Monica Dutta, Deepali Gupta, Sumegh Tharewal
et al.
The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper aims to explore the opportunities and challenges of IoT-based soilless farming. It also highlights its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, these methods also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By highlighting the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations.
Mutaz Ryalat, N. Almtireen, Ghaith Al-refai
et al.
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers.
Lean Six Sigma 4.0 (LSS 4.0) represents a transformative evolution of Lean Six Sigma, integrating Industry 4.0 technologies to drive smart manufacturing excellence. By leveraging Artificial Intelligence (AI), the Internet of Things (IoT), Digital Twins, and Big Data Analytics, LSS 4.0 enables realtime decision-making, predictive intelligence, and autonomous process optimization, enhancing efficiency, agility, and resilience in modern industrial environments. This paper introduces a conceptual framework for LSS 4.0, redefining the DMAIC (Define-Measure-Analyze-Improve-Control) methodology through IoT-driven process monitoring, AI-powered predictive analytics, and digital twin simulations. This transformation shifts manufacturing from reactive control to predictive and autonomous optimization, reducing variability, defects, and waste while maximizing productivity, resource efficiency, and sustainability. By leveraging data-driven decision-making, intelligent automation, and predictive maintenance, the framework enhances process reliability, prevents defects, and improves operational performance. Despite its advantages, LSS 4.0 presents challenges, including technological complexity, workforce upskilling, and organizational resistance. This study underscores the critical role of leadership-driven digital transformation, AI-augmented decision-making, and targeted skill development in fostering an innovation-driven manufacturing culture. Additionally, blockchain for secure supply chain traceability, augmented reality (AR) for enhanced humanmachine collaboration, and edge computing for decentralized intelligence are explored as key enablers of LSS 4.0’s full potential. Leadership commitment, cross-functional collaboration, and AI-driven Lean workflows are identified as essential success factors. Aligning digital transformation strategies with Lean principles and fostering a culture of continuous innovation is crucial for realizing LSS 4.0’s full benefits. Finally, this study highlights future research directions, emphasizing Industry 5.0 advancements such as human-centric automation, collaborative robotics, and sustainable smart manufacturing—key drivers in building adaptive, intelligent, and resilient industrial ecosystems.
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.
Sabur Butt, Sandra Dennis Núñez Daruich, Joanna Alvarado-Uribe
et al.
Aspect-Based Sentiment Analysis (ABSA) has emerged as a powerful tool for deriving actionable insights from qualitative feedback in education. This study presents a multitask learning framework to analyze student evaluations of teaching (SET) by extracting and classifying opinions on specific aspects of teaching performance. Leveraging a novel and first open-sourced dataset of 6,025 Spanish-language comments, the proposed framework integrates opinion segmentation and multi-label classification to capture nuanced feedback on nine predefined aspects, such as "Teaching Quality" and "Classroom Atmosphere." Applications of this approach extend beyond SET analysis, offering valuable insights for course improvement, faculty assessment, and institutional decision-making in higher education. The paper compares the performance of fine-tuned transformers (BERT and RoBERTa) with large language models (LLMs), including GPT-4o, GPT4o-mini, and LLama-3.1-8B, using both fine-tuned and Few-shot Chain of Thought (CoT) methodologies. Evaluation results reveal that fine-tuned GPT-4o outperformed all other models, achieving a weighted F1-score of 0.69 for positive aspects and 0.79 for negative aspects, while Few-shot CoT approaches demonstrated competitive performance with greater scalability and interpretability. Our findings demonstrate the framework's potential to transform unstructured feedback into structured insights, aiding educators and institutions in enhancing teaching quality and student engagement.
This article explores the concept of technological singularity and the factors that could accelerate or hinder its arrival. The butterfly effect is used as a framework to understand how seemingly small changes in complex systems can have significant and unpredictable outcomes. In section II, we discuss the various factors that could hasten the arrival of technological singularity, such as advances in artificial intelligence and machine learning, breakthroughs in quantum computing, progress in brain-computer interfaces and human augmentation, and development of nanotechnology and 3D printing. In section III, we examine the factors that could delay or impede the arrival of technological singularity, including technical limitations and setbacks in AI and machine learning, ethical and societal concerns around AI and its impact on jobs and privacy, lack of sufficient investment in research and development, and regulatory barriers and political instability. Section IV explores the interplay of these factors and how they can impact the butterfly effect. Finally, in the conclusion, we summarize the key points discussed and emphasize the importance of considering the butterfly effect in predicting the future of technology. We call for continued research and investment in technology to shape its future and mitigate potential risks.
This paper develops a theory-driven automation exposure index based on Moravec's Paradox. Scoring 19,000 O*NET tasks on performance variance, tacit knowledge, data abundance, and algorithmic gaps reveals that management, STEM, and sciences occupations show the highest exposure. In contrast, maintenance, agriculture, and construction show the lowest. The positive relationship between wages and exposure challenges the notion of skill-biased technological change if AI substitutes for workers. At the same time, tacit knowledge exhibits a positive relationship with wages consistent with seniority-biased technological change. This index identifies fundamental automatability rather than current capabilities, while also validating the AI annotation method pioneered by Eloundou et al. (2024) with a correlation of 0.72. The non-positive relationship with pre-LLM indices suggests a paradigm shift in automation patterns.
This short paper provides a means to classify augmentation technologies to reconceptualize them as sociotechnical, discursive and rhetorical phenomena, rather than only through technological classifications. It identifies a set of value systems that constitute augmentation technologies within discourses, namely, the intent to enhance, automate, and build efficiency. This short paper makes a contribution to digital literacy surrounding augmentation technology emergence, as well as the more specific area of AI literacy, which can help identify unintended consequences implied at the design stages of these technologies.
L. Hrytsenko, O. Pakhnenko, Aleksandra Kuzior
et al.
The article is aimed at the current issues of using smart technologies and innovative approach during evolution and transformation processes in banking. The study identifies the special place of this topic for achieving a high level of efficiency and competitiveness of banks and characterizes the impact of the introduction of technological approaches on the customer base and its perception of banking products. The main functions of banking innovations in this area are analyzed and the justification of their feasibility at the present stage of economic development is provided. A number of the most promising technologies and approaches to banking activities are allocated, namely: contactless payment, digital wallets, biometric identification, person-to-person payments, collective financing, omnichannel banking, interaction with FinTech companies, blockchain, big data, artificial intelligence, smart machines, Internet of Things, behavioral banking, retail bank, application programming interfaces, multi-component bank, open banking, augmented reality, robotic automation, hybrid clouds. The relevance of the identified areas is proved based on their perception by analyzing the popularity of the identified topics in Google search queries using the Google Trends tool. The perception of smart technologies in banking by Internet users in the world and specifically in Ukraine is investigated, which gave grounds to conclude that there is a significant interest in them, and therefore the expediency of further study and implementation in the activities of banks. It is identified that the most perspective technologies are biometric identification, blockchain, Internet of Things, big data analysis, artificial intelligence, etc. Several technologies have been identified, namely, collective financing (crowdfunding), application programming interfaces (APIs) and digital wallets, which are less popular in Ukraine than in the world in general, and therefore require detailed research and study of the relevance of their application in the domestic banking market. Possible directions for further innovative development of banking institutions based on the use of smart technologies are proposed. Based on panel data for 60 banks of Ukraine for the period 2014-2022, the author analyzes the correlations between the indicators of the use of digital technologies and the financial performance of banks and builds regression dependencies of financial indicators of banks on the indicator of the number of electronic means of payment in active circulation. The theoretical value of the study is to identify the most promising smart technologies and innovative approaches to banking business in modern conditions. The practical value lies in studying the level of perception of high-tech innovations in the field of banking services by the active public and identifying further directions for the development of this process. We consider it advisable to direct further research in the context of a detailed study of the possibilities of applying the identified technologies in specific banking products or business processes.
The intricate nexus between artificial intelligence (AI) and employment encapsulates a dynamic interplay influenced by technological, social, and business dynamics. AI’s emergence not only fosters novel job prospects but also reshapes conventional work structures across diverse sectors. It empowers computers to emulate human-level tasks like problem-solving and pattern recognition through machine learning, natural language processing, and robotics. While AI enhances productivity and efficiency across industries, concerns arise regarding job displacement, skill mismatches, and economic disparities. The evolving landscape of AI-driven automation underscores the imperative for workforce adaptation and structural adjustments within labour markets. Despite challenges, AI catalyzes job creation and economic growth, particularly in sectors leveraging software engineering, data analysis, and machine learning expertise. Moreover, AI fuels innovation across industries, fostering new employment avenues and business models. Realizing the symbiotic relationship between AI and employment necessitates proactive measures by policymakers, businesses, educators, and workers to foster resilience, innovation, and social equity, thereby shaping a more sustainable future of work in the era of AI.
Digital Twin (DT) technology is a cutting-edge innovation in Industry 4.0 that combines virtual and physical worlds to create real-time representations of scenarios. Simulation, monitoring, and maximizing efficiency are crucial in industrial automation and have been acquiring significant momentum. Fluid Power Application (FPA) is essential in automation, providing accurate control and significant force through hydraulic and pneumatic systems, fluently merging into current configurations. Although there have been significant improvements in DT technology and its prospective applications in fluid power (FP) systems, there is still a lack of understanding regarding its practical deployment in many sectors. Thus, this study examines the practical applications of DT in FP systems, using the knowledge presented in the previous review papers as a foundation. Although recognizing the importance of emerging DT technology and technological enablers, this study focuses on present FPAs where the adoption of DT is still in its early stages. Further, explores the FP systems usage, and outlines the deployment of DT, including advantages over conventional methods, challenges, and potential implications. Furthermore, the article explicitly outlines the technical challenges. This review study highlights literature gaps and drives DT in FPA's research and developments.
In the United States, long-term changes in the nature of the economy – including advances in technological innovation and automation, declines in the extraction of certain energy resources, increases in globalization, and a shift to the "knowledge-based" economy – have coincided with disproportionately negative employment outcomes in many rural, or "nonmetro," communities, especially for prime working-age men and those with less than a high school degree.
The research systematically examines recent literature on digital transformation in internal audit and its effect on operational efficiency, considering that the adoption of technologies such as big data, artificial intelligence and robotic automation redefines traditional control methods. Under an exploratory qualitative approach, a review of publications indexed between 2015 and 2024 in Scopus, Web of Science and ScienceDirect was conducted, prioritizing empirical studies and systematic reviews. The results show that digitization increases the speed and accuracy of data analysis, reduces operating costs and strengthens the timely detection of risks, contributing to a more proactive and strategic audit. However, significant barriers remain, such as cultural resistance to change, lack of technological skills among auditors and integration problems with legacy systems, which limit the impact of these tools. The discussion underlines that the success of digital transformation does not depend only on technological investment, but also on a comprehensive management that combines leadership, innovation culture and continuous training. It is concluded that digitalization represents a strategic opportunity to strengthen the audit function, provided that organizational barriers are addressed in a coordinated manner.
: Modern banking, accompanied by rapid technological development, is evolving towards the comprehensive implementation of Artificial Intelligence. Throughout this article, the ongoing integration of AI into most banking procedures will be discussed, concentrating on the implementation of AI to enhance the optimal customer experience, affordable data privacy, effective bank performance, and adherence to all existing rules and restrictions. Contrary to widespread belief, AI is no longer limited to the automation of routine activities: it has the potential to conduct cybersecurity, risk evaluation, and real-time transaction oversight. Not only do testing methodologies evolve through automated test creation and automation of intelligent data integrity, but even security measures and regulation are bolstered, minimizing human negligence and overheads. Developments in the work of banks imply a further utilization of AI in the creation of smart systems that enhance risk assessment, customer relationships, and court proceedings, according to the survey. This research examines current banking trends and provides recommendations for utilizing AI to improve cost savings and protection. It argues that shortly, AI will be a key focus of banking industry innovation and advancement.
M. Cuomo, Cinzia Genovino, Federico de Andreis
et al.
PurposeThe aim of this research is to elucidate the correlation between open innovation, digital strategies and networking in enhancing agricultural enterprises within the new perspective of Agrifood 5.0. As such, it contributes to making businesses more competitive, especially in the Italian agricultural sector, where small and medium-sized enterprises are highly fragmented. Numerous studies have asserted that the competitiveness of actors operating within a specific territory is closely linked to local identity and image enhancement. Agricultural organizations are undergoing a profound transformation, with technological assets emerging as catalysts for new synergies. Advanced technologies such as robotics, the Internet of Things (IoT) and automation (AI) are emerging as differentiating elements capable of further advancing the agricultural sector, transitioning it from Agrifood 4.0 to Agrifood 5.0. The empirical analysis of the research shows a positive correlation between a collaborative attitude and a propensity for innovation. Indeed, the data demonstrated that digital strategies and open innovation positively influence competitiveness in agricultural SMEs.Design/methodology/approachThe methodology employed in this study is mixed, incorporating both qualitative and quantitative approaches. The quantitative aspect involves analysis of the dataset from the Italian Statistical Institute (ISTAT) through logistic regression, while the qualitative component entails analysis of semi-structured interviews conducted with a sample of 174 agricultural cooperatives in southern Italian regions (Campania). This approach allows for a comprehensive understanding of the research topic, capturing both numerical trends and nuanced insights from interviews.FindingsAfter analyzing the data from the 7th General Census of Agriculture conducted by ISTAT, a clear understanding of the sector has emerged, revealing several potential research avenues. It is evident that innovation in the agricultural sector is often driven by the largest and best-capitalized production entities, primarily located in Italy. Conversely, smaller agricultural entities can benefit from networking as new technological assets act as catalysts for new synergies, innovation and competitiveness.Practical implicationsEnhancing the relational contribution within the network and humanizing a fragmented sector are crucial elements for promoting open innovation. Network structuring facilitates the transmission of managerial knowledge, contributing to an overall increase in the intellectual and relational capital of the agricultural sector. These factors, combined with open innovation, enhance the competitiveness of individual firms and elevate the brand of the entire sector, creating a conducive environment for transitioning toward Agrifood 5.0. This transition is characterized by increased interconnection, continuous innovation and overall prosperity. Specific studies on this topic are lacking in Italy, particularly in the southern regions. Therefore, this contribution focuses on investigating the Campania region.Originality/valueThe novelty of this study lies in its investigation of the relationship between agricultural enterprises and innovation in the context of enterprises networking strategies (i.e. associationism and/or cooperation), promoting competitiveness. The limitations of this study are related to the dimension of the sample selected and its relationship with other productive sectors.
Arvind Chauhan, Shivansu Sachan, Aishwarya Arya
et al.
The age of AI is characterized by significant changes in the realms of work and technology. Rapid technological advances and automation are reshaping industries, highlighting the need for individuals to learn new skills. In the age of AI, upskilling has become an essential strategy to navigate the ever-changing career landscape. Upskilling involves continuous learning and gaining expertise in areas that complement or leverage AI technology. Upskilling has become a necessity, referring to the process of improving one’s skills and knowledge to remain relevant and effective in an environment increasingly dominated by artificial intelligence (AI) and automation. This post discusses the importance of upskilling in the context of the AI era, highlighting its benefits, challenges, and potential options. It enables workers to adapt to AI-driven changes, allowing them to remain relevant and competitive. This change promotes career longevity and nurtures a workforce capable of harnessing the potential of AI. In this study, the authors have attempted to articulate the goal and importance of AI in upskilling employees who can collaborate effectively with AI systems, leading to increased productivity and improved innovation. In addition, recommendations have been made for HR managers on upskilling employees through AI intervention.
The digital substation is an emerging concept adopted by utilities globally. This concept employs two predominant standards - IEC 61850-9-2 and IEC 61869–9, defining a path for technological innovation in power systems, leveraging digital communication to enhance automation, monitoring, and control capabilities. The paper provides a comparative review of the IEC 61850-9-2 and IEC 61869–9 standards, critical in the design and implementation of process bus technology. The IEC 61850-9-2 standard defines the specifics for Ethernet-based communication of sampled values, emphasizing interoperability and real-time data exchange in substation automation systems. In turn, the IEC 61869–9 standard establishes the protocols for the digital interface of instrument transformers, facilitating the accurate and efficient transmission of measurement data. The paper highlights the functionalities and applications of both standards and the resultant implications on substation design and operation. Paper discusses benefits and limitations inherent to each standard family, offering substation engineers guiding principles to align standard selection with application specific requirements and operational efficacy.