Hasil untuk "Engineering machinery, tools, and implements"

H. Sinan Bank, Daniel R. Herber

The proliferation of data across the system lifecycle presents both a significant opportunity and a challenge for Engineering Design and Systems Engineering (EDSE). While this "digital thread" has the potential to drive innovation, the fragmented and inaccessible nature of existing datasets hinders method validation, limits reproducibility, and slows research progress. Unlike fields such as computer vision and natural language processing, which benefit from established benchmark ecosystems, engineering design research often relies on small, proprietary, or ad-hoc datasets. This paper addresses this challenge by proposing a systematic framework for a "Map of Datasets in EDSE." The framework is built upon a multi-dimensional taxonomy designed to classify engineering datasets by domain, lifecycle stage, data type, and format, enabling faceted discovery. An architecture for an interactive discovery tool is detailed and demonstrated through a working prototype, employing a knowledge graph data model to capture rich semantic relationships between datasets, tools, and publications. An analysis of the current data landscape reveals underrepresented areas ("data deserts") in early-stage design and system architecture, as well as relatively well-represented areas ("data oases") in predictive maintenance and autonomous systems. The paper identifies key challenges in curation and sustainability and proposes mitigation strategies, laying the groundwork for a dynamic, community-driven resource to accelerate data-centric engineering research.

Zainatul Sirti, Neny Rosmawarni, Musthofa Galih Prada et al.

The solar system is a Basic Competency for grade VI students at SDN 06 Taluak IV Suku. This material encourages students to recognize planets and their characteristics in the solar system, thus requiring interactive learning media. This research develops solar system flashcard learning media based on AR technology to enhance learning interactivity. Using the MDLC method, the application was built with Unity Editor and Vuforia SDK for Android and iOS devices. The application utilizes marker-based and markerless tracking technology to display 3D models of the planets. Flashcards are equipped with engaging images and brief information, as well as a quiz feature for evaluation. Testing showed that the application successfully displayed 3D objects and interactive quiz features. The application is considered to have an attractive appearance, appropriate material, ease of use, and provides an in-depth learning experience about the solar system.

Nikolay Gueorguiev, Atanas Nachev, Yavor Boychev et al.

The reliability of the electro-power equipment of electrical power transmission systems is essential in ensuring an uninterrupted power supply with the necessary voltage and frequency stability. This is especially important when performing lengthy procedures requiring the serviceability of the electrical equipment used, such as those related to foundries and metallurgical processes, or with the processes of testing complex means for the remote control of electromagnetic radiation within the implementation of the Sustainable development of the Competence Center “Quantum Communication, Intelligent Security Systems and Risk Management” (QUASAR) Project, funded with the participation of the EU under the “Research, Innovation and Digitalization for Smart Transformation” Program 2021.2027 according to procedure BG16RFPR002-1.014. One of the main issues in this case is related to the availability of information regarding the technical condition of the deployed or reserve energy resources. In this connection, this study proposes methods for determining the quantity of operational equipment that is either in use or in storage, based on the reliability testing of a representative sample of it.

Erick Loyaga, Estefano Quinatoa, Edgar Haro et al.

This paper introduces a structured methodology for bridging the gap between theoretical modeling and high-fidelity simulation of multirotor Unmanned Aerial Systems (UAS) through the construction of digital twins in PX4 v1.12 Software-in-the-Loop (SITL) environments. A key challenge addressed is the absence of standardized procedures for translating physical UAV characteristics into simulation-ready parameters, which often results in inconsistencies between virtual and real-world behavior. To overcome this, we propose a hybrid parametric identification pipeline that combines analytical modeling with experimental characterization. Critical parameters—such as inertial properties, thrust and torque coefficients, drag factors, and motor response profiles—are obtained through a combination of physical measurements and theoretical derivation. The proposed methodology is demonstrated on a custom-built heavy-lift quadrotor, and the resulting digital twin is validated by executing autonomous missions and comparing simulated outputs against flight logs from real-world tests.

Vincenzo La Battaglia, Valerio Mussi, Stefano Marini et al.

Several car manufacturers have encountered corrosion in certain mechanical components caused by chlorine in fuel. The current regulations governing the quality of fuel allowed for trade are briefly described. Next, this paper analyzes the possible origin of chlorine in damaged components. In particular, the phenomenon of corrosion found in EGR valves and EGR coolers is analyzed. The analyses conducted to determine the nature of the corrosion and its origin are illustrated. Finally, the effects of the phenomenon on engine operation are analyzed, depending on the type of damaged component.

Davide Venturelli, Erik Gustafson, Doga Kurkcuoglu et al.

We review the prospects to build quantum processors based on superconducting transmons and radiofrequency cavities for testing applications in the NISQ era. We identify engineering opportunities and challenges for implementation of algorithms in simulation, combinatorial optimization, and quantum machine learning in qudit-based quantum computers.

Wouter Groeneveld

Today's software engineering (SE) complexities require a more diverse tool set going beyond technical expertise to be able to successfully tackle all challenges. Previous studies have indicated that creativity is a prime indicator for overcoming these hurdles. In this paper, we port results from creativity research in the field of cognitive psychology to the field of SE. After all, programming is a highly creative endeavour. We explore how to leverage creativity as a practical problem solving tool to wield for software developers. The seven distinct but intertwined creative problem solving themes unfolded in this paper are accompanied with practical perspectives, specifically geared for software professionals. Just like technical skills such as knowledge of programming languages, we believe that creativity can be learned and improved with practice.

Nir Somech, Guy Katz

Software obfuscation techniques make code more difficult to understand, without changing its functionality. Such techniques are often used by authors of malicious software to avoid detection. Reverse Engineering of obfuscated code, i.e., the process of overcoming obfuscation and answering questions about the functionality of the code, is notoriously difficult; and while various tools and methods exist for this purpose, the process remains complex and slow, especially when dealing with layered or customized obfuscation techniques. Here, we present a novel, automated tool for addressing some of the challenges in reverse engineering of obfuscated code. Our tool, called ReSMT, converts the obfuscated assembly code into a complex system of logical assertions that represent the code functionality, and then applies SMT solving and simulation tools to inspect the obfuscated code's execution. The approach is mostly automatic, alleviating the need for highly specialized deobfuscation skills. In an elaborate case study that we conducted, ReSMT successfully tackled complex obfuscated code, and was able to solve reverse-engineering queries about it. We believe that these results showcase the potential and usefulness of our proposed approach.

Houcine Abdelkader Cherief, Brahim Mahmoudi, Zacharie Chenail-Larcher et al.

Grey literature is essential to software engineering research as it captures practices and decisions that rarely appear in academic venues. However, collecting and assessing it at scale remains difficult because of their heterogeneous sources, formats, and APIs that impede reproducible, large-scale synthesis. To address this issue, we present GLiSE, a prompt-driven tool that turns a research topic prompt into platform-specific queries, gathers results from common software-engineering web sources (GitHub, Stack Overflow) and Google Search, and uses embedding-based semantic classifiers to filter and rank results according to their relevance. GLiSE is designed for reproducibility with all settings being configuration-based, and every generated query being accessible. In this paper, (i) we present the GLiSE tool, (ii) provide a curated dataset of software engineering grey-literature search results classified by semantic relevance to their originating search intent, and (iii) conduct an empirical study on the usability of our tool.

Shalini Chakraborty, Sebastian Baltes

The IT industry provides supportive pathways such as returnship programs, coding boot camps, and buddy systems for women re-entering their job after a career break. Academia, however, offers limited opportunities to motivate women to return. We propose a diverse multicultural research project investigating the challenges faced by women with software engineering (SE) backgrounds re-entering academia or related research roles after a career break. Career disruptions due to pregnancy, immigration status, or lack of flexible work options can significantly impact women's career progress, creating barriers for returning as lecturers, professors, or senior researchers. Although many companies promote gender diversity policies, such measures are less prominent and often under-recognized within academic institutions. Our goal is to explore the specific challenges women encounter when re-entering academic roles compared to industry roles; to understand the institutional perspective, including a comparative analysis of existing policies and opportunities in different countries for women to return to the field; and finally, to provide recommendations that support transparent hiring practices. The research project will be carried out in multiple universities and in multiple countries to capture the diverse challenges and policies that vary by location.

Bianca Trinkenreich, Fabio Calefato, Geir Hanssen et al.

The adoption of Large Language Models (LLMs) is not only transforming software engineering (SE) practice but is also poised to fundamentally disrupt how research is conducted in the field. While perspectives on this transformation range from viewing LLMs as mere productivity tools to considering them revolutionary forces, we argue that the SE research community must proactively engage with and shape the integration of LLMs into research practices, emphasizing human agency in this transformation. As LLMs rapidly become integral to SE research - both as tools that support investigations and as subjects of study - a human-centric perspective is essential. Ensuring human oversight and interpretability is necessary for upholding scientific rigor, fostering ethical responsibility, and driving advancements in the field. Drawing from discussions at the 2nd Copenhagen Symposium on Human-Centered AI in SE, this position paper employs McLuhan's Tetrad of Media Laws to analyze the impact of LLMs on SE research. Through this theoretical lens, we examine how LLMs enhance research capabilities through accelerated ideation and automated processes, make some traditional research practices obsolete, retrieve valuable aspects of historical research approaches, and risk reversal effects when taken to extremes. Our analysis reveals opportunities for innovation and potential pitfalls that require careful consideration. We conclude with a call to action for the SE research community to proactively harness the benefits of LLMs while developing frameworks and guidelines to mitigate their risks, to ensure continued rigor and impact of research in an AI-augmented future.

Mia Mohammad Imran, Tarannum Shaila Zaman

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.

Diva Gabriela Prawiro, Fourmando Butar Butar, Jimmy Gotomo et al.

Hanko or signature seals are widely used by Japanese companies to sign business contracts. However, the COVID-19 pandemic has caused Hanko culture in Japan to become ineffective, especially in teleworking. The pandemic has also negatively affected the global economy, including Japan. To face this issue, the Japanese government has expanded the use of electronic signatures since 2020. Regardless of the government’s efforts, many Japanese companies have not implemented the use of electronic signatures. Nevertheless, the prospects of utilizing electronic signatures in Japanese companies have increased due to post-COVID-19 socio-economic factors. We examined the problem of Hanko culture and how Hanko is replaced by electronic signatures in Japan’s society through an inductive, descriptive, and qualitative method. By using those approaches, we discovered that, in the short run, Japanese companies will be forced to adapt to the use of electronic signatures to increase productivity post-economic crisis. In the long run, Japanese companies will be inclined to use electronic signatures to adapt to global trade conditions.

Gogulamudi Pradeep Reddy, Duppala Rohan, Yellapragada Venkata Pavan Kumar et al.

Parkinson’s disease (PD) is a neurodegenerative illness that affects the central nervous system and leads to a gradual degeneration of neurons that results in movement slowness, mental health problems, speaking difficulties, etc. In the past 20 years, the frequency of PD has doubled. Global estimates revealed that over 8.5 million cases have been identified so far. Thus, early and accurate detection of PD is crucial for treatment. Traditional detection methods are subjective and prone to delays, as they are reliant on clinical evaluation and imaging. Alternatively, artificial intelligence (AI) has recently emerged as a transformative technology in the healthcare sector, showing decent and promising results. However, an effective algorithm needs to be investigated for the most accurate prediction of a particular disease. Thus, this paper explores the ability of different machine learning algorithms in regard to the effective detection of PD. A total of 26 algorithms were implemented using the Scikit-Learn library on the Oxford PD detection dataset. This is a collection of 195 voice measurements recorded from 31 individuals, of which 23 have PD. The implemented algorithms are logistic regression, decision tree, k-nearest neighbors, random forest, support vector machine, Gaussian naïve bayes, multi-layered perceptron (MLP), extreme gradient boosting, adaptive boosting, stochastic gradient descent, gradient boosting machine, extra tree classifier, light gradient boosting machine, categorical boosting, Bernoulli naïve bayes, complement naïve bayes, multinomial naïve bayes, histogram-based gradient boosting, nearest centroid, radius neighbors classifier, logistic regression with elastic net regularization, extreme learning machine, ridge classifier, huber classifier, perceptron classifier, and voting classifier. Among them, MLP outperformed the other algorithms with a testing accuracy of 95%, precision of 94%, sensitivity of 100%, F1 score of 97%, and AUC of 98%. Thus, it successfully discriminates healthy individuals from those with PD, thereby helping for accurate early detection of PD for new patients using their voice measurements.

Pedro Orvalho, Mikoláš Janota, Vasco Manquinho

Due to the substantial number of enrollments in programming courses, a key challenge is delivering personalized feedback to students. The nature of this feedback varies significantly, contingent on the subject and the chosen evaluation method. However, tailoring current Automated Assessment Tools (AATs) to integrate other program analysis tools is not straightforward. Moreover, AATs usually support only specific programming languages, providing feedback exclusively through dedicated websites based on test suites. This paper introduces GitSEED, a language-agnostic automated assessment tool designed for Programming Education and Software Engineering (SE) and backed by GitLab. The students interact with GitSEED through GitLab. Using GitSEED, students in Computer Science (CS) and SE can master the fundamentals of git while receiving personalized feedback on their programming assignments and projects. Furthermore, faculty members can easily tailor GitSEED's pipeline by integrating various code evaluation tools (e.g., memory leak detection, fault localization, program repair, etc.) to offer personalized feedback that aligns with the needs of each CS/SE course. Our experiments assess GitSEED's efficacy via comprehensive user evaluation, examining the impact of feedback mechanisms and features on student learning outcomes. Findings reveal positive correlations between GitSEED usage and student engagement.

Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco et al.

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.

Takumi SHIBATA, Yohei MICHITSUJI

In the railway sector, efforts are being made to improve the efficiency of operations. One of these is the automatic operation of trains. To keep the train running safely, it is necessary to make sure that there are no obstacles in front of the railway vehicle. Therefore, automatic train operation requires obstacle detection system in front of the railway vehicle. In recent years, some railway operators have introduced onboard cameras as drive recorders in order to understand the situation at the time of an accident and to investigate the cause. In addition, efforts are being made in the railway sector to improve operational efficiency through the use of onboard cameras. In this paper, we propose an obstacle detection method using an onboard stereo camera. The proposed system can be divided into three parts. First, the center position of the track is estimated using RGB images, and the search area for obstacle detection is set. Second, proposed system checks for the presence of objects within the set search area and creates a candidate area of obstacle. Finally, the area of each neighboring candidate area of obstacle is calculated, and if the area is greater than a certain value, the object is determined to be an obstacle. Furthermore, the proposed system is verified by using a 1/10 scale model experimental vehicle. Validation results show that the proposed system is capable of detecting obstacles in front of railway vehicles.

Lukas Balcarik, Bohdana Simackova, Samaneh Shaghaghi et al.

This article deals with the use of ash from biomass burning for the remediation of thermally active dump in Heřmanice, Czech Republic. Nowadays, various chemical, physical, or biological methods of remediation are used for the remediation of dumps. The authors discuss the complex use of ash from biomass as a possibility for biological remediation of the Heřmanice dump. The main advantage of obtaining ash when burning biomass is primarily the fact that it is a renewable energy source, which produces electricity and large amounts of ash, which can be used, for example, for the remediation of the Heřmanice dump. Tailings are characterized by their acidity, while fly ash is characterized by high alkalinity. This study deals with which ratio (tailings:ash) would achieve the necessary neutral values in order to prevent the release of heavy metals into the surroundings of the Heřmanice dump. The value of the active soil reaction (pH/H₂O), the value of the exchange soil reaction (pH/CaCl₂), the value of hydrolytic acidity (H_a), together with the value of soluble salts in the tailings, i.e., electrical conductivity, were also studied. Based on the obtained results, it can be concluded that the addition of biomass combustion ash had a positive effect on the pH value of the tailings. Based on this fact, Al³⁺ is excreted more slowly into the environment. A higher content of aluminum is toxic to plants, while in a smaller amount, its content is necessary and at the same time an important factor in the process of plant growth. The mixing of alkaline ash with unburned surface tailings from the thermally active Heřmanice dump significantly influenced its acidity, which had a positive effect on increasing the active and exchange acidity.

Adel Alqudhaibi, Ashish Krishna, Sandeep Jagtap et al.

The food industry stands as one of the most vital manufacturing sectors globally, with an ever-increasing reliance on digitalization and technology-driven processes. However, this advancement comes with an inherent risk of cyberattacks, encompassing data breaches and system disruptions, which can severely impact production and disrupt the entire food supply chain. Consequently, such cyberthreats can lead to consumer fear and mistrust, potentially tarnishing a company’s brand image. Additionally, the sector is becoming the focus of cyberthreat actors owing to the current crisis in Ukraine, revealing the severity of the rippling effects of these disruptions. This research aims to delve into the current perception of cyberthreats within the food industry, emphasizing the importance of cybersecurity and analyzing the measures taken by stakeholders to mitigate the risks associated with cyberattacks. The findings reveal that although the food industry acknowledges the potential threats posed by inadequate cybersecurity measures, these risks are perceived as insignificant due to the unique nature of the industry. Moreover, an extensive literature review highlights that the food industry places great emphasis on adopting innovative information technologies to enhance operational efficiency and cost-effectiveness. However, it remains vulnerable to cyberattacks, necessitating continuous employee education and training to strengthen the security landscape. This holistic approach fosters a seamless, reliable, and sustainable growth environment for the industry. By analyzing the existing challenges and requirements, this study underscores the need for proactive measures to safeguard the food industry against cyberthreats. It emphasizes the significance of implementing robust cybersecurity protocols and cultivating a culture of awareness and preparedness within organizations. Furthermore, the research emphasizes the importance of employee education and training, equipping them with the necessary knowledge and skills to identify and mitigate potential cyber risks. In conclusion, while cognizant of the risks posed by cyberattacks, the food industry must prioritize cybersecurity measures to protect its production and supply chain. Enhancing the security environment through ongoing employee education and training is crucial for fostering consumer trust and enabling seamless growth within the industry. By adopting a proactive approach to cybersecurity, the food industry can ensure the sustainability and reliability of its operations in the face of evolving cyberthreats.

Fernando Lara, Ricardo Mena, Antonio Flores et al.

This research article presents a comprehensive study focusing on advancing radar systems for unmanned aerial vehicle (UAV) surveillance in cluttered environments. The proliferation of UAV technology and its diverse applications have raised concerns about airspace security. To tackle this issue, this article introduces a novel simulator designed to evaluate the performance of primary monopulse radar processors. The simulator accurately replicates scenarios involving clutter Weibull distributions, stationary and moving targets, as well as pulse compression situations, thereby enabling precise and controlled evaluations. The study employs the simulator to assess radar processors, including a variant of moving target detection (MTD) and a constant false alarm rate (CFAR) processor. By implementing a rigorous methodology, the article underscores the significance of simulating cluttered conditions in refining the effectiveness of radar processors. The results yield valuable insights, facilitating objective interpretations. The proposed simulator and its implications contribute to enhancing UAV surveillance and airspace security, thereby pushing forward the capabilities of radar systems.