The implementation of self-service automation technology is acknowledged as an essential strategy to enhance service quality and profitability in the fast-food industry. This study, informed by Cognitive Appraisal Theory, examines the factors influencing the continuing intention to use smart self-service automated kiosks among Thai young adults. Based on Partial Least Squares-Structural Equation Modeling, the study found substantial empirical evidence linking perceived responsiveness, customization, convenience, and innovativeness with attitudes toward smart self-service technologies, which then affect continuous usage intentions. Interestingly, perceived functionality, interface design, and payment security did not significantly influence attitudes toward smart self-service technologies. The current study enhances both theoretical and practical comprehension of the elements influencing prolonged technology utilization in the digital service domain. Theoretically, the findings highlight the essential importance of smart self-service technology (SST) features in maintaining user engagement with smart kiosks. This study offers significant implications for fast-food companies, highlighting the necessity to improve customer sentiment through dependable and intuitive kiosk interfaces. Subsequent study ought to investigate these links within diverse demographic contexts and employ longitudinal methodologies to track the progression of user behaviors.

Purpose: This systematic literature review investigated the impact of workflow automation — with emphasis on Robotic Process Automation (RPA) and financial technology tools — on the operational efficiency and cost structure of small and medium-sized enterprises (SMEs). The research focused on the financial sub-processes of accounts payable, accounts receivable, and bank reconciliation, analyzing how the digital transformation of these workflows affects organizational liquidity and profitability. Methodology: The PRISMA 2020 protocol was adopted for conducting and reporting the review. Searches were carried out in the Web of Science, Scopus, EBSCO Business Source Complete, and CAPES Periódicos databases, covering the period from 2019 to 2024. After applying inclusion and exclusion criteria, 52 primary studies were selected for qualitative and quantitative analysis. Findings: The literature evidences an average reduction of operational costs between 30% and 70% in automated financial processes, with productivity gains of up to 85% in repetitive tasks. The automation of accounts payable and receivable demonstrated a shortened financial cycle, improved working capital, and reduced manual errors. Barriers such as implementation costs, cultural resistance, and skill gaps were identified as predominant limiting factors for SMEs. Contributions: The study proposes an empirically validated financial digital maturity framework, structured in four stages — Diagnosis, Gradual Implementation, Optimization, and Strategic Expansion — specifically designed for SMEs seeking to initiate or deepen financial automation processes. The results expand the body of knowledge on digital transformation in smaller organizations and provide practical insights for managers, consultants, and policymakers.

To autonomously control a robot's movement in complex and unpredictable environments, adaptive models must be developed that can continuously learn about and refine their future physical dynamics. This paper introduces a Self-Organising Physics Discovered Neural Architecture (SOPDNA) that unites Physics-Informed Neural Networks (PINNs) with Robotics Process Automation (RPA) and Digital Twin DT technologies to create an entirely automated, robust robotic motion control system. In contrast to traditional robotic motion control systems that rely on predefined mathematical models or offline training methods, SOPDNA automatically discovers previously unknown physical relationships between robotic motion components and continuously updates the controller policy during operation. The Digital Twin software acts as a nearly identical simulation environment to enable extensive self-experimentation and safety verification of SOPDNA while the RPA software automates all aspects of the SOPDNA learning process, including acquiring experimental data, detecting anomalies, retraining the learning model, and deploying optimised controllers, without requiring any human oversight. The core of SOPDNA consists of a PINN that incorporates a comprehensive mathematical representation of the robot's physical constraints and uses symbolic regression techniques to estimate unmodelled dynamic variables, such as variable friction coefficients and environmental disturbances. Experimental validation using a robotic manipulator performing trajectory tracking and obstacle avoidance under changing payloads shows the proposed approach's capabilities. The SOPDNA framework provides an overall trajectory-tracking accuracy of 98.4%, a 22.7% reduction in energy usage, and a 30.9% improvement in disturbance rejection when compared to traditional deep reinforcement learning (DRL) and Proportional-Integral-Derivative (PID)-based control systems. Results from the fault-injection experiments show SOPDNA's ability to recover from degradation of actuators 41.3% faster than conventional methods. Overall, these findings support the potential for self-organising physics-discovered neural network architectures to enhance the development of robust, adaptive, intelligent robotic motion control systems capable of autonomously executing complex tasks.

The legal sector is undergoing a rapid transformation driven by automation and digital technologies. Some traditional legal tasks, such as contract drafting, document sorting, legal research, case management, and legal data analysis, can now be performed using smart systems and specialized software. The impact of automation is no longer limited to expediting procedures; it has extended to reshaping the role of the lawyer, the nature of the relationship between the lawyer and the client, and the limits of professional responsibility when using technological tools. This research discusses the impact of automation on lawyers in the digital environment. It examines the concept of legal automation, its practical applications, advantages, and professional and ethical challenges, focusing on the Saudi legal environment, particularly regarding client data confidentiality, personal data protection, and restricting the provision of legal services to licensed professionals. The research concludes that automation does not eliminate the role of the lawyer, but rather redefines it. The modern lawyer is no longer merely an executor of traditional procedures; they are now required to understand technology, manage its risks, and utilize it in a way that preserves their professional independence, the rights of their clients, and the quality of legal services.

Clinical embryologists are pivotal to assisted reproductive technology (ART) success, performing critical procedures from gamete manipulation to embryo transfer, yet they face multifaceted and under recognized occupational health challenges in a high-stakes environment. This review examines a variety of occupational risks and diseases affecting embryologists, synthesizing literature on physical, psychological, and ergonomic hazards. Musculoskeletal disorders are prevalent, with shoulder pain increasing in a dose-dependent manner as career length progresses. Psychological morbidity is similarly significant, as a subset of embryologists have stress-related mental health issues and elevated rates of emotional exhaustion. Furthermore, gaps in safety compliance persist, including insufficient protection during the handling of semen or liquid nitrogen. This review ultimately offers two concrete contributions: (1) reframing embryologist health as a quality assurance imperative rather than solely a personnel issue, and (2) leveraging automation and digital technology as a targeted strategy to reduce ergonomic and psychological hazards. Building on this framework, this review also proposes a multi-level intervention strategy encompassing individual, ergonomic, organizational, and technological dimensions to alleviate the aforementioned risks, directly linking embryologist well-being to treatment safety and clinical outcomes.

Zebrafish swimming towards cures: a scalable NAM platform for drug discovery.

The dairy industry has seen remarkable increases in milk yield per cow; however, these gains are often offset by declining fertility and a reduced lifespan. This review discusses strategies to enhance lifetime productivity, focusing on three key research areas: 1) Age at first calving (AFC): Retrospective analysis indicates that reducing AFC to <22.5 months enhances lifetime daily milk yield without adverse effects through the third lactation, provided that the heifer reaches a body weight of ≥ 600 kg before first calving. 2) Double Ovulation: Sequential ultrasonic observation of the ovaries characterizes the pathophysiology of spontaneous double ovulation in high-producing cows during the early postpartum period. Multiparous cows exhibit a higher incidence of double ovulation that leads to twin pregnancies, which is associated with a negative energy balance. This metabolic state can be monitored using milk components such as lactose as a potential metabolic indicator of follicular dynamics. 3) Postpartum uterine health: Diagnostic strategies for endometritis have been optimized. Integrating vaginal discharge scoring and ultrasonography ensures a reliable assessment. This multimodal approach significantly improves the sensitivity of predicting reproductive performance compared to single-method assessments. Improving the lifetime productivity of modern dairy cattle requires precise management across various life stages. Automation and labor-saving technologies, particularly in routine husbandry, reproductive management, and veterinary practices, will become increasingly important for maintaining sustainable dairy operations.

A decade of innovation in healthcare: Automation, bio-printing and digital twin technologies for personalized therapies.

ObjectiveThis study investigates how users' trust evolves during their first ride in a fully driverless robotaxi and how it can be affected by user characteristics, system design, and traffic scenarios.BackgroundAs driving automation technology matures, driverless robotaxis have become available. Despite its immense economic and social potential, public acceptance can be strongly influenced by user trust. Previous research on trust in autonomous vehicles often relied on surveys, driving simulators, or "Wizard of Oz" methods, potentially introducing biases.MethodAn on-road experiment was conducted in commercially operating fully driverless robotaxis on public urban roads. In total, 30 participants with no prior experience riding fully driverless robotaxis were recruited, comprising nondrivers (n = 10), and drivers with (n = 10) and without (n = 10) driving automation experience. Dynamic trust was collected at a 2-min interval during the ride, along with participants' think-aloud for changes in trust. A cumulative link mixed model was used to assess the impact of past driving experience, demographics, and riding time on trust development.ResultsOur findings revealed that dynamic trust increased gradually and stabilized over time, with user heterogeneity playing a moderating role in this process. Further think-aloud data analysis identified key factors in trust formation, including driving style, riding safety and comfort, and user interface design.ConclusionTrust in driverless robotaxis builds progressively with real-world exposure, shaped by user characteristics, vehicle control, and interface design.ApplicationOur findings underscore the importance of considering user heterogeneity in fostering trust and acceptance of robotaxis.

Unintended carbon cost of automation technology in transforming economies: The role of capital dependence and structure change

Pneumonia is a major health problem and the most important causes of mortality in all age groups worldwide. We investigated new automation technology to detect plasma biomarkers, including thrombinantithrombin complex (TAT), α2-plasmininhibitor-plasmin complex (PIC), soluble thrombomodulin (sTM), and tissue plasminogen activator-inhibitor complex (t-PAI·C), and evaluated their diagnostic performance and prognostic value for severe pneumonia patients. We collected 414 patients date with pneumonia. sTM, t-PAI·C, TAT, PIC were measured by qualitative chemiluminescence immunoassay performed on HISCL analyzers. Other laboratory tests were evaluated on the day of non-severe pneumonia and severe pneumonia diagnosis. There were significant differences in sTM, t-PAI·C, TAT, PIC (p < 0.0001), WBC (p = 0.023), PCT (p = 0.007) and IL-6 (p = 0.002) between the severe pneumonia and non-severe pneumonia groups, Logistic regression analysis showed that sTM (p = 0.001), t-PAI·C (p = 0.001), TAT (p = 0.022), PIC (p = 0.000) and APTT (p = 0.013) were independent risk factors for severe pneumonia. Logistic regression analysis showed that t-PAI·C (p = 0.006) was an independent risk factor for hospital mortality in severe pneumonia. The AUC of sTM combined with t-PAI·C, TAT and PIC on diagnosis of patients with severe pneumonia was 0.868 (95% CI: 0.837, 0.899). Kaplan-Meier survival analysis with a log-rank test showed the in-hospital death rate of severe pneumonia was higher in the high TAT (≥ 5.58ng/mL) level than in group with low TAT (< 5.58ng/mL) level (log rank < 0.029). The same trend with high t-PAI·C was also found in severe pneumonia patients (log rank < 0.021). The thrombosis markers are helpful for the diagnosis and prognostic assessment of severe pneumonia.

This paper aims to identify how Industry 4.0 technologies adopted by small and medium-sized enterprises (SMEs) contribute to the development of Industry 5.0. It also identifies which technologies are most suitable and should be prioritised for implementation. Correspondence Analysis was used to map the alignment between specific technologies and the Industry 5.0 pillars. The Fuzzy-DEMATEL method was also applied to identify and prioritise the most prominent and influential technologies. The results show a strong alignment of IoT, Big Data analytics, and AI with the Sustainability pillar, while augmented reality, automation, and mobile technologies align more with Human-centricity. The Fuzzy-DEMATEL analysis identifies AI and IoT as the primary ‘cause’ technologies, holding the highest influence and prominence. This research offers novel insights to guide strategic technology adoption in resource-constrained environments, making theoretical and practical contributions. Theoretically, the study provides valuable insights into which technologies managers should align with their strategic objectives. For practical application, the findings offer significant support to managers, enabling them to create a clear roadmap for implementation and to prioritise technologies most aligned with their specific goals for the Industry 5.0 pillars. This insight helps managers avoid wasting valuable financial and time resources on non-priority technologies.

The global nursing shortage crisis presents a significant challenge to healthcare systems. Digital health technologies, such as communication tools, automation systems, monitoring devices, and information platforms have been proposed as one solution to alleviate the issue by optimizing nursing resources. However, a comprehensive overview of the use and potential of these technologies in optimizing nurses' work and resources is still lacking. The aim of this review is to provide an overview of (a) the digital health technologies used in nursing that may have potential to save nursing resources, (b) which indicators are used to measure the effectiveness of these technologies, and (c) which technologies are found to be effective in terms of saving nursing resources. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews in accordance with the Joanna Briggs Institute (JBI) Manual for Evidence Synthesis. The databases PubMed, CINAHL, and Web of Science were searched. Studies were included if they were peer-reviewed, quantitative, addressed nursing professionals, and considered digital health technologies that specifically aimed to save nursing resources. Relevant data were extracted and synthesized narratively, using an emerging framework. A total of 115 studies were included in this review. Digital health technologies fell into four categories: communication, automation, monitoring, and information technologies. All showed potential for saving resources, with monitoring technologies most often reported as having high potential. Indicators used to measure effectiveness focused on temporal, workforce, and workload resources, and related outcomes including quality of care, patient safety, and cost efficiency. Workforce resources were the most frequently optimized, followed by improvements in patient safety and quality of care. While positive effects were predominant, some studies reported adverse and insignificant effects, indicating variability in effectiveness. Digital health technologies offer promising opportunities to alleviate nursing resource shortages, but their potential seem to vary by type. Monitoring technologies showed the most consistent benefits, while communication and information technologies had mixed effects and automation technologies require further research to clarify their potential. Given the wide range of indicators used to measure potential resource savings, defining common, standardized indicators is essential for systematically assessing the impact of digital health technologies on nursing work and resources. This would enable comparability across studies, strengthen evidence-based decision-making and guide implementation strategies.

With the development of modern science and technology, automation technology has been gradually applied to various industries, and has gradually begun to realize automatic control. In the process of using this technology, it can not only reduce the investment in human and financial resources, but also greatly reduce the project duration. At present, in the power system, the high voltage power system has been widely used and has achieved good results. High voltage transmission lines are easy to cause accidents, which would directly cause huge damage to the interests of the people and enterprises, so they must be regularly maintained. This paper analyzed the optimization and application of artificial intelligence (AI) in the automatic grid overhead line project, and compared the differences of angle, average speed and communication distance. It was concluded that the average speed of robot automation optimization by using AI was 26.1% higher than that before optimization, and the use of AI can increase the communication distance of robot automation by 25%.

This paper presents an approach to designing a locally deployed smart home control system as an educational platform based on the integration of Artificial Intelligence (AI), Natural Language Processing (NLP), and Internet of Things (IoT) technologies. The relevance of the study is driven by the widespread reliance of modern smart home solutions on cloud-based platforms, which results in increased response latency, limited offline functionality, higher security risks, and insufficient support for less common natural languages, including Ukrainian, as well as the growing need for practical educational platforms that enable students to gain hands-on experience with AI-driven automation systems and intelligent interfaces. The primary objective is to improve the reliability, responsiveness, and privacy of smart home systems while developing a versatile educational tool for teaching modern technologies in intelligent systems design and natural language processing. The proposed system is built on the open-source Home Assistant platform and the MQTT messaging protocol, enabling low-latency message delivery, reduced network overhead, and stable operation in environments with limited internet connectivity. Special attention is given to system security through MQTT broker configuration with authentication mechanisms and encrypted credentials. User interaction is enhanced through an AI-driven NLP module implemented in Python using the spaCy library and the Pymorphy3 morphological analyzer. The module performs text normalization, tokenization, lemmatization, and morphological analysis of Ukrainian-language commands, allowing accurate extraction of user intent and target devices. A Telegram bot provides a cross-platform user interface without additional client-side software. The modular architecture allows students to modify individual components, experiment with NLP models, and develop custom extensions, making the platform suitable for laboratory work and research activities. Experimental validation demonstrates reduced command execution latency of 150–200 milliseconds compared to cloud-dependent solutions (500–800 milliseconds). The NLP module achieved accuracy rates of 94% і for single-device commands, 91% і for multi-device commands, and 87% і for complex conditional commands. The results indicate that the system represents both a viable alternative to proprietary cloud-based platforms and an effective educational platform for developing professional competencies in AI-driven automation technologies.

With the advancement of communication and automation technologies, urban roadways in the near future will feature the coexistence of conventional human-driven vehicles alongside connected and autonomous vehicles. To enhance safety and efficiency for vehicles navigating unsignalized intersections in mixed traffic environments, this paper proposes a novel strategy based on a cooperative vehicle-infrastructure system for multi-vehicle trajectory planning. In this strategy, the unsignalized intersection area is divided into a preparation zone, an adjustment zone, and a conflict zone, with a corresponding planning method designed for each zone. The generation of multi-vehicle trajectories in a mixed-traffic environment is formulated as an optimization problem. Subsequently, a novel collision avoidance method and an improved particle swarm optimization algorithm combined with genetic algorithm are proposed to address the problem. Furthermore, the strategy accounts for the treatment of vehicles with different priority levels. The experimental results on multi-vehicle trajectory planning at unsignalized intersections under varying operational conditions demonstrate that this method enables the safe and efficient passage of potentially conflicting vehicles through unsignalized intersections in mixed-traffic scenarios.

Extracellular vesicles (EVs) are nanoscale particles secreted by cells that carry diverse biomolecules reflecting their cell of origin. Single-EV imaging approaches have enabled precise characterization of heterogeneous EV populations; however, their broader application is limited by low-throughput workflows and cumbersome EV isolation procedures. Here, we introduce a streamlined, high-throughput imaging platform capable of analyzing protein expression of individual intact EVs directly from unprocessed biological samples at the single-vesicle level. Our approach employs a functionalized glass surface optimized for high-throughput single-EV imaging, facilitating specific capture of EVs and enabling integration with existing automation technologies. We evaluate the platform's analytical capabilities by characterizing various recombinant EV samples and demonstrate its clinical utility by analyzing EVs in a total of 191 human plasma samples with high-throughput efficiency. This technology will offer a pathway for high-precision and large-scale characterization of EVs in clinical samples.

ABSTRACT Logistics cost management has been an important part of the supply chain performance in all developed as well as developing countries. In India, which has high logistics costs comparatively, companies face constant pressure to improve their efficiency in operations and maintain their quality. This study focuses on the connections between components of logistics cost, like transportation, warehousing and administrative expenses and their impact on how efficiently a business runs. The study is based on secondary data collected from company reports, academic literature and case studies from leading companies. The analysis shows that the inadequate logistics systems considerably increase the delays in operation and the cost of the total supply. However, companies that adopt systematic strategies for cost management, supported by the advanced digital tools like automation technologies, route optimisation systems, and real-time monitoring systems, indicate improved speed in delivery, decreased wastage and better use of resources. Case studies of companies like Flipkart and Amazon further explain how a merged logistics network plays a role in cost reduction and develops efficiency in service. The study states that logistics cost management is not a necessity in operation but a strategic function that affects the competition directly and the long-term viability of the supply chain. Keywords: Logistics cost management; Supply chain performance; Operational efficiency; Digital technologies; Route optimisation systems; Automation; Cost reduction; Integrated logistics networks.

Medication errors in hospital pharmacies are still a major source of patient injury and healthcare expenditure globally. There has been a growing trend of implementing pharmacy automation technologies to improve the efficiency of work processes and minimise errors. However, there is a lack of evidence on the cost-effectiveness of these technologies. The objective of this systematic review was to assess the cost-effectiveness of pharmacy automation technologies such as automated dispensing cabinets (ADCs), robotic dispensing systems, automated compounding systems, barcode-assisted medication administration (BCMA) and unit-dose dispensing systems (UDDS) compared with traditional manual dispensing systems in inpatient and outpatient hospital pharmacy settings. Literature searches were performed using PubMed, Scopus, ScienceDirect and Google Scholar on 11 January 2026. The search terms were developed using Boolean operators and key words for pharmacy automation and economic evaluation. Articles were independently screened by two authors using Covidence software and any disagreements were resolved by consensus. The articles were selected based on predefined inclusion and exclusion criteria, focusing on cost-effectiveness outcomes in hospital pharmacy settings. The findings were qualitatively synthesised. A total of 613 articles were screened for title and abstract, and 7 of 118 full-text articles were selected based on the inclusion criteria. The included articles assessed various automation technologies such as ADCs, robots, UDDS, BCMA and LED-guided picking systems in hospital settings in Taiwan, Germany, the Netherlands, Singapore, Denmark and Brazil. Cost-effectiveness analysis showed large error reduction rates, with incremental cost-effectiveness ratios ranging from €2.01 to €386 per error prevented, and large efficiency gains, including time savings of up to 25.68 min per patient and equivalent to 11.7 full-time equivalent nurses. Automation reduced clinical, procedural and potentially harmful errors in comparison to manual systems, thereby supporting better patient safety and efficiency. Pharmacy automation technologies are highly cost-effective and efficient compared with manual dispensing systems. These technologies reduce errors, improve efficiency and can also provide cost savings, thereby providing rationale for their use as a strategy for optimising hospital pharmacy operations.

The construction sector is a major generator of solid waste, with construction and demolition waste (CDW) posing significant challenges to sustainable resource management. Accurate quantification is vital for achieving circular economy and waste reduction goals; however, traditional estimation methods remain manual, fragmented and inconsistent. This state-of-the-art review synthesizes developments in automation and digital technologies that are transforming CDW quantification and management. A total of 125 peer-reviewed articles published between 1993 and 2025 were systematically analysed to identify trends, methods and emerging tools. Advances across artificial intelligence, computer vision, building information modelling and the Internet of Things are categorized, focusing on automated waste recognition, volumetric estimation and real-time monitoring. Persistent challenges, including limited dataset diversity, model generalization, interoperability and implementation cost, are critically examined. By linking technological innovation with practical waste management, this review highlights how automation can enhance efficiency, traceability and sustainability in the construction industry's transition towards circular practices.