Hasil untuk "Engineering design"

Gerry L. Koons, M. Diba, A. Mikos

Benjamín Sánchez-Lengeling, Alán Aspuru-Guzik

The discovery of new materials can bring enormous societal and technological progress. In this context, exploring completely the large space of potential materials is computationally intractable. Here, we review methods for achieving inverse design, which aims to discover tailored materials from the starting point of a particular desired functionality. Recent advances from the rapidly growing field of artificial intelligence, mostly from the subfield of machine learning, have resulted in a fertile exchange of ideas, where approaches to inverse molecular design are being proposed and employed at a rapid pace. Among these, deep generative models have been applied to numerous classes of materials: rational design of prospective drugs, synthetic routes to organic compounds, and optimization of photovoltaics and redox flow batteries, as well as a variety of other solid-state materials.

Zhengchen Wu, Hang Cheng, Chen Jin et al.

Electromagnetic (EM) wave absorption materials possess exceptionally high EM energy loss efficiency. With vigorous developments in nanotechnology, such materials have exhibited numerous advanced EM functions, including radiation prevention and antiradar stealth. To achieve improved EM performance and multifunctionality, the elaborate control of microstructures has become an attractive research direction. By designing them as core–shell structures with different dimensions, the combined effects, such as interfacial polarization, conduction networks, magnetic coupling, and magnetic–dielectric synergy, can significantly enhance the EM wave absorption performance. Herein, the advances in low‐dimensional core–shell EM wave absorption materials are outlined and a selection of the most remarkable examples is discussed. The derived key information regarding dimensional design, structural engineering, performance, and structure–function relationship are comprehensively summarized. Moreover, the investigation of the cutting‐edge mechanisms is given particular attention. Additional applications, such as oxidation resistance and self‐cleaning functions, are also introduced. Finally, insight into what may be expected from this rapidly expanding field and future challenges are presented.

B. Schleich, N. Anwer, L. Mathieu et al.

Vivian Liu, Lydia B. Chilton

Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range of generations, they also must engage in brute-force trial and error with the text prompt when the result quality is poor. We conduct a study exploring what prompt keywords and model hyperparameters can help produce coherent outputs. In particular, we study prompts structured to include subject and style keywords and investigate success and failure modes of these prompts. Our evaluation of 5493 generations over the course of five experiments spans 51 abstract and concrete subjects as well as 51 abstract and figurative styles. From this evaluation, we present design guidelines that can help people produce better outcomes from text-to-image generative models.

D. Richerson

Jeanie L. Drury, D. Mooney

R. Budynas, J. Nisbett

S. Hollister

N. Barton, R. Lien, J. Lunde

M. Meloni, Jianguo Cai, Qian Zhang et al.

Origami‐based designs refer to the application of the ancient art of origami to solve engineering problems of different nature. Despite being implemented at dimensions that range from the nano to the meter scale, origami‐based designs are always defined by the laws that govern their geometrical properties at any scale. It is thus not surprising to notice that the study of their applications has become of cross‐disciplinary interest. This article aims to review recent origami‐based applications in engineering, design methods and tools, with a focus on research outcomes from 2015 to 2020. First, an introduction to origami history, mathematical background and terminology is given. Origami‐based applications in engineering are reviewed largely in the following fields: biomedical engineering, architecture, robotics, space structures, biomimetic engineering, fold‐cores, and metamaterials. Second, design methods, design tools, and related manufacturing constraints are discussed. Finally, the article concludes with open questions and future challenges.

Mairieli Wessel, Daniel Feitosa, Sangeeth Kochanthara

Rising publication pressure and the routine use of generative AI tools are reshaping how software engineering research is produced, assessed, and taught. While these developments promise efficiency, they also raise concerns about skill degradation, responsibility, and trust in scholarly outputs. This vision paper employs Design Fiction as a methodological lens to examine how such concerns might materialise if current practices persist. Drawing on themes reported in a recent community survey, we construct a speculative artifact situated in a near future research setting. The fiction is used as an analytical device rather than a forecast, enabling reflection on how automated assistance might impede domain knowledge competence, verification, and mentoring practices. By presenting an intentionally unsettling scenario, the paper invites discussion on how the software engineering research community in the future will define proficiency, allocate responsibility, and support learning.

Esteban Parra, Sonia Haiduc, Preetha Chatterjee et al.

Peer review is the main mechanism by which the software engineering community assesses the quality of scientific results. However, the rapid growth of paper submissions in software engineering venues has outpaced the availability of qualified reviewers, creating a growing imbalance that risks constraining and negatively impacting the long-term growth of the Software Engineering (SE) research community. Our vision of the Future of the SE research landscape involves a more scalable, inclusive, and resilient peer review process that incorporates additional mechanisms for: 1) attracting and training newcomers to serve as high-quality reviewers, 2) incentivizing more community members to serve as peer reviewers, and 3) cautiously integrating AI tools to support a high-quality review process.

Himon Thakur, Armin Moin

The discussion around AI-Engineering, that is, Software Engineering (SE) for AI-enabled Systems, cannot ignore a crucial class of software systems that are increasingly becoming AI-enhanced: Those used to enable or support the SE process, such as Computer-Aided SE (CASE) tools and Integrated Development Environments (IDEs). In this paper, we study the energy efficiency of these systems. As AI becomes seamlessly available in these tools and, in many cases, is active by default, we are entering a new era with significant implications for energy consumption patterns throughout the Software Development Lifecycle (SDLC). We focus on advanced Machine Learning (ML) capabilities provided by Large Language Models (LLMs). Our proposed approach combines Retrieval-Augmented Generation (RAG) with Prompt Engineering Techniques (PETs) to enhance both the quality and energy efficiency of LLM-based code generation. We present a comprehensive framework that measures real-time energy consumption and inference time across diverse model architectures ranging from 125M to 7B parameters, including GPT-2, CodeLlama, Qwen 2.5, and DeepSeek Coder. These LLMs, chosen for practical reasons, are sufficient to validate the core ideas and provide a proof of concept for more in-depth future analysis.

Yang Yue, Zheng Jiang, Yi Wang

The heterogeneity in the organization of software engineering (SE) research historically exists, i.e., funded research model and hands-on model, which makes software engineering become a thriving interdisciplinary field in the last 50 years. However, the funded research model is becoming dominant in SE research recently, indicating such heterogeneity has been seriously and systematically threatened. In this essay, we first explain why the heterogeneity is needed in the organization of SE research, then present the current trend of SE research nowadays, as well as the consequences and potential futures. The choice is at our hands, and we urge our community to seriously consider maintaining the heterogeneity in the organization of software engineering research.

杨世明1,刘克瑾2,姚辉江2,黄硕硕2,杜春来1,章敬波1 YANG Shiming1, LIU Kejin2, YAO Huijiang2, HUANG Shuoshuo2, DU Chunlai1, ZHANG Jingbo1

旨在为筒仓的设计和优化提供参考，基于自主设计的半圆柱形有机玻璃平底筒仓模型，进行了平均粒径分别为15、3.5 mm和5.5 mm陶球颗粒的室内筒仓中心卸料试验和离散元数值模拟。采用流态观察、速度分析、颗粒位移追踪3种方法探究了3组粒径颗粒的流态演变过程，分析了仓壁压力分布及变化规律，通过PFC 2D得到孔隙率、力链等细观变量分布并联合宏观层次的物理试验探讨了粒径大小对流态及仓壁压力的影响。结果表明：粒径对颗粒流态的整体演化过程无显著影响，不同粒径颗粒的流态均由整体流经漏斗流过渡为管状流；大粒径颗粒完成卸料过程耗时更久，卸料速率更慢；粒径对颗粒的流动轨迹无显著影响；边界区并不是一成不变的，在卸料过程中随着粒径的增大而逐渐上移；不同粒径颗粒组的峰值卸料压力最大值均位于距离仓底约1/3的位置；粒径越大，仓壁的压力波动越剧烈，峰值卸料压力也越大。综上，粒径对平底筒仓中心卸料的流态无显著影响，不同粒径的颗粒流态演化过程和颗粒流动轨迹具有相似性，粒径越大产生的仓壁卸料压力也越大。在实际工程中，需考虑粒径对筒仓结构安全性的影响。 To provide a reference for the design and optimization of silos, based on a self-designed semi-cylindrical plexiglass flat-bottom silo model, indoor silo center discharge tests and discrete element numerical simulations were conducted using ceramic ball particles with average particle size of 15, 3.5 mm and 5.5 mm. Three methods of flow pattern observation, velocity analysis, and particle displacement tracking were used to explore the flow pattern evolution of the three groups of particles with different particle sizes. The pressure distribution and variation of the silo wall were analyzed, and the distribution of microscopic variables such as porosity and force chain obtained from PFC 2D, along with macroscopic physical tests, were used to investigate the effect of particle size on flow pattern and silo wall pressure. The results showed that particle size had no significant effect on the overall evolution of particle flow pattern, and the flow pattern of particles with different particle size shifted from mass flow through funnel flow to tubular flow. Larger particle sizes of particles resulted in a longer discharge process with slower discharge rates. Particle size had no significant effect on the particle flow trajectories. The boundary zone was not fixed and gradually moved upwards with the increase of particle size during the discharge process. The peak of discharge pressure for different particle size groups was located approximately one-third of the way from the silo bottom. The larger the particle size, the more severe the pressure fluctuations on the silo wall, and the higher the peak of discharge pressure. In summary, particle size has no significant effect on the flow pattern of the silo center discharge. The flow pattern evolution and particle flow trajectories of particles with different particle sizes are similar. Larger particles generate higher discharge pressure on the silo wall. In practical engineering, the impact of particle size on silo structure safety should be considered.

Oguz Emrah Turgut, Mustafa Asker, Hayrullah Bilgeran Yesiloz et al.

This theoretical research study proposes a novel hybrid algorithm that integrates an improved quasi-dynamical oppositional learning mutation scheme into the Mountain Gazelle Optimization method, augmented with chaotic sequences, for the thermal and economical design of a shell-and-tube heat exchanger operating with nanofluids. The Mountain Gazelle Optimizer is a recently developed metaheuristic algorithm that simulates the foraging behaviors of Mountain Gazelles. However, it suffers from premature convergence due to an imbalance between its exploration and exploitation mechanisms. A two-step improvement procedure is implemented to enhance the overall search efficiency of the original algorithm. The first step concerns substituting uniformly random numbers with chaotic numbers to refine the solution quality to better standards. The second step is to develop a novel manipulation equation that integrates different variants of quasi-dynamic oppositional learning search schemes, guided by a novel intelligently devised adaptive switch mechanism. The efficiency of the proposed algorithm is evaluated using the challenging benchmark functions from various CEC competitions. Finally, the thermo-economic design of a shell-and-tube heat exchanger operated with different nanoparticles is solved by the proposed improved metaheuristic algorithm to obtain the optimal design configuration. The predictive results indicate that using water + SiO₂ instead of ordinary water as the refrigerant on the tube side of the heat exchanger reduces the total cost by 16.3%, offering the most cost-effective design among the configurations compared. These findings align with the demonstration of how biologically inspired metaheuristic algorithms can be successfully applied to engineering design.

Hussain Z, Abdulrahim Abdul Moti L, Jagal J et al.

Zahid Hussain,1,2 Lama Abdulrahim Abdul Moti,1 Jayalakshmi Jagal,2 Hnin Ei Thu,3 Shahzeb Khan,4 Mohsin Kazi5 1Department of Pharmaceutics and Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates; 2Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah, 27272, United Arab Emirates; 3Department of Pharmacology, Faculty of Dentistry, Universiti Teknologi MARA, Sungai Buloh Campus, Sungai Buloh, Selangor, Malaysia; 4Center for Pharmaceutical Engineering Science, Faculty of Life Sciences, School of Pharmacy and Medical Sciences, University of Bradford, West Yorkshire Bradford, BD7 1DP, UK; 5Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh, 11451 Saudi ArabiaCorrespondence: Zahid Hussain, Department of Pharmaceutics and Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates, Email zhussain@sharjah.ac.aeIntroduction: Owing to its high prevalence, colossal potential of chemoresistance, metastasis, and relapse, breast cancer (BC) is the second leading cause of cancer-related fatalities in women. Several treatments (eg, chemotherapy, surgery, radiations, hormonal therapy, etc.) are conventionally prescribed for the treatment of BC; however, these are associated with serious systemic aftermaths. In this research, we aimed to design a multiprong targeting strategy for concurrent action against different phenotypes of BC (MCF-7 and SK-BR-3) and tumor-associated macrophages (TAMs) for relapse-free treatment of BC.Methods: Paclitaxel (PTX) and tamoxifen (TMX) co-loaded chitosan (CS) nanoparticles (NPs) were prepared using the ionic-gelation method and optimized using the Design Expert® software by controlling different material attributes. For selective targeting through CD44-receptors that are heavily expressed on the BC cells and TAMs, the fabricated NPs (PTX-TMX-CS-NPs) were functionalized with hyaluronic acid (HA) as a targeting ligand.Results: The optimized HA-PTX-TMX-CS-NPs exhibited desired physicochemical properties (PS ~230 nm, PDI 0.30, zeta potential ~21.5 mV), smooth spherical morphology, high encapsulation efficiency (PTX ~72% and TMX ~97%), good colloidal stability, and biphasic release kinetics. Moreover, the lowest cell viability depicted in MCF-7 (~25%), SK-BR-3 (~20%), and RAW 264.7 cells (~20%), induction of apoptosis, cell cycle arrest, enhanced cell internalization, and alleviation of MCF-7 and SK-BR-3 migration proved the superior anticancer potential of HA-PTX-TMX-CS-NPs compared to unfunctionalized NPs and other control medicines.Conclusion: HA-functionalization of NPs is a promising multiprong strategy for CD44-receptors-mediated targeting of BC cells and TAMs to mitigate the progression, metastasis, and relapse in the BC. Keywords: paclitaxel, tamoxifen, hyaluronic acid, polymeric nanoparticles, CD44-receptors, breast cancer, cell uptake, anticancer efficacy

Chakrit Suvanjumrat, Jetsadaporn Priyadumkol, Kunthakorn Khaothong et al.

Enhancing natural convection heat transfer in heated electrical devices, particularly those with curved geometries and limited space for cooling systems is a crucial area of research. This study experimentally evaluated the performance of a corona wind generator—an electrohydrodynamic (EHD) system—employing needle-to-cylinder configurations to improve natural convection around a heated cylinder. Three configurations were investigated: a single vertical wire electrode, a single lateral wire electrode, and two lateral wire electrodes, positioned perpendicular to the cylindrical surface at varying distances. Voltages ranging from 0 to 9000 V were applied to produce a corona wind jet. The findings revealed that lateral wire electrode configurations significantly enhanced natural convection heat transfer, achieving an average Nusselt number improvement exceeding 51.17 % at 8000 V compared to natural convection alone. Among these, the single lateral electrode configuration demonstrated superior performance, yielding a 13.87 % higher average Nusselt number than the vertical electrode configuration. It was observed that the corona wind jet initially impinged on the heated cylinder; however, increasing the distance between the electrode tip and the cylinder caused the jet to rise due to buoyancy, reducing its cooling effectiveness. Despite this limitation, the lateral electrode configurations effectively enhanced natural convection. The experimental results were utilized to develop a practical Nusselt number correlation that integrates voltage, electrode tip distance, distance of two electrodes, and cylinder diameter. The proposed model demonstrated high accuracy, with R2 values ranging from 0.81 to 0.94, offering a valuable tool for designing efficient cooling systems for electrical devices.

Maher Abuhussain, Ali Hussain Alhamami, Khaled Almazam et al.

This study introduces a comprehensive framework combining building information modeling (BIM), project management body of knowledge (PMBOK), and machine learning (ML) to optimize energy efficiency and reduce environmental impacts in Riyadh’s construction sector. The suggested methodology utilizes BIM for dynamic energy simulations and design visualization, PMBOK for integrating sustainability into project-management processes, and ML for predictive modeling and real-time energy optimization. Implementing an integrated model that incorporates building-management strategies and machine learning for both commercial and residential structures can offer stakeholders a thorough solution for forecasting energy performance and environmental impact. This is particularly essential in arid climates owing to specific conditions and environmental limitations. Using a simulation-based methodology, the framework was evaluated based on two representative case studies: (i) a commercial complex and (ii) a residential building. The neural network (NN), reinforcement learning (RL), and decision tree (DT) were implemented to assess performance in energy prediction and optimization. Results demonstrated notable seasonal energy savings, particularly in spring (15% reduction for commercial buildings) and fall (13% reduction for residential buildings), driven by optimized heating, ventilation, and air conditioning (HVAC) systems, insulation strategies, and window configurations. ML models successfully predicted energy consumption and greenhouse gas (GHG) emissions, enabling targeted mitigation strategies. GHG emissions were reduced by up to 25% in commercial and 20% in residential settings. Among the models, NN achieved the highest predictive accuracy (R² = 0.95), while RL proved effective in adaptive operational control. This study highlights the synergistic potential of BIM, PMBOK, and ML in advancing green project management and sustainable construction.