Engineering Domain Research Articles

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.

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.

Artificial Intelligence and the Future of Communication Sciences and Disorders: A Bibliometric and Visualization Analysis.

As artificial intelligence (AI) takes an increasingly prominent role in health care, a growing body of research is being dedicated to its application in the investigation of communication sciences and disorders (CSD). This study aims to provide a comprehensive overview, serving as a valuable resource for researchers, developers, and professionals seeking to comprehend the evolving landscape of AI in CSD research. We conducted a bibliometric analysis of AI-based research in the discipline of CSD published up to December 2023. Utilizing the Web of Science and Scopus databases, we identified 15,035 publications, with 4,375 meeting our inclusion criteria. Based on the bibliometric data, we examined publication trends and patterns, characteristics of research activities, and research hotspot tendencies. From 1985 onwards, there has been a consistent annual increase in publications, averaging 16.51%, notably surging from 2012 to 2023. The primary communication disorders studied include autism, aphasia, dysarthria, Parkinson's disease, and Alzheimer's disease. Noteworthy AI models instantiated in CSD research encompass support vector machine, convolutional neural network, and hidden Markov model, among others. Compared to AI applications in other fields, the adoption of AI in CSD has lagged slightly behind. While CSD studies primarily use classical machine learning techniques, there is a growing trend toward the integration of deep learning methods. AI technology offers significant benefits for both research and clinical practice in CSD, but it also presents certain challenges. Moving forward, collaboration among technological, research, and clinical domains is essential to empower researchers and speech-language pathologists to effectively leverage AI technology for the study, diagnosis, assessment, and rehabilitation of CSD. https://doi.org/10.23641/asha.27162564.

Study of Self-Locking Structure Based on Surface Microstructure of Dung Beetle Leg Joint.

Dung beetle leg joints exhibit a remarkable capacity to support substantial loads, which is a capability significantly influenced by their surface microstructure. The exploration of biomimetic designs inspired by the surface microstructure of these joints holds potential for the development of efficient self-locking structures. However, there is a notable absence of research focused on the surface microstructure of dung beetle leg joints. In this study, we investigated the structural characteristics of the surface microstructures present in dung beetle leg joints, identifying the presence of fish-scale-like, brush-like, and spike-like microstructures on the tibia and femur. Utilizing these surface microstructural characteristics, we designed a self-locking structure that successfully demonstrated functionality in both the rotational direction of the structure and self-locking in the reverse direction. At a temperature of 20 °C, the biomimetic closure featuring a self-locking mechanism was capable of generating a self-locking force of 18 N. The bionic intelligent joint, characterized by its unique surface microstructure, presents significant potential applications in aerospace and various engineering domains, particularly as a critical component in folding mechanisms. This research offers innovative design concepts for folding mechanisms, such as those utilized in satellite solar panels and solar panels for asteroid probes.

Research status and development direction of smart heritage: a bibliometric review (1994-2024)

ABSTRACT Smart heritage, an emerging and sustainable paradigm, utilises technology to conserve and enhance cultural heritage. Despite integration into smart city development, full implementation remains unrealised. Consequently, this study aims to investigate the status and trends of smart heritage research, explore the development direction of smart heritage, bridge existing knowledge gaps, and guide its integration into smart city development. This study employs a bibliometric analysis method, supported by machine algorithms, to obtain scientific and quantifiable results. The study utilises the bibliometric analysis software CiteSpace 6.2R6 to analyze 981 articles from the Web of Science Core Collection, covering 1994 to 2024. We observe a notable increase in scholarly output over time, especially in smart cities and digital cultural strategies, where smart heritage has emerged as a research hotspot across various disciplines and domains. Smart heritage crosses three stages: digital technology application, technology integration, and multidisciplinary integration, showcasing diverse technological applications, service scenarios, and application domains. Moving forward, we must prioritize a comprehensive research agenda, including theoretical constructs, smart cultural infrastructure, robust policy, public engagement, and expanded application realms. Additionally, fostering interdisciplinary collaboration between international entities and driving technological innovation is vital for promoting the sustainable development of cultural heritage.

Critical lateral‐torsional buckling loads of porous orthotropic rectangular beams containing warping effects

AbstractDue to their elemental porosity, porous materials display individual mechanical properties, rendering them essential components in various engineering applications. So, this study investigates the lateral‐torsional buckling (LTB) sensitivity of porous orthotropic rectangular beams subjected to a concentrated load, including the warping effects. Porosity‐dependent material properties vary along the beam's height via four different porosity distribution patterns. The constitutive equations are obtained using the principle of virtual work based on the classical beam theory containing the warping effect, and the governing equations are solved by performing the Ritz method to obtain the critical LTB load. The porous orthotropic beams and the warping effects add complexity to the analysis. So, the influence of some parameters, such as porosity coefficients, porosity distribution patterns, orthotropy, height‐to‐width ratio, slenderness ratio, and warping coefficients, on the LTB characteristics of porous orthotropic rectangular beams are investigated in detail. The novelty of this study lies in its comprehensive LTB analysis of orthotropic rectangular beams under specific effects, such as porosity and warping factors. The findings offer valuable insights into the LTB characteristics of orthotropic porous beams, contributing to an improved comprehension of their structural behavior and facilitating the design of engineering structures. This study contributes to the composite materials and structural stability analysis, offering potential applications in many engineering domains.

Programmable Truncated Cuboctahedral Origami Metastructures Actuated by Shape Memory Polymer Hinges

AbstractOver the past few decades, origami‐inspired structures have attracted great attention across various engineering fields due to their unique geometric and mechanical characteristics. Additionally, combining origami structures with active materials is employed to achieve programmable mechanical properties and self‐reconfigurability under external stimuli. In this work, a novel family of truncated cuboctahedral origami metastructures is proposed. These designs integrate shape memory polymers (SMPs) to actively achieve programmable mechanical properties and shape memory behavior. By utilizing SMPs for the creases and stiff materials for the panels, this approach enables deformation along the creases while enhancing the overall structural robustness. The mechanical properties and shape memory processes of these structures are investigated in detail. The proposed origami metastructures exhibit a negative Poisson's ratio and demonstrate excellent energy storage capabilities. Notably, their mechanical properties can be programmed by controlling both temperature and geometric parameters. More particularly, their Poisson's ratio can be tuned within a range of zero to −1. As a result, these truncated cuboctahedral origami metastructures hold significant potential for applications across various engineering domains, particularly in composite structures and active metamaterials.

Navigating complex riskscapes: the Koudiet Eddraouch GIS substation project in Sub-Saharan Africa

Learning outcomes Risk identification and qualitative assessment are the learning outcomes. Case overview/synopsis The Bharat Bijlee Construction Limited (BBCL) was one of the largest construction companies operating in the power sector in India. After successfully completing a few projects in the Middle East, BBCL decided to expand its presence into African region. The BBCL was awarded a $85m contract for three sub-station projects to modernise Algeria’s power grid system by the “Shariket Karhaba Koudiet Eddraouch Spa”, a state-owned company in charge of power generation, transmission and distribution in Algeria. The project, which is the first of BBCL in Saharan region in Algeria’s, presents many unique situations that company had never encountered before, including sand dunes, severe weather, remote locations, supply chain & logistics, strict contractual deadlines and a high level of construction risk. The project manager for BBCL was sceptic about how well his company would perform under the present project circumstances. How could he better align himself with the client, the various on-site local contractors and the numerous suppliers spread around the world? The case emphasises the identification of various project risks that the project manager might encounter in the project. What do the PESTLE and ASCE frameworks for risk identification each represent, and how are they helpful for the project team in understanding various risks? How should the project’s qualitative risk assessment be conducted? And how can a heat map be a better tool for comprehending the criticality of each risk in the project? Complexity academic level Undergraduate and post graduate courses in project management, civil engineering and architecture domain. Supplementary material Teaching notes are available for educators only. Subject code CSS 2: Built Environment.

Systematic Review of Adapting Immersive Technology in Enhancing Teaching and Learning for Students in Higher Education

The integration of immersive technology into educational settings has garnered substantial interest as a potential means to improve teaching and learning (TnL)experiences. The dynamic landscape of education demands innovative approaches to engage, motivate, and empower both educators and learners. Immersive technology, including Virtual Reality (VR), Augmented Reality (AR), as well as Mixed Reality (MR), provides uncommon probabilities to transcend traditional classroom boundaries and provide interactive and immersive learning experiences. However, to harness the full potential of immersive technology, a clear understanding of the conceptual frameworks guiding its implementation is essential. This study utilizes the PRISMA approach. Furthermore, the systematic search of academic databases using the Web of Science (WoS) as well as the Scopus database resulted in an extensive selection of studies, reviews and articles related to immersive learning. Following the implementation of an advanced search approach utilizing keywords (1. Immersive learning, 2. AR, as well as 3. Technology Impact on TnL), the systematic review process yielded a diverse array of articles. These articles were identified and included in the review due to their exploration of the conceptual frameworks that underlie the integration of immersive technology in the field of education. Additionally, the review reveals common themes and emerging trends within the field, shedding light on the diverse approaches to leveraging immersive technology for TnL. The final finding data is (n: 29), which reviews identified key themes. Expect validation to be divided into three themes, which are (1) Immersive learning, (2) AR, and (3) Technology Impact on TnL. Meanwhile, the findings emphasize the importance of well-defined theoretical foundations and best practices when implementing immersive technology for educational purposes. The review underscores the importance of ongoing research as well as innovation in the domain of immersive technology to fully exploit its potential for enhancing TnL experiences. In essence, this systematic review adds to the continued conversation about the efficient utilization of immersive technology in education and paves the way for further exploration and advancement in this field.

Mathematical Information Retrieval: A Review

Mathematical formulas are commonly used to demonstrate theories and basic fundamentals in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning research in the STEM domain results in the mass production of scientific documents that contain both textual and mathematical terms. In scientific information, the definition of mathematical formulas is expressed through context and symbolic structure that adheres to strong domain-specific notions. Whereas the retrieval of textual information is well-researched, and numerous text-based search engines are present. However, textual information retrieval systems are inadequate for searching scientific information containing mathematical formulas, including simple symbols to complicated mathematical structures. The retrieval of mathematical information is infancy, and it requires the inclusion of new technologies and tools to promote the retrieval of scientific information and the management of digital libraries. This paper provides a comprehensive study of mathematical information retrieval, highlights their challenges and future opportunities.

(Non-)terrestrial and (Non-)local pathways of behavioral policy diffusion in European cities’ climate action plans: Contextual, cultural, and leadership framing

This study investigates the spread of innovative behavioral (green nudging) policies within city-level Climate Action Plans (CAPs) across the European Union, focusing on how these innovations diffuse and the factors influencing their adoption. Using textual analysis with a dataset consisting of CAPs from 40 cities across Europe, we categorized various green nudging innovations and then tracked their origins and uptake. Then, we employed fsQCA (Fuzzy set qualitative comparative analysis) to identify the key factors driving diffusion. The findings reveal that while certain innovations, particularly in the building and transportation sectors, have achieved widespread adoption, other initiatives like community co-creation and urban parks have seen lower diffusion. Local terrestrial factors, especially sectoral carbon emissions, are significant drivers, with cities facing higher emissions more likely to adopt these policies. Interestingly, local emissions levels and strong climate leadership emerge as more critical determinants than economic status or climate similarities. The study identifies two primary diffusion pathways—Cultural Leadership for Emission Reduction and Local Adaptive Synergy—demonstrating the diverse strategies cities employ based on their unique contexts. This research highlights the importance of expanding green nudging measures in CAPs beyond technological and infrastructure domains to promote low-carbon behaviors comprehensively.

CO-TSM: A Flexible Model for Secure Embedded Device Ownership and Management

The Consumer-Oriented Trusted Service Manager (CO-TSM) model has been recognised as a significant advancement in managing applications on Near Field Communication (NFC)-enabled mobile devices and multi-application smart cards. Traditional Trusted Service Manager (TSM) models, while useful, often result in market fragmentation and limit widespread adoption due to their centralised control mechanisms. The CO-TSM model addresses these issues by decentralising management and offering greater flexibility and scalability, making it more adaptable to the evolving needs of embedded systems, particularly in the context of the Internet of Things (IoT) and Radio Frequency Identification (RFID) technologies. This paper provides a comprehensive analysis of the CO-TSM model, highlighting its application in various technological domains such as smart cards, HCE-based NFC mobile phones, TEE-enabled smart home IoT devices, and RFID-based smart supply chains. By evaluating the CO-TSM model’s architecture, implementation challenges, and practical deployment scenarios, this paper demonstrates how CO-TSM can overcome the limitations of traditional TSM approaches. The case studies presented offer practical insights into the model’s adaptability and effectiveness in real-world scenarios. Through this examination, the paper aims to underscore the CO-TSM model’s role in enhancing scalability, flexibility, and user autonomy in secure embedded device management, while also identifying areas for future research and development.

Managing Induced Seismicity Risks From Enhanced Geothermal Systems: A Good Practice Guideline

AbstractGeothermal energy is a green source of power that could play an important role in climate‐conscious energy portfolios; enhanced geothermal systems (EGS) have the potential to scale up exploitation of thermal resources. During hydraulic fracturing, fluids injected under high‐pressure cause the rock mass to fail, stimulating fractures that improve fluid connectivity. However, this increase of pore fluid pressure can also reactivate pre‐existing fault systems, potentially inducing earthquakes of significant size. Induced earthquakes are a significant concern for EGS operations. In some cases, ground shaking nuisance, building damages, or injuries have spurred the early termination of projects (e.g., Basel, Pohang). On the other hand, EGS operations at Soultz‐sous‐Forêts (France), Helsinki (Finland), Blue Mountain (Nevada, USA), and Utah FORGE (USA) have adequately managed induced earthquake risks. The success of an EGS operation depends on economical reservoir enhancements, while maintaining acceptable seismic risk levels. This requires state‐of‐the‐art seismic risk management. This article reviews domains of seismology, earthquake engineering, risk management, and communication. We then synthesize “good practice” recommendations for evaluating, mitigating, and communicating the risk of induced seismicity. We advocate for a modular approach. Recommendations are provided for key technical aspects including (a) a seismic risk management framework, (b) seismic risk pre‐screening, (c) comprehensive seismic hazard and risk evaluation, (d) traffic light protocol designs, (e) seismic monitoring implementation, and (f) step‐by‐step communication plans. Our recommendations adhere to regulatory best practices, to ensure their general applicability. Our guidelines provide a template for effective earthquake risk management and future research directions.

Mathematical Analysis of a Navier–Stokes Model with a Mittag–Leffler Kernel

In this paper, we establish the existence and uniqueness results of the fractional Navier–Stokes (N-S) evolution equation using the Banach fixed-point theorem, where the fractional order β is in the form of the Atangana–Baleanu–Caputo fractional order. The iterative method combined with the Laplace transform and Sumudu transform is employed to find the exact and approximate solutions of the fractional Navier–Stokes equation of a one-dimensional problem of unsteady flow of a viscous fluid in a tube. In the domains of science and engineering, these methods work well for solving a wide range of linear and nonlinear fractional partial differential equations and provide numerical solutions in terms of power series, with terms that are simple to compute and that quickly converge to the exact solution. After obtaining the solutions using these methods, we use Mathematica software Version 13.0.1.0 to present them graphically. We create two- and three-dimensional plots of the obtained solutions at various values of β and manipulate other variables to visualize and model relationships between the variables. We observe that as the fractional order β becomes closer to the integer order 1, the solutions approach the exact solution. Lastly, we plot a 2D graph of the first-, second-, third-, and fourth-term approximations of the series solution and observe from the graph that as the number of iterations increases, the approximate solutions become close to the series solution of the fourth-term approximation.

A Digital Twin of a University Campus from an Urban Sustainability Approach: Case Study in Madrid (Spain)

The development of geographic information systems has grown significantly over the past decade. Simultaneously, the concept of smart cities based on the management of large volumes of data has also spread worldwide. The digital twin concept has recently been incorporated into the technological domain of urban management. However, currently, phases such as technological integration, standardization, data and process interconnection, the development of tools and utilities, professional training, and the application of digital urban development in real-world situations are converging. This paper presents the experience developed on a university campus, detailing each of the phases carried out from the initial design to a fully operational pilot phase model. The article highlights the importance of certain aspects to consider in each phase, demonstrating that there are barriers and limitations and at the same time, great strengths and opportunities in applying the digital twin model in urban management, considering aspects such as mobility, accessibility, energy management, and involving students and university administrators in the process.

Damage detection and localization in plate-like structures using sideband peak count (SPC) technique

This paper addresses the critical issue of detecting and localizing damage in plate-like structures, which are commonly encountered in aerospace, marine and other engineering applications. To address this challenge, the current study introduces the sideband peak count (SPC) technique as the foundation for diagnostic imaging for damage detection in plate structures. The proposed damage detection algorithm requires only a limited number of sensor responses, streamlining the detection process. It does not rely on a reference baseline, thereby enhancing its efficiency and accuracy. This approach enables rapid and precise identification of damage and its location within the plate structure. To validate the effectiveness and applicability of the proposed method, finite element simulation results are utilized. These results demonstrate the capability of the proposed technique to accurately detect and localize damage, providing a promising solution for enhancing the structural health monitoring of plate-like structures in various engineering domains.