Efficient Scheme Research Articles

Dynamic analysis of Hashimoto’s Thyroiditis bio-mathematical model using artificial neural network

This article establishes an efficient solution scheme for a mathematical model of Hashimoto’s Thyroiditis (HT) employing artificial neural networks. HT is an auto-immune disorder hostile to the thyroid follicle cells, effectuating hypothyroid or hyperthyroidism. Under this condition, the thyroid-stimulating hormone (TSH) alters incomparably to the free thyroxine (FT4) interrupts the functioning of the hypothalamus-pituitary-thyroid (HPT) axis, implicating the thyroid follicle cells getting destroyed. We primarily focus on utilizing artificial neural network (ANN) to perform numerical simulations for the system of ordinary differential equations describing the dynamics of an existing 4D model of HT. The presented model comprises four time-dependent variables: TSH, FT4, anti-thyroid antibodies (Ab), and size of the thyroid gland (T). We utilize ND-Solver and ANN scheme in the Mathematica software to acquire the computational data and illustrate thus retrieved results with essential performance plots. Further, mean square error has been considered in validating the proposed ANN-based approach accurately. The plot for training and validation loss exhibits the effectiveness of the proposed methodology, and substantiate that the suggested ANN approach is a good fit for the solving the mathematical model of HT.

A priori and a posteriori error estimates for efficient numerical schemes for coupled systems of linear and nonlinear singularly perturbed initial-value problems

This work considers the numerical approximation of linear and nonlinear singularly perturbed initial value coupled systems of first-order, for which the diffusion parameters at each equation of the system are distinct and also they can have a different order of magnitude. To do that, we use two efficient discretization methods, which combine the backward differences and an appropriate splitting by components. Both a priori and a posteriori error estimates are proved for the proposed discretization methods. The developed numerical methods are more computationally efficient than those classical methods used to solve the same type of coupled systems. Extensive numerical experiments strongly confirm in practice the theoretical results and corroborate the superior performance of the current approach compared with previous existing approaches.

A stabilized finite volume method based on the rotational pressure correction projection for the time-dependent incompressible MHD equations

This paper presents an efficient numerical scheme for solving the time-dependent incompressible Magnetohydrodynamics (MHD) equations based on the rotational pressure correction projection method within the finite volume framework. Utilizing the lowest equal-order mixed finite element pair (P1−P1−P1) to approximate the velocity, magnetic and pressure fields, our numerical scheme satisfies the discrete inf-sup condition by the pressure projection stabilization. To tackle the coupling inherent in the time-dependent incompressible MHD equations,the rotational pressure correction projection method is introduced to split the original problem into several linear subproblems. The unconditional stability of numerical schemes are provided,optimal error estimates in both L2 and H1-norms of numerical solutions are also presented.Finally, some numerical results are given to verify the established theoretical findings and show the performances of the considered numerical schemes.

Estimation of Denudation Parameters and River Capture Events From Neural Network Inverse Modeling of River Profiles and Thermo‐ and Geochronology Data

AbstractEarth's topography represents the cumulative effects of tectonics and surface processes modulated by climate and lithology. These factors shape landscapes through time. River profiles can be inverted to estimate the rock uplift histories or lithology‐specific erodibilities. However, river systems are dynamic and evolve in response to spatial and temporal internal dynamics, such as river capture events. Here, we present a modeling framework to infer denudation rates from the inversion of river profiles and thermo‐ and geochronology data. We achieve this by coupling a landscape evolution model and an efficient inverse modeling scheme to infer poorly resolved erosional and tectonic parameters. An application of the approach is presented for the Neckar catchment, southwest Germany, characterized by stark lateral variation in bedrock erodibility and rock uplift, and that have demonstrably undergone multiple river capture events. Different end‐member scenarios are explored in the simulations. First, we test uniform and spatial variability in rock uplift rate and bedrock erodibility, and second, temporal variations in rock uplift rate and base level. Finally, we simulate river capture events by adding upstream sections (drainage area) at specific times and locations within the fluvial network. We find that spatial variation in rock uplift rate is necessary to reproduce the Neckar's river profile while honoring analytical observations. Simulations integrating river captures allow improved river profile predictions of specific tributaries of the Neckar catchment, leading to potentially more realistic erodibility and rock uplift history estimates. The time and location of the capture events determined from the modeling agree with previous estimations from geological evidence.

Non-Hermitian mode management in periodically modulated waveguide amplifiers

We propose an efficient mode management scheme in active nonlinear multimode fibers based on non-Hermitian potentials in the longitudinal direction with an antisymmetric transverse profile. The proposal takes advantage of the nonlinear saturation toward particular mode configurations, which can be tuned by the non-Hermitian potential. We demonstrate flexible control of the beam profile within the parameter space of the applied potential with various possibilities, such as improving the beam quality by condensing photons to the lowest order Hermite mode, exciting higher order modes, or engineering a desired mode profile as a combination of modes. The effect is also analytically predicted using a mode-expansion approach, showing good agreement with the full model calculations based on the complex Ginzburg–Landau equation. This study was performed for 1D planar guiding structures, yet the results can be extended to 2D fibers and could be very useful in applications of fiber amplifiers and lasers.

Acoustic emission monitoring of a large-scale 50-year-old prestressed concrete bridge girder during an eccentric three-point bending test

Deterioration of reinforced and prestressed concrete structures is one of the main challenges in structural engineering. As the majority of our infrastructure is built around 1960-1980, most structures have reached or will soon reach their design lifetime, giving rise to more structural problems and costs in the coming decades. It is therefore important to develop efficient assessment schemes allowing to assure the safety and reliability of these existing structures and to estimate their residual lifetime. Within the framework of the Bridge|50 research project, a 50-year-old prestressed concrete bridge showing signs of corrosion damage was decommissioned. The bridge girders were recovered and have been subjected to large-scale load tests with varying loading patterns. This paper presents the results acquired during acoustic emission (AE) monitoring of one of the I-shaped girders during a monotonic three-point bending test with an eccentric point load. The results show that the failure mode and location could be detected based on AE activity and zonal AE localization. The onset of concrete cracking was observed by a decrease in peak frequency of the AE signals. Average frequency (AF) and rise angle (RA) value analysis was used to characterize the crack mode, which shifted from tensile to shear mode during bending.

An efficient hierarchical attribute-based encryption scheme with cross-domain data sharing

With the rapid advancement of data sharing technology, an increasing amount of data is being stored on cloud servers. To enable fine-grained access control over the data stored on cloud servers, the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology has been widely adopted. Recognizing that shared data and files often possess a hierarchical structure, hierarchical CP-ABE technology has been proposed recently. However, most existing schemes are restricted to single-domain data access, which limits their flexibility and universal applicability in practical applications. To address this limitation, an access control scheme based on hierarchical CP-ABE, named CDS-CP-ABE, is proposed to facilitate secure and efficient cross-domain data sharing. The scheme is capable of not only realizing fine-grained hierarchical access control within a single domain but also enabling cross-domain data sharing. Security analysis confirms that our scheme effectively resists chosen-plaintext attack. Furthermore, empirical results indicate that the time consumption associated with our scheme is lower compared to other existing schemes.

Scheme of preparing cluster states with cat qubits

Abstract Cluster states are essential quantum resources for one-way quantum computations and quantum networks. The reliable generation of cluster states in specific quantum systems is crucial for initializing complex quantum operations. In this paper, we introduce an efficient scheme for the deterministic preparation of a cluster state via circuit quantum electrodynamics (QED). Our scheme involves four individual microwave resonators, each of which is coupled to a superconducting transmon qutrit. We demonstrated that a four-cqubit cluster state can be achieved using three controlled-phase gate operations. The cluster state is prepared deterministically, eliminating the need for measurement-based feedback. Throughout these operations, the qutrit remains in its ground state, effectively minimizing decoherence from the qutrit. Numerical simulations suggest that our scheme can generate high-fidelity cluster states using current-circuit QED technology. We believe that our model will facilitate exploration of future large-scale continuous-variable quantum information processing systems.

Coordination of energy loss and fish friendliness for a low-head tubular-pump blade based on a multi-objective optimization model

Tubular pumps and turbines are the water-to-electricity conversion devices widely used in pumped hydro storage systems for low heads and high flow rates. An ecological concern is the high proportion of fish injuries caused by the rotating blades. In this study, a tubular pump blade is retrofitted to balance the energy loss and fish friendliness through multi-objective optimization, combining computational fluid dynamics with a mathematical blade strike model. Sensitivity analysis identifies nine geometric variables out of thirteen for optimizing blade design. An approximation model is developed to implement optimization algorithm and achieve the objective function. By artificially assigning weights to targeted performance metrics, three scenarios are obtained: the optimal efficiency scheme (A), the optimal fish friendliness scheme (C) and the balanced scheme (B). Scheme C achieves a fish survival ratio of 100 % at multiple flow rates (for fish lengths of Lf/D=1/12), but results in a 6.4 % decrease in efficiency compared to scheme A. Additionally, a negative blade attack angle can improve flow pattern. Considerable reduction in fish mortality can be obtained by equipping the rotor with a larger size but lower shaft speed, while also limiting the fish size to pass through the pump running at a reduced flow rate.

Understanding Leakage in Searchable Encryption: a Quantitative Approach

Searchable encryption, or more generally, structured encryption, permits search over encrypted data. It is an important cryptographic tool for securing cloud storage. The standard security notion for structured encryption mandates that a protocol leaks nothing about the data or queries, except for some allowed leakage, defined by the leakage function. This is due to the fact that some leakage is unavoidable for efficient schemes.\\ Unfortunately, it was shown by numerous works that even innocuous-looking leakage can often be exploited by attackers to undermine users' privacy and recover their queries and/or data, despite the structured encryption schemes being provably secure. Nevertheless, the standard security remains the go-to notion used to show the 'security' of structured encryption schemes. While it is not likely that researchers will design practical structured encryption schemes with no leakage, it is not satisfactory that very few works study ways to assess leakage.This work proposes a novel framework to quantify leakage. Our methodology is inspired by the quantitative information flow, and we call our method q-leakage analysis. We show how $q$-leakage analysis is related to the standard security. We also demonstrate the usefulness of q-leakage analysis by analyzing the security of two existing schemes with complex leakage functions.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba's capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

A robust approach to satellite image encryption using chaotic map and circulant matrices

AbstractIn the modern era, where satellite imagery is vital for applications like ecological monitoring and national security, ensuring the safety and integrity of these data repositories is crucial. This study presents an improved satellite image encryption technique that combines the cryptographic strength of the circulant matrix in the Hill cipher with the dynamic characteristics of the hyperbolic tangent tent map, further enhanced by the Kronecker XOR product. The algorithm initiates with computing alterations by a shift amount. After preserving the leftmost pixel in each row, it executes XOR operations between alternating rows, combining the value of the current even or odd row with corresponding pixels in the adjacent rows followed by encryption using the Hill cipher. The resulting image undergoes a diffusion process utilizing a hyperbolic tangent tent map. The Kronecker XOR product operation is then applied to individual pixels to produce a secure image followed by additional diffusion with keys from the hyperbolic tangent tent map to achieve the final encrypted image. We conducted simulations using MATLAB to assess the efficiency of the proposed satellite image encryption from theoretical and statistical perspectives. The results exhibit robust encryption performance as demonstrated by metrics such as an entropy value of 7.9982, a UACI of 33.5333%, and an NPCR of 99.6038%. The experiment results demonstrate the proposed image encryption scheme's reliability, practicability, and efficiency in securing satellite images during data storage and transmission. Comprehensive testing against various attacks including correlation, histogram, chi‐square, NPCR, PSNR, UACI, SSIM, key space and key sensitivity analysis confirms the scheme's robustness, efficiency and speed. These findings verify the scheme's ability to come across the most stringent encryption and decryption standards, making it an effective solution for securing sensitive satellite image data.

An accurate wavelets‐collocation technique for neutral delay distributed‐order fractional optimal control problems

AbstractIn this study, a new class of optimal control problems called neutral delay distributed‐order fractional optimal control problems is introduced, this problem is solved based on an efficient computational scheme. To solve the problem, we derive an exact formula for the Riemann–Liouville fractional integral operator of Genocchi wavelets based on beta functions for the first time. By taking into account this operator, collocation method, and Gauss–Legendre integration formula, the solution of fractional optimal control problems (FOCPs) under consideration is converted to a nonlinear programming one to which existing well‐developed algorithms may be applied. The mentioned scheme is applied to both FOCPs with or without delay. Error analysis associated with the proposed idea is also investigated under several mild conditions. The effectiveness of the strategy is showed by several illustrative examples, furthermore, a comparison with the previous methods highlights the preference of this scheme.

RT-PPS: Real-time privacy-preserving scheme for cloud-hosted IoT data

The Internet of Things (IoT) is a rapidly growing network of devices that can communicate with each other and with cloud-based services. These devices generate vast amounts of data that can be used to provide valuable insights into user behavior, environmental conditions, and other important factors. However, as this data is collected and processed by cloud-hosted services, there is a growing concern about privacy and security. Without adequate protection, sensitive information could be exposed to hackers or other malicious actors, putting both individuals and organizations at risk. To address this challenge, real-time privacy-preserving techniques can be used to protect IoT data without compromising its value. This paper introduces an efficient Real-time privacy-preserving scheme (RT-PPS) for cloud-hosted IoT data. RT-PPS employs multi-authority attribute-based encryption on a hybrid cloud environment to keep data secure and private, while still allowing it to be processed and analyzed by cloud-hosted services. RT-PPS has efficient response time and resource consumption, which gives it the ability to handle a huge number of concurrent users at the same time without notable delay. The proposed RT-PPS has been validated through extensive experimental evaluation on a variety of configurations. Moreover, the proposed scheme has been computationally compared with the state-of-the-artwork. RT-PPS has shown excellent performance, effectiveness, and efficiency. The RT-PPS encryption time for a 1 GB dataset while considering 1024 slices is approximately 1000 ms. Also, the RT-PPS decryption time for a 1 GB ciphertext while considering 1024 slices are approximately 235 ms. Finally, RT-PPS is proven secure against any polynomial-time attacks and their variations that have at most a negligible advantage in the introduced security model. Moreover compared to most of the state-of-the-artwork, RT-PPS reduced the ciphertext size and lowered the computations in the encryption, key generation, decryption, and ciphertext update while assuring their security. By implementing RT-PPS, organizations can take advantage of the benefits of IoT data while protecting the privacy of their users and maintaining the security of their systems.

On the analysis and deeper properties of the fractional complex physical models pertaining to nonsingular kernels.

This study solves the coupled fractional differential equations defining the massive Thirring model and the Kundu Eckhaus equation using the Natural transform decomposition method. The massive Thirring model is a dynamic component of quantum field theory, consisting of a coupled nonlinear complex differential equations. Initially, we study the suggested equations under the fractional derivative of Caputo-Fabrizio. The Atangana-Baleanu derivative is then used to evaluate the comparable equations. The results are significant and necessary for exploring a range of physical processes. This paper uses modern approach and the fractional operators in this situation to develop satisfactory approximations to the offered problems. The proposed approach combines the natural transform technique with the efficient Adomian decomposition scheme. Obtaining numerical findings in the form of a fast-converge series significantly improves the scheme's accuracy. Some graphical plot distributions are presented to show that the present approach is very simple and straightforward. We performed a fractional order analysis of assumed phenomena to demonstrate and validate the effectiveness of the future technique. The behaviour of the approximate series solution for several fractional orders is shown visually. Additionally, the nature of the derived outcome has been observed for various fractional orders. The derived results demonstrate how simple and efficient the proposed method is to apply for analysing the behaviour of fractionally-order complex nonlinear differential equations that arise in related fields of engineering and science.

Optimizing personalized treatments for targeted patient populations across multiple domains.

Learning individualized treatment rules (ITRs) for a target patient population with mental disorders is confronted with many challenges. First, the target population may be different from the training population that provided data for learning ITRs. Ignoring differences between the training patient data and the target population can result in sub-optimal treatment strategies for the target population. Second, for mental disorders, a patient's underlying mental state is not observed but can be inferred from measures of high-dimensional combinations of symptomatology. Treatment mechanisms are unknown and can be complex, and thus treatment effect moderation can take complicated forms. To address these challenges, we propose a novel method that connects measurement models, efficient weighting schemes, and flexible neural network architecture through latent variables to tailor treatments for a target population. Patients' underlying mental states are represented by a compact set of latent state variables while preserving interpretability. Weighting schemes are designed based on lower-dimensional latent variables to efficiently balance population differences so that biases in learning the latent structure and treatment effects are mitigated. Extensive simulation studies demonstrated consistent superiority of the proposed method and the weighting approach. Applications to two real-world studies of patients with major depressive disorder have shown a broad utility of the proposed method in improving treatment outcomes in the target population.

Implicit-Explicit Schemes for Compressible Cahn–Hilliard–Navier–Stokes Equations

The isentropic compressible Cahn–Hilliard–Navier–Stokes equations are a system of fourth-order partial differential equations that model the evolution of some binary fluids under convection. The purpose of this paper is the design of efficient numerical schemes to approximate the solution of initial-boundary value problems with these equations. The efficiency stems from the implicit treatment of the high-order terms in the equations. Our proposal is a second-order linearly implicit-explicit time stepping scheme applied in a method of lines approach, in which the convective terms are treated explicitly and only linear systems have to be solved. Some experiments are performed to assess the validity and efficiency of this proposal.

Efficient control scheme development for a highly integrated multi-component distillation process

This research aims to address intricate control challenges in a highly integrated multi-component distillation process within a polyolefin elastomers plant. The studied process incorporates various process intensification techniques into a unified unit, resulting in strong interactivity and presenting unique control challenges. To tackle these challenges, a rigorous mathematical model of the distillation process is established, and index reduction is employed to handle the high-index DAE system. Various control structures and algorithms are utilized to comprehensively assess their feasibility and effectiveness in disturbance rejection, setpoint tracking, and handling inaccurate measurements. The control structures include temperature inferential control, temperature-composition hybrid control, and composition control. The control algorithms encompass PI control, linear model predictive control (LMPC), and nonlinear model predictive control (NMPC). Results indicate that both temperature-composition hybrid control and composition control exhibit comparable and superior performance. This suggests that temperature-composition hybrid control is an effective and cost-efficient configuration for complex distillation systems. The dynamic responses of MPC outperform those of PI control, as evidenced by a reduction in the integral of the absolute error. NMPC exhibits the most outstanding performance in all scenarios, showcasing the smallest maximum deviations and shortest settling times.

LiDAR point cloud simplification strategy utilizing probabilistic membership

With the continuous progress of information acquisition technology, the volume of LiDAR point cloud data is also expanding rapidly, which greatly hinders the subsequent point cloud processing and engineering applications. In this study, we propose a point cloud simplification strategy utilizing probabilistic membership to address this challenge. The methodology initially develops a feature extraction scheme based on curvature to identify the set of feature points. Subsequently, a combination of k-means clustering and Possibilistic C-Means is employed to partition the point cloud into subsets, and to simultaneously acquire the probabilistic membership information of the point cloud. This information is then utilized to establish a rational and efficient simplification scheme. Finally, the simplification results of the feature point set and the remaining point set are merged to obtain the ultimate simplification outcome. This simplification method not only effectively preserves the features of the point cloud while maintaining uniformity in the simplified results but also offers flexibility in balancing feature retention and the degree of simplification. Through comprehensive comparative analysis across multiple point cloud models and benchmarking against various simplification methods, the proposed approach demonstrates superior performance. Finally, the proposed algorithm was critically discussed in light of the experimental results.

Multi-label remote sensing classification with self-supervised gated multi-modal transformers.

With the great success of Transformers in the field of machine learning, it is also gradually attracting widespread interest in the field of remote sensing (RS). However, the research in the field of remote sensing has been hampered by the lack of large labeled data sets and the inconsistency of data modes caused by the diversity of RS platforms. With the rise of self-supervised learning (SSL) algorithms in recent years, RS researchers began to pay attention to the application of "pre-training and fine-tuning" paradigm in RS. However, there are few researches on multi-modal data fusion in remote sensing field. Most of them choose to use only one of the modal data or simply splice multiple modal data roughly. In order to study a more efficient multi-modal data fusion scheme, we propose a multi-modal fusion mechanism based on gated unit control (MGSViT). In this paper, we pretrain the ViT model based on BigEarthNet dataset by combining two commonly used SSL algorithms, and propose an intra-modal and inter-modal gated fusion unit for feature learning by combining multispectral (MS) and synthetic aperture radar (SAR). Our method can effectively combine different modal data to extract key feature information. After fine-tuning and comparison experiments, we outperform the most advanced algorithms in all downstream classification tasks. The validity of our proposed method is verified.