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A hyaluronic acid-containing ethosomes (HA-ES) as the transdermal drug delivery system was prepared in this work, and rhodamine B (RB) was used as a model drug to be encapsulated. The obtained HA-ES-RB was then characterized by the surface morphology, entrapment efficiency, drug loading and the stability. Results showed that the prepared HA-ES-RB was spherical and showed good dispersion as well as the stability, with a particle size of below 100 nm. The skin permeation experiments were carried out in vitro with the Franz diffusion cells and the rat dorsal skins were used. It was found that the penetration effect of HA-ES-RB was much better than that of ES-RB. The fluorescence microscopy image showed that HA-ES-RB penetrated into the deepest dermis. The excellent transdermic drug delivery effect of HA-ES-RB maybe attributed from its smaller size, hydration of hyaluronic acid as well as greater potential targeting to skin and skin appendages of liposomal carriers. Moreover, the HA-ES delivery system showed non-cytotoxicity to normal cells, indicating a good biocompatibility. This work provded a hyaluronic acid-containing ethosomes which can offer a quick, high efficient, safe and self-administered transdermal drug delivery system.

In the work, the non-woven cellulose acetate (CA) nanofiber mats were prepared via electrospinning, and CA nanofiber were incorporated into the core layer of the high-pressure laminates (HPLs). When the concentration of CA was 16 wt%, SEM images demonstrated that the morphology of the CA nanofiber mat was the best, with an average diameter of 654±246 nm. When CA nanofiber mats were incorporated into the core layer of HPLs, the mechanical properties of the resulted HPLs composites were significantly improved. Specifically, the tensile strength and elongation at break of the nanofiber mats reinforced HPLs composites increased remarkably to 40.8 ±1.1 MPa and 27.9 ± 0.9 %, respectively, which were nearly 6 times and 4.4 times higher than those of the pure HPLs. Furthermore, the incorporation of the CA nanofiber mats also significantly improved the flame retardancy of the HPLs, which was revealed from the thermogravimetric analysis (TGA) results.

Vibration characteristics of heavy CNC machine tools are directly affected by their foundations. To analyze vibrations of heavy CNC machine tools caused by internal and external loads, a system dynamics model of a rigid-flexible coupled heavy-duty CNC machine tool-foundation system was established based on the multibody transfer matrix method. Since joint surfaces can seriously affect the accuracy of system mechanics models, the substructure synthesis method was first used to establish a dynamic model of the joint surface. The frequency response function was then used to identify model parameters. Moreover, to improve the accuracy of parameter identification of the joint surface, a residual frequency compensation function was used to reconstruct the frequency response function. Finally, the multibody system model was implemented by combining surface elements. To verify the system dynamics model, an experimental model of the heavy-duty machine-foundation system was built, taking into consideration joint surface factors, and the theoretical model was validated by comparing theoretical, simulation, and experimental results. Using the theoretical model, the influence of different forms of concrete foundations, materials, and soil properties on the vibration characteristics of heavy-duty CNC machine tools was analyzed, thus providing a theoretical basis for optimizing and improving CNC machine tools.

Deep neural networks (DNNs) are inherently vulnerable to adversarial inputs: such maliciously crafted samples trigger DNNs to misbehave, leading to detrimental consequences for DNN-powered systems. The fundamental challenges of mitigating adversarial inputs stem from their adaptive and variable nature. Existing solutions attempt to improve DNN resilience against specific attacks; yet, such static defenses can often be circumvented by adaptively engineered inputs or by new attack variants. Here, we present EagleEye, an attack-agnostic adversarial tampering analysis engine for DNN-powered systems. Our design exploits the {\em minimality principle} underlying many attacks: to maximize the attack's evasiveness, the adversary often seeks the minimum possible distortion to convert genuine inputs to adversarial ones. We show that this practice entails the distinct distributional properties of adversarial inputs in the input space. By leveraging such properties in a principled manner, EagleEye effectively discriminates adversarial inputs and even uncovers their correct classification outputs. Through extensive empirical evaluation using a range of benchmark datasets and DNN models, we validate EagleEye's efficacy. We further investigate the adversary's possible countermeasures, which implies a difficult dilemma for her: to evade EagleEye's detection, excessive distortion is necessary, thereby significantly reducing the attack's evasiveness regarding other detection mechanisms.

Many of today's machine learning (ML) systems are not built from scratch, but are compositions of an array of {\em modular learning components} (MLCs). The increasing use of MLCs significantly simplifies the ML system development cycles. However, as most MLCs are contributed and maintained by third parties, their lack of standardization and regulation entails profound security implications. In this paper, for the first time, we demonstrate that potentially harmful MLCs pose immense threats to the security of ML systems. We present a broad class of {\em logic-bomb} attacks in which maliciously crafted MLCs trigger host systems to malfunction in a predictable manner. By empirically studying two state-of-the-art ML systems in the healthcare domain, we explore the feasibility of such attacks. For example, we show that, without prior knowledge about the host ML system, by modifying only 3.3{\textperthousand} of the MLC's parameters, each with distortion below $10^{-3}$, the adversary is able to force the misdiagnosis of target victims' skin cancers with 100\% success rate. We provide analytical justification for the success of such attacks, which points to the fundamental characteristics of today's ML models: high dimensionality, non-linearity, and non-convexity. The issue thus seems fundamental to many ML systems. We further discuss potential countermeasures to mitigate MLC-based attacks and their potential technical challenges.

In order to prolong NO release and enhance the antibacterial effect, N-Carboxy propionyl chitosan sodium (CPCS) was cross-linked to branched polyethylenimine (bPEI) (CPCS-bPEI) and used as a NO donor. The CPCS-bPEI has been successfully synthesized at different molar ratios of CPCS to bPEI, followed with the reaction with NO gas under a parr high-pressure reactor to yield CPCS-bPEI-NO. Successful preparation of CPCS-bPEI-NO was confirmed by elemental analysis, FITR and SEM. The effective release of NO was determined by in vitro release. The antibacterial assay showed that CPCS-bPEI-NO can effectively inhibit Gram-negative Escherichia coli (E. coli) and Gram-positive Staphylococcus aureus (S. aureus). We found that the NO loading amount and antibacterial effect can be adjusted by the molar ratio of CPCS to bPEI, and there was no significant toxicity of CPCS-bPEI-NO, suggesting that it may serve as an ideal antimicrobial agent.

An intriguing property of deep neural networks is their inherent vulnerability to adversarial inputs, which significantly hinders their application in security-critical domains. Most existing detection methods attempt to use carefully engineered patterns to distinguish adversarial inputs from their genuine counterparts, which however can often be circumvented by adaptive adversaries. In this work, we take a completely different route by leveraging the definition of adversarial inputs: while deceiving for deep neural networks, they are barely discernible for human visions. Building upon recent advances in interpretable models, we construct a new detection framework that contrasts an input's interpretation against its classification. We validate the efficacy of this framework through extensive experiments using benchmark datasets and attacks. We believe that this work opens a new direction for designing adversarial input detection methods., Comment: 6 pages, 6 figures

Through analyzing the influence factors of assembly process, an evaluation index system is proposed, According to assembly precedence relationship and the parallelism characteristics of actual assembly sequence, a new algorithm-"Optimal order reference set" is proposed to calculate assembly membership degree of unit level index. Concrete application steps are verified through a case study. Analysis of the example shows that the proposed method satisfied the requirements of practical process rules and the integrity of index system. By the new method, optimal selection of geometric feasible assembly sequence is realized. This method has realistic significance to automatic assemble application.