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Appl. Sci., Volume 12, Issue 8 (April-2 2022) – 390 articles

Cover Story (view full-size image): Nowadays, there also exists a considerable amount of interest in monitoring large-scale and long-term geoscience phenomena using the Moon-based SAR (MBS). The paper first proposes a fast back-projection (FBP) algorithm in time domain for MBS, a platform that has long transmission time and long synthetic aperture time. The original method, that projected echo on all pixels in imaging area, is changed to projected echo on a centerline instead. A suitable interpolation for points on the centerline is adopted to reduce the projected error. Through theoretical analysis, the detailed range difference mainly at apogee, perigee, ascending, and descending nodes indicate the necessity to separately calculate the dual-path for MBS’s single pulse transmission in Earth-Moon motion, with real ephemeris been adopted; then, the high-order polynomial fitting will better describe the motion trajectory. View this paper
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Article
Performance Index for in Home Assessment of Motion Abilities in Ataxia Telangiectasia: A Pilot Study
Appl. Sci. 2022, 12(8), 4093; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084093 - 18 Apr 2022
Viewed by 523
Abstract
Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of [...] Read more.
Background. It has been shown in the very recent literature that human walking generates rhythmic motor patterns with hidden time harmonic structures that are represented (at the subject’s comfortable speed) by the occurrence of the golden ratio as the the ratio of the durations of specific walking gait subphases. Such harmonic proportions may be affected—partially or even totally destroyed—by several neurological and/or systemic disorders, thus drastically reducing the smooth, graceful, and melodic flow of movements and altering gait self-similarities. Aim. In this paper we aim at, preliminarily, showing the reliability of a technologically assisted methodology—performed with an easy to use wearable motion capture system—for the evaluation of motion abilities in Ataxia-Telangiectasia (AT), a rare infantile onset neurodegenerative disorder, whose typical neurological manifestations include progressive gait unbalance and the disturbance of motor coordination. Methods. Such an experimental methodology relies, for the first time, on the most recent accurate and objective outcome measures of gait recursivity and harmonicity and symmetry and double support subphase consistency, applied to three AT patients with different ranges of AT severity. Results. The quantification of the level of the distortions of harmonic temporal proportions is shown to include the qualitative evaluations of the three AT patients provided by clinicians. Conclusions. Easy to use wearable motion capture systems might be used to evaluate AT motion abilities through recursivity and harmonicity and symmetry (quantitative) outcome measures. Full article
(This article belongs to the Special Issue Performance Analysis in Sport and Exercise)
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Article
Propeller Slipstream Effect on Aerodynamic Characteristics of Micro Air Vehicle at Low Reynolds Number
Appl. Sci. 2022, 12(8), 4092; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084092 - 18 Apr 2022
Viewed by 427
Abstract
A numerical investigation on propeller-induced flow effects in tractor configurations on a Zimmerman wing-fuselage using the cambered thin airfoil is presented in this paper. The Reynolds number based on the mean aerodynamic chord was 1.3 × 105. Significant aerodynamic performance benefits [...] Read more.
A numerical investigation on propeller-induced flow effects in tractor configurations on a Zimmerman wing-fuselage using the cambered thin airfoil is presented in this paper. The Reynolds number based on the mean aerodynamic chord was 1.3 × 105. Significant aerodynamic performance benefits could be found for a propeller in the tractor configuration. The numerical results showed that the propeller slipstream effect on the wings was highly dependent on the size of the propeller, and the major slipstream effect was working at 60% inboard wingspan, whereas less effects were observed towards the wingtip. The propeller slipstream increased the local angle of attack on the up-going blade side. This effect simultaneously augmented the section lift. The unsteady Reynolds-averaged Navier–Stokes (URANS) simulations helped to improve understanding of the interaction of the propeller wake and the wing-fuselage, which is an important aspect to guide the design of future efficient and controllable micro air vehicles. The results indicated that, in MAV designs, the slipstream from the propeller had a significant effect on the wing aerodynamics, regarding both performance and stability of the vehicle. Full article
(This article belongs to the Special Issue Advances in Computational Fluid Dynamics: Methods and Applications)
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Article
Throughput/Area Optimized Architecture for Elliptic-Curve Diffie-Hellman Protocol
Appl. Sci. 2022, 12(8), 4091; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084091 - 18 Apr 2022
Viewed by 455
Abstract
This paper presents a high-speed and low-area accelerator architecture for shared key generation using an elliptic-curve Diffie-Hellman protocol over GF(2233). Concerning the high speed, the proposed architecture employs a two-stage pipelining and a Karatsuba finite field multiplier. [...] Read more.
This paper presents a high-speed and low-area accelerator architecture for shared key generation using an elliptic-curve Diffie-Hellman protocol over GF(2233). Concerning the high speed, the proposed architecture employs a two-stage pipelining and a Karatsuba finite field multiplier. The use of pipelining shortens the critical path which ultimately improves the clock frequency. Similarly, the employment of a Karatsuba multiplier decreases the required number of clock cycles. Moreover, an efficient rescheduling of point addition and doubling operations avoids data hazards that appear due to pipelining. Regarding the low area, the proposed architecture computes finite field squaring and inversion operations using the hardware resources of the Karatsuba multiplier. Furthermore, two dedicated controllers are used for efficient control functionalities. The implementation results after place-and-route are provided on Virtex-7, Spartan-7, Artix-7 and Kintex-7 FPGA (field-programmable gate arrays) devices. The utilized FPGA slices are 5102 (on Virtex-7), 5634 (on Spartan-7), 5957 (on Artix-7) and 6102 (on Kintex-7). In addition to this, the time required for one shared-key generation is 31.08 (on Virtex-7), 31.68 (on Spartan-7), 31.28 (on Artix-7) and 32.51 (on Kintex-7). For performance comparison, a figure-of-merit in terms of throughputarea is utilized which shows that the proposed architecture is 963.3 and 2.76 times faster as compared to the related architectures. In terms of latency, the proposed architecture is 302.7 and 132.88 times faster when compared to the most relevant state-of-the-art approaches. The achieved results and performance comparison prove the significance of presented architecture in all those shared key generation applications which require high speed with a low area. Full article
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Article
The Assessment of COVID-19 Vulnerability Risk for Crisis Management
Appl. Sci. 2022, 12(8), 4090; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084090 - 18 Apr 2022
Cited by 1 | Viewed by 414
Abstract
The subject of this article is to determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. The aim was to determine the COVID-19 Vulnerability Index [...] Read more.
The subject of this article is to determine COVID-19 vulnerability risk and its change over time in association with the state health care system, turnover, and transport to support the crisis management decision-making process. The aim was to determine the COVID-19 Vulnerability Index (CVI) based on the selected criteria. The risk assessment was carried out with methodology that includes the application of multicriteria analysis and spatiotemporal aspect of available data. Particularly the Spatial Multicriteria Analysis (SMCA) compliant with the Analytical Hierarchy Process (AHP), which incorporated selected population and environmental criteria were used to analyse the ongoing pandemic situation. The influence of combining several factors in the pandemic situation analysis was illustrated. Furthermore, the static and dynamic factors to COVID-19 vulnerability risk were determined to prevent and control the spread of COVID-19 at the early stage of the pandemic situation. As a result, areas with a certain level of risk in different periods of time were determined. Furthermore, the number of people exposed to COVID-19 vulnerability risk in time was presented. These results can support the decision-making process by showing the area where preventive actions should be considered. Full article
(This article belongs to the Special Issue Big Data for eHealth Applications)
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Article
Improving Semantic Dependency Parsing with Higher-Order Information Encoded by Graph Neural Networks
Appl. Sci. 2022, 12(8), 4089; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084089 - 18 Apr 2022
Viewed by 478
Abstract
Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-order information in many graph learning tasks. Inspired by the success of GNNs, [...] Read more.
Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-order information in many graph learning tasks. Inspired by the success of GNNs, we investigate improving semantic dependency parsing with higher-order information encoded by multi-layer GNNs. Experiments are conducted on the SemEval 2015 Task 18 dataset in three languages (Chinese, English, and Czech). Compared to the previous state-of-the-art parser, our parser yields 0.3% and 2.2% improvement in average labeled F1-score on English in-domain (ID) and out-of-domain (OOD) test sets, 2.6% improvement on Chinese ID test set, and 2.0% and 1.8% improvement on Czech ID and OOD test sets. Experimental results show that our parser outperforms the previous best one on the SemEval 2015 Task 18 dataset in three languages. The outstanding performance of our parser demonstrates that the higher-order information encoded by GNNs is exceedingly beneficial for improving SDP. Full article
(This article belongs to the Special Issue Applied and Innovative Computational Intelligence Systems)
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Article
Classifying Malicious Documents on the Basis of Plain-Text Features: Problem, Solution, and Experiences
Appl. Sci. 2022, 12(8), 4088; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084088 - 18 Apr 2022
Viewed by 402
Abstract
Cyberattacks widely occur by using malicious documents. A malicious document is an electronic document containing malicious codes along with some plain-text data that is human-readable. In this paper, we propose a novel framework that takes advantage of such plaintext data to determine whether [...] Read more.
Cyberattacks widely occur by using malicious documents. A malicious document is an electronic document containing malicious codes along with some plain-text data that is human-readable. In this paper, we propose a novel framework that takes advantage of such plaintext data to determine whether a given document is malicious. We extracted plaintext features from the corpus of electronic documents and utilized them to train a classification model for detecting malicious documents. Our extensive experimental results with different combinations of three well-known vectorization strategies and three popular classification methods on five types of electronic documents demonstrate that our framework provides high prediction accuracy in detecting malicious documents. Full article
(This article belongs to the Special Issue Advances in Big Data and Machine Learning)
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Article
Obstacle Avoidance Path Planning for the Dual-Arm Robot Based on an Improved RRT Algorithm
Appl. Sci. 2022, 12(8), 4087; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084087 - 18 Apr 2022
Viewed by 443
Abstract
In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be [...] Read more.
In the future of automated production processes, the manipulator must be more efficient to complete certain tasks. Compared to single-arm robots, dual-arm robots have a larger workspace and stronger load capacity. Coordinated motion planning of multi-arm robots is a problem that must be solved in the process of robot development. This paper proposes an obstacle avoidance path planning method for the dual-arm robot based on the goal probability bias and cost function in a rapidly-exploring random tree algorithm (GA_RRT). The random tree grows to the goal point with a certain probability. At the same time, the cost function is calculated when the random state is generated. The point with the lowest cost is selected as the child node. This reduces the randomness and blindness of the RRT algorithm in the expansion process. The detection algorithm of the bounding sphere is used in the process of collision detection of two arms. The main arm conducts obstacle avoidance path planning for static obstacles. The slave arm not only considers static obstacles, but also takes on the role of the main arm at each moment as a dynamic obstacle for path planning. Finally, MATLAB is used for algorithm simulation, which proves the effectiveness of the algorithm for obstacle avoidance path planning problems for the dual-arm robot. Full article
(This article belongs to the Topic Industrial Robotics)
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Editorial
Editorial for Special Issue on Reliability Analysis of Electrotechnical Devices
Appl. Sci. 2022, 12(8), 4086; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084086 - 18 Apr 2022
Viewed by 320
Abstract
Advancement in electrotechnical devices has indeed revolutionize our daily lives [...] Full article
(This article belongs to the Special Issue Reliability Analysis of Electrotechnical Devices)
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Article
The Impact of Rising Reservoir Water Level on the Gravity Field and Seismic Activity in the Reservoir Area: Evidence from the Impoundment of the Three Gorges Reservoir (China)
Appl. Sci. 2022, 12(8), 4085; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084085 - 18 Apr 2022
Viewed by 355
Abstract
Using the test–retest data of the relative gravity field and earthquake monitoring catalog of the Three Gorges Reservoir (TGR) from October 2001 to October 2009, this paper systematically analyzes the changes in the gravity field in the head area of the reservoir and [...] Read more.
Using the test–retest data of the relative gravity field and earthquake monitoring catalog of the Three Gorges Reservoir (TGR) from October 2001 to October 2009, this paper systematically analyzes the changes in the gravity field in the head area of the reservoir and the temporal and spatial distribution characteristics of seismic activity during the impoundment process. It also employs the surrogate reshuffling tests to calculate the cross-correlation between the reservoir water level and the seismic activity sequence and discusses the influence of the rising reservoir water level on the gravity field and seismic activity in the reservoir region. Then, by constructing a three-dimensional finite-difference model based on the theory of fluid–solid coupling, the mechanism of reservoir-induced earthquakes is discussed from the aspects of direct reservoir water load and reservoir water infiltration. The results show that: (1) The rising reservoir water level has had a critical impact on the gravity field and seismic activity in the reservoir’s head area. The cumulative changes in the gravity field from October 2001 to November 2008 show that water impounding has led to a huge banded positive anomaly of gravity along the river near Xiangxi, which reached 450 × 10−8 ms−2. The seismicity activity dominated by micro-earthquakes after a 135 m water level rose rapidly, and the monthly average earthquake frequency increased from 2.00 before the impoundment to 92.60 after the 175 m stage. (2) From the beginning of the impoundment to the experimental impoundment stage of 175 m, the time series correlation test result between the monthly frequency of earthquakes and the water level of the reservoir also changed from uncorrelated before the water storage to correlated when the time lag was 0 months at a 95% confidence threshold. This indicates that the seismic activity obviously has a direct relationship with the load pressure produced by the rapid rise of the reservoir water level, which causes the instability of the mines, karst caves, shallow rock strata, and faults within 10 km along the river and near the reservoir bank, and consequently induces earthquakes. (3) As the TGR enters the 175 m high-level operation stage, the cross-correlation test confirmed that the seismic activity and the reservoir water level show negative correlation characteristics under the time lag of 4 to 5 months, indicating that the seismic activity has a lagging response to the reservoir water level change. The continued infiltration of the reservoir water, followed by the softening of the faults and other actions, triggered the Xiangxi M4.1 earthquake at the center of the four quadrants of gravity anomalies near Xiangxi on 22 November 2008. The Xiangxi segment of the reservoir and its periphery, a triangular geological region where the Xiannvshan faults, the Jiuwanxi fault, and the Yangtze River meet, might be at risk of having reservoir-induced tectonic earthquakes. Full article
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Article
The Role of Acetyl-Carnitine and Rehabilitation in the Management of Patients with Post-COVID Syndrome: Case-Control Study
Appl. Sci. 2022, 12(8), 4084; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084084 - 18 Apr 2022
Viewed by 400
Abstract
Post-COVID syndrome is characterized by the persistence of nonspecific disabling symptoms, even several months after the resolution of the infection, with clinical characteristics similar to fibromyalgia (FM) and a prevalence of 31%. We evaluated the effectiveness of physical exercise, in association with L-acetyl-carnitine [...] Read more.
Post-COVID syndrome is characterized by the persistence of nonspecific disabling symptoms, even several months after the resolution of the infection, with clinical characteristics similar to fibromyalgia (FM) and a prevalence of 31%. We evaluated the effectiveness of physical exercise, in association with L-acetyl-carnitine (ALC) therapy, in patients with Post-COVID syndrome, on musculoskeletal pain, dyspnea, functional capacity, quality of life, and depression. We conducted an observational case-control study on patients with Post-COVID syndrome. The patients were randomly divided into two groups: a treatment group that received rehabilitation treatment in combination with ALC 500 mg therapy; a control group that received only rehabilitation treatment. Patients were assessed at the time of recruitment (T0) and one month after the end of therapy (T1), with the administration of rating scales: NRS, Barthel Dyspnea Index (NPI), 12-Item Short Form Survey (SF-12) scale, Fibromyalgia Impact Questionnaire (FIQ), and Patient Health Questionnaire (PHQ-9). The treatment group showed statistically higher variations in pain scores, quality of life, and depression. No statistically significant differences between the two groups emerged regarding changes in dyspnea and functional capacity scores. Combining exercise with ALC is a promising and effective treatment in the management of Post-COVID syndrome, especially for musculoskeletal pain, depression, and quality of life. Full article
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Article
Burapha-TH: A Multi-Purpose Character, Digit, and Syllable Handwriting Dataset
Appl. Sci. 2022, 12(8), 4083; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084083 - 18 Apr 2022
Viewed by 394
Abstract
In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai [...] Read more.
In handwriting recognition research, a public image dataset is necessary to evaluate algorithm correctness and runtime performance. Unfortunately, in existing Thai language script image datasets, there is a lack of variety of standard handwriting types. This paper focuses on a new offline Thai handwriting image dataset named Burapha-TH. The dataset has 68 character classes, 10 digit classes, and 320 syllable classes. For constructing the dataset, 1072 Thai native speakers wrote on collection datasheets that were then digitized using a 300 dpi scanner. De-skewing, detection box and segmentation algorithms were applied to the raw scans for image extraction. The experiment used different deep convolutional models with the proposed dataset. The result shows that the VGG-13 model (with batch normalization) achieved accuracy rates of 95.00%, 98.29%, and 96.16% on character, digit, and syllable classes, respectively. The Burapha-TH dataset, unlike all other known Thai handwriting datasets, retains existing noise, the white background, and all artifacts generated by scanning. This comprehensive, raw, and more realistic dataset will be helpful for a variety of research purposes in the future. Full article
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Article
Protonic Transport in Layered Perovskites BaLanInnO3n+1 (n = 1, 2) with Ruddlesden-Popper Structure
Appl. Sci. 2022, 12(8), 4082; https://doi.org/10.3390/app12084082 - 18 Apr 2022
Cited by 1 | Viewed by 393
Abstract
The work focused on the layered perovskite-related materials as the potential electrolytic components of such devices as proton conducting solid oxide fuel cells for the area of clean energy. The two-layered perovskite BaLa2In2O7 with the Ruddlesden–Popper structure was [...] Read more.
The work focused on the layered perovskite-related materials as the potential electrolytic components of such devices as proton conducting solid oxide fuel cells for the area of clean energy. The two-layered perovskite BaLa2In2O7 with the Ruddlesden–Popper structure was investigated as a protonic conductor for the first time. The role of increasing the amount of perovskite blocks in the layered structure on the ionic transport was investigated. It was shown that layered perovskites BaLanInnO3n+1 (n = 1, 2) demonstrate nearly pure protonic conductivity below 350 °C. Full article
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Article
Zircon Hf-Isotopic Mapping Applied to the Metal Exploration of the Sanjiang Tethyan Orogenic Belt, Southwestern China
Appl. Sci. 2022, 12(8), 4081; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084081 - 18 Apr 2022
Viewed by 302
Abstract
Zircon Hf-isotopic mapping can be regarded as a useful tool for evaluating the coupling relationship between lithospheric structure and metallic mineralization. Hence, this method shows important significance for mineral prediction. To explore this potential, the published granite zircon Hf isotope data from the [...] Read more.
Zircon Hf-isotopic mapping can be regarded as a useful tool for evaluating the coupling relationship between lithospheric structure and metallic mineralization. Hence, this method shows important significance for mineral prediction. To explore this potential, the published granite zircon Hf isotope data from the Sanjiang Tethyan Orogen were systematically compiled. This study uses the Kriging weighted interpolation in the Mapgis software system to contour Hf isotopes, revealing a relation between the crustal structure and metallogenesis. The mapping results suggest that the Changning–Menglian suture zone is the boundary between ancient and juvenile crust, viz., the western terranes have ancient crust attributes, whereas the eastern terranes exhibit the properties of new juvenile crust. In addition, this study also found that the mineralization and element types in the Sanjiang Tethyan Orogen have a coupling relationship with the crustal structure. The distribution of porphyry Cu-Mo-Au deposits is mainly controlled by the new juvenile crust, whereas the magmatic-hydrothermal Sn-W and porphyry Mo-W(-Cu) deposits are closely related to the reworked ancient crust. The results of zircon Hf isotope mapping prove that the formation and spatial distribution of deposits are related to the composition and properties of the crust. Hf isotope mapping can reveal the regional metallogenic rules and explore metallogenic prediction and metallogenic potential evaluation. Full article
(This article belongs to the Special Issue Critical Metal Occurrence, Enrichment, and Application)
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Article
Fault Diagnosis of Induction Motors with Imbalanced Data Using Deep Convolutional Generative Adversarial Network
Appl. Sci. 2022, 12(8), 4080; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084080 - 18 Apr 2022
Viewed by 363
Abstract
A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional generative adversarial network (DCGAN) [...] Read more.
A homemade defective model of an induction motor was created by the laboratory team to acquire the vibration acceleration signals of five operating states of an induction motor under different loads. Two major learning models, namely a deep convolutional generative adversarial network (DCGAN) and a convolutional neural network, were applied for fault diagnosis of the induction motor to the problem of an imbalanced training dataset. Two datasets were studied and analyzed: a sufficient and balanced training dataset and insufficient and imbalanced training data. When the training datasets were adequate and balanced, time–frequency analysis was advantageous for fault diagnosis at different loads, with the diagnostic accuracy achieving 95.06% and 96.38%. For the insufficient and imbalanced training dataset, regardless of the signal preprocessing method, the more imbalanced the training dataset, the lower the diagnostic accuracy was for the testing dataset. Samples generated by DCGAN were found to exhibit 80% similarity with the actual data through comparison. By oversampling the imbalanced dataset, DCGAN achieved a 90% diagnostic accuracy, close to the accuracy achieved using a balanced dataset. Among all oversampling techniques, the pro-balanced method yielded the optimal result. The diagnostic accuracy reached 85% in the cross-load test, indicating that the generated data had successfully learned the different fault features that validate the DCGAN’s ability to learn parts of input signals. Full article
(This article belongs to the Topic Machine and Deep Learning)
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Article
Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
Appl. Sci. 2022, 12(8), 4079; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084079 - 18 Apr 2022
Viewed by 341
Abstract
Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps [...] Read more.
Mammography is a first-line imaging examination approach used for early breast tumor screening. Computational techniques based on deep-learning methods, such as convolutional neural network (CNN), are routinely used as classifiers for rapid automatic breast tumor screening in mammography examination. Classifying multiple feature maps on two-dimensional (2D) digital images, a multilayer CNN has multiple convolutional-pooling layers and fully connected networks, which can increase the screening accuracy and reduce the error rate. However, this multilayer architecture presents some limitations, such as high computational complexity, large-scale training dataset requirements, and poor suitability for real-time clinical applications. Hence, this study designs an optimal multilayer architecture for a CNN-based classifier for automatic breast tumor screening, consisting of three convolutional layers, two pooling layers, a flattening layer, and a classification layer. In the first convolutional layer, the proposed classifier performs the fractional-order convolutional process to enhance the image and remove unwanted noise for obtaining the desired object’s edges; in the second and third convolutional-pooling layers, two kernel convolutional and pooling operations are used to ensure the continuous enhancement and sharpening of the feature patterns for further extracting of the desired features at different scales and different levels. Moreover, there is a reduction of the dimensions of the feature patterns. In the classification layer, a multilayer network with an adaptive moment estimation algorithm is used to refine a classifier’s network parameters for mammography classification by separating tumor-free feature patterns from tumor feature patterns. Images can be selected from a curated breast imaging subset of a digital database for screening mammography (CBIS-DDSM), and K-fold cross-validations are performed. The experimental results indicate promising performance for automatic breast tumor screening in terms of recall (%), precision (%), accuracy (%), F1 score, and Youden’s index. Full article
(This article belongs to the Special Issue Advances in Biomedical Image Processing and Analysis)
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Article
Design of Third-Order Dispersion Compensation for the SG PW Laser System Using a Birefringent Crystal
Appl. Sci. 2022, 12(8), 4078; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084078 - 18 Apr 2022
Viewed by 306
Abstract
This study aims to update the existing SG PW laser system and improve the temporal contrast and shape fidelity of a compressed pulse with a 150 fs level for multi-PW (5–10 PW). The design of third-order dispersion (TOD) compensation via a birefringent crystal [...] Read more.
This study aims to update the existing SG PW laser system and improve the temporal contrast and shape fidelity of a compressed pulse with a 150 fs level for multi-PW (5–10 PW). The design of third-order dispersion (TOD) compensation via a birefringent crystal was studied through numerical simulations and experiments. The dispersions introduced by the birefringent crystal were calculated using the Jones matrix element by changing the in-plane rotation angle ϕ, thickness d, incident angle θ, and temperature T, while also considering the transmission spectral bandwidth. The group-velocity dispersion (GVD), TOD, and fourth-order dispersion (FOD) of the existing SG PW laser system and its influence on the compressed pulse with different pulse durations were analyzed. The results suggest that a TOD of 1.3×106 fs3 needs to compensate for the multi-PW design. The compensation scheme is designed using a quartz crystal of d = 6.5 mm, θ = 90°, ϕ = 17°, and T = 21 °C, corresponding to the thickness, inclination angle, in-plane rotation angle, and temperature, respectively. Furthermore, we show a principle-proof experiment offline and measure the GVD and TOD by the Wizzler, which is based on theoretical simulations. These results can be applied to independently and continuously control the TOD of short-pulse laser systems. Full article
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Article
A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions
Appl. Sci. 2022, 12(8), 4077; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084077 - 18 Apr 2022
Viewed by 472
Abstract
The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many [...] Read more.
The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project). Full article
(This article belongs to the Collection Innovation in Information Security)
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Article
Analysis of Slip Failure Characteristics and Support Deformation Law of Structural Planes and Rock Foundation Pits with Developed Karst Caves
Appl. Sci. 2022, 12(8), 4076; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084076 - 18 Apr 2022
Viewed by 338
Abstract
This paper establishes a numerical analysis model for the slope of a high-inclined angle stratified foundation pit using support methods including row piles, pile-anchor supports, and combined pile-bracing-anchor supports. The reliability of the analysis conclusion was verified by comparing the stress and deformation [...] Read more.
This paper establishes a numerical analysis model for the slope of a high-inclined angle stratified foundation pit using support methods including row piles, pile-anchor supports, and combined pile-bracing-anchor supports. The reliability of the analysis conclusion was verified by comparing the stress and deformation laws of the support structures on the bedding rock side and the toppling rock side in different schemes, in conjunction with the measured data from Sanhuan South Road Station of Xuzhou Metro Line 3. In addition, on the basis of summarizing the deformation characteristics of the support structures on the bedding rock side and the toppling rock side, the design concept of sectionalized support based on the spatial effect was proposed, and the advantages of the sectionalized support design were elaborated in combination with the numerical analysis results. Full article
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Article
One-Step Synthesis of Cross-Linked Esterified Starch and Its Properties
Appl. Sci. 2022, 12(8), 4075; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084075 - 18 Apr 2022
Viewed by 290
Abstract
Cross-linked esterified starch (CES) was prepared using a one-step method, where maize starch was selected as the raw material, sodium trimetaphosphate as the cross-linking agent, and acetic anhydride as the esterifying agent, respectively. A response surface experiment was systematically conducted for analyzing the [...] Read more.
Cross-linked esterified starch (CES) was prepared using a one-step method, where maize starch was selected as the raw material, sodium trimetaphosphate as the cross-linking agent, and acetic anhydride as the esterifying agent, respectively. A response surface experiment was systematically conducted for analyzing the correlation of the experimental variables (cross-linked temperature, pH, reaction time, sodium trimetaphosphate and acetic anhydride dosage) and properties of the product (peak and final viscosity). The Brabender viscosity, freeze-thaw stability, shearing resistance, and acid tolerance of the cross-linked acetylated dual modified starch were studied under different conditions of crosslinking degree and acetyl content. Meanwhile, the granular structure and morphology of the modified starch were analyzed. The results indicated that: after cross-linked acetylated dual modification, the starch had a distinct birefringence and granular structure, along with the creation of new carbonyl groups. The low degree of crosslinking and high acetyl contents were beneficial to the viscosity, which was significantly increased at both low and high temperatures. Moreover, the freeze-thaw stability of CES was elevated sharply after five cycles. In addition, CES displayed increased shear and acid tolerance compared to the original waxy maize, and their lowest differences between waxy maize and CES were only 0.62% and 0.59%, respectively. In summary, a novel method for starch modification was provided, and the synthesized CES was suggested to have exceptional performance for the food industry. Full article
(This article belongs to the Special Issue Bioavailability of Main Food Bioactives)
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Article
The Association between Influenza Vaccination and Stroke Risk in Patients with Hypertension: A Nationwide Population-Based Study
Appl. Sci. 2022, 12(8), 4074; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084074 - 18 Apr 2022
Viewed by 381
Abstract
There is evidence of strong association between influenza infections and stroke; however, the influenza vaccination and its effect on strokes is currently unclear. In the present study, Taiwan’s National Health Insurance Database was used in obtaining data for study subjects 55 years and [...] Read more.
There is evidence of strong association between influenza infections and stroke; however, the influenza vaccination and its effect on strokes is currently unclear. In the present study, Taiwan’s National Health Insurance Database was used in obtaining data for study subjects 55 years and older diagnosed with hypertension (n = 59,251; 25,266 vaccinated and 33,985 unvaccinated subjects) from 2001–2012. Propensity scores were calculated using a logistic regression model to determine the effects of vaccination by accounting for covariates that predict receiving the intervention (vaccine). A time-dependent Cox proportional hazard model was used to calculate the hazard ratios (HRs) for stroke in vaccinated and unvaccinated patients. Influenza vaccination was associated with a 42%, 40% and 44% stroke risk reduction in the entire cohort for all seasons, the influenza season and the non-influenza season, respectively (Adjust hazard ratio [aHR]: 0.58, 95% confidence interval [CI]: 0.56–0.61; aHR: 0.60, 95% CI: 0.56–0.63; aHR: 0.56, 95% CI: 0.52–0.60, for all seasons, the influenza season and the non-influenza season, respectively). The effect of risk reduction by vaccination also revealed a trend of dose dependency. Among subjects between 55 to 64 years old with four or more vaccinations during the study period, there is a 73% risk reduction for stroke during the non-influenza season (aHR: 0.27, 95% CI: 0.20–0.34). In conclusion, the influenza vaccination exerts dose-dependent and synergistic protective effects against stroke in individuals 55 years and older with hypertension. Full article
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Article
A New Method of Inland Water Ship Trajectory Prediction Based on Long Short-Term Memory Network Optimized by Genetic Algorithm
Appl. Sci. 2022, 12(8), 4073; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084073 - 18 Apr 2022
Cited by 1 | Viewed by 407
Abstract
Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the [...] Read more.
Ship position prediction plays a key role in the early warning and safety of inland waters and maritime navigation. Ship pilots must have in-depth knowledge of the future position of their ship and target ship in a specific time period when maneuvering the ship to effectively avoid collisions. However, prediction accuracy and computing efficiency are crucial issues that need to be worked out at present. To solve these problems, in this paper, the deep long short-term memory network framework (LSTM) and genetic algorithm (GA) are introduced to predict the ship trajectory of inland water. Firstly, the collected actual automatic identification system (AIS) data are preprocessed and a series of typical trajectories are extracted from them; then, the LSTM network is used to predict the typical trajectories in real time. Considering that the hyperparameters of the LSTM network have difficulty obtaining the optimal solution manually, the GA is used to optimize hyperparameters of LSTM; finally, the GA-LSTM trajectory prediction model is constructed with the optimal network parameters and compared with the traditional support vector machine (SVM) model and LSTM model. The experimental results show that the GA-LSTM model effectively improves the accuracy and speed of trajectory prediction, with outstanding performance and good generalization, which possess certain reference values for the development of collision avoidance of unmanned ships. Full article
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Article
Can AI Automatically Assess Scan Quality of Hip Ultrasound?
Appl. Sci. 2022, 12(8), 4072; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084072 - 18 Apr 2022
Viewed by 330
Abstract
Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality [...] Read more.
Ultrasound images can reliably detect Developmental Dysplasia of the Hip (DDH) during early infancy. Accuracy of diagnosis depends on the scan quality, which is subjectively assessed by the sonographer during ultrasound examination. Such assessment is prone to errors and often results in poor-quality scans not being reported, risking misdiagnosis. In this paper, we propose an Artificial Intelligence (AI) technique for automatically determining scan quality. We trained a Convolutional Neural Network (CNN) to categorize 3D Ultrasound (3DUS) hip scans as ‘adequate’ or ‘inadequate’ for diagnosis. We evaluated the performance of this AI technique on two datasets—Dataset 1 (DS1) consisting of 2187 3DUS images in which each image was assessed by one reader for scan quality on a scale of 1 (lowest quality) to 5 (optimal quality) and Dataset 2 (DS2) consisting of 107 3DUS images evaluated semi-quantitatively by four readers using a 10-point scoring system. As a binary classifier (adequate/inadequate), the AI technique gave highly accurate predictions on both datasets (DS1 accuracy = 96% and DS2 accuracy = 91%) and showed high agreement with expert readings in terms of Intraclass Correlation Coefficient (ICC) and Cohen’s kappa coefficient (K). Using our AI-based approach as a screening tool during ultrasound scanning or postprocessing would ensure high scan quality and lead to more reliable ultrasound hip examination in infants. Full article
(This article belongs to the Special Issue Applications of Deep Learning and Artificial Intelligence Methods)
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Article
Fractal Contact Mechanics Model for the Rough Surface of a Beveloid Gear with Elliptical Asperities
Appl. Sci. 2022, 12(8), 4071; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084071 - 18 Apr 2022
Cited by 1 | Viewed by 339
Abstract
Understanding the contact mechanics of rough tooth surfaces is critical in order to understand phenomena such as tooth surface flash temperature, tooth surface wear, and gear vibration. In this paper, the contact behavior between the meshing tooth surfaces of beveloid gear pairs with [...] Read more.
Understanding the contact mechanics of rough tooth surfaces is critical in order to understand phenomena such as tooth surface flash temperature, tooth surface wear, and gear vibration. In this paper, the contact behavior between the meshing tooth surfaces of beveloid gear pairs with elliptical asperities is the focus. The contact area distribution function of the elliptical asperity was proposed for the point contact of curved surfaces by transforming the elastic contact problem between gear meshing surfaces into the contact between elastic curved surfaces with an arbitrary radius of curvature. In addition, a fractal contact mechanics model for the rough surface of a beveloid gear with elliptical asperities was established. The influence of tooth surface topography on the contact load and contact stiffness under different fractal parameters was investigated, and the results demonstrated that the real contact load and the contact stiffness of curved surfaces increase with the increase in the fractal dimension D and the contact coefficient λ. Conversely, the real contact load and normal contact stiffness decrease with the increase in the fractal roughness G and eccentricity e. Full article
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Article
Simplified Two-Dimensional Generalized Partial Response Target of Holographic Data Storage Channel
Appl. Sci. 2022, 12(8), 4070; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084070 - 18 Apr 2022
Viewed by 285
Abstract
With a high capacity and fast data access rate, holographic data storage (HDS) is a potential candidate for future storage systems. However, for page-oriented data processing, two-dimensional (2D) interference appears intensely in the HDS systems. Therefore, the new 2D generalized partial response (GPR) [...] Read more.
With a high capacity and fast data access rate, holographic data storage (HDS) is a potential candidate for future storage systems. However, for page-oriented data processing, two-dimensional (2D) interference appears intensely in the HDS systems. Therefore, the new 2D generalized partial response (GPR) target is introduced to estimate the 2D interference. In addition, we also propose a method to analyze the 2D GPR target into two serial one-dimensional (1D) GPR targets. It makes us design a simple detection scheme composed of two serial 1D detectors instead of a complicated 2D detector. In simulations, the results show that our proposed scheme can improve the BER performance compared to the conventional 1D GPR target model. Full article
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Article
Experimental Investigation and Modelling of the Droplet Size in a DN300 Stirred Vessel at High Disperse Phase Content Using a Telecentric Shadowgraphic Probe
Appl. Sci. 2022, 12(8), 4069; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084069 - 18 Apr 2022
Viewed by 322
Abstract
In this work, steady-state droplet size distributions in a DN300 stirred batch vessel with a Rushton turbine impeller are investigated using an insertion probe based on the telecentric transmitted light principle. High-resolution droplet size distributions are extracted from the images using a convolutional [...] Read more.
In this work, steady-state droplet size distributions in a DN300 stirred batch vessel with a Rushton turbine impeller are investigated using an insertion probe based on the telecentric transmitted light principle. High-resolution droplet size distributions are extracted from the images using a convolutional neural network for image-analysis in order to investigate the influence of impeller speed and phase fraction (up to 50 vol.-%). In addition, Sauter mean diameters were calculated and correlated with two semi-empirical approaches, while the standard approach only accomplished 5.7% accuracy, and the correlation of Laso et al. provided a relative mean error of 4.0%. In addition, the correlated exponent in the Weber number was fitted to the experimental data of this work yielding a slightly different value than the theoretical (−0.6), which allows a better representation of the low coalescence tendency of the system, which is usually neglected in standard procedures. Full article
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Article
MSPNet: Multi-Scale Strip Pooling Network for Road Extraction from Remote Sensing Images
Appl. Sci. 2022, 12(8), 4068; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084068 - 18 Apr 2022
Viewed by 358
Abstract
Extracting roads from remote sensing images can support a range of geo-information applications. However, it is challenging due to factors such as the complex distribution of ground objects and occlusion of buildings, trees, shadows, etc. Pixel-wise classification often fails to predict road connectivity [...] Read more.
Extracting roads from remote sensing images can support a range of geo-information applications. However, it is challenging due to factors such as the complex distribution of ground objects and occlusion of buildings, trees, shadows, etc. Pixel-wise classification often fails to predict road connectivity and thus produces fragmented road segments. In this paper, we propose a multi-scale strip pooling network (MSPNet) to learn the linear features of roads. Motivated by the strip pooling being more aligned with the shape of roads, which are long-span and narrow, we develop a multi-scale strip pooling (MSP) module that utilizes strip pooling layers with long but narrow kernel shapes to capture multi-scale long-range context from horizontal and vertical directions. The proposed MSP module focuses on establishing relationships along the road region to guarantee the connectivity of roads. Considering the complex distribution of ground objects, the spatial pyramid pooling is applied to enhance the learning ability of complex features in different sub-regions. In addition, to alleviate the problem caused by an imbalanced distribution of road and non-road pixels, we use binary cross-entropy and dice-coefficient loss functions to jointly train our proposed deep learning model. Then, we perform ablation experiments to adjust the loss contributions to suit the task of road extraction. Comparative experiments on a popular benchmark DeepGlobe dataset demonstrate that our proposed MSPNet establishes new competitive results in both IoU and F1-score. Full article
(This article belongs to the Topic Machine and Deep Learning)
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Article
Stock Portfolio Management in the Presence of Downtrends Using Computational Intelligence
Appl. Sci. 2022, 12(8), 4067; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084067 - 18 Apr 2022
Viewed by 344
Abstract
Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of [...] Read more.
Stock portfolio management consists of defining how some investment resources should be allocated to a set of stocks. It is an important component in the functioning of modern societies throughout the world. However, it faces important theoretical and practical challenges. The contribution of this work is two-fold: first, to describe an approach that comprehensively addresses the main activities carried out by practitioners during portfolio management (price forecasting, stock selection and portfolio optimization) and, second, to consider uptrends and downtrends in prices. Both aspects are relevant for practitioners but, to the best of our knowledge, the literature does not have an approach addressing them together. We propose to do it by exploiting various computational intelligence techniques. The assessment of the proposal shows that further improvements to the procedure are obtained when considering downtrends and that the procedure allows obtaining portfolios with better returns than those produced by the considered benchmarks. These results indicate that practitioners should consider the proposed procedure as a complement to their current methodologies in managing stock portfolios. Full article
(This article belongs to the Topic Applied Metaheuristic Computing)
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Article
Multi-Aspect Oriented Sentiment Classification: Prior Knowledge Topic Modelling and Ensemble Learning Classifier Approach
Appl. Sci. 2022, 12(8), 4066; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084066 - 18 Apr 2022
Viewed by 443
Abstract
User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or [...] Read more.
User-generated content on numerous sites is indicative of users’ sentiment towards many issues, from daily food intake to using new products. Amid the active usage of social networks and micro-blogs, notably during the COVID-19 pandemic, we may glean insights into any product or service through users’ feedback and opinions. Thus, it is often difficult and time consuming to go through all the reviews and analyse them in order to recognize the notion of the overall goodness or badness of the reviews before making any decision. To overcome this challenge, sentiment analysis has been used as an effective rapid way to automatically gauge consumers’ opinions. Large reviews will possibly encompass both positive and negative opinions on different features of a product/service in the same review. Therefore, this paper proposes an aspect-oriented sentiment classification using a combination of the prior knowledge topic model algorithm (SA-LDA), automatic labelling (SentiWordNet) and ensemble method (Stacking). The framework is evaluated using the dataset from different domains. The results have shown that the proposed SA-LDA outperformed the standard LDA. In addition, the suggested ensemble learning classifier has increased the accuracy of the classifier by more than ~3% when it is compared to baseline classification algorithms. The study concluded that the proposed approach is equally adaptable across multi-domain applications. Full article
(This article belongs to the Topic Methods for Data Labelling for Intelligent Systems)
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Article
Metal Release Mechanism and Electrochemical Properties of Lix(Ni1/3Mn1/3Co1/3)O2
Appl. Sci. 2022, 12(8), 4065; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084065 - 18 Apr 2022
Viewed by 389
Abstract
Complex metal oxides (CMOs) are used broadly in applications including electroreactive forms found in lithium-ion battery technology. Computational chemistry can provide unique information about how the properties of CMO cathode materials change in response to changes in stoichiometry, for example, changes of the [...] Read more.
Complex metal oxides (CMOs) are used broadly in applications including electroreactive forms found in lithium-ion battery technology. Computational chemistry can provide unique information about how the properties of CMO cathode materials change in response to changes in stoichiometry, for example, changes of the lithium (Li) content during the charge–discharge cycle of the battery. However, this is difficult to measure experimentally due to the small cross-sectional area of the cations. Outside of operational conditions, the Li content can influence the transformations of the CMO when exposed to the environment. For example, metal release from CMOs in aqueous settings has been identified as a cross-cutting mechanism important to CMO degradation. Computational studies investigating metal release from CMOs show that the thermodynamics depend on the oxidation states of lattice cations, which is expected to vary with the lithium content. In this work, computational studies track changes in metal release trends as a function of Li content in Lix(Ni1/3Mn1/3Co1/3)O2 (NMC). The resulting dataset is used to construct a random forest tree (RFT) machine learning (ML) model. A modeling challenge in delithiation studies is the large configurational space to sample. Through investigating multiple configurations at each lithium fraction, we find structural features associated with favorable energies to chemically guide the identification of relevant structures and adequately predict voltage values. Full article
(This article belongs to the Special Issue Complex Oxides: Freestanding, Interfaces, and Tunnel Junctions)
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Article
Effects of Ultrafine Blast Furnace Slag on the Microstructure and Chloride Transport in Cementitious Systems under Cyclic Drying–Wetting Conditions
Appl. Sci. 2022, 12(8), 4064; https://0-doi-org.brum.beds.ac.uk/10.3390/app12084064 - 18 Apr 2022
Viewed by 357
Abstract
This paper presents experimental investigations into the effects of ultrafine blast furnace slag on microstructure improvements against chloride penetration in saturated and unsaturated cementitious systems exposed to cyclic drying–wetting conditions. The hydration kinetics of ultrafine slag powders and pore solution chemistry in slag-blended [...] Read more.
This paper presents experimental investigations into the effects of ultrafine blast furnace slag on microstructure improvements against chloride penetration in saturated and unsaturated cementitious systems exposed to cyclic drying–wetting conditions. The hydration kinetics of ultrafine slag powders and pore solution chemistry in slag-blended cementitious systems at different ages, together with the main hydration products and pore structure characteristics, were determined. The chloride profiles accounting for different slag contents and drying–wetting cycles were measured. The results reveal that the reactivity of ultrafine slag can be well described with Avrami’s equation. The dilution effect of the slag predominated the pore solution chemistry, and the pH value decreased with a higher inclusion of slag. An optimal inclusion of 65% slag by mass of the binder corresponding to the finest pore structure and highest hydrotalcite content was found, which provides a reasonable basis for the slow chloride diffusion and high chloride binding. Under drying–wetting exposure, the specimen with a lower saturation exhibited a higher chloride transport caused by capillary absorption in the skin layer. The chloride transport tended to be diffusion controlled after sufficient drying–wetting cycles. Full article
(This article belongs to the Topic Innovative Construction and Building Materials)
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