Applications Deal Research Articles

Nets4Learning: A Web Platform for Designing and Testing ANN/DNN Models

Nowadays, any research discipline is interested in tackling its problems with artificial intelligence and, therefore, is demanding knowledge and frameworks with the aim of developing and using intelligent methods. Within this scenario, neural networks stand out for the important results they have achieved. This paper introduces Nets4Learning, a web platform for designing, training and testing artificial/deep neural network models. The application deals with some of the most popular tasks in the data science field such as tabular classification, regression, image classification and object detection. Nets4Learning has been designed so that any researcher from any discipline can easily develop neural network models without special programming or digital skills. In fact, the user does not have to install anything as the application is publicly available and can be accessed from any device. The site also has manuals, glossaries, etc., and all this code is available on GitHub.

A Dominance Approach for Influence Maximization with Incomplete Information in Social Network

Influence maximization plays an important role in social network analysis and has been extensively explored due to its emergence in a great deal of applications. However, a vast majority of researchers have only considered the influence of one factor on the spread of information among individuals. In addition, this factor which is mainly the influence probability, has been considered to be fixed which is estimated based on the number of neighbors of each individual. While, in reality, we do not have complete information about this probability and it is not a fixed value. Moreover, in the spread of information, other factors including trust, are also involved, which cannot be easily combined due to their different nature. In this paper, a dominance approach to influence maximization is studied under the condition, in which, complete information about influence of users on each other is not available and users infect each other based on an unknown influence probability. Furthermore, in order to make the proposed model closer to reality, trust factor is also considered as an independent factor. The proposed method is compared to several state-of-the-art influence maximization algorithms and attained results on several synthetic and real social network datasets verify effectiveness of the proposed method compared to previous methods in the context of influence maximization.

Photonic Crystal Materials with Full Spectrum Structural Color Modulated by Co‐Assembly Strategy

AbstractPhotonic materials have drawn considerable attention for a great deal of promising applications with versatile optical characteristics and functionalities. This review provides a concise overview of co‐assembly strategies for the creation of photonic crystals, offering precise control over structural colors throughout the entire visible spectrum by manipulating the interaction of light with complex micro‐nano structures. Specifically, the utilization of block copolymers (BCPs) and colloidal nanoparticles as wavelength‐scale building blocks facilitates the modulation of defined nanostructures with functionality and tailorability for diverse coloration effects. Additionally, by facilely adjusting the relative ratios, a flexible balance can be struck between photonic crystals and photonic glasses for dynamic control of arrangements. Finally, this review concludes by discussing the future outlook and challenges in advancing co‐assembly‐based photonic functional materials.

IIoT Protocols for Edge/Fog and Cloud Computing in Industrial AI

Various industrial applications deal with high-frequency data. Traditionally, these systems have analyzed high-frequency data directly on the data source or at the commanding PLC. However, currently, Industry 4.0 technologies support new monitoring scenarios for high-frequency data monitoring where raw data is transmitted in soft-real time to an Edge/Fog or Cloud node for processing, enabling centralized computing. This demands efficient communication protocols capable of handling high-frequency, high-throughput data. This paper focuses on analyzing the performance of key IIoT (Industrial Internet of Things) messaging protocols—AMQP, MQTT, KAFKA, ZeroMQ, and OPCUA—to evaluate their suitability, in terms of latency and jitter, for transmitting high-frequency data within these new scenarios. The analysis reveals MQTT, AMQP, and ZeroMQ as top performers in Edge/Fog computing, while ZeroMQ exhibits the lowest latency and jitter in Cloud computing. Finally, a guideline for protocol selection is proposed, aiding industrial enterprises in protocol selection for specific AI use cases.

A modeling strategy for the simulation of box sections with layered shell elements

This paper presents a numerical strategy to model box sections from bridge girders using horizontal layered shell finite elements. The basic idea is to avoid the construction of more computational expensive meshes based on the use of folded shell and brick finite elements. The numerical strategy imposes the deactivation of those layers related to empty spaces within the bridge cross-section. The performance of this technique is explored by means of two demanding practical applications related to bridge structures with constant and variable deck thicknesses. The first application deals with the static truck load test of the Caynarachi Bridge, located in Peru, and for which measured field data exists, while the second application is related to the construction stage analysis of a box bridge structure including time effects due to concrete creep, shrinkage and steel relaxation. The results demonstrate that the studied technique acceptably correlates with the measured field data,expressed in terms of vertical displacements,with correlation coefficients exceeding 0.94.Additionally, the outcomes align well with the results of other numerical techniques, allowing applicants to use this simplified modeling approach in daily design office.

Exploring the Literature of Data Analytics Services on Cloud Computing: A Comprehensive Summary

Data generated from the internet and modern applications is extensive and rapidly expanding. So, all modern applications must successfully perform tasks using this massive data. Therefore, one of the significant factors for the success of any application is understanding and extracting meaningful information using digital analytics tools to positively impact the application's performance and deal with challenges that can be encountered. On the other hand, cloud computing is simply an environment comprising a collection of high-performance services from various vendors. These services can frequently access and process massive amounts of data faster than a traditional computer. One of these services is cloud analytics, which applies cloud-based analytic algorithms against data in a private or public cloud to get the desired outcome. This paper differentiates between several analytics methodologies to create highly consistent, logical, and information-rich summaries. This research has discussed and analyzed many studies related to this field. Therefore, the results of this paper can be utilized to determine the advantages of these methods, which will help future researchers in their research for a more organized and thorough analysis when dealing with such applications. In addition, this paper attempts to discover new directions and propose new guidelines in this evolving field.

Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques

In the era of automated and digitalized information, advanced computer applications deal with a major part of the data that comprises audio-related information. Advancements in technology have ushered in a new era where cutting-edge devices can deliver comprehensive insights into audio content, leveraging sophisticated algorithms such such as Mel Frequency Cepstral Coefficients (MFCCs) and Short-Time Fourier Transform (STFT) to extract and provide pertinent information. Our study helps in not only efficient audio file management and audio file retrievals but also plays a vital role in security, the robotics industry, and investigations. Beyond its industrial applications, our model exhibits remarkable versatility in the corporate sector, particularly in tasks like siren sound detection and more. Embracing this capability holds the promise of catalyzing the development of advanced automated systems, paving the way for increased efficiency and safety across various corporate domains. The primary aim of our experiment is to focus on creating highly efficient audio classification models that can be seamlessly automated and deployed within the industrial sector, addressing critical needs for enhanced productivity and performance. Despite the dynamic nature of environmental sounds and the presence of noises, our presented audio classification model comes out to be efficient and accurate. The novelty of our research work reclines to compare two different audio datasets having similar characteristics and revolves around classifying the audio signals into several categories using various machine learning techniques and extracting MFCCs and STFTs features from the audio signals. We have also tested the results after and before the noise removal for analyzing the effect of the noise on the results including the precision, recall, specificity, and F1-score. Our experiment shows that the ANN model outperforms the other six audio models with the accuracy of 91.41% and 91.27% on respective datasets.

Dissipative exoskeletons for seismic rehabilitation of RC buildings

Relevant buildings like schools, public offices, assembly halls, and so on, should retain their structural integrity since their collapse could cause major human losses and significant economic impact. Moreover, such buildings should remain operational even during seismic retrofit work. This has stimulated the development of seismic retrofit solutions based on rapid, low-impact, and reversible interventions that can be done while the building is in use, removed and rapidly replaced if damaged due to earthquake, and integrated with energy efficiency measures. This situation has encouraged the use of external additive structures, commonly called exoskeletons, as a feasible solution for seismic retrofit. Typically, the research and applications deal with non-dissipative steel exoskeletons based on diagrids or external braces. This paper outlines the design and evaluation of dissipative exoskeletons. It focuses on a real school building case study, where both parallel and orthogonal exoskeleton configurations are employed. The dissipative exoskeletons are designed using a displacement-based design procedure. The effectiveness of the retrofit strategy for ductile failure modes is finally demonstrated by nonlinear time-history analyses under different sets of earthquake-ground motions.

Simple algebraic groups with the same maximal tori, weakly commensurable Zariski-dense subgroups, and good reduction

We provide a new condition for an absolutely almost simple algebraic group to have good reduction with respect to a discrete valuation of the base field which is formulated in terms of the existence of maximal tori with special properties. This characterization, in particular, shows that the Finiteness Conjecture for forms of an absolutely almost simple algebraic group over a finitely generated field that have good reduction at a divisorial set of places of the field (cf. [66]) would imply the finiteness of the genus of the group at hand. It also leads to a new phenomenon that we refer to as “killing the genus by a purely transcendental extension.” Yet another application deals with the investigation of “eigenvalue rigidity” of Zariski-dense subgroups (cf. [64]), which in turn is related to the analysis of length-commensurable Riemann surfaces and general locally symmetric spaces. Finally, we analyze the Finiteness Conjecture and the genus problem for simple algebraic groups of type F4.

Optimized derivative fast Fourier transform with high resolution and low noise from encoded time signals: Ovarian NMR spectroscopy

The unfiltered derivative fast Fourier transform (dFFT) of degrees higher than two fails flagrantly for encoded time signals. These data are always dominated by noise at larger times of encodings. Such a breakdown is due to processing the unweighted product of the time signal and the time power function. The latter is generated by the frequency derivative operator applied to the fast Fourier transform (FFT). As a result, the unfiltered dFFT cannot separate the overlapped resonances and it dramatically decreases signal-to-noise ratio (SNR) relative to the FFT. This problem is solved by a derivative-adapted optimization with the properly attenuated filters. The ensuing optimized dFFT achieves the long sought simultaneous enhancement of both resolution and SNR. It uncovers the genuine resonances hidden within overlapping peaks to enable quantitative interpretations. It does not impose any model on the input time signals nor on the output lineshape in the spectra. It is computationally expedient as it uses the Cooley-Tukey fast algorithm. The present applications deal with time signals encoded by in vitro NMR spectroscopy from human malignant ovarian cyst fluid. A remarkably successful performance of the optimized dFFT is demonstrated for reconstructed spectra of potentially added value in clinical decision-making.

Benefits of Non-Destructive Evaluation and Probabilistic Damage Tolerance Cosimulation for Fracture Risk Reduction

In the context of the damage tolerance approach used to drive aircraft maintenance operations, it is essential to demonstrate the reliability of NDE inspections in detecting structural damage especially for additively manufactured components since this process produces components that may introduce material anomalies at any location within a component. The Probability Of Detection curve (POD) that links the probability to detect a detrimental flaw to its size is generally used as a key indicator for that purpose by giving the maximum flaw size that a NDE process can miss with a given level of probability and confidence. This information can then be used along with other inputs such as component geometries, mechanical properties, constraints, service and residual stresses and damage evolution speed to assess fracture risk and then adapt maintenance scenarios to optimize safety and component service life. NDE reliability and risk assessment are based on statistical indicators that need a large amount of data to provide reliable metrics. It is difficult to obtain such indicators using a purely empirical approach that is based on physical trials and measurements as it may involve many mock-ups and costly processes. Simulation tools can achieve this goal via their ability to include and precisely monitor many parameters as well as high computing capacities that are now available. The work presented in this paper involved cosimulations performed between the DARWIN® probabilistic damage tolerance software and the CIVA NDE simulation software. DARWIN computes fracture risk levels throughout a component, while CIVA can efficiently provide Probability Of Detection curves at different locations in a component and for various NDE methods. The presented application deals with a titanium gas turbine engine impeller disk and involves an Ultrasonic NDE inspection technique. It appears to be a very interesting approach to connect together NDE and Fracture mechanics simulations, two disciplines that generally work on their own. Indeed, DARWIN helps the user to determine detrimental flaw types, locations and sizes which are key inputs to develop an effective NDE inspection method. CIVA can provide location-specific POD curves that enable DARWIN to quantity the effect of dedicated NDE on potential risk reduction. This highlights the importance of NDE simulation for safety and helps to identify potential changes in the physical NDE process and the maintenance cycle that provide the best compromise between detection performance and cost.

Topological Characterization of Hexagonal Network and Non-Kekulean Benzenoid Hydrocarbon

Graph theory has great deal of application in disciplines like biology, computer science, informatics, engineering, mathematics, social science, linguistics and medicine. Topological indices are created by labeling the chemical structure through numerical parameters. These notations link chemical compounds physicochemical features such as boiling temperature, stability, strain energy etc, to its molecular structure. Different topological features of chemical structure have been explored. One of the molecular graphs topological indices are the connection indices defined on the number of connection between vertices of distance two. In this work, we compute the first Zagreb connection index, second Zagreb connection index, and modified first Zagreb connection index for hexagonal network and non kekulean benzenoid hydrocarbon.

Mobility and Privacy-Aware Offloading of AR Applications for Healthcare Cyber-Physical Systems in Edge Computing

Cyber-physical systems (CPSs) can be regarded as a new generation of systems which have been widely used for healthcare system. The introduction of Augmented Reality (AR) can further enhance the effectiveness of healthcare CPSs. AR applications can provide a better user experience in the health treatment process for both patients and clinicians. However, AR applications are computation-intensive, putting a substantial computational burden on AR devices. Fortunately, offloading AR applications to edge nodes can enable AR to be suitable for real-time applications. Nevertheless, AR applications deal with the patient’s private information; placing it on edge raises serious privacy concerns. Besides, the network structure of AR applications has spatio-temporal uncertainty. To tackle these issues, we jointly investigate the computation offloading for AR applications in the healthcare CPSs in edge computing considering user privacy protection and mobility. We propose a novel multi-objective meta-heuristic method based on the R2 indicator-II, which preserves privacy, and minimizes the Motion-to-photon latency, energy consumption, and maintain load balancing. Eventually, it verifies the efficiency and superiority of our proposed approach based on a certain scale of the experiments.

A Review of Prognostic and Health Management (PHM) Methods and Limitations for Marine Diesel Engines: New Research Directions

Prognostic and health management (PHM) methods focus on improving the performance and reliability of systems with a high degree of complexity and criticality. These systems include engines, turbines, and robotic systems. PHM methods involve managing technical processes, such as condition monitoring, fault diagnosis, health prognosis, and maintenance decision-making. Various software and applications deal with the processes mentioned above independently. We can also observe different development levels, making connecting all of the machine’s technical processes in one health management system with the best possible output a challenging task. This study’s objective was to outline the scope of PHM methods in real-time conditions and propose new directions to develop a decision support tool for marine diesel engines. In this paper, we illustrate PHM processes and the state of the art in the marine industry for each technical process. Then, we review PHM methods and limitations for marine diesel engines. Finally, we analyze future research opportunities for the marine industry and their role in developing systems’ performance and reliability. The main added value of the research is that a research gap was found in this research field, which is that new advanced PHM methods have to be implemented for marine diesel engines. Our suggestions to improve marine diesel engines’ operation and maintenance include implementing advanced PHM methods and utilizing predictive analytics and machine learning.

Recent Advances, Development, and Impact of Using Phase Change Materials as Thermal Energy Storage in Different Solar Energy Systems: A Review

The efficient utilization of solar energy technology is significantly enhanced by the application of energy storage, which plays an essential role. Nowadays, a wide variety of applications deal with energy storage. Due to the intermittent nature of solar radiation, phase change materials are excellent options for use in several types of solar energy systems. This overview of the relevant literature thoroughly discusses the applications of phase change materials, including solar collectors, solar stills, solar ponds, solar air heaters, and solar chimneys. Despite the complexity of their availability and high costs, phase change materials are utilized in the majority of solar energy techniques because of the considerable technical improvements they provide. While numerous studies have investigated the progress of phase change materials used in solar energy applications such as photovoltaic systems, it is vital to understand the conceptual knowledge of employing phase change materials in various types of solar thermal energy systems. Investigations into the use of phase change materials in solar applications for the purpose of storing thermal energy are still being carried out to upgrade the overall performance. This paper briefly reviews recently published studies between 2016 and 2023 that utilized phase change materials as thermal energy storage in different solar energy systems by collecting more than 74 examples from the open literature. This study focuses on demonstrating the maturity of phase change materials and their integration into solar energy applications. Based on the findings, proposals for new research projects are made.

Thermal stability of twin graphene: A Reaxff molecular dynamics study

Twin graphene, a new phase of two-dimensional semiconducting carbon allotrope, is theorized to exist and show a great deal of potential applications due to its superior electronic and mechanical properties. In this investigation, the thermal stability of twin graphene is explored via molecular dynamic (MD) simulations based on a reactive force field (ReaxFF). We show that, before twin graphene totally transforms into amorphous graphene, the initial structural evolution of twin graphene shows unique behavior that leads to a mesophase consisting of amorphous graphene, carbon atom chains and complex structures from 1500 to 1700 K. In particular, a new twin structure, named v-twin graphene, is observed in the mesophase. Detailed simulations show that v-twin graphene possesses a more stable molecular structure than the original twin graphene. These findings provide fundamental understandings for the thermal stability and structural evolution of these two types of twin graphene.

Effects of ionic liquid on micellar aggregate formed by pluronic (F-127) and non-ionic surfactant (TX-100) in aqueous solution

Although ionic liquids have a great deal of application, a few of the most popular ones are in separations, battery electrolyte development, and electrocatalysis. The synergistic interaction between surface-active ionic liquid (1-hexadecyl-3-methylimidazolium chloride, SAIL) and pluronic (F-127)/non-ionic surfactant (TX-100) has been investigated at 298.15 K. The examined mixture's critical micelle concentration (cmc) values for various compositions are significantly lower than those of pure amphiphiles (due to the strong attractive interaction between components). In this study, the surface tension method was used to calculate the cmc values of single and mixed amphiphiles. The mole fractions of the components in the mixed micelle as well as the mixed monolayer and interaction parameter were specified by regular solution theory (RST) using the data from experimental observations. Several thermodynamic parameters were also calculated for the mixed micellization of SAIL with F-127/TX-100. In the current mixed systems, the data obtained show that the ΔGM and ΔGIdealM are negative while ΔSM is positive, implying a spontaneous process of micellization. Using Maeda's theory, it was found that the stability of mixed micelles in these systems depended profoundly on chain-chain interactions. UV-visible spectroscopy further supports the positive interaction between SAIL and nonionic surfactants. The SAIL absorption spectra demonstrate an increase in absorption with increasing surfactant concentrations without any shift, supporting the creation of a new complex. The F-127+SAIL mixture has a higher binding constant value, indicating that SAIL interacts with pluronic more effectively.

In Vivo Toxicity of Oral Administrated Nano-SiO2: Can Food Additives Increase Apoptosis?

Nano-silicon dioxide (nano-SiO2) has a great deal of application in food packaging, as antibacterial food additives, and in drug delivery systems but this nanoparticle, despite its wide range of utilizations, can generate destructive effects on organs such as the liver, kidney, and lungs. This study is aimed at investigating the toxicological effects of nano-SiO2 through apoptotic factors. For this purpose, 40 female rats in 4 groups (n = 10) received 300, 600, and 900mg/kg/day of nano-SiO2 at 20-30nm size orally for 20days. Relative expression of Caspase3, Bcl-2, and BAX genes in kidney and liver was evaluated in real time-PCR. The results indicated the overexpression of BAX and Caspase3 genes in the liver and kidney in groups receiving 300 and 900mg/kg/day of nano-SiO2. Bcl-2 gene was up-regulated in the liver and kidney at 600mg/kg/day compared to the control group. Overexpression of the Bcl-2 gene in the kidney in 300 and 900mg/kg/day recipient groups was observed (P ≤ 0.05). Histopathological examination demonstrated 600mg/kg/day hyperemia in the kidney and lungs. In addition, at 900mg/kg/day were distinguished scattered necrosis and hyperemia in the liver. The rate of epithelialization in the lungs increased. The nano-SiO2 at 300 and 900mg/kg/day can induce more cytotoxicity in the liver and lung after oral exposure. However, cytotoxicity of nano-SiO2 at 600mg/kg/day in the kidney and lung was noticed. Hence, the using of nano-SiO2 as an additive and food packaging should be more considered due to their deleterious effects.

Optical Character Recognition System for Digit Recognition Using Deep Learning

Because it is so difficult to distinguish handwritten digits, digit identification is one of the most critical applications in computer vision. This is one of the reasons why it is so tough. The field of handwritten character recognition is one in which a great deal of application of numerous deep learning models has occurred. The startling parallels that can be drawn between deep learning and the brain are primarily responsible for its meteoric rise in popularity. In this study, the Artificial Neural Network and the Convolutional Neural Network, two of the most used Deep Learning algorithms, were investigated with an eye toward the recognition process's feature extraction and classification phases. With the assistance of the categorical cross-entropy loss and the ADAM optimizer, the models were trained on the MNIST dataset. Backpropagation and gradient descent are the two methods utilized during the training process of neural networks that contain reLU activations and carry out automatic feature extraction. In computer vision, one of the most common and widely used classifiers is the Convolution Neural Network, sometimes referred to as ConvNets or Convolutional neural networks. This network is used for the recognition and categorization of images.

Contractions Use as the New Trend in PR Professional Language: Linguistic and Linguodidactic

The article deals with the analysis of PR professional language variety as referred to teaching English to university undergraduates. PR terminology encompasses various contracted forms, which contain structurally and semantically encoded information that refers to language economy in terms of heuristic approach to language. The analysis of the vocabulary in question brings the subject matter into the focus of interdisciplinary research in the field of humanitarian studies, with reference to its linguistic and linguodidactic aspects. Certain spheres dealing with PR communication are subject to the analysis and synthesis in terms of linguistics, political science, economic theory, philosophy, psychology, sociology, rhetoric, and cognitive science. To elaborate the methods of teaching L2 to PR undergraduates, it is necessary to carry out consistent analysis of all language levels representatives in their interaction. The analysis of PR terminology, especially abbreviations and acronyms, is carried out to facilitate the process of professionally oriented L2 teaching. The results application deal with practical issues of linguodidactics and language teaching methodology as referred to PR studies with respect to the content and structure of teaching process, material choice and exercise samples with abbreviated forms in different types of activities. The article provides examples of possible tasks aimed at developing students' communicative and general cultural competencies based on the work with the language material under consideration. The shift of PR procedures implementation into Web 3.0 is discussed in terms of LSP teaching, which concerns with appropriate Internet materials use and processes of adequate interpretation as well as contracted realizations creation, determined by students’ abilities and their proficiency level.