Piecewise Acoustic Source Imaging with Unknown Speed of Sound Using a Level-Set Method

We investigate the following inverse problem:starting from the acoustic wave equation,reconstruct a piecewise constant passive acoustic source from a single boundary temporal measurement without knowing the speed of sound.When the amplitudes of the source are known a priori,we prove a unique determination result of the shape and propose a level set algorithm to reconstruct the singularities.When the singularities of the source are known a priori,we show unique determination of the source amplitudes and propose a least-squares fitting algorithm to recover the source amplitudes.The analysis bridges the low-frequency source inversion problem and the inverse problem of gravimetry.The proposed algorithms are validated and quantitatively evaluated with numerical experiments in 2D and 3D.

Adaptive Sparse Grid Discontinuous Galerkin Method:Review and Software Implementation

This paper reviews the adaptive sparse grid discontinuous Galerkin(aSG-DG)method for computing high dimensional partial differential equations(PDEs)and its software implementation.The C++software package called AdaM-DG,implementing the aSG-DG method,is available on GitHub at https://github.com/JuntaoHuang/adaptive-multiresolution-DG.The package is capable of treating a large class of high dimensional linear and nonlinear PDEs.We review the essential components of the algorithm and the functionality of the software,including the multiwavelets used,assembling of bilinear operators,fast matrix-vector product for data with hierarchical structures.We further demonstrate the performance of the package by reporting the numerical error and the CPU cost for several benchmark tests,including linear transport equations,wave equations,and Hamilton-Jacobi(HJ)equations.

Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans.This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis.This paper proposes a novel deep learning approach,called Conformer Network,for explainable discrimination of viral pneumonia depending on the lung Region of Infections(ROI)within a single modality radiographic CT scan.Firstly,an efficient U-shaped transformer network is integrated for lung image segmentation.Then,a robust transfer learning technique is introduced to design a robust feature extractor based on pre-trained lightweight Big Transfer(BiT-L)and finetuned on medical data to effectively learn the patterns of infection in the input image.Secondly,this work presents a visual explanation method to guarantee clinical explainability for decisions made by Conformer Network.Experimental evaluation of real-world CT data demonstrated that the diagnostic accuracy of ourmodel outperforms cutting-edge studies with statistical significance.The Conformer Network achieves 97.40% of detection accuracy under cross-validation settings.Our model not only achieves high sensitivity and specificity but also affords visualizations of salient features contributing to each classification decision,enhancing the overall transparency and trustworthiness of our model.The findings provide obvious implications for the ability of our model to empower clinical staff by generating transparent intuitions about the features driving diagnostic decisions.

MOALG: A Metaheuristic Hybrid of Multi-Objective Ant Lion Optimizer and Genetic Algorithm for Solving Design Problems

This study proposes a hybridization of two efficient algorithm’s Multi-objective Ant Lion Optimizer Algorithm(MOALO)which is a multi-objective enhanced version of the Ant Lion Optimizer Algorithm(ALO)and the Genetic Algorithm(GA).MOALO version has been employed to address those problems containing many objectives and an archive has been employed for retaining the non-dominated solutions.The uniqueness of the hybrid is that the operators like mutation and crossover of GA are employed in the archive to update the solutions and later those solutions go through the process of MOALO.A first-time hybrid of these algorithms is employed to solve multi-objective problems.The hybrid algorithm overcomes the limitation of ALO of getting caught in the local optimum and the requirement of more computational effort to converge GA.To evaluate the hybridized algorithm’s performance,a set of constrained,unconstrained test problems and engineering design problems were employed and compared with five well-known computational algorithms-MOALO,Multi-objective Crystal Structure Algorithm(MOCryStAl),Multi-objective Particle Swarm Optimization(MOPSO),Multi-objective Multiverse Optimization Algorithm(MOMVO),Multi-objective Salp Swarm Algorithm(MSSA).The outcomes of five performance metrics are statistically analyzed and the most efficient Pareto fronts comparison has been obtained.The proposed hybrid surpasses MOALO based on the results of hypervolume(HV),Spread,and Spacing.So primary objective of developing this hybrid approach has been achieved successfully.The proposed approach demonstrates superior performance on the test functions,showcasing robust convergence and comprehensive coverage that surpasses other existing algorithms.

Energy Stable Nodal DG Methods for Maxwell’s Equations of Mixed-Order Form in Nonlinear Optical Media

In this work,we develop energy stable numerical methods to simulate electromagnetic waves propagating in optical media where the media responses include the linear Lorentz dispersion,the instantaneous nonlinear cubic Kerr response,and the nonlinear delayed Raman molecular vibrational response.Unlike the first-order PDE-ODE governing equations considered previously in Bokil et al.(J Comput Phys 350:420–452,2017)and Lyu et al.(J Sci Comput 89:1–42,2021),a model of mixed-order form is adopted here that consists of the first-order PDE part for Maxwell’s equations coupled with the second-order ODE part(i.e.,the auxiliary differential equations)modeling the linear and nonlinear dispersion in the material.The main contribution is a new numerical strategy to treat the Kerr and Raman nonlinearities to achieve provable energy stability property within a second-order temporal discretization.A nodal discontinuous Galerkin(DG)method is further applied in space for efficiently handling nonlinear terms at the algebraic level,while preserving the energy stability and achieving high-order accuracy.Indeed with d_(E)as the number of the components of the electric field,only a d_(E)×d_(E)nonlinear algebraic system needs to be solved at each interpolation node,and more importantly,all these small nonlinear systems are completely decoupled over one time step,rendering very high parallel efficiency.We evaluate the proposed schemes by comparing them with the methods in Bokil et al.(2017)and Lyu et al.(2021)(implemented in nodal form)regarding the accuracy,computational efficiency,and energy stability,by a parallel scalability study,and also through the simulations of the soliton-like wave propagation in one dimension,as well as the spatial-soliton propagation and two-beam interactions modeled by the two-dimensional transverse electric(TE)mode of the equations.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

Deep learning(DL)plays a critical role in processing and converting data into knowledge and decisions.DL technologies have been applied in a variety of applications,including image,video,and genome sequence analysis.In deep learning the most widely utilized architecture is Convolutional Neural Networks(CNN)are taught discriminatory traits in a supervised environment.In comparison to other classic neural networks,CNN makes use of a limited number of artificial neurons,therefore it is ideal for the recognition and processing of wheat gene sequences.Wheat is an essential crop of cereals for people around the world.Wheat Genotypes identification has an impact on the possible development of many countries in the agricultural sector.In quantitative genetics prediction of genetic values is a central issue.Wheat is an allohexaploid(AABBDD)with three distinct genomes.The sizes of the wheat genome are quite large compared to many other kinds and the availability of a diversity of genetic knowledge and normal structure at breeding lines of wheat,Therefore,genome sequence approaches based on techniques of Artificial Intelligence(AI)are necessary.This paper focuses on using the Wheat genome sequence will assist wheat producers in making better use of their genetic resources and managing genetic variation in their breeding program,as well as propose a novel model based on deep learning for offering a fundamental overview of genomic prediction theory and current constraints.In this paper,the hyperparameters of the network are optimized in the CNN to decrease the requirement for manual search and enhance network performance using a new proposed model built on an optimization algorithm and Convolutional Neural Networks(CNN).

X-ray imaging of 30 year old wine grape wood reveals cumulative impacts of rootstocks on scion secondary growth and Ravaz index

Annual rings from 30 year old vines in a California rootstock trial were measured to determine the effects of 15 different rootstocks on Chardonnay and Cabernet Sauvignon scions.Viticultural traits measuring vegetative growth,yield,berry quality,and nutrient uptake were collected at the beginning(1995 to 1999)and end(2017 to 2020)of the lifetime of a vineyard initially planted in 1991 and removed in 2021.X-ray Computed Tomography(CT)was used to measure ring widths in 103 vines.Ring width was modeled as a function of ring number using a negative exponential model.Early and late wood ring widths,cambium width,and scion trunk radius were correlated with 27 traits.Modeling of annual ring width shows that scions alter the width of the first rings but that rootstocks alter the decay of later rings,consistently shortening ring width throughout the lifetime of the vine.Ravaz index,juice pH,photosynthetic assimilation and transpiration rates,and instantaneous water use efficiency are correlated with scion trunk radius.Ultimately,our research indicates that rootstocks modulate secondary growth over years,altering physiology and agronomic traits.Rootstocks act in similar but distinct ways from climate to modulate ring width,which borrowing techniques from dendrochronology,can be used to monitor both genetic and environmental effects in woody perennial crop species.

Behavior of Delivery Robot in Human-Robot Collaborative Spaces During Navigation

Navigation is an essential skill for robots.It becomes a cumbersome task for the robot in a human-populated environment,and Industry 5.0 is an emerging trend that focuses on the interaction between humans and robots.Robot behavior in a social setting is the key to human acceptance while ensuring human comfort and safety.With the advancement in robotics technology,the true use cases of robots in the tourism and hospitality industry are expanding in number.There are very few experimental studies focusing on how people perceive the navigation behavior of a delivery robot.A robotic platform named“PI”has been designed,which incorporates proximity and vision sensors.The robot utilizes a real-time object recognition algorithm based on the You Only Look Once(YOLO)algorithm to detect objects and humans during navigation.This study is aimed towards evaluating human experience,for which we conducted a study among 36 participants to explore the perceived social presence,role,and perception of a delivery robot exhibiting different behavior conditions while navigating in a hotel corridor.The participants’responses were collected and compared for different behavior conditions demonstrated by the robot and results show that humans prefer an assistant role of a robot enabled with audio and visual aids exhibiting social behavior.Further,this study can be useful for developers to gain insight into the expected behavior of a delivery robot.

Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

Recent security applications in mobile technologies and computer sys-tems use face recognition for high-end security.Despite numerous security tech-niques,face recognition is considered a high-security control.Developers fuse and carry out face identification as an access authority into these applications.Still,face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user.In the existing spoofing detection algorithm,there was some loss in the recreation of images.This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems.This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure.First,this pro-posed method is tested with the Cross-ethnicity Face Anti-spoofing(CASIA),Fetal alcohol spectrum disorders(FASD)dataset.This database has three models of attacks:distorted photographs in printed form,photographs with removed eyes portion,and video attacks.The images are taken with three different quality cameras:low,average,and high-quality real and spoofed images.An extensive experimental study was performed with CASIA-FASD,3 Diagnostic Machine Aid-Digital(DMAD)dataset that proved higher results when compared to existing algorithms.

Proof of Activity Protocol for IoMT Data Security

The Internet of Medical Things(IoMT)is an online device that senses and transmits medical data from users to physicians within a time interval.In,recent years,IoMT has rapidly grown in the medicalfield to provide healthcare services without physical appearance.With the use of sensors,IoMT applications are used in healthcare management.In such applications,one of the most important factors is data security,given that its transmission over the network may cause obtrusion.For data security in IoMT systems,blockchain is used due to its numerous blocks for secure data storage.In this study,Blockchain-assisted secure data management framework(BSDMF)and Proof of Activity(PoA)protocol using malicious code detection algorithm is used in the proposed data security for the healthcare system.The main aim is to enhance the data security over the networks.The PoA protocol enhances high security of data from the literature review.By replacing the malicious node from the block,the PoA can provide high security for medical data in the blockchain.Comparison with existing systems shows that the proposed simulation with BSD-Malicious code detection algorithm achieves higher accuracy ratio,precision ratio,security,and efficiency and less response time for Blockchain-enabled healthcare systems.

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of recovery.Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition.This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies.Additionally,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers.In this research,the tri-stage wrapper method is used because it reduces the computation time.The proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this scenario.To start,this research pre-processed the information.Next for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is used.Then,various classification methods are tested and trained.Deep learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for categorization.The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

基于注意力机制与自适应特征融合的群养猪身份识别

针对群养猪攻击过程中身体形变和重叠影响猪身份识别精度的问题,开发一种基于注意力机制和自适应特征融合的深度学习方法以提高猪身份识别精度。录制猪栏中8只猪每天8小时共3天的视频,并筛选含攻击的16830帧作为数据集。首先,采用ResNet50提取猪的卷积神经网络(CNN)特征;然后,采用特征金字塔网络(FPN)在ResNet50中选择3层不同尺度的特征,以优化这些特征的定位和语义信息;接着,采用自注意力机制提高这些特征的区分度,并采用自适应空间特征融合(ASFF)以融合不同尺度的特征;最后,利用卷积和Sigmoid函数相结合的检测器对猪身份进行识别。使用该方法后,猪身份识别的均值平均精度(mAP)达到95.59%,速度达到37.6 f/s。结果表明,该方法能够在攻击场景下有效识别猪身份,有助于将攻击识别从群体级细化为个体级。

Research on Multi-Scale Feature Fusion Network Algorithm Based on Brain Tumor Medical Image Classification

Gliomas have the highest mortality rate of all brain tumors.Correctly classifying the glioma risk period can help doctors make reasonable treatment plans and improve patients’survival rates.This paper proposes a hierarchical multi-scale attention feature fusion medical image classification network(HMAC-Net),which effectively combines global features and local features.The network framework consists of three parallel layers:The global feature extraction layer,the local feature extraction layer,and the multi-scale feature fusion layer.A linear sparse attention mechanism is designed in the global feature extraction layer to reduce information redundancy.In the local feature extraction layer,a bilateral local attention mechanism is introduced to improve the extraction of relevant information between adjacent slices.In the multi-scale feature fusion layer,a channel fusion block combining convolutional attention mechanism and residual inverse multi-layer perceptron is proposed to prevent gradient disappearance and network degradation and improve feature representation capability.The double-branch iterative multi-scale classification block is used to improve the classification performance.On the brain glioma risk grading dataset,the results of the ablation experiment and comparison experiment show that the proposed HMAC-Net has the best performance in both qualitative analysis of heat maps and quantitative analysis of evaluation indicators.On the dataset of skin cancer classification,the generalization experiment results show that the proposed HMAC-Net has a good generalization effect.

Comprehensive Level One Trauma Center Could Lower In-hospital Mortality of Severe Trauma in China

Trauma is a major health and social problem in the US and China, It constitutes the main cause of death in people aged 45 or under in both countries112]. There is clear evidence from clinical studies that a large percentage of these deaths are needless and preventable if better treatment and prevention programs are available12-3].

An Improved Jellyfish Algorithm for Multilevel Thresholding of Magnetic Resonance Brain Image Segmentations

Image segmentation is vital when analyzing medical images,especially magnetic resonance(MR)images of the brain.Recently,several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation;however,the algorithms become trapped in local minima and have low convergence speeds,particularly as the number of threshold levels increases.Consequently,in this paper,we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm(JSA)(an optimizer).We modify the JSA to prevent descents into local minima,and we accelerate convergence toward optimal solutions.The improvement is achieved by applying two novel strategies:Rankingbased updating and an adaptive method.Ranking-based updating is used to replace undesirable solutions with other solutions generated by a novel updating scheme that improves the qualities of the removed solutions.We develop a new adaptive strategy to exploit the ability of the JSA to find a best-so-far solution;we allow a small amount of exploration to avoid descents into local minima.The two strategies are integrated with the JSA to produce an improved JSA(IJSA)that optimally thresholds brain MR images.To compare the performances of the IJSA and JSA,seven brain MR images were segmented at threshold levels of 3,4,5,6,7,8,10,15,20,25,and 30.IJSA was compared with several other recent image segmentation algorithms,including the improved and standard marine predator algorithms,the modified salp and standard salp swarm algorithms,the equilibrium optimizer,and the standard JSA in terms of fitness,the Structured Similarity Index Metric(SSIM),the peak signal-to-noise ratio(PSNR),the standard deviation(SD),and the Features Similarity Index Metric(FSIM).The experimental outcomes and the Wilcoxon rank-sum test demonstrate the superiority of the proposed algorithm in terms of the FSIM,the PSNR,the objective values,and the SD;in terms of the SSIM,IJSA was competitive with the others.

Optimization of Cognitive Radio System Using Self-Learning Salp Swarm Algorithm

CognitiveRadio(CR)has been developed as an enabling technology that allows the unused or underused spectrum to be used dynamically to increase spectral efficiency.To improve the overall performance of the CR systemit is extremely important to adapt or reconfigure the systemparameters.The Decision Engine is a major module in the CR-based system that not only includes radio monitoring and cognition functions but also responsible for parameter adaptation.As meta-heuristic algorithms offer numerous advantages compared to traditional mathematical approaches,the performance of these algorithms is investigated in order to design an efficient CR system that is able to adapt the transmitting parameters to effectively reduce power consumption,bit error rate and adjacent interference of the channel,while maximized secondary user throughput.Self-Learning Salp Swarm Algorithm(SLSSA)is a recent meta-heuristic algorithm that is the enhanced version of SSA inspired by the swarming behavior of salps.In this work,the parametric adaption of CR system is performed by SLSSA and the simulation results show that SLSSA has high accuracy,stability and outperforms other competitive algorithms formaximizing the throughput of secondary users.The results obtained with SLSSA are also shown to be extremely satisfactory and need fewer iterations to converge compared to the competitive methods.

Efcient MCDM Model for Evaluating the Performance of Commercial Banks: A Case Stud

Evaluation of commercial banks(CBs)performance has been a signicant issue in the nancial world and deemed as a multi-criteria decision making(MCDM)model.Numerous research assesses CB performance according to different metrics and standers.As a result of uncertainty in decision-making problems and large economic variations in Egypt,this research proposes a plithogenic based model to evaluate Egyptian commercial banks’performance based on a set of criteria.The proposed model evaluates the top ten Egyptian commercial banks based on three main metrics including nancial,customer satisfaction,and qualitative evaluation,and 19 subcriteria.The proportional importance of the selected criteria is evaluated by the Analytic Hierarchy Process(AHP).Furthermore,the Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS),Vlse Kriterijumska Optimizacija Kompro-misno Resenje(VIKOR),and COmplex PRoportional ASsessment(COPRAS)are adopted to rank the top ten Egyptian banks based on their performance,comparatively.The main role of this research is to apply the proposed integrated MCDM framework under the plithogenic environment to measure the performance of the CBs under uncertainty.All results show that CIB has the best performance while Faisal Islamic Bank and Bank Audi have the least performance among the top 10 CBs in Egypt.

In-Situ Characterization of Three-Dimensional Optical Matters by Light Diffraction

Three-dimensional optical matters are created by combining the single beam optical trapping with the conventional Z-scan technique. Dynamic light diffraction is employed to evaluate the structure and quality of the optical matter formed at the optimum trapping power. The lattice constant of the optical matter is extracted based on the Bragg and Snell laws, showing that polystyrene spheres are nearly close-packed in the optical matter, confirmed by comparing the diffraction pattern of the optical matter with that of a colloidal photonic crystal fabricated by the self-assembled technique. The relatively broad diffraction peaks observed in the optical matter indicate that the density of disorders in it is higher than that in the photonic crystal. It is suggested that the optical matter possesses a random close-packed structure rather than a face centered cubic one.Three-dimensional optical matters are created by combining the single beam optical trapping with the conven- tional Z-scan technique. Dynamic light diffraction is employed to evaluate the structure and quality of the optical matter formed at the optimum trapping power. The lattice constant of the optical matter is extracted based on the Bragg and Snell laws, showing that polystyrene spheres are nearly close-packed in the optical matter, confirmed by comparing the diffraction pattern of the optical matter with that of a colloidal photonic crystal fabricated by the self-assembled technique. The relatively broad diffraction peaks observed in the optical matter indicate that the density of disorders in it is higher than that in the photonic crystal. It is suggested that the optical matter possesses a random close-packed structure rather than a face centered cubic one.

Efficient Model for Emergency Departments:Real Case Study

There are several challenges that hospitals are facing according to the emergency department(ED).Themain two issues are department capacity and lead time.However,the lack of consensus on performance criteria to evaluateEDincreases the complication of this process.Thus,this study aims to evaluate the efficiency of the emergency department in 20 Egyptian hospitals(12 private and 8 general hospitals)based on 13 performance metrics.This research suggests an integrated evaluation model assess ED under a framework of plithogenic theory.The proposed framework addressed uncertainty and ambiguity in information with an efficient manner via presenting the evaluation expression by plithogenic numbers.Data Envelopment Analysis(DEA)technique is used in order to measure the efficiency of the emergency department of 20 hospitals according to the number of treated patients and effect on patient’s life quality based on 11 factors.Using the Analytic Hierarchy Process(AHP),the weight of efficiency factors will be measured based on neutrosophic linguistic scale pairwise comparison.Plithogenic operations provide more accurate aggregation result according to contradiction degree between criteria values.The results show that ten of the hospitals are providing efficient service in their emergency department,while the other ten are less efficient.The analysis of the results shows that 58%of private hospitals emergency department is operating efficiently,while the efficient general hospitals represent 38%.