Numerical Search Research Articles

Factors Influencing Stock Market Participation: A Review

Purpose: The most pertinent source for generating wealth is to invest in stock market, however numerous studies have proved that there is low stock market participation. Hence, the extensive review of existing literatures on stock market involvement has been conducted to identify the various factors influencing stock market participation and also to identify the gap in this body of research. Approach: The current study is conducted with thorough analysis of the body of literature from numerous search engines, including Google Scholar, Scopus, Research Gate, etc. these gathered data on financial literacy, stock market participation, social inclusion and digital inclusion includes journal articles, working paper, reports, magazines, books and websites etc. In order to search the pertinent information for the study many keywords were used. Literatures were then divided into conceptual review, hypothetical relationship review, and theoretical review. Results/ Findings: Numerous studies have exposed that financial literacy and social inclusion significantly contribute to stock market participation, and also digitization promotes individuals involvement in the financial market. The extensive review directed a researcher towards low touched area under stock market participation i.e. to determine how literacy level on financial aspects and social inclusion will improve one’s involvement in the stock market, when it is mediated through digital inclusion. Originality/Value: This study will provide policy guidelines to Government, financial policy makers, practitioners and academician in the area of finance to encourage stock market participation by highlighting the sustainable inclusive growth through socialization and digitalization in order to generate wealth which again contributes to economic development of a country. Type of Paper: Literature Review

Tall Building Installed With Bio-Inspired TMD With Inerter

This paper explores the combined effect of inerter and bio-inspired tuned mass damper (BTMD) on enhancing the performance the wind excited benchmark building. Initially, the performance of the building installed with BTMD on the top floor, under across wind loads is evaluated. In addition to this, an inerter is attached to the BTMD (BTMDI) and the performance of the benchmark building is studied. A mechanical device called an inerter has two terminals and generates an equal and opposing force proportionate to the relative acceleration between the terminals. One terminal of the inerter is linked to the BTMD and the other terminal is connected to the 76th floor of the structure in order to improve the performance of the structure. The response quantities of the building installed with BMTDI are studied, and compared with those installed with TMD and BTMD. Comparative study of various performance criteria of the building installed with BMTDI, BMTD and TMD is also carried out. Optimum values of parameters such as, damping ratio, frequency ratio and mass ratio of BTMDI are found by numerical search technique. The performance of the 76th - storey benchmark tower installed with BMTDI is enhanced considerably as compared to that with BMTD.

Closed-Form Derivation of Optimum Tuned Mass Damper Parameter Based on Modal Multiplicity Criteria

It is presently established that properly designed tuned mass dampers (TMD) systems can effectively control the vibrations induced due to the wind and/or the seismicity. The TMD is one of the most used passive control systems in high-rise structural systems in an era of recent developments. The optimum parameters for the damped system to date require a numerical search technique or shootout technique, which is the trial-error method, of-course add complexity in selecting parameters obtained at optimality conditions. The study of optimum parameters based on modal multiplicity criteria showcases the complete closed-form solution, independent to another optimum parameter. The parameters presented in this study solely depend on structural damping and mass ratio. The derived optimum parameters are compared with the existing parameters in this field. This study also investigates parametric uncertainty for structural configuration under the harmonic excitation. To confirm its robustness under random vibration, various earthquake time history loads are applied to study the effect of structural displacement and acceleration response. In the end, the energy concept is deployed to explain how effectively the optimum TMD parameters help to reduce the kinetic energy, damping energy, strain energy, and input energy.

Optimal Geometries for AOA Localization in the Bayesian Sense

This paper considers the optimal sensor placement problem for angle-of-arrival (AOA) target localization in the 2D plane with a Gaussian prior. Optimal sensor locations are analytically determined for a single AOA sensor using the D- and A-optimality criteria and an approximation of the Bayesian Fisher information matrix (BFIM). Optimal sensor placement is shown to align with the minor axis of the prior covariance error ellipse for both optimality criteria. The approximate BFIM is argued to be valid for a sufficiently small prior covariance compared with the target range. Optimal sensor placement results obtained for Bayesian target localization are extended to manoeuvring target tracking. For sensor trajectory optimization subject to turn-rate constraints, numerical search methods based on the D- and A-optimality criteria as well as a new closed-form projection algorithm that aims to achieve alignment with the minor axis of the prior error ellipse are proposed. It is observed that the two optimality criteria generate significantly different optimal sensor trajectories despite having the same optimal sensor placement for the localization of a stationary target. Analysis results and the performance of the sensor trajectory optimization methods are demonstrated with simulation examples. It is observed that the new closed-form projection algorithm achieves superior tracking performance compared with the two numerical search methods.

Targeted modal response control of structures with inerter-based vibration absorbers considering modal interaction effects

Inerter-based vibration absorbers (IVAs) have been widely studied for the vibration control of multi-degree-of-freedom (MDOF) structures. Common analytical optimization strategies of IVAs for controlling the modal response of a specific mode neglect the contributions from non-resonant modes (denoted as modal interaction effects hereafter), which could weaken the validity of the optimum solution, while directly employing numerical search methods usually implies expensive computational costs in repeatedly evaluating control objectives under different sets of IVA parameters. To aim at both accuracy and efficiency of optimum design solution of IVAs for targeted modal response control, the Sherman-Morrison inversion (SMI) is exploited in this paper to derive explicit expressions of frequency response functions (FRFs) of IVA-controlled structures based on an MDOF model established in modal coordinates. In this way, the non-zero off-diagonal elements and precise expressions of diagonal elements in the FRF matrix are explicitly incorporated in the design process, with full account for modal interaction effects. The SMI is further implemented to calculate the FRF matrix of structures controlled by multi-IVA (MIVA) in an iterative manner, and subsequently, design strategies for targeted modal control using (M)IVA are proposed with particular focuses on wind-resistance and seismic design. Based on a high-rise building taken as the case study, the accuracy and efficiency in calculating the FRF matrix are quantitively assessed by Monte Carlo simulations. Two design examples, i.e., using a single tuned inerter damper (TID) for wind-resistance design and using a multi-TID for seismic design, are presented to validate the precision and efficiency of proposed design strategies. The results indicate that modal interaction effects are increasingly important for the design of (M)IVAs having larger inertance or being tuned to higher modes.

Active control of structures using genetic algorithm with dynamic weighting factors using in the constrained objective function

Active control method by improving specification of well-known intelligent numerical search method i.e. genetic algorithm is developed here. This method reduces displacement of the structure by optimizing the control forces at each time step. The efficiency of the genetic algorithm as a part of nature-inspired metaheuristic methods is highly dependent on the constrained objective function. The constrained objective function is achieved by combining the constraints of the optimization problem. There are several methods to numerically combine these constraints. Using appropriate weighting factors to generate this function has been suggested by many researchers. In previous studies, the selection of these factors has been based on experimental or try and error methods and were constant throughout the control period. Proper selection of weighting factors increases the efficiency of the control method. Presenting a new genetic algorithm method in a way selecting weighting factors dynamically over the structural control period is the aim of this paper. Here, weighting factors are non-static in nature and are dynamically selected at each time step according to the memory of the previous step. Numerical results clearly prove the accuracy and efficiency of the proposed control process in comparison with Constant weighting factors methods.

Understanding the enhancement of scintillation light in xenon-doped liquid argon

Measuring the scintillation light in noble gases is an important detection technique in particle physics. Numerous rare event searches like neutrino beam experiments, neutrino-less double beta-decay, and dark matter searches use argon-based detectors. In liquid argon, the light yield can be enhanced by the addition of a small quantity of xenon, where ∼10−1000 ppm are added. The general enhancement mechanism and its pathway via an energy transfer between argon and xenon excimers is well known, however the importance of absorption of argon excimer emission by atomic xenon has not been fully appreciated. This absorption significantly reduces the light yield in commercially available argon (extracted from air) which contains trace amounts (∼ 0.1 ppm) of xenon. The addition of a small xenon dopant of ∼10 ppm recovers this lost light resulting in an increased light yield over un-doped argon of about a factor of two. In this paper we introduce a model for the light production in xenon doped argon, including absorption and re-emission, and compare it to the measured time dependence of light emission in xenon-doped argon.

CFD Body Force Propeller Model with Blade Rotational Effect

The purpose of this study is to consider propeller geometry and blade rotation in the propeller model in a CFD code. To predict propeller performance, a body force propeller model was developed based on blade element theory and coupled with URANS (unsteady Reynolds-averaged Navier–Stokes) solver CFDSHIP-IOWA V4.5 both implicitly and interactively. The model was executed inside the flow solver every inner iteration. The grid points inside each 2D blade geometry were identified by a numerical search algorithm. To calculate the lift coefficient, the total flow velocities at 25% foil chord length were obtained using the inverse distance weighting interpolation from the RANS solution. The body forces were distributed linearly along the chord length with the maximal value located at the leading edge and zero at the trailing edge. The main achievements are: (1) for a KP505 propeller in an open water condition, the error of the thrust coefficient generally is around or less than 3%, which is a better prediction than the previous model. (2) For a behind-hull condition, the error is about 1%. (3) For an E1619 propeller in an open water condition, the error is around 6%. (4) The blade-to-blade effect and unsteady flow field between blades are sufficiently resolved by the model.

Performance Bounds of Coded Slotted ALOHA Over Erasure Channels

In this paper, we derive an upper bound on load-threshold of coded slotted ALOHA (CSA) for slot erasure channel (SEC) and that for packet erasure channel (PEC), respectively. In SEC, by exploiting the analogy between Gaussian elimination of solving linear equations and the iterative successive interference-cancellation (SIC) decoding procedure of CSA, we derive the upper performance bound for SEC. Subsequently, in PEC, utilizing density evolution to track the average probability of packet-decoded failure within the SIC decoding process, we obtain the upper bound for PEC. Moreover, we propose a numerical search algorithm of these bounds under a given calculating precision. The numerical results indicate that the asymptotic upper limit on load-threshold of CSA over SEC is always less than that over PEC with any given erasure probability and average transmission rate.

JFLN: Joint Feature Learning Network for 2D sketch based 3D shape retrieval

Cross-modal retrieval attracts much research attention due to its wide applications in numerous search systems. Sketch based 3D shape retrieval is a typical challenging cross-modal retrieval task for the huge divergence between sketch modality and 3D shape view modality. Existing approaches project the sketches and shapes into a common space for feature update and data alignment. However, these methods contain several disadvantages: Firstly, the majority approaches ignore the modality-shared information for divergence compensation in descriptor generation process. Secondly, traditional fusion method of multi-view features introduces much redundancy, which decreases the discrimination of shape descriptors. Finally, most approaches only focus on the cross-modal alignment, which omits the modality-specific data relevance. To address these limitations, we propose a Joint Feature Learning Network (JFLN). Firstly, we design a novel modality-shared feature extraction network to exploit both modality-specific characteristics and modality-shared information for descriptor generation. Subsequently, we introduce a hierarchical view attention module to gradually focus on the effective information for multiview feature updating and aggregation. Finally, we propose a novel cross-modal feature learning network, which can simultaneously contribute to modality-specific data distribution and cross-modal data alignment. We conduct exhaustive experiments on three public databases. The experimental results validate the superiority of the proposed method. Full Codes are available at https://github.com/dlmuyy/JFLN.

Evolutionary Algorithm-Based Iterated Local Search Hyper-Heuristic for Combinatorial Optimization Problems

Hyper-heuristics are widely used for solving numerous complex computational search problems because of their intrinsic capability to generalize across problem domains. The fair-share iterated local search is one of the most successful hyper-heuristics for cross-domain search with outstanding performances on six problem domains. However, it has recorded low performances on three supplementary problems, namely knapsack, quadratic assignment, and maximum-cut problems, which undermines its credibility across problem domains. The purpose of this study was to design an evolutionary algorithm-based iterated local search (EA-ILS) hyper-heuristic that applies a novel mutation operator to control the selection of perturbative low-level heuristics in searching for optimal sequences for performance improvement. The algorithm was compared to existing ones in the hyper-heuristics flexible (HyFlex) framework to demonstrate its performance across the problem domains of knapsack, quadratic assignment, and maximum cut. The comparative results have shown that the EA-ILS hyper-heuristic can obtain the best median objective function values on 22 out of 30 instances in the HyFlex framework. Moreover, it has achieved superiority in its generalization capability when compared to the reported top-performing hyper-heuristic algorithms.

How Have the Diagnosis and Treatment of Chronic Recurrent Multifocal Osteomyelitis Changed Over Time?

Chronic recurrent multifocal osteomyelitis (CRMO) is a rare inflammatory condition characterized by sterile bone lesions. There appears to be a shift in the diagnostic modalities and treatment over the past decades despite insufficient published data. The purpose of this study was to document: 1) the number of patients diagnosed with CRMO, 2) patient demographics, 3) disease characteristics at presentation, 4) diagnostic modalities employed, and 5) treatments prescribed at our institution over a 30-year period. This single-center, retrospective cohort study included children diagnosed with CRMO who presented between 1990 and 2020. The electronic medical records were queried using numerous search terms. Patients were excluded if CRMO was included in the differential diagnosis but was not confirmed at the time of chart review or if CRMO was suspected early in the disease course but the patient was ultimately diagnosed with another condition. The predictor (time in years) and outcome variables (diagnostic modalities and treatment types) were tested using bivariate analyses using IBM SPSS, Version 27 (IBM Corp., Armonk, NY). A total of 224 patients were diagnosed with CRMO during the observation period (68.3% female; 67.4% white). The number of patients diagnosed over the past decade rose by 215%, as compared to the previous 2decades (1990 to 2010). Regional magnetic resonance imaging (83.8%) and biopsy (66.5%) were the most utilized diagnostic modalities over the past decade, with a statistically significant decline in the proportion of biopsies performed (66.5% during the past decade vs 84.9% in the previous 2decades, P=.01). Over the past decade, nonsteroidal anti-inflammatory drugs (40.1%), disease-modifying antirheumatic drugs (27.1%), and tumor necrosis factor inhibitors (21.1%) were the most commonly used treatments, with a statistically disproportionate increase in the use of tumor necrosis factor inhibitors (21.1% during the past decade vs 3.8% in the previous 2decades, P<.001). This is one of the largest CRMO cohort studies and the only study to observe changes in diagnostic modalities and treatment over a 30-year period. Future studies should assess the impact of variations in clinical presentation, time to diagnosis, diagnostic modalities, and management as predictors of disease outcomes.

Kepler in search of the “Anaclastic”

This article was written as a tribute to Enrique Tirapegui who passed away on 10 March 2020. We were working on these issues with him. He will always be remembered.We describe Kepler’s attempt to discover the law of refraction. Its very elegant analysis can be seen as a numerical search for the solution of a differential equation. Kepler’s arguments and the reasons for his failure are discussed. This analysis, which precedes Descartes’ solution of the inverse tangent problem posed by Florimond de Beaune, is an opportunity to briefly recall some aspects of the history of differential equations. It is also an opportunity to propose a pedagogical activity that consists of introducing the law of refraction to students in the wake of Kepler and Descartes. This article is intended to be pedagogical. It is self-consistent and all the properties of the hyperbola necessary to follow Kepler’ arguments are demonstrated using elementary geometry. It will also be the material for the chapter of a book intended to revisit the elementary laws of physics using Euclidean geometry. This project started with Enrique Tirapegui. This work was presented at the XVIII Instabilities and Nonequilibrium Structures Workshop in tribute to Enrique held in December 2021 in Valparaiso, Chile.

Ballistic Object Trajectory and Impact Point Estimation in the Reentry Phase From a Moving Passive Sensor

This article considers the problem of estimating the trajectory of a ballistic target in the reentry phase using 2-D measurements (azimuth and elevation angles) from a moving passive sensor. Previous works investigated the estimation problem of an object in the thrusting and initial ballistic phase from a single fixed passive sensor. This article shows that the 3-D trajectory in the reentry phase can be obtained by estimating the target’s state at the end of the observation interval. The 7-d motion parameter vector (velocity azimuth angle, velocity elevation angle, drag coefficient, target speed, and 3-D position) is estimated by the maximum likelihood (ML) estimator with numerical search. Then we can predict the future position at an arbitrary time and the impact point of the target. The observability of the system for a sensor on a fast aircraft moving with constant velocity or maneuvering is verified via the invertibility of the Fisher information matrix. This is a major extension of the applicability of the recent observability proof for a stationary passive sensor observing a target in a gravitational field. The Cramer–Rao lower bound for the estimated parameters is evaluated and it shows that the estimates are statistically efficient. The angle estimation performance for the ML estimator is also compared with that of the polynomial fitting method. Simulation results illustrate the effectiveness of the proposed method.

Path Following for Unmanned Combat Aerial Vehicles Using Three-Dimensional Nonlinear Guidance

An effective path-following guidance algorithm for unmanned combat aerial vehicles by pursuing a look-ahead target-point along the desired path is devised. The concept of look-ahead pursuit for 3-D path-following was extended from the planar <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal {L}_1}$</tex-math></inline-formula> guidance, which has been widely adopted in the application of lateral guidance of fixed-wing drone. Decoupled implementation of the planar guidance action on each of the longitudinal (vertical) and lateral (horizontal) planes supplies longitudinal-lateral acceleration commands in two perpendicular planes which is well accepted by aviation engineers. The computation of the look-ahead point utilizes a finite and iterative numerical search method by introducing an auxiliary path-related parameter. Furthermore, additional implementation-specific details such as the modification of adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mathcal {L}_1}$</tex-math></inline-formula> length and the protection strategies ensuring wide applicability of the algorithm indeed guarantee certain necessity to be considered in real implementation of the guidance law. The improved schemes eliminate complicated coordinate transformations and are built on the path-independent assumption, thus making them easier to be adjusted between various types of paths, including discrete optimal trajectories. Monte Carlo simulations and real-world flight tests have confirmed the effectiveness of the nonlinear guidance algorithm.

A hyper-heuristic based reinforcement-learning algorithm to train feedforward neural networks

Artificial Neural Networks (ANNs) offer unique opportunities in numerous research fields. Due to their remarkable generalization capabilities, they have grabbed attention in solving challenging problems such as classification, function approximation, pattern recognition and image processing that can be quite complex to model mathematically in practice. One of the most vital issues regarding the ANNs is the training process. The aim at this stage is to find the optimum values of ANN parameters such as weights and biases, which indeed embed the whole information of the network. Traditional gradient-descent-based training methods include various algorithms, of which the backpropagation is one of the best-known. Such methods have been shown to exhibit outstanding results, however, they are known have two major theoretical and computational limitations, which are slow convergence speed and possible local minima issues. For this purpose, numerous stochastic search algorithms and heuristic methods have been individually used to train ANNs. However, methods, bringing diverse features of different optimizers together are still lacking in the related literature. In this regard, this paper aims to develop a training algorithm operating based on a hyper-heuristic (HH) framework, which indeed resembles reinforcement learning-based machine learning algorithm. The proposed method is used to train Feed-forward Neural Networks, which are specific forms of ANNs. The proposed HH employs individual metaheuristic algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE) Algorithm and Flower Pollination Algorithm (FPA) as low-level heuristics. Based on a feedback mechanism, the proposed HH learns throughout epochs and encourages or discourages the related metaheuristic. Thus, due its stochastic nature, HH attempts to avoid local minima, while utilizing promising regions in search space more conveniently by increasing the probability of invoking relatively more promising heuristics during training. The proposed method is tested in both function approximation and classification problems, which have been adopted from UCI machine learning repository and existing literature. According to the comprehensive experimental study and statistically verified results, which point out significant improvements, the developed HH based training algorithm can achieve significantly superior results to some of the compared optimizers.

Optimal two-qubit gates in recurrence protocols of entanglement purification

We propose and investigate a method to optimize recurrence entanglement purification protocols. The approach is based on a numerical search in the whole set of SU(4) matrices with the aid of a quasi-Newton algorithm. Our method evaluates average concurrences where the probabilistic occurrence of mixed entangled states is also taken into account. We show for certain families of states that optimal protocols are not necessarily achieved by bilaterally applied controlled-NOT gates. As we discover several optimal solutions, the proposed method offers some flexibility in experimental implementations of entanglement purification protocols and interesting perspectives in quantum information processing.

Numerical search for the stationary quasi-breather of the graphene superlattice equation

The propagation of electromagnetic solitons in a graphene superlattice device is governed by a modified sine-Gordon equation, referred to as the graphene superlattice equation. Kink-antikink collisions suggest the existence of a quasi-breather solution. Here, a numerical search for static quasi-breathers is undertaken by using a new initial condition obtained by a regular perturbation of the null solution. Our results show that the frequency of the initial condition has a minimum critical value for the appearance of a robust quasi-breather able to survive during more than one thousand periods. The amplitude and energy of the quasi-breather solution decrease, but its frequency increases, as time grows. The robustness of the new quasi-breather supports its experimental search in real graphene superlattice devices.

STRATEGIC ADVERTISING MANAGEMENT DURING THE PRODUCT LIFE CYCLE

The subject of the research is the development of a strategy for optimal management of the company's advertising expenditures when promoting a new product to the market throughout its entire life cycle. To model the sales dynamics of a new product, the article proposes a modification of the Bass diffusion of innovations model, which makes it possible to numerically simulate the impact of advertising expenditures on the intensity of sales of the two groups of consumers identified in the model, namely innovators and imitators. The problem of optimal control of advertising expenditures for product promotion throughout its entire life cycle is formulated. As an optimality criterion, the expected net present value of cash flows from the sale of the product is chosen. It is shown how to solve this problem by means of dynamic programming techniques. An algorithm for numerical search for the optimal control program of advertising expenditures based on the method of contraction mapping has been developed and implemented. As a result of the algorithm, the optimal level of advertising expenditures is established in the form of a law with feedback, where the degree of market saturation acts as a state variable of the system. On this basis, one can find the optimal level of advertising expenditures in each time period and compare sales trajectories with different advertising strategies. Two cases of advertising aimed at innovators and imitators were studied separately. Numerical experiments were carried out, on the basis of which the qualitative characteristics of the strategy for optimal control of advertising expenditures for these two cases were determined. The influence of the chosen management strategy on the form and duration of the product life cycle has been studied. Conclusions are drawn about the comparative economic efficiency of various advertising management strategies. Optimization of the advertising budget using the developed methodology allows enterprises to check whether the planned level of advertising spending is excessive or insufficient. The application of the methodology allows not only to find the optimal size of advertising expenditures depending on the stage of the product life cycle, but also to predict sales and profits for future periods.

Optimum Parameters of Tuned Inerter Damper for Damped Structures

This study evaluates optimal parameters for a damped system with a tuned inerter damper (TID). TID is a device that works similarly to a tuned mass damper, except that the actual mass is substituted with apparent mass, i.e. the inertance of the inerter. Fixed point theory is used to derive optimal parameters of TID attached to an undamped single degree of freedom (SDOF) structure considering objective responses of relative displacement, relative velocity, absolute acceleration and the force transmitted by the primary structure to the ground. Optimal TID parameters attached to the viscously damped SDOF main system are derived by minimising the four objective responses using a numerical search technique. Based on the results from the numerical search and optimal parameters obtained from the fixed point theory, a curve-fitting technique is utilised to establish explicit formulations for TID parameters for the damped primary system. The closed-form expressions thus obtained are based on the minimum mean square error between optimal parameters obtained by numerical search and corresponding explicit formulae. The expressions proposed have a negligible inaccuracy, making them useful for the design process in the damped systems. The damping of the primary system has a negligible effect on the TID's optimal damping ratio. The damping of the primary system, on the other hand, has a substantial impact on the optimum tuning frequency of TID. Finally, an optimally designed TID is applied to SDOF damped system with a fixed and flexible base as a supplemental damper. It is seen optimal TID is significantly effective in controlling the seismic responses.