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This study presents a two-step numerical iteration method specifically designed to solve absolute value equations. The proposed method is valuable and efficient for solving absolute value equations. Several numerical examples were taken to demonstrate the accuracy and efficiency of the proposed method.

We present a two-step numerical iteration method for solving absolute value equations. Our two-step method consists of a predictor-corrector technique. The new method uses the generalized Newton method as the predictor step. The four-point open Newton-Cotes formula is considered the corrector step. The convergence of the proposed method is discussed in detail. This new method is highly effective for solving large systems due to its simplicity and effectiveness. We consider the beam equation, using the finite difference method to transform it into a system of absolute value equations, and then solve it using the proposed method.

The paper provides empirical insights into how to solve a system of absolute value equations.

This paper fulfills an identified need to study absolute value equations.

This study examines the Robo-Advisors (RA) based on Artificial Intelligence (AI), a new service that digitises and automates investment decisions in the financial and banking industries to provide low-cost and personalised financial advice. The RAs use objective algorithms to select portfolios, reduce behavioural biases, and improve transactions. They are inexpensive, accessible, and transparent platforms. Objective algorithms improve the believability of portfolio selection.

This study adopts a qualitative approach consisting of an exploratory examination of seven different RA case studies and analyses the RA platforms used in the banking industry.

The findings provide two different approaches to running a business that are appropriate for either fully automated or hybrid RAs through the realisation of two platform model frameworks. The research reveals that relying solely on algorithms and not including any services involving human interaction in a company model is inadequate to meet the requirements of customers in decision-making.

This study emphasises key robo-advisory features, such as investor profiling, asset allocation, investment strategies, portfolio rebalancing, and performance evaluation. These features provide managers and practitioners with new information on enhancing client satisfaction, improving services, and adjusting to dynamic market demands.

This study fills the research gap related to the analysis of RA platform models by providing a meticulous analysis of two different types of RAs, namely, fully automated and hybrid, which have not received adequate attention in the literature.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

Parallelogram linkages are used to increase the stiffness of manipulators and allow precise control of end-effectors. They help maintain the orientation of connected links when the manipulator changes its position. They are implemented in many palletizing robots connected with binary, ternary and quaternary links through both active and passive joints. This limits the motion of some joints and hence results in relative and negative joint angles when assigning coordinate axes. This study aims to provide a simplified accurate model for manipulators built with parllelogram linkages to ease the kinematics calculations.

This study introduces a simplified model, replacing each parallelogram linkage with a single (binary) link with an active and a passive joint at the ends. This replacement facilitates countering motion while preserving subsequent link orientations. Validation of kinematics is performed on palletizing manipulators from five different OEMs. The validation of Dobot Magician and ABB IRB1410 was carried out in real time and in their control software. Other robots from ABB, Yaskawa, Kuka and Fanuc were validated using control environments and simulators.

The proposed model enables the straightforward derivation of forward kinematics and transforms hybrid robots into equivalent serial-link robots. The model demonstrates high accuracy streamlining the derivation of kinematics.

The proposed model facilitates the use of classical methods like the Denavit–Hartenberg procedure with ease. It not only simplifies kinematics derivation but it also helps in robot control and motion planning within the workspace. The approach can also be implemented to simplify the parallelogram linkages of robots with higher degrees of freedom such as the IRB1410.

The purpose of this study is to investigate the thermoforming process of 3D-printed parts made from polylactic acid (PLA) and explore its application in producing wrist-hand orthoses. These orthoses were 3D printed flat, heated and molded to fit the patient’s hand. The advantages of such an approach include reduced production time and cost.

The study used both experimental and numerical methods to analyze the thermoforming process of PLA parts. Thermal and mechanical characteristics were determined at different temperatures and infill densities. An equivalent material model that considers infill within a print is proposed. Its practical use was proven using a coupled finite-element analysis model. The simulation strategy enabled a comparative analysis of the thermoforming behavior of orthoses with two designs by considering the combined impact of natural convection cooling and imposed structural loads.

The experimental results indicated that at 27°C and 35°C, the tensile specimens exhibited brittle failure irrespective of the infill density, whereas ductile behavior was observed at 45°C, 50°C and 55°C. The thermal conductivity of the material was found to be linearly related to the temperature of the specimen. Orthoses with circular open pockets required more time to complete the thermoforming process than those with hexagonal pockets. Hexagonal cutouts have a lower peak stress owing to the reduced reaction forces, resulting in a smoother thermoforming process.

This study contributes to the existing literature by specifically focusing on the thermoforming process of 3D-printed parts made from PLA. Experimental tests were conducted to gather thermal and mechanical data on specimens with two infill densities, and a finite-element model was developed to address the thermoforming process. These findings were applied to a comparative analysis of 3D-printed thermoformed wrist-hand orthoses that included open pockets with different designs, demonstrating the practical implications of this study’s outcomes.

The purpose of this study is to determine the psychological features of providing initial psychotherapeutic assistance to victims, in particular to military servants.

Theoretical analysis of scientific works; observation of servicemen in hospital conditions; conversations with servicemen and doctors; and interviews with foreign colleagues.

As a result of theoretical and empirical research, it was found that initial psychological assistance is more effective if it is provided in a timely and comprehensive manner in cooperation with medical assistance, if the recommended exercises are performed systematically by the victim and if a certain algorithm for providing initial psychotherapeutic assistance is used.

The authors present their own algorithm for providing initial psychological assistance to military servants: psychophysiological stabilization; adjustment of emotional balance; restoration of cognitive processes and acquisition of constructive coping strategies; and formation of life meanings that provide an opportunity to survive the crisis period of life. In this paper, the authors also note the importance of providing psychological first aid to victims of extreme situations in a timely manner, which helps reduce the intensity of symptoms of acute stress disorder and reduces the likelihood of post-traumatic stress disorder.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

This paper aims to highlight the expanding link between facility management (FM) and building automation and control systems (BACS) through a review of literature. It examines the opportunities and challenges of BACS for facility managers and proposes solutions for mitigating the risks associated with BACS implementation.

This paper reviews various research papers to explore the positive influences of BACS on FM, such as support with strategic decision-making, predictive maintenance, energy efficiency and comfort improvement. It also discusses the challenges of BACS, including obsolescence, interoperability, vendor lock-in, reliability and security risks and suggests potential solutions based on existing literature.

BACS offers numerous opportunities for facility managers, such as improved decision-making, energy efficiency and comfort levels in office buildings. However, there are also risks associated with BACS implementation, including obsolescence, interoperability, vendor lock-in, reliability and security risks. These risks can be mitigated through measures such as hardware and software obsolescence management plans, functional requirement lists, wireless communication protocols, advanced feedback systems and increased awareness about BACS security.

To the best of the authors’ knowledge, no prior academic research has been conducted on the expanding link between FM and BACS. Although some papers have touched upon the opportunities and challenges of BACS for FM, this paper aims to provide a comprehensive overview of these findings by consolidating existing literature.