Abstract Implementing practices for a circular economy transforms the way companies do business, notably in the manufacturing industry. However, a circular economy requires a transformation of both production and consumption systems; the standard approach for creation, fabrication, and commerce of products is challenged. Authors repeatedly call for the development of new proficiencies to attend to system transformations, but these so far have not been described for design and engineering. Given that the design of a product directly influences the way a value chain will be managed, building circular, globally sustainable value chains inevitably signifies a fundamental change in the practice of design. Comprehensive analyses were conducted on case studies from a variety of multinational enterprises that are transforming their product strategies for climate change. Changes in design processes were identified, revealing a growing necessity for industry to employ new proficiencies that support closure of material loops. This paper contributes to existing literature by depicting successful practices being implemented in industry. A variety of new capabilities are key to design for a sustainable future; these range from deeper knowledge of material composition to rich understanding of social behaviour. Resulting from this research, learning goals are proposed to serve as guidance for manufacturing companies seeking to tackle climate change. Conclusions aim to encourage researchers and academics to respond to emerging needs by re-thinking education in design and engineering.
Exploring highly-effective and nonprecious electrocatalysts for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is urgent and challenging for developing the hydrogen economy. Interface engineering is a feasible approach for regulating the surface electronic distribution, thereby promoting the catalytic performance. Herein, the CoP/FeP heterostructure is fabricated via the oxidation and phosphating treatments of Fe-decorated Ni(OH)2 nanoflakes. The hierarchically porous nanoflakes can expose more active species, while the formation of CoP/FeP heterojunctions have provided extra catalytic active sites and accelerated the charge transfer process. Theoretical calculations reveal that the interfacial electron coupling between CoP and FeP in the heterostructure has promoted the adsorption of intermediate species on catalytic sites, thereby decreasing the Gibbs free energy during the catalysis. The as-fabricated CoP/FeP catalyst requires small overpotentials of 190 mV and 280 mV to realize a current density of 10 mA cm-2 for alkaline HER and OER, respectively. The electrolytic cell with CoP/FeP as catalyst needs a voltage of 1.61 V to reach 10 mA cm-2, and can run stably for over 25 h. The present study highlights a superiority of interfacial engineering to construct efficient electrocatalysts for water electrolysis.
Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rigorous criterion for unambiguous boundary semantics, context-dependent interaction traceability to successful stakeholder goal satisfaction, and sufficiency of problem-space specification over which disciplined reasoning can occur independent of solution design. It offers a clear distinction between what is true of the problem domain and what is chosen as a solution. The paper concludes by discussing the significance of the theory on practitioners and provides a dialogue-based hypothetical case study between a stakeholder and an engineer, demonstrating how the theory guides problem framing before designing any prescriptive artifacts.
The discovery of plastics as useful materials for human existence, providing comfort and ease has now turned into a menace in the society and a pain in the neck as plastics have also become co-habitat of the human ecosystem. Plastics were made by men, but plastics now live way longer than the men who gave them the privilege of existence. Since these colonies of materials cannot be eradicated, it is very pertinent to discover possible ways of useful diversion through recycling by converting plastic bottle waste into useful raw materials for other sectors of the economy. This paper has stirred the pool of relevant literature to extract some of the innovative efforts that have been deployed into redirecting the potential of plastic waste into useful applications like engineering and building construction, horticulture in agriculture, and the most advanced application is the 3D printing using plastics as filaments. Following the trend of technology, 3D printing is a grey area of plastic recycling, it is quite expensive however, it worth exploring.
Hydrogen embrittlement reduces the durability of the structural steels required for the hydrogen economy. Understanding how hydrogen interacts with the materials plays a crucial role in managing the embrittlement problems. Theoretical models have indicated that carbon vacancies in metal carbide precipitates are effective hydrogen traps in steels. Increasing the number of carbon vacancies in individual metal carbides is important since the overall hydrogen trapping capacity can be leveraged by introducing abundant metal carbides in steels. To verify this concept, we compare a reference steel containing titanium carbides (TiCs), which lack carbon vacancies, with an experimental steel added with molybdenum (Mo), which form Ti-Mo carbides comprising more carbon vacancies than TiCs. We employ theoretical and experimental techniques to examine the hydrogen trapping behavior of the carbides, demonstrating adding Mo alters the hydrogen trapping mechanism, enabling hydrogen to access carbon vacancy traps within the carbides, leading to an increase in trapping capacity. Understanding how hydrogen embrittles steels and developing the solutions are crucial for enabling the hydrogen economy. Here, the authors report a materials design strategy that can increase the hydrogen trapping capacity by creating carbon vacancies in metal carbide precipitates via microalloying.
Anshuman Mishra, T. Omoyeni, Pravin Kumar Singh
et al.
Eco-friendly materials have emerged in biomedical engineering, driving major advances in chitosan-based hydrogels. These hydrogels offer a promising green alternative to conventional polymers due to their non-toxicity, biodegradability, biocompatibility, environmental friendliness, affordability, and easy accessibility. Known for their remarkable properties such as drug encapsulation, delivery capabilities, biosensing, functional scaffolding, and antimicrobial behavior, chitosan hydrogels are at the forefront of biomedical research. This paper explores the fabrication and modification methods of chitosan hydrogels for diverse applications, highlighting their role in advancing climate-neutral healthcare technologies. It reviews significant scientific advancements and trends chitosan hydrogels focusing on cancer diagnosis, drug delivery, and wound care. Additionally, it addresses current challenges and green synthesis practices that support a circular economy, enhancing biomedical sustainability. By providing an in-depth analysis of the latest evidence on climate-neutral management, this review aims to facilitate informed decision-making and foster the development of sustainable strategies leveraging chitosan hydrogel technology. The insights from this comprehensive examination are pivotal for steering future research and applications in sustainable biomedical solutions.
Mohammad Hakim Khalili, Rujing Zhang, Sandra Wilson
et al.
In this brief review, we discuss the recent advancements in using poly(ethylene glycol) diacrylate (PEGDA) hydrogels for tissue engineering applications. PEGDA hydrogels are highly attractive in biomedical and biotechnology fields due to their soft and hydrated properties that can replicate living tissues. These hydrogels can be manipulated using light, heat, and cross-linkers to achieve desirable functionalities. Unlike previous reviews that focused solely on material design and fabrication of bioactive hydrogels and their cell viability and interactions with the extracellular matrix (ECM), we compare the traditional bulk photo-crosslinking method with the latest three-dimensional (3D) printing of PEGDA hydrogels. We present detailed evidence combining the physical, chemical, bulk, and localized mechanical characteristics, including their composition, fabrication methods, experimental conditions, and reported mechanical properties of bulk and 3D printed PEGDA hydrogels. Furthermore, we highlight the current state of biomedical applications of 3D PEGDA hydrogels in tissue engineering and organ-on-chip devices over the last 20 years. Finally, we delve into the current obstacles and future possibilities in the field of engineering 3D layer-by-layer (LbL) PEGDA hydrogels for tissue engineering and organ-on-chip devices.
To facilitate the adoption of the circular economy (CE) in the architecture, engineering and construction (AEC) sector, some authors have demonstrated the potential of recent designs that take into account the sustainable management of an asset’s end-of-life (EOL), providing an alternative to the dominant designs that end with demolition. However, there is no review of the literature that encompasses a large range of sustainable designs in the current CE context. This paper provides a critical review of journal papers that deal with the barriers to implementing sustainable designs and approaches to the EOL management of assets that have the potential to fulfil the principles of the CE. Eighteen approaches related to prefabrication, design for change, design for deconstruction, reverse logistics, waste management and closed-loop systems were found. Through an analysis of the barriers that are common among these 18 approaches, we classified them into six different categories (organisational, economical, technical, social, political and environmental). Two Sankey diagrams illustrate the interrelation between the barriers, their categories and the 18 approaches. The diagrams clearly show that most of the barriers are common to multiple approaches and that most of the barriers relate to organisational concerns. The study gives a detailed map of the barriers that would help stakeholders from the AEC sector develop strategies to overcome the current obstacles in the shift to a CE.
Vladyslav Bulhakov, Giordano d'Aloisio, Claudio Di Sipio
et al.
The introduction of large language models (LLMs) has enhanced automation in software engineering tasks, including in Model Driven Engineering (MDE). However, using general-purpose LLMs for domain modeling has its limitations. One approach is to adopt fine-tuned models, but this requires significant computational resources and can lead to issues like catastrophic forgetting. This paper explores how hyperparameter tuning and prompt engineering can improve the accuracy of the Llama 3.1 model for generating domain models from textual descriptions. We use search-based methods to tune hyperparameters for a specific medical data model, resulting in a notable quality improvement over the baseline LLM. We then test the optimized hyperparameters across ten diverse application domains. While the solutions were not universally applicable, we demonstrate that combining hyperparameter tuning with prompt engineering can enhance results across nearly all examined domain models.
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.
Kusnadi Kusnadi, Muhammad Hatta, Goenawan Brotosaputro
et al.
The integration of Information Technology (IT) has significantly transformed modern education, particularly classroom dynamics, by enhancing accessibility to information and enabling personalized learning experiences. This paper aims to explore both the positive impacts and challenges of IT adoption in the classroom, focusing on the importance of Computer Science in shaping effective teaching practices. The study analyzes tools like Learning Management Systems (LMS), simulation software, and data analysis platforms, which improve engagement between students and teachers but also highlight challenges such as the digital divide and less interactive learning. Understanding fundamental Computer Science concepts, including algorithms, programming, and networking, is key to developing innovative solutions that enhance classroom learning. The results show that while IT has revolutionized education by facilitating online learning and collaboration, it also presents challenges that must be addressed, such as access to resources and the need for more interactive learning experiences. To optimize IT’s impact, the paper recommends continuous teacher training, better integration of technology with curricula, and improved access to devices and internet connectivity, ensuring a more inclusive and innovative learning environment in the digital age.
Energy Efficiency Contracting (EEC) enables structural improvements in buildings by financing upgrades through the savings generated, eliminating the need for upfront investment by property owners. Although the model supports the energy transition and the reduction in GHG emissions, its adoption in the private sector faces relevant barriers such as the lack of information from the Energy Service Companies (ESCOs), distrust from clients in benefits with no upfront costs, and legal and behavioral barriers. To overcome these challenges, the FinSESCo platform, funded by Era-Net 2020 joint call, aims to channel private investments into building renovations and renewable energy installations via a crowdfunding portal. The platform allows individuals and organizations to finance small-scale renewable energy installations and energy efficiency measures for homeowners, tenants, and apartment owners. The new platform is likely to change the way EE investments are made and reach out to new audiences. A survey of 2585 German households sought to understand the drivers of EE investments, factors affecting the decisions, and their relationships with several demographic variables. Using a stepwise backward regression model, the study found significant differences between traditional investors in EE and those who would use the FinSESCo platform. Low- and medium-income households were more likely to take up the platform, and previous renewable energy ownership, experience with EEC models, and knowledge of crowdfunding further raised willingness to participate. The results point to the potential of the FinSESCo platform to expand EEC to new audiences, underlining its role of democratization and diversification of investments in building energy efficiency.
Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in effectively capturing the multifractality inherent in carbon market prices. Inspired by the self-similarity and scale invariance inherent in fractal structures, this study proposes a novel multifractal-aware model, MF-Transformer-DEC, for carbon market price prediction. The multi-scale convolution (MSC) module employs multi-layer dilated convolutions constrained by shared convolution kernel weights to construct a scale-invariant convolutional network. By projecting and reconstructing time series data within a multi-scale fractal space, MSC enhances the model’s ability to adapt to complex nonlinear fluctuations while significantly suppressing noise interference. The fractal attention (FA) module calculates similarity matrices within a multi-scale feature space through multi-head attention, adaptively integrating multifractal market dynamics and implicit associations. The dynamic error correction (DEC) module models error commonality through variational autoencoder (VAE), and uncertainty-guided dynamic weighting achieves robust error correction. The proposed model achieved an average R<sup>2</sup> of 0.9777 and 0.9942 for 7-step ahead predictions on the Shanghai and Guangdong carbon price datasets, respectively. This study pioneers the interdisciplinary integration of fractal theory and artificial intelligence methods for complex engineering analysis, enhancing the accuracy of carbon market price prediction. The proposed technical pathway of “multi-scale deconstruction and similarity mining” offers a valuable reference for AI-driven fractal modeling.
Matthew J. Takle, David M. Maurer, Philipp Staehle
et al.
Abstract In recent years, industrial biocatalysis has significantly advanced, largely due to innovations in DNA sequencing, bioinformatics, and protein engineering. However, the challenge of implementing biocatalysis at an industrial scale while ensuring sustainability and cost-effectiveness remains a critical barrier. This study presents the development of a flash thermal racemization protocol for chemoenzymatic dynamic kinetic resolution (FTR-CE-DKR) of chiral amines, encompassing an investigation of substrate scope, catalyst screening, and optimization studies. The outcomes of this research facilitated the successful scale-up of an industrially relevant amide within a recycle-batch platform, achieving unprecedented scales of up to 100 grams and space-time yield (STY) values of up to 73.2 g L⁻¹ h⁻¹. Furthermore, the process exhibited very favorable sustainability metrics when benchmarked against previous reports, including atom economy, reaction mass efficiency, and process mass intensity. These findings represent a significant milestone in the biocatalytic production of optically active amines.