Applied Sciences

13 pages, 3271 KiB

Open AccessArticle

The Various Forms of Cow Manure Waste as Adsorbents of Heavy Metals

by Oviyanti Mulyani, Benny Joy and Dikdik Kurnia

Appl. Sci. 2022, 12(11), 5763; https://doi.org/10.3390/app12115763 - 6 Jun 2022

Cited by 5 | Viewed by 2808

In recent years, the application of cow manure waste as an adsorbent of heavy metals in water and soil has increased. The analysis of the most effective adsorbents from cow manure as materials that can reduce heavy metals, while being low-cost and easy [...] Read more.

In recent years, the application of cow manure waste as an adsorbent of heavy metals in water and soil has increased. The analysis of the most effective adsorbents from cow manure as materials that can reduce heavy metals, while being low-cost and easy to produce, is important in the agricultural field. This study investigated adsorbents from cow manure, such as compost, biochar and humic acid, and analyzed the capability of the adsorption mechanisms of Cr, Pb and Cd. The experiments were performed as a function of pH, adsorbent dose, initial metal ion concentration, and contact time. To investigate the mechanism of the adsorption process, the Langmuir and Freundlich models were used. The results showed that the optimum conditions of Cr, Cd and Pb ions were achieved by compost, biochar and humic acid with 83–99% removal. An adsorption isotherm model for compost, biochar and humic acid generally followed the Langmuir and Freundlich models. This study ranks the different forms of cow manure waste in the following order based on their ease of production, high adsorption capacity, and low cost: biochar > compost > humic acid. Full article

(This article belongs to the Special Issue Composts and Organic Wastes: Analytical Methods and Applications)

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23 pages, 5792 KiB

Open AccessArticle

Application of EEG Signals Integration to Proprietary Classification Algorithms in the Implementation of Mobile Robot Control with the Use of Motor Imagery Supported by EMG Measurements

by Dawid Pawuś and Szczepan Paszkiel

Appl. Sci. 2022, 12(11), 5762; https://doi.org/10.3390/app12115762 - 6 Jun 2022

Cited by 11 | Viewed by 3310

Abstract

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The [...] Read more.

This article is a continuation and extension of research on a new approach to the classification and recognition of EEG signals. Their goal is to control the mobile robot through mental commands, using a measuring set such as Emotiv Epoc Flex Gel. The headset, despite its relative advancement, is rarely found in this type of research, which makes it possible to search for its advanced and innovative applications. The uniqueness of the proposed approach is the use of an EMG measuring device located on the biceps, i.e., MyoWare Muscle Sensor. This is to verify pure mental commands without additional muscle contractions. The participants of the study were asked to imagine the forearm movement that was responsible for triggering the movement command of the LEGO Mindstorms EV3 robot. The change in direction of movement is controlled by artifacts in the signal caused by the blink of an eyelid. The measured EEG signal was subjected to meticulous analysis by an expert system containing a classic classification algorithm and an artificial neural network. It was supposed to recognize mental commands, as well as detect artifacts in the form of blinking and change the direction of the robot’s movement. In addition, the system monitored the analysis of the EMG signal, detecting possible muscle tensions. The output of the expert algorithm was a control signal sent to the mobile robot. Full article

(This article belongs to the Special Issue Advances in Technology of Brain-Computer Interface)

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19 pages, 4673 KiB

Open AccessArticle

An Intelligent Optimization Back-Analysis Method for Geomechanical Parameters in Underground Engineering

by Jianhe Li, Weizhe Sun, Guoshao Su and Yan Zhang

Appl. Sci. 2022, 12(11), 5761; https://doi.org/10.3390/app12115761 - 6 Jun 2022

Cited by 4 | Viewed by 1868

Abstract

The geomechanical parameters in underground engineering are usually difficult to determine, which can pose great obstacles in underground engineering. A novel displacement back-analysis method is proposed to determine the geomechanical parameters in underground engineering. In this method, the problem of geomechanical parameter determination [...] Read more.

The geomechanical parameters in underground engineering are usually difficult to determine, which can pose great obstacles in underground engineering. A novel displacement back-analysis method is proposed to determine the geomechanical parameters in underground engineering. In this method, the problem of geomechanical parameter determination is converted into an optimization problem, regarding the geomechanical parameters as the optimization parameters, and the error between the calculated results and the field measurement information as the optimization objective function. The grasshopper optimization algorithm (GOA), which offers excellent global optimization performance, and the Gaussian process regression (GPR) machine learning, offering powerful fitting ability, are combined to address the time-consuming numerical calculations. Furthermore, the proposed method is combined with the 3D numerical calculation software FLAC^3D to form the GOA-GPR-FLAC^3D method, which can be used in the displacement back-analysis of geomechanical parameters in underground engineering. The results of a case study show that the proposed method can greatly improve computational efficiency while ensuring high precision compared with the GOA. When applied to the Tai’an Pumped Storage Power Station, this method can obtain more accurate results compared with the GOA under the same evaluation times and is more suitable for the back-analysis of rock parameters in underground engineering. Full article

(This article belongs to the Section Civil Engineering)

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18 pages, 6001 KiB

Open AccessArticle

Spectroscopic and Microscopic Characterization of Flashed Glasses from Stained Glass Windows

by Teresa Palomar, Marina Martínez-Weinbaum, Mario Aparicio, Laura Maestro-Guijarro, Marta Castillejo and Mohamed Oujja

Appl. Sci. 2022, 12(11), 5760; https://doi.org/10.3390/app12115760 - 6 Jun 2022

Cited by 5 | Viewed by 2678

Abstract

Flashed glasses are composed of a base glass and a thin colored layer and have been used since medieval times in stained glass windows. Their study can be challenging because of their complex composition and multilayer structure. In the present work, a set [...] Read more.

Flashed glasses are composed of a base glass and a thin colored layer and have been used since medieval times in stained glass windows. Their study can be challenging because of their complex composition and multilayer structure. In the present work, a set of optical and spectroscopic techniques have been used for the characterization of a representative set of flashed glasses commonly used in the manufacture of stained glass windows. The structural and chemical composition of the pieces were investigated by optical microscopy, field emission scanning electron microscopy-energy dispersive X-ray spectrometry (FESEM-EDS), UV-Vis-IR spectroscopy, laser-induced breakdown spectroscopy (LIBS), and laser-induced fluorescence (LIF). Optical microscopy and FESEM-EDS allowed the determination of the thicknesses of the colored layers, while LIBS, EDS, UV-Vis-IR, and LIF spectroscopies served for elemental, molecular, and chromophores characterization of the base glasses and colored layers. Results obtained using the micro-invasive LIBS technique were compared with those retrieved by the cross-sectional technique FESEM-EDS, which requires sample taking, and showed significant consistency and agreement. In addition, LIBS results revealed the presence of additional elements in the composition of flashed glasses that could not be detected by FESEM-EDS. The combination of UV-Vis-IR and LIF results allowed precise chemical identification of chromophores responsible for the flashed glass coloration. Full article

(This article belongs to the Special Issue Interdisciplinary Researches for Cultural Heritage Conservation)

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15 pages, 4123 KiB

Open AccessArticle

An Adaptive Bi-Mutation-Based Differential Evolution Algorithm for Multi-Threshold Image Segmentation

by Yu Sun and Yingying Yang

Appl. Sci. 2022, 12(11), 5759; https://doi.org/10.3390/app12115759 - 6 Jun 2022

Cited by 11 | Viewed by 2123

Abstract

Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposite learning strategy is introduced into the initial population to improve [...] Read more.

Aiming at solving the problems of large calculation, time-consuming, and low segmentation accuracy of multi-threshold image segmentation, an adaptive threshold value based differential evolution algorithm is proposed in this paper. Firstly, an opposite learning strategy is introduced into the initial population to improve the quality of the initial population; secondly, a threshold-value-based mutation strategy is proposed to balance the exploration and development capabilities of the algorithm, and the number of successfully evolved individuals is considered as a threshold value to adaptively adjust the evolution of superior and inferior individuals. Experiments demonstrate that the proposed algorithm has better performance in enhancing accuracy and speeding up the convergence. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications)

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16 pages, 259 KiB

Open AccessConference Report

International Symposium on New Frontiers in Reef Coral Biotechnology (5 May 2022, Taiwan)

by Chiahsin Lin

Appl. Sci. 2022, 12(11), 5758; https://doi.org/10.3390/app12115758 - 6 Jun 2022

Viewed by 2271

Abstract

Given the global threats towards coral reefs, this conference’s central theme, “Reef coral biotechnology”, is particularly timely. Our goal is to promote communication and dialogue in this field among marine researchers within and outside of Taiwan, and we have invited experts in the [...] Read more.

Given the global threats towards coral reefs, this conference’s central theme, “Reef coral biotechnology”, is particularly timely. Our goal is to promote communication and dialogue in this field among marine researchers within and outside of Taiwan, and we have invited experts in the fields of coral reef ecology, physiology, conservation, and biotechnology to discuss their recent findings with a cadre of both local and foreign scientists, as well as students (undergraduate, Master’s, and Ph.D. students). We envision that these presentations will segue into discussions and collaborations that stimulate innovation in reef coral biotechnology, and particularly in the development of tools and approaches that improve the odds of conserving coral reefs and biopreserving reef corals. Full article

(This article belongs to the Special Issue New Frontiers in Reef Coral Biotechnology)

10 pages, 1855 KiB

Open AccessArticle

Implementation Details for Controlling Contactless 3D Virtual Endoscopy

by Martin Zagar, Ivica Klapan, Alan Mutka and Zlatko Majhen

Appl. Sci. 2022, 12(11), 5757; https://doi.org/10.3390/app12115757 - 6 Jun 2022

Cited by 1 | Viewed by 1789

Abstract

In the medical world, with the innovative application of medical informatics, it is possible to enable many aspects of surgeries that were not able to be addressed before. One of these is contactless surgery planning and controlling the visualization of medical data. In [...] Read more.

In the medical world, with the innovative application of medical informatics, it is possible to enable many aspects of surgeries that were not able to be addressed before. One of these is contactless surgery planning and controlling the visualization of medical data. In our approach to contactless surgery, we adopted a new framework for hand and motion detection based on augmented reality. We developed a contactless interface for a surgeon to control the visualization options in our DICOM (Digital Imaging and Communications in Medicine) viewer platform that uses a stereo camera as a sensor device input that controls hand/finger motions, in contactless mode, and applied it to 3D virtual endoscopy. In this paper, we will present our proposal for defining motion parameters in contactless, incisionless surgeries. We enabled better surgeon’s experience, more precise surgery, real-time feedback, depth motion tracking, and contactless control of visualization, which gives freedom to the surgeon during the surgery. We implemented motion tracking using stereo cameras with depth resolution and precise shutter sensors for depth streaming. Our solution provides contactless control with a range up to 2–3 m that enables the application in the operating room. Full article

(This article belongs to the Topic eHealth and mHealth: Challenges and Prospects)

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8 pages, 502 KiB

Open AccessArticle

Maxillary and Mandibular Third Molars Impaction with Associated Pathologies in a North Cyprus Population: A Retrospective Study

by Bedriye Gizem Çelebioğlu Genç, Kaan Orhan and Evren Hıncal

Appl. Sci. 2022, 12(11), 5756; https://doi.org/10.3390/app12115756 - 6 Jun 2022

Cited by 4 | Viewed by 2408

Abstract

This study aimed to find out the incidence of impacted third molars and associated pathologies in people who live in the North Cyprus population. Dr. Burhan Nalbantoglu State Hospital is the only hospital that has an oral and maxillofacial surgery department among the [...] Read more.

This study aimed to find out the incidence of impacted third molars and associated pathologies in people who live in the North Cyprus population. Dr. Burhan Nalbantoglu State Hospital is the only hospital that has an oral and maxillofacial surgery department among the state hospitals in North Cyprus. Patients who were referred to this department during a one-year period due to the complaints regarding their third molar were included in our study. This retrospective study involved 550 patients aged 16 to 65 years (1752 third molars). Chi-square tests were done for bilateral comparison between age, gender, and third molar (p < 0.05). Among the groups included in the study, the highest number of third molars originated from the 20–29 age group (n = 1050). Among all 1752 third molars, 716 (40%) of them erupted, while 1036 (60%) were impacted molar teeth, with significant differences between genders (p > 0.05). The most often impacted position in the mandible was the mesioangular type (42%) and in the maxilla was the vertical type (62%). Partially and completely impacted mandibular third molars showed a significant difference between the left and right sides (p < 0.05). A huge proportion of third molars are impacted in the North Cyprus population. The degree of impaction of wisdom teeth and the problems they cause should be well evaluated, and the surgical approach should be considered according to the baseline of this data. Full article

(This article belongs to the Section Applied Dentistry and Oral Sciences)

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22 pages, 4475 KiB

Open AccessArticle

Exploring the Nonlinear Effects of Built Environment on Bus-Transfer Ridership: Take Shanghai as an Example

by Ding Liu, Wuyue Rong, Jin Zhang and Ying-En (Ethan) Ge

Appl. Sci. 2022, 12(11), 5755; https://doi.org/10.3390/app12115755 - 6 Jun 2022

Cited by 6 | Viewed by 2384

Abstract

In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% [...] Read more.

In this paper, the nonlinear effects of the built environment on bus–metro-transfer ridership are explored, based on Shanghai metro data, with an extreme gradient-boosting decision-trees (XGBoost) model. It was found that the bus-network density had the largest influence on transfer ridership, contributing 27.56% predictive power for transfer ridership, followed by closeness centrality and bus-stop density, and their contribution rates are 21.6% and 17.27%, respectively. Local explanations for the model reveal the following conclusions: most built-environment variables have nonlinear and threshold effects on bus–metro ridership. The suggested values for the dominant contributors to bus–metro-transfer ridership are obtained. For example, bus-network density, bus-stop density, and closeness centrality were 12.8 km/sq. km, 11 counts/sq. km, and 0.18 km/sq. km, respectively, for maximizing bus–metro-transfer ridership. The interaction impacts of the bus–metro connection characteristics and the closeness centrality of metro stations on transfer ridership were, also, examined. The result showed that the setting of bus–metro-transfer facilities depended on the location of metro stations. It was necessary to improve the bus–metro-connection system, in metro stations with high closeness centrality. Full article

(This article belongs to the Special Issue Transportation Big Data and Its Applications)

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11 pages, 836 KiB

Open AccessArticle

Impact of Rapid pH Changes on Activated Sludge Process

by Kristina Kokina, Linda Mezule, Kamila Gruskevica, Romans Neilands, Ksenija Golovko and Talis Juhna

Appl. Sci. 2022, 12(11), 5754; https://doi.org/10.3390/app12115754 - 6 Jun 2022

Cited by 14 | Viewed by 5765

Abstract

The inhibition effect of rapid variations of pH in wastewater on activated sludge was investigated in laboratory-scale sequencing batch reactors (SBR). The toxic influence of pH 6.5 and 8.5 was examined. The experiment with pH 8.5 was preferable to formation of high FA [...] Read more.

The inhibition effect of rapid variations of pH in wastewater on activated sludge was investigated in laboratory-scale sequencing batch reactors (SBR). The toxic influence of pH 6.5 and 8.5 was examined. The experiment with pH 8.5 was preferable to formation of high FA concentration and showed a low risk of inhibition of second step nitrification (conversion of nitrites to nitrates). However, the reactor at pH 6.5 showed inhibition of first-step nitrification (conversion of ammonia to nitrites) caused by FNA formation. High ammonia levels caused a decrease in the overall microfauna population, whereas low–enhanced gymnamoebae, Zoogloea, and Chilodonella sp. population increased after 72 h of inhibition. Destructive acidic pH influence caused sludge washout from the reactor and, therefore, higher organic load on ASP and intensive sludge foam due to Zoogloea higher population. Full article

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11 pages, 2245 KiB

Open AccessArticle

Robust Automatic Segmentation of Inflamed Appendix from Ultrasonography with Double-Layered Outlier Rejection Fuzzy C-Means Clustering

by Kwang Baek Kim, Doo Heon Song and Hyun Jun Park

Appl. Sci. 2022, 12(11), 5753; https://doi.org/10.3390/app12115753 - 6 Jun 2022

Cited by 5 | Viewed by 2206

Abstract

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In [...] Read more.

Accurate diagnosis of acute appendicitis from abdominal ultrasound is a challenging task, since traditional sonographic diagnostic criteria for appendicitis, such as diameter, compressibility, and wall thickness, rely on complete identification or visualization of the appendix and the diagnosis is frequently operator subjective. In this paper, we propose a robust automatic segmentation method for inflamed appendix identification to mitigate abovementioned difficulties. We use outlier rejection fuzzy c-means clustering (FCM) algorithm within a double-layered learning structure to extract the target inflamed appendix area. The proposed method extracts the target appendix in 98 cases out of 100 test images, which is far better than traditional FCM, standard outlier FCM, and double-layered learning with FCM in correct extraction rate. Furthermore, we investigate the outlier rejection effect and double layered learning effect by comparing our proposed method with standard double-layered FCM and the standard outlier-rejection FCM. In this comparison, the proposed method exhibits robust segmentation results in accuracy, precision, and recall by 2.5~5.6% over two standard methods in quality with human pathologists’ marking as the ground truth. Full article

(This article belongs to the Special Issue Future Information & Communication Engineering 2022)

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30 pages, 6116 KiB

Open AccessReview

A Detailed Review on Foam Concrete Composites: Ingredients, Properties, and Microstructure

by Osman Gencel, Turhan Bilir, Zeynep Bademler and Togay Ozbakkaloglu

Appl. Sci. 2022, 12(11), 5752; https://doi.org/10.3390/app12115752 - 6 Jun 2022

Cited by 35 | Viewed by 8924

Abstract

With the development of new cement-based raw materials, foaming agents and fillers used for special applications of foam concrete, the use of foam concretes has become widespread. Foam concrete is a type of concrete that stands out with its lightness, waste potential, controlled [...] Read more.

With the development of new cement-based raw materials, foaming agents and fillers used for special applications of foam concrete, the use of foam concretes has become widespread. Foam concrete is a type of concrete that stands out with its lightness, waste potential, controlled low strength, thermal insulation, acoustics performance, and durability. The knowledge base is still developing for this particular building material. This article describes in detail the fresh, hardened, and physical properties of foam concrete. The properties of materials such as cement, aggregate, foam, and fiber used in foam concrete production are explained and their effects on microstructure are discussed. In addition, physical properties, such as fresh state properties, fresh state and consistency, stability, workability, drying shrinkage, air void system, and water absorption, as well as strength and durability properties are emphasized. The main findings of the presented study are to show the current level of the cement-based foam concretes and their shortcomings, which needs more investigations. The effect of fibers on the characteristics of foam concrete and acoustic characteristic of foam concretes are seen as the main topics to be focused on in the studies. Full article

(This article belongs to the Special Issue Design, Synthesis and Characterization of Hybrid Composite Materials)

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13 pages, 1432 KiB

Open AccessFeature PaperArticle

Assessment of the Genetic and Phytochemical Variability of Italian Wild Hop: A Route to Biodiversity Preservation

by Margherita Rodolfi, Matteo Marieschi, Benedetta Chiancone and Tommaso Ganino

Appl. Sci. 2022, 12(11), 5751; https://doi.org/10.3390/app12115751 - 6 Jun 2022

Cited by 4 | Viewed by 1962

Abstract

Background: Northern Italy has an enormous heritage of hop biodiversity that need to be exploited and studied. The preservation and valorization through the characterization of the existent biodiversity is a primary goal of the European Green Deal 2023–2030. The aim of this study [...] Read more.

Background: Northern Italy has an enormous heritage of hop biodiversity that need to be exploited and studied. The preservation and valorization through the characterization of the existent biodiversity is a primary goal of the European Green Deal 2023–2030. The aim of this study was to acquire information on the biodiversity of Italian wild hops. Methods: Genetic characterization of sixty accessions was done resorting to Single Sequence Repeated (SSR) markers. Phytochemical characterization of wild hops was achieved using: (i) high-performance liquid chromatography with ultraviolet detection for bitter acids quantification, (ii) steam distillation for essential oils quantification and (iii) Gas Chromatography-Mass Spectrometry for the determination of the aromatic profile. Results: The eight SSR primers showed high Polymorphic Information Content (PIC), especially HlGA23. α-Acids reached values between 0 and 4.125. The essential oils analysis highlighted variability within the studied population, with some accessions characterized by important spicy fraction, and others by fruity and floral notes. Conclusions: The present study allowed the characterization of Italian wild hops and demonstrated an interesting biodiversity. Part of this biodiversity have been shown to be potentially suitable for use in brewing. Moreover, several genotypes could be used in breeding programs to obtain new more sustainable varieties. Full article

(This article belongs to the Special Issue Frontier Research in Hop)

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8 pages, 795 KiB

Open AccessArticle

Nest Material Preference of Wild Mouse Species in Laboratory Housing

by Boróka Bárdos, István Nagy, Zsolt Gerencsér and Vilmos Altbacker

Appl. Sci. 2022, 12(11), 5750; https://doi.org/10.3390/app12115750 - 6 Jun 2022

Cited by 2 | Viewed by 2779

Abstract

Our research examined the nest-building characteristics of two mouse species native to Hungary, the mound-building mouse (Mus spicilegus) and the house mouse (Mus musculus), under laboratory housing conditions. In indoor housing, the nest-building material plays a very important role [...] Read more.

Our research examined the nest-building characteristics of two mouse species native to Hungary, the mound-building mouse (Mus spicilegus) and the house mouse (Mus musculus), under laboratory housing conditions. In indoor housing, the nest-building material plays a very important role in the welfare of the animals. The present study examined how wild mouse species choose from natural nest material. In a three-way test, mice were able to choose whether to make their nest from long blades of hay, nonfibrous cotton, or paper strips. In addition, the effect of nest composition on its quality was also investigated. The test was run at the standard laboratory (21 °C) and lower (10 °C) temperatures, assuming that temperature influences the choice. Based on the results of the three-way selection tests, both species of wild mice chose hay nest material in the highest proportion, and it was also found that the increasing the hay proportion coincided with better nest quality. Mice kept in colder places used more hay nest material for their nests and built better quality nests. Our results show that wild mouse species prefer natural nest-building materials that meet their ecological needs even under laboratory conditions, resulting in a good quality nest. This finding is worth considering in designing appropriate enclosures for wild rodent species. Full article

(This article belongs to the Special Issue Animal Behavior in Intensive Culture Environment)

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24 pages, 1404 KiB

Open AccessArticle

Effective Conversion of a Convolutional Neural Network into a Spiking Neural Network for Image Recognition Tasks

by Huynh Cong Viet Ngu and Keon Myung Lee

Appl. Sci. 2022, 12(11), 5749; https://doi.org/10.3390/app12115749 - 6 Jun 2022

Cited by 11 | Viewed by 5250

Abstract

Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN–SNN conversion is considered one of the [...] Read more.

Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, the CNN–SNN conversion is considered one of the most successful approaches to training SNNs. However, previous works assume a rather long inference time period called inference latency to be allowed, while having a trade-off between inference latency and accuracy. One of the main reasons for this phenomenon stems from the difficulty in determining proper a firing threshold for spiking neurons. The threshold determination procedure is called a threshold balancing technique in the CNN–SNN conversion approach. This paper proposes a CNN–SNN conversion method with a new threshold balancing technique that obtains converted SNN models with good accuracy even with low latency. The proposed method organizes the SNN models with soft-reset IF spiking neurons. The threshold balancing technique estimates the thresholds for spiking neurons based on the maximum input current in a layerwise and channelwise manner. The experiment results have shown that our converted SNN models attain even higher accuracy than the corresponding trained CNN model for the MNIST dataset with low latency. In addition, for the Fashion-MNIST and CIFAR-10 datasets, our converted SNNs have shown less conversion loss than other methods in low latencies. The proposed method can be beneficial in deploying efficient SNN models for recognition tasks on resource-limited systems because the inference latency is strongly associated with energy consumption. Full article

(This article belongs to the Special Issue Advances in Intelligent Systems)

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12 pages, 1492 KiB

Open AccessArticle

Computational-Model-Based Biopharmaceutics for p53 Pathway Using Modern Control Techniques for Cancer Treatment

by Amina Yasin, Abdul Rehman Yasin, Muhammad Rizwan Azam, Ali Raza, Robina Nazir and Ridab Adlan Elamin Abdalla

Appl. Sci. 2022, 12(11), 5748; https://doi.org/10.3390/app12115748 - 6 Jun 2022

Cited by 2 | Viewed by 2073

Abstract

The p53 pathway has been the focus of many researchers in the last few decades owing to its pivotal role as a frontline cancer suppressant protein. It plays a vital role in maintaining cell cycle checkpoints and cell apoptosis in response to a [...] Read more.

The p53 pathway has been the focus of many researchers in the last few decades owing to its pivotal role as a frontline cancer suppressant protein. It plays a vital role in maintaining cell cycle checkpoints and cell apoptosis in response to a broken DNA strand. This is why it is found in the mutated form in more than 50% of malignant tumors. To overcome this, various drugs have been proposed to revive the p53 pathway in cancer patients. Small-molecule-based drugs, such as Nutlin 3a, which are capable of performing this stimulation, are at the fore of advanced clinical trials. However, the calculation of their dosage is a challenge. In this work, a method to determine the dosage of Nutlin 3a is investigated. A control-systems-based model is developed to study the response of the wild-type p53 protein to this drug. The proposed strategy regulates the p53 protein along with negative and positive feedback loops mediated by the MDM2 and MDM2 mRNA, respectively, along with the reversible repression of MDM2 caused by Nutlin 3a. For a broader perspective, the reported PBK dynamics of Nutlin 3a are also incorporated. It has been reported that p53 responds to stresses in two ways in terms of concentration to this drug: either it is a sustained (constant) or an oscillatory response. The claimed dosage strategy turned out to be appropriate for sustained p53 response. However, for the induction of oscillations, inhibition of MDM2 is not enough; rather, anti-repression of the p53–MDM2 complex is also needed, which opens new horizons for a new drug design paradigm. Full article

(This article belongs to the Special Issue Biopharmaceutics and Multivariate Modeling of Complex Systems)

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20 pages, 3873 KiB

Open AccessArticle

Rolling Bearing Health Indicator Extraction and RUL Prediction Based on Multi-Scale Convolutional Autoencoder

by Zijian Ye, Qiang Zhang, Siyu Shao, Tianlin Niu and Yuwei Zhao

Appl. Sci. 2022, 12(11), 5747; https://doi.org/10.3390/app12115747 - 6 Jun 2022

Cited by 20 | Viewed by 3008

Abstract

Rolling bearings are some of the most crucial components in rotating machinery systems. Rolling bearing failure may cause substantial economic losses and even endanger operator lives. Therefore, the accurate remaining useful life (RUL) prediction of rolling bearings is of tremendous research importance. Health [...] Read more.

Rolling bearings are some of the most crucial components in rotating machinery systems. Rolling bearing failure may cause substantial economic losses and even endanger operator lives. Therefore, the accurate remaining useful life (RUL) prediction of rolling bearings is of tremendous research importance. Health indicator (HI) construction is the critical step in the data-driven RUL prediction approach. However, existing HI construction methods often require extraction of time-frequency domain features using prior knowledge while artificially determining the failure threshold and do not make full use of sensor information. To address the above issues, this paper proposes an end-to-end HI construction method called a multi-scale convolutional autoencoder (MSCAE) and uses LSTM neural networks for RUL prediction. MSCAE consists of three convolutional autoencoders with different convolutional kernel sizes in parallel, which can fully exploit the global and local information of the vibration signals. First, the raw vibration data and labels are input into MSCAE, and then, MSCAE is trained by minimizing the composite loss function. After that, the vibration data of the test bearings are fed into the trained MSCAE to extract HI. Finally, RUL prediction is performed using the LSTM neural network. The superiority of the HI extracted by MSCAE was verified using the PHM2012 challenge dataset. Compared to state-of-the-art HI construction methods, RUL prediction using MSCAE-extracted HI has the highest prediction accuracy. Full article

(This article belongs to the Special Issue Industrial AI: Applications in Fault Detection, Diagnosis, and Prognosis)

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12 pages, 3428 KiB

Open AccessArticle

Complexity Analysis in the PR, QT, RR and ST Segments of ECG for Early Assessment of Severity in Cardiac Autonomic Neuropathy

by Sharanya Senthamil Selvan, Sridhar P. Arjunan, Ramakrishnan Swaminathan and Dinesh Kant Kumar

Appl. Sci. 2022, 12(11), 5746; https://doi.org/10.3390/app12115746 - 6 Jun 2022

Cited by 2 | Viewed by 2206

Abstract

Early-stage detection of cardiac autonomic neuropathy (CAN) is important for better management of the disease and prevents hospitalization. This study has investigated the complex nature of PR, QT, RR, and ST time segments of ECG signals by computing the fractal dimension (FD) of [...] Read more.

Early-stage detection of cardiac autonomic neuropathy (CAN) is important for better management of the disease and prevents hospitalization. This study has investigated the complex nature of PR, QT, RR, and ST time segments of ECG signals by computing the fractal dimension (FD) of all segments from 20 min ECG recordings of people with different severity of the disease and healthy individuals. The mean computed for each ECG time segment to distinguish between subjects was insufficient for an early diagnosis. Statistical analysis shows that the change of FD in various time segments of ECG throughout the recording was most suitable to assess the steps for severity in symptoms of CAN between the healthy and the subjects with early symptoms of CAN. The complexity of ECG features was evaluated using various classifier models, namely, support vector machine (SVM), naïve Bayes, random forest, K-nearest neighbor (KNN), AdaBoost, and neural networks. Performance measures were computed on all models, with a maximum neural network classifier having an accuracy of 96.9%. Feature ranking results show that fractal features have more significance than the time segments of ECG in differentiating the subjects. The results of statistical validation show that all the selected features based on ECG physiology proved to have an evident complexity change between normal and severity stages of CAN. Thus, this work reports the complexity analysis in all the selected time segments of ECG that can be an effective tool for early diagnostics for CAN. Full article

(This article belongs to the Section Biomedical Engineering)

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8 pages, 6579 KiB

Open AccessArticle

Divergence of High-Order Harmonic Generation by a Convex Plasma Surface

by Chun Yang, Chuliang Zhou, Yinghui Zheng, Dongdong Zhang, Jixing Gao, Yafeng Bai, Rong Qi, Jiayi Qian, Jiayan Gui, Zongxin Zhang, Ye Tian and Zhinan Zeng

Appl. Sci. 2022, 12(11), 5745; https://doi.org/10.3390/app12115745 - 6 Jun 2022

Viewed by 2251

Abstract

The electron density profile on a plasma surface has a decisive influence on the mechanism and characteristics of the plasma high-order harmonic generation. When the pre-pulse has a similar spatial and temporal distribution as the main laser pulse, the plasma surface on the [...] Read more.

The electron density profile on a plasma surface has a decisive influence on the mechanism and characteristics of the plasma high-order harmonic generation. When the pre-pulse has a similar spatial and temporal distribution as the main laser pulse, the plasma surface on the target will expand to form a convex profile of the similar size as the focal spot of the main pulse. We experimentally observed that the divergence of the harmonics generated by the relativistic laser light incident on a silica target has a saddle-shaped structure. The two-dimensional particle-in-cell simulation with convex plasma surfaces explains the experimental results very well and infers a 0.12

λ_{L}

plasma scale length around the center of the convex profile. Further, we qualitatively explained that the asymmetry of the saddle-shaped harmonic divergence is caused by oblique incidence. Full article

(This article belongs to the Special Issue Progress on Laser Plasma Interaction)

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18 pages, 8307 KiB

Open AccessArticle

A Novel Brain Tumor Detection and Coloring Technique from 2D MRI Images

by Inayatul Haq, Najib Ullah, Tehsen Mazhar, Muhammad Amir Malik and Iqra Bano

Appl. Sci. 2022, 12(11), 5744; https://doi.org/10.3390/app12115744 - 6 Jun 2022

Cited by 8 | Viewed by 3937

Abstract

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems [...] Read more.

The early automated identification of brain tumors is a difficult task in MRI images. For a long time, continuous research efforts have floated a new idea of replacing different grayscale anatomic regions of diagnostic images with appropriate colors that could overcome the problems being faced by radiologists. The colorization of grayscale images is challenging for enhancing various regions’ contrasts by transforming grayscale images into high-contrast color images. This study investigates standard solutions in discriminating between normal and abnormal regions by assigning colors to grayscale human brain MR images to differentiate different kinds of tissues. The proposed approach is influenced by connected component and index-based colorization methods for applying colors to different regions and abnormal areas. It is an automated approach that varies its inputs using luminance and pixel matrix values and provides the possible outcome. After segmentation, a specific algorithm is devised to colorize the region-of-interest (ROI) areas, which distinguishes and applies colors to differentiate the regions. Results show that implementing the watershed-based area segmentation method and ROI selection method based on the morphological operation helps identify tissues during processing. Moreover, the colorization approach based on luminance and pixel matrix after segmentation and ROI selection is beneficial due to better PSNR and SSIM values and visible contrast improvement. Our proposed algorithm works with less processing overhead and uses less time than those of the industry’s previously used color transfer method. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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18 pages, 7763 KiB

Open AccessArticle

CFD Analysis of Sine Baffles on Flow Mixing and Power Consumption in Stirred Tank

by Shuiqing Zhou, Qizhi Yang, Laifa Lu, Ding Xia, Weitao Zhang and Hao Yan

Appl. Sci. 2022, 12(11), 5743; https://doi.org/10.3390/app12115743 - 6 Jun 2022

Cited by 4 | Viewed by 3043

Abstract

In order to enhance the fluid mixing in the stirred tank and reduce the power consumption under the condition of full baffle, a sinusoidal sawtooth baffle was established in the present study. Based on the Eulerian–Eulerian method, a numerical simulation of the mixed [...] Read more.

In order to enhance the fluid mixing in the stirred tank and reduce the power consumption under the condition of full baffle, a sinusoidal sawtooth baffle was established in the present study. Based on the Eulerian–Eulerian method, a numerical simulation of the mixed flow in the stirred tank was conducted, and the reliability of the simulation method was verified by means of PIV experiments. The different structural characteristics of a standard baffle and the sine baffle were compared, to explore the effect of the modified baffle on flow mixing and power consumption in the tank. The outcomes indicate that the sinusoidal sawtooth structure had the effect of reducing drag and shunting, which could lessen the backflow on the backside of the baffle, strengthen the intensity of fluid turbulence and strain rate, improve the uniformity of particle distribution, and significantly lower the power consumption. When the relative tooth height was 0.333 and the relative tooth width was 0.028, the power consumption was reduced by 11.7%. Full article

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20 pages, 2429 KiB

Open AccessArticle

FLOWSA: A Python Package Attributing Resource Use, Waste, Emissions, and Other Flows to Industries

by Catherine Birney, Ben Young, Mo Li, Melissa Conner, Jacob Specht and Wesley W. Ingwersen

Appl. Sci. 2022, 12(11), 5742; https://doi.org/10.3390/app12115742 - 5 Jun 2022

Cited by 2 | Viewed by 3198

Abstract

Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once [...] Read more.

Quantifying industry consumption or production of resources, wastes, emissions, and losses—collectively called flows—is a complex and evolving process. The attribution of flows to industries often requires allocating multiple data sources that span spatial and temporal scopes and contain varied levels of aggregation. Once calculated, datasets can quickly become outdated with new releases of source data. The US Environmental Protection Agency (USEPA) developed the open-source Flow Sector Attribution (FLOWSA) Python package to address the challenges surrounding attributing flows to US industrial and final-use sectors. Models capture flows drawn from or released to the environment by sectors, as well as flow transfers between sectors. Data on flow use and generation by source-defined activities are imported from providers and transformed into standardized tables but are otherwise numerically unchanged in preparation for modeling. FLOWSA sector attribution models allocate primary data sources to industries using secondary data sources and file mapping activities to sectors. Users can modify methodological, spatial, and temporal parameters to explore and compare the impact of sector attribution methodological changes on model results. The standardized data outputs from these models are used as the environmental data inputs into the latest version of USEPA’s US Environmentally Extended Input–Output (USEEIO) models, life cycle models of US goods and services for ~400 categories. This communication demonstrates FLOWSA’s capability by describing how to build models and providing select model results for US industry use of water, land, and employment. FLOWSA is available on GitHub, and many of the data outputs are available on the USEPA’s Data Commons. Full article

(This article belongs to the Special Issue Advanced Data Engineering for Life Cycle Applications)

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12 pages, 1513 KiB

Open AccessFeature PaperArticle

Conversion of Waste Biomass into Activated Carbon and Evaluation of Environmental Consequences Using Life Cycle Assessment

by Muhammad Amin, Hamad Hussain Shah, Amjad Iqbal, Zia Ur Rahman Farooqi, Marek Krawczuk and Adeel Zia

Appl. Sci. 2022, 12(11), 5741; https://doi.org/10.3390/app12115741 - 5 Jun 2022

Cited by 22 | Viewed by 3736

Abstract

In this article, activated carbon was produced from Lantana camara and olive trees by H₃PO₄ chemical activation. The prepared activated carbons were analyzed by characterizations such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, Brunauer–Emmett–Teller, X-ray diffraction, thermogravimetric analysis, and Fourier [...] Read more.

In this article, activated carbon was produced from Lantana camara and olive trees by H₃PO₄ chemical activation. The prepared activated carbons were analyzed by characterizations such as scanning electron microscopy, energy-dispersive X-ray spectroscopy, Brunauer–Emmett–Teller, X-ray diffraction, thermogravimetric analysis, and Fourier transform infrared spectroscopy. H₃PO₄ is used as an activator agent to create an abundant pore structure. According to EDX analysis, the crystalline structure destroys and increases the carbon content of the olive tree and Lantana camara by 77.51 and 76.16%, respectively. SEM images reveal a porous structure formed as a result of H₃PO₄ activation. The Brunauer–Emmett–Teller (BET) surface area of the olive tree and Lantana camara activated carbon was 611.21 m²/g and 167.47 m²/g, respectively. The TGA analysis of both activated carbons shows their thermal degradation starts at 230 °C but fully degrades at temperatures above 450 °C. To quantify the potential environmental implications related to the production process of the activated carbon (AC) from olive trees, the life cycle assessment (LCA) environmental methodology was employed. For most of the tested indicators, chemical activation using H₃PO₄ showed the greatest ecological impacts: the ozone layer depletion potential (42.27%), the acidification potential (55.31%), human toxicity (57.00%), freshwater aquatic ecotoxicity (85.01%), terrestrial ecotoxicity (86.17%), and eutrophication (92.20%). The global warming potential (5.210 kg CO₂ eq), which was evenly weighted between the phases, was shown to be one of the most significant impacts. The total energy demand of the olive tree’s AC producing process was 70.521 MJ per Kg. Full article

(This article belongs to the Special Issue Environmental Friendly Technologies in Power Engineering)

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16 pages, 2571 KiB

Open AccessArticle

Closed-Circuit Pump-Controlled Electro-Hydraulic Steering System for Pure Electric Wheel Loader

by Tong Guo, Biao Wu, Tianliang Lin, Honghui Chen and Qihuai Chen

Appl. Sci. 2022, 12(11), 5740; https://doi.org/10.3390/app12115740 - 5 Jun 2022

Cited by 6 | Viewed by 3683

Abstract

Traditional construction machinery’s full hydraulic steering system has high energy consumption. An electro-hydraulic flow matching steering system for electric wheel loaders based on closed-circuit pump control is proposed to solve the problem. The transfer function of the electro-hydraulic system is established, and the [...] Read more.

Traditional construction machinery’s full hydraulic steering system has high energy consumption. An electro-hydraulic flow matching steering system for electric wheel loaders based on closed-circuit pump control is proposed to solve the problem. The transfer function of the electro-hydraulic system is established, and the system is stable according to analysis via Routh matrix. A test platform is built to verify the effectiveness of the system and the control strategy. Taking a 1.6T wheel loader as an example, the energy consumption of the traditional steering system and the new steering system under zero-load, positive-load (shovel loaded with 600 kg gravel), and offset-load (the center of gravity of the gravel is off the center of the bucket) conditions is compared. The results show that the energy consumption of the proposed steering system is greatly reduced compared to the traditional system. Under the condition of zero-load with medium steering speed, compared to the traditional system, consumption is reduced by 22.8%. Full article

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24 pages, 7052 KiB

Open AccessArticle

Study of Rock Crack Extension under Liquid Nitrogen Low-Temperature Fracturing

by Chunyan Bao, Meng Zhou and Yuexiang Cai

Appl. Sci. 2022, 12(11), 5739; https://doi.org/10.3390/app12115739 - 5 Jun 2022

Cited by 1 | Viewed by 2304

Abstract

Shale gas is a promising new energy source stored in shale. This research aims to study the laws of rock crack initiation and propagation under the low-temperature fracturing of liquid nitrogen, explore the influencing factors of the shale reservoir fracturing effect, and identify [...] Read more.

Shale gas is a promising new energy source stored in shale. This research aims to study the laws of rock crack initiation and propagation under the low-temperature fracturing of liquid nitrogen, explore the influencing factors of the shale reservoir fracturing effect, and identify the method that achieves the best fracturing effect and obtains the highest economic benefits. Herein, a visualized physical experiment of the liquid nitrogen effect is carried out, and the fracture process of a numerical model under cold shock is simulated to analyze the effect of homogeneity on shale crack propagation. The results show that two different crack development modes could be observed in the field test. The first one was the horizontal plane radial crack caused by longitudinal thermal shrinkage, and the other one was the vertical tensile crack caused by circumferential shrinkage. A certain interval length was frequently found between the horizontal cracks. The crack propagation of the specimens with different homogenization degrees was basically distributed in the direction perpendicular to the liquid nitrogen contact surface. When the homogenization degrees were m = 2 and 5, the crack surface was rough and the microfracture was disordered and dotted around the crack tip. When m ≥ 10, the dotted damage around the crack tip did not appear, and the crack propagation was close to the results obtained from using the homogeneous materials. Finally, this work simulates the fracture process of a circular hole plane model under cold shock, analyzes the influences of heat transfer coefficient, in situ stress and other parameters on shale temperature, minimum principal stress distribution, and crack propagation, and discusses the optimal method to improve the heat transfer coefficient. The results show that increasing the heat transfer coefficient can increase the tensile stress value and influence the range of the contact boundary, making the rock more prone to cracking and resulting in greater crack development and a better crack initiation effect. The lateral stress coefficient affects the propagation direction of the cracks, and the propagation depths of low-temperature cracks were found to be deeper in the direction of larger principal stress. Full article

(This article belongs to the Special Issue Fracture and Failure of Jointed Rock Mass)

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23 pages, 23884 KiB

Open AccessArticle

Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning

by Jiangang Yang, Kaiguo Ruan, Jie Gao, Shenggang Yang and Lichao Zhang

Appl. Sci. 2022, 12(11), 5738; https://doi.org/10.3390/app12115738 - 5 Jun 2022

Cited by 23 | Viewed by 2733

Abstract

Three-dimensional ground penetrating radar (3D GPR) is a non-destructive examination technology for pavement distress detection, for which its horizontal plane images provide a unique perspective for the task. However, a 3D GPR collects thousands of horizontal plane images per kilometer of the investigated [...] Read more.

Three-dimensional ground penetrating radar (3D GPR) is a non-destructive examination technology for pavement distress detection, for which its horizontal plane images provide a unique perspective for the task. However, a 3D GPR collects thousands of horizontal plane images per kilometer of the investigated pavement. The existing detection methods using GPR images are time-consuming and risky for subjective judgment. To solve the problem, this study used deep learning methods and 3D GPR horizontal plane images to detect pavement structural distress, including cracks, repairs, voids, poor interlayer bonding, and mixture segregation. In this study, two deep learning methods, called CP-YOLOX and SViT, were used to achieve the aim. A dataset for anomalous waveform localization (3688 images) was first created by pre-processing 3D-GPR horizontal plane images. A CP-YOLOX model was then trained to localize anomalous waveforms. Five SViT models with different numbers of encoders were adopted to perform the classification of anomalous waveforms using the localization results from the CP-YOLOX model. The numerical experiment results showed that 3D GPR horizontal plane images have the potential to be an assistant for pavement structural distress detection. The CP-YOLOX model achieved 87.71% precision, 80.64% mAP, and 33.57 sheets/s detection speed in locating anomalous waveforms. The optimal SViT achieved 63.63%, 68.12%, and 75.57% classification accuracies for the 5-category, 4-category, and 3-category datasets, respectively. The proposed models outperformed other deep learning methods on distress detection using 3D GPR horizontal plane images. In the future, more radar images should be collected to improve the accuracy of SViT. Full article

(This article belongs to the Special Issue Advances in Nondestructive Testing and Evaluation)

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18 pages, 11033 KiB

Open AccessArticle

Failure Mechanism Analysis and Optimization Analysis of Tunnel Joint Waterstop Considering Bonding and Extrusion

by Yimin Wu, Haiping Wu, Dinghai Chu, Sheng Feng, Junjian Zhang and Haoran Wu

Appl. Sci. 2022, 12(11), 5737; https://doi.org/10.3390/app12115737 - 5 Jun 2022

Cited by 6 | Viewed by 2051

Abstract

In waterproofing mountain tunnels, the tunnel joint is the weak link. To explore the waterproof failure mechanism of the tunnel joint waterstop belt and to propose an optimization method for the waterstop belt, this paper combines tests and numerical simulations, summarizes the waterproofing [...] Read more.

In waterproofing mountain tunnels, the tunnel joint is the weak link. To explore the waterproof failure mechanism of the tunnel joint waterstop belt and to propose an optimization method for the waterstop belt, this paper combines tests and numerical simulations, summarizes the waterproofing mechanism of the waterstop belt, establishes a finite element model of the waterstop belt considering bonding and extrusion, and studies the waterproofing ability and mechanical properties of the waterstop. The main conclusions are as follows: (1) The waterproofing capacity of the water stop belt depends on its surface contact pressure and bonding force. (2) Waterstop deformation will partially destroy the bonding between the waterstop and concrete, reducing the reliability of the waterproofing mechanism. (3) When the deformation of the waterstop belt reaches a certain degree, its stress value is too large to meet the requirements of its service life. (4) The design can be optimized from two aspects: the bond between the waterstop and concrete, and the size of the waterstop. Full article

(This article belongs to the Special Issue Advanced Underground Space Technology)

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19 pages, 2573 KiB

Open AccessArticle

Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

by Alaa M. Adel El-Shazli, Sherin M. Youssef and Abdel Hamid Soliman

Appl. Sci. 2022, 12(11), 5736; https://doi.org/10.3390/app12115736 - 5 Jun 2022

Cited by 7 | Viewed by 2146

Abstract

Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect [...] Read more.

Digital breast tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms; however, since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learning architecture (Mod_AlexNet). To the proposed modified deep learning architecture (Mod AlexNet), an optimization layer with multiple high performing optimizers is incorporated so that it can be evaluated and optimised using various optimization techniques. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT augmentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the performance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The optimization techniques included adaptive moment estimation (Adam), root mean squared propagation (RMSProp), and stochastic gradient descent with momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark dataset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenario, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with improvement rates of 3.23%, 1.79% and 1.34% when compared using SGDM, Adam, and RMSProp optimizers, respectively. Full article

(This article belongs to the Section Computing and Artificial Intelligence)

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11 pages, 2837 KiB

Open AccessArticle

Relationship between Induced Polarization Relaxation Time and Hydraulic Characteristics of Water-Bearing Sand

by Zhao Ma, Lichao Nie, Zhaoyang Deng, Xiaobin Xu, Xin Yin, Junfeng Shen, Kai Wang and Ningbo Li

Appl. Sci. 2022, 12(11), 5735; https://doi.org/10.3390/app12115735 - 5 Jun 2022

Viewed by 1800

Abstract

The induced polarization method has become a popular method for evaluating formation permeability characteristics in recent years because of its sensitivity to water and water-bearing porous media. In particular, the induced polarization relaxation time can reflect the macroscopic characteristics of the porous media [...] Read more.

The induced polarization method has become a popular method for evaluating formation permeability characteristics in recent years because of its sensitivity to water and water-bearing porous media. In particular, the induced polarization relaxation time can reflect the macroscopic characteristics of the porous media of rock and soil. Therefore, in order to study the relationship between relaxation time and permeability, eight quartz sand samples of different grain sizes were used to simulate water-bearing sand layers under different geological conditions, and the induced polarization experiment and the Darcy seepage experiment were carried out on the same sand sample. The experimental results show that relaxation time and permeability are closely correlated with the grain size of quartz sand samples. According to the experimental data, the power function equation is a better fit for describing the relationship between permeability and relaxation time. It is worth noting that the equations obtained are only empirical equations for quartz sand samples, and they may not be applicable to all geological conditions. Full article

(This article belongs to the Section Civil Engineering)

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14 pages, 3358 KiB

Open AccessArticle

Investigation of the Heat Transfer and Pressure Drop in Tubes with Transverse Ribs of Zigzag Configurations

by Mohamed H. Ahmed

Appl. Sci. 2022, 12(11), 5734; https://doi.org/10.3390/app12115734 - 5 Jun 2022

Viewed by 1820

Abstract

Heat transfer through tube walls can be promoted using a ribbed tube configuration. Most of the ribs used in previous reports have equal height along the tube length. In this numerical study, we investigate the heat transfer and pressure drop in a transverse [...] Read more.

Heat transfer through tube walls can be promoted using a ribbed tube configuration. Most of the ribs used in previous reports have equal height along the tube length. In this numerical study, we investigate the heat transfer and pressure drop in a transverse ribbed tube where ribs of unequal heights are mounted such that the tops of the ribs form a zigzag shape. Four configurations were studied. Each configuration had a set of two neighboring ribs of different heights. The set was repeated along the tube length to form a zigzag shape. The rib height ratios, e₂/e₁, of the four sets were 0.25, 0.5, 0.75, and 1.0. The ratios of the height of the taller rib, pitch, and width to the tube diameter were kept constant at values of e₁/d = 0.1, p/d = 1.0, and w/d = 0.05, respectively. The Reynolds number ranged from 10,000 to 60,000, while the Prandtl number ranged from 0.71 to 7.0. The results from the k-ε and k-ω models were first validated and compared with the experimental results of smooth and ribbed tubes. The two models showed comparable results, with the k-ε showing slightly better performance and was thus selected to perform the current study. It was found that the average Nusselt number increases along with increases in the rib height ratio, Prandtl number, and Reynolds number. The friction factor changed exponentially with the rib height ratio, while the Reynolds number showed a minor effect. At the same pumping power, a maximum thermal performance enhancement of approximately 8% was achieved at rib height ratios of 0.25 and 0.5. The rib height ratio of 0.5 has an advantage over that of 0.25 as it has a higher average Nusselt number. Two correlations were introduced to estimate the Nusselt number and friction factor for the current ribbed tube of zigzag configurations. Full article

(This article belongs to the Section Mechanical Engineering)

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