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The presence of probability-driven models is highly influential in setting the stage for vital decision-making in domains including reliability, engineering, music engineering, and other closely interconnected scenarios. With a deep understanding of the consequential roles played by probability-arisen models, we have developed and implemented a new probabilistic model. This model is constructed by utilizing the sine-based function and the exponentiated Weibull distribution, and it is known as the exponent power sine exponentiated Weibull (EPSE-Weibull) distribution. Point estimators are derived for the EPSE-Weibull distribution. These estimators are then evaluated through a simulation study. The significance of the EPSE-Weibull distribution is demonstrated through the analysis of reliability and music engineering data sets. In addition to the above, we also utilize two deep learning algorithms, namely Artificial Neural Networks (ANN) and Support Vector Regression (SVR), to forecast the same data sets. The findings indicate that the ANN model consistently exhibits higher levels of accuracy, as evidenced by its lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values compared to the SVR model for both data sets. These findings indicate that ANN is better at capturing the fundamental patterns in the underlying data sets. In addition, visual representations, such as bar charts and line charts, further emphasize the superior performance of the ANN across both data sets. [ABSTRACT FROM AUTHOR]

Probability-arisen models play a considerable role in preparing a crucial stage for decision-making concerning reliability, engineering, and more closely related scenarios. Bearing in mind the consequential roles of probability-arisen models, we introduce and implement a new probabilistic model that has arisen by using the sine function, namely, the sine very flexible Weibull (SVF-Weibull) distribution. The proposed SVF-Weibull distribution is a result of a combination of the very flexible Weibull distribution with the sine-based strategy. For the SVF-Weibull distribution, point estimates are obtained. The assessment of the point estimates of the SVF-Weibull distribution is done via a simulation study. Finally, the consequential role of the SVF-Weibull distribution, illustrated by considering reliability and music engineering data sets. Furthermore, we implement some machine learning tools for predicting the reliability and music engineering data sets. The performances of the machine learning tools are assessed across many hidden variables. Our findings suggest that the artificial neural network method is more optimal than other methods for predicting the reliability and music engineering data sets. [ABSTRACT FROM AUTHOR]

Statistical modeling of the practical data sets is a useful method that guides researchers to make decisions under uncertainty in applied sectors. To date, a rich number of probability distributions have appeared that have been developed in different ways and implemented in different fields. A vast literature of the available distributions have been constructed by adding new additional parameters, varying from one to eight. This specific piece of the research work also prompts a new probabilistic model, namely, the weighted sine flexible-Weibull distribution. The weighted sine flexible-Weibull distribution consists of the flexible Weibull extension model and a sine-oriented methodology. For the weighted sine flexible-Weibull distribution, the quartile-based properties are derived. Besides these properties, the estimators for the new model are also derived. The estimation part of the weighted sine flexible-Weibull distribution is further extended by conducting a simulation study. Finally, the weighted sine flexible-Weibull distribution is illustrated by considering the injury rate in logistics and public transport systems that are reported after the safety factors are considered. Based on several evaluation criteria, it is established that the weighted sine flexible-Weibull distribution provides the optimal fitting to the injury rate data. [ABSTRACT FROM AUTHOR]

This study proposes a novel tracking differentiator and applies it to the sliding-mode control (SMC) algorithm to address the unsatisfactory disturbance suppression and low tracking accuracy of magnetic levitation (maglev) systems. First, to assess performance in terms of filtering, tracking, and differentiation, an inverse hyperbolic sine function and a two-phase power function are introduced to improve the tracking differentiator. This can accelerate the global convergence speed, ensure smooth convergence at the equilibrium point, reduce system jitter, and enhance the noise-suppression ability of the system. The differentiator parameter-adjustment rules are derived from a system sweep. A comparison of the simulation results show that the proposed differentiator effectively suppresses noise and performs signal tracking and differentiation. Finally, the new differentiator is applied to the SMC of a maglev system. Simulation and experimental results show that the response speed of the maglev system under the SMC based on the new tracking differentiator is high, the jitter is effectively reduced, and the noise-suppression ability is improved. • Inverse hyperbolic sine function as differentiator acceleration function. • Introduction of two-phase power functions to weaken high frequency signals. • New tracking differentiator weakens jitter in slide film control algorithm. • New tracking differentiator improves the performance of magnetic levitation system. [ABSTRACT FROM AUTHOR]

Probability distributions are frequently used for statistical modeling of real-life phenomena in every field of life. Most often, real-life data sets are skewed in nature, and therefore, asymmetrical probability distributions are very competent for such scenarios. In the recent gains in distribution theory, researchers are focusing on developing new symmetrical and asymmetrical trigonometric-based probability distributions for data modeling in various fields. In the literature, however, there is no published work on statistical modeling of the transaction confirmation times on blockchain technology using trigonometric-originated probability distributions. For the first time covering the aforesaid research gap, this paper introduces a new trigonometric-based probabilistic approach for modeling the transaction confirmation times on blockchain technology. The new approach is called a new sine- G family of distributions. Using the proposed method, a new probability distribution called a new sine-Weibull distribution is studied. The proposed model is very flexible and obeys the symmetrical and asymmetrical shapes of its density function. The estimators of the new sine-Weibull distribution are derived, and their evaluation is tested through a simulation study. The applicability of the new sine-Weibull distribution is demonstrated by analyzing the average waiting time until the Bitcoin transaction is confirmed on blockchain technology. [ABSTRACT FROM AUTHOR]

So far in the literature, the two-parameter Weibull distribution and its other extensions are frequently implemented to analyze the wind speed energy data sets. However, based on our study of the literature, there is no published work about analyzing the wind speed energy data sets using new probability distributions that are developed via trigonometric functions. In this paper, I attempt to cover this amazing and interesting research gap. Therefore, I incorporate a trigonometric function, especially, the sine function to introduce a new statistical distributional method. The proposed method is called a new modified sine- G family of distributions. Some distributional properties of the new modified sine- G method are obtained. Using the new modified sine- G method, a new extension of the Weibull distribution called a new modified sine-Weibull distribution is studied. The new modified sine-Weibull distribution is applied to analyze four wind speed energy data sets. All three data sets are taken from the weather station at Sotavento Galicia in the Canary Islands, Spain, located at 43.3544 North and 7.8812 West. [ABSTRACT FROM AUTHOR]

This article introduces a novel model of asymmetric neural networks combined with fractional difference memristors, which has both theoretical and practical implications in the rapidly evolving field of computational intelligence. The proposed model includes two types of fractional difference memristor elements: one with hyperbolic tangent memductance and the other with periodic memductance and memristor state described by sine functions. The authenticity of the constructed memristor is confirmed through fingerprint verification. The research extensively investigates the dynamics of a coupled neural network model, analyzing its stability at equilibrium states, studying bifurcation diagrams, and calculating the largest Lyapunov exponents. The results suggest that when incorporating sine memristors, the model demonstrates coexisting state variables depending on the initial conditions, revealing the emergence of multi-layer attractors. The article further demonstrates how the memristor state shifts through numerical simulations with varying memductance values. Notably, the study emphasizes the crucial role of memductance (synaptic weight) in determining the complex dynamical characteristics of neural network systems. To support the analytical results and demonstrate the chaotic response of state variables, the article includes appropriate numerical simulations. These simulations effectively validate the presented findings and provide concrete evidence of the system's chaotic behavior. [ABSTRACT FROM AUTHOR]

In this work, our aim is to ensure the existence of mild solutions and study the controllability of fractional evolution inclusions of Clarke's subdifferential type of order q ∈ (1 , 2) with nonlocal conditions in the setting of Hilbert spaces. Using fixed point techniques, fractional calculus, multivalued maps, cosine and sine function operators, we discuss the existence of mild solutions for the considered system. Moreover, under suitable conditions, we investigate the controllability of the proposed system. Finally, we present an illustrated example. [ABSTRACT FROM AUTHOR]

This paper investigates the prescribed-time leader-following output-feedback consensus problem for second order multiagent systems without velocity measurement. Firstly, by introducing a time-scaling function, novel prescribed-time state observers are designed to estimate the second-order states of the agents. Then, a distributed output-feedback scheme is proposed to achieve leader-following consensus, where the transient performance, including the convergence rate and the overshoot, can be offline pre-assigned. It should be noted that the singularity-like problem is solved for the system under measurement errors by adopting a form of piecewise functions. Moreover, the control strategy is modified by introducing an auxiliary system when taking the common saturation problem into account. Finally, the efficiency of the proposed schemes is illustrated by numerical simulation examples. • A novel kind of prescribed-time performance functions is designed. • A form of sine functions is introduced to deal with the singularity-like problem. • The prescribed-time stability of the system is guaranteed by the proposed scheme. [ABSTRACT FROM AUTHOR]

p , q -quasirung orthopair fuzzy set (p , q -QOFS) is an extension of the q -rung orthopair fuzzy set (q -ROFS) theory used to represent uncertainty and vagueness in decision-making processes. The paper aims to utilize robust sine-trigonometric operational laws to investigate the group decision-making process within the p , q -QOFSs framework. The p , q -QOFS possess a distinctive characteristic of handling uncertain information by utilizing a wider space for membership representation compared to q -ROFS. Consequently, the current paper has been categorized into three distinct phases. In the initial phase, novel operational laws will be presented for p , q -QOFSs. The fundamental concept behind these newly proposed operations is to integrate the properties of the sine function, which include being periodic and symmetric about the origin, into the decision-making process for objects. Then, based on these laws, several operators for aggregating information will be obtained, along with their necessary properties and relationships. Lastly, an algorithm will be presented for interpreting the problem of multi attribute group decision-making (MAGDM), utilizing the operators, and demonstrating it with an illustrative example. A comprehensive comparative analysis will be conducted with some of the existing methods to uncover their impacts. [ABSTRACT FROM AUTHOR]

This study focuses on a novel family of distributions inspired by the hyperbolic sine function. The Rayleigh distribution is the base model for the newly formed family of distributions known as the new hyperbolic Sine-Rayleigh distribution. The recommended distribution's distinct structural traits have been examined. The behaviors of the distributional functions of the proposed model are depicted in several figures. The maximum likelihood estimation procedure is employed to estimate the specified distribution parameters. A simulation study was carried out to examine and evaluate the behavior of the estimators. Moreover, the efficacy of the specified distribution is supported by realistic data sets pertaining to engineering science. [ABSTRACT FROM AUTHOR]

The role of Membranolytic Anticancer Peptides (ACPs) as breast cancer therapeutics is gaining popularity because of their capacity to delay cellular resistance development and eliminate some common chemotherapy challenges, like cytotoxicity, aftereffect, etc. As breast cancer is a leading cause of death among women globally and designing effective treatment mechanisms for it is a crucial step, identifying the potent ACPs is a vital contribution towards cancer treatment. Since ACPs are small protein sequences, it is essential to devise efficient and effective sequence analysis methods for them. In this regard, Chaos game representation (CGR) is a popular technique used to visualize protein sequences as 2D images. In CGR, amino acids of a sequence are projected on a pre-defined axis, and the resulting image is used as input for machine learning/deep learning models. The traditional method of projecting amino acids uses the sine and cosine functions, but in this study, we explore the use of secant and cosecant functions as an alternative. Moreover, we use Spaced k-mers of a sequence as an alternate way to manipulate its amino acids rather than the original sequence, which is proven to show better results than traditional methods which deal with each amino acid within the sequence separately. In this study, we propose a new approach for ACP classification based on the concept of CGR and spaced k-mers. We generate images of the peptide sequences and use them as input to the deep learning (DL) models to perform classification. The results show that our proposed approach achieves high predictive performance for ACP classification, and provides a new direction for identifying the potent ACPs which can be used for breast cancer treatment. [ABSTRACT FROM AUTHOR]

Moldy core is a common internal disease in apples, with current visible/near-infrared (Vis/NIR) spectroscopy-based detection methods often suffering from inaccuracies due to variations in fruit diameter and soluble solids content (SSC). To address this issue, an online detection system was developed that integrates spectral corrections for both diameter and SSC information. The spectra were first corrected using a hyperbolic sine function based on fruit diameter, followed by a second correction utilizing the spectral features of SSC through the Variational Mode Decomposition (VMD) algorithm. This two-step correction process effectively mitigated the influence of diameter and SSC variations on the spectral data. A Partial Least Squares Discriminant Analysis (PLS-DA) model was subsequently constructed using the corrected spectra and moldy core labels. Compared to the uncorrected model, the improved model exhibited substantial enhancements in accuracy, recall, and precision, achieving 94.44%, 92.59%, and 96.15%, respectively. Additionally, online validation with independent samples demonstrated an accuracy of 88.33%, highlighting the system's stability and robustness for efficient online detection of apple moldy core. • Developed an online detection system for apple moldy core disease, combining internal and external quality assessments. • Proposed a method to correct fruit size effects on spectral attenuation. • Developed an equation to calculate the attenuation coefficient for spectral correction. • Proposed a correction method for spectra based on fruit soluble solids content. [ABSTRACT FROM AUTHOR]

In the scenario of linguistic neutrosophic multivalued sets (LNMSs), LNMS can capture true, false, and indeterminate linguistic multivalued information and effectively represent group decision making information in multiple criteria group decision-making (MCGDM) issues. Therefore, the objective of this study is to develop an MCGDM strategy using linguistic confidence interval neutrosophic number (LCINN) trigonometric aggregation operators to effectively tackle MCGDM problems with periodicity in an LNMS scenario. To do so, this study first presents a transformation approach from LNMS to the linguistic confidence interval neutrosophic set to ensure the confidence level of linguistic term sequences (LTSs) in LNMS from the perspective of probability estimation and to address the operation issue between different LTS lengths. Next, the linguistic sine t-norm, cosine t-conorm, and trigonometric operation laws of LCINNs are defined to include the periodic features of the sine and cosine functions. Then, the LCINN trigonometric weighted average and geometric operators are constructed to provide periodic aggregation tools for LCINNs in the LNMS scenario. Furthermore, an MCGDM strategy using the proposed aggregation operators is constructed to effectively tackle MCGDM problems with periodicity in LNMS scenarios. Finally, the constructed MCGDM strategy is applied to a choice case of energy storage technologies, and then its validity of the ranking results is presented by comparing it with the existing MCGDM methods in the scenarios of linguistic neutrosophic uncertain sets and LNMSs. In addition, the proposed strategy (no learning process) is simpler in the decision-making process than machine learning or artificial intelligence decision-making algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing offers a variety of on-demand services to users and has gained significant prominence in the contemporary era. The security of information stored in cloud data centers has become a central concern, especially for sensitive data like medical images, videos, and multimedia content that require heightened protection when stored in data centers. Cloud users have a responsibility to ensure the security of their data through strategic measures. Focusing on maintaining the privacy of images stored in the cloud, this research introduces an innovative image encryption technique that is based on a modified Skew Tent chaotic map. The suggested modification to the chaotic map includes combining the Skew Tent map with both the sine trigonometric function and perturbation technology. This results in enhanced randomness, more intricate dynamical behavior, a broader chaotic range, and increased sensitivity to initial values in contrast to alternative chaotic maps. The incorporation of this adapted map into a stepwise procedure, involving two rounds of permutation followed by diffusion, effectively accomplishes image data encryption for cloud storage systems. These consecutive operations collectively enhance the encryption method's randomness and robustness. Through simulations conducted using Cloudsim, the cipher-image exhibits a uniform distribution and achieves a commendable information entropy score of 7.996749. The encryption algorithm significantly reduces correlation coefficients from 1 in the original image to 0 in the encrypted image, while maintaining NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) values within the critical range. Additionally, both theoretical analysis and practical evaluations confirm the algorithm's resilience against exhaustive, occlusion, and classical attacks. Moreover, extending this encryption framework to video data, a novel video encryption approach is proposed. • Novel image encryption with modified Skew Tent map for cloud privacy. • Method merges Skew Tent map, sine function, and perturbation technology. • The technique boosts chaos, complexity, range, and initial value sensitivity. • Cloudsim simulations show cipher-image uniformity & high entropy of 7.996749. • Encryption lowers correlation from 1 to 0, keeps NPCR and UACI in critical range. [ABSTRACT FROM AUTHOR]

We consider Presburger arithmetic extended by the sine function, call this extension sine-Presburger arithmetic (sin-PA), and systematically study decision problems for sets of sentences in sin-PA. In particular, we detail a decision algorithm for existential sin-PA sentences under assumption of Schanuel's conjecture. This procedure reduces decisions to the theory of the ordered additive group of real numbers extended by sine, which is decidable under Schanuel's conjecture. On the other hand, we prove that four alternating quantifier blocks suffice for undecidability of sin-PA sentences. To do so, we explicitly interpret the weak monadic second-order theory of the grid, which is undecidable, in sin-PA. [ABSTRACT FROM AUTHOR]

This study explores the application of Physics-Informed Neural Networks (PINNs) in modeling fluid flow and heat transfer dynamics within intricate geometric configurations, focusing on manifold microchannel (MMC) heat sinks designed for efficient high-power IGBT cooling. A deep neural network architecture comprising two sub-PINNs, one for flow dynamics and another for thermal behavior, is developed, each initialized with a sine activation function to capture high-order derivatives and address the vanishing gradient problem. Comparisons between PINN and CFD simulations reveal close agreement, with both methods showing an increase in pressure drop and a decrease in temperatures as inlet velocity increases. Discrepancies arise in scenarios with rapid flow pattern or gradient changes, highlighting PINNs' sensitivity to geometric complexity and numerical stability. Overall, this study underscores PINNs' potential as a promising tool for advancing thermal management strategies across various engineering applications. • Physics-Informed Neural Networks (PINNs) as an innovative approach to model fluid flow and heat transfer in complex geometries is introduced. • Close agreement between PINN and CFD simulations, affirming PINNs' accuracy, bolstering their credibility is achieved. • PINNs' sensitivity to geometric intricacies and numerical stability, particularly in abrupt flow pattern or gradient changes is identified. [ABSTRACT FROM AUTHOR]

Visual tracking is one of the pre-eminent tasks in several computer vision applications. Particle filter (PF) is extensively used in visual tracking for intelligent surveillance system applications, hugely significant. But the re-sampling procedure of PF will result in sample impoverishment, which will affect the precision of tracking simultaneously. In this paper, a new tracking technique, called Trigonometric Particle Filter (TPF), based on PF optimized by Sine Cosine Algorithm (SCA), which contains trigonometric sine and cosine functions, is proposed. An enhanced method for improving the number of target particles used in a Sine Cosine Algorithm for trigonometric particle filter includes SCA ahead of the re-sampling step. This step ensures a more extensive particle set Achievement of the proposed TPF tracker is inspected and assessed on Visual Tracker Benchmark (VOT) databases. The proposed TPF tracker is compared with evolutionary-based methods like the Spider monkey optimization assisted PF (SMO-PF), Firefly algorithm-based PF (FAPF) method, Particle swarm optimization-based PF (PSO-PF) and Particle filter, recent four correlation filter-based trackers, and also with other ten state-of-the-art tracking methods. We demonstrate that visual tracking using TPF delivers additional consistent and proficient tracking outcomes than compared trackers. • A new tracking technique, called TPF, based on PF optimized by SCA, is proposed. • The SCA replaces the normal re-sampling process and improves the PF. • TPF compared with evolutionary-based approaches and 10 other leading-edge methods. • TPF visual tracking is more consistent and proficient than other trackers. [ABSTRACT FROM AUTHOR]

This article proposes using in-phase and quadrature frequency-shift keying (IQFSK) modulation for low-power optical wireless communications (OWC). IQFSK independently leverages both cosine and sine basis functions to enhance the system's spectral efficiency (SE). It uses only the odd harmonic frequencies for these basis functions, allowing the clipping of negative amplitude excursions without losing information, making the waveform compatible with OWC The work presents optimal maximum likelihood and low-complexity sub-optimal detection mechanisms for IQFSK. The proposed scheme is analyzed analytically and with numerical simulations. The simulation and analytical results indicate that the proposed scheme is more energy-efficient, can attain a better energy and SE trade-off by exploiting the frame structure of the waveform, and has a lower minimum squared Euclidean distance relative to other state-of-the-art FSK-based counterparts, thus establishing it as one of the most efficient FSK approaches for low-power OWCs. [ABSTRACT FROM AUTHOR]

This paper presents a coordinated trajectory planning method for a free-floating dual-arm space robot system. The research purpose of this paper is to enable a space robot to complete the specified goals while avoiding collision with an obstacle by planning the motion of the joints. The coordinated trajectory planning problem for a space robot is transformed into an optimization problem to find the optimal trajectory of the base and manipulators by using particle swarm optimization (PSO). Since the optimization method is used to find an optimal trajectory, it is unnecessary to calculate the position of the base and the inverse kinematics of the joints. Therefore, the kinematic singular problem in seeking the inverse solution can be avoided. The joint trajectories are parametrized by a sine function with seventh-order polynomial parameters. Multiple constraints are added to the objective function as penalty items, and a new increasing strategy for a penalty factor is proposed to balance the penalty and search ability under multiple constraints. Finally, numerical simulations are carried out to verify the effectiveness of the proposed method. • Constraints are added to a trajectory optimization problem as penalty items. • Joint trajectories are parametrized by a sine function with polynomial parameters. • A new increasing strategy for a penalty factor is proposed. [ABSTRACT FROM AUTHOR]

This paper formulates a new fractional order (FO) integral terminal sliding mode control algorithms for the stabilization and synchronization of N-dimensional FO chaotic/hyper-chaotic systems, which are perturbed with unknown uncertainties. In order to render closed loop robustness, a novel efficient double hyperbolic functions based uncertainty estimator is developed for the estimation and mitigation of unknown uncertainties. Moreover, a double hyperbolic reaching law comprising of tangent hyperbolic and inverse sine hyperbolic functions is incorporated in the presented control techniques for the practical convergence of various chaotic system states and tracking errors to infinitesimally close to equilibrium. Examples such as FO Lu, FO Chen and FO Lorenz systems are taken to investigate robustness, finite time convergence, tracking accuracy and closed loop stability properties of the devised methodologies. Last but not least, comparative analysis is also carried out between the proposed and prior control techniques through various time domain performances such as settling time, error indices and measure of control energy. • Novel fractional order double hyperbolic integral terminal sliding mode control is developed. • Control of uncertain fractional order chaos is dealt. • Novel hyperbolic uncertainty estimator is also developed for estimation of unknown uncertainties. • Three examples are also validated along with comparative analysis with respect to prior techniques. [ABSTRACT FROM AUTHOR]

It is well known that multi-UAV cooperative dynamic target path planning is a challenging field. In this field, multi-UAV cooperative dynamic target path planning is very important to achieve efficient task completion. However, the existing algorithms have some limitations in solving the problems of insufficient search range, premature convergence, local optimization and insufficient population diversity. In order to solve these problems and improve the efficiency and accuracy of path planning, this paper proposes an innovative method. Firstly, we use A* algorithm to obtain the initial assignment result of UAV target, and then use Hungarian algorithm and genetic algorithm to optimize the assignment result and expand the target assignment range. Secondly, in the path planning stage, the bat algorithm is improved, and sine function, dynamic expansion factor and nonlinear function are introduced to solve the problems of insufficient search range, premature convergence and local optimization. At the same time, genetic algorithm is used to solve the problem of insufficient population. By optimizing target assignment and path planning, the performance of multi-UAV cooperative system is improved, so as to better adapt to the task requirements in dynamic environment and provide more reliable solutions for UAV applications. The experimental results show that the tracking and path planning are more effective than BA, CCWOA and OUMPOA under the condition of good stability. Compared with BA, the fitness function and convergence speed are improved by 34.64% and 30.29% respectively. [ABSTRACT FROM AUTHOR]

Startup and shutdown are quite common processes in the operation of fluid machinery. Therefore, it is of great significance to clarify the mechanism of pumps during start-up periods for their safe and stable operations. To this end, the transient performance of a centrifugal under start-up conditions is investigated experimentally and theoretically. A theoretical prediction model is established based on the pipeline balance between the transient pump head and pipeline resistance, combined with the corresponding time-stepping algorithm for solving the pipeline balance equation. The independence test of the transient time step for the prediction method is conducted to determine a proper value. The predicted results show that sufficient prediction accuracy can be achieved, with an average relative error of 11.13 % compared with experimental results. A thorough mathematical analysis is performed to demonstrate the convexity of the characteristics of the transient pump head according to its second derivative from the pipeline balance equation. Then, a unified hyperbolic sine function with a normalized transient pump head and normalized time is established, which works well for the same pump under start-up conditions with various acceleration time. • A theoretical model is established and applied to predict the transient performance of a centrifugal pump. • A unified hyperbolic sine function is established to model the time evolution of the transient pump head. • An average relative error of 11.13 % for theoretical model in comparison of experimental results. [ABSTRACT FROM AUTHOR]

First-order compartment models are common tools for modelling many biological processes, including pharmacokinetics. Given the compartments and the transfer rates, solutions for the time-dependent quantity (or concentration) curves can normally be described by a sum of exponentials. This paper investigates cases that go beyond simple sums of exponentials. With specific relations between the transfer rate constants, two exponential rate constants can be equal, in which case the normal solution cannot be used. The conditions for this to occur are discussed, and advice is provided on how to circumvent these cases. An example of an analytic solution is given for the rare case where an exact equality is the expected result. Furthermore, for models with at least three compartments, cases exist where the solution to a real-valued model involves complex-valued exponential rate constants. This leads to solutions with an oscillatory element in the solution for the tracer concentration, i.e., there are cases where the solution is not a simple sum of (real-valued) exponentials but also includes sine and cosine functions. Detailed solutions for three-compartment cases are given. As a tentative conclusion of the analysis, oscillatory solutions appear to be tied to cases with a cyclic element in the model itself. [ABSTRACT FROM AUTHOR]

Differential Evolution (DE), as a powerful population-based stochastic optimization algorithm, has attracted the attention of researchers from various fields due to its advantages such as simple operation, strong robustness, and few control parameters. However, many existing DE variants often suffer from drawbacks such as premature convergence and stagnation when solving complicated optimization problems. In view of the aforementioned issues, this paper proposes an adaptive DE with opposition learning-based diversity enhancement (OLBADE). The main contributions can be summarized as follows: Firstly, a new adaptive parameter control is proposed with a non-linear weighting strategy incorporating into the framework of parameter adaptation. Secondly, a donor vector perturbation strategy is introduced to complement existing strategy for increasing population diversity. Thirdly, a novel stagnation indicator is proposed, and then opposition learning strategy is employed to renew stagnated individuals in the population when stagnation occurs. OLBADE is compared with five excellent DE variants under a large test-bed containing CEC2013, CEC2014, CEC2017 and CEC2022 test suites to verify its effectiveness. In addition, OLBADE is applied in parameter identification problem of photovoltaic model to verify its feasibility. Experimental results demonstrate that OLBADE achieves higher solution accuracy, faster convergence speed and better stability. • A hybrid method combining the logistic and sine functions is used for the adjustment of F. • A donor vector perturbation strategy is introduced. • A diversity enhancement mechanism based on opposition learning is proposed. • To avoid over-fitting, a larger test suite is employed in algorithm validation. [ABSTRACT FROM AUTHOR]

Arithmetic optimization algorithm (AOA) is a meta -heuristic algorithm inspired by mathematical operations. AOA has been diffusely used for optimization issues on continuous domains, but few scholars have studied discrete optimization problems. In this paper, we proposed Binary AOA (BAOA) based on two strategies to handle the feature selection problem. The first strategy used S-shaped and V-shaped shift functions to map continuous variables to discrete variables. The second strategy was to combine four logical operations (AND, OR, XOR, XNOR) on the basis of the transfer function, and constructed a parameter model based on the sine and cosine function. An enhanced logic binary sine–cosine function arithmetic optimization algorithm (LBSCAOA) was proposed to realize the position update of variables. Its purpose was to improve the algorithm's global search capabilities and local exploitation capabilities. In the simulation experiments, 20 datasets were selected to testify the capability of the proposed algorithm. Since KNN had the advantages of easy understanding and low training time complexity, this classifier was selected for evaluation. The performance of the improved algorithm was comprehensively evaluated by comparing the average classification accuracy, the average number of selected features, the average fitness value and the average running time. Simulation results showed LBSCAOA with V-Shaped "V4" stood out among many improved algorithms. On the other hand, LBSCAOA with V-Shaped "V4" was used as a representative to compare with other typical feature selection algorithms to verify its competitivenes. [ABSTRACT FROM AUTHOR]

The passenger volumes of cross-Strait air travel between Taiwan and China have grown rapidly, largely due to the gradual introduction of direct flights. This study captures passengers' preferences for airlines and flight departure times in the Taiwan–China market. The model structures examined include the multinomial logit, nested logit, and mixed nested logit models. In particular, trigonometric functions are incorporated into choice modeling to identify passenger preferences regarding flight departure times. A stated preference survey was conducted at Taiwan Taoyuan International Airport. A mixed nested logit model, which captures substitution patterns among competitive alternatives as well as individual heterogeneity, outperforms the other models. The estimation results indicate high degrees of competition among airlines and flight-departure times, especially highly substitutable departure times within the same airline. The continuous departure-time specification, which uses a series of sine and cosine functions, adequately captured passenger preferences. From the qualitative aspect of airline services, a premium for service improvement is highest for cabin crew service, followed by onboard food and check-in service. Airlines can use these results to improve their service quality and adjust flight schedules. • Passengers' preferences for airlines and flight departure times were studied. • Stated choice modeling and trigonometric function identified departure time preferences. • Quantitative and qualitative aspects of service attributes influence air-travel itinerary choice. • Strong competition among airlines and flight departure times was observed. • Mixed nested logit model confirms significant heterogeneity. [ABSTRACT FROM AUTHOR]

We consider a superconductor-barrier-superconductor configuration built with Rarita-Schwinger-Weyl semimetal, which features four bands crossing at a single nodal point. Assuming a homogeneous s-wave pairing in each superconducting region, and the barrier region created by applying a voltage of magnitude V 0 across a piece of normal state semimetal, we apply the BdG formalism to compute the discrete energy spectrum ε of the subgap Andreev bound states in the short-barrier regime. In contrast with the two-band semimetals studied earlier, we find upto four pairs of localized states (rather than one pair for two-band semimetals) in the thin-barrier limit, and each value of ε has a complicated dependence on the phase difference φ 12 via cosine and sine functions, which cannot be determined analytically. These are artifacts of multi-band nodes hosting quasiparticles of pseudospin values greater than 1/2. Using the bound state energies, we compute the Josephson current across the junction configuration. • We consider a superconductor-barrier-superconductor (S-B-S) configuration built with Rarita-Schwinger-Weyl semimetal. • We compute the discrete energy spectrum of the subgap Andreev bound states in the short-barrier regime. • The numerical solutions show the existence of more than one pair of Andreev bound states. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) can be used to inversely model complex physical systems by encoding the governing partial differential equations and training data into the neural network. However, neural networks are known to be biased towards learning less complex functions, called spectral bias. This has important implications in modeling cardiovascular flows, where spatial frequencies can vary substantially across anatomies and pathologies (e.g., aneurysms or stenoses). Recent evidence suggests that Fourier-based activation functions have desirable properties, and can potentially reduce spectral bias; however, the performance and adequacy of such Fourier activation functions have not yet been evaluated in patient-specific cardiovascular flow applications. The performance of s i n e activation function was evaluated against t a n h and s w i s h activation functions in a 1D advection-diffusion problem, an eccentric 2D stenosis model (Re=5000), and a patient-specific 3D aortic model (Re=823) under pulsatile flow conditions. CFD simulations were performed at high spatio-temporal resolution and data points were extracted for training the neural network. The number of training data points were normalized by L / D. The performance of the PINNs framework was evaluated with increasing number of training data points and across all three activation functions. Our results demonstrate that s i n e activation function presents desirable characteristics, such as monotonic reduction in errors, relatively faster convergence, and accurate eigen spectra at higher modes, compared to t a n h and s w i s h activation functions. Interestingly, for all activation functions, the domain-averaged errors tended to asymptote at ≈ 15 − 20 % despite substantial increase in training point density. For 2D eccentric stenosis, errors asymptoted at a sensor point density of 40 L / D. For 3D patient-specific aorta, this asymptote was achieved at 180 L / D for all three activation functions with an error of ≈ 15 % although s i n e activation function demonstrated relatively faster convergence. We have demonstrated that Fourier-based activation functions have higher performance in terms of accuracy and convergence properties for cardiovascular flow applications; however, inherent challenges of neural networks (e.g., spectral bias) can limit the accuracy to ≈ 15 % under physiological, 3D patient-specific blood flow conditions. • Sine activation functions have better performance in modeling highly-dynamical blood flows. • Sensor point density of 180 D/L shows converged errors in 3D patient-specific aortic models. • PINNs show ∼20% errors in patient-specific models regardless of the activation functions used. [ABSTRACT FROM AUTHOR]

This paper is devoted to the multistability problem for complex-valued recurrent neural networks (CVRNNs) with a specific class of piecewise nonlinear activation functions. Firstly, a general class of piecewise nonlinear activation functions is presented to facilitate further analysis. Then, by resorting to the fixed point theorem and Lagrange's mean value theorem, sufficient criteria are established which ascertain that the existence of (2 k + 1) 2 n equilibrium points. Meanwhile, it also verifies that (k + 1) 2 n equilibrium points of the considered CVRNNs with a piecewise nonlinear activation function are stable, where k stands for a positive real number, which is associated with the frequency of sine and cosine functions. Moreover, the utilization of the activation functions provides a larger storage capacity in associative memory application. In the end of paper, three numerical examples are presented to demonstrate the feasibility and validity of the achieved theoretical results. [ABSTRACT FROM AUTHOR]

• Chaotic maps are widely used in pseudo-random number generators, and other cryptographic applications. • STM is a 1-D map, which needs fewer computations to create a pseudo-random sequence, but it can face chaotic annulling traps. • With the use of the sine function, the modified chaotic map doesn't have chaotic annulling traps and has complex behaviour. • XOR function is a one-way function that hides the two different original sequences. Everyday, a vast amount of information is created and shared on the internet. Security steps and methods are needed to ensure the data is sent and stored safely. Random numbers are essential to cryptography because they are crucial to securing data. In recent years, the use of chaos theory has become increasingly important in producing pseudo-random number sequences in the field of cryptography. But the majority of fundamental chaotic maps have a variety of limitations, such as constrained chaotic regions, a low Lyapunov Exponent (LE), chaotic annulling conditions, and high computational cost. In this research, we construct a new chaotic map based on the skew tent map (STM) in order to find a better solution to these problems. The proposed chaotic map includes significantly enhanced chaotic behaviour and has a more comprehensive chaotic range and higher LE. Furthermore, two novels Pseudo-random Number Generators (PRNGs) based on a new M-STM chaotic map, have been built to investigate its application in security-related fields. The performance evaluations of these generators demonstrate their ability to generate pseudo-random number sequences that exhibit improved statistical properties efficiently. [ABSTRACT FROM AUTHOR]

Due to its outstanding efficiency and high precision, Harris hawks optimization (HHO for short) is suitable for solving the problem of visual target tracking under conditions of occlusion, deformation, rotation and in other complicated tracking scenes. A visual target tracker based on HHO is proposed in this study. To further promote the efficiency and stability of the standard HHO method and reduce the probability of the iteration falling into local optima and algorithm prematurity, in this study we propose an improved method called Super-HHO and apply it to visual target tracking. Compared with standard HHO, Super-HHO is superior due to its parameter optimization and updating strategy. We first optimize the random parameters of HHO via chaos theory to avoid frequent repeated exploration of the feasible region. Next, we design a nonlinear renewal strategy for the escape energy, which solves the problem in traditional HHO in which the fixed escape energy cannot accurately reflect the real hunting process of Harris hawks. Mutation strategies are also designed for the locations of the prey and the hunters to improve the optimization ability and eliminate the risk of falling into local extremes. In addition, a frame scale adjustment method model is developed to address the the issue in which the use of a size-fixed tracking frame makes it easy to include too many invalid features, which reduces the efficiency. Finally, we use the OTB2015, and VOT2018 tracking evaluation datasets, which contain hundreds of visual sequences and more than 10 complex interference scenes to conduct a qualitative analysis, a quantitative analysis and a statistical analysis of ours and other classic trackers, and to effectively test and compare the success ratio, precision and stability of each tracker. The proposed method was also compared with other classic trackers using classic large-scale benchmarks such as LaSOT and TrackingNet. Experimental data prove that ours performs well in terms of robustness, precision and efficiency. [Display omitted] • The optimized random parameters based on a chaotic tent map. • The nonlinear renewal strategy for the escape energy. • The position mutation strategy of the prey using a sine function. • An adaptive adjustment method for the tracking frame. [ABSTRACT FROM AUTHOR]

Restraints along slender structures, such as pipes conveying fluid and beams, play important roles in enhancing their bending stiffness. The current work investigates the influence of the midpoint restraint on the buckled axial force and dynamic characteristics for the first time. A continuous model is introduced by treating the midpoint restraint force as a concentrated force using Dirac function. The influence of the midpoint restraint is discussed based on modal expansion, and a partitioned model is proposed for validation. The conclusion is that the continuous model has good accuracy. Based on this model, it finds out two stable buckled states changing with the midpoint stiffness. The first stable buckled state occurs under weak midpoint restraint and has a cambered shape. The zero- and double frequency components occur in symmetric modal shapes and the response is softened along the whole beam. However, in the second stable buckled state, it turns into a whole sine function shape. The zero- and double frequency components just occur in the second modal shape, and the soften phenomenon doesn't occur at the midpoint. With increasing excitation amplitude, a bubble appears on the primary resonance peak while another resonance peak emerges nearby it. This work explains the influence of midpoint stiffness on nonlinear resonance and enhances the study of the nonlinear behavior of buckled slender structures. • The model of a beam with the midpoint flexibly restraint is established. • It finds out two stable buckled states changing with the midpoint stiffness. • Influence of the midpoint flexible restraint on dynamics of double-span beams is studied. [ABSTRACT FROM AUTHOR]

• An advanced time-marching PINN method (AT-PINN) is proposed in this work. • The method is valid for structural vibration analysis in long-duration simulation. • Four key techniques are incorporated into the AT-PINN approach. • The method can offer accurate solutions with lower computational cost for dynamic analysis. Solving partial differential equations through deep learning has recently received wide attention, with physics-informed neural networks (PINNs) being successfully used and showing great potential. This study focuses on the development of an efficient PINN approach for structural vibration analysis in " long-duration " simulation that is still a technical but unresolved issue of PINN. The accuracies of the standard PINN (STD-PINN) and conventional time-marching PINN (CT-PINN) methods in solving vibration equations, especially free-vibration equations, are shown to decrease to varying degrees with the simulation time. To resolve this problem, an advanced time-marching PINN (AT-PINN) approach is proposed. This method is used to solve structural vibration problems over successive time segments by adopting four key techniques: normalization of the spatiotemporal domain in each time segment, a reactivating optimization algorithm, transfer learning and the sine activation function. To illustrate the advantages of the AT-PINN approach, numerical simulations for the forced and free vibration analysis of strings, beams and plates are performed. In addition, the vibration analysis of plates under multi-physics loads is also studied. The results show that the AT-PINN approach can provide accurate solutions with lower computational cost even in long-duration simulation. The techniques adopted are verified to effectively avoid the offset of the spatiotemporal domain, reduce the accumulative error and enhance the training efficiency. The present one overcomes the drawback of the existing PINN methods and is expected to become an effective method for solving time-dependent partial differential equations in long-duration simulation. [ABSTRACT FROM AUTHOR]

• Comprehensive model is introduced to estimate hourly resolved power load curves. • 57 real load data sets are used for calibrating for various climates and levels of development. • Key influencing factors for the power load curve shape are identified and analysed. • Extensive data are generated and accessible for further research and energy system planning. Electricity demand modelling is the central and integral issue for the planning and operation of power systems. Load projection provides important information for electricity network planning, and it is essential for the electricity system development. This work investigates the impact of specific economic, technical and climate characteristics on the shape of the electricity demand and introduces a methodology to project electricity demand in hourly resolution within a single framework for all countries. The method used is a multiple linear regression in terms of spectral analysis. 57 real load data profiles of diverse countries were decomposed into a set of sine functions to analyse the cyclical pattern of the data. Fourier coefficients contain information about frequencies and amplitudes in these sinusoids. The sum of various sine functions can be used to calibrate and project hourly electricity demand for any country with available input data for any year in the addressed period. The accuracy of proposed model function is represented in terms of R-squared error. The proposed model is flexible to be applied to different socio-economic scenarios based on alternative assumptions regarding both long-term trends as well as short-term projections. [ABSTRACT FROM AUTHOR]

Time-interleaving has been a popular choice for multi-GHz analog-to-digital converters (ADCs). Unfortunately, inherent defects such as offset, gain, timing-skew mismatches among sub-ADCs degrade overall performance seriously. At present, the method for eliminating offset and gain mismatch is fairly straightforward, however, calibration for timing-skew is still in a state of exploration. An efficient digital-mixing-based method to estimate timing skew in time-interleaved analog-to-digital converters (TI-ADCs) is proposed and verified. The timing skew can be estimated by digital-mixing and inverse sine function processing. The proposed method can greatly alleviate the effects of phase ambiguity. It is also insensitive to offset mismatch. A four-channel TI-ADC behavioral system of 10-bits 2 GS/s has been simulated to verify the performance of the method. Simulation results demonstrate that the method greatly improves the TI-ADC′s performance and is effective up to the severe timing skew mismatch. [ABSTRACT FROM AUTHOR]

Silicate glasses have no long range order and a short range order similar to their crystalline counter-parts. Therefore, their key structurally distinct information lies in the medium range. The statistics of rings, describing the 3D connection of tetrahedral units, is a crucial indicator of the medium range structure. However, no experimental techniques have succeeded in quantifying the ring statistics. Here we show a heuristic method to extract ring structure information from the first sharp diffraction peak (FSDP) of the neutron scattering structure factor. We demonstrate that, for 81 commercially important silicate glasses, the real space representation I(r)-s of their FSDPs, can be consistently represented by a sum of three compressed exponentially decaying sine functions with three fixed periodicities. We propose these three characteristic periodicities are commensurate with the statistically averaged sizes of ≤4- , 5- and ≥6 -membered rings, with their relative amplitudes reflecting the relative fractions. Our results are validated using Molecular Dynamics simulated glass structures. The derived ring structure information provides an insight into the structural origin of the anomaly in hardness of aluminosilicate glasses. • Ring structure information is carried by FSDP. • Real space representation I(r) of FSDP can be represented by a sum of three sine functions with fixed periodicities. • Three characteristic periodicities in the FSDP correspond to sizes of ≤4, 5 & ≥6-membered rings. • RingFSDP method is developed to extract ring structure information from FSDP. [ABSTRACT FROM AUTHOR]

Highlights • Analysis of sinusoidal model for handwriting generation. • Proposal of multi-component sinusoidal model for representing online handwriting. • Exploration of sinusoidal parameters as features in handwriting recognition system. • Proposal is evaluated for signature reconstruction and its synthetic variation. Abstract The representation of online handwriting is an important aspect of handwriting applications, which involves the extraction of various spatial and temporal attributes for analysis and individualization of handwritten patterns. In this work, a model based representation is proposed for online handwriting using a multi-component sinusoidal model. The method extracts sinusoidal parameters from handwriting by modeling its horizontal and vertical velocities between each successive pair of zero crossing points with a half period of the sine function. Thus, each velocity profile is represented by the sinusoidal oscillations whose parameters are modulated at the zero-crossing points. The use of multiple oscillations to model the velocities results in a better representation of the complex trajectories. The parameters of the proposed model are computed iteratively from its residual signals. We hypothesize that the analysis of the sinusoidal components and its parameters may provide added dynamic information about the handwriting. The efficacy of the proposal is demonstrated for online signature representation and synthetic variability generation by modifying the extracted parameters. Further, the proposed feature set is also employed for online handwriting recognition task. It is observed that the multi-component sinusoidal representation combined with existing point-based features provide an improvement in the recognition performance. [ABSTRACT FROM AUTHOR]

Abstract Trajectory tracking control of a quadcopter drone is a challenging work due to highly-nonlinear dynamics of the system, coupled with uncertainties in the flight environment (e.g. unpredictable wind gusts, measurement noise, modelling errors, etc). This paper addresses the aforementioned research challenges by proposing evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang-type fuzzy logic controller (FLC). We consider three major state-of-the-art optimisation algorithms, namely, Genetic Algorithm, Particle Swarm Optimisation, and Artificial Bee Colony to facilitate automatic tuning. The effectiveness of the proposed control schemes is tested and compared under several different flight conditions, such as, constant, varying step and sine functions. The results show that the ABC-FLC outperforms the GA-FLC and PSO-FLC in terms of minimising the settling time in the absence of overshoots. Highlights • Evolutionary algorithms-based self-tuning for first-order Takagi–Sugeno–Kang fuzzy logic autopilots are proposed. The proposed controllers are applied for position control of quadcopter drone, which is a multi-input multi-output (MIMO) system, with highly non-linear rigid body dynamics and severe cross-couplings. • Given constant and varying step functions, ABC-FLC has slower rise and peak time, and faster settling time in the absence of overshoots. On the contrary, GA-FLC with a mutation rate of 0.1 has faster rise time and bigger overshoots compared to GA-FLC with a mutation rate of 0.4 and PSO-FLC. • Given constant and varying step functions, all proposed controllers yield ununiformity in the shape and position of the fuzzy sets although there are overlap zones among several tuned fuzzy sets. However, under sine function, ABC-FLC yields uniformity in the shape of fuzzy sets, and the overlap zone is almost fifty percent. • Overall, all three-dimensional control surfaces appear to have the same tendency, which is nonlinear and the volume of ABC-FLC is smaller than the other controllers. However, under constant and varying step functions, the shapes of control surface of ABC-FLC has less distinctive small bumps in the plane of e and Δ e , while under sine function, ABC-FLC has distinctive smaller bumps compared to other controllers. Graphical abstract [ABSTRACT FROM AUTHOR]

A constrained squared sine derived adaptive (CSSDA) algorithm is proposed in this paper, which provides better steady-state behavior than existing algorithms in impulsive noise environments. The devised CSSDA works by constructing a squared sine function as the constrained cost function in solving the constrained adaptive filtering problem. Theoretical analysis of the CSSDA is presented and compared with the simulations. The simulation results show that the theoretical analysis agrees well with the simulations, helping to verify the effectiveness and correctness of the analysis. Also, the performance of the CSSDA is superior to the recent popular constraint adaptive algorithms for system identifications in a variety of environments with non-Gaussian impulsive noises. [ABSTRACT FROM AUTHOR]

• Radiation asymmetry calculates the radiation information of four positions, each of which is about 90° apart, covering the whole tokamak. • There are more pre-existing locking mode phases in the experiment. • Two disruption mitigation systems, MGI and SPI, were used in the experiment and compared with each other. • The possible explanation of the effect of pre-existing locking mode phase on radiation asymmetry is found and verified from the perspective of radiation energy. Plasma disruption in the tokamak is a huge threat to the safe operation of ITER, which will cause serious damage to the tokamak device. At present, massive impurity injection is the main disruption mitigation method, include massive gas injection (MGI) and shattered pellet injection (SPI) which can dissipate most of the internal thermal energy via radiation. However, radiation asymmetry will still cause damage to the device when it results the concentration of radiation in a small area. It has been observed on many devices that n = 1 mode has a strong influence on radiation asymmetry. In this paper, the experimental results of radiation asymmetry on J-TEXT tokamak have been introduced, and there is a close to sine function relationship between toroidal peaking factor (TPF) and the phase of 2/1 tearing mode. It is found that when the injection position is aligned with the X-point of the pre-existing magnetic island, TPF has the maximum value, and while the injection position is between the X-point and the O-point, TPF has the minimum value. This will be helpful for the study of disruption mitigation of future large devices. [ABSTRACT FROM AUTHOR]