Hasil untuk "Engineering machinery, tools, and implements"

Massimiliano Di Penta, Kelly Blincoe, Marsha Chechik et al.

As software engineering conferences grow in size, rising costs and outdated formats are creating barriers to participation for many researchers. These barriers threaten the inclusivity and global diversity that have contributed to the success of the SE community. Based on survey data, we identify concrete actions the ACM Special Interest Group on Software Engineering (SIGSOFT) can take to address these challenges, including improving transparency around conference funding, experimenting with hybrid poster presentations, and expanding outreach to underrepresented regions. By implementing these changes, SIGSOFT can help ensure the software engineering community remains accessible and welcoming.

Youssef Charkaoui, Dounia Tebr, Zeineb El Hammoumi et al.

In this age of consumerism, most companies are doing their utmost to convince their customers of their products and to attract new customers. The IT development we see today is a perfect solution for strengthening the relationship between companies and their customers, giving them the opportunity to expand their customer base. The Internet of Things refers to an inter-connected system of smart devices that communicate and exchange data and big data analytics over the internet. As this involves the process of the treating data to unlock hidden information, patterns, and insights, the combination of both tools creates a revolution in customer relations and gives us the opportunity to understand our customers’ needs before they do themselves. This article presents an exploratory literature review of studies analyzing the relationship between IOT and big data in marketing. It provides a deep analysis of various scholars’ works that examine the methodology used by these tools to reinforce customer relations and acquire new ones. This review provides an overview of the most interesting research on this topic and the methods and techniques employed as well as an analysis of the obstacles and challenges involved. The results of this research show that IOT and big data analytics are key factors for an efficient analysis of clients’ needs.

Wissal Ed-Dehbi, Mustapha Ahlaqqach, Jamal Benhra

This review investigates the application of Artificial Intelligence (AI), deep learning (DL), and the Internet of Things (IoT) in water resource management, focusing on distribution optimization, demand prediction, and water quality enhancement. The study synthesizes findings from 2015 to 2024, encompassing experimental and applied research published in English or French in recognized scientific outlets. By analyzing the prevalent algorithms, IoT technologies, and their impacts, this systematic review highlights research gaps and proposes directions for future work. The results show significant advancements in predictive analytics and real-time monitoring through AI and the IoT. However, challenges remain in scalability, interdisciplinary integration, and contextual adaptation.

Krishnaveni Manubolu, Raveesha Peeriga

This study explores the potential of <i>Vigna mungo</i> gum as a sustainable and innovative natural polymer for developing microbeads for the controlled delivery of vildagliptin, a widely used antidiabetic agent. Unlike conventional natural polymers, <i>Vigna mungo</i> gum offers unique biocompatibility, biodegradability, and an eco-friendly production process, distinguishing it as a superior candidate for drug delivery systems. Microbeads were prepared by combining <i>Vigna mungo</i> gum with sodium alginate and inducing gelation using calcium carbonate. Scanning electron microscopy (SEM) revealed a rough, porous microbead surface, advantageous for drug encapsulation and controlled release. Drug release studies demonstrated sustained release kinetics, highlighting the effectiveness of this formulation. These findings underscore the novelty of <i>Vigna mungo</i> gum as a promising platform for antidiabetic drug delivery, providing a sustainable alternative to existing polymer systems.

Masafumi OKADA, Morito SATO

A robot motion tracking control system consists of feedback and feedforward controller. The former is realized by state feedback such as PD controller or a dynamic controller such as PID controller, while the latter is obtained by inverse dynamics analysis based on a robot dynamics model. To obtain the robot dynamics model, an approximate solution of minimum set of dynamics parameters is identified from experimental data using the least-squares method, etc. The accuracy of the approximation, i.e., which parameters have priority, is highly dependent on the robot motion and feedback controller used. We have proposed a stochastic parameter identification method using sensitivity analysis to determine the priority of the parameters. However, a state feedback controller is assumed to be used for this approach. In this paper, we derive a stochastic parameter identification method for small periodic motions of the end-effector, such as weaving motion of a welding robot, using a dynamic controller, and verify the effectiveness of the proposed method through experiments using a planar three-link manipulator. Based on the error ellipsoid of the parameters, we show that the priority of the parameters to be identified changes as the controller changes, and that the priority of the parameters that the controller will compensate decreases.

Debopriyo Roy, George Fragulis, Peter Ilic

In submitting conference proceedings to <i>Engineering Proceedings</i>, the Volume Editors of the proceedings would like to certify to the publisher that all papers published in this volume have been subjected to peer review by the designated expert referees and were administered by the Volume Editors strictly following the policies announced on the conference website [...]

Yining Hong, Christopher S. Timperley, Christian Kästner

Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Shraddha Surana, Ashwin Srinivasan, Michael Bain

Engineering information systems for scientific data analysis presents significant challenges: complex workflows requiring exploration of large solution spaces, close collaboration with domain specialists, and the need for maintainable, interpretable implementations. Traditional manual development is time-consuming, while "No Code" approaches using large language models (LLMs) often produce unreliable systems. We present iProg, a tool implementing Interactive Structured Inductive Programming. iProg employs a variant of a '2-way Intelligibility' communication protocol to constrain collaborative system construction by a human and an LLM. Specifically, given a natural-language description of the overall data analysis task, iProg uses an LLM to first identify an appropriate decomposition of the problem into a declarative representation, expressed as a Data Flow Diagram (DFD). In a second phase, iProg then uses an LLM to generate code for each DFD process. In both stages, human feedback, mediated through the constructs provided by the communication protocol, is used to verify LLMs' outputs. We evaluate iProg extensively on two published scientific collaborations (astrophysics and biochemistry), demonstrating that it is possible to identify appropriate system decompositions and construct end-to-end information systems with better performance, higher code quality, and order-of-magnitude faster development compared to Low Code/No Code alternatives. The tool is available at: https://shraddhasurana.github.io/dhaani/

Roberto Verdecchia, Justus Bogner

From its first adoption in the late 80s, qualitative research has slowly but steadily made a name for itself in what was, and perhaps still is, the predominantly quantitative software engineering (SE) research landscape. As part of our regular column on empirical software engineering (ACM SIGSOFT SEN-ESE), we reflect on the state of qualitative SE research with a focus group of experts. Among other things, we discuss why qualitative SE research is important, how it evolved over time, common impediments faced while practicing it today, and what the future of qualitative SE research might look like. Joining the conversation are Rashina Hoda (Monash University, Australia), Carolyn Seaman (University of Maryland, United States), and Klaas Stol (University College Cork, Ireland). The content of this paper is a faithful account of our conversation from October 25, 2025, which we moderated and edited for our column.

Dipin Khati, Yijin Liu, David N. Palacio et al.

Applications of Large Language Models (LLMs) are rapidly growing in industry and academia for various software engineering (SE) tasks. As these models become more integral to critical processes, ensuring their reliability and trustworthiness becomes essential. Consequently, the concept of trust in these systems is becoming increasingly critical. Well-calibrated trust is important, as excessive trust can lead to security vulnerabilities, and risks, while insufficient trust can hinder innovation. However, the landscape of trust-related concepts in LLMs in SE is relatively unclear, with concepts such as trust, distrust, and trustworthiness lacking clear conceptualizations in the SE community. To bring clarity to the current research status and identify opportunities for future work, we conducted a comprehensive review of $88$ papers: a systematic literature review of $18$ papers focused on LLMs in SE, complemented by an analysis of 70 papers from broader trust literature. Additionally, we conducted a survey study with 25 domain experts to gain insights into practitioners' understanding of trust and identify gaps between existing literature and developers' perceptions. The result of our analysis serves as a roadmap that covers trust-related concepts in LLMs in SE and highlights areas for future exploration.

Veronika Anna Szabó, Gábor Böcz, Gábor Dogossy

This research investigates the correlation between tensile strength, tensile strain, and porosity in chemically foamed recycled poly(ethylene-terephthalate) (rPET) samples. Tensile strength and strain were measured on non-foamed samples to predict the porosity of the foamed samples. Utilizing various flame retardants and additives, we analyzed their impact on mechanical properties and structures. However, neither tensile strength nor strain demonstrated a reliable correlation with the resulting porosity. Strain values did not consistently predict porosity, indicating the significant role of additive types and concentrations.

Krisztián Enisz, Ernő Horváth, Norbert Markó et al.

There are several global and local position estimation and refinement techniques based on the GNSS (Global Navigation Satellite System) and environmental monitoring (e.g., LIDAR, Light Detection and Ranging). These are usually based on a combination of multiple sensors using some form of sensor fusion, together with a filtering or observation technique. The behavior of these algorithms may vary depending on the applied sensor signals and on their accuracy under different environmental conditions and for different vehicle types. In the case of systems that also use GNSS signals, different procedures must also be prepared for signal dropouts and, in the worst case, drastic fluctuations in accuracy. The aim of this research is to present and compare the performance of different estimation procedures for different vehicles and environmental conditions.

Ebube Alor, Ahmad Abdellatif, SayedHassan Khatoonabadi et al.

Software engineering (SE) chatbots are increasingly gaining attention for their role in enhancing development processes. At the core of chatbots are Natural Language Understanding platforms (NLUs), which enable them to comprehend user queries but require labeled data for training. However, acquiring such labeled data for SE chatbots is challenging due to the scarcity of high-quality datasets, as training requires specialized vocabulary and phrases not found in typical language datasets. Consequently, developers often resort to manually annotating user queries -- a time-consuming and resource-intensive process. Previous approaches require human intervention to generate rules, called labeling functions (LFs), that categorize queries based on specific patterns. To address this issue, we propose an approach to automatically generate LFs by extracting patterns from labeled user queries. We evaluate our approach on four SE datasets and measure performance improvement from training NLUs on queries labeled by the generated LFs. The generated LFs effectively label data with AUC scores up to 85.3% and NLU performance improvements up to 27.2%. Furthermore, our results show that the number of LFs affects labeling performance. We believe that our approach can save time and resources in labeling users' queries, allowing practitioners to focus on core chatbot functionalities rather than manually labeling queries.

Aline de Campos, Jorge Melegati, Nicolas Nascimento et al.

Generative Artificial Intelligence (GenAI) has become an emerging technology with the availability of several tools that could impact Software Engineering (SE) activities. As any other disruptive technology, GenAI led to the speculation that its full potential can deeply change SE. However, an overfocus on improving activities for which GenAI is more suitable could negligent other relevant areas of the process. In this paper, we aim to explore which SE activities are not expected to be profoundly changed by GenAI. To achieve this goal, we performed a survey with SE practitioners to identify their expectations regarding GenAI in SE, including impacts, challenges, ethical issues, and aspects they do not expect to change. We compared our results with previous roadmaps proposed in SE literature. Our results show that although practitioners expect an increase in productivity, coding, and process quality, they envision that some aspects will not change, such as the need for human expertise, creativity, and project management. Our results point to SE areas for which GenAI is probably not so useful, and future research could tackle them to improve SE practice.

K. M. And, T. Teixeira

B. Ziganshin, Nikolay Semushkin, Denis Semushkin et al.

The article analyzes the effectiveness of scientific and technical developments in obtaining biologically pure products in agriculture, identifies aspects of improving the effectiveness of scientific and technical developments in the agricultural sector used in obtaining agricultural products in organic farming, in particular, the specifics of the development agricultural machines and tools for organic agriculture, the conditions that the developed equipment must satisfy are determined. The analysis of factors contributing to the increase in the cost of work on the creation of a single technical tool for the conditions of organic farming was carried out. The proportions that have developed at the present time in the structure of R&D performed in the agricultural engineering industry are also considered. It is concluded that insufficient attention is paid to the work on the creation of a strategic scientific and technical reserve, inertia of R&D subjects and organizational and staff structures of the sectoral research institutes of agricultural engineering. The necessity of creating organizational forms of integration of science and production, which allow for a clear and quick passage of ideas from their inception to widespread use in practice, the widespread use of new progressive forms of organization of scientific activity is considered. Measures are proposed to improve the systematic organization of R&D management. The necessity of introducing documents on the procedure for conducting joint work on the creation of new types of agricultural machinery, carried out by subordinate research and technological organizations, is shown. A significant place in the article is given to the directions and results of the work of Kazan State Agrarian University in the field of R&D and its contribution to the scientific support of the agro-industrial complex of the Republic of Tatarstan and the Russian Federation. The currently used forms of R&D integration are considered from the point of view of the completeness of coverage of all potential performers, especially the academic and university sectors of science. Ways are proposed to expand the communication systems for scientific and technical cooperation through the widespread use of forms of interdepartmental cooperation, using progressive developments in the field of scientific and technical developments of Russian research institutes and educational institutions. Recommendations are proposed to increase the amount of funding for work on creating a strategic scientific and technical reserve in agricultural machinery and the implementation of a certain economic management mechanism. It is concluded that there are all the necessary prerequisites for a decisive transition to fundamentally new technologies, machines and tools in organic agricultural production, a significant improvement in the organization of work on their creation and implementation, which will dramatically accelerate the pace of scientific and technological progress in the industry.

Pankaj Sarsia, Akhileshwer Munshi, Fiza Sheikh et al.

The global energy shift towards sustainability and renewable power sources is pressing. Large-scale electric vehicles (EVs) play a pivotal role in accelerating this transition. They significantly curb carbon emissions, especially when charged with renewable energy like solar or wind, resulting in near-zero carbon footprints. EVs also enhance grid flexibility, acting as mobile energy storage, stabilizing power supply. Integrating EVs into renewable systems offers demand response programs, optimizing energy use. However, extensive infrastructure development, particularly charging networks, is a significant challenge. Collaboration among governments, utility companies, and private sectors is crucial to ensure a smooth transition to electric mobility.

Sanjay Mate, Vikas Somani, Prashant Dahiwale

Agriculture has a good stake in the world’s GDP. In many countries, agriculture and allied sectors have a good stake in national GDP. This paper covers details related to livestock since 1960s. The workforce has managed livestock for many decades. The workforce increases as the number of animals increases; it is an energy, time-consuming, and economically costly approach. Apart from it, there is no assurance about animal welfare in case of diseases, breeding, and feed intake issues. In the 21st century of digitalization, technology has a key role in improving overall monitoring, controlling, and processing in livestock management. This paper has gone thoroughly into the manual and automated livestock farm management, aiming welfare of animals, livestock products, consumers’ benefit, and sustainable environmental approaches.

Lvyang Yang, Jiankang Zhang, Huaiqiang Li et al.

The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.