In industrial equipment containing hydraulic lines for power transmission, the lines have boundary conditions defined by components such as pumps, valves, and actuators located at the ends of the lines. Sudden changes in any of the boundary conditions may result in significant pressure/flow dynamics (fluid transients) in the lines that may be detrimental or favorable to the performance of the equipment. Accurate models for line transients are defined by a set of simultaneous partial differential equations. In this paper, analytical solutions to the partial differential equations provide Laplace transform transfer functions applicable to any set of boundary conditions yet to be specified that satisfy the requirements of causality. Analytical solutions from previous publications are reviewed for cases of laminar and turbulent flow for Newtonian and a class of non-Newtonian fluids. When obtaining time domain simulations for specific boundary conditions, complexities associated with the inverse Laplace transform are avoided by using an inverse frequency algorithm. Examples with pumps, valves, and actuators demonstrate the process of coupling equations for components at the ends of a line to get total system transfer functions and then obtaining time domain solutions for outputs-of-interest associated with system inputs and load variations.
The use of Light Fidelity (LiFi) can enable the reduction of satellite mass by reducing the wiring harness while avoiding electromagnetic interference. In this paper, a LiFi-transceiver suitable for Telemetry and Telecommand (TMTC) intra-satellite communication is developed and evaluated. The focus of the implementation is on miniaturization and energy-efficiency. First test results with a simple Transimpedance-Amplifier and an investigation of the achievable eff. data rate depending on different distances and Error-Correcting-Codes and the energy-consumption of the developed transceiver are presented. The results show that the LiFi-transceiver achieves a payload data rate of 77.6 kbit/s with Error-Correcting-Code protection and thus can be used for a reliable TMTC communication within the satellite bus.
Within the project management context, project scheduling serves as an indispensable component, functioning as a fundamental tool for planning, monitoring, controlling, and managing projects more broadly. Although the resource-constrained project scheduling problem (RCPSP) lies at the core of project management activities, it remains largely disconnected from the broader literature on model-based systems engineering (MBSE), thereby limiting its integration into the design and management of complex systems. The original contribution of this paper is twofold. First, the paper seeks to reconcile the RCPSP with the broader literature and vocabulary of model-based systems engineering and hetero-functional graph theory (HFGT). A concrete translation pipeline from an activity-on-node network to a SysML activity diagram, and then to an operand net is constructed. Using this representation, it specializes the hetero-functional network minimum-cost flow (HFNMCF) formulation to the RCPSP context as a systematic means of HFGT for quantitative analysis and proves that the RCPSP is recoverable as a special case of a broader model. Secondly, on an illustrative instance with renewable and non-renewable operands, the specialized HFNMCF, while producing similar schedules, yields explicit explanations of the project states that enable richer monitoring and control. Overall, the framework preserves the strengths of the classical RCPSP while accommodating real-world constraints and enterprise-level decision processes encountered in large, complex megaprojects.
The reconstruction, management, and optimization of gas pipelines is of significant importance for solving modern engineering problems. This paper presents innovative methodologies aimed at the effective reconstruction of gas pipelines under unstable conditions. The research encompasses the application of machine learning and optimization algorithms, targeting the enhancement of system reliability and the optimization of interventions during emergencies. The findings of the study present engineering solutions aimed at addressing the challenges in real-world applications by comparing the performance of various algorithms. Consequently, this work contributes to the advancement of cutting-edge approaches in the field of engineering and opens new perspectives for future research. A highly reliable and efficient technological Figure has been proposed for managing emergency processes in gas transportation based on the principles of the reconstruction phase. For complex gas pipeline systems, new approaches have been investigated for the modernization of existing control process monitoring systems. These approaches are based on modern achievements in control theory and information technology, aiming to select emergency and technological modes. One of the pressing issues is to develop a method to minimize the transmission time of measured and controlled data on non-stationary flow parameters of gas networks to dispatcher control centers. Therefore, the reporting Figures obtained for creating a reliable information base for dispatcher centers using modern methods to efficiently manage the gas dynamic processes of non-stationary modes are of particular importance.
Foundation models, such as large language models (LLMs), have the potential to streamline evaluation workflows and improve their performance. However, practical adoption faces challenges, such as customisability, accuracy, and scalability. In this paper, we present DOCUEVAL, an AI engineering tool for building customisable DOCUment EVALuation workflows. DOCUEVAL supports advanced document processing and customisable workflow design which allow users to define theory-grounded reviewer roles, specify evaluation criteria, experiment with different reasoning strategies and choose the assessment style. To ensure traceability, DOCUEVAL provides comprehensive logging of every run, along with source attribution and configuration management, allowing systematic comparison of results across alternative setups. By integrating these capabilities, DOCUEVAL directly addresses core software engineering challenges, including how to determine whether evaluators are "good enough" for deployment and how to empirically compare different evaluation strategies. We demonstrate the usefulness of DOCUEVAL through a real-world academic peer review case, showing how DOCUEVAL enables both the engineering of evaluators and scalable, reliable document evaluation.
Research has highlighted the valuable contributions of autistic individuals in the Information and Communication Technology (ICT) sector, particularly in areas such as software development, testing, and cybersecurity. Their strengths in information processing, attention to detail, innovative thinking, and commitment to high-quality outcomes in the ICT domain are well-documented. However, despite their potential, autistic individuals often face barriers in Software Engineering (SE) roles due to a lack of personalised tools, complex work environments, non-inclusive recruitment practices, limited co-worker support, challenging social dynamics and so on. Motivated by the ethical framework of the neurodiversity movement and the success of pioneering initiatives like the Dandelion program, corporate Diversity, Equity, and Inclusion (DEI) in the ICT sector has increasingly focused on autistic talent. This movement fundamentally reframes challenges not as individual deficits but as failures of environments designed for a neurotypical majority. Despite this progress, there is no synthesis of knowledge reporting the full pathway from software engineering education through to sustainable workplace inclusion. To address this, we conducted a Systematic Review of 30 studies and identified 18 success factors grouped into four thematic categories: (1) Software Engineering Education, (2) Career and Employment Training, (3) Work Environment, and (4) Tools and Assistive Technologies. Our findings offer evidence-based recommendations for educational institutions, employers, organisations, and tool developers to enhance the inclusion of autistic individuals in SE. These include strategies for inclusive meeting and collaboration practices, accessible and structured work environments, clear role and responsibility definitions, and the provision of tailored workplace accommodations.
In this research, we present a novel approach for cone clustering, path planning, and path visualization in autonomous Formula Student race cars, utilizing the YOLOv8 model and a ZED 2 camera, executed on a Jetson Orin computer. Our system first identifies and then deprojects the positions of cones in space, employing an advanced clustering mechanism to generate midpoints and draw connecting lines. In previous clustering algorithms, cones were stored separately by color and connected based on relevance to create the lane edges. However, our proposed solution adopts a fundamentally different approach. Cones on the left and right sides within a dynamically changing maximum and minimum distance are connected by a central line, and the midpoint of this line is marked distinctly. Cones connected in this manner are then linked by their positions to form the edges of the track. The midpoints on these central lines are displayed as markers, facilitating the visualization of the optimal path. In our research, we also cover the analysis of the clustering algorithm on global maps. The implementation utilizes the ROS 2 framework for real-time data handling and visualization. Our results demonstrate the system’s efficiency in dynamic environments, highlighting potential advancements in the field of autonomous racing. The limitation of our approach is the dependency on precise cone detection and classification, which may be affected by environmental factors such as lighting and cone positioning.
Recent advancements in deep learning for healthcare and computer-aided laboratory services have sparked a renewed interest in making medical data more accessible. Elevating the quality of healthcare services and delivering improved patient care necessitates a knowledge base rooted in data-driven insights. Deep learning models have proven to excel in this regard, as they are specifically designed to embrace a data-driven approach. These models thrive on exposure to larger datasets, which enables them to continuously improve their performance. However, as healthcare organizations strive to aggregate clinical records onto central servers to construct robust deep learning models, concerns surrounding privacy, data ownership, and legal restrictions have emerged. Safeguarding sensitive medical data while harnessing collective knowledge from multiple healthcare centers is a challenging balancing act. One promising approach to address these concerns is the use of privacy-preserving techniques that allow for the utilization of data from multiple centers without compromising security. Federated learning (FL) is a technique that has emerged to enable the deployment of large machine learning models trained across multiple data centers without the necessity of sharing sensitive information. In this article, we present the most recent findings derived from a systematic literature review focusing on the application of federated learning in healthcare settings. This review offers insights into the current state of research and practical implementations of FL within the healthcare domain. By leveraging federated learning, healthcare institutions can harness the collective power of their data while upholding privacy and data security standards, ultimately leading to more effective and data-driven healthcare solutions.
Lola Burgueño, Davide Di Ruscio, Houari Sahraoui
et al.
Model-Driven Engineering (MDE) provides a huge body of knowledge of automation for many different engineering tasks, especially those involving transitioning from design to implementation. With the huge progress made in Artificial Intelligence (AI), questions arise about the future of MDE, such as how existing MDE techniques and technologies can be improved or how other activities that currently lack dedicated support can also be automated. However, at the same time, it has to be revisited where and how models should be used to keep the engineers in the loop for creating, operating, and maintaining complex systems. To trigger dedicated research on these open points, we discuss the history of automation in MDE and present perspectives on how automation in MDE can be further improved and which obstacles have to be overcome in both the medium and long-term.
6G will revolutionize the software world allowing faster cellular communications and a massive number of connected devices. 6G will enable a shift towards a continuous edge-to-cloud architecture. Current cloud solutions, where all the data is transferred and computed in the cloud, are not sustainable in such a large network of devices. Current technologies, including development methods, software architectures, and orchestration and offloading systems, still need to be prepared to cope with such requirements. In this paper, we conduct a Systematic Mapping Study to investigate the current research status of 6G Software Engineering. Results show that 18 research papers have been proposed in software process, software architecture, orchestration and offloading methods. Of these, software architecture and software-defined networks are respectively areas and topics that have received the most attention in 6G Software Engineering. In addition, the main types of results of these papers are methods, architectures, platforms, frameworks and algorithms. For the five tools/frameworks proposed, they are new and not currently studied by other researchers. The authors of these findings are mainly from China, India and Saudi Arabia. The results will enable researchers and practitioners to further research and extend for 6G Software Engineering.
[Context and Motivation]: The quality of requirements specifications impacts subsequent, dependent software engineering activities. Requirements quality defects like ambiguous statements can result in incomplete or wrong features and even lead to budget overrun or project failure. [Problem]: Attempts at measuring the impact of requirements quality have been held back by the vast amount of interacting factors. Requirements quality research lacks an understanding of which factors are relevant in practice. [Principal Ideas and Results]: We conduct a case study considering data from both interview transcripts and issue reports to identify relevant factors of requirements quality. The results include 17 factors and 11 interaction effects relevant to the case company. [Contribution]: The results contribute empirical evidence that (1) strengthens existing requirements engineering theories and (2) advances industry-relevant requirements quality research.
The Ganga is a river and a representation of morality and purity for the people of India. From a geographical perspective, it is also India’s main river. A significant part of Patna’s population used ganga water for a variety of uses, including domestic, agricultural, and industrial. This study aims to evaluate the Ganga River’s water quality for different Ghats of Patna urban area from Digha to Gai Ghat. Samples of water were taken from 15 distinct Ghats. The biological, chemical, and physical characteristics of water have significantly changed as a result of heavy municipal waste discharge and anthropogenic activities in the river. All the Ghats were classified as unfit for drinking purposes, and it was suggested that water be made available only after thorough treatment. People’s habitual usage of Ganga water for various purposes raises the potential of human health hazards.
In this paper, the model predictive control technique is proposed to control the voltage balancing for the subway train 1500 V DC system for variable loads. This paper compares the conventional neutral point clamped converter (NPC) using the control technique of a PI controller with model predictive control in variable load conditions. MPC enhances the stability of the system during variable loads in comparison with the conventional technique. Consequently, the suggested control technique using MPC can maintain the DC bus output voltage dynamics at variable loads for the subway. Simulation results are provided to demonstrate the accuracy of the DC bus output voltage dynamics for the proposed control method.
Conventional steel bars are mostly used as the main load transfer mechanism between jointed slabs in rigid pavements; however, they are generally prone to corrosion which reduces the load transfer efficiency at the joints. This study evaluates the performance of steel bars wrapped with Carbon Fiber Reinforced Polymer (CFRP) sheets, introducing a corrosion-free alternative to conventional steel bars while maintaining the required strength. This paper explains the test setup of large-scale shear strength and load transfer efficiency tests that are currently conducted on the slab samples to evaluate the structural performance of the proposed dowel bars and concrete mix designs.
The primary objective of this study is to evaluate the adaptability and inclination of industrial sectors of Pakistan with respect to Industry 4.0. A questionnaire with nine questions was developed and disseminated to 20 sampled industries. To analyze the variability in responses, a one-way analysis of variance test was used. The statistical analysis revealed that there is an awareness of the basic concept behind Industry 4.0 in Pakistani industries, but there is a reluctance to adopt digitization and to shift from conventional production systems. This study will be helpful and will provide a guide for new and already existing enterprises for achieving Industry 4.0 requisite attributes precisely.
Krishna Ronanki, Beatriz Cabrero-Daniel, Jennifer Horkoff
et al.
[Context]: Companies are increasingly recognizing the importance of automating Requirements Engineering (RE) tasks due to their resource-intensive nature. The advent of GenAI has made these tasks more amenable to automation, thanks to its ability to understand and interpret context effectively. [Problem]: However, in the context of GenAI, prompt engineering is a critical factor for success. Despite this, we currently lack tools and methods to systematically assess and determine the most effective prompt patterns to employ for a particular RE task. [Method]: Two tasks related to requirements, specifically requirement classification and tracing, were automated using the GPT-3.5 turbo API. The performance evaluation involved assessing various prompts created using 5 prompt patterns and implemented programmatically to perform the selected RE tasks, focusing on metrics such as precision, recall, accuracy, and F-Score. [Results]: This paper evaluates the effectiveness of the 5 prompt patterns' ability to make GPT-3.5 turbo perform the selected RE tasks and offers recommendations on which prompt pattern to use for a specific RE task. Additionally, it also provides an evaluation framework as a reference for researchers and practitioners who want to evaluate different prompt patterns for different RE tasks.
Chenyang Yang, Rachel Brower-Sinning, Grace A. Lewis
et al.
In spite of machine learning's rapid growth, its engineering support is scattered in many forms, and tends to favor certain engineering stages, stakeholders, and evaluation preferences. We envision a capability-based framework, which uses fine-grained specifications for ML model behaviors to unite existing efforts towards better ML engineering. We use concrete scenarios (model design, debugging, and maintenance) to articulate capabilities' broad applications across various different dimensions, and their impact on building safer, more generalizable and more trustworthy models that reflect human needs. Through preliminary experiments, we show capabilities' potential for reflecting model generalizability, which can provide guidance for ML engineering process. We discuss challenges and opportunities for capabilities' integration into ML engineering.
Using models for requirements engineering (RE) is uncommon in systems engineering, despite the widespread use of model-based engineering in general. One reason for this lack of use is that formal models do not match well the trend to move towards agile developing methods. While there exists work that investigates challenges in the adoption of requirements modeling and agile methods in systems engineering, there is a lack of work studying successful approaches of using requirements modelling in agile systems engineering. To address this gap, we conducted a case study investigating the application of requirements models at Ericsson AB, a Swedish telecommunications company. We studied a department using requirements models to bridge agile development and plan-driven development aspects. We find that models are used to understand how requirements relate to each other, and to keep track with the product's evolution. To cope with the effort to maintain models over time, study participants suggest to rely on text-based notations that bring the models closer to developers and allow integration into existing software development workflows. This results in tool trade-offs, e.g., losing the possibility to control diagram layout.
During the conceptual and preliminary design stage of a ship, designers need to ensure that the selected principal dimensions and parameters are good enough to deliver a stable ship (statically and dynamically) besides deadweight and speed. To support this, the initial intact stability of the proposed ship is required to be calculated, and in doing so, the lightship weight and its detailed breakdown are necessary to be known. After hull steel weight, machinery weight, mainly, marine propulsion and power generation machinery, play a vital role in the lightship weight estimate of a ship due to its robustness. The correct estimation of respective weights improves the accuracy of calculating a ship's initial stability typically to be designed and built. Hence, it would be advantageous for the designer to convince the ship owner. A total of 3006 marine propulsion (main marine diesel) engines and 348 power generation (auxiliary marine diesel) engines/generators of various power output (generators output for auxiliary engines), engine RPM and cylinder number of different engine makers are collected. These are analyzed and presented in both tabular and graphical forms to demonstrate the possible relationship between marine propulsion engine weight and power generation engine weight, and their respective power output, RPM, cylinder number, power-RPM ratio and power-RPM ratio per cylinder.In this article, the authors have attempted to investigate the behavior of marine propulsion engine weight and power generation engine/generator weight regarding engine power output, generator power output, engine RPM and cylinder number (independent variables). Further attempts have been made to identify those independent variables that influence the weight of the marine propulsion engine and power generation engine/generator (dependent variables), and their interrelationships. A mathematical model has thus been developed and proposed, as a guiding tool, for the designer to estimate the weight of main and auxiliary engines more accurately during the conceptual and preliminary design stage.
Abdullah A. Talal, Wisam H. Khaleel, Yahyia M. Hameed
et al.
This paper represents a parametric study utilizing finite element analysis for twenty-five reinforced concrete semi-circular deep beams. The parameters that were taken into consideration in the current work are radius, height, width, concrete compressive strength and number of supports. It is found that decreasing radius of beam by 16-66% leads to decrease the midspan positive moment, support negative moment, torsional moment and midspan deflection by about 0.3-20%, 2.4-25%, 2-24% and 29-85%, respectively, while the load capacity increases by about 23-158%. The midspan positive moment, support negative moment, torsional moment and load capacity increase by about 20-682%, 20-81%, 20-81% and 21-84%, respectively, whereas midspan deflection decreases by 7-17% when the beam height increases by about 16-66%. The positive moment, negative moment, torsional moment and load capacity increases by about 43-197%, 40-185%, 29-187% and 46-214%, respectively, whereas deflection decreases by about 1.4-3.3% when the beam width increases by about 16-66%. The positive moment, negative moment, torsional moment and load capacity increases by about 10-84%, 9-77%, 9-79% and 11-92%, respectively, whereas deflection decreases by about 0.1-0.5% when the compressive strength increases by 20-220%. Finally, it is found that the positive moment increases by about 36-47% when number of supports increased by 33-66%, while the negative moment increases by about 16-31% when number of supports decreases by 14-29%, whereas the torsional moments and deflection decreases by about 6-55% and 37-84%, respectively when number of supports increases by 33-133%, while load capacity increases by 156-969% when number of support increases by 33-133%.
Conclusions
Most of the parameters influencing the behavior and strength of the semicircular reinforced concrete deep beams were investigated in the current study. The use of the ETABS 2016 software, which relies on the finite element method, was easy and practical. Accordingly, the following conclusions were reached:
• The maximum positive bending moment, maximum negative bending moment, maximum torsional moments and midspan deflection decrease by about 0.3-20%, 2.4-25%, 2-24% and 29-85%, respectively, when beam radius decreases by 16-66%. Whereas this radius decrease leads to increase the load capacity by about 23-158%. That can be attributed to span length shortening that caused be radius decrease.
• The maximum positive bending moment, maximum negative bending moment, torsional moment and load capacity increase by about 20-82%, 20-81%, 20-81% and 21-84% respectively, when the beam height decreases by 16-66%. While this height decrease leads to midspan deflection decrease by about 7-17%. The beam sectional area increases when increasing beam height, i.e., causes strength increase.
• The maximum positive bending moment, maximum negative bending moment, torsional moment and load capacity increase by about 200-1113%, 167-958%, 172-989% and 230-1368%, respectively when beam width increases by 50-200%. While this width increase leads to midspan deflection decrease by about 3-5%. The beam sectional area increases when increasing width, i.e., more strength.
• The maximum positive bending moment, maximum negative bending moment, torsional moment and load capacity increase by about 10-84%, 9-77%, 9-79% and 11-92%, respectively, when concrete compressive strength increase by 20-220%. While this concrete compressive strength increase leads to deflection decrease by about 0.1-0.5%. Increasing concrete compressive strength increases shear stress resistance.
• The maximum positive bending moment increases by about 36-47% when increasing number of supports by 33-66%. Maximum negative bending moment increases by about 16-31% when decreasing number of supports by 14-29%. Torsional moment and midspan deflection decrease by about 6-55% and 37-84%, respectively when number of supports increases by 33-133%. Whereas the load capacity increases by about 156-969%, when number of supports increases by 33-133%. The length of span decreases when increasing number of supports, resulting in higher strength capacity.
Engineering machinery, tools, and implements, Mechanics of engineering. Applied mechanics