The Leadership in Energy and Environmental Design (LEED) certification process is characterized by labor-intensive requirements for data handling, simulation, and documentation. This paper presents an automated platform designed to streamline key aspects of LEED certification. The platform integrates a PySide6-based user interface, a review Manager for process orchestration, and multiple analysis engines for credit compliance, energy modeling via EnergyPlus, and location-based evaluation. Key components include an OpenCV-based preprocessing pipeline for document analysis and a report generation module powered by the Gemma3 large language model with a retrieval-augmented generation framework. Implementation techniques - including computer vision for document analysis, structured LLM prompt design, and RAG-based report generation - are detailed. Initial results from pilot project deployment show improvements in efficiency and accuracy compared to traditional manual workflows, achieving 82% automation coverage and up to 70% reduction in documentation time. The platform demonstrates practical scalability for green building certification automation.
Richard Karl, Meissa Diouf, Aleksandr Shlykov
et al.
Modern experiments with cold molecular ions have reached a high degree of complexity requiring frequent sample preparation, state initialization and protocol execution while demanding precise control over multiple devices and laser sources. To maintain a high experimental duty cycle and robust measurement conditions, automation becomes essential. We present a fully automated control system for the preparation of trapped state-selected molecular ions and subsequent quantum logic-based experiments. Adaptive feedback routines based on real-time image analysis introduce and identify single molecular ions in atomic-ion Coulomb crystals. By appropriate manipulation of the trapping potentials, excess atomic ions are released from the trap to produce dual-species two-ion strings, here Ca$^+-$N$_2^+$. After mass and state identification of the molecular ion, nanosecond-level synchronization of laser pulses employing the Sinara/ARTIQ framework and real-time data analysis enable quantum-logic-spectroscopic measurements. The present automated control system enables robust, unsupervised operation over extended periods resulting in an increase of the number of experimentation cycles by about a factor of ten compared to manual operation and a factor of about eight in loaded molecules in typical practical situations. The modular, distributed design of the system provides a scalable blueprint for similar molecular-ion experiments.
The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for CICIDS2017 datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.
Tommaso Compagnucci, Franco Callegati, Saverio Giallorenzo
et al.
Ransomware poses a significant threat to individuals and organisations, compelling tools to investigate its behaviour and the effectiveness of mitigations. To answer this need, we present SAFARI, an open-source framework designed for safe and efficient ransomware analysis. SAFARI's design emphasises scalability, air-gapped security, and automation, democratising access to safe ransomware investigation tools and fostering collaborative efforts. SAFARI leverages virtualisation, Infrastructure-as-Code, and OS-agnostic task automation to create isolated environments for controlled ransomware execution and analysis. The framework enables researchers to profile ransomware behaviour and evaluate mitigation strategies through automated, reproducible experiments. We demonstrate SAFARI's capabilities by building a proof-of-concept implementation and using it to run two case studies. The first analyses five renowned ransomware strains (including WannaCry and LockBit) to identify their encryption patterns and file targeting strategies. The second evaluates Ranflood, a contrast tool which we use against three dangerous strains. Our results provide insights into ransomware behaviour and the effectiveness of countermeasures, showcasing SAFARI's potential to advance ransomware research and defence development.
Tanmay Khule, Stefan Marksteiner, Jose Alguindigue
et al.
In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive, error-prone task that has seen limited automation in the context of testing vehicular systems. This paper introduces STAF (Security Test Automation Framework), a novel approach to automating security test case generation. Leveraging Large Language Models (LLMs) and a four-step self-corrective Retrieval-Augmented Generation (RAG) framework, STAF automates the generation of executable security test cases from attack trees, providing an end-to-end solution that encompasses the entire attack surface. We particularly show the elements and processes needed to provide an LLM to actually produce sensible and executable automotive security test suites, along with the integration with an automated testing framework. We further compare our tailored approach with general purpose (vanilla) LLMs and the performance of different LLMs (namely GPT-4.1 and DeepSeek) using our approach. We also demonstrate the method of our operation step-by-step in a concrete case study. Our results show significant improvements in efficiency, accuracy, scalability, and easy integration in any workflow, marking a substantial advancement in automating automotive security testing methodologies. Using TARAs as an input for verfication tests, we create synergies by connecting two vital elements of a secure automotive development process.
The Fourth Industrial Revolution commonly refers to the accelerating technological transformation that has been taking place in the 21st century. Economic growth theories which treat the accumulation of knowledge and its effect on production endogenously remain relevant, yet they have been evolving to explain how the current wave of advancements in automation and artificial intelligence (AI) technology will affect productivity and different occupations. The work contributes to current economic discourse by developing an analytical task-based framework that endogenously integrates knowledge accumulation with frictions that describe technological lock-in and the burden of knowledge generation and validation. The interaction between production (or automation) and growth (or knowledge accumulation) is also described explicitly. To study how automation and AI shape economic outcomes, I rely on high-throughput calculations of the developed model. The effect of the model's structural parameters on key variables such as the production output, wages, and labor shares of output is quantified, and possible intervention strategies are briefly discussed. An important result is that wages and labor shares are not directly linked, instead they can be influenced independently through distinct policy levers. Generally, labor share depends sensitively on capital-labor ratio, while wages respond positively to larger knowledge stocks.
Automated algorithm design is entering a new phase: Large Language Models can now generate full optimisation (meta)heuristics, explore vast design spaces and adapt through iterative feedback. Yet this rapid progress is largely performance-driven and opaque. Current LLM-based approaches rarely reveal why a generated algorithm works, which components matter or how design choices relate to underlying problem structures. This paper argues that the next breakthrough will come not from more automation, but from coupling automation with understanding from systematic benchmarking. We outline a vision for explainable automated algorithm design, built on three pillars: (i) LLM-driven discovery of algorithmic variants, (ii) explainable benchmarking that attributes performance to components and hyperparameters and (iii) problem-class descriptors that connect algorithm behaviour to landscape structure. Together, these elements form a closed knowledge loop in which discovery, explanation and generalisation reinforce each other. We argue that this integration will shift the field from blind search to interpretable, class-specific algorithm design, accelerating progress while producing reusable scientific insight into when and why optimisation strategies succeed.
Teleoperation enables remote human support of automated vehicles in scenarios where the automation is not able to find an appropriate solution. Remote assistance concepts, where operators provide discrete inputs to aid specific automation modules like planning, is gaining interest due to its reduced workload on the human remote operator and improved safety. However, these concepts are challenging to implement and maintain due to their deep integration and interaction with the automated driving system. In this paper, we propose a solution to facilitate the implementation of remote assistance concepts that intervene on planning level and extend the operational design domain of the vehicle at runtime. Using arbitration graphs, a modular decision-making framework, we integrate remote assistance into an existing automated driving system without modifying the original software components. Our simulative implementation demonstrates this approach in two use cases, allowing operators to adjust planner constraints and enable trajectory generation beyond nominal operational design domains.
At present, the development of the global energy internet is occurring in depth and the construction of a distributed power supply is rapid, and the energy router (ER), as a key device for integrating energy flow and information flow, has important application value in microgrids. In this paper, a multi-port energy router based on a 710 V DC bus is designed and developed with a modular structure design, including core components such as a total controller, a power converter, a hybrid energy storage system, and an auxiliary power supply. Flexible access and the management of multiple-voltage-level ports (690 V AC, 380 V AC, 220 V DC, and 24 V DC) are realized through rational topology design. The test results of the device show that the system performance indexes meet the design requirements. The operation is stable and reliable, displaying strong practical engineering value, and at the same time provides a technical solution that can be borrowed for other special scenarios such as the microgrid system.
Urban systems are transforming as artificial intelligence (AI) evolves from automation to Agentic Urban AI (AI systems with autonomous goal-setting and decision-making capabilities), which independently define and pursue urban objectives. This shift necessitates reassessing governance, planning, and ethics. Using a conceptual-methodological approach, this study integrates urban studies, AI ethics, and governance theory. Through a literature review and case studies of platforms like Alibaba’s City Brain and CityMind AI Agent, it identifies early agency indicators, such as strategic adaptation and goal re-prioritisation. A typology distinguishing automation, autonomy, and agency clarifies AI-driven urban decision-making. Three trajectories are proposed: fully autonomous Agentic AI, collaborative Hybrid Urban Agency, and constrained Non-Agentic AI to mitigate ethical risks. The findings highlight the need for participatory, transparent governance to ensure democratic accountability and social equity in cognitive urban ecosystems.
Miguel Chicchon, Francisco James Leon Trujillo, Ivan Sipiran
et al.
An accurate land-cover segmentation of very-high-resolution aerial images is essential for a wide range of applications, including urban planning and natural resource management. However, the automation of this process remains a challenge owing to the complexity of images, variability in land surface features, and noise. In this study, a method for training convolutional neural networks and transformers to perform land-cover segmentation on very-high-resolution aerial images in a regional context was proposed. We assessed the U-Net-scSE, FT-U-NetFormer, and DC-Swin architectures, incorporating transfer learning and active contour loss functions to improve performance on semantic segmentation tasks. Our experiments conducted using the OpenEarthMap dataset, which includes images from 44 countries, demonstrate the superior performance of U-Net-scSE models with the EfficientNet-V2-XL and MiT-B4 encoders, achieving an mIoU of over 0.80 on a test dataset of urban and rural images from Peru.
ABSTRACT To increase their throughput, reduce laboratory work and improve reproducibility, automation of bioprocesses is gaining in importance nowadays. This applies in particular to microbioreactors (MBRs), which can be easily integrated in highly parallelized and automated platforms and, therefore, be applied for screenings, cell‐based assays, and bioprocess development. One promising pharmaceutical application for MBRs is the performance of phage sensitivity tests called phagograms in phage therapy. However, there is no automated and parallelized platform available so far that fulfills the requirements of phagograms. Therefore, a novel highly parallelizable capillary‐wave microbioreactor (cwMBR) with a volume of 7 µL, which has already been successfully applied for phagograms, was extended by an in‐house built platform for automated fluid addition in the single‐digit nanoliter range. The cwMBR has a phage‐repellent hydrophilic glass surface. Furthermore, a custom‐made highly parallelizable device for biomass measurement in the lower microliter scale was developed and validated in the cwMBR. To prove the applicability of the platform for the generation of phagograms, a phagogram using Escherichia coli and automated phage addition was performed. The results indicate a clear lysis of the bacteria by the phages and thus confirm the applicability of performing automated phagograms in the highly parallelizable cwMBR platform.