Showing papers by "University of Rhode Island published in 2017"

Instrumented cardiac microphysiological devices via multimaterial three-dimensional printing

Abstract: Biomedical research has relied on animal studies and conventional cell cultures for decades. Recently, microphysiological systems (MPS), also known as organs-on-chips, that recapitulate the structure and function of native tissues in vitro, have emerged as a promising alternative. However, current MPS typically lack integrated sensors and their fabrication requires multi-step lithographic processes. Here, we introduce a facile route for fabricating a new class of instrumented cardiac microphysiological devices via multimaterial three-dimensional (3D) printing. Specifically, we designed six functional inks, based on piezo-resistive, high-conductance, and biocompatible soft materials that enable integration of soft strain gauge sensors within micro-architectures that guide the self-assembly of physio-mimetic laminar cardiac tissues. We validated that these embedded sensors provide non-invasive, electronic readouts of tissue contractile stresses inside cell incubator environments. We further applied these devices to study drug responses, as well as the contractile development of human stem cell-derived laminar cardiac tissues over four weeks.

Genome sequencing of the sweetpotato whitefly Bemisia tabaci MED/Q

Abstract: National Natural Science Foundation of China [31420103919, 31672032]; Chinese Academy of Agricultural Sciences (CAAS-ASTIP-IVFCAAS) the China Agriculture Research System [CARS-26-10]; Beijing Training Project for the Leading Talents in S T [LJRC201412]; Beijing Key Laboratory for Pest Control and Sustainable Cultivation of Vegetables; Beijing Nova Program [Z171100001117039]

New Literacies: A Dual-Level Theory of the Changing Nature of Literacy, Instruction, and Assessment:

Abstract: This chapter suggests that a dual-level theory of new literacies is a useful approach to theory building in a world where the nature of literacy continuously changes. It begins by making a central point: Social contexts have always shaped both the function and form of literate practices and been shaped by them in return. The chapter discusses the social context of the current period and explain how this has produced new information and communication technologies, and the new literacies that the technologies demand. It explores several lowercase new literacies perspectives that are emerging. The chapter argues that a dual-level New Literacies theory is essential to take full advantage of this important and diverse work. It presents one lowercase theory of new literacies, the new literacies of online research and comprehension, to illustrate how a dual-level theory of new literacies can inform new literacies research that takes related but different theoretical perspectives.

Non-alcoholic fatty liver disease (NAFLD) - pathogenesis, classification, and effect on drug metabolizing enzymes and transporters.

Abstract: Non-alcoholic fatty liver disease (NAFLD) is a spectrum of liver disorders. It is defined by the presence of steatosis in more than 5% of hepatocytes with little or no alcohol consumption. Insulin ...

The Effect of Molecular Rapid Diagnostic Testing on Clinical Outcomes in Bloodstream Infections: A Systematic Review and Meta-analysis

Abstract: Background Previous reports on molecular rapid diagnostic testing (mRDT) do not consistently demonstrate improved clinical outcomes in bloodstream infections (BSIs). This meta-analysis seeks to evaluate the impact of mRDT in improving clinical outcomes in BSIs. Methods We searched PubMed, CINAHL, Web of Science, and EMBASE through May 2016 for BSI studies comparing clinical outcomes between mRDT and conventional microbiology methods. Results Thirty-one studies were included with 5920 patients. The mortality risk was significantly lower with mRDT than with conventional microbiology methods (odds ratio [OR], 0.66; 95% confidence interval [CI], .54-.80), yielding a number needed to treat of 20. The mortality risk was slightly lower with mRDT in studies with antimicrobial stewardship programs (ASPs) (OR, 0.64; 95% CI, .51-.79), and non-ASP studies failed to demonstrate a significant decrease in mortality risk (0.72; .46-1.12). Significant decreases in mortality risk were observed with both gram-positive (OR, 0.73; 95% CI, .55-.97) and gram-negative organisms (0.51; .33-.78) but not yeast (0.90; .49-1.67). Time to effective therapy decreased by a weighted mean difference of -5.03 hours (95% CI, -8.60 to -1.45 hours), and length of stay decreased by -2.48 days (-3.90 to -1.06 days). Conclusions For BSIs, mRDT was associated with significant decreases in mortality risk in the presence of a ASP, but not in its absence. mRDT also decreased the time to effective therapy and the length of stay. mRDT should be considered as part of the standard of care in patients with BSIs.

Interpersonal Autonomic Physiology A Systematic Review of the Literature

Abstract: Interpersonal autonomic physiology is defined as the relationship between people's physiological dynamics, as indexed by continuous measures of the autonomic nervous system. Findings from this field of study indicate that physiological activity between two or more people can become associated or interdependent, often referred to as physiological synchrony. Physiological synchrony has been found in both new and established relationships across a range of contexts, and it correlates with a number of psychosocial constructs. Given these findings, interpersonal physiological interactions are theorized to be ubiquitous social processes that co-occur with observable behavior. However, this scientific literature is fragmented, making it difficult to evaluate consistency across reports. In an effort to facilitate more standardized scholarly approaches, this systematic review provides a description of existing work in the area and highlights theoretical, methodological, and statistical issues to be addressed in future interpersonal autonomic physiology research.

Rapid adaptive responses to climate change in corals

Abstract: Pivotal to projecting the fate of coral reefs is the capacity of reef-building corals to acclimatize and adapt to climate change. Transgenerational plasticity may enable some marine organisms to acclimatize over several generations and it has been hypothesized that epigenetic processes and microbial associations might facilitate adaptive responses. However, current evidence is equivocal and understanding of the underlying processes is limited. Here, we discuss prospects for observing transgenerational plasticity in corals and the mechanisms that could enable adaptive plasticity in the coral holobiont, including the potential role of epigenetics and coral-associated microbes. Well-designed and strictly controlled experiments are needed to distinguish transgenerational plasticity from other forms of plasticity, and to elucidate the underlying mechanisms and their relative importance compared with genetic adaptation.

Comparative Genomic Analysis of the Class Epsilonproteobacteria and Proposed Reclassification to Epsilonbacteraeota (phyl. nov.)

Abstract: The Epsilonproteobacteria is the fifth validly described class of the phylum Proteobacteria, known primarily for clinical relevance and for chemolithotrophy in various terrestrial and marine environments, including deep-sea hydrothermal vents. As 16S rRNA gene repositories have expanded and protein marker analysis become more common, the phylogenetic placement of this class has become less certain. A number of recent analyses of the bacterial tree of life using both 16S rRNA and concatenated marker gene analyses have failed to recover the Epsilonproteobacteria as monophyletic with all other classes of Proteobacteria. In order to address this issue, we investigated the phylogenetic placement of this class in the bacterial domain using 16S and 23S rRNA genes, as well as 120 single-copy marker proteins. Single- and concatenated-marker trees were created using a data set of 4,170 bacterial representatives, including 98 Epsilonproteobacteria. Phylogenies were inferred under a variety of tree building methods, with sequential jackknifing of outgroup phyla to ensure robustness of phylogenetic affiliations under differing combinations of bacterial genomes. Based on the assessment of nearly 300 phylogenetic tree topologies, we conclude that the continued inclusion of Epsilonproteobacteria within the Proteobacteria is not warranted, and that this group should be reassigned to a novel phylum for which we propose the name Epsilonbacteraeota (phyl. nov.). We further recommend the reclassification of the order Desulfurellales (Deltaproteobacteria) to a novel class within this phylum and a number of subordinate changes to ensure consistency with the genome-based phylogeny. Phylogenomic analysis of 658 genomes belonging to the newly proposed Epsilonbacteraeota suggests that the ancestor of this phylum was an autotrophic, motile, thermophilic chemolithotroph that likely assimilated nitrogen from ammonium taken up from the environment or generated from environmental nitrate and nitrite by employing a variety of functional redox modules. The emergence of chemoorganoheterotrophic lifestyles in several Epsilonbacteraeota families is the result of multiple independent losses of various ancestral chemolithoautotrophic pathways. Our proposed reclassification of this group resolves an important anomaly in bacterial systematics and ensures that the taxonomy of Proteobacteria remains robust, specifically as genome-based taxonomies become more common.

Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Cities

Abstract: Data intensive analysis is the major challenge in smart cities because of the ubiquitous deployment of various kinds of sensors. The natural characteristic of geodistribution requires a new computing paradigm to offer location-awareness and latency-sensitive monitoring and intelligent control. Fog Computing that extends the computing to the edge of network, fits this need. In this paper, we introduce a hierarchical distributed Fog Computing architecture to support the integration of massive number of infrastructure components and services in future smart cities. To secure future communities, it is necessary to integrate intelligence in our Fog Computing architecture, e.g., to perform data representation and feature extraction, to identify anomalous and hazardous events, and to offer optimal responses and controls. We analyze case studies using a smart pipeline monitoring system based on fiber optic sensors and sequential learning algorithms to detect events threatening pipeline safety. A working prototype was constructed to experimentally evaluate event detection performance of the recognition of 12 distinct events. These experimental results demonstrate the feasibility of the system's city-wide implementation in the future.

Adaptive Critic Nonlinear Robust Control: A Survey

Abstract: Adaptive dynamic programming (ADP) and reinforcement learning are quite relevant to each other when performing intelligent optimization. They are both regarded as promising methods involving important components of evaluation and improvement, at the background of information technology, such as artificial intelligence, big data, and deep learning. Although great progresses have been achieved and surveyed when addressing nonlinear optimal control problems, the research on robustness of ADP-based control strategies under uncertain environment has not been fully summarized. Hence, this survey reviews the recent main results of adaptive-critic-based robust control design of continuous-time nonlinear systems. The ADP-based nonlinear optimal regulation is reviewed, followed by robust stabilization of nonlinear systems with matched uncertainties, guaranteed cost control design of unmatched plants, and decentralized stabilization of interconnected systems. Additionally, further comprehensive discussions are presented, including event-based robust control design, improvement of the critic learning rule, nonlinear $ {H_{\infty }}$ control design, and several notes on future perspectives. By applying the ADP-based optimal and robust control methods to a practical power system and an overhead crane plant, two typical examples are provided to verify the effectiveness of theoretical results. Overall, this survey is beneficial to promote the development of adaptive critic control methods with robustness guarantee and the construction of higher level intelligent systems.

Genomic adaptation to polyphagy and insecticides in a major East Asian noctuid pest

Abstract: The tobacco cutworm, Spodoptera litura, is among the most widespread and destructive agricultural pests, feeding on over 100 crops throughout tropical and subtropical Asia. By genome sequencing, physical mapping and transcriptome analysis, we found that the gene families encoding receptors for bitter or toxic substances and detoxification enzymes, such as cytochrome P450, carboxylesterase and glutathione-S-transferase, were massively expanded in this polyphagous species, enabling its extraordinary ability to detect and detoxify many plant secondary compounds. Larval exposure to insecticidal toxins induced expression of detoxification genes, and knockdown of representative genes using short interfering RNA (siRNA) reduced larval survival, consistent with their contribution to the insect’s natural pesticide tolerance. A population genetics study indicated that this species expanded throughout southeast Asia by migrating along a South India–South China–Japan axis, adapting to wide-ranging ecological conditions with diverse host plants and insecticides, surviving and adapting with the aid of its expanded detoxification systems. The findings of this study will enable the development of new pest management strategies for the control of major agricultural pests such as S. litura. Genome of the tobacco cutworm, Spodoptera litura, which is one of the most widespread and destructive agricultural pests in tropical and subtropical Asia.

Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming

Abstract: In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.

Global analysis of depletion and recovery of seabed biota after bottom trawling disturbance

Abstract: Bottom trawling is the most widespread human activity affecting seabed habitats. Here, we collate all available data for experimental and comparative studies of trawling impacts on whole communities of seabed macroinvertebrates on sedimentary habitats and develop widely applicable methods to estimate depletion and recovery rates of biota after trawling. Depletion of biota and trawl penetration into the seabed are highly correlated. Otter trawls caused the least depletion, removing 6% of biota per pass and penetrating the seabed on average down to 2.4 cm, whereas hydraulic dredges caused the most depletion, removing 41% of biota and penetrating the seabed on average 16.1 cm. Median recovery times posttrawling (from 50 to 95% of unimpacted biomass) ranged between 1.9 and 6.4 y. By accounting for the effects of penetration depth, environmental variation, and uncertainty, the models explained much of the variability of depletion and recovery estimates from single studies. Coupled with large-scale, high-resolution maps of trawling frequency and habitat, our estimates of depletion and recovery rates enable the assessment of trawling impacts on unprecedented spatial scales.

Blood-based biomarkers in Alzheimer disease: Current state of the science and a novel collaborative paradigm for advancing from discovery to clinic.

Abstract: The last decade has seen a substantial increase in research focused on the identification of blood-based biomarkers that have utility in Alzheimer's disease (AD). Blood-based biomarkers have significant advantages of being time- and cost-efficient as well as reduced invasiveness and increased patient acceptance. Despite these advantages and increased research efforts, the field has been hampered by lack of reproducibility and an unclear path for moving basic discovery toward clinical utilization. Here we reviewed the recent literature on blood-based biomarkers in AD to provide a current state of the art. In addition, a collaborative model is proposed that leverages academic and industry strengths to facilitate the field in moving past discovery only work and toward clinical use. Key resources are provided. This new public-private partnership model is intended to circumvent the traditional handoff model and provide a clear and useful paradigm for the advancement of biomarker science in AD and other neurodegenerative diseases.

An Event-Triggered ADP Control Approach for Continuous-Time System With Unknown Internal States

Abstract: This paper proposes a novel event-triggered adaptive dynamic programming (ADP) control method for nonlinear continuous-time system with unknown internal states. Comparing with the traditional ADP design with a fixed sample period, the event-triggered method samples the state and updates the controller only when it is necessary. Therefore, the computation cost and transmission load are reduced. Usually, the event-triggered method is based on the system entire state which is either infeasible or very difficult to obtain in practice applications. This paper integrates a neural-network-based observer to recover the system internal states from the measurable feedback. Both the proposed observer and the controller are aperiodically updated according to the designed triggering condition. Neural network techniques are applied to estimate the performance index and help calculate the control action. The stability analysis of the proposed method is also demonstrated by Lyapunov construct for both the continuous and jump dynamics. The simulation results verify the theoretical analysis and justify the efficiency of the proposed method.

Q-Learning-Based Vulnerability Analysis of Smart Grid Against Sequential Topology Attacks

Abstract: Recent studies on sequential attack schemes revealed new smart grid vulnerability that can be exploited by attacks on the network topology. Traditional power systems contingency analysis needs to be expanded to handle the complex risk of cyber-physical attacks. To analyze the transmission grid vulnerability under sequential topology attacks, this paper proposes a Q-learning-based approach to identify critical attack sequences with consideration of physical system behaviors. A realistic power flow cascading outage model is used to simulate the system behavior, where attacker can use the Q-learning to improve the damage of sequential topology attack toward system failures with the least attack efforts. Case studies based on three IEEE test systems have demonstrated the learning ability and effectiveness of Q-learning-based vulnerability analysis.

Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis

Abstract: Currently, vibration analysis has been widely considered as an effective way to fulfill the fault diagnosis task of gearboxes in wind turbines (WTs) However, vibration signals are usually with abundant noise and characterized as nonlinearity and nonstationarity Therefore, it is quite challenging to extract robust and useful fault features from complex vibration signals to achieve an accurate and reliable diagnosis This paper proposes a novel feature representation learning approach, named stacked multilevel-denoising autoencoders (SMLDAEs), with the aim to learn robust and discriminative fault feature representations through a deep network architecture for diagnosis accuracy improvement In our proposed approach, we design an MLD training scheme, which uses multiple noise levels to train AEs It enables to learn more general and detailed fault feature patterns simultaneously at different scales from the complex frequency spectra of the raw vibration data, and therefore helps enhance the feature learning and fault diagnosis capability Furthermore, SMLDAE-based fault diagnosis is performed with an unsupervised representation learning procedure followed by a supervised fine-tuning process with label information for classification Our approach is evaluated by using the field vibration data collected from a self-designed WT gearbox test rig The results show that our proposed approach learned more robust and discriminative fault feature representations and achieved the best diagnosis accuracy compared with the traditional approaches

Effect of exercise training on C reactive protein: a systematic review and meta-analysis of randomised and non-randomised controlled trials

Abstract: Purpose C-reactive protein (CRP) is a marker of chronic systemic inflammation frequently used in cardiovascular disease risk assessment. The purpose of this meta-analysis was to provide a quantitative estimate of the magnitude of change in CRP following participation in physical exercise interventions. Methods All studies included in the meta-analysis were peer reviewed and published in English. Human participants were assigned to a non-exercise comparison group or exercise training group, with the intervention lasting ≥2 weeks. CRP levels were measured at baseline, during and/or after completion of the exercise training programme. Random-effects models were used to aggregate a mean effect size (ES), 95% CIs and potential moderators. Results 83 randomised and non-randomised controlled trials met the inclusion criteria and resulted in 143 effects (n=3769). The mean ES of 0.26 (95% CI 0.18 to 0.34, p Conclusions These results suggest that engaging in exercise training is associated with a decrease in CRP levels regardless of the age or sex of the individual; however, greater improvements in CRP level occur with a decrease in BMI or %Fat.

A global biogeographic classification of the mesopelagic zone

Abstract: We have developed a global biogeographic classification of the mesopelagic zone to reflect the regional scales over which the ocean interior varies in terms of biodiversity and function An integrated approach was necessary, as global gaps in information and variable sampling methods preclude strictly statistical approaches A panel combining expertise in oceanography, geospatial mapping, and deep-sea biology convened to collate expert opinion on the distributional patterns of pelagic fauna relative to environmental proxies (temperature, salinity, and dissolved oxygen at mesopelagic depths) An iterative Delphi Method integrating additional biological and physical data was used to classify biogeographic ecoregions and to identify the location of ecoregion boundaries or inter-regions gradients We define 33 global mesopelagic ecoregions Of these, 20 are oceanic while 13 are ‘distant neritic’ While each is driven by a complex of controlling factors, the putative primary driver of each ecoregion was identified While work remains to be done to produce a comprehensive and robust mesopelagic biogeography (ie, reflecting temporal variation), we believe that the classification set forth in this study will prove to be a useful and timely input to policy planning and management for conservation of deep-pelagic marine resources In particular, it gives an indication of the spatial scale at which faunal communities are expected to be broadly similar in composition, and hence can inform application of ecosystem-based management approaches, marine spatial planning and the distribution and spacing of networks of representative protected areas

Improved Sliding Mode Design for Load Frequency Control of Power System Integrated an Adaptive Learning Strategy

Abstract: Randomness from the power load demand and renewable generations causes frequency oscillations among interconnected power systems. Due to the requirement of synchronism of the whole grid, load frequency control (LFC) has become one of the essential challenges for power system stability and security. In this paper, by modeling the disturbances and parameter uncertainties into the LFC model, we propose an adaptive supplementary control scheme for the power system frequency regulation. An improved sliding mode control (SMC) is employed as the basic controller, where a new sliding mode variable is specifically proposed for the LFC problem. The adaptive dynamic programming strategy is used to provide the supplementary control signal, which is beneficial to the frequency regulation by adapting to the real-time disturbances and uncertainties. The stability analysis is also provided to guarantee the reliability of the proposed control strategy. For comparison, a particle swarm optimization-based SMC scheme is developed as the optimal parameter controller for the frequency regulation problem. Simulation studies are performed on single-area and multiarea benchmark systems, and comparative results illustrate the favorable performance of the proposed adaptive approach for the frequency regulation under load disturbances and parameter uncertainties.

Lyme disease ecology in a changing world: Consensus, uncertainty and critical gaps for improving control

Abstract: Lyme disease is the most common tick-borne disease in temperate regions of North America, Europe and Asia, and the number of reported cases has increased in many regions as landscapes have been altered. Although there has been extensive work on the ecology and epidemiology of this disease in both Europe and North America, substantial uncertainty exists about fundamental aspects that determine spatial and temporal variation in both disease risk and human incidence, which hamper effective and efficient prevention and control. Here we describe areas of consensus that can be built on, identify areas of uncertainty and outline research needed to fill these gaps to facilitate predictive models of disease risk and the development of novel disease control strategies. Key areas of uncertainty include: (i) the precise influence of deer abundance on tick abundance, (ii) how tick populations are regulated, (iii) assembly of host communities and tick-feeding patterns across different habitats, (iv) reservoir competence of host species, and (v) pathogenicity for humans of different genotypes of Borrelia burgdorferi . Filling these knowledge gaps will improve Lyme disease prevention and control and provide general insights into the drivers and dynamics of this emblematic multi-host–vector-borne zoonotic disease. This article is part of the themed issue ‘Conservation, biodiversity and infectious disease: scientific evidence and policy implications'.

Immune-mediated cytokine storm and its role in severe dengue

Abstract: Dengue remains one of the most important mosquito-borne diseases worldwide. Infection with one of the serologically related dengue viruses (DENVs) can lead to a wide range of clinical manifestations and severity. Severe dengue is characterized by plasma leakage and abnormal bleeding that can lead to shock and death. There is currently no specific treatment for severe dengue due to gaps in understanding of the underlying mechanisms. The transient period of vascular leakage is usually followed by a rapid recovery and is suggestive of the effects of short-lived biological mediators. Both the innate and the adaptive immune systems are activated in severe dengue and contribute to the cytokine production. We discuss the immunological events elicited during a DENV infection and identify candidate cytokines that may play a key role in the severe manifestations of dengue and possible interventions.

Event-Triggered Optimal Control for Partially Unknown Constrained-Input Systems via Adaptive Dynamic Programming

Abstract: Event-triggered control has been an effective tool in dealing with problems with finite communication and computation resources. In this paper, we design an event-triggered control for nonlinear constrained-input continuous-time systems based on the optimal policy. Constraints on controls are handled using a bounded function. To learn the optimal solution with partially unknown dynamics, an online adaptive dynamic programming algorithm is proposed. The identifier network, the critic network, and the actor network are employed to approximate the unknown drift dynamics, the optimal value, and the optimal policy, respectively. The identifier is tuned based on online data, which further trains the critic and actor at triggering instants. A concurrent learning technique repeatedly uses past data to train the critic. Stability of the closed-loop system, and convergence of neural networks to the optimal solutions are proved by Lyapunov analysis. In the end, the algorithm is applied to the overhead crane system to observe the performance. The event-triggered optimal controller with constraints stabilizes the system and consumes much less sampling times.

Microplastics are not important for the cycling and bioaccumulation of organic pollutants in the oceans—but should microplastics be considered POPs themselves?

Abstract: The role of microplastic particles in the cycling and bioaccumulation of persistent organic pollutants (POPs) is discussed. Five common concepts, sometimes misconceptions, about the role of microplastics are reviewed. While there is ample evidence that microplastics accumulate high concentrations of POPs, this does not result in microplastics being important for the global dispersion of POPs. Similarly, there is scant evidence that microplastics are an important transfer vector of POPs into animals, but possibly for plastic additives (flame retardants). Last, listing microplastics as POPs could help reduce their environmental impact. Integr Environ Assess Manag 2017;13:460-465. © 2017 SETAC.

Event-Triggered Adaptive Dynamic Programming for Continuous-Time Systems With Control Constraints

Abstract: In this paper, an event-triggered near optimal control structure is developed for nonlinear continuous-time systems with control constraints. Due to the saturating actuators, a nonquadratic cost function is introduced and the Hamilton–Jacobi–Bellman (HJB) equation for constrained nonlinear continuous-time systems is formulated. In order to solve the HJB equation, an actor-critic framework is presented. The critic network is used to approximate the cost function and the action network is used to estimate the optimal control law. In addition, in the proposed method, the control signal is transmitted in an aperiodic manner to reduce the computational and the transmission cost. Both the networks are only updated at the trigger instants decided by the event-triggered condition. Detailed Lyapunov analysis is provided to guarantee that the closed-loop event-triggered system is ultimately bounded. Three case studies are used to demonstrate the effectiveness of the proposed method.