size, aiming to address the aforementioned issue. It develops a strategy to control collisions and mitigate the impact of these collisions on model performance. A Collision-Aware Key Recovery (CAKR) framework is proposed tailored for high-collision data based on this strategy. This framework leverages the distribution characteristics of labels, eliminating the need for training neural distinguishers and significantly reducing both time and resource consumption. Experimental results show that the CAKR framework reduces the key recovery time by 96.8%, 95.5%, and 91.0% for the Speck32/64, Speck64/96, and Speck96/128, respectively. Additionally, a bit search algorithm is proposed that incorporates a differential evolution strategy and uses the non-uniformity of the ciphertext difference distribution among positive samples as the fitness criterion. Frequent calls to the neural distinguisher are avoided by our method, reducing the search time from 3.286 h to 7.464 s for 8-bit combinations in Speck32/64. The CAKR framework also offers a quantum version that theoretically further reduces time complexity.

Erosion of metallic surfaces due to the permanent impact of high-speed water droplets is a significant concern in diverse industrial applications like turbine blades, among others. In the initial stage of water droplet erosion, there is an incubation regime with negligible mass loss whose duration is strongly dependent on water droplet sizes and velocities, the initial state of the surface, and the material properties of the target. The prediction of the incubation period duration is one of the main topics of research in the field. In this work, the interaction of the water droplets with a metallic surface is simulated using a hybrid Smoothed-Particle Hydrodynamics/Finite Element Method modeling scheme. The effect of multiple random impacts on representative target areas for certain ranges of impact angles and velocities was studied using a combination of simple material and damage models for the target surface of Ti-6Al-4V titanium alloy. The simulation is able to reproduce the main dependencies of the incubation regime and the first stages of water droplet erosion on the impact angle and velocity as reported in the literature. This framework can be considered a foundation for more advanced models with the goal of a better understanding of the physical mechanisms behind the incubation regime in order to devise strategies to extend it in real applications.

Background Chimeric antigen receptor (CAR)-T cell therapy has demonstrated remarkable success in the treatment of hematologic malignancies, while the success has not yet been replicated in solid tumors. To some extent, the disappointing results can be attributed to the paucity and heterogeneity of target antigens in solid tumors since adequate antigens are the cornerstone for CAR-T cells to recognize and attack tumor cells. Methods We established a target-redirected universal CAR-T (TRUE CAR-T) cell therapeutic modality, in which exogenous antigens are loaded onto fusogenic nanoparticles to achieve in situ modification of cell membrane in solid tumors, providing targets for subsequent CAR-T cell therapy. The modification effect was evaluated by flow cytometry and confocal microscopic imaging. The in vivo metabolism and biodistribution of fusogenic antigen loaded nanoparticles (F-AgNPs) was explored using near infrared living imaging. Then F-AgNPs mediated in situ antigen modification were cooperated with corresponding CAR-T cell therapy, and its antitumor efficacy was evaluated using immune function experiments and further investigated in different tumor models. Results Using F-AgNPs, exogenous antigens were selectively modified onto tumor cell membranes through membrane fusion, spread deeper into tumor tissues through intercellular lipid transfer, further activating corresponding CAR-T cells and mediating antitumor immune responses towards multiple types of tumor cells, despite of their inherent antigen profiles. The cooperative treatment of F-AgNPs and CAR-T cell therapy successfully suppressed tumor proliferation and prolonged survival in both subcutaneous and peritoneally disseminated tumor models. Conclusion The fusogenic nanoparticle-based in situ antigen modification overcome the limitation of target antigens paucity and heterogeneity in solid tumors, improving the efficacy and broadening the applications of CAR-T cells, thus establishing a novel TRUE CAR-T cell therapeutic modality with universal application and translational potential in immunotherapies for solid tumors.