The concept of sustainable competitiveness is becoming increasingly relevant, as it combines the investigation of the factors that determine the competitive advantages of economic entities, as well as management strategies that ensure economic and environmental efficiency in the face of modern global requirements and challenges. The main hypothesis of the research is that technological innovations are key determinants of the sustainable competitiveness of economic entities, increase their resilience to various challenges and threats, and therefore contribute to sustainable competitiveness in the long run. Accordingly, the object of the research is a comprehensive science literature review at the intersection of the issues of competitiveness, technological innovations, and sustainable development using the Scopus database and PRISMA statement in order to substantiate the importance of technological innovation in ensuring sustainable competitiveness. The scientific research pinpoints three key questions shaping the scientific discussion: Are technological innovations key determinants of sustainable competitiveness? How do advanced technologies contribute across sectors? What strategies and measures stimulate sustainable competitiveness? By answering the research questions based on the methodology of nonempirical systematic scientific analysis, this review article provides scientific and practical insights for businesses and policymakers aiming to harness technological advancements to sustain their business in the long run.
Для проведення експериментальних досліджень процесу різання робочого середовища просторово орієнтованими ножами відвального обладнання, доопрацьовано динамометричний стенд реєстрації силового навантаження авторської конструкції КНУБА, що дозволило провести повноцінні експериментальні дослідження з врахуванням всіх чинних факторів взаємодії робочого середовища та робочого органу під час різання. В якості робочого середовища запропоновано використання ґрунтів III, IV, та V категорії. В результаті проведених досліджень для динамометричного стенда реєстрації силового навантаження при дослідженні процесу різання просторово орієнтованим ножем аналітично визначено сили різання при різних кутах α її відхилення, які виконують роботу по руйнуванню і подоланню опору ґрунту різанню. За результатами теоретичних досліджень встановлено, що межі сили різання, визначеної для натуральної установки з просторово орієнтованими ножами та для лабораторного стенду, однакові, а характер їх зміни також подібний і пов’язаний між собою коефіцієнтом подібності. З метою перевірки адекватності теоретичних розрахунків на динамометричному стенді проведено експериментальні дослідження різання робочого середовища. При проведенні експериментальних досліджень одночасно проводилось вимірювання нормальних та ортогональних зусиль, які виконують роботу з руйнування і подолання опору ґрунту різанню. Проведені експериментальні дослідження в повній мірі підтверджують адекватність теоретичних розрахунків, а порівняння теоретичних та експериментальних результатів визначення сили різання показало їх достатню збіжність і, відповідно, правомірність використання аналітичних виразів при розрахунку силових параметрів машин з просторово орієнтованими ножами відвального обладнання. Величини сили різання, що виконують роботу по руйнуванню і подоланню опору ґрунту різанню, що визначались теоретичним шляхом із врахуванням коефіцієнтів подібності, використаних при фізичному моделюванні для наведеного лабораторного стенда реєстрації сил різання просторово орієнтованими ножами відвального обладнання, порівняно з результатами сили різання, визначених експериментальним шляхом на даному стенді. Максимальне значення похибки визначення сили різання теоретичним та експериментальним шляхом на лабораторному стенді реєстрації сил різання просторово орієнтованими ножами відвального обладнання становить Δδ=10,06%.
This monograph presents a comprehensive analysis of the technological foundations of management decision-making in the reconstruction of complex gas pipeline systems. The study addresses the challenges posed by the aging infrastructure of gas supply networks and explores advanced strategies to improve their reliability, efficiency, and automation. Particular attention is given to the reconstruction of pipelines with various configurations linear, looped, and parallel systems under non-stationary gas flow conditions. The proposed models and methodologies offer solutions for optimizing operational parameters, improving emergency valve response, and ensuring uninterrupted gas supply through advanced management systems and data-driven decision support tools. Emphasis is placed on the integration of modern technologies, system theory, and feedback mechanisms in the design and operation of reconstructed pipeline systems. This work is intended for engineers, system designers, and researchers in the fields of gas supply, systems engineering, and energy infrastructure.
Shriyank Somvanshi, Anannya Ghosh Tusti, Mahmuda Sultana Mimi
et al.
The increasing presence of automated vehicles (AVs) presents new challenges for crash classification and safety analysis. Accurately identifying the SAE automation level involved in each crash is essential to understanding crash dynamics and system accountability. However, existing approaches often overlook automation-specific factors and lack model sophistication to capture distinctions between different SAE levels. To address this gap, this study evaluates the performance of three advanced tabular deep learning models MambaAttention, TabPFN, and TabTransformer for classifying SAE automation levels using structured crash data from Texas (2024), covering 4,649 cases categorized as Assisted Driving (SAE Level 1), Partial Automation (SAE Level 2), and Advanced Automation (SAE Levels 3-5 combined). Following class balancing using SMOTEENN, the models were trained and evaluated on a unified dataset of 7,300 records. MambaAttention demonstrated the highest overall performance (F1-scores: 88% for SAE 1, 97% for SAE 2, and 99% for SAE 3-5), while TabPFN excelled in zero-shot inference with high robustness for rare crash categories. In contrast, TabTransformer underperformed, particularly in detecting Partial Automation crashes (F1-score: 55%), suggesting challenges in modeling shared human-system control dynamics. These results highlight the capability of deep learning models tailored for tabular data to enhance the accuracy and efficiency of automation-level classification. Integrating such models into crash analysis frameworks can support policy development, AV safety evaluation, and regulatory decisions, especially in distinguishing high-risk conditions for mid- and high-level automation technologies.
This article examines how unequal access to AI innovation creates systemic challenges for developing countries. Differential access to AI innovation results from the acute competition between domestic and global actors. While developing nations contribute significantly to AI development through data annotation labor, they face limited access to advanced AI technologies and are increasingly caught between divergent regulatory approaches from democratic and authoritarian tendencies. This brief paper analyzes how more affordable AI engagement and Western countries' development cooperation present developing nations with a complex choice between accessibility and governance standards. I argue this challenge entails new institutional mechanisms for technology transfer and regulatory cooperation, while carefully balancing universal standards with local needs. In turn, good practices could help developing countries close the deepening gap of global technological divides, while ensuring responsible AI development in developing countries.
As the innovative potential of quantum technologies comes into focus, so too does the urgent need to address their ethical implications. While many voices highlight the importance of ethical engagement, less attention has been paid to the conditions that make such engagement possible. In this article, I argue that technological understanding is a foundational capacity for meaningful ethical reflection on emerging technology like quantum technologies. Drawing on De Jong & De Haro's account of technological understanding (2025a; 2025b), I clarify what such understanding entails and how it enables ethical enquiry. I contend that ethical assessment, first and foremost, requires an understanding of what quantum technologies can do - their functional capacities and, by extension, their potential applications. Current efforts to build engagement capacities among broader audiences - within and beyond academic contexts - tend, however, to focus on explaining the underlying quantum mechanics. Instead, I advocate a shift from a physics-first to a functions-first approach: fostering an understanding of quantum technologies' capabilities as the basis for ethical reflection. Presenting technological understanding as an epistemic requirement for meaningful ethical engagement may appear to raise the bar for participation. However, by decoupling functional understanding from technical expertise, this condition becomes attainable for a broader group, contributing not only to a well-informed but also to a more inclusive ethical debate.
With the rapid pace of technological innovation, traditional methods of policy formation and legislating are becoming conspicuously anachronistic. The need for regulatory choices to be made to counter the deadening effect of regulatory lag is more important to developing markets and fostering growth than achieving one off regulatory perfection. This article advances scholarship on innovation policy and the regulation of technological innovation in the European Union. It does so by considering what building an agile yet robust anticipatory governance regulatory culture involves. It systematically excavates a variety of tools and elements that are being put into use in inventive ways and argues that these need to be more cohesively and systemically integrated into the regulatory toolbox. Approaches covered include strategic foresight, the critical embrace of iterative policy development and regulatory learning in the face of uncertainty and the embrace of bottom up approaches to cocreation of policy such as Policy Labs and the testing and regulatory learning through pilot regulation and experimentation. The growing use of regulatory sandboxes as an EU policy tool to boost innovation and navigate regulatory complexity as seen in the EU AI Act is also probed
This analysis provides a comprehensive overview of current research regarding prefabricated construction costs, explained under three main categories: collaboration, intellectual basis, and research trends. The collaboration network covers country, institution, and journal distribution. Intellectual basis includes a cited journal, cited reference, and cited author, while research trends cover research category, keyword and keyword cluster analysis, and cited reference cluster. Through bibliometric analysis, we find that this field has garnered significant attention in the academic community and has developed rapidly. China dominates the field of prefabricated construction, with Curtin University, Chongqing University, and Deakin University being the leading research institutions, while Automation in Construction is the most cited journal. Although technology integration is widely regarded as a key means of cost optimization, its high implementation costs and complexity have limited its widespread application. The challenges of technology integration lie in the need to address high capital costs, complex management practices, and the demand for advanced technology integration, which have become significant barriers to the promotion of prefabricated construction. Moreover, current research also focuses on how to enhance risk control and management practices in cost management to promote sustainable development. Future research will focus on green and sustainable technologies, multidisciplinary engineering, energy and fuel, construction technologies to optimize prefabricated construction techniques, advance low-carbon building practices, and improve decision analysis and risk management. The key factors influencing costs include technological factor, policy factors, market and environmental factors, and organizational management. By systematically controlling these factors, cost pressures can be effectively alleviated, construction efficiency improved, and the sustainability of prefabricated buildings enhanced. This study not only provides a comprehensive analysis of the current state and trends in research on the costs of prefabricated construction but also highlights the critical role of technological innovation, policy optimization, and interdisciplinary collaboration in promoting the sustainable development of prefabricated construction globally.
This article provides an in-depth exploration of the multifaceted impact of technology on sales performance within B2B companies. It delves into how digital transformation and the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics have revolutionized traditional sales processes, enhancing efficiency, customer engagement, and ultimately, sales outcomes. The discussion spans several key areas, including the pivotal role of customer relationship management (CRM) systems in improving sales processes, the significance of digital marketing in reaching and engaging with potential customers, and the transformative effects of automation and chatbots in streamlining sales operations and providing superior customer service. The article also touches on the emerging trend of IoT-enabled selling and its potential to offer personalized and proactive sales experiences. Through a series of case studies, the article illustrates successful implementations of technology in B2B sales, showcasing the tangible benefits and improvements in sales performance. However, it also addresses the challenges and barriers to technology adoption, such as resistance to change and integration difficulties, while offering strategies to overcome these obstacles. The future trends section anticipates further advancements in tech-driven sales practices, highlighting the ongoing evolution of the B2B sales landscape driven by technological innovation.
PurposeIn the tourism sector, fully unmanned and partially unmanned hotel models serving customer segments from different income groups are increasing. Analyzing examples of unmanned hotels worldwide and their practices is crucial for understanding the automation systems used, the smart technologies employed, and the opportunities and challenges these hotels present, as well as for gaining insights into their impacts on the tourism sector.Design/methodology/approachThe data used in this research were obtained from secondary sources. One of the qualitative research methods, document analysis, was used for the analysis of these sources. The content analysis technique was used in the analysis of the data. A seven-stage systematic review process was used in the research. This seven-stage review process consists of the following stages: (1) determining the review objectives and formulating research questions; (2) identifying search terms and selection criteria; (3) conducting a search for unmanned hotel applications before clarifying exclusion and inclusion criteria; (4) evaluating the quality and relevance of unmanned hotel applications; (5) identifying content analysis review variables; (6) conducting content analysis; and (7) analyzing and reporting the findings.FindingsIn traditional hotel management, the innovations brought by digitalization and automation are transforming the guest experience and increasing operational efficiency. Unmanned smart hotels are equipped with various technological solutions, such as voice-controlled AI assistants, smart room control systems, AI-based concierge services, and robotic room service. These hotels are redefining roles and expectations within traditional hotel management, while simultaneously reducing costs and enhancing efficiency. Analyses indicate that unmanned smart hotels particularly appeal to specific customer segments, such as business travelers, and are becoming increasingly popular. These hotels offer advantages such as allowing guests to perform self-check-in, control their rooms, and receive necessary services via robots.Research limitations/implicationsThe universe of the research consists of all currently operating unmanned hotels worldwide. As a result of the research, 18 examples of unmanned smart hotels were identified. Hotels within the same chain with identical applications and processes were considered as a single example. Therefore, the research sample consists of 18 hotels.Originality/valueBy integrating these technological advancements, the hospitality and tourism industries can mitigate the impact of staff shortages, maintain high service standards, and improve operational efficiency. This approach allows businesses to adapt to changing workforce dynamics while continuing to deliver exceptional guest experiences. In conclusion, the significance and impact of unmanned smart hotels in the travel industry are growing. These hotels have the potential to shape the role of technology in the hospitality sector and influence future trends. Therefore, the adoption and development of unmanned smart hotels are important considerations for hotel operators and industry experts.
This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture
Shih-Chih Chen, Ria Sari Pamungkas, Daniel Schmidt
Machine learning, specifically through Convolutional Neural Networks (CNNs) and Reinforcement Learning (RL), significantly enhances robotic perception and decision-making capabilities. This research explores the integration of CNNs to improve object recognition accuracy and employs sensor fusion for interpreting complex environments by synthesizing multiple sensory inputs. Furthermore, RL is utilized to refine robots real-time decision-making processes, which reduces task completion times and increases decision accuracy. Despite the potential, these advanced methods require extensive datasets and considerable computational resources for effective real-time applications. The study aims to optimize these machine learning models for better efficiency and address the ethical considerations involved in autonomous systems. Results indicate that machine learning can substantially advance robotic functionality across various sectors, including autonomous vehicles and industrial automation, supporting sustainable industrial growth. This aligns with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation, and Infrastructure) and SDG 8 (Decent Work and Economic Growth), by promoting technological innovation and enhancing industrial safety. The conclusion suggests that future research should focus on improving the scalability and ethical application of these technologies in robotics, ensuring broad, sustainable impact.
The article conducts a theoretical and methodological analysis of digitalization as a crucial factor in the development of a digital society and democratic governance in Ukraine. Digitalization is characterized as a tool for modernizing and introducing a new model of public administration based on cooperation with the population, protection of public interests, advocacy for citizens' interests, and public-private partnerships with commercial business structures.
It is determined that the digitalization of public administration can be understood as a concept for developing public administration through the automation of decision-making processes and the provision of e-services; as a process of transitioning to digital public administration through the use of digital technologies; and as a tool for transforming the system of public administration at all levels of government-state, regional, and municipal – with the aim of strengthening communication between authorities and the public.
It is noted that digitalization has changed the approach to public administration, directing it towards the provision of e-services and the exchange of benefits through the adoption of modern digital technologies. It is substantiated that the digitalization of public administration is a complex technological process: the creation of entirely new developments based on the introduction of IT solutions, digital platforms, and innovations contributes to increasing the productivity of managerial labor, enhancing communication between government entities, business structures, and the population.
It is emphasized that the digitalization of public administration has made it possible to combine efforts in achieving the global goal of sustainable development of the state. Sustainable development is a voluntary initiative of individuals, the state, and the world as a whole, while increasing the openness of public administration allows all stakeholders to access necessary information, thereby reducing the possibility of falsification.
Political institutions and public administration (General)
Generative AI (GenAI) or large language models (LLMs) have been running the world since 2022, but despite all the trends surrounding the use of generative models, these cannot yet be used professionally. While they are most valued for ‘knowing everything’, nonetheless GenAI models cannot explain and prove. In this way we conceptualize the most recent problem of LLMs as the general trend of mistakes even in the core of knowledge and non-causality of mistake via the complexity of question, as the mistake can be named as an accident and be everywhere as the most limitation of professionalism. At their current stage of development, LLMs are not widely used in a professional context, nor have they replaced human workers. They do not event extend workers’ professional abilities.. These limitations of GenAI have one general: non-repayment. This article seeks to analyze GenAI’s professional viability by examining two models (GigaChatPro, GPT-4) in three fields of knowledge (economics, law, education) based on our unique Bloom’s taxonomy benchmark. To prove our assumption concerning the low possibility of its professional usage, we test three hypotheses: 1) the number of parameters of models have low elasticity regarding difficulty and taxonomy with even the right answer; 2) difficulty and taxonomy jointly have no effect on the correctness of an answer, 3) multiple choice is a factor that decreases the number of right answers of a model. We also present the results of GPT-4 and GigaChat MAX on our benchmark. Finally, we suggest what can be done about the limitations of GenAI’s architecture to reach at least a quasi-professional use.
Marvin Schieseck, Philip Topalis, Lasse Reinpold
et al.
The integration of Artificial Intelligence (AI) into automation systems has the potential to enhance efficiency and to address currently unsolved existing technical challenges. However, the industry-wide adoption of AI is hindered by the lack of standardized documentation for the complex compositions of automation systems, AI software, production hardware, and their interdependencies. This paper proposes a formal model using standards and ontologies to provide clear and structured documentation of AI applications in automation systems. The proposed information model for artificial intelligence in automation systems (AIAS) utilizes ontology design patterns to map and link various aspects of automation systems and AI software. Validated through a practical example, the model demonstrates its effectiveness in improving documentation practices and aiding the sustainable implementation of AI in industrial settings.
This research introduces an innovative artificial intelligence-driven educational concept designed to optimize self-directed learning through personalized course delivery and automated teaching assistance. The system leverages fine-tuned AI models to create an adaptive learning environment that encompasses customized roadmaps, automated presentation generation, and three-dimensional modeling for complex concept visualization. By integrating real-time virtual assistance for doubt resolution, the platform addresses the immediate educational needs of learners while promoting autonomous learning practices. This study explores the psychological advantages of self-directed learning and demonstrates how AI automation can enhance educational outcomes through personalized content delivery and interactive support mechanisms. The research contributes to the growing field of educational technology by presenting a comprehensive framework that combines automated content generation, visual learning aids, and intelligent tutoring to create an efficient, scalable solution for modern educational needs. Preliminary findings suggest that this approach not only accommodates diverse learning styles but also strengthens student engagement and knowledge retention through its emphasis on self-paced, independent learning methodologies.
Purpose: The purpose of this study is to investigate the potential of Large Language Models (LLMs) in transforming technical customer service (TCS) through the automation of cognitive tasks. Design/Methodology/Approach: Using a prototyping approach, the research assesses the feasibility of automating cognitive tasks in TCS with LLMs, employing real-world technical incident data from a Swiss telecommunications operator. Findings: Lower-level cognitive tasks such as translation, summarization, and content generation can be effectively automated with LLMs like GPT-4, while higher-level tasks such as reasoning require more advanced technological approaches such as Retrieval-Augmented Generation (RAG) or finetuning ; furthermore, the study underscores the significance of data ecosystems in enabling more complex cognitive tasks by fostering data sharing among various actors involved. Originality/Value: This study contributes to the emerging theory on LLM potential and technical feasibility in service management, providing concrete insights for operators of TCS units and highlighting the need for further research to address limitations and validate the applicability of LLMs across different domains.
The effectiveness of a test suite in detecting faults highly depends on the correctness and completeness of its test oracles. Large Language Models (LLMs) have already demonstrated remarkable proficiency in tackling diverse software testing tasks, such as automated test generation and program repair. This paper aims to enable discussions on the potential of using LLMs for test oracle automation, along with the challenges that may emerge during the generation of various types of oracles. Additionally, our aim is to initiate discussions on the primary threats that SE researchers must consider when employing LLMs for oracle automation, encompassing concerns regarding oracle deficiencies and data leakages.
Sebastian Musslick, Laura K. Bartlett, Suyog H. Chandramouli
et al.
Automation transformed various aspects of our human civilization, revolutionizing industries and streamlining processes. In the domain of scientific inquiry, automated approaches emerged as powerful tools, holding promise for accelerating discovery, enhancing reproducibility, and overcoming the traditional impediments to scientific progress. This article evaluates the scope of automation within scientific practice and assesses recent approaches. Furthermore, it discusses different perspectives to the following questions: Where do the greatest opportunities lie for automation in scientific practice?; What are the current bottlenecks of automating scientific practice?; and What are significant ethical and practical consequences of automating scientific practice? By discussing the motivations behind automated science, analyzing the hurdles encountered, and examining its implications, this article invites researchers, policymakers, and stakeholders to navigate the rapidly evolving frontier of automated scientific practice.