Real-Space Approach for the Electronic Calculation of Twisted Bilayer Graphene Using the Orthogonal Polynomial Technique

We discuss technical issues involving the implementation of a computational method for the electronic structure of material systems of arbitrary atomic arrangement. The method is based on the analysis of time evolution of electron states in the real lattice space. The Chebyshev polynomials of the first kind are used to approximate the time evolution operator.  We demonstrate that the developed method is powerful and efficient since the computational scaling law is linear. We invoked the method to study the electronic properties of special twisted bilayer graphene whose atomic structure is quasi-crystalline. We show the density of states of an electron in this graphene system as well as the variation of the associated time auto-correlation function. We find the fluctuation of electron density on the lattice nodes forming a typical pattern closely related to the typical atomic pattern of the quasi-crystalline bilayer graphene configuration

On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors

Recently, there has been a growing focus and interest in applying machine learning (ML) to the field of cybersecurity, particularly in malware detection and prevention. Several research works on malware analysis have been proposed, offering promising results for both academic and practical applications. In these works, the use of Generative Adversarial Networks (GANs) or Reinforcement Learning (RL) can aid malware creators in crafting metamorphic malware that evades antivirus software. In this study, we propose a mutation system to counteract ensemble learning-based detectors by combining GANs and an RL model, overcoming the limitations of the MalGAN model. Our proposed FeaGAN model is built based on MalGAN by incorporating an RL model called the Deep Q-network anti-malware Engines Attacking Framework (DQEAF). The RL model addresses three key challenges in performing adversarial attacks on Windows Portable Executable malware, including format preservation, executability preservation, and maliciousness preservation. In the FeaGAN model, ensemble learning is utilized to enhance the malware detector's evasion ability, with the generated adversarial patterns. The experimental results demonstrate that 100\% of the selected mutant samples preserve the format of executable files, while certain successes in both executability preservation and maliciousness preservation are achieved, reaching a stable success rate

MirrorNet: Bio-Inspired Camouflaged Object Segmentation

Camouflaged objects are generally difficult to be detected in their natural environment even for human beings. In this paper, we propose a novel bio-inspired network, named the MirrorNet, that leverages both instance segmentation and mirror stream for the camouflaged object segmentation. Differently from existing networks for segmentation, our proposed network possesses two segmentation streams: the main stream and the mirror stream corresponding with the original image and its flipped image, respectively. The output from the mirror stream is then fused into the main stream's result for the final camouflage map to boost up the segmentation accuracy. Extensive experiments conducted on the public CAMO dataset demonstrate the effectiveness of our proposed network. Our proposed method achieves 89% in accuracy, outperforming the state-of-the-arts. Project Page: https://sites.google.com/view/ltnghia/research/camoComment: Under Revie

Evaluation of Phacoemulsification Cataract Surgery Outcomes After Penetrating Keratoplasty

BACKGROUND: Cataract is one of the reasons which causes impaired visual acuity (VA) of the eyes after penetrating keratoplasy (PK), which can be treated by cataract surgery after PK or triple procedure. Cataract surgery after PK has advantages that parameters of the eyes such as axial length, anterior chamber depth (ACD) as well as corneal curvature are stabilized after removing all sutures postoperatively, and intraocular lens (IOL) power can be calculated correctly. Therefore, postoperative VA will be improved significantly. In Vietnam, there have not been any study about cataract surgery after PK, therefore we conduct this research. AIM: To evaluate the outcomes of phacoemulsification cataract surgery following primary PK. METHODS: Non-randomized controlled intervention study. Ninteen eyes (19 patients) that underwent phacoemulsification plus IOL insertion after initial PK in Cornea department, Vietnam National Institute of Ophthalmology, from December 2013 to September 2014. RESULTS: All patients presented with reduced VA, including 17 eyes (89.9%) with VA ≤ 20/200, mean astigmatism was 7.9 ± 1.0 D. Clear corneal grafts in 16 eyes while corneal opacity was seen in 3 eyes. All eyes with cataract were diagnosed from grade 2. After cataract surgery, improved VA > 20/200 was achieved in 72.22% of cases. There was a markable reduce of postoperative astigmatism with 1,8 ± 0.8 D (p < 0.05). However, the immunologic graft reaction was presented in one eye, and two edematous corneas also reported after cataract surgery. After treatment, there was one cornea achieved its clarity. CONCLUSION: Phacoemulsification cataract surgery following initial PK showed good outcomes with improved postoperative VA, reduced astigmatism, and the ultimate graft survival rate was high

Investigation of Sodium Manganese Oxide Nanowires Synthesized by Hydrothermal Method for Alkaline Ion Battery

Sodium Manganese Oxide (NaxMnO2) has attracted much attention as cathode materials for alkaline ion battery due to the ability of fast charge and discharge ion Na+, in particular in nanoscale. We report on the synthesis of NaxMnO2 nanowires via hydrothermal synthesis route from Mn2O3 and NaOH solution. The morphological observation indicates that the obtained Na0.44MnO2 nanowires with diameters of about 20-30 nm, length up to several micrometers were formed by this process. The electrochemical properties of fabricated materials were investigated by means of cyclic voltammetry technique and show that Sodium Manganese Oxide (NaxMnO2) is a promising material in the field of research and fabrication alkaline ion battery

A modern purification by accelerated solvent extraction and centrifugal partition chromatography and biological evaluation of capsaicin from Capsicum chinense

A special alkaloid compound known as capsaicin, which can only be found in the fruit of the Capsicum plant, was isolated and tested for its anti-inflammatory activity. The purpose of this work is to establish a simple and quick approach for capsaicin purification utilizing centrifugal partition chromatography (CPC) as well as an effective method - accelerated solvent extraction (ASE), for extracting capsaicin from Capsicum chinense. After purification, capsaicin was validated by HPLC-DAD at 281 nm to be > 90% purity. The in vivo anti-inflammatory activity of the isolated capsaicin was also investigated, and the IC50 value of the capsaicin was determined to be 57.61 µg/mL. The current work emphasizes how an ASE and CPC system may combine to extract high-purity capsaicin from Capsicum chinense, which have the anti-inflammatory activity, as we evaluated in the experiment

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland