Applications In Computing Systems Research Articles

Simulation of hybrid boiling nano fluid flow with convective boundary conditions through a porous stretching sheet through Levenberg Marquardt artificial neural networks approach

In the domain of artificial neural networks, the Levenberg-Marquardt technique stands out for its innovative approach and convergent stability. It generates a numerical approach for the progression of a hybrid Cu–Al2CO3/water nanofluid on a porous stretched sheet (HNF-PSS), employing regression plots, state transition measures, histogram representations, and mean squared errors. This work explores the analysis of the fluid flow problem associated with HNF-PSS, introducing a novel application of an intelligent computing system that utilizes the effectiveness of neural networks trained by the Levenberg-Marquardt algorithm (NN-TLMA). The initial mathematical expression, expressed in terms of partial differential equations (PDEs) for HNF-PSS, undergoes transformation into dimensionless nonlinear ordinary differential equations (ODEs). The collection of data for the envisioned NN-TLMA involves parameters influencing the velocity of the HNF-PSS system model, generated using the Lobatto IIIa formula. The efficacy of the suggested NN-TLMA for solving the HNF-PSS is robustly verified by examinations of state transition dynamics, regression analyses, mean square error, and error histogram studies. The consistent alignment of the obtained results with corresponding solutions attests to the legitimacy of the structure, achieving a high level of accuracy within the range of 10–6 to 10–8. Insightful findings demonstrate that the thermal Biot number contributes significantly to an upsurge in fluid temperature. Furthermore, an augmentation in nanofluid concentration and thermal profile is found to increase the porosity strength of the medium. Nevertheless, an attenuation in the velocity profile is noted under these conditions. The application of this simulation work can be found in optimizing the designs of heat exchanger, in biomedical application and in renewable energy processes.

Synthesis of composite films for ZnO-based memristors with superior stability

Memristors have unique non-volatile characteristics that potentially can emulate biological synapses for applications in neural computing systems. However, the random formation of conductive filaments in these devices can cause various unreliability problems. In this work, films of a composite of ZnO nanoparticles and carbon nanotubes were prepared as functional layers for memristors by an in-situ growing strategy (ZnO@CNT-IS) using a straightforward high-temperature annealing treatment. This approach allowed for the formation of a high-quality films with uniform loading of ZnO nanoparticles on the carbon nanotubes, which contributed to a lower formation energy for oxygen vacancies and increased electron transfer rate. As a result, the memristors exhibited faster switching response speed, lower power consumption, and a stabilised switching ratio even after 2000 switching cycles. Based on the analog switching behaviour, the ZnO@CNT-IS-based devices showed significant biological synapse functions and plasticity, indicating their potential for high-density storage and neuromorphic computing.

A Hybrid Organic‐Crystal‐Based Curved Waveguide Network for Producing, Splitting, and Transducing Multi‐Color Outputs

AbstractHybrid optical components and circuits that deal with multiple signal generation and processing are quintessential for neural networking systems. Herein, the study reports the fabrication of one such component, a hybrid directional coupler (HDC) from blue emissive (4,4′‐bis(2,6‐di(1H‐pyrazol‐1‐yl)pyridin‐4‐yl)biphenyl) (BPP) and green emissive (E)‐1‐(((5‐bromopyridin‐2‐yl)imino)methyl)naphthalen‐2‐ol (BPyIN) pseudo‐plastic molecular crystals. Initially, a BPyIN microcrystal optical waveguide (OW1) is shaped into a strained waveguide with five bends using an atomic force microscopy cantilever‐tip aided mechanophotonics approach. Later, a singly bent BPyIN waveguide (OW2) is integrated into one of the bends at the coupling region on a strained waveguide to produce a 2 × 2 monolithic directional coupler (DC). Later, this 2 × 2 DC is extended to a HDC by integrating a blue emissive BPP waveguide (OW3) at another bend on the deformed waveguide. The fabricated HDC can effectively split the incident light into three parts with different split ratios and deliver multi‐color outputs depending on the port receiving the input signal. The spontaneous generation and processing of various signals produced in the circuit helps in understanding the functioning of complex optical neural networks. The demonstration of such innovative optical components manifests their utility for diverse applications in neural networking and quantum computing systems.

Modeling the Process of Evaluation of Information Security Risks and its Methodology by the Application of Computer Systems

The main aim of the article is to study modeling the process of evaluation of information security risks by the application of computer systems. Thus, information security risk assessment and analysis of existing methods of information security risk assessment had been also studied in the article. The term "risk of information security" (IS) applies to the damage that can attack the information technology systems. IS risk is a wide range of potential threats, especially includes data violations, control measures, financial costs, reputation damage, etc. cover issues such as. IS risks include the failure of hardware and software, human errors, spams, viruses and harmful attacks, as well as natural disasters such as fires, cyclones or floods. Security risk assessment determines, assesses and implements key security controls in applications. It also focuses on prevention of software security defects and vulnerabilities. Information security risk is the potential probability of using vulnerabilities of an asset or group of assets as a specific threat to damage the organization.

High‐Entropy Perovskite Oxide Photonic Synapses

AbstractHigh‐entropy oxides are garnering increased attention due to their unique properties engineered by chemical disorders. Chemical disorders can cause random potential spatial fluctuations and the resulting anomalous photoconductivity, while they have not yet been exploited in high‐entropy oxides for usefulness. In this work, persistent photoconductivity (PPC) behavior in high‐entropy perovskite Ba(Zr0.2Sn0.2Ti0.2Hf0.2Nb0.2)O3 thin films is reported and used it as a basis to develop artificial photonic synapses. This photonic synapse can emulate distinctive biological synaptic features such as excitatory postsynaptic current, paired‐pulse facilitation, transition from short‐term memory to long‐term memory, and learning‐forgetting processes. This photonic synapse can also implement the basic “AND” and “OR” logic operations. In addition, a 5×5 photonic synaptic array is built to mimic the spatiotemporal sensing and memory functions of a visual sensing‐memory system, exhibiting the improved accuracy of dynamic machine vision classification tasks as compared to traditional visual sensing systems. This study highlights the potential of high‐entropy oxides for application in neuromorphic computing and dynamic machine vision systems.

Development of computer application system and database testing based on data encryption technology

Abstract In this paper, we use the MapReduce programming framework for the development of a computer application system and design the vision module, control module and transaction module of the system based on MVC architecture. Focusing on the encryption design of the database, the data stored in the database is chunked through the Map function, and all the chunking results are aggregated based on the Reduce function, and synchronized encryption of the database is combined with the Paillier homomorphic encryption algorithm. On this basis, the system environment is configured, and the application system developed in this paper is tested, focusing on exploring the stability of the database. The test results show that the decryption output time is 21s when the Paillier encryption algorithm is 200, and the first set of database peak is 76% of the server load, which is within the tolerable range. Security test on the database: the results show that the probability of an attack on all ports with a database security factor less than 6 is around 0.3. There is no significant difference, and the database test is good.

Two-dimensional materials memory devices with floating metal gate for neuromorphic applications

Emerging technologies such as neuromorphic computing and nonvolatile memories based on floating gate field-effect transistors (FETs) hold promise for addressing a wide range of artificial intelligence tasks. For example, neuromorphic computing seeks to emulate the human brain's functionality and employs a device that mimics the role of a synapse in the brain. However, achieving a high current ON/OFF ratio for the program and erase states of nonvolatile memory and neuromorphic computing device with a metal gate is necessary. This study demonstrates a multi-functional device based on heterostructures of transition metal dichalcogenides (TMDCs) with a metal floating gate. Five different channel materials (SnS2, WSe2, MoS2, WS2, and MoTe2) were employed, and hexagonal boron nitride (h-BN) was used as a tunneling layer. The study found that n-type SnS2 exhibits high endurance (15,000 cycles), good retention (2.4 × 105 s), and the highest current ON/OFF ratio (∼2.58 × 108) among the materials for the program and erase states. Moreover, the SnS2 device exhibits synaptic behavior and offers highly stable operation at room temperature. Furthermore, the device shows high linearity in both potentiation and depression, with good retention time and repeatable results with low cycle-to-cycle variations. Additionally, the study used an artificial neural network (ANN) for MNIST simulation of image recognition and achieved the highest accuracy of ∼92 % based on the SnS2 synaptic device experimental results. These findings pave the way for developing nonvolatile memory devices and their applications in brain-inspired neuromorphic computing and artificial intelligence systems.

Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications.

The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals.

Python-based social science applications’ profiling and optimization on HPC systems using task and data parallelism

This research addresses the pressing need to optimize Python-based social science applications for high-performance computing (HPC)systems, emphasizing the combined use of task and data parallelism techniques. The paper delves into a substantial body of research,recognizing Python’s interpreted nature as a challenge for efficient social science data processing. The paper introduces a Pythonprogram that exemplifies the proposed methodology. This program uses task parallelism with multi-processing and data parallelismwith dask to optimize data processing workflows. It showcases how researchers can effectively manage large datasets and intricatecomputations on HPC systems. The research offers a comprehensive framework for optimizing Python-based social science applicationson HPC systems. It addresses the challenges of Python’s performance limitations, data-intensive processing, and memory efficiency.Incorporating insights from a rich literature survey, it equips researchers with valuable tools and strategies for enhancing the efficiencyof their social science applications in HPC environments.

Photonic synaptic transistors with new electron trapping layer for high performance and ultra-low power consumption

Photonic synaptic transistors are being investigated for their potential applications in neuromorphic computing and artificial vision systems. Recently, a method for establishing a synaptic effect by preventing the recombination of electron–hole pairs by forming an energy barrier with a double-layer consisting of a channel and a light absorption layer has shown effective results. We report a triple-layer device created by coating a novel electron-trapping layer between the light-absorption layer and the gate-insulating layer. Compared to the conventional double-layer photonic synaptic structure, our triple-layer device significantly reduces the recombination rate, resulting in improved performance in terms of the output photocurrent and memory characteristics. Furthermore, our photonic synaptic transistor possesses excellent synaptic properties, such as paired-pulse facilitation (PPF), short-term potentiation (STP), and long-term potentiation (LTP), and demonstrates a good response to a low operating voltage of − 0.1 mV. The low power consumption experiment shows a very low energy consumption of 0.01375 fJ per spike. These findings suggest a way to improve the performance of future neuromorphic devices and artificial vision systems.

Penerapan Sistem Cloud Computing Dalam Meningkatkan Efisiensi Kerja Pada Organisasi Kesatuan Aksi Mahasiswa Sibolga Tapteng (KAMISTA) dengan Menggunakan Layanan Google Drive

Application of cloud computing systems in increasing work efficiency in the Sibolga Tapteng Student Action Unit (KAMISTA) organization. We use the Google Drive cloud computing service as a platform to implement the system. The purpose of this study is to evaluate how the application of a cloud computing system can improve work efficiency at KAMISTA.The research method we use is descriptive qualitative, conducting a survey of KAMISTA members to find out the level of use of the cloud computing system before and after implementing the system. We also collect data on the time needed by KAMISTA members to complete tasks before and after system implementation.The results show that there is a significant increase in work efficiency after the implementation of the cloud computing system. KAMISTA members show improvements in the speed of completing tasks and reducing the time it takes to search and access data. In addition, there has been an increase in collaboration and communication between KAMISTA members after the implementation of the system.Based on the results of this study, other organizations can also implement cloud computing systems to improve their work efficiency. And in this KAMISTA organization to continue to develop and increase the use of cloud computing systems to obtain greater benefits.

Automatic Restaurant Ordering System

Abstract: Automation is a technology concerned with the application of mechanical, electronic and computer systems. This article provides an overview of the various methods and techniques used for restaurant automation. In restaurants, menus are available on every table. The customer can refer it and order through the waiter. The customer has to wait for the waiter. Even a restaurant manager finds it difficult to keep up with changing menu prices. At the same time, adding a new menu to the same card becomes a tedious job for whoever is responsible for this work, as changing the menu within a short period of time can lead to an increase in costs.

Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.

Carbon Nanodots Memristor: An Emerging Candidate toward Artificial Biosynapse and Human Sensory Perception System.

In the era of big data and artificial intelligence (AI), advanced data storage and processing technologies are in urgent demand. The innovative neuromorphic algorithm and hardware based on memristor devices hold a promise to break the von Neumann bottleneck. In recent years, carbon nanodots (CDs) have emerged as a new class of nano-carbon materials, which have attracted widespread attention in the applications of chemical sensors, bioimaging, and memristors. The focus of this review is to summarize the main advances of CDs-based memristors, and their state-of-the-art applications in artificial synapses, neuromorphic computing, and human sensory perception systems. The first step is to systematically introduce the synthetic methods of CDs and their derivatives, providing instructive guidance to prepare high-quality CDs with desired properties. Then, the structure-property relationship and resistive switching mechanism of CDs-based memristors are discussed in depth. The current challenges and prospects of memristor-based artificial synapses and neuromorphic computing are also presented. Moreover, this review outlines some promising application scenarios of CDs-based memristors, including neuromorphic sensors and vision, low-energy quantum computation, and human-machine collaboration.

Machine learning and deep learning based on the small FT-MIR dataset for fine-grained sampling site recognition of boletus tomentipes

This study proposed the necessity of identifying the sampling sites for Boletus tomentipes (B.tomentipes) in combination with cadmium content and environmental factors. Based on fourier transform mid-infrared spectroscopy (FT-MIR) preprocessing by 1st, 2nd, MSC, SNV and SG, five machine learning (ML) algorithms (NB, DT, KNN, RF, SVM) and three Gradient Boosting Machine (GBM) algorithms (XGBoost, LightGBM, CatBoost) were built. To avoid complex preprocessing, we construct BoletusResnet model, propose the concepts of 3DCOS, 3DCOS projected images, index images in addition to 2DCOS, and combine them with deep learning (DL) for classification for the first time. It shows that GBM has higher accuracy than ML and DL has better accuracy than GBM. The four DL models presented in this paper achieve fine-grained sampling sites recognition based on small samples with 100 % accuracy, and a computer application system was developed on them. Therefore, spectral image processing combined with DL is a rapid and efficient classification method which can be widely used in food identification.

Penetration Testing and Data Privacy: An In-Depth Review

This research paper provides a comprehensive review of penetration testing and data privacy. Penetration testing is a simulation of an attack on a computer system, network, or web application to identify vulnerabilities and assess the level of security. The objective of this review is to highlight the importance of penetration testing in ensuring the privacy and security of sensitive data. The paper will cover the different types of penetration testing, the processes involved, and the tools and techniques used in the testing. Additionally, the paper will also discuss the various challenges faced by organizations in implementing penetration testing and the measures that can be taken to overcome them. Furthermore, the paper will delve into data privacy and the role of penetration testing in ensuring the confidentiality, integrity, and availability of sensitive data. The review concludes by highlighting the significance of penetration testing in today's increasingly digital world and the need for organizations to invest in it.

Kullback–Leibler Divergence of an Open-Queuing Network of a Cell-Signal-Transduction Cascade

Queuing networks (QNs) are essential models in operations research, with applications in cloud computing and healthcare systems. However, few studies have analyzed the cell's biological signal transduction using QN theory. This study entailed the modeling of signal transduction as an open Jackson's QN (JQN) to theoretically determine cell signal transduction, under the assumption that the signal mediator queues in the cytoplasm, and the mediator is exchanged from one signaling molecule to another through interactions between the signaling molecules. Each signaling molecule was regarded as a network node in the JQN. The JQN Kullback-Leibler divergence (KLD) was defined using the ratio of the queuing time (λ) to the exchange time (μ), λ/μ. The mitogen-activated protein kinase (MAPK) signal-cascade model was applied, and the KLD rate per signal-transduction-period was shown to be conserved when the KLD was maximized. Our experimental study on MAPK cascade supported this conclusion. This result is similar to the entropy-rate conservation of chemical kinetics and entropy coding reported in our previous studies. Thus, JQN can be used as a novel framework to analyze signal transduction.

Plasmonic Generation of Spatiotemporal Optical Vortices

We investigate the transformation of spatiotemporal optical signals using the Kretschmann configuration with an additional dielectric layer, which can be referred to as the “generalized Kretschmann setup”. We demonstrate that in the considered structure, it is possible to achieve the condition of generating a reflected optical pulse containing a spatiotemporal optical vortex, which appears to be impossible in the conventional Kretschmann configuration. High-quality generation of spatiotemporal optical vortices using the investigated structure was confirmed by the results of rigorous numerical simulations. The obtained results are promising for applications in analog optical computing and optical information processing systems.

Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems.

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human-machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.

An Article Review in Computer System Application

An Article Review in Computer System Application