Applied Sciences

Open AccessArticle

A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes

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Appl. Sci. 2023, 13(7), 4371; https://doi.org/10.3390/app13074371 (registering DOI) - 29 Mar 2023

Abstract

The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder [...] Read more.

The conventional soft decision decoding (SDD) methods require various hard decision decoders (HDDs) based on different codes or re-manipulate the generator matrix by the complicated Gaussian elimination technique according to the bit reliability. This paper presents a general multi-class neural network (NN)-based decoder for the short linear block codes, where no HDD and Gaussian elimination are required once the NN is constructed. This network architecture performs multi-classification to select the messages with high occurrence probabilities and chooses the best codeword on a maximum likelihood basis. Simulation results show that the developed approach outperforms the existing deep neural network (DNN)-based decoders in terms of decoding time and bit error rate (BER). The error-correcting performance is also superior to the conventional Chase-II algorithm and is close to the ordered statistics decoding (OSD) in most cases. For Bose–Chaudhuri–Hocquenghem (BCH) codes, the SNR is improved by 1dB to 4dB as the BER is 10⁻⁴. For the (23, 12) quadratic residue (QR) code, the SNR is improved by 2dB when the BER is 10⁻³. The developed NN-based decoder is quite general and applicable to various short linear block codes with good BER performance. Full article

Open AccessSystematic Review

MRONJ Treatment Strategies: A Systematic Review and Two Case Reports

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Angelo Michele Inchingolo

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Merigrazia Campanelli

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Alessio Danilo Inchingolo

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Gianna Dipalma

Appl. Sci. 2023, 13(7), 4370; https://doi.org/10.3390/app13074370 (registering DOI) - 29 Mar 2023

Abstract

MRONJ is a serious drug-related side effect that is most common in people using antiresorptive and/or angiogenic medications. Therapy options for this condition include conservative treatments, surgical procedures with varied degrees of invasiveness, and adjuvant therapies. The aim of the present study is [...] Read more.

MRONJ is a serious drug-related side effect that is most common in people using antiresorptive and/or angiogenic medications. Therapy options for this condition include conservative treatments, surgical procedures with varied degrees of invasiveness, and adjuvant therapies. The aim of the present study is to identify the most successful and promising therapy alternatives available to clinicians. PubMed, Cochrane, Scopus, Web of Science, and Embase were searched for works on our topic published between 8 January 2006 and 8 January 2023. The search was restricted to randomized clinical trials, retrospective studies, clinical studies, and case series involving human subjects with at least five cases and no age restriction on participants. A total of 2657 was found. After the selection process, the review included 32 publications for qualitative analysis. Although conservative treatments (pharmacological, laser, and minimally invasive surgery) are effective in the early stages of MRONJs or as a supplement to traditional surgical resection therapy, most studies emphasize the importance of surgical treatment for the resolution or downstaging of advanced lesions. Fluorescence-guided surgery, PRP, PRF, CGF, piezosurgery, VEGF, hyaluronic acid, and ozone therapy all show significant potential for improving treatment outcomes. Full article

(This article belongs to the Special Issue Dentistry, Orthodontics and Maxillo-Facial Surgery, Traditional and Smart Approaches)

Open AccessArticle

Study on the Dynamic Generation of Subcooled Water Using a Compact Heat Exchanger

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Pengwei Cheng

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Kaijian Wang

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Solomon Sinkolongo

Appl. Sci. 2023, 13(7), 4369; https://doi.org/10.3390/app13074369 (registering DOI) - 29 Mar 2023

Abstract

The dynamic generation of ice slurry from subcooled water is one of the most promising ways to make ice; this process is utilized widely in ice storage air-conditioning systems. However, the random occurrence of ice blockage during the generation of subcooled water using [...] Read more.

The dynamic generation of ice slurry from subcooled water is one of the most promising ways to make ice; this process is utilized widely in ice storage air-conditioning systems. However, the random occurrence of ice blockage during the generation of subcooled water using conventional heat exchangers prevents the increase in subcooling, thereby reducing the efficiency of the release of the subcooled water and converting it into ice slurry. A more efficient approach to reducing the fluid passage time is to employ a compact heat exchanger with a highly efficient heat transfer performance, a heat transfer length of only 21.5 mm, and a hydraulic diameter of 0.32 mm. A compact heat exchanger was used to build a dynamic generation setup for subcooled water, and 40 wt% of non-freezing liquid and tap water was used as the working fluid for heat exchange to generate subcooled water. The results show that the compact heat exchanger can achieve a greater subcooling degree (3.8 K) and longer duration (108 min). This study further explored the potential for dynamic ice making from deep, subcooled water and improved the overall structure of the compact heat exchanger used. The experimental setup is recommended based on the analysis of the results. Full article

(This article belongs to the Special Issue The10th Asian Conference on Refrigeration and Air-Conditioning (ACRA2022))

Open AccessArticle

A Low Frequency Noise Source Localization and Identification Method Based on a Virtual Open Spherical Vector Microphone Array

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Appl. Sci. 2023, 13(7), 4368; https://doi.org/10.3390/app13074368 (registering DOI) - 29 Mar 2023

Abstract

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA (’p+v’ joint processing method of pressure and [...] Read more.

Aiming at the problem of poor spatial resolution of low-frequency noise sources in a small-aperture spherical microphone array (SMA), this paper proposes a method for localizing and identifying low-frequency noise sources based on virtual-vector open SMA (’p+v’ joint processing method of pressure and velocity). Firstly, a virtual open SMA with a larger aperture is obtained using a virtual array extrapolation method. In this method, the virtual SMA and the actual SMA are regarded as a dual-radius SMA, and velocity information is obtained using finite difference elements of the same direction (azimuth and elevation) array of the virtual and actual SMA. At the same time, the sound pressure at the velocity position is obtained using the virtual SMA extrapolation method and the virtual vector array element SMA, whereby both velocity and sound pressure information is obtained. Finally, the vector signal processing technology is introduced into the generalized inverse beamforming algorithm (GIB). After determining the vector transfer function of the ’p+v’ joint processing mode, a low-frequency-noise-source localization and identification method based on the vector signal processing GIB is proposed. The simulation and experiment results show that a virtual SMA with a large aperture can be obtained using a virtual array extrapolation method, and the GIB with sound pressure and velocity joint processing has a better spatial resolution. Full article

(This article belongs to the Special Issue Underwater Acoustic Signal Processing)

Open AccessArticle

Analysis of the Influence of Thermal Loading on the Behaviour of the Earth’s Crust

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Ivo Wandrol

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Karel Frydrýšek

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Daniel Čepica

Appl. Sci. 2023, 13(7), 4367; https://doi.org/10.3390/app13074367 (registering DOI) - 29 Mar 2023

Abstract

The article focuses on the deformation and strain-stress analysis of the Earth’s crust under external thermal loading. More specifically, the influence of cyclic changes in the surface temperature field on the stress and displacement inside the crust over a two-year time span is [...] Read more.

The article focuses on the deformation and strain-stress analysis of the Earth’s crust under external thermal loading. More specifically, the influence of cyclic changes in the surface temperature field on the stress and displacement inside the crust over a two-year time span is investigated. The finite element program MSC.Marc Mentat was used to calculate the stresses and displacements. For practical analysis reasons, the Earth’s crust is simplified as a planar, piecewise homogeneous, isotropic model (plane strain), and time-varying temperature functions of illumination (thermal radiation) from the Sun are considered in the local isotropy sections of the model. Interaction between the Earth’s crust and mantle is defined by the Winkler elastic foundation. By applying a probabilistic approach (Monte Carlo Method), a new stochastic model of displacements and stresses and new information on crustal displacements relative to the Earth’s mantle were obtained. The results proved the heating influence of the Sun on the Earth’s crust and plate tectonics. Full article

(This article belongs to the Section Mechanical Engineering)

Open AccessArticle

Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide

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Appl. Sci. 2023, 13(7), 4366; https://doi.org/10.3390/app13074366 (registering DOI) - 29 Mar 2023

Abstract

The effect of temperature (from 288 to 308 K) and concentration of sulfide ions (up to 40 ppm) on the corrosion behavior of AISI 304L and AISI 316L stainless steels in seawater was studied with measurements of open-circuit potential, linear and potentiodynamic polarization, [...] Read more.

The effect of temperature (from 288 to 308 K) and concentration of sulfide ions (up to 40 ppm) on the corrosion behavior of AISI 304L and AISI 316L stainless steels in seawater was studied with measurements of open-circuit potential, linear and potentiodynamic polarization, and electrochemical impedance spectroscopy. An increase in temperature and pollutant concentration negatively affects the corrosion stability of stainless steels at the open circuit (the resistance, compactness, and thickness of the surface layer decrease and the corrosion current increases), in the passive region (the passivation current increases, the depassivation potential decreases, and the passive potential region narrows), and in the transpassive potential region (the rate of metal dissolution increases). The occurrence of pitting corrosion on the surface of the samples was confirmed with optical microscopy and a non-contact 3D profilometer. A few large pits (depth 80–100 μm and width 100 μm) were formed on the surface of AISI 304L steel, while several smaller pits (depth 40–50 μm and width 50 μm) were formed on the surface of AISI 316L steel. With increasing temperature and sulfide ion concentration, the width, depth, and density of the pits increased on both steel samples. In the studied temperature and concentration range of sulfide ions, the AISI 316L steels exhibited higher corrosion resistance. Overall, the influence of sulfide ions on steel corrosion was more pronounced than the influence of temperature. Full article

(This article belongs to the Special Issue Corrosion Inhibitors and Protective Coatings)

Open AccessArticle

Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks

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Fawaz Khaled Alarfaj

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Nayeem Ahmad Khan

Appl. Sci. 2023, 13(7), 4365; https://doi.org/10.3390/app13074365 (registering DOI) - 29 Mar 2023

Abstract

SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing [...] Read more.

SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing research uses machine learning techniques that have the capability of detecting previously unknown and novel attack types. Taking advantage of deep learning to improve detection accuracy, we propose using a probabilistic neural network (PNN) to detect SQL injection attacks. To achieve the best value in selecting a smoothing parament, we employed the BAT algorithm, a metaheuristic algorithm for optimization. In this study, a dataset consisting of 6000 SQL injections and 3500 normal queries was used. Features were extracted based on tokenizing and a regular expression and were selected using Chi-Square testing. The features used in this study were collected from the network traffic and SQL queries. The experiment results show that our proposed PNN achieved an accuracy of 99.19% with a precision of 0.995%, a recall of 0.981%, and an F-Measure of 0.928% when employing a 10-fold cross-validation compared to other classifiers in different scenarios. Full article

(This article belongs to the Special Issue Security and Privacy of Big Data: Issues, Challenges, and Future Perspectives)

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Open AccessEditorial

Reliability Techniques in Engineering Projects

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Justo García-Sanz-Calcedo

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Gonzalo Sánchez-Barroso

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Jaime González-Domínguez

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Manuel Botejara-Antúnez

Appl. Sci. 2023, 13(7), 4364; https://doi.org/10.3390/app13074364 (registering DOI) - 29 Mar 2023

Abstract

In engineering projects, reliability is conceived as physical equipment’s ability to function without failure. [...] Full article

(This article belongs to the Special Issue Reliability Techniques in Engineering Projects)

Open AccessArticle

Modeling the Effects of Particle Shape on Damping Ratio of Dry Sand by Simple Shear Testing and Artificial Intelligence

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Abolfazl Baghbani

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Susanga Costa

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Roohollah Shirani Faradonbeh

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Amin Soltani

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Hasan Baghbani

Appl. Sci. 2023, 13(7), 4363; https://doi.org/10.3390/app13074363 (registering DOI) - 29 Mar 2023

Abstract

This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and [...] Read more.

This study investigates the effects of sand particle shape, in terms of roundness, sphericity and regularity, on the damping ratio of a dry sand material. Twelve different cyclic simple shear testing scenarios were considered and applied using vertical stresses of 50, 150 and 250 kPa and cyclic stress ratios (CSR) of 0.2, 0.3, 0.4 and 0.5 in both constant- and controlled-stress modes. Each testing scenario involved five tests, using the same sand that was reconstructed from its previous cyclic test. On completion of the cyclic tests, corresponding hysteresis loops were established to determine the damping ratio. The results indicated that the minimum and maximum damping ratios for this sand material were 6.9 and 25.5, respectively. It was observed that the shape of the sand particles changed during cyclic loading, becoming progressively more rounded and spherical with an increasing number of loading cycles, thereby resulting in an increase in the damping ratio. The second part of this investigation involved the development of artificial intelligence models, namely an artificial neural network (ANN) and a support vector machine (SVM), to predict the effects of sand particle shape on the damping ratio. The proposed ANN and SVM models were found to be effective in predicting the damping ratio as a function of the particle shape descriptors (i.e., roundness, sphericity and regularity), vertical stress, CSR and number of loading cycles. Finally, a sensitivity analysis was conducted to identify the importance of the input variables; the vertical stress and regularity were, respectively, ranked as first and second in terms of importance, while the CSR was found to be the least important parameter. Full article

(This article belongs to the Special Issue Experimental Works in Geotechnical Engineering: Challenges on Innovation and Reliability)

Open AccessArticle

Static Strength and Buckling Analysis of an Aircraft Support

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Daqian Zhang

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Guoxiong Zhang

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Yueyang Chen

Appl. Sci. 2023, 13(7), 4362; https://doi.org/10.3390/app13074362 (registering DOI) - 29 Mar 2023

Abstract

At the initial stage of design, the structural strength and stability of the load-bearing structural members of the aircraft are mainly considered. In this paper, the structural strength and stability of a support of aircraft assembly are analyzed. First, structural strength analysis, under [...] Read more.

At the initial stage of design, the structural strength and stability of the load-bearing structural members of the aircraft are mainly considered. In this paper, the structural strength and stability of a support of aircraft assembly are analyzed. First, structural strength analysis, under the design load of 30.151 KN, and numerical analysis shows that, in the stress concentration area, the stress peak value is 571.9 Mpa. The experimental value of the strain sensor S6 position is compared with the numerical results, and the error is within 2.5%, which verifies the validity of the numerical model and the structural strength to meet the design requirements. Second, structural stability adopts the method of buckling analysis. Linear buckling analysis shows that the first six order buckling modes and critical loads. The first order critical load is 464.9 KN. Risk arc length method is used in the nonlinear buckling analysis. Considering the geometric imperfection of the support, 1% and 10% initial imperfection values are used. Nonlinear buckling critical load values—without imperfection and considering the initial imperfection—are obtained, which are 110.6 KN, 108.4 KN, and 106.2 KN, respectively. There is little difference between the three values, indicating that the geometric imperfection of the support is not obvious to the nonlinear buckling, and they have structural stability. In the case of no imperfection, the maximum deformation of the main area reaches 21.4 mm, and the support enters the instability state. This paper comprehensively discusses the results of structural strength and structural stability analysis, which can provide reference for the design and optimization of bearing components. Full article

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Open AccessArticle

Feature Identification, Solution Disassembly and Cost Comparison of Intelligent Driving under Different Technical Routes

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Appl. Sci. 2023, 13(7), 4361; https://doi.org/10.3390/app13074361 (registering DOI) - 29 Mar 2023

Abstract

Technical route decision making of intelligent driving has always been the focus of attention of automotive enterprises and even the industry. Firstly, this study combs the main technical routes of intelligent driving at different levels from three dimensions: development strategy, intelligence allocation and [...] Read more.

Technical route decision making of intelligent driving has always been the focus of attention of automotive enterprises and even the industry. Firstly, this study combs the main technical routes of intelligent driving at different levels from three dimensions: development strategy, intelligence allocation and sensor combination. Then, the methodology of technical component combination is designed to disassemble different technical routes into corresponding technical component combinations. Finally, an improved evaluation model of total cost of ownership of intelligent driving is developed and the total cost of ownership of intelligent driving system under different technical routes is compared. For the development strategy, even if the function superposition can follow some research and development achievements of low-level intelligent driving, scenario-driven is still the option with lower cost and better sustainability. For intelligence allocation, collaborative intelligence can effectively reduce the cost of the vehicle compared with single-vehicle intelligence by up to 46%, but the cost reduction depends on the original on-board hardware. For sensor combination, the multi-source fusion always has the cost advantage compared with vision-only, but the advantage is more obvious in the medium-level and high-level stage of single-vehicle intelligence. Full article

(This article belongs to the Special Issue Autonomous Vehicles: Technology and Application)

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Open AccessArticle

Characterizing Variances of Adulterated Extra Virgin Olive Oils by UV-Vis Spectroscopy Combined with Analysis of Variance-Projected Difference Resolution (ANOVA-PDR) and Multivariate Classification

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Boyan Gao

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Jingyao Zhang

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Weiying Lu

Appl. Sci. 2023, 13(7), 4360; https://doi.org/10.3390/app13074360 (registering DOI) - 29 Mar 2023

Abstract

The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined [...] Read more.

The analysis of variance-projected difference resolution (ANOVA-PDR) was proposed and compared with multivariate classification for its potential in detecting possible food adulteration in extra virgin olive oils (EVOOs) by UV-Vis spectra. Three factors including origin, adulteration level, and adulteration type were systematically examined by the ANOVA-derived methods. The ANOVA-PDR quantitatively presented the separation of the internal classes according to the three main factors. Specifically, the average ANOVA-derived PDRs of the EVOO origination and adulteration level, respectively, is 4.01 and 1.78, while the conventional PDRs of the three factors are all less than 1.5. Furthermore, the partial least-squares-discriminant analysis (PLS-DA) and the PLS regression (PLSR) modeling with the selected sub-datasets from different origins were used to verify the results. The resulting models suggested that the three main factors and their interactions were all important sources of spectral variations. Full article

(This article belongs to the Special Issue Advances in Chemometrics in Analytical Chemistry)

Open AccessArticle

Assessment of Deformation Flow in 1050 Aluminum Alloy by the Implementation of Constitutive Model Parameters

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Appl. Sci. 2023, 13(7), 4359; https://doi.org/10.3390/app13074359 (registering DOI) - 29 Mar 2023

Abstract

The behavior of technically pure aluminum was examined, and this investigation allowed the determination of the material constants by various models. The model parameters derived were subsequently used for the finite element simulations (FEM) of a cold rolling process. To determine the tuning [...] Read more.

The behavior of technically pure aluminum was examined, and this investigation allowed the determination of the material constants by various models. The model parameters derived were subsequently used for the finite element simulations (FEM) of a cold rolling process. To determine the tuning parameters such as the strain-hardening coefficient K, strain-hardening exponent n, or elastic constant E, a tensile test was performed on the heat-treated sheet of 1050 Al alloy and the experimentally observed deformation behavior was compared to the simulated counterpart. The results of the FEM calculations reveal that the strain-hardening characteristics can be alternatively derived from the Brinell indentation. Additionally, the determined constitutive model parameters (E = 69.8 GPa, K = 144.6 MPa, and n = 0.3) were verified by simulating both the symmetric and asymmetric rolling processes. The distribution of the equivalent strain across the sheet thickness was computed by the FEM, and it was found that the modeled deformation profiles tend to reproduce the experimentally observed ones with high accuracy for different strain modes inasmuch as the mentioned rolling trials accommodate diverse amounts of shear and normal strain components. Full article

(This article belongs to the Special Issue Alloys: Evolution of Microstructure and Texture)

Open AccessArticle

Delamination Effect on the Buckling Behaviour of Carbon–Epoxy Composite Typical Aeronautical Panels

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Appl. Sci. 2023, 13(7), 4358; https://doi.org/10.3390/app13074358 (registering DOI) - 29 Mar 2023

Abstract

The instability of structures due to compression is one of the most critical issues related to aircraft components. Especially in composite materials, which have poor out-of-plane mechanical properties, the buckling load must be assessed to ensure that the structures are within the safe [...] Read more.

The instability of structures due to compression is one of the most critical issues related to aircraft components. Especially in composite materials, which have poor out-of-plane mechanical properties, the buckling load must be assessed to ensure that the structures are within the safe limits compared to the operating loads. In the presence of delamination, the compression instability of structures becomes catastrophic, as the propagation of delamination can dramatically reduce the stiffness of the structure almost instantaneously. During the operational life of composite aircraft components, one of the most common events that can occur is low-velocity impact with foreign objects, which is one of the primary reasons for delamination. In this paper, a sensitivity analysis is presented on a typical aerospace reinforced panel with a circular delamination, representative of an impact damage. Different configurations have been analysed, varying the radius and position along the thickness of the delamination. Furthermore, some geometric parameters of the panel have been modified to evaluate how the buckling load and the propagation of interlaminar damage evolve. Full article

(This article belongs to the Special Issue Improvement of Composite Components Performances according to Damage Tolerant Design Philosophies)

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Open AccessReview

A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems

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Appl. Sci. 2023, 13(7), 4357; https://doi.org/10.3390/app13074357 (registering DOI) - 29 Mar 2023

Abstract

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role [...] Read more.

Knowledge graph (KG) technology is a newly emerged knowledge representation method in the field of artificial intelligence. Knowledge graphs can form logical mappings from cluttered data and establish triadic relationships between entities. Accurate derivation and reasoning of knowledge graphs play an important role in guiding power equipment operation and decision-making. Due to the complex and weak relations from multi-source heterogeneous data, the use of KGs has become popular in research to represent potential information in power knowledge reasoning. In this review, we first summarize the key technologies of knowledge graph representation and learning. Then, based on the complexity and real-time changes of power system operation and maintenance, we present multiple data processing, knowledge representation learning, and the graph construction process. In three typical power operation and fault decision application scenarios, we investigate current algorithms in power KG acquisition, representation embedding, and knowledge completion to illustrate accurate and exhaustive recommendations. Thus, using KGs to provide reference solutions and decision guidance has a significant role in improving the efficiency of power system operations. Finally, we summarize the achievements and difficulties of current research and give an outlook for future, promising roles of KG in power systems. Full article

(This article belongs to the Special Issue Grid Innovation in the Era of Smart Grids, Volume Ⅱ)

Open AccessSystematic Review

PREFMoDeL: A Systematic Review and Proposed Taxonomy of Biomolecular Features for Deep Learning

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Jacob L. North

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Victor L. Hsu

Appl. Sci. 2023, 13(7), 4356; https://doi.org/10.3390/app13074356 (registering DOI) - 29 Mar 2023

Abstract

Of fundamental importance in biochemical and biomedical research is understanding a molecule’s biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction [...] Read more.

Of fundamental importance in biochemical and biomedical research is understanding a molecule’s biological properties—its structure, its function(s), and its activity(ies). To this end, computational methods in Artificial Intelligence, in particular Deep Learning (DL), have been applied to further biomolecular understanding—from analysis and prediction of protein–protein and protein–ligand interactions to drug discovery and design. While choosing the most appropriate DL architecture is vitally important to accurately model the task at hand, equally important is choosing the features used as input to represent molecular properties in these DL models. Through hypothesis testing, bioinformaticians have created thousands of engineered features for biomolecules such as proteins and their ligands. Herein we present an organizational taxonomy for biomolecular features extracted from 808 articles from across the scientific literature. This objective view of biomolecular features can reduce various forms of experimental and/or investigator bias and additionally facilitate feature selection in biomolecular analysis and design tasks. The resulting dataset contains 1360 nondeduplicated features, and a sample of these features were classified by their properties, clustered, and used to suggest new features. The complete feature dataset (the Public Repository of Engineered Features for Molecular Deep Learning, PREFMoDeL) is released for collaborative sourcing on the web. Full article

(This article belongs to the Special Issue Machine Learning in Bioinformatics: Latest Advances and Prospects)

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Open AccessArticle

Data Rights Confirmation Scheme Based on Auditable Ciphertext CP-ABE in the Cloud Storage Environment

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Appl. Sci. 2023, 13(7), 4355; https://doi.org/10.3390/app13074355 (registering DOI) - 29 Mar 2023

Abstract

Advances in information technology have made data accessible anytime and anywhere. Currently, data confirmation is a popular area of research. Many current approaches to data confirmation rely on submitting certificates of ownership, embedding digital watermarks, or using blockchain. However, none of these approaches [...] Read more.

Advances in information technology have made data accessible anytime and anywhere. Currently, data confirmation is a popular area of research. Many current approaches to data confirmation rely on submitting certificates of ownership, embedding digital watermarks, or using blockchain. However, none of these approaches can avoid exposing source data to third parties that are not fully trusted. To address this issue, this paper proposes a new data confirmation method based on ciphertext policy attribute-based encryption (CP-ABE), which is widely used in cloud storage environments. The unique identifier of the data owner is encrypted by Paillier encryption and embedded into the ciphertext, so that the ownership corresponding to the plaintext is converted to the ownership corresponding to the ciphertext. During the entire confirmation process, third-party organizations cannot access the source data, reducing the risk of source data leakage. Finally, the feasibility of the scheme is proved by security proof and experiment comparison. Full article

(This article belongs to the Special Issue Secure Cloud Storage)

Open AccessArticle

Study on Crack Propagation of Rock Bridge in Rock-like Material with Fractures under Compression Loading with Sudden Change Rate

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Appl. Sci. 2023, 13(7), 4354; https://doi.org/10.3390/app13074354 (registering DOI) - 29 Mar 2023

Abstract

In order to study the influence of sudden change of loading rate on crack propagation and failure mode of rock bridge of fractured rock mass, the specimens used in this paper were rock-like materials containing two prefabricated fractures. The mechanical properties and the [...] Read more.

In order to study the influence of sudden change of loading rate on crack propagation and failure mode of rock bridge of fractured rock mass, the specimens used in this paper were rock-like materials containing two prefabricated fractures. The mechanical properties and the failure mode of the specimens under different constant loading rates and sudden changes in loading rate were tested. The photographic monitoring and acoustic emission were carried out at the same time. The results show that: (1) In the process of sudden change loading, the specimen shows the characteristics of approximate elastic deformation, and the stress-strain curve after sudden loading is similar to the corresponding stress-strain curve at the corresponding constant loading rate. (2) Combined with acoustic emission detection, it is found that when the loading rate is not much different before the mutation, the loading rates is the same after the mutation and the stress-strain curve of the specimen is similar. In the low-speed loading stage, the acoustic emission count is generally low, while in the high-speed loading stage, the acoustic emission count is generally high. The sudden change from low-speed loading to high-speed loading easily induces stress drop, resulting in crack generation and increase of acoustic emission count. (3) The rate of the specimen during the crack development period plays a decisive role in the failure mode of the specimen. Before the sudden change of loading rate, the low-speed loading within a certain range has little effect on the specimen. When the high-speed loading is carried out when the stress is low in the early stage of the mutation, the acoustic emission count of the specimen is high, which will cause some damage inside the specimen. As a result, even if the rate is the same after the mutation, the final peak stress and failure mode may be different. Full article

Open AccessArticle

A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks

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Xinzhe Wang

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Wenbin Yao

Appl. Sci. 2023, 13(7), 4353; https://doi.org/10.3390/app13074353 (registering DOI) - 29 Mar 2023

Abstract

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and [...] Read more.

The dynamic-scheduling problem of transmission tasks (DSTT) is an important problem in the daily work of radio and television transmission stations. The transmission effect obtained by the greedy algorithm for task allocation is poor. In the case of more tasks and equipment and smaller time division, the precise algorithm cannot complete the calculation within an effective timeframe. In order to solve this problem, this paper proposes a discrete particle swarm optimization algorithm (DPSO), builds a DSTT mathematical model suitable for the DPSO, solves the problem that particle swarm operations are not easy to describe in discrete problems, and redefines the particle motion strategy and adds random disturbance operation in its probabilistic selection model to ensure the effectiveness of the algorithm. In the comparison experiment, the DPSO achieved much higher success rates than the greedy algorithm (GR) and the improved genetic algorithm (IGA). Finally, in the simulation experiment, the result data show that the accuracy of the DPSO outperforms that of the GR and IGA by up to 3.012295% and 0.11115%, respectively, and the efficiency of the DPSO outperforms that of the IGA by up to 69.246%. Full article

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

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