This paper presents a substrate-dependent performance evaluation of a defected ground structure (DGS)-integrated multiband hexagonal microstrip patch antenna operating in the 22–28 GHz millimetre-wave band for 5G FR2 applications. To examine the influence of dielectric properties on electromagnetic behaviour, the same antenna geometry is implemented on three commonly used substrates—Duroid (relative permittivity εr ≈ 2.2, loss tangent tanδ ≈ 0.0009), Rogers (εr ≈ 2.94, tanδ ≈ 0.0012), and FR4 (εr ≈ 4.4, tanδ ≈ 0.02). A controlled substrate-based comparison is conducted with respect to the reflection coefficient, impedance bandwidth, gain, and radiation efficiency. The results indicate that substrate characteristics significantly affect resonance depth, impedance stability, and radiation performance at millimetre-wave frequencies. The Duroid-based configuration achieves S₁₁ below −32 dB, peak gain of 8–8.5 dBi, and high radiation efficiency due to reduced dielectric loss. The Rogers substrate exhibits stable multiband behaviour with moderate gain, whereas the FR4-based design shows reduced resonance depth and lower gain due to increased dielectric dissipation. By maintaining identical geometry across all substrates, the study isolates the direct impact of dielectric constant and loss tangent on modal excitation and efficiency degradation in the 22–28 GHz band. The presented analysis supports informed substrate selection for compact multiband mmWave antenna designs in next-generation wireless systems.
H. Sinan Bank, Daniel R. Herber, Thomas H. Bradley
Engineering system design -- whether mechatronic, control, or embedded -- often proceeds in an ad hoc manner, with requirements left implicit and traceability from intent to parameters largely absent. Existing specification-driven and systematic design methods mostly target software, and AI-assisted tools tend to enter the workflow at solution generation rather than at problem framing. Human--AI collaboration in the design of physical systems remains underexplored. This paper presents Design-OS, a lightweight, specification-driven workflow for engineering system design organized in five stages: concept definition, literature survey, conceptual design, requirements definition, and design definition. Specifications serve as the shared contract between human designers and AI agents; each stage produces structured artifacts that maintain traceability and support agent-augmented execution. We position Design-OS relative to requirements-driven design, systematic design frameworks, and AI-assisted design pipelines, and demonstrate it on a control systems design case using two rotary inverted pendulum platforms -- an open-source SimpleFOC reaction wheel and a commercial Quanser Furuta pendulum -- showing how the same specification-driven workflow accommodates fundamentally different implementations. A blank template and the full design-case artifacts are shared in a public repository to support reproducibility and reuse. The workflow makes the design process visible and auditable, and extends specification-driven orchestration of AI from software to physical engineering system design.
While the search for novel superconductors toward higher current carrying capacities and lower transport losses in different types of superconducting wires continues, so far, there is no record of any “liquid superconductor”. Superfluid helium (He-II), however, “conducts” heat without thermal resistance due to its very high thermal conductivity below its Lambda transition point. For superconducting magnets, different cooling schemes are employed using superfluid helium. These systems usually relate to internal or forced convection modes capable of transferring high heat loads. The inherent hydraulic quantum properties of He-II, like viscosity and density e.g., are therefore used in pumps (so-called fountain effect pumps (FEPs)), as described by the London equation, and enable the generation of a self-sustaining forced flow when using filters, optimized to work as “superleaks”. Those pumps have successfully been integrated in large superconducting applications e.g., like fusion magnets, accelerators, or dedicated gyroscopes. All these applications primarily depend on the peculiar flow characteristics of the superfluid helium component. As of today, there is no technical application that solely relies on the high thermal conductivity of the superfluid helium film as a heat transfer medium through copper/steel interfaces at temperatures below 1 K. To fully utilize that specific physical quantum property however, the interposing superconducting film needs to be well controlled in static, as well as transient cryogenic operating conditions. In this paper we present cryogenic engineering insights of trials and tribulations faced, when implementing, containing, and operating those thin superfluid helium films in a clinical environment for a medical Healthcare platform, that takes full advantage of this unique thermal conductivity and sound properties, that superfluid helium provides.
Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt engineering techniques. This paper presents an extensive empirical study that reevaluates various prompt engineering techniques within the context of these advanced LLMs. Focusing on three representative SE tasks, i.e., code generation, code translation, and code summarization, we assess whether prompt engineering techniques still yield improvements with advanced models, the actual effectiveness of reasoning models compared to non-reasoning models, and whether the benefits of using these advanced models justify their increased costs. Our findings reveal that prompt engineering techniques developed for earlier LLMs may provide diminished benefits or even hinder performance when applied to advanced models. In reasoning LLMs, the ability of sophisticated built-in reasoning reduces the impact of complex prompts, sometimes making simple zero-shot prompting more effective. Furthermore, while reasoning models outperform non-reasoning models in tasks requiring complex reasoning, they offer minimal advantages in tasks that do not need reasoning and may incur unnecessary costs. Based on our study, we provide practical guidance for practitioners on selecting appropriate prompt engineering techniques and foundational LLMs, considering factors such as task requirements, operational costs, and environmental impact. Our work contributes to a deeper understanding of effectively harnessing advanced LLMs in SE tasks, informing future research and application development.
This study analyzed the effects of compressor speed, expansion valve opening, and drying temperature on the thermal performance of a closed-loop heat pump drying system through single-factor experiments. Subsequently, system performance variation characteristics were obtained. The results show that when the compressor speed was increased from 2 000 r/min to 3 000 r/min, the power consumption increased by 89.6%, the heating capacity increased by 11.4%, and the COP（coefficient of performance） decreased by 41.3%. When the expansion valve was opened from 30% to 80%, the power consumption decreased by 5.2%, heating capacity increased by 73.5%, and COP decreased by 82.9%. When the drying temperature was increased from 40 °C to 60 °C, the power consumption increased by 38.1%, the heating capacity increased by 3.1%, and the COP decreased by 25.3%. Based on the analysis of the research results, it can be concluded that increasing the opening of the expansion valve and reducing the drying temperature and compressor speed can effectively reduce the system power consumption and improve the system performance.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Quantum software engineering (QSE) is receiving increasing attention, as evidenced by increasing publications on topics, e.g., quantum software modeling, testing, and debugging. However, in the literature, quantum software requirements engineering (QSRE) is still a software engineering area that is relatively less investigated. To this end, in this paper, we provide an initial set of thoughts about how requirements engineering for quantum software might differ from that for classical software after making an effort to map classical requirements classifications (e.g., functional and extra-functional requirements) into the context of quantum software. Moreover, we provide discussions on various aspects of QSRE that deserve attention from the quantum software engineering community.
Alexander E. I. Brownlee, James Callan, Karine Even-Mendoza
et al.
Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI's API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI.
Emilio Vital Brazil, Eduardo Soares, Lucas Villa Real
et al.
Data is a critical element in any discovery process. In the last decades, we observed exponential growth in the volume of available data and the technology to manipulate it. However, data is only practical when one can structure it for a well-defined task. For instance, we need a corpus of text broken into sentences to train a natural language machine-learning model. In this work, we will use the token \textit{dataset} to designate a structured set of data built to perform a well-defined task. Moreover, the dataset will be used in most cases as a blueprint of an entity that at any moment can be stored as a table. Specifically, in science, each area has unique forms to organize, gather and handle its datasets. We believe that datasets must be a first-class entity in any knowledge-intensive process, and all workflows should have exceptional attention to datasets' lifecycle, from their gathering to uses and evolution. We advocate that science and engineering discovery processes are extreme instances of the need for such organization on datasets, claiming for new approaches and tooling. Furthermore, these requirements are more evident when the discovery workflow uses artificial intelligence methods to empower the subject-matter expert. In this work, we discuss an approach to bringing datasets as a critical entity in the discovery process in science. We illustrate some concepts using material discovery as a use case. We chose this domain because it leverages many significant problems that can be generalized to other science fields.
Registered reports are scientific publications which begin the publication process by first having the detailed research protocol, including key research questions, reviewed and approved by peers. Subsequent analysis and results are published with minimal additional review, even if there was no clear support for the underlying hypothesis, as long as the approved protocol is followed. Registered reports can prevent several questionable research practices and give early feedback on research designs. In software engineering research, registered reports were first introduced in the International Conference on Mining Software Repositories (MSR) in 2020. They are now established in three conferences and two pre-eminent journals, including Empirical Software Engineering. We explain the motivation for registered reports, outline the way they have been implemented in software engineering, and outline some ongoing challenges for addressing high quality software engineering research.
The demand for product customization and flexible manufacturing techniques is growing day by day to meet the rapid changes in customer requirements. The current review presents the developments in the domains of incremental sheet forming (ISF) and deformation machining (DM) strategies to obtain thin monolithic geometries. The study focuses on the literature on room temperature single point incremental forming that can be applied to the DM. Thin structural parts are challenging to produce by machining, because they have inadequate static and dynamic stiffness and low thermal stability. Significant research work on the evolution of diverse theories that emerged to address the fundamental mechanisms of ISF and DM processes has been reported in the literature. This paper presents an outline of the significant process and response parameters, experimental strategies, deformation mechanics and fracture behavior, toolpath generation techniques, and processes' applications. The paper reports the motivation, research directions, existing gaps, and expansion in the domains of DM processes. The paper also outlines the evolution of incremental forming for deformation machining in the context of future critical applications in the domains of biomedical, aerospace, and automotive engineering.
Low-temperature geothermal heat sources have the highest share of geothermal energy in the world. Utilization of these heat sources for energy and freshwater generation can play an important role in meeting energy and freshwater demands. To do so, this study aims to propose a novel trigeneration cycle powered by low-temperature geothermal sources. The proposed system, which is an integration of Kalina and humidification-dehumidification (HDH) cycles, is used for the generation of electricity, heating, and freshwater. For the Kalina cycle, an evaporative condenser is used. It also acts as a humidifier and heater of the humidification-dehumidification desalination cycle, resulting in a reduction in the Journal of Thermal Engineering Web page info: https://jten.yildiz.edu.tr DOI: 10.18186/thermal.1067015
The paper presents a new efficient and robust method for rare event probability estimation for computational models of an engineering product or a process returning categorical information only, for example, either success or failure. For such models, most of the methods designed for the estimation of failure probability, which use the numerical value of the outcome to compute gradients or to estimate the proximity to the failure surface, cannot be applied. Even if the performance function provides more than just binary output, the state of the system may be a non-smooth or even a discontinuous function defined in the domain of continuous input variables. In these cases, the classical gradient-based methods usually fail. We propose a simple yet efficient algorithm, which performs a sequential adaptive selection of points from the input domain of random variables to extend and refine a simple distance-based surrogate model. Two different tasks can be accomplished at any stage of sequential sampling: (i) estimation of the failure probability, and (ii) selection of the best possible candidate for the subsequent model evaluation if further improvement is necessary. The proposed criterion for selecting the next point for model evaluation maximizes the expected probability classified by using the candidate. Therefore, the perfect balance between global exploration and local exploitation is maintained automatically. The method can estimate the probabilities of multiple failure types. Moreover, when the numerical value of model evaluation can be used to build a smooth surrogate, the algorithm can accommodate this information to increase the accuracy of the estimated probabilities. Lastly, we define a new simple yet general geometrical measure of the global sensitivity of the rare-event probability to individual variables, which is obtained as a by-product of the proposed algorithm.
This paper represents preliminary work in identifying the foundation for the discipline of Software Engineering and discovering the links between the domains of Software Engineering and Information Technology (IT). Our research utilized IEEE Transactions on Software Engineering (IEEE-TSE), ACM Transactions on Software Engineering and Methodology (ACM-TOSEM), Automated Software Engineering (ASE), the International Conference on Software Engineering(ICSE), and other related journal publication in the software engineering domain to address our research questions. We explored existing frameworks and described the need for software engineering as an academic discipline. We went further to clarify the distinction difference between Software Engineering and Computer Science. Through this efforts we contribute to an understanding of how evidence from IT research can be used to improve Software Engineering as a discipline.
We study low-energy scattering of spin-1/2 baryons from the perspective of quantum information science, focusing on the correlation between entanglement minimization and the appearance of accidental symmetries. The baryon transforms as an octet under the SU(3) flavor symmetry and its interactions below the pion threshold are described by contact operators in an effective field theory (EFT) of QCD. Despite there being 64 channels in the 2-to-2 scattering, only six independent operators in the EFT are predicted by SU(3). We show that successive entanglement minimization in SU(3)-symmetric channels are correlated with increasingly large emergent symmetries in the EFT. In particular, we identify scattering channels whose entanglement suppression are indicative of emergent SU(6), SO(8), SU(8), and SU(16) symmetries. We also observe the appearance of non-relativistic conformal invariance in channels with unnaturally large scattering lengths. Improved precision from lattice simulations could help determine the degree of entanglement suppression, and consequently the amount of accidental symmetry, in low-energy QCD.