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Recently, symplectic totally isotropic subspace inclusion graph ℐ n () on a 2 ν -dimensional symplectic space was introduced in [J. He, S. Zhang and G. Zhang, Symplectic totally isotropic subspace inclusion graph, Linear Multilinear Algebra (2022), doi:10.1080/03081087.2022.2112016], which is a graph whose vertices are all totally isotropic subspaces of and two distinct vertices are adjacent if and only if one is contained in the other. In that paper, the authors studied the diameter, girth, clique number and chromatic number of ℐ n () and also studied some other properties of ℐ n (). In this paper, we determine the automorphism group of ℐ n (). [ABSTRACT FROM AUTHOR]

This paper proposes an image compression algorithm based on Swin Transformer and residual network (STRN), aiming to reduce blurring and distortions in traditionally compressed images. The algorithm utilizes a dual-channel mechanism to remove artifacts from the image, which takes advantage of the complementary features of the transform and residual networks. The Swin Transformer networks address the issue of long-range dependency, leading to an enhanced and improved reconstructed image quality. The residual network is an effective network that mitigates gradient loss and recovers image details during the image compression process. The paper demonstrates that image compression can be achieved by training a convolutional network based on a transformer and residuals network, which significantly reduces artifacts and provides better reconstructed image quality compared to previously used and current mainstream methods based on traditional convolutional neural networks. The proposed approach can remove blocking artifacts by subtracting estimated artifacts from the input image, while still preserving most of the original details. Therefore, our proposed method is highly effective in improving image quality and reducing visual artifacts caused by traditional compression methods. Moreover, this method is useful for enhancing image transmission and storage efficiency in various computer vision systems that employ digital visual codecs. [ABSTRACT FROM AUTHOR]

Welding, despite being the most efficient joining process, has significant negative impact on the environment and worker health. This paper presents the environmental impact of various conventional welding processes and sustainability of Friction Stir Welding–a Green technology which requires no filler material, no shielding gas, no flux and involves no gas emissions. It is an energy-efficient process that saves significant amount of power and energy when compared to conventional welding processes. The defect-free welds produced have better mechanical and metallurgical properties than other processes. The higher temperature distributed due to the friction heat is established as the source of weld failure. Cooling the weld joint controls the heat conduction thus further enhancing the weld properties. This review paper explores the different cooling techniques employed for cooling the joint during Friction Stir Welding process. The changes in temperature distribution and microstructure caused by the cooling applied and the consequential transformation in material flow, tensile strength, hardness, corrosion, wear, residual stress are discussed. The changes in weld properties obtained by using different cooling media on different materials are reviewed. [ABSTRACT FROM AUTHOR]

Numerous papers have studied the problem of determining upper bounds for the number of integer points on elliptic curves of the form y 2 = x 3 − m x , and quartic curves of the form X 2 − d Y 4 = k. Bounds for the number of integer solutions to such quartic equations typically depend on both of the coefficients d , k. The purpose of this paper is to examine more closely how the number of integer points on such quartic curves seems to depend almost entirely on the number of prime factors of k. This is done by focusing in on the problem of bounding the number of squares in certain recurrence sequences. In particular, using some arithmetic on elliptic curves, it is proved that infinitely many such sequences can have four squares, but that a fifth square remains elusive after extensive computation, suggesting that an absolute bound for the number of squares is more than likely, and also suggesting that the above assertion regarding the dependence on the number of prime factors of k. [ABSTRACT FROM AUTHOR]

In this paper, we study Scholte's waves propagating along the interface of micropolar solid and liquid media, considering the effect of initial stress and impedance boundary surface. We obtained the phase velocities of basic elastic waves in both media and used them to derive the secular equation of Scholte's wave. We found that the initial stress in the solid material does not affect the speed of basic longitudinal waves. After solving the secular equation, we obtained the speed of Scholte's wave and the corresponding attenuation. We observed that Scholte's surface wave obtained at the micropolar solid–liquid interface is inhomogeneous and dispersive. We investigated the effects of initial stress, Tiersten's impedance boundary condition, and micropolar solid parameters on Scholte's wave and basic shear waves. Numerically, these effects are computed for a particular model using the appropriate value of the parameters, and the results are illustrated graphically. This paper also contributes to not only understanding the behavior of Scholte's waves in micropolar solid–liquid interfaces but also the factors that affect the propagation. [ABSTRACT FROM AUTHOR]

The energy content of ground motion records obtained during the 2019 M6.4, M7.1 Ridgecrest, California earthquakes which occurred closely in space is analyzed in this paper. Wavelet transform as a powerful tool, that is used to evaluate the energy content characteristics including cumulative energy profiles and the total energy for different components of records, was obtained at three stations named China Lake Christmas Canyon, China Lake and Tower 2. Due to the fact that the energy-based parameters could be well correlated with damage distribution, this path has been taken into consideration in this research work with the light of the powerful wavelet tool. For this purpose, the discrete wavelet transform is efficiently used for multi-resolution decomposition of acceleration time history of ground motions and a new framework is presented to estimate the energy level of earthquake sequences. Finally, the pseudo-period corresponding to each corresponding level is introduced and the distribution of energy vs. the pseudo-period is discussed. The results of this paper provide a platform to compare the energy levels of different earthquake sequences in different periods. Analysis showed that wide-band energy distribution could be identified at China Lake Christmas Canyon and Tower 2 stations. [ABSTRACT FROM AUTHOR]

The laws governing life should be as simple as possible; however, theoretical investigations into allometric laws have become increasingly complex, with the long-standing debate over the scaling exponent in allometric laws persisting. This paper re-examines the same biological phenomenon using two different scales. On a macroscopic scale, a cell surface appears smooth, but on a smaller scale, it exhibits a fractal-like porous structure. To elaborate, a few examples are given. Employing the two-scale fractal theory, we theoretically predict and experimentally verify the scaling exponent values for basal, active, and maximal metabolic rates. This paper concludes that Rubner's 2/3 law and Kleiber's 3/4 law are two facets of the same truth, manifested across different scale approximations. [ABSTRACT FROM AUTHOR]

Scientists worldwide have always been interested in the study of networks that resemble trees and porous materials. Therefore, based on fractal theory, this paper systematically studies the heat transfer problem of the damaged tree-like networks under different saturations in multi-medium composite materials and derives their dimensionless thermal conductivity (DTC). The research has shown that the dimensionless thermal conductivity (DTC) decreases with an increase in damaged channels. The saturation significantly impacts the heat transfer of the damaged tree networks, and it exhibits linear changes with the ratio of the gas phase, liquid phase, and solid phase. Additionally, it can be observed that the fractal dimension of length distribution and diameter distribution, bifurcation numbers, bifurcation series, porosity, and other parameters in the networks are related to thermal conductivity. The comparison between the data derived from this model and experimental data shows that the proposed model can effectively deepen our understanding of the heat transfer mechanism of the damaged networks in composite materials under different saturation levels. Additionally, the model in this paper does not have an empirical constant, which avoids the influence of potential factors. [ABSTRACT FROM AUTHOR]

Data-driven learning of the fractional discrete-time unified system is studied in this paper. A neural network method is suggested in the parameter estimation of fractional discrete-time chaotic systems. An optimization problem is obtained and the famous Adam algorithm is employed to train the neural network's weights and parameters. The parameter estimation result is compared with that of the stepwise response sensitivity approach (SRSA). This paper provides a high accuracy method for parameter inverse problems. The method also can be applied to data-driven learning of other fractional chaotic systems. [ABSTRACT FROM AUTHOR]

The Kozeny–Carman (KC) equation is a well-known semi-empirical formula, which is used to calculate the permeability of porous media in the seepage field. The KC constant is an empirical constant in the KC equation. In this paper, based on the fractal theory, the Fractal-Monte Carlo technique is used to simulate the KC constant of the roughened fibrous porous media (RFPM) with micro-scale effects. There is no empirical constants in the proposed model, and each parameter has its physical meaning. The KC constant model of RFPM can be expressed as a function of structural parameters, including relative roughness (), porosity (ϕ), pore area fractal dimension ( D f ), tortuosity fractal dimension ( D T ), capillary diameter (λ) and Knudsen number (Kn). The result shows that the KC constant increases with increases in ϕ , , D f and D T . On the contrary, with increases in λ and Kn , the KC constant decreases. In addition, the KC constant model constructed in the paper agrees well with the existing experimental data and the model. According to the proposed Fractal-Monte Carlo technique, it is possible to better clarify the transmission physical mechanism in RFPM with micro-scale effects. [ABSTRACT FROM AUTHOR]

Researchers and authorities have become increasingly interested in how the COVID-19 pandemic has profoundly impacted the real economy and financial markets around the world since its outbreak in late 2019. Applying the methods of multifractal analysis, this paper investigates the fluctuation characteristics and market risks of Chinese industry and stock markets under the COVID-19 pandemic, and reveals the whole dynamics of industry and stock markets from the perspective of system theory. The empirical results show that the multifractal strength of the industry market has significantly increased during the pandemic with elevated systematic risk, while the situation is different for the stock market. Specifically, the Hurst surfaces generated using the multiscale technique intuitively visualize the dynamical behaviors of the systematic fluctuation of the Chinese industry and stock markets at various scales under the COVID-19 pandemic. Furthermore, it is found that the sources of multifractality of the two types of markets include long-range correlation and fat-tailed distribution, with the contribution of fat-tailed distribution being greater. The chi-square test is promoted in this paper to measure the contribution of the internal components of the multivariate system to the multifractality sources of the whole system, revealing that the real estate industry has a greater impact on the multifractality of the whole industry system, while the Shanghai Composite Index has a stronger influence on the whole stock system. [ABSTRACT FROM AUTHOR]

In this paper, we consider a simple, purely stochastic model characterized by two conserved quantities (mass density a and energy density h) which is known to display a condensation transition when h > 2 a 2 : in the localized phase a single site hosts a finite fraction of the whole energy. Its equilibrium properties in the thermodynamic limit are known and in a recent paper [G. Gotti, S. Iubini and P. Politi, Finite-size localization scenarios in condensation transitions, Phys. Rev. E 103 (2021) 052133] we studied the transition for finite systems. Here, we analyze finite-size effects on the energy distribution and on the relaxation dynamics, showing that extremely large systems should be studied in order to observe the asymptotic distribution and even larger systems should be simulated in order to observe the expected relaxation dynamics. [ABSTRACT FROM AUTHOR]

In this paper, a portable low-power low dropout regulator (LDO) with no output capacitance has been designed in 0.18- μ m CMOS technology. In this paper, a PMOS power transistor is used and combined with an overshoot and undershoot suppression circuit to improve the transient response of the circuit. A modified Class-AB operational transconductance amplifier (OTA) is used to reduce power consumption and act as an error amplifier to eliminate low frequency poles. At the same time, a super source follower and a Miller capacitor are used for frequency compensation of the system. Finally, the system can provide a stable voltage for a load step from 0.2 to 25 mA in 500 ns edge-time for 140 pF-load capacitance. The test results show that the circuit achieves a good voltage regulation rate and high transient response. [ABSTRACT FROM AUTHOR]

Our paper is a continuation of a previous work referenced as [Katsanikas & Wiggins, 2024b]. In this new paper, we present a second method for computing three-dimensional generating surfaces in Hamiltonian systems with three degrees of freedom. These 3D generating surfaces are distinct from the Normally Hyperbolic Invariant Manifold (NHIM) and have the unique property of producing dividing surfaces with no-recrossing characteristics, as explained in our previous work [Katsanikas & Wiggins, 2024b]. This second method for computing 3D generating surfaces is valuable, especially in cases where the first method is unable to achieve the desired results. This research aims to provide alternative techniques and solutions for addressing specific challenges in Hamiltonian systems with three degrees of freedom and improving the accuracy and reliability of generating surfaces. This research may find applications in the broader field of dynamical systems and attract the attention of researchers and scholars interested in these areas. [ABSTRACT FROM AUTHOR]

In our earlier research (see [Katsanikas & Wiggins, 2021a, 2021b, 2023a, 2023b, 2023c]), we developed two methods for creating dividing surfaces, either based on periodic orbits or two-dimensional generating surfaces. These methods were specifically designed for Hamiltonian systems with three or more degrees of freedom. Our prior work extended these dividing surfaces to more complex structures such as tori or cylinders, all within the energy surface of the Hamiltonian system. In this paper, we introduce a new method for constructing dividing surfaces. This method differs from our previous work in that it is based on 3D surfaces or geometrical objects, rather than periodic orbits or 2D generating surfaces (see [Katsanikas & Wiggins, 2023a]). To explain and showcase the new method and to present the structure of these 3D surfaces, the paper provides examples involving Hamiltonian systems with three degrees of freedom. These examples cover both uncoupled and coupled cases of a quadratic normal form Hamiltonian system. Our current paper is the first in a series of two papers on this subject. This research is likely to be of interest to scholars and researchers in the field of Hamiltonian systems and dynamical systems, as it introduces innovative approaches to constructing dividing surfaces and exploring their applications. [ABSTRACT FROM AUTHOR]

This study contributes knowledge on the detection of depression through handwriting/drawing features, to identify quantitative and noninvasive indicators of the disorder for implementing algorithms for its automatic detection. For this purpose, an original online approach was adopted to provide a dynamic evaluation of handwriting/drawing performance of healthy participants with no history of any psychiatric disorders (n = 2 8), and patients with a clinical diagnosis of depression (n = 2 7). Both groups were asked to complete seven tasks requiring either the writing or drawing on a paper while five handwriting/drawing features' categories (i.e. pressure on the paper, time, ductus, space among characters, and pen inclination) were recorded by using a digitalized tablet. The collected records were statistically analyzed. Results showed that, except for pressure, all the considered features, successfully discriminate between depressed and nondepressed subjects. In addition, it was observed that depression affects different writing/drawing functionalities. These findings suggest the adoption of writing/drawing tasks in the clinical practice as tools to support the current depression detection methods. This would have important repercussions on reducing the diagnostic times and treatment formulation. [ABSTRACT FROM AUTHOR]

In recent years, plasma surface treatment has seen tremendous breakthroughs, which have revolutionized both the field of material engineering and the procedures used to modify surfaces. This paper offers a thorough discussion of the most recent discoveries and developments that have been made in the field of plasma surface treatment. It investigates cutting-edge technologies, innovative plasma sources and sophisticated process control approaches, all of which have greatly improved the effectiveness and adaptability of the therapy. The research also explores how these breakthroughs solve earlier obstacles, provide new opportunities for industrial applications and pave the way for future developments in the area. Moreover, the study looks at how these advancements open the door for future developments in the sector. This study gives useful insights into the emerging landscape of plasma surface treatment and its potential influence on a variety of sectors by summarizing and assessing current research. These insights may be found in this paper. [ABSTRACT FROM AUTHOR]

Dye-Sensitized Solar Cells (DSSC) and optical fiber-based-sensors sensitized with organic dyes play a fundamental role in modern technology, particularly in the family of photovoltaic power generation devices and measurement of chemical variables. DSSC is low-cost, highly efficient, and easy to manufacture. Therefore, they are a suitable option for many engineering applications. This paper deals with natural pigment extraction (spirulina, carrots (beta-carotene), and beetroot) at different milling and temperature conditions. Nanoparticles were fabricated using an SPEX mill and a planetary ball mill. The particle size distribution, absorbance (UV-Vis), and powder morphology were obtained using Field Emission Scanning Electron Microscopy (FESEM). Herein, the optical characterization of modified TiO2 powder at different temperatures and milling conditions is performed. Results indicate that each natural dye is sensitive to operational temperature. In addition, the absorbance of the pigments is affected by milling conditions and particle size distribution. During SEM characterization, rounded particles were observed in the starting materials with average sizes of more than 15 microns in diameter until they were reduced to nanometer ranges close to 100 using SPEX milling. The observed absorption spectra range from 400nm to 642 nm for spirulina. Moreover, the experimental results show that the intensity of the absorption peaks is affected by the temperature, which indicates a degradation of the dye. Therefore, different combinations of natural dyes will be feasible to improve the wide range of light absorption of the visible spectra and stability of DSSCs and optical fiber-based sensors. This paper focuses on the manufacturing route of powder and liquid state dyes extracted from natural sources (i.e., spirulina, cacti) and their performance under controlled conditions. Likewise, this paper addresses the absorbance data of the dyes and the processing of nanometric-size powder through mechanical milling. This study will provide information that could lead to future research on solid-state natural dye-sensitized solar cells and natural dye-sensitized optical sensors. [ABSTRACT FROM AUTHOR]

Gait analysis is one of the highly focused research areas inviting attention of academicians and practitioners. Being a prominent analytical tool to detect the abnormalities and onset of musculoskeletal disorders (MSDs), abundant literature is published in this area. Though a large number of gait analysis methods (GMSs) for motion analysis are available in the literature, very few studies are available to showcase the comparative analysis and applicability of various techniques. This study seeks to provide useful recapitulation of various gait measurement methods and interrogates the relative prevalence of various GMS based on the applications by various researchers. The paper summarizes the findings of a systematic review on gait measurement methods carried out using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. Fifty-three research papers published during 1998–2021 were included for critical review. The categories of gait measure ment systems were further compared based on certain criteria such as portability, cost, and accuracy, and ranked using analytic hierarchy process (AHP). The review helped to encapsulate various aspects of gait analysis, especially, the types of tasks, types of gait parameters (GPs), and the gait measurement systems adopted. The study provides suitable mappings between these factors and offers comparison between the gait measurement systems using a specially devised capability index. It further provides useful guidelines for selecting the gait systems based on the criteria. [ABSTRACT FROM AUTHOR]

The spacecraft’s (SC) electric power system (EPS) is one of the vital systems. It could limit the SC capabilities or reduce the SC lifetime. The EPS should provide the required energy for the SC loads to perform their tasks successfully until End-of-Life (EOL). Furthermore, the EPS should guarantee the power needed to execute the customer requirements under all degradations (battery degradation, radiation, etc.) and other external factors (orbital degradation). The SC’s primary power generation sources are solar arrays and storage batteries. This paper investigates a SC with a Nickel Hydrogen Storage Battery (NHSB) consisting of 17 cells: 4 measured cells and 13 unmeasured cells. In this paper, two distinct objectives have been achieved: The first objective is formulating a control algorithm for battery testing and reconditioning (CABTR). This algorithm is applied to an NHSB in an orbital setting mode, rectifying the memory effect in the NHSB and restoring the battery’s total capacity. The second objective constructs an environment that effectively simulates the dynamic behavior of the battery during in-orbit operations. This simulated environment calculates crucial parameters such as the state of charging (SOC), battery temperature, voltage, and pressure of the NHSB during reconditioning. This computational analysis assures the robustness of the CABTR, and confirms the optimal battery performance. Subsequent validation encompasses ground tests and real-time telemetry data, which affirms the battery’s dynamic behavior and the developed algorithms’ efficacy. [ABSTRACT FROM AUTHOR]

This paper presents in-depth research and analysis of the optimization method of tourist traffic routes using the adaptive cuckoo algorithm. The traditional cuckoo algorithm has the disadvantages of slow convergence speed and easy falling into local extremes. This paper proposes the iterative adaptive CHSACS algorithm based on iteration. Based on the original CS algorithm, a dynamic adaptive step control amount and a segmented weighted position update formula are introduced to give a class of improved CS algorithms to coordinate the problem of local search and global search of the CS algorithm and speed up the convergence speed at the later stage. To address the problem that the out-of-bounds nests interfere with the convergence of the algorithm, a memory strategy is introduced to relocate the out-of-bounds nests in the search space to improve the stability of the algorithm. Experiments are conducted on the iterative adaptive-based conductive CHSACS algorithm with test function sets. Compared with the original CS algorithm, and ACO algorithm, the CHSACS algorithm has faster convergence, higher search accuracy and a better ability to avoid local optima when dealing with continuous function optimization problems. For the dynamic travel path problem of travel time optimization, the road travel time and sightseeing time of different periods are predicted based on historical data, and the most traveled distance of travel time between two sights is dynamically searched by using the search mechanism based on time windows. The iterative adaptive CHSACS algorithm is used to continuously find the tourist traffic route with the shortest travel time travel path from all combinations of tour sequences. This paper combines the characteristics of bus vehicle scheduling itself, takes into account the interests of both bus companies and tourists, establishes a bus vehicle scheduling model with the departure interval as the independent variable, introduces this hybrid cuckoo algorithm into bus scheduling, verifies the scientific and feasibility of the algorithm through examples, and provides a new idea for solving bus scheduling optimization problems. [ABSTRACT FROM AUTHOR]

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

This paper proposes a method that considers the bus net Fuzzy control strategy quickly and accurately for power. Due to the intermittent and fluctuating characteristics of distributed generation units in microgrid, the power quality will be reduced and the normal operation of microgrid will be affected. In this paper, empirical modal analysis fused with convolutional neural network algorithm is applied to the power control strategy of microgrid energy storage system to improve the economy and reliability of microgrid. The design process of the controller is analyzed, and fuzzy rules and membership functions are designed according to the effect requirements of the controller. Then, the center of gravity method is used to obtain the output power of the energy storage system. The simulation results show that the configuration scheme based on the optimization of the hybrid energy storage system in the whole life cycle in this paper not only ensures the stability of the energy storage system but also effectively avoids power fluctuations and stabilizes the peak shaving and valley filling rate. While improving the economy of the energy storage system, it also increases the service life of the energy storage system. [ABSTRACT FROM AUTHOR]

Active suspension technique normally provides the best vibration mitigation performance at the cost of high-energy consumption. In contrast, the energy consumption of a semi-active suspension is normally much smaller than that of an active suspension, whereas the control performance is compromised as well. Observing the core issue that caused such a phenomenon is that the existing semi-active control force, unlike the active control force, shall always oppose to the relative motion of the actuator (i.e. clipping phenomenon). This paper subsequently proposes a novel semi-active vehicle suspension system that incorporates a passive negative stiffness (NS) spring and a semi-active damper (SD) to realize uncompromised active control force while consuming energy of a typical semi-active suspension system. In specific, the proposed system allows for decomposition of the target active control force and tracked via the collaboration of the NS and SD components. Herein, the NS element is capable of releasing the store potential energy that subsequently eases the aforementioned clipping phenomenon of a traditional semi-active suspension. In this paper, besides the relevant clarifications on the system topology and working mechanism, its feasibility and performance enhancement are also validated via systematic numerical simulations of a vehicle suspension; and the results indicate, for the first time, that the proposed semi-active suspension can fully track the active control force and subsequently achieve unprecedented control performance comparable to an active controller. [ABSTRACT FROM AUTHOR]

The Π -shaped open section girder is a very common section, which has been widely used in the construction of modern bridges. However, compared with a streamlined box girder, it is easy to cause vortex-induced vibration (VIV) in bridges due to its bluff section shape and poor aerodynamic stability. In this paper, a typical open section with a height–width ratio of 10:1 is taken as the research object. Through wind tunnel tests and numerical simulation technology, the time-history data of VIV of the Π -shaped section are obtained. By selecting three different neural network models to establish the relationship between external parameters (wind attack angle, wind speed, turbulence) and structural response parameters (the onset wind speed of VIV, the length of the lock-in interval). After comparing different machine learning methods, the most suitable machine learning model is selected to predict the VIV response characteristics of the main girder. The research results show that the typical open section has a serious VIV phenomenon. The BP neural network model has the best performance in predicting the VIV performance of the open section in terms of calculation speed, prediction accuracy and solving nonlinear adaptability. At the same time, it is found that different characteristic parameters of the incoming wind field have different influences on the prediction results of the VIV response. Among them, the wind attack angle is the key parameter that affects the onset wind speed of VIV, but the turbulence intensity has a greater impact on the maximum amplitude of VIV. At the same time, the optimized BP model can predict the characteristic parameters of VIV response under unknown wind fields. This paper provides a new idea to study the effect of different incoming flow parameters on the performance of section VIV and further improves people's understanding of VIV system. [ABSTRACT FROM AUTHOR]

A linear response relation between metric and fluid perturbations driven by a background internal noise source is used as a framework for addressing stochastic effects in order to establish a mesoscopic theory for dense matter relativistic stars. In this paper, nonradial polar perturbations are worked out, which are important from the point of view of detection in future. We present qualitative first results in this paper, numerical estimates have to await further progress in theoretical modeling. These perturbations carry a new generalized stochastic nature and are obtained as solutions of the classical Einstein–Langevin equation which has been recently proposed. The significance of these stochastic nonradial polar perturbations lies at probing the intermediate sub-hydro scales inside the dense fluid. This formalism extends towards a mesoscopic scale nonequilibrium/near-equilibrium statistical mechanics study for relativistic star interiors. The generalized stochastic noise originates as the remnant of collapse mechanism and dynamical effects at intermediate scales in isolated massive stars which drives these polar perturbations. More specifically, for cold dense matter which is our focus in this paper, it is either the interplay between the degeneracy pressure and the gravitational pressure, or the multiscale phenomena like turbulences giving rise to the seeds of stochastic effects in the gravitating body. Characterizing such stochastic effects can lead to an improved understanding of the nature of dense matter and help to probe multiple scales which are yet untouched. [ABSTRACT FROM AUTHOR]

This paper focuses on the study of Blasius–Rayleigh–Stokes nanofluid dusty flow with generalized Fourier and Fick laws under the influence of a transitive magnetic field. The Blasius–Rayleigh–Stokes nanofluid dusty flow has significant applications in various fields, including the design of heat exchangers, microfluidic devices and industrial processes involving the transportation of suspended particles in fluids. This paper considers the impact of melting heat transfer at the surface boundary. By employing relevant transformations, the mathematical model is transformed into a system of self-similar equations. The solution to this set of highly nonlinear equations is obtained using the bvp4c numerical method in combination with the response surface methodology (RSM) statistical approach. The results are presented through graphical illustrations and numerically calculated tabulated values. It is observed that the fitted model for the skin friction coefficient Rex1∕2Cf optimal. Moreover, the model parameters Pr, Q, βv, M and L are linearly and quadratically significant in explaining the variation presented for skin friction coefficient Rex1∕2Cf. This indicates the response skin friction coefficient Rex1∕2Cf is minimized to 0.005 at Pr=2.2027, Q=0.252, βv=0.22, M=0.217 and L=0.252 and is maximized to 1.387 at Pr=2.066, Q=0.254, βv=0.021, M=0.056 and L=0.252. [ABSTRACT FROM AUTHOR]

Szemerédi’s Regularity Lemma (SRL) is a crucial tool in the analysis of large graphs, having made significant contributions in the proof of some sensational results in mathematics. Traditional methods for studying proteins in Protein–Protein Interaction (PPI) networks typically only extract the first-order or second-order neighbor information of proteins, ignoring the potential third-order or higher-order neighbor information between proteins, which may hide certain relationships between proteins. To explore more in-depth insights for PPI networks, we take into account the fourth-order neighbor information of proteins and reconstruct the network in this paper, naming it the weighted dense PPI network. We then partition it using SRL, which primarily utilizes the structural information and corresponds to a unique partition of the original network. Bioinformatics analyses such as those for pathway enrichment analysis and multiple sequence alignment show that our method can classify interacting protein pairs, grouping proteins with functional association, disease association, and sequence similarity together. Overall, this paper has three essential contributions: (1) we present a new model to overcome the astronomically large demand of vertices in applying SRL, and achieve protein classification; (2) we reconstruct a weighted dense PPI network which can make SRL work and mine potential interactions more efficiently; and (3) proteins in the same class partitioned by our method not only have sequence similarity, but also have functional associations. [ABSTRACT FROM AUTHOR]

In this paper, we propose a verifiable multi-party secret sharing protocol based on an entanglement swapping theorem of GHZ states. In our protocol, all receivers only need to perform simple GHZ measurements to recover the secret. The quantum-safe system proposed in this paper is feasible with current quantum technologies, and we show that it is secure against both external and internal attacks. In addition, the secret sender can verify whether the secret is correctly recovered. [ABSTRACT FROM AUTHOR]

A subset S of an integral domain is called a semidomain if the pairs (S, +) and (S∖{0},⋅) are commutative and cancellative semigroups with identities. The multiplication of S extends to the group of differences 풢(S), turning 풢(S) into an integral domain. In this paper, we study the arithmetic of semisubtractive semidomains (i.e. semidomains S for which either s ∈ S or − s ∈ S for every s ∈풢(S)). Specifically, we provide necessary and sufficient conditions for a semisubtractive semidomain to be atomic, to satisfy the ascending chain condition on principals ideals, to be a bounded factorization semidomain, and to be a finite factorization semidomain, which are subsequent relaxations of the property of having unique factorizations. In addition, we present a characterization of factorial and half-factorial semisubtractive semidomains. Throughout the paper, we present examples to provide insight into the arithmetic aspects of semisubtractive semidomains. [ABSTRACT FROM AUTHOR]

In recent years, cloud computing technologies have been developed rapidly in this computing world to provide suitable on-demand network access all over the world. A cloud service provider offers numerous types of cloud services to the user. But the most significant issue is how to attain optimal virtual machine (VM) allocation for the user and design an efficient big data storage platform thereby satisfying the requirement of both the cloud service provider and the user. Therefore, this paper presents two novel strategies for optimizing VM resource allocation and cloud storage. An optimized cloud cluster storage service is introduced in this paper using a binarization based on modified fuzzy c-means clustering (BMFCM) algorithm to overcome the negative issues caused by the repetitive nature of the big data traffic. The BMFCM algorithm utilized can be implemented transparently and can also address problems associated with massive data storage. The VM selection is optimized in the proposed work using a hybrid COOT-reverse cognitive fruit fly (RCFF) optimization algorithm. The main aim of this algorithm is to improve the massive big data traffic and storage locality. The CPU utilization, VM power, memory dimension and network bandwidth are taken as the fitness function of the hybrid COOT-RCFF algorithm. When implemented in CloudSim and Hadoop, the proposed methodology offers improvements in terms of completion time, overall energy consumption, makespan, user provider satisfaction and load ratio. The results show that the proposed methodology improves the execution time and data retrieval efficiency by up to 32% and 6.3% more than the existing techniques. [ABSTRACT FROM AUTHOR]

This paper introduces a Multi-Level Cascade Inverter (MLI) based on Enhanced Quasi-Z Source Inverter (MQZSI) to connect photovoltaic (PV) systems based on the proposed method. Usually the interface among PV power supply and load is completed with MQZS-CMLI. In this paper, the proposed control scheme is comprehensive implementation of Recalling Enhanced Recurrent Neural Network (RERNN) and Quasi-Opposite Chemical Reaction Optimization (QOCRO) named as RENCO. The main objective of proposed method is to decide the efficiency of the PV system by the maximal power extraction. Here, MQZS-CMLI's modeling design contains suitable number of components, other than their capacitors and semiconductors comply with low-voltage stress. It is improved for providing that maximum power of the PV power generation system. At first, the goal function is described according to the parameters and limitations of controller (like voltage, current, power, modulation index, so on). These parameters apply to recommend RENCO technology input. The proposed RENCO technology improves voltage distribution, power transmission, and minimizes power fluctuations while sharing power with load. The proposed MPPT-based technology ensures that the maximum power is provided to load. The proposed method adjust the duty cycle of MQZS-CMLI and reduces the modulation load. Finally, the proposed technology is executed on the MATLAB/Simulink platform, and its output efficiency is compared to existing systems under dissimilar load circumstances. [ABSTRACT FROM AUTHOR]

In this paper, we present a denoising method for path tracing using residual learning with convolutional neural networks (CNNs). Noisy artifacts in path tracing are inherited from insufficient sampling, which often generates over- or under-exposed values when integrating the limited bright or dark samples in a pixel. In this paper, we introduce a dual channel residual learning CNNs which separates the over and under-exposed signals in order to provide an efficient denoising filter for the path tracing rendering. Furthermore, we present an advanced CNN comprised of variable-sized kernels in each convolutional layer. Our CNN detects features in different scales providing an adaptive denoising filter capability which is optimal for extracting various contextual details in a complex scene. The experiments demonstrate that our method generates better visual quality than other compared approaches across various rendering effects. [ABSTRACT FROM AUTHOR]

In a previous paper, we showed theoretically that the total cross-section of the top-quark pair production by electron–positron annihilation is strongly reduced by the presence of a circularly polarized laser field. In this paper, we present the result for the case of a linearly polarized laser field. This time, the total cross-section is significantly enhanced by the laser field. [ABSTRACT FROM AUTHOR]

We consider a new coloring as follows such that a subset of V (G) needs very few colors. Let G be a graph with vertex set V (G) and a,b,i be three integers with a ≥ b ≥ i ≥ 0. S is a vertex subset of G, graph G is said to be i-weak (a : b)-choosable about set S, if the following can be satisfied: For any list assignment L = {L(v) : |L(v)| = a,v ∈ V (G)}, there is a way that each vertex v of S is assigned i colors of L(v) and each vertex u of V (G) ∖ S is assigned b colors of L(v) such that the color sets corresponding to adjacent vertices do not intersect. In this paper, we consider the i-weak (a : b)-choosable problem of 1-planar graphs without short cycles and prove our results by the discharging method. In this paper, we prove that every 1-planar graph without 3-cycles, 5-cycles and adjacent 4-cycles is i-weak (4b + i : b)-choosable about set S = {v : v ∈ V (G),d(v) = 4}. [ABSTRACT FROM AUTHOR]

In this paper, we study the pulsating traveling fronts for a spatially discrete periodic reaction–diffusion system with a quiescent stage. It is known that there exists a critical number c∗ > 0 (called minimal wave speed) such that a pulsating traveling front exists if and only if its speed is above c∗. In this paper, we derive a uniqueness theorem for supercritical pulsating traveling fronts. Further, we show that all supercritical pulsating traveling fronts are exponentially stable. [ABSTRACT FROM AUTHOR]

Along with the implementation of reform and opening up in the 1970s, China’s economy entered a period of rapid development. During this period, the economic structure of our country has gradually changed from the primary industry to the secondary industry. Even today, the industrial industry still plays a very important role in the economic structure of our country. The industrial chain such as the real estate industry, chemical industry, and manufacturing industry is still the main industrial structure that promotes our economic growth for a long time. However, as the energy of industrial development is obtained by massive consumption of fossil fuels, the economic development brought by industrial development is behind the serious environmental pollution problem. We gradually realize that economic development cannot be based on environmental damage, but although our country has been committed to reversing the situation of environmental sacrifice for economic development at the beginning of the discovery of problems, the effect is very little. Therefore, how to maximize environmental protection under the current economic structure is the issue to be discussed in this paper. This paper mainly summarizes relevant experience through domestic and foreign literature, and clarifies the macro relationship between environmental governance and economic structure, and then discusses it from three dimensions: technological innovation, consumer demand, and international trade. The final results show that different types of environmental regulations have different effects on industrial structure, and the effectiveness of policy incentive regulation is about 23% higher than that of command control regulation. At the same time, according to the analysis results of economic structure upgrading in the Pearl River Delta region, the effect of environmental regulation on the regional industrial structure is also different, and the average effect can improve the overall industrial structure optimization rate by about 36%. [ABSTRACT FROM AUTHOR]

The authors of A. Benhissi and S. Hizem [Nonnil-Noetherian rings and SFT property, Rocky Mountain J. Math. 41 (2011) 1483–1500] proved that the primary decomposition of a nonnil-ideal of a nonnil-Noetherian ring always exists. We will show in this paper that this primary decomposition is minimal. We next prove that over any nonnil-Noetherian ring, a sub-class of modules admit a primary decomposition. In the second part of this paper, we give some properties related to ϕ-chained and ϕ-Prüfer rings. [ABSTRACT FROM AUTHOR]

In this paper, a new detection method of pin defects based on image segmentation and ResNe-50 is proposed, which realizes the defect detection of faulty pins in many aviation connectors. In this paper, a new dataset image segmentation method is used to segment many aviation connectors in a single image to generate a dataset, which reduces the tedious work of manually labeling the dataset. In the defect detection model, based on ResNet-50, a ResNet-B residual structure is introduced to reduce the loss of features during information extraction; a continuously differentiable CELU is used as the activation function to reduce the neuron death problem of ReLU; a new deformable convolution network (DCN v2) is introduced as the convolution kernel structure of the model to improve the recognition of aviation connectors with prominent geometric deformation pin recognition. The improved model achieved 97.2% and 94.4% accuracy for skewed and missing pins, respectively, in the experiments. The detection accuracy improved by 1.91% to 96.62% compared to the conventional ResNet-50. Compared with the traditional model, the improved model has better generalization ability. [ABSTRACT FROM AUTHOR]

The theme of the paper is to emphasize the detection and classification of faults and their site location in the transmission line using machine learning techniques which help to indemnify the foul-up of the humans in identifying the site and type of occurrence of fault. Moreover, the transient stability is a supreme one in power systems and so the disturbances like faults are required to be separated to preserve the transient stability. In general, the protection of the transmission line includes the installation of relays at both ends of the line that constantly monitor voltages and currents and operate unless a fault occurs on a line. Therefore, this paper intends to introduce a novel transmission line protection model by exploiting the hybrid optimization concept to train the Convolutional Neural Network (CNN). Here, the fault detection, classification and site location are diagnosed by using CNN which is trained and tested by making use of diverse synthetic field data derived from the simulation models of distinct types of transmission lines. Hence, the location and the type of faults will be predicted by the CNN depending on the fault signal characteristics which are optimally trained by a new hybrid algorithm named Chicken Swarm Insisted Spotted Hyena (CSI-SH) Algorithm that hybrids both the concept of Spotted Hyena Optimization (SHO) and Chicken Swarm Optimization (CSO). Finally, the proposed method based on CNN for fault classification and site location of transmission lines is implemented in MATLAB/Simulink and the performances are compared with various measures like classification accuracy, fault detection rate and so on. [ABSTRACT FROM AUTHOR]

This paper aims to develop the simulated numerical methodology for sloping beach with nonlinear free-surface waves of sloshing water in a 2D rectangular tank. The current computational tool would be the first step in accurately modeling wave energy converters. The 2D tank model was developed by ANSYS FLUENT software, and Navier–Stokes equations are discretized on a regular structured grid based on the finite volume method (FVM). The model is validated for nonlinear sloshing cases. In addition, tank flexibility is analyzed for its impact on liquid sloshing. The test was conducted in five different wave steepness conditions. There were five conditions of wave steepness used in the test. This study looked at fluid–structure interaction around moving plates. The output power of the plate wave energy converter for different plate heights and streamline motion around the plate are important studies in the flow field. Variations of drag coefficient with the wave steepness are the important study in this paper. It shows that the drag coefficient increases with the increase in wave steepness and plate length. In particular, the new structure can give a new, highly accurate formula for determining the wave power of the ocean structures. Its application is seen in wave energy converter (WEC), where the interaction of solid objects, such as wind turbines with water waves, extracts renewable wave energy from the ocean. A highly efficient CFD approach method is introduced to capture the interaction of solid object with a two-phase flow. The effect of wave steepness using second-order stokes wave theory on a moving thin plate in intermediate water is a new discussion. This research is a highly relevant source of information in the development of many practical systems and has direct employment in the design of WEC in order to extract ocean energy. [ABSTRACT FROM AUTHOR]

This paper focuses on Herbert A. Simon's visionary theory of the Artificial World. The artificial world evolves over time as a result of various actions, including interactions with the external world as well as interactions among its internal components. This paper proposes a mathematical theory of the conceptual framework of the artificial world. This goal requires the development of new mathematical tools, inspired in some way by statistical physics and stochastic game theory. The mathematical theory is applied in particular to the study of the dynamics of organizational learning, where cooperation and competition evolve through decomposition and recombination of organizational structures; the effectiveness of the evolutionary changes depends on the dynamic prevalence of cooperative over selfish behaviors, showing features common to the evolution of all living systems. [ABSTRACT FROM AUTHOR]

This editorial is dedicated to presenting the papers published in a special issue focused on modeling, qualitative analysis and simulation of the collective dynamics of systems in engineering and life sciences. All papers have a minor or major reference to living, i.e. complex systems, and a critical analysis of the overall content of the issue is proposed, leading to a forward look at research perspectives. This paper first defines the goals of the issue. Then, a brief description of the scientific contribution of the papers published in this issue is given. Finally, a look into the future is proposed. [ABSTRACT FROM AUTHOR]

The paper traces the origin of the Middle East conflict and discusses the gradual evolution of the Israel-Palestine conflict from a historical perspective. The paper discusses the peace negotiations undertaken for the region during different periods and the contribution of the international community in reaching a compromise formula. The paper discusses the emergence and the role of Hamas and how it gained political legitimacy. The paper concludes with the views of Israel and India perspective. [ABSTRACT FROM AUTHOR]

The use of ideal memristors in a continuous-time (CT) nonlinear circuit is known to greatly enrich the dynamic behavior with respect to the memristorless counterpart, which is a crucial property for applications in future analog electronic circuits. This can be explained via the flux–charge analysis method (FCAM), according to which CT circuits with ideal memristors have for structural reasons first integrals (or invariants of motion, or conserved quantities) and their state space can be foliated in infinitely many invariant manifolds where they can display different dynamics. The paper introduces a new discretization scheme for the memristor which, differently from those adopted in the literature, guarantees that the first integrals of the CT memristor circuits are preserved exactly in the discretization, and that this is true for any step size. This new scheme makes it possible to extend FCAM to discrete-time (DT) memristor circuits and rigorously show the existence of invariant manifolds and infinitely many coexisting attractors (extreme multistability). Moreover, the paper addresses standard bifurcations varying the discretization step size and also bifurcations without parameters, i.e. bifurcations due to varying the initial conditions for fixed step size and circuit parameters. The method is illustrated by analyzing the dynamics and flip bifurcations with and without parameters in a DT memristor–capacitor circuit and the Poincaré–Andronov–Hopf bifurcation in a DT Murali–Lakshmanan–Chua circuit with a memristor. Simulations are also provided to illustrate bifurcations in a higher-order DT memristor Chua's circuit. The results in the paper show that DT memristor circuits obtained with the proposed discretization scheme are able to display even richer dynamics and bifurcations than their CT counterparts, due to the coexistence of infinitely many attractors and the possibility to use the discretization step as a parameter without destroying the foliation in invariant manifolds. [ABSTRACT FROM AUTHOR]

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain. [ABSTRACT FROM AUTHOR]

This document serves as a preface to a special issue of the International Journal on Artificial Intelligence Tools (IJAIT). The articles in this issue build upon papers presented at the 36th International Florida AI Research Society Conferences in May 2023. The lead article introduces the theme of the special issue, which is explainable, fair, and Trustworthy AI. Other articles in the issue cover topics such as improving the bounds of a neuron's Boolean function, ethical explanations and transparency in AI systems, fairness measurement and mitigation in deep learning systems, and the dynamics of human-agent collaboration in ad hoc teams. The paper emphasizes the importance of positive and negative contrastive explanations in enhancing motivation and performance. It also addresses the challenges of fairness and reliability in predictive policing systems, with a focus on reducing bias related to race, age, and sex. The authors propose tailoring predictive models to specific causal queries and utilizing causal structure instead of relying solely on pre-trained models. The paper also explores how machine learning models can balance accuracy and understandability, as well as ensure fairness. The findings indicate that more complex models tend to outperform current guidelines, but fair models have lower complexity. The paper concludes by expressing gratitude to the reviewers for their contributions and support. [Extracted from the article]

The radio frequency (RF) argon plasma induced surface modification of paper revealed novel surface characteristics and substantially changed surface topography. It was found that RF-argon glow discharge affects surface properties resulting in photo-degradation and chain-scission mechanism on paper network structure. High-power and extended treatment time caused increasing elemental carbon, while decreasing the oxygen concentration on paper surface. However, increased hydroxyls and the creation of new carbonyl functionalities were verified by trifluoroacetoanhydride and pentafluorophenylhydrazine in vapor-phase derivatization reactions. [ABSTRACT FROM AUTHOR]

In the last 30 years, several distinguished researchers have proposed and discussed different fuzzy versions of well-known Saaty's Analytic Hierarchy Process (AHP). The paper recently published by K. Zhü in the European Journal of Operational Research heavily criticizes the fuzzy approaches to AHP, claiming the fallacy of all of them. Therefore, it seems to be necessary to clarify whether the criticisms are well-founded or not. The present paper aims to rebut Zhü's claims by showing that the evidences and the reasonings in his paper are very poor and far from proving the fallacy of fuzzy AHP. [ABSTRACT FROM AUTHOR]