Hasil untuk "Engineering"

Jun Zhang, Chuan Zhang, Mingtao Zhang

ABSTRACT This study integrates embodied intelligence (EI) with a two‐stage two‐sided Hotelling duopoly model to reveal how physical intelligence reshapes digital platform equilibrium in intelligent logistics. By embedding EI‐driven efficiency parameters into market cost functions, the model demonstrates that improved perception and coordination reduce the effective transportation cost and transform pricing dynamics between competing platforms. Experiments in a digital twin warehouse show that when EI strength η increases from 0 to 0.6, throughput rises by 37.5%, Dock‐to‐Stock time decreases by 30.9%, and unit energy consumption drops by 7%–8%, verifying that EI directly enhances operational and economic efficiency. Further analysis confirms that asymmetric advantages in action or data lead to discriminatory pricing as the optimal strategy. Complementary encryption experiments indicate that lightweight security algorithms such as SHA‐1/SHA‐256 add less than 3% latency overhead, maintaining real‐time performance.

Jianjun Zhao

Quantum computing has demonstrated the potential to solve computationally intensive problems more efficiently than classical methods. Many software engineering tasks, such as test case selection, static analysis, code clone detection, and defect prediction, involve complex optimization, search, or classification, making them candidates for quantum enhancement. In this paper, we introduce Quantum-Based Software Engineering (QBSE) as a new research direction for applying quantum computing to classical software engineering problems. We outline its scope, clarify its distinction from quantum software engineering (QSE), and identify key problem types that may benefit from quantum optimization, search, and learning techniques. We also summarize existing research efforts that remain fragmented. Finally, we outline a preliminary research agenda that may help guide the future development of QBSE, providing a structured and meaningful direction within software engineering.

Thomas J. Misa

The field of software engineering is embedded in both engineering and computer science, and may embody gender biases endemic to both. This paper surveys software engineering's origins and its long-running attention to engineering professionalism, profiling five leaders; it then examines the field's recent attention to gender issues and gender bias. It next quantitatively analyzes women's participation as research authors in the field's leading International Conference of Software Engineering (1976-2010), finding a dozen years with statistically significant gender exclusion. Policy dimensions of research on gender bias in computing are suggested.

Haoxuan Wang, Yifan Wang, Xu Liang et al.

Dislocations are line defects in crystalline solids and often exert a significant influence on the mechanical properties of metals. Recently, there has been a growing interest in using dislocations in ceramics to enhance materials performance. However, dislocation engineering has frequently been deemed uncommon in ceramics owing to the brittle nature of ceramics. Contradicting this conventional view, various approaches have been used to introduce dislocations into ceramic materials without crack formation, thereby paving the way for controlled ceramics performance. However, the influence of dislocations on functional properties is equally complicated owing to the intricate structure of ceramic materials. Furthermore, despite numerous experiments and simulations investigating dislocation-controlled properties in ceramics, comprehensive reviews summarizing the effects of dislocations on ceramics are still lacking. This review focuses on some representative dislocation-controlled properties of ceramic materials, including mechanical and some key functional properties, such as transport, ferroelectricity, thermal conductivity, and superconducting properties. A brief integration of dislocations in ceramic is anticipated to offer new insights for the advancement of dislocation engineering across various disciplines.

Mikio Nakano, Hironori Takeuchi, Sadahiro Yoshikawa et al.

This paper proposes to refer to the field of software engineering related to the life cycle of dialogue systems as Dialogue Systems Engineering, and surveys this field while also discussing its future directions. With the advancement of large language models, the core technologies underlying dialogue systems have significantly progressed. As a result, dialogue system technology is now expected to be applied to solving various societal issues and in business contexts. To achieve this, it is important to build, operate, and continuously improve dialogue systems correctly and efficiently. Accordingly, in addition to applying existing software engineering knowledge, it is becoming increasingly important to evolve software engineering tailored specifically to dialogue systems. In this paper, we enumerate the knowledge areas of dialogue systems engineering based on those of software engineering, as defined in the Software Engineering Body of Knowledge (SWEBOK) Version 4.0, and survey each area. Based on this survey, we identify unexplored topics in each area and discuss the future direction of dialogue systems engineering.

Qing Zhang, Jiaoyang Cheng, Jinxin Cao et al.

Lithium‐rich manganese‐based oxides are accepted as a promising cathode material for high‐energy density batteries. However, they suffer from irreversible structural transformations and detrimental interfacial reactions, especially under deep charge/discharge states, causing severe voltage fade and capacity degradation. Herein, Li‐rich binary oxide Li1.16(Ni0.25Mn0.75)0.84O2 is proposed to dual‐coated by superionic conductor Li1.4Al0.4Ti1.6(PO4)3 and conductive polymer polyaniline, displaying nearly two orders of magnitude promotion for lithium ion transmission coefficient (10−9.5 cm2 S−1) at the end of charge/discharge. COMSOL Multiphysics simulation indicates the synergistic interfacial coating elevates the homogeneous distribution of lithium–ions and current density, improving utilization rates of lithium–ions, mitigating irreversible structural transformation, and suppressing the dissolution of transition metal ions and side reactions between the cathode and electrolyte. Therefore, Li1.16(Ni0.25Mn0.75)0.84O2 with the significantly promoted charge transfer kinetics exhibits greatly strengthened specific capacity of 293.6 mAh g−1 at 20 mA g−1 within the range of 2.0–4.8 V, with an increased initial Coulombic efficiency of 84.42% and capacity retention of 88.94% in 150 cycles, alongside with a low voltage decay (0.23 V within 150 cycles) and a high rate capability of 160 mAh g−1 at 5 C.

Li-Tao Che, Li-Tao Che, Ahmed H. El-Sappah et al.

IntroductionThis study investigated whether glycyrrhetinic acid (GA) can alleviate the reproductive toxicity of Zearalenone (ZEN) in replacement gilts.MethodsEighty Landrace × Yorkshire gilts were randomly assigned to four dietary groups: control (basal diet), ZEN (1 mg/kg), GA (400 mg/kg), and ZEN + GA (1 mg/kg ZEN + 400 mg/kg GA).ResultsThe onset of estrus advanced significantly in all treatment groups, with the GA and ZEN + GA groups showing the most pronounced changes. Puberty onset occurred earlier in the ZEN group and was further advanced by GA supplementation. ZEN exposure impaired uterine and ovarian development, while GA improved organ development and mitigated the abnormalities in the ZEN + GA group. Hormonal analysis revealed that ZEN reduced estradiol (E2) and luteinizing hormone (LH), whereas GA elevated all measured hormones. The ZEN + GA group showed a partial recovery in hormone levels, excluding E2. Histological examination of liver tissue in the ZEN group revealed focal hepatocellular necrosis and lymphocyte infiltration, which GA notably attenuated. ZEN upregulated 3α/3β/17β-hydroxysteroid dehydrogenase (HSD) gene expression in the liver and duodenum, while GA co-administration downregulated most HSD genes except hepatic 3α-HSD.Discussion and conclusionThese findings suggest that GA can alleviate ZEN-induced reproductive toxicity via modulation of endocrine and hepatic metabolic pathways.

Toshifumi Fujioka, Tamako Nishimura, Hiroki Kawana et al.

Abstract Extracellular vesicles (EVs) mediate the transfer of intracellular proteins from producer to recipient cells. EVs originate either from plasma membrane protrusions or endosomes, with endosome-derived EVs being extensively studied and engineered. However, the efficiency and functionality of protein transfer via both types of EVs remain poorly understood. Here, we demonstrate that natural EVs derived from cell protrusions dependent on the I-BAR protein MIM, rather than from endosomes, deliver the functional small GTPase Rac1 protein at levels similar to microinjection. Rac1-containing EVs are internalized via endocytosis, trafficked through endosomal compartments, and subsequently released into the cytosol, where they enhance cell motility. To evaluate broader applicability, the genome-editing protein Cas12f is packaged into protrusion-derived EVs by MIM and endosome-derived EVs by endosomal tetraspanin CD63. Notably, protrusion-derived EVs deliver Cas12f with significantly higher efficiency than endosome-derived EVs, highlighting their superior capability for functional protein transfer. Our findings establish the protrusion-derived EVs as a powerful platform for the efficient and bioactive delivery of both native and engineered proteins, expanding the EV-based therapeutic strategies.

Rasmus Adler, Frank Elberzhager, Florian Balduf

Engineering a sustainable world requires to consider various systems that interact with each other. These systems include ecological systems, economical systems, social systems and tech-nical systems. They are loosely coupled, geographically distributed, evolve permanently and generate emergent behavior. As these are characteristics of systems of systems (SoS), we discuss the engi-neering of a sustainable world from a SoS engineering perspective. We studied SoS engineering in context of a research project, which aims at political recommendations and a research roadmap for engineering dynamic SoS. The project included an exhaustive literature review, interviews and work-shops with representatives from industry and academia from different application domains. Based on these results and observations, we will discuss how suitable the current state-of-the-art in SoS engi-neering is in order to engineer sustainability. Sustainability was a major driver for SoS engineering in all domains, but we argue that the current scope of SoS engineering is too limited in order to engineer sustainability. Further, we argue that mastering dynamics in this larger scope is essential to engineer sustainability and that this is accompanied by dynamic adaptation of technological SoS.

LIU Shuxian, XU Huan, WANG Wei, DENG Le

Social network provides a medium for information dissemination,leading to the rapid development of public opinion.Controlling the development direction of public opinion is one of the core issues of public opinion dynamics.However,the public opinion dynamics model mainly studies the way of updating the opinions of the subject so as to deduce the law of public opinion evolution.This paper classifies the current public opinion dynamics models,analyzes their advantages and disadvantages,and their applications in different fields,and summarizes the future research direction of public opinion dynamics.It is helpful to understand the law of the evolution of public opinion,so as to provide better guidance for the government and other institutions to control the direction of public opinion.

Wenqiang Liu, Li Yan, Ningning Ma et al.

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.

Joost C. F. de Winter, Dimitra Dodou, Yke Bauke Eisma

The processes underlying human cognition are often divided into System 1, which involves fast, intuitive thinking, and System 2, which involves slow, deliberate reasoning. Previously, large language models were criticized for lacking the deeper, more analytical capabilities of System 2. In September 2024, OpenAI introduced the <i>o1</i> model series, designed to handle System 2-like reasoning. While OpenAI’s benchmarks are promising, independent validation is still needed. In this study, we tested the <i>o1-preview</i> model twice on the Dutch ‘Mathematics B’ final exam. It scored a near-perfect 76 and 74 out of 76 points. For context, only 24 out of 16,414 students in the Netherlands achieved a perfect score. By comparison, the <i>GPT-4o</i> model scored 66 and 62 out of 76, well above the Dutch students’ average of 40.63 points. Neither model had access to the exam figures. Since there was a risk of model contamination (i.e., the knowledge cutoff for <i>o1-preview</i> and <i>GPT-4o</i> was after the exam was published online), we repeated the procedure with a new Mathematics B exam that was published after the cutoff date. The results again indicated that <i>o1-preview</i> performed strongly (97.8th percentile), which suggests that contamination was not a factor. We also show that there is some variability in the output of <i>o1-preview</i>, which means that sometimes there is ‘luck’ (the answer is correct) or ‘bad luck’ (the output has diverged into something that is incorrect). We demonstrate that the self-consistency approach, where repeated prompts are given and the most common answer is selected, is a useful strategy for identifying the correct answer. It is concluded that while OpenAI’s new model series holds great potential, certain risks must be considered.

Hanna Abi Akl

The "Information Retrieval in Software Engineering (IRSE)" at FIRE 2023 shared task introduces code comment classification, a challenging task that pairs a code snippet with a comment that should be evaluated as either useful or not useful to the understanding of the relevant code. We answer the code comment classification shared task challenge by providing a two-fold evaluation: from an algorithmic perspective, we compare the performance of classical machine learning systems and complement our evaluations from a data-driven perspective by generating additional data with the help of large language model (LLM) prompting to measure the potential increase in performance. Our best model, which took second place in the shared task, is a Neural Network with a Macro-F1 score of 88.401% on the provided seed data and a 1.5% overall increase in performance on the data generated by the LLM.

Jongho Lee, Jiuk Shin, Jaewook Lee et al.

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.

G. Li, E. K. Wilbourn, Z. Cheng et al.

<p>Ice-nucleating particles (INPs) initiate primary ice formation in Arctic mixed-phase clouds (MPCs), altering cloud radiative properties and modulating precipitation. For atmospheric INPs, the complexity of their spatiotemporal variations, heterogeneous sources, and evolution via intricate atmospheric interactions challenge the understanding of their impact on microphysical processes in Arctic MPCs and induce an uncertain representation in climate models. In this work, we performed a comprehensive analysis of atmospheric aerosols at the Arctic coastal site in Ny-Ålesund (Svalbard, Norway) from October to November 2019, including their ice nucleation ability, physicochemical properties, and potential sources. Overall, INP concentrations (<span class="inline-formula"><i>N</i>_INP</span>) during the observation season were approximately up to 3 orders of magnitude lower compared to the global average, with several samples showing degradation of <span class="inline-formula"><i>N</i>_INP</span> after heat treatment, implying the presence of proteinaceous INPs. Particle fluorescence was substantially associated with INP concentrations at warmer ice nucleation temperatures, indicating that in the far-reaching Arctic, aerosols of biogenic origin throughout the snow- and ice-free season may serve as important INP sources. In addition, case studies revealed the links between elevated <span class="inline-formula"><i>N</i>_INP</span> and heat lability, fluorescence, high wind speeds originating from the ocean, augmented concentration of coarse-mode particles, and abundant organics. Backward trajectory analysis demonstrated a potential connection between high-latitude dust sources and high INP concentrations, while prolonged air mass history over the ice pack was identified for most scant INP cases. The combination of the above analyses demonstrates that the abundance, physicochemical properties, and potential sources of INPs in the Arctic are highly variable despite its remote location.</p>

Tomislav Maric, Dennis Gläser, Jan-Patrick Lehr et al.

University research groups in Computational Science and Engineering (CSE) generally lack dedicated funding and personnel for Research Software Engineering (RSE), which, combined with the pressure to maximize the number of scientific publications, shifts the focus away from sustainable research software development and reproducible results. The neglect of RSE in CSE at University research groups negatively impacts the scientific output: research data - including research software - related to a CSE publication cannot be found, reproduced, or re-used, different ideas are not combined easily into new ideas, and published methods must very often be re-implemented to be investigated further. This slows down CSE research significantly, resulting in considerable losses in time and, consequentially, public funding. We propose a RSE workflow for Computational Science and Engineering (CSE) that addresses these challenges, that improves the quality of research output in CSE. Our workflow applies established software engineering practices adapted for CSE: software testing, result visualization, and periodical cross-linking of software with reports/publications and data, timed by milestones in the scientific publication process. The workflow introduces minimal work overhead, crucial for university research groups, and delivers modular and tested software linked to publications whose results can easily be reproduced. We define research software quality from a perspective of a pragmatic researcher: the ability to quickly find the publication, data, and software related to a published research idea, quickly reproduce results, understand or re-use a CSE method, and finally extend the method with new research ideas.

Daniele Di Pompeo, Michele Tucci

Software model optimization is the task of automatically generate design alternatives, usually to improve quality aspects of software that are quantifiable, like performance and reliability. In this context, multi-objective optimization techniques have been applied to help the designer find suitable trade-offs among several non-functional properties. In this process, design alternatives can be generated through automated model refactoring, and evaluated on non-functional models. Due to their complexity, this type of optimization tasks require considerable time and resources, often limiting their application in software engineering processes. In this paper, we investigate the effects of using a search budget, specifically a time limit, to the search for new solutions. We performed experiments to quantify the impact that a change in the search budget may have on the quality of solutions. Furthermore, we analyzed how different genetic algorithms (i.e., NSGA-II, SPEA2, and PESA2) perform when imposing different budgets. We experimented on two case studies of different size, complexity, and domain. We observed that imposing a search budget considerably deteriorates the quality of the generated solutions, but the specific algorithm we choose seems to play a crucial role. From our experiments, NSGA-II is the fastest algorithm, while PESA2 generates solutions with the highest quality. Differently, SPEA2 is the slowest algorithm, and produces the solutions with the lowest quality.

Jeroen C. J. GROOT, Xiaolin YANG

&lt;List&gt; &lt;ListItem&gt;&lt;ItemContent&gt;&lt;p&gt;● Impacts of 30 cropping systems practiced on the North China Plain were evaluated.&lt;/p&gt;&lt;/ItemContent&gt;&lt;/ListItem&gt; &lt;ListItem&gt;&lt;ItemContent&gt;&lt;p&gt;● Trade-offs were assessed among productive, economic and environmental indicators.&lt;/p&gt;&lt;/ItemContent&gt;&lt;/ListItem&gt; &lt;ListItem&gt;&lt;ItemContent&gt;&lt;p&gt;● An evolutionary algorithm was used for multi-objective optimization.&lt;/p&gt;&lt;/ItemContent&gt;&lt;/ListItem&gt; &lt;ListItem&gt;&lt;ItemContent&gt;&lt;p&gt;● Conflict exists between productivity and profitability versus lower ground water decline.&lt;/p&gt;&lt;/ItemContent&gt;&lt;/ListItem&gt; &lt;ListItem&gt;&lt;ItemContent&gt;&lt;p&gt;● Six strategies were identified to jointly mitigate the trade-offs between objectives.&lt;/p&gt;&lt;/ItemContent&gt;&lt;/ListItem&gt;&lt;/List&gt;&lt;/p&gt; &lt;p&gt;Since the Green Revolution cropping systems have been progressively homogenized and intensified with increasing rates of inputs such as fertilizers, pesticides and water. This has resulted in higher crop productivity but also a high environmental burden due to increased pollution and water depletion. To identify opportunities for increasing the productivity and reducing the environmental impact of cropping systems, it is crucial to assess the associated trade-offs. The paper presents a model-based analysis of how 30 different crop rotations practiced in the North China Plain could be combined at the regional level to overcome trade-offs between indicators of economic, food security, and environmental performance. The model uses evolutionary multi-objective optimization to maximize revenues, livestock products, dietary and vitamin C yield, and to minimize the decline of the groundwater table. The modeling revealed substantial trade-offs between objectives of maximizing productivity and profitability versus minimizing ground water decline, and between production of livestock products and vitamin C yield. Six strategies each defining a specific combination of cropping systems and contributing to different extents to the various objectives were identified. Implementation of these six strategies could be used to find opportunities to mitigate the trade-offs between objectives. It was concluded that a holistic analysis of the potential of a diversity cropping systems at a regional level is needed to find integrative solutions for challenges due to conflicting objectives for food production, economic viability and environmental protection.

Peng Xu, Alejandro Salado, Xinwei Deng

Verification is a critical process in the development of engineered systems. Through verification, engineers gain confidence in the correct functionality of the system before it is deployed into operation. Traditionally, verification strategies are fixed at the beginning of the system's development and verification activities are executed as the development progresses. Such an approach appears to give inferior results as the selection of the verification activities does not leverage information gained through the system's development process. In contrast, a set-based design approach to verification, where verification activities are dynamically selected as the system's development progresses, has been shown to provide superior results. However, its application under realistic engineering scenarios remains unproven due to the large size of the verification tradespace. In this work, we propose a parallel tempering approach (PTA) to efficiently explore the verification tradespace. First, we formulate exploration of the verification tradespace as a tree search problem. Second, we design a parallel tempering (PT) algorithm by simulating several replicas of the verification process at different temperatures to obtain a near-optimal result. Third, We apply the PT algorithm to all possible verification states to dynamically identify near-optimal results. The effectiveness of the proposed PTA is evaluated on a partial model of a notional satellite optical instrument.

Yasemin Çiftci

The structural, electronic, elastic and optic properties of NaYSi formed in the half-Heusler structure are studied by using ab-initio density-functional theory. The calculated equilibrium lattice constants are compaired with the available experimental and theoretical data. The elastic parameters have calculated. Computed elastic results prove that this compound were mechanically stable. The band gap of this compound predicted to be semiconductor. Further the phonon spectra and optical analysis have been obtained for the energy range of 0–40 eV.