Search results

A Robo Advisor (RA) is a fully automated investment advisory service. Its development in recent years has been very relevant within the financial industry. Although most RAs comply with most investment principles (diversification, cost efficiency, personalization and contextualization of investment opportunities to the current environment), their need for standardization reduces their ability to find portfolios that fit the investors’ constraints or needs. The main objective of this paper is to analyze the possibility of eliminating this shortcoming of the RA by including new types of financial instruments or generating different investment portfolios.

This study performs a bottleneck analysis of all activities related to the management of financial instruments to detect the most affected activities when incorporating new types of instruments. This study also presents a case study on including fixed-income bonds to increase RA personalization and proposes two types of investment portfolios to promote personalization.

The bottleneck analysis has allowed us to identify that “instrument data validation” and “order management” are the most affected activities if new types of instruments are incorporated. In addition, the liquidity level of financial instruments is a critical variable that must be integrated into an RA.

The results indicate the possibility of designing a new RA with a higher level of personalization. This study helps to understand the difficulties and opportunities when customizing an RA.

The objective of this article is to evaluate and compare the performance of two machine learning (ML) algorithms, i.e. support vector machines (SVMs) and random forests (RFs), when classifying seven states of operation of an electric motor using the Mel-frequency cepstral coefficients (MFCCs) as extracted representative features.

The extracted MFCCs are calculated using the motor’s vibration and audio signals separately.

After the training, the SVM model obtained a mean accuracy of 100% for the MFCCs obtained from database vibration signals and 69.6% for the database of audio signals.

The ML strategies and results reported are limited to the well-known data for industrial electric motors used in the evaluations, although it was performed tests and cross-validations with unseen data and the information from the confusion matrix.

The success of these methodologies in defect classification, where the RF presented a mean accuracy of 99.15% for the vibration signals and 63.82% for the audio signal, enables the use of this ML and extracted features as a predictive tool for failure and anomaly detection, lifetime predictions and online real-time monitoring.

It is the first time that the MFCCs are being used for anomaly detection in vibration and audio signals for electrical motors, as this extracted feature is usually used for human speech identification in the literature.

The purpose of this research is to evaluate the effectiveness of different repair patch materials in reducing the stresses at the crack tip of a 2024-T3 aluminum plate. This involves a numerical analysis using the finite element method (FEM) to estimate the reduction in the J-integral value, with the goal of identifying how various parameters related to the patch materials, adhesive properties and loading conditions influence the structural integrity of the repaired plate.

The methodology of this research involves conducting a numerical analysis using the FEM to estimate the reduction in the J-integral value at the crack tip of a 2024-T3 aluminum plate. Three types of patches – metal, composite and functionally graded material (FGM) – were examined under tensile loading conditions, and Adekit-A140 adhesive was used to bond these repair patches to the aluminum plate.

The analysis considered various parameters, including crack length, the nature of fibers in the composite material, the gradation exponent for FGM patches and the nature of the face in contact with the adhesive for the FGM patch. Additionally, stress analysis was conducted, examining the J-integral values for the plate, shear stress in the adhesive layer and peel stress in the composite patch. The findings highlight that modifying the nature of the repair patch used can significantly enhance the structural integrity of the repaired plate.

The study analyzed J-integral values, shear stress in the adhesive and peel stress in the composite patch. Various parameters, including crack length, fiber type, gradation exponent and adhesive contact face nature, were considered. Results demonstrate that the J-integral value can be significantly reduced by altering the repair patch type, highlighting the effectiveness of customized patch materials in enhancing structural integrity.

Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.

The main objectives of this paper are to develop a novel perturbation method (PM) to solve the complex-orthogonal eigenvalue problem and further propose an exact complex mode superposition method (CMSM) for the non-proportionally rate-independent damped systems.

A novel PM is developed to solve the eigenvalue problem. The PM reduced the N-order generalized complex eigenvalue problem into a set of n algebraic equations by the perturbation theory. The convergence and accuracy of the PM are demonstrated by several numerical examples. Further, an exact CMSM is presented. The influences of the imaginary part response of the modal coordinate and the off-diagonal elements of the damping matrix as well as the modal truncation on the solution by CMSM are discussed to illustrate the effectiveness of the developed CMSM.

The eigenvalues obtained by PM would converge to the exact ones with the increase of the modal numbers. For seismic response, the influence of the imaginary part solutions of the modal coordinate would increase with the increase of the coupling factor. The contribution of higher modes to acceleration response is greater than that to the displacement. The cumulative mode contribution coefficient of acceleration is developed to estimate the numbers of the complex modes for the acceleration seismic response by the CMSM.

1. An eigenvalue perturbation method for a rate-independent damped system is proposed. 2. PM is carried out by the real mode and accomplishes the reduction of the matrix. 3. CMSM is established for rate-independent damped systems. 4. CMSM considers the effect of imaginary part solutions of the modal coordinate. 5. Modal truncation index is developed to estimate the complex mode number for CMSM.

This work focuses on optimizing and predicting the tenacity of twin-sheath single-core hybrid yarn. This study aims to predict and maximize yarn performance by investigating key factors influencing yarn tenacity.

Three critical parameters − ultra-high molecular weight polyethylene (HPPE) denier, stainless steel micron size and twist per meter − were considered for making multicomponent yarn and optimized using the Box-Behnken design (BBD), a response surface methodology variant. The hybrid yarn studied consists of a stainless-steel core, a polyester inner layer and an HPPE outer layer with opposite twists. The ASTM D2256 method was applied on Instron 3365 machine to measure yarn tenacity.

The optimized yarn setup involved 200 twists per meter, 400 Den HPPE and 45-micron stainless steel, resulting in a 127.5 cN/Tex tenacity. The quadratic model best fits the data, with R² values close to 1.00 (R² = 0.9935, adjusted R² = 0.9817, projected R² = 0.8956), a lower PRESS value of 445, a higher adequacy precision of 19.6816 and a higher TPC percentage of 35.23%. The analysis of variance results confirmed the model significance (F-value = 84.75, P-value < 0.0001), and the average relative error was found to be 3.43%, indicating predictive accuracy.

This study demonstrates the effectiveness of the BBD in optimizing hybrid yarn tenacity, providing valuable insights in terms of core yarn and outer sheath yarn linear density with twist per meter. The work presents a novel approach to hybrid yarn optimization and prediction, expanding the potential for further research and development in textile engineering.

Sudan’s challenges are deeply rooted in a complex web of societal, political, and health-related issues. The nation is currently experiencing a challenging period that is impacting various aspects of human life, including the healthcare system for the youth, who are significantly impacted by this crisis that has persisted for a decade. Sudan, located in northeastern Africa, is an Arab nation and a member of the Greater Horn of Africa, along with Ethiopia and South Sudan. Despite its strategic location near the Nile River – a hub for global oil trade and other merchandise – it does not possess any military equipment. Its history, unfortunately, is marked by prolonged social conflicts. These conflicts have significantly affected essential infrastructures, social services, and the overall well-being of the population. We examine the conflict through humanitarian and socio-economic factors affecting Sudan and its people. This chapter explores the role of global economic forces and international relations in Sudan’s ability to address these challenges. Secondly, we explore the social structures, power dynamics, and interdependencies, examining how institutions and societal functions hinder comprehensive issue resolution through structural functionalism. Lastly, we will analyze the legal recognition and protection as foundational elements for human rights and assess the acknowledgment of rights, its absence influences the endurance of multifaceted challenges like health care and educational system by the Right to have Rights.