Organizations across finance, healthcare, transportation, content moderation, and critical infrastructure are rapidly deploying highly automated AI systems, yet they lack principled methods to quantify how increasing automation amplifies harm when failures occur. We propose a parsimonious Bayesian risk decomposition expressing expected loss as the product of three terms: the probability of system failure, the conditional probability that a failure propagates into harm given the automation level, and the expected severity of harm. This framework isolates a critical quantity -- the conditional probability that failures propagate into harm -- which captures execution and oversight risk rather than model accuracy alone. We develop complete theoretical foundations: formal proofs of the decomposition, a harm propagation equivalence theorem linking the harm propagation probability to observable execution controls, risk elasticity measures, efficient frontier analysis for automation policy, and optimal resource allocation principles with second-order conditions. We motivate the framework with an illustrative case study of the 2012 Knight Capital incident ($440M loss) as one instantiation of a broadly applicable failure pattern, and characterize the research design required to empirically validate the framework at scale across deployment domains. This work provides the theoretical foundations for a new class of deployment-focused risk governance tools for agentic and automated AI systems.
The widespread adoption of electric vehicles (EVs) has turned charging demand into a substantial load on the power grid. To satisfy the rapidly growing demand of EVs, the construction of charging infrastructure has received sustained attention in recent years. As charging stations become more widespread, how to attract EV users in a competitive charging market while optimizing the internal charging process is the key to determine the charging station’s operational efficiency. This paper tackles this issue by presenting the following contributions. Firstly, a simulation method based on prospect theory is proposed to simulate EV users’ preferences in selecting charging stations. The selection behavior of EV users is simulated by establishing coupling relationship among the transportation network, power grid, and charging network as well as the model of users’ preference. Secondly, a two-stage joint stochastic optimization model for a charging station is developed, which considers both charging pricing and energy control. At the first stage, a Stackelberg game is employed to determine the day-ahead optimal charging price in a competitive market. At the second stage, real-time stochastic charging control is applied to maximize the operational profit of the charging station considering renewable energy integration. Finally, a scenario-based Alternating Direction Method of Multipliers (ADMM) approach is introduced in the first stage for optimal pricing learning, while a simulation-based Rollout method is applied in the second stage to update the real-time energy control strategy based on the latest pricing. Numerical results demonstrate that the proposed method can achieve as large as 33% profit improvement by comparing with the competitive charging stations considering 1000 EV integration.
Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry
Recent advances in artificial intelligence (AI) and quantum computing are accelerating automation in scientific and engineering processes, fundamentally reshaping research methodologies. This perspective highlights parallels between scientific automation and established Computer-Aided Engineering (CAE) practices, introducing Quantum CAE as a framework that leverages quantum algorithms for simulation, optimization, and machine learning within engineering design. Practical implementations of Quantum CAE are illustrated through case studies for combinatorial optimization problems. Further discussions include advancements toward higher automation levels, highlighting the critical role of specialized AI agents proficient in quantum algorithm design. The integration of quantum computing with AI raises significant questions about the collaborative dynamics among human scientists and engineers, AI systems, and quantum computational resources, underscoring a transformative future for automated discovery and innovation.
This systematic literature review provides a comprehensive analysis of the most critical cybersecurity challenges in DevOps environments. Through a rigorous examination of 62 peer-reviewed articles published between 2016 and 2025, we identified recurring threats, active attack vectors, structural vulnerabilities, mitigation strategies, and their technical impact on system performance and operational resilience. The analysis revealed that the most significant threats are related to uncontrolled automation, exposure of sensitive secrets in CI/CD pipelines, lack of mutual authentication between distributed services, supply chain attacks, and the use of unauthorized tools (Shadow IT). These threats simultaneously compromise core security principles, including integrity, confidentiality, and traceability. The most frequent attack vectors include code injection in CI/CD pipelines, unrestricted access to public repositories, remote execution via default configurations, and lateral movement in flat architectures. We identified 27 recurrent vulnerabilities throughout the DevOps lifecycle. The most critical include the absence of automated security testing, poor management of secrets, and reliance on unverified third-party components. More than 30 technical and organizational countermeasures were documented, such as SAST/DAST/IAST scans, infrastructure-as-code validation, secure credential storage via vaults, and integrated practices like DevSecOps and compliance-as-code. When properly implemented, these strategies do not degrade system performance and may even enhance resilience and stability. Nonetheless, a lack of comparative empirical validation in most reviewed studies limits the generalizability of proposed solutions. These findings establish a foundation for future research in emerging domains, such as the Internet of Things, where continuous, adaptive, and verifiable security is paramount for automated and dynamic environments.
Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is still going on, and as the epidemic situation continues, the genome of SARS-CoV-2 is also mutating and evolving, resulting in more and more SARS-CoV-2 mutant strains, which have brought serious pressure on the prevention and control of COVID-19. Given that the COVID-19 is still spreading, it is extremely important to rapidly identify SARS-CoV-2 variants by nucleic acid assays. Thus, developing highly sensitive and specific assays that are suitable for field testing, high-throughput, and automation, as well as other diagnostic applications for SARS-CoV-2 variants, is urgently needed. This paper reviews the research progress of novel CRISPR-based diagnostic methods for SARS-CoV-2 variants.
Occupational mobility is an emergent strategy to cope with technological unemployment by facilitating efficient labor redeployment. However, previous studies analyzing networks show that the boundaries to smooth mobility are constrained by a fragmented structure in the occupation network. In this study, positing that this structure will significantly change due to automation, we propose the skill automation view, which asserts that automation substitutes for skills, not for occupations, and simulate a scenario of skill automation drawing on percolation theory. We sequentially remove skills from the occupation-skill bipartite network and investigate the structural changes in the projected occupation network. The results show that the accumulation of small changes (the emergence of bridges between occupations due to skill automation) triggers significant structural changes in the occupation network. The structural changes accelerate as the components integrate into a new giant component. This result suggests that automation mitigates the bottlenecks to smooth occupational mobility.
Recently, large language models (LLMs) have demonstrated exceptional capabilities in serving as the foundation for AI assistants. One emerging application of LLMs, navigating through websites and interacting with UI elements across various web pages, remains somewhat underexplored. We introduce Steward, a novel LLM-powered web automation tool designed to serve as a cost-effective, scalable, end-to-end solution for automating web interactions. Traditional browser automation frameworks like Selenium, Puppeteer, and Playwright are not scalable for extensive web interaction tasks, such as studying recommendation algorithms on platforms like YouTube and Twitter. These frameworks require manual coding of interactions, limiting their utility in large-scale or dynamic contexts. Steward addresses these limitations by integrating LLM capabilities with browser automation, allowing for natural language-driven interaction with websites. Steward operates by receiving natural language instructions and reactively planning and executing a sequence of actions on websites, looping until completion, making it a practical tool for developers and researchers to use. It achieves high efficiency, completing actions in 8.52 to 10.14 seconds at a cost of $0.028 per action or an average of $0.18 per task, which is further reduced to 4.8 seconds and $0.022 through a caching mechanism. It runs tasks on real websites with a 40% completion success rate. We discuss various design and implementation challenges, including state representation, action sequence selection, system responsiveness, detecting task completion, and caching implementation.
Göök Alf, Andersson-Sundén Erik, Hansson Joachim
et al.
In this paper, we discuss the development of a nuclear data evaluation pipeline, based around the TALYS code system. The pipeline focuses on the evaluation of the fast neutron energy range, above the resolved resonances. A strong focus in development lies on automation and reproducibility, as well as the efficient use of large-scale computational infrastructure, to enable rapid testing of new algorithms and modified assumptions. Several novel concepts for nuclear data evaluation methodology are implemented. A particular problem in evaluating the neutron-induced reaction cross-section using TALYS, relates to the intermediate energy range. While TALYS only predicts the smooth energy-averaged cross-section, experiments reveal unresolved resonance-like structures. In this paper, we explore ways to treat this type of model defect using heteroscedastic Gaussian processes to automatically determine the distribution of experimental data around an energy-averaged cross-section curve.
At the Walisongo Vocational School Semarang Semarang SPP payment system that is carried out at this time is by manual method where payment of SPP is still conventional where administrative staff must look for student data and record transactions in the ledger containing student data, then fill in the student payment card column. and come as proof that students have paid. However, the payment system is less optimistic.Seeing these situations and conditions, the author makes a web-based response system for spp payment automation based on the SMS gateway at Walisongo Vocational School Semarang by using the Research And Development (R & D) method where this application can help administrative administrators in this institution to facilitate payment and can make notifications directly to parents of students automatically.This application the author uses the HTML and PHP programming language with the MySQL database, where later the data will be entered and stored in the database and the author also uses the SMS gateway hardware as a notification media to the parents of students so that the use can be more easier and optimal.
Large-scale software development has become a highly collaborative and geographically distributed endeavour, especially in open-source software development ecosystems and their associated developer communities. It has given rise to modern development processes (e.g., pull-based development) that involve a wide range of activities such as issue and bug handling, code reviewing, coding, testing, and deployment. These often very effort-intensive activities are supported by a wide variety of tools such as version control systems, bug and issue trackers, code reviewing systems, code quality analysis tools, test automation, dependency management, and vulnerability detection tools. To reduce the complexity of the collaborative development process, many of the repetitive human activities that are part of the development workflow are being automated by CI/CD tools that help to increase the productivity and quality of software projects. Social coding platforms aim to integrate all this tooling and workflow automation in a single encompassing environment. These social coding platforms gave rise to the emergence of development bots, facilitating the integration with external CI/CD tools and enabling the automation of many other development-related tasks. GitHub, the most popular social coding platform, has introduced GitHub Actions to automate workflows in its hosted software development repositories since November 2019. This chapter explores the ecosystems of development bots and GitHub Actions and their interconnection. It provides an extensive survey of the state-of-the-art in this domain, discusses the opportunities and threats that these ecosystems entail, and reports on the challenges and future perspectives for researchers as well as software practitioners.
Within recent times, cybercriminals have curated a variety of organised and resolute cyber attacks within a range of cyber systems, leading to consequential ramifications to private and governmental institutions. Current security-based automation and orchestrations focus on automating fixed purpose and hard-coded solutions, which are easily surpassed by modern-day cyber attacks. Research within Automated Cyber Defence will allow the development and enabling intelligence response by autonomously defending networked systems through sequential decision-making agents. This article comprehensively elaborates the developments within Automated Cyber Defence through a requirement analysis divided into two sub-areas, namely, automated defence and attack agents and Autonomous Cyber Operation (ACO) Gyms. The requirement analysis allows the comparison of automated agents and highlights the importance of ACO Gyms for their continual development. The requirement analysis is also used to critique ACO Gyms with an overall aim to develop them for deploying automated agents within real-world networked systems. Relevant future challenges were addressed from the overall analysis to accelerate development within the area of Automated Cyber Defence.
A central question in economics is whether automation will displace human labor and diminish standards of living. Whilst prior works typically frame this question as a competition between human labor and machines, we frame it as a competition between human consumers and human suppliers. Specifically, we observe that human needs favor long tail distributions, i.e., a long list of niche items that are substantial in aggregate demand. In turn, the long tails are reflected in the goods and services that fulfill those needs. With this background, we propose a theoretical model of economic activity on a long tail distribution, where innovation in demand for new niche outputs competes with innovation in supply automation for mature outputs. Our model yields analytic expressions and asymptotes for the shares of automation and labor in terms of just four parameters: the rates of innovation in supply and demand, the exponent of the long tail distribution and an initial value. We validate the model via non-linear stochastic regression on historical US economic data with surprising accuracy.
This study examines the relationship between automation and income inequality across different countries, taking into account the varying levels of technological adoption and labor market institutions. The research employs a panel data analysis using data from the World Bank, the International Labour Organization, and other reputable sources. The findings suggest that while automation leads to an increase in productivity, its effect on income inequality depends on the country's labor market institutions and social policies.
Automation systems that can autonomously drive application user interfaces to complete user tasks are of great benefit, especially when users are situationally or permanently impaired. Prior automation systems do not produce generalizable models while AI-based automation agents work reliably only in simple, hand-crafted applications or incur high computation costs. We propose UINav, a demonstration-based approach to train automation agents that fit mobile devices, yet achieving high success rates with modest numbers of demonstrations. To reduce the demonstration overhead, UINav uses a referee model that provides users with immediate feedback on tasks where the agent fails, and automatically augments human demonstrations to increase diversity in training data. Our evaluation shows that with only 10 demonstrations UINav can achieve 70% accuracy, and that with enough demonstrations it can surpass 90% accuracy.