Abstract Nowadays, the magnetic materials with special functional characteristics are played very important roles in the development of our present modern society. The magnetic refrigeration (MR) technology which is based on the magnetocaloric effect (MCE) of magnetic solids has been considered as an energy-efficient alternative method to our present well used gas compression/expression refrigeration technology. The commercial products of magnetic refrigerators are still in the developing stage, searching and designing magnetic solids with outstanding MCE performances is one of the most important tasks at present. This paper briefly reviewed our recent progress in the investigation of the crystal structure, magnetic properties and magnetocaloric performances in several series of binary and ternary rare earth (RE) based intermetallic compounds. Some of them have been found to exhibit promising magnetocaloric performances at low temperatures which make them be considerable for cryogenic MR application.
We explore the concept of folklore within software engineering, drawing from folklore studies to define and characterize narratives, myths, rituals, humor, and informal knowledge that circulate within software development communities. Using a literature review and thematic analysis, we curated exemplar folklore items (e.g., beliefs about where defects occur, the 10x developer legend, and technical debt). We analyzed their narrative form, symbolic meaning, occupational relevance, and links to knowledge areas in software engineering. To ground these concepts in practice, we conducted semi-structured interviews with 12 industrial practitioners in Sweden to explore how such narratives are recognized or transmitted within their daily work and how they affect it. Synthesizing these results, we propose a working definition of software engineering folklore as informally transmitted, traditional, and emergent narratives and heuristics enacted within occupational folk groups that shape identity, values, and collective knowledge. We argue that making the concept of software engineering folklore explicit provides a foundation for subsequent ethnography and folklore studies and for reflective practice that can preserve context-effective heuristics while challenging unhelpful folklore.
Material selection problem of cryogenic storage tank the material selection for cryogenic storage tanks is a critical and complex problem due to the extreme operating conditions and specific requirements of cryogenic applications. Cryogenic storage tanks are designed to store liquefied gases at very low temperatures, typically below -150°C (-238°F) Common materials used for cryogenic storage tanks include stainless steels, aluminum alloys, and certain low-temperature carbon steels. Additionally, specialized materials such as nickel alloys or composite materials may be utilized for specific applications where enhanced properties are required. The material selection process for cryogenic storage tanks involves a comprehensive evaluation of the aforementioned factors, often utilizing material databases, testing, and simulation tools. It is essential to consult with experts in cryogenic engineering and consider the specific requirements of the intended application to ensure the optimal material choice for cryogenic storage tanks. he Weighted Aggregated Sum Product Assessment (WASPAS) methodology is a multi-criteria decision-making (MCDM) technique used to solve problems where multiple criteria need to be considered for decision-making. The WASPAS methodology allows decision-makers to systematically evaluate alternatives based on multiple criteria and their relative importance. It provides a structured approach for decision-making that takes into account the preferences and priorities of the decision-maker. The weights assigned to the criteria play a crucial role in determining the final ranking of the alternatives. 2024 aluminium in the T6 temper, 2024 aluminium in the O temper, 301 Full Hard Tempered Stainless Steel, Stainless Steel 310, TC4, Ti64, or ASTM Grade 5, nickel-chromium-molybdenum super alloy, Brass. Yield Strength, Elastic Modulus, Toughness Index, Density, Specific Heat, Thermal Expansion. From the result we can see that ss301-FH got 1st rank and AL5052-0 got last rank. by using waspas method we obtained that ss301-FH got 1st rank and AL5052-0 got last rank .
Lalatendu Pattanayak, Dr. Taraprasad Mohapatra, B. Padhi
Liquefied natural gas (LNG) regasification releases a significant amount of cold energy. As a result, the use of LNG for cold energy has gained attention in both academic and engineering study. In the case of high-pressure LNG (meeting the demands of the gas supply networks following re-gasification), applying organic Rankine cycle (ORC) and seawater as heat source results in a significant exergy loss and relatively poor power generation. The purpose of this work was to propose a cryogenic power production system based on direct expansion cycle (DEC) for the effective use of LNG cold and pressure energy. The proposed system utilizes the low-temperature condensate from heat recovery steam generator (HRSG) served as a low-grade heat source to reheat the re-gasified LNG to eliminate the use of seawater as a heat source. The LNG flow rate m˙LNG, temperature T4 and pressure P4 at the expander inlet selected for sensitivity analysis. Then a multi-objective optimization technique using response surface methodology (RSM) is employed to maximize the thermal efficiency ηth and exergy efficiency ηex and minimizing the exergy destruction. ANOVA analysis is used to verify the model adequacy, and the constructed model capacity to accurately predict the output responses is examined. Sensitivity analysis is used to recognize and rank different key parameters in order of relevance. The proposed system design demonstrated ηex increased up to 15.43% and ηth of 16.2% at m˙LNG/P4 of 100 kg s−1/100 bar.
To simulate and optimize an enhanced vapor-injection system, it is necessary to develop a vapor-injection scroll compressor model with fast calculation speed, high accuracy, good extrapolation accuracy, and few parameters for computation. However, existing models cannot meet these demands simultaneously. In this study, a physics-based explicit form semi-empirical model of a scroll compressor with vapor injection was developed to predict its mass flow rate, input power, and discharge temperature. In this model, the suction mass flow rate was derived by correcting the pressure ratio using the specific heat ratio and multiplying it by the quadratic function of frequency. The injection mass flow rate was based on the assumption of an isochoric mixing process and obtained by expanding the coefficients. The discharge flow rate was the sum of the suction and injection mass flow rates. The input power was based on the assumption of isentropic compression and corrected by pressure, and the discharge temperature model was based on the heat leakage factor. The model was validated based on experimental data, and the results showed that the model had a calculation speed of milliseconds, and was able to accurately predict the performance of the compressor, with the average deviations of the suction mass flow rate and discharge mass flow rate both within 2%, and the average deviations of the injection mass flow rate, input power, and discharge temperature within 5%, 3%, and 3 ℃, respectively. The model can provide reasonable results outside the range of fitted conditions, and the amount of data required for model fitting has been reduced by more than 50% compared to that of existing models.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Sentiment analysis is an essential technique for investigating the emotional climate within developer teams, contributing to both team productivity and project success. Existing sentiment analysis tools in software engineering primarily rely on English or non-German gold-standard datasets. To address this gap, our work introduces a German dataset of 5,949 unique developer statements, extracted from the German developer forum Android-Hilfe.de. Each statement was annotated with one of six basic emotions, based on the emotion model by Shaver et al., by four German-speaking computer science students. Evaluation of the annotation process showed high interrater agreement and reliability. These results indicate that the dataset is sufficiently valid and robust to support sentiment analysis in the German-speaking software engineering community. Evaluation with existing German sentiment analysis tools confirms the lack of domain-specific solutions for software engineering. We also discuss approaches to optimize annotation and present further use cases for the dataset.
Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski
et al.
Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.
The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to enable the seamless evolution of AI-native software systems as part of the emerging Software Engineering 3.0 era. Unlike traditional static compilers, Compiler.next takes human-written intents and automatically generates working software by searching for an optimal solution. This process involves dynamic optimization of cognitive architectures and their constituents (e.g., prompts, foundation model configurations, and system parameters) while finding the optimal trade-off between several objectives, such as accuracy, cost, and latency. This paper outlines the architecture of Compiler.next and positions it as a cornerstone in democratizing software development by lowering the technical barrier for non-experts, enabling scalable, adaptable, and reliable AI-powered software. We present a roadmap to address the core challenges in intent compilation, including developing quality programming constructs, effective search heuristics, reproducibility, and interoperability between compilers. Our vision lays the groundwork for fully automated, search-driven software development, fostering faster innovation and more efficient AI-driven systems.
While mastered by some, good scientific writing practices within Empirical Software Engineering (ESE) research appear to be seldom discussed and documented. Despite this, these practices are implicit or even explicit evaluation criteria of typical software engineering conferences and journals. In this pragmatic, educational-first document, we want to provide guidance to those who may feel overwhelmed or confused by writing ESE papers, but also those more experienced who still might find an opinionated collection of writing advice useful. The primary audience we had in mind for this paper were our own BSc, MSc, and PhD students, but also students of others. Our documented advice therefore reflects a subjective and personal vision of writing ESE papers. By no means do we claim to be fully objective, generalizable, or representative of the whole discipline. With that being said, writing papers in this way has worked pretty well for us so far. We hope that this guide can at least partially do the same for others.
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.
study. ABSTRACT Please Creating mechanically robust tissue engineering scaffolds capable of delivering growth factors and stem cells in situ for hard tissue repair remains a significant challenge. Inspired by the spiral structure of ice cream, our group developed an advanced 3D printing technique known as cryogenic 3D printing to fabricate polyester-based scaffolds with embedded growth factors. This method utilizes water-in-oil (w/o) polyester emulsions containing growth factors as the printing ink, which is patterned onto a cryogenic substrate. The resulting scaffolds feature a hierarchically porous structure, allowing mesenchymal stem cells (MSCs) to easily attach and proliferate. Additionally, the biological activity of the growth factors is well-preserved throughout the printing process, promoting efficient MSC differentiation. To further enhance the scaffold’s properties, a collagen hydrogel containing an angiogenic peptide can be coated onto cryogenic 3D printed bone scaffolds loaded with osteogenic growth factors. This modification endows the scaffolds with both osteoinductive and angiogenic properties, improving bone formation and vascularization. Beyond the in situ delivery of growth factors, incorporating MSCs directly into the scaffolds is highly desirable, as conventional post-cell seeding strategies often lead to low cell seeding density and uneven distribution. To address this, a hybrid cryogenic 3D printing approach was developed. This method involves the alternating deposition of polyester emulsion ink and MSC-laden GelMA/gelatin hydrogel bioink onto the cryogenic substrate, resulting in scaffolds that simultaneously deliver growth factors and MSCs. The GelMA/gelatin hydrogel acts as a protective barrier, shielding MSCs from the toxic effects of organic solvents present in adjacent polyester struts until the solvents evaporate. Furthermore, the dissolution of gelatin within the hydrogel enables the rapid release of MSCs, facilitating their migration and integration into the surrounding polyester structures. By combining this hybrid cryogenic 3D printing strategy with sequential multi-material 3D printing, bi-phasic MSC-laden osteochondral scaffolds with a heterogeneous structure can be generated. When different material matrices are employed to construct zonal microenvironments, MSCs adopt distinct cellular organizational structures in each zone, forming MSC microspheres in the cartilage region and fusiform MSCs in the subchondral zone. Additionally, the spatial delivery of TGF-β 1 and osteogenic peptides independently enhances the chondrogenic and osteogenic differentiation of MSCs within their respective zones.
Abstract Nb–Ti–Al base alloy is an important structural material with low density and high temperature. However, as with other niobium alloys, the weak oxidation resistance is the bottleneck of its engineering application. Surface coating technology is considered an ideal method to solve the oxidation resistance of Nb–Ti–Al-based alloys. In this article, the progress of research on high-temperature antioxidation silicide coatings on Nb–Ti–Al alloy in recent years is reviewed. The microstructure, phase composition, and oxidation properties of different silicide coatings are analyzed. The failure mechanism and applications of Nb–Ti–Al-based alloy silicide coating are summarized. The existing problems and future development of Nb–Ti–Al-based alloy silicide coatings are analyzed and prospected.
Sonja Hyrynsalmi, Ella Peltonen, Fanny Vainionpää
et al.
In the extant literature, there has been discussion on the drivers and motivations of minorities to enter the software industry. For example, universities have invested in more diverse imagery for years to attract a more diverse pool of students. However, in our research, we consider whether we understand why students choose their current major and how they did in the beginning decided to apply to study software engineering. We were also interested in learning if there could be some signs that would help us in marketing to get more women into tech. We approached the topic via an online survey (N = 78) sent to the university students of software engineering in Finland. Our results show that, on average, women apply later to software engineering studies than men, with statistically significant differences between genders. Additionally, we found that marketing actions have different impacts based on gender: personal guidance in live events or platforms is most influential for women, whereas teachers and social media have a more significant impact on men. The results also indicate two main paths into the field: the traditional linear educational pathway and the adult career change pathway, each significantly varying by gender
Large Language Models (LLMs) have recently shown remarkable capabilities in various software engineering tasks, spurring the rapid growth of the Large Language Models for Software Engineering (LLM4SE) area. However, limited attention has been paid to developing efficient LLM4SE techniques that demand minimal computational cost, time, and memory resources, as well as green LLM4SE solutions that reduce energy consumption, water usage, and carbon emissions. This paper aims to redirect the focus of the research community towards the efficiency and greenness of LLM4SE, while also sharing potential research directions to achieve this goal. It commences with a brief overview of the significance of LLM4SE and highlights the need for efficient and green LLM4SE solutions. Subsequently, the paper presents a vision for a future where efficient and green LLM4SE revolutionizes the LLM-based software engineering tool landscape, benefiting various stakeholders, including industry, individual practitioners, and society. The paper then delineates a roadmap for future research, outlining specific research paths and potential solutions for the research community to pursue. While not intended to be a definitive guide, the paper aims to inspire further progress, with the ultimate goal of establishing efficient and green LLM4SE as a central element in the future of software engineering.
Ethnography has become one of the established methods for empirical research on software engineering. Although there is a wide variety of introductory books available, there has been no material targeting software engineering students particularly, until now. In this chapter we provide an introduction to teaching and learning ethnography for faculty teaching ethnography to software engineering graduate students and for the students themselves of such courses. The contents of the chapter focuses on what we think is the core basic knowledge for newbies to ethnography as a research method. We complement the text with proposals for exercises, tips for teaching, and pitfalls that we and our students have experienced. The chapter is designed to support part of a course on empirical software engineering and provides pointers and literature for further reading.
Juan M. Murillo, Jose Garcia-Alonso, Enrique Moguel
et al.
As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.
Ting Zhang, Ivana Clairine Irsan, Ferdian Thung
et al.
Software development involves collaborative interactions where stakeholders express opinions across various platforms. Recognizing the sentiments conveyed in these interactions is crucial for the effective development and ongoing maintenance of software systems. For software products, analyzing the sentiment of user feedback, e.g., reviews, comments, and forum posts can provide valuable insights into user satisfaction and areas for improvement. This can guide the development of future updates and features. However, accurately identifying sentiments in software engineering datasets remains challenging. This study investigates bigger large language models (bLLMs) in addressing the labeled data shortage that hampers fine-tuned smaller large language models (sLLMs) in software engineering tasks. We conduct a comprehensive empirical study using five established datasets to assess three open-source bLLMs in zero-shot and few-shot scenarios. Additionally, we compare them with fine-tuned sLLMs, using sLLMs to learn contextual embeddings of text from software platforms. Our experimental findings demonstrate that bLLMs exhibit state-of-the-art performance on datasets marked by limited training data and imbalanced distributions. bLLMs can also achieve excellent performance under a zero-shot setting. However, when ample training data is available or the dataset exhibits a more balanced distribution, fine-tuned sLLMs can still achieve superior results.