Hasil untuk "Engineering machinery, tools, and implements"

Ian A. Cosden, Elizabeth Holtz, Joel U. Bretheim

Research Software Engineers (RSEs) have become indispensable to computational research and scholarship. The fast rise of RSEs in higher education and the trend of universities to be slow creating or adopting models for new technology roles means a lack of structured career pathways that recognize technical mastery, scholarly impact, and leadership growth. In response to an immense demand for RSEs at Princeton University, and dedicated funding to grow the RSE group at least two-fold, Princeton was forced to strategize how to cohesively define job descriptions to match the rapid hiring of RSE positions but with enough flexibility to recognize the unique nature of each individual position. This case study describes our design and implementation of a comprehensive RSE career ladder spanning Associate through Principal levels, with parallel team-lead and managerial tracks. We outline the guiding principles, competency framework, Human Resources (HR) alignment, and implementation process, including engagement with external consultants and mapping to a standard job leveling framework utilizing market benchmarks. We share early lessons learned and outcomes including improved hiring efficiency, clearer promotion pathways, and positive reception among staff.

André Takeshi Endo, Anders Møller

Chung-Shun Feng, Chao-Ming Wang

According to the World Health Organization, the global population is aging, with cognitive and memory functions declining from the age of 40–50. Individuals aged 65 and older are particularly prone to dementia. Therefore, we developed an interactive system for visual cognitive training to prevent dementia and delay the onset of memory loss. The system comprises three “three-dimensional objects” with printed 2D barcodes and near-field communication (NFC) tags and operating software processing text, images, and multimedia content. Electroencephalography (EEG) data from a brainwave sensor were used to interpret brain signals. The system operates through interactive games combined with real-time feedback from EEG data to reduce the likelihood of dementia. The system provides feedback based on textual, visual, and multimedia information and offers a new form of entertainment. Thirty participants were invited to participate in a pre-test questionnaire survey. Different tasks were assigned to randomly selected participants with three-dimensional objects. Sensing technologies such as quick-response (QR) codes and near-field communication (NFC) were used to display information on smartphones. Visual content included text-image narratives and media playback. EEG was used for visual recognition and perception responses. The system was evaluated using the system usability scale (SUS). Finally, the data obtained from participants using the system were analyzed. The system improved hand-eye coordination and brain memory using interactive games. After receiving visual information, brain function was stimulated through brain stimulation and focused reading, which prevents dementia. This system could be introduced into the healthcare industry to accumulate long-term cognitive function data for the brain and personal health data to prevent the occurrence of dementia.

Masae HAYASHI, Hiroshi OKUDA, Kazuhisa INAGAKI et al.

Short fiber reinforced rubber composites (SFRRCs) are crucial, lightweight, and durable materials used across various industries. We've developed a simulator for full rubber belt-pulley friction contact behavior utilizing the large-scale parallel FE structural software FrontISTR. However, accurately integrating SFRRC's complex material behavior into this already large-scale simulation presented a significant challenge: traditional solid element modeling for both rubber and fibers proved impractically expensive. To overcome this, we developed and added an insert element function to FrontISTR. This function represents short fibers as embedded truss elements within the solid rubber matrix and supports distributed-parallel computation based on domain decomposition. The proposed method was verified by comparing the results obtained using the specimen model with actual short fiber orientations to those from commercial software. Our approach significantly reduces the computation time, achieving a speed-up of 400 times compared to conventional analysis with solid elements.

Manar Ashkanani, Alanoud AlAjmi, Aeshah Alhayyan et al.

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.

Victor Norrefeldt, Michael Buschhaus, Sabine Johann et al.

This paper describes an experimental investigation on the spread of a virus in a business jet cabin and the potential of ionization to reduce the pathogen load. In contrast to priorly investigated recirculation air cleaning, ionization can act directly in the cabin by introducing ions into the supply air. Tests were performed by emitting a surrogate virus through a breathing head in a business jet mock-up. The results allow for the conclusion that ionization technology, along with increased airflow, is a well-suited tool to sanitize cabins. Additionally, the effect of ionization on particles was investigated where it became obvious that the presence of particles reduces the ion level; however, the presence of ions hardly impact particles.

Ziyou Li, Agnia Sergeyuk, Maliheh Izadi

Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt reuse and quality, Prompt-with-Me suggests language refinements, masks sensitive information, and extracts reusable templates from a developer's prompt library. Our taxonomy study of 1108 real-world prompts demonstrates that modern LLMs can accurately classify software engineering prompts. Furthermore, our user study with 11 participants shows strong developer acceptance, with high usability (Mean SUS=73), low cognitive load (Mean NASA-TLX=21), and reported gains in prompt quality and efficiency through reduced repetitive effort. Lastly, we offer actionable insights for building the next generation of prompt management and maintenance tools for software engineering workflows.

Paris Avgeriou, Nauman bin Ali, Marcos Kalinowski et al.

Increasingly, courses on Empirical Software Engineering research methods are being offered in higher education institutes across the world, mostly at the M.Sc. and Ph.D. levels. While the need for such courses is evident and in line with modern software engineering curricula, educators designing and implementing such courses have so far been reinventing the wheel; every course is designed from scratch with little to no reuse of ideas or content across the community. Due to the nature of the topic, it is rather difficult to get it right the first time when defining the learning objectives, selecting the material, compiling a reader, and, more importantly, designing relevant and appropriate practical work. This leads to substantial effort (through numerous iterations) and poses risks to the course quality. This chapter attempts to support educators in the first and most crucial step in their course design: creating the syllabus. It does so by consolidating the collective experience of the authors as well as of members of the Empirical Software Engineering community; the latter was mined through two working sessions and an online survey. Specifically, it offers a list of the fundamental building blocks for a syllabus, namely course aims, course topics, and practical assignments. The course topics are also linked to the subsequent chapters of this book, so that readers can dig deeper into those chapters and get support on teaching specific research methods or cross-cutting topics. Finally, we guide educators on how to take these building blocks as a starting point and consider a number of relevant aspects to design a syllabus to meet the needs of their own program, students, and curriculum.

Marina Araújo, Júlia Araújo, Romeu Oliveira et al.

[Context] Domain knowledge is recognized as a key component for the success of Requirements Engineering (RE), as it provides the conceptual support needed to understand the system context, ensure alignment with stakeholder needs, and reduce ambiguity in requirements specification. Despite its relevance, the scientific literature still lacks a systematic consolidation of how domain knowledge can be effectively used and operationalized in RE. [Goal] This paper addresses this gap by offering a comprehensive overview of existing contributions, including methods, techniques, and tools to incorporate domain knowledge into RE practices. [Method] We conducted a systematic mapping study using a hybrid search strategy that combines database searches with iterative backward and forward snowballing. [Results] In total, we found 75 papers that met our inclusion criteria. The analysis highlights the main types of requirements addressed, the most frequently considered quality attributes, and recurring challenges in the formalization, acquisition, and long-term maintenance of domain knowledge. The results provide support for researchers and practitioners in identifying established approaches and unresolved issues. The study also outlines promising directions for future research, emphasizing the development of scalable, automated, and sustainable solutions to integrate domain knowledge into RE processes. [Conclusion] The study contributes by providing a comprehensive overview that helps to build a conceptual and methodological foundation for knowledge-driven requirements engineering.

Jake Zappin, Trevor Stalnaker, Oscar Chaparro et al.

This position paper examines the substantial divide between academia and industry within quantum software engineering. For example, while academic research related to debugging and testing predominantly focuses on a limited subset of primarily quantum-specific issues, industry practitioners face a broader range of practical concerns, including software integration, compatibility, and real-world implementation hurdles. This disconnect mainly arises due to academia's limited access to industry practices and the often confidential, competitive nature of quantum development in commercial settings. As a result, academic advancements often fail to translate into actionable tools and methodologies that meet industry needs. By analyzing discussions within quantum developer forums, we identify key gaps in focus and resource availability that hinder progress on both sides. We propose collaborative efforts aimed at developing practical tools, methodologies, and best practices to bridge this divide, enabling academia to address the application-driven needs of industry and fostering a more aligned, sustainable ecosystem for quantum software development.

Timo Kehrer, Robert Haines, Guido Juckeland et al.

Anecdotal evidence suggests that Research Software Engineers (RSEs) and Software Engineering Researchers (SERs) often use different terminologies for similar concepts, creating communication challenges. To better understand these divergences, we have started investigating how SE fundamentals from the SER community are interpreted within the RSE community, identifying aligned concepts, knowledge gaps, and areas for potential adaptation. Our preliminary findings reveal opportunities for mutual learning and collaboration, and our systematic methodology for terminology mapping provides a foundation for a crowd-sourced extension and validation in the future.

Lekshmi Murali Rani, Richard Berntsson Svensson, Robert Feldt

The integration of AI for Requirements Engineering (RE) presents significant benefits but also poses real challenges. Although RE is fundamental to software engineering, limited research has examined AI adoption in RE. We surveyed 55 software practitioners to map AI usage across four RE phases: Elicitation, Analysis, Specification, and Validation, and four approaches for decision making: human-only decisions, AI validation, Human AI Collaboration (HAIC), and full AI automation. Participants also shared their perceptions, challenges, and opportunities when applying AI for RE tasks. Our data show that 58.2% of respondents already use AI in RE, and 69.1% view its impact as positive or very positive. HAIC dominates practice, accounting for 54.4% of all RE techniques, while full AI automation remains minimal at 5.4%. Passive AI validation (4.4 to 6.2%) lags even further behind, indicating that practitioners value AI's active support over passive oversight. These findings suggest that AI is most effective when positioned as a collaborative partner rather than a replacement for human expertise. It also highlights the need for RE-specific HAIC frameworks along with robust and responsible AI governance as AI adoption in RE grows.

Sustainable energy sources like solar, wind, hydropower, biomass, geothermal, tidal, and wave energy can take the place of fossil fuels. They replenish organically and aid in the fight against climate change. Solar energy harvests the sun's energy using photovoltaic panels or concentrated solar power plants. Wind energy converts the kinetic energy of the wind into electricity by using turbines. Hydropower uses water that is either flowing or falling to generate electricity. You may generate energy from organic material using biomass. Geothermal energy harnesses the heat of the Earth to produce heat or electricity. Utilising the strength of tides and ocean waves to produce electricity is known as tidal and wave energy. These tools aid in the development of a cleaner, greener future by lowering emissions and enhancing air quality. Our energy mix needs to be more diverse in order to lessen our dependency on fossil fuels, and renewable energy sources are essential for this. Solar power is widely available and can be used in rooftop installations or massive solar farms. Building wind farms in windy areas has significantly increased the use of wind energy. An established technology called hydropower uses water sources to make electricity, whereas biomass uses organic waste to produce both heat and power. Geothermal energy uses the Earth's interior heat as a source of power, making it dependable and continuous. With the ability to harness the energy of the ocean to produce electricity, tidal and wave energy offer tremendous promise. Adopting renewable energy sources contributes to the development of a resilient and sustainable energy system for a cleaner and better future. Hydropower is a well-known technique that uses water to generate electricity, whereas biomass uses organic waste to generate both heat and power. Geothermal energy is dependable and continuous because it harnesses the heat from deep inside the Earth. Tidal and wave energy hold great potential since they can use ocean energy to generate electricity. Utilising renewable energy sources helps build a robust and sustainable energy system for a better and cleaner future.Due to our reliance on diminishing fossil fuel reserves, we are susceptible to price swings and geopolitical unrest. By varying our energy mix and lowering our dependency on foreign fuels, research into renewable energy sources fosters greater energy independence and thereby supports energy security. Environmental Protection: The exploitation and burning of fossil fuels have negative consequences on ecosystems, causing pollution of the air and water, the destruction of habitats, and the extinction of species. We can reduce environmental damage and safeguard natural resources by investigating and implementing renewable energy sources. The renewable energy industry has the ability to stimulate economic growth and employment creation. Research in this area paves the way for the creation of cutting-edge technology, lowers costs, and boosts productivity, making renewable energy more competitive with fossil fuels and economically viable. Energy Access in Developing Regions: Many areas, particularly in developing nations, do not have consistent access to energy. Researching renewable energy sources, especially decentralised ones like solar energy, can produce clean and economical energy solutions, enhancing socioeconomic development and quality of life.Technological Advancements: Ongoing research into renewable energy has made it possible to make strides in energy storage, solar panel efficiency, and wind turbine design. These developments improve the overall efficiency and dependability of renewable energy systems, increasing their viability and efficiency. Research offers insightful analysis into the policy and regulatory frameworks required to facilitate the integration of renewable energy into current power systems. It aids in identifying obstacles, evaluating the results of the deployment of renewable energy, and creating efficient policies to encourage the use of renewable energy. Conduct resource assessments to determine a region's potential for renewable energy. Decide on the appropriate renewable energy technology based on the needs of the location and the available resources. Utilise the technical, environmental, and economic factors to analyse the viability. Consider the system's size, capacity, and necessary infrastructure when designing it. It is necessary to buy and install the necessary infrastructure and machinery for the renewable energy system. Integrate the system into the existing electrical grid to ensure that it is compatible and compliant. Establish operational, maintenance, and performance-enhancing routines. Follow up on problems with and inefficiencies in the system. Continue your research and development efforts to advance technologies. Work with research organisations and stakeholders to advance the production of renewable energy.

Zemenay Hailegebreal, Metie Assefinaw

In work environments and industries, there are several factors that contribute to the fall of productivity and these factors have been the cause of several investigations so that one can minimize the issue of cleaner and complexity of production in mechanical machines workshops and other engineering branches, in order to provide satisfaction, safety and increased production part of the organization customer satisfaction. The planning of the physical arrangement presents itself as an aspect that can confer decisive improvements, since in addition to defining the flow of materials in the production process. The present case study aims to present a proposal for the implementation of the improved layout in the mechanical workshop of the Ethiopia “Selam workshop” located in Kotbe. The current layout of the Selam machinery workshop has exerted negative influence on issues such as production volume, system flexibility and even material and labor costs. In machining shops specifically, there are many process variables that make it difficult to work on improving the productivity index. Here, it analyzed the production process in the machining sector in which the process variables are from the high amount of shapes of the parts to the position of the machines, tools, and differentiated production devices. It analyzes the production process in two moments based on the production time. In the first, productivity before and, in the other, after the study and process changes focused on approximation of sectors, tools, using as work philosophy the Lean Manufacturing (takt time) to control the production time, especially with regard to the techniques of continuous improvement through the SPL Muther (approximation of sectors). Here are presented three layouts proposals, the first, is general occupation with new compartments or arranged compartments, the second, layout of the machinery in the line of Machining and a third layout for the flow of people and goods. Therefore, with the implementation of this project were met general aspects to the needs of the projected, bringing about changes like: reduction of complexity in production in the line of machining, creation of warehouses for raw material and finished products, increased sectorial cooperation, increase of the final quality of the service provided, enhancement of the workers' ergonomic settings.

K. Bello, E. Ajisegiri, O. Awoegbemi et al.

Predictive maintenance, which uses machine learning to optimize maintenance schedules, reduce operating costs, and increase reliability, is a significant advancement in the management of industrial machinery. The predictive maintenance methods for milling machines using machine learning algorithms are the main topic of this study. To develop predictive models that can anticipate possible failures, a dataset containing operational parameters like temperature, rotational speed, torque, and tool wear was analyzed. To determine how well they predicted failures, a number of machine learning algorithms were tested, including Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM). To improve model accuracy, data preprocessing techniques such as exploratory data analysis (EDA), feature engineering, and normalization were used. Model performance was evaluated using metrics like precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). According to the results,  predictive maintenance can significantly reduce unscheduled downtime, increase machine longevity, and simplify maintenance plans. For increased efficiency, real-time monitoring and ongoing model training are advised. With a cost-effective solution for manufacturing sectors that rely on milling machines, this study highlights the revolutionary potential of integrating artificial intelligence into industrial maintenance. Future studies should look into adaptability and real-time implementation for different kinds of industrial machinery.

D. Golub, S. N. Grechanik, A. Pashkevich et al.

The article discusses the scientific and technical developments of OJSC “BELNIILIT” aimed at the creation of modern technologies and equipment for the machine‑based production of sand‑resin cores for foundries. It outlines the approaches of specialists in the design of core machine assemblies, mechanisms, and tooling. The paper presents practical implementation results of core‑making equipment, examples of complex sand cores produced using OJSC “BELNIILIT” machinery at various mechanical engineering enterprises, and highlights the prospects for future development in this field.

Carlos Alberto González Almaguer, Verónica Saavedra Gastélum, Anders Berglund et al.

The COVID-19 pandemic was a great opportunity for learning and solving challenges to develop tools that enable excellence in distance education. This research summarizes the “journey” of research professors in educational innovation, detailing the results of different classroom implementations of virtual and augmented reality lessons at both Tecnologico de Monterrey and Mälardalen University for industrial engineering and design programs. In this paper, we describe the methodology for designing virtual reality (VR) and augmented reality (AR) lessons from two different approaches: designing a lesson for a specific topic and the design of a lesson using virtual and augmented reality lessons already realized and adapted to enhance learning using this type of emerging methodologies. The obtained results show the implementation of the lessons in different training units and how enhanced engagement and deeper understanding can be promoted through extended reality (XR).

Lateef Dheyab Nsaif Murshedi, Inna P. Solyanikova

Green synthesis is one of the lowest energy processes for constructing nanomaterials because it is clean, safe, and cost-effective. The aim of this research is to prepare green nanoparticles using eggplant extract and then optimize and characterize these particles using different techniques. This research also includes a study of total antioxidants and their ability to scavenge free radicals. The method of green synthesis of silver nanoparticles with eggplant extract was elaborated for the first time. The best conditions for this were 1 mM of argentum nitrate. The obtained green nanoparticles possess high activity against oxidation.

Johan Cederbladh, Antonio Cicchetti

In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that promote specific and general V&V activities earlier to support more effective decision-making. As a result, there are incentives to introduce new technologies to remain competitive with the recently drastic changes in system complexity and heterogeneity. Performing V&V early on in development is a means of reducing risk for later error detection while moving key activities earlier in a process. We present a summary of the literature on early V&V and position existing challenges regarding potential solutions and future investigations. In particular, we reason that the software engineering community can act as a source for inspiration as many emerging technologies in the software domain are showing promise in the wider systems domain, and there already exist well formed methods for early V&V of software behaviour in the software modelling community. We conclude the paper with a road-map for future research and development for both researchers and practitioners to further develop the concepts discussed in the paper.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Without that model connectivity, requirement quality can suffer due to imprecision and inconsistent terminology, frustrating communication during system development. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. The Model-Based Structured Requirement SysML Profile was extended to comply with the INCOSE Guide to Writing Requirements updated in 2023 while conforming to the ISO/IEC/IEEE 29148 standard requirement statement templates. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition and requirements V&V. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to explore its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support with the system architecture modeling software.