Engineering Domain Research Articles

Environmental Impact of Digital Technologies and Artificial Intelligence

Very often arguments are put forward that replacing in-person meetings by online events or using digital technologies to do collaborative research will lower the pressure on the environment (Spencer 2023). But is this actually true? The obvious impacts, such as the carbon footprint of using digital technologies, are often overlooked. The electricity (even if green alternatives exist) used to run and cool the ever-growing number of large data centers has been regularly cited (Monteclaro 2023) as the largest contributor to this impact. Recent studies (Bordage 2019), however, tend to show that other actors (mostly the users) in the domain of digital technologies, appear to have an even greater impact. Additionally, ethical considerations and habitat degradation issues resulting from mining for the components to build professional and personal digital devices contribute to the problems. The deployments of networks have also a non-negligible share in the calculation of the actual digital impact on the environement (Bordage 2019). The tremendous increase of generated data, information exchanges, algorithms to run digital applications due to the larger use of AI in all scientific fields, in all sectors of the policy making, industry and civil society will increase the environmental impact of digital technologies even more. However, depending on the purpose of the AI technology, if its usage can demonstrate a reduction of negative environmental impacts, mitigation solutions can be envisaged. For example, if AI allows better land planning or better identification of species, it could balance out the digital environmental impact. In terms of online versus in-person or hybrid meetings, the issue is not as easy as one may think. For in-person meetings, especially in topics like biodiversity information, almost everyone is using devices such as laptops, tablets or smartphones, even encouraged by the organizers and speakers, making live demos, or doing online polls requiring the use of digitial technologies (e.g., Mentimeter). Totally online meetings make heavy use of the networks, dataflows and devices, even if it can be argued that the impact of the flights is removed from the equation. Hybrid meetings combine both the impacts of fully in-person or fully online options, also encouraging local participants to interact with the online participants and potentially demultiplying the environemental impact even more. Mitigation aspects to take into account are the added value of social interactions, learning about localhost's culture and biodiversity, and input to the local economies and infrastructures to welcome business tourism for large conferences. Efforts made to make onsite meetings greener play an important role, by wisely choosing the location and how catering is handled with local products and diminishing the use of one-use-only cups and cutlery. The debate on which meeting options are the best, is far from being closed and will continue into the future. This presentation will show recent analyses conducted, among others, by the European Institutes for Sustainable IT (information technology), to demystify common assumptions on major responsibilities in terms of environmental impact or carbon footprints of digital technologies. Tips and tricks to lower everyone's overall digital impact will be provided as well as encouraging the actors in biodiversity information to sign the Sustainable IT charter or even act further to get the highest Sustainable IT label for their institutions.

Research on the Sensing Properties and Vibration Reduction Performance of Self-Sensing Self-Tuning Magnetic Fluid Damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper's natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit's output induced electromotive force model is created, followed by an expression of the damper's natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage.
Finally, an experimental platform was developed to assess the damper's self-sensing and self-tuning vibration reduction performance. The experimental results demonstrate that the suggested self-tuning magnetic fluid damper exhibits excellent self-sensing capabilities and effectively reduces vibrations. This offers an innovative research concept for using magnetic fluid in self-sensing technology and vibration reduction domains.

Darcy-Forchheimer magnetohydrodynamic flow of non-Newtonian Reiner-Rivlin hybrid nanofluid bounded by double-revolving disks

The applications of fluid flow and heat transfer between coaxial double-revolving disks are diverse and can be found in various engineering and scientific domains. In general, the utilization of engine oil ( EO) mixed with hybrid nanoparticles serves various purposes in engine cooling, such as maintaining viscosity-temperature properties, preventing corrosion, and achieving other cooling-related objectives. This investigation focuses on the Darcy-Forchheimer flow of a non-Newtonian Reiner-Rivlin hybrid nanofluid confined between double-revolving disks. The hybrid nanoparticles used include cadmium telluride ( CdTe) and titanium dioxide ( TiO2), combined with the base fluid engine oil. The dimensional flow equations are converted into dimensionless forms using suitable similarity transformations. Subsequently, a semi-analytical methodology known as the homotopy analysis method ( HAM) is utilized to tackle this problem. Graphs are used to determine the effects of the various flow parameters of the hybrid nanofluids on the velocities, temperature, skin friction coefficient, and the Nusselt number. After validating the findings against previous research, an attractive agreement was observed. Some major outcomes from this present problem are that axial, radial, and temperature profiles increase with the rise of the cross-viscosity parameter while the tangential velocity decreases. Radial velocity increased, and tangential velocity diminished with an enhanced magnetic field parameter. Radial velocity is increased for higher values of the dimensionless forced parameter. For higher values of the porosity parameter, the temperature profile improved. Also, the exploration of the Nusselt number and skin friction for the disks, incorporating the effects of a magnetic field and porous medium, reveals that skin friction for both disks increase with the rising Reynolds number and volume fraction of the cadmium telluride nanoparticles.

Review on geopolymer concrete incorporating Alccofine-1203

Abstract The emergence of geopolymer concrete (GPC) has occurred as a sustainable substitute for conventional cement-based concrete, offering enhanced mechanical characteristics and reduced environmental impact. In the quest to further optimize GPC, incorporating supplementary cementitious materials has gained attention. For the research, Alccofine-1203 was considered as the material for incorporation into the geo-polymer concrete. This review study specifically examines the utilization of Alccofine-1203 as a material in GPC within the domain of structural engineering. Alccofine-1203, a high-performance mineral additive, exhibits pozzolanic and reactive properties, making it a promising candidate for enhancing GPC performance. This paper provides a comprehensive analysis of the chemical compositions and physical properties of Alccofine 1203, highlighting its potential benefits in GPC. Furthermore, it explores the influence of Alccofine-1203 on various aspects of GPC, including workability, compressive strength, flexural strength, durability, microstructure, and thermal properties. The review encompasses an analysis of relevant tests conducted to evaluate these properties. In summary, this review article is a great resource for researchers, engineers, and practitioners who are interested in the development and application of GPC containing Alccofine-1203.

New Estimates of the Potential Schrödinger Equation

The Schrödinger equation, a fundamental construct in quantum mechanics, plays a pivotal role in understanding the properties and behaviors of quantum systems across various scientific fields, including but not limited to physics, economics, and geophysics. This research paper delves into the exploration of novel invariants associated with the Schrödinger equation, uncovering previously unrecognized symmetries and properties that open new pathways for theoretical and applied scientific exploration. The investigation reveals that the energy of bound states remains invariant under transformations of the coordinate system, highlighting a universal symmetry embedded within the equation. This discovery, coupled with an analysis of the variation in scattering amplitudes’ phase with different coordinate systems, not only enriches the theoretical framework of quantum mechanics but also has significant practical implications across various domains such as fluid dynamics, material science, and medical imaging. Furthermore, by applying the principles of the Poincaré-Riemann-Hilbert boundary-value problem, the study offers a novel approach to estimate potentials within the Schrödinger equation, advancing the three-dimensional inverse problem of quantum scattering theory. This methodological innovation allows for a more profound understanding of the unitary scattering operator, facilitating enhanced modeling of complex systems and phenomena. The implications of these findings extend beyond theoretical physics, offering transformative insights and applications in areas ranging from fluid dynamics, where it aids in refining models for the Navier-Stokes equations, to seismic exploration, tomography, and ultrasound imaging, where it enhances phase selection and interpretation of measurement results. In essence, this research not only contributes to a deeper understanding of the Schrödinger equation’s fundamental properties but also paves the way for interdisciplinary advancements, demonstrating the profound impact of theoretical discoveries on practical applications in diverse scientific and technological domains.

Bridging Technology, Design, and Management: An Interdisciplinary Approach to Innovation in the AI Era

Not a single domain of technology, design, or management can fully tackle AI's ethical, practical, and strategic dimension.A service design approach is used to explore the intersection of technology, design, and management for a responsible AI implementation that fosters innovation but is also ethically aligned and strategically integrated.

Examining bias perpetuation in academic search engines: An algorithm audit of Google and Semantic Scholar

Researchers rely on academic Web search engines to find scientific sources, but search engine mechanisms may selectively present content that aligns with biases embedded in queries. This study examines whether confirmation biased queries prompted into Google Scholar and Semantic Scholar will yield results aligned with a query’s bias. Six queries (topics across health and technology domains such as ‘vaccines’, ‘Internet use’) were analyzed for disparities in search results. We confirm that biased queries (targeting ‘benefits’ or ‘risks’) affect search results in line with bias, with technology-related queries displaying more significant disparities. Overall, Semantic Scholar exhibited fewer disparities than Google Scholar. Topics rated as more polarizing did not consistently show more disparate results. Academic search results that perpetuate confirmation bias have strong implications for both researchers and citizens searching for evidence. More research is needed to explore how scientific inquiry and academic search engines interact.

Benchmarking Big Data Systems: Performance and Decision-Making Implications in Emerging Technologies

Systems for graph processing are a key enabler for insights from large-scale graphs that are critical to many new advanced technologies such as Artificial Intelligence, Internet of Things, and blockchain. In this study, we benchmark another two widely utilized graph processing systems, Apache Spark GraphX and Apache Fink, concerning the key performance criterion by means of response time, scalability, and computational complexity. We demonstrate our results which show the capability of each system for real-world graph applications, and hence, providing a quantitative understanding to select the system for our purpose. GraphX’s strength was in processing batch in-memory workloads typical of blockchain and machine learning model optimization, while Flink excelled in processing stream data, which is timely and important to the IoT world. These performance characteristics emphasize how the capabilities of graph processing systems can match the requirements for the performance of different emerging technology applications. Our findings ultimately inform practitioners about system efficiencies and limitations, but also the recent advances in hardware accelerators and algorithmic improvements aimed at shaping the new graph processing frontier in diverse technology domains.

AI-driven data fusion modeling for enhanced prediction of mixed-mode I/III fracture toughness

This research evaluates the use of artificial intelligence to enhance the accuracy of predictions for mixed-mode I/III fracture toughness in polymethyl methacrylate . Traditionally, assessing fracture toughness relies heavily on destructive testing methods, specifically using edge-notch disc bend specimens subjected to three-point bending tests. These established methods are not only expensive and time-consuming but also frequently limited by the availability of data. To address these challenges, this study introduces a data fusion source modeling approach. This approach integrates primary fracture toughness test data with secondary predictive data derived from the maximum tangential stress criterion and the local strain energy density criterion. By employing adaptive boosting and general regression neural network algorithms, the models developed in this research demonstrate a marked improvement in predictive performance compared to traditional primary source models. Additionally, a feature importance analysis using Shapley Additive exPlanations values reveals that the mode mixity parameter, specimen thickness, and radius are critical factors influencing fracture toughness. The study highlights that while mode mixity emerges as the most significant factor, a reduction in specimen thickness generally leads to decreased fracture toughness, whereas an increase in radius has a more complex, often negative, effect. The results of this study indicate that AI-powered models using data fusion can overcome limitations related to data scarcity, enabling more accurate predictions in fracture mechanics. Furthermore, this approach provides a pathway for utilizing AI in other engineering domains where data sets are limited.

Quantum Neural Networks: A New Frontier

Abstract. In recent years, there has been remarkable progress in improving the availability of resources and refining algorithms for quantum computing. Since the late 1980s, the scientific community has been fascinated by the idea of harnessing quantum phenomena to tackle computational problems. This article provides a comprehensive exploration of the foundational theories and practical applications of quantum neural networks (QNNs), highlighting their potential to transform machine learning through unique features like quantum parallelism and entanglement. It delves into various QNN architectures, such as quantum circuits and hybrid quantum-classical models, showcasing their effectiveness in handling intricate computational tasks more efficiently than traditional neural networks. Furthermore, the article examines the current challenges and future prospects in this rapidly advancing field, emphasizing the pivotal role of QNNs in driving forward research in both quantum computing and artificial intelligence. Quantum neural networks are poised to not only enhance computational capabilities but also pave the way for groundbreaking innovations in diverse technological domains.

Innovation’s false spring: U.S. export controls and Chinese patent quality

This paper evaluates the effect of U.S. export restrictions, enacted in 2018, on China’s patent production. An analysis of patent-level datasets indicates a significant surge in Chinese patent filings in sanctioned technological domains. This quantitative growth, however, is accompanied by a decrease in patent quality, evidenced by reduced citation counts and market worth. Specifically, state-owned enterprises (SOEs) have witnessed a substantial rise in patent filings post-2018, while private firms exhibit a more modest increase. Despite the surge in patent quantity, the overall quality of patents—particularly those originating from SOEs—has deteriorated significantly. This study contributes to the body of research concerning the economic consequences of the U.S.-China trade dispute and offers original insights into the implications of trade sanctions on innovation quality.

Elasticity study of SLA Additively Manufactured Composites

This study investigates the mechanical properties of ceramic and photopolymer composites created using stereolithography (SLA) 3D printing. It aims to evaluate and compare the performance of two SLA materials: Liqcreate Composite-X and Phrozen's Water-Washable Resin, using dynamic flexural vibration tests to determine Young's modulus. Liqcreate Composite-X, a photopolymer resin with excellent mechanical properties, including high tensile and flexural strength, showed a first resonance frequency of 318 Hz and a Young's modulus of 0.93 x 1010 Pa. Phrozen's Water-Washable Resin, designed for easy post-processing, had a first resonance frequency of 210 Hz and a Young's modulus of 0.30 x 1010 Pa. In conclusion, Liqcreate Composite-X demonstrates superior mechanical performance, making it suitable for high-strength applications, while Phrozen's Water-Washable Resin, with lower mechanical properties, is more suited for less demanding uses. This study enhances the understanding and application of SLA-based ceramic composites in various engineering domains.

Impact of E-Governance on Public Service Efficiency

This study examines the complex relationship between e-governance implementation and public service efficiency across diverse socio-economic contexts. Through a comprehensive review of recent literature, the research investigates how digital technologies reshape government operations and citizen interactions. The analysis reveals that while e-governance generally leads to improvements in service delivery, cost reduction, and increased transparency, these benefits vary significantly based on factors such as technological infrastructure, digital literacy, and specific service domains. The study challenges assumptions about immediate efficiency gains, particularly in developing countries where initial costs may increase due to the need for parallel traditional systems. The research emphasizes the importance of context-specific approaches, continuous evaluation, and addressing digital divide issues for successful e-governance implementation. These findings contribute to a nuanced understanding of e-governance's impact on public service efficiency, offering valuable insights for policymakers and public administrators navigating the digital transformation of government services.

The Influence of Curatorial Art on Visual Art

As time progressed and societal structures underwent transformation, there was a notable increase in interest in the memories encapsulated by objects. This resulted in a significant increase in the development and popularity of museums and art exhibitions, which in turn gave rise to a multitude of innovative spatial art forms, and the term “curation” was born. This research program examines the impact of the curatorial space on the visual presentation of artworks and its role in the visual arts as a whole. This is achieved using an analysis of several specific cases. Moreover, it demonstrates the evolution and advancement of the utilisation of curatorial space for the presentation of artworks. The concept of curatorial space is both flexible and complex, with implications for both the visual arts and their presentation. The utilisation of new media and technology in curatorial environments has been demonstrated to offer several advantages over physical curation. These include enhanced accessibility, flexibility, and the capacity to reach a broader audience. Consequently, curators and artists are presented with the chance to interact with audiences and cultivate innovative artistic practices and prospects in the domain of digital media and technology.

A Decade in Blockchain: A Bibliometric Reflection on the Growth and Interdisciplinary Reach of a Disruptive Technology

This study seeks to deliver a comprehensive bibliometric analysis of blockchain research undertaken from 2013 to 2023. It seeks to map the field’s evolution, highlight emerging trends, and identify key contributors and thematic areas. Understanding these aspects is crucial for guiding future research and policymaking in the rapidly growing domain of blockchain technology. The study utilises a systematic bibliometric analysis approach and the Scopus database was used for collecting data. The analysis includes keyword trends, citation patterns, author productivity, and network analysis. The results reveal significant expansion and diversification in blockchain research, with 13,369 publications and 52,009 contributing authors. Key metrics include a total citation count of 266,483 and an average of 19.93 citations per paper, underscoring the field’s scholarly impact. The study identifies core research themes such as smart contracts, security, IoT integration, and emerging topics like AI and sustainability. The findings highlight the interdisciplinary nature of blockchain research and its global distribution, with notable contributions from China and the United States. The study’s reliance on selected databases and predefined keywords may exclude relevant grey literature and industry reports. Future research should integrate these sources for a more complete view. This study provides a panoramic view of blockchain research, offering valuable insights into its development, current state, and future directions. Identifying key trends, influential contributors, and emerging themes contributes to the academic discourse and supports strategic planning for researchers, practitioners, and policymakers engaged in blockchain technology.

Understanding the Inherently Self-Adjusting Mechanism and Load Adaptability of the Mem-Damper

The tapered dashpot (damper) has been the earliest memristor found in the mechanical engineering domain, named mem-dashpot (mem-damper), since the missing memristor was postulated by Leon Chua. In this paper, a displacement-dependent viscous damper device with internal channels is investigated and recognized as a physical realization of the mem-damper by analyzing the mathematical and dynamic relationships of the device’s complementary port variables theoretically and experimentally. A vehicle suspension equipped with a mem-damper is taken as a carrier for presenting load adaptability of the mem-damper. An investigation reveals the inherently self-adjusting mechanism behind load adaptability from the viewpoint of energy storage, that is, a mem-damper with different initial displacement values can be equivalent to a semi-active damper performing an initial-position-dependent damping control strategy. Finally, the load adaptability of the mem-damper is demonstrated through both simulation and experiment of the suspension system with the mem-damper prototype. Due to such load adaptability, the suspension equipped with the mem-damper can offer more constant and smoother ride comfort than the one with the linear damper.

An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection

Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.

Cantilever configurations in vibration-based piezoelectric energy harvesting: A comprehensive review on beam shapes and multi-beam formations.

Abstract Vibration-based energy harvesting technology is a well-established research area that has attracted tremendous interest over the last decade. This interest is primarily owing to its extension into a wide range of engineering domains, particularly in Microelectromechanical Systems (MEMS). The cantilever beam is the most common and widely used model for vibration-based energy harvester, driven by two key factors: a) simplicity in design, and b) high output power density. Numerous studies over the years have focused on optimizing the cantilever beam design to increase output power capacity and/or widen the frequency bandwidth of the harvester. While researchers have proposed a plethora of cantilever beam configurations for specific purposes (e.g., low-frequency harvesting, multi-directional frequency harvesting, etc.), there is a notable lack of detailed literature on the types and configurations of cantilever beams. This gap hinders researchers from gaining a comprehensive understanding of the cantilever beams already introduced. Following the need, in this article a comprehensive review is made to list the types of cantilever beams proposed by the researchers over the years. This review covers the working principles of piezoelectric energy harvesting, analyses existing solutions geared towards increasing power output and widening working frequency, and discusses diverse configurations including single and multiple beam setups. The listed beams are categorized based on their structural shape and organization such that it can be helpful for a reader to anticipate which cantilever beam design can be suitable for a specific need. Power output capacity and operating frequency for every beam design are also presented in a tabular form, under each beam category. This would enable the researchers to tailor their designs for specific applications, enhance material efficiency, drive innovation, and open new application possibilities.

Machine Learning Modeling for Predicting Tensile Strain Capacity of Pipelines and Identifying Key Factors

Abstract Machine learning (ML) techniques have recently gained great attention across a multitude of engineering domains, including pipeline materials. However, their application to tensile strain capacity (TSC) modeling remains unexplored. To bridge this gap, this study developed and evaluated an ML model to predict the tensile strain capacity of girth-welded pipelines. The model was trained on over 20,000 data points derived from a TSC equation available in the literature. The ML model demonstrated robust performance in predicting tensile strain capacities. Evidence of this lies in the near-zero means, minimal standard deviations, and normal distribution of residuals for both the training and test datasets. These collectively suggest that the model provides a good fit for the data. Furthermore, the model's loss behavior indicates successful convergence and generalization, without signs of overfitting or underfitting. An analysis using the random forest method revealed that the geometry of the flaw, specifically the flaw depth, is the most influential variable in predicting the TSC. This could be attributed to its significant impact on the fracture toughness of materials. In contrast, material properties and fracture toughness exert less influence relatively, despite their contributions to the model. This finding underscores the importance of flaw geometry in TSC prediction models. Overall, the development of a data-driven TSC model has shown efficient TSC modeling. This model leverages ML techniques, allowing for continuous updates with new data via deep learning.

Agriculture and Automation: Potential Benefits, Difficulties and Economic Impacts

In contemporary agriculture, automation and robotics are the main players. They present viable answers to issues with the world's expanding population, changing demographics, and economic standing. This analysis examines the potential and difficulties of utilizing new technologies as well as the frequently overlooked connection between automation technology and agricultural economics. This study examines the potential of automation and robots in farming techniques, as well as their socio-economic impacts, through a methodical analysis of the literature. It also offers practical advice for those involved. For this reason, a variety of robot types used in diverse agricultural fields have been examined, along with the difficulties and technical viability of adopting automation. Additional significant aspects have been examined, such as changes in the labour market, economic conditions, and demographic trends. This study also looks into the social effects of automation, specifically with regard to worker adaption and employment. It discovers that although automation increases sustainability and efficiency, it also results in labour displacement and necessitates significant technological investment. This in-depth analysis closes a significant gap by evaluating precision agriculture's future, labour market change, and economic viability. Along with outlining a future roadmap for research and policy, it also plots a course for future studies and policy-making at the nexus of agricultural technology and socioeconomic domains.