We argue theoretically and document empirically that aging leads to greater (industrial) automation, because it creates a shortage of middle-aged workers specializing in manual production tasks. We show that demographic change is associated with greater adoption of robots and other automation technologies across countries and with more robotics-related activities across U.S. commuting zones. We also document more automation innovation in countries undergoing faster aging. Our directed technological change model predicts that the response of automation technologies to aging should be more pronounced in industries that rely more on middle-aged workers and those that present greater opportunities for automation and that productivity should improve and the labor share should decline relatively in industries that are more amenable to automation. The evidence supports all four of these predictions.
V. Chamola, Mritunjay Shall Peelam, M. Guizani
et al.
The evolution from 1G to 6G networks has transformed global communication, progressing from basic voice calls in 1G to the immersive, AI-enabled experiences of 6G. As emerging AI-driven applications like autonomous systems, the Internet of Everything (IoE), and immersive technologies demand unprecedented capabilities, 7G networks are set to redefine connectivity by overcoming the limitations of earlier generations. This paper comprehensively reviews the innovations and challenges in 7G networks, focusing on integrating advanced AI and machine learning paradigms such as meta-learning, incremental learning, distributed intelligence, and reinforcement learning to enhance adaptability, resource allocation, and edge performance. The review also examines the role of Large Language Models (LLMs) in enabling real-time actionable intelligence and optimizing edge devices within 7G. The paper highlights the use of technologies, including blockchain for decentralized security, quantum computing for robust encryption, terahertz communication for ultra-fast data transfer, zero-energy solutions for sustainability, and generative AI for intelligent network optimization and automation. By addressing these challenges and exploring cutting-edge strategies, this paper envisions 7G networks as the foundation for a secure, intelligent, and sustainable digital future, equipped to combat emerging cyber warfare threats, enhance resilience against technological disruptions, and support innovations across smart cities, autonomous systems, healthcare, and industrial IoT.
This scoping review examines the evolving landscape of European Union (EU) legislation, as it pertains to the implementation of artificial intelligence (AI) in smart grid systems. By outlining the current regulatory landscape, including the General Data Protection Regulation (GDPR), the EU Artificial Intelligence Act, the EU Data Act, the EU Data Governance Act, the ePrivacy framework, the Network and Information Systems (NIS2) Directive, the EU Cyber Resilience Act, the EU Network Code on Cybersecurity for the electricity sector, and the EU Cybersecurity Act, it highlights both constraints and opportunities for stakeholders, including energy utilities, technology providers, and end-users. The analysis delves into regulatory barriers such as data protection requirements, algorithmic transparency mandates, and liability concerns that can limit the scope and scale of AI deployment. Technological challenges are also addressed, ranging from the integration of distributed energy resources and real-time data processing to cybersecurity and standardization issues. Despite these challenges, this review emphasizes how compliance with EU laws may ultimately boost consumer trust, promote ethical AI usage, and streamline the roll-out of robust, scalable smart grid solutions. The paper further explores stakeholder benefits, including enhanced grid stability, cost reductions through automation, and improved sustainability targets aligned with the EU’s broader energy and climate strategies. By synthesizing these findings, the review offers insights into policy gaps, technological enablers, and collaborative frameworks critical for accelerating AI-driven innovation in the energy sector, helping stakeholders navigate a complex regulatory environment while reaping its potential rewards.
The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances cognitive decision-making, challenges such as organizational resistance, interoperability, and ethical governance hinder scalable and sustainable implementation. The envisioned scenario involves seamless RPA-AI integration, fostering human–machine collaboration, operational resilience, and sustainability. Expected outcomes include (1) hyperautomation for efficiency gains, (2) agile, data-driven decision-making, (3) sustainable resource optimization, and (4) an upskilled workforce focusing on innovation. This study proposes a structured five-stage framework for RPA-AI deployment in Industry 5.0, combining automation, cognitive enhancement, and human–machine symbiosis. A systematic literature review (PICO method) identifies gaps and supports the framework’s design, validated through operational, human-impact, and sustainability metrics. Incorporating ethical governance and continuous upskilling, the model ensures technological advancement aligns with societal and environmental values. Results demonstrate its potential as a roadmap for responsible digital transformation, balancing efficiency with human-centricity. Future research should focus on empirical validation and sector-specific adaptations.
Kristina Cvetković, Aleksandar Lazarević, Sandra Stamenković Stojanović
et al.
This study aims to develop and evaluate pH-sensitive food packaging films, based on starch and enriched with aqueous wild blackberry extract (Rubus sp.). The extract was selected for its high anthocyanin content due to color changes in different pH environments. Extract analysis revealed a dry matter content of 23 mg/mL and a polyphenol concentration of 21.10 mg GAE/g (dry extract), with high antioxidant activity, measured to be an 86.57% DPPH radical neutralizer. Films were produced with wild blackberry extract at concentrations of 0%, 5%, 10%, and 15%. The analyses determined the barrier, mechanical, physical, and intelligent properties of biodegradable films. The introduction of the extract resulted in a substantial rise in water content (9.6–21.36%), swelling capacity (35.27–43.06%), dissolution rate in water (288.05–459.89%), and permeability to water vapor (1.99–3.69 × 10−10 g/(Pa × m × s)). The bioactive compounds in the extract enhanced the films’ antimicrobial and antioxidant properties, with the highest effectiveness observed in the film containing 15% extract. These starch-based films, enriched with aqueous wild blackberry extract, demonstrated strong potential for packaging foods prone to pH changes during fermentation, such as fruits, dairy products, meat, and fish.
This paper introduces the Environmental Justice in Technology (EJIT) Principles, a framework to help reorient technological development toward social and ecological justice and collective flourishing. In response to prevailing models of technological innovation that prioritize speed, scale, and profit while neglecting systemic injustice, the EJIT principles offer an alternative: a set of guiding values that foreground interdependence, repair, and community self-determination. Drawing inspiration from the 1991 principles of environmental justice, this framework extends their commitments into the technological domain, treating environmental justice not as a peripheral concern but as a necessary foundation for building equitable and regenerative futures. We situate the EJIT principles within the broader landscape of environmental justice, design justice, and post-growth computing, proposing them as a values infrastructure for resisting extractive defaults and envisioning technological systems that operate in reciprocity with people and the planet. In doing so, this article aims to support collective efforts to transform not only what technologies we build, but how, why, and for whom.
This paper presents an intelligent work automation approach in the context of contemporary digital transformation by integrating generative AI and Intelligent Document Processing (IDP) technologies with an Automation Agent to realize End-to-End (E2E) automation of corporate financial expense processing tasks. While traditional Robotic Process Automation (RPA) has proven effective for repetitive, rule-based simple task automation, it faces limitations in handling unstructured data, exception management, and complex decision-making. This study designs and implements a four-stage integrated process comprising automatic recognition of supporting documents such as receipts via OCR/IDP, item classification based on a policy-driven database, intelligent exception handling supported by generative AI (large language models, LLMs), and human-in-the-loop final decision-making with continuous system learning through an Automation Agent. Applied to a major Korean enterprise (Company S), the system demonstrated quantitative benefits including over 80% reduction in processing time for paper receipt expense tasks, decreased error rates, and improved compliance, as well as qualitative benefits such as enhanced accuracy and consistency, increased employee satisfaction, and data-driven decision support. Furthermore, the system embodies a virtuous cycle by learning from human judgments to progressively improve automatic exception handling capabilities. Empirically, this research confirms that the organic integration of generative AI, IDP, and Automation Agents effectively overcomes the limitations of conventional automation and enables E2E automation of complex corporate processes. The study also discusses potential extensions to other domains such as accounting, human resources, and procurement, and proposes future directions for AI-driven hyper-automation development.
Motivated by concerns that AI-driven entry-level automation may deprive new generations of valuable work experience, this paper studies how technological change affects the intergenerational transmission of tacit knowledge -- practical, hard-to-codify skills acquired through workplace interaction. I develop a task-based overlapping-generations model in which novices acquire tacit knowledge by working alongside experts. Knowledge-transfer contracts are incomplete because tacit knowledge is embodied and non-verifiable. In equilibrium, endogenous growth arises because only the most knowledgeable experts manage production and transmit their expertise to multiple novices, diffusing best practices. I show that improvements in entry-level automation increase output on impact but can reduce growth and welfare, even without reducing entry-level employment. This occurs when such improvements reallocate novices away from the most productive experts, weakening the diffusion of best practices. By contrast, technological improvements that increase the span of control of the most productive experts -- such as those that create new labor-intensive tasks -- strengthen knowledge transmission and raise growth.
Classical estimators for ARIMA parameters (MLE, CSS, OLS) assume Gaussian innovations, an assumption frequently violated in financial and economic data exhibiting asymmetric distributions with heavy tails. We develop and validate the second-order polynomial maximization method (PMM2) for estimating ARIMA$(p,d,q)$ models with non-Gaussian innovations. PMM2 is a semiparametric technique that exploits higher-order moments and cumulants without requiring full distributional specification. Monte Carlo experiments (128,000 simulations) across sample sizes $N \in \{100, 200, 500, 1000\}$ and four innovation distributions demonstrate that PMM2 substantially outperforms classical methods for asymmetric innovations. For ARIMA(1,1,0) with $N=500$, relative efficiency reaches 1.58--1.90 for Gamma, lognormal, and $χ^2(3)$ innovations (37--47\% variance reduction). Under Gaussian innovations PMM2 matches OLS efficiency, avoiding the precision loss typical of robust estimators. The method delivers major gains for moderate asymmetry ($|γ_3| \geq 0.5$) and $N \geq 200$, with computational costs comparable to MLE. PMM2 provides an effective alternative for time series with asymmetric innovations typical of financial markets, macroeconomic indicators, and industrial measurements. Future extensions include seasonal SARIMA models, GARCH integration, and automatic order selection.
Louison Duboz, Ioan Cristinel Raileanu, Jette Krause
et al.
The deployment of Automated Vehicles (AVs) is expected to address road transport externalities (e.g., safety, traffic, environmental impact, etc.). For this reason, a legal framework for their large-scale market introduction and deployment is currently being developed in the European Union. Despite the first steps towards road transport automation, the timeline for full automation and its potential economic benefits remains uncertain. The aim of this paper is twofold. First, it presents a methodological framework to determine deployment pathways of the five different levels of automation in EU27+UK to 2050 under three scenarios (i.e., slow, medium baseline and fast) focusing on passenger vehicles. Second, it proposes an assessment of the economic impact of AVs through the calculation of the value-added. The method to define assumptions and uptake trajectories involves a comprehensive literature review, expert interviews, and a model to forecast the new registrations of different levels of automation. In this way, the interviews provided insights that complemented the literature and informed the design of assumptions and deployment trajectories. The added-value assessment shows additional economic activity due to the introduction of automated technologies in all uptake scenarios.
Artificial intelligence (AI) enabled technologies are now corporate organisations' top priorities due to the availability of large data and the advent of the Internet of Things during the past ten years. AI is becoming a crucial component of business model innovation, process transformation, disruption, and gaining a competitive edge for companies adopting digital and data-centric cultures. This study investigates the implications of smart technology, artificial intelligence, robotics, and algorithms (STARA) on the future of work, with a particular emphasis on employee well-being and workplace dynamics. As futurists project that by 2025, 52% of all work functions will be automated, replacing one-third of current jobs, the rapid advancement of STARA creates both opportunities and risks. While automation has the potential to produce 133 million new jobs, it also threatens to eliminate 75 million employments, raising employee anxieties about job security and future roles. Despite the rising volume of studies on smart automation, there is still a major vacuum in our understanding of its implications on employees' mental health, well-being, and the entire workplace. This study investigates STARA's dual influence: while technology reduces physical strain and automates tedious jobs, it also raises new difficulties such as job displacement concerns and shifts in worker dynamics. The study emphasizes the need of human resource professionals to develop methods that strike a balance between technological integration and employee assistance. Key areas of focus include providing reskilling opportunities, adopting mental health initiatives, and encouraging open conversation regarding AI's expanding role in the workforce. By addressing these concerns, organisations may build a more resilient workforce that is better suited to the fourth industrial revolution. The study intends to contribute to a better understanding of how organisations may safeguard and improve employee well-being in the face of fast technological change, ensuring that STARA integration encourages innovation while simultaneously supporting a healthy and engaged workforce
Fish-cutting products are widely loved by consumers due to the unique nutrient composition and flavor in different cuts. However, fish-cutting faces the issue of labor shortage due to the harsh working environment, huge workload, and seasonal work. Hence, some automatic, efficient, and large-scale cutting technologies are needed to overcome these challenges. Accompanied by the development of Industry 4.0, the Internet of Things (IoT), artificial intelligence, big data, and blockchain technologies are progressively applied in the cutting process, which plays pivotal roles in digital production monitoring and product safety enhancement. This review focuses on the main fish-cutting schemes and delves into advanced automatic cutting techniques, showing the latest technological advancements and how they are revolutionizing fish cutting. Additionally, the production monitoring architecture based on IoT in the fish-cutting process is discussed. Fish cutting involves a variety of schemes tailored to the specific characteristics of each fish cut. The cutting process includes deheading and tail removal, filleting, boning, skinning, trimming, and bone inspection. By incorporating sensors, machine vision, deep learning, and advanced cutting tools, these technologies are transforming fish cutting from a manual to an automated process. This transformation has significant practical implications for the industry, offering improved efficiency, consistent product quality, and enhanced safety, ultimately providing a modernized manufacturing approach to fish-cutting automation within the context of Industry 4.0.
With the deep application of automation and digitalization technologies, the global automobile value chains are undergoing a new round of large-scale restructuring, and the traditional supply chain structure taken vehicle manufacturers as the core has been broken. The dual-credit policy (DCP), taking over the subsidies for new energy vehicles (NEVs), plays a vital role in reconstructing, transforming, and upgrading the automobile industry. The target group of DCP is passenger vehicle manufacturers, but it is unclear how its implementation will affect the NEV industry chain. To address this issue, this study examined the impact of the DCP on the innovation performance of automobile manufacturing enterprises using a DID (difference-in-difference) model based on the data of 693 listed advanced manufacturing enterprises in China A-shares from 2014 to 2021. The empirical results show that the DCP has significantly promoted the innovation performance of automobile manufacturing enterprises. In terms of supply chain role heterogeneity, the impact of the DCP on the innovation performance of parts manufacturers is more significant. Regarding enterprise ownership heterogeneity, the DCP has a greater impact on the innovation performance of SOEs(state-owned enterprises). In addition, regarding regional heterogeneity, enterprises in eastern and middle regions are significantly affected by the DCP to improve innovation performance.
This research investigates the impact of various fertilization methods on the bioactive compound content and antioxidant activity of celery (Apium graveolens L.) root and leaf. Mineral fertilizer, poultry manure, cattle manure, sheep manure, supercompost, and molasses were applied. Total dry weight, phenolic and flavonoid compounds, and antioxidant activity were assessed, along with fiber, protein, fat, sugar, and starch in celery root. Principal component analysis (PCA) and cluster analysis were used to correlate production conditions with the parameters. The highest fiber and protein contents were found in mineral-fertilized roots, while total fat and sugar were highest in cattle-manure-fertilized roots, and starch was highest in supercompost-fertilized roots. Fertilization with supercompost yielded the highest total phenolic and flavonoid contents in leaves, while mineral fertilizer resulted in the highest antioxidant activity in roots. Notably, the highest dry weight in leaves and the highest total phenolic and flavonoid contents in roots were also observed with supercompost. PCA and cluster analysis demonstrated significant correlations between plant parts, i.e., the celery root and leaf samples, cultivation conditions, and the observed parameters, emphasizing the importance of selecting suitable cultivation methods to optimize celery’s nutritional properties. Also, these findings suggest that supercompost, a byproduct of breweries, could potentially replace animal-based organic fertilizers, addressing the problem of reduced availability due to declining livestock numbers.
The advent of technological advancements has brought about significant transformations in various domains, including education, particularly in electrical engineering and its automation. This paper explores how schools and teachers are adapting to these changes through technology-driven curriculum reforms in electrical engineering and automation education. The shift from traditional lecture-based methods to student-centered learning models is no longer an option but a necessity given the rapid pace of innovation and industry demands. The integration of virtual reality, artificial intelligence, and online education platforms in electrical engineering and automation courses provides immersive experiences and flexible learning options, allowing students to explore complex concepts more interactively and engage deeply with subject matter. Furthermore, technology-driven curriculum reforms emphasize close collaboration with industry enterprises to ensure graduates possess the practical skills and innovative thinking abilities required by today's job market. This paper highlights the importance of harnessing modern information technology in electrical engineering and automation education and the need for a symbiotic relationship between schools, teachers, and industries to develop talents capable of contributing meaningfully to society. The use of technology-driven courses will not only promote the development of high-quality educational programs but also improve students' comprehensive understanding and practical abilities, preparing them for successful careers in this dynamic field.
This paper explores the transformative journey of the Duolingo language learning application from automation to Artificial Intelligence (AI) integration. Through secondary data analysis, it traces the historical trajectory of Computer Assisted Language Learning (CALL) in conjunction with the pedagogical and technological advancements. By examining Duolingo as a case study, the paper investigates the motivations behind its shift to AI and the impact of evolving educational methods on this transition. Key findings highlight that Duolingo’s adoption of AI, particularly through Automatic Speech Recognition (ASR), has significantly enhanced language learning by offering personalized, interactive experiences, and immediate feedback. The study delves into the five key stages of automated language assessment, and details Duolingo’s migration from Phyton to Scala, addressing the challenges and strategic decisions that improved scalability, performance, and stability. Despite obstacles, like documentation gaps and compatibility issues, Duolingo’s technological advancements have been well-received by users, indicating high usability and user satisfaction. Finally, Duolingo’s evolution mirrors broader trends in technology-driven language education, emphasizing the importance of innovation in meeting the evolving needs of learners in the digital age.
Purpose: The general objective of the study was to examine the impact of technological advancements on human existence. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to impact of technological advancements on human existence. Preliminary empirical review revealed that technology exerted a dual influence on society, both enhancing and detracting from human well-being. Empirical findings revealed that while innovations like smartphones and social media platforms offered unprecedented connectivity, they also posed risks to mental health and social relationships. Similarly, workplace automation and AI integration improved productivity but raised concerns about job displacement. Furthermore, the study emphasized the importance of considering socio-cultural, economic, and environmental factors in understanding technology's impact. Overall, the findings underscored the need for a balanced approach to technological innovation that prioritizes human well-being, equity, and sustainability, suggesting initiatives such as promoting digital literacy and fostering inclusive technological development. Unique Contribution to Theory, Practice and Policy: The Technological Determinism, Social Construction of Technology (SCOT) and Actor-Network Theory (ANT) may be used to anchor future studies on technological advancements on human existence. The study provided recommendations that contributed to theory, practice, and policy. It suggested further exploration of interdisciplinary theoretical frameworks to understand technology's complex dynamics, advocated for proactive strategies in organizations to mitigate negative impacts, and called for regulatory frameworks balancing innovation with societal values. These recommendations aimed to foster digital literacy, ethical innovation, and equitable access to technology, guiding stakeholders in navigating the complexities of technology's influence on society. Keywords: Technological Advancements, Human Existence, Interdisciplinary, Digital Literacy, Ethical Innovation, Regulation, Equity, Innovation, Societal Values, Technology's Influence