Hasil untuk "Engineering machinery, tools, and implements"

Hamza Moumad, Maryam Radgui

Process mining has become an essential technique for analyzing and optimizing business processes by leveraging digital traces recorded by enterprise systems. However, traditional process mining methods rely heavily on the concept of case identifiers, assuming that each event is associated with only one process instance. This assumption often limits their applicability in complex, real-world environments where multiple objects interact concurrently. This study seeks to connect conventional process mining approaches with the growing domain of object-centric process mining, which provides a broader perspective by considering events linked to multiple business entities. We review the conceptual foundations of both approaches and identify the challenges in transitioning from a case-centric to an object-centric perspective. Our findings demonstrate that object-centric process mining provides richer insights into interconnected process behavior. We conclude that object-centric paradigms mark a significant advancement in process analytics, paving the way for more adaptive and intelligent process improvement frameworks. This study not only bridges conventional process mining approaches with the emerging field of object-centric process mining (OC-PM) but also explores how recent advancements, particularly in Generative AI, are being leveraged within OC-PM frameworks. Specifically, we highlight approaches that integrate Generative AI techniques, including Large Language Models (LLMs), to enhance process understanding and prediction. The integration of AI—especially Generative AI—enables researchers and practitioners to move beyond the limitations and challenges of classical, case-centric process mining, offering more flexible, intelligent, and context-aware process analysis capabilities.

Xiangwu Ding, Jiale Feng, Mengke Ding

The address is a key element in the construction of smart cities. When receiving reports from citizens, public security officers need to quickly and accurately locate a crime scene based on the address provided by the reporter. The address from the reporter may be a standard address or it may be a point of interest, abbreviation, or common name. The difficulty in converting the address into a standard address can be solved through the analysis of address elements and address matching. We developed a bidirectional encoder representations from transformers (BERT)-based address feature resolution method and an address-matching algorithm. On this basis, a police force address-matching system for City A was designed and implemented. A Web application system was also developed based on the address database of City A. The developed address resolution and matching method with the database maintenance module successfully matched the reported address to the standard one.

Chavdar Pashinski

Although the field of industrial coatings grows rapidly, some classic solutions do not lose their relevance. In this work, the creation and study of a TiN coating are described, taking into account the influence of two important factors in the process—deposition temperature and N₂ flow rate. The method shown here could also be applied to other coatings, especially in the initial stage of their development.

Ragupathi Indhumathi, Arumugasamy Sathiya Priya, Baskar Sumathi Samyuktha

ZnSnO₃ ceramics were prepared via chemical precipitation at various calcination temperatures of 200, 300, 400, 500, and 600 °C. The prepared ceramics were analyzed using thermogravimetric analysis–differential scanning calorimetry (TGA–DSC), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and UV-visible spectroscopy (UV-Vis). Thermal analysis identified critical phase transitions, including the decomposition of ZnSn(OH)₆ into ZnSnO₃ and its subsequent transformation into Zn₂SnO₄ at elevated temperatures. XRD confirmed the orthorhombic crystal structure of the prepared ceramics. Further, increasing calcination temperatures led to enhanced crystallinity and reduced crystallite sizes, with the average crystallite size ranging from 22 to 45 nm. FTIR analysis revealed the chemical bonding and functional groups present in ZnSnO₃. The energy band gap values observed from UV-Vis spectroscopy ranged from 3.64 eV to 3.53 eV. These findings show the role of calcination temperature in tailoring the structural and optical properties of ZnSnO₃ ceramics, with potential applications in energy conversion technologies, including solar cells and optoelectronic devices. The optimization and development of ZnSnO₃-based materials hold promise for efficient energy harvesting and storage applications.

Mohamed Maktabi, Eugen Rusu

The objective of this paper is to carry out response analyses of eight floating wind turbines and compare them together; this is something that is not seen in previous research papers. From this perspective, this paper will compare the response offset regarding the motions of the six degrees of freedom of the respective floating wind turbines. The applied forces that these analyses consider come mainly from constant wind forces applied on the wind turbines’ blades, as well as forces from waves and currents. Different response offset values are considered and compared regarding the different constant wind speeds, as well as the different velocities of waves and currents. This paper also provides various innovative references related to floating wind turbine analyses and software. Validation and verification studies are left for future work due to the complexity of the data provided in this paper. However, some comparisons are made between the obtained analysis results and some external references. The mentioned external references unfortunately have floating wind turbines with different wind and wave environmental conditions, power capacities, and dimensional characteristics. The results of the constant wind dynamic analysis of the eight floating wind turbines studied in this paper have shown that the maximum surge, sway, and heave response offset corresponds to the DTU Spar 1 floating wind turbine. The maximum roll and yaw response offset corresponds to the INO-WINDMOOR floating wind turbine. The maximum pitch response offset corresponds to the WindFloat floating wind turbine. The aero-hydro-servo-elastic method was used in the Sima software to run the analyses. It is a time-domain dynamic analysis, and it uses meters [m] and degrees [°] to describe the response offsets of the different floating wind support structures studied in this paper.

Allysson Allex Araújo, Marcos Kalinowski, Maria Teresa Baldassarre

In recent years, Software Engineering (SE) scholars and practitioners have emphasized the importance of integrating soft skills into SE education. However, teaching and learning soft skills are complex, as they cannot be acquired passively through raw knowledge acquisition. On the other hand, hackathons have attracted increasing attention due to their experiential, collaborative, and intensive nature, which certain tasks could be similar to real-world software development. This paper aims to discuss the idea of hackathons as an educational strategy for shaping SE students' soft skills in practice. Initially, we overview the existing literature on soft skills and hackathons in SE education. Then, we report preliminary empirical evidence from a seven-day hybrid hackathon involving 40 students. We assess how the hackathon experience promoted innovative and creative thinking, collaboration and teamwork, and knowledge application among participants through a structured questionnaire designed to evaluate students' self-awareness. Lastly, our findings and new directions are analyzed through the lens of Self-Determination Theory, which offers a psychological lens to understand human behavior. This paper contributes to academia by advocating the potential of hackathons in SE education and proposing concrete plans for future research within SDT. For industry, our discussion has implications around developing soft skills in future SE professionals, thereby enhancing their employability and readiness in the software market.

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.

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.

Jonathan Ullrich, Matthias Koch, Andreas Vogelsang

With the advent of generative LLMs and their advanced code generation capabilities, some people already envision the end of traditional software engineering, as LLMs may be able to produce high-quality code based solely on the requirements a domain expert feeds into the system. The feasibility of this vision can be assessed by understanding how developers currently incorporate requirements when using LLMs for code generation-a topic that remains largely unexplored. We interviewed 18 practitioners from 14 companies to understand how they (re)use information from requirements and other design artifacts to feed LLMs when generating code. Based on our findings, we propose a theory that explains the processes developers employ and the artifacts they rely on. Our theory suggests that requirements, as typically documented, are too abstract for direct input into LLMs. Instead, they must first be manually decomposed into programming tasks, which are then enriched with design decisions and architectural constraints before being used in prompts. Our study highlights that fundamental RE work is still necessary when LLMs are used to generate code. Our theory is important for contextualizing scientific approaches to automating requirements-centric SE tasks.

Maarten Vlaswinkel, Duarte Antunes, Frank Willems

Decarbonization of the transport sector sets increasingly strict demands to maximize thermal efficiency and minimize greenhouse gas emissions of Internal Combustion Engines. This has led to complex engines with a surge in the number of corresponding tunable parameters in actuator set points and control settings. Automated calibration is therefore essential to keep development time and costs at acceptable levels. In this work, an innovative self-learning calibration method is presented based on in-cylinder pressure curve shaping. This method combines Principal Component Decomposition with constrained Bayesian Optimization. To realize maximal thermal engine efficiency, the optimization problem aims at minimizing the difference between the actual in-cylinder pressure curve and an Idealized Thermodynamic Cycle. By continuously updating a Gaussian Process Regression model of the pressure's Principal Components weights using measurements of the actual operating conditions, the mean in-cylinder pressure curve as well as its uncertainty bounds are learned. This information drives the optimization of calibration parameters, which are automatically adapted while dealing with the risks and uncertainties associated with operational safety and combustion stability. This data-driven method does not require prior knowledge of the system. The proposed method is successfully demonstrated in simulation using a Reactivity Controlled Compression Ignition engine model. The difference between the Gross Indicated Efficiency of the optimal solution found and the true optimum is 0.017%. For this complex engine, the optimal solution was found after 64.4s, which is relatively fast compared to conventional calibration methods.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Yuma ADACHI, Masashi YOKOTA, Tetsuya AKAGI et al.

A home healthcare support device that can give passive exercise to keep the treated joint moving area has been desired. In the previous study, a Tetrahedral-shaped Flexible Pneumatic Actuator (it is called TFPA for short) using three extension type flexible pneumatic actuators (it is called EFPAs for short) was proposed and tested as a healthcare support device for wrist. As a core training machine while playing video game, the six-legged mobile robot using TFPAs that can translate and rotate as a movable cushion was also developed. However, the tested robot lacks the load capacity to carry the patient on board. In this paper, to increase the carrying load per plane unit area of the robot, a miniaturized TFPA was proposed and tested. The generated lifting force of the improved TFPA was investigated. As a result, it can be confirmed that the improved TFPA increases the generated lifting force by increasing the setting angle of the EFPA and it improves the rigidity due to miniaturization. The 18-legged mobile robot / seat simulator using miniaturized TFPAs was proposed and tested. It was confirmed that the improved robot can translate and rotate by changing driving pattern of each EFPA. By increasing the number of legs per plane unit to get enough carrying force of human, the estimated lifting force of the 18-legged robot is 2700N, that is 3.2 times compared to the previous one. In addition, both translational and rotational motions using the improved robot could be realized.

Emeralda Sesari, Federica Sarro, Ayushi Rastogi

Software practitioners discuss problems at work with peers, in-person and online. These discussions can be technical (e.g., how to fix a bug?) and social (e.g., how to assign work fairly?). While there is a growing body of knowledge exploring fairness problems and solutions in the human and social factors of software engineering, most focus has been on specific problems. This study provides fairness discussions by software practitioners on Stack Exchange sites. We present an exploratory study presenting the fairness experience of software practitioners and fairness expectations in software teams. We also want to identify the fairness aspects software practitioners talk about the most. For example, do they care more about fairness in income or how they are treated in the workplace? Our investigation of fairness discussions on eight Stack Exchange sites resulted in a list of 136 posts (28 questions and 108 answers) manually curated from 4,178 candidate posts. The study reveals that the majority of fairness discussions (24 posts) revolve around the topic of income suggesting that many software practitioners are highly interested in matters related to their pay and how it is fairly distributed. Further, we noted that while not discussed as often, discussions on fairness in recruitment tend to receive the highest number of views and scores. Interestingly, the study shows that unfairness experiences extend beyond the protected attributes. In this study, only 25 out of 136 posts mention protected attributes, with gender mainly being discussed.

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.

Daniel R. Clarkson, Lawrence A. Bull, Chandula T. Wickramarachchi et al.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g.\ structural health monitoring), feature-label pairs used to learn such mappings are of limited availability which hinders the effectiveness of traditional supervised machine learning approaches. The current paper proposes a methodology for overcoming the issue of data scarcity by combining active learning with hierarchical Bayesian modelling. Active learning is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g.\ inspection and maintenance). Hierarchical Bayesian modelling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modelling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modelling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost -- maintaining predictive performance while reducing the number of inspections required.

Ikuo TANABE

In the 21st century, as it is important to produce products with high quality, high dignity and high accuracy, mirror-like surface was requested for many products and prats. Therefore, polishing process, lapping process and buffing were performed for mirror-like surface as in the past. However, both polishing process and lapping process need long working time and high cost, and buffing accuracies except surface roughness were very low. Therefore, mirror-like surface turning technology with swift and easy working was developed and evaluated using only one lathe. Surface roughness on the turning was firstly considered for mirror-like surface. Then the simple grinder using polishing for mirror-like surface turning insert was also developed and installed on the turning lathe with linear motor. Cutting property using the proposed technology was evaluated in the several experiments. It is concluded from the results that; (1) The proposed technology can machine mirror-like surface in the turning. Its surface roughness was Rz 0.1μm, (2) The process for mirror-like surface becomes swift and easy working.

Stavros T. Miloulis, Ioannis Zorzos, Ioannis Kakkos et al.

The efficient translation of brain signals into an output device is an essential characteristic to establish a Brain-computer Interface (BCI) link. This research investigates the applicability of diverse correlation indices for the differentiation of specific movements (left, right, both, or none) and states (real or imaginary) in a private BCI dataset, including EEG recordings of 32 participants. As such, the recorded brain activation data were employed to illustrate the differences between visual- and auditory-event-related responses during task performance. Our methodology involved a two-pronged approach. Firstly, EEG data were collected, capturing both the visual- and auditory-event-related signals that corresponded to each of the four movement classes. Secondly, we performed a comparative analysis of the collected dataset using various correlation algorithms, such as Pearson, Spearman, and Kendall, among others, to evaluate their effectiveness in differentiating between movements and states. The results demonstrated distinctive correlation patterns, as the selected indices effectively distinguished between real and imaginary movements, as well as between different lower limp movements in most cases. Moreover, the correlation schemas of certain individuals presented greater sensitivity in discerning nuances within the dataset. In this regard, it can be inferred that the chosen correlation indices can provide valuable insights into the aforementioned differentiation in EEG data. The results open up potential paths for improving BCI interfaces and contributing to more accurate prediction models.

Soya SATO, Tsunehisa SUZUKI, Tatsuya FUJII et al.

This study unveils the effect of ion beam (IB) irradiation on mechanical properties and adhesion of titanium nitraide (TiN) thin films on ultrafine-grained WC-Co cemented carbide substrates. TiN thin films are used for improving wear resistance and lubricity of molds and cutting tools consisted of WC-Co cemented carbides. It is known that the ion beam assisted deposition (IBAD) improve the adhesion strength between the coatings and substrates. In this paper, TiN coatings were deposited on the ultrafine-grained WC-Co cemented carbide substrates by IBAD method that titanium was evaporated by an electron beam (EB) and nitrogen gas was introduced with/without IB. The chemical composition of TiN coatings deposited by IB were investigated by EPMA, XRD and XPS. The nitrogen ratio of TiN increased by IB irradiation in deposition process. The mechanical properties of TiN coatings with/without IB were evaluated by nanoindentation tests and ball-on-disc tests. Dynamic hardness and Young's modulus of TiN deposited by IBAD was 100% and 50% higher than that by conventional vapor deposition (VD), respectively. Friction coefficient of TiN deposited by IBAD was lower than that by VD. The adhesion of TiN coatings was measured by scratch tests. The adhesion strength of TiN deposited by IBAD was 21% higher than that by VD.

Yassine Yazid, Antonio Guerrero-González, Ahmed El Oualkadi et al.

Robot vision, enabled by deep learning breakthroughs, is gaining momentum in the industry 4.0 digitization process. The present investigation describes a robotic grasp detection application that makes use of a two-finger gripper and an RGB-D camera linked to a collaborative robot. The visual recognition system, which is integrated with edge computing units, conducts image recognition for faulty items and calculates the position of the robot arm. Identifying deformities in object photos, training and testing the images with a modified version of the You Only Look Once (YOLO) method, and establishing defect borders are all part of the process. Signals are subsequently sent to the robotic manipulator to remove the faulty components. The adopted technique used in this system is trained on custom data and has demonstrated a high accuracy and low latency performance as it reached a detection accuracy of 96% with 96.6% correct grasp accuracy.

Bendib Riad, Mechhoud El-Arkam, Kassah Maroua et al.

Fuzzy logic is the most effective mathematical solution that has been presented in the last few years to deal with the problems in which imprecise and uncertain data exist. It can be defined as a generalization of classical binary logic, which admits only two logical states, true or false, by adding degrees of truth between the extreme values. Its basics were initiated by Lotfi Zadeh in the mid-1960s. However, in the last few years, a question was raised by different authors: yes, by using a fuzzy set or membership functions we can solve the problem of some kinds of uncertainties, but what is the situation in cases where the uncertainties exist in the membership function itself? To handle this situation, researchers introduce type 2 fuzzy logic. In our paper, we will introduce the design of a type 2 fuzzy logic controller to control the level in a FESTO process workstation. The mathematical model is first deduced, and after that the controller is designed. The simulation results show that the obtained controller gives very good transient characteristics for the system response work.