Owing to the limited amount of test data available for flow coefficient correlation fitting, there is a large deviation when applying the correlation formula to the flow coefficient calculation of valves with different diameters. To solve this problem, the flow characteristics of an electronic expansion valve were studied experimentally. The experimental results indicated that the valve diameter, valve opening, and subcooling were the main factors affecting the flow coefficient of the electronic expansion valve. The flow coefficient decreased with an increase in the valve opening. The flow coefficient of a valve with a large diameter (DPF2.0) was greater than that of a valve with a small diameter (DPF1.65) under the same opening degree, and the flow coefficient increased with the subcooling degree. A correlation formula for the flow coefficient that considered subcooling degree <italic>T</italic><sub>sub</sub> and flow area <italic>A</italic> was fitted using the experimental data, and the relative deviation was calculated to be within ±5% when using this fitting correlation formula.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Chaos Engineering (CE) is an engineering technique aimed at improving the resiliency of distributed systems. It involves artificially injecting specific failures into a distributed system and observing its behavior in response. Based on the observation, the system can be proactively improved to handle those failures. Recent CE tools implement the automated execution of predefined CE experiments. However, defining these experiments and improving the system based on the experimental results still remain manual. To reduce the costs of the manual operations, we propose ChaosEater, a system for automating the entire CE operations with Large Language Models (LLMs). It predefines the agentic workflow according to a systematic CE cycle and assigns subdivided operations within the workflow to LLMs. ChaosEater targets CE for Kubernetes systems, which are managed through code (i.e., Infrastructure as Code). Therefore, the LLMs in ChaosEater perform software engineering tasks to complete CE cycles, including requirement definition, code generation, debugging, and testing. We evaluate ChaosEater through case studies on both small and large Kubernetes systems. The results demonstrate that it stably completes reasonable single CE cycles with significantly low time and monetary costs. The CE cycles are also qualitatively validated by human engineers and LLMs.
Bastiaan Heeren, Fabiano Dalpiaz, Mazyar Seraj
et al.
Software engineering educators strive to continuously improve their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them by benchmarking against observed practices, and ultimately enhance their curricula. In this study, we aim to provide an encompassing analysis of higher education on software engineering by considering the higher educational offering of an entire European country, namely the Netherlands. We leverage a crowd-sourced analysis process by considering 10 Dutch universities and 207 university courses. The courses are analysed via knowledge areas adopted from the SWEBOK. The mapping process is refined via homogenisation and internal consistency improvement phases, and is followed by a data analysis phase. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively covered at Master level. We identify three clusters of tightly coupled knowledge areas: (i) requirements, architecture, and design, (ii) testing, verification, and security, and (iii) process-oriented and DevOps topics. Dutch universities generally cover all knowledge areas uniformly, with minor deviations reflecting institutional research strengths. Our results highlight correlations among key knowledge areas and their potential for enhancing integrated learning. We also identify underrepresented areas, such as software engineering economics, which educators may consider including in curricula. We invite researchers to use our research method in their own geographical region, in order to contrast software engineering education programs across the globe.
Artificial Intelligence (AI) technology such as Large Language Models (LLMs) have become extremely popular in creating code. This has led to the conjecture that future software jobs will be exclusively conducted by LLMs, and the software industry will cease to exist. But software engineering is much more than producing code -- notably, \emph{maintaining} large software and keeping it reliable is a major part of software engineering, which LLMs are not yet capable of.
Nguyen Thi My Duc, Hariharan Srikanth, Manh-Huong Phan
The magnetocaloric effect (MCE) provides a promising foundation for the development of solid-state refrigeration technologies that could replace conventional gas compression-based cooling systems. Current research efforts primarily focus on identifying cost-effective magnetic materials that exhibit large MCEs under low magnetic fields across broad temperature ranges, thereby enhancing cooling efficiency. However, practical implementation of magnetic refrigeration requires more than bulk materials; real-world devices demand efficient thermal management and compact, scalable architectures, often achieved through laminate designs or miniaturized geometries. Magnetocaloric materials with reduced dimensionality, such as ribbons, thin films, microwires, and nanostructures, offer distinct advantages, including improved heat exchange, mechanical flexibility, and integration potential. Despite these benefits, a comprehensive understanding of how size, geometry, interfacial effects, strain, and surface phenomena influence the MCE remains limited. This review aims to address these knowledge gaps and provide guidance for the rational design and engineering of magnetocaloric materials tailored for high-performance, energy-efficient magnetic refrigeration systems.
Cristina Martinez Montes, Robert Feldt, Cristina Miguel Martos
et al.
As artificial intelligence advances, large language models (LLMs) are entering qualitative research workflows, yet no reproducible methods exist for integrating them into established approaches like thematic analysis (TA), one of the most common qualitative methods in software engineering research. Moreover, existing studies lack systematic evaluation of LLM-generated qualitative outputs against established quality criteria. We designed and iteratively refined prompts for Phases 2-5 of Braun and Clarke's reflexive TA, then tested outputs from multiple LLMs against codes and themes produced by experienced researchers. Using 15 interviews on software engineers' well-being, we conducted blind evaluations with four expert evaluators who applied rubrics derived directly from Braun and Clarke's quality criteria. Evaluators preferred LLM-generated codes 61% of the time, finding them analytically useful for answering the research question. However, evaluators also identified limitations: LLMs fragmented data unnecessarily, missed latent interpretations, and sometimes produced themes with unclear boundaries. Our contributions are threefold. First, a reproducible approach integrating refined, documented prompts with an evaluation framework to operationalize Braun and Clarke's reflexive TA. Second, an empirical comparison of LLM- and human-generated codes and themes in software engineering data. Third, guidelines for integrating LLMs into qualitative analysis while preserving methodological rigour, clarifying when and how LLMs can assist effectively and when human interpretation remains essential.
In contrast with the conventional drying technology, a novel air cycle heat pump drying (ACHPD) system, which uses air as its working medium, is proposed to solve the problems of low energy efficiency and eliminate the use of HCFCs/HFCs. A simulation model is established, and its verification via experimental results from a constructed test bench indicates errors of the drying rate within ±0.01% and the system power within ±7%. Compared with a conventional electric heater drying (EHD) system, the energy-saving rate of the ACHPD system reaches 15%–27% when the water evaporation rate increases from 0.75 kg/h to 3.45 kg/h. The increase of the inlet air temperature is 10–40 ℃ and the relative humidity is 30%–80%, which reduces the moisture extraction ratio (MER) by approximately 7% and 21%, respectively. The efficiency improvements of the expander and compressor are 0.55–0.8 and 0.4–0.9, respectively, which can deteriorate the energy efficiency of the system by 17% and 36%, respectively. The research results provide new technical references for the application of heat pump technology in the drying field.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Disability studies is an interdisciplinary field of studies based on the grounds of humanities and social sciences rather than viewing disability through the prism of medicine or psychology. It is an academic discipline that surfaced in the late 20th century, emanating from the disability rights movement and critical theory. This paper tries to examine Preeti Monga’s novels, The Other Senses: An Inspiring True Story of a Visually Impaired and Flight Without Sight, considering disability studies, marginalization, and oppression that disable the experience of people in private and public lives.
Taiwo A. Adebiyi, Nafeezat A. Ajenifuja, Ruda Zhang
Digital twin (DT) technology has received immense attention over the years due to the promises it presents to various stakeholders in science and engineering. As a result, different thematic areas of DT have been explored. This is no different in specific fields such as manufacturing, automation, oil and gas, and civil engineering, leading to fragmented approaches for field-specific applications. The civil engineering industry is further disadvantaged in this regard as it relies on external techniques by other engineering fields for its DT adoption. A rising consequence of these extensions is a concentrated application of DT to the operations and maintenance phase. On another spectrum, Building Information Modeling (BIM) is pervasively utilized in the planning/design phase, and the transient nature of the construction phase remains a challenge for its DT adoption. In this paper, we present a phase-based development of DT in the Architecture, Engineering, and Construction industry. We commence by presenting succinct expositions on DT as a concept and as a service, and establish a five-level scale system. Furthermore, we present separately a systematic literature review of the conventional techniques employed at each civil engineering phase. In this regard, we identified enabling technologies such as computer vision for extended sensing and the Internet of Things for reliable integration. Ultimately, we attempt to reveal DT as an important tool across the entire life cycle of civil engineering projects, and nudge researchers to think more holistically in their quest for the integration of DT for civil engineering applications.
Resilient cyber-physical systems comprise computing systems able to continuously interact with the physical environment in which they operate, despite runtime errors. The term resilience refers to the ability to cope with unexpected inputs while delivering correct service. Examples of resilient computing systems are Google's PageRank and the Bubblesort algorithm. Engineering for resilient cyber-physical systems requires a paradigm shift, prioritizing adaptability to dynamic environments. Software as a tool for self-management is a key instrument for dealing with uncertainty and embedding resilience in these systems. Yet, software engineers encounter the ongoing challenge of ensuring resilience despite environmental dynamic change. My thesis aims to pioneer an engineering discipline for resilient cyber-physical systems. Over four years, we conducted studies, built methods and tools, delivered software packages, and a website offering guidance to practitioners. This paper provides a condensed overview of the problems tackled, our methodology, key contributions, and results highlights. Seeking feedback from the community, this paper serves both as preparation for the thesis defense and as insight into future research prospects.
Michael Unterkalmsteiner, Robert Feldt, Tony Gorschek
Requirements Engineering and Software Testing are mature areas and have seen a lot of research. Nevertheless, their interactions have been sparsely explored beyond the concept of traceability. To fill this gap, we propose a definition of requirements engineering and software test (REST) alignment, a taxonomy that characterizes the methods linking the respective areas, and a process to assess alignment. The taxonomy can support researchers to identify new opportunities for investigation, as well as practitioners to compare alignment methods and evaluate alignment, or lack thereof. We constructed the REST taxonomy by analyzing alignment methods published in literature, iteratively validating the emerging dimensions. The resulting concept of an information dyad characterizes the exchange of information required for any alignment to take place. We demonstrate use of the taxonomy by applying it on five in-depth cases and illustrate angles of analysis on a set of thirteen alignment methods. In addition, we developed an assessment framework (REST-bench), applied it in an industrial assessment, and showed that it, with a low effort, can identify opportunities to improve REST alignment. Although we expect that the taxonomy can be further refined, we believe that the information dyad is a valid and useful construct to understand alignment.
A dynamic model is proposed for photopolymerization of 1,6-hexanediol diacrylate (HDDA) using bifunctional initiator (BAPO). The proposed model accounts for branching, backbiting and cyclization reactions, and for diffusion-dependent reaction rates during photopolymerization. The proposed model contains 40 adjustable kinetic and free-volume parameters. Experimental data available for parameter estimation are vinyl group conversions obtained using a variety of light intensities and exposure times, and monomer conversions for three experiments. Systematic parameter ranking and estimation is used to evaluate the influence of phenomena included in the model on the quality of the fit. Estimation and ranking results indicate that branching, backbiting, and cyclization reactions have important influences on conversion. Reactions involving two large molecules and propagation reactions become diffusion-dependent. Incorporating diffusion-dependent initiator efficiency results in improved model predictions. The homopolymer and copolymers of 2-(dimethylamino)ethyl methacrylate (DMAEMA) are triggered by external stimuli (e.g., pH, temperature, ionic strength and carbon dioxide (CO 2 )). There are numerous applications of these polymers such as coatings, drug delivery, gene delivery, tissue engineering and polymeric surfactants. The development of the polymers have been widely investigated for stimuli-responsiveness; however, the reaction mechanisms and kinetics have not been fully explored for process and product development. In this work, the solution free radical polymerization of DMAEMA is investigated in organic, organic-aqueous and aqueous solvents. The propagation rate coefficient, 𝑘 𝑝 , of DMAEMA is probed by the pulsed laser polymerization – size exclusion chromatography (PLP-SEC) technique. Bulk and solution homopolymerizations are conducted in 1-butanol (BuOH), ethanol (EtOH) and EtOH/H 2 O mixture. When 50 wt% DMAEMA was present in BuOH, the 𝑘 𝑝 decreased slightly from the bulk values (7%). A similar trend was observed in EtOH, where a further decrease occurred in the polar solvent (12%). In this study, graphene hydrogels formed through the reduction of a graphene oxide (GO) dispersion in water are used as electrodes in a supercapacitor. Graphene has a theoretical surface area of 2600 m 2 /g and using graphene gels formed of reduced graphene oxide a high surface area of 1300m 2 /g was achieved to give high energy storage. Combining these electrodes with a non-aqueous electrolyte with a wide electrochemical window even higher energy storage can be achieved. 1, 3- ethyl methylimidazolium Tetrafluoroborate is used as the ionic liquid in this study for its combination of high ionic conductivity of 16 mS/cm and wide electrochemical window of 3.6V. the spreading of the spikes, the oblate prolate capsids. rigid bead-rod role of such coronavirus cross-sectional ellipticity on its rotational diffusivity, the transport property around which its cell attachment revolves. We learn that coronavirus ellipticity drastically decreases its rotational diffusivity, it oblate or prolate. Low-power multi high gain observers (LP MHGO) are proven to be effective in reducing the peaking of state estimation of nonlinear systems to an arbitrarily small magnitude. Moreover, they reduce the sensitivity of estimates to measurement noise. They also relax the numerical implementation problem of high gain observers by using gains powered up to the order of 2 instead of n. In this work, we aim to further improve the noise sensitivity of these observers by employing low-pass filters in the observer dynamics. The main results establish the convergence of the estimation error to zero with an arbitrarily small decay rate in the absence of noise, as well as an input to state stability feature when the noise is present. We also demonstrate in the linear case that the proposed observer improves the upper bound on the estimates. Simulation results compare the performance of the proposed observer with similar works and show the effectiveness of the proposed method. Cellulose nanocrystals (CNCs) are attractive reinforcing agents for polymer nanocomposites, due their excellent mechanical properties, natural abundance, and sustainable value proposition. Applications are limited, as the hydrophilicity of the CNC surface prevents effective dispersion in hydrophobic polymer matrices. Grafting hydrophobic polymers to the CNC surface has facilitated the formation of CNC-reinforced polymer nanocomposites, while introducing complexity and cost. This work aims to simplify the modification of CNCs by substituting polymer grafting in favour of non-covalent techniques. We demonstrate that cationic copolymers can electrostatically adsorb to anionic CNCs and form stable CNC dispersions in non-polar solvents. These results demonstrate that non-covalent modifications have the potential to improve the economic feasibility of CNC-reinforced polymer nanocomposites. Linear model predictive control is used in a wide variety of process industries. Model predictive controllers rely on optimizers to calculate adjustments in manipulated variables to ensure that operating constraints are obeyed and that controlled variables are maintained near their setpoints. There is significant industrial interest in preventing erratic behaviour of model predictive controllers caused by imperfect models with ill-conditioned gain matrices. Currently, control practitioners use time-consuming processes based on the relative gain array or singular-value thresholding to condition their gain matrices to prevent degraded controller performance. In the proposed new approach, we extend a widely-used parameter ranking algorithm to identify manipulated variables with highly correlated influence. A linear optimization algorithm is then used to find optimal gain adjustments to condition the gain matrix. To test the effectiveness of the proposed algorithm, we investigate 3 case studies based on a fluid catalytic cracker.
Accurate chiller models are important for the energy conservation of chillers. The DOE-2 model is the most common chiller model. Parameter identification and error compensation are crucial for the development of an accurate DOE-2 model. However, the parameter identification of a DOE-2 model of an actual chiller is usually challenging because chillers are usually equipped with limited sensors and the quality of actual data is usually low. To address the above issues, a genetic algorithm based on an external knowledge base for parameter identification and artificial-neural-network-based (ANN-based) error compensation method are proposed. The results show that the proposed genetic algorithm can significantly reduce the computation load of parameter identification of the DOE-2 model. It can also significantly improve the accuracy of the DOE-2 model. Moreover, the accuracy of the DOE-2 model with the ANN-based error compensation is significantly higher than that of the DOE-2 model without error compensation. The MAE, RMSE, MAPE, and CV-RMSE of the model with error compensation in predicting the chilled water outlet temperature were reduced by 36.49%, 46.00%, 33.16%, and 45.73%, respectively, while R2 of the model with error compensation was increased by 25.75%.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Many scientific and practical areas have shown increasing interest in reaping the benefits of blockchain technology to empower software systems. However, the unique characteristics and requirements associated with Blockchain Based Software (BBS) systems raise new challenges across the development lifecycle that entail an extensive improvement of conventional software engineering. This article presents a systematic literature review of the state-of-the-art in BBS engineering research from a software engineering perspective. We characterize BBS engineering from the theoretical foundations, processes, models, and roles and discuss a rich repertoire of key development activities, principles, challenges, and techniques. The focus and depth of this survey not only gives software engineering practitioners and researchers a consolidated body of knowledge about current BBS development but also underpins a starting point for further research in this field.
Rohit Gupta, Sieglinde Kranz, Nikolaus Regnat
et al.
Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling.
While the consequences of mathematically-based software, algorithms and strategies have become ever wider and better appreciated, ethical reflection on mathematics has remained primitive. We review the somewhat disconnected suggestions of commentators in recent decades with a view to piecing together a coherent approach to ethics in mathematics. Calls for a Hippocratic Oath for mathematicians are examined and it is concluded that while lessons can be learned from the medical profession, the relation of mathematicians to those affected by their work is significantly different. There is something to be learned also from the codes of conduct of cognate but professionalised quantitative disciplines such as engineering and accountancy, as well as from legal principles bearing on professional work. We conclude with recommendations that professional societies in mathematics should sponsor an (international) code of ethics, institutional mission statements for mathematicians and syllabuses for incorporation into mathematics degrees.
Ehsan Zabardast, Julian Frattini, Javier Gonzalez-Huerta
et al.
During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product's final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets' characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.