The vitrification preservation methods for cell microdroplets have received considerable attention in the field of cryopreservation. However， the vitrification process for microdroplets has limitations such as uncontrollable cooling/rewarming processes. In this study， a cryopreservation system for controllable rapid cooling and rewarming during the vitrification of cell microdroplets was constructed by combining the lifting of a moving slide table with the Joule heating rewarming method. This system achieves rapid and controllable cooling by adjusting the speed of immersion in liquid nitrogen and realizes a fast rewarming process by controlling the heating time and current intensity of Joule heating. Thus， it realizes the vitrification of microdroplets and significantly prevents damage to cells caused by devitrification during the droplet rewarming process. The results show that the cooling rate controlled by this system attained 1.8 × 10⁴ ℃/min. This enabled the vitrification preservation of cell microdroplets with a relatively low concentration of cryoprotectant. The rewarming rate control attained 4.0 × 10⁴ ℃/min. This effectively prevented the occurrence of devitrification and ice crystal regrowth during the rewarming process of the relatively low concentration of cryoprotectant. Cryopreservation of A549 cells in microdroplets verified that the survival rate after cryopreservation and resuscitation using this system is significantly higher than that after conventional straw preservation. The research presented in this paper is likely to provide new solutions for the automatic vitrification preservation and rewarming of cell microdroplets.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
This paper investigates how software professionals perceive the economic implications of diversity in software engineering teams. Motivated by a gap in software engineering research, which has largely emphasized socio-technical and process-related outcomes, we adopted a qualitative interview approach to capture practitioners' reasoning about diversity in relation to economic and market-oriented considerations. Based on interviews with ten software professionals, our analysis indicates that diversity is perceived as economically relevant through its associations with cost reduction and containment, revenue generation, time to market, process efficiency, innovation, and market alignment. Participants typically grounded these perceptions in concrete project experiences rather than abstract economic reasoning, framing diversity as a practical resource that supports project delivery, competitiveness, and organizational viability. Our findings provide preliminary empirical insights into how economic aspects of diversity are understood in software engineering practice.
Nathaniel O’Connor, Jacob Adams, Matthew Jones
et al.
Abstract Cryogenic refrigerators can be broadly classified as either continuous flow machines or oscillating flow machines. The acoustic expander is a new hybrid approach that combines the best aspects of these two machines. Globally, the working fluid moves continuously through the recuperative heat exchanger of the cycle while locally the working fluid oscillates in the acoustic expander. This concept has been demonstrated experimentally through the use of reed-valves coupled to an acoustic resonator. This work develops a high-fidelity numerical model that captures non-linear acoustic effects for the future design and optimization of these acoustic expanders. The numerical model solves the fully compressible Navier-stokes equations for various 2D and 3D resonator geometries including both harmonic quarter wave resonators and non-harmonic resonators. The reed valve behavior is simplified using pressure-dependent boundary conditions that can be tuned to represent a variety of reed characteristics. The coupled reed-resonator system spontaneously oscillates at its natural resonance frequency and the numerical model predicts the resonator quality factor and isentropic expansion efficiency. Finally, the numerical model predictions are validated by experimental data. The acoustic expander unlocks new cryogenic cooling paradigms with applications to superconducting magnets and electronics, infrared imaging, quantum sensing, and cryogenic propellant management.
Because the volume of the variable base circular involute scroll compressor can be reduced under the premise of obtaining the same cooling capacity, it can meet the small and lightweight requirements of the vehicle air conditioning compressor. In order to improve the isentropic efficiency and volumetric efficiency of the scroll compressor, a mathematical and geometric model of the scroll disc with variable base circular involute is established. With the variable index k and the modified increment δ0 as variables, the internal flow field of the scroll compressor is numerically simulated and hydrodynamic analysis is carried out. The numerical results show that when the parameters of the variable base circular line are k=1 and δ0 =-0.03, the specific dissipation rate of the fluid in the compressor working chamber is 180.28 s-1, which is 103.11 s-1 lower than that of 283.39 s-1 of the flow field in the fixed base circular compressor. The isentropic efficiency of scroll compressor can be improved by reducing the energy loss caused by turbulent kinetic energy dissipation. The performance test of electric vehicle air conditioning scroll compressor is carried out. Compared with fixed base scroll compressor, the input power of variable base scroll compressor with k=1 and δ0 =-0.03 decreases by 1.392%, and the performance coefficient COPel increases by 4.204%.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
The adoption of large language models (LLMs) and autonomous agents in software engineering marks an enduring paradigm shift. These systems create new opportunities for tool design, workflow orchestration, and empirical observation, while fundamentally reshaping the roles of developers and the artifacts they produce. Although traditional empirical methods remain central to software engineering research, the rapid evolution of AI introduces new data modalities, alters causal assumptions, and challenges foundational constructs such as "developer", "artifact", and "interaction". As humans and AI agents increasingly co-create, the boundaries between social and technical actors blur, and the reproducibility of findings becomes contingent on model updates and prompt contexts. This vision paper examines how the integration of LLMs into software engineering disrupts established research paradigms. We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments. Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.
Joshua Owotogbe, Indika Kumara, Dario Di Nucci
et al.
Chaos engineering aims to improve the resilience of software systems by intentionally injecting faults to identify and address system weaknesses that cause outages in production environments. Although many tools for chaos engineering exist, their practical adoption is not yet explored. This study examines 971 GitHub repositories that incorporate 10 popular chaos engineering tools to identify patterns and trends in their use. The analysis reveals that Toxiproxy and Chaos Mesh are the most frequently used, showing consistent growth since 2016 and reflecting increasing adoption in cloud-native development. The release of new chaos engineering tools peaked in 2018, followed by a shift toward refinement and integration, with Chaos Mesh and LitmusChaos leading in ongoing development activity. Software development is the most frequent application (58.0%), followed by unclassified purposes (16.2%), teaching (10.3%), learning (9.9%), and research (5.7%). Development-focused repositories tend to have higher activity, particularly for Toxiproxy and Chaos Mesh, highlighting their industrial relevance. Fault injection scenarios mainly address network disruptions (40.9%) and instance termination (32.7%), while application-level faults remain underrepresented (3.0%), highlighting for future exploration.
Vivekanand Tiwari, Zhaojin Liu, Hao-Cheng Weng
et al.
Nitrogen-vacancy centres in nanodiamonds (NDs) provide a promising resource for quantum photonic systems. However, developing a technology beyond proof-of-principle physics requires optimally engineering its component parts. In this work, we present a hybrid materials platform by photolithographically positioning ball-milled isotopically-enriched NDs on broadband metal reflectors. The structure enhances the photonic collection efficiency, enabling cryogenic characterisation despite the limited numerical aperture imposed by our cryostat. Our device, with SiO$_2$ above a silver reflector, allows us to perform spectroscopic characterisation at 16 K and measure autocorrelation functions confirming single-photon emission (g$^2$(0)<0.5). Through comparative studies of similar hybrid device configurations, we can move towards optimally engineered techniques for building and analysing quantum emitters in wafer-scale photonic environments.
Vincenzo De Martino, Mohammad Amin Zadenoori, Xavier Franch
et al.
Language Models are increasingly applied in software engineering, yet their inference raises growing environmental concerns. Prior work has examined hardware choices and prompt length, but little attention has been paid to linguistic complexity as a sustainability factor. This paper introduces Green Prompt Engineering, framing linguistic complexity as a design dimension that can influence energy consumption and performance. We conduct an empirical study on requirement classification using open-source Small Language Models, varying the readability of prompts. Our results reveal that readability affects environmental sustainability and performance, exposing trade-offs between them. For practitioners, simpler prompts can reduce energy costs without a significant F1-score loss; for researchers, it opens a path toward guidelines and studies on sustainable prompt design within the Green AI agenda.
Egor Klimov, Muhammad Umair Ahmed, Nikolai Sviridov
et al.
Bus factor (BF) is a metric that tracks knowledge distribution in a project. It is the minimal number of engineers that have to leave for a project to stall. Despite the fact that there are several algorithms for calculating the bus factor, only a few tools allow easy calculation of bus factor and convenient analysis of results for projects hosted on Git-based providers. We introduce Bus Factor Explorer, a web application that provides an interface and an API to compute, export, and explore the Bus Factor metric via treemap visualization, simulation mode, and chart editor. It supports repositories hosted on GitHub and enables functionality to search repositories in the interface and process many repositories at the same time. Our tool allows users to identify the files and subsystems at risk of stalling in the event of developer turnover by analyzing the VCS history. The application and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/bus-factor-explorer. The demonstration video can be found on YouTube: https://youtu.be/uIoV79N14z8
Eriks Klotins, Michael Unterkalmsteiner, Tony Gorschek
Background - Startup companies are becoming important suppliers of innovative and software intensive products. The failure rate among startups is high due to lack of resources, immaturity, multiple influences and dynamic technologies. However, software product engineering is the core activity in startups, therefore inadequacies in applied engineering practices might be a significant contributing factor for high failure rates. Aim - This study identifies and categorizes software engineering knowledge areas utilized in startups to map out the state-of-art, identifying gaps for further research. Method - We perform a systematic literature mapping study, applying snowball sampling to identify relevant primary studies. Results - We have identified 54 practices from 14 studies. Although 11 of 15 main knowledge areas from SWEBOK are covered, a large part of categories is not. Conclusions - Existing research does not provide reliable support for software engineering in any phase of a startup life cycle. Transfer of results to other startups is difficult due to low rigor in current studies.
Background: Risk-taking is prevalent in a host of activities performed by software engineers on a daily basis, yet there is scant research on it. Aims and Method: We study if software engineers' risk-taking is affected by framing effects and by software engineers' personality. To this end, we perform a survey experiment with 124 software engineers. Results: We find that framing substantially affects their risk-taking. None of the "Big Five" personality traits are related to risk-taking in software engineers after correcting for multiple testing. Conclusions: Software engineers and their managers must be aware of framing effects and account for them properly.
AbstractIn this study, the tribological properties of drawn PTFE were tested on a CSM tribometer at room temperature and cryogenic temperature. The orientation degree and worn surface morphology of drawn PTFE were studied and correlated with the friction and wear behaviors. It was found that the sample with low friction coefficient corresponded to high orientation degree in the parallel direction at room temperature, while the friction coefficient barely changed with increasing draw ratio in the perpendicular direction. The friction coefficient of drawn PTFE at liquid nitrogen temperature is higher than that at room temperature in the perpendicular direction, while the two values are almost undistinguishable within error range in the parallel direction. The wear volume of drawn PTFE at liquid nitrogen temperature is lower than that at room temperature in both perpendicular and parallel directions due to the mitigation of adhesive wear of drawn PTFE under liquid nitrogen temperature.
In this study, simulation and experimental testing were carried out in a 28 kW variable refrigerant flow system to evaluate the contributions to annual performance factor (APF) from key factors, including heat exchanger area, compressor efficiency, refrigerant charge, system pressure loss, and oil return system. The APF values obtained experimentally and numerically are 4.59 and 4.61, respectively. Meanwhile, the maximum deviation between simulation and experiment is 5.1% at a single test condition, and the deviation of the APF is 0.5%. The results show that for the experimental system, the improvement in a single factor has a limited effect on the APF. For example, if the compressor efficiency increases by 3%, the APF increases by 2.8%. The optimization of the oil return system leads to an approximately 5% increase in the APF. A 10% increase in KA of the heat exchanger results in a 1% improvement of the APF. Therefore, many factors must be considered simultaneously to improve the APF.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
As software engineering research becomes more concerned with the psychological, sociological and managerial aspects of software development, relevant theories from reference disciplines are increasingly important for understanding the field's core phenomena of interest. However, the degree to which software engineering research draws on relevant social sciences remains unclear. This study therefore investigates the use of social science theories in five influential software engineering journals over 13 years. It analyzes not only the extent of theory use but also what, how and where these theories are used. While 87 different theories are used, less than two percent of papers use a social science theory, most theories are used in only one paper, most social sciences are ignored, and the theories are rarely tested for applicability to software engineering contexts. Ignoring relevant social science theories may (1) undermine the community's ability to generate, elaborate and maintain a cumulative body of knowledge; and (2) lead to oversimplified models of software engineering phenomena. More attention to theory is needed for software engineering to mature as a scientific discipline.
Based on a quantum thermodynamic protocol for shortcut to isothermality that smoothly modify the system-reservoir interaction can significantly speed up an isothermal process while keeping the overall dissipation constant [Phys. Rev. X. 10, 031015 (2020)], we extend the study of optimization performance of Carnot-like heat engines and refrigerators in a straightforward and unified way. We derive the universal optimization efficiency of heat engines and the optimization coefficient of performance of refrigerators under two unified optimization criterions, i.e., chi criterion and omega criterion. We also derived the universal lower and upper bounds for heat engines and refrigerators, and found that these bounds can be reached under extremely asymmetric cases.
Background: Academic search engines (i.e., digital libraries and indexers) play an increasingly important role in systematic reviews however these engines do not seem to effectively support such reviews, e.g., researchers confront usability issues with the engines when conducting their searches. Aims: To investigate whether the usability issues are bugs (i.e., faults in the search engines) or constraints, and to provide recommendations to search-engine providers and researchers on how to tackle these issues. Method: Using snowball-sampling from tertiary studies, we identify a set of 621 secondary studies in software engineering. By physically re-attempting the searches for all of these 621 studies, we effectively conduct regression testing for 42 search engines. Results: We identify 13 bugs for eight engines, and also identify other constraints. We provide recommendations for tackling these issues. Conclusions: There is still a considerable gap between the search-needs of researchers and the usability of academic search engines. It is not clear whether search-engine developers are aware of this gap. Also, the evaluation, by academics, of academic search engines has not kept pace with the development, by search-engine providers, of those search engines. Thus, the gap between evaluation and development makes it harder to properly understand the gap between the search-needs of researchers and search-features of the search engines.
The use of conceptual models to foster requirements engineering has been proposed and evaluated as beneficial for several decades. For instance, goal-oriented requirements engineering or the specification of scenarios are commonly done using conceptual models. Bringing such model-based requirements engineering approaches into industrial practice typically requires industrial training. In this paper, we report lessons learned from a training program for teaching industry professionals model-based requirements engineering. Particularly, we as educators and learners report experiences from designing the training program, conducting the actual training, and applying the instructed material in our day-to-day work. From these findings we provide guidelines for educators designing requirements engineering courses for industry professionals.
Vinicius G. Goecks, Nicholas Waytowich, David Watkins-Valls
et al.
Real-world tasks of interest are generally poorly defined by human-readable descriptions and have no pre-defined reward signals unless it is defined by a human designer. Conversely, data-driven algorithms are often designed to solve a specific, narrowly defined, task with performance metrics that drives the agent's learning. In this work, we present the solution that won first place and was awarded the most human-like agent in the 2021 NeurIPS Competition MineRL BASALT Challenge: Learning from Human Feedback in Minecraft, which challenged participants to use human data to solve four tasks defined only by a natural language description and no reward function. Our approach uses the available human demonstration data to train an imitation learning policy for navigation and additional human feedback to train an image classifier. These modules, combined with an estimated odometry map, become a powerful state-machine designed to utilize human knowledge in a natural hierarchical paradigm. We compare this hybrid intelligence approach to both end-to-end machine learning and pure engineered solutions, which are then judged by human evaluators. Codebase is available at https://github.com/viniciusguigo/kairos_minerl_basalt.
Given the data-intensive and collaborative trend in science, the software engineering community also pays increasing attention to obtaining valuable and useful insights from data repositories. Nevertheless, applying data science to software engineering (e.g., mining software repositories) can be blindfold and meaningless, if lacking a suitable knowledge architecture (KA). By observing that software engineering practices are generally recorded through a set of factors (e.g., programmer capacity, different environmental conditions, etc.) involved in various software project aspects, we propose a factor-based hierarchical KA of software engineering to help maximize the value of software repositories and inspire future software data-driven studies. In particular, it is the organized factors and their relationships that help guide software engineering knowledge mining, while the mined knowledge will in turn be indexed/managed through the relevant factors and their interactions. This paper explains our idea about the factorial KA and concisely demonstrates a KA component, i.e. the early-version KA of software product engineering. Once fully scoped, this proposed KA will supplement the well-known SWEBOK in terms of both the factor-centric knowledge management and the coverage/implication of potential software engineering knowledge.