Methods and Applications for Modeling and Simulation of Complex Systems

In this paper, a systematic method for determining the mathematical model for the pH neutralization process is proposed by applying system identification techniques to the controller design requirements. The identification is conducted experimentally using an actual pilot plant and analysis is performed using MATLAB toolbox for system identification. The identification is based on the first order plus dead time (FOPDT) method and subsequent application of model estimation and validation tests. In this study, pH is a controlled variable and alkali dosing pump stroke rate is a manipulated variable. The acid dosing pump stroke rate is fixed at 10%. Four open-loop step response experiments were conducted in which the alkali dosing pump stroke rate was set at 30% (Set A), 40% (Set B), 50% (Set C), and 60% (Set D). Based on the best fit performance criteria, Set B, Set C and Set D show approximately the same results. Frequency response analysis was performed using Bode plot and Nyquist diagram to determine the stability. The results show that Set B is more stable than Set C and Set D. The stability criterion is important because the obtained FOPDT model is used for controller design.

Metastasis is responsible for 90% of all cancer-related fatalities. CTCs are difficult to detect due to their rarity. Currently, the devices that aid in the detection of CTCs have limitations, such as a high price, complex apparatus, or low sample purity. Consequently, the purpose of this research is to construct and enhance a microfluidic device with a tapered angle. This device aims to outperform conventional microfluidic devices in terms of cost, sample purity, and apparatus. Using the finite element simulation software COMSOL Multiphysics, two studies are conducted, the first of which examines the effect of taper angle on particle separation and the second of which examines the effect of flow rate on particle separation. This design enables particle separation based on hydrodynamic theory and the sedimentation process. A mixture of 3 μm and 10 μm polystyrene (PS) microbeads were effectively separated when the taper angle approached 20 degrees, and separation continued until the taper angle reached 89 degrees. Using 12° and 6° taper angles, 3 μm, and 10 μm polystyrene microbeads were not effectively separated using finite element simulation. The proposed product is functionally enticing despite the fact that this design utilizes a passive separation technique. This technology provides uncomplicated, label-free, continuous separation of numerous particles in a self-contained device without the need for cumbersome equipment. Consequently, both point-of-care diagnostic instruments and micro total analytic systems could utilize this device.

Flexible Manufacturing Systems (FMS) comprised a number of machine tools alongside other material handling devices that forms a synergistic combination of productivity-efficiency transport and flexibility. In FMS, mobile robots are commonly deployed in the material handling system for the purpose of increasing the manufacturing process’ productivity and efficiency. Due to the necessity of navigating from one location to another, it is crucial to properly designed the AMR’s schedule in accordance to the real-time situation prior to planning its path. A reliable, efficient, and optimally scheduling is the most important in such manufacturing system. This paper presents the metaheuristic approaches i.e., Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to deal with the NP-hard problem of scheduling mobile robot in Job-Shop FMS environment. These algorithms are developed to find the feasible solution for the integrated problem with the goal to obtain a minimum completion time of all tasks (or makespan). The results indicated that the performance of GA provided the better solution quality in terms of minimal makespan, while PSO exhibited better convergence times.

Traditional train control systems experience problems such as low efficiency, tedious operation, high labor costs, poor visibility, and difficulty in designing and maintaining test cases during manual functional testing of onboard automatic train protection (ATP) equipment in a semi-physical testing environment. In the study, we conducted in-depth research on the functions of various ATP products and designed and implemented a general automatic test platform for ATP. Additionally, we developed a general script language for onboard ATP testing that can be converted into a set of specific script instructions based on train type. For the hardware, an extensible interface platform was designed and developed and combined with a robotic arm control unit and driver–machine interface image recognition unit to simulate ATP external input signals and driver operation. For the software, we designed a general communication protocol between devices; developed a series of toolsets including script parsing and execution, communication protocol conversion, and test process management; and realized the automatic control of the test process. An actual engineering application indicates that this ATP general automatic test platform can be used to reduce development costs and improve test production efficiency, thereby showing high practical value.

In this study on satellite-based train control systems, the data structure of electronic maps was analyzed. Moreover, a semi-physical simulation system for laboratory system integration testing was designed and implemented. By parsing and computing electronic maps and combining them with train control engineering data, the proposed design swiftly constructs a system integration testing environment, improves the real-time calculation efficiency of satellite simulation data, supports the testing and verification of the functions and data of satellite-based train control systems, and provides agile and powerful technical support for equipment development and laboratory testing. The design’s viability was confirmed via system deployment and laboratory tests in an engineering project, fulfilling the laboratory testing criteria.

The purpose of this research is to improve the accuracy of the lunar surface high-precision landing technology for small unmanned probes in the SLIM (Smart Lander for Investigating Moon) project [1] of JAXA. As a method for realizing high precision landing technology, matching by image collation using the images taken by the space probe and the lunar map image is being studied. However, shooting in outer space tends to cause positional errors due to low resolution caused by disturbances. Conventionally, position estimation has been performed by matching based on k-NN Matching and Ratio Test. However, the accuracy of the estimated position depends on the selected correspondence points, and no error correction is performed in this method. Therefore, we propose a matching model that minimizes the error of position estimation by using Bayesian estimation. Bayesian estimation is an estimation approach that uses the likelihood calculated from observed data and the prior distribution of parameters. In the proposed method, the estimation problem based on the feature values of local features is formulated as a constrained optimization problem, and a Bayesian model is constructed by designing the likelihood and prior distribution. In this paper, we realize a matching algorithm with the function of conventional self-localization by Bayesian estimation.

This paper presents a study on calculating Stress Intensity Factors (SIFs) under mixed mode using a simulation method based on Smoothed Particle Hydrodynamics (SPH). By modeling crack behaviors using the total Lagrangian SPH (TLSPH) method, we can obtain displacement and stress fields at the crack tip and further calculate the SIFs. This study used the TLSPH method to simulate cracks with mode-I loading to obtain displacement and stress field data. Using these data, we calculated SIFs under mode-I using different methods. Better results can be achieved using the extrapolation method with a relatively short calculation time. This study obtained displacement and stress field data for calculating SIFs under mode-I through SPH simulation. The simulation results show that the extrapolation stress method delivers better accuracy and time-efficiency with errors of 0.20%, 1.97%, and 4.39% for particle diameters of 0.125 mm, 0.25 mm, and 0.5 mm, respectively. These findings are essential for the study and practical engineering applications of SIFs under mixed mode.

On-orbit servicing (OOS) is a crucial research area with vast application prospects. There is currently a lack of studies focusing on a novel type of OOS scenario. In this scenario, In-Space Additive Manufacturing (ISAM) and Debris Recycling (DR) are incorporated into OOS. Traditional OOS obtains the necessary products for failed satellites through recycling space debris and ISAM, thereby improving sustainability and efficiency in the servicing process. We construct an optimization model. In this model, the total cost of velocity is chosen as the optimization objective. And mission sequences, orbital transfer time, service position, and service time are considered as design variables. Compared to traditional OOS, the novel optimization model is more prone to trapping optimizers in local optima. Therefore, we employ the Whale Optimization Algorithm (WOA) due to its ability to escape local optima. Simulation results demonstrate the effectiveness of the optimization model we have developed in representing the novel problem accurately. Moreover, the solution obtained through the utilization of the WOA closely approximates the optimal solution.

This paper presents the implementation of a Fractional Order Proportional-Integral-Derivative (FOPID) controller for attitude and altitude stabilization of a quadrotor Micro Air Vehicle (MAV). FOPID extends the conventional Proportional-Integral-Derivative (PID) form by introducing fractional orders to enhance control. It maintains the basic structure of PID with the addition of $$\lambda$$ λ for integration order and $$\mu$$ μ for differential order, allowing for the study of system performance and increased flexibility in controller design. The simulation in this study utilizes the Fractional-order Modeling and Control (FOMCON) toolbox in MATLAB. At the same time, the system’s performance is assessed through the measurement of Integral Square Error (ISE) and Integral Square Control Input (ISCI). Regarding attitude control, the FOPID controller performs better transient response, and ISE is comparable to the PD controller. However, when considering ISCI, the Proportional-Derivative (PD) controller consumes less energy. Regarding altitude control, the FOPID controller performs better ISE but exhibits a 35% overshoot compared to the PD controller. However, the energy consumption of the FOPID controller is not too significant compared to the energy consumption of the PD controller.

Researchers and control engineers have long found controlling quadrotor helicopters to be quite difficult. Due to the significant nonlinearities of this type of helicopter, numerous algorithms have been devised to control it. The dynamic model of the quadrotor has been developed in this article, and a sturdy controller has been created to handle the issue of altitude tracking and stabilization in the presence of external disturbances. Backstepping, which contains switching function as part of the controller, allows for the development of a robust control system. The switching function is utilized in the control law design to lessen the effects of external disturbances. To demonstrate the value and efficacy of the theoretical development, the proposed method is assessed in a quadrotor simulation environment. The results of the simulations demonstrate that even in the face of external perturbations, the suggested control system may produce favourable control performances for an autonomous quadrotor helicopter.

The planar electrical capacitance tomography (ECT) sensor has gained popularity for its ability to reconstruct two-dimensional (2D) and three-dimensional (3D) images in applications such as defect and landmines detection. Besides, miniature planar ECT has been investigated for biomedical imaging and lab-on-chip monitoring in terms of the design of electrode to improve the quality of reconstructed image. However, the study on the noise performance of the sensor is limited. Therefore, this paper compared the noise performance of peripheral, single plane distributed and dual plane distributed sensors in 3D image reconstruction using simulation approach. A detailed simulation procedure involving the modelling of sensors, generation of sensitivity map and simulating the capacitance measurement are described in this paper. The simulated capacitance data of the sensors were added with White Gaussian noise to produce noisy capacitance data that was used for 3D image reconstruction. Then, quality of the reconstructed 3D image at different noise level were compared quantitatively using the correlation coefficient. The simulation results revealed that the increase in the number of electrode pair combinations and the distributed electrode arrangement enables the miniature planar ECT sensor to reconstruct 3D image at low SNR of 20 dB.

Concrete is a composite material that is widely used in a construction project. The evaluation of concrete structure is very important in order to determine its strength and quality. Concrete is commonly evaluated by using the ultrasonic pulse velocity (UPV) method, which adopted the concept of measuring time of a first arrival of the received signal. Hence, this paper aims to evaluate the first arrival time of the detected ultrasonic signals based on different conditions of concrete structure. A simulation study was conducted by using COMSOL Multiphysics software version 5.6. Data collected were categorized into three sections, including in concrete model with inclusion of air hole, crack, and rust. From the simulation results, concrete models with inclusion of air hole showed an increment in the arrival time as the size of air hole increase. For the concrete models with rust, the arrival time were significantly increased in 20-mm and 40-mm rust, however it turns down as the size of rust reached 60-mm. The results also indicated that transverse crack took a longer arrival time compared to other orientation of crack.

In order to meet the requirement of fast calculation of the ground coverage time window of observation satellites, a method of fast calculation of the ground coverage time window of satellites based on the segmented dichotomy is proposed. Firstly, the method uses a 2D map with the geographic longitude of ascending node and the argument of latitude as the horizontal and vertical axes, and constructs a mathematical model for the satellite trajectory on the 2D map based on the SGP4. Subsequently, an applicable segmented dichotomy method is proposed according to the characteristics of window calculation, which can solve all intersections of satellite trajectories with the coverage area on the 2D map quickly and accurately. Finally, the intersection correction formula and the window calculation formula are proposed, and through the above two formulas, the satellite to ground coverage window is deduced. The accuracy of the algorithm is evaluated by taking one satellite to multiple points of coverage as an example, and the results show that the method is equal to the calculation results of commercial software within the error tolerance; in addition, 100 observation satellites are selected for algorithm efficiency evaluation, and Harbin city is selected as the coverage area for coverage window calculation and timing, and the results show that the calculation time of the method in this paper is reduced by more than 62% on average compared with the fixed-step method.

Soft field sensor is common with their forward problem which is the distribution of the sensitivity field throughout the target medium is not uniform. This paper concise the effect of single and segmented excitation in Electrical Capacitance Tomography for Gas-solid flow. The electrodes excitation for Protocol 1, Protocol 2, Protocol 3, and Protocol 4 were done to observe the potential value reading at the center of the pipe flow. The analysis on the potential value at the center of ECT sensor was done in COMSOL Multiphysics to determine the best excitation method. Results indicate that protocol 4 has 299.83% difference when compared to the single excitation in both homogenous and non-homogenous ECT system.

Underactuated robotic and mechatronic systems have been employed in many practical applications for a long time. It is crucial to increase crane efficiency for practical applications; yet the primary factor limiting crane efficiency is the payload swing driven on by inertia or outside disturbances. The swing of the crane's payload mass, which moves like a pendulum, has created numerous challenges since it can collide with the operator and result in accidents. This paper presents the simulation implementation of an open-loop input-shaper controller to control the swing angle of an overhead crane. A mathematical model of the two-dimensional overhead crane and input shaper controller was constructed. The model of the overhead crane and the input shaper was created in MATLAB/Simulink and the simulation was executed. This paper evaluated the performance and robustness of input shaping techniques with constant cable length using the zero vibration (ZV), zero vibration derivative (ZVD), zero vibration derivative-derivative (ZVDD), and zero vibration derivative-derivative-derivative (ZVDDD). The payload mass varied in two cases which are 1 kg and 0.3 kg. Based on the simulation results, ZVDDD controller showed the highest reductions in the overall and residual payload swing with 91% for both cases. It is envisaged that the proposed method can be used for improving the robustness of input shapers for payload swing suppression of an overhead crane.

This paper presents the development of input shaping techniques for effective sway control of a tower crane, considering varying cable lengths. The nonlinear dynamic model of the tower crane is derived using the Lagrange equation, and simulations confirm its close agreement with experimental results. To design and assess the input shaping control schemes, an unshaped square-pulse input is initially employed to determine the system’s characteristic parameters. Zero Vibration (ZV) and Zero Vibration Derivative (ZVD) shapers are specifically devised based on the tower crane’s inherent properties, and their performance is examined under various operating conditions, encompassing payload hoisting scenarios. The effectiveness of these shapers is evaluated through criteria such as maximum oscillation and reduction of mean squared error (MSE) for overall oscillations. The outcomes of this study demonstrate that implementing input shapers can offer practical and advantageous solutions for enhancing tower crane performance and ensuring smoother operation.

The need for sensing technology has arisen as a result of the growing environmental concern over microplastic pollution in water systems. In this work, a potential microplastic sensor based on an optical planar doped polymer waveguide was theoretically investigated by using Wave Optics Module-COMSOL Multiphysics® software over a range of 2 μm to 16 μm waveguide core diameters. The microplastic refractive index of the analytes employed in this research ranged from 1.48 RIU to 1.50 RIU reflecting the microplastic refractive index. The results revealed that a 2 μm core diameter of the waveguide sensor achieved the best sensitivity value of 5.12 × 10–5 at fixed cladding depth and wavelength of 0 μm and 617 nm, respectively. The simulation work offers useful information for designing a potential waveguide for real world applications to detect microplastics in water. This study is believed to contribute towards the development of cutting-edge sensing technologies through the optimized optical waveguide sensor design, which shows promise in addressing the serious problem of microplastic pollution in water systems.

Fiber Bragg Grating (FBG) has garnered significant interest in the field of bend sensors. However, one major drawback of the FBG is its inherent structure fragility due to structural deformation. Hence, this paper seeks to.remedy this problem by presenting a stress simulation of a polydimethylsiloxane (PDMS)-coated FBG by utilizing Ansys Workbench 2023 R1 software. The FBG’s design optimization was performed by varying the PDMS thickness from 0 mm to 2 mm. To evaluate the stress performance of the FBG, the coated and uncoated FBGs were compared with those subjected to an additional force of 0.010 N. The simulation results revealed that increasing the PDMS thickness slightly reduces structural deformation but significantly improves the maximum combined stress. The uncoated FBG exhibited the largest deformation of 0.28926 mm and the highest maximum combined stress of 73.309 MPa, whereas the smallest deformation of 0.27419 mm and the lowest maximum combined stress of 61.885 MPa were achieved with a PDMS thickness of 2 mm. Therefore, the minimal changes in deformation, up to 5.21% of the PDMS-coated FBG compared with the uncoated FBG, make it a suitable candidate to sustain the curvature structure of the FBG. Meanwhile, the maximum combined stress of the PDMS-coated FBG was 15.58% higher than that of the uncoated FBG, proving that the presence of PDMS improves the mechanical performance of the FBG by providing enhanced physical robustness, thereby showcasing its potential as a new bend sensor.

The largest theoretical impact of the circulating tumour DNA (ctDNA) discovery is a non-invasive liquid biopsy which could substitute surgical tumour biopsy. This work describes the numerical simulation of ctDNA extraction and separation from the blood plasma of stage I and II cancer patients using superparamagnetic bead particles in a microfluidic platform. The ctDNA extraction and separation have great importance in early detection of cancer, especially in identifying precision medicine that can be prescribed. An average 5.7 ng of circulating tumor DNA was separated efficiently for every 10 µL blood plasma input based on the simulation result from the model using the COMSOL Multiphysics 5.3a software tool. The result provides a highly valuable tool for early evaluation of cancer management.

This study proposes an Ensemble Differential Evolution with Simulation-Based Hybridization and Self-Adaptation (EDESH-SA) approach for inventory management (IM) under uncertainty. In this study, DE with multiple runs is combined with a simulation-based hybridization method that includes a self-adaptive mechanism that dynamically alters mutation and crossover rates based on the success or failure of each iteration. Due to its adaptability, the algorithm is able to handle the complexity and uncertainty present in IM. Utilizing Monte Carlo Simulation (MCS), the continuous review (CR) inventory strategy is examined while accounting for stochasticity and various demand scenarios. This simulation-based approach enables a realistic assessment of the proposed algorithm’s applicability in resolving the challenges faced by IM in practical settings. The empirical findings demonstrate the potential of the proposed method to improve the financial performance of IM and optimize large search spaces. The study makes use of performance testing with the Ackley function and Sensitivity Analysis with Perturbations to investigate how changes in variables affect the objective value. This analysis provides valuable insights into the behavior and robustness of the algorithm.

People in industries like manufacturing require, use, and produce knowledge on a daily basis. Tremendous quantity of data with difference in formats, structures and linkages need to be cautiously explored. However, the most valuable knowledge is not easy to identify or share because it is deep within the minds of experts. In manufacturing, it is very common to see dashboards on business performance, however, very few literatures available on technical knowledge management. Technical knowledge of an expert can be effectively managed and transferred by having an interface or dashboard that provides adequate information for the learners. Hence, this project aims to establish intelligent Decision Support System (iDSS) that can strategically manage, transfer, and share valuable knowledge of experts within the manufacturing organization based on machine learning and deep learning models. This study used English text data that is properly phrased to build a deep learning model in Natural Language Processing (NLP) for maintenance factory reports. As a result, interactive visualizations are presented to aid decision-makers in making knowledgeable decisions that includes the display of failure diagnostic and Named Entity Recognition (NER). These findings may provide troubleshooting insights as an assistance to new employees and deliver a precise management of decisions in looking back in history and preparing ahead. The investigation of this study will be further explored for complex numeric parameters from sensors data, integration of predictive maintenance in the dashboard, and utilizing a more sophisticated training model for better predictions.

Recently, triboelectric nanogenerators (TENGs) have emerged as promising technology to generate electricity from wasted mechanical energies based on charge transfer between two suitably selected dielectric surfaces. Simulation studies play a key role in pre-fabrication processes to understand and optimize the TENGs performance. In this work, contact-separation TENG (CS-TENG) is reported based on finite element modeling simulation. The influence of the thickness and relative permittivity of triboelectric materials on the CS-TENG performance under open-circuit (OC) and short-circuit (SC) conditions was investigated. It was found that under the OC condition, the output voltage $$V_{OC}$$ V OC shows unsignificant change (slight reduction) upon increasing the thickness of tribo-materials from 100 to 500 µm and the relative permittivity of negative tribo-material from 2.7 to 7.5. On the other hand, under the SC condition, the air gap voltage ( $$V_{gap,SC}$$ V g a p , S C ) was significantly affected (remarkably decreased) by increasing the thickness of tribo-materials 100 to 500 µm and the relative permittivity of negative tribo-material from 2.7 to 7.5. In addition, the influence of the thickness and relative permittivity on the electric field along with the distribution of electric potential and electric field within the CS-TENG structure was explored to bring an in-depth understanding of the fundamental physics of the TENGs.

Despite the growing adoption of cloud computing, security challenges continue to persist in its implementation. In this study, we delve into the specific security challenges associated with cloud computing and explore the use of machine learning algorithms like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression for anomaly detection. Our study leverages the MawiLab dataset to develop supervised machine learning models and evaluates their performance using key metrics such as accuracy, precision, recall, and F1-score. The results of our analysis showcase promising outcomes, with accuracy, precision, recall, and F1-score achieving impressive values of 96.3%, 93.8%, 95.2%, and 95.9% respectively. Nevertheless, the acquisition of real-time and unbiased datasets presents significant challenges. These findings underscore the importance of further research to enhance the applicability of machine learning techniques in effectively addressing the diverse operational conditions inherent in cloud environments.

In data-driven world, organizations face challenges in managing and analyzing large volumes of data in real-time to make informed decisions. This paper proposes a scalable and efficient big data management and analytics framework for real-time deep decision support. The framework leverages advanced technologies such as distributed computing, parallel processing, and machine learning algorithms to enable organizations to process and analyze massive amounts of data quickly and accurately. By combining real-time data processing with deep decision support capabilities, the framework empowers decision-makers with timely insights and actionable intelligence to make informed decisions. The scalability and efficiency of the framework are demonstrated through experimental evaluations using real-world big data sets. The results show that the proposed framework outperforms existing solutions in terms of processing speed, resource utilization, and decision accuracy, making it an ideal choice for organizations seeking to harness the power of big data analytics for real-time decision support.

Traditional methods of operation and resource management are unable to keep up with the growth of air traffic and passenger numbers. Delays, congestion, and suboptimal resource allocation have become urgent problems requiring efficient solutions. RBF networks offer the potential to optimize various aspects of aviation and transportation systems by leveraging historical data, real-time information, and predictive modeling. The optimization of flight routes is a complex endeavor that requires consideration of numerous factors, including weather conditions, air traffic congestion, fuel consumption, and flight schedules. By utilizing RBF networks, we intend to analyze these factors and provide recommendations for optimal flight routes that minimize travel time, fuel consumption, and emissions while ensuring passenger safety and convenience. We propose integrating intelligent RBF networks into existing aviation and transportation infrastructure to address this issue. By analyzing real-time data and historical patterns, RBF networks can identify optimal flight routes, suggest alternate routes when necessary, and aid in adjusting routes based on dynamic conditions. This integration seeks to streamline operations, reduce flight times, improve fuel efficiency, and contribute to the overall effectiveness of the system. The originality of this study resides in its application of RBF networks to the optimization of flight routes in aviation and transportation systems. While RBF networks have been utilized in a variety of domains, their incorporation into the complexities of flight route optimization remains understudied. By leveraging the power of RBF networks, we intend to provide intelligent solutions that improve efficiency, reduce costs, and contribute to the development of sustainable transportation systems. The novelty of this study lies in its application of RBF networks to optimize flight routes within aviation and transportation systems. While RBF networks have seen varied applications, their adaptation to the intricate domain of flight route optimization remains an underexplored area. By harnessing the capabilities of RBF networks, our objective is to offer intelligent solutions that enhance efficiency, cut costs, and foster the growth of sustainable transportation systems. The abstract would be enriched by including specific quantifiable benefits, such as potential percentage reductions in travel time, fuel consumption, emissions, and expenses, that RBF networks can potentially bring to the aviation and transportation industries.

The advent of Computer-Aided Design (CAD) has brought about a significant transformation in the realm of design, facilitating the efficient and accurate development of intricate objects. Conventional CAD systems are heavily dependent on centralized data processing, which gives rise to apprehensions regarding computational scalability and data privacy. The present study introduces a novel methodology for enhancing CAD through the utilization of FML in the context of innovative design procedures. The FML system facilitates the cooperation of numerous design participants while upholding the privacy of their respective local design data. Through the utilization of distributed computing power, FML facilitates the scalable training of machine learning models on decentralized data sources. The present study introduces a comprehensive framework that incorporates FML into CAD workflows. This integration facilitates designers to acquire knowledge from each other design experiences in a collaborative manner, while ensuring the confidentiality of sensitive design information. In this discourse, we examine the technical obstacles associated with the integration of FML into CAD systems and proffer remedies to mitigate them. Furthermore, our approach efficacy is illustrated through a sequence of experiments conducted on diverse design domains, exhibiting enhanced design excellence and expedited iteration cycles. The study presents novel prospects for collaborative design procedures that prioritize privacy preservation. This enables designers to collectively augment their competencies and ingenuity while ensuring the confidentiality and safety of data.

The utilization of flame synthesis as a viable method for the large-scale production of carbon nanotubes (CNTs) holds great promise. Nevertheless, works related to the optimization of the synthesis process is still limited in this study, a computational fluid dynamics (CFD) model at the flame-scale has been developed to predict the growth of CNTs within a synthesis chamber placed on top of a diffusion burner using a growth rate model (GRM). The primary objective is to analyse the effects of fuel concentration on the length of CNTs synthesized in the synthesis chamber using methane as a fuel. Generally, the length of the diffusion flame above the burner is reduced as the concentration of methane decreases which leads to a reduction in temperature within the synthesis chamber. Interestingly, about a 60% increase in maximum CNT length is predicted for flame with a reduction of methane concentration from 100 to 97 vol% which can be attributed to the favourable thermochemical conditions within the synthesis chamber. However, further decreases in fuel concentration will result in a reduction in CNT length. The growth of CNTs is not feasible with fuel concentrations below 92 vol% due to the low temperature and minimal carbon concentration. Furthermore, the optimal temperature range for CNT growth is found to be between 875K and 905K which facilitates the formation of nanoparticle catalysts and the growth of CNTs with lengths ranging from approximately 2.6 to 4.7 μm.

This study focuses on investigating the impact of using Aluminium Oxide (Al2O3) nanofluid on heat transfer within a rectangular channel featuring different protrusion heights. The primary objectives include analysing fluid flow characteristics and heat transfer performance in the channel and determining the optimal parameters and specifications. Additionally, this study places emphasis on examining the influence of protrusion height on fluid flow and heat transfer in conjunction with nanofluids. The obtained numerical results are compared with other relevant findings. The findings reveal a positive correlation between the heat transfer coefficient and both the protrusion height and Reynolds number. Similarly, the Nusselt number demonstrates an increase with the protrusion height. Furthermore, the friction coefficient displays a slight increase with both the protrusion height and Reynolds number, although the differences are not statistically significant.

This paper investigates the integral role of ultrasonic sensors in developing multi-sensor companion robots. It focuses on environment mapping, landmark-based localization, and path reconstruction. Despite the significant advancements in the field, the research identifies notable gaps that hinder the full potential of ultrasonic sensors. Addressing these gaps could revolutionize companion robots’ functionality, reliability, and efficiency, particularly in real-world applications such as healthcare, elderly care, and home assistance. The research emphasizes the importance of sensor fusion techniques and the potential improvements in environment mapping and landmark-based localization, which could significantly enhance the navigation capabilities of companion robots. Furthermore, advancements in path reconstruction could transform how companion robots retrace their steps, increasing their operational efficiency. The findings of this research contribute significantly to the field of robotics and pave the way for future studies, highlighting the transformative potential of advanced environment mapping techniques, sensor fusion, and path reconstruction in propelling advancements in robotic navigation.

Le-fort I osteotomy has been widely used by maxillofacial surgeons as a procedure due to its versatility and simplicity to treat patient with dentofacial deformity. Technology such as virtual planning software has greatly aided surgeon to prepare for the surgery by allowing them to work with clinical engineer to plan for the surgery and create custom implants design on the patient’s scanned anatomy in form of three-dimensional (3D) model. Most of convergence study for skull was done by analyzing the total number of mesh elements. Hence, this study was conducted to determine the optimum mesh size by performing mesh convergence study using h-refinement method on the upper skull 3D model. The 2D Computed-Tomography (CT) images in DICOM format were used to create the 3D model by using Mimics software. The mesh refinement process of the segmented upper skull 3D model was performed in the 3-Matic software, and the mesh size used from 5 mm to 2.5 mm with 0.5 mm interval. Marc Mentat software was used to perform finite element analysis (FEA) where a fixed load was set on superior part of the skull meanwhile point load with 125N stress was applied on incisors. Results from the mesh convergence analysis showed that percentage difference of upper skull of 4.0 mm–3.5 mm mesh size is below 5% and from the convergence plot the line graph start to converge at 3.5 mm to 4 mm. Therefore, it can be concluded that 4 mm mesh size is the optimum mesh size for upper skull 3D model.

Education has always been a crucial and concerning issue in human civilization. With the increasing popularity of games, various instructional theories based on game-based learning have been proposed. However, whether in gaming or learning, scaffolding plays a significant supportive role. Therefore, this study adopts the four dimensions of FSLSM as the classification of learning styles and designs four different types of scaffolding for players to use. By analyzing the behavior of players with different learning styles in using scaffolding during the game, we aim to tailor the most suitable scaffolding for players with different learning styles. The research findings indicate that when the educational content in game-based learning relates to logic and engineering, providing more visualized and detailed scaffolding will enhance learners’ motivation to actively use scaffolding and improve the overall learning quality.

Robot Operating System (ROS) is a framework platform that runs on UNIX operating systems and enables more efficient operation and administration of different kinds of robots. The Jaguar 4 $$\,\times \,$$ × 4 Wheel Mobile Robot by Dr. Robot Inc. is one example of an unmanned ground vehicle (UGV) that can be controlled using ROS. Dr. Robot Inc. has released a Windows-based software development kit (SDK) and open-source code to allow developers to develop this kind of robot. However, to manipulate this robot’s control and monitoring by ROS, a set of Linux command lines should be executed to launch the robot without the ability to use Dr. Robot’s SDK. Therefore, in order to benefit from the provided open-source code with ROS packages with Python and to avoid the need for running multiple Linux command lines, this paper presents a Python-based SDK and a real-time simulation for controlling and monitoring the movement of the Jaguar robot. The methodology technique that is considered in this paper is to analyze the open-source code to access all the robot’s sensor data and nodes using the proposed SDK, then simulate the hardware (the Jaguar Mobile Robot) with ROS Visualization (RViz) 3D software in real-time. The practical experiment is carried out by using a keyboard and a gamepad as controllers to achieve mobility control. The results show the robot’s position and orientation visualized by RViz, as well as its turn angle (theta), linear and angular velocities in a 2D space x-y plot. Using the RViz visualization tool and with the aid of the proposed SDK, it is straightforward to access and monitor all topics and nodes in real-time. Consequently, algorithms such as obstacle avoidance and object tracking could be readily tested and implemented in future works.

Simulations focusing on wave propagation and inverse problems involving the estimation of a scattering source have been conducted for a long time. As a method for estimating a scattering source of wave propagation, there are known techniques such as time-reversal methods that utilize simulation results and inverse scattering analysis methods based on the Born approximation. However, these methods are mathematically complex and require much computation time. Therefore, in this study, we aim to develop a scattering source estimation method using deep learning, which has been receiving increasing attention in recent years. However, the waveforms used for this scattering source estimation are generated using simulations. In this paper, we first simulate the 2-D scalar waves from the scattering source using the convolution quadrature time-domain boundary element method (CQBEM). The received waveforms at observation points are transformed into image data. These image data are utilized for the deep learning to estimate the actual position of the scattering source. As numerical examples, some unlearned waveforms by a scattering source are given to the created deep learning model and the position and size of the scattering source are estimated to verify the proposed method.

Gantry cranes are commonly utilized to transfer huge loads in construction projects as well as essential sectors such as petrochemical and nuclear power. The objective of their operation is to achieve high levels of precision in trolley positioning while simultaneously reducing the amplitudes of sway oscillations. The goal of control approaches is to achieve optimum operational efficiency by maintaining accurate trolley placement while simultaneously meeting safety requirements by reducing sway-induced oscillatory oscillations. To satisfy control objective this paper considered the design of fractional order PID (FOPID) with help of genetic algorithm to compute controller parameters using MATLAB Software. Simulation results showed the controller performance is better than classic PID and also it was capable of providing good response for different payload masses.

Preserving location privacy in Vehicular Ad hoc NETwork (VANET) is crucial for gaining public acceptance of this emerging technology. Many privacy schemes focus on periodically changing pseudonyms to prevent message linking. However, the spatiotemporal information contained in beacons allows vehicles to be traced, compromising driver privacy. Therefore, pseudonym changes should be performed in a mix-context, which disrupts the spatial and temporal correlation of subsequent beacons. This mix-context is commonly achieved through methods such as a silence period or predetermined locations (e.g., Mix-Zone). In this paper propose a new privacy scheme named SHADOW (Silent-based Hybrid Approach for Dynamic Pseudonymization and Privacy Preservation in Vehicular Networks), which is a location privacy scheme that allows vehicles to determine when to change their pseudonyms based on the number of neighboring vehicles and a silent period. Evaluated this scheme against a global multitarget tracking adversary using simulated and realistic vehicle traces and compare it with twelve previous privacy schemes in the Enhanced Privacy Extension Model (EPREXT) by using privacy metrics. The simulation results demonstrate that SHADOW achieves the highest level of security and privacy compared to the other twelve schemes.

Cardiac alternans is a beat-to-beat alternation in the electrical properties of the heart, such as membrane potential and intracellular calcium (Ca) cycling in myocytes. Due to the bi-directional coupling between voltage and calcium, it is not easy to decide which mechanism drives the alternans and thus, affect the effectiveness of controlling alternans. In this study, the control of cardiac alternans in a one-dimensional short cable was investigated numerically using the $$ T \pm \epsilon $$ T ± ϵ feedback control, where $$ T $$ T is the basic cycle length and $$ \epsilon $$ ϵ is a pre-set control parameter. The effectiveness of controlling alternans between action potential duration (APD) and peak value of intracellular calcium concentration (peak-[Ca]) being used as the feedback control variables were compared. Results showed that the effectiveness of APD-based and Ca-based feedback controls were the same when $$ \epsilon $$ ϵ was less than the critical value ( $$ \epsilon _\textrm{c} $$ ϵ c ), however, the APD-based control performed better than the Ca-based feedback control when $$ \epsilon > \epsilon _\textrm{c} $$ ϵ > ϵ c . This study may improve the understanding of which method is a better feedback control and lead to the development of a better alternans control scheme.

The prevalence of stress among individuals has become increasingly common, as approximately 40% of the population experiences stress. Given that decision-making under stressful conditions can have catastrophic consequences, it is imperative to devote additional attention to investigating the impact of acute stress on decision-making processes. The present study aims to explore the effects of acute stress, induced in a controlled laboratory environment, on decision-making. The study employed the Balloon Analog Risk Task (BART), a task designed to assess individuals’ behaviour and coping strategies during three distinct stages. The participants in this study were individuals aged between 30 and 34 years. During the first stage, participants engaged in the decision-making task without any time constraints, whereas the second stage introduced time limitations. In the third stage, both time constraints and the N-Back memory task were presented simultaneously. Participants were outfitted with the Function Near Infrared Spectroscopy (fNIRS) device throughout the experiment. Multiple repetitions of tasks and measurements were conducted. The findings of this preliminary experiment revealed that participants performed poorly in the second stage and exhibited the lowest scores in the third stage. This diminished performance was attributed to their inability to process all available information due to limited cognitive resources, resulting in increased errors and misconceptions about the main task. Consequently, these cognitive lapses facilitated the occurrence of unsafe decision-making behaviours.

While the distributor concerned striving to provide quality product and service to achieve customer satisfaction with opportunities given by the rapidly developing supplement market and e-commerce, the sales of their bestselling health supplement, bitter melon supplement dropped. Research was conducted to discover the customer satisfaction level, customers’ latent profile, opinion, and perspectives towards their purchase of the bitter melon supplement. 130 random samples of data collected from a total of 600 customers approached via a questionnaire. The data were analyzed with exploratory data analysis, association rule mining, and text analysis. The result indicated that the customers are mostly satisfied but knowing the ineffectiveness of the supplement will increase the expected dissatisfaction of customer by 1.68 times. It is found that the customers were mainly chased away by the low effectiveness of the product instead of customer service. Male at age of 45 to 54 are suggested to be considered in customer segmentation with emphasis on the natural ingredient when increasing the product visibility. The findings suggested that the enhancement in product quality and lower price is necessary for repositioning in terms of product and price.

Security is a major concern for all premise properties, such as residences, industries, etc. Lack of a secure system will open a window of opportunity for theft that causes damage, loss of property, and emotional misery. This paper mainly focused on the OWASP development of an embedded live surveillance camera with instant notification. The system functions by capturing video of any motion detected in restricted areas, such as user belongings and property. The system will immediately alert the user via Telegram and email, for further action to be taken as the result proves within an average of 2.3 s of motion detection and a user alert system of 3.8 s. The monitoring system with a high alarm rate is able to integrate with the system and communicate with the user by using an embedded system of Raspberry Pi 4 Model B. The algorithms used in this research include the dynamic programming algorithm (DPA) and the application programming interface (API). The integration and embed system indicate the system efficiency of 92.65% with error tolerance below 3.1% using Support Vector Machine (SVM) as a tool to analyze system performance of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).