Industrial Domains Research Articles

Study of the dynamic process in a nonlinear mathematical model of the transverse oscillations of a moving beam under perturbed boundary conditions

The study of transverse oscillations of systems moving along their axis is a very difficult, but at the same time a very important task. Mathematical models of nonlinear transverse oscillations of a beam moving along its axis are analyzed in this paper work, both for non-resonant and resonant cases. The task becomes even more complicated if we additionally take into account the method of fastening the ends of the beam or the perturbation at its ends. We have obtained dependencies that can be used in construction, transport, industry, mechanical engineering and other domains of technology, ensuring the stability and safety of the operation of such mechanical systems. Mathematical models have been obtained for structural engineers to determine the amplitude–frequency response of relevant structures. These mathematical models are key to researching the dynamics of moving media. The obtained results allow considering not only the influence of kinematic and physical-mechanical parameters on the amplitude–amplitude frequency response of the medium, but also the fastening method. In addition, the correlations obtained in the paper make it possible to study not only the influence of the moving medium parameters on the nature of changes in the frequency and amplitude of oscillations, but also to consider the movement at the points of support of the medium. Namely, even at the stage of designing a pipeline for a liquid flowing at a certain speed, it is possible to consider the influence of the oscillation of the supports or their fastening method on the dynamics of the oscillatory process. The resulting dependencies allow designers to consider the influence of the characteristics given in the paper with a high level of accuracy and predict dynamic phenomena in them. In engineering calculations of various mechanical systems, the resulting dependencies can be used to optimize parameters to avoid negative destructive phenomena during operation.

Triage Software Update Impact via Release Notes Classification

In the rapidly evolving domain of Industry 4.0, effective management of software updates is crucial for maintaining system continuity and security. This paper presents a novel machine learning-based approach for a prompt and effective triage of software updates, leveraging an evaluation of six release note classifiers to categorize updates by component type, release type, and security risk. Our methodology, tested on a dataset of 1,000 release notes commonly encountered in Industry 4.0 ecosystems, demonstrates Logistic Regression as the most accurate classifier. The findings not only highlight the practical applicability of our approach in real-world data but also set the foundation for future enhancements to streamline the machine learning triage process further.

Artificial Intelligence in the Printing Industry: A Systematic Review of Industrial Applications, Challenges and Benefits

The commercial printing industry is undergoing a significant transformation with the integration of Artificial Intelligence (AI), yet there is limited literature on its specific applications and impact. This systematic review addresses two critical research questions: What are the key industrial domains where AI is applied in the printing industry, and what are the associated benefits and challenges? Our study identifies the primary AI applications across Production Planning and Control (PPC), Quality Management (QM), Maintenance Management (MM), and Supply Chain Management (SCM). PPC employs optimization algorithms to optimise scheduling and resource allocation, improving production efficiency. In QM, Machine Learning and Computer Vision detect defects and optimize print quality. MM focuses on minimizing downtime by ensuring machines operate efficiently through predictive maintenance. Despite these advancements, challenges such as high-quality data requirements, algorithmic complexity, and integration difficulties persist. This research fills a critical gap in the literature by providing a comprehensive overview of AI’s role in the printing industry, offering valuable insights into its potential to drive efficiency, cost savings, and quality improvements. Our findings suggest that while AI holds substantial promise, its benefits are contingent upon overcoming significant implementation challenges. This study contributes to the ongoing discourse on digital transformation by providing a robust framework for understanding AI’s current and future impact on the printing sector. Our methodology followed PRISMA guidelines, with a thorough review and thematic analysis of peer-reviewed studies. The insights gained from this review can guide future research and practical implementations, positioning AI as a crucial enabler in the evolving landscape of the printing industry.

Security Issues and Defensive Approaches in Deep Learning Frameworks

Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issues and corresponding defensive approaches within deep learning frameworks. By establishing clear connections between specific attacks and their defenses, we aim to enhance the robustness of these frameworks. In addition to theoretical considerations, our study extends to real-world applications, exploring a case involving the practical implications of deep learning security issues. Looking ahead, we discuss future directions and open issues within the realm of deep learning frameworks, aspiring to inspire further developments. We anticipate that our research will not only contribute valuable insights but also attract attention from both academic and industrial domains, fostering a collective commitment to fortifying the security of deep learning frameworks.

Non-similar modeling and numerical simulations of microploar hybrid nanofluid adjacent to isothermal sphere

Abstract In today’s era of rapid technological development, there is an increasing requirement for high-functioning investiture solutions, working liquids and materials that can satisfy the benchmarks of energy efficacy. Specifically, within the domain of heat transference-based industries, an essential challenge is to fabricate a cooling medium that can effectually cope with dissipation of substantial heat flux engendered by high-energy utilizations. At present, nanoliquids are extensively deliberated as some of the most promising aspirants for such effectual cooling mediums. The current investigation features hybrid nanoliquid flow adjacent to magnetized non-isothermal incompressible sphere. Rheological expressions representing micropolar liquid are accounted for flow formulation. The rheological analysis is developed using the boundary-layer concept. Buoyancy impact is accounted for heat transference analysis. Nanoparticles with distinct shapes are considered. The developed nonlinear systems are computed numerically and non-similar simulations are performed.

Mechatronics in modern industrial applications: Delving into the integration of electronics, mechanics, and informatics

This study delves into the integration of electronics, mechanics, and informatics within the realm of mechatronics, emphasizing its pivotal role in modern industrial applications. The primary objective is to dissect the synergistic interplay of these disciplines and assess their collective impact on various industrial sectors. Employing a systematic literature review as the core methodology, the research draws on a wide array of peer-reviewed articles and academic journals, focusing on publications from the last two decades to ensure relevance and contemporaneity. Key findings reveal that mechatronics has significantly evolved from its conceptual inception to a cornerstone technology in industries such as manufacturing, automotive, and healthcare. The study highlights the advancements in mechatronic systems, including the integration of AI and IoT, which have led to enhanced efficiency, precision, and adaptability in industrial processes. Furthermore, the research identifies the economic, technological, and environmental impacts of these systems, underscoring their role in driving innovation and sustainability. The study concludes with actionable recommendations for industry stakeholders and policymakers, emphasizing the need for continuous investment in R&D, workforce training, and the development of ethical standards and regulations. It also proposes future research directions, particularly focusing on the scalability, sustainability, and long-term societal impacts of mechatronic systems. This research contributes to a deeper understanding of mechatronics in the industrial domain, offering insights into its current applications and future potential.

The role of viscoplastic drop shape in impact

The impact of fluid drops on solid substrates is a cardinal fluid dynamics phenomenon intrinsically related to many fields. Although these impacting objects are very often non-spherical and non-Newtonian, previous studies have mainly focused on spherical Newtonian drops. As a result, both shape and rheological effects on the drop-spreading dynamics remain largely unexplored. In the present work we use a mixed approach combining experiments with multiphase three-dimensional numerical simulations to extend the work reported by Luu & Forterre (J. Fluid Mech., vol. 632, 2009, pp. 301–327) by highlighting the fundamental role of shape in the normal impact of viscoplastic drops. Such complex fluids are highly common in various industrial domains and ideally behave either like a rigid body or a shear-rate-dependent liquid, according to the stress solicitation. Spherical, prolate, cylindrical and prismatic drops are considered. The results show that, under negligible capillary effects, the impacting kinetic energy of the drop is dissipated through viscoplastic effects during the spreading process, giving rise to three flow regimes: (i) inertio-viscous, (ii) inertio-plastic, and (iii) mixed inertio-visco-plastic. These regimes are deeply affected by the drop initial aspect ratio, which in turn reveals the possibility of using drop shape to control spreading. The physical mechanisms driving the considered phenomenon are underlined by energy budget analyses and scaling laws. The results are summarised in a two-dimensional diagram linking the drop maximum spreading, minimum height and final shape with different spreading regimes through a single dimensionless parameter, here called the impact number.

A multi-appointment patient scheduling system with machine learning and optimization

Appointment scheduling is critical to increasing resource utilization and operational performance in various industry domains, especially healthcare. Costs to care for several serious diseases are projected to grow due to the aging population and rising drug prices. Thus, there is an urgent need for efficient operational planning and scheduling to reduce expenses. This research explores ways to effectively schedule outpatient chemotherapy visits with multiple appointments requiring different resources. The study aims to assess the impact of patient no-shows and individual stochastic appointment durations in scheduling performance and determine if overbooking is viable to mitigate the adverse effects of patient no-shows. The study first applies artificial neural networks (ANN) to calculate patient no-show probabilities and individualized appointment durations. Then, it builds several optimization models that use the ANN models’ outcomes to schedule outpatient chemotherapy visits. The performance of patient schedules obtained from these optimization models is assessed using simulation analysis to identify the effectiveness of overbooking to combat patient no-shows and determine if individual stochastic appointment durations produce better key performance indicators.

Numerical analysis of a metal-insulator-metal waveguide-integrated magnetic field sensor operating at sub-wavelength scales

This article introduces a novel plasmonic magnetic field sensor (MFS) that utilizes a Metal-Insulator-Metal (MIM) waveguide configuration with a W-shaped cavity filled with magnetic fluid (MF). The MFS's unique design combines the advantages of plasmonic sensing, offering a promising solution for the detection of magnetic field strength. It operates based on the inherent properties of surface plasmon polaritons and the magneto-optical properties of MF, resulting in a shift in resonant wavelength. The performance of the proposed MFS has been investigated through numerical calculation employing the finite element method (FEM). Remarkably, the MFS exhibits a maximum magnetic field sensitivity of 49.11 pm/Oe, covering a detection range from 33 Oe to 200 Oe. The recorded figure of merit (FOM) and Q-factor of the MFS are 18.39 and 18.4 respectively, attesting to its high performance and reliability. This innovation has the potential to revolutionize fields such as navigation, medical diagnostics, and robotics technologies by seamlessly integrating optical sensing into traditional devices. The proposed sensor's excellent performance, compact size, and cost-effectiveness position it as a promising technology for widespread adoption, contributing to advancements in magnetic field sensing across scientific, industrial, and technological domains.

EDL Induced Electro-magnetized Modified Hybrid Nano-blood Circulation in an Endoscopic Fatty Charged Arterial Indented Tract.

The electrokinetic process for streaming fluids in magnetic environments is emerging due to its immense applications in medical and biochemical industrial domains. In this context, our proposed model seeks to inquire into the hemodynamic characteristics of electro-magnetized blood blended with trihybrid nanoparticles circulation induced by electro-osmotic forces in an endoscopic charged arterial annular indented tract. This steaming model also invokes the consequences of variable Lorentz attractive force, buoyancy force, heat source, viscous and Joule warming, arterial wall properties, and sliding phenomena for featuring more realistic problems in blood flows. Different shapes of suspended trihybrid nanoparticles, such as spheres, bricks, cylinders, and platelets, are included in the model formation. Electro-magnetized modified hybrid nano-blood is an electro-conductive solution comprising blood as base fluid and magnetized trihybrid nanoparticles (copper, gold, and alumina). Closed-form solution in terms of Bessel's functions is gotten for electro-osmotic potential due to the electric double layer (EDL). The homotopy perturbation methodology is implemented in order to track down the convergent series solutions of non-linear coupled flow equations being elicited. The physical attributes of distinct evolving parameters on the different dimensionless hemodynamic profiles and quantities of interest are elucidated evocatively via a sort of graphs and charts. The ancillary outcomes proved that the Debye-Hückel parameter and Helmholtz-Smoluchowski velocity have a dual impact on the ionized bloodstream. The bloodstream rapidity is alleviated/boosted for the assisting/opposing electroosmosis process. Cooling of ionized blood in the endoscopic arterial conduit is achieved with lower Hartmann numbers. Copper-gold-alumina/blood exhibits a superior heat transmission rate across the arterial wall than copper-gold-blood, copper-blood, and pure blood. Additionally, the contour topology for the bloodstream in the flow domain is briefly elaborated. The contour distribution is significantly amended due to the variant of the Debye-Hückel parameter. The model's new findings may be invaluable in electro-magneto-endoscopic operation, electro-magneto-treatment for cancer, surgical process, etc.

Enhancing Multi-Modal Perception and Interaction: An Augmented Reality Visualization System for Complex Decision Making

Visualization systems play a crucial role in industry, education, and research domains by offering valuable insights and enhancing decision making. These systems enable the representation of complex workflows and data in a visually intuitive manner, facilitating better understanding, analysis, and communication of information. This paper explores the potential of augmented reality (AR) visualization systems that enhance multi-modal perception and interaction for complex decision making. The proposed system combines the physicality and intuitiveness of the real world with the immersive and interactive capabilities of AR systems. By integrating physical objects and virtual elements, users can engage in natural and intuitive interactions, leveraging multiple sensory modalities. Specifically, the system incorporates vision, touch, eye-tracking, and sound as multi-modal interaction methods to further improve the user experience. This multi-modal nature enables users to perceive and interact in a more holistic and immersive manner. The software and hardware engineering of the proposed system are elaborated in detail, and the system’s architecture and preliminary function testing results are also included in the manuscript. The findings aim to aid visualization system designers, researchers, and practitioners in exploring and harnessing the capabilities of this integrated approach, ultimately leading to more engaging and immersive user experiences in various application domains.

The investigation of printing parameters effect on tensile characteristics for triply periodic minimal surface designs by Taguchi

AbstractThe advent of additive manufacturing also referred to as 3D printing, has brought about substantial changes in the industrial domain, as it possesses the capability to fabricate intricate items with enhanced cost‐efficiency and productivity. Fused Filament Fabrication (FFF) is a 3D printing process that has gained significant popularity due to its versatile capabilities and cost‐effectiveness. This paper investigates the impact of the printing parameters on the tensile characteristics of Triply Periodic Minimal Surface (TPMS) manufactured using FFF 3D printing. TPMS patterns have unique geometric properties and potential applications, making them an intriguing subject of study. The behavior of three different TPMS lattices with three printing parameters is investigated. Finding the best testing settings is done with the Taguchi method, and the data is analyzed with the Analysis of Variance (ANOVA) test. TPMS pattern was found to be the most effective parameter with 83.78%. While the highest strength was obtained in Schwarz diamond, the highest energy absorption was observed in the Gyroid structure. The contributions of printing parameters to tensile strength are line thickness, printing speed, and layer height, respectively. As line width and printing speed increase, both energy absorption and strength increase. Therefore, 0.40 mm line width and 60 mm/s printing speed give optimum values. When considered for energy absorption, the optimum value is 0.20 mm layer height, while when considered for strength, 0.10 mm layer height is the optimum value. The findings emphasize the importance of optimizing printing parameters for desired mechanical characteristics in 3D printed components and highlight the potential of TPMS structures in various applications. This research contributes to the growing knowledge in additive manufacturing and provides insights into optimizing FFF 3D printing for improved mechanical performance.

Semi-automated dataset creation for semantic and instance segmentation of industrial point clouds.

The current practice for creating as-built geometric Digital Twins (gDTs) of industrial facilities is both labour-intensive and error-prone. In aged industries it typically involves manually crafting a CAD or BIM model from a point cloud collected using terrestrial laser scanners. Recent advances within deep learning (DL) offer the possibility to automate semantic and instance segmentation of point clouds, contributing to a more efficient modelling process. DL networks, however, are data-intensive, requiring large domain-specific datasets. Producing labelled point cloud datasets involves considerable manual labour, and in the industrial domain no open-source instance segmentation dataset exists. We propose a semi-automatic workflow leveraging object descriptions contained in existing gDTs to efficiently create semantic- and instance-labelled point cloud datasets. To prove the efficiency of our workflow, we apply it to two separate areas of a gas processing plant covering a total of 40000m2. We record the effort needed to process one of the areas, labelling a total of 260 million points in 70 h. When benchmarking on a state-of-the-art 3D instance segmentation network, the additional data from the 70-hour effort raises mIoU from 24.4% to 44.4%, AP from 19.7% to 52.5% and RC from 45.9% to 76.7% respectively.

Fuzzy Logic, Neural Network, and Adaptive Neuro-Fuzzy Inference System in Delegation of Standard Concrete Beam Calculations

Machine learning (ML) has been proven effective in various scientific and industrial domains. Nevertheless, its practical application in the construction industry requires further investigation. Leveraging ML capabilities conserves human resources, reduces errors, and speeds up computation and interpretation tasks. The efficacy of ML algorithms depends on factors like ability, result accuracy, analysis cost, and sensitivity to parameter count and available data volume. This study explores the potential of using ML to delegate structural calculation processes, which is an aspect with limited attention. Concrete beam section calculations based on the American Concrete Institute (ACI) standards were chosen as a case study to assess ML’s capacity to emulate a structural designer’s role. Initially, manual design steps and standard considerations for a concrete beam section were parametrically coded in MATLAB. Validation against structural design references ensured code accuracy in calculating shear and bending capacities. The parametric results served as initial data (lookup table) for training ML operators. Various ML techniques, including fuzzy logic (FL), neural network (NN), and adaptive neuro-fuzzy inference system (ANFIS), were coded in MATLAB. A comparative analysis of the three ML operators assessed their performance in replacing standard calculations. Parametric examples illustrated each operator’s precision in delegation compared to direct calculations. The study also explored the impact of the number of parameters and lookup table size on the accuracy of each ML operator. The findings revealed that while all three operators could delegate standard calculations, their precision varied. Notably, when the lookup table was optimal, ANFIS operators demonstrated the ability to represent standard calculations with varying parameter counts and high precision. Focused on beam calculations, this study provides insights into ML operator performance. The outcomes, including selecting the most capable operator and their sensitivity to parameters and lookup table size, offer valuable guidance for researchers interpreting experimental and numerical analysis results.

Определение готовности территорий Арктической зоны к цифровизации строительной отрасли на основе рейтингования регионов Российской Федерации

The relevance of developing the construction sector in Russia stems from the imperative to 1) enhance productivity, 2) reduce costs and improve quality and safety, and 3) address future challenges such as climate change and economic diversification. This article assesses the readiness of regions for digitalizing the construction sector through a ranking system based on the geometric mean. Digitalization readiness in the construction industry is the current state of territorial socio-economic systems defined across four areas of development: the socio-economic domain, research and innovations,the digital sphere, and the industrial domain. The study aims to establish a regional ranking to assess digitalization readiness in the construction sector, with a specific focus on the Arctic zone of the Russian Federation. Regional specialization in construction-related economic activities is identified, revealing that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) present opportunities for creating an industrial cluster. A benchmark ranking is proposed, indicating that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) exhibit attractiveness for strategic initiatives aimed at digitalizing the construction industry.

Влияние пандемии COVID-19 на цифровизацию экономики арктических регионов России

The relevance of developing the construction sector in Russia stems from the imperative to 1) enhance productivity, 2) reduce costs and improve quality and safety, and 3) address future challenges such as climate change and economic diversification. This article assesses the readiness of regions for digitalizing the construction sector through a ranking system based on the geometric mean. Digitalization readiness in the construction industry is the current state of territorial socio-economic systems defined across four areas of development: the socio-economic domain, research and innovations, the digital sphere, and the industrial domain. The study aims to establish a regional ranking to assess digitalization readiness in the construction sector, with a specific focus on the Arctic zone of the Russian Federation. Regional specialization in construction-related economic activities is identified, revealing that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) present opportunities for creating an industrial cluster. A benchmark ranking is proposed,indicating that the Yamalo-Nenets Autonomous Okrug and the Republic of Sakha (Yakutia) exhibit attractiveness for strategic initiatives aimed at digitalizing the construction industry.

Mathematical modelling of entropy generation in radiative non-Darcy flow of a Carreau-Yasuda nanofluid in a rotating channel with activation energy, Hall current and the Cattaneo-Christov heat flux

The current research investigates entropy generation in magnetohydrodynamic rotating multiple slip flow of Carreau-Yasuda nanofluid between parallel plates within a non-Darcy porous medium, considering factors such as viscous dissipation, Joule heating, Hall current, and nonuniform heat generation. The Carreau-Yasuda model, applicable to non-Newtonian fluids like blood and shampoo, is employed to comprehend rheological behavior. This study holds significance in industrial, technological, and medical domains, impacting areas like drilling muds, metallurgy, and cardiac MRI. Nonlinear thermal radiation enhances model realism. The research delves into chemical reactions influenced by activation energy. The mathematical model is formed using nonlinear partial differential equations with multiple slip boundary conditions, and addressed using a compatibility-based similarity transformation. Numerical solutions employ the successive linearization coupled with Chebyshev spectral collocation method. MATLAB-generated graphs and tables illustrate the influence of flow parameters on the physical quantities. The investigation reveals inverse and favoring trends of the Weissenberg number to the energy field in shear-thinning and shear-thickening scenarios, respectively. Velocity slip is found to diminishes entropy production in the channel.

VTubers and pandemic in China

Considering the current landscape of cultural product consumption through digital platforms, this study aimed to understand the exponential growth process of the VTuber segment in China, amidst the challenges posed by the COVID-19 pandemic. This research revealed the complexity involved in this development due to the intersection of factors unique to China, stemming from a very particular context regarding the degree of participation and the consequent intensity, as well as the networks established, albeit indirectly, by public and private entities. Through a literature review from a narrative perspective, case studies on prominent Chinese VTubers, and an analysis of the breadth of their engagement beyond live interactions with their fans, the study unveiled the emergence of new aesthetics and new possibilities for para-social relationships within this virtual realm. It also indicated a greater involvement in other sectors, such as services and public relations, thereby hybridizing and generating a distinct model for this phenomenon. This led us to conclude that this cultural product has found additional niches in China and is likely to expand into more, especially as the offering of an increasingly potent internet becomes more widespread among the population. Lastly, the unique Chinese context for products in this cultural industry domain underscored the robustness of its hybrid model and points toward a paradigm shift in the creation, performance, and consumption patterns related to VTubers, which may be replicable in other countries.

Impact of Showing Robot Demonstration on Introducing Social Robotics Field to University Students

Despite the social robotics’ potential to revolutionize human life, the field of social robotics has been less appealing and less popular than other robotics fields in both industrial and research domains. Therefore, many students are unfamiliar with this field. To expedite the development of the social robotics sector, numerous efforts must be made, including introducing students to the topic. In this study, we developed teaching materials and a procedure for introducing university students to the topic of social robotics. A robot demonstration was also utilized to provide participants with a more engaging experience and a deeper knowledge of the importance of social robotics. The ultimate objective of this paper is to provide a reference for lecturers and instructors in introducing social robots to students in the hope that they will become interested in the field, whether for the purposes of research or simply to learn more about this topic. We evaluated the proposed teaching materials and procedures for a small class size based on behavioral and subjective measurements. We also compared the outcome of presentations with and without a robot demonstration. In addition, we performed semi-structured interviews with 10 random participants who wanted to expand our qualitative analysis. Both presentations, with and without robot demonstration, successfully delivered the material. The experimental findings indicated that the robot demonstration had a significant impact on students’ satisfaction with the presentation and their desire to participate in similar presentations in the future.

Systems engineering issues for industry applications of large language model

Large language model (LLM) is an important direction in the development of AGI, but its technology is still in rapid change, and its capabilities still have obvious deficiencies and imbalances, with persistent problems such as hallucination, value non-alignment, weak specialization, and black-box effect. In this case, how to apply LLM to different professional fields and develop high-quality AIGC industry applications has become a great challenge for ISVs. Building AIGC industry applications based on LLM is not simply a matter of functional realization. Although researchers and open-source communities have proposed numerous application development frameworks or tool components, there is a lack of overall architecture design for systems engineering and a lack of discussion on theories and methods of LLM application development in large-scale industry domains, such as healthcare, government affairs, finance, and media. This paper analyzes the basic ideas of LLM industry applications development, defines the functional requirements and feature requirements of LLM industry applications, puts forward the concept of Large Language Model Systems Engineering (LLM-SE), and develops an AI assisted clinical risk prediction system for amyloidosis disease based on the architecture of LLM-SE, which adopt knowledge engineering, quality engineering, etc., and verifies the LLM-SE development architecture and methodology.