Engineering workflows such as design optimization, simulation-based diagnosis, control tuning, and model-based systems engineering (MBSE) are iterative, constraint-driven, and shaped by prior decisions. Yet many AI methods still treat these activities as isolated tasks rather than as parts of a broader workflow. This paper presents Agentic Engineering Intelligence (AEI), an industrial vision framework that models engineering workflows as constrained, history-aware sequential decision processes in which AI agents support engineer-supervised interventions over engineering toolchains. AEI links an offline phase for engineering data processing and workflow-memory construction with an online phase for workflow-state estimation, retrieval, and decision support. A control-theoretic interpretation is also possible, in which engineering objectives act as reference signals, agents act as workflow controllers, and toolchains provide feedback for intervention selection. Representative automotive use cases in suspension design, reinforcement learning tuning, multimodal engineering knowledge reuse, aerodynamic exploration, and MBSE show how diverse workflows can be expressed within a common formulation. Overall, the paper positions engineering AI as a problem of process-level intelligence and outlines a practical roadmap for future empirical validation in industrial settings.
A CO<sub>2</sub> data center cooling and heating system integrated with dedicated mechanical subcooling and dual-temperature evaporation technology (DMS-DE) is proposed to realize green and efficient cooling for data centers and improve comprehensive energy efficiency. A thermodynamic and carbon emission performance system model was established and compared with the basic CO<sub>2</sub> system (Base) and a single evaporating-temperature CO<sub>2</sub> system with dedicated mechanical subcooling (DMS-SE). The results demonstrated that the DMS-DE system had the maximum coefficient of performance (COP), optimal subcooling degree, and discharge pressure. Therefore, adopting the DMS-DE can significantly increase the system COP and exergy efficiency. Compared with Base and DMS-SE, COP increased by 14.1% and 9.0%, and the exergy efficiency increased by 13.24% and 4.31%, respectively. The life cycle carbon emissions of the DMS-DE system were reduced by 16.1% and 9.3% compared with Base and DMS-SE, respectively. This study can provide a technical reference for the highly efficient and clean operation of combined heating and cooling utilization for data center scenarios.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Immersion battery cooling involves immersing the battery directly in a coolant and has the advantages of a simple structure, rapid cooling, and better temperature uniformity than conventional indirect liquid cooling, air cooling, and two-phase cooling. This study summarizes the relevant technologies for immersion battery cooling, including screening of immersion liquid, cooling system structure design, and thermal safety, and then analyzes the technical applications of the immersion battery cooling system based on this work. The results show that immersion cooling can rapidly reduce the battery temperature and effectively improve the temperature uniformity of the battery pack. However, this technology requires a high sealing performance of battery modules, and challenges such as leakage and corrosion need to be addressed.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices. 6G will enable a shift towards a continuous edge-to-cloud architecture. Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices. Current technologies, including development methods, software architectures, and orchestration and offloading systems, still need to be prepared to cope with such requirements. In this paper, we conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering. Results show that 18 research papers have been proposed in software process, software architecture, orchestration and offloading methods. Of these, software architecture and software-defined networks are respectively areas and topics that have received the most attention in 6G Software Engineering. In addition, the main types of results of these papers are methods, architectures, platforms, frameworks and algorithms. For the five tools/frameworks proposed, they are new and not currently studied by other researchers. The authors of these findings are mainly from China, India and Saudi Arabia. The results will enable researchers and practitioners to further research and extend for 6G Software Engineering.
In the social and organizational sciences, accountability has been linked to the efficient operation of organizations. However, it has received limited attention in software engineering (SE) research, in spite of its central role in the most popular software development methods (e.g., Scrum). In this article, we explore the mechanisms of accountability in SE environments. We investigate the factors that foster software engineers' individual accountability within their teams through an interview study with 12 people. Our findings recognize two primary forms of accountability shaping software engineers individual senses of accountability: institutionalized and grassroots. While the former is directed by formal processes and mechanisms, like performance reviews, grassroots accountability arises organically within teams, driven by factors such as peers' expectations and intrinsic motivation. This organic form cultivates a shared sense of collective responsibility, emanating from shared team standards and individual engineers' inner commitment to their personal, professional values, and self-set standards. While institutionalized accountability relies on traditional "carrot and stick" approaches, such as financial incentives or denial of promotions, grassroots accountability operates on reciprocity with peers and intrinsic motivations, like maintaining one's reputation in the team.
This paper introduces reAnalyst, a framework designed to facilitate the study of reverse engineering (RE) practices through the semi-automated annotation of RE activities across various RE tools. By integrating tool-agnostic data collection of screenshots, keystrokes, active processes, and other types of data during RE experiments with semi-automated data analysis and generation of annotations, reAnalyst aims to overcome the limitations of traditional RE studies that rely heavily on manual data collection and subjective analysis. The framework enables more efficient data analysis, which will in turn allow researchers to explore the effectiveness of protection techniques and strategies used by reverse engineers more comprehensively and efficiently. Experimental evaluations validate the framework's capability to identify RE activities from a diverse range of screenshots with varied complexities. Observations on past experiments with our framework as well as a survey among reverse engineers provide further evidence of the acceptability and practicality of our approach.
Various ideation methods, such as morphological analysis and design-by-analogy, have been developed to aid creative problem-solving and innovation. Among them, the Theory of Inventive Problem Solving (TRIZ) stands out as one of the best-known methods. However, the complexity of TRIZ and its reliance on users' knowledge, experience, and reasoning capabilities limit its practicality. To address this, we introduce AutoTRIZ, an artificial ideation system that integrates Large Language Models (LLMs) to automate and enhance the TRIZ methodology. By leveraging LLMs' vast pre-trained knowledge and advanced reasoning capabilities, AutoTRIZ offers a novel, generative, and interpretable approach to engineering innovation. AutoTRIZ takes a problem statement from the user as its initial input, automatically conduct the TRIZ reasoning process and generates a structured solution report. We demonstrate and evaluate the effectiveness of AutoTRIZ through comparative experiments with textbook cases and a real-world application in the design of a Battery Thermal Management System (BTMS). Moreover, the proposed LLM-based framework holds the potential for extension to automate other knowledge-based ideation methods, such as SCAMPER, Design Heuristics, and Design-by-Analogy, paving the way for a new era of AI-driven innovation tools.
This position paper for an invited talk on the "Future of eScience" discusses the Research Software Engineering Movement and where it might be in 2030. Because of the authors' experiences, it is aimed globally but with examples that focus on the United States and United Kingdom.
Rebeca C. Motta, Káthia M. de Oliveira, Guilherme H. Travassos
Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. Results: The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. Conclusions: The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems.
Jacky Chen Long Chai, Tiong-Sik Ng, Cheng-Yaw Low
et al.
Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition devices. In this paper, we study principled approaches to elevate the recognizability of a face in the embedding space instead of the visual quality. We first formulate a robust learning-based face recognizability measure, namely recognizability index (RI), based on two criteria: (i) proximity of each face embedding against the unrecognizable faces cluster center and (ii) closeness of each face embedding against its positive and negative class prototypes. We then devise an index diversion loss to push the hard-to-recognize face embedding with low RI away from unrecognizable faces cluster to boost the RI, which reflects better recognizability. Additionally, a perceptibility attention mechanism is introduced to attend to the most recognizable face regions, which offers better explanatory and discriminative traits for embedding learning. Our proposed model is trained end-to-end and simultaneously serves recognizability-aware embedding learning and face quality estimation. To address VLRFR, our extensive evaluations on three challenging low-resolution datasets and face quality assessment demonstrate the superiority of the proposed model over the state-of-the-art methods.
Thermoelectric refrigeration is a solid-state refrigeration technology that uses electric energy to achieve refrigeration via the Peltier effect. Compared with vapor compression refrigeration, thermoelectric refrigeration offers the advantages of scalability, vibration-free, high reliability, and high temperature-control accuracy. Based on the development history and basic principles of thermoelectric refrigeration, this study focuses on the research progress of thermoelectric materials, refrigerator structures, functional layer interfaces, and heat sinks. Subsequently, applications of thermoelectric refrigeration are introduced based on their advantages. Finally, investigations regarding thermoelectric refrigeration are summarized and discussed.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
A solar air dual-source heat collecting evaporator (condenser) using a micro heat pipe array and a dual-source direct expansion heat pump system was designed. This system effectively reduces the temperature of the photovoltaic module to improve its power conversion efficiency and simultaneously achieves heating and refrigeration functions. The multiple energy complements each other in such an energy system for building applications. An experimental platform for a solar-air energy direct expansion heat pump system was established and the performance of the power generation, cooling supply, and domestic hot water supply was tested and analyzed. When the heating system in the dual-source direct expansion heat pump system operated, the PV/T assembly had a significant cooling effect on the photovoltaic module. Compared to the rest conditions, the photoelectric power and photoelectric efficiency were improved by 56.43% and 53.15%, respectively. During the heating process, the dual-source direct expansion heat pump heated 180 L water from 20 ℃ to 50 ℃. The average system heating COP, heating power, total power generation, and total power consumption were 4.59, 3.20 kW, 1.43 kW·h, and 1.36 kW·h, respectively. The average refrigeration EER was 2.29 when cooling from 12 ℃ to 7 ℃.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Internet of Things (IoT) is transforming the industry by bridging the gap between Information Technology (IT) and Operational Technology (OT). Machines are being integrated with connected sensors and managed by intelligent analytics applications, accelerating digital transformation and business operations. Bringing Machine Learning (ML) to industrial devices is an advancement aiming to promote the convergence of IT and OT. However, developing an ML application in industrial IoT (IIoT) presents various challenges, including hardware heterogeneity, non-standardized representations of ML models, device and ML model compatibility issues, and slow application development. Successful deployment in this area requires a deep understanding of hardware, algorithms, software tools, and applications. Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies. SeLoC-ML enables non-experts to easily model, discover, reuse, and matchmake ML models and devices at scale. The project code can be automatically generated for deployment on hardware based on the matching results. Developers can benefit from semantic application templates, called recipes, to fast prototype end-user applications. The evaluations confirm an engineering effort reduction by a factor of at least three compared to traditional approaches on an industrial ML classification case study, showing the efficiency and usefulness of SeLoC-ML. We share the code and welcome any contributions.
Steam (water vapor) is an important heat carrier with a latent heat value over 2000 kJ kg , which has advantages such as uniform heating, convenient temperature regulation, clean and nontoxic, convenient transportation. Therefore, it has been widely used in industrial production and is mainly provided by industrial boilers. Currently, the research on steam generation mainly focuses on supply energy and efficiency optimization. Fossil fuel, biomass, solar heat, and electric (direct heating) are the input energy source of most boilers. Fossil-fuel boiler is the most widely used boiler in industrial process. The existing boilers are mainly based on the combustion of coal or natural gas. Recently in China, the coal-fired boiler has been restricted to use due to its severe environmental pollution impacts. In this case, natural gas is regarded as a cleaner energy source; however, it is indicated that the gas-fired boilers will also emit gaseous pollutants such as NOX; [3] the high cost of natural gas has increased the cost of production process. Biomass is regarded as carbon-neutral fuel because the CO2 it produced during the combustion process is equal to the amount which was taken from the atmosphere during the growing stage. However, the biomass will also emit PM2.5 and CO during the combustion process, and its resources are not possible to meet the demand for industry heating. Solar heating could also serve as an energy source for steam generation, and current research can be mainly divided into solar heating for industrial processes (SHIP) and small-scale solar-driven steam generation. SHIP with solar collector converts solar irradiation hitting on its surface into thermal energy by heating a suitable heat-transport medium. However, the unavailability at night and the large collector area make this technology difficult for stable industrial steam demand and also the high initial cost may deter the customer. Electricity can be directly used for steam generation in electric boiler, which is quite flexible and easy to use if low-cost electricity is accessible. However, in terms of thermodynamics, electrical boilers have a lower efficiency of its first energy resources (fossil fuel and biomass with efficiency typically between 30% and 50%), whereas electrical-driven heat pump could increase electric heating performance as it absorbs heat from low-temperature heat sources (waste heat, solar heat, or even ambient heat, etc.). There are several heat-pump methods of steam generation appeared to meet the high-temperature industrial steam requirement. Kobe Steel. Ltd. developed two heatpump-based steam supply systems: the high-efficiency steamsupply system with a steam temperature of 120 C and the standard steam-supply system with a steam temperature of 165 C. The heat pump system, which is equipped with a newly developed screw compressor and a high-efficiency motor resistant to high temperatures, uses a refrigerant suitable for high temperature supply. Chamoun et al. proposed a new R718 high-temperature heat-pump system to realize the high temperature output of 145 C. The water vapor heat pump is investigated to satisfy different temperature demands for industrial use with high performance. Wu et al. studied a water vapor high-temperature heat-pump system for waste heat recovery H. Z. Yan, Prof. R. Z. Wang, Dr. S. Du, Dr. B. Hu, Prof. Z. Y. Xu Institution of Refrigeration and Cryogenics MOE Engineering Research Center of Solar Energy Shanghai Jiao Tong University Shanghai 200240, China E-mail: rzwang@sjtu.edu.cn
Cloud native information systems engineering enables scalable and resilient service infrastructures for all major online offerings. These are built following agile development practices. At the same time, a growing demand for privacy-friendly services is articulated by societal norms and policy through effective legislative frameworks. In this paper, we identify the conceptual dimensions of cloud native privacy engineering and propose an integrative approach to be addressed in practice to overcome the shortcomings of existing privacy enhancing technologies. Furthermore, we propose a reference software development lifecycle called DevPrivOps to enhance established agile development methods with respect to privacy. Altogether, we show that cloud native privacy engineering advances the state of the art of privacy by design and by default using latest technologies.
Abstract The swelling pressure of compacted bentonite under complex thermo-chemical conditions is a critical safety parameter of the engineering barrier system for a deep geological repository. To investigate the cyclic thermal and saline effects on the swelling pressure of densely compacted Gaomiaozi (GMZ) bentonite, a series of constant-volume swelling pressure tests were performed using deionized water and NaCl solutions. At 20 °C and 60 °C, the multi-step salinization-desalinization process generated a hysteresis of the swelling pressure, and multiple saline cycles led to its diminishing attenuation. In the framework of the double structure theory, the observed hysteresis of the swelling pressure was ascribed to the irreversible collapse of inter-aggregate pores under osmotic pressure, while the gradual attenuation was attributed to the further homogenization of the clay structure. Soaking tests were also conducted at various thermal and saline conditions to provide evidence for the cyclic saline effects. No salt precipitation was identified due to the negligible changes in the total suctions of the specimens after saline cycles conducted at 20 °C and 60 °C. The membrane effect (resistance of compacted clay to salt migration) was considered, and its degree reflected the changes in the osmotic suctions of the specimens occurred after the soaking tests. The resistance of compacted GMZ bentonite to salt migration was relatively strong during wetting with low saline solutions (0–0.5 mol/L) at 20 °C, and became weak during wetting with high saline solutions (1.0–2.0 mol/L) or at 60 °C. The variation of the swelling pressure during thermal cycling depended on the maximum temperature and concentration of the presaturation solution. By assuming the validity of the membrane effect at the maximum temperatures (valid at 40 °C and invalid at 60 °C), the inconsistent changes in the swelling pressure during thermal cycling were explained by considering the incomplete initial saturation and role of thermal osmosis. However, further verification of the proposed hypothesis by X-ray diffraction and membrane testing at elevated temperatures is required.
Abstract This paper investigates transient heat transfer processes of a vehicle under-bonnet region during natural soak condition using computer aided engineering (CAE). Heat reserved within the engine bay is beneficial to the engine cold-start for potentially reductions in friction losses, CO2 emissions and fuel consumption. Buoyancy-driven convection, thermal radiation and conduction are key contributors to heat transfer processes of engine compartments during soak. In this study, a coupled transient 3D computational fluids dynamics (CFD) – heat transfer modelling method was studied in a passenger vehicle to simulate its 9 h cool-down behaviours. The developed CAE method was able to predict the temperature cool-down of the key fluids of good agreement with experiments. Potential air and heat leakage paths around the engine bay were identified. The flow development during the early stage (0–2 h) of the soak was vital to accurate prediction of the heat transfer coefficients for the heat retention modelling, where convection and radiation have played important parts. Optimum simulation strategy was obtained with reduced simulation time and good prediction accuracy. This further allows the integration of engine encapsulation design for optimising fuel consumption and emissions in a timely and robust manner, aiding the development of low-carbon transport technologies.
Knowledge of the dissociation conditions of mixed-gas hydrate systems is of great importance for scientific understanding (e.g. Clathrate hydrates in the outer solar system) and engineering applications (e.g. flow assurance, refrigeration and separation processes). In this work, CO2+O2 hydrate dissociation points were measured at different O2 mole fractions (11%, 32% and 50%) using isochoric pressure search method. The consistency of these new data was verified using the Clausius-Clapeyron relationship. The measurements performed for pressures up to 19 MPa overcome the lack of data for this system, and also allows to evaluate the model predictions from pure CO2 hydrate to pure O2 hydrate. To predict gas hydrate stability curves, in this work, the well-established hydrate theory of van der Waals and Platteeuw (vdWP) is combined with an electrolyte CPA-type Equation of State (e-PR-CPA EoS) which has been successfully used to represent with high accuracy the fluid phase equilibria (including gas solubility and water content) of complex systems containing gas, water and salt. The resulting model (e-PR-CPA + vdWP) was applied to the O2+H2O and CO2+H2O+(NaCl) systems by comparing with literature data. In the studied temperature range (>270K), the model predicts as expected a hydrate structure of type I for O2, CO2 and their mixtures. An excellent reproduction of the measured data by this complete model was obtained without any additional adjustable parameters.