bluuebunny/crossref_metadata_2025 huggingface dataset:
Levelized Cost of Energy Assessment for Offshore Wind Farms—An Examination of Different Methodologies, Input Variables, and Uncertainty
Authors: McAuliffe Fiona Devoy , Noonan Miriam , Murphy Jimmy
Published: August 2021

Levelized cost of energy (LCoE) is the most common metric used in renewable energy assessments. However, this can be a very complex calculation with numerous methodologies depending on the perspective taken. Inputs including costs, energy production are generally forecasts and predictions based on publicly available information; therefore, they are key areas of uncertainty. Elements of the calculation are site or region specific such as the tax rate or inclusion of grid connection costs. The business case and financial assumptions applied will be very project specific, e.g., the discount rate applied. These numerous variables and uncertainties must be fully understood in order to effectively apply the metric or review and compare LCoEs. Therefore, this paper provides a comprehensive set of LCoE methodologies that provide a reference basis for researchers. A case study demonstrates the application of these methods and the variation in results illustrates the importance of correctly selecting the discount rate and cash flow based on the perspective and motivation of the user. Sensitivity studies further investigates the potential impact of key variables and areas of uncertainty on results. Analysis indicates that the energy production and discount rate applied will have the most significant impact on LCoE, followed by Capital Expenditure (CAPEX) costs. While the key areas of uncertainties cannot necessarily be solved, this paper promotes consistency in the application and understanding of the metric, which can help overcome its limitations.
State of Health Estimation Method for Lithium-Ion Batteries Based on Nonlinear Autoregressive Neural Network Model With Exogenous Input
Authors: Che Yanbo , Cai Yibin , Li Hongfeng , Liu Yushu , Jiang Mingda , Qin Peijun
Published: August 2021

The working state of lithium-ion batteries must be estimated accurately and efficiently in the battery management system. Building a model is the most prevalent way of predicting the battery's working state. Based on the variable order equivalent circuit model, this article examines the attenuation curve of battery capacity with the number of cycles. It identifies the order of the equivalent circuit model using Bayesian information criterion (BIC). Based on the correlation between capacity and resistance, this article concludes that there is a nonlinear correlation between model parameters and state of health (SOH). The nonlinear autoregressive neural network with exogenous input (NARX) is used to fit the nonlinear correlation for capacity regeneration. Then, the self-adaptive weight particle swarm optimization (SWPSO) method is suggested to train the neural network. Finally, single-battery and multibattery tests are planned to validate the accuracy of the SWPSO-NARX estimate of SOH. The experimental findings indicate that the SOH estimate effect is significant.
Cross-reactive CD4
+
T cells enhance SARS-CoV-2 immune responses upon infection and vaccination
Authors: Loyal Lucie , Braun Julian , Henze Larissa , Kruse Beate , Dingeldey Manuela , Reimer Ulf , Kern Florian , Schwarz Tatjana , Mangold Maike , Unger Clara , Dörfler Friederike , Kadler Shirin , Rosowski Jennifer , Gürcan Kübrah , Uyar-Aydin Zehra , Frentsch Marco , Kurth Florian , Schnatbaum Karsten , Eckey Maren , Hippenstiel Stefan , Hocke Andreas , Müller Marcel A. , Sawitzki Birgit , Miltenyi Stefan , Paul Friedemann , Mall Marcus A. , Wenschuh Holger , Voigt Sebastian , Drosten Christian , Lauster Roland , Lachman Nils , Sander Leif-Erik , Corman Victor M. , Röhmel Jobst , Meyer-Arndt Lil , Thiel Andreas , Giesecke-Thiel Claudia
Published: August 2021

Cross-reactive T cells aid in the fight

There is mounting evidence that immunological memory after infection with seasonal human coronaviruses (hCoVs) contributes to cross-protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Loyal
et al
. identified a universal immunodominant coronavirus peptide found within the fusion peptide domain of coronavirus spike protein. This peptide is recognized by CD4
+
T cells in 20% of unexposed individuals, more than 50% of SARS-CoV-2 convalescents, and 97% of subjects treated with the Pfizer–BioNTech COVID-19 vaccine. Although ubiquitous, these coronavirus-reactive T cells decreased with age, which may explain in part the increased susceptibility of elderly people to COVID-19. —STS
Infusing 21st Century Skill Development into the Undergraduate Curriculum: The Formation of the iBEARS Network
Authors: St. Louis Alex T. , Thompson Penny , Sulak Tracey N. , Harvill Marty L. , Moore Michael E.
Published: August 2021

The demonstrated gap between skills needed and skills learned within a college education places both undergraduates seeking gainful employment and the employers seeking highly skilled workers at a disadvantage. Recent and up-and-coming college graduates should possess 21st century skills (i.e., communication, collaboration, problem solving), skills that employers deem necessary for the workplace. Research shows that the development of this skillset can help narrow the gap in producing highly skilled graduates for the science, technology, engineering, and mathematics (STEM) workforce.
Active faulting controls bedform development on a deep-water fan
Authors: Maselli Vittorio , Micallef Aaron , Normandeau Alexandre , Oppo Davide , Iacopini David , Green Andrew , Ge Zhiyuan
Published: August 2021

Tectonically controlled topography influences deep-water sedimentary systems. Using 3-D seismic reflection data from the Levant Basin, eastern Mediterranean Sea, we investigate the spatial and temporal evolution of bedforms on a deep-water fan cut by an active normal fault. In the footwall, the fan comprises cyclic steps and antidunes along its axial and external portions, respectively, which we interpret to result from the spatial variation in flow velocity due to the loss of confinement at the canyon mouth. Conversely, in the hanging wall, the seafloor is nearly featureless at seismic scale. Numerical modeling of turbidity currents shows that the fault triggers a hydraulic jump that suppresses the flow velocity downstream, which thus explains the lack of visible bedforms basinward. This study shows that the topography generated by active normal faulting controls the downslope evolution of turbidity currents and the associated bedforms and that seafloor geomorphology can be used to evince syn-tectonic deposition.
Noether symmetries, dynamical constants of motion, and spectrum generating algebras
Authors: Balondo Iyela Daddy , Govaerts Jan
Published: August 2021

When discussing consequences of symmetries of dynamical systems based on Noether’s first theorem, most standard textbooks on classical or quantum mechanics present a conclusion stating that a global continuous Lie symmetry implies the existence of a time-independent conserved Noether charge which is the generator of the action on phase space of that symmetry, and which necessarily must as well commute with the Hamiltonian. However this need not be so, nor does that statement do justice to the complete scope and reach of Noether’s first theorem. Rather a much less restrictive statement applies, namely, that the corresponding Noether charge as an observable over phase space may in fact possess an explicit time dependency, and yet define a constant of the motion by having a commutator with the Hamiltonian which is nonvanishing, thus indeed defining a dynamical conserved quantity. Furthermore, and this certainly within the Hamiltonian formulation, the converse statement is valid as well, namely, that any dynamical constant of motion is necessarily the Noether charge of some symmetry leaving the system’s action invariant up to some total time derivative contribution. This contribution revisits these different points and their consequences, straightaway within the Hamiltonian formulation which is the most appropriate for such issues. Explicit illustrations are also provided through three general but simple enough classes of systems.
Collision dynamics of cytosine under proton irradiation
Authors: Qian Chaoyi , Wang Zhiping , Xu Xuefen , Wang Yanbiao , Zhang Fengshou
Published: August 2021

In the framework of the time-dependent density-functional theory, applied to valence electrons, coupled non-adiabatically to molecular dynamics of ions, the collision dynamics of cytosine impacted by proton is studied. We especially focus on the effect of the collision orientations on the damage of cytosine by choosing two collision orientations taking the oxygen atom on the double bond CO as the collision site with the incident energy of proton ranging from 150 eV to 1000 eV. First, two collision dynamical processes are explored by analyzing the molecular ionization, the ionic position and the kinetic energy, the energy loss of proton and the electronic density evolution. The results show that the damage process of cytosine induced by proton impact is mainly the capture of electrons by proton, the departments of ions and groups as well as the opening of ring. It is found that the orientation has little effect on the loss of the kinetic energy of proton, which is about 21.5[Formula: see text] of the incident energy of proton. Although the scattering angle [Formula: see text] has a polynomial relationship with [Formula: see text] in both cases, it is greatly affected by the orientation. When [Formula: see text] eV, the scattering angle of proton colliding with O along the [Formula: see text]-axis is greater than that of proton colliding with O along the [Formula: see text]-axis. The orientation also has a great effect on the mass distribution of the fragments and the fragmentation route. When proton moves along the [Formula: see text]-axis, the fragmentation route is that O leaves the cytosine and the rest keeps on vibration, while products are not only related to the incident kinetic energy, but also show diversity when proton moves along the [Formula: see text]-axis.
A new modeling and existence–uniqueness analysis for Babesiosis disease of fractional order
Authors: Jena Rajarama Mohan , Chakraverty Snehashish , Yavuz Mehmet , Abdeljawad Thabet
Published: August 2021

In this study, we consider the dynamics of the Babesiosis transmission on bovine populations and ticks. The most important role in the transmission of the parasite is the ticks from the Ixodidae family. The vector tick takes factors (merozoites in erythrocytes) from the diseased animal while sucking the blood. To model and investigate the transmissions of this parasite and address this important issue, we have considered the disease in a fractional epidemiological model. This paper, therefore, discusses the mechanisms of transmission of Babesiosis defined in the fractional derivative sense. The Caputo–Fabrizio (CF) derivative is considered to study the propagation mechanisms of Babesiosis. First, the important characteristics of the model have been presented, and then the transmission of the Babesiosis model defined in CF is discussed. The application of fixed-point theory is used to derive the concept of the qualitative properties of the mentioned model. The solution is obtained by using the Homotopy perturbation Elzaki transform method (HPETM). Numerical simulations are performed, and the effects of the arbitrary-order derivatives are investigated graphically.
Truchet Tiles and Combinatorial Arabesque
Authors: Nabli Hédi
Published: August 2021

In this paper, we introduce a new topic on geometric patterns issued from Truchet tile, that we agree to call combinatorial arabesque. The originally Truchet tile, by reference to the French scientist of 17th century Sébastien Truchet, is a square split along the diagonal into two triangles of contrasting colors. We define an equivalence relation on the set of all square tiling of same size, leading naturally to investigate the equivalence classes and their cardinality. Thanks to this class notion, it will be possible to measure the beauty degree of a Truchet square tiling by means of an appropriate algebraic group. Also, we define many specific arabesques such as entirely symmetric, magic and hyper-maximal arabesques. Mathematical characterizations of such arabesques are established facilitating thereby their enumeration and their algorithmic generating. Finally the notion of irreducibility is introduced on arabesques.
EFFECT OF EMBEDDED TUNGSTEN PARTICLES ON IMPACT RESISTANCE OF DIAMOND COATING
Authors: CHEN NAICHAO , LIU DONGMEI , ZHOU FAN , HE PING , LIU MUSEN
Published: August 2021

For diamond coating, natural fragile property easily leads to fracture, delamination and peeling, which seriously inhibits the applications in many industrial fields. In order to prolong the lifetime, improving the toughness under impact load is essential for diamond coating. In this work, a novel method was proposed by the conventional CVD diamond technique combining with the particles, with which the nanocrystalline diamond (NCD) coating with W particles (W-NCD) was fabricated to evaluate the impact behavior. The pure NCD coating was also produced for comparison. Repeating impact testing was performed to evaluate the impact resistance of the as-deposited NCD coatings. The results showed that the diamond coating can be fabricated on the substrate with W particles. The indentation scar revealed that the W-NCD coating had the stronger impact resistance than the NCD coating. Ratcheting effect was employed to discuss the impact properties of NCD coating for the first time. The coating integrity played a vital role in ratcheting displacement. Repeating impact can make the NCD and W-NCD coatings soft, and the W particles can accelerate the softening process. Hence, embedding particles can provide a potential and valid method to enhance the impact resistance of diamond coating that was very important for the fragile coating.
Mixed H∞ and Passivity Performance Analysis of Interfered Digital Filters with Markovian Jumping Parameters and Delays
Authors: Kumar Mani Kant
Published: August 2021

This paper deals with the problem of mixed [Formula: see text] and passivity performance analysis of digital filters subject to Markovian jumping parameters, external disturbances, time delays and bounds of the nonlinearity functions. By employing Lyapunov theory and matrix decomposition technique, a novel sufficient condition is established. The proposed criterion ensures that the underlying system is stochastically stable and satisfies a mixed [Formula: see text] and passivity performance index simultaneously. The obtained criterion can also be employed to solve the [Formula: see text] problem or the passivity problem in a unified framework. Moreover, the problem is formulated to obtain optimal mixed [Formula: see text] and passivity performance index of the interfered digital filters. The effectiveness and superiority of our proposed results are illustrated by three examples.
Performance Study on Indexing and Accessing of Small File in Hadoop Distributed File System
Authors: Rodrigues Anisha P , Fernandes Roshan , Vijaya P. , Chander Satish
Published: August 2021

Hadoop Distributed File System (HDFS) is developed to efficiently store and handle the vast quantity of files in a distributed environment over a cluster of computers. Various commodity hardware forms the Hadoop cluster, which is inexpensive and easily available. The large number of small files stored in HDFS consumed more memory which lags the performance because small files consumed heavy load on NameNode. Thus, the efficiency of indexing and accessing the small files on HDFS is improved by several techniques, such as archive files, New Hadoop Archive (New HAR), CombineFileInputFormat (CFIF), and Sequence file generation. The archive file combines the small files into single blocks. The new HAR file combines the smaller files into a single large file. The CFIF module merges the multiple files into a single split using NameNode, and the sequence file combines all the small files into a single sequence. The indexing and accessing of a small file in HDFS are evaluated using performance metrics, such as processing time and memory usage. The experiment shows that the sequence file generation approach is efficient when compared to other approaches concerning file access time is 1.5[Formula: see text]s, memory usage is 20 KB in multi-node, and the processing time is 0.1[Formula: see text]s.
A Galerkin–Newton Algorithm for Solution of a Kirchhoff-Type Static Equation
Authors: Kachakhidze Nikoloz , Peradze Jemal , Tsiklauri Zviad
Published: August 2021

In this paper, an algorithm is proposed to find an approximate solution for the Kirchhoff -type nonlinear differential equation, which describes the static state of a beam. The solution of the problem consists of two parts. First, we apply the Galerkin method. Next, to solve the obtained discrete system of equations, we use the Newton iteration method. The algorithm total error is estimated. The results of the numerical experiment are given.
A Novel Constitutive Parameters Identification Procedure for Hyperelastic Skeletal Muscles Using Two-Way Neural Networks
Authors: Li Yang , Sang Jianbing , Wei Xinyu , Wan Zijian , Liu G. R.
Published: August 2021

Muscle soreness can occur after working beyond the habitual load, especially for people engaged in high-intensity work load. Prediction of hyperelastic material parameters is essentially an inverse process, which possesses challenges. This work presents a novel procedure that combines nonlinear finite element method (FEM), two-way neural networks (NNs) together with experiments, to predict the hyperelastic material parameters of skeletal muscles. FEM models are first established to simulate nonlinear deformation of skeletal muscles subject to compressions. A dataset of nonlinear relationship between nominal stress and principal stretch of skeletal muscles is created using our FEM models. The dataset is then used to establish two-way NNs, in which a forward NN is trained and it is in turn used to train the inverse NN. The inverse NN is used to predict the hyperelastic material parameters of skeletal muscles. Finally, experiments are carried out using fresh skeletal muscles to validate the predictions in great detail. In order to examine the accuracy of the two-way NNs predicted values against the experimental ones, a decision coefficient [Formula: see text] with penalty factor is introduced to evaluate the performance. Studies have also been conducted to compare the present two-way NNs approach with the other existing methods, including the directly (one-way) inverse problem NN, and improved niche genetic algorithm (INGA). The comparison results show that two-way NNs model is an accurate approach to identify the hyperelastic parameters of skeletal muscles. The present two-way NNs method can be further expanded to the predictions of constitutive parameters of other type of nonlinear materials.
Numerical modeling and optimization of process parameters in continuous extrusion process through response surface methodology, artificial neural network & genetic algorithm
Authors: Desta Tariku , Sinha Devendra Kumar , Ramulu Perumalla Janaki , Singh Ram Sewak
Published: August 2021

The product of high complex profile, high strength, high productivity and excellent material properties with infinite length can be produced by Continuous Extrusion (CE) process. The numerical simulation of Aluminum (AA 1100) feedstock material at different wheel velocities, product diameter, feedstock temperature, die temperature and friction condition has been carried out using 3D simulation tool Design Environment for Forming (DEFORM-3D) in this paper. The development of mathematical model is carried out to investigate the influence of wheel velocity, extrusion ratio, feedstock temperature, die temperature and friction conditions on total load required for the deformation and extrusion of feedstock material through Response Surface Methodology (RSM). The statistical significance of mathematical model is verified through analysis of variance (ANOVA). The most optimum value of extrusion load has been found to be 136.4[Formula: see text]kN through iterative process of Genetic Algorithm (GA) using Artificial Neural Network (ANN). The optimized value of input process variables for minimum value of extrusion load obtained has been found to be 13 Revolutions per Minute (RPM) as wheel velocity, 5[Formula: see text]mm as product diameter, 0.95 as friction condition, 650[Formula: see text]C as feedstock temperature and 550[Formula: see text]C as die temperature. This paper with proposed methodology will be helpful for industries working in the area of CE in terms of minimizing energy consumption during production process of bus bars, tubes, wires, cables, sheets, plates, strips, etc.
Causes, Consequences, and Conservation of Ungulate Migration
Authors: Kauffman Matthew J. , Aikens Ellen O. , Esmaeili Saeideh , Kaczensky Petra , Middleton Arthur , Monteith Kevin L. , Morrison Thomas A. , Mueller Thomas , Sawyer Hall , Goheen Jacob R.
Published: August 2021

Our understanding of ungulate migration is advancing rapidly due to innovations in modern animal tracking. Herein, we review and synthesize nearly seven decades of work on migration and other long-distance movements of wild ungulates. Although it has long been appreciated that ungulates migrate to enhance access to forage, recent contributions demonstrate that their movements are fine tuned to dynamic landscapes where forage, snow, and drought change seasonally. Researchers are beginning to understand how ungulates navigate migrations, with the emerging view that animals blend gradient tracking with spatial memory, some of which is socially learned. Although migration often promotes abundant populations—with broad effects on ecosystems—many migrations around the world have been lost or are currently threatened by habitat fragmentation, climate change, and barriers to movement. Fortunately, new efforts that use empirical tracking data to map migrations in detail are facilitating effective conservation measures to maintain ungulate migration.
Oral Manifestations of Exudative Erythema Multiforme in a Patient with COVID-19
Authors: Dalipi Zana Sllamniku , Dragidella Fatmir , Dragidella Donika Kastrati
Published: August 2021

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a viral infection with multiorgan manifestations that may affect the oral mucosa. The full range of oral manifestations of COVID-19 are unknown, and there are limited reports describing the features of oral manifestations of COVID-19, including taste loss, oral lesions, and xerostomia. The aim of this study is to report a case of oral erythema multiforme (EM) manifesting as oral, lip, and skin lesions in a COVID-19 patient. The presence of oral lesions in the late stage of COVID-19 could be related to weak patient immunity or related therapies.
Numerical Study on the Behavior of Square Stiffened Caissons Penetrating into Normally Consolidated Clay
Authors: Liu Wei , Huang Zhihuai , Zhou Mi
Published: August 2021

Significant difference between predicted and measured installation resistance of stiffened suction caissons was identified due to the existing uncertainty regarding the mobilized soil flow mechanisms. This paper describes an extensive investigation of square stiffened caisson penetration in nonhomogeneous clays undertaken through large deformation FE (LDFE) analysis to identify the soil flow mechanisms around and between lateral ring stiffeners. A detailed parametric study has been carried out, exploring a range of nondimensional parameters related to stiffened caisson geometry, caisson roughness, and soil strength. The LDFE results were compared with centrifuge test data in terms of soil flow mechanisms, with good agreement obtained. Two interesting features of soil flow inside the caisson were observed including soil backflow into the gaps between the embedded stiffeners and soil heaving at the surface. It shows that the cavity depth can reach ∼5 m. Finally, simple expressions were proposed for estimating the critical depths of soil backflow and cavity formation.
Application of Multiattention Mechanism in Power System Branch Parameter Identification
Authors: Wang Zhiwei , Weng Liguo , Lu Min , Liu Jun , Pan Lingling
Published: August 2021

Maintaining accuracy and robustness has always been an unsolved problem in the task of power grid branch parameter identification. Therefore, many researchers have participated in the research of branch parameter identification. The existing methods of power grid branch parameter identification suffer from two limitations. (1) Traditional methods only use manual experience or instruments to complete parameter identification of single branch characteristics, but they are only used to identify a single target and cannot make full use of the historical information of power grid data. (2) Deep learning methods can complete model training through historical data, but these methods cannot consider the constraints of power grid topological structure, which is equivalent to identifying connected power grid branches separately. To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. Specifically, we input the global features and topology information of branch nodes into our GTN model. In the process of parameter identification, the multihead attention mechanism is used to fuse the branch feature information of different subspaces, which highlights the importance of different branches and enhances the ability of local feature extraction. Finally, the fitting and prediction of each branch feature are completed through the decoding layer. The experiment shows that our proposed GTN is superior to other machine learning methods and deep learning methods and can still realize accurate branch parameter identification under various noise conditions.
[Retracted] English Feature Recognition Based on GA‐BP Neural Network Algorithm and Data Mining
Authors: Wu Dan , Shen Yuanjun
Published: August 2021

With the development of society and the promotion of science and technology, English, as the largest universal language in the world, is used by more and more people. In the life around us, there is information in English all the time. However, because the process of manual recognition of English letters is very labor‐intensive and inefficient, the demand for computer recognition of English letters is increasing. This paper studies the influence of the parameters of BP neural network and genetic algorithm on the whole network, including the input, output, and number of hidden layer nodes. Finally, it improves and determines the settings and values of the relevant parameters. On this basis, it shows the rationality of the selected parameters through experiments. The results show that only GA‐BP neural network and feature data mining algorithm can complete feature extraction and become the main function of feature classification at the same time. After enough initial data sample analysis training, the GA‐BP neural network was found to have good data fault tolerance and feature recognition. The experimental results show that the genetic algorithm can find the best weights and thresholds and the weights and thresholds are given to the BP neural network. After training, the recognition of handwritten letters can be realized. Finally, the convergence of the two algorithms is compared through experiments, which shows that the overall performance of the BP neural network algorithm is improved after genetic algorithm optimization. It can be seen that the genetic algorithm has a good effect in improving the BP neural network and this method has a broad prospect in English feature recognition.
J-Net: Asymmetric Encoder-Decoder for Medical Semantic Segmentation
Authors: Shi Yanli , Sheng Pengpeng
Published: August 2021

With the development of deep learning, breakthroughs have been made in the field of semantic segmentation. However, it is difficult to generate a fine mask on the same medical images because medical images have low contrast, high resolution, and insufficient semantic information. In most scenarios, existing approaches mostly use a pooling layer to reduce the resolution of feature maps. Therefore, it is difficult for them to consider the whole image features, resulting in information loss and performance degradation. In this paper, a multiscale asymmetric encoder-decoder semantic segmentation network is proposed. The network consists of two parts, which perform feature extraction and image restoration on the input, respectively. The encoder network obtains multiscale feature information by connecting multiple ASPP modules to form a feature pyramid. Meanwhile, the upsampling layer of each decoder can be connected to the feature map generated by the corresponding ASPP module. Finally, the classification information of each pixel is obtained through the sigmoid function. The performance of the proposed method can be verified on publicly available datasets. The experimental evidence shows that the proposed method can take full advantage of multiscale feature information and achieve superior performance with less inference computational cost.
Protective Effect of Opuntia dillenii (Ker Gawl.) Haw. Seed Oil on Gentamicin-Induced Nephrotoxicity: A Biochemical and Histological Analysis
Authors: Bouhrim Mohamed , Bencheikh Noureddine , Imtara Hamada , Daoudi Nour Elhouda , Mechchate Hamza , Ouassou Hayat , Kharchoufa Loubna , Elachouri Mostafa , Mekhfi Hassane , Ziyyat Abderrahim , Legssyer Abdelkhaleq , Aziz Mohammed , Bnouham Mohamed
Published: August 2021

Opuntia dillenii is a medicinal plant with frequent usage in folk medicine to treat many illnesses. The present study aims to investigate the protective effect of Opuntia dillenii seed oil against gentamicin-induced nephrotoxicity in rats. The animals (rats) were randomly divided into three groups (i) the normal control group treated only with distilled water (10 mL/kg), (ii) the gentamicin group treated with distilled water (10 mL/kg) and received an intraperitoneal injection of gentamicin (80 mg/kg), and (iii) the group treated with the Opuntia dillenii seed oil (2 mL/kg) and also received an intraperitoneal injection of gentamicin (80 mg/kg). The rats received their following treatments for 14 consecutive days orally. Serum urea, creatinine, gamma-glutamyl transferase, albumin, and electrolyte levels were quantified as the markers of acute renal and liver failure. Besides, the kidney and liver relative weight, kidney malondialdehydes, and kidney histological analysis were determined. The results have shown that daily pretreatment with Opuntia dillenii seed oil (2 mL/kg) prevented severe alterations of biochemical parameters and disruptions of kidney tissue structures. In addition, the results of the present study showed for the first time that Opuntia dillenii seed oil reduced renal toxicity in gentamicin-induced nephrotoxicity in rats. Therefore, Opuntia dillenii seed oil may represent a new therapeutic avenue to preserve and protect renal function in gentamicin-treated patients.
Joint Optimization in Condition-Based Maintenance and Inventory Policy for the Repairable System
Authors: Deng Huayang , Shi Quan , Wang Yadong
Published: August 2021

In the modern industry, to reduce support pressure, a variety of suppliers have begun to use the same kind of units in many types of equipment. However, highly integrated equipment is more difficult to use in traditional reliability-centered maintenance (RCM) models, and researchers start steering based on the state monitoring method. This paper mainly discusses the remaining useful life (RUL) prediction method based on the Wiener process and introduces the improved condition-based maintenance policy. Combined with the (S-1, S) policy, the joint policy and the model considering the repairable policy are built for the multiunit system. Finally, the optional decision combination is obtained. Except that, the advantage of the joint optimization in a multiunit system and the characteristics of repairable policy in the joint model are analyzed.
A New Scale-Invariant Lindley Extension Distribution and Its Applications
Authors: Kayid Mohamed , Alskhabrah Rayof , Alshangiti Arwa M.
Published: August 2021

A new scale-invariant extension of the Lindley distribution and its power generalization has been introduced. The moments and the moment-generating functions of the proposed models have closed forms. The failure rate, the mean residual life, and the

α

-quantile residual life functions have been explored. The failure rate function of these models accommodates increasing, bathtub-shaped, and increasing then bathtub-shaped forms. The parameters of the models have been estimated by the maximum likelihood method for the complete and right-censored data. In a simulation study, the efficiency and consistency of the maximum likelihood estimator have been investigated. Then, the proposed models were fitted to four data sets to show their flexibility and applicability.
Theoretical Study of Excitonic Complexes in GaAs/AlGaAs Quantum Dots Grown by Filling of Nanoholes
Authors: Omri Mohamed , Sayari Amor , Sfaxi Larbi
Published: August 2021

In this work, a theoretical study of the electronic and the optical properties of a new family of strain-free GaAs/AlGaAs quantum dots (QDs) obtained by AlGaAs nanohole filling is presented. The considered model consists of solving the three-dimensional effective-mass Schrödinger equation, thus providing a complete description of the neutral and charged complex excitons’ fine structure. The QD size effect on carrier confinement energies, wave functions, and s-p splitting is studied. The direct Coulomb interaction impact on the calculated s and p states’ transition energies is investigated. The behaviour of the binding energy of neutral and charged excitons (X− and X+) and biexciton XX versus QD height is studied. The addition of the correlation effect allows to explain the nature of biexcitons often observed experimentally.
Control System Design for 16/6/8 Double-Stator Bearingless Switched Reluctance Motor
Authors: Sun Chuanyu , Yang Hang , Han Shangke , Ding Hongchang , Li Jiaqing , Han Ning
Published: August 2021

The 16/6/8 double-stator bearingless switched reluctance motor (DSBSRM) is used as the object of study in this paper. To solve the problem of torque and levitation force ripples in this motor, a control system direct force control (DFC) and direct instantaneous torque control (DITC) based on the torque sharing function (TSF) are proposed. With the strong nonlinearity and approximation capability of radial basis function neural networks, the torque and levitation force observer are designed. The observed torque and levitation forces are used as feedback for the internal loop control, which is combined with the external loop control to make a double closed-loop control. In order to further improve the output torque and system robustness and suppress the torque ripple in steady-state process, the motor winding method is optimized and a set of switching angles is added on the basis of TSF. The simulation results verify the effectiveness and superiority of the proposed control method. It effectively suppresses speed ripple and reduces torque and levitation force fluctuations and rotor radial displacement jitter.
An Intelligent Garbage Sorting System Based on Edge Computing and Visual Understanding of Social Internet of Vehicles
Authors: Shen Xuehao , Wu Yuezhong , Chen Shuhong , Luo Xueming
Published: August 2021

In order to enable Social Internet of Vehicles devices to achieve the purpose of intelligent and autonomous garbage classification in a public environment, while avoiding network congestion caused by a large amount of data accessing the cloud at the same time, it is therefore considered to combine mobile edge computing with Social Internet of Vehicles to give full play to mobile edge computing features of high bandwidth and low latency. At the same time, based on cutting-edge technologies such as deep learning, knowledge graph, and 5G transmission, the paper builds an intelligent garbage sorting system based on edge computing and visual understanding of Social Internet of Vehicles. First of all, for the massive multisource heterogeneous Social Internet of Vehicles big data in the public environment, different item modal data adopts different processing methods, aiming to obtain a visual understanding model. Secondly, using the 5G network, the model is deployed on the edge device and the cloud for cloud-side collaborative management, aiming to avoid the waste of edge node resources, while ensuring the data privacy of the edge node. Finally, the Social Internet of Vehicles devices is used to make intelligent decision-making on the big data of the items. First, the items are judged as garbage, and then the category is judged, and finally the task of grabbing and sorting is realized. The experimental results show that the system proposed in this paper can efficiently process the big data of Social Internet of Vehicles and make valuable intelligent decisions. At the same time, it also has a certain role in promoting the promotion of Social Internet of Vehicles devices.
A Review on Biosurfactant Applications in the Petroleum Industry
Authors: Xi Wei , Ping Yuanye , Alikhani Masoome Agha
Published: August 2021

The inadequacy of worldwide fossil fuel resources, combined with increasing energy demands, encourages global attention to either using alternative energy resources or improving the recovery factor and produce larger quantities from present reservoirs. Among all enhanced oil recovery (EOR) methods, surfactant injection is a well-known technique that reduces the interfacial tension (IFT) between oil and water and increases oil production. Despite numerous advantages of using surfactants, there are also a few obstacles like environmental impacts, high cost, effect on humans and other organisms due to toxicological potential, and availability from nonrenewable resources. Biosurfactants are microbial surface-active agents that decrease the surface tension (ST) of a liquid phase and the IFT of two diverse phases. They are biotechnological products of high value owing to their widespread applications, low toxicity, relatively easy preparation, and specific performance, applied in different industries like organic chemicals and fertilizers, agrochemicals, metallurgy and mining, cosmetics, foods, medical and pharmaceuticals, beverages, environmental management, and petroleum and petrochemical applications in emulsifying and demulsifying wetting agents, detergent spreading and foaming agents, and functional food ingredients. Biosurfactants are synthesized by microbes; therefore, various genetic diversities of microorganisms provide the considerable capability to produce new types of biosurfactants, which can develop EOR technology. Biosurfactants are classified into ex situ and in situ MEOR processes. The genera Pseudomonas, Bacillus, Sphingomonas, and Actinobacteria are the foremost biosurfactant-producing bacteria. This paper reviews relevant reports and results from various presented papers by researchers and companies on applications of microorganisms and biosurfactant technology with specific emphasis on EOR and MEOR processes, based on recently published articles since 2010 until now.
Occupational Health Risk of Waste Pickers: A Case Study of Northern Region of South Africa
Authors: Uhunamure Solomon E. , Edokpayi Joshua N. , Shale Karabo
Published: August 2021

In South Africa, waste pickers play a significant role in the management of waste at landfill sites. Waste picking is an income-generating venture for most people with low-income base. The activity of sorting waste at landfill sites is, however, associated with occupational health risks to waste pickers which this study has examined. The study adopted a cross-sectional survey with a convenience sampling method which was conducted among 114 waste pickers in three landfill sites in Limpopo Province of South Africa. A validated questionnaire was used in eliciting responses from the participants. The statistical technique employed includes the ANOVA, simple, and multiple regression. The results indicated that, in the last one year, waste pickers exposed to landfill sites were 1.7 times more likely to develop a common health disorder (AOR: 1.733; 95% CI: 1.069, 2.755;

P

value: 0.041). There was statistical significance between the number of days worked at the landfill and the health conditions of the waste pickers (

P

 ≤ 0.001). The cofounders were adjusted for age and years worked, and the result revealed that days worked by the waste pickers’ increased their chances of occupational health risks by 1.4 times. It is unlikely that waste pickers will have a risk-free environment, but supportive policies such as provision of adequate personal protective equipment and more awareness programmes on the health risks related to such enterprises will aid in abating the associated risks.
Extraction of Optimal Measurements for Drowsy Driving Detection considering Driver Fingerprinting Differences
Authors: Sun Yifan , Wu Chaozhong , Zhang Hui , Zhang Yijun , Li Shaopeng , Feng Hongxia
Published: August 2021

Contributions of measurements for detecting drowsy driving are determined by calculation parameters, which are directly related to the accuracy of drowsiness detection. The previous studies utilized the same Unified Calculation Parameters (UCPs) to compute each driver’s measurements. However, since each driver has unique driving behavior characteristics, namely, driver fingerprinting, Individual Drivers’ Best Calculation Parameters (IDBCPs) making measurements more discriminative for drowsiness are various. Regardless of the difference in driver fingerprinting among the drivers being tested, using UCPs instead of IDBCPs to compute measurements will limit the drowsiness-detection performance of the measurements and reduce drowsiness-detection accuracies at the individual driver level. Thus, this paper proposed a model to optimize calculation parameters of individual driver’s measurements and to extract individual driver’s measurements that effectively distinguish drowsy driving. Through real vehicle experiments, we collected naturalistic driving data and subjective drowsy levels evaluated by the Karolinska Sleepiness Scale. Eight nonintrusive drowsiness-related measurements were calculated by double-layer sliding time windows. In the proposed model, we firstly applied the Wilcoxon test to analyze differences between measurements of the awake state and drowsy state, and constructed the fitness function reflecting the relationship between the calculation parameters and measurement’s drowsiness-detection performance. Secondly, the genetic algorithms were used to optimize fitness functions to obtain measured IDBCPs. Finally, we selected measurements calculated by IDBCPs that can distinguish drowsy driving to constitute individual drivers’ optimal drowsiness-detection measurement set. To verify the advantages of IDBCPs, the measurements calculated by UCPs and IDBCPs were, respectively, used to build driver-specific drowsiness-detection models: DF_U and DF_I based on the Fisher discriminant algorithm. The mean drowsiness-detection accuracies of DF_U and DF_I were, respectively, 85.25% and 91.06%. It indicated that IDBCPs could enhance measurements’ drowsiness-detection performance and improve the drowsiness-detection accuracies. This paper contributed to the establishment of personalized drowsiness-detection models considering driver fingerprinting differences.
Effect of RF Sputtering Power and Deposition Time on Optical and Electrical Properties of Indium Tin Oxide Thin Film
Authors: Tchenka Abdelaziz , Agdad Abdelali , Samba Vall Mohamed Cheikh , Hnawi Salma Kaotar , Narjis Abdelfattah , Nkhaili Lahcen , Ibnouelghazi Elalami , Ech-Chamikh Elmaati
Published: August 2021

Indium tin oxide (ITO) films are widely used as transparent conducting electrodes in solar cells, gas sensors, and car windows because of their high electrical conductivity and good optical transparency in the visible region. In this work, ITO thin films were prepared by cathodic radio‐frequency (RF) sputtering using an ITO target with 90% In2O3 and 10% SnO2. The structural properties were studied by X‐ray diffraction (XRD), scanning electronic microscopy (SEM), and X‐ray reflectometry (XRR). Electrical measurements were performed by applying the four‐point method and studying the Hall Effect. Finally, optical properties were taken by the UV‐Vis‐NIR spectrophotometry. The effect of the RF power and deposition time on optical and electrical properties was investigated. It is shown that by using a RF power of 110–80 W, one can prepare crystalline samples with low resistivity, which is an aimed property for TCO semiconductors. Electrical measurements revealed that the resistivity decreases by increasing the RF power and/or the deposition time.
The Antimicrobial Effect of Pomegranate Peel Extract versus Chlorhexidine in High Caries Risk Individuals Using Quantitative Real-Time Polymerase Chain Reaction: A Randomized Triple-Blind Controlled Clinical Trial
Authors: Jacob Benoy , Malli Sureshbabu Nivedhitha , Ranjan Manish , Ranganath Aishwarya , Siddique Riluwan
Published: August 2021

The aim of the present study was to compare the antibacterial effectiveness of chlorhexidine and PPE oral rinse on S. mutans, Lactobacilli, and Veillonella, in clinical salivary samples of patients with advanced stages of dental caries at baseline and two and four weeks with PCR technique. This triple-blind randomized clinical trial involved 60 high caries risk adult patients, 19–59 years of age, randomly allocated into two groups of 30 subjects each. The intervention group received pomegranate peel extract mouthwash, whereas the control group received chlorhexidine mouthwash. Unstimulated pooled saliva was collected from the floor of the mouth before and after the intervention. The quantitative real-time polymerase chain reaction was employed to analyze the bacterial copies of each salivary sample at baseline and two and four weeks. The significance level was fixed at 5% (α = 0.05). Overall comparison of antimicrobial effectiveness across both groups revealed insignificant outcomes. The control group evinced a significant reduction in S. mutans between a specific time, i.e., baseline and 4 weeks (

p
=
0.043

). PPE oral rinse as a natural product or ecological alternative was effective in disrupting activity across all microorganisms tested in this triple-blind RCT; however, the nutraceutical, when compared to chlorhexidine, was not as effective against S. mutans.
Efficient Investigation of Rock Crack Propagation and Fracture Behaviors during Impact Fragmentation in Rockfalls Using Parallel DDA
Authors: Zheng Lu , Wu Yihan , Wu Wei , Zhang Hong , Peng Xinyan , Zhang Xuelue , Wu Xuezhen
Published: August 2021

The study of the rock crack propagation and fracture behaviors during impact fragmentation is important and necessary for disaster evaluation of rockfalls. Discontinuous Deformation Analysis (DDA) incorporating virtual joints can offer a powerful tool to solve such a problem. In the analysis process, the computational efficiency is critical because the mesh must be very dense to make crack propagation more realistic. Thus, parallel DDA using OpenMP is applied. The flattened and precrack Brazilian disc tests are first reproduced, respectively, to verify the accuracy and efficiency of the parallel DDA with virtual joints. Then, the impact fragmentation process is simulated and validated with corresponding laboratory experiments in terms of crack propagation results. Furthermore, the effects of joint‐slope angle, joint connectivity rate, and impact velocity on rock fracture behaviors are investigated. It is concluded that the peak number of cracks occurs when the joint‐slope angle ranges between 30° and 45°; the higher impact velocity and joint connectivity rate tend to cause more cracks and larger damages to the specimen.