Hasil untuk "Engineering machinery, tools, and implements"

Tsvetomir Gechev, Veselin Tsonev, Petar Ivanov et al.

The paper investigates experimentally the influence of infill density, infill pattern, layer height, wall number, printing orientation, and material color on the impact strength of 3D-printed PLA (polylactic acid) samples by using the Charpy test method. The used printing method is FDM (Fused Deposition Modeling) performed on a desktop printer. For each parameter changed in the study, five separate unnotched specimens were produced and tested, and the average impact strength value was taken into account. The filament rolls went through a drying process before printing and were then stored in a low-humidity environment filled with desiccant in order to minimize the effect of absorbed humidity in the filament during the experiments. The conditioning and testing of samples were performed according to the EN ISO 179-1 standard. Dimensional accuracy, print times, and filament consumption were also estimated in the study. The results revealed that the infill density, infill pattern, and wall number have a larger influence on the impact energy absorbed by the samples in comparison to the layer height, printing orientation, and the PLA filament color. The best optimization of the studied mechanical property was obtained by increasing the infill percentage and the number of walls. Applying different PLA colors has a slight effect on the impact strength, yet it should be taken into consideration when designing 3D-printed products that are intended to withstand impact. Moreover, it was found out that the studied parameters have an insignificant effect on the dimensional accuracy of the produced samples.

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.

Dina Koishiyeva, Jeong Won Kang, Teodor Iliev et al.

Class imbalance is a persistent limitation in polyp segmentation, commonly resulting in biased predictions and reduced accuracy in identifying clinically relevant structures. This study systematically evaluated 12 loss functions, including standard, weighted, and compound formulas, applied to colon polyp segmentation using the UNet-VGG16 fixed architecture on the Kvasir-SEG dataset. The encoder was frozen to isolate the effect of loss functions under the same training conditions. A fixed random seed was used in all experiments to ensure reproducibility and control variance during training. The results reveal that the combined loss functions, namely WBCE combined with Dice and Tversky combined with Focal, achieved the top Dice scores of 0.8916 and 0.8917, respectively. Tversky plus Focal also provided the highest sensitivity of 0.8885, and WBCE obtained the best average IoU of 0.8120. Tversky loss showed the lowest error rate of 4.99, indicating stable optimization. These results clarify the influence of loss function selection on segmentation performance in scenarios characterized by considerable class imbalance.

Van-Khang Nguyen, Chiung-An Chen, Cheng-Yu Hsu et al.

We applied processing technology to detect and diagnose liver tumors in patients. The cancer imaging archive (TCIA) was used as it contains images of patients diagnosed with liver tumors by medical experts. These images were analyzed to detect and segment liver tumors using advanced segmentation techniques. Following segmentation, the images were converted into binary images for the automatic detection of the liver’s shape. The tumors within the liver were then localized and measured. By employing these image segmentation techniques, we accurately determined the size of the tumors. The application of medical image processing techniques significantly aids medical experts in identifying liver tumors more efficiently.

Po-Liang Liu

In submitting conference proceedings to <i>Engineering Proceedings</i>, the volume editors of the proceedings certify to the publisher that all papers published in this volume have been subjected to peer review administered by the volume editors [...]

Sonja M. Hyrynsalmi, Grischa Liebel, Ronnie de Souza Santos et al.

The discipline of software engineering (SE) combines social and technological dimensions. It is an interdisciplinary research field. However, interdisciplinary research submitted to software engineering venues may not receive the same level of recognition as more traditional or technical topics such as software testing. For this paper, we conducted an online survey of 73 SE researchers and used a mixed-method data analysis approach to investigate their challenges and recommendations when publishing interdisciplinary research in SE. We found that the challenges of publishing interdisciplinary research in SE can be divided into topic-related and reviewing-related challenges. Furthermore, while our initial focus was on publishing interdisciplinary research, the impact of current reviewing practices on marginalized groups emerged from our data, as we found that marginalized groups are more likely to receive negative feedback. In addition, we found that experienced researchers are less likely to change their research direction due to feedback they receive. To address the identified challenges, our participants emphasize the importance of highlighting the impact and value of interdisciplinary work for SE, collaborating with experienced researchers, and establishing clearer submission guidelines and new interdisciplinary SE publication venues. Our findings contribute to the understanding of the current state of the SE research community and how we could better support interdisciplinary research in our field.

Mohammed Latif Siddiq, Arvin Islam-Gomes, Natalie Sekerak et al.

Reproducibility is a cornerstone of scientific progress, yet its state in large language model (LLM)-based software engineering (SE) research remains poorly understood. This paper presents the first large-scale, empirical study of reproducibility practices in LLM-for-SE research. We systematically mined and analyzed 640 papers published between 2017 and 2025 across premier software engineering, machine learning, and natural language processing venues, extracting structured metadata from publications, repositories, and documentation. Guided by four research questions, we examine (i) the prevalence of reproducibility smells, (ii) how reproducibility has evolved over time, (iii) whether artifact evaluation badges reliably reflect reproducibility quality, and (iv) how publication venues influence transparency practices. Using a taxonomy of seven smell categories: Code and Execution, Data, Documentation, Environment and Tooling, Versioning, Model, and Access and Legal, we manually annotated all papers and associated artifacts. Our analysis reveals persistent gaps in artifact availability, environment specification, versioning rigor, and documentation clarity, despite modest improvements in recent years and increased adoption of artifact evaluation processes at top SE venues. Notably, we find that badges often signal artifact presence but do not consistently guarantee execution fidelity or long-term reproducibility. Motivated by these findings, we provide actionable recommendations to mitigate reproducibility smells and introduce a Reproducibility Maturity Model (RMM) to move beyond binary artifact certification toward multi-dimensional, progressive evaluation of reproducibility rigor.

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.

Florian Sihler, Lukas Pietzschmann, Raphael Straub et al.

Context The R programming language has a huge and active community, especially in the area of statistical computing. Its interpreted nature allows for several interesting constructs, like the manipulation of functions at run-time, that hinder the static analysis of R programs. At the same time, there is a lack of existing research regarding how these features, or even the R language as a whole are used in practice. Objective In this paper, we conduct a large-scale, static analysis of more than 50 million lines of real- world R programs and packages to identify their characteristics and the features that are actually used. Moreover, we compare the similarities and differences between the scripts of R users and the implementations of package authors. We provide insights for static analysis tools like the lintr package as well as potential interpreter optimizations and uncover areas for future research. Method We analyze 4 230 R scripts submitted alongside publications and the sources of 19 450 CRAN packages for over 350 000 R files, collecting and summarizing quantitative information for features of interest. Results We find a high frequency of name-based indexing operations, assignments, and loops, but a low frequency for most of R's reflective functions. Furthermore, we find neither testing functions nor many calls to R's foreign function interface (FFI) in the publication submissions. Conclusion R scripts and package sources differ, for example, in their size, the way they include other packages, and their usage of R's reflective capabilities. We provide features that are used frequently and should be prioritized by static analysis tools, like operator assignments, function calls, and certain reflective functions like loadCCS CONCEPTS•General and reference → Empirical studies; • Software and its engineering → Language features.

Lydia Tsiami, Christos Makropoulos, Dragan Savic

Effectively planning the design of a water distribution network for the long term is a challenging task for water utilities, mainly due to the deep uncertainty that characterizes some of its most important design parameters. In an effort to navigate this challenge, this work investigates the potential of reinforcement learning in the lifecycle design of water networks. To this end, a deep reinforcement learning agent was trained to identify a sequence of cost-effective interventions across multiple construction phases within a network’s lifecycle under both deterministic and uncertain conditions. Our approach was tested on a modified benchmark of the New York Tunnels problem with promising results. The agent achieved comparable performance with the baseline heuristic algorithm in the deterministic setting and devised a flexible design strategy when multiple future scenarios were considered. These preliminary findings highlight the potential of reinforcement learning in the lifecycle design of water networks and represent a step towards the integration of more adaptive planning approaches in the field.

Daniel Sutandio, Sheng-Jung Ou

Theme parks have been constantly updated and modified to maintain their attractiveness while managing seasonality and fluctuating visitors. Flexibility must be considered in spatial planning, design, and operations to meet the evolving daily and seasonal requirements and ensure long-term adjustability. Nonetheless, the flexibility remains unclear due to variations in its interpretation among different authors, thus hindering its adoption. Its application in spatial planning and design is still limited, primarily seen in the housing sector. This research aims to establish a framework for integrating and evaluating flexibility in the planning and design of a theme park based on the diverse interpretations of flexibility in residential and urban sectors. These interpretations were distilled into three primary dimensions: function, access, and transformation.

Livio Fenga

In this paper, we address the challenge of early fake news detection within the framework of anomaly detection for time-dependent data. Our proposed method is computationally intensive, leveraging a resampling scheme inspired by maximum entropy principles. It has a hybrid nature, combining a sophisticated machine learning algorithm augmented by a bootstrapped versions of binomial statistical tests. In the presented approach, the detection of fake news through the anomaly detection system entails identifying sudden deviations from the norm, indicative of significant, temporary shifts in the underlying data-generating process.

Jorge Mario MONSALVE GUARACAO, Sergiu LANGA, Michael STOLZ et al.

Variability in the features produced by microfabrication processes, as well as uncertainty in some material properties, may cause a significant deviation in the performance of micromachines within the same fabrication run. Based on an estimation of the expected process variations, the design of such devices can be optimised to achieve the design goals, even under this uncertainty. Learning from previous works on the design of microresonators, we formulate this design problem as a case of chance-constrained optimisation and expand it to a general case where both the dynamic sensitivity ought to be maximised and the natural frequency should be close to a given target. Constraints to ensure a safe operation under both static and dynamic conditions are included by means of penalty functions. We implement the ‘Sample-Average Approximation’ (SAA), known in the field of stochastic programming, to solve the problem with a single-objective genetic algorithm (CMA-ES), requiring only a numerical evaluation of the objective function—no computation of its gradient is required nor a specific analytic form. We apply this optimisation strategy to the design case of an ultrasonic transducer—‘lateral CMUT’—, using optical measurements of trench variability to estimate process variations in a hypothetical design. Comparison of different optimisation results reveals that the implementation of SAA enables the choice of a more conservative design that meets the targets in spite of variability in its features.

Bence Molnár, Krisztián Kun

The configuration under investigation consists of a car and a trailer connected by a coupling mechanism at a hinge point. Due to the dual-unit design, car–trailer combinations are prone to poor lateral stability at high speeds, often resulting in trailer sway, which is a significant factor in road accidents near the upper speed limit. This issue is exacerbated by the fact that drivers receive feedback primarily from the car, making it difficult to detect and respond to the trailer’s movements. To address this problem, vehicle manufacturers advocate for the use of active safety systems such as active trailer braking or steering. The comprehensive study of vehicle dynamics is essential for improving road safety, particularly in the context of car–trailer systems. This research aims to analyze the dynamic behavior of these systems using a specialized Kistler force and torque measurement instrument mounted on the vehicle’s wheels. By varying the position of the cargo mass forwards and backwards on the trailer, the effect of different load distributions on vehicle stability and handling will be evaluated. The findings of this study are expected to provide valuable insights into the role of mass distribution in dynamic performance, contributing to the development of more effective safety measures and enhanced vehicle performance.

Yvonne Dittrich, Johan Bolmsten, Catherine Seidelin

Action research provides the opportunity to explore the usefulness and usability of software engineering methods in industrial settings, and makes it possible to develop methods, tools and techniques with software engineering practitioners. However, as the research moves beyond the observational approach, it requires a different kind of interaction with the software development organisation. This makes action research a challenging endeavour, and it makes it difficult to teach action research through a course that goes beyond explaining the principles. This chapter is intended to support learning and teaching action research, by providing a rich set of examples, and identifying tools that we found helpful in our action research projects. The core of this chapter focusses on our interaction with the participating developers and domain experts, and the organisational setting. This chapter is structured around a set of challenges that reoccurred in the action research projects in which the authors participated. Each section is accompanied by a toolkit that presents related techniques and tools. The exercises are designed to explore the topics, and practise using the tools and techniques presented. We hope the material in this chapter encourages researchers who are new to action research to further explore this promising opportunity.

Nikhil Patnaik, Joseph Hallett, Awais Rashid

Writing secure code is challenging and so it is expected that, following the release of code-generative AI tools, such as ChatGPT and GitHub Copilot, developers will use these tools to perform security tasks and use security APIs. However, is the code generated by ChatGPT secure? How would the everyday software or security engineer be able to tell? As we approach the next decade we expect a greater adoption of code-generative AI tools and to see developers use them to write secure code. In preparation for this, we need to ensure security-by-design. In this paper, we look back in time to Saltzer & Schroeder's security design principles as they will need to evolve and adapt to the challenges that come with a world of AI-generated code.

Anthony Mbata, Yaji Sripada, Mingjun Zhong

Currently, a variety of pipeline tools are available for use in data engineering. Data scientists can use these tools to resolve data wrangling issues associated with data and accomplish some data engineering tasks from data ingestion through data preparation to utilization as input for machine learning (ML). Some of these tools have essential built-in components or can be combined with other tools to perform desired data engineering operations. While some tools are wholly or partly commercial, several open-source tools are available to perform expert-level data engineering tasks. This survey examines the broad categories and examples of pipeline tools based on their design and data engineering intentions. These categories are Extract Transform Load/Extract Load Transform (ETL/ELT), pipelines for Data Integration, Ingestion, and Transformation, Data Pipeline Orchestration and Workflow Management, and Machine Learning Pipelines. The survey also provides a broad outline of the utilization with examples within these broad groups and finally, a discussion is presented with case studies indicating the usage of pipeline tools for data engineering. The studies present some first-user application experiences with sample data, some complexities of the applied pipeline, and a summary note of approaches to using these tools to prepare data for machine learning.

U. Khasanov, Y. Rajabov, R. Xudoydotov et al.

This investigation explores the optimization of the backhoe blade's entry angle, addressing its crucial role in enhancing energy efficiency and operational effectiveness in subsoil manipulation - a fundamental aspect of agricultural tillage technologies. Utilizing an integrated approach that combines detailed field experiments with robust theoretical simulations, the study methodically quantifies the effects of angular variations on energy demands and mechanical performance during soil dredging. Our results reveal precise angular configurations that offer optimal reductions in energy use while significantly improving the disruption and aeration of the subsoil layer. This optimization contributes directly to the development of advanced, precision-engineered agricultural implements aimed at boosting sustainability and productivity in farming practices. Furthermore, the outcomes of this research provide pivotal insights into soil health management strategies, potentially influencing crop yield through improved root penetration and water absorption. These findings are expected to serve as a benchmark for future innovations in agricultural machinery design, aligning with global trends towards more sustainable agricultural practices and enhanced food security. This study sets a new standard for agricultural tool engineering, paving the way for transformative changes in the sector.

Rongkang Su

In today's dual-carbon economy, an increasing number of manufacturing companies are seeking ways to incorporate economic and environmental benefits into their improvement strategies. This has led to the concept of integrating green principles into lean improvement. Value Stream Mapping is a key analytical tool in lean manufacturing. This paper introduces Environmental Value Stream Mapping, which builds on this tool to address the lack of environmental factors in traditional value stream mapping analysis. Additionally, the environmental value stream mapping method is combined with simulation technology to enhance the objectivity of the future value stream mapping process. Using the production process of wear-resistant guide rods in a machinery factory as an example, this study applies the environmental value stream mapping method to identify problems in the production process from the perspectives of time and equipment energy consumption indexes. The study then improves the production line's value-added rate of time from 2.21% to 7.53% and the value-added rate of energy consumption from 43.92% to 58.39% through the implementation of industrial engineering improvement methods and Flexsim simulation technology. The goal of improving the efficiency of the production line while reducing energy consumption and protecting the environment was achieved.

Z. Abil, Ana María Restrepo Sierra, C. Danelon

In cell-free gene expression, low input DNA concentration severely limits the phenotypic output, which may impair in vitro protein evolution efforts. We address this challenge by developing CADGE, a strategy that is based on clonal isothermal amplification of a linear gene-encoding dsDNA template by the minimal Φ29 replication machinery and in situ transcription-translation. We demonstrate the utility of CADGE in bulk and in clonal liposome microcompartments to boost up the phenotypic output of soluble and membrane-associated proteins, as well as to facilitate the recovery of encapsulated DNA. Moreover, we report that CADGE enables the enrichment of a DNA variant from a mock gene library via either a positive feedback loop-based selection or high-throughput screening. This new biological tool can be implemented for cell-free protein engineering and the construction of a synthetic cell.