Hasil untuk "Engineering"

Meenakshi Sudarvizhi Seenipeyathevar, Prasath Palaniappan, Vijayakumar Arumugam et al.

ABSTRACT This study deals with an integrated experimental‐machine learning framework for wear estimation in functionally graded composites made from recycled magnesium machining chips, using low‐cost ceramic fibers as reinforcement with the radial Modeling technique. The primary hurdle that is being addressed is the accurate prediction of wear behavior in spatially graded magnesium matrix composites, while simultaneously avoiding extensive experimental testing. Under varying degrees of applied loads (4.4 to 39 N), sliding speeds (0.45 to 4.5 m/s), and sliding distances (500 to 4500 m), the wear performance was experimentally assessed. Results demonstrate a hardness increment of 26.26% in the outer region compared to the inner region, while resistance to wear was enhanced by 19.8% in the outer zone due to the grading of ceramic fibers. A limited experimental dataset consisting of wear measurements from the inner, middle, and outer zones of the composite was utilized in developing and validating four machine‐learning models for wear rate prediction. The tree‐based ensemble methods significantly outperformed deep‐learning strategies, with the LightGBM model providing the best prediction performance across all zones and achieving optimization with a maximum tree depth of 5, 480 leaves, and a feature fraction of 0.05. Moreover, zone‐specific XGBoost models were also developed, employing customized learning rates and minimal loss reduction parameters in order to elevate prediction accuracy. The proposed machine‐learning framework thus provides a pathway for rapid and reliable wear rate estimation for ceramic fiber‐reinforced magnesium composites, significantly lessening experimental burden. Results highlight that recycled magnesium waste, when combined with ceramic reinforcement, can be effectively employed to produce sustainable and economically viable materials with improved wear resistance, particularly for automotive and industrial applications.

S. Kim, Y. Lee, Y. Lee et al.

This study presents a Historic Building Information Modeling (HBIM)-based approach for the digital restoration and documentation of lost wooden architectural heritage. The approach was applied to Building 1-2 of Hyeumwonji, the site of a temporary Goryeo Dynasty palace in Paju, South Korea. To reconstruct this lost structure, we combined historical and archaeological analyses to estimate the original design and generated blueprints that guided the HBIM-based 3D model of the building. We collected LiDAR point cloud data from the site, aligned them with the HBIM model, and visualized the integrated result using Unreal Engine 5. The outcome was a comprehensive virtual restoration comprising 13,814 individual building elements. This case study demonstrates that, even with minimal physical remains, wooden heritage sites can be digitally restored by leveraging HBIM and historical reasoning. It also highlights the strengths of HBIM in version tracking, incorporation of historical updates, and systematic documentation throughout the restoration process. Compared to traditional 2D CAD-based restoration methods, the HBIM approach offers significant advantages in terms of updatability, data integration, and long-term preservation of restoration data. Overall, the project illustrates how combining rigorous historical analysis with advanced digital modeling can revive lost heritage architecture in virtual form, providing a rich resource for research and conservation.

Deyang Zhang, Binhe Feng, Wenbo Guo et al.

Abstract A cost-effective and large-scale method for synthesizing ZnCo2O4 nanoflowers with surface oxygen vacancies as electrode materials for supercapacitors is presented. The existence of oxygen vacancies on the surface of the ZnCo2O4 nanoflowers has been confirmed through X-ray photoelectron spectroscopy (XPS). The energy bands and density of states (DOS) of ZnCo2O4 are examined using density functional theory, revealing that treatment with NaBH4 reduces the band gap of ZnCo2O4 while increasing the DOS near the Fermi level compared to pristine ZnCo2O4. Furthermore, the specific capacitance of reduced ZnCo2O4 is nearly double that of its unmodified counterpart. This straightforward and practical approach significantly enhances both conductivity and specific capacitance in metal oxides, making it applicable to other similar materials.

Kun Huang, Shuoshuo Hu, Yilin Zhou et al.

Introduction: Adult exposure to endocrine-disrupting chemicals (EDCs) may reduce muscle mass and strength; however, few studies considered EDC mixtures and their potential mechanisms. Objectives: We aimed to explore associations of EDC mixtures with adult muscle mass and strength, the modifying effects of diet and exercise, as well as the potential metabolic perturbations through plasma metabolome. Methods: We included 127 adults from a panel study that repeated measures across 3 seasons. We measured 110 EDCs spanning 12 groups in plasma and urine, along with the plasma metabolome. Bayesian kernel machine regression (BKMR), Bayesian weighted quantile sum regression, and quantile-based g-computation were employed to assess the mixture effects and relative contributions. Key EDCs were defined as those with weights exceeding the group average in at least two models. Stratified analyses were employed to investigate the modifying effects of diet and exercise. A meet-in-the-middle (MITM) approach was applied to characterize the underlying mechanisms. Results: BKMR results revealed significant negative associations between 7 EDC groups and both appendicular skeletal muscle mass (ASM) and hand-grip strength (HGS), namely per- and polyfluoroalkyl substances, polycyclic aromatic hydrocarbons, organophosphate pesticides, bisphenols, neonicotinoids, atrazine, and parabens. Three multi-exposure models identified 22 and 17 key EDCs linked to decreased ASM and HGS, respectively. Mixtures of these key EDCs were associated with decreases in both ASM and HGS, with significantly attenuated effects observed among participants with healthy diets or regular exercise. MITM approach identified overlapping pathways linking key EDC mixtures to ASM, including arachidonic acid, linoleic acid, and alpha-linolenic acid metabolism. Key EDC Mixtures were negatively associated with glycocyamine, which was positively associated with ASM. Conclusions: Adult exposure to EDC mixtures was linked to reduced ASM and HGS, whereas healthy diets and regular exercise mitigated such impairment. Downregulated glycocyamine and altered fatty acid metabolism were potential mechanisms underlying the decreased ASM.

Justus Bogner, Roberto Verdecchia

From its early foundations in the 1970s, empirical software engineering (ESE) has evolved into a mature research discipline that embraces a plethora of different topics, methodologies, and industrial practices. Despite its remarkable progress, the ESE research field still needs to keep evolving, as new impediments, shortcoming, and technologies emerge. Research reproducibility, limited external validity, subjectivity of reviews, and porting research results to industrial practices are just some examples of the drivers for improvements to ESE research. Additionally, several facets of ESE research are not documented very explicitly, which makes it difficult for newcomers to pick them up. With this new regular ACM SIGSOFT SEN column (SEN-ESE), we introduce a venue for discussing meta-aspects of ESE research, ranging from general topics such as the nature and best practices for replication packages, to more nuanced themes such as statistical methods, interview transcription tools, and publishing interdisciplinary research. Our aim for the column is to be a place where we can regularly spark conversations on ESE topics that might not often be touched upon or are left implicit. Contributions to this column will be grounded in expert interviews, focus groups, surveys, and position pieces, with the goal of encouraging reflection and improvement in how we conduct, communicate, teach, and ultimately improve ESE research. Finally, we invite feedback from the ESE community on challenging, controversial, or underexplored topics, as well as suggestions for voices you would like to hear from. While we cannot promise to act on every idea, we aim to shape this column around the community interests and are grateful for all contributions.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.

Dinesh Eswararaj, Ajay Babu Nellipudi, Vandana Kollati

The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparative analysis of Delta Parquet, Iceberg, and Hudi using real-world time-series automotive telemetry data with fields such as vehicle ID, timestamp, location, and event metrics. The evaluation considers modeling strategies, partitioning, CDC support, query performance, scalability, data consistency, and ecosystem maturity. Key findings show Delta Parquet provides strong ML readiness and governance, Iceberg delivers high performance for batch analytics and cloud-native workloads, while Hudi is optimized for real-time ingestion and incremental processing. Each format exhibits tradeoffs in query efficiency, time-travel, and update semantics. The study offers insights for selecting or combining formats to support fleet management, predictive maintenance, and route optimization. Using structured datasets and realistic queries, the results provide practical guidance for scaling data pipelines and integrating machine learning models in automotive applications.

Daisuke Kikuta, Hiroki Ikeuchi, Kengo Tajiri

Chaos Engineering (CE) is an engineering technique aimed at improving the resilience of distributed systems. It involves intentionally injecting faults into a system to test its resilience, uncover weaknesses, and address them before they cause failures in production. Recent CE tools automate the execution of predefined CE experiments. However, planning such experiments and improving the system based on the experimental results still remain manual. These processes are labor-intensive and require multi-domain expertise. To address these challenges and enable anyone to build resilient systems at low cost, this paper proposes ChaosEater, a system that automates the entire CE cycle with Large Language Models (LLMs). It predefines an agentic workflow according to a systematic CE cycle and assigns subdivided processes within the workflow to LLMs. ChaosEater targets CE for software systems built on Kubernetes. Therefore, the LLMs in ChaosEater complete CE cycles through software engineering tasks, including requirement definition, code generation, testing, and debugging. We evaluate ChaosEater through case studies on small- and large-scale Kubernetes systems. The results demonstrate that it consistently completes reasonable CE cycles with significantly low time and monetary costs. Its cycles are also qualitatively validated by human engineers and LLMs.

Sharon Guardado, Risha Parveen, Zheying Zhang et al.

The integration of Large Language Models (LLMs) in Requirements Engineering (RE) education is reshaping pedagogical approaches, seeking to enhance student engagement and motivation while providing practical tools to support their professional future. This study empirically evaluates the impact of integrating LLMs in RE coursework. We examined how the guided use of LLMs influenced students' learning experiences, and what benefits and challenges they perceived in using LLMs in RE practices. The study collected survey data from 179 students across two RE courses in two universities. LLMs were integrated into coursework through different instructional formats, i.e., individual assignments versus a team-based Agile project. Our findings indicate that LLMs improved students' comprehension of RE concepts, particularly in tasks like requirements elicitation and documentation. However, students raised concerns about LLMs in education, including academic integrity, overreliance on AI, and challenges in integrating AI-generated content into assignments. Students who worked on individual assignments perceived that they benefited more than those who worked on team-based assignments, highlighting the importance of contextual AI integration. This study offers recommendations for the effective integration of LLMs in RE education. It proposes future research directions for balancing AI-assisted learning with critical thinking and collaborative practices in RE courses.

Andrew Moses Obeti, Lawrence Muhwezi, John Muhumuza Kakitahi et al.

Force Account Mechanism (FAM) is the predominant road maintenance system in Uganda’s local government setup and a similar, though slightly different approach, is used in some large private sector agriculture plantations. With the Uganda Road Fund (URF) 2021/2022 annual report and previous research citing challenges in cost management and efficiency of the FAM method of road maintenance, it becomes paramount to analyse how FAM is implemented in government-led operations, in comparison to similar private sector approaches, while proposing possible solutions to these challenges. This research offered to analyse unpaved road maintenance cost drivers alongside providing a cost model solution to improve on cost prediction of the FAM system. Gulu District Local Government (DLG) and Kakira Sugar Limited (KSL) were selected as case study areas. Two descriptive research methods were used: observations and case study approach. The selected case study areas were accessible and reachable in terms of data. Control parameters affecting unpaved mechanized road maintenance were identified as machine repair costs, tool costs, labour costs, material costs, fuel costs and machine fuel costs. Unpaved mechanized road maintenance costs at KSL and Gulu DLG were computed as a cost/km ratio of 26,442,032Ugx/km (6,958.4USD/km) and 32,674,895Ugx/km (8,598.65USD/km) respectively. The Uganda National Roads Authority (UNRA) unpaved road maintenance costs were calculated as an average of 34,987,122.9Ugx/km (9,165USD/km) while the World Bank ROCKS database provided a comparable figure of 7,971USD/km (30,553,440.83Ugx/km). A USD to Ugx conversion rate of 3,800 was used. Two linear regression cost models with a 0.679 and 0.687 R2 value were computed, and these can be used in preliminary road maintenance cost prediction. The study recommends the need for an effective, digital road maintenance cost database system for mechanized unpaved road maintenance works, cost driver analytics and management, alongside improvement in aspects of maintenance processes at both the DLG and KSL. Further research can be conducted on equipment condition level prediction and analytics in the private sector and at the DLG.

M. F. Nabiullina, G. R. Mingaleeva, O. V. Afanaseva et al.

RELEVANCE. Agricultural enterprises generate vegetable waste, which is difficult to utilize. Such waste can be used for combustion in boilers, providing thermal and electric energy to an agricultural energy complex. A hybrid mini-thermal power plant combining renewable energy sources and plant biofuels will be able to provide more economical, environmentally friendly and reliable supplies of heat and electricity under any demand conditions compared to using one of these systems.OBJECTIVE. Determination of fuel consumption during combustion of various types of vegetative agricultural waste in a hybrid mini-thermal power plant with parallel connection of solar energy concentrators under conditions of solar insolation of the Republic of Tatarstan.METHODS. The article considers the chemical composition and characteristics of various types of plant waste from agriculture. The average total energy consumption load of an agricultural enterprise has been determined. RESULTS. To determine the consumption of biofuels for the operation of the KE10-14CO boiler, the calculation of the theoretical volumes of combustion products and the thermal calculation of the boiler were carried out. The need for auxiliary fuel at mini-thermal power plants with parallel connection of solar energy concentration plants has been determined. The calculation of the heat collected by the solar collector has been performed.CONCLUSION. The use of solar energy concentrators when connected in parallel with a biofuel boiler makes it possible to evenly supply energy to the enterprise and form biofuel reserves. Calculations have shown that the use of hybrid biomass combustion plants and solar collectors helps to reduce fuel consumption.

Tukhtaeva Zebo, Abulova Parvina

This article outlines the issues of providing the medium-sized industrial sector with natural fiber products, analyzing the aesthetic properties of fabrics in the production of fabrics that meet the aesthetic and hygienic requirements of consumers for a wide range of modern clothing, and determining the use of silk fabrics according to physical and mechanical characteristics. In the production of consumer goods in accordance with the needs of the population, the specific characteristics of gauze, including the connections between the air permeability of gauze and its filling with fiber, hygienic and hygroscopic, air and vapor permeability, the type of fabric, fiber composition, aesthetic properties, quality indicators are also analyzed. Aesthetic properties of gauzes are physical and morphological indicators, that is, color and given pattern of gauzes, hardness and layer formation; glossiness, depending on the structure of the surface level and evaluated parameters - it is determined that it depends on the requirements such as application, meeting the customs of the time and fashion trends. As a result of the research, in order to expand the scope of consumption of gas products, proposals were made to improve the production assortment, after studying the needs of consumers of different categories and nationalities.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan et al.

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.

Johannes Schleiss, Aditya Johri, Sebastian Stober

Building up competencies in working with data and tools of Artificial Intelligence (AI) is becoming more relevant across disciplinary engineering fields. While the adoption of tools for teaching and learning, such as ChatGPT, is garnering significant attention, integration of AI knowledge, competencies, and skills within engineering education is lacking. Building upon existing curriculum change research, this practice paper introduces a systems perspective on integrating AI education within engineering through the lens of a change model. In particular, it identifies core aspects that shape AI adoption on a program level as well as internal and external influences using existing literature and a practical case study. Overall, the paper provides an analysis frame to enhance the understanding of change initiatives and builds the basis for generalizing insights from different initiatives in the adoption of AI in engineering education.

Horia Mărgărit, Amanda Bowman, Krishnageetha Karuppasamy et al.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems.

Haider K. AMMASH, Noora N. SHAFFAF

Based on the experimental test results of the authors, this investigation is concerned with the finite element analysis to examine and compare the load values and failure modes of the authors’ results. This research was conducted using the Abaqus software. The experimental work included the fabrication of twelve plate girders with honeycomb and flat web plate corrugation patterns, which were then tested under a single concentrated load at the midspan. According to the corrugation dimension or outer honeycomb web thickness, the honeycomb steel plate web girder is divided into three groups (60 mm, 80 mm and 100 mm). The specimens also involved plate girders with a flat web. The specimens were created with three lengths (600 mm, 1,200 mm and 1,800 mm). The Abaqus software was used in finite element models to simulate the concentrated load. The numerical results demonstrated that the 60 mm thick honeycomb web provides a greater load-bearing capacity and shear strength than other girders. The 20 mm honeycomb corrugation on the steel plate girder indicates the increased and improved shear resistance. The conclusion was that as the width of the corrugation increased, so did the steel web’s ultimate load and shear strength, resulting in a positive relationship between the critical shear buckling load of the web and the moment of inertia at the strong axis. When the dimension of the corrugation increases, the moment of inertia of the Y axis (Iy) decreases; thus, the plate girder will fail with a less critical buckling load (Pcr). Also, it can be concluded that as the steel plate thickness of the honeycomb web increases, the shear resistance increases as well. However, the spacing between the intermediate stiffener or the horizontal spacing of the web panel can enhance the shear resistance of honeycomb web girder if it was decreased due to increasing the action of tension field force that resists the diagonal tension developed at the web panel by the applied midspan concentrated force.

Mafalda Reis Pereira, Mafalda Reis Pereira, Filipe Neves dos Santos et al.

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine – SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants’ defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher et al.

Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.

Dongwei Xiao, Zhibo Liu, Shuai Wang

A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.

Hossein Safi, Ali Mohammad Montazeri, Javane Rostampoor et al.

Abstract In this paper, the problem of opportunistic spectrum sharing for the next generation of wireless systems empowered by the cloud radio access network (C‐RAN) is studied. More precisely, low‐priority users employ cooperative spectrum sensing to detect a vacant portion of the spectrum that is not currently used by high‐priority users. The authors' aim is to maximize the overall throughput of the low‐priority users while guaranteeing the quality of service of the high‐priority users. This objective is attained by optimally adjusting spectrum sensing time, with respect to target probabilities of detection and false alarm, as well as dynamically allocating C‐RAN resources, that is, powers, sub‐carriers, remote radio heads, and base‐band units. To solve this problem, which is non‐convex and NP‐hard, a low‐complex iterative solution is proposed. Numerical results demonstrate the necessity of sensing time adjustment as well as effectiveness of the proposed solution.