Modern Computer Research Articles

Neural net analysis of NMR spectra from strongly-coupled spin systems

Extracting parameters such as chemical shifts and coupling constants from proton NMR spectra is often a first step in using spectra for compound identification and structure determination. This can become challenging when scalar couplings between protons are comparable in size to chemical shift differences (strongly coupled), as is often the case with low-field (bench top) spectrometers. Here we explore the potential utility of AI methods, in particular neural networks, for extracting parameters from low-field spectra. Rather than seeking large experimental sets of spectra for training a network, we chose quantum mechanical simulation of sets, something that is possible with modern software packages and computer resources. We show that application of a network trained on 2-D J-resolved spectra and applied to a spectrum of iduronic acid, shows some promise, but also meets with some obstacles. We suggest that these may be overcome with improved pulse sequences and more extensive simulations.

CLOUD DATA PROTECTION USING WEIBULL DISTRIBUTED RECURRENT NEURAL ERGODIC SIGNCRYPTION

Cloud computing has become an integral part of modern computing, offering scalable storage and processing resources. However, the security of data stored in the cloud remains a major concern, especially when dealing with sensitive information. Traditional encryption schemes, while effective, often face limitations in terms of computational overhead and vulnerability to advanced attacks. To address these challenges, we propose a novel Weibull Distributed Recurrent Neural Ergodic Skewed Certificateless Signcryption scheme aimed at enhancing data protection in cloud environments. The key problem addressed by this work is the inherent inefficiency of existing cryptographic solutions that either rely on certificate-based systems or suffer from high computational and communication costs. This is especially crucial in cloud systems where real-time data processing is essential. Our approach integrates Weibull distribution for key management and optimization, recurrent neural networks (RNNs) for secure data transmission prediction, and ergodic skewed signcryption to eliminate the need for certificate authorities. This results in improved security, reduced computational overhead, and efficient communication, ensuring that the data remains secure even in dynamic cloud environments. The proposed scheme was tested using various metrics, including encryption/decryption time, data throughput, and attack resistance. Results demonstrate a significant reduction in computational cost by approximately 28% compared to traditional certificateless encryption. Furthermore, encryption times decreased from an average of 1.8 ms to 1.2 ms, and the scheme showed robustness against man-in-the-middle and chosen-ciphertext attacks with a detection accuracy of 98.6%. These results confirm the efficacy of the proposed system for enhancing security in cloud computing environments while maintaining high performance.

Proof Complexity and Beyond

Proof complexity is a multi-disciplinary research area that addresses questions of the general form “how difficult is it to prove certain mathematical facts?” The current workshop focussed on recent advances in our understanding that the analysis of an appropriately tailored concept of “proof” underlies many of the arguments in algorithms, geometry or combinatorics research that make the core of modern theoretical computer science. These include the analysis of practical Boolean satisfiability (SAT) solving algorithms, the size of linear or semidefinite programming formulations of combinatorial optimization problems, the complexity of solving total NP search problems by local methods, and the complexity of describing winning strategies in two-player round-based games, to name just a few important examples.

Analysis of the Effectiveness of Using a High-Accuracy Numerical Algorithm in the Simulation of Two-Phase Flow Problems

The continuous technical development of weapons and ammunition, as well as the growing requirements placed on new equipment, require the use of modern tools for their designing and testing. The analysis of the operation of powder propellant systems, which is the focus of interior ballistics, requires particularly specialised tools due to the complexity of the phenomena occurring during a shot. The development of numerical methods and the increase in the computing power of modern computers have enabled the modelling and simulation of highly complex physical problems and the development of numerical schemes with a theoretically arbitrary high order of accuracy. In this study, an attempt was made to develop a one-dimensional model of two-phase flows with a high order of solution accuracy in time, based on the finite volume method. The use of a two-phase flow model in the context of interior ballistics allows, among other things, the analysis of wave phenomena occurring during firing, which is crucial for safety reasons, especially in tank guns. The developed algorithm for solving two-phase systems was checked using recognised test problems for this type of model. The results of the work will be used to develop a numerical model to solve the main problem of interior ballistics.

Інформаційно-комп’ютерна технологія підтримки психічного здоров’я людини з урахуванням її характерологічних властивостей

Introduction. The current stage of the development of society is characterized by an avalanche-like increase in information, which, due to the continuous and rapidly growing pace, has acquired the character of the information industry. Currently, the problem of developing and using information systems in those areas of activity that are related to human health, especially in the areas of psychosocial status of a person who was under the negative influence of stressogenic factors caused by the state of war, is gaining relevance. Therefore, the development of information technology, which includes methods of operative diagnosis of the psychological state of a person, methods of qualifying corrective and rehabilitation measures with the involvement of the willpower of the victims, taking into account their characterological properties and the strength of the personal "I", are of great importance. Providing psychological support to persons who have been under the negative influence of psychogenic factors is an urgent and rather complex problem. We consider it expedient to involve the means of modern computer technologies in its solution. The purpose of the paper is to develop a computer decision-making support system for the rehabilitation of the psychosocial state of health of a person who was under the negative influence of psychogenic factors, taking into account personal character traits. Results. A hierarchical structure of health assessment technology was developed, taking into account personal characterological properties. On the basis of a hierarchical approach, an information-technological structure of classification and evaluation of human character properties has been developed. A program algorithm for the application of rehabilitation measures depending on the strength of manifestation of characterological properties of the individual has been synthesized. A computer system was developed to support rehabilitation decision-making by a person with psychological problems, taking into account his personal character traits. Conclusions. The developed classification of the working information array, which is carried out using a hierarchical approach, systematizes and transforms a huge amount of unordered data related to the psychological component, taking into account the character, into an information product convenient for further computer-technological transformations. The developed information technology, implemented in a computer decision support system, is a constructive tool for increasing the accessibility and efficiency of providing the necessary specific information to the user when choosing rehabilitation measures, taking into account the personal characteristics of his character. Taking into account the characterological components of mental status in the developed computer support system for making rehabilitation decisions by persons who were under the influence of psychological overstrain in connection with martial law increases the purposefulness of recommendations in the case of independent selection of health measures. Keywords: human character, hierarchical modeling, negative psychogenic factors, mental health, computer decision support system.

Bio-mimicking DNA fingerprint profiling for HLS watermarking to counter hardware IP piracy

The multifaceted, multivendor-based global design supply chain induces hardware threats of intellectual property (IP) piracy for modern computing and electronic systems. Current hardware watermarking techniques fall short either in terms of watermark strength (size of covert constraints generated) or number of security layers/variables involved in the security constraints generation process. This paper presents a novel approach for high level synthesis (HLS) watermarking by bio-mimicking DNA fingerprint profiling to counter hardware IP piracy. The proposed approach effectively captures the vital DNA fingerprint profiling phases such as DNA sequencing, DNA fragmentation, fragment replication, DNA ligase, etc. and bio-mimics them to generate a digital watermarking framework. The presented approach has been demonstrated on convolutional layer and JPEG compression-decompression (CODEC) algorithms that are widely used in several medical and machine learning applications. The proposed approach has been thoroughly compared with several state-of-the-art approaches. The proposed approach depicts superior security in the probability of coincidence of up to ~ 104 and tamper tolerance of up to ~ 10368 at 0% overhead as compared to the prior approaches.

Analyze, monitoring and testing of computer information systems

The high importance and criticality of the efficiency and reliability of the functioning of modern computer information systems require efforts to ensure them, which are no less, than the efforts to develop the systems themselves. Offline and online monitoring and testing of computer information systems, which are often used to ensure efficiency and reliability of functioning, involve the development and implementation of appropriate hardware, software and information tools, as part of automated technical diagnostic systems. Features of both the computer information systems themselves and the means of monitoring and testing are their network distribution, autonomy and at the same time cooperation of all their components. Such features appear already in the system-functional presented systems and impose requirements for a special analysis of all their properties and functions, in that case also energy ones. The proposed work is focused on the implementation of energy-behavioral monitoring and testing of computer information systems, defines a formal presentation of a special analysis of the relevant hardware and software tools and is based on asynchronous-event models of extended Petri nets and behavioral experiments. This representation is based on models of energy-behavioral specification and testing, parallel, associative-evolutionary methods of monitoring and testing with the accumulation of blanks and results that differ in special features. The proposed presentation makes it possible to create a new system energy-behavioral technology for monitoring and testing computer information systems.

Horizon picking algorithm using global optimization and coherence measurement in poststacked seismic data

Accurate horizon recognition within poststack seismic sections is important in seismic interpretation. Horizon picking techniques influence diverse seismic structural analyses and inversion methodologies. Despite encountering challenges such as computational demands and time constraints, the past few years have witnessed the development of numerous 2D and 3D methods. With the progress of modern computing, more robust and efficient techniques, harnessing artificial intelligence, have come to the fore. In this context, we develop an innovative cost-effective algorithm grounded in a global optimization framework. This algorithm combines the very fast simulated annealing method with a set of stability parameters and coherence measurements between neighboring seismic traces, which is used as the objective function. The search is executed on continuous and sequential clusters of seismic traces, with a focus on maximizing coherence amidst the presented events. We evaluate the algorithm’s efficacy by applying it to two distinct input data sets — enveloped and nonenveloped seismic traces. The initial application is to the time-migrated Marmousi data set, a synthetic marine data set originated from a complex geologic sedimentary basin situated in Angola, Africa. Subsequently, we apply the algorithm to depth-migrated land data extracted from a past survey conducted in the Tacutu Basin in northern Brazil. Outcomes arising from the application to 2D synthetic and field data sets underscore the method’s viability as a compelling alternative for seismic horizon picking within the time or depth domain.

Predicting the Consequences of an Emergency at a Railway Station

Purpose. The paper considers the problem of determining the size of the damage zones in the event of an emergency at a railway station due to a tanker fire. The task of forecasting is to determine the zones of thermal pollution, as well as chemical and mechanical pollution. The main objective of the study is to create numerical models for calculating the zones of mechanical and thermal pollution in the event of a fire at a railway station. Methodology. To analyze the size and intensity of zones of thermal, chemical, and mechanical environmental pollution in the event of an extreme situation at a railway station, we used the equations of heat and mass transfer and Newton's second law for modeling mechanical environmental pollution. To solve the equations, numerical methods such as Euler's method and finite difference schemes were used. On the basis of the developed numerical models, a computer code was created to conduct a computational experiment. Findings. Modern computer models for assessing the zones of chemical, thermal, and mechanical pollution in the event of an extreme situation have been developed. The results of computer modeling are presented. Originality. A set of numerical models for computer simulation of heat and mass transfer processes and dynamics of point motion has been created, which allows conducting a computational experiment to determine the contamination zones during a fire at a railway station. Practical value. A computer code was developed on the basis of the created mathematical models. This code is a tool for solving important problems in the field of environmental safety and civil protection. The computer code makes it possible to quickly determine the intensity and size of environmental pollution zones in the event of an extreme situation.

An in-depth analysis of data reduction methods for sustainable deep learning.

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

Сегнетоэлектрические материалы в современных вычислениях

Background: Ferroelectric materials, known for their spontaneous electric polarization, are reshaping the landscape of modern computing. These materials have unique qualities, such as non-volatility, high-speed operation, and energy efficiency, which are critical for advancing modern computer technology. Objective: This study intends to investigate integrating ferroelectric materials into current computer systems, emphasizing to improve memory devices and processors and solve the accompanying technological obstacles and possibilities. Methods: A thorough analysis of existing literature and experimental data was carried out to determine the capabilities and limitations of ferroelectric materials in computing applications. Key performance indicators examined include polarization retention, energy consumption, scalability, and integration with existing semiconductor technology. Results: The results show that ferroelectric materials considerably increase memory device performance and efficiency by allowing quicker write/read operations and lowering power use. However, concerns such as material deterioration, data retention, and integration complexity with silicon-based technology remain. Conclusion: Ferroelectric materials provide exciting prospects for the next generation of computer technology. While they provide significant gains in memory and processing power, addressing the technological obstacles is critical to their effective adoption into mainstream computer applications. Further research and development are needed to solve these issues and fully realize the promise of ferroelectric materials in improving computer performance.

COMPUTER TECHNOLOGIES AND MEDIA LITERACY IN THE EDUCATIONAL SPACE OF GEORGIA

In the twenty-first century, digital technologies are an integral part of daily life. The technology used has added a whole new dimension to society regarding communication and relationships, learning and cognition, development, and self-realization. Virtual reality has often replaced the real one, making humanity even more dependent on technology. The merits of digital technologies are unlimited access to information, inclusiveness, and availability from anywhere in the world, breaking down cultural, social, and gender barriers, self-realization and development of creative skills, social communication through various multimedia means, promotion of education and science development, and others. At the same time, the technological age has added a whole new dimension to safety in the form of suicidal online challenges, cyberbullying, and other threats that the younger generation may face as they enter adulthood. The older generation has the responsibility to protect them in the digital environment and ensure a safe digital life. Therefore, it is a priority for the education system to develop the digital awareness of young people and prepare the young generation to deal with the challenges in the digital world. This preparation is necessary for living in both the real and virtual worlds. The paper examines the current situation and peculiarities of using modern computer technologies, digital citizenship, and media literacy in Georgia's general education space and determines the possibilities for improving it.

Modern Tensor-Spinor Symbolic Algebra Algorithms and Computing Non-Closure Geometry & Holoraumy in 11D, 𝒩 = 1 Supergravity

Modern Tensor-Spinor Symbolic Algebra Algorithms and Computing Non-Closure Geometry & Holoraumy in 11D, 𝒩 = 1 Supergravity

Simplifying IP multimedia systems by introducing next-generation networks with scalable architectures

The IP Multimedia Subsystem (IMS) is essential to providing multimedia services over IP networks. However, maintaining its scalability in the face of increasing demand and changing next-generation networks is still a significant concern. In light of growing loads and service diversity, scalable solutions are required, as this article explores the shortcomings of the IMS designs in use today. To present an innovative, scalable architecture for IMS that integrates cutting-edge computer science techniques, enabling effective service delivery in next-generation networks. By bridging the gap between the static nature of previous IMS designs and the dynamic demands of contemporary telecommunications, this article hopes to deliver results. A mixed-method approach was utilized, integrating evaluations of architectural flexibility with network loads and service needs analysis. In order to evaluate the suggested designs' performance to current benchmarks, the study used simulations to predict the architectures' performance under various scenarios. Compared to conventional IMS frameworks, the suggested design showed outstanding scalability, enabling a tenfold increase in simultaneous connections and services. It notably improved fault tolerance and service delay and increased flexibility for network modifications and service diversification. Next-generation network demands are met by IMS designs that use modern computer science methodologies to improve scalability and flexibility significantly. More practical, scalable, and reliable service delivery methods will be possible thanks to the foundation our work creates for future research into dynamic, resilient telecommunications infrastructures.

GIS-technologies and algorithm for developing regional systems for handling medical waste on the example of three regions

Introduction. GIS is a modern computer technology designed for collecting, storing, analyzing and visualizing spatial data – mapping. GIS is also actively used in healthcare to solve many regional problems, including optimization and management of healthcare resources. Over last decade, many researchers have used mathematical software models for healthcare waste management. A review of the literature shows facility location, risk to the public, and economic feasibility to be among the issues that have been extensively covered in the literature. Designing a reverse logistics network using mathematical software tools is an efficient and effective way to manage healthcare waste. The objective of the study. To determine the optimal approach to creating a functional model of a system for managing medical waste of class B and C using GIS-technologies for each of the three regions, based on the characteristics of each. Materials and methods. Medical institutions in three regions (St. Petersburg, Leningrad Region and Krasnoyarsk Territory) as sources of medical waste generation were considered taking into account their capacity and specialization. The composition and volumes of waste by class were determined using a calculation method based on indicative waste generation standards. The identification of local technologies for the neutralization of medical waste existing in medical institutions was carried out. The obtained data was processed statistically and displayed on electronic maps for subsequent use in GIS technologies, as well as for the development and justification of a waste management system for medical institutions, taking into account the prospects for the development of healthcare. Results. Based on the analysis of data on medical waste of classes B and C in individual medical institutions, as well as an analysis of the power of existing technologies for neutralizing this waste using GIS technologies, proposals were developed to improve the medical waste management system in three regions. The proposals are aimed at improving the sanitary and epidemiological situation in the regions over a five-year period and take into account the features of the existing transport infrastructure and the requirements of sanitary legislation. Limitations. The model of a medical waste management system using GIS technologies was used only for managing waste of classes B and C. Conclusion. To ensure the safe and environmentally sustainable functioning of the waste management system of classes B and C in the regions, it is necessary to apply an integrated approach based on GIS technologies. This approach should take into account the regional characteristics of the management of the medical waste disposal system and its economic feasibility and take into account the regional characteristics of transport logistics and the prospects for the development of healthcare.

Custom RISC-V architecture incorporating memristive in-memory computing

Due to the rise in data-intensive applications, the von Neumann bottleneck is increasingly restricting modern computer architectures, resulting to latency and energy consumption. Addressing this challenge necessitates a CMOS-compatible solution with high energy efficiency and significant parallelism. Utilizing resistive switching components within a 1T1R crossbar array and the application of Stanford RRAM model, this paper suggests an original method for in-memory computing. Moreover, this work shows a new way to advance the popular RISC-V architecture by including memristive crossbar array. It does this by adding a custom instruction set, special hardware blocks, and the Scouting Logic Scheme. These modifications serve both as a comprehensive testbed for the memory system and a proof of concept for the future integration of memristors in computing architectures. The proposed design undergoes extensive testing and power analysis to validate its functionality and performance under various conditions. The results demonstrate significant improvements in computational efficiency and energy savings, highlighting the potential of memristor-based in-memory computing systems to overcome current architectural limitations.

ADVANCING MEMORY DENSITY: A NOVEL DESIGN FOR MULTIPLE-BIT-PER-CELL PHASE CHANGE MEMORY

Multiple-bit-per-cell phase-change memory (MPCM) has emerged as a promising solution to address the escalating demands for high-density, low-power, and fast-access memory in modern computing and data storage systems. This paper presents a novel device design aimed at enabling multiple bits per cell in phase-change memory, thereby significantly enhancing memory density while maintaining performance and reliability. Leveraging innovative material compositions and advanced fabrication techniques, the proposed design demonstrates the potential to push the boundaries of memory capacity, efficiency, and scalability. Through comprehensive simulation analysis and performance evaluations, we showcase the feasibility and advantages of the new device design, highlighting its potential to revolutionize memory architectures and meet the evolving needs of next-generation computing systems.

Bangladeshi crops leaf disease detection using YOLOv8

The agricultural sector in Bangladesh is a cornerstone of the nation's economy, with key crops such as rice, corn, wheat, potato, and tomato playing vital roles. However, these crops are highly vulnerable to various leaf diseases, which pose significant threats to crop yields and food security if not promptly addressed. Consequently, there is an urgent need for an automated system that can accurately identify and categorize leaf diseases, enabling early intervention and management. This study explores the efficacy of the latest state-of-the-art object detection model, YOLOv8 (You Only Look Once), in surpassing previous models for the automated detection and categorization of leaf diseases in these five major crops. By leveraging modern computer vision techniques, the goal is to enhance the efficiency of disease detection and management. A dataset comprising 19 classes, each with 150 images, totaling 2850 images, was meticulously curated and annotated for training and evaluation. The YOLOv8 framework, known for its capability to detect multiple objects simultaneously, was employed to train a deep neural network. The system's performance was evaluated using standard metrics such as mean Average Precision (mAP) and F1 score. The findings demonstrate that the YOLOv8 framework successfully identifies leaf diseases, achieving a high mAP of 98% and an F1 score of 97%. These results underscore the significant potential of this approach to enhance crop disease management, thereby improving food security and promoting agricultural sustainability in Bangladesh.

HTSA: A novel hybrid task scheduling algorithm for heterogeneous cloud computing environment

Cloud computing provides users and programs with scalable resources and on-demand services virtually in real time, making it a fundamental paradigm in modern computing. The concept for using remote computing resources is novel. Cloud computing relies on task scheduling to boost system performance, reduce execution time, and optimise resource use. Due to exponential task increase and problem complexity, the search space is huge. Optimization tasks like this are NP-hard. This work aims to find a near-optimal solution for a multi-objective task scheduling problem in the cloud while lowering search time. Using the Genetic Algorithm (GA) and Gravitational Search Algorithms (GSA) benefits while avoiding their drawbacks, we offer a standard cloud computing task scheduling method to improve system performance and optimise the Quality of service (QoS) parameters like energy, makespan, resource utilization and throughput. We use CloudSim to test standard functions, real-time, and synthetic workloads. The obtained results are compared to other similar, metaheuristic-based techniques that were evaluated under the same conditions. The designed technique outperforms Gravitational Search Algorithms (GSA), Ant Colony Optimizaion(ACO), and Particle Swarm optimization(PSO) in Degree Of Imbalance (12%), resource utilization (9%), Mean Response Time (7%) and energy consumption (6%).

A Comparative Analysis and Mathematical Modelling of Data Migration Tools to Evaluate Performance Parameters using Machine Learning Methods

Abstract: Transferring data between storage systems, platforms, or places is a common reason why data movement is an important part of modern computing. The effectiveness and dependability of these tasks can be greatly affected by how well data transfer tools work. Using machine learning techniques, we compare and contrast different data transfer tools in this work. First, we will talk about some important performance metrics that are needed to properly evaluate data transfer tools. Such elements include the type of data, its size, the network's speed, and other important factors like delay and flow. Next, we get data from different data transfer tools and prepare it before reviewing their performance in a range of situations and setups. The data is analyzed using machine learning techniques like classification, regression, and grouping. Finding groups of tools that work similarly is easier with clustering. This gives you information about which tools are best for different data transfer jobs. We can describe the link between performance factors and tool performance under different situations using regression analysis. The performance traits of tools help with classification, which makes it easier to choose the right tool for a transfer job. Our findings demonstrate that machine learning techniques can correctly evaluate and contrast data transfer tools by looking at how well they do certain tasks. The paper study shows how these approaches can help companies pick the best tool for their data transfer needs by giving them useful information about tool choices. Furthermore, our research shows how important it is to use machine learning when assessing data transfer tools and sets the stage for more research to be done in this area in the future.