Requirements Engineering Research Articles

Cooperative Optimization Design Method of Multitrajectory Profile in “Well Factory” for Oil and Gas Development

Summary A “well factory” refers to the arrangement of multiple or even a large number of similar wells on the same platform and the drilling and completion of these wells with standardized equipment and a similar operational approach. The well factory mode can effectively handle the challenges posed by complex terrains in oceans and mountainous areas and achieve economical and efficient development of oil and gas resources. Conventional studies mainly focus on the optimization of a single trajectory or the sequential optimization of multiple trajectories. However, there is a lack of cooperative optimization of multiple trajectories, making it difficult to obtain better design results for well trajectories of the well factory mode. Owing to the complicated interdependencies among the design variables of each trajectory (such as the kickoff point, target section length, well inclination, azimuth, and buildup rate), traditional methods struggle to directly address the cooperative optimization problem. This problem involves collaboration and interaction between different trajectories to achieve common objectives and enhance the overall optimization effectiveness. This study established a multitrajectory profile cooperative optimization design model with the objective of minimizing the overall drilling footage, trajectory complexity, trajectory similarity, and collision risk. Next, a cooperative coevolution (CC) approach was introduced and combined with a directed artificial bee colony (DABC) algorithm to develop a collaborative update strategy for multitrajectory optimization. Finally, the CC-DABC algorithm was tested, and a case study was conducted using the new design method. The results indicated that the new method could effectively handle multitrajectory profile cooperative optimization design for various well layouts, including single-layer, multilayer, and extremely small well-spacing scenarios. The CC-DABC algorithm significantly outperformed other combined algorithms in terms of optimization effectiveness, robustness, and overall search efficiency and exhibited excellent performance under small well spacing conditions. In the case study, the trajectories optimized with the new method had less drilling footage and smaller trajectory complexity than the original designs. Additionally, the anticollision risk and staggered arrangement of the kickoff points between adjacent wells effectively met the engineering requirements, demonstrating the application potential of this new method.

The influence of mechanical constraints on Li diffusion and stress in bilayer electrode of lithium-ion batteries

Lithium-ion batteries (LIBs) are widely used in portable electronic devices, electric vehicles, and other fields. With the rapid development of its application fields, there is an urgent need to further improve its energy density and safety. During the charging/discharging process of the LIBs, the diffusion of Li will cause local volumetric change in the electrode material. The degradation and damage of the electrode material structure caused by diffusion-induced deformation is a major obstacle to the development of LIBs. Generally speaking, the electrode materials in LIBs are always subject to specific external constraints, including both inevitable passive structural constraints within the battery and external active constraints that may be imposed by emerging technology application scenarios, which can also affect the mechanical properties of the electrode materials. Therefore, a deeper understanding of the diffusion-induced stress and Li concentration changes in the electrode material is an engineering requirement for developing new material design paradigms to enhance the overall performance of LIBs. In this work, a two-way diffusion-stress coupling model is used to discuss the effect of the four different levels of idealized deformation constraints on the Li concentration and stress in the bilayer plate electrode during the charging process through the numerical solution. From a mechanical perspective, the bilayer plate electrode structure has two degrees of freedom: lateral expansion and bending deformation. Weakened constraint conditions can partially or completely activate these stress release mechanisms, thereby reducing the overall stress level of the electrode structure and improving its mechanical stability. However, from an electrochemical perspective, the stress gradient generated by the forward bending deformation of the electrode structure can hinder the Li intercalation process. Enhanced constraints can partially or completely suppress the forward bending of the electrode, making the Li concentration in the active layer more uniform and thus improving the capacity utilization efficiency of the active layer. These results not only provide theoretical references for further understanding the chemical-mechanical response of the bilayer electrodes under more realistic or extreme service conditions, but also indicate from a design perspective that compromised external constraints are beneficial for balancing the structural durability and electrochemical performance of electrodes.

Experimental Analysis on Quality of Coarse and Fine Aggregate Produce from Crusher Plants of Syangja District of Nepal

Does quality address grade? No, quality standards do not demand the best quality. However, they establish the minimum requirements for engineering uses or conformance with the code provisions or standards, free from faults and defects. Quality control by detection, quality assurance by prevention, and total quality by improving the quality of materials using quality policy and quality planning are necessary for any crusher plant or entrepreneur to achieve customer and society satisfaction. Aggregate must meet specific standards or code provisions for optimum engineering use to get better quality and higher strength concrete. The aggregates' types and quality significantly influence the concrete's properties; hence, the aggregates used to produce concrete that meets the required specifications are a prime concern. The aggregates' quality is significantly related to the concrete's strength, workability, and durability. The physical and mechanical properties of coarse and fine aggregate were analyzed in this research. The test results found that the P1, P3, and P4 crusher plants comply with all physical and mechanical parameters of the IS 383:2016 code provision. In contrast, the crusher plant of P2 does not comply with IS 383:2016 standard on optimizing the coarse and fine aggregate proportion in all-inaggregate analysis. The Gradation of fine aggregates of all the crusher plants does not lie in the grading zone as classified by IS code 383:2016. The inert material satisfying code provisions and standards helps to gain strength, durability, and other concrete parameters during mixed design for concrete production.

Physical Properties and Marshall Stability of High-Density Polyethylene and Rubber Crumb Modified Bitumen for Road Application

Standard bitumen used in road construction may not meet engineering requirements due to it becoming brittle in cold weather and softening in hot weather. This study investigates the mechanical and physical properties of bitumen modified with high-density polyethylene (HDPE) and rubber crumb for road applications. Bitumen sourced from Ilaje, Ondo State, Nigeria, underwent characterisation tests including penetration, softening point, ductility, and fire point. Waste HDPE and rubber crumb, collected from dumpsites in Zaria, Kaduna State, Nigeria, were sorted, washed, dried, and milled to a size of 600 microns. The waste plastic was then blended with the bitumen using an optimised mix design obtained from the design of experiment. Characterisation tests (Marshall stability, penetration, softening point, ductility, and fire point) were performed on the resulting waste plastic-modified bitumen. The test results showed that the stability of the road asphalt increases as the proportion of HDPE and rubber crumb used in the bitumen modification increases. The control group (100% bitumen) exhibited the lowest Marshall stability (19.81 kN, 13.12 kN, and 17.82 kN for samples A, B, and C, respectively). The stability of sample A increased by 62.2%, the stability of sample B increased by 75.6%, and that of sample C increased by 56.6%. Based on the research findings, modifying bitumen with waste materials offers several advantages for road construction, which are reducing material costs as waste plastics and tires are readily available, waste utilisation reduces environmental impact, and modified bitumen enhances road durability.

REQUIREMENTS ENGINEERING CHALLENGES IN OPEN-SOURCE SOFTWARE DEVELOPMENT: A MULTIVOCAL LITERATURE REVIEW PROTOCOL WITH PRELIMINARY RESULTS

In the dynamic landscape of software development, open source software (OSS) has acquired traction due to its collaborative and transparent nature. In OSS, source code of the software is accessible to users under a copyright license. The license allows the users to examine, improve, alter and distribute the software either in modified or unmodified form. Richard Stallman, programmer at MIT, presented the idea of making source code freely accessible in the year 1983, from an ideological movement. Users must accept the conditions of a license before the use of OSS. Requirement engineering (RE) is the process of gathering requirements statements, along with the documentation and analysis for a project. Requirements that are insufficient, unclear, inconsistent, and incomplete, may adversely affect software development. In OSS, requirements emerge through online conversations, as well as discussions evolve continually in different online forums and emails. Requirements’ gathering is a complex process and its practitioners may face different challenges while practicing it. A Multivocal literature review (MLR) will be conducted to find out challenges, success factors, and practices/solutions for the identified challenges in RE process of OSS. This research will help academic researcher, OSS practitioners and managers to improve RE process, leading to more efficient, effective and successful software applications.

Alkali-Activated Permeable Concretes with Agro-Industrial Wastes for a Sustainable Built Environment.

This research presents a proposal for alkali-activated permeable concrete composites with the use of industrial by-products, including ground granulated blast-furnace slag (GGBS) and waste-foundry sand, as well as agro-desecrate product, i.e., sugarcane bagasse ash (SBA). GGBS and SBA served as binders, with crushed granite as coarse aggregate and waste-foundry sand as fine aggregate. The novelty of this proposal is in examining the influence of SBA, in combination with slag, on the fresh- and hardened-state properties of the proposed permeable concretes. Experiments were conducted to optimize the SBA percentage based on hydraulic conductivity and compressive and tensile strength after 28 days of air curing. The hardened density, compaction factor (workability), and saturated water absorption were also measured for all the mixes. Furthermore, the control and optimal mixes were subjected to evaluate the microstructure analysis (EDX, XRD, and FESEM) after 28 days of air curing. The mix containing 100% GGBS and 0% SBA served as the reference, with the optimal 10% SBA mix (with 90% GGBS) used for comparative analysis to understand its effect on the properties of permeable composites. The results showed positive or acceptable mechanical performance at a mix ratio of 10% SBA to 90% GGBS as binders. This study aims to enhance the understanding of the engineering behavior of alkali-activated permeable composites, facilitating the rational design of permeable pavement systems through the effective use of agro-industrial waste products, thereby conserving ecosystems while meeting engineering requirements.

Selection of the Process of Arc Welding of Sealing Surfaces of Power Valves with a Consumable Electrode in the Shielding Gas

Introduction. One of the main requirements to the methods of weld overlay of sealing surfaces of power valve trim parts is to obtain a high-quality wear-resistant pad with minimal penetration and optimal process performance. Currently, arc, electroslag, plasma, beam, induction and other surfacing techniques have been developed and introduced into production. However, the influence of various arc welding processes with a consumable electrode in shielding gas on the geometric parameters of weld beads and metal hardness of sealing surfaces is understudied. The presented research is intended to fill this gap. The objective of its authors is to select such a process of arc welding of beads on parts of the power valve trim with a consumable electrode in shielding gases, which would provide the best workability of the deposited metal.Materials and Methods. Arc surfacing with a consumable electrode in a mixture of gases was performed on steel plates. The welding torch was moved in a straight line, without transverse oscillations, using the FRC-9 mechanism (Fronius). A microprocessor-controlled inverter-type digital current source TransPulsSynergic 3200 CMT (Fronius) was used as the power supply. The following welding processes were analyzed: MIG/MAG process with self-regulation (Standard mode), synergic process of MIG/MAG method (Synergic mode), short arc process with mechanical separation of electrode metal droplets (CMT-ColdMetalTransfer), and synergic pulse-arc process (PulseSynergic). The short-cut process of bead surfacing was evaluated by the stability of the values of the energy parameters of the bead surfacing mode in time at the same electrode wire feed rates, which were recorded by oscilloscopes, as well as by comparing the geometric characteristics of the deposited beads and the hardness of the deposited metal.Results. The analysis of experimental data of the geometrics of the weld beads and their complex dimensional characteristics made it possible to establish that the welding engineering requirements for the welded beads are most fully met by long-arc surfacing by the PulseSynergic pulse-arc process.Discussion and Conclusion. The study and the resulting data make a certain contribution to solving the problem of the influence of arc welding processes on the parameters of weld beads and on the hardness of the metal of sealing surfaces. A detailed analysis of the modes of arc surfacing of beads with a consumable electrode in shielding gases on the trim parts of power valves can be used in further research on this topic. The conclusions of the authors will not only provide considerable theoretical assistance to scientists, but will also make adjustments to the activities of practitioners.

Study and Simulation of Switched Reluctance Motor Drive Circuit for EV Application

In recent years, an increase in emissions and a decrease in fossil fuels cause environmental pollution and its main air pollution, so we must restore the emissions of vehicles, and this can be achieved with the help of electric propulsion. An electric vehicle is a vehicle powered by an electric motor and powered by a rechargeable battery that can be charged with electricity. It also promotes the use of renewable energy. A rare earth magnet is a primary requirement for most engines. China is the supplier of more than 90% of rare earth magnets in the world and its price is high due to the scarcity of this magnet. So, we need to reduce the use of magnets and this can be achieved by using SRM motors in electric cars due to their reliable structure, high speed, fault tolerance and magnet less design. The main disadvantages of SRM are torque ripple, low power density, low power factor and small extended speed range. To solve this problem, we use an asymmetric bridge converter topology for a 0.5 kW, 2000 rpm, 8/6 SRM drive. Thus, the analysis and problem of Switched Reluctance Motor and the use of power transformers to minimize torque ripple were identified using Ansys Maxwell software (RMxprt tool).

A Systematic Literature Review of Multi-Label Learning in Software Engineering

In this paper, we provide the first systematic literature review of the intersection of two research areas, Multi-Label Learning (MLL) and Software Engineering (SE). We refer to this intersection as MLL4SE. In recent years, MLL problems have increased in many applications and research areas because real-world datasets often have a multi-label nature. For multi-label data, simplifying the assumption of traditional classification approaches that an instance can only be associated with one class only leads to worse accuracy. Thus, a better match of methods and assumptions about the data is required. We identified 50 primary studies in our systematic literature review in the MLL4SE domain. Based on this review, we identified six main SE application domains where MLL has been applied. These domains include Software Requirement Engineering, Issue Tracking and Management, Community and Knowledge Management, API Usage and Management, Code Quality and Maintenance, and Mobile Application Development . We summarized the methods used and the data nature of the MLL4SE applications. Moreover, we separately provide taxonomies of future work directions from machine learning and software engineering perspectives. In general, we highlight current trends, research gaps, and shortcomings.

SSMBERT: A Space Science Mission Requirement Classification Method Based on BERT

Model-Based Systems Engineering (MBSE) has demonstrated importance in the aerospace field. However, the MBSE modeling process is often tedious and heavily reliant on specialized knowledge and experience; thus, a new modeling method is urgently required to enhance modeling efficiency. This article focuses on the MBSE modeling in space science mission phase 0, during which the mission requirements are collected, and the corresponding dataset is constructed. The dataset is utilized to fine-tune the BERT pre-training model for the classification of requirements pertaining to space science missions. This process supports the subsequent automated creation of the MBSE requirement model, which aims to facilitate scientific objective analysis and enhances the overall efficiency of the space science mission design process. Based on the characteristics of space science missions, this paper categorized the requirements into four categories: scientific objectives, performance, payload, and engineering requirements, and constructed a requirements dataset for space science missions. Then, utilizing this dataset, the BERT model is fine-tuned to obtain a space science mission requirements classification model (SSMBERT). Finally, SSMBERT is compared with other models, including TextCNN, TextRNN, and GPT-2, in the context of the space science mission requirement classification task. The results indicate that SSMBERT performs effectively under Few-Shot conditions, achieving a precision of 95%, which is at least 10% higher than other models, demonstrating superior performance and generalization capabilities.

Thermal Hazard Evaluation and Prediction in Deep Excavations for Sustainable Underground Mining

With the advent of the deep mining era, thermal damage in mines has become increasingly significant. The high-temperature environment in underground mines adversely impacts the physiological and psychological health of operators, reduces work efficiency, elevates the risk of accidents, and disrupts sustainable mining operations. Consequently, the development of accurate and reliable methods for classifying thermal hazards is essential for enabling mining enterprises to implement effective prevention strategies. Furthermore, such methods provide a theoretical basis for the sustainable management and utilization of geothermal energy. This study systematically considered factors influencing underground thermal damage and selected 10 quantitative indicators, encompassing both natural and human factors, as evaluation criteria. The CRITIC method was employed to determine the weight of each indicator, which was then integrated with uncertainty measurement theory to develop a novel thermal hazard assessment framework (CRITICUM). This framework enables the classification of thermal hazards in deep mine roadways. The evaluation results generated by the CRITICUM system were subsequently used to train machine learning predictive models. During the training process, the particle swarm optimization algorithm (PSO) was utilized to identify the most suitable prediction model parameters for the complex thermal environment of deep mines by leveraging its capability for continuous iterative evolution. The optimized parameters replaced the original random forest (RF) model parameters, resulting in an enhanced thermal damage prediction model (PSO-RF) with an accuracy of 96.55%, outperforming the standard RF model by 3%. Finally, the prediction model’s accuracy was validated using engineering case data, demonstrating that the results met practical engineering requirements. In summary, the proposed CRITICUM-PSO-RF evaluation and prediction model can accurately classify thermal damage in deep mines and provide a valuable reference for ensuring site safety and supporting the sustainable utilization of geothermal energy.

A Study on the Teaching Reform of GIS Courses in Higher Vocational Engineering Surveying Based on Work Process Orientation

To cultivate students' professional skills, enhance their practical ability, exploratory skills, and spirit of cooperation, this paper, through extensive research, adopts a work process-oriented curriculum design method to analyze the GIS course for the higher vocational engineering surveying major. The course content is reconstructed to align with the job requirements of data processing engineers, and typical work tasks are determined based on the actual work process.

A Comparative Performance Evaluation of Mainstream Multiphase Models for Aerated Flow on Stepped Spillways

A systematic comparative evaluation of mainstream multiphase models for specific hydraulic structures is essential to investigate aerated flow characteristics and promote the models’ application. However, such evaluations remain scarce. In this paper, four multiphase models, namely the VOF, Mixture, and Eulerian models in Ansys Fluent and the aerated flow model in FLOW-3D (AFM-F3D), were comparatively introduced and tested for the aerated flow over stepped spillways. Simulation results, including water surface profiles, inception points of aeration, air concentrations, velocities, and turbulent kinetic energies from four models, are compared with each other and with available experimental data. It is discovered that both the VOF and Mixture models fail to reproduce the self-aeration and subsequent downward transport phenomenon. In contrast, AFM-F3D and the Eulerian models predict reliable aerated water surface profiles. AFM-F3D and the Eulerian model demonstrate superior performance in velocity and air concentration calculations, respectively. Based on overall performance, the Eulerian model is recommended for simulating aerated stepped spillway flows. These insights provide valuable guidance for selecting appropriate multiphase models based on specific engineering requirements.

End to End Leaf Detection Using Convolutional Neural Network

Global food security is seriously threatened by crop diseases, yet in many places, it is still difficult to identify them quickly because of a lack of infrastructure. In tackling this problem, recent developments in leaf-based image categorization methods have produced encouraging outcomes. This study investigates how to differentiate between healthy and unhealthy leaves using curated datasets using the Random Forest algorithm. The development of the dataset, feature extraction, classifier training, and classification are the various stages of the implementation process. To efficiently distinguish between diseased and healthy samples, the Random Forest classifier is used to train the dataset, which includes pictures of both diseased and healthy leaves. A significant obstacle to agricultural production, plant diseases have an impact on crop yield and quality. Precision farming and sustainable agriculture depend on the timely and precise identification of leaf diseases. In this work, we present a complete system that automatically detects and classifies leaf diseases using Convolutional Neural Networks (CNN), starting with picture capture. The technology offers a quicker method of identifying leaf illnesses and does away with the requirement for manual feature engineering. Our tests, carried out on several public datasets, show the model's efficacy and achieve high accuracy for a range of illness categories. These results underline the practical uses that might improve crop management and early intervention techniques. Using machine learning on massive datasets that are accessible to the public, this approach offers a scalable solution for detecting plant diseases on a large scale.

Exploring Student Perspectives on Generative AI in Requirements Engineering Education

The rapid development of generative AI (GenAI) technologies in recent years has enabled new opportunities as well as new challenges in higher education. While many studies in computer science have focused on GenAI in programming education, fewer have examined its possibilities and challenges in requirements engineering (RE). This study aims to explore the impact of GenAI on the pedagogical aspects of RE in higher education, focusing on the student perspective, to analyse how GenAI might influence learning experiences, knowledge acquisition, and skill development. The main research question to answer was: "What are the students’ perspectives of the integration of GenAI in the educational practices of requirements engineering?" An Action research strategy was employed, with one of the authors also serving as teacher in the investigated course. A mixed-methods approach was used to collect both qualitative and quantitative data from workshops and surveys. During the workshops, students used ChatGPT to generate and evaluate software requirements and compared these to manually crafted requirements. Thematic analysis of the qualitative data captured students’ perspectives, while survey data identified trends and preferences. Findings show that while students generally had a positive experience with GenAI, valuing its efficiency and the quality of generated requirements, they also recognized the need for human oversight to maintain accuracy. The study highlights both opportunities and challenges of using GenAI in RE education. While GenAI increased learning engagement and helped with brainstorming, students faced difficulties in creating effective prompts and found it time-consuming to refine AI-generated requirements. A hybrid approach, combining AI-generated and manually created requirements, proved most effective by balancing AI's advantages with human insights. Further research is needed on how GenAI could be effectively integrated into computer science education.

Problems and tasks for the development of the Russian standards and requirements for engineering and design hydrographic surveying

The current state and issues of standardization of hydrographic surveys performed for engineering purposes on inland water areas, coastal zone of the seas and continental shelf examined. There is a variety of existing regulatory documents based on outdated norms and rules of traditional analog hydrography issued more than forty years ago. There are a number of regulatory documents governing the survey of the bottom relief in order to ensure hydraulic construction, dredging, construction of structures on the continental shelf, as well as special surveys on the downstream of hydroelectric power plants that do not meet modern requirements. These documents have been updated several times in recent years, however, their content has not fundamentally changed and still remained inconsistent with modern technological capabilities of international hydrography. In the 90s of the 20th century, there was a technological breakthrough in the development of means and methods of hydrographic research, which was first reflected in the fourth edition of the S‑44 Standards of the International Hydrographic Organization. The fourth edition of the S‑44 Standards marked the beginning of a new era of modern digital and electronic hydrography, based on the concept of “survey orders,” due to the new capabilities of global navigation satellite systems, multibeam echo sounders and electronic hydrographic information systems. The new paradigms of digital hydrography, first introduced in the S‑44 Standards, Edition 4 (1998), were developed in the 5th (2008) and 6th (2022) editions. The paper analyzed the main norms of the current regulatory documents of engineering hydrography adopted at the beginning of the 21st century: RD31.74.04–2002, SNiP 11–02–96, SP 11–104–97, SP 11–114–2004, as well as similar documents of the last decade: SP 317.1325800.2017, SP 47.13330.2016 and SP 504.1325800.2021. It is shown that all these documents are based on the conceptual provisions of analog hydrography contained in the Rules of the Hydrographic Service № 4, part 2, published by the Main Directorate of Navigation and Oceanography of the Ministry of Defense in 1984. Particular attention is paid to the criteria for assessing the survey quality (precision, accuracy and reliability) of the results of bottom topography. In particular, the assessment of the precision of the horizontal and vertical position in analog hydrography according to the Rules of the Hydrographic Service № 4, part 2, is carried out with a confidence probability of 63–68 %, while the S‑44 Standards use a confidence probability of 95 %. As a result, the permissible uncertainty of the corrected depth according to the S‑44 Standards is twice as high as according to the domestic rules of the hydrographic service and some current regulatory documents of engineering hydrography. It is noted that some of the current regulatory documents of engineering hydrography do not take into account the new capabilities of hydrographic hydroacoustic equipment, including multibeam echo sounders, bathymetric side scan sonars and lowfrequency echo sounders for studying the bottom relief and the upper part of the bottom section for engineering and other scientific purposes. It is indicated that the new paradigm of the S‑44 Standards (2022) is the introduction of the concepts of “bathymetric coverage,” “object detection” and “object search” which led to the possibility of implementing a new tool the “specification matrix” for design and formation of brief hydrographic survey characteristics, also appropriate for the engineering surveys. It is noted that the new Russian specifications for the performance of both traditional and engineering hydrographic surveys, based on the Standards of the S‑44 International Hydrographic Organization, are absent, and their development, coordination and approval is urgently needed.

A novel approach to elicit distributed requirements for IOT system using SVM classifier

Internet of Things (IoT) is one of the growing technologies embedded in most application systems. It aims to solve real-world problems in different environmental fields such as industry, education, healthcare etc. IoT is becoming an integral part of daily devices and technologies opening a need for efficient and novel solutions to meet functional requirements that are more complex than those in traditional requirement engineering (RE). In addition, remotely smart systems present new challenges and lack in RE process that needs a solution. IoT systems open new research issues in RE such as elicitation, analysis, specification and management of IoT RE. To solve this lack of RE new smart techniques based on AI must be applied in the elicitation RE process. This paper presents a new smart dynamic approach in the RE elicitation phase to build dynamic functional requirements based on AI models. New stakeholder expectation needs from the smart IOT system are collected and stored in the requirements dataset. These new requirements are analyzed and classified into requirement features using the Support Vector Machine classifier. These classified functional requirements are compared to the IoT system, and the positive training requirements are added to the smart functional requirement presented in the IoT system. The proposed approach shows a significant accuracy of 95.64%, where 395 features were classified and detected from 413 entered features. This paper measures the gap between stakeholder expectations and device requirements in smart systems; these proposed measures can be implemented to optimize smart device specifications for manufacturers.

A Social-Network-Based Crowd Selection Approach for Crowdsourcing Mobile Apps Requirements Engineering Tasks

Mobile apps have revolutionized almost every aspect of our daily lives, shaping the way we shop, learn and work. The transformative and unprecedented impact they have made on our lifestyle and the convenience they have offered have increased their adoption in diverse domains. Therefore, it is of paramount importance to hear from the interested audience about their desires and requirements in mobile apps. This has stressed the need to employ crowdsourcing in requirements engineering (RE) activities to harness the scattered talent in the crowd. RE tasks require certain software domain knowledge, hence, selecting a suitable subset of the crowd is crucial to obtain high-quality contributions. For that, we propose a crowd selection approach for crowdsourcing mobile app requirements engineering tasks which leverages the untapped crowd available on the social network Twitter (recently changed to X). This article is an extension of our previous work, where we present the proposed social-network-based crowd selection approach design, continue to work on the remaining component of the approach and evaluate the approach through a controlled experiment. For evaluation, the approach was utilized to select a real crowd that were invited to contribute to crowdsourcing requirements elicitation tasks for a fitness mobile app. The quality of the crowdsourced requirements was assessed by experts and the results have provided encouraging and compelling insights about the effectiveness of the proposed approach. The obtained assessment scores for the five quality factors clarity, creativity, relatedness, feasibility and diversity were respectively 4.36, 4.01, 4.29, 4.45 and 4.43 out of 5. Overall, we believe that the proposed social-network-based crowd selection approach could help in eliciting mobile app requirements and features that could cater to the needs of a large audience.

Three-Dimensional Pulsating Flow Simulation in a Multi-Point Gas Admission Valve for Large-Bore CNG Engines

This study examines the dynamic fluid behavior of a PWM-controlled Solenoid-Operated Gas Admission Valve (SOGAV) for large-bore CNG engines using 3D Computational Fluid Dynamics (CFD) simulations with dynamic mesh techniques. The research focuses on the influence of orifice geometry variations in the multi-hole restrictor and pressure differentials between the inlet and outlet on flow stability, turbulence, and valve performance. Results demonstrate that multi-hole restrictors with different-sized orifices improve flow uniformity and reduce turbulence, thereby mitigating flow resistance. Transient simulations further reveal standing wave formation and pressure wave interference, emphasizing that steady-state models cannot capture critical transient phenomena, such as accelerated and decelerated jet-like flows and flow separation. These findings provide key insights into SOGAV optimization, contributing to enhanced fuel efficiency and engine responsiveness, meeting the performance requirements of modern gas engines.

Enhancing the Geotechnical Properties of Expansive Soils through Coconut Shell Ash Treatment: An Experimental Investigation

Expansive Soils (ES) present a significant challenge to civil engineering projects worldwide due to their propensity to undergo volumetric changes in response to fluctuations in the moisture content. This study examined the potential of Coconut Shell Ash (CSA) as a soil stabilizer to mitigate the adverse effects of ES. The objective was to conduct a systematic evaluation of the impact of CSA on a range of soil properties, including the plasticity index, compressive strength, shear strength, swelling potential, and compaction characteristics, across a diverse array of soil types. This study adopted a comprehensive methodology, which involved the laboratory testing of soil samples with varying proportions of CSA. The tests included the determination of the Atterberg limits, the evaluation of the compaction properties, unconfined compression tests, and swelling tests. The findings revealed significant variations in the soil properties in response to the CSA content. The plasticity index responses exhibited a range of subtle changes, with a downward trend at lower CSA concentrations and more complex behaviors at higher concentrations. The compaction characteristics exhibited alterations in the optimum moisture content and maximum dry unit weight, indicating changes in soil density and stability. Similarly, the compressive and shear strength properties exhibited fluctuations with varying CSA content, underscoring the necessity for a comprehensive assessment of soil stability under CSA-treated conditions. Additionally, swelling tests demonstrated the potential of CSA to mitigate soil expansiveness, with lower swelling percentages observed in treated soils. This study highlights the importance of considering the soil type, CSA content, and engineering requirements to optimize the effectiveness of CSA in soil stabilization applications.