Analysis Module Research Articles

Performance Evaluation and Analysis of a Vehicle

The rapid advancements in technology and the growing demand for personalized solutions have revolutionized the automobile industry. This report introduces a Vehicle Performance and Analysis Application, designed to streamline the process of finding vehicle details based on user-defined parameters. The application provides comprehensive insights into vehicle specifications, performance metrics, and associated company information, enabling users to make well-informed decisions. Additionally, it offers manufacturers and dealers valuable analytics on user preferences and frequently searched criteria. The application features an intuitive search mechanism that allows users to filter vehicles based on parameters such as fuel efficiency, engine power, seating capacity, price range, and more. It also includes a comparison tool to evaluate multiple vehicles simultaneously. For manufacturers and dealers, the app provides real-time data on market trends and user preferences, helping them tailor their offerings and marketing strategies.By integrating advanced technologies such as dynamic database systems and analytical modules, this application enhances the user experience and facilitates data-driven decision-making for companies. The Vehicle Performance and Analysis App thus bridges the gap between consumer demands and market offerings, fostering transparency, efficiency, and innovation in the automobile industry.

Integrated bioinformatics analysis identified cuproptosis-related hub gene Mpeg1 as potential biomarker in spinal cord injury

Spinal cord injury (SCI) is a profound ailment lacking a well-defined molecular mechanism and effective treatments. Cuproptosis, identified as a recently discovered cell death pathway, exhibits diverse roles in various cancers. Nevertheless, its involvement in SCI is yet to be elucidated. Firstly, the RNA sequencing data of 1, 3, 7 dpi SCI samples were collected from GEO database. We performed differential expression analysis on these samples with varying cuproptosis-related scores calculating by ssGSEA. Subsequently, we conducted enrichment analyses with KEGG, GO, and GSEA. Simultaneously, we executed WGCNA analysis using cuproptosis-related scores, selecting the most relevant module for enrichment analysis. Hub genes were identified at the intersection of PPI analysis results from two modules and cuproptosis-related DEGs. Additionally, relying on the immune infiltration landscape associated with cuproptosis, we carried out immune cell correlation analysis on hub genes. Finally, to corroborate our earlier findings, we utilized single-cell RNA-seq analysis and in vitro experimental validation. Based on ssGSEA, differential expression analysis and WGCNA analysis, we identified two modules that were highly relevant to cell division and immune processes, respectively. From these modules, we identified two hub genes, Cd48 and Mpeg1, which exhibited a strong positive correlation (R = 0.92) and shared similar pathways. Furthermore, we observed a positive correlation between M2 macrophages and Cd48/Mpeg1. To validate our findings, we performed external cohort validation using a single-cell RNA sequencing dataset. The results confirmed that Mpeg1 was highly expressed in microglia (macrophages in center nervous system) following spinal cord injury. Additionally, we conducted in vitro experiments to further validate the molecular functions of Mpeg1 in SCI. In summary, targeting Mpeg1, as well as cuproptosis and immune cell infiltration, holds promise as a potential strategy for reducing spinal cord tissue damage and promoting recovery after SCI. These findings provide valuable insights for future therapeutic interventions.

Enhancing supply chain resilience through supervised machine learning: supplier performance analysis and risk profiling for a multi-class classification problem

PurposeThis paper explores the application of supervised machine learning (ML) classification models to address supplier performance analysis and risk profiling as a multi-class classification problem. The research highlights that current applications of machine learning in supplier selection primarily focus on binary classification problems, underscoring a significant gap in the literature.Design/methodology/approachThis research paper opts for a structured approach to solve supplier selection and risk profiling using supervised machine learning multi-class classification models and prediction probabilities. The study involved a synthetic data set of 1,600 historical data points, creating a supplier selection framework that simulates current supply chain (SC) performance. The “Supplier Analysis and Selection ML Module” guided supplier selection recommendations based on ML analysis. Real-world variability is introduced through random seeds, impacting actual delivery dates, quantity delivered and quality performance. Supervised ML models, with hyperparameter tuning, enable multi-class classification of suppliers, considering past delivery performance and risk calculations.FindingsThe study demonstrates the effectiveness of the supervised ML-based approach in ensuring consistent supplier selection across multi-class classification problems. Beyond evaluating past delivery performance, it introduces a new dimension by predicting and assessing supplier risks through ML-generated prediction probabilities. This can enhance overall SC visibility and help organizations optimize strategies associated with risk mitigation, inventory management and customer service.Research limitations/implicationsThe findings highlight the adaptability of ML-based methodologies in dynamic SC environments, providing a proactive means to identify and manage risks. These insights are vital for organizations aiming to bolster SC resilience, particularly amid uncertainties.Practical implicationsThe practical implications of this study are significant for both commercial and humanitarian supply chain management (SCM). For commercial applications, the ML-based methodology allows businesses to make more informed supplier selection decisions, reducing risks and improving operational efficiency. In disaster and humanitarian SC contexts, the use of ML can improve preparedness and resource allocation, ensuring that critical supplies reach affected areas promptly.Social implicationsThe study’s implications extend to disaster and humanitarian SCM, where timely and efficient delivery is critical for saving lives and alleviating suffering. ML tools can improve preparedness, resource allocation and coordination in these contexts, enhancing the resilience and responsiveness of humanitarian supply chains.Originality/valueUnlike conventional methods focused on quality, cost and delivery performance aspects, the current study introduces supervised ML to identify and assess supplier risks through prediction probabilities for multi-class classification problems (delivery performance as late, on-time and ahead), offering a refined understanding of supplier selection in dynamic SC environments.

Sentiment Analysis for Movie Database Management System Using Naive Bayes Algorithm

Abstract: According to F. Furtado and A. Singh (2021), movie recommendation systems aim to assist viewers by suggesting movies to watch, thereby avoiding the time-consuming and complex process of selecting from a vast library of movies, which can number in the thousands or even millions. This article seeks to reduce human effort by recommending movies based on user interests. To address these challenges, we propose a technique that combines content-based and collaborative approaches. This method produces more precise results than systems that rely solely on content-based methods. Content-based recommendation systems are limited to individual preferences and do not recommend items outside the user’s immediate interests, restricting exploration. To overcome these limitations, we developed a system that addresses these concerns. The proposed movie recommendation system uses Cosine Similarity to suggest movies like the one chosen by the user. While existing algorithms generate recommendations, they often fail to answer the critical question, “Is this a film worth watching?” To enhance the overall experience, our system incorporates sentiment analysis of selected movie reviews using machine learning techniques. This study employs two supervised learning approaches, Naïve Bayes (NB) and Support Vector Machine (SVM), to improve accuracy and efficacy. A comparison of the two methods reveals that SVM achieved an accuracy score of 98.63%, while NB scored 97.33%, as reported by N. Pavitha et al. (2022). The project integrates a sentiment analysis module into the movie management system to enhance decision-making and user experience. The Naïve Bayes approach is used to classify and evaluate user reviews and feedback into positive, negative, and neutral categories. This analysis provides insights into audience preferences, overall satisfaction, and movie reception. The system utilizes this information to refine movie recommendations, analyze trends, and enhance content selection, all of which contribute to a more personalized user experience. The system is evaluated using the ISO 25010 framework, focusing on functionality, reliability, usability, effectiveness, and robustness. Findings show that respondents actively participated in the review process across multiple domains. Key results indicate high satisfaction levels in terms of ease of use, interface design, and system performance. Feedback also highlighted areas for improvement, such as optimizing loading times and enhancing specific features for a smoother user experience. Overall, the findings reflect positive engagement with the system, demonstrating its effectiveness in meeting user needs while identifying opportunities for future enhancements.

Aesthetic evaluation underpinning brand love relationship development: an activation likelihood estimation meta-analysis and multivariate analysis

ObjectivesBrand love is a crucial construct in marketing strategies. Building brand love can generate stable profits for enterprises. Although the marketing literature points out that aesthetic factors contribute to establishing the relationship as a trigger, to what stage of the relationship do they influence the minds of consumers? The present study attempts to reveal the involvement of aesthetic experiences in brand love developmental dynamics.MethodologyUsing the activation likelihood estimation method, we address this issue by assessing overlapping brain regions between brand love at each stage and aesthetic experiences. We adopted three major meta-analytic decoding analysis modules to objectively interpret these brain regions, namely, Neurosynth, NeuroQuery, and the Behavioral Analysis plugin (BrainMap platform). Moreover, we performed a correspondence analysis to identify relationships of mental processes between aesthetic experiences and brand love in each developmental stage of brand love.FindingsOur results suggest that the same neural mechanism and mental processes may be underlaid between brand love and aesthetic experiences across all stages. Although reward- and emotion-related mental processes are commonly underlaid between brand love at the first-half stage and aesthetic experiences, exteroceptive and interoceptive signals may drive those mental processes between the early and migration stages of brand love, respectively, and aesthetic experiences. Overlapping regions of brand love at the stable stage and aesthetic experiences may be associated with semantic processing.ConclusionWe demonstrate that several brain regions overlapped between brand love and aesthetic experiences across all the brand love developmental stages. Therefore, aesthetic experiences might be associated with the mental processes of brand love development through all the developmental stages. Our results suggest that aesthetic experiences are essential elements for developing brand-love relationships.ImplicationsOur findings indicate that marketers should recognize that aesthetic experiences play a crucial role in building a bond between brands and consumers, not only when choosing brands. Thus, marketers need to design visual strategies from the view of nurturing brand-love relationships.

자모비(jamovi) 통계 프로그램 환경에서 잠재전이분석 통계 모듈 개발 및 적용

This study presents the development and implementation of a novel statistical module for latent transition analysis within the jamovi software environment. jamovi is an open-source platform designed to make statistical analysis accessible to users with varying levels of expertise. This module integrates latent transition analysis, a complex method used for examining changes in latent variables over time. This integration significantly enhances the software's analytical capabilities. The primary objective of this development is to democratize access to advanced statistical methods, thereby facilitating their use in educational settings and diverse research fields such as psychology, sociology, and education. This paper discusses the technical aspects of the module's integration, evaluates its usability through user feedback, and highlights its potential impact on both teaching and research. The findings suggest that the module not only simplifies the execution of sophisticated analyses but also encourages broader adoption of latent transition analysis by reducing technical barriers. Consequently, this work contributes to the ongoing efforts to make advanced statistical tools more accessible and user-friendly, promoting their integration into everyday research practices.

Study of a Grid-Based Urban Drainage System Analysis Model

As the focus shifts from responding to climate change to adapting to the climate crisis, the need for detailed water adaptation strategies is increasing. Local governments are required to implement measures to address urban flooding, and models such as XP-SWMM are being utilized. However, these models have limitations in terms of precision owing to the resolution of the available data. To address this issue, this study developed a grid-based urban flood analysis model capable of predicting urban flooding using high-resolution data based on a detailed grid of less than 10 m, adequately considering watershed characteristics. It integrates surface runoff analysis using the Clark Unit Hydrograph method and the D8 algorithm, conduit transport analysis linked with EPA-SWMM, and flood analysis modules utilizing the WCA2D method. Unlike existing urban flood analysis models, which require complex parameters and specialized expertise, this model minimizes the number of input parameters to enhance accessibility for practitioners while maintaining reliability in the analysis results. A grid-based model based on high-resolution spatial data can be employed by local governments to establish practical guidelines. It effectively identifies vulnerable flood areas within cities and supports the development of specific flood response strategies, thereby contributing significantly to urban flood management and resilience planning.

HemaCisDB: An Interactive Database for Analyzing Cis-Regulatory Elements Across Hematopoietic Malignancies.

Noncoding cis-regulatory elements (CREs), such as transcriptional enhancers, are key regulators of gene expression programs. Accessible chromatin and H3K27ac are well-recognized markers for CREs associated with their biological function. Deregulation of CREs is commonly found in hematopoietic malignancies yet the extent to which CRE dysfunction contributes to pathophysiology remains incompletely understood. Here, we developed HemaCisDB, an interactive, comprehensive, and centralized online resource for CRE characterization across hematopoietic malignancies, serving as a useful resource for investigating the pathological roles of CREs in blood disorders. Currently, we collected 922 ATAC-seq, 190 DNase-seq, and 531 H3K27ac ChIP-seq datasets from patient samples and cell lines across different myeloid and lymphoid neoplasms. HemaCisDB provides comprehensive quality control metrics to assess ATAC-seq, DNase-seq, and H3K27ac ChIP-seq data quality. The analytic modules in HemaCisDB include transcription factor (TF) footprinting inference, super-enhancer identification, and core transcriptional regulatory circuitry analysis. Moreover, HemaCisDB also enables the study of TF binding dynamics by comparing TF footprints across different disease types or conditions via web-based interactive analysis. Together, HemaCisDB provides an interactive platform for CRE characterization to facilitate mechanistic studies of transcriptional regulation in hematopoietic malignancies. HemaCisDB is available at https://hemacisdb.chinablood.com.cn/.

საქართველოს ელექტროენერგეტიკული სისტემის დინამიკური მდგრადობის ანალიზი ბატარეის ელექტროენერგიის დამაგროვებელ სისტემებში ინტეგრირებული ორმხრივი ნახევარგამტარული გარდამსახების და ენერგიის მართვის სისტემის გამოყენებით

In practice, a directional converter integrated into the battery energy storage systems - an inverter, which only supplies energy to the network for frequency and voltage regulation when necessary, and cannot receive energy from the network. Increasing the reliability of the stability of the electric power system will be especially effective when in the battery energy storage systems will be used a Bi-directional AC/DC Converter, which in turn includes a rectifier and an inverter, which will supply the grid through the energy management system and also consume energy from it if necessary through the upgraded SCADA/EMS. The high-level architecture of the Supervisory Control and Data Acquisition systems/Energy Management System includes a number of capabilities, including a dynamic stability analysis module, also "High numerical aperture extreme ultraviolet lithography - lithography of advanced semiconductor materials" - "High-NA-EUV lithography" provides opportunities for manufacturing such microcircuits, each nanocell includes a nanotransistor and a nanocapacitor, and their chipset size is 1 nanometer. The mentioned rate of scientific development provides an opportunity to create such technologies that will give wide opportunities to modern the battery energy storage systems to fully respond to the requirements that arise during the continuous operational management of the country's electric power system.

Analysis of differential weathering patterns and causes on the top of hollow enemy towers’ facades of the Great Wall – a case study of section in Haigang District, Qinhuangdao City

ABSTRACT The hollow enemy towers are iconic structures of the Great Wall. During a survey of the Ming Great Wall in Haigang District of Qinhuangdao, researchers observed differential weathering phenomena in the facade tops of 21 out of 51 well-preserved hollow enemy towers. Initially, high-precision 3D data of enemy towers was acquired using close-range photogrammetry from drones. The 3D surface metrology analysis software Mountains was introduced, in which the particle analysis module can use false-color images to calibrate and measure depressions on the 3D model surface, thereby enabling the identification and quantitative analysis of weathering and erosion areas on the top of the enemy towers’ facades. The study revealed that the upper regions of facades with the drainage troughs exhibited more pronounced weathering erosion compared to the areas without drainage troughs. Upon conducting an analysis of the structural integrity of the top drainage components and evaluating the slope and surface of the drainage system, it was determined that the primary water collection zones were located near the drainage troughs at both ends of enemy tower tops. Differential weathering at the top of enemy towers’ facades is primarily influenced by the varying amount of water accumulation, which serves as the decisive factor.

A Machine-Learning-Based and IoT-Enabled Robot Swarm System for Pipeline Crack Detection

In today’s expanding cities, pipeline networks are becoming an essential part of the industrial infrastructure. Monitoring these pipelines autonomously is becoming increasingly important. Inspecting pipelines for cracks is one specific task that poses a huge burden on humans. Undetected cracks may pose multi-dimensional risks. In this paper, we introduce the Pipeline Leak Identification Emergency Robot Swarm (PLIERS) system, an industrial system that deploys Internet-of-Things (IoT), robotics, and neural network technologies to detect cracks in emptied water and sewage pipelines. In PLIERS, a swarm of robots inspect emptied pipelines from the inside to detect cracks, collect images of them, and register their locations. When the images are taken, they are fed into a cloud-based module for analysis by a convolutional neural network (CNN). The CNN is used to detect cracks and identify their severity. Through extensive training and testing, the CNN model performance showed promising scores for accuracy (between 80% and 90%), recall (at least 95%), precision (at least 95%), and F1 (at least 96%). Additionally, through the careful design of a prototype for a water/sewage pipeline structure with several types of cracks, the robots used managed to exchange information among themselves and convey crack images to the cloud-based server for further analysis. PLIERS is a system that deploys modern technologies to detect and recognize cracks in pipeline grids. It adds to the efforts of improving instrumentation and measurement approaches by using robots, sensory, IoT principles, and the efficient analysis of CNNs.

Development of an information and calculation complex for assessing the radioecological situation on radioactively contaminated agricultural land

To organize agriculture on radioactively contaminated agricultural lands, taking into account the levels of contamination and landscape features, an information and calculation complex for assessing the radioecological situation is tested. In the NextGIS Collector mobile application for field operators, a pilot version of the working environment has been created and requirements for the information content of various blocks of the complex have been developed. In the NextGIS QGIS software environment, a GIS project of the test farm of the Tula Research Institute of Agriculture has been created, in which calculation and analytical modules will be created. This project contains attribute data bases, as well as electronic maps of various thematic focus created on their basis, which will be used in the future for a comprehensive analysis of the radioecological situation. Also, the mathematical foundations of calculation and analytical modules have been developed, which are planned to be implemented in the form of built-in software blocks. The presented materials are supposed to be used for practical testing on the territory of a test agricultural enterprise and the subsequent development of recommendations for the design of targeted protective and rehabilitation measures of landscape-ecological focus for different types of agrolandscapes in radioactively contaminated areas.

A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure.

LearningViz: a dashboard for visualizing, analyzing and closing learning performance gaps—a case study approach

The availability of large-scale learning data presents unprecedented opportunities for investigating student learning processes. However, it is challenging for instructors to fully make sense of this data and effectively support their teaching practices. This study introduces LearningViz, an interactive learning analytics dashboard to help instructors identify, analyze, and close performance gaps among students in their classes. In this dashboard, we incorporated three modules to enhance human and computer interactions for better supporting the teaching practices: the Student Overall Performance Analysis Module, which provides a comprehensive understanding of students’ learning in the course; the Student Group Performance Analysis Module, which examines performance gaps across different groups and identifies factors contributing to these gaps; and the Final Exam Item Analysis Module, which evaluates the quality of exam questions and identifies strategies for closing performance gaps. The overall design of the platform follows a user-centered approach, integrating data analysis with various visualization strategies in a unified platform. A case study is then conducted to highlight the effectiveness of LearningViz in supporting instructors analyzing students’ learning patterns and associated factors impacting learning performance. We further conduct a usability test with several domain experts, to evaluate the usefulness and effectiveness of this platform in supporting the teaching practices. Our findings underscore the platform's ability to support instructors in detecting performance gaps among students, investigating influential factors, evaluating assessment quality and implementing targeted instructional strategies for closing performance gaps.

Clinical-Inspired Framework for Automatic Kidney Stone Recognition and Analysis on Transverse CT Images.

The stone recognition and analysis in CT images are significant for automatic kidney stone diagnosis. Although certain contributions have been made, existing methods overlook the promoting effect of clinical knowledge on model performance and clinical interpretation. Thus, it is attractive to establish methods for detecting and evaluating kidney stones originating from the practical diagnostic process. Inspired by this, a novel clinical-inspired framework is proposed to involve the diagnostic process of urologists for better analysis. The diagnostic process contains three main steps, the localization step, the identification step and the evaluation step. Three modules integrating the decision-making mode of urologists are designed to mimic the diagnosis process. The object attention module simulates the localization step to provide the position of kidneys by embedding weight feature factor and angle loss. The feature-driven discriminative module mimics the identification step to detect stones by extracting geometric and positional features. The analysis module based on the principle of clustering and graphic combination is a quantitative analysis strategy for simulating the evaluation step. This work constructed a clinical dataset collecting 27,885 transverse CT images with stones and/or clinical interference. Experiments on the dataset show that the object attention module outperforms the well-performing Yolov7 model by +1% mAP.5:.95, and the analysis module outperforms the well-performing AR-DBSCAN model and the formula method by +21.9% average cluster accuracy and -17.35% average error. Experiments demonstrate that the proposed framework is recently the most effective solution for recognizing and evaluating kidney stones.

Flow-models 2.2: Efficient and parallel elephant flow modeling with machine learning

This article introduces the latest version of the flow-models framework for IP network flow analysis. Key improvements include support for Dask to enable parallel computing, dataset reduction techniques for efficient training, and new modules for entropy analysis and granular flow table simulations. The codebase has been refined, with improved documentation and the incorporation of automated testing via ruff. The framework is now compatible with forthcoming releases of Python and NumPy, making it a useful resource for researchers and professionals involved in network flow analysis and machine learning-driven traffic classification.

Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0.

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.

Temporal Dynamic Synchronous Functional Brain Network for Schizophrenia Classification and Lateralization Analysis.

Available evidence suggests that dynamic functional connectivity can capture time-varying abnormalities in brain activity in resting-state cerebral functional magnetic resonance imaging (rs-fMRI) data and has a natural advantage in uncovering mechanisms of abnormal brain activity in schizophrenia (SZ) patients. Hence, an advanced dynamic brain network analysis model called the temporal brain category graph convolutional network (Temporal-BCGCN) was employed. Firstly, a unique dynamic brain network analysis module, DSF-BrainNet, was designed to construct dynamic synchronization features. Subsequently, a revolutionary graph convolution method, TemporalConv, was proposed based on the synchronous temporal properties of features. Finally, the first modular test tool for abnormal hemispherical lateralization in deep learning based on rs-fMRI data, named CategoryPool, was proposed. This study was validated on COBRE and UCLA datasets and achieved 83.62% and 89.71% average accuracies, respectively, outperforming the baseline model and other state-of-theart methods. The ablation results also demonstrate the advantages of TemporalConv over the traditional edge feature graph convolution approach and the improvement of CategoryPool over the classical graph pooling approach. Interestingly, this study showed that the lower-order perceptual system and higher-order network regions in the left hemisphere are more severely dysfunctional than in the right hemisphere in SZ, reaffirmings the importance of the left medial superior frontal gyrus in SZ. Our code was available at: https://github.com/swfen/Temporal-BCGCN.

An efficient test case selection framework for multitenant SaaS applications using RTS4MTS

The augmentation of multitenant Software as a Service (SaaS) has significantly contributed to the success of cloud computing. To stay competitive, multitenant SaaS applications are frequently updated, necessitating regression tests. Regression testing ensures modifications do not alter existing functionalities or introduce new bugs. Rerunning all test cases is resource intensive. To minimize efforts, it's crucial to select a relevant subset of test cases. Thus, a novel framework, Regression Test Selection for Multitenant SaaS (RTS4MTS), is proposed for selective regression testing in multitenant SaaS applications. This framework uses the Functional Coverage Module (FCM) and Dependency Analysis Module (DAM) to select relevant test cases. It classifies existing test cases into two groups: those affected by modifications (need to test) and those unaffected (no need to test). Applying RTS4MTS to the multitenant SaaS MtBookTrip application demonstrated that the framework reduces the test suite size by 68% and lowers test execution time by 70%, thereby saving resources and testing costs.

Design and Implementation of a High-Performance VLSI Architecture for Canonical Huffman Encoding

A key component of contemporary computer systems, data compression makes it possible for information to be stored and transmitted efficiently. A popular technique for lossless data compression, Huffman coding can drastically cut down on data size without sacrificing intensity. But conventional Huffman encoding techniques can have scalability and speed issues, especially when used in hardware. A high-throughput Very Large-Scale Integration (VLSI) design for a Canonical Huffman Encoder is shown in this study. To accomplish quick and effective encoding, the suggested approach makes use of parallel processing and improved algorithms. The system's use of the canonical Huffman algorithm lowers computational complexity and streamlines hardware implementation without sacrificing compression performance. The architecture incorporates real-time operation-optimized modules for frequency analysis, code generation, and symbol encoding. Results from simulations show that the suggested design achieves significantly higher throughput compared to conventional approaches, making it suitable for applications in data storage, multimedia, and communication systems. This study contributes to advancing VLSI designs for high-performance hardware Key Words: Data Compression, Huffman Coding, Lossless Compression, Very Large-Scale Integration (VLSI), Canonical Huffman Encoder, High-Throughput Encoding, Parallel Processing, Efficient Encoding, Hardware Implementation, Computational Complexity, Code Generation, Symbol Encoding, Frequency Analysis, Real-Time Operation, Throughput Optimization, Multimedia Systems, Communication Systems, Data Storage, Hardware Architecture, VLSI Design, Performance Optimization, Simulations, Compression Performance, Hardware Design, Throughput Enhancement, System-Level Optimization, Power Efficiency, Resource Management, Data Transfer Efficiency, Low-Cost Implementation.