Abstract The emergence of blockchain technology has created a number of potential innovations in handling business activities across various industries. However, few studies discuss the potential influence of blockchain technology from a business process re-engineering perspective. This study focuses on the feasibility and inceptive application of supply chain processes. We proposed a blockchain-based framework along with the use of an affiliated technology, i.e., smart contracts, to derive the feasible benefits of the supply chain process design. Through the illustrative design of an integrated process, we provide an achievable use case of the disintermediation of business processes via a conceptual, shared information ledger. This ledger not only facilitates the sharing of tracking information but also promotes a network for multilateral collaboration among supply chain members. The pursuit of transparency and accountability across supply chain processes can potentially influence decentralization and automation. A comparative analysis of the current and proposed frameworks is conducted to support the core reasoning of this study. Additionally, future implications on managerial practice and academic research are explored to provide pervasive suggestions for similar attempts in different sectors. We conclude with an evaluation of the potential influence of blockchain technology on supply chain management.
Fatemeh Haghayegh, Alireza Norouzi Azad, Elnaz Haghani
et al.
Early‐stage disease detection, particularly in Point‐Of‐Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision‐medicine. Public benefits of early detection extend beyond cost‐effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground‐breaking innovations enabling automation of operations, conducting advanced large‐scale data analysis, generating predictive models, and facilitating remote and guided clinical decision‐making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
Modern agricultural technologies are crucial for addressing global food security and environmental sustainability challenges amidst a growing population and climate change. These innovations, including precision agriculture, biotechnology, smart irrigation, automation, vertical farming, and artificial intelligence (AI), significantly enhance productivity and land use efficiency. Precision agriculture, utilizing GPS, drones, and IoT, improves yields by 20–30% and cuts input waste by 40–60%. Biotechnology, with CRISPR and GMOs, delivers drought and pest-resistant crops, stabilizing yields, as seen with Bt cotton reducing pesticide use by 50% in India. Smart irrigation boosts water efficiency by 40–60%, while automation and robotics mitigate labor shortages and reduce costs by 25%. Vertical farming increases yields 10–20 times with 95% less land and water, supporting urban food security. AI analytics enhance decision-making with over 90% accuracy in forecasting and resource allocation. Despite these benefits, high costs, technological illiteracy, and regulatory issues hinder adoption, especially among smallholders. Policy support, public-private partnerships, and training are vital for broader technology access and fair benefits. Integrating renewable energy and circular economy principles into aggrotech presents a path to sustainability. This review highlights the transformative potential of modern technologies for sustainable intensification, increasing productivity without expanding farmland, while lessening environmental impacts. It underscores the need for coordinated efforts to overcome adoption challenges and harness these innovations for global food security and climate resilience.
The Fourth Industrial Revolution and artificial intelligence (AI) technology are driving the transformation of the meat industry from mechanization and automation to intelligence and digitization. This paper provides a systematic review of key technological innovations in this field, including physical technologies (such as smart cutting precision improved to the millimeter level, pulse electric field sterilization efficiency exceeding 90%, ultrasonic-assisted marinating time reduced by 12 h, and ultra-high-pressure processing extending shelf life) and digital technologies (IoT real-time monitoring, blockchain-enhanced traceability transparency, and AI-optimized production decision-making). Additionally, it explores the potential of alternative meat production technologies (cell-cultured meat and 3D bioprinting) to disrupt traditional models. In application scenarios such as central kitchen efficiency improvements (e.g., food companies leveraging the “S2B2C” model to apply AI agents, supply chain management, and intelligent control systems, resulting in a 26.98% increase in overall profits), end-to-end temperature control in cold chain logistics (e.g., using multi-array sensors for real-time monitoring of meat spoilage), intelligent freshness recognition of products (based on deep learning or sensors), and personalized customization (e.g., 3D-printed customized nutritional meat products), these technologies have significantly improved production efficiency, product quality, and safety. However, large-scale application still faces key challenges, including high costs (such as the high investment in cell-cultured meat bioreactors), lack of standardization (such as the absence of unified standards for non-thermal technology parameters), and consumer acceptance (surveys indicate that approximately 41% of consumers are concerned about contracting illnesses from consuming cultured meat, and only 25% are willing to try it). These challenges constrain the economic viability and market promotion of the aforementioned technologies. Future efforts should focus on collaborative innovation to establish a truly intelligent and sustainable meat production system.
Santosh Kumar, Anurag Shrivastava, R. Praveen
et al.
The union of LLMs and AR is revolutionizing human-AI engagement, providing immersive, smarter, and more personalized interactions. On October 2023, you train on data. As AI systems become increasingly context-aware and multimodal, the integration of the digital and physical experience is one of the great challenges to address. LLMs allow natural language understanding and AR offers spatial and visual augmentation—combine those and you have a natural way to improve user engagement and enhance decision-making across industries, including education, healthcare, and remote collaboration. Nonetheless, challenges remain, including real-time processing, ethical implications, and user adaptation. Thus, we present this paper to address emerging trends, potential applications, and the technological innovations to be developed to maximize the synergy between LLMs and AR..highlight the convergence of state-of-the-art developments in natural language processing, spatial computing, and real-time data fusion towards accessibility, automation, and interactive learning. It is also vital to dive deeper and leverage future research to enhance optimization potential around hardware, latency, and security risks to guarantee a seamless and ethical AI-augmented experience. As LLMs and AR continue to evolve, they will change how we interact with the digital world and usher in a new age of more natural and human-centered AI applications.
This paper investigates frequency regulation of an airport microgrid (AIM) through the application of an integral absolute error (IAE)-assisted control approach. The islanded AIM is initially captured using a linearized transfer function model to accurately reflect its dynamic characteristics. This model is then simplified using a first-order plus dead time (FOPDT) approximation derived via a reaction-curve-based method, which balances between model simplicity and accuracy. Two different proportional–integral–derivative (PID) controllers are designed to meet distinct objectives: one focuses on set-point tracking (SPT) to maintain the target frequency levels, while the other addresses load disturbance rejection (LDR) to reduce the effects of load fluctuations. A thorough comparison of these controllers demonstrates that the SPT-mode PID controller outperforms the LDR-mode controller by providing an improved transient response and notably lower error measures. The results underscore the effectiveness of combining IAE-based control with reaction curve modeling to tune PID controllers for islanded AIM systems, contributing to enhanced and reliable frequency regulation for microgrid operations.
Engineering machinery, tools, and implements, Technological innovations. Automation
The weights and structure determination (WASD) neuronet (or neural network) is a single-hidden-layer feedforward neuronet that exhibits an excellent approximation ability, despite its simple structure. Thanks to its strong generalization, fast speed, and ease of implementation, the WASD neuronet has been the subject of many modifications, including metaheuristics, and applications in a wide range of scientific fields. As it has garnered significant attention in the last decade, the aim of this study is to provide an extensive overview of the WASD framework. Furthermore, the WASD has been effectively used in numerous real-time learning tasks like regression, multiclass classification, and binary classification due to its exceptional performance. In addition, we present WASD’s applications in social science, business, engineering, economics, and medicine. We aim to report these developments and provide some avenues for further research.
Engineering machinery, tools, and implements, Technological innovations. Automation
Janneke Pieters, Ana Kujundzic, Rulof Burger
et al.
Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.
Advanced Planning and Scheduling (APS) systems have become indispensable for modern manufacturing operations, enabling optimized resource allocation and production efficiency in increasingly complex and dynamic environments. While algorithms for solving abstracted scheduling problems have been extensively investigated, the critical prerequisite of specifying manufacturing requirements into formal constraints remains manual and labor-intensive. Although recent advances of generative models, particularly Large Language Models (LLMs), show promise in automating constraint specification from heterogeneous raw manufacturing data, their direct application faces challenges due to natural language ambiguity, non-deterministic outputs, and limited domain-specific knowledge. This paper presents a constraint-centric architecture that regulates LLMs to perform reliable automated constraint specification for production scheduling. The architecture defines a hierarchical structural space organized across three levels, implemented through domain-specific representation to ensure precision and reliability while maintaining flexibility. Furthermore, an automated production scenario adaptation algorithm is designed and deployed to efficiently customize the architecture for specific manufacturing configurations. Experimental results demonstrate that the proposed approach successfully balances the generative capabilities of LLMs with the reliability requirements of manufacturing systems, significantly outperforming pure LLM-based approaches in constraint specification tasks.
Serverless computing is increasingly adopted for its ability to manage complex, event-driven workloads without the need for infrastructure provisioning. However, traditional resource allocation in serverless platforms couples CPU and memory, which may not be optimal for all functions. Existing decoupling approaches, while offering some flexibility, are not designed to handle the vast configuration space and complexity of serverless workflows. In this paper, we propose AARC, an innovative, automated framework that decouples CPU and memory resources to provide more flexible and efficient provisioning for serverless workloads. AARC is composed of two key components: Graph-Centric Scheduler, which identifies critical paths in workflows, and Priority Configurator, which applies priority scheduling techniques to optimize resource allocation. Our experimental evaluation demonstrates that AARC achieves substantial improvements over state-of-the-art methods, with total search time reductions of 85.8% and 89.6%, and cost savings of 49.6% and 61.7%, respectively, while maintaining SLO compliance.
Anna-Maria Theodora Andreescu, Daniel Eugeniu Crunteanu, Maximilian Vlad Teodorescu
et al.
This paper outlines the development phases of a wave-driven Helicon Plasma Thruster for cutting-edge Low Earth Orbit (LEO) constellations. The two-stage ambipolar electric propulsion (EP) system combines the efficient ionization of an ultra-compact helicon reactor with plasma acceleration based on an ambipolar electric field provided by a magnetic nozzle. This paper reveals maturation challenges associated with an emerging EP system in the hundreds-watt class, followed by outlook strategies. A 3 cm diameter helicon reactor was operated using argon gas under a time-modulated RF power envelope ranging from 250 W to 500 W with a fixed magnetic field strength of 400 G. Magnetically enhanced inductively coupled plasma reactor characteristics based on half-wavelength right helical and Nagoya Type III antennas under capacitive (E-mode), inductive (W-mode), and wave coupling (W-mode) were systematically investigated based on Optical Emission Spectroscopy. The operation characteristics of a wave-heated reactor based on helicon configuration were investigated as a function of different operating parameters. This work demonstrates the ability of two-stage HPT using a compact helicon reactor and a cusped magnetic field to outperform today’s LEO spacecraft propulsion.
Engineering machinery, tools, and implements, Technological innovations. Automation
Lying in the space of human curiosity, this issue of CIJ experiments with the boundaries of scientific exploration to foster technological development. To cultivate experimental innovation, it is imperative to translate research into tangible action, explore multifaceted problems, offer support for implementation, and effectuate meaningful changes.
Holographic Optical Tweezers (HOT) are powerful tools that can manipulate micro and nano-scale objects with high accuracy and precision. They are most commonly used for biological applications, such as cellular studies, and more recently, micro-structure assemblies. Automation has been of significant interest in the HOT field, since human-run experiments are time-consuming and require skilled operator(s). Automated HOTs, however, commonly use point traps, which focus high intensity laser light at specific spots in fluid media to attract and move micro-objects. In this paper, we develop a novel automated system of tweezing multiple micro-objects more efficiently using multiplexed optical traps. Multiplexed traps enable the simultaneous trapping of multiple beads in various alternate multiplexing formations, such as annular rings and line patterns. Our automated system is realized by augmenting the capabilities of a commercially available HOT with real-time bead detection and tracking, and wavefront-based path planning. We demonstrate the usefulness of the system by assembling two different composite micro-structures, comprising 5 $μm$ polystyrene beads, using both annular and line shaped traps in obstacle-rich environments.
David A. Robb, Donald Risbridger, Ben Mills
et al.
The alignment of optical systems is a critical step in their manufacture. Alignment normally requires considerable knowledge and expertise of skilled operators. The automation of such processes has several potential advantages, but requires additional resource and upfront costs. Through a case study of a simple two mirror system we identify and examine three different automation approaches. They are: artificial neural networks; practice-led, which mimics manual alignment practices; and design-led, modelling from first principles. We find that these approaches make use of three different types of knowledge 1) basic system knowledge (of controls, measurements and goals); 2) behavioural skills and expertise, and 3) fundamental system design knowledge. We demonstrate that the different automation approaches vary significantly in human resources, and measurement sampling budgets. This will have implications for practitioners and management considering the automation of such tasks.