Complicated System Research Articles

Application of systems modeling language (SysML) and discrete event simulation to address patient waiting time issues in healthcare

A robust health care system is crucial to reducing patient stress and contributing to economic growth. The future of the health care industry depends upon a reliable and efficient system to deal with the increasing number of patients. However, in today's healthcare system, patients face negative experiences as a result of long wait times. Now the pressing question is how to develop an effective healthcare system? To address this issue, this study uses systems engineering modeling language (SysML) coupled with a simulation approach to assess the performance of the healthcare system, identify the problem, and offer recommended alternatives. To elaborate, a formal modeling language, SysML will be used to visualize the complicated system architecture and interactions between its different components through a set of diagrams. A blood laboratory within an outpatient clinic located at southern US State is considered a testing bed. The detailed architecture of the system of interest is studied, and required data are collected for modeling and simulation. This article will provide a reference point for practitioners who would like to apply the SysML approach to address health sector-related issues.

Automating Rey Complex Figure Test scoring using a deep learning-based approach: a potential large-scale screening tool for cognitive decline

BackgroundThe Rey Complex Figure Test (RCFT) has been widely used to evaluate the neurocognitive functions in various clinical groups with a broad range of ages. However, despite its usefulness, the scoring method is as complex as the figure. Such a complicated scoring system can lead to the risk of reducing the extent of agreement among raters. Although several attempts have been made to use RCFT in clinical settings in a digitalized format, little attention has been given to develop direct automatic scoring that is comparable to experienced psychologists. Therefore, we aimed to develop an artificial intelligence (AI) scoring system for RCFT using a deep learning (DL) algorithm and confirmed its validity.MethodsA total of 6680 subjects were enrolled in the Gwangju Alzheimer’s and Related Dementia cohort registry, Korea, from January 2015 to June 2021. We obtained 20,040 scanned images using three images per subject (copy, immediate recall, and delayed recall) and scores rated by 32 experienced psychologists. We trained the automated scoring system using the DenseNet architecture. To increase the model performance, we improved the quality of training data by re-examining some images with poor results (mean absolute error (MAE) ge 5 [points]) and re-trained our model. Finally, we conducted an external validation with 150 images scored by five experienced psychologists.ResultsFor fivefold cross-validation, our first model obtained MAE = 1.24 [points] and R-squared ({R}^{2}) = 0.977. However, after evaluating and updating the model, the performance of the final model was improved (MAE = 0.95 [points], {R}^{2} = 0.986). Predicted scores among cognitively normal, mild cognitive impairment, and dementia were significantly different. For the 150 independent test sets, the MAE and {R}^{2} between AI and average scores by five human experts were 0.64 [points] and 0.994, respectively.ConclusionWe concluded that there was no fundamental difference between the rating scores of experienced psychologists and those of our AI scoring system. We expect that our AI psychologist will be able to contribute to screen the early stages of Alzheimer’s disease pathology in medical checkup centers or large-scale community-based research institutes in a faster and cost-effective way.

On a simplified multi-layered plate model of growth: Asymptotic analyses and numerical implementation

In this paper, a simplified multi-layered plate model is proposed, which can be used to study the growth-induced deformations of multi-layered hyperelastic plates. First, we introduce a full-form multi-layered plate theory of growth, which has a relatively complicated plate equation system. To simplify the plate equations, systematic asymptotic analyses are conducted with some specified magnitudes of growth functions and surface tractions. It is found that the terms containing a high-order stress tensor always have higher asymptotic orders. By dropping these higher-order terms from the original plate equations, a simplified multi-layered plate model can be derived. Despite its simplicity, this new plate model is valid in a wide range of growth and loading conditions. To be prepared for practical applications, the associated weak forms of the simplified plate equations are derived and implemented in a finite element software. Some typical examples are further studied to show the efficiency of the plate model. By virtue of the plate model, the growth deformations of multi-layered plate samples are simulated, which show very good consistency with the results of 3D volume model. Especially, the distributions of residual stresses on the interfaces of different layers can be predicted accurately, which has an important meaning for the design of soft devices with multi-layered plate structures.

Modeling and second-order sliding mode control for a full three-dimensional ridable ballbot

ABSTRACT The full 3D ridable ballbot (RB) system is a typical higher-order, multi-variable, underactuated, nonlinear, nonholonomic constraint, and strongly coupled control system. The dynamics of the RB also consists of uncertainties of the system parameters and unknown frictions. Such a complicated system requires a sophisticated control algorithm that can handle these difficulties to control the balancing and transferring of the RB. This study introduces a nonlinear robust control algorithm based on the sliding mode technique. The robust controller with sliding mode surfaces is designed to control the position of the ball on the floor and the angles of the body. The simulation and experimental results of the full 3D RB setup. The acquired results demonstrate the superior performance of the proposed controller. Meanwhile, the experimental performance of the proposed controller is validated on an actual RB.

Single-Actuated Camshaft Robot With Multiple Sequential Motions

Abstract Pipeline inspection in unknown environments is challenging for robots, and various in-pipe crawling robots have been developed in recent years to perform pipeline inspection. Most of these robots comprise multiple parts and require multiple actuators to realize the pipeline locomotion, resulting in complicated system composition and large energy consumption. In this paper, inspired by the crawling principles of earthworm locomotion, we propose a single-actuated camshaft robot that can realize multiple sequential motions for pipeline crawling. The proposed single-actuated camshaft robot contains one actuator and three parts: head anchoring, body elongation, and rear anchoring part. The multiple sequential motions of these three parts are realized based on the cam mechanisms. Umbrella-shaped elastic rubbers are circumferentially around the head and rear anchoring parts. Each part contains a cam bracket. The camshaft’s rotatory motion pushes the cam brackets to generate the axial translational motion, resulting in the umbrella-shaped elastic rubbers being expanded or contracted. The proposed camshaft robot’s expansion and contraction motion are sequentially realized by the phase deviation of the camshafts. First, the structures of the proposed robot are designed. Then, the cam curves are modeled, the expansion/contraction ratio of the rear/head anchoring part is calculated, the phase deviation of the camshafts is determined, and multiple sequential motions of the proposed robot are simulated. Finally, we fabricate the proposed camshaft robot and carry out crawling experiments in pipelines with different shapes and diameters.

THE STUDENTS’ ERRORS IN WRITING EXPOSITORY TEXT AT THE ELEVENTH GRADE OF MAS DARUL FALAH LANGGAPAYUNG

<p>This research aimed to analyze the students’ errors in writing expository text at the eleventh grade of MAS Darul Falah, Langga Payung, Labuhan Batu Selatan. In this research, the researcher used a qualitative method. The participant of this research were eleventh grade students of MAS Darul Falah. This research were conducted by snowball sampling and the researcher only took 10 students to write expository text based on the topic they choose in sixty minutes. The data were collected by giving test to the students. The data were analyzed by using documentary technique of Miles and Huberman. It was consisted of three steps were data reduction, and data display and conclusion drawing or verification. The result of the study indicating that there are some errors that made by students such as 6 errors of auxiliary verb (13%), 3 errors of subject (7%), 4 errors of conjuction (9%), 4 errors of article (9%), 3 errors of misformation (7%), 3 errors of misordering (7%), 5 errors of preposition(11%), 4 errors of singular and plural (9%), 2 errors of possesive adjective (4%), 6 errors of lexical (13%), 5 errors of capitalization (11%). From the frequency of each error types, errors of auxiliary verb and lexical errors was the errors which most frequently produced by the students. The students made errors because of their first language (Indonesian logical thinking), or we should say the students mother tongue interference (interlingual source), some students tried to translate the words one by one from Indonesian language into English without paying attention to the English structure rules, and intralingual errors that is caused of errors resulting from complicated system of the target language itself. Besides that the students still confused to arranging the sentences correctly, it is because of the lack of knowledge, grammar, pronoun or poor vocabularies and its use.</p>

Sensitivity Analysis of Mathematical Models

The construction of a mathematical model of a complicated system is often associated with the evaluation of inputs’ (arguments, factors) influence on the output (response), the identification of important relationships between the variables used, and reduction of the model by decreasing the number of its inputs. These tasks are related to the problems of Sensitivity Analysis of mathematical models. The author proposes an alternative approach based on applying Analysis of Finite Fluctuations that uses the Lagrange mean value theorem to estimate the contribution of changes to the variables of a function to the output change. The article investigates the presented approach on an example of a class of fully connected neural network models. As a result of Sensitivity Analysis, a set of sensitivity measures for each input is obtained. For their averaging, it is proposed to use a point-and-interval estimation algorithm using Tukey’s weighted average. The comparison of the described method with the computation of Sobol’s indices is given; the consistency of the proposed method is shown. The computational robustness of the procedure for finding sensitivity measures of inputs is investigated. Numerical experiments are carried out on the neuraldat data set of the NeuralNetTools library of the R data processing language and on data of the healthcare services provided in the Lipetsk region.

A Study on the System of Residence Preferences for Foreign Investors in France

As a member of the European Union (EU), France's legal system for foreign investment includes the provisions of EU law and relevant domestic legislation. EU law is the prerequisite and framework for domestic legislation to promote foreign investment. Without violating the relevant treaties signed by the EU and EU law, France can formulate its own investment management policy according to its own national conditions. In recent years, in order to promote foreign investment, France has been seeking for innovation and change, and has carried out a series of reforms on the legal system for foreign investment on the basis of compliance with the investment-related laws and regulations at the EU level. The reform of France's legal system for promoting foreign investment mainly focuses on the issuance of visas for high-end talents and foreign students to attract foreign investors, as well as multi-type and multi-level tax incentives to attract foreign-invested enterprises. However, the effectiveness of the reform of France's legal system for promoting foreign investment has been affected to some extent by the complicated tax collection and management system and the tilted protection of employees in the labor law. Through a series of studies, this paper attempts to sort out the overall thinking and specific initiatives of France's reform of its legal system for promoting foreign investment, and objectively assess its impact and effectiveness, with a view to summarizing the lessons that can be drawn from China's efforts to improve its legal system for promoting foreign investment..

EDUCATION FROM THE SYSTEMIC POINT OF VIEW: THE CONTEXT OF (NON) - CONSTANT TRANSFORMATIONS

It is generally undeniable that education is a complicated and complex system. Moreover, it is an integrated and integral system of teaching and learning at different levels and in different contexts. In the public academic educational discourse, the need to search for modern teaching and learning methods is constantly expressed so that educational institutions can better respond to the changing educational needs of learners and society. However, there is no unequivocal answer to what is “modern”. Complex knowledge of education systems (and education as a system) guarantees effective educational changes in them. If we are talking about scientific knowledge of educational phenomena, it cannot be otherwise. Based on this point of view, most educational phenomena and processes can be called systems.

Model-free adaptive robust control based on TDE for robot with disturbance and input saturation

AbstractA model-free adaptive robust control based on time delay estimation (TDE) is proposed for robot in the presence of disturbance and input saturation. TDE is utilized to estimate the complicated nonlinear terms of the robot including unknown dynamics and disturbance, and a TDE error observer is developed to estimate the inevitable TDE error. When the input torque of the robot exceeds the upper or lower limit of the input saturation, an auxiliary system and a saturation deviation boundary adaptive law are employed to mitigate the negative impact of input saturation on the position tracking. Finally, the robust control law is obtained by backstepping. The stability of the closed-loop system is proved by Lyapunov functions, and the validity of the proposed method is demonstrated by comparative simulations and experiments. Compared with the model-based controllers and other model-free controllers, the proposed method does not necessitate the accurate dynamic model of the complicated system and with lower computation. Moreover, it can guarantee the desired position tracking performance of the robot even subject to disturbance and input saturation simultaneously.

Predicting Stock Prices using Data Mining Technique

The stock market, for a long time, has been known as a complicated yet captivating system. It is a mainstream investment platform for both beginners and financially savvy people to grow and hold their assets. While it remains a good way to earn profit, the stock market is often considered as one of the risky approaches, mostly due to the nature of the field, and an enormous number of various factors that not often welcome the naïve investors. Therefore, the demand for using a tool that can support us on an overall view of the market trends, facilitating the financial analysis and strategies to identify the optimal time to purchase stocks and the actual stocks to purchase has risen for many years recently. In this study, we focus on using data mining techniques that can support investors in predicting the stock price with existing data from previous phrases. Given data is taken from Yahoo Finance within the 7-year period from 2015-2022. This data will be used to train the algorithms, then we can decide which one is the most suitable for the data mining tools to give the best suggestions for investors.

Tectonic controls on effective elastic thickness of lithospheres around the Southeast Asian subduction zones

Southeast Asia has the most complicated convergent system on the Earth. The effective elastic thickness (Te) can be a constraint on the long-term flexural strength and deformation mechanism of the lithosphere. In this paper, we estimate Te over the Southeast Asia via the free-air admittance function calculated by the continuous wavelet transform. Sedimentary correction is performed to avoid the biases induced by the low density of sediments. The spatial variations of Te coincide well with the tectonic settings. The Northeast Indian Seamount Province and young oceanic basins correspond to the lowest Te (≤10km). The Sunda Shelf displays relatively low Te (10–20 km), where Cenozoic rifting and extension weakened the lithosphere. The Molucca Sea and most continental regions show high Te (≥40 km). Overall, Te shows dependence on the oceanic crustal age in our study area, and is influenced by intensive tectonic activities. Low Te (≤10km) in northern Sumatra and central Java subduction zone is consistent with the presence of a slab tear/window and mantle upwelling. Moreover, Te in Southeast Asia is either non-correlated with surface heat flow, or weakly correlated to Curie-point depth and seismic velocities at the 100 km depth. In some regions, e.g., the South China Sea and Sumatra-Java Trench, Te shows poor correlation with all geothermal proxies we employed. We argue that the geothermal state of lithosphere exerts very limited effects on integrated lithospheric strength in the tectonically active Southeast Asia, and apart from the temperature and composition, tectonic factors dynamically control the rigidity of the lithosphere.

Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.

Broadband microwave photonic channelizer with high optical spectrum efficiency based on odd- and even-order local optical frequency combs

Microwave photonic channelizers assisted by coherent dual optical frequency combs (OFCs) have been widely considered for wideband radio frequency (RF) spectrum analysis. However, it requires dual OFCs with a large free spectral range (FSR) and multiple comb lines to support a wide RF coverage, leading to a complicated system structure and low optical spectrum efficiency. To this end, we propose a simplified, high optical spectrum-efficiency microwave photonic channelizer, wherein the odd-order and even-order OFCs with a double frequency spacing are generated and isolated to directly slice two frequency-shifted modulated optical spectra into a series of subbands. N comb lines can support 4 × N channels but take up a significantly reduced optical spectrum range. Partial experiments and simulations are presented to demonstrate the scheme. By exploiting a four-lines odd-order OFC and three-lines even OFC, a wideband RF spectrum range of 0–56 GHz is divided into 28 channels with a channel bandwidth of 2 GHz, and various wideband signals are spectrally analyzed with a high measurement accuracy and large image rejection suppression higher than 30 dB. Moreover, a 16-quadrature amplitude modulation (16QAM) vector signal with a center frequency of 7 GHz and a bit rate of 1 Gbps is channelized and successfully recovered with a good constellation diagram.

A State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations

Load frequency management (LFM) work has become more difficult due to hybrid power systems. These power systems are responsible for increased power quality issues, exacerbating control problems. Frequency changes and power quality issues are the central concerns caused by these modern power systems. Frequency deviations occur mainly due to variations in generation, consumption, or both, and one of the critical reasons is the low inertia of the modern renewable penetrated hybrid power systems. So, to handle all these issues related to frequency and many more, the need for the best controller arises, which can easily handle any nonlinearities. This article comprehensively analyzes various load frequency control (LFC) architectures in various power system configurations. It focuses on the frequency management features of various power systems, including microgrids that use renewable energy and electric vehicles, with a complete understanding of current development states. Different controllers and tactics underwent comparative investigations. The detailed tabular analysis of frequency control architectures provides a deep insight into the LFC assessment of various power system configurations. The importance of better controllers for LFC planning in complicated power system domains is also emphasized in this work.

System Requirements Development Under a Dual‐Prime Contracting Model

AbstractDeveloping requirements for a highly coupled complicated system is a challenging prospect for any project. The task gets more complicated if multiple organisations are needed to deliver the required solution. This paper relates the practical experience of ‘dual prime contracting’ where two organisations take the lead for the delivery of a system in response to the requirements of a common customer. The first part of the paper describes real motivations and contractual elements that underpin a dual prime contracting model. The technical body of the paper describes how a single requirement set is allocated and flowed to two prime contractors and introduces the common product breakdown structure. The allocation and flow down of non‐functional transversal requirements is given special treatment. The paper closes with useful insights for dual‐prime contractors, emphasising the value of early planning and preparation of joint collaboration strategies.

Stock Price Prediction Using LSTM and GRU

Abstract: Because the stock market is a very complicated nonlinear movement system whose fluctuation law is influenced by a wide range of factors, forecasting the stock price index is challenging. Numerous examples show how neural network algorithms can accurately forecast time series and frequently produce results that are adequate. The two stocks' short-term closing values were predicted using a Regularised GRULSTM neural network model that we created in this paper based on existing models. Experiments show that our suggested model predicts stock time series better than the existing GRU and LSTM network models.

Frequency-Domain Analysis and Joint Torque Vibration Suppression Control on Two-Input-Two-Output Torque Difference Amplification Motor Drive System of Electrified Vehicles

<div>To improve the cornering performance, a torque vectoring differential (TVD) that generates a torque difference between the left and right wheels has been developed. Particularly, the use of multiple electric traction motors can easily achieve this. A TVD with a two-motor-torque difference amplification mechanism (TDA-TVD), which employs a unique alignment of planetary gears, has been studied and it can generate a larger torque difference compared to an individual-wheel-drive (IWD) system in the case of using the equal torque output from the traction motors. However, due to the mechanically complicated driving force transmission system including driveshafts and planetary gears, TDA-TVD is prone to cause torsional vibrations of the driveshaft, and the yaw rate of the vehicle body while differential torque is generated. A previous study derived a dynamic model TDA-TVD and designed a vibration suppression feedforward controller. While the study demonstrated a certain vibration suppression performance, it did not strictly consider the load side characteristics during the yaw motion. In addition, there was no frequency-domain analysis either. On the other hand, this study will analyze TDA-TVD in the frequency domain and propose a novel modeling method to construct vibration suppression controllers that deal with both longitudinal motion and yaw motion simultaneously. First, this article shows a theoretical frequency-domain analysis using matrices and obtains resonance frequencies of TDA-TVD. Second, TDA-TVD is modeled based on a summation-differential mode transformation (SDMT). Third, simulations and experimental evaluations of vibration suppression controls using a real vehicle equipped with the TDA-TVD are demonstrated.</div>

Create of a Conceptual Framework for the Use of an Agent-Based Modeling in Urban Studies with an Emphasis on the Walkable City

Agent-based modeling by a bottom-up approach has shown that it has enough capacity to provide a natural definition for urban complicated system. During the last two decades, numerous studies have been dedicated to investigate the influence of urban policies on people’s tendency to walk using an agent-based approach. Therefore, it is considered to be the right time to meta-synthesize experiences and aggregating data into a unit in order to create a comprehensive framework for these studies. With this in mind, in this study, we aim to meta-synthesize these experiences to create a comprehensive framework in order to use agent-based modeling to investigate the influence of urban design and planning policies on people's tendency to walk. This study revealed that “basic model”, “disruptor” and “operation” made up the crucial elements of framework.

A novel combined model for energy consumption performance prediction in the secondary air system of gas turbine engines based on flow resistance network

Predicting the comprehensive energy consumption is challenging for the complicated secondary air system. To solve this problem, a universal flow resistance element has been constructed to describe different components more accurately. And a novel flow resistance network is proposed to calculate the system's energy consumption. This network can be analyzed by the flow resistance characteristics of its components to estimate the energy consumption of each component and the energy loss of the system. Furthermore, a flow resistance parameter is defined as a unique factor to determine the magnitude of flow resistance for each component. Compared to experimental and numerical results, both models demonstrate sufficient accuracy in calculating mass flow rates and entropy increments, with the maximum deviation less than 1.2%. The above two models are also applied to predict flow losses of elements with changes in pressure and inlet total temperature, with the maximum deviation less than 4.5%. Based on the flow resistance network, the aerodynamic performance of each element can be easily computed by the inlet and outlet boundary of this system, regardless of whether the elements are arranged in series or parallel.