Many Fields Of Engineering Research Articles

Using generative Artificial Intelligence tools in Public Relations: Ethical concerns and the impact on the profession in the Romanian context

The controversy surrounding ChatGPT has reopened the debate about the impact of new technologies in many fields of activities, including communication and PR. This study mapped Romanian PR practitioners’ use of generative AI and their perception of it, placing a special focus on the ethical concerns involved and the implications for the profession itself. We took a quantitative-qualitative approach by using both a survey and semi-structured interviews. Our goal was to determine the impact of generative artificial intelligence (AI) in the Romanian PR industry and to understand the reasons and challenges behind integrating generative AI in PR practice. The survey findings revealed a substantial adoption (73.5%) of AI within the Romanian PR community, with an overwhelming 91.6% of them using ChatGPT. The satisfaction level was remarkably high, with 92% expressing satisfaction with generative AI application efficacy. Benefits included timesaving, work simplification, and the reduction of repetitive tasks. Surprisingly, not only did 67.3% of respondents not perceive AI as an immediate threat to PR jobs, but 80.5% believed AI represents an opportunity for the industry. Indeed, almost all our interviewees admitted relief and satisfaction when using generative AI tools to complete their tasks. However, some concerns were expressed regarding the quality of the generative AI content and, in particular, the need to always check this kind of content by a human editor before using it. Moreover, PR professionals’ main ethical concerns are related to the issue of transparency towards their clients when using AI tools to produce different types of content.

Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.

This paper proposes a scheme for predicting ground reaction force (GRF) and center of pressure (CoP) using low-cost FSR sensors. GRF and CoP data are commonly collected from smart insoles to analyze the wearer's gait and diagnose balance issues. This approach can be utilized to improve a user's rehabilitation process and enable customized treatment plans for patients with specific diseases, making it a useful technology in many fields. However, the conventional measuring equipment for directly monitoring GRF and CoP values, such as F-Scan, is expensive, posing a challenge to commercialization in the industry. To solve this problem, this paper proposes a technology to predict relevant indicators using only low-cost Force Sensing Resistor (FSR) sensors instead of expensive equipment. In this study, data were collected from subjects simultaneously wearing a low-cost FSR Sensor and an F-Scan device, and the relationship between the collected data sets was analyzed using supervised learning techniques. Using the proposed technique, an artificial neural network was constructed that can derive a predicted value close to the actual F-Scan values using only the data from the FSR Sensor. In this process, GRF and CoP were calculated using six virtual forces instead of the pressure value of the entire sole. It was verified through various simulations that it is possible to achieve an improved prediction accuracy of more than 30% when using the proposed technique compared to conventional prediction techniques.

Path toward "Net Zero Organic Synthesis".

Researchers over the past ∼200 years have accomplished the synthesis of simple to very complex molecules; however, the concept of ideal synthesis has still not reached maturity. Of late, the "Net Zero" concept has captured the imagination of many fields of technology, in tune with Ideal Synthesis. The current Viewpoint covers the principles of ideal synthesis being discussed in the literature and how one could take up the synthesis of organic molecules considering the Net Zero concept to make this central science well-accepted by critics of this important field.

ДОСЛІДЖЕННЯ МЕТОДУ ФРАКТАЛЬНОГО СТИСНЕННЯ ЗОБРАЖЕНЬ З МЕТОЮ ПОКРАЩЕННЯ ЯКОСТІ СТИСНЕННЯ

The development of the Internet, along with the availability of increasingly powerful computers and other digital devices, cameras, scanners and printers, has led to the wide-spread use of digital images. In this regard, interest in improving data compression algo-rithms, such as images, is growing. Data compression is important for both transfer speed and storage efficiency. In addition to many commercial uses, compression technologies are also of interest in the military industry, such as applications for processing telemetry data from missile inter-ceptors or for archiving terrain image data for defense simulations. Solving the problem of image compression, or, more generally, image coding, has used advances and stimulated the development of many fields of engineering and math-ematics. The article examines fractal image compression — a data compression method based on the use of self-similar patterns in an image. This method allows you to achieve a high degree of compression while preserving image details. Fractal image compression is a unique and efficient approach to data compression based on the mathematical theory of fractals. Nowadays, it has important applications and advantages that make it a valuable tool in image processing. The main advantages include: 1. Preservation of details during compression. One of the key advantages of fractal compression is its ability to preserve a high degree of image detail in a relatively small amount of storage. This is especially important in situations where image quality must be preserved with limited storage and data transfer resources. 2. Efficiency of transmission through the network. Fractal compression allows for compact images, making it suitable for image transmission over a low-bandwidth network. This is especially true for mobile devices, the Internet of Things, and other scenarios where high bandwidth is not always available. 3. Adaptive compression for different resolutions. Fractal compression allows you to adapt the level of compression depending on the resolution and details of the image. This means that it can be used to compress various image sizes without significant loss of quality. 4. Data archiving and storage. Fractal compression can be useful for archiving and long-term storage of images, as it allows you to effectively reduce the amount of data without losing important information. This is especially important for libraries, archives, research databases and other data repositories. Fractal image compression remains a relevant and valuable tool in today's envi-ronment, thanks to its ability to efficiently compress, preserve details, and adapt to dif-ferent usage scenarios. Therefore, the study of its efficiency, the optimization of the soft-ware code to obtain a faster and better compression result, is an urgent task.

A Cost-Effective Integrated Methodology for Electromagnetic Actuation via Visual Feedback.

Electromagnetic actuation can support many fields of technology, such as robotics or biomedical applications. In this context, fully understanding the system behavior and proposing a low-cost package for feedback control is challenging. Modeling the electromagnetic force is particularly tricky because it is a nonlinear function of the actuated object's position and coil's current. Measuring in real time the position of the actuated object with the precision required for accurate motion control is also nontrivial. In this study, we propose a novel, cost-effective electromagnetic set-up to achieve position control via visual feedback. We actuated vertically and under different experimental conditions a 10 mm diameter steel ball hanging on a low-stiffness spring, demonstrating good tracking performance (the position error remained within ±0.5 mm, with a negligible phase delay in the best scenarios). The experimental results confirm the feasibility of the proposed set-up, which is characterized by minimum complexity and realized with off-the-shelf and cost-effective components. For these reasons, such a contribution helps to understand and apply electromagnetic actuation even further.

Prediction of Losses Due to Dust in PV Using Hybrid LSTM-KNN Algorithm: The Case of Saruhanlı

Sustainable and renewable energy sources are of great importance in today’s world. In this respect, renewable energy sources are used in many fields of technology. In order to minimize dust on PV panels and ensure their sustainability, power losses due to dust must be estimated accurately. In this way, the efficiency of a sustainable energy source will increase and serious economic savings can be achieved. In this study, a hybrid deep learning model was designed to predict losses caused by dust in PV panels installed in the Manisa Saruhanlı district. The hybrid deep learning model consists of Long Short-Term Memory (LSTM) and K-Nearest-Neighbors (KNN) algorithms. The performance of the proposed hybrid deep learning model was compared with LSTM and KNN algorithms. Sensitivity analysis was performed to statistically evaluate the prediction results. The input variables of the models were time, sunshine duration, humidity, ambient temperature and solar radiation. The output variable was the losses caused by dust in the PV panels. Hybrid LSTM-KNN, LSTM and KNN models predicted losses caused by dust in PV panels with 98.22%, 95.51% and 61.49% accuracy. The hybrid LSTM-KNN model predicted losses caused by dust in PV panels with higher accuracy than other models. Using LSTM and KNN algorithms together improved the performance of the hybrid deep learning model. With sensitivity analysis, it was found that solar radiation is the most important variable affecting the losses caused by dust in PV panels.

Modifying the false discovery rate procedure based on the information theory under arbitrary correlation structure and its performance in high-dimensional genomic data

BackgroundControlling the False Discovery Rate (FDR) in Multiple Comparison Procedures (MCPs) has widespread applications in many scientific fields. Previous studies show that the correlation structure between test statistics increases the variance and bias of FDR. The objective of this study is to modify the effect of correlation in MCPs based on the information theory. We proposed three modified procedures (M1, M2, and M3) under strong, moderate, and mild assumptions based on the conditional Fisher Information of the consecutive sorted test statistics for controlling the false discovery rate under arbitrary correlation structure. The performance of the proposed procedures was compared with the Benjamini–Hochberg (BH) and Benjamini–Yekutieli (BY) procedures in simulation study and real high-dimensional data of colorectal cancer gene expressions. In the simulation study, we generated 1000 differential multivariate Gaussian features with different levels of the correlation structure and screened the significance features by the FDR controlling procedures, with strong control on the Family Wise Error Rates.ResultsWhen there was no correlation between 1000 simulated features, the performance of the BH procedure was similar to the three proposed procedures. In low to medium correlation structures the BY procedure is too conservative. The BH procedure is too liberal, and the mean number of screened features was constant at the different levels of the correlation between features. The mean number of screened features by proposed procedures was between BY and BH procedures and reduced when the correlations increased. Where the features are highly correlated the number of screened features by proposed procedures reached the Bonferroni (BF) procedure, as expected. In real data analysis the BY, BH, M1, M2, and M3 procedures were done to screen gene expressions of colorectal cancer. To fit a predictive model based on the screened features the Efficient Bayesian Logistic Regression (EBLR) model was used. The fitted EBLR models based on the screened features by M1 and M2 procedures have minimum entropies and are more efficient than BY and BH procedures.ConclusionThe modified proposed procedures based on information theory, are much more flexible than BH and BY procedures for the amount of correlation between test statistics. The modified procedures avoided screening the non-informative features and so the number of screened features reduced with the increase in the level of correlation.

AI-Powered ChatGPT in Dental Practice: A Meteoric Rise

The future of humanity in many fields of engineering and health care is being changed by artificial intelligence and this is creating a lot of enthusiasm around the world. It forms the cornerstone of the latest technological advancements encompassing big data robotics and the Internet of Things and is poised to continue driving innovation in the foreseeable future. The ChatGPT Chat-Generative Pretrained Transformer is the latest innovation concerning AI that has emerged in the market. This language model has the unremarkable ability to generate and think like a human which could also provide human-like text. Even though ChatGPT is currently in the beta testing phase it could be a breakthrough in medicine as well as in dentistry. This review focuses on explaining the prospects and providing insights regarding the future of ChatGPT in dental practice.

Irreversibility mixed convection Jeffrey fluid flow analysis in a vertical channel with an inclined magnetic field and Soret effects under slip conditions

Jeffrey fluid flow is found to have extensive applications in many fields of engineering. There are numerous applications of Jeffrey fluid in polymer industries and industrial fluids such as paints, paper, toothpaste, ketchup, etc. The present work explains the irreversibility of Jeffrey fluid in mixed convection Navier–slip flow through a vertical channel having an inclined magnetic field. The model equations of linear momentum, energy, and concentration are formulated and numerically solved using the spectral quasi-linearization method. The outcomes are depicted graphically and quantitatively discussed for active parameters that appeared in mathematical formulations. Moreover, flow and cross-flow velocity increase with an enhancement in Soret number and magnetic parameter, whereas heat transfer rate decreases with these parameters. Temperature, cross-flow velocity, and heat transfer decrease as the inclination angle increases. Flow velocity, concentration, and mass transfer rise as the inclination angle rises. The results demonstrate that as the magnetic parameter, hall parameter, angle of inclination, and slip condition at the right plate increase, the entropy generation number also increases. Conversely, it decreases as the slip condition at the left plate and Jeffrey fluid parameter increase.

Technological Aspects of Manufacturing and Control of Gears—Review

Gear drives are widely used in various fields and applications due to their properties and capacity. Their versatility, durability, and ability to transmit high torques as well as precision and reliability make them extremely useful in many fields of technology. They are widely used in industrial and energy machinery, vehicle drive systems, aerospace, medical devices, and many other areas. Gears can be manufactured using many technologies. This work focuses mainly on machining with particular emphasis on high-performance new technologies. The process of mathematical modeling of the gear and the machined profile is strongly related to CNC machining technologies. A robust correlation of systems supporting the design and modeling of sliding gears needed for the manufacturing process is presented in the article. It is very important to properly assess gears with correct manufacturing in accordance with a specific standard. The article presents an analysis of available methods for controlling gears using coordinate measurement techniques. Gear machining methods were assessed in terms of the technologies used as well as their productivity and manufacturing tolerance.

Face2PPG: An Unsupervised Pipeline for Blood Volume Pulse Extraction from Faces.

Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurable. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.

A massive MPI parallel framework of smoothed particle hydrodynamics with optimized memory management for extreme mechanics problems

The dynamic failure process of structures under extreme loadings is very common in many fields of engineering and science. The smoothed particle hydrodynamics (SPH) method offers inherent benefits in dealing with complex interfaces and large material deformations in extreme mechanics problems. However, SPH simulations for 3D engineering applications are time-consuming. To address this issue, we introduce MPI (Message Passing Interface) in our SPH scheme to reduce computational time. Some optimizations are adopted to ensure the massive computation of the SPH method. In particular, an optimized memory management strategy is developed to control the memory footprint. With the present MPI-based massive parallelization of the SPH method, several validation examples are tested and analyzed. By comparing the present numerical results with the reference data, the dynamic failure process of complex structures subjected to extreme loadings like explosive and impact loadings can be well captured. A large number of particles, up to 2.04 billion, are adopted in the present simulations. The scaling tests show that the scalability of the massively parallel SPH program achieves a maximum parallel efficiency of 97% on 10020 CPU cores.

The future of photonic crystal surface-emitting lasers

Semiconductor lasers are the heart for the development of technologies in many fields. In recent decades, a new type of large-area surface-emitting lasers based on the in-plane photonic crystal modulation and feedback have emerged to show superior advantages of high output power, low-divergence, high beam quality and brightness, and compact and monolithic structure. In this paper, we review the design and development of photonic crystal surface-emitting laser and discuss the future investigation and improvements.

An expanding pipeline: 20 + years of Acentech internships

Acoustics interns at Acentech, a multidisciplinary acoustics, technology, and noise & vibration control consultancy based in Cambridge, Massachusetts, USA, have included undergraduate and graduate students as well as graduates in many fields of engineering, physics, and architecture. Acentech interns work on a deliberate mix of both project work and internal research and development, often inspired by research presented by academics in our field. For a time, interns were typically from one of approximately a dozen US-based graduate programs in acoustic, and more rarely, from one or two undergraduate programs with acoustics concentrations. In recent years, successful interns have hailed from an increasingly wide array of university programs, and the results have exceeded expectations: an increasingly diverse pipeline of skilled, intellectually curious individuals with a passion for music, buildings, and acoustical design.

Exact solution for thermal vibration of multi-directional functionally graded porous plates submerged in fluid medium

An analytical method for analyzing the thermal vibration of multi-directional functionally graded porous rectangular plates in fluid media with novel porosity patterns is developed in this study. Mechanical properties of MFG porous plates change according to the length, width, and thickness directions for various materials and the porosity distribution which can be widely applied in many fields of engineering and defence technology. Especially, new porous rules that depend on spatial coordinates and grading indexes are proposed in the present work. Applying Hamilton’s principle and the refined higher-order shear deformation plate theory, the governing equation of motion of an MFG porous rectangular plate in a fluid medium (the fluid-plate system) is obtained. The fluid velocity potential is derived from the boundary conditions of the fluid-plate system and is used to compute the extra mass. The Galerkin-Vlasov solution is used to solve and give natural frequencies of MFG porous plates with various boundary conditions in a fluid medium. The validity and reliability of the suggested method are confirmed by comparing numerical results of the present work with those from available works in the literature. The effects of different parameters on the thermal vibration response of MFG porous rectangular plates are studied in detail. These findings demonstrate that the behavior of the structure within a liquid medium differs significantly from that within a vacuum medium. Thereby, they offer appropriate operational approaches for the structure when employed in various mediums.

Space heating performance analysis of air source heat pump integrated with image gray recognition-based defrosting controller

In practice, owing to the unreasonable defrosting initiating method of air source heat pumps (ASHPs), mal–defrost occurs frequently, further leading to the increase of building heating energy consumption. In recent years, with the development of image recognition technology in many fields, it has been expected to be used in defrosting initiating control methods of ASHPs. Although former investigations demonstrate that it is feasible and promising, there is still lack of a control strategy to make it become reality. To promote the application of image recognition in defrosting initiating method of ASHPs, a control strategy for the defrosting initiating method based on image gray recognition was proposed in the present work and used in an image gray recognition equipment (IGRE). Then, nine groups of experiments were conducted based on this new IGRE method. Results indicate that the proposed control strategy for the IGRE method is feasible and practical. By employing this new IGRE method, the defrosting accuracy rate and the average COP of the ASHPs are respectively 42.86% and 36.60% higher, compared with those using the conventional T–T method. In complex and variable environments, the defrosting accuracy rate of this new IGRE method reaches 93.33% in a 10–day test.

Spontaneous formation of a nest-like frame structure in sprayed coatings for superhydrophobic applications

The accretion of ice, contaminants, oxidants, and organisms on exposed surfaces has been a perpetual concern in many fields of engineering and often presents a serious safety hazard. Green methods to make superhydrophobic (SH) coatings via spraying nanoparticles are attracting routes to resolve these issues, especially considering the low energy inputs and capital costs. However, these nanoparticle coatings do not yet meet the demanding standards of practical applications. Here, we show that spraying SiC whiskers (SiCw) with optimized dispersity in water spontaneously forms nest-like frame structures that readily host hydrophobic nanoparticles (NPs), yielding SH coatings with excellent durability and usability. The SiCw frame structure improves the wear resistance of the NPs coating by two orders of magnitude and withstands repeated bending and twisting (180°), making it more appealing than the state-of-the-art SH surfaces fabricated by sophisticated techniques. Exhibiting a low surface friction coefficient, superior hardness, and adequate adhesive strength and fracture toughness, the SiCw frame structure endows our novel SiCw/NPs composite coating with the necessary properties to meet the stringent demands for efficient SH coating fabrication.

Characteristics of changes in microstructure and mechanical characteristics under high energy load

Purpose. Verification of the hypothesis about the possibility of obtaining a fine-grained structure of a metal as a result of processing with high-density energies. The methods. The research was carried out on cylindrical samples with a diameter of 50 mm and a length of 300 mm, which were made of structural steel 45 in a normalized state. The thickness of the inner coaxial layer BP1 with a detonation speed D=7.5 km/s was 3 mm, the thickness of the outer layer BP2 with a detonation speed D=3.5 km/s was 40 mm. The two-layer BP charge used in the experiment increases the duration of the shock wave and at the same time protects the samples from destruction. Findings. The mechanism of nanostructuring, in general terms, consists in the accumulation of the degree of deformation without destruction, which leads to the defragmentation of the structure with a significant increase in the density of dislocations. One of the methods that allows you to achieve a high density of dislocations, comparable to IPD, is treatment with shock waves. The change in the grain structure in the places of the pores indicates extremely high degrees of inhomogeneous deformation in these areas. During the passage of the shock wave in the middle of the sample, at the boundaries of the interface of phases with different densities, diffraction wave effects occur, which lead to the occurrence of shear deformations. It can be assumed that in the area of pores and microcracks, the deformation conditions are similar to those realized by some methods of intensive plastic deformation. This leads to a sharp grinding of the grain structure and a corresponding change in mechanical properties. The originality. The paper found that the processing of steel parts with the help of high-density energy leads to a sharp grinding of the grain to the nanostructure size and, as a result, to an increase in mechanical properties. The revealed regularities make it possible to obtain optimal parameters of high-energy processing, which lead to an increase in hardness, a redistribution of internal stresses and a decrease in surface roughness. Practical implementation. Obtaining grains with nanostructural characteristics in the structure of the material will allow to create fundamentally new devices and materials. Such materials will have properties significantly exceeding their achieved level – which is important for many fields of engineering, medicine, biotechnology, environmental protection, defense, etc.

Towards U-statistics clustering inference for multiple groups

Inference in clustering is paramount to uncovering inherent group structure in data. Clustering methods that assess statistical significance have recently drawn attention due to their role in identifying patterns in high-dimensional data with applications in many scientific fields. Towards developing a general framework for clustering in multiple groups, we present here a U-statistics-based approach, specially tailored for high-dimensional datasets, that clusters the data into three groups while assessing the significance of such partitions. We also consider theoretical aspects of allowing for an outlier group. Our approach stands on the U-statistics-based clustering framework of the methods in R package uclust and inherits its properties being a non-parametric method relying on very few assumptions about the data. Thus it can be applied to a wide range of datasets. Furthermore our method aims to be a statistically powerful tool to find the best partitions of the data into three groups when that particular structure is present. To do so, we first propose an extension of the test U-statistic and develop its asymptotic theory. Additionally we propose a ternary non-nested significance clustering method. Our approach is tested through multiple simulations and is shown to be comparable or have more statistical power to competing alternatives in all scenarios considered. An application to image recognition data showcases our method.

OPTIMIZATION OF STIFFENED COMPOSITE PLATE USING BALANCING COMPOSITE MOTION OPTIMIZATION

Recently, metaheuristic optimization algorithms have been studied and applied in many fields of engineering, especially in solving structural optimization problems. For structures with complex behaviors, the selection of a suitable optimization algorithm significantly increases the computational efficiency and the accuracy of the solution. In this paper, we investigate the optimization problem of stiffened composite plates using Balancing Composite Motion Optimization. The core idea of the Balancing Composite Motion Optimization algorithm is that the searching movements of candidate solutions are compositely equalized in both global and local ones in the solution space of solving the fiber optimization problem of stiffened composite plates. A candidate solution can move closer to better ones to exploit the local regions and move further to explore the search space also. The method does not require algorithm parameters. The calculation results are compared with many other metaheuristic optimization methods to prove the accuracy and efficiency of the method.