Engineering Domain Research Articles

Exploring the presence of Cymraeg on TikTok

Many of the world’s languages are endangered or vulnerable. For some of these languages, a presence in technological domains can illustrate vitality and demonstrate relevance to the lives of younger speakers. A presence on social media is often seen as particularly significant for younger speakers due to their high levels of social media use. This article explores the presence of Cymraeg (the Welsh language) on TikTok. Through the analysis of a corpus of 200 videos, the article reveals a complex and richly bilingual content space with considerable intermingling of Cymraeg and English. Commenting on videos and engaging in a text-based conversation with the content creator and other audience members is shown to be a significant activity. The corpus demonstrates that Cymraeg is being used, taught, discussed and promoted on TikTok. This article contributes to an ongoing assessment of the digital vitality of Cymraeg and extends our understanding of the role that social media can play in supporting the sustainability and vitality of minority languages.

Innovative encapsulation of alkali activators in alkali-activated slag concrete: A sustainable strategy for regulating setting time and durability

The scientific community has devoted considerable attention to alkali-activated slag binder systems, due to the low-carbon footprint and eco-friendly attributes. However, the regulation of setting time poses significant challenges to the widespread application in practical engineering environments. This study proposes an innovative approach to encapsulate alkali activators in capsules or tablets, followed by the incorporation into the alkali slag concrete matrix. This strategy aims to regulate and control the early dissolution and hydration rate of slag, thereby altering the setting time of the alkali-activated material. Specifically, this method can prolong the setting time by approximately 40%. Importantly, this intervention does not significantly damage the mechanical properties of materials over the 28 days due to the continuous outward diffusion of the active ingredients of the alkali activator. This encapsulation technique harbors the capacity for quantitative modulation of the setting time in alkali-activated slag concrete. The findings of this research offer valuable theoretical insights, paving the way for the application of alkali-activated hydration systems in diverse engineering domains.

Experimental investigation of hydraulic jump characteristics in sloping rough surfaces for sustainable development

Understanding the intricate dynamics of hydraulic jumps in sloped channels holds pivotal importance in various engineering applications. This research explores the intricate relationship between the size of the bed material and the basic properties of hydraulic jumps, providing insight into the relative jump length, height, and energy efficiency. The goal of the research is to get important knowledge that will be useful for optimizing hydraulic systems and enhancing their overall efficiency in diverse engineering domains. This study used an open-channel flow arrangement with four-bed slopes (0° to 6°) and three irregularity heights (10 to 30 mm). During the investigation, the Froude number differed from 2.30 to 8.85 and the Reynolds number differed from 5450 to 25500. A novel instinctive technique was used to create correlations for different hydraulic jump characteristics in roughen-bed inclined channels. The study examines the combined effects of roughness and slope of the bed, and it was discovered that the relative jump height and efficiency of hydraulic jump increase by 19.37% and 8.44% respectively while the relative jump length decreases by 23.05% with an increase in bed slope from 0° to 6°. The relative jump height and efficiency of the hydraulic jump increase by 14.20% and 21.06% respectively while the relative jump length decreases by 29.09% with a rise in bed roughness from 0 to 30 mm.

Optimizing Fund Performance: A Communication Systems Approach to Data Mining in Large- Cap Equity Mutual Funds

Mutual funds represent a secure and technologically driven conduit for Indian investors to engage with the dynamic Indian financial markets. These professionally managed funds strategically allocate investments across a spectrum of securities, including equities, bonds, government securities, and gold, leveraging cutting-edge information technology infrastructure for seamless operation and optimization. Within this landscape, performance emerges as a pivotal metric, driving the flow of savings into these digitally enabled funds, indicative of investors' confidence and expectations in the realm of data-driven decision-making. The core objective of this paper is to construct a robust mutual fund performance evaluation model utilizing the Statistical Package for the Social Sciences (SPSS), a sophisticated software platform renowned for its advanced analytics and predictive modeling capabilities in the information technology domain. The work focused specifically on equity mutual funds, this study meticulously examines top-rated funds within the large-cap category, drawing insights from a comprehensive validation period spanning the last fifteen years from April 2007 to March 2021. Through the systematic evaluation of various performance metrics within the context of information technology-driven analysis, this model aims to deliver actionable insights and strategic foresight, benefiting researchers, society, and financial analysts alike, while catalyzing innovation and driving positive impacts in the intersection of technology and finance.

Characteristic Mode Solution of Complex-Coefficient Complex-Solution Differential Equations

Computation of complex-coefficient complex-solution differential equations is a problem that arises in various domains of science and engineering. This paper aims at applying the Theory of Characteristic Modes (TCM) approach along with the Method of Moments (MoM) in solving these problems with emphasis on procedures for higher differential equations. Several available methods, known in literatures, are available for solving the problem. The complexity of the available methods differs based on the accuracy of the solution. In this paper, the general method is first presented and then a simplified version of it is proposed to solve high order differential equations. Two examples are illustrated, a third and a fourth order complex-coefficients complex-solution differential equations, to show the simplicity of the proposed method. The proposed approach can be also introduced along with other methods to solve these special occurrences differential equations and other boundary value problems.

Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling

Forecasting citations of scientific patents and publications is a crucial task for understanding the evolution and development of technological domains and for foresight into emerging technologies. By construing citations as a time series, the task can be cast into the domain of temporal point processes. Most existing work on forecasting with temporal point processes, both conventional and neural network-based, only performs single-step forecasting. In citation forecasting, however, the more salient goal is n -step forecasting: predicting the arrival of the next n citations. In this article, we propose Dynamic Multi-Context Attention Networks (DMA-Nets), a novel deep learning sequence-to-sequence (Seq2Seq) model with a novel hierarchical dynamic attention mechanism for long-term citation forecasting. Extensive experiments on two real-world datasets demonstrate that the proposed model learns better representations of conditional dependencies over historical sequences compared to state-of-the-art counterparts and thus achieves significant performance for citation predictions.

Differential evolution with adaptive niching and reinitialisation for nonlinear equation systems

ABSTRACT Nonlinear equation systems (NESs) are ubiquitous in scientific and engineering domains, necessitating efficient methods for discovering multiple roots simultaneously. Employing evolutionary algorithms to solve NESs has received significant attention. To simultaneously locate multiple roots in a single run, this paper proposes a differential evolution algorithm with adaptive niching and reinitialisation (DE-ANR). The proposed differential evolution algorithm integrates four strategies to address the challenges posed by NESs. Firstly, DE-ANR employs a dynamic niche size control mechanism to adjust the niche size during evolution, striking a balance between exploration and exploitation. Secondly, a lifetime mechanism is introduced to identify stagnating individuals for reinitialisation, ensuring diversity of the population and preventing premature convergence. Thirdly, an improved archive technique is employed to conserve computational resources by eliminating redundant solutions, directing the algorithm’s efforts toward unexplored regions of the solution space. Finally, an adaptive strategy is introduced to dynamically adjust the scaling factor F based on the feedback of evolving population, simplifying parameter tuning. Extensive evaluations on 30 diverse NES problems and the comparison results with 12 state-of-the-art algorithms clearly show the superiority of proposed method.

Centrifugal Pump Fault Detection with Convolutional Neural Network Transfer Learning.

The centrifugal pump is the workhorse of many industrial and domestic applications, such as water supply, wastewater treatment and heating. While modern pumps are reliable, their unexpected failures may jeopardise safety or lead to significant financial losses. Consequently, there is a strong demand for early fault diagnosis, detection and predictive monitoring systems. Most prior work on machine learning-based centrifugal pump fault detection is based on either synthetic data, simulations or data from test rigs in controlled laboratory conditions. In this research, we attempted to detect centrifugal pump faults using data collected from real operational pumps deployed in various places in collaboration with a specialist pump engineering company. The detection was done by the binary classification of visual features of DQ/Concordia patterns with residual networks. Besides using a real dataset, this study employed transfer learning from the image detection domain to systematically solve a real-life problem in the engineering domain. By feeding DQ image data into a popular and high-performance residual network (e.g., ResNet-34), the proposed approach achieved up to 85.51% classification accuracy.

Fintech: Digital Transformation of Financial Services and Financial Regulation

This study offers a comprehensive analysis of critical aspects within the domains of financial technology (Fintech) and online payments, with a focal point on the regulatory dimension. It commences by exploring how Fintech, propelled by technological innovations, has orchestrated a paradigm shift, ushering in the digital economy and digital civilization. It underscores Fintech's role in mitigating longstanding issues in traditional finance, particularly information asymmetry and high-risk premiums, harnessing the potential of big data, cloud computing, blockchain, and artificial intelligence. The study extends to a detailed evaluation of the intelligent investment advisory sector (Robo-Advisor) and online payments, delving into security concerns and regulatory considerations. A pivotal highlight is the transformative potential of digital currencies, including central bank digital currencies (CBDCs) and cryptocurrencies, in revamping payment systems, fortifying security, and reducing cross-border transaction costs. Regulatory upgrades play a paramount role in shaping this transformation, ensuring the security of online payments and fostering compliance.

Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility

Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.

The Industrial Internet of Things (IIoT) and its roles in the Fourth Industrial Revolution: A review

The Industrial Internet of Things and Industry 4.0 are now two highly sought-after areas of research and development, attracting significant interest from both academic and industrial sectors. The two ideas, Industry 4.0 and IIoT, share significant similarities, with Industry 4.0 being seen as the use of IIoT specifically in the automation and manufacturing sectors. Within the framework of the present Industry 4.0 paradigm, many growth pathways have emerged, collectively leading to notable enhancements in terms of efficiency, flexibility, communication, adaptability, customization, and modularity in the industrial sector. The Industry 4.0 is rapidly evolving within the framework of the Industrial Internet of Things (IIoT), and the authors are recognizing the necessity for a comprehensive and in-depth overview of the many research areas that are currently expanding. The area will remain intriguing in the foreseeable future due to its significant potential for enhancing the existing industrial technologies. An exhaustive evaluation of the current systems in the automotive sector, emergency response, and chain management on IIoT has been conducted, revealing that IIoT has been widely adopted across several technological domains. Industry 4.0 is the term used to describe the present automation and data sharing trend in businesses. Presently, there is a dearth of agreement about the assessment of an organization's readiness for Industry 4.0. Industry 4.0 encompasses a diverse array of digital technologies that profoundly influence industrial enterprises. The literature on Industry 4.0 has had significant exponential growth during the previous decade. The results of our research confirm the idea of Industry 4.0 as a concept that goes beyond the Smart Manufacturing sector, hence opening up possibilities for collaboration with other interconnected disciplines.

Formation of Singularity for Isentropic Irrotational Compressible Euler Equations

The domain of science and engineering relies heavily on an in-depth comprehension of fluid dynamics, given the prevalence of fluids such as water, air, and interstellar gas in the universe. Euler equations form the basis for the study of fluid motion. This paper is concerned with the Cauchy problem of isentropic compressible Euler equations away from the vacuum. We use the integration method with the general test function f=f(r), proving that there exist the corresponding blowup results of C1 irrotational solutions for Euler equations and Euler equations with time-dependent damping in Rn (n≥2), provided the density-independent initial functional is sufficiently large. We also provide two simple and explicit test functions f(r)=r and f(r)=1+r, to demonstrate the blowup phenomenon in the one-dimensional case. In particular, our results are applicable to the non-radial system.

ScrumSpiral: An Improved Hybrid Software Development Model

In the lightning-quick world of software development, it is essential to find the most effective and efficient development methodology. This thesis represents "Scrum Spiral" which is an improved hybrid software development model that combines the features of Scrum and Spiral approach to enhance the software development process. This thesis aims to identify the usefulness of "ScrumSpiral" methodology and compare it with other hybrid software development models to encourage its use in software development projects. To develop this hybrid model, we did extensive research on the software engineering domain and decided to create a hybrid model by using Scrum and Spiral, named "Scrum Spiral" which is suitable for complicated projects and also for those projects whose requirements are not fixed. Traditional software development models face numerous challenges in rapidly changing markets. By developing this kind of hybrid model, we want to overcome these kinds of limitations and present the software development community with a novel concept for better project results. Final outcome of this thesis was that we developed a model that should be able to complete the project according to the expected schedule, satisfy customer requirements, and obtain productivity through team coordination. The significance of the hybrid model "Scrum Spiral" is reflected in its ability to offer flexibility towards various size projects, proactive risk management to identify all risks before developing the system, and result in higher-quality outcomes for those projects whose requirements are not properly described initially in the project.

Forecasting Future Research Trends in the Construction Engineering and Management Domain Using Machine Learning and Social Network Analysis

Construction Engineering and Management (CEM) is a broad domain with publications covering interrelated subdisciplines and considered a key source of knowledge sharing. Previous studies used scientometric methods to assess the current impact of CEM publications; however, there is a need to predict future citations of CEM publications to identify the expected high-impact trends in the future and guide new research efforts. To tackle this gap in the literature, the authors conducted a study using Machine Learning (ML) algorithms and Social Network Analysis (SNA) to predict CEM-related citation metrics. Using a dataset of 93,868 publications, the authors trained and tested two machine learning classification algorithms: Random Forest and XGBoost. Validation of the RF and XGBoost resulted in a balanced accuracy of 79.1% and 79.5%, respectively. Accordingly, XGBoost was selected. Testing of the XGBoost model revealed a balanced accuracy of 80.71%. Using SNA, it was found that while the top CEM subdisciplines in terms of the number of predicted impactful papers are “Project planning and design”, “Organizational issues”, and “Information technologies, robotics, and automation”; the lowest was “Legal and contractual issues”. This paper contributes to the body of knowledge by studying the citation level, strength, and interconnectivity between CEM subdisciplines as well as identifying areas more likely to result in highly cited publications.

Extreme fine-tuning and explainable AI model for non-destructive prediction of concrete compressive strength, the case of ConcreteXAI dataset

This groundbreaking study introduces a novel approach employing Extreme Fine-Tuning (XFT) combined with Explainable Artificial Intelligence (XAI) for the accurate, non destructive prediction of concrete compressive strength. By analyzing a state-of-the-art dataset containing 18,480 data points, this study developed a deep neural network that, through extensive hyperparameter optimization, achieves unprecedented prediction accuracy of approximately 98.7%. The novelty of this research lies in the sophisticated integration of XFT and XAI techniques, which not only significantly enhances prediction accuracy but also provides insightful explanations of the model’s decision-making process, shedding light on the factors influencing concrete strength. This dual focus on accuracy and explainability represents a significant advancement in the application of Artificial Intelligence (AI) in material science and civil engineering, offering a powerful tool for researchers and practitioners.This study culminates in a model that outperforms existing methodologies in predicting concrete compressive strength, with an accuracy superior to 98.5% in both instances, testing and validation. By integrating XAI into this approach, we have also opened new avenues for understanding the complex relationships between concrete composition and its mechanical properties. This study marks a substantial step forward in the non-destructive evaluation of construction materials. It sets a new benchmark for transparency and interpretability in AI models within the engineering domain.

A review of the frequency-amplitude formula for nonlinear oscillators and its advancements

The frequency-amplitude relationship is pivotal in understanding oscillatory systems, dictating their dynamic behaviors and responses. This paper delves into the intricacies of the frequency amplitude formula, elucidating its foundational role in both linear and nonlinear oscillations. Through comprehensive analysis, we highlight the formula’s significance in predicting system behaviors, especially in environments with varying amplitudes. Our findings underscore the formula’s potential as a robust tool for enhanced system characterization, offering profound implications for diverse applications across scientific and engineering domains. This study delves deep into the formulation of the frequency-amplitude relationship, extending its application beyond the conventional cubic powers of the restoring force. We introduce three novel and equivalent formulations of the generalized frequency amplitude, breaking traditional boundaries by not confining the restoring force to just odd functions. The formula to determine the frequency of the singular oscillator has been set forth. Our approach, characterized by its simplicity, offers a robust tool for analyzing high nonlinearity vibrations, ushering in a richer, multidimensional perspective to vibration analysis.

Characterization of adults concerning the use of a hypothetical mHealth application addressing stress-overeating: an online survey

BackgroundAbout 40% of people respond to stress by consuming more unhealthy foods. This behavior is associated with increased energy intake and the risk of obesity. As mobile health (mHealth) applications (apps) have been shown to be an easy-to-use intervention tool, the characterization of potential app users is necessary to develop target group-specific apps and to increase adherence rates.MethodsThis cross-sectional online survey was conducted in the spring of 2021 in Germany. Sociodemographic data and data on personality (Big Five Inventory, BFI-10), stress-eating (Salzburg Stress Eating Scale, SSES), and technology behavior (Personal Innovativeness in the Domain of Information Technology, PIIT; Technology Acceptance Model 3, TAM 3) were collected.ResultsThe analysis included 1228 participants (80.6% female, mean age: 31.4 ± 12.8 years, mean body mass index (BMI): 23.4 ± 4.3 kg/m2). Based on the TAM score, 33.3% (409/1228) of the participants had a high intention to use a hypothetical mHealth app to avoid stress-overeating. These persons are characterized by a higher BMI (24.02 ± 4.47 kg/m2, p < 0.001), by being stress-overeaters (217/409, 53.1%), by the personality trait “neuroticism” (p < 0.001), by having specific eating reasons (all p < 0.01), and by showing a higher willingness to adopt new technologies (p < 0.001).ConclusionThis study suggests that individuals who are prone to stress-overeating are highly interested in adopting an mHealth app as support. Participants with a high intention to use an mHealth app seem to have a general affinity towards new technology (PIIT) and appear to be more insecure with conflicting motives regarding their diet.Trial registrationThis survey was registered in the German Clinical Trials Register (Registration number: DRKS00023984).

A numerical study on MHD Maxwell fluid with nanoparticles over a stretching surface: Impacts of thermal radiation, convective boundary condition and induced magnetic field

The current study investigates the influence of induced magnetic field and convective boundary condition on the behavior of a magnetohydrodynamic (MHD) Maxwell fluid including nanoparticles flowing through a stretching sheet. Furthermore, the analysis considers the existence of radiation. Numerical solutions to the basic governing equations are achieved using the Runge–Kutta–Fehlberg technique. The impact of various physical parameters on concentration, velocity, and temperature profiles is deployed through graphs. The primary conclusions of this study are that increasing the Magnetic parameter decreases the Induced Magnetic field while increasing the Deborah parameter improves the velocity profile. Temperature distribution is growing due to increased estimates of the Radiation, Biot number, and Deborah number values. When the Lewis number values are raised, the concentration profiles get smaller but there is an opposite scenario deployed as the Thermophoresis parameter upsurges. The findings of this study are consistent with those that have been previously reported. The phenomenon of flow induced by a stretched surface finds several applications in various industrial and technological domains, including copper wire production, polymer extrusion, article manufacturing, and fiber synthesis. Induced magnetic fields are extremely prominent in many areas of science and technology, including electromagnetism, magnetic materials, and magnetic resonance imaging (MRI).

Some m-fold symmetric bi-univalent function classes and their associated Taylor-Maclaurin coefficient bounds

The Ruscheweyh derivative operator is used in this paper to introduce and investigate interesting general subclasses of the function class Σm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\Sigma_{m}$\\end{document} of m-fold symmetric bi-univalent analytic functions. Estimates of the initial Taylor-Maclaurin coefficients |am+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{m+1} \\vert $\\end{document} and |a2m+1|\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$\\vert a_{2 m+1} \\vert $\\end{document} are obtained for functions of the subclasses introduced in this study, and the consequences of the results are discussed. Additionally, the Fekete-Szegö inequalities for these classes are investigated. The results presented could generalize and improve some recent and earlier works. In some cases, our estimates are better than the existing coefficient bounds. Furthermore, within the engineering domain, the utilization of the Ruscheweyh derivative operator can encompass a broad spectrum of engineering applications, including the robotic manipulation control, optimizing optical systems, antenna array signal processing, image compression, and control system filter design. It emphasizes the potential for innovative solutions that can significantly enhance the reliability and effectiveness of engineering applications.

Computational Analysis of the Dissipative Casson Fluid Flow Originating from a Slippery Sheet in Porous Media

This research paper examines the characteristics of a two-dimensional steady flow involving an incompressible viscous Casson fluid past an elastic surface that is both permeable and convectively heated, with the added feature of slip velocity. In contrast to Darcy’s Law, the current model incorporates the use of Forchheimer’s Law, which accounts for the non-linear resistance that becomes significant at higher flow velocities. The accomplishments of this study hold significant relevance, both in terms of theoretical advancements in mathematical modeling of Casson fluid flow with heat mass transfer in engineering systems, as well as in the context of practical engineering cooling applications. The study takes into account the collective influences of magnetic field, suction mechanism, convective heating, heat generation, viscous dissipation, and chemical reactions. The research incorporates the consideration of fluid properties that vary with respect to temperature or concentration, and solves the governing equations by employing similarity transformations and the shooting approach. The heat transfer process is significantly affected by the presence of heat generation and viscous dissipation. Furthermore, the study illustrates and presents the impact of various physical factors on the dimensionless temperature, velocity, and concentration. From an engineering perspective, the local Nusselt number, the skin friction, and local Sherwood number are also depicted and provided in graphical and tabular formats. In the domains of energy engineering and thermal management in particular, these results have practical relevance in improving our understanding of heat transmission in similar settings. Finally, the thorough comparison analysis reveals a significant level of alignment with the outcomes of the earlier investigations, thus validating the reliability and effectiveness of our obtained results.