Computational Components Research Articles

NOA-LSTM: An efficient LSTM cell architecture for time series forecasting

The application of Machine learning and deep learning techniques for time series forecasting has gained significant attention in recent years. Numerous endeavors have been devoted to automating forecasting through the utilization of cutting-edge neural networks. Notably, the recurrent neural network (LSTM – Long Short-Term Memory) has emerged as a central concept in most research endeavors. Although LSTM was initially introduced in 1997 for sequence modeling, subsequent updates have primarily focused on language learning tasks. These updates have introduced various computational mechanisms within the LSTM cell, including the forget gate, input gate, and output gate. In this study, we investigate the impact of each computational component in isolation to analyze their effects on time series forecasting tasks. Our experiments utilize the Jena weather dataset and Appliance Energy Usage time series for evaluation. The experimental results reveal that variations of the LSTM model outperform the most popular LSRM cell format in terms of error rate and training time. Specifically, the variations identified in this paper demonstrate superior generalization capabilities and yield reduced forecasting errors.

BatTool: projecting bat populations facing multiple stressors using a demographic model

Bats provide ecologically and agriculturally important ecosystem services but are currently experiencing population declines caused by multiple environmental stressors, including mortality from white-nose syndrome and wind energy development. Analyses of the current and future health and viability of these species may support conservation management decision making. Demographic modeling provides a quantitative tool for decision makers and conservation managers to make more informed decisions, but widespread adoption of these tools can be limited because of the complexity of the mathematical, statistical, and computational components involved in implementing these models. In this work, we provide an exposition of the BatTool R package, detailing the primary components of the matrix projection model, a publicly accessible graphical user interface (https://rconnect.usgs.gov/battool) facilitating user-defined scenario analyses, and its intended uses and limitations (Wiens et al., US Geol Surv Data Release 2022; Wiens et al., US Geol Surv Softw Release 2022). We present a case study involving wind energy permitting, weighing the effects of potential mortality caused by a hypothetical wind energy facility on the projected abundance of four imperiled bat species in the Midwestern United States.

New computer components for proximity assessment and recognition of double objects, which are encoded with symbols of the binary alphabet

Numerous binary measures of similarity and distance make it possible to effectively solve various tasks of recognition, classification, estimation of proximity of binary sequences, etc. Since the performance of the proposed systems largely depends on the choice of appropriate measures and distances, many researchers have spent considerable effort to find more efficient binary ratios for solving the mentioned tasks. Numerous binary relations, especially binary ones, have been proposed and investigated in various fields of science. However, it was not possible to determine one or a group of distances or similar measures effective in solving any tasks. In this regard, a number of new methods were proposed, based on the use of algorithms for learning, and on the external similarity of ratios that have different names of methods and proposed by different specialists, and the introduction of pre-processing of the initial data in order to determine the weighting factors when using them. Il.: 2; Bibliogr.: 15 titles.

Analisis Sistem Bus USB Dan PCI Pada Organisasi Arsitektur Komputer

Bus systems are an important component of modern computer organization and architecture. In this context, the two types of buses that stand out are Universal Serial Bus (USB) and Peripheral Component Interconnect (PCI). The USB bus is an interface standard commonly used to connect various external devices to a computer, while PCI is an internal bus that connects computer components. This study aims to analyze the role and characteristics of the USB and PCI bus systems in computer organization and architecture. The results of this analysis provide an in-depth understanding of the role of USB and PCI bus systems in modern computer organization and architecture. This information can help IT professionals and system designers make decisions regarding hardware connectivity and performance in a variety of computing environments.

SISTEM PAKAR UNTUK DIAGNOSA KERUSAKAN KOMPUTER MENGGUNAKAN METODE FORWARD CHAINING DAN CERTAINTY FACTOR BERBASIS WEB

Damage to the computer is caused by several problematic components such as problematic RAM, hard disk or VGA. This condition is a condition of minor damage which sometimes users consider this to be fatal damage due to a lack of knowledge about computer components, which is why an expert system is needed to help users understand. damage to the computer. In this research, the Forward chaining method will be used in the system that will be used in the program. This research will focus on a system that produces and adopts knowledge from an expert in the field and to be able to diagnose computer damage using a web-based method. An expert system is an application that uses the expertise or knowledge of an expert in a particular field and this expertise is implemented in a system.

The influence of the cooling system on the efficiency of thermal management and the performance of computer components

The article attempts to evaluate the existing methods of cooling computer components in terms of ensuring their efficient operation. Issues related to heat exchange were discussed and individual computer components were characterized in terms of heat reception and the available methods of cooling computer components were reviewed. Based on the analysis of cooling methods, the most appropriate solution was selected in terms of: efficiency, costs and generated noise. A liquid cooling system was designed for the model computer, allowing for a significant increase in the clock speed of the computer’s main components. In order to determine the efficiency of the system, i.e. the maximum flow that can be discharged by the system, and to determine the efficiency of the required coolers, appropriate calculations were made regarding: thermal conductivity between each of the cooled elements and the corresponding block and heat transfer to the cooling liquid inside each of the blocks.

ENHANCED DIAGNOSTIC TECHNIQUES FOR MARINE DIESEL ENGINES: ACHIEVING IMO DECARBONIZATION COMPLIANCE

This paper discusses hardware-based methods for monitoring the operational parameters of marine diesel engines and online mathematical modeling techniques (digital twins) for calculating the emissions of CO2, NOx, soot, and other harmful pollutants. It is demonstrated that measuring and analyzing the engine's vibrational diagrams in parallel with gas pressure diagrams in the cylinders allows the evaluation of the actual fuel injection and gas distribution timings. The obtained data is used to refine the mathematical model of the engine's working process, which determines the engine's indicator parameters and power, as well as the emission of harmful substances that need to be monitored in compliance with current IMO requirements. The authors have accumulated experience in using the discussed hardware-based methods for diagnosing marine engines during operation. The hardware and software methods discussed have been implemented into a real-time system for in-service diagnostics of marine engines. The system is designed based on a modern dual-core controller with high performance and low power consumption, incorporating a high-speed ADC with sufficient capability to monitor the working process with a 0.1-degree crankshaft rotation resolution for all types of marine main and auxiliary engines. The system also utilizes wireless data transmission technology. A contemporary Android/iOS smartphone or tablet serves as the computational and graphical component of the system. This real-time system enables the utilization of all the advantages of parallel pressure and vibroacoustic analysis, including real-time determination of key operating parameters, identification of top dead center position, and evaluation of fuel delivery and gas distribution phases. Additionally, it harnesses the benefits of employing a digital twin—an online mathematical model of the engine cylinder's working process. These solutions will enhance diagnostic quality and, ultimately, improve the operational efficiency of marine engines by reducing operational costs and extending the period of reliable, trouble-free operation.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization.

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

Enhancement of Product-Inspection Accuracy Using Convolutional Neural Network and Laplacian Filter to Automate Industrial Manufacturing Processes

The automation of the manufacturing process of printed circuit boards (PCBs) requires accurate PCB inspections, which in turn require clear images that accurately represent the product PCBs. However, if low-quality images are captured during the involved image-capturing process, accurate PCB inspections cannot be guaranteed. Therefore, this study proposes a method to effectively detect defective images for PCB inspection. This method involves using a convolutional neural network (CNN) and a Laplacian filter to achieve a higher accuracy of the classification of the obtained images as normal and defective images than that obtained using existing methods, with the results showing an improvement of 11.87%. Notably, the classification accuracy obtained using both a CNN and Laplacian filter is higher than that obtained using only CNNs. Furthermore, applying the proposed method to images of computer components other than PCBs results in a 5.2% increase in classification accuracy compared with only using CNNs.

ATFA: Adversarial Time–Frequency Attention network for sensor-based multimodal human activity recognition

Human activity recognition (HAR) is a key component of mobile and ubiquitous computing. With the increasing number of sensors embedded in devices such as smartphones and smartwatches, models are enhanced to recognize more complex human behaviors. However, this also leads to increased data heterogeneity, caused by diverse user backgrounds, health conditions, and sensing environments, which makes it difficult to align the multimodal data distributions across individuals. This paper proposes a novel unsupervised domain adaptation framework via Adversarial Time-Frequency Attention (ATFA) to efficiently adapt models to new users. In particular, the proposed attention-based modality fusion module captures and fuses important modalities based on their context to reduce redundant information. Additionally, the network explores frequency domain features to improve performance in recognizing human activities. Extensive experiments are conducted on three publicly available HAR datasets to demonstrate the superiority of our proposed method compared to state-of-the-art baselines.

Clinical and computational speech measures are associated with social cognition in schizophrenia spectrum disorders.

In this study, we compared three domains of social cognition (emotion processing, mentalizing, and attribution bias) to clinical and computational language measures in 63 participants with schizophrenia spectrum disorders. Based on the active inference model for discourse, we hypothesized that emotion processing and mentalizing, but not attribution bias, would be related to language disturbances. Clinical ratings for speech disturbance assessed disorganized and underproductive dimensions. Computational features included speech graph metrics, use of modal verbs, use of first-person pronouns, cosine similarity of adjacent utterances, and measures of sentiment; these were represented by four principal components. We found that higher clinical ratings for disorganized speech were predicted by greater impairments in both emotion processing and mentalizing, and that these relationships remained significant when accounting for demographic variables, overall psychosis symptoms, and verbal ability. Similarly, a computational speech component reflecting insular speech was consistently predicted by impairment in emotion processing. There were notable trends for computational speech components reflecting underproductive speech and decreased content-rich speech predicting mentalizing ability. Exploratory longitudinal analyses in a small subset of participants (n=17) found that improvements in both emotion processing and mentalizing predicted improvements in disorganized speech. Attribution bias did not demonstrate strong relationships with language measures. Altogether, our findings are consistent with the active inference model of discourse and suggest greater emphasis on treatments that target social cognitive and language systems.

Enhanced Au bioleaching from waste central processing unit (CPU) slots in a bioreactor with 10 mA direct current

As the core component of computers, waste central processing unit slots contain recyclable precious metals as well as a small amount of toxic and hazardous substances. Bioleaching is considered as a green technology for recovering precious metals. However, the limited cyanogenic capacity and long leaching time limit its industrialization. To break this bottleneck, this paper presents an innovative bioleaching technique that increases the cyanide production to 2.38 times and reduces the leaching time to a few hours with 10 mA direct current. Meanwhile, part of Au+ was electrodeposited on the cathode, and Au recovery was achieved. Under the electric field, the improvement of biological activity, the enhancement of cyanogenic capacity, and the change of microbial community greatly contributed to the leaching of Au. This work presents a new approach to achieve efficient leaching in bioleaching, providing key scientific information for the recovery of precious metals from waste central processing unit slots.

Preferential fault-tolerance multiplier design to mitigate soft errors in FPGAs

FPGAs are the most prominent electronic devices used in space, military, and commercial applications. This is because of the reprogrammable feature in FPGAs. The electronic and computational components are sensitive to various types of events caused by ionisation effects. The problem at hand is effectively addressed by a fault-tolerant method known as Triple Modular Redundancy (TMR). Triple Modular Redundancy (TMR) has been demonstrated as an effective and dependable technique for mitigating the impacts of single-event upsets (SEUs) and single-event transients (SETs). However, to ensure effective mitigation of SETs, an additional delay element is required. However, the implementation of a TMR system may incur higher costs due to the requirement for additional hardware resources. Subsequently, numerous studies have focused on conventional fault-tolerant techniques with the aim of reducing the hardware overhead associated with TMR, although these efforts have not yielded satisfactory results. In order to tackle this issue more effectively, this paper introduces a novel error analysis approach. This approach works based on the error percentage algorithm and also proposes the other method, a preferential method for reducing the overhead of the hardware in existing fault-tolerant techniques. The proposed work is applied to the multiplier circuit to analyse its efficiency. The proposed work reduces the hardware resources by 48% when compared with the existing work.

BERPIKIR KOMPUTASIONAL SISWA SMP DALAM MENYELESAIKAN MASALAH MATEMATIKA

The important skill to be applied in the 21st century is computational thinking (CT). Students need to develop computational thinking skills by being trained to solve open-ended problems. The descriptive qualitative method is the method used in this research. This research aims to describe the computational thinking skills of junior high school students in solving open-ended mathematical problems by explaining each component of computational thinking, namely abstraction, decomposition, algorithmic thinking, generalization, and debugging. The subjects of the research were several 8th-grade students from a private junior high school in Surabaya, consisting of 55 students who had been given TKM and TPM. Then, 3 students with different categories who provided unique answers were selected. Student in the low computational thinking category (BK1) do not fulfill all computational components. Student in the moderate category (BK2) have incomplete results in the final solution, and the problem-solving methods provided are also inadequate, resulting in imperfect final outcomes. Student in the high computational thinking category fulfill all computational components. Student (BK3) provides correct and varied problem-solving methods and solutions.
 

Tracing The Timeline of Network Security From the 1950s To the Present (2023)

The field of network security has evolved significantly over the years, with new threats emerging and security measures adapting in response. Network security problems exist through all the layers of the computer network, and the network security objective is to maintain the confidentiality, authenticity, integrity, dependability, availability, and suitability of the network. The rapid development of computer network systems brings both great convenience and new security threats for users. Network security problems generally include network system security and data security. Specifically, it refers to the reliability of the network system, confidentiality, integrity, and availability of data information in the system. This paper aims to trace the timeline of network security in chronological order, starting from the early days of computing to the present day. The paper will explore the major milestones in the development of network security, including the emergence of viruses, the introduction of firewalls, and the rise of cybercrime. By examining the historical context of these developments, this paper will provide a deeper understanding of the evolution of network security and the challenges that continue to shape the field today. Ultimately, this paper will demonstrate how network security has become an essential component of modern computing, and how its evolution has helped shape the digital world we live in today. Index Terms — Access Control, Anti-Virus, Asymmetric Key, Authentication, Availability, Cipher, Clearance levels, Confidentiality, Data encryption, DevSecOps, Firewalls, Firmware, Hash Function, IDS, Integrity, Intrusion, NAT, Security, Security Audits, SOAR, Symmetric Key, VPN, XDR

20-Mode Universal Quantum Photonic Processor

Integrated photonics is an essential technology for optical quantum computing. Universal, phase-stable, reconfigurable multimode interferometers (quantum photonic processors) enable manipulation of photonic quantum states and are one of the main components of photonic quantum computers in various architectures. In this paper, we report the realization of the largest quantum photonic processor to date. The processor enables arbitrary unitary transformations on its 20 input modes with an amplitude fidelity of FHaar=97.4% and FPerm=99.5% for Haar-random and permutation matrices, respectively, an optical loss of 2.9 dB averaged over all modes, and high-visibility quantum interference with VHOM=98%. The processor is realized in Si3N4 waveguides and is actively cooled by a Peltier element.

Fundamentals Plasmonic Logic Gates at Nano Scale Structure and 1550 nm Wavelength

This article posits, simulates, designs, and implements all-optical logic gates that use nano-rings Insulator-Metal-Insulator (IMI) plasmonic waveguides (NOT, OR, AND). The suggested plasmonic gates' structure has been designed and numerically simulated by two dimensions (2-D) structure using the Finite Element Method (FEM). The proposed device's performance is evaluated using four criteria: transmission, modulation depth, extension ratio, and insertion loss. The proposed structure was built with silver and glass materials. The suggested plasmonic gates' functionality was achieved by using the concepts of destructive and constructive interferences. Numerical simulations show that at 1550 nm operating wavelength, the suggested plasmonic gates could be implemented in a single structure with a transmission threshold of (0.5). This device had the following characteristics: transmission exceeding 100% in one state of NOT and exceeding 200% in one state of OR and AND gates, medium and high Extension Ratio values, so small footprint, and very high MD values. Future access to nanophotonic integrated circuits will be made possible by this technology, which is also thought to be a crucial component of all-optical computers.

Efficient in-situ workflow planning for geographically distributed heterogeneous environments

In-situ workflows are a particular class of scientific workflows where different components (such as simulation, visualization, machine learning, and data analysis) run concurrently. In an in-situ workflow, data continuously flows between components during the whole workflow lifetime in a pipeline fashion. The overall throughput of an in-situ workflow is limited by the slowest moving part of the workflow: either a time-consuming computation component or an expensive data transfer operation between different components. Given geographically distributed heterogeneous computing environments and an in-situ workflow comprising various component applications, it is challenging to produce optimized deployment plans to decide where to launch each component application and how much computing resources to allocate for each component. In this work, we first formulate and define the in-situ workflow planning problem and then discuss the uniqueness of the problem. We design a heuristic-based solver based on our new “Scheduled-Neighbors-Lookup” (SNL) algorithm, which produces effective deployment plans with much less time, compared with the mathematical-optimization-based solver using CPLEX. Our experiments with both synthetic and real-world workflows show that the SNL algorithm can find optimized solutions whose qualities are comparable to that of the CPLEX optimization method with significantly reduced problem-solving time (e.g., more than 20,000 times faster on average for in-situ workflows with 14 components). Compared with existing methods such as HEFT, R-Storm, and T3 schedulers, the SNL algorithm can generate resource plans with higher throughput, with increased time and resource efficiency.

Macroprogramming: Concepts, State of the Art, and Opportunities of Macroscopic Behaviour Modelling

Macroprogramming refers to the theory and practice of expressing the macro(scopic) behaviour of a collective system using a single program. Macroprogramming approaches are motivated by the need of effectively capturing global/system-level aspects and the collective behaviour of multiple computational components, while abstracting over low-level details. Previously, this programming style had been primarily adopted to describe the data-processing logic in sensor networks; recently, research forums on spatial computing, collective systems, and the Internet of Things have provided renewed interest in macro approaches. However, related contributions are still fragmented and lack conceptual consistency. Therefore, to foster principled research, an integrated view of the field is provided, together with opportunities and challenges.

State-of-the-art computational methods to predict protein-protein interactions with high accuracy and coverage.

Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.