Automatic Tool Research Articles

Library automation trends at City University of New York (CUNY): transforming face-to-face services into remote access during and post-pandemic − a case study of York College’s Library

Purpose This paper examines the transformation of York College Library’s services through automation before, during and after the COVID-19 pandemic. This paper aims to evaluate how automation tools such as Integrated Library Systems (ILS) and e-resource platforms facilitated the transition to remote services, while identifying challenges related to staff training, accessibility and digital engagement. By analyzing user feedback, operational data and automation trends, the study provides actionable insights for optimizing academic library services. The findings offer scalable recommendations to enhance service delivery, ensure inclusivity and strengthen the resilience of libraries in adapting to evolving educational demands in a post-pandemic landscape. Design/methodology/approach This study uses a mixed-methods approach to analyze York College Library’s automation trends across three phases: pre-pandemic, during the pandemic and post-pandemic. Quantitative data was collected through surveys and library usage statistics, measuring e-resource downloads and virtual reference inquiries. Qualitative insights were obtained via interviews with library staff and users, exploring challenges and the impact of automation systems on service delivery. The combined analysis provides a comprehensive understanding of the library’s transition to remote services, identifying key strengths and areas for improvement to inform future strategies for academic library operations. Findings This study revealed that York College Library’s pre-pandemic investment in automation tools, such as ILS and e-resource platforms, enabled a rapid shift to remote services during the COVID-19 pandemic. Virtual reference inquiries surged by 200%, and e-resource usage increased by 45%, demonstrating high user reliance on digital services. However, gaps in staff training and accessibility highlighted areas for improvement. Post-pandemic, the library adopted a hybrid model that blends physical and virtual services to meet diverse user needs. The findings underscore the importance of continuous professional development, robust digital infrastructure and accessibility-focused strategies to ensure resilient and inclusive library operations. Originality/value This paper provides a unique case study of York College Library’s transition to remote services during the COVID-19 pandemic, offering insights into how automation tools were leveraged to maintain academic support. It highlights the dual role of technology in enabling service continuity and exposing gaps in training and accessibility. By integrating user feedback, operational data and automation trends, the study offers actionable recommendations for libraries navigating post-pandemic challenges. Its focus on scalable solutions for hybrid service models, staff development and digital equity adds significant value to the evolving discourse on academic library automation and resilience in higher education.

Artificial intelligence (AI) for reinforced autoclaved aerated concrete (RAAC) crack defect identification

PurposeReinforced autoclaved aerated concrete (RAAC) panels have been extensively used in the UK since the 1960s as structural roofs, floors and walls. The lack of a longitudinal, objective, consistent defect data capture process has led to inaccurate, invalid and incomplete RAAC data, which limits the ability to survey RAAC within buildings and monitor performance. Therefore, an accurate, complete and valid digital data capture process is needed to facilitate better RAAC performance and defect monitoring. This paper presents the development of an artificial intelligence (AI)-driven RAAC crack defect capture tool for improving the quality of RAAC survey data.Design/methodology/approachRAAC crack defect image data were collected, curated and trained. A deep learning approach was employed to train RAAC surveyed defects (cracks) images from two hospitals. This approach mitigated unavoidable occlusions/obstructions and unintended “foreign” objects and textures.FindingsAn automatic RAAC crack identification tool has been developed to be integrated into RAAC survey processes via an executable code. The executable code categorises RAAC survey images into “crack” or “non-crack” and can provide longitudinal graphical evidence of changes in the RAAC over time.Originality/valueThis paper identifies the role of AI in addressing the intrinsic defects data capture issues for RAAC and extends current debates on data-driven solutions for defect capture and monitoring.

Mastering Dynamic Web Element Handling and Locator Strategies in Selenium

Web automation testing is essential for ensuring software quality and delivering seamless user experiences. Selenium, a leading automation tool, empowers testers to create robust scripts for handling diverse and dynamic web applications. However, challenges such as identifying dynamic web elements and crafting effective locator strategies require advanced techniques. This article explores key solutions, including the use of relative locators, dynamic XPath expressions, and custom attributes for stable element identification. It also highlights the importance of employing the Page Object Model (POM) framework to enhance script maintainability and scalability while addressing dynamic element challenges. Strategies like explicit waits, retry mechanisms, and modular frameworks are discussed to improve test reliability and minimize failures. By adopting these approaches, testers can overcome the complexities of modern web applications, streamline test automation processes, and ensure the accuracy and resilience of their scripts. This enables faster testing cycles and contributes to delivering high-quality software in dynamic environments. Keywords: Web Automation Testing, Selenium, Dynamic Web Elements, Locator Strategies, Relative Locators, Dynamic XPath, Page Object Model (POM), Custom Attributes, Test Automation Framework, Script Maintainability.

Magnitude, risk factors and economic impacts of diabetic emergencies in developing countries: A systematic review.

Diabetic ketoacidosis (DKA), hyperglycemic hyperosmolar syndrome (HHS) and severe hypoglycemia are considered as the life-threatening diabetic emergencies of diabetic patients worldwide. As the prevalence of diabetes grows in developing countries, so too does the impact of these costly human and economic complications. Noticeable scarcity of data concerning the magnitude, the cost expenditures as well as well unidentified predictors of these complications made the management more difficult in the resource limited health care settings. Thus, this systematic review aimed to assess the magnitude, risk factors and economic impacts of diabetes emergencies among diabetic patients in the developing countries. Following PRISMA (2020) guidelines, databases of PubMed, EMBASE, Cochrane and Scopus were searched for studies reporting on prevalence, risk factors, and direct costs of diabetes emergencies published in English from 2000 to 2023. Forty eligible studies were extracted and retrieved using manual data extraction form and automation tools. Studies were analyzed and combined in a narrative synthesis. The estimations of direct cost expenditure were standardized to 2023 USD. A comprehensive examination was conducted on the 40 eligible studies, with the majority originating from African sources. The review shows the prevalence of diabetic emergencies; DKA episodes in the range of (3.8%-73.4%), HHS (0.9%-58%) and Severe hypoglycemia (3.3%-64.7%) per year in the developing countries. Infection, new onset of the diabetes, and non-compliance to medications and diets were reported as the most common risk factors of theses diabetic emergencies. Besides, the costs of hospitalization taken from the patients' perspective, that were associated per one diabetic emergency event per patient was reported in the range of 105-230 USD in the developing countries. The rising prevalence of diabetic emergencies in poor nations, where infections, non-compliance, and new onset of diabetes are major causes, highlighted the urgent need for preventative interventions. Identifying high-risk individuals is crucial for implementing tailored strategies to reduce emergency visits and hospital admissions. The significant economic burden of these emergencies exacerbates the strain on already limited healthcare resources. In order to enhance health outcomes and lessen the financial strain on healthcare systems in these areas, preventive strategies must be incorporated into diabetes management programs.

Enumeration Approach to Atom-to-Atom Mapping Accelerated by Ising Computing.

Chemical reactions are regarded as transformations of chemical structures, and the question of which atoms in the reactants correspond to which atoms in the products has attracted chemists for a long time. Atom-to-atom mapping (AAM) is a procedure that establishes such correspondence(s) between the atoms of reactants and products in a chemical reaction. Currently, automatic AAM tools play a pivotal role in various chemoinformatics tasks. However, achieving accurate automatic AAM for complex or unknown reactions within a reasonable computation time remains a significant challenge due to the combinatorial nature of the problem and the difficulty in applying appropriate reaction rules. In this study, we propose a rule-free AAM algorithm, which enumerates all atom-to-atom correspondences that minimize the number of bond cleavages and formations during the reaction. To reduce the computational burden associated with the combinatorial optimization (i.e., minimizing bond changes), we introduce Ising computing, a computing paradigm that has gained significant attention for its efficiency in solving hard combinatorial optimization problems. We found that our Ising computing framework outperforms conventional combinatorial optimization algorithms in terms of computation times, making it feasible to solve the AAM problem without reaction rules in an acceptable time. Furthermore, our AAM algorithm successfully found the correct AAM solution for all problems in a benchmark data set. In contrast, conventional AAM algorithms based on chemical heuristics failed for several problems. Specifically, these algorithms either failed to find the optimal solution in terms of bond changes, or they identified only one optimal solution, which was incorrect when multiple optimal solutions exist. These results emphasize the importance of enumerating all optimal correspondences that minimize bond changes, which is effectively achieved by our Ising-computing framework.

Automatic base-dose planning for a robust field junction in total marrow irradiation.

Total marrow (lymph-node) irradiation (TMI/TMLI) is a radiotherapy technique irradiating the whole body of a patient. The limited couch travel range in modern linacs (130-150cm) forces to split the TMI/TMLI delivery into two plans with opposite orientation. A dedicated field junction is necessary to achieve satisfactory target coverage in the overlapping region of the two plans. In a recent study, we implemented an automatic tool (AT) using the Eclipse Scripting API for the creation of a field junction and optimization of TMI/TMLI. In this work, we improved the AT by developing an automatic base-dose planning approach. Ten patients were selected to compare the manual procedure, original automatic planning approach, and new base-dose approach. Treatment plans were evaluated with the D98%, Dmean, and D2% for the planning target volume at the junction (PTV_J), while Dmean and D2% were considered for the PTV without the junction (PTVNoJ) and healthy tissues. Base-dose planning significantly improved the PTV_J coverage compared with the manual approach, with unaltered Dmean, consistently lower D2% (2.24Gy vs. 2.30Gy) and higher D98% (1.98Gy vs. 1.89Gy). Moreover, it significantly reduced the hotspots in healthy tissues (2.02Gy vs. 2.15Gy). No significant differences for PTVNoJ dose statistics were observed among the procedures, demonstrating that the automatic approaches produced a target coverage similar to the one obtained manually. Base-dose planning improved the field junction optimization of TMI/TMLI. Automatic planning tools can incrementally improve procedures that would be challenging or error-prone to achieve manually.

Embracing Industry 4.0: Navigating the Evolution of Entrepreneurship in Romania

Entrepreneurship constitutes one of the oldest and most profitable occupations. It has evolved alongside the Industrial Revolutions (IR), directly influencing each other. One of the fundamental pillars of technological development is represented by inventive and innovative entrepreneurs who had the courage to investigate and implement what initially seemed to be mere dreams. The first three IRs brought major and decisive changes on a global level. The fourth revolution, called Revolution 4.0 - R 4.0 (or Industry 4.0 - I 4.0), which we consider an evolution, has brought to the forefront new fields of interest and heterogeneous areas of concern: artificial intelligence, robotics, automation, the Internet of Things, 3D printing, etc. In our research, we aimed, on the one hand, to analyze the perspective of Romanian entrepreneurs regarding IR 4.0, whether they implement measures specific to new technologies, and what the impact of these measures is. On the other hand, following the analysis of the collected data, we sought to identify possibilities for stimulating local entrepreneurship under the new R 4.0 conditions. Noting that, still, in Romania, there is insufficient investment in the necessary technological infrastructure, we began our research with a theoretical analysis of the data and the state of the specialized literature. The specific infrastructure that Romania lacks includes reliable broadband internet connectivity, advanced digital technologies such as IoT devices, and accessible automation tools. These gaps significantly hinder the ability of entrepreneurs to leverage Industry 4.0 advancements, thereby limiting their capacity to innovate and compete globally. Addressing these issues will require targeted government investments in digital infrastructure, incentives for technology adoption in key sectors, and partnerships between the private and public sectors to accelerate the deployment of Industry 4.0 technologies. In the next stage, we analyzed the data collected through a semi-structured questionnaire disseminated via crowdsourcing, to which 1026 subjects responded. Before conducting the econometric analysis of the data, three research hypotheses were formulated, which were ultimately validated. The working methodology included quantitative data analysis, descriptive statistics methods, categorical principal component analysis (CATPCA), and correspondence analysis. Data processing was performed using the IBM SPSS 20 application. A limitation of our research is that we did not select the questionnaire participants based on their competencies, allowing individuals with still limited knowledge to intuitively express opinions on a field they do not know or only think they know. The lack of competency-based selection for survey participants means that respondents may not have had the requisite knowledge to provide informed opinions, potentially skewing the results. This limitation impacts the reliability of the data and could mean that the findings are not fully reflective of expert perspectives in Industry 4.0. Future studies should consider a more rigorous selection criterion, ensuring participants possess relevant competencies. This approach would help in obtaining more accurate insights, leading to more actionable conclusions. As a future action direction, we aim to analyze the issue of local entrepreneurship by involving respondents from different professional categories who possess the necessary competencies, so that their opinions can form the basis of a more detailed analysis. This would facilitate establishing viable action directions as a basis for the sustainable and enduring development of entrepreneurship in Romania in the context of the Fourth Industrial Revolution Keywords: Entrepreneurship, Industrial Revolution, Revolution 4.0, Artificial Intelligence, Technology, Investments

A scoping review of automatic and semi-automatic MRI segmentation in human brain imaging.

AI-based segmentation techniques in brain MRI have revolutionized neuroimaging by enhancing the accuracy and efficiency of brain structure analysis. These techniques are pivotal for diagnosing neurodegenerative diseases, classifying psychiatric conditions, and predicting brain age. This scoping review synthesizes current methodologies, identifies key trends, and highlights gaps in the use of automatic and semi-automatic segmentation tools in brain MRI, particularly focusing on their application to healthy populations and clinical utility. A scoping review was conducted following Arksey and O'Malley's framework and PRISMA-ScR guidelines. A comprehensive search was performed across six databases for studies published between 2014 and 2024. Studies focused on AI-based brain segmentation in healthy populations, and patients with neurodegenerative diseases, and psychiatric disorders were included, while reviews, case series, and studies without human participants were excluded. Thirty-two studies were included, employing various segmentation tools and AI models such as convolutional neural networks for segmenting gray matter, white matter, cerebrospinal fluid, and pathological regions. FreeSurfer, which utilizes algorithmic techniques, are also commonly used for automated segmentation. AI models demonstrated high accuracy in brain age prediction, neurodegenerative disease classification, and psychiatric disorder subtyping. Longitudinal studies tracked disease progression, while multimodal approaches integrating MRI with fMRI and PET enhanced diagnostic precision. AI-based segmentation techniques provide scalable solutions for neuroimaging, advancing personalized brain health strategies and supporting early diagnosis of neurological and psychiatric conditions. However, challenges related to standardization, generalizability, and ethical considerations remain. The integration of AI tools and algorithm-based methods into clinical workflows can enhance diagnostic accuracy and efficiency, but greater focus on model interpretability, standardization of imaging protocols, and patient consent processes is needed to ensure responsible adoption in practice.

Features of the work activity and health status in station and shunting dispatchers at the railway transport

Introduction. One of the urgent problems of occupational hygiene is the optimization of working conditions and the protection of the health in workers at the railway transport. Technical progress at the railway transport, the introduction of modern automation tools, and the gain in the operational capacity of railways place special requirements for such occupations referred to the group of dispatching and operator occupations. One of the leading occupations among this category of workers is the profession of station and maneuver dispatchers. Chronic exposure to nervous and emotional stress in station and maneuver dispatchers can contribute to the development of pathological conditions in various organs and systems of the body and cause corresponding diseases. Therefore, an in-depth study of the working conditions of station and shunting dispatchers is relevant and timely. The purpose of the study to investigate the working conditions in station and shunting dispatchers of the railway transport with the development of preventive measures. Materials and methods. Time-lapse studies were carried out (photo of the working day). Measurements of harmful production factors in the workplace were carried out using the method of special assessment of working conditions. The structure of morbidity has been studied. Results. According to the conducted research, it was found that the duration of concentrated attention for shunting dispatchers is 93.3%, for station dispatchers 87.7%. The average signal density per hour is 304 for the station dispatcher, and 311 for the maneuvering dispatcher. The load on the organs of hearing and vision was established. The hearing load for station dispatchers was 4–8 hours, for maneuver dispatchers – 5–7 hours, including talking on the “phone” and “selector”. During the work of the dispatcher, complex tasks are solved with a choice according to known algorithms (work according to a series of instructions), which takes 73.5–79.3% of the working time for station dispatchers, 70.8–96.3% for maneuvering dispatchers. Morbidity rates with temporary disability in the above-mentioned occupations are much higher than the industry average. Limitations. Stations and branches were selected as the basic facilities for conducting hygienic research at the Moscow, Oktyabrskaya, and Sverdlovskaya railways. Conclusion. The work activity in station and maneuver dispatchers is characterized by a significant amount of work, including speech (semantic), signal (auditory), sign and visual information. This allows characterizing it as operator labour against the background of a high degree of stress factors. According to the indicators of the intensity of the labour process, the working conditions in station and maneuver dispatchers are classified as strenuous work of the 3rd grade of harmfulness, class 3.3. Employees of this occupation category have a high incidence of temporary loss of ability to work in terms of the number of cases of disability of 149.0 and the number of days of disability of 1,800, 8 per 100 employees. The average duration of the one case is 12.1 days. Based on the conducted research there were developed preventive recommendations, including rehabilitative psychohygienic means, and methods of improving the health in operator personnel and knowledge workers.

Green IT in Healthcare: Leveraging DevOps for Scalable Patient Services

Healthcare organizations encounter rising environmental threats that force them to use sustainable methods while maintaining top patient care standards. This research examines how healthcare uses Green IT to save energy and materials and build environmentally friendly technology networks. DevOps applications help unite developers and operations teams through automated tools and regular software delivery techniques. Through containerization, microservices, and cloud computing technologies, healthcare organizations can create scalable systems that reduce idle resources, which leads to less energy waste. Technology-enhanced monitoring and automation tools help organizations execute routine work more accurately while improving their operations. Healthcare providers who move their data centers to the cloud can make their operations more environmentally friendly while keeping their service quality high. Our research shows organizations how to build a sustainable healthcare system that protects nature and provides excellent medical care while providing practical ways to unite Green IT and DevOps.

Sustainable DevOps: Minimizing the carbon footprint of banking data centers

Sustainable DevOps methods drive results in banking, given data centers account for most energy usage and environmental damage. Our study demonstrates how sustainability practices join with DevOps techniques to lower the ecological impact of banking data centers while achieving performance and security requirements. The paper illustrates how microservices and containerization technology help banking data centers work better with their resources by automatically scaling and lowering electricity consumption. The evaluation compares Jenkins and Ansible with other automation tools to show how they simplify repetitive work and shut down systems when usage drops to save energy. The study proves why advanced monitoring tools are necessary to track real-time power usage so data center managers can redistribute or stop using servers when needed. The analysis shows how banks can use cloud solutions that depend on green energy and infrastructure architectures to build sustainability practices. Beyond new technology tools, this research pushes for cultural change by supporting business units that add environmental effect measures to their new product development process. This study examines how banking industries can use sustainable DevOps practices to reduce energy use and environmental damage while maintaining regulatory standards and stable operations. The study provides technical and action-based methods that allow DevOps processes to support ecological preservation for finance companies.

Diagnosis of depression based on facial multimodal data

IntroductionDepression is a serious mental health disease. Traditional scale-based depression diagnosis methods often have problems of strong subjectivity and high misdiagnosis rate, so it is particularly important to develop automatic diagnostic tools based on objective indicators.MethodsThis study proposes a deep learning method that fuses multimodal data to automatically diagnose depression using facial video and audio data. We use spatiotemporal attention module to enhance the extraction of visual features and combine the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM) to analyze the audio features. Through the multi-modal feature fusion, the model can effectively capture different feature patterns related to depression.ResultsWe conduct extensive experiments on the publicly available clinical dataset, the Extended Distress Analysis Interview Corpus (E-DAIC). The experimental results show that we achieve robust accuracy on the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.51 in estimating PHQ-8 scores from recorded interviews.DiscussionCompared with existing methods, our model shows excellent performance in multi-modal information fusion, which is suitable for early evaluation of depression.

A prospective pragmatic evaluation of automatic trial matching tools in a molecular tumor board

Publicly available trial matching tools can improve the access to therapeutic innovations, but errors may expose to over-solicitation and disappointment. We performed a pragmatic non-interventional prospective evaluation on sequential patients at the Molecular Tumor Board of Centre Leon Berard. During 10 weeks in 2024, we analysed 157 patients with four clinical trial matching tools from the 19 screened: Klineo, ScreenAct, Trialing and DigitalECMT. Each patient had 2.19 trials proposed on average, and 38% had no trials suggested. The mean performances were precision = 0.33, recall = 0.32, AP@3 = 0.45, and NDCG@3 = 0.34. Using all the tools can increase to 26% the clinical trial options. The most frequent error concerned the type of gene variants required by the selection criteria. We showed that using a Large Language Model on the patients’ molecular reports could improve the performance by up to 5%. We recommend that experts supervise the results and we advocate for improved technologies.

A Python-Based Automation Script to Mark Computer-Aided Design Assessments

The integration of automated tools in engineering education has the potential to improve student assessments, ensuring consistency and reducing instructor workload. This study introduces a Python-based automation tool designed to evaluate student Computer-Aided Design (CAD) submissions. The tool utilises software API and Open Cascade library to calculate model parameters. These parameters are compared against expected values from a solution file and marks are assigned based on deviations relative to the solution file. As a use case, seventy-five Solid Edge CAD files were assessed for geometric properties such as volume, surface area, and centre of gravity location to evaluate inter- and intra-marker reliability. The results showed perfect agreement, with a Cohen kappa of 1.0 for both metrics. Furthermore, the automated tool reduced grading time by 89.7% compared to manual evaluation. The potential of automation in improving marking efficiency, consistency, and objectivity in engineering education has been shown, providing a foundation for further integration of software. The python-based automation script is openly available on GitHub.

A remote monitoring approach for coastal engineering projects

High costs and project-based (short-term) financing mean that coastal engineering projects are often undertaken in the absence of appropriate post-construction monitoring programmes. Consequently, the performance of shoreline-stabilizing structures or beach nourishments cannot be properly quantified. Given the high value of beaches and the increase in erosion problems and coastal engineering responses, managers require as much accurate data as possible to support efficient decision-making. This work presents a methodological approach to characterise coastline position changes as a result of engineering actions. We describe a new, low-cost method based on satellite remote sensing to monitor shoreline evolution at high temporal and spatial resolution pre-, during and post-implementation. Initially, satellite-derived waterlines are identified and extracted from publicly available satellite imagery of the Landsat 5, 7, 8, and 9, and Sentinel-2 constellations using the automatic shoreline extraction tool SHOREX. The waterline positions are then compiled, differences over time are quantified, and a matrix is constructed that allows easy depiction and interpretation of spatial and temporal patterns of erosion/accretion. This allows the access and the comprehension of the morphological data by the non-expert. Two examples of application on the Valencian coast of Spain at different spatial scales demonstrate how beach response to coastal engineering actions can be characterised at different levels of detail (from local to regional) and over different periods of time. These applications evidence the utility of the approach as it allows analysis of pre- and post-intervention coastal change and offers a means to overcome the widespread lack of monitoring and hence to improve coastal engineering practice.

Integrated BIM + Parametric Modelling: A decision-support tool for human-centric campus simulation, automation, and optimization

Developing masterplans of large-scale areas such as campuses involves formulating multiple scenarios, considering various social, functional, environmental, and aesthetic factors, and pursuing multi-objective optimization to fulfill various conflicting quantifiable and non-quantifiable design objectives. However, current practice lacks an integrated methodology that enables automatic evaluation of those scenarios and visualization of their results to stakeholders. Using computer-performed simulations with innovative design methods to dynamically investigate and empirically assess how campus characteristics influence its inclusiveness, users’ behavior, and social interactions can help identify designs that stimulate human-centered principles. This paper introduces the integrated parametric BIM-based campus life simulation as a decision-support tool. It unifies the design and analysis cycles with scenario generation and design automation through a human-centered methodology. The supports interactive design processes that involve space exploration, analysis, and optimization, allowing the usage of multi-objective optimization methods to develop detailed design alternatives that fulfill conflicting quantifiable and non-quantifiable objectives. Thus, it functions as a descriptive and predictive tool and supports theory-testing and development.

Digital Entrepreneurship and Business Innovation: Strategies for Indonesian SMEs in the Era of Industry 4.0

The rapid advancement of Industry 4.0 has significantly transformed the entrepreneurial landscape, particularly for small and medium-sized enterprises (SMEs) in Indonesia. Digital entrepreneurship has emerged as a critical driver of business innovation, enabling SMEs to enhance operational efficiency, expand market reach, and develop competitive advantages. This study explores the role of digital entrepreneurship in fostering business innovation, focusing on strategies that Indonesian SMEs adopt to navigate the digital economy. By utilizing a qualitative research approach, this paper examines case studies of SMEs that have successfully integrated digital technologies, such as e-commerce platforms, cloud computing, artificial intelligence, and blockchain, into their business models. The findings reveal that digital transformation enhances SMEs’ adaptability, productivity, and customer engagement. Key success factors include digital literacy, strategic use of online marketplaces, and the adoption of automation tools to streamline operations. However, SMEs also face significant challenges, including digital infrastructure limitations, cybersecurity risks, financial constraints, and resistance to technological change. The study highlights the importance of government policies, financial incentives, and digital training programs in supporting SMEs’ digital transition. This paper argues that a comprehensive and collaborative approach—where businesses, policymakers, and technology providers work together—is essential for maximizing the potential of digital entrepreneurship in Indonesia. By embracing digital transformation, SMEs can drive innovation, strengthen economic resilience, and contribute to sustainable economic growth in the digital era.

Development of a Genetic Algorithm Tool for the Optimization of the Methanol Oxidation

AbstractThis work presents an automatic optimization tool using the genetic algorithm (GA) for the chemical kinetic model of methanol (CH3OH) oxidation. A total of 54 parameters of 40 important reactions of the reaction model have been optimized. Ignition delay times measured in shock tubes, concentration profiles measured in plug flow reactors, and laminar flame speeds were used for the model validation. Compared to the results of the initial model, the optimized model exhibits a significantly improved predictive capability for the experimental targets. The GA tool developed in this study has been proven effective for optimizing detailed chemical kinetics models.

ІНСТРУМЕНТАРІЙ ВИЯВЛЕННЯ ПЛАГІАТУ В НАУКОВИХ РОБОТАХ: АНАЛІЗ ПРОГРАМНИХ РІШЕНЬ

The article deals with the concept of plagiarism, self-plagiarism and their basic types. It is described a classification of plagiarism automatic detection tools for text documents and the basic methods that underlie their functioning. It is considered a number of popular software and online resources to check text similarity, carried out their detailed analysis, testing and evaluation according to defined software specifications, productivity of functioning and obtained results during the checking. There are presented recommendations of the best tools that can be used by the research institutions and scientific journals’ editors.

How are We Detecting Inconsistent Method Names? An Empirical Study from Code Review Perspective

Proper naming of methods can make program code easier to understand, and thus enhance software maintainability. Yet, developers may use inconsistent names due to poor communication or a lack of familiarity with conventions within the software development lifecycle. To address this issue, much research effort has been invested into building automatic tools that can check for method name inconsistency and recommend consistent names. However, existing datasets generally do not provide precise details about why a method name was deemed improper and required to be changed. Such information can give useful hints on how to improve the recommendation of adequate method names. Accordingly, we construct a sample method-naming benchmark, ReName4J, by matching name changes with code reviews. We then present an empirical study on how state-of-the-art techniques perform in detecting or recommending consistent and inconsistent method names based on ReName4J. The main purpose of the study is to reveal a different perspective based on reviewed names rather than proposing a complete benchmark. We find that the existing techniques underperform on our review-driven benchmark, both in inconsistent checking and the recommendation. We further identify potential biases in the evaluation of existing techniques, which future research should consider thoroughly.