Hasil untuk "Technological innovations. Automation"

Lixiang Yan, Lele Sha, Linxuan Zhao et al.

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. What is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks. Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks. Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring. What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation. A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks. Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts. Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks. The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved. Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education.

Damilola Emmanuel Ogedengbe, James Olakunle Oladapo, Oluwafunmi Adijat Elufioye et al.

Strategic Human Resource Management (HRM) plays a critical role in the success and sustainability of organizations, particularly in dynamic and competitive industries such as logistics and shipping. This review explores the challenges and opportunities inherent in implementing Strategic HRM practices within the logistics and shipping sector. In recent years, the logistics and shipping industry has witnessed significant transformations due to globalization, technological advancements, and evolving customer demands. Consequently, HRM strategies must adapt to these changes to effectively manage human capital and drive organizational performance. One of the primary challenges faced by HR professionals in the logistics and shipping sector is the recruitment and retention of skilled talent. The industry's high demand for specialized skills, coupled with a shortage of qualified professionals, intensifies competition among companies. Moreover, the transient nature of employment in this sector, characterized by seasonal fluctuations and contract-based arrangements, complicates talent management efforts. Strategic HRM approaches must address these challenges by implementing robust recruitment strategies, offering competitive compensation packages, and providing opportunities for continuous learning and career development. Additionally, the logistics and shipping industry operates in a highly regulated environment with stringent safety and compliance standards. HRM practices must ensure that employees are adequately trained and compliant with industry regulations to mitigate risks and maintain operational efficiency. This necessitates the implementation of rigorous training programs, safety protocols, and performance management systems to uphold organizational standards and ensure employee well-being. Despite these challenges, Strategic HRM in the logistics and shipping sector presents significant opportunities for innovation and growth. Advancements in technology, such as automation and data analytics, enable HR professionals to streamline processes, enhance workforce productivity, and make data-driven decisions. Moreover, strategic partnerships with educational institutions and vocational training centers can facilitate the development of a skilled workforce tailored to the industry's evolving needs. Strategic HRM in the logistics and shipping sector is vital for addressing challenges, optimizing workforce performance, and capitalizing on opportunities for sustainable growth. By adopting a strategic approach to human resource management, organizations can navigate the complexities of the industry landscape and achieve competitive advantage in the global market.

Thijs Van Gerrewey, N. Boon, D. Geelen

Vertical farming is on its way to becoming an addition to conventional agricultural practices, improving sustainable food production for the growing world population under increasing climate stress. While the early development of vertical farming systems mainly focused on technological advancement through design innovation, the automation of hydroponic cultivation, and advanced LED lighting systems, more recent studies focus on the resilience and circularity of vertical farming. These sustainability objectives are addressed by investigating water quality and microbial life in a hydroponic cultivation context. Plant growth-promoting rhizobacteria (PGPR) have been shown to improve plant performance and resilience to biotic and abiotic stresses. The application of PGPRs to plant-growing media increases microbial functional diversity, creating opportunities to improve the circularity and resilience of vertical farming systems by reducing our dependency on chemical fertilizers and crop protection products. Here, we give a brief historical overview of vertical farming, review its opportunities and challenges in an economic, environmental, social, and political context, and discuss advances in exploiting the rhizosphere microbiome in hydroponic cultivation systems.

Neeraj Kumar Bhoi, Murlidhar Patel, S. K. Kamarapu et al.

The management of electronic waste (e-waste) has become gradually critical due to the quick growth in electronic device usage and the lack of satisfactory recycling setup. In 2022, global e-waste generation touched 62 billion kilograms, raising serious ecological and public health concerns. Toxic elements such as lead, mercury, and cadmium released from improperly handled e-waste can cause neurological damage, respiratory illnesses, and environmental degradation. This study highlights the pressing need for sustainable e-waste management practices and explores recent mechatronic innovations aimed at improving recycling efficiency and minimizing human exposure to hazardous substances. It examines the health and ecological impacts of current recycling methods and emphasizes the development of safer, more environmentally responsible alternatives. The paper provides a comprehensive overview of state-of-the-art approaches to e-waste processing based on mechatronic principles, including automated sorting, material recovery, and pollution control technologies. It further reviews the application of robotic manipulators, advanced sensors, and modern automation tools for the efficient sorting and segregation of e-waste. By analysing diverse technological approaches, this work proposes a sustainable framework for recycling applications that enhances operational efficiency and reduce the detrimental effects on environment.

Jose F Quesada

While language technologies have advanced significantly, current approaches fail to address the complex sociocultural dimensions of linguistic preservation. AI Thinking proposes a meaning-centered framework that would transform technological development from creating tools FOR communities to co-creating solutions WITH them. This approach recognizes that meaningful solutions emerge through the interplay of cultural understanding, community agency, and technological innovation. The proposal articulates a holistic methodology and a five-layer technological ecosystem where communities maintain control over their linguistic and cultural knowledge representation. This systematic integration of community needs, cultural preservation, and advanced capabilities could revolutionize how we approach linguistic diversity preservation in the digital age.

R. M. Aratchige, W. M. K. S. Ilmini

This survey investigates foundational technologies essential for developing effective Large Language Model (LLM)-based multi-agent systems. Aiming to answer how best to optimize these systems for collaborative, dynamic environments, we focus on four critical areas: Architecture, Memory, Planning, and Technologies/Frameworks. By analyzing recent advancements and their limitations - such as scalability, real-time response challenges, and agent coordination constraints, we provide a detailed view of the technological landscape. Frameworks like the Mixture of Agents architecture and the ReAct planning model exemplify current innovations, showcasing improvements in role assignment and decision-making. This review synthesizes key strengths and persistent challenges, offering practical recommendations to enhance system scalability, agent collaboration, and adaptability. Our findings provide a roadmap for future research, supporting the creation of robust, efficient multi-agent systems that advance both individual agent performance and collective system resilience.

Yang Li, Frank Neffke

Achieving net-zero emissions requires rapid innovation, yet the necessary technological knowhow is scattered across industries and countries. Comparing functionally similar green and nongreen patents, we identify "Green Building Blocks" (GBBs): modular components that can be added to reduce existing technologies' carbon footprints. These GBBs depict the anatomy of the green transition as a network that connects problems -- nongreen technologies -- to GBBs that mitigate their climate-change impact. Node degrees in this network are highly unequal, showing that the scope for climate-change mitigating innovation varies substantially across domains. The network also helps predict which green technologies firms develop themselves, and which alliances they form to do so. This reveals a critical dependence on international collaboration: optimal innovation partners for 84% of US, 87% of German, and 92% of Chinese firms are foreign, providing quantitative evidence that rising economic nationalism threatens the pace of innovation required to meet global climate goals.

Amir Mirzadeh Phirouzabadi

This research unravels the stationary or transitionary dilemma of hybrid technologies in transitions processes. A system dynamics technology interaction framework is built and simulated based on Technological Innovation System and Lotka-Volterra to investigate the inter-technology relationship impacts and modes that hybrid technologies establish with incumbent and emerging technologies. This is conducted for the case of conventional, hybrid and battery electric vehicles under various scenarios . Results reveal that, by acting as an exploration-hybrid solution, hybrid technologies maintain a transitionary role by supporting mainly the technological development side of emerging technology. On the contrary, by acting as an exploitation-hybrid solution, they hardly (or never) sustain an inhibitive role against both the technological and market development sides of incumbent technology. While hybrid technologies may play a stationary role on the market development side in transitions processes, simulation results show that maintaining all inter-technology relationship modes as business-as-usual (i.e., baseline scenario) but instead simultaneously strengthening the various socio-technical dimensions of emerging technology and destabilising the various socio-technical dimensions of incumbent technology (i.e., sociotechnical scenario) is a more promising pathway in both short term (e.g., an accelerated uptake of emerging technology and decline of incumbent technology) and long term (e.g., highest emission reduction). Findings, additionally, reinforce the existence of both spillover and try-harder versions of 'sailing-ship effect', which are either seriously doubted in the literature or partially validated using raw bibliometric and patents data.

Abdelghani Maddi, Ahmad Yaman Abdin, Francesco Fdp de Pretis

Scientific knowledge is a key driver of technological innovation, shaping industrial development and policy decisions worldwide. Understanding how patents incorporate scientific research is essential for assessing the role of academic discoveries in technological progress. Non-Patent References (NPRs) provide a useful indicator of this relationship by revealing the extent to which patents draw upon scientific literature. Here, we show that reliance on scientific research in patents varies significantly across regions. Oceania and Europe display stronger engagement with scientific knowledge, while the Americas exhibit lower reliance. Moreover, NPRs are more likely to be open access than the average scientific publication, a trend that intensifies when Sci-Hub availability is considered. These results highlight the transformative impact of Open Science on global innovation dynamics. By facilitating broader access to research, Open Science strengthens the link between academia and industry, underscoring the need for policies that promote equitable and science-based innovation, particularly in developing regions.

chao zhang, chong han, guanghui zhou et al.

Background Intelligent manufacturing is perceived as a manufacturing mode with powerful learning and cognitive capabilities empowered by information technologies such as the internet of things, edge computing, and cloud computing. The mode can be used to address the problems of low intelligence and poor timeliness of traditional process planning. Methods The framework includes the multi-objective process planning method based on real-time data, and the process closed-loop optimization mechanism of “cloud-based theoretical process planning plus edge MEC side online simulation verification and real-time feedback adjustment”, which realizes online process planning and iterative optimization in mass customization. Results The feasibility of the online analysis method for thin-walled part milling deformation is verified by taking the finishing process of aerospace thin-walled parts as an example. The experimental results show that the simulation time on the single analysis step is reduced from 6s to 1s, and the accuracy rate is 86.9%. Conclusions A new intelligent process planning theoretical framework integrating with online process planning and autonomous collaborative control, namely, digital twin and multi-access edge computing process planning (DT-MEC-PP) is proposed in this paper.

Niraj Chaudhary, Luis G. Villa-Diaz, Luis G. Villa-Diaz

The advancement of human pluripotent stem cell (hPSC) culture systems has revolutionized the landscape of preclinical drug discovery and toxicological evaluation. Progressing innovations from feeder-dependent and xenogeneic matrices to chemically defined, xeno-free, and fully synthetic platforms have addressed long-standing challenges in reproducibility, safety, and clinical translation. Developments in recombinant extracellular matrix proteins, synthetic peptide substrates, and polymer-based coatings have enabled the generation of Good Manufacturing Practice (GMP)-compliant, scalable hPSC cultures while minimizing biological variability and immunogenic risks. Integration of automation, artificial intelligence (AI), and three-dimensional (3D) bioprocessing technologies aims at further enhancement of standardization, quality control, and throughput. In the context of pharmaceutical research, hPSC-derived cellular models now underpin high-throughput drug screening and mechanistic toxicological assays, offering superior human relevance compared to traditional animal models. Despite these advances, barriers such as cellular immaturity, inter-batch variability, and limited regulatory acceptance persist, underscoring the need for further protocol standardization and technological refinement. This review provides a comprehensive analysis of current animal-free hPSC culture platforms, critically examines their strengths and limitations, and discusses future directions for advancing their application in drug discovery and predictive toxicology. The ongoing evolution of hPSC technologies promises to accelerate the development of safer, more effective therapeutic agents and to reshape the future of human disease modeling and pharmacological research.

Tiago Hennemann Hilario da Silva, Simone Sehnem

Sustainable business models tend to have greater potential for the transition to the circular economy. The present study aims to investigate how Brazilian startups in the food segment foodtech are integrating Industry 4.0 technologies to boost the circular economy, emphasizing the importance of stakeholder engagement in the process. The choice of this unit of analysis is associated with companies that were born recently, with a high chance of having sustainable and circular business models. The research involved eight food technology startups, using questionnaires, interviews, and content analysis to collect data on circular supply chain practices, Industry 4.0 technologies adopted, and the level of stakeholder engagement. The results indicate that foodtech startups are progressively incorporating technologies such as automation, big data, biotechnology, and the Internet of Things, to promote circular economy initiatives. The active participation of stakeholders has been fundamental, contributing to innovation and efficiency in processes. However, the complete transition to the circular economy and the full adoption of Industry 4.0 technologies are challenged by obstacles, including high costs and regulatory barriers. The study found that startups play a crucial role in this transition, due to their agility and capacity for innovation, which can serve as an example for larger companies. Collaboration between businesses, regulators, academic institutions, and society is essential to overcome these challenges and facilitate effective transformation. This work offers valuable practical insights for foodtech startups looking to adopt circular practices, highlighting technologies, and practices that can result in economic and environmental benefits. Startups emerge not only as adopters of new technologies but also as agents of change in industrial practices, shaping a more sustainable future for the food sector. It is recommended that future research explore the impact of regulatory policies and incentives on accelerating the adoption of these technologies by startups, in addition to evaluating the long‐term economic viability of these technological innovations in promoting a more circular economy.

R. Rahmani, Cristiano Jesus, S. Lopes

In the era of rapid technological advancement and evolving industrial landscapes, embracing the concept of the factory of the future (FoF) is crucial for companies seeking to optimize efficiency, enhance productivity, and stay sustainable. This case study explores the concept of the FoF and its role in driving the energy transition and digital transformation within the automotive sector. By embracing advancements in technology and innovation, these factories aim to establish a smart, sustainable, inclusive, and resilient growth framework. The shift towards hybrid and electric vehicles necessitates significant adjustments in vehicle components and production processes. To achieve this, the adoption of lighter materials becomes imperative, and new technologies such as additive manufacturing (AM) and artificial intelligence (AI) are being adopted, facilitating enhanced efficiency and innovation within the factory environment. An important aspect of this paradigm involves the development and utilization of a modular, affordable, safe human–robot interaction and highly performant intelligent robot. The introduction of this intelligent robot aims to foster a higher degree of automation and efficiency through collaborative human–robot environments on the factory floor and production lines, specifically tailored to the automotive sector. By combining the strengths of human and robotic capabilities, the future factory aims to revolutionize manufacturing processes, ultimately driving the automotive industry towards a more sustainable and technologically advanced future. This study explores the implementation of automation and the initial strides toward transitioning from Industry 4.0 to 5.0, focusing on three recognized, large, and automotive companies operating in the north of Portugal.

Evaldo Costa

The convergence of Industry 5.0 (I5.0) and Sustainable Development Goal 9 (SDG 9) signifies a transformative shift in global industries, propelled by a new triple bottom line approach– human-centric, sustainable, and resilient. Departing from traditional models, I5.0, an evolution from Industry 4.0, strategically aligns with SDG 9 to reshape industrial landscapes and promote global sustainable, resilient, and inclusive development. I5.0’s emphasis on resource optimization and collaboration between humans and machines marks a departure from technologically driven manufacturing (I4.0), ushering in a sustainable production model. Cutting-edge technologies, including Artificial Intelligence (AI), Machine Learning (ML), and automation, optimize resource utilization, enhancing operational efficiency to support sustainability goals. Yet, challenges like initial implementation costs and a lack of global sustainability standards pose obstacles. The human-centric integration within I5.0 prioritizes human needs throughout the manufacturing process. Collaborations with Cobots and AI-ML technologies optimize workflows, contribute to customization, and align with SDG 9’s vision, necessitating robust training programs and strategic considerations for workforce adaptation and financial investments. Exploring I5.0 resilience within SDG 9 unveils its pivotal role during crises, such as the COVID-19 pandemic. Discussions navigate challenges related to supply chain disruptions, economic impacts, and geopolitical factors, emphasizing the need for strategic resilience, sustainability, and human-centric approaches. I5.0 resilience, guided by Cobots, aligns with SDG 9’s focus on resilient infrastructure. Sustainable Business Model Innovation (SBMI) emerges as a central point of contention in the I5.0 and SDG 9 interplay. Advocates tout its transformative potential for sustainability goals, while skeptics question scalability and adaptability, reflecting the complexity of factors in achieving sustainable and resilient industrial development. Therefore, the strategic imperative of I5.0 and SDG 9 unfolds as a transformative force for positive change, embedded in SBMI. This collaborative journey transcends the confines of a production system, ushering in a future where technology management, supported by SBMI, proactively reinforces resilience, societal well-being, and environmental stewardship. The future of I5.0 raises questions about innovative ecosystems, collaboration practices, geopolitical impacts, circular production models, and extending I5.0 beyond current geographical limits.

Chidiogo Uzoamaka Akpuokwe, Adekunle Oyeyemi Adeniyi, Seun Solomon Bakare et al.

The paper presents an insightful overview of the intricate legal challenges posed by the proliferation of Artificial Intelligence (AI) and Robotics. This comprehensive review explores the multifaceted dimensions of the evolving legal landscape, addressing issues at the intersection of technology and law. Key focal points include the accountability and liability frameworks for autonomous AI systems, ethical considerations in the deployment of intelligent machines, and the complex dynamics of data privacy in the age of pervasive automation. The review delves into the intricate legal nuances surrounding intellectual property rights, particularly as AI systems contribute to creative outputs and innovation. It navigates the blurred lines between human and machine authorship, raising fundamental questions about ownership and protection in this digital era. Moreover, the paper emphasizes the global nature of these challenges, highlighting the imperative for international cooperation to formulate harmonized legal standards. As AI and robotics revolutionize industries and societal frameworks, the analysis underscores the critical need for adaptive and anticipatory legal frameworks. It explores how existing legal paradigms are grappling with the unprecedented speed of technological advancements and the ethical dilemmas arising from the delegation of decision-making to intelligent algorithms. The paper sets the stage for a thorough examination of the legal intricacies surrounding AI and robotics. It advocates for a proactive and collaborative approach, involving legal experts, technologists, ethicists, and policymakers in crafting robust frameworks that balance innovation with ethical, privacy, and accountability considerations. This review serves as a foundational resource for understanding and addressing the legal challenges inherent in the transformative era of Artificial Intelligence and Robotics. Keywords: Artificial intelligence, Robotics, Legal, AI challenges, Ethics, Review.

E. Novozhilova, Kate K. Mays, James E. Katz

The present study explores people’s attitudes towards an assortment of occupations on high and low-likelihood of automation probability. An omnibus survey ( N  = 1150) was conducted to measure attitudes about various emerging technologies, as well as demographic and individual traits. The results showed that respondents were not very comfortable with AI’s management across domains. To some degree, levels of comfort corresponded with the likelihood of automation probability, though some domains diverged from this pattern. Demographic traits explained the most variance in comfort with AI revealing that men and those with higher perceived technology competence were more comfortable with AI management in every domain. With the exception of personal assistance, those with lower internal locus of control were more comfortable with AI managing in almost every domain. Age, education, and employment showed little influence on comfort levels. The present study demonstrates a more holistic approach of assessing attitudes toward AI management at work. By incorporating demographic and self-efficacy variables, our research revealed that AI systems are perceived differently compared to other recent technological innovations.

A. Sharabati, Heba Ziad Awawdeh, Samer Sabra et al.

The research aims to investigate the potential impact of Artificial Intelligence (AI) on the digital supply chain in light of extant literature on the Decision-Oriented Information (DOI) theory and the Technology-Oriented Enterprise (TOE) framework. The research further attempts to unpack the strategic implications of AI integration in supply chain management, and its association with operational excellence and business model innovation. The study is exploratory and employs a mixed-methods approach. We develop propositions that examine the decision-making processes within AI-enhanced supply chains based on an analysis of concepts central to the DOI theory. We also employ the TOE framework to develop further propositions regarding the technological infrastructure required for AI implementation. Empirical case studies encompassing AI applications in different industries (e.g. manufacturing, healthcare, and pharmaceuticals) are presented to gain a broad perspective of the impact of AI on the digital supply chain. AI technologies inherently make supply chains more agile, transparent, and responsive. Machine Learning algorithms allow for more accurate forecasting and demand management under conditions of supply chain risk and volatility. Robotics and automation, allow for greater flexibility and efficiency in executing operations and logistics. Additionally, the successful implementation of AI is heavily contingent on the organization’s current level of technological infrastructure and its alignment with its current and future business objectives. Furthermore, the DOI theory and TOE framework may serve as a blueprint for how one could evaluate AI implementation beyond the scope of supply chain management.

Amanda Michaud

Alexei Makhovikov, Yulia Filyasova

Introduction. The research of the industrial information technologies (IT) is relevant for an active implementation of innovations into solid mineral extraction. The low human adaptation level to the hostile Arctic environmental conditions explains the high relevance of the IT in terms of breadth of their functional application. The aim of this research was to study the international and domestic experience of the IT deployment and the attempt to identify their role. Research methods and materials. Results of geophysical, physical, and environmental research as well as production mining and operations made by Russian and foreign scientists served as the material for analysis. The research was fulfilled with the use of the following methods: the analysis of scientific and technical findings, the study of core business activities, the classification and comparison of digital data. Research results. The main trend is the intention of a complete automation of the mining process, which has led to the emergence of multiple achievements in the technological process automation. The second trend is the elimination of workers’ participation on extraction sites. The third trend is the environmental maintenance in the mining area. Conclusions. It was found that the development of algorithmized systems for complete automation of the solid mineral extraction process is a highly relevant task. Nowadays, the IT are predominantly used at the stage of solids mineral exploration, the prediction of mineral deposit size, and the economic evaluation of mining operations. In the process of solid mineral extraction IT are used to a lower extent, due to high risks of cyber security and robotized integration of all technological objects and processes. At the completion stage of mineral excavation, IT have a significant value for landscape modeling and environmental parameter measuring.

Mohammad Ali Tareq, Piyush Tripathi, Nurhayati Md Issa et al.

Although blockchain technology was first introduced in 2008 and materialised in 2009, the early usage of blockchain were mainly limited to financial technologies, particularly cryptocurrencies. Later, blockchain became a widespread emerging technology, utilised in multifaceted sectors and applications. In fact, various new and innovative application of blockchain and distributed ledger technologies are still continuously being researched and explored. On the other hand, smart-contracts were first introduced in 1990s, however, it did not gain enough popularity until being integrated with blockchain technologies lately. The duo lately been seen as the key to many innovations in various industries and sectors. So, we took data from 1445 blockchain-related patent documents and tried to map out the historical and current trends in patenting activities in the blockchain field. This helps us get a better grasp of how blockchain technologies are evolving and being tracked. In addition to serving as an indicator of science and technology growth, patents are also used to judge the research potential and development of a particular technology.