Ballistic behavior of plain and reinforced concrete slabs under high velocity impact

This work presents a numerical simulation of ballistic penetration and high velocity impact behavior of plain and reinforced concrete slabs. In this paper, we focus on the comparison of the performance of the plain and reinforced concrete slabs of unconfined compressive strength 41 MPa under ballistic impact. The concrete slab has dimensions of 675 mm × 675 mm × 200 mm, and is meshed with 8-node hexahedron solid elements in the impact and outer zones. The ogive-nosed projectile is considered as rigid element that has a mass of 0.386 kg and a length of 152 mm. The applied velocities vary between 540 and 731 m/s. 6 mm of steel reinforcement bars were used in the reinforced concrete slabs. The constitutive material modeling of the concrete and steel reinforcement bars was performed using the Johnson-Holmquist-2 damage and the Johnson-Cook plasticity material models, respectively. The analysis was conducted using the commercial finite element package Abaqus/Explicit. Damage diameters and residual velocities obtained by the numerical model were compared with the experimental results and effect of steel reinforcement and projectile diameter were studies. The validation showed good agreement between the numerical and experimental results. The added steel reinforcements to the concrete samples were found efficient in terms of ballistic resistance comparing to the plain concrete sample.     

An Abstract Reachability Approach by Combining HOL Induction and Multiway Decision Graphs

In this paper, we provide a necessary infrastructure to define an abstract state exploration in the HOL theorem prover. Our infrastructure is based on a deep embedding of the Multiway Decision Graphs (MDGs) theory in HOL. MDGs generalize Reduced Ordered Binary Decision Diagrams (ROBDDs) to represent and manipulate a subset of first-order logic formulae. The MDGs embedding is based on the logical formulation of an MDG as Directed Formulae (DF). Then, the MDGs operations are defined and the correctness pro...     

Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 10³⁸ unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system of the security of a network that detects suspicious activities and deals with a massive amount of data comprised of repetitive and inappropriate features which affect the detection rate. A feature selection (FS) technique helps to reduce the computation time and complexity by selecting the optimum subset of features. This paper proposes a method for detecting DDoS flooding attacks (FA) based on ICMPv6 messages using a Binary Flower Pollination Algorithm (BFPA-FA). The proposed method (BFPA-FA) employs FS technology with a support vector machine (SVM) to identify the most relevant, influential features. Moreover, The ICMPv6-DDoS dataset was used to demonstrate the effectiveness of the proposed method through different attack scenarios. The results show that the proposed method BFPA-FA achieved the best accuracy rate (97.96%) for the ICMPv6 DDoS detection with a reduced number of features (9) to half the total (19) features. The proven proposed method BFPA-FA is effective in the ICMPv6 DDoS attacks via IDS.     

Rheological characteristics of modified calcium grease with graphene nanosheets

The effect of graphene nanosheets (GNS) on the rheological characteristics and thermal conductivity of calcium grease used in marine applications, and water pumps for lower demanding applications, has been experimentally evaluated in this paper. Various volume fractions of GNS (0.5, 1, 2, 3, and 4% wt.) have been immersed in the grease aiming at finding the better percentages that improve the properties of nanogrease. The rheological characteristics and thermal conductivity were evaluated with a Brookfield Rheometer DV-III ULTRA and KD2 thermal analyzer, respectively. The results of the modified calcium grease indicated that the best concentration of GNS is 3% wt. The rheological characteristics of the grease with a different volume fraction of GNS indicated a non-Newtonian behavior. Thermal conductivity of nanogreas also increases with an increase of GNS volume fraction. Moreover, the apparent viscosity, shear stress and dropping point increase by 59%, 52%, and 65%, respectively, with increasing GNS volume fraction.     

Generation of sodium hypochlorite (NaOCl) from sodium chloride solution using C/PbO_2 and Pb/PbO_2 electrodes

Two modified electrodes (Pb/PbO2 and C/PbO2) were prepared by electrodepositing a lead oxide layer on lead and carbon sub-strates. These modified electrodes were used as anodes for the generation of sodium hypochlorite (NaOCl) from sodium chloride solution. Different operating conditions and factors affecting the treatment process of NaOCl generation, including current density, pH values, con-ductive electrolytes, and electrolysis time, were studied and optimized. By comparison the C/PbO2 electrode shows a higher efficiency than the Pb/PbO2 electrode for the generation of NaOCl.     

Implicit algorithm for finite deformation hypoelastic–viscoplasticity in fcc metals

An implicit objective stress update algorithm is proposed for a hypoelastic–viscoplastic model. A thermal/dynamic yield function, which is derived based on the thermal activation analysis and dislocation interaction mechanisms, is used, along with the Consistency approach and the framework of additive viscoplasticity, in deriving the proposed model for fcc metals. The corotational formulation approach is utilized in developing the proposed model in the finite deformation field. For the case of the Newton–Raphson iteration method, a new expression for the consistent (algorithmic) tangent stiffness matrix of rate‐dependent metals is derived by direct linearization of the stress update algorithm. Finite element simulations are performed by implementing the proposed viscoplasticity constitutive models in the commercial finite element program ABAQUS. Numerical implementation for a simple tensile problem is used for validating the material parameters of the OFHC Copper under low and high strain rates and temperatures. The numerical results of the adiabatic true stress–true strain curves compare very well with the experimental data. The effectiveness of the present approach is tested by studying strain localization in a simple plane strain problem. Results indicate excellent performance of the present framework in describing the strain localization problem and in obtaining mesh‐independent results. Copyright © 2006 John Wiley & Sons, Ltd.     

Quantitative and reliable in vitro method combining scanning electron microscopy and image analysis for the screening of osteotropic modulators

The increased generation and up-regulated activity of bone resorbing cells (osteoclasts) play a part in the impairment of bone remodeling in many bone diseases. Numerous drugs (bisphosphonates, calcitonin, selective estrogen receptor modulators) have been proposed to inhibit this increased osteoclastic activity. In this report, we describe a pit resorption assay quantified by scanning electron microscopy coupled with image analysis. Total rabbit bone cells with large numbers of osteoclasts were cultured on dentin slices. The whole surface of the dentin slice was scanned and both the number of resorption pits and the total resorbed surface area were measured. Resorption pits appeared at 48 h and increased gradually up to 96 h. Despite the observation of a strong correlation between the total resorption area and the number of pits, we suggest that area measurement is the most relevant marker for osteoclastic activity. Osteotropic factors stimulating or inhibiting osteoclastic activity were used to test the variations in resorption activity as measured with our method. This reproducible and sensitive quantitative method is a valuable tool for screening for osteoclastic inhibitors and, more generally, for investigating bone modulators.     

Hierarchical steganography using novel optimum quantization technique

Security and imperceptibility are fundamental issues for steganography. This paper presents a multi-level steganography system based on concealing and deception. Basically, the secret image is embedded in a cover image, which in turn is embedded in another higher-level cover image. Each steganography level provides additional security, with lower levels implemented using stronger steganography methods. Furthermore, in the event that the higher level steganography is compromised, it would include misleading information to deceive the attacker that he/she is successfully eavesdropping. One challenge of hierarchical system is retaining hidden data in lower level in a higher level stenographic technique. To mitigate this issue, we designed a novel technique which combines discrete cosine transform and least significant bit (LSB). The technique objective is to maximize the capacity and invisibility of the secret image with minimal modification to the cover image (at most k-bits per block). The performance (capacity and peak signal-to-noise ratio) of the proposed method has superior performance compared with LSB since it combines the benefits of 1-bit and 3-bit LSBs. Furthermore, the paper demonstrates that the secret message is successfully embedded and extracted in two-level steganography system with peak signal-to-nose ratio greater than 20 dB.     

Application-independent feature construction based on almost-closedness properties

Feature construction has been studied extensively, including for 0/1 data samples. Given the recent breakthroughs in closedness-related constraint-based mining, we are considering its impact on feature construction for classification tasks. We investigate the use of condensed representations of frequent itemsets based on closedness properties as new features. These itemset types have been proposed to avoid set counting in difficult association rule mining tasks, i.e. when data are noisy and/or highly correlated. However, our guess is that their intrinsic properties (say the maximality for the closed itemsets and the minimality for the δ-free itemsets) should have an impact on feature quality. Understanding this remains fairly open, and we discuss these issues thanks to itemset properties on the one hand and an experimental validation on various data sets (possibly noisy) on the other hand.