Project-specific Requirements Research Articles

Research Progress on the Characteristics and Recovery of Phosphorus from Sludge

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="line-height:200%"><span style="font-family:等线"><a name="_Hlk170675397"><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:200%"><span style="font-family:"Times New Roman",serif"><span style="color:black">     Phosphorus is a non-metallic element essential for all forms of life, and the issue of phosphorus resource scarcity is increasingly gaining attention. Understanding the content and forms of phosphorus in sludge, as well as the technologies for phosphorus recovery from sludge, is of great importance. </span></span></span></span></a><span lang="EN-US" style="font-size:12.0pt"><span style="line-height:200%"><span style="font-family:"Times New Roman",serif"><span style="color:black">Firstly, this paper provided a comprehensive overview of various pre-treatment methods (biological, oxidation, acid-base, microwave, ultrasonic, and hydrolysis) for sludge phosphorus recovery, highlighting the potential for combining these techniques to enhance recovery efficiency. Secondly, it introduced the concept of mixed recovery technology for phosphorus in sludge, which involved the strategic arrangement and combination of different treatment technologies. This approach represented a novel and innovative way of addressing the phosphorus recovery challenge. Furthermore, by discussing the effects of various factors on the phosphorus content in sludge, the paper offered an integrated perspective on the complex interplay of these factors, facilitating a more nuanced understanding of the phosphorus recovery process. This review provides a good reference for recovery processes of sludge phosphorus, enabling engineers to select the most appropriate techniques based on specific project requirements.</span></span></span></span></span></span></span></p>

Fracture characteristics of SMA mixtures with hydrated lime through the semi-circular bending approach

Stone Mastic Asphalt (SMA) is a composite mixture made up of high-quality crushed stone, fine aggregates, an asphalt binder, and a significant percentage of mineral filler in contrast to conventional mixtures. Renowned for its durability and rolling resistance, it is predominantly employed as a surface “wearing” course on main roads subjected to heavy traffic, but it can also be used as a binder course under specific project requirements. In this study, we investigated the effect of hydrated lime as a partial replacement of limestone filler at various concentrations on the mechanical and cracking resistance characteristics of 10-mm SMA mixtures. The semi-circular bending (SCB) test, which is pivotal for understanding the cracking mechanism was used to assess the mixtures performance-related response to this distress type. The mixture cores were produced with granite aggregates, limestone filler as natural filler, hydrated lime as natural filler replacement, and a 30/45 penetration asphalt type. Mixtures containing 1.1 %, 2.2 %, and 3.4 % of hydrated lime by weight of aggregates were evaluated, in addition to a control mixture without hydrated lime. The factors considered for comparing the mixtures included: load and deformation curves, stiffness, fracture toughness, fracture energy, and flexibility index of the mixtures. The experimental program also comprised a set of various test temperatures (0°C, 10°C, and 20°C) to better understand the cracking mechanism. Experimental results indicated a significant performance-dependency on the HL concentration and the test temperature. Precisely, it was found that the mixture with the highest hydrated lime content (3.4 %) exhibited the best cracking performance at 0°C and 20°C, making it a suitable filler replacement concentration. However, at 10°C, the mixture containing 2.2 % hydrated lime showed the most consistent performance overall, suggesting and important improvement in asphalt mixtures’ cracking resistance. Overall, these findings obtained from the SCB test approach substantiate the potential benefit of hydrated lime filler replacement in optimizing the cracking performance and durability of SMA mixtures under varying temperature conditions and loading regimes.

Machine Learning Models for Predicting Heart Failure: Unveiling Patterns and Enhancing Precision in Cardiac Risk Assessment

Purpose This study aims to evaluate the efficacy of various machine learning models in predicting heart failure incidence using medical data, focusing on the innovative aspect of a novel dataset. Unlike previous studies that predominantly examined features such as smoking status or age, this research explores novel features. The primary challenge addressed is the utilization of these new features, coupled with machine learning techniques, to accurately diagnose heart failure. Methods Five machine learning models, including logistic regression, support vector classifier, decision tree classifier, random forest classifier, and K-nearest neighbors, were applied to analyze medical data from a dataset comprising over 900 individuals. The dataset encompasses diverse parameters such as age, sex, chest pain severity, blood pressure, cholesterol levels, blood sugar levels, and electrocardiogram results, introducing a novel approach to feature selection. Results The evaluation of machine learning models unveiled varying performances in predicting heart failure. Logistic regression and support vector classifier exhibited the highest accuracy of 88%, followed by the decision tree classifier (below 85%), random forest classifier (84%), and K-nearest neighbors (82%). Additionally, the analysis revealed a balanced dataset distribution and highlighted sex-based disparities in heart failure incidence, along with significant correlations with factors such as age, chest pain severity, blood glucose levels, and physical activity. Conclusions The findings underscore the potential of integrating multiple machine learning models for early detection of heart failure, leveraging the inclusion of novel features in the dataset. However, careful model selection is crucial to account for discrepancies in accuracy among different models, emphasizing the importance of tailoring approaches based on specific project requirements.

A methodology for evaluating feature selection and clustering methods with project-specific requirements

This paper describes a methodology for ranking feature selection and clustering methods with user-specific preferences and taking data properties into account. For a better understanding of this paper, the developed methodology is referred to as the Two Machine Learning Procedures, Preferences and Properties (2ML3P) methodology. The 2ML3P methodology aims to support users from multiple domains, such as engineers, who have little expertise in machine learning (ML). It is also independent from the disciplinary core competencies of the manufacturer, with a strong focus on employability in small and mid-sized enterprises (SME). The foundation of the methodology to evaluate the combination of the two machine learning classes is described. It focuses on a range of feature selection and clustering methods, their limitations, and their challenges. The paper covers the concept phase by defining the inputs, such as the specific characteristics of machine learning classes or the properties of the production data and the user preferences. With applied methodologies such as the analytic hierarchy process (AHP) and the technique for order preference by similarity to ideal solution (TOPSIS), the preferences of the user as valid input are integrated. The scientific contribution of this methodology is the approach to include user preferences and specific data properties in the selection process of two ML methods. As digitalisation progresses, making data-driven decisions in the domains of production and logistics is a goal for many SMEs. This methodology can support a data-driven decision-aid model by providing a guided method, which requires relatively little ML knowledge on the part of the engineer. It allows the user(s) to select the best suited combination of ML methods for a clustering use case.

Enriching Building Information Modeling Models through Information Delivery Specification

The efficient acquisition and dissemination of information are crucial in building information modeling (BIM). Current BIM models face significant challenges, including inadequate modeling techniques, poorly defined information requirements, and low interoperability. These issues result in poor information quality and complicate the transition from information acquisition to model processing. Public authorities often provide documentation in various formats, requiring manual transfer to software, which is error-prone and burdensome. This process is particularly difficult for small and medium enterprises lacking resources and knowledge. To address these issues, the IDS (Information Delivery Specification) Collab Tool is under development. This tool aims to automate the import of requirements into authoring software, perform automated compliance checks, and enhance interoperability among stakeholders. It will assist designers in providing accurate information according to requirements through the IDS standard, improving model quality and efficiency from early design stages. Adapting BIM models to specific project requirements and aligning new IDS capabilities with traditional industry practices remain significant challenges. Preliminary evaluations indicate the tool’s potential to significantly improve workflow efficiency and compliance in BIM modeling. However, broader awareness and adoption of the IDS standard are needed. Further research and refinement are essential to fully realize the benefits of digital tools in revolutionizing design and construction practices.

Automated Code Review Tool

As software development undergoes continual evolution, the assurance of code quality remains paramount in the development lifecycle. This research paper introduces an innovative Automated Code Review Tool engineered to augment and streamline the code review process. The tool capitalizes on advanced static code analysis, machine learning algorithms, and industry best practices to automate the detection of code quality issues, security vulnerabilities, and compliance with coding standards. The Automated Code Review Tool addresses the inherent challenges of manual code reviews, including human error, time constraints, and inconsistencies. Through automation, developers can expedite the identification and resolution of potential issues, thereby enhancing overall code quality and software reliability. Key features of the tool encompass intelligent pattern recognition, real-time feedback, and customizable rule sets, enabling development teams to tailor the tool to their project-specific requirements. Leveraging machine learning algorithms facilitates the tool's continuous learning from past reviews, thereby adapting to evolving coding practices and emerging security threats. This paper presents the architectural framework and design principles underpinning the Automated Code Review Tool, emphasizing its capacity to foster collaboration among development teams, reduce time-to-market, and ultimately contribute to the development of more robust and secure software. A comprehensive evaluation of the tool's efficacy across diverse development environments is conducted, offering insights into the practical implications of embracing automated solutions in real-world contexts. Keywords: Automated Code Review Tool, Code quality, Static code analysis, Machine learning algorithms, Coding standards, Manual code reviews, Human error, Time constraints, etc.

Influential factors for risk assessment and allocation on complex design-build infrastructure projects; the Texas experience

Introduction: The design-build (DB) delivery method is used to deliver increasingly complex transportation infrastructure projects associated with higher uncertainty. As such, allocating risks in the contract between the owner and design-builder becomes challenging and often leads to higher initial bids, increased contingency, or claims. Learnings from implementation worldwide have underlined the need for improving risk allocation in DB contracts. Most existing studies address risk allocation mechanisms to manage contingency at the contract level. Other studies have recognized the need for owners to adapt their processes to better allocate risks in DB contracts. This study explored the influential factors for risk assessment and allocation for complex DB infrastructure projects, addressing the opportunity to improve transportation owners’ risk allocation processes before the design-builder is selected and the DB contract is awarded.Method: The objectives of this work were achieved by utilizing empirical data collected through 20 interviews with Texas Department of Transportation and private sector experts. The interview data were analyzed using inductive and axial coding. Inductive coding allowed themes to emerge without a pre-existing framework, identifying six influential factors and six pertinent risks on complex DB projects.Results: These factors include the (i) Quality of DB teams, (ii) Level of up-front investigation, (iii) Limitations on the timing of letting, (iv) Design optimization opportunities, (v) Project-specific requirements, and (vi) Relationships with third parties. Through axial coding, the interaction and frequency between the factors and risks were also examined. The coded interactions demonstrated how the identified factors influence allocation for six pertinent risks including right-of-way acquisition, stakeholder approval, site conditions, permits and third-party agreements, railroad interaction, and utility adjustments and coordination. Findings indicate that the evaluation of these interactions can shift the risk allocation from baseline norms established by an agency to correspond to project-specific needs.Contribution: In contributing to the infrastructure project management, this is the first study to examine the factors that influence risk allocation in complex DB projects and examine interactions with pertinent risks, setting the foundation for optimizing allocation based on project-specific needs. In practice, the findings presented in this study can guide owners in adapting their allocation practices, managing, and developing their strategic plan for delivering complex DB projects. The findings can also assist contractors in pricing risks more efficiently and increase competitive bidding.

Unlocking the potential of ponds and pondscapes as nature-based solutions for climate resilience and beyond: Hundred evidences

Unlocking the full potential of ponds (small water bodies) and pondscapes (network of ponds) as Nature-based Solutions (NbS) is critical pursuit for enhancing ecosystems and societal resilience to climate change and other societal challenges. Despite scattered initiatives for pond/pondscape creation, restoration and management—each considered here a distinct NbS—there is a significant knowledge gap in utilising ponds/pondscapes as effective NbS. We aimed to assess these NbS in terms of their objectives, outcomes, effectiveness, multifunctionality, delivery of potentially conflicting effects, and the implementation process while considering their Nature's Contributions to People (NCPs, i.e., benefits to society). We compiled data on 183 NbS actions implemented across 93 ponds/pondscapes from 24 countries, predominantly from Europe, via a questionnaire distributed to experienced stakeholders implementing NbS in ponds/pondscapes. One single pond/pondscape may imply more than one NbS action. Two-thirds were in rural areas, and one-third in urban settings. Our analysis revealed that Creation of habitat for biodiversity was a primary delivery objective (targeted NCP) in the implementation of most NbS in ponds/pondscapes, often also combined with other NCPs such as Learning and inspiration, Regulation of water quantity, and Physical and psychological experiences, showcasing their intended multifunctionality. Implemented NbS primarily focused on climate change adaptation (especially Regulation of hazards and extreme events, and water quantity) rather than mitigation, with less emphasis on measures like direct greenhouse gas emissions reduction or enhancing carbon sinks. The costs associated with pond's NbS varied significantly depending on factors such as project scope, objectives, location, socio-economic-cultural system, and specific implementation requirements. The creation of ponds/pondscapes often entailed the highest financial investment, much more than their restoration or their management. In conclusion, our study underscores the multifunctionality of ponds/pondscapes and provides insights about their significant potential as cost-effective NbS for enhancing ecosystem and societal resilience to climate change and biodiversity. It underscores the importance of further research to fully understand and measure the diverse range of NbS they offer, particularly in the context of climate change mitigation. Standardised measurements of the NCP provided by these NbS in ponds/pondscapes are essential for validating managers' claims and exploring their role in addressing climate change.

Comparative Study of G+6 Irregular Multistoryed Building of Different Shape of Diaphragm

Abstract: This study investigates the effectiveness of incorporating openings in the roof of re-entrant corner plan irregular buildings at different locations under earthquake loads. Five models with varying degrees of diaphragm discontinuity were analysed using STAAD Pro software. Node displacement, storey shear in X and Z directions, stress values, and maximum bending moment were evaluated to assess structural performance. Results indicate that models with higher diaphragm discontinuity exhibit increased node displacements, storey shear, and stress levels. Model M3, with 17% diaphragm discontinuity, consistently demonstrates superior performance in terms of lower displacements and stress values compared to other models. However, final model selection should consider specific project requirements and design considerations.

Comparative Study of G+6 Irregular Multistoryed Building of L Shape of Diaphragm Discontinuity

Abstract: This study investigates the effectiveness of incorporating roof openings in re-entrant corner plan irregular buildings at various locations under earthquake loads. Three models L-shaped diaphragm discontinuity were analyzed using STAAD Pro software. Node displacement and storey shear in both the X and Z directions were evaluated to assess structural performance. The results indicate that models with square diaphragm discontinuity exhibit increased node displacements and storey shear compared to those with L-shaped diaphragm discontinuity. The model with L-shaped diaphragm discontinuity consistently demonstrates superior performance in terms of lower displacements and shear stress values. However, the final model selection should take into account specific project requirements and design considerations.

Cost-Efficient Deployment Strategies: A Comparative Analysis of Feature Flagging Services and Blue/Green Deployments

Aims: This study provides a detailed comparison of two leading feature flagging services, LaunchDarkly and ConfigCat, and also examines the cost-efficiency and operational implications of using Blue/Green deployment strategies. It seeks to aid stakeholders in understanding the trade-offs and benefits of each approach to make informed decisions based on project-specific needs, budget constraints, and developmental objectives. Study Design: The study employs a comparative analysis framework, focusing on features, usability, scalability, architectural design, and the financial impacts of adopting feature flagging services versus Blue/Green deployment strategies. Place and Duration of Study: The analysis was conducted over a period of two years, encompassing a broad range of software development environments and project scenarios to ensure comprehensive coverage and relevance. Methodology: The methodology employed in this paper includes a detailed examination of LaunchDarkly and ConfigCat's service offerings, an evaluation of Blue/Green deployment strategies, and an analysis of cost-efficiency of each approach. The study synthesizes information from product documentation, user feedback, and performance metrics, alongside interviews with industry experts and case studies from diverse software development projects. Results: The results highlight the nuanced differences between feature flagging services in terms of scalability, ease of use, and the suitability for various project sizes. LaunchDarkly emerges as optimized for large-scale, complex projects due to its extensive feature set and scalability, while ConfigCat is favored for its simplicity and ease of use in smaller projects. The analysis also uncovers the cost benefits of feature flagging over Blue/Green deployments, emphasizing the savings on infrastructure and operational expenses while offering dynamic feature management capabilities. Conclusion: The study concludes that the choice between feature flagging services like LaunchDarkly and ConfigCat, and the utilization of Blue/Green deployment strategies, should be guided by specific project requirements, financial constraints, and desired operational efficiency. Feature flagging services provide a cost-effective, flexible solution for dynamic feature management, whereas Blue/Green deployments offer a straightforward, though potentially more resource-intensive, approach to minimizing deployment risks. This comparative analysis aims to assist stakeholders in selecting the most appropriate deployment strategy to meet their development goals efficiently.

Proposal of Multicriteria Decision-Making Models for Biogas Production

While biogas production offers promising solutions for waste management, energy diversification, and sustainable development, effective project implementation requires comprehensive evaluation criteria that encompass diverse aspects, such as the problem to be addressed, biodigester technology selection, business model development, investment considerations, and final product utilization. A preliminary study involving an integrative review of 58 articles yielded 499 unique criteria. These criteria were categorized into four groups: economic, environmental, social, and technical, encompassing a total of 39 subcriteria. Six stages of the biogas production cycle were considered in the analysis: project, initiation, biodigester type selection, location determination, operational cycle definition, and final product utilization. The analysis revealed that existing decision-making models often prioritize technical and economic considerations while neglecting broader social and environmental perspectives. This paper addresses this gap by proposing, for the first time, stage-specific, multicriteria decision-making (MDCA) models tailored to each phase of a biogas production cycle. These models empower project managers and policymakers to optimize resource allocation, minimize the environmental impact, maximize social benefits, and ensure project viability and profitability. The models’ adaptability allows for tailored prioritization based on specific project requirements and contexts. This groundbreaking research fills a critical void in biogas decision making by bridging the gap between existing technical and economic model limitations and the growing need for truly sustainable project development.

Auditive Emotion Recognition for Empathic AI-Assistants

AbstractThis paper briefly introduces the Project “AudEeKA”, whose aim is to use speech and other bio signals for emotion recognition to improve remote, but also direct, healthcare. This article takes a look at use cases, goals and challenges, of researching and implementing a possible solution. To gain additional insights, the main-goal of the project is divided into multiple sub-goals, namely speech emotion recognition, stress detection and classification and emotion detection from physiological signals. Also, similar projects are considered and project-specific requirements stemming from use-cases introduced. Possible pitfalls and difficulties are outlined, which are mostly associated with datasets. They also emerge out of the requirements, their accompanying restrictions and first analyses in the area of speech emotion recognition, which are shortly presented and discussed. At the same time, first approaches to solutions for every sub-goal, which include the use of continual learning, and finally a draft of the planned architecture for the envisioned system, is presented. This draft presents a possible solution for combining all sub-goals, while reaching the main goal of a multimodal emotion recognition system.

A Model Driven Framework for Collaborative and Dynamic Design and Implementation of NoSQL-Oriented Data warehouses

Nowadays, modernizing the data warehouse ecosystem is a key challenge in decision support systems. This modernization is crucial for ensuring scalability and meeting evolving business requirements, especially with the advent of big data. A promising solution involves implementing data warehouses with contemporary data stores, such as NoSQL. In this context, we introduce in this paper a framework that leverages Model-Driven Architecture (MDA) to design and implement modern data warehouses across NoSQL data stores. Our MDA approach aims to offer a collaborative, dynamic, and reusable process for developing NoSQL-oriented data warehouses tailored to specific project requirements. It facilitates the automatic and dynamic generation of a hybrid data warehouse model from its conceptual model, which encompasses structural, domain, and access parameters. Moreover, our framework includes the generation of implementation code for the data warehouse, along with a set of files to validate, document, and illustrate the data warehouse schema on a target platform. Finally, we present a detailed case study to highlight the effectiveness of our MDA framework.

Algorithmic analysis and application of structural tessellation in design and optimization

This paper explores the application of structural tessellation in architectural and structural design, where surfaces or spaces are divided into smaller repeating shapes or units to create aesthetic and functional structures. Structural tessellation offers various advantages, including improved stability, increased load-bearing capacity, and enhanced aesthetic appeal. The growing use of digital tools and advanced numerical algorithms has facilitated the creation of complex and intricate tessellations that can be tailored to suit specific project requirements. This research focuses on algorithmic methods for generating tessellations and their utilization in structural engineering and design optimization. Diverse patterns and configurations of tessellation are investigated through mesh generation algorithms, parametric approaches, and pattern gradation and repetitions, with data visualization accomplished using computing scripts and Grasshopper. The tessellation elements are employed to discretize the design domain in structural optimization and create initial patterns. The paper demonstrates the feasibility of proposed frameworks for structural design and application through the examination of various numerical examples. In conclusion, the strategic use of structural tessellation proves to be effective in producing unique and functional structures that seamlessly combine visual appeal with material and resource efficiency.

Project Oriented Management (POM) and Logistical Challenges in Maintenance and Network Development Systems

Maintenance, Refurbishment and Network Upgrading Systems occupy a prominent place among service systems, especially due to their dispersed location and large territorial extent. For the efficient opertaion of these systems, it is essential to apply Project Oriented Management (POM), which aims at optimizing logistic processes and meeting project specific requirements.

Evaluating Text Quality of GPT Engine Davinci-003 and GPT Engine Davinci Generation Using BLEU Score

The improvement of text generation based on language models has witnessed significant progress in the field of natural language processing with the use of Transformer-based language models, such as GPT (Generative Pre-trained Transformer). In this study, we conduct an evaluation of text quality using the BLEU (Bilingual Evaluation Understudy) score for two prominent GPT engines: Davinci-003 and Davinci. We generated questions and answers related to Python from internet sources as input data. The BLEU score comparison revealed that Davinci-003 achieved a higher score of 0.035, while Davinci attained a score of 0.021. Additionally, for the response times, with Davinci demonstrating an average response time of 4.20 seconds, while Davinci-003 exhibited a slightly longer average response time of 6.59 seconds. The decision of whether to use Davinci-003 or Davinci for chatbot development should be made based on the specific project requirements. If prioritizing text quality is paramount, Davinci-003 emerges as the superior choice due to its higher BLEU score. However, if faster response times are of greater importance, Davinci may be the more suitable option. Ultimately, the selection should align with the unique needs and objectives of the chatbot development project.

Improvement in Long Term Bonding and Mechanical Performance by Using Glass Concrete in Combination with Xanthan Gum Exposed to Harsh Environment

Abstract Concrete is the mostly used construction material composed of a mixture of cement, water, aggregates (such as sand, gravel, or crushed stone), and often additional additives or admixtures. It is widely used in the construction industry for various applications due to its strength, durability, and versatility. Key characteristics of concrete include strength, durability, versatility, fire resistant, cost effective, weather resistant, insulation and decorative options. Concrete plays a vital role in the construction industry, providing the foundation for most buildings, infrastructure, and many other structures worldwide. Its composition and properties can be tailored to meet specific project requirements, making it an indispensable material in modern construction. Various fibers can be used to enhance the mechanical and bonding properties of concrete. Also waste fibers after recycling can be reduced the environmental burden. Keeping in this view, glass powder sodium silicate glass (SSG) is used as replacement of cement with different percentages 0%,4%,8%, and 12% in combination of xanthan gum 0.2% for all mixes. An experimental study is conducted to investigate the mechanical and durability properties of concrete by performing compression test, flexural test, alkali silica reactivity test, sulfate resistivity test and drying shrinkage test. For this, forty-eight concrete cylinders are prepared for compression test, forty-eight concrete prisms for flexural test and thirty-six mortar bars of four mixes are prepared for durability testing. Workability is checked of fresh concrete during the pouring of concrete cylinders. Poured cylinders’ samples are left for 7, 14, 21 and 28 days of curing. Different tests are performed on hardened concrete and mortar samples to evaluate the mechanical and durability properties. Results concluded that workability of four mixes lies between 60-80mm and compressive strength of concrete has been improved using glass powder (SSG). Optimum results have been achieved at 12% as compared to other mixes 4% and 8% of concrete samples. Fibrous material is used as a binding agent and fibrous concrete is suitable for humid environment where high strength and voids less concrete are required. Quantity of cement can be reduced by using different fibers as a replacement of cement. Research recommended that recycled glass powder can be used in concrete as construction material and 12% replacement is suitable for optimum results.

Comparing Sustainability and Affordability: a Study of Prefabricated Versus Conventional Construction for Housing of Economically Weaker Sections

This research aims to compare the sustainability and affordability of prefabricated housing with conventional construction for economically weaker sections (EWS) in India. The study analyzes the embodied energy, embodied carbon, and cost per functional unit for both construction methods. The findings reveal that prefabricated construction exhibits lower embodied energy and carbon emissions. However, it is observed that the initial cost of prefabricated construction is higher compared to conventional methods. The long-term cost implications are subject to specific project requirements and local conditions. This research highlights the potential of prefabrication in addressing the housing crisis and emphasizes the need for further exploration to optimize its cost-effectiveness. The outcomes contribute to the growing body of knowledge on sustainable housing solutions for EWS communities in India.

Statistical analysis software

Purpose. Analysis of existing software to perform statistical analysis for further use as part of the selection of the necessary software for data processing. Methodology. To conduct a software review, an analysis of scientific articles and open sources on statistical analysis software was conducted. Findings. Choosing the right statistical software is a key decision in the field of data analysis, with numerous options to meet a variety of needs. This article provides a comprehensive overview of five leading statistical software tools: IBM SPSS Statistics, RStudio, Stata, Minitab, and Python. This paper reveals key insights into the capabilities, functions, and suitability of each tool for various analytical tasks. This review concludes that the choice of statistical software should be consistent with specific project requirements, data complexity, and user experience. Researchers and analysts should consider their analytical goals and preferences when choosing the most appropriate tool. In addition, to make informed decisions in this dynamic field, it is important to stay abreast of new trends in data analysis and machine learning. Originality. The conducted analysis revealed the possibilities and application of the most popular software for solving problems of statistical analysis. The work provides a comprehensive overview of current trends and innovations in the field of software for statistical analysis, offering readers a deeper understanding of existing tools. Practical value. The conducted analysis will allow to choose software for solving a specific task of statistical analysis based on its characteristics and existing requirements. This work helps to identify the practical benefits of statistical analysis software and promotes the implementation of these tools in various fields of activity, providing improvements in analysis and decision-making processes.