Hasil untuk "Chemical engineering"

Tanja E. J. Vos, Tijs van der Storm, Alexander Serebrenik et al.

Software engineering is the invisible infrastructure of the digital age. Every breakthrough in artificial intelligence, quantum computing, photonics, and cybersecurity relies on advances in software engineering, yet the field is too often treated as a supportive digital component rather than as a strategic, enabling discipline. In policy frameworks, including major European programmes, software appears primarily as a building block within other technologies, while the scientific discipline of software engineering remains largely absent. This position paper argues that the long-term sustainability, dependability, and sovereignty of digital technologies depend on investment in software engineering research. It is a call to reclaim the identity of software engineering.

Lili Chen, Winn Wing-Yiu Chow, Stella Peng et al.

PURPOSE OR GOAL: This study investigates how GenAI can be integrated with a criterion-referenced grading framework to improve the efficiency and quality of grading for mathematical assessments in engineering. It specifically explores the challenges demonstrators face with manual, model solution-based grading and how a GenAI-supported system can be designed to reliably identify student errors, provide high-quality feedback, and support human graders. The research also examines human graders' perceptions of the effectiveness of this GenAI-assisted approach. ACTUAL OR ANTICIPATED OUTCOMES: The study found that GenAI achieved an overall grading accuracy of 92.5%, comparable to two experienced human graders. The two researchers, who also served as subject demonstrators, perceived the GenAI as a helpful second reviewer that improved accuracy by catching small errors and provided more complete feedback than they could manually. A central outcome was the significant enhancement of formative feedback. However, they noted the GenAI tool is not yet reliable enough for autonomous use, especially with unconventional solutions. CONCLUSIONS/RECOMMENDATIONS/SUMMARY: This study demonstrates that GenAI, when paired with a structured, criterion-referenced framework using binary questions, can grade engineering mathematical assessments with an accuracy comparable to human experts. Its primary contribution is a novel methodological approach that embeds the generation of high-quality, scalable formative feedback directly into the assessment workflow. Future work should investigate student perceptions of GenAI grading and feedback.

Carolyn Seaman, Rashina Hoda, Robert Feldt

The paper entitled "Qualitative Methods in Empirical Studies of Software Engineering" by Carolyn Seaman was published in TSE in 1999. It has been chosen as one of the most influential papers from the third decade of TSE's 50 years history. In this retrospective, the authors discuss the evolution of the use of qualitative methods in software engineering research, the impact it's had on research and practice, and reflections on what is coming and deserves attention.

Zhimin Zhao

Foundation models (FMs), particularly large language models (LLMs), have shown significant promise in various software engineering (SE) tasks, including code generation, debugging, and requirement refinement. Despite these advances, existing evaluation frameworks are insufficient for assessing model performance in iterative, context-rich workflows characteristic of SE activities. To address this limitation, we introduce \emph{SWE-Arena}, an interactive platform designed to evaluate FMs in SE tasks. SWE-Arena provides a transparent, open-source leaderboard, supports multi-round conversational workflows, and enables end-to-end model comparisons. The platform introduces novel metrics, including \emph{model consistency score} that measures the consistency of model outputs through self-play matches, and \emph{conversation efficiency index} that evaluates model performance while accounting for the number of interaction rounds required to reach conclusions. Moreover, SWE-Arena incorporates a new feature called \emph{RepoChat}, which automatically injects repository-related context (e.g., issues, commits, pull requests) into the conversation, further aligning evaluations with real-world development processes. This paper outlines the design and capabilities of SWE-Arena, emphasizing its potential to advance the evaluation and practical application of FMs in software engineering.

Marc Bruni, Fabio Gabrielli, Mohammad Ghafari et al.

Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code security. Our benchmark leverages two peer-reviewed prompt datasets and employs static scanners to evaluate code security at scale. We tested multiple prompt engineering techniques on GPT-3.5-turbo, GPT-4o, and GPT-4o-mini. Our results show that for GPT-4o and GPT-4o-mini, a security-focused prompt prefix can reduce the occurrence of security vulnerabilities by up to 56%. Additionally, all tested models demonstrated the ability to detect and repair between 41.9% and 68.7% of vulnerabilities in previously generated code when using iterative prompting techniques. Finally, we introduce a "prompt agent" that demonstrates how the most effective techniques can be applied in real-world development workflows.

Fei Li, Qinye Zhang, Yuntong Lu et al.

IntroductionCodon usage bias (CUB) can influence host-microbe interactions and stress adaptation. In this study, we aimed to investigate how codon usage bias (CUB) similarity between Arabidopsis thaliana and Bacillus amyloliquefaciens influences their interaction and contributes to the adaptation of A. thaliana to high calcium stress.MethodsThe CUB indices of both species were computed, and genes with high correlations were identified. The transcriptome sequencing data of gene expression in A. thaliana cultured under normal and high calcium conditions, with and without B. amyloliquefaciens treatment was used to analyze the expression of A. thaliana genes with CUB similar to that of B. amyloliquefaciens in relation with the adaptation of A. thaliana to high calcium stress and the interaction between both organisms.ResultsWe identified 19210 A. thaliana genes with CUB similar to B. amyloliquefaciens and 95 B. amyloliquefaciens-responsive and calcium-responsive genes in A. thaliana, which were involved in transport, carbohydrate metabolism, and response to chemical, and cellular homeostasis. Differential expression analysis showed a total of 733 A. thaliana genes with CUB similar to B. amyloliquefaciens to be dysregulated, among which 47 changed when A. thaliana was cultivated in the presence of the B. amyloliquefaciens LZ04 strain, 643 under high calcium condition and 43 with calcium treatment and the presence of the B. amyloliquefaciens LZO4 strain. The gene ontology (GO) biological processes termed among others of response to endogenous stimulus, response to oxygen containing compound, response to organic substance, response to abiotic and biotic stimuli, response to stress, and response to light stimulus, regulation of hormone levels, response to nutrient levels, post-embryonic plant morphogenesis, metabolic process, cell growth.DiscussionThese findings highlight the importance of CUB in the interaction between A. thaliana and B. amyloliquefaciens as well as in the adaptation of A. thaliana to high calcium stress. They also show the underlying regulatory role of B. amyloliquefaciens, which could help develop new tactics for improving A. thaliana growth and yield in karst regions. A more elaborate analysis of the value of CUB in the interaction of these two organisms could assist in engineering host- sensitive micro-organism strains and enhance the microbial-based approaches for the improvement of A. thaliana growth and yield in such areas and for managing abiotic stress in crops.

Lynn Maria Schneider, Benedikt Sochor, Marcus Johansen et al.

Kushagra Singh, Souradeep Gupta

The application of carbon-rich char-based admixtures, including biochar and plastic char, in construction products has received substantial attention from global industries due to their potential to “lock in” carbon for the long term, thus mitigating the climatic impacts of future constructions. Furthermore, a sharp rise in plastic waste generation and uncontrolled landfilling threatens natural ecosystems. Depending on type, plastic waste can be used as fuel, and the generated char (solid residue) can be reintegrated into the construction value chain by utilizing it as a carbon-sequestering admixture in construction materials. This article discusses critical factors, including the synthesis temperature, heating rate, and different activation pathways, for tuning plastic char’s porosity and surface properties, contributing to enhanced carbon fixation and CO2 uptake. Chemical pyrolysis using alkaline agents produces microporous structure (< 2 nm) with high surface areas (> 1000 m2g−1) and CO2 uptake, ranging up to 4.6 mmolg−1 while acidic agents produce a higher fraction of mesopores (> 2 nm) with lower surface areas < 1500 m2g−1 and CO2 uptake capacities (up to 1.8 mmolg−1). The review finds that surface functionalization of plastic char and altering its physicochemical properties improve the engineering properties of construction binders. The locked carbon in the char, complemented by additional CO2 uptake in the engineered pore and surface sites, can be instrumental in mitigating the embodied carbon of construction products. However, future investigations should study the microstructural interactions of engineered char within construction binders and conduct a holistic life-cycle assessment to fully realize the benefits of using engineered plastic char as a supplementary additive.

Eduard C. Groen, Kazi Rezoanur Rahman, Nikita Narsinghani et al.

The farming domain has seen a tremendous shift towards digital solutions. However, capturing farmers' requirements regarding Digital Farming (DF) technology remains a difficult task due to domain-specific challenges. Farmers form a diverse and international crowd of practitioners who use a common pool of agricultural products and services, which means we can consider the possibility of applying Crowd-based Requirements Engineering (CrowdRE) for DF: CrowdRE4DF. We found that online user feedback in this domain is limited, necessitating a way of capturing user feedback from farmers in situ. Our solution, the Farmers' Voice application, uses speech-to-text, Machine Learning (ML), and Web 2.0 technology. A preliminary evaluation with five farmers showed good technology acceptance, and accurate transcription and ML analysis even in noisy farm settings. Our findings help to drive the development of DF technology through in-situ requirements elicitation.

Feriehiwote Weldeyohanis Gebremariam, Eneyew Tadesse Melaku, Venkatesa Prabhu Sundramurthy et al.

To develop high quality cookies, even seemingly smallest changes depended on factors that can affect taste, texture, and nutritional value. In this light, this study aimed to investigate the upshot of refined wheat flour and pumpkin seed flour on properties of cookies such as antioxidant activity, thermal and oxidative stability. In view of the foregoing, the roasted pumpkin seeds of particle size below 500 μm were blended with wheat flour at different ratios (BR) to bake at selected pre-determined temperatures (T) and time durations (TD). The synergetic effect of aforesaid parameters on cookie development, BR, T, and TD was studied by varying the parameters between the range 6–15 %, 180–200 °C and from 8 to 12 min, respectively, for the baking process of cookies. Further, the process was modelled and scrutinized using numerical optimization to achieve a highly acceptable product. On that account, it was deduced that the optimal condition for BR, T, and TD were 12.87 %, 186 °C and 9.5 min, respectively, that could pave to beget the excellent quality cookies with overall acceptance score of 8, protein content 14.28 %, fat 17.85 %, ash 2.23 %, moisture 2.46 %, fiber 2.38 % and total color difference 12.01. The optimized cookies (OCs) were found to have higher protein (11.49–14.28 %), fiber (0.93–2.41 %), ash (2.19–1.77 %), total antioxidant activity (38.7158–43.1860 %), oxidative stability (28.61–51.24 h), Zn (1.42–2.63 mg/100g), and Fe (2.12–3.20 mg/100g) content as compared to the control. Laconically, the study results provided the optimized processing condition for developing high quality cookies with respect to improved nutritional value and comparable overall acceptability.

Liang Anqi, Zeng Shuang, Ren Jiahang et al.

To improve the comprehensive utilization of regional energy and promote low-carbon development, this study constructs an integrated energy system for typical areas, such as parks, including a new energy power generation system driven by photovoltaic and wind power, heating and cooling energy supply systems for ground-source/air-source heat pumps, water chillers, and energy storage equipment. TRNSYS? software is used to simulate and study the dynamic characteristics of the system under six climate conditions in Beijing, and the game theory is used for intelligent operation, which is then compared with the logic control method. The results show that the logic control method can meet the load demand but cannot realize the efficient operation of the heat pump unit and the charge and discharge balance of the energy storage device. The integrated energy system after optimization via game theory can not only realize flexible energy scheduling and distribution through electric-thermal coordination, but also save the entire energy consumption of the heat pump unit and achieve the goal of regional energy economic benefits. The research presented in this paper provides an important theoretical basis for the intelligent operation of heat pump systems in integrated electric-thermal cooperative grids.

Younggill Son, Woongchul Choi

With the global surge in electric vehicle (EV) deployment, driven by enhanced environmental regulations and efforts to reduce transportation-related greenhouse gas emissions, managing the life cycle of Li-ion batteries becomes more critical than ever. A crucial step for battery reuse or recycling is the precise estimation of static capacity at retirement. Traditional methods are time-consuming, often taking several hours. To address this issue, a machine learning-based approach is introduced to estimate the static capacity of retired batteries rapidly and accurately. Partial discharge data at a 1 C rate over durations of 6, 3, and 1 min were analyzed using a machine learning algorithm that effectively handles temporally evolving data. The estimation performance of the methodology was evaluated using the mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). The results showed reliable and fairly accurate estimation performance, even with data from shorter partial discharge durations. For the one-minute discharge data, the maximum RMSE was 2.525%, the minimum was 1.239%, and the average error was 1.661%. These findings indicate the successful implementation of rapidly assessing the static capacity of EV batteries with minimal error, potentially revitalizing the retired battery recycling industry.

Abdelfattah Amari, Ahmad Ismael Saber, Haitham Osman et al.

Abstract Water pollution is a pressing global concern, with per- and polyfluoroalkyl substances (PFAS) being considered as “forever contaminants.” Among them, perfluorooctanesulfonic acid (PFOS) has received significant attention for its adverse effects on human health and aquatic ecosystems. This study aimed to design an innovative adsorbent for effective PFOS removal with exceptional water stability, improving its cost-performance trade-off. The current work simultaneously improved the stability of water of Cu-based metal–organic framework (CMOF) and increased its PFOS removal capacity by modifying it with amine-functionalized SiO2 nanoparticles (AF-CMOF). AF-CMOF presented a lower specific surface area of 999 m2 g−1 compared to CMOF with a surface area of 1098 m2 g−1. AF-CMOF showed remarkable PFOS uptake performance of 670 mg/g compared to the performance of the Cu-based MOF which exhibited a PFOS uptake capacity of only 22 mg/g. The most suitable pH for PFOS removal using both adsorbents was determined to be 3. In addition, AF-CMOF demonstrated excellent water stability, retaining its structural integrity even after seven days of water contact, while CMOF structure collapsed rapidly after four days of water exposure. Moreover, the study identified the significant pH influence on the PFOS uptake process, with electrostatic interactions between protonated amine functionalities and PFOS molecules identified as the dominant mechanism. The study’s findings present the potential of synthesized adsorbent as a superior candidate for PFOS uptake and contribute to the development of effective water treatment technologies.

Woojeong Kim, Canice Chun‐Yin Yiu, Yong Wang et al.

Abstract This review highlights the development of plant proteins from a wide variety of sources, as most of the research and development efforts to date have been limited to a few sources including soy, chickpea, wheat, and pea. The native structure of plant proteins during production and their impact on food colloids including emulsions, foams, and gels are considered in relation to their fundamental properties, while highlighting the recent developments in the production and processing technologies with regard to their impacts on the molecular properties and aggregation of the proteins. The ability to quantify structural, morphological, and rheological properties can provide a better understanding of the roles of plant proteins in food systems. The applications of plant proteins as dairy and meat alternatives are discussed from the perspective of food structure formation. Future directions on the processing of plant proteins and potential applications are outlined to encourage the generation of more diverse plant‐based products.

Jiace Sun, Lixue Cheng, Weitang Li

Achieving chemical accuracy with shallow quantum circuits is a significant challenge in quantum computational chemistry, particularly for near-term quantum devices. In this work, we present a Clifford-based Hamiltonian engineering algorithm, namely CHEM, that addresses the trade-off between circuit depth and accuracy. Based on variational quantum eigensolver and hardware-efficient ansatz, our method designs Clifford-based Hamiltonian transformation that (1) ensures a set of initial circuit parameters corresponding to the Hartree--Fock energy can be generated, (2) effectively maximizes the initial energy gradient with respect to circuit parameters, (3) imposes negligible overhead for classical processing and does not require additional quantum resources, and (4) is compatible with any circuit topology. We demonstrate the efficacy of our approach using a quantum hardware emulator, achieving chemical accuracy for systems as large as 12 qubits with fewer than 30 two-qubit gates. Our Clifford-based Hamiltonian engineering approach offers a promising avenue for practical quantum computational chemistry on near-term quantum devices.

Yuhui Xiong, Sijie Ma, Xiaodong Hong et al.

Various thicknesses of TiO₂ films were prepared by the sol–gel method and spin-coating process. These prepared TiO₂ films exhibit thickness-dependent photoelectrochemical performance. The 1.09-μm-thickTiO₂ film with 20 spin-coating layers (TiO₂-20) exhibits the highest short circuit current of 0.21 mAcm⁻² and open circuit voltage of 0.58 V among all samples and exhibits a low PEC reaction energy barrier and fast kinetic process. Photoelectrocatalytic (PEC) degradation of methyl orange (MO) by TiO₂ films was carried out under UV light. The roles of bias, film thickness, pH value, and ion properties were systematically studied because they are the four most important factors dominating the PEC performance of TiO₂ films. The optimized values of bias, film thickness, and pH are 1.0 V, 1.09 μm, and 12, respectively, which were obtained according to the data of the PEC degradation of MO. The effect of ion properties on the PEC efficiency of TiO₂-20 was also analyzed by using halide as targeted ions. The “activated” halide ions significantly promoted the PEC efficiency and the order was determined as Br > Cl > F. The PEC efficiency increased with increasing Cl content, up until the optimized value of 30 × 10⁻³ M. Finally, a complete degradation of MO by TiO₂-20 was achieved in 1.5 h, with total optimization of the four factors: 1.0 V bias, 1.09-μm-thick, pH 12, and 30 × 10⁻³ M Cl ion content. The roles of reactive oxygen species and electric charge of photoelectrodes were also explored based on photoelectrochemical characterizations and membrane-separated reactors. Hydrogen peroxide, superoxide radical, and hydroxyl radical were found responsible for the decolorization of MO.

Alexander I. Ikeuba

The pH effect on the surface and interfacial films on η-phase (MgZn2) in aqueous solutions under acidic, neutral, and alkaline conditions has been evaluated using time of flight-secondary ion spectroscopy (TOF-SIMS), Atomic force microscopy (AFM) and scanning electron microscopy/energy dispersive X-ray spectroscopy (SEM/EDX). TOF-SIMS depth profile plots reveal that under an acidic environment (pH2) deep corrosion penetration occurs with a dispersion of corrosion products which claims a considerable depth matrix cross-section. Under near neutral environments (pH 6), the corrosion film is seen to be stratified into two layers of different compositions, while in a slightly alkaline environment (pH 10) the film appears not to be distinctly differentiated, whereas in a very alkaline environment (pH 13) a compact film rich in hydroxides develops. TOF-SIMs surface and depth profile maps were consistent with the depth profile plots. SEM and AFM images reveal that the surface roughness increased in with a decrease in pH value from the acidic to the alkaline environments. EDX elemental composition analysis also indicated a severe drop in the zinc content of the film in the alkaline environment. Largely, metallic zinc enrichment occurs following the initial magnesium dissolution whose stability is greatly affected by the near-surface pH of the bulk solution, thus, giving rise to different film structures.

Firuz Kamalov, Ho-Hon Leung

The rapid advances in technology over the last decade have significantly altered the nature of engineering knowledge and skills required in the modern industries. In response to the changing professional requirements, engineering institutions have updated their curriculum and pedagogical practices. However, most of the changes in the curriculum have been focused on the core engineering courses without much consideration for the auxiliary courses in mathematics and sciences. In this paper, we aim to propose a new, augmented mathematics curriculum aimed at meeting the requirements of the modern, technology-based engineering workplace. The proposed updates require minimal resources and can be seamlessly integrated into the existing curriculum.

Johan Bergelin, Per Erik Strandberg

There is an increasing interest in research on the combination of AI techniques and methods with MDE. However, there is a gap between AI and MDE practices, as well as between researchers and practitioners. This paper tackles this gap by reporting on industrial requirements in this field. In the AIDOaRt research project, practitioners and researchers collaborate on AI-augmented automation supporting modeling, coding, testing, monitoring, and continuous development in cyber-physical systems. The project specifically lies at the intersection of industry and academia collaboration with several industrial use cases. Through a process of elicitation and refinement, 78 high-level requirements were defined, and generalized into 30 generic requirements by the AIDOaRt partners. The main contribution of this paper is the set of generic requirements from the project for enhancing the development of cyber-physical systems with artificial intelligence, DevOps, and model-driven engineering, identifying the hot spots of industry needs in the interactions of MDE and AI. Future work will refine, implement and evaluate solutions toward these requirements in industry contexts.

Ahmed A. Al-Amiery, Abu Bakar Mohamad, Abdul Amir H. Kadhum et al.

Abstract The corrosion performance of mild steel (MS) in 1M HCl solution was examined by weight loss (WL), potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), electrochemical frequency modulation (EFM), and open circuit potential (OCP) measurements in the absence and presence of nonanedihydrazide. PDP measurements indicated that nonanedihydrazide acts as a mixed inhibitor due to its adsorption on the MS surface, exhibiting an inhibition efficiency of more than 97%. The surface morphology investigation of the protective layer on the MS surface confirmed that adsorption of nonanedihydrazide molecules occurred via chemical adsorption following Langmuir’s isotherm model. The effect of temperature on the corrosion performance in the presence of nonanedihydrazide was investigated in the range of 303–333 K, showing that the inhibition efficiency increased with an increase in the inhibitor concentration and decreased with an increase in temperature. A new green corrosion inhibitor was synthesised and theoretical computations were conducted to completely understand the inhibition mechanism. Nonanedihydrazide molecules were investigated by DFT (density functional theory) using the B3LYP functional to evaluate the relationship of corrosion inhibition performance and the molecular structure. The computed theoretical parameters presented significant support for understanding the inhibitive mechanism revealed by the inhibitory molecules and are in good agreement with WL, PDP, EIS, (EFM), and OCP results.