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This research is based on the present missile detection technologies in the world and the analysis of these technologies to find a cost effective solution to implement the system in Bangladesh. The paper will give an idea of the missile detection technologies using the electro-optical sensor and the pulse doppler radar. The system is made to detect the target missile. Automatic detection and destruction with the help of ultrasonic sonar, a metal detector sensor, and a smoke detector sensor. The system is mainly based on an ultrasonic sonar sensor. It has a transducer, a transmitter, and a receiver. Transducer is connected with the connected with controller. When it detects an object by following the algorithm, it finds its distance and angle. It can also assure whether the system can destroy the object or not by using another algorithm's simulation., Comment: 67 pages

Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of Group IV, III-V, and II-VI zinc blende (ZB) semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98 % given the range of values within the dataset, improving significantly on the state-of-the-art. Models are tested for different defect types as well as for defect charge transition levels. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially-optimized defective structures, helping identify electronically active defects in technologically-important semiconductors.

Firewalls are critical components in securing communication networks by screening all incoming (and occasionally exiting) data packets. Filtering is carried out by comparing incoming data packets to a set of rules designed to prevent malicious code from entering the network. To regulate the flow of data packets entering and leaving a network, an Internet firewall keeps a track of all activity. While the primary function of log files is to aid in troubleshooting and diagnostics, the information they contain is also very relevant to system audits and forensics. Firewalls primary function is to prevent malicious data packets from being sent. In order to better defend against cyberattacks and understand when and how malicious actions are influencing the internet, it is necessary to examine log files. As a result, the firewall decides whether to 'allow,' 'deny,' 'drop,' or 'reset-both' the incoming and outgoing packets. In this research, we apply various categorization algorithms to make sense of data logged by a firewall device. Harmonic mean F1 score, recall, and sensitivity measurement data with a 99% accuracy score in the random forest technique are used to compare the classifier's performance. To be sure, the proposed characteristics did significantly contribute to enhancing the firewall classification rate, as seen by the high accuracy rates generated by the other methods., Comment: Accepted and presented in International Conference on Computational Intelligence and Sustainable Engineering (CISES-2023), 2022, 7 pages, 13 figures

Three-dimensional (3D) image cytometers may significantly improve the cell analysis accuracy to facilitate biological discoveries and clinical diagnosis, but their development is curbed by the low imaging throughput. Here we report SIngle-frame LAbel-free Cell Tomography (SILACT) with diffraction-limited resolution and unprecedented imaging speed of over 10,000 volumes/second. SILACT is built on a unique interferometric microscope with angle-multiplexing illumination and a pre-trained physics-incorporating Deep Neural Network for efficient 3D Refractive Index (RI) reconstruction, from which 3D morphological and biophysical parameters of cells are extracted. With microfluidics and a high-speed camera, SILACT is capable of imaging over 20,000 cells/second and distinguishing different cell species during rapid measurements of large cell quantities, as well as visualizing shear-induced 3D transient deformation of red blood cells on a sub-millisecond scale., Comment: Baoliang Ge, Yanping He, Mo Deng contributed equally to this work

Nowadays, Software Process Improvement popularly known as SPI has been able to receive an immense concern in the continuous process to purify software quality. Several Agile methodologies previously have worked with Extreme programming (XP). Before improving the process, defect prevention (DP) is inevitable. In addition, DP largely depends on defect detection either found earlier in the design and implementation stages or held in the testing phases. However, testing maturity model integration (TMMI) has a crucial aspect in DP as well as process improvement of the software. In particular, when software gets validated by being tested and fixed the defects up, it achieves the maximum capability maturity model integration (CMMI) aiming the process improvement. Here, the article has proposed an improved defect detection and prevention model to enhance the software process following the approach of XP. Besides, as a unique contribution, we have united the capability and testing model integration to ensure better SPI., Comment: 10 Pages, 4 Figures, 2 Tables

We used molecular dynamics (MD) simulations to investigate the mechanical properties of cubic zinc blende (ZB) Si0.5Ge0.5 alloy nanowire (NW). Tersoff potential is employed to elucidate the effect of nanowire size, crystal orientations, and temperature on the material properties. We found that the reduction in the cross-sectional area results in lower ultimate tensile strength and Youngs modulus of this alloy which can be attributed to the increased surface to volume ratio. The [111] oriented Si0.5Ge0.5 NW exhibits the highest fracture strength compared to other crystal orientations but [110] orientation possesses the highest fracture toughness. The effect of temperature depicts an inverse relationship with the ultimate tensile strength and Youngs modulus. The increased temperature facilitates the failure of the material, thus degrades the materials strength. Our study reveals that the vacancy defects introduced via removal of either Si or Ge atoms exhibit similar behavior, and with the increase in vacancy concentration, both ultimate tensile strength and Youngs modulus reduces linearly. We further illustrate the failure characteristics of Si0.5Ge0.5 NW at two extremely low and high temperatures. The intrinsic failure characteristics of Si0.5Ge0.5 alloy is found to be insensitive to the temperature. Interestingly, at both temperatures, with the increasing strain, the cross-section of Si0.5Ge0.5 eventually resembles a neck as typically observed in ductile materials, although the NW failure is brittle in nature. Overall, this work offers a new perspective on understanding material properties and failure characteristics of ZB Si0.5Ge0.5 NW that will be a guide for designing Si-Ge based nanodevices., Comment: 13 pages, 5 figures. arXiv admin note: text overlap with arXiv:2004.08023

The mechanical properties of Cadmium Selenide (CdSe) nanowire is an emerging issue due to its application in semiconductor and optoelectronics industries. In this paper, we conducted molecular dynamics (MD) simulations to investigate the temperature-dependent mechanical properties and failure behavior of Zinc-Blende (ZB) CdSe nanowire under uniaxial tensile deformation. We employed Stillinger-Weber (SW) potential to describe the inter-atomic interactions. The effect of variation of temperatures (100 K-600 K), sizes, and crystal orientation on the tensile response of the CdSe nanowires is investigated. Our simulation results suggest that both ultimate tensile strength and Youngs modulus of CdSe have an inverse relationship with temperature. From 100K to 600K, the ZB CdSe exhibits brittle type failure thus there is no brittle to ductile transition temperature found. Results also suggest that size has a significant effect on the mechanical properties of CdSe nanowire. It has been found that as the cross-sectional area increases both ultimate tensile stress and Youngs modulus increases as well. The [111] oriented ZB CdSe shows the largest ultimate tensile strength, Youngs modulus and fracture toughness whereas the values are lowest for [100] orientation. The [110] orientation shows the largest failure strain compared to other orientations. Finally, failure mechanisms of CdSe nanowire are also investigated at 100K and 600K. We noticed that at 100K temperature [100] oriented ZB CdSe fails along {111} cleavage plane however in the case of 600 K temperature, both {111} and {100} planes are activated and cause fracture of CdSe nanowire at lower strain value. This study can guide to design ZB CdSe based solar cell, optoelectronic and semiconductor devices by presenting a comprehensive understanding of the mechanical and fracture characteristics of this nanowire., Comment: 12 pages, 6 figures

Functionally graded materials (FGMs), have drawn considerable attention of the worldwide researchers and scientific community because of its unique mechanical, thermal and electrical properties which may be exploited by varying the compositions gradually over volume. This makes FGM multifunctional material (properties changing continuously in a certain direction) for specific purpose without creating any phase interface thus making it superior to its composite counterparts. In this paper, we applied Molecular Dynamics (MD) approach to investigate the mechanical properties of functional graded Ni-Al alloy with Ni coating by applying uniaxial tension. Nickel-Aluminum (Ni-Al) alloy has been used extensively in the industry due to its remarkable mechanical and thermal properties. Our aim is to find the difference in material behavior when we change the grading function (linear, elliptical and parabolic), temperature and crystallographic direction. We also observe distinct type of failure mechanism for different grading function at different temperature. Close observation reveals that elliptically graded Ni-Al alloy has high tensile strength at low temperature whereas at high temperature, the highest tensile strength is found for parabolic grading. Besides, at any temperature, the parabolically graded Ni-Al alloy shows superior elasticity than its elliptical and linear counterpart. Moreover, it is also observed that [111] crystallographic direction for this alloy demonstrates more resistivity towards failure than any other crystallographic direction. It is found that lattice disorder plays a significant role on the mechanical properties of Functionally Graded Materials (FGMs). This paper details a pathway to tune the mechanical properties like Young's Modulus, plasticity and yield strength at molecular level by varying the composition of materials along different grading functions., Comment: 13 pages, 8 figures