Industrial Domains Research Articles

Mapping the Complexity of Legal Challenges for Trustworthy Drones on Construction Sites in the United Kingdom

Drones, unmanned aircraft controlled remotely and equipped with cameras, have seen widespread deployment across military, industrial, and commercial domains. The commercial sector, in particular, has experienced rapid growth, outpacing regulatory developments due to substantial financial incentives. The UK construction sector exemplifies a case where the regulatory framework for drones remains unclear. This article investigates the state of UK legislation on commercial drone use in construction through a thematic analysis of peer-reviewed literature. Four main themes, including opportunities, safety risks, privacy risks, and the regulatory context, were identified along with twenty-one sub-themes such as noise and falling materials. Findings reveal a fragmented regulatory landscape, combining byelaws, national laws, and EU regulations, creating business uncertainty. Our study recommends the establishment of specific national guidelines for commercial drone use, addressing uncertainties and building public trust, especially in anticipation of the integration of “autonomous” drones. This research contributes to the responsible computing domain by uncovering regulatory gaps and issues in UK drone law, particularly within the often-overlooked context of the construction sector. The insights provided aim to inform future responsible computing practices and policy development in the evolving landscape of commercial drone technology.

Aligning technical knowledge to an industry domain in global software development: A systematic mapping

AbstractFinding software developers with expertise in specific technologies that align with industry domains is an increasingly critical requirement. However, due to the ever‐changing nature of the technology industry, locating these professionals has become a significant challenge for companies and institutions. This research presents a comprehensive overview of studies exploring suitable recommendation systems that can assist companies in addressing this pressing need. To conduct this study, we employ a hybrid systematic mapping approach with an initial number of 1,251 studies and a final selection of 21 studies. Our work focuses on collecting data on key technologies, methodologies, and data sets utilized in proposed recommendation systems, to design a new recommendation system that can effectively identify specialists capable of aligning specific technical knowledge with industry domains. The outcomes of this study include insights into the current research trends in this field, alongside a practical overview of considerations necessary for developing a recommendation system that successfully meets the criteria for aligning technical skills with industry domains. By following a hybrid systematic mapping methodology and presenting the outcomes in the form of insights, this research addresses the challenge of finding software developers with domain‐specific expertise in a rapidly changing technology industry, laying the groundwork for aligning technical skills with industry domains.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

DTAG: A Methodology for Aggregating Digital Twins Using WoTDT Ontology

The advancement of digital twins (DTws) has been instrumental in various scientific and industrial domains, facilitating real-time monitoring, analysis, and optimisation of complex systems. However, it remains difficult to describe precisely the architectural models and their characteristics of DTws and the aggregation of lower-level DTws to higher-level DTws. This article introduces two contributions with the goal of addressing challenges in describing DTws architectures and aggregating DTws. Firstly, it presents the development of “WoTDT” (WoT digital twin) ontology, an extension of the W3C Web of Things descriptions ontology, designed to semantically describe the five-dimensional model architecture of DTws. This ontology enhances data interoperability and accessibility across dimensions, promoting a deeper understanding of DTws. Secondly, it introduces the “DTAG” (digital twin aggregation) methodology for aggregating multiple DTws into an unified DTw aggregate (DTwA). This methodology considers whether the DTws contain semantics or not and employs the WoTDT ontology to conceptualise the architecture and features of the resulting DTwA. Finally, an example of WoTDT ontology together with the DTAG methodology is shown in the context of the European H2020 construction-related project COGITO.

Landscaping the digital twin technology: Patent-based networks and technology reference model

The digital twin (DT) is a core technology for supporting digital transformation (DX) across industrial domains that has functions of monitoring, control, simulation, visualization, and prediction. However, there exist conceptual and terminological inconsistencies in DT due to industrial heterogeneity and technological complexity. We conduct patent-based network analysis to develop a technology reference model for DT technologies in order to uncover the core enabling technologies and technological structure. Two types of network analysis, co-word and co-citation, are constructed based on the 1201 DT-related patents. The co-word network analysis identifies 13 technology clusters, and the co-citation network analysis reveals 16 technology clusters. The combined results yield 17 technology components structured into three layers: (a) enabling technology, (b) core functionality, and (c) service according to technological role. A DT technology reference model is then developed based on the structured technology components. The proposed DT technology reference model offers comprehensive insights into the landscape of DT technologies and serves as a guide for researchers and policymakers seeking to systematically understand DT technologies for the DX paradigm.

Advancing multimodal diagnostics: Integrating industrial textual data and domain knowledge with large language models

The rapid advancement and application of large language models (LLMs) in various domains prompt an investigation into their potential in the field of prognostics and health management (PHM), particularly for enhancing data-driven model capabilities. This study explores the integration of domain knowledge accumulated in unstructured text data such as technical documents and maintenance logs into diagnostics models using LLMs. The study demonstrates the new possibilities to exploit data that are traditionally underutilized due to their complexity and the presence of domain-specific jargon. By leveraging LLMs for contextual understanding and information extraction from such texts, this study proposes a novel approach that combines textual data with existing condition monitoring systems to improve the accuracy of diagnostics models. A case study on hydrogenerators illustrates the feasibility and value of integrating LLMs into PHM systems. The findings suggest that the incorporation of LLMs can lead to more informed, accurate diagnostics, ultimately enhancing operational efficiency and safety within industrial environments.

Numerical simulation for MHD slip flow with heat transfer over a stretching bullet‐shaped object

AbstractThis paper investigates magnetohydrodynamic (MHD) boundary layer slip flow with heat transfer over a bullet shaped object. The study addresses a significant problem in fluid dynamics and heat transport with applications in various engineering and industrial domains, including aerospace, material processing, and energy systems. The governing equations are resolved using the bvp4c, an inbuilt MATLAB tool, and the arithmetic computation for the momentum and thermotic equations are executed. The results are exhibited graphically. Numerical outcomes are graphically depicted with the aid of velocity and temperature profiles for several model variables. The achieved results exhibit a promising agreement with the previously established findings available in the open literature. The heat transfer processes and fluid flow are remarkably influenced by means of the ratio of surface thickness and stretching potential. The results obtained designated that the Mixed convection parameter λ increases momentum BL thickness, whereas the temperature profile diminishes. Furthermore, momentum and temperature profile improve for surface thickness parameter . The current investigation highlights the potential utility of heat transport rate and friction factor in the industrial divisions for regulating cooling rates and enhancing the quality of end products.

ITRPL: An intelligent and trusted RPL protocol based on Multi-Agent Reinforcement Learning

Routing Protocol for Low Power and Lossy Networks (RPL) is the de-facto routing standard in IoT networks. It enables nodes to collaborate and autonomously build ad-hoc networks modeled by tree-like destination-oriented direct acyclic graphs (DODAG). Despite its widespread usage in industry and healthcare domains, RPL is susceptible to insider attacks. Although the state-of-the-art RPL ensures that only authenticated nodes participate in DODAG, such hard security measures are still inadequate to prevent insider threats. This entails a need to integrate soft security mechanisms to support decision-making. This paper proposes iTRPL, an intelligent and behavior-based framework that incorporates trust to segregate honest and malicious nodes within a DODAG. It also leverages multi-agent reinforcement learning (MARL) to make autonomous decisions concerning the DODAG. The framework enables a parent node to compute the trust for its child and decide if the latter can join the DODAG. It tracks the behavior of the child node, updates the trust, computes the rewards (or penalties), and shares them with the root. The root aggregates the rewards/penalties of all nodes, computes the overall return, and decides via its ϵ-Greedy MARL module if the DODAG will be retained or modified for the future. A simulation-based performance evaluation demonstrates that iTRPL learns to make optimal decisions with time.

Unlocking underrepresented use-cases for large language model-driven human-robot task planning

Large language models (LLM) are now the de facto task planners for Embodied AI (EAI) systems. This shift can be attributed to LLMs' powerful, emergent properties which enable their adaptation to downstream tasks with minimal to no fine tuning via prompting. However, we find that LLM-driven task planning is not a solved problem. In this work we measure the extent to which these models can be adapted to complex and domain-specific task planning via few-shot prompting. Additionally, we contribute quantitative and qualitative analysis on prompt robustness. Lastly, to meet the challenges of adapting EAI systems to real-world, industrial domains, we adopt a human-in-the-loop approach to guarantee safe and interpretable task planning and execution. We successfully demonstrate co-located, human-robot teaming where an Augmented Reality (AR) headset mediates information exchanged between an EAI agent and human operator for a variety of inspection tasks. To our knowledge the use of an AR headset for multimodal grounding and the application of EAI to industrial tasks are novel contributions within Embodied AI research.

Recent Advancements and Challenges in High‐Power Thulium‐Doped Laser

AbstractHigh‐power all‐fiber thulium lasers have gained considerable interest in recent times due to their distinct characteristics and versatile applications in the medical and industrial sectors. This review article presents a comprehensive examination of the advancements and challenges in this field. It begins with an overview of thulium‐doped silica fiber, which is a critical component for high‐power lasers operating at the 2 µm (micrometer) wavelength band. The research progress of essential high‐power thulium laser sources, including continuous‐wave (CW), quasi‐continuous wave (QCW), and pulsed lasers, is then thoroughly analyzed, highlighting their respective strengths and limitations. Additionally, the diverse applications of high‐power thulium fiber lasers in medical and industrial domains are summarized. Furthermore, the article emphasizes the current challenges in the advancement of high‐power thulium‐doped fiber lasers (TDFLs) and outlines potential avenues for future development. Despite TDFLs being the predominant laser source in lithotripsy and material processing applications, optimizing their performance and expediting further progress in thulium laser technology remain crucial objectives. This review article aims to provide valuable insights for researchers, engineers, and professionals working in the field of high‐power fiber lasers operating at 2 µm.

Optimal production plan in a production inventory model under the coexistence of both green and regular products: A study on Indian textile industry

The necessity to restrict environmental pollution worldwide has led to the development and implementation of green products. Various sectors within the industrial domain are now placing emphasis on the creation of environmentally friendly products in order to minimize their impact on the environment. The textile industry, both in India and globally, significantly contributes to environmental pollution through the release of untreated effluents into water and the emission of carbon into the atmosphere. Consequently, the popularity of green apparel is steadily increasing as a means to alleviate this crisis. It has been reported that, despite customer preference for green apparel, factors such as the cost of these products and a lack of consumer awareness regarding their importance deter a significant portion of potential buyers from making purchases. In order to stimulate demand for green apparel, the government could implement a subsidy scheme and initiate advertising campaigns highlighting the benefits of using such products. In the market, green apparel and regular apparel are viewed as substitutes for one another and coexist. As a result, there is a constant price competition between these two types of products. This research examines the optimal inventory decisions of manufacturers in a production inventory model where both green and regular products are present and product substitution is allowed. The demand for both types of products is influenced by their respective selling prices. Furthermore, the level of greenness and the efforts put into advertising green products have positive effects on generating demand for these products. The resulting objective function in this model is non-concave, and the quantum behaved particle swarm optimization technique is utilized to solve the corresponding optimization problem. Additionally, Mathematica software has also been employed to verify the efficiency of the proposed solution algorithm. To provide economic validation of the proposed model, a numerical example is presented. Finally, a sensitivity analysis of the key parameters is conducted to gain insights into their impact on the optimal solution. The results indicate that strategic and well-executed advertising campaigns, along with government subsidy schemes, can boost the market for green products and generate greater profits for manufacturers.

Investigating the effects of partitioning temperature fluctuations on the mechanical properties of ASTM A36 carbon steel using Q-P-T heat treatment: an experimental study

In the continuum of time and technological advancement, the use of metals, specifically carbon steel, has significantly increased as primary materials in various operational and industrial domains, including tool fabrication and automotive components. To meet the evolving demands of industries, precise heat treatment processes have been developed to enhance the metallic properties. This study specifically focused on the application of the Quenching-Partitioning-Tempering (Q-P-T) method to ASTM A36 steel. The study investigated different partitioning temperatures, namely 300℃, 350℃, and 400℃, with 15-minute intervals. A comprehensive set of mechanical tests, including hardness, tensile, and microstructural analyses, were conducted to assess the response of the material to the treatment. The results reveal significant findings: a partitioning temperature of 300℃ yields the highest hardness value of 164 Vickers Hardness Number (VHN). Furthermore, the tensile tests demonstrate that a partitioning temperature of 300℃ is optimal, achieving a maximum stress value of 515.73 MPa. Conversely, a partitioning temperature of 400℃ exhibits the highest strain value at 21.08% and the highest elastic modulus value at 11.47 GPa. Microstructural evaluations highlighted the presence of pearlite and ferrite phases, with the partitioning temperature of 300°C displaying the highest proportion of pearlite phase at 38.5%. This meticulous investigation expands our understanding of metallurgy and underscores the intricate relationship between partitioning temperatures and the mechanical properties of ASTM A36 steel. It provides valuable insights for material design and application methodologies and facilitates advancements in industrial practices

Nanosecond laser-assisted fabrication of Ti6Al4V surfaces with gradient wettability and robust cross-linked microstructures

Abstract Surfaces with gradient wettability outperform singular superhydrophobic surfaces in terms of self-cleaning efficiency, anti-contamination properties, and fluid manipulation. These attributes offer extensive application potential across various industrial and scientific domains. This study introduces and employs nanosecond pulsed laser ablation to create wettability gradient surfaces on Ti6Al4V alloys, featuring robust cross-linked frame microstructures. Experimental results demonstrate that by varying the ridge width (w), associated with the liquid-solid contact fraction, we can achieve varying wettability and mechanical durability in these cross-linked frame microstructures. Wettability tests indicate static contact angles ranging from 150.7° to 105.45°. Furthermore, the sandpaper linear abrasion test illustrates a decrease in material wear rate with an increase in w. Under similar test conditions, the proposed surfaces demonstrate superior mechanical durability compared to two other prevalent wettability surface structures. The proposed surfaces, efficiently and eco-friendly produced through nanosecond laser fabrication, hold tremendous potential for diverse applications.

Evaluation of innovative thermal performance augmentation techniques in heat sinks & heat pipes: Electronics & renewable energy applications

Heat sinks and heat pipes have vast applications including industrial domains requiring high heat flux. Due to numerous uses of these heat transfer devices, there have been great advancement in this area through innovation. This study focuses use of nanofluids and hybrid nanofluids for thermal performance enhancement of heat pipes and heat sinks. There are five different arrangements finless sintered copper wicked heat pipe, water based TiO2 filled heat pipe, TiO2 and SiO2 based hybrid nanofluid filled heat pipe, water cooled cylindrical pin finned heat sink and Fe2O3 and TiO2 hybrid nanofluid cooled heat sink. There are two test rigs one for testing heat pipe configurations and other for heat sinks. All the five elements are tested at constant heat flux ranging from 900 W/m2 to 3600 W/m2, in four equal steps. It’s observed that nanofluids play a very significant role in thermal performance augmentation, the affect is even more significant in heat pipes compared to heat sinks. Interestingly, nanofluids based heat pipes are proved more significant for high heat flux applications, although, nanofluid cooled heat sinks have slight better performance compared to the said but at a higher expense of weight, auxiliaries, space and cost. At 3600 W/m2 nanofluid based heat pipes have 33 % lower thermal resistance compared to water cooled heat sink. Therefore, nanofluid based heat pipes are primarily significant unless use of heat sink deemed dire. Nusselt number of heat sink is calculated at various heat flux and at variable mass flux conditions and found increasing with both parameter.

Comparative Analysis of Social and University Incubators from The Perspective of Public-Private Partnership

As the concept of corporate sustainability is being advocated, social innovation is also gradually gaining importance. Many startups encounter difficulties in their early stages of development, both in terms of operations and capabilities. Companies with business support can shorten the time to adapt to the market and increase the survival rate of startups. Meanwhile, incubators supporting entrepreneurship have evolved into different forms with the development of various industries. Incubators, which support entrepreneurship, have evolved alongside various industries, with some focusing specifically on social issues. Incubators focusing on social issues have also emerged, yet only a few studies have investigated social incubators. Therefore, this study conducts multiple case studies focusing on startups in the creative industry. weIt examines the processes of participating in both social incubators and university incubators, employing a comparative analysis framework based on the theory of Public-private partnership (PPPs). We selecting potential candidates from the list of supported companies listed on the incubator websites and choosing those within the creative industry domain, inviting them to participate in interviews. The study found that clustering similar types of enterprises helps deepen and enhance their growth. On the other hand, social incubators, due to their goals, resources, and activities, can provide more opportunities for collaboration with private enterprises and access to industry resources. Additionally, participation in university incubators not only provides comprehensive business support but also access to key resources for collaboration with the public sector. The experiential evidence from case studies of creative industry incubators in Asia fills the research gap related to social incubators. Through the comparison of different types of incubators, it enriches the knowledge base of stakeholders (private enterprises, government, universities, and business incubators) regarding PPPs. It aids researchers and practitioners in gaining a more comprehensive understanding of the current status and future directions of incubator development, highlighting the distinct roles and contributions of social and university incubators in supporting startup growth and innovation.

STFT-TCAN: A TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems

Networks and industrial systems play a pivotal role in modern society, and their security has garnered increasing attention. Anomalies within industrial equipment may propagate through fault transmission, leading to a cascade of failures. Additionally, cyberattacks on equipment can result in significant losses. Therefore, in the realm of industrial and cyberspace domains, an effective multivariate time series anomaly detection system for monitoring equipment is instrumental in ensuring the healthy operation of the machinery. Nevertheless, detecting anomalies in numerous time series remains challenging, stemming from the absence of anomaly labels and the complexity of the data patterns. Existing algorithms predominantly concentrate on modeling within the time domain, falling short in fully leveraging the informative features present in frequency domain data, resulting in diminished detection performance. This paper introduces STFT-TCAN, a model for anomaly detection in time series that seamlessly integrates information from both time and frequency domains for extracting data features. Sliding windows and the Short Time Fourier Transform (STFT) are utilized to construct a frequency matrix, effectively amalgamating the characteristics of both time and frequency domains within the time series. Furthermore, the model employs Temporal Convolutional Networks (TCN) and Transformer attention mechanisms (which combined to form the TCAN module) to capture the features of multivariate time series, thereby resulting in heightened detection accuracy. The proposed model undergoes validation on six publicly available datasets, showcasing the superior performance of the STFT-TCAN model in comparison to current baseline methods. It adeptly extracts features from both frequency and time domains in sequential data, thereby achieving state-of-the-art performance in tasks related to anomaly detection in multivariate time series.

Automatic beam optimization method for scanning electron microscopy based on electron beam Kernel estimation

Scanning Electron Microscopy (SEM) leverages electron wavelengths for nanoscale imaging, necessitating precise parameter adjustments like focus, stigmator, and aperture alignment. However, traditional methods depend on skilled personnel and are time-consuming. Existing auto-focus and auto-stigmation techniques face challenges due to interdependent nature of these parameters and sample diversity. We propose a beam kernel estimation method to independently optimize SEM parameters, regardless of sample variations. Our approach untangles parameter influences, enabling concurrent optimization of focus, stigmator x, y, and aperture-align x, y. It achieves robust performance, with average errors of 1.00 μm for focus, 0.30% for stigmators, and 0.79% for aperture alignment, surpassing sharpness-based approach with its average errors of 6.42 μm for focus and 2.32% for stigmators and lacking in aperture-align capabilities. Our approach addresses SEM parameter interplay via blind deconvolution, facilitating rapid and automated optimization, thereby enhancing precision, efficiency, and applicability across scientific and industrial domains.

INFLUENCES OF NANOPARTICLES ON DIELECTRIC FLUID TRANSFORMERS: A REVIEW

Applications of nanoscience & nanotechnology have produced advanced materials & demonstrated impressive development in a number of industrial domains. Many technological advancements & research & development initiatives involving nanotechnology concepts are underway these days in an effort to ace the performance & dependability of traditional materials. The electrical sector has been benefited from these nanotechnology initiatives by seeking applications for its various apparatuses, such as electrical transformers, which are regarded as one of essential parts of the electrical grid. This paper presents an extensive assessment of the literature on the uses of nanotechnology principles for transformers, with a focus on studies pertaining to dielectric fluids, monitoring systems, solid or outdoor insulators & many other components. The most recent research on the use of nanomaterials in transformer applications has been examined and documented. Furthermore, prospects for further investigation

The Relationships Between Two Indicators of International Research Collaboration: Co-Publication and Co-Patent

ABSTRACT This study investigates international research collaboration (IRC) dynamics between the United States and the United Kingdom, examining co-publication and co-patent indicators. While IRC is crucial for enhancing research competitiveness and economic development, the choice of indicators remains a challenge. The paper addresses this gap by examining the association between co-publications and co-patents on the measurement of IRC. Findings indicate a robust positive correlation between co-publications and co-patents, reflecting increased collaboration in academic and industrial domains. Despite distinct growth rates, their aligned trends suggest potential redundancy in using both indicators. Notably, co-patents have a lasting positive effect on co-publications, emphasising the role of industrial innovations in forecasting scientific collaboration. Acknowledging study limitations, particularly its focus on core countries, prompts future research avenues to broaden insights into IRC dynamics. This study connects theoretical understanding with practical implications, offering nuanced perspectives for policymakers to formulate strategic plans for global scientific collaboration. Researchers are encouraged to consider these insights when selecting indicators, aligning choices with data availability and analytical ease to enhance the relevance and impact of their studies.

Water strategies and management: current paths to sustainable water use

Freshwater is unquestionably the most crucial resource essential for the sustenance and advancement of humankind. This invaluable entity surpasses all societal, economic, and environmental domains, consequently rendering it a ubiquitous good. Globally, it has been estimated that the industrial sector employs approximately 20% of the available freshwater. The principal aim within the industrial domain is to maximize production efficiency, rather than prioritizing the enhancement of water conservation and efficiency. Research suggests that a favorable association exists between the monetary investments made in technological improvements for industrial water treatment and reuse and a profitable return on investment that is sustained over a prolonged duration. This could plausibly explain the dearth of willingness exhibited by some corporations in dedicating resources to this vital issue. The objective of this study is to explicate the notion of sustainability concerning water management that can be operationalized in the context of Pakistan, while delving into the latest advancements in the sphere of sustainable management practices. This research endeavor shall serve as an instructive source for executives, entrepreneurs, and vested parties in various industrial domains to propel their endeavors toward sustainable practices while simultaneously achieving optimization and surpassing the benchmarks set by national regulations and international establishments. This investigation has illuminated the imperative of executing an all-encompassing water management strategy that incorporates the ecological, financial, and societal dimensions as the essential constituents of viability in industrial water utilization. This work ought to possess a worldwide scope, bearing in mind the ubiquitous character of industrial practices in the epoch of globalization.