Field Of Engineering Research Articles

AI-Powered Innovations in Electrical Engineering: Enhancing Efficiency, Reliability, and Sustainability

Artificial Intelligence (AI) has emerged as a transformative force in the field of Electrical Engineering, revolutionizing traditional practices and unlocking unprecedented possibilities. This research paper investigates the integration of AI-powered innovations to enhance efficiency, reliability, and sustainability within electrical engineering systems. Through a comprehensive review of existing literature, this study delves into key applications of AI, including predictive maintenance, optimal resource allocation, and fault detection, among others. Utilizing advanced machine learning algorithms and data analytics techniques, AI facilitates real-time decision-making processes, enabling proactive maintenance strategies and optimizing system performance. Moreover, AI-driven approaches contribute to the enhancement of reliability by predicting potential failures and implementing pre-emptive measures, consequently reducing downtime, and improving operational continuity. Furthermore, the implementation of AI in electrical engineering fosters sustainability by optimizing energy consumption, mitigating environmental impacts, and facilitating the integration of renewable energy sources into power grids. By leveraging AI technologies, electrical engineering systems can adapt to dynamic operational conditions, maximize resource utilization, and minimize environmental footprints, thereby paving the way for a more efficient, reliable, and sustainable future. This paper underscores the transformative potential of AI in shaping the landscape of electrical engineering and provides insights into future research directions to harness the benefits of AI-powered innovations further.

Experiences of female early-career professionals in male-dominated STEM companies in Kazakhstan

ABSTRACT After the collapse of the Soviet Union, Kazakhstan put forward elaborate initiatives to address gender segregation in the labour market. However, female professionals are still heavily underrepresented in the fields of science, technology, engineering and mathematics. Considering conflicting cultural influences on the role of women in the country and guided by the social cognitive theory, the present study explores the early career experiences of female professionals working in STEM companies in Kazakhstan. The thematic analysis of 24 semi-structured interviews with early-career female professionals showed that social structural challenges with recruitment and promotion, organization integration and work-life balance prevent women from building successful careers in STEM. It was also found that to counterbalance these challenges, female professionals develop a variety of coping strategies such as projecting a professional image and adopting a masculine interaction style, conforming to the existing status quo and challenging gender discrimination and stigmatization. Drawing from the social cognitive theory, this study revealed that conflicting gender role expectations based on traditional, Western and Soviet cultural values might disempower and undermine women’s agency in transitioning to employment in STEM.

A rational approach for the scantling design of GRP pleasure crafts

In various fields of industrial engineering, including naval architecture, structural design is considered one of the most challenging steps by designers as it shall accommodate all other design aspects, likely already defined in earlier steps. Moreover, structures need to comply with specific requirements, i.e. limit states, while also performing in accordance with user's demands in terms of mission profile. In a goal-based design perspective, limit state criteria have to be defined accounting for both, structural robustness, functional and operational demands. Construction feasibility is an issue as well, further constraining the scantling design. Balancing construction needs, ship performances and structural aspects is not always an easy task.The environment in which a ship operates is indeed complex and verification of safety and robustness is usually carried out in accordance with classification societies and other rules, possibly applying direct calculations whenever applicable rules do not provide suitable solutions. At the same time, the asset needs to fulfill various attributes specified by the owner. As a matter of facts, the scantling design is mathematically a multivariate and over-constrained problem whose feasible solutions are in fact rather few. In the case of composite pleasure craft hull structures, while the number of design variables increases, feasibility constraints also largely reduce the design space.To address these challenges, designers may nowadays exploit new procedures able to reduce structural weight, though ensuring that the asset performs safely and adequately meeting client's requirements. To achieve this, designers may combine a more rational design philosophy with currently available numerical problem-solving applications to obtain a rational trade-off among all actually feasible solutions, which are relatively few.Thus, the objective of this study is to present a dependable numerical method for determining the scantling while reducing the weight of hull composite structures of pleasure crafts. This method aims to provide the most suitable, high-performing, and safe layout in the quickest and easiest possible manner. It turns out that counterintuitive, yet feasible and fit-for-purpose, structural lay-outs can be obtained.

Construction and Exploration of Experimental Teaching Content in Software Engineering from a Systems Theory Perspective

Software engineering, as a branch of traditional computer science, distinguishes itself from other computer-related disciplines by its emphasis on practical application. Experimental teaching in software engineering, integral to professional education, serves as a crucial means to enhance students’ practical skills. The content of experimental teaching, serving as a vessel for such instruction, plays a pivotal role in the holistic development of students. Addressing the current fragmented state of experimental teaching in applied universities, this study introduces a holistic and systematic view based on systems theory. By integrating the fundamental principles and methods of systems theory with the distinctive characteristics of software engineering, this study focuses on analyzing the holistic technology of software implementation and the interrelationships among various technical elements of software systems within the field of software engineering. From a holistic-part-holistic analytical perspective, this research constructs an analytical model for experimental teaching content in software engineering under the lens of systems theory. The study elaborates on the construction method of this model and its specific implementation details from the perspectives of talent cultivation programs, engineering elements, and concentrated practical exercises. Practical evidence indicates that the implementation of this experimental teaching content effectively enhances students’ practical skills in software engineering, elevates students’ recognition of their field of study, boosts their confidence in job placement, and provides robust support for seamless transition from graduation to employment. Furthermore, this study sheds light on the construction of experimental teaching content in computer-related fields in engineering disciplines at other applied universities.

Bamboo structures: Innovative methods and applications for structural health monitoring and dissemination

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Enhanced Typhoon-Induced Design Wave Height Estimation for Ocean Engineering Applications Using High Performance Computing

Coastal construction heavily depends on accurately estimating design wave parameters. This paper presents a technique for calculating ocean engineering design wave heights that addresses the challenges of inadequate wind speed and measured wave data in the field using High Performance Computing (HPC). The proposed method combines the Simulating WAves Nearshore (SWAN) wave model driven by wind with two different wind field models: the NCEP/NCAR reanalysis wind field and the Jelesnianski typhoon model wind field. By combining these models, the study reduces the underestimation of wind speed and inaccurate depiction of typhoon-related details. The method's effectiveness is demonstrated by applying it to calculate the design wave elements of the deep-sea breeding platform in Dongluo Island, Fujian. The study results show that the platform can experience a maximum wave height of 9.42 m during a 50-year recurrence period. The research shows that the platform's southwest corner is the most vulnerable location. This new calculation method is a significant improvement in ocean engineering design since it overcomes the limitations that come with the lack of wind speed and measured waves. By utilizing multiple wind field models, the calculated wave heights' accuracy and reliability is considerably enhanced. The application of this method to the breeding platform in Dongluo Island has yielded valuable insights for designing resilient structures in similar oceanic environments. This study demonstrates the important application of computational numerical simulations in the field of ocean engineering, providing high-precision predictive capabilities for structural design and risk assessment.

Point Cloud Reduction Method Based on Curvature Grading and Voxel Filtering

In the context of automated assembly engineering based on reverse engineering in the aerospace field, redundant data will make it impossible to complete 3D model reconstruction efficiently and quickly, and point cloud data must be streamlined. A single simplification method cannot accurately retain the feature information of the scanned point cloud. Aiming at this background, a point cloud simplification method combining curvature grading and octree voxel filtering is introduced. The obtained curvature is divided into different levels by using the properties of logarithmic function, and the strong and weak feature points are distinguished. The weak feature points are filtered by octree voxel, and the filtered results are merged with the strong feature points to obtain the final streamlined results. This method is compared with the random sampling method and the point cloud simplification method combining 3D-SIFT feature extraction and voxel filtering. The experimental results show that this method not only greatly reduces the amount of data, but also preserves the local details of the original point cloud data, so as to achieve the purpose of efficiently compressing point cloud data.

PCR Independent Strategy-Based Biosensors for RNA Detection

RNA is an important information and functional molecule. It can respond to the regulation of life processes and is also a key molecule in gene expression and regulation. Therefore, RNA detection technology has been widely used in many fields, especially in disease diagnosis, medical research, genetic engineering and other fields. However, the current RT-qPCR for RNA detection is complex, costly and requires the support of professional technicians, resulting in it not having great potential for rapid application in the field. PCR-free techniques are the most attractive alternative. They are a low-cost, simple operation method and do not require the support of large instruments, providing a new concept for the development of new RNA detection methods. This article reviews current PCR-free methods, overviews reported RNA biosensors based on electrochemistry, SPR, microfluidics, nanomaterials and CRISPR, and discusses their challenges and future research prospects in RNA detection.

A novel current-based identification method for dynamic performance of high-speed on/off valve

Digital hydraulic systems (DHS) are widely used in complex engineering fields. Their response speed and control accuracy depend on the dynamic performance of High-speed on/off valve (HSV), which serves as the core control component of DHS. However, the compact structure and embedded installation configuration of the HSV make it difficult to accurately measure its dynamic performance. To address the problem, a novel current-based identification method (CBIM) is proposed in this study. Considering the B-H hysteresis characteristic of the soft magnetic alloy, the influence of the spool moving velocity on the coil current derivative is explored. On this basis, each feature point obtained from the current derivative model can be matched with every switching state of the HSV. Compared with the laser displacement detection method, results show that the proposed CBIM can accurately measure the moving characteristic of spool under different temperature rise conditions. Of these, the maximum identification error of individual switching state time is 3.96 % (compare 0.97 ms with 1.01 ms). Meanwhile, the maximum identification error of total dynamic time is only 1.49 % (compare 8.57 ms with 8.70 ms). Additionally, under different supply pressures, the feature points of the coil derivative curves are also obvious. Therefore, the experimental results all validate the effectiveness of CBIM, and it has scalable applications in the future.

Solving a class of multi-scale elliptic PDEs by Fourier-based mixed physics informed neural networks

Deep neural networks have garnered widespread attention due to their simplicity and flexibility in the fields of engineering and scientific calculation. In this study, we probe into solving a class of elliptic partial differential equations (PDEs) with multiple scales by utilizing Fourier-based mixed physics informed neural networks (dubbed FMPINN), its solver is configured as a multi-scale deep neural network. In contrast to the classical PINN method, a dual (flux) variable about the rough coefficient of PDEs is introduced to avoid the ill-condition of neural tangent kernel matrix caused by the oscillating coefficient of multi-scale PDEs. Therefore, apart from the physical conservation laws, the discrepancy between the auxiliary variables and the gradients of multi-scale coefficients is incorporated into the cost function, obtaining a satisfactory solution of PDEs by minimizing the defined loss through some optimization methods. Additionally, a trigonometric activation function is introduced for FMPINN, which is suited for representing the derivatives of complex target functions. Handling the input data by Fourier feature mapping will effectively improve the capacity of deep neural networks to solve high-frequency problems. Finally, to validate the efficiency and robustness of the proposed FMPINN algorithm, we present several numerical examples of multi-scale problems in various dimensional Euclidean spaces. These examples cover low-frequency and high-frequency oscillation cases, demonstrating the effectiveness of our approach. All code and data accompanying this manuscript will be publicly available at https://github.com/Blue-Giant/FMPINN.

Dynamic and controlled stretching of macroscopic crystalline membranes towards unprecedented levels

Imposing and controlling strain in materials such as semiconductors or ferroelectrics is a promising way to obtain new or enhance existing properties. Although the field of strain engineering has seen a rapid expansion over the last two decades, straining semiconductor membranes over large areas remains a challenge. A generic way of tuning strain and hence band structure and electric or magnetic properties of any crystalline material can be obtained by compression of a composite structure involving poorly compressible elastomers. Mechanically similar to the principle of a hydraulic press, this work proposes a device and describes analytically a methodology to easily strain macroscopic membranes up to unprecedented values. Using in-situ X-ray diffraction and Raman spectroscopy, we tuned the biaxial strain in silicon membranes up to a value of 2.1 %, paving the way for new studies in the field of strain related physics, from semiconductors to perovskite oxide multiferroics.

Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Dynamics of the traveling wave solutions of conformable time-fractional ISLW and DJKM equations via a new expansion method

This work searches the new wave solutions of the time-fractional ion sound and Langmuir waves (ISLWs) equation and the time-fractional Date–Jimbo–Kashiwara–Miwa (DJKM) equation with the conformable derivative. The m+1G′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( m+\\frac{1}{G^\\prime }\\right)$$\\end{document}-expansion method is employed for these models for the first time. Getting support from symbolic software, some new solutions are presented, which will be constructive for studies in the field of nonlinear physical phenomena. New wave solutions obtained by using the m+1G′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\left( m+\\frac{1}{G^\\prime }\\right)$$\\end{document}-expansion method have applications in engineering, physics, and other scientific fields. The regarded method may provide a framework for the exact solutions of several evolution equations in applied sciences. Moreover, the graphical illustrations are also demonstrated to observe the dynamical behaviours of the solutions, which may benefit the development of new technologies.

Advanced synthetic routes of metal organic frameworks and their diverse applications

Abstract Metal-organic frameworks (MOFs) are crystalline materials characterized by their porous structures, formed through coordination bonding between metal ions/clusters and multidentate organic linkers. MOFs have emerged as a significant class of materials with applications in energy storage, CO2 adsorption, and catalysis. This study serves as a brief introduction to the currently available synthesis methods of MOFs, aimed at acquainting beginners in the field of chemical engineering with the ongoing developments in MOF research. The discussed synthesis methods encompass traditional solvothermal/hydrothermal approaches, microwave synthesis of MOFs, one-pot synthesis, MOF nanocomposites, isothermal synthesis, and fluid-fluid synthesis. Notably, the MOF/NH2/Fe3O4 combination exhibited enhanced adsorption capacity of 618 mg/g and retained an efficiency of over 90 %. This study displays a valuable technique for designing functional MOF hybrid composites. By combining MOFs with specific materials, numerous advantages can be achieved in the newly created compounds (MOF composites), including synergistic effects beneficial in catalytic applications, and overcoming the challenges associated with using bare MOFs.

Platelet-rich fibrin as an autologous biomaterial for bone regeneration: mechanisms, applications, optimization.

Platelet-rich fibrin, a classical autologous-derived bioactive material, consists of a fibrin scaffold and its internal loading of growth factors, platelets, and leukocytes, with the gradual degradation of the fibrin scaffold and the slow release of physiological doses of growth factors. PRF promotes vascular regeneration, promotes the proliferation and migration of osteoblast-related cells such as mesenchymal cells, osteoblasts, and osteoclasts while having certain immunomodulatory and anti-bacterial effects. PRF has excellent osteogenic potential and has been widely used in the field of bone tissue engineering and dentistry. However, there are still some limitations of PRF, and the improvement of its biological properties is one of the most important issues to be solved. Therefore, it is often combined with bone tissue engineering scaffolds to enhance its mechanical properties and delay its degradation. In this paper, we present a systematic review of the development of platelet-rich derivatives, the structure and biological properties of PRF, osteogenic mechanisms, applications, and optimization to broaden their clinical applications and provide guidance for their clinical translation.

Design and optimization of double-layer structure for improved electromagnetic wave absorbing characteristic of single-layer foam cement-based materials containing carbon black

Electromagnetic wave (EMW) absorbing properties are increasingly needed in cement-based materials; however, lightweight and broadband absorption properties are challenging to realize. This study aims to create lightweight, broadband, and highly efficient EMW-absorbing materials by investigating a foam cement-based material with single- and double-layer structures containing carbon black (CB). The results revealed that CB significantly affects the mechanical properties and electrical conductivity of foam cement-based materials. The compressive strength of the foam cement-based material containing 1.5 wt% CB with a density of 0.9 g cm−3 can reach 7.0 MPa and has satisfactory workability and conductivity. The dielectric properties of the foam cement-based material can be synergistically controlled by its density and CB content, and obtain favorable electric loss ability. For a single-layer structure, the optimal reflection loss (RL) value of a specimen with 10 mm-thick can reach −29.67 dB with an efficient bandwidth of 0.99 GHz. In contrast, the double-layer structure exhibited remarkable broadband characteristics. When the total thickness of double-layer structure is 10 mm, the optimal two specimens can achieve a minimum RL value of −28.62 and −25.52 dB with a maximum effective bandwidth of 1.49 and 1.87 GHz, respectively. This study proposes single- and double-layer foam cement-based structures with excellent impedance matching and loss attenuation ability, and constructs an effective design method for cement-based materials to achieve lightweight and broadband absorption. In addition, these cement-based materials can be regarded as a new generation of EMW-absorbing materials in the field of construction engineering.

Experimental Study on Mechanical Behavior of Skirt–Pile Foundations in Saturated Clay under Horizontal Load

Skirt–pile foundations have gained widespread attention in the field of offshore engineering due to their ease of installation and high bearing capacity. In this study, the ultimate bearing capacity, pile bending moment distribution and development, cumulative deformation characteristics, and cyclic stiffness development of skirt–pile foundations were investigated using physical model tests. The experimental results indicate that the ultimate bearing capacity and deformation resistance of the foundation can effectively be improved by increasing the skirt diameter. The cumulative deformation of the skirt–piles exhibited rapid development during the initial stages of cyclic loading, eventually stabilizing. Under long-term cyclic loading, the existence of the skirt can share the bending moment, which then affects the internal force distribution of the pile foundation along the axis. The pile foundation’s cyclic stiffness reduces as the loading cycles increase and increases as the skirt diameter and length grow. Meanwhile, the horizontal cyclic stiffness decreases as the number of cycles increases, stabilizing after 3000 cycles. This study can not only deepen the understanding of the deformation laws of skirt–pile foundations in clay soil but also offers some references for the design of offshore pile foundations.

Data-driven intelligent prediction of TBM surrounding rock and personalized evaluation of disaster-inducing factors

With the widespread application of tunnel boring machines (TBM) in underground tunnel construction, the accurate and real-time identification of geological indicators has become a key factor for ensuring construction safety. In this study, we introduced the nomogram method into the field of civil engineering for the first time to predict surrounding rock grades. Against the backdrop of a major water conservancy project in China, an in-depth analysis of construction data from the ascending phase was conducted to establish the relationship between surrounding rock grade predictions and key construction parameters. Compared with traditional models, the nomogram method has shown advantages in terms of prediction accuracy. Furthermore, by providing a personalized scoring mechanism for specific construction data, the nomogram method not only achieves the real-time prediction of rock grades but also clearly reveals the dynamic relationships between input parameters and rock grades, clarifying the entire prediction process. We identified the main factors related to rock grade prediction among TBM construction parameters, including the slurry conveyor belt speed, jacking pressure, cutterhead torque, cutterhead thrust, thrust pressure, and actual rotation speed. The training process revealed that the nomogram prediction model achieved optimal performance with seven input parameters, balancing accuracy and training time. According to the prediction results, the nomogram model’s area under the curve index is 0.92, providing greater accuracy than the traditional random forest model (0.89). Therefore, this study enhances the transparency and interpretability of model predictions by providing personalized assessments of construction parameters for predicting surrounding rock grades, overcoming the black-box issue of existing prediction methods and offering a novel and effective tool for rock grade prediction.

Review of Image-Processing-Based Technology for Structural Health Monitoring of Civil Infrastructures.

The continuous monitoring of civil infrastructures is crucial for ensuring public safety and extending the lifespan of structures. In recent years, image-processing-based technologies have emerged as powerful tools for the structural health monitoring (SHM) of civil infrastructures. This review provides a comprehensive overview of the advancements, applications, and challenges associated with image processing in the field of SHM. The discussion encompasses various imaging techniques such as satellite imagery, Light Detection and Ranging (LiDAR), optical cameras, and other non-destructive testing methods. Key topics include the use of image processing for damage detection, crack identification, deformation monitoring, and overall structural assessment. This review explores the integration of artificial intelligence and machine learning techniques with image processing for enhanced automation and accuracy in SHM. By consolidating the current state of image-processing-based technology for SHM, this review aims to show the full potential of image-based approaches for researchers, engineers, and professionals involved in civil engineering, SHM, image processing, and related fields.

Transient analyses of underwater acoustic propagation with the modified meshfree radial point interpolation method and newmark time integration techniques

Underwater acoustic propagation plays a very important role in underwater communication and ocean engineering field. The radial point interpolation method (RPIM) is an effective numerical approach to solve underwater acoustic propagations though considerable numerical errors can always be caused due to the discontinuously differentiable nodal interpolation function in one integration cell. The origin of this numerical error primarily comes from the mismatch between the background integration cell and the nodal interpolation function support. To address this issue, a modified meshfree RPIM is developed in this work for transient analyses of underwater acoustic propagations. In the proposed modified RPIM, the construction of the interpolation function supports are dependent on the integration cells. In consequence, in one background cell all the quadrature points share one mutual support domain and the continuously differentiable interpolation functions can be obtained. Therefore, the numerical integration errors can be evidently decreased and the related numerical dispersion effects in wave analysis can be largely suppressed. Owing to the adequately small spatial dispersion errors from the proposed modified RPIM, it is observed that the modified RPIM with the Newmark time domain discretization scheme can monotonically improve the solution precision by directly reducing the temporal discretization interval. This property makes the modified RPIM clearly outperform the original RPIM and is especially suitable for handling very complex underwater acoustic propagations.