Effect of Pre-Induced Mesenchymal Stem Cell-Coated Cellulose/Collagen Nanofibrous Nerve Conduit on Regeneration of Transected Facial Nerve

(1) Objective: In order to evaluate the effect of a pre-induced mesenchymal stem cell (MSC)-coated cellulose/collagen nanofibrous nerve conduit on facial nerve regeneration in a rat model both in vitro and in vivo. (2) Methods: After fabrication of the cellulose/collagen nanofibrous conduit, its lumen was coated with either MSCs or pre-induced MSCs. The nerve conduit was then applied to the defective main trunk of the facial nerve. Rats were randomly divided into three treatment groups (n = 10 in each): cellulose/collagen nanofiber (control group), cellulose/collagen nanofiber/MSCs (group I), and cellulose/collagen nanofiber/pre-induced MSCs (group II). (3) Results Fibrillation of the vibrissae of each group was observed, and action potential threshold was compared 8 weeks post-surgery. Histopathological changes were also observed. Groups I and II showed better recovery of vibrissa fibrillation than the control group. (4) Conclusions: Group II, treated with the pre-induced MSC-coated cellulose/collagen nanofibrous nerve conduit, showed the highest degree of recovery based on functional and histological evaluations.     

Local outlier factor and stronger one class classifier based hierarchical model for detection of attacks in network intrusion detection dataset

Identification of attacks by a network intrusion detection system (NIDS) is an important task. In signature or rule based detection, the previously encountered attacks are modeled, and signatures/rules are extracted. These rules are used to detect such attacks in future, but in anomaly or outlier detection system, the normal network traffic is modeled. Any deviation from the normal model is deemed to be an outlier/ attack. Data mining and machine learning techniques are widely used in offline NIDS. Unsupervised and supervised learning techniques differ the way NIDS dataset is treated. The characteristic features of unsupervised and supervised learning are finding patterns in data, detecting outliers, and determining a learned function for input features, generalizing the data instances respectively. The intuition is that if these two techniques are combined, better performance may be obtained. Hence, in this paper the advantages of unsupervised and supervised techniques are inherited in the proposed hierarchical model and devised into three stages to detect attacks in NIDS dataset. NIDS dataset is clustered using Dirichlet process (DP) clustering based on the underlying data distribution. Iteratively on each cluster, local denser areas are identified using local outlier factor (LOF) which in turn is discretized into four bins of separation based on LOF score. Further, in each bin the normal data instances are modeled using one class classifier (OCC). A combination of Density Estimation method, Reconstruction method, and Boundary methods are used for OCC model. A product rule combination of the threemethods takes into consideration the strengths of each method in building a stronger OCC model. Any deviation from this model is considered as an attack. Experiments are conducted on KDD CUP’99 and SSENet-2011 datasets. The results show that the proposed model is able to identify attacks with higher detection rate and low false alarms.     

Convergence on iterative learning control of Hilfer fractional impulsive evolution equations

In this paper, impulsive fractional differential equations with Hilfer fractional derivatives of order and type $0\le \nu \le 1$ is considered. Convergence analysis of $P$-type and $P{I}^{\mu }$-type open-loop iterative learning scheme is studied in the sense of $\lambda$-norm. Examples are provided to explain the theory developed.     

Glass Fiber-Reinforced Composites: From Formulation to Application

Glass fiber-reinforced composite materials are attractive because their properties can be tailored to meet the specific needs of a variety of applications. The mechanical and thermal properties of a composite generally follow the rule of mixtures. As glass fiber is the major component at 70–75% by weight (50–60% by volume), selection of the correct glass product is critical. Glass fiber reinforcement is available in many forms, including continuous rovings, chopped fibers, fabrics, and nonwoven mats. In addition to form, selection of a reinforcement product involves choosing a glass type, chemistry on the glass (sizing) filament diameter, and tex. Glass formulation or type governs mechanical, thermal, and corrosion properties, whereas sizing protects the glass during handling and gives compatibility with the resin system. Filament diameter and strand tex are chosen to balance physical properties and manufacturing efficiency. A significant amount of tensile strength, up to 50%, may be lost from a pristine single filament to a multi-filament roving. To minimize this degradation, the utmost care and consistency must be exercised in the fiber forming process. This, coupled with selection of a high-performance glass formulation, enables use of composites in highly demanding applications, such as pressure vessels and ballistic armor.     

Investigation of Single Event Transients on RingFET using 3D TCAD Simulations

Silicon - In this work, the effect of heavy ion radiation on ringFET structures with gate lengths of 32 nm and 20 nm is explored using 3D TCAD simulations. The sensitive position...     

An optimal cluster based security and resilience of smart home environments using hybrid soft computing techniques

Smart home is the main part of smart intelligent system here the remote users share the sensitive information through an insecure medium to access such smart devices, which becomes security issues. The recent user authentication protocols have used to solve those problems and provide secure communication. Consumer traffic increase the risk of illegal user as legal user and radio channels are extra vulnerable to listeners. For further security enhancement, we proposed an optimal cluster based remote user authentication (OCRUA) protocol for smart home environment using hybrid soft computing techniques. The first contribution of proposed protocol is to introduce squirrel induced butterfly optimization (SBO) algorithm for cluster formation, which groups the smart devices. Then, we compute the cluster head (CH) using the teacher learning based deep neural network (TL-DNN) based on multiple design constraints. The second contribution is to illustrate remote user authentication using optimal elliptic curve cryptography (OECC) which encrypts the sensitive information before forward to gateway. At long last, the concert of planned OCRUA protocol evaluates use different replication scenarios and shows the effectiveness over the existing state-of-art protocols.     

A Study of Added SiC Powder in Kerosene for the Blind Square Hole Machining of CFRP Using Electrical Discharge Machining

Silicon - Carbon Fiber Reinforced Polymers (CFRPs) have been applied potentially for various application components owing to their lightweight and better mechanical properties. However, the...     

Heat-Resistant,Robust, and Hydrophilic Separators Based on Regenerated Cellulose for Advanced Supercapacitors

Separators in supercapacitors (SCs) typically suffer from defects of low mechanical property, limited ion transport, and electrolyte wettability, and poor thermal stability, impeding the development of SCs. Herein, high-performance regenerated cellulose (RC) based separators are designed that are fabricated by effective hydrolytic etching of inorganic CaCO₃ nanoparticles from a filled RC membrane. The as-prepared RC separator displays excellent comprehensive performances such as higher tensile strength (75.83 MPa) and thermal stability (200 °C), which is superior to commercial polypropylene-based separator (Celgard 2500) and sufficient to maintain their structural integrity even at temperatures in excess of 200 °C. Benefiting from its hydrophilicity, high porosity, and outstanding electrolyte uptake rate (208.5%), the RC separator exhibits rapid transport and permeability of ions, which is 2.5× higher than that of the commercial nonwoven polypropylene separator (NKK -MPF30AC-100) validated by electrochemical tests in the 1.0 m Na₂SO₄ electrolyte. Results show that porous RC separator with unique advantages of superior electrolyte wettability, mechanical robustness, and high thermal stability, is a promising separator for SCs with high-performance and safety.     

Effect of Partial Replacement of Retort with an Insulation Material on Mc-Silicon Grown in Directional Solidification Furnace: Numerical Modeling

Silicon - In this paper, the transient global numerical simulation was used in order to study the four types of insulation surrounding the retort within directional solidification furnace which are...