software configurations Research Articles (Page 1)

Purpose: The purpose of this study is to explore end-to-end Linux administration practices, focusing on installation, configuration, performance tuning, and production reliability in heterogeneous computing environments. With the increasing complexity of distributed systems, Linux administrators must manage diverse infrastructures efficiently while ensuring system stability and scalability. Design/methodology/approach: This research adopts a qualitative and analytical approach by reviewing existing literature, system administration frameworks, and real-world case studies. It evaluates Linux system lifecycle stages, including deployment, configuration management, performance optimization, and reliability engineering across heterogeneous platforms such as cloud, edge, and enterprise systems. Findings: The findings reveal that effective Linux administration requires integrated tools, automation, and proactive monitoring. Performance tuning and configuration optimization significantly enhance system efficiency, while reliability frameworks improve fault tolerance. Heterogeneous environments demand adaptive strategies due to varied hardware and software configurations. Practical implications: This study provides system administrators and IT professionals with structured methodologies to improve operational efficiency. It highlights best practices for automation, monitoring, and system tuning to achieve high availability and performance in production environments. Originality/value: This paper contributes a unified perspective on Linux administration by integrating installation, configuration, tuning, and reliability into a single framework tailored for heterogeneous systems, bridging gaps in prior fragmented studies.

The study aimed to develop a theoretical methodology for evaluating and selecting software and hardware for creating a cloud environment with a single information space, based on the use of fuzzy cluster analysis. The research issue was the difficulty of choosing the optimal solution in the face of uncertainty, which was typical for the process of cloud infrastructure creation. The methodology included an analysis of existing approaches, the formation of a system of evaluation criteria, theoretical justification and construction of a mathematical model for fuzzy evaluation of alternatives and involved the use of fuzzy cluster analysis to group alternative solutions based on multi-criteria assessments, including parameters such as performance, cost, scalability, reliability and security. Testing was conducted on real data from five leading cloud platform providers. The results demonstrated that the use of fuzzy cluster analysis increased the accuracy of cloud infrastructure options assessment by 23% compared to traditional methods. The application of the methodology on real-life examples demonstrated its effectiveness in the decision-making process when creating cloud environments, which was confirmed by the achieved increase in productivity and reduction in the cost of implementing and maintaining the system. The fuzzy cluster analysis identified three optimal hardware and software configurations that used computing resources 18% more efficiently. The developed algorithm demonstrated the processing capabilities of uncertainty in input data, reducing the impact of the subjectivity of expert opinions by 31%. The conclusions of this study emphasised that the fuzzy cluster analysis method is a substantial technology for evaluating and selecting software and hardware in modern IT projects, ensuring adaptability and accuracy in decision-making and increasing the validity of decision-making in planning corporate cloud environments, reduce the risks of choosing suboptimal configurations, and ensure more efficient use of resources in a single information space.

Traditional centralized systems cannot deal with the big data context. Distributed computing platforms such as Apache Spark have been widely adopted, but configuring their parameters is challenging given the number of factors and their interactions. This work employs Design of Experiments (DoE) techniques to screening most relevant factors regarding execution time of a Naïve Bayes machine learning distributed task on a subset of the PT7 Web Corpus, which has 14.88 GB of data. Employing a fractional factorial design with 192 experimental units and linear regression techniques with backward elimination, we obtained (i) the most relevant factors based on statistical significance and (ii) a model capable of predicting execution time according to parameters' values in the analyzed context. Our results also include a visualization technique based on Inselberg's Parallel Coordinates to comprehend the impact on performance facing various configuration possibilities.

Surface electromyography (sEMG) is a non-invasive tool that measures the electrical signals of masticatory and related muscles, allowing for the quantitative assessment of muscle activity, asymmetry, coordination, muscle tension, and fatigue. It plays a significant role in the clinical diagnosis and treatment evaluation in dentistry. sEMG is widely used to diagnose and assess treatment outcomes in temporomandibular disorders, oromandibular dystonia, orthodontic treatment, prosthetic rehabilitation, and implant-supported restorations. This article explains the basic principles and characteristics of sEMG. Furthermore, it provides a detailed presentation of the configuration of devices and software, types and attachment methods of surface electrodes, sEMG testing protocols, and methods for analyzing results. This comprehensive review offers guidelines to help clinicians and researchers understand and effectively utilize the sEMG testing process in clinical and research applications.

ABSTRACT Leakages in water supply networks can be minimized through pressure reduction or by repairing the resulting damage. The effective implementation of the latter approach necessitates the localization of leaks, for which various methods exist. This paper presents a specific software and hardware configuration for constructing a digital twin of pressurized piping systems that can be utilized for this purpose. The proposed method can identify both the flow and position of leaks and is based on solving an equivalent optimization problem, where the objective function depends on the difference between the measured pressure values at specific points and those predicted at the same points under the same boundary conditions by a computational model. Given the multimodality of the formulated problem, particle swarm optimization is implemented for this task. To enable real-time localization, the measured pressure values are transmitted to a microcontroller, which processes the data and sends it to a Python script. This script employs a while loop to continuously monitor the criteria for leak detection, and when these criteria are met, it initiates a procedure for leak localization. The theoretical aspect of the procedure, as well as its practical implementation, is presented below as an example.

Symmetry in IT system design is essential for improving efficiency, consistency, and manageability in data center operations. Symmetry guarantees that all system elements—be it hardware, software, or network configurations—are crafted to be consistent, thereby minimizing variability and streamlining operations. This principle is especially pertinent in cluster computing, where uniform server configurations facilitate efficient maintenance and consistent system performance. Symmetric designs reduce variations among nodes, alleviating performance discrepancies and resource imbalances commonly encountered in heterogeneous environments. This paper examines the advantages of symmetric configurations via an experimental analysis of the lifecycle management process. The findings indicate that clusters constructed with a symmetric server architecture enhance operational efficiency. From a lifecycle management standpoint, symmetry streamlines hardware provisioning and maintenance, diminishing complexities related to Day-1 and Day-2 operations. Furthermore, by guaranteeing consistent performance across all servers, symmetric designs facilitate a more predictable quality of service (QoS), reducing bottlenecks and improving overall system stability. Experimental results indicate that, when properly configured, symmetric clusters surpass asymmetric configurations in sustaining QoS, especially during peak loads or hardware failures, owing to their enhanced resource allocation and failover mechanisms. This research highlights the significance of symmetry as a fundamental principle in cluster-based data center architecture.

Purpose: The aim of this article is to analyze and assess the possibilities of building and implementing flexible Business Intelligence systems for modern industrial analytics. Design/methodology/approach: The article presents a project of a system supporting multi criteria analysis of production processes. The project included analytical work, the development of a prototype (data warehouse, OLAP server application, user interface), and its verification. The conclusions provide an assessment of selected technical solutions implemented in the prototype, as well as an evaluation of the expected software flexibility. Findings: The flexibility of the system prototype was validated through pilot studies conducted in a large furniture manufacturing company. The experiment confirmed the feasibility of creating new analytical objects within a spreadsheet environment equipped with active connections to the OLAP server. The implementation process included software installation and configuration, training sessions for industrial analysts and development work carried out in the form of workshops. The development work was carried out by industrial analysts without the involvement of IT specialists. Research limitations/implications: Due to the experimental nature of the project, the selection of appropriate techniques for industrial data visualization was deliberately omitted during the prototype implementation process. Future research will focus on evaluating the functionality, ergonomics and flexibility of the data visualization system. Practical implications: The results of the experiment can be practically used for the designing and implementation of BI systems in the industrial sector. The flexibility of BI software will enable a reduction in costs associated with its maintenance and development, which will contribute to the popularization of this technology in the market economy. Social implications: Data analysis using BI technologies can be a significant element in the process of implementing new research programs in the area of human-centric production management, in line with the Industry 5.0 perspective. Originality/value: The article addresses the issues related to the design, implementation and development of specialized systems supporting industrial data analysis. It is intended for analysts, architects, and designers of Business Intelligence class systems. Keywords: industrial analytics, software flexibility, Business Intelligence. Category of the paper: Research paper.

The effective functioning of the national security system during disasters, accidents and other crisis situations related to cyber security and cyber defence requires the realization of opportunities for inter-institutional interaction of all elements of the system. This paper presents certain opportunities as part of the methodology to apply NATO models to develop the system design of information infrastructure with capabilities to achieve the required levels of operational interoperability to respond to cyber threats to national security. For the architectural configuration, the NATO Technical Reference Model (NTRM) is applied and the NATO Common Operating Environment Component Model (NCOECM) is used for defining the information infrastructure components of the system design. By implementing the proposed methodology, a model of architectural configuration of an information infrastructure of a Security Operations Centre component has been developed – the Security Information Event Management (SIEM) Server. A specification of the technical and software configuration of the SIEM server is developed.

The authors examine the theoretical and practical problems connected with determining the concept and the possibilities of using digital fingerprints of computer devices, found in the information-communication networks, in court proceedings. The authors present key goals of identifying computer devices by their digital fingerprints based on selected key and specific features from the standpoint of digital criminalistics. It is stated that a device fingerprint is a digital trace formed as an arbitrary value of parameters, the configurations of software and hardware of a specific computer device. It is virtually impossible to falsify a digital fingerprint. Key areas of using device fingerprints are examined. The list of parameters used for identifying a digital device by its computer fingerprint in Russian and foreign practice is analyzed. In Russian legal practice, a digital fingerprint is formed by the identifiers of the device’s hardware, operation system’s version, browser’s version, and others. The authors point out that there are two types of device fingerprints: browser fingerprint (helps identify both desktop and mobile devices) and mobile device’s fingerprint. Due to a complex formation mechanism of this type of fingerprints connected with its unique character and frequency of changes in its features, the authors describe the circumstances that should be taken into consideration when obtaining it. They present some methods for improving the effectiveness of this work and further identification of a specific device by its digital fingerprints, as well as a system of criminalistic situations of using device fingerprints. In conclusion it is stated that device fingerprints have a considerable identification potential that could be used not only within the framework of anti-fraud systems, but also in preventing and investigating computer-related incidents and cybercrimes, user de-anonymization, as well as protection of copyright and development of targeted advertising.

The interactions between software and hardware are increasingly important to computer system security. This research collected microprocessor control signal sequences to develop machine learning models that identify software tasks. In contrast with prior work that relies on hardware performance counters to collect data for task identification, this research is based on creating additional digital logic to record sequences of control signals inside a processor’s microarchitecture. The proposed approach considers software task identification in hardware as a general problem, with attacks treated as a subset of software tasks. Three lines of effort are presented. First, a data collection approach is described to extract sequences of control signals labeled by task identity during actual (i.e., non-simulated) system operation. Second, experimental design selects hardware and software configurations to train and evaluate machine learning models. The machine learning models significantly outperform a naïve classifier based on Euclidean distances from class means. Various experiment configurations produced a range of balanced accuracy scores. Third, task classification is addressed using decision boundaries defined with thresholds chosen by an optimization strategy to develop non-neural network classifiers. When implemented in hardware, the non-neural network classifiers could require less digital logic to implement compared to neural network models.

PurposeHealthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address these issues is the development of new telemedicine applications. The currently researched network standard 6G promises to deliver many new features which could be beneficial to leverage the full potential of emerging telemedical solutions and overcome the limitations of current network standards.MethodsWe developed a telerobotic examination system with a distributed robot control infrastructure to investigate the benefits and challenges of distributed computing scenarios, such as fog computing, in medical applications. We investigate different software configurations for which we characterize the network traffic and computational loads and subsequently establish network allocation strategies for different types of modular application functions (MAFs).ResultsThe results indicate a high variability in the usage profiles of these MAFs, both in terms of computational load and networking behavior, which in turn allows the development of allocation strategies for different types of MAFs according to their requirements. Furthermore, the results provide a strong basis for further exploration of distributed computing scenarios in medical robotics.ConclusionThis work lays the foundation for the development of medical robotic applications using 6G network architectures and distributed computing scenarios, such as fog computing. In the future, we plan to investigate the capability to dynamically shift MAFs within the network based on current situational demand, which could help to further optimize the performance of network-based medical applications and play a role in addressing the increasingly critical challenges in healthcare.

The extreme weather and natural disasters will cause power grid outage. In disaster relief, mobile emergency energy storage vehicle (MEESV) is the significant tool for protecting critical loads from power grid outage. However, the on-site online expansion of multiple MEESVs always faces the challenges of hardware and software configurations through communications. In order to simplify the on-site operation, the online expansion without communication line is always expected to realize the effect of plug-and-play. For this purpose, this article proposes an online expansion method, which focuses on the voltage coordinate control at two key parts to avoid the circulating current: one is for the system reset after new MEESV is joined, while the other one is for the bus voltage synchronously gradually restored to the rated value. For the former one, a mutual small-signal-based resetting method is proposed, for which the injection and identification of small signal are studied to ensure the safe expansion with acceptable disturbances. For the latter one, event-triggered method is suggested which synchronously restores the bus voltage to nominal value at its specific zero crossings. The effectiveness of the proposed methods is validated through an experimental platform, with three prototypes online expanded one by one.

Software products have many configurations to meet different environments and diverse needs. Building software with multiple software configurations typically incurs high costs in terms of build time and computing resources. Incremental builds could reuse intermediate artifacts if configuration settings affect only a portion of the build artifacts. The efficiency gains depend on the strategic ordering of the incremental builds as the order influences which build artifacts can be reused. Deriving an efficient order is challenging and an open problem, since it is infeasible to reliably determine the degree of re-use and time savings before an actual build. In this paper, we propose an approach, called BUDDI—BUild Declaration DIstance, for C-based and Make-based projects to derive an efficient order for incremental builds from the static information provided by the build scripts (i.e., Makefile). The core strategy of BUDDI is to measure the distance between the build declarations of configurations and predict the build size of a configuration from the build targets and build commands in each configuration. Since some artifacts could be reused in the subsequent builds if there is a close distance between the build scripts for different configurations. We implemented BUDDI as an automated tool called BuddiPlanner and evaluated it on 20 popular open-source projects, by comparing it to a baseline that randomly selects a build order. The experimental results show that the order created by BuddiPlanner outperforms 96.5% (193/200) of the random build orders in terms of build time and reduces the build time by an average of 305.94s (26%) compared to the random build orders, with a median saving of 64.88s (28%). BuddiPlanner demonstrates its potential to relieve practitioners of excessive build times and computational resource burdens caused by building multiple software configurations.

This study focuses on the use of the Internet of Things (IoT) for non-intrusive monitoring of electricity consumption. Using Internet of Things technology, this study attempts to develop and evaluate an affordable and easy-to-navigate energy monitoring system. Literature review, system design, hardware development, software configuration, system integration, and system testing are all included in the methodology. The results demonstrate the successful use of an IoT device to track electrical energy consumption. The system demonstrates promising levels of accuracy in tracking power consumption patterns, despite ongoing testing and calibration challenges. This study provides an affordable and secure IoT-based smart energy monitoring solution.

Research motivation. When developing microcontrollers, manufacturers try to include as many different types of peripherals as possible in order to increase the marketing attractiveness of their products. On the one hand, with a large assortment of various peripheral modules, it is very difficult to implement several devices of the same type in the microcontroller: manufacturers are mainly limited to 1-2 instances, in rare cases 4 modules of the same type are included. On the other hand, most software projects do not use all the peripherals of modern microcontrollers and many devices are left unused, while there may be a shortage of other types of modules. Another problem that has become especially noticeable for microcontrollers used in the field of IoT is the cryptographic protection of data that is transmitted through built-in information exchange interfaces. The main efforts of researchers and developers of cryptographic data protection methods were aimed at reducing energy-intensive operations, memory access iterations and speeding up encryption processes while maintaining a high level of cryptographic protection and enabling efficient data distribution within IoT devices networks. Research results. This paper presents an alternative approach to the manufacture of peripheral modules as part of microcontrollers. The authors propose to use a configurable software processor module based on the MIPS architecture with a reduced instruction set and limited capabilities. Conclusions. This approach would make it possible to dynamically change the functionality of peripheral modules in accordance with the requirements of the developed software solution, which in turn will increase the efficiency of the microcontroller chips capabilities utilization. In addition, the transfer of data stream encryption functions to the reconfigurable core of the peripheral module will provide fast and transparent cryptographic protection, as well as allow offloading the microcontroller core and increasing the energy efficiency of chips while reducing their production cost.

SummaryMobile devices play a vital role in people's day‐to‐day activities and are essential for communication, accessing Internet resources, entertainment, and so forth. In most cases, the performance of mobile applications is restricted due to the inadequate resources of mobile specifications such as controlled battery power, predetermined storage, and limited processing competence. Thus to improve the processing efficiency of resource‐constrained mobile devices, this research proposes a model developed on offloading technique based on the recent literature observations which transfer the resource‐consuming tasks from mobile devices to proximate computing entities. This generic architecture aiming for resource augmentation is formulated with five fundamental components including a mobile device, offloading engine, resource augmentation engine, scheduler, and synchronizer. The offloading engine identifies the resource‐intensive tasks that need to be executed on external entities using soft computing techniques such as neural network training algorithms, fuzzy logic, and neuro‐fuzzy logic processing. The augmentation engine chooses feasible edge computing entities such as Arduino, Raspberry PI controller, and fog devices. The scheduler prioritizes the resource‐consuming tasks for offloading based on their importance. The synchronizer coordinates all the central components by updating the execution status of tasks at regular intervals of time. Further extension of this research work, based on the proposed architecture three resource augmentation frameworks are developed namely (a) an adaptive proximate computing framework (b) a web service‐based IoT framework, and (c) a neuro‐fuzzy hybrid framework. Moreover, these frameworks are realized through the development of resource‐efficient intelligent edge computing IoT mobile apps namely (a) an inventory monitoring app (b) a lecture recording app, and (c) an intelligent transport app. The performance of these three resource augmentation frameworks is analyzed through the simulation results and it illustrates that the neuro‐fuzzy hybrid framework outperforms the other two. The outcome of the proposed research contributions improves the effectiveness of mobile communication by providing seamless services to mobile users regardless of their resource‐limited devices. Besides the devised edge computing architecture supports mobile application developers in the design and deployment of resource‐rich mobile applications in low‐specification mobile devices. Also, there is a strong scope for implementing the proposed generic architecture as an industrial smart product with adequate hardware and software configurations.

An increasing amount of Industry 4.0 data storages is highly configurable. As each variant includes individual features and interactions, ensuring data security becomes increasingly challenging. However, we are missing an analysis of research on security and configurable storages in Industry 4.0, especially those based on product-line engineering. To address this gap, we conducted a literature study covering relevant state-of-the-art publications (2013–2022). Overall, security for configurable systems seems under-explored. We highlighted that security standards and concrete mitigations techniques are usually not considered. In addition, we are missing an analysis of configurable storage and software systems in concert to identify threats, risks, and vulnerabilities caused by variability.

The aim of the present study was to establish the normal range for the radiographic distance between cemento-enamel junction and the marginal bone level in the primary dentition using bitewing digital images. The samples examined in this study were 120 Iraqi children 7 and 8 years old (60 male and 60 female). The parameters were measured using the DIXI2 configuration software, which is automatically installed from Dimaxis software. Results showed that the mean of marginal bone height of the normal primary molars was (1.37 ± 0.48 mm). In addition, individual surfaces displayed distance ranging from (0.5-2.5). It was concluded that the normal marginal bone level in posterior primary dentition was (0.0-2.0) and bone loss occured when the distance between cemento-enamel junction and the marginal bone level more than 2 mm.

Biopharma Is Going Digital … Bit by Bit

Object-oriented logic configuration platform design for large power grid security and stability simulation