Qualitative Simulation Research Articles

Bifurcation and Chaos of a Discrete-Time Population Model

A Leslie population model for two generations is investigated by qualitative analysis and numerical simulation. For the different parameters a and b in the model, the dynamics of the system are studied, respectively. It shows many complex dynamic behavior, including several types of bifurcations leading to chaos, such as period-doubling bifurcations and Neimark–Sacker bifurcations. With the change of parameters, attractor crises and chaotic bands with periodic windows appear. The largest Lyapunov exponents are numerically computed and can verify the rationality of the theoretical analysis.

Coupled Simulation-Optimization Model for the Management of Groundwater Resources by Considering Uncertainty and Conflict Resolution

Determining the optimized policies in the exploitation of groundwater water resources is a complicated issue, especially when there are several different managers with conflicting goals. The current study presents a new multi-purpose method to reach a compromise among different stakeholders by determining optimal social policies and sustainable hydro-environmental management of underground water resources. This method simultaneously considers qualitative and quantitative simulation and optimization, stakeholders’ preferences, and uncertainty analysis. In this study, the recharge was determined and incorporated in MODFLOW groundwater current model and MT3DMS pollution transfer model by using the hydrological model SWAT. In addition, DREAM (zs) algorithm (derived from algorithms based on Markov chain Monte Carlo) was used to examine the uncertainty of MODFLOW model parameters. The optimal head and TDS rate were determined in the studied aquifer by linking the model with MOPSO. Then, the Pareto frontier derived from the previous step, was utilized to determine the allocation rate of groundwater resources among a set of non-dominated solutions using Social Choice Rules (SCR) including Condorcet, Median Voting Rule (MVR), and Fallback Bargaining (FB) including unanimity fallback bargaining and fallback bargaining with impasse. The results showed that almost all the selected methods of conflict resolution in this research behaved similarly, and their results were not significantly different from each other. However, the comparison of these methods indicated that the MVR with the minimum reduction in withdrawal discharge and the maximum elevation in response to optimal allocation policies had the best performance. The amount of water extracted from the study area is about 540 million m3/year, which reaches 395 million m3/year.

The Time-Driven Activity-Based Costing Model for a Small Startup in Indonesia

The research proposed a Time-Driven Activity-Based Costing (TDABC) model for cost and human capital analysis in a small startup in Indonesia. TDABC discussed and illustrated the integration of capabilities into cost and performance in startups. The researchers applied a qualitative simulation method. The data were taken from actual regulatory data of wage and salary from the local government of Jakarta. The other data were simulated data based on a software development process for a small startup. The result indicates that the TDABC can assist the company to trace its performance and costs. Additional factors in implementing the costing system are provided for further research and practical considerations in adopting the costing system.

Learning to Control a Quadcopter Qualitatively

Qualitative modeling allows autonomous agents to learn comprehensible control models, formulated in a way that is close to human intuition. By abstracting away certain numerical information, qualitative models can provide better insights into operating principles of a dynamic system in comparison to traditional numerical models. We show that qualitative models, learned from numerical traces, contain enough information to allow motion planning and path following. We demonstrate our methods on the task of flying a quadcopter. A qualitative control model is learned through motor babbling. Training is significantly faster than training times reported in papers using reinforcement learning with similar quadcopter experiments. A qualitative collision-free trajectory is computed by means of qualitative simulation, and executed reactively while dynamically adapting to numerical characteristics of the system. Experiments have been conducted and assessed in the V-REP robotic simulator.

Optimal Robust Search for Parameter Values of Qualitative Models of Gene Regulatory Networks.

Computational and mathematical models are a must for the in silico analysis or design of Gene Regulatory Networks (GRN)as they offer a theoretical context to deeply address biological regulation. We have proposed a framework where models of network dynamics are expressed through a class of nonlinear and temporal multiscale Ordinary Differential Equations (ODE). To find out models that disclose network structures underlying an observed or desired network behavior, and parameter values that enable the candidate models to reproduce such behavior, we follow a reasoning cycle that alternates procedures for model selection and parameter refinement. Plausible network models are first selected via qualitative simulation, and next their parameters are given quantitative values such that the ODE model solution reproduces the specified behavior. This paper gives algorithms to tackle the parameter refinement problem formulated as an optimization problem. We search, within the parameter space symbolically expressed, for the largest hypersphere whose points correspond to parameter values such that the ODE solution gives an instance of the given qualitative trajectory. Our approach overcomes the limitation of a previously proposed stochastic approach, namely computational load and very reduced scalability. Its applicability and effectiveness are demonstrated through two benchmark synthetic networks with different complexity.

Object Tracking Algorithm based on Improved Siamese Convolutional Networks Combined with Deep Contour Extraction and Object Detection Under Airborne Platform

Abstract Object detection and tracking is an indispensable module in airborne optoelectronic equipment, and its detection and tracking performance is directly related to the accuracy of object perception. Recently, the improved Siamese network tracking algorithm has achieved excellent results on various challenging data sets. However, most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will lead to tracking drift and eventually cause tracking failure. In order to solve these problems, this article proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection, which uses the contour template of the object instead of the bounding-box template to reduce the background clutter interference. First, the contour detection network automatically obtains the closed contour information of the object and uses the flood-filling clustering algorithm to obtain the contour template. Then, the contour template and the search area are fed into the improved Siamese network to obtain the optimal tracking score value and adaptively update the contour template. If the object is fully obscured or lost, the YoLo v3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process. A large number of qualitative and quantitative simulation results on benchmark test data set and the flying data set show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which has high engineering application value.

Fabrication of micro rotary structures by wire electrochemical grinding

Micro rotary structures have attracted increasing attention in many industrial applications. However, the fabrication of complex micro rotary structures with high strength, high hardness, and low rigidity, poses a significant challenge to traditional machining methods. To solve this difficult problem, this study proposes a novel method of fabricating complex micro rotary structures by using wire electrochemical grinding (WECG), which is an anodic dissolution process and has no machining force. WECG, a type of ECM, has some major advantages over wire electrical discharge grinding (WEDG): no electrode wear, absence of residual stress, good surface quality, low machining cost, and high machining efficiency. Firstly, this study investigated the influences of the process parameters on the dimensional accuracy of the micro grooves produced, then optimized these process parameters. A qualitative simulation of WECG was conducted to reveal the reason for the trumpet-like micro grooves fabricated by WECG. Finally, based on the optimal process parameters, several different kinds of rotary structures were successfully produced by WECG. The experimental results demonstrate that WECG is a promising method for fabricating complex rotary structures of difficult-to-cut materials at low cost and with high efficiency.

A Fast Subpixel Registration Algorithm Based on Single-Step DFT Combined with Phase Correlation Constraint in Multimodality Brain Image.

Multimodality brain image registration technology is the key technology to determine the accuracy and speed of brain diagnosis and treatment. In order to achieve high-precision image registration, a fast subpixel registration algorithm based on single-step DFT combined with phase correlation constraint in multimodality brain image was proposed in this paper. Firstly, the coarse positioning at the pixel level was achieved by using the downsampling cross-correlation model, which reduced the Fourier transform dimension of the cross-correlation matrix and the multiplication of the discrete Fourier transform matrix, so as to speed up the coarse registration process. Then, the improved DFT multiplier of the matrix multiplication was used in the neighborhood of the coarse point, and the subpixel fast location was achieved by the bidirectional search strategy. Qualitative and quantitative simulation experiment results show that, compared with comparison registration algorithms, our proposed algorithm could greatly reduce space and time complexity without losing accuracy.

How Workplace Bullying and Incivility Impacts Patient Safety: A Qualitative Simulation Study Using BSN Students

BackgroundHealth care systems strive to provide safe and quality environments. Bullying and incivility are found internationally in health care environments and begins in academic education programs. This study evaluates the impact of a bullying simulation when students participated in different participant roles. MethodA convenience sample of 169 nursing students participated in a simulation and completed a postevaluation survey. Qualitative inductive methods were used to analyze responses. ResultsFour major themes were revealed: nurse and patient/family experience, advocacy strategies, and impact on patient outcomes. ConclusionsThe students learned that patient safety and outcomes are affected when bullying occurs. Students found the simulation meaningful.

Guided Identification of Failed Nodes Based on Failure Presentation

Sensor nodes and Internet of Things devices may experience long-term loss of functional roles. The objective of this article is to efficiently identify the failed sensors in a large-scale sensor network. The problem addressed is overcoming information constraints in identifying the location of a failed sensor using only local interactions between low-cost sensors and a network maintainer. The proposed method in this article allows the sensors to self-organize into a functional structure in which a supporting sensor set facilitates the autonomous maintainer (AM) to find the failed sensors. The supporting sensor set presents the failure information to the AM, which then rescales its search area for quick discovery of the failed sensor. Both a qualitative analysis and simulation evaluation are conducted to verify the effectiveness of the proposed approach. The results show that an appropriate presenting area of the failed sensor facilitates fast and localized discovery of failed sensors by the AM.

Superlattice period dependence on nonradiative recombination centers in the n-AlGaN layer of UV-B region revealed by below-gap excitation light

Nonradiative recombination (NRR) centers in n-AlGaN layers of UV-B AlGaN samples with different numbers of superlattice (SL) periods (SLPs), grown on the c-plane sapphire substrate at 1150 °C by the metalorganic chemical vapor deposition technique, have been studied by using below-gap-excitation (BGE) light in photoluminescence (PL) spectroscopy at 30 K. The SLP affects the lattice relaxation of the SL and n-AlGaN layer. The PL intensity decreased by the superposition of BGE light of energies from 0.93 eV to 1.46 eV over the above-gap-excitation light of energy 4.66 eV, which has been explained by a two-level model based on the Shockley–Read–Hall statistics. The degree of PL quenching from n-AlGaN layers of the sample with SLP 100 is lower than those of other samples with SLP 50, 150, and 200. By a qualitative simulation with the dominant BGE energy of 1.27 eV, the density ratio of NRR centers in n-AlGaN layers of 50:100:150:200 SLP samples is obtained as 1.7:1.0:6.5:3.4. This result implies that the number of SLP changes lattice relaxation and determines the density of NRR centers in the n-AlGaN layer, which affects the performance of LEDs.

CONSOLIDATION OF URBAN VILLAGE SETTLEMENT PATTERNS USING HYBRID ARCHITECTURE CONCEPT APPROACH; Case Study: Densely Populated Settlement of Sindulang Satu Village, Manado

The hybrid concept that develops in urban community settlements in an unplaning manner has led to various forms of settlement patterns that are adapted to social, economic and environmental conditions. This hybridity condition is potential for more innovative settlement consolidation efforts. This research was conducted to examine the quality of the hybridity formed in urban village settlements in Sindulang Satu Village, Manado, and to find the consolidation principle of settlement patterns by developing hybridity values at the site location. The quality of the hybridity was examined based on the perspective of programming hybrid, operational hybrid and spatial hybrid. This study used a qualitative research method, with modeling and experimental simulation technique approach. The analysis of settlement space performance was done by statistical calculations and graphical analysis using space syntax method. The research data was obtained using questionnaire, interviews and observations methods of samples that met the criteria of purposive sampling. The results of this study showed the potential of the hybridity in site locations with good quality, so that it became a consideration in formulating the principle of settlement pattern consolidation to increase site hybridity.

Mean-Square Exponential Stability for Stochastic Control Systems With Discrete-Time State Feedbacks and Their Numerical Schemes in Simulation

Aiming at the qualitative numerical simulation, this article concerns the mean-square exponential stability (MSES) of the stochastic systems with discrete-time state feedbacks and their numerical schemes. A time delay dependent stability criterion is obtained for the underlying system. The Euler–Maruyama and backward Euler–Maruyama schemes are established, and their MSES are proved under the same criterion, which means that the numerical schemes preserve the stability of the continuous model. The illustrative example at the end of this letter shows that the theoretical upper bound for the sampling period limited in the stability criterion is much greater than that obtained by the stability criteria from the literature.

Special Issue: Highlights From ASME CIE 2018

Special Issue: Highlights From ASME CIE 2018

Nonradiative recombination centers in deep UV-wavelength AlGaN quantum wells detected by below-gap excitation light

Nonradiative recombination (NRR) centers in AlGaN multiple quantum well samples, grown on sapphire substrate at two different growth temperatures by the MOCVD technique, have been studied by using below-gap excitation (BGE) light in photoluminescence (PL) measurements at about 25 K. The PL intensity decreased by the superposition of BGE light of energies between 0.93 and 1.46 eV over above-gap excitation light of energy 4.66 eV. This is explained by a two-level model based on Shockley–Read–Hall statistics. The model indicates the presence of a pair of NRR centers in both samples, which are activated by the BGE. The degree of PL quenching for the sample grown at 1140 °C is higher than that of the sample grown at 1180 °C for BGE energies 0.93, 1.17, and 1.27 eV. The density ratio of 1.5, for the BGE energy of 1.27 eV, was obtained from a qualitative simulation. This result implies that a slight difference in growth conditions changes defect densities.

Stochastic Optimal Reservoir Operation Management, Applying Group Conflict Resolution Model

In the present study, qualitative simulation model of reservoir and genetic algorithm optimization model were combined based on fuzzy logic to determine the trade off curve with stochastic quantitative and qualitative objectives. According to contradictory utilities of decision makers with respect to the obtained optimum trade off curve, a model of group conflict resolution was developed in order to determine the best point on the trade off curve from the perspective of decision makers. Using the outcome of conflict resolution model which was indicative of acceptable water quality for all decision makers, the optimization model was re-run, and time series of monthly water opeartion was determined for the agreed quality of beneficiaries. Finally, with the aim of formulating timely policies of operation of the reservoir, the use of probabilistic support vector machines that have the ability to create output possibilities was suggested. The proposed model was used for the operation of 15-Khordad Dam Reservoir, Iran. The results demonstrate the efficiency and accuracy of the fuzzy optimization model as well as the importance of using group conflict resolution model to provide the utility of decision makers in the operation of the reservoir.

Modelling and characterization of mechanical properties of optimized honeycomb structure

The honeycomb structure can be applied to the design of new lightweight composites materials due to its excellent properties such as high strength, strong energy absorption ability, low thermal conductivity, etc. In recent years, researchers have noticed that cell joint performance optimization can improve the performance of honeycomb structures. In this study, an optimized honeycomb structure with cell joint thickened was obtained by a theoretical analysis in which two principal geometrical parameters were adjusted, and then fabricated with additive manufacturing technology for compression testing. The results were obtained through experimentation and qualitative simulation, and then compared with those of a traditional honeycomb structure with uniform wall thickness. Finally, the mechanism of the optimized thickened-joint honeycomb structure was investigated, which could be a design guideline for the application of honeycomb structures in various engineering fields.

A Generative Human-in-the-Loop Approach for Conceptual Design Exploration Using Flow Failure Frequency in Functional Models1

A challenge systems engineers and designers face when applying system failure risk assessment methods such as probabilistic risk assessment (PRA) during conceptual design is their reliance on historical data and behavioral models. This paper presents a framework for exploring a space of functional models using graph rewriting rules and a qualitative failure simulation framework that presents information in an intuitive manner for human-in-the-loop decision-making and human-guided design. An example is presented wherein a functional model of an electrical power system testbed is iteratively perturbed to generate alternatives. The alternative functional models suggest different approaches to mitigating an emergent system failure vulnerability in the electrical power system's heat extraction capability. A preferred functional model configuration that has a desirable failure flow distribution can then be identified. The method presented here helps systems designers to better understand where failures propagate through systems and guides modification of systems functional models to adjust the way in which systems fail to have more desirable characteristics.

Action Research as a Method to Find Solutions for the Burden of Caregiving at Hospital Discharge

The role played by family caregivers in delivering long-term care is crucial: they enhance the quality of care perceived by the patients and support the shift to out-of-hospital care. However, taking care of a relative entails a huge burden that usually begins with the patient’s hospital discharge and may mean that caregivers become patients in need of care as well. Owing to socio-demographic trends, informal caregiving is the most important source of care in community settings; hence targeting the caregivers’ burden properly is crucial. This study explores how action research (AR) can be used to develop new hospital practices to manage the burden borne by family caregivers when patients are discharged from hospital. The 7-month-long action research reported in this paper consisted of three stages (burden identification, burden sharing, and burden management) and it was conducted in a teaching hospital in Rome (Italy). Both quantitative (surveys) and qualitative (focus groups and simulation sessions) techniques were used to engage participants. The AR demonstrates that there is a feasible way in which hospital managers can address proactively the caregivers’ needs in the hospital discharge process, to the advantage of the patients and the entire community.

Multiple Sclerosis multimodal lesion simulation tool (MS-MIST)

Background: Multiple Sclerosis (MS) diagnosis and evaluation is often a challenging task due to its growing need for multimodal MRI acquisition protocol. Recently, the scientific community offers several computational alternatives to the time-consuming and subjective task of manual MS lesion segmentation. Although there is an increasing number of MS lesion segmentation methods, a controlled and realistic simulation environment can benefit the community for a reliable evaluation procedure. Methods: This study proposes an automatic parametric MS lesion simulation framework (MS-MIST) with the objective to emulate real MS-like pattern on MRI data of healthy individuals. The voxel gray-level patterns, spatial location, and shapes extracted from MS patient allow consistent simulation features. We used both visual evaluation from an expert radiologist in the field of MS diagnosis and SPM Lesion Segmentation Tool (LST) for qualitative and quantitative simulation quality, respectively. Results: Our results show that both the agreement between the automatic segmentation with the simulated lesions (Pearson’s correlation R = 0.977) and the segmentation quality scores, i.e., sensitivity (mean = 0.9050), specificity (mean = 0.9992), dice similarity (mean = 0.8972), and accuracy (mean = 0.9984), are consistent between MS-MIST and real clinical settings. Conclusions: MS-MIST proposes a practical solution to common issues discussed in the literature, such as inconsistency with real lesions geometry, imprecise lesion spatial and signal variability, lack of multiple MRI modalities and restrictions to simulate different lesion loads. It is worth noting that the simulation platform is freely available as an open-source code to the community.