Yi-Shen Huang, Shibiru Yadeta Ejeta, Michael Kneidinger
et al.
Typical hydrogels generally display poor mechanical properties due to their high water content, limiting their practical applications. To address this, we designed a double-network hydrogel (DNH) with superior strength while retaining high water content. The first network was constructed using 2-acrylamido-2-methylpropane sulfonic acid (AMPS) and single styryl-terminated polyphosphazene with jeffamine sidechains (St-PPzjeff) monomers, along with N,N′-methylene bisacrylamide (NMBA) crosslinker and 2-oxoglutaric acid (OGA) photo-initiator, producing PPzjeff@SN hydrogels. These SN samples underwent further internal polymerization to build the second network from acrylamide (AAm), OGA, and NMBA. To reinforce the system, high-molecular-weight PPz-based bottlebrush polymers (BBPs, Mn ≈ 200 and 400 kDa) with varied side chains were incorporated into the first network of the DN hydrogels. This unique brush-like architecture, containing PPz grafted with hydrophilic jeffamine chains (ethylene oxide (EO) and propylene oxide (PO) units), synergistically stabilized the matrix via a hydrophilic/hydropobic balance. Characterization confirmed a dense, uniform network and chemical integration to the network through the styryl end of PPzjeff BBPs. Mechanical testing revealed significant enhancements, including tensile strength up to ∼ 4 MPa, toughness ∼ 540 kJ/m3, and markedly higher compression resistance. These improvements arise from BBP architecture and network embedding. This strategy demonstrates that HMW PPz BBPs enable the fabrication of mechanically robust and tough hydrogels.
Materials of engineering and construction. Mechanics of materials
Modern engineering design platforms excel at discipline-specific tasks such as CAD, CAM, and CAE, but often lack native systems engineering frameworks. This creates a disconnect where system-level requirements and architectures are managed separately from detailed component design, hindering holistic development and increasing integration risks. To address this, we present the conceptual framework for the GenAI Workbench, a Model-Based Systems Engineering (MBSE) environment that integrates systems engineering principles into the designer's workflow. Built on an open-source PLM platform, it establishes a unified digital thread by linking semantic data from documents, physical B-rep geometry, and relational system graphs. The workbench facilitates an AI-assisted workflow where a designer can ingest source documents, from which the system automatically extracts requirements and uses vision-language models to generate an initial system architecture, such as a Design Structure Matrix (DSM). This paper presents the conceptual architecture, proposed methodology, and anticipated impact of this work-in-progress framework, which aims to foster a more integrated, data-driven, and informed engineering design methodology.
Songkran Wiriyasart, Nathawat Unsomsri, Pichai Asadamongkon
et al.
Co-pyrolysis gas produced from the co-pyrolysis of fresh palm fruit bunches and medical waste plastic bottles using a batch pyrolyzer integrated with a downdraft gasifier was evaluated for sustainable electricity generation. Increasing the plastic fraction enhanced the gas heating value (41.43–55.53 MJ/Nm3) but decreased gas yield from 22.7 % to 9.0 %. Electrical efficiency was calculated based on the ratio of output power to syngas energy content, achieving 8.96–9.01 %. Engine tests showed a reduction in CO emissions (30,149–17,419 ppm) and comparable NOx levels to gasoline combustion. These results demonstrate the co-pyrolysis gas's potential as a renewable fuel for decentralized power.
Configuring trams with hybrid power systems of appropriate capacity can effectively improve the operational efficiency of trams. The traditional capacity configuration depends on the engineering experience, which leads to the problem of high configuration cost. In this paper, based on the remaining useful life (RUL) prediction of lithium batteries, a capacity configuration method of tramway hybrid power system considering lithium battery RUL is proposed. Prediction of lithium battery RUL based on particle filtering (PF) algorithm and
comparative analysis with extended kalman filter (EKF) algorithm. Analyzing the cost structure of the power system, constructing a system capacity configuration model, including objective function, constraints, and solving the model by using honey badger algorithm (HBA). Finally, the calculation example compares and analyzes the impact of different energy storage methods on the total annual average minimum cost of the system to verify the feasibility of the proposed method. The results show that the total annual average minimum cost of hybrid energy storage is only 14.603 million yuan, which decreases by 38.2% compared with the single-type battery energy storage capacity configuration, and greatly improves the system economy.
Engineering (General). Civil engineering (General), Chemical engineering
This study investigates the dynamic changes in wheat canopy spectral characteristics across seven critical growth stages (Tillering, Pre-Jointing, Jointing, Post-Jointing, Booting, Flowering, and Ripening) using UAV-based multispectral remote sensing. By analyzing four key spectral bands—green (G), red (R), red-edge (RE), and near-infrared (NIR)—and their combinations, we identify spectral features that reflect changes in canopy activity, health, and structure. Results show that the green band is highly sensitive to chlorophyll activity and low canopy coverage during the Tillering stage, while the NIR band captures structural complexity and canopy density during the Jointing and Booting stages. The combination of G and NIR bands reveals increased canopy density and spectral concentration during the Booting stage, while the RE band effectively detects plant senescence and reduced spectral uniformity during the ripening stage. Time-series analysis of spectral data across growth stages improves the accuracy of growth stage identification, with dynamic spectral changes offering insights into growth inflection points. Spatially, the study demonstrates the potential for identifying field-level anomalies, such as water stress or disease, providing actionable data for targeted interventions. This comprehensive spatio-temporal monitoring framework improves crop management and offers a cost-effective, precise solution for disease prediction, yield forecasting, and resource optimization. The study paves the way for integrating UAV remote sensing into precision agriculture practices, with future research focusing on hyperspectral data integration to enhance monitoring models.
Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.
Bianca Trinkenreich, Fabio Calefato, Geir Hanssen
et al.
The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.
Background: Harnessing advanced computing for scientific discovery and technological innovation demands scientists and engineers well-versed in both domain science and computational science and engineering (CSE). However, few universities provide access to both integrated domain science/CSE cross-training and Top-500 High-Performance Computing (HPC) facilities. National laboratories offer internship opportunities capable of developing these skills. Purpose: This student presents an evaluation of federally-funded postgraduate internship outcomes at a national laboratory. This study seeks to answer three questions: 1) What computational skills, research skills, and professional skills do students improve through internships at the selected national laboratory. 2) Do students gain knowledge in domain science topics through their internships. 3) Do students' career interests change after these internships? Design/Method: We developed a survey and collected responses from past participants of five federally-funded internship programs and compare participant ratings of their prior experience to their internship experience. Findings: Our results indicate that participants improve CSE skills and domain science knowledge, and are more interested in working at national labs. Participants go on to degree programs and positions in relevant domain science topics after their internships. Conclusions: We show that national laboratory internships are an opportunity for students to build CSE skills that may not be available at all institutions. We also show a growth in domain science skills during their internships through direct exposure to research topics. The survey instrument and approach used may be adapted to other studies to measure the impact of postgraduate internships in multiple disciplines and internship settings.
The thermal management performance of a commercial vehicle engine compartment is crucial for improving overall vehicle performance and promoting green transportation. In this study, a one-dimensional (1D) and three dimensional (3D) CFD co-simulation method has been proposed for the modeling and simulation of thermal management of a commercial vehicle engine compartment. The steady-state temperature and velocity vector distribution in engine compartment has been obtained through 3D CFD approach. With data exchange between the cooling circuit and air conditioning circuit, the mathematical characterizations of the thermal management performance have been further investigated through 1D simulation and validated using test results. The comparison show that the obtained results for thermal management performance are consistent with test results, validating the accuracy of the co-simulation model. The results indicate an obvious thermal backflow phenomenon in the upper area of the intercooler module, causing a high outlet water temperature of the engine cooler. Thus, a deflector structure for eliminating the thermal backflow problem has been presented, and the heat dissipation effect has been analyzed. The co-simulation results show an improvement in cooling performance that the ATO value of cooling circuit decreased by 2.5% and intercooler constant of air conditioning circuit decreased by 12.1%. The proposed 1D/3D co-simulation method provides a reliable reference for developing the thermal management system of commercial vehicle engine compartment.
A flaky test yields inconsistent results upon repetition, posing a significant challenge to software developers. An extensive study of their presence and characteristics has been done in classical computer software but not quantum computer software. In this paper, we outline challenges and potential solutions for the automated detection of flaky tests in bug reports of quantum software. We aim to raise awareness of flakiness in quantum software and encourage the software engineering community to work collaboratively to solve this emerging challenge.
Jhordan Silveira Borba, Sebastian Gonçalves, Celia Anteneodo
We analyze inequality aspects of the agent-based model of capitalist economy named it Social Architecture of Capitalism that has been introduced by Ian Wright. The model contemplates two main types of agents, workers and capitalists, which can also be unemployed. Starting from a state where all agents are unemployed and possess the same initial wealth, the system, governed by a few simple rules, quickly self-organizes into two classes. After a transient, the model reproduces the statistics of many relevant macroeconomic quantities of real economies worldwide, notably the two regimes of the distributions of wealth and income. We perform extensive simulations testing the role of the model parameters (number of agents, total wealth, and salary range) on the resulting distribution of wealth and income, the social distribution of agents, and other stylized facts of the dynamics. Our main finding is that, according to the model, in an economy where total wealth is conserved and with a fixed average wage, the increase in wealth per capita comes with more inequality.
Metal–organic frameworks (MOFs) have emerged as ideal multifunctional platforms for renewable hydrogen (H2) energy applications owing to their tunable chemical compositions and structures and high porosity. Their advanced component species and porous structure contribute greatly to the enhanced activity, electrical conductivity, photo response, charge‐hole separation efficiency, and structural stability of MOF materials, which are promising for practical H2 economy. In this review, we mainly introduce design strategies for the enhancement of electro‐/photochemical behaviors or adsorption performance of porous MOF materials for H2 production, storage, and utilization from compositional perspective. Following these engineering strategies, the correlation between composition and property‐structure‐performance of pristine MOFs and their composite with advanced components is illustrated. Finally, challenges and directions of future development of related MOFs and MOF composites for H2 economy are provided.
The selection and engineering of materials is a critical component towards the development of a circular economy model. The redesign of both consumer commodity goods and advanced products may not only require engineering feats in terms of advanced structures but also the implementation of safer and more facile to recycle materials. Although such endeavours include the engineering of goods generated from clever components assemblies, easier to dismantle and separate, new avenues to move beyond planned obsolescence towards triggered obsolescence, whereby materials may degrade on command, is required. Circular Materials must be designed to enable complete recycling of materials and novel synthesis strategies free from toxic precursors or by-products to regenerate raw materials. Circular materials shall therefore be processed first at the local level for local needs. Key supply-chain challenges arising from the COVID-19 lockdowns have further stressed the relevance of this issue and the need to have develop well dispatched geographically manufacturing hubs. Changes towards Circular Materials considerations will depend on the development of repurposing and recycling platforms as well as from the rebirth of delocalized manufacturing capabilities. This chapter will present current solutions to develop sustainable materials engineering strategies and focus on greener fabrication and recycling routes. Focus on smarter designs and life-cycle analysis will reflect on how Circular Design of materials may contribute to the Circular Economy.
a Institute of Climate Change and Sustainable Development, Tsinghua University, Beijing, 100084, China b Institute of Energy, Environment and Economy, Tsinghua University, Beijing, 100084, China c Energy Research Institute, Chinese Academy of Macroeconomic Research, Beijing, 100038, China d Department of Energy and Power Engineering, Tsinghua University, Beijing, 100084, China e Division of Public Policy, Hong Kong University of Science and Technology, Hong Kong, China
R. J. Wicker, Gopalakrishnan Kumar, Eakalak Khan
et al.
Abstract The current COVID-19 pandemic is forcing radical change in the global energy economy. All energy sectors have experienced profound economic contraction in 2020, with the notable exception of renewable energy, which has grown by nearly 3%. These unprecedented circumstances offer a pristine opportunity to create new jobs, technologies, and infrastructure, aimed at engineering a climate-friendly and sustainable energy future. This review explores several pathways to renewable bioenergy by developing microalgal biorefinery systems which capitalize upon the unique abilities of microalgae to sequester carbon and produce biomass for bioenergy feedstock, without compromising food security or land use. This review further highlights the necessity of synergistically coupled upstream and downstream techniques to realize the economic viability of microalgal biorefinery systems, and details possible product pathways using either whole or fractionated biomass. The zero-waste circular biorefinery approach, when specifically tailored to local conditions, in terms of regional climate, economics, infrastructure, and available resources, is the answer to economically competitive microalgal bioenergy. The most promising emergent methods for microalgal biomass valorization to fungible bioenergy are reviewed herein.
The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.
Experimental studies of the CPO (Crude Palm Oil) fuel pre-treatment system for diesel power plants with a capacity of 2 544 kW were carried out to determine the fuel heating temperature requirements and to evaluate the reliability of the pre-treatment system. The fuel pre-treatment consists of the heating process, mechanical separation process and filtering process. The CPO at the booster module was heated from 64 °C to 81 °C by set the thermal oil temperature. The result shows that a minimum CPO heating of 78 °C is required in the booster module to produced kinematic viscosity CPO less than 13 cSt at the engine inlet. When there is a disturbance in the thermal boiler system, CPO deposits are found in the booster module strainer, which clogs the fuel supply to the engine, causing hunting loads. Redundancy equipment is needed to increase the reliability of the pre-treatment system. In addition, it is also necessary to add a heating process through the CPO fuel line starting from the storage tank to the engine inlet so that the thermal loss that occurs can be minimized and the work of the thermal boiler is reduced.