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Naive Bayes (NB) is one of the top ten machine learning algorithms whereas its attribute independence assumption rarely holds in practice. A feasible and efficient approach to improving NB is relaxing the assumption by adding augmented edges to the restricted topology of NB. In this paper we prove theoretically that the generalized topology may be a suboptimal solution to model multivariate probability distributions if its fitness to data cannot be measured. Thus we propose to apply log-likelihood function as the scoring function, then introduce an efficient heuristic search strategy to explore high-dependence relationships, and for each iteration the learned topology will be improved to fit data better. The proposed algorithm, called log-likelihood Bayesian classifier (LLBC), can respectively learn two submodels from labeled training set and individual unlabeled testing instance, and then make them work jointly for classification in the framework of ensemble learning. Our extensive experimental evaluations on 36 benchmark datasets from the University of California at Irvine (UCI) machine learning repository reveal that, LLBC demonstrates excellent classification performance and provides a competitive approach to learn from labeled and unlabeled data. [ABSTRACT FROM AUTHOR]

Feature selection is an optional preprocessing procedure and is frequently used to improve the classification accuracy of a machine learning algorithm by removing irrelevant and/or redundant features. However, in many real-world applications, the test cost is also required for making optimal decisions, in addition to the classification accuracy. To the best of our knowledge, thus far, few studies have been conducted on test-cost-sensitive feature selection (TCSFS). In TCSFS, the objectives are twofold: 1) to improve the classification accuracy and 2) to decrease the test cost. Therefore, in fact, it constitutes a multiobjective optimization problem. In this paper, we transformed this multiobjective optimization problem into a single-objective optimization problem by utilizing a new evaluation function and in this paper, we propose a new general wrapper framework for TCSFS. Specifically, in our proposed framework, we add a new term to the evaluation function of a wrapper feature selection method so that the test cost of measuring features is taken into account. We experimentally tested our proposed framework, using 36 classification problems from the University of California at Irvine (UCI) repository, and compared it to some other state-of-the-art feature selection frameworks. The experimental results showed that our framework allows users to select an optimal feature subset with the minimal test cost, while simultaneously maintaining a high classification accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a correlation index-based clustering algorithm (COIN) is proposed for clustering the categorical data. The proposed algorithm was tested on nine datasets gathered from the University of California at Irvine (UCI) repository. The experiments were made in two ways, one by specifying the number of clusters and another without specifying the number of clusters. The proposed COIN algorithm is compared with five existing categorical clustering algorithms such as Mean Gain Ratio (MGR), Min–Min-Roughness (MMR), COOLCAT, K-ANMI, and G-ANMI. The result analysis clearly reports that COIN outperforms other algorithms. It produced better accuracies for eight datasets (88.89%) and slightly lower accuracy for one dataset (11%) when compared individually with MMR, K-ANMI, and MGR algorithms. It produced better accuracies for all nine datasets (100%) when it is compared with G-ANMI and COOLCAT algorithms. When COIN was executed without specifying the number of clusters, it outperformed MGR for 88.89% of the test instances and produced lower accuracy for 11% of the test instances. [ABSTRACT FROM AUTHOR]

Averaged one-dependence estimators (AODE) is a state-of-the-art machine learning tool for classification due to its simplicity, high computational efficiency, and excellent classification accuracy. Weighting provides an effective mechanism to ensemble superparent one-dependence estimators (SPODEs) in AODE by linearly aggregating their weighted probability estimates. Supervised weighting and unsupervised weighting are proposed to learn weights from labeled or unlabeled data, whereas their interoperability has not previously been investigated. In this paper, we propose a novel weighting paradigm in the framework of semi-supervised learning, called semi-supervised weighting (SSW). Two different versions of weighted AODEs, supervised weighted AODE (SWAODE) which performs weighting at training time and unsupervised weighted AODE (UWAODE) which performs weighting at classification time, are built severally. Log likelihood function is introduced to linearly aggregate the outcomes of these two weighted AODEs. The proposed algorithm, called SSWAODE, is validated on 38 benchmark datasets from the University of California at Irvine (UCI) machine learning repository and the experimental results prove the effectiveness and robustness of SSW for weighting AODE in terms of zero-one loss, bias, variance and etc. SSWAODE well achieves the balance between the ground-truth dependencies approximation and the effectiveness of probability estimation. [ABSTRACT FROM AUTHOR]

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• A general model of sagged-cable-crosstie is established by introducing spatial and temporal nondimensionalization. • The general expression of the characteristic equation's 2N(M+1)-order coefficient matrix is given for the first time. • A minimal set of fundamental key dimensionless parameters that govern the system's modal characteristics are intuitively found. • The general model is degenerated into three models and is high-precisely verified by literature and FEM. Though crosstie has become a promising approach for vibration mitigation of long cables, its mechanism is yet to be fully understood, restricting the set of engineering design theory. Scholars worldwide have continuously proposed various mechanical models to investigate modal characteristics of cable-crosstie structures. However, first, dynamic equations were dimensional or not fully dimensionalized, hindering the definition of essential independent key parameters that govern the modal characteristics of the system; second, no general expression of the coefficient matrix in characteristic equations was given. This paper proposes a general sagged-cable-crosstie model applicable to structures with a generic number of cables and crossties. A universal dimensionless dynamic equation is derived by thorough dimensionless treatment, and the universal expression of a 2 N (M + 1) -order coefficient matrix is obtained by introducing boundary, equilibrium, and continuity conditions. A minimal set of dimensionless parameters that govern modal characteristics of any sagged-cable-crosstie system is found, i.e., crosstie positions ε j , p , Irvine parameters of cables λ j 2 , dimensionless wave speed parameters α j , and dimensionless crosstie stiffnesses k j , p. Subsequently, the general model degenerates into three representative models: double-cable-single-crosstie, three-cable-single-crosstie, and double-cable-double crosstie. Frequencies of each degenerated model are compared with that of literature and FEM, and good consistency is obtained. [ABSTRACT FROM AUTHOR]

• The simultaneous existence of cross-over and veering that commonly wouldnt co-existence in single classical cables is found. • The setting of crosstie(s) can only significantly increase the out-of-phase modal frequency of specific orders. • The increment limits of dimensionless frequencies for systems with identical sagged cables are found. • Wave speed difference of sagged cables can enhance the effect of crosstie(s) to further increase the high-order modal frequencies. Analysis of key parameters is an effective means to understanding modal characteristics of a system. However, because of the large number of components, there are various geometric, material, and physical parameters in a sagged-cable-crosstie structure, which inevitably hinders the reasonable selection of key parameters. Based on the companion (Part 1) paper, the effect of four fundamental key parameters, i.e., the Irvine parameter, position and stiffness of crosstie(s), and wave speed ratio, on the modal characteristics of three representative models: double-cable-single-crosstie, three-cable-single-crosstie, and double-cable-double-crosstie, are investigated by mechanical modeling-based parametric analysis. The simultaneous existence of cross-over and veering phenomena that commonly wouldn't co-exist in single classical cables is found in sagged-cable-crosstie structures. Generally, the frequency curves of all the in-phase modes, out-of-phase modes with symmetrically arranged crossties, and specific out-of-phase modes with crossties just at modal nodes show cross-over phenomena, while that of out-of-phase modes with non-symmetrically arranged crossties show veering phenomena. Setting one or two crossties can only significantly increase the out-of-phase modal frequencies of specific orders, and the increment limits of dimensionless frequency for systems with identical sagged cables are found to be 1 and 2 respectively, no matter how the crosstie stiffness and position are adjusted and how many cables are connected. However, the wave speed difference between sagged cables can enhance the effect of crosstie(s) to further increase modal frequencies, especially for high-order ones, and hence break through the above increment limits. Moreover, the more cables with wave speed differences connected by crossties and the greater the difference in wave speed, the more the system frequency increases. [ABSTRACT FROM AUTHOR]

• We have developed a clustering algorithm for an unknown number of clusters by MOGA. • It works with continuous and categorical featured data sets. • It can work with data sets having missing values. • The final solution is selected by majority vote by all non-dominated solutions. • Context-sensitive and cluster-orient genetic operators are designed. Many clustering algorithms categorized as K -clustering algorithm require the user to predict the number of clusters (K) to do clustering. Due to lack of domain knowledge an accurate value of K is difficult to predict. The problem becomes critical when the dimensionality of data points is large; clusters differ widely in shape, size, and density; and when clusters are overlapping in nature. Determining the suitable K is an optimization problem. Automatic clustering algorithms can discover the optimal K. This paper presents an automatic clustering algorithm which is superior to K -clustering algorithm as it can discover an optimal value of K. Iterative hill-climbing algorithms like K -Means work on a single solution and converge to a local optimum solution. Here, Genetic Algorithms (GA s) find out near global optimum solutions, i.e. optimal K as well as the optimal cluster centroids. Single-objective clustering algorithms are adequate for efficiently grouping linearly separable clusters. For non-linearly separable clusters they are not so good. So for grouping non-linearly separable clusters, we apply Multi-Objective Genetic Algorithm (MOGA) by minimizing the intra-cluster distance and maximizing inter-cluster distance. Many existing MOGA based clustering algorithms are suitable for either numeric or categorical features. This paper pioneered employing MOGA for automatic clustering with mixed types of features. Statistical testing on experimental results on real-life benchmark data sets from the University of California at Irvine (UCI) machine learning repository proves the superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Background: Pulmonary embolism (PE) is a common disease with an annual incidence of about 1/1000 persons. About every sixth patient dies within the first 30 days after diagnosis. The electrocardiogram (ECG) is one of the first diagnostic tests performed, and is able to confirm the suspicion of PE with typical electrocardiographic signs. Some ECG signs and their regression are also prognostically relevant. Endovascular mechanical thrombectomy is one option for PE treatment, and aims to relieve right heart strain immediately. The first studies on endovascular mechanical thrombectomy using a dedicated device (FlowTriever System, Inari Medical, Irvine, CA, USA) yielded promising results. Methods: In the following, we report the case of a 66-year-old male patient who presented with New York Heart Association III dyspnea in our emergency department. Among typical clinical and laboratory results, he displayed very impressive electrocardiographic and radiological findings at the time of PE diagnosis. Results: After endovascular mechanical thrombectomy, the patient's complaints and pulmonary hemodynamics improved remarkably. In contrast, the ECG worsened paradoxically 18 h after intervention. Nevertheless, control echocardiography 4 days after the intervention no longer showed any signs of right heart strain, and dyspnea had disappeared completely. At a 4-month follow-up visit, the patient presented as completely symptom-free with a high quality of life. His ECG and echocardiography were normal and excluded recurrent right heart strain. Conclusions: Overall, the patient benefitted remarkably from endovascular mechanical thrombectomy, resulting in an almost complete resolution of electrocardiographic PE signs at the 4-month follow-up after exhibiting multiple typical electrocardiographic PE signs at time of diagnosis and initial electrocardiographic worsening 18 h post successful intervention. [ABSTRACT FROM AUTHOR]

After the presentation of 60 papers at the conference “Advancing Aerosol Dosimetry Research” (October 24–25, 2014 in Irvine, CA, USA), attendees submitted written descriptions of needed research. About 40 research needs were submitted. The suggestions fell into six broad categories: 1) Access to detailed anatomic data; 2) Access to subject-specific aerosol deposition datasets; 3) Improving current inhaled aerosol deposition models; 4) Some current experimental data needs and hot topics; 5) Linking exposure and deposition modeling to health endpoints; and 6) Developing guidelines for appropriate validation of dosimetry and risk assessment models. Summaries of suggestions are provided here as an update on research needs related to inhaled aerosol dosimetry modeling. Taken together, the recommendations support the overarching need for increased collaborations between dose modelers and those that use the models for risk assessments, aerosol medicine applications, design of toxicology experiments, and extrapolation across species. This paper is only a snapshot in time of perceived research needs from the conference attendees; it does not carry the approval of any agency or other group that plans research priorities or that funds research. [ABSTRACT FROM AUTHOR]

Data splitting is an important step in artificial neural network (ANN) models, which is found in the form of training and testing subsets. In general, a random data splitting method is favored to divide a pool of samples into subsets, without considering the quality of data for the training step of a neural network. The drawback of poor data splitting methods is that they poses ill effects to the performance of the neural network when the data involves complex matrices or multivariate modeling. In order to overcome this drawback, the current paper presents our proposed M-SPXY method. It is based on a modified version of Sample Set Partitioning, which relies on a joint X-y distances (SPXY) method. The proposed method has resulted in better performance, compared to the modified Kennard-Stone (KS) method, using Mahalanobis distances (MDKS). In our experiments, the proposed approach was employed to compare various data splitting methods using data sets from the repository of the University of California in Irvine (UCI), processed through an Extreme Learning Machine (ELM) neural network. Performance was measured in terms of classification accuracy. The results indicate that the classification accuracy of the proposed M-SPXY process is superior to that of the MDKS data splitting method. [ABSTRACT FROM AUTHOR]

In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods. [ABSTRACT FROM AUTHOR]

STERIX is a new data-intensive storage and computing platform project spanning UC Irvine, UC Riverside, and UC San Diego. In this paper we provide an overview of the ASTERIX project, starting with its main goal-the storage and analysis of data pertaining to evolving-world models. We describe the requirements and associated challenges, and explain how the project is addressing them. We provide a technical overview of ASTERIX, covering its architecture, its user model for data and queries, and its approach to scalable query processing and data management. ASTERIX utilizes a new scalable runtime computational platform called Hyracks that is also discussed at an overview level; we have recently made Hyracks available in open source for use by other interested parties. We also relate our work on ASTERIX to the current state of the art and describe the research challenges that we are currently tackling as well as those that lie ahead. [ABSTRACT FROM AUTHOR]

This paper presents a new learning algorithm for multitask pattern recognition (MTPR) problems. We consider learning multiple multiclass classification tasks online where no information is ever provided about the task category of a training example. The algorithm thus needs an automated task recognition capability to properly learn the different classification tasks. The learning mode is "online" where training examples for different tasks are mixed in a random fashion and given sequentially one after another. We assume that the classification tasks are related to each other and that both the tasks and their training examples appear in random during "online training." Thus, the learning algorithm has to continually switch from learning one task to another whenever the training examples change to a different task. This also implies that the learning algorithm has to detect task changes automatically and utilize knowledge of previous tasks for learning new tasks fast. The performance of the algorithm is evaluated for ten MTPR problems using five University of California at Irvine (UCI) data sets. The experiments verify that the proposed algorithm can indeed acquire and accumulate task knowledge and that the transfer of knowledge from tasks already learned enhances the speed of knowledge acquisition on new tasks and the final classification accuracy. In addition, the task categorization accuracy is greatly improved for all MTPR problems by introducing the reorganization process even if the presentation order of class training examples is fairly biased. [ABSTRACT FROM AUTHOR]

Predictive emission monitoring systems (PEMS) are software solutions for the validation and supplementation of costly continuous emission monitoring systems for natural gas electrical generation turbines. The basis of PEMS is that of predictive models trained on past data to estimate emission components. The gas turbine process dataset from the University of California at Irvine open data repository has initiated a challenge of sorts to investigate the quality of models of various machine learning methods to build a model for predicting CO and NOx emissions depending on ambient variables and the parameters of the technological process. The novelty and features of this paper are: (i) a contribution to the study of the features of the open dataset on CO and NOx emissions for gas turbines, which will enable one to more objectively compare different machine learning methods for further research; (ii) for the first time for the CO and NOx emissions, a model based on symbolic regression and a genetic algorithm is presented—the advantage of this being the transparency of the influence of factors and the interpretability of the model; (iii) a new classification model based on the symbolic regression model and fuzzy inference system is proposed. The coefficients of determination of the developed models are: R 2 = 0.83 for NOx emissions, R 2 = 0.89 for CO emissions. [ABSTRACT FROM AUTHOR]

The exponential growth in the use of smartphones means that users must constantly be concerned about the security and privacy of mobile data because the loss of a mobile device could compromise personal information. To address this issue, continuous authentication systems have been proposed, in which users are monitored transparently after initial access to the smartphone. In this study, the authors address the problem of user authentication by considering human activities as behavioural biometric information. The authors convert the behavioural biometric data (considered as time series) into a 2D colour image. This transformation process keeps all the characteristics of the behavioural signal. Time series does not receive any filtering operation with this transformation, and the method is reversible. This signal‐to‐image transformation allows us to use the 2D convolutional networks to build efficient deep feature vectors. This allows them to compare these feature vectors to the reference template vectors to compute the performance metric. The authors evaluate the performance of the authentication system in terms of Equal Error Rate on a benchmark University of Californy, Irvine Human Activity Recognition dataset, and they show the efficiency of the approach. [ABSTRACT FROM AUTHOR]

Inhaled aerosols whether beneficial or harmful express their effects depending on the amount initially deposited in the body (i.e. the deposition dose), with clearance mechanisms including dissolution, mucociliary transport and translocation then determining retained dose in the respiratory tract. The many regions of the respiratory tract differ in their shapes, cellular makeups, defenses, and pathologies. As a result, predicting, measuring, and simulating initial deposition doses and subsequent events for deposited particles, vapors and gases presents formidable challenges. Interest in supplementing and in some cases replacing laboratory animals in inhalation studies with in vitro cellular exposures, challenges researchers with both designing exposure systems and defining the internal doses to exposed cells. Addressing these challenges requires the cooperation and collaboration of many specialists that do not normally meet at discipline-specific conferences. To address that historical limitation, research related to the dosimetry of inhaled aerosols has been addressed at a series of three issue oriented international conferences. This paper addresses the research-related needs and recommendations from the third, October 2019 conference held in Irvine, CA, USA. Conference participants were invited to submit suggestions related to research. This paper includes those suggestions plus some offered by e-mail after the conference. Over 40 submitted suggestions are organized into several categories. Sample suggestions include; 1. research-related activities should address an identified health related problem; 2. in vitro aerosol systems require development in order to have better-defined doses to cells and to represent actual in vivo exposures; 3. additional knowledge on respiratory tract anatomy is needed for several species and for the variations within humans and other species; 4. dosimetric models should be expanded in scope, and should improve their usability by non-modelers; and 5. there is value in introducing newcomers to the field of inhaled aerosols. Collaborations among several disciplines are emphasized in order to advance inhaled dosimetry models and their applications to laboratory and real-life inhalation exposures. The suggestions reported here are a snapshot in time from experts from a variety of disciplines. The categories represent the major topic areas discussed at the meeting, but within each area the recommendations are not prioritized. As such, they do not carry the approval of any research institutions, research funding agencies, private entities, or regulatory agencies. The authors are solely responsible for the contents of this paper. • Presents several suggestions for improving inhaled aerosol dosimetry modeling. • Considers computational fluid dynamic and traditional mechanistic model approaches. • Includes both in vivo and in vitro aerosol exposure scenarios. • Includes applications of aerosol models for risk assessments. • Stresses the importance of collaborative multi-disciplinary efforts. [ABSTRACT FROM AUTHOR]

Hepatitis C is a liver infection caused by a virus, which results in mild to severe inflammation of the liver. Over many years, hepatitis C gradually damages the liver, often leading to permanent scarring, known as cirrhosis. Patients sometimes have moderate or no symptoms of liver illness for decades before developing cirrhosis. Cirrhosis typically worsens to the point of liver failure. Patients with cirrhosis may also experience brain and nerve system damage, as well as gastrointestinal hemorrhage. Treatment for cirrhosis focuses on preventing further progression of the disease. Detecting cirrhosis earlier is therefore crucial for avoiding complications. Machine learning (ML) has been shown to be effective at providing precise and accurate information for use in diagnosing several diseases. Despite this, no studies have so far used ML to detect cirrhosis in patients with hepatitis C. This study obtained a dataset consisting of 28 attributes of 2038 Egyptian patients from the ML Repository of the University of California at Irvine. Four ML algorithms were trained on the dataset to diagnose cirrhosis in hepatitis C patients: a Random Forest, a Gradient Boosting Machine, an Extreme Gradient Boosting, and an Extra Trees model. The Extra Trees model outperformed the other models achieving an accuracy of 96.92%, a recall of 94.00%, a precision of 99.81%, and an area under the receiver operating characteristic curve of 96% using only 16 of the 28 features. [ABSTRACT FROM AUTHOR]

Many investigations on the response phase of cables mainly focus on the phase shift value in the linear solution, while the effect of the higher-order approximate terms (HOAT) is often omitted. To ascertain the effect of the HOAT on response phases, instantaneous phase-frequency characteristics of a classical externally- and parametrically-excited suspended cable are investigated. The Galerkin method is used to discretize the motion equations into ordinary differential equations, and the Multiple Scales Method (MSM) is used to solve these equations. Afterward, cable responses under these two types of excitation with different Irvine parameters λ 2 and excitation frequency Ω are numerically solved, and instantaneous phase differences between the responses and excitations are obtained by using the Hilbert transform. Then, variation characteristics of the instantaneous phase differences and corresponding amplitudes are analyzed in the ( λ 2 , Ω ) plane. It is shown that if the HOAT are not considered, the phase shifts of cable response would be constants. On the contrary, if they are included, the drift term (DT) and the doubling-frequency term (DFT) in the HOAT would vary periodically with time. Due to the difference in the frequency-response equation's right-hand terms between these two excitations, response amplitudes are different, affecting the phase-frequency characteristics through the DT and the DFT. The response-excitation instantaneous phase difference amplitude p max under external and parametric excitation are both suddenly increase in the local region centered on λ 2 ≈ 3.0 and Ω ≈ 1.125 and present a near-antisymmetrical distribution. However, the sudden-change-region of the former is a long and narrow band along the axis of λ 2 in the ( λ 2 , Ω ) plane, while that of the latter is a point field. Besides, values of the former are significantly larger than of the latter. [ABSTRACT FROM AUTHOR]

The modern industrial and consumer applications in accordance with the concepts of Industry 4.0 and the Internet of Things are characterized by autonomy and self-sufficiency. This has led to an increase in the interest for the so-called smart materials, capable of combining the functionalities of sensors, actuators and, in some applications, control systems. An important group of smart materials are shape-memory alloys, among which nickel–titanium (NiTi) alloys are the most known. In this article, the influence of phase transformation on the mechanical properties of thin NiTi alloy wires was investigated. During the test, the influence of the heating currents on the displacement and the force generated by the thin NiTi wires were analyzed. The temperature of the wires during heating was measured by a thermographic camera. This study proved the maximum value of the wires' displacement was related to the value of the heating current. During the research, the dependence of the transformation dynamics on the value of the heating currents was also proved. In addition, the influence of the surface inhomogeneity of the thin NiTi alloy wires on the accuracy of the thermographic measurements was analyzed. For the experimental research described in this article, we used the NiTi alloy whose trade name is Flexinol, produced by DYNALLOY (Inc. 2801 McGaw Ave. Irvine, CA, USA). [ABSTRACT FROM AUTHOR]

This paper introduces a novel classification rule mining model based on Pareto-based Multiobjective Optimization called CRM-PM. The process of rule extraction is a challenging classification task in data mining since it has several constraints and conflicting objectives such as accuracy and comprehensibility. In this study, this task is accepted as a multi-objective optimization problem. Classification accuracy and misclassification ratio are assigned as evaluation criteria. The candidate solutions are generated in the direction of a proposed strategy to determine optimal ranges of the attributes that form the rules. The proposed approach is applied on eight benchmark datasets (Iris Plants, Wine Quality, Glass Identification, Stat log (Heart), Haberman's Survival, E-coli, Wisconsin Breast Cancer, and Pima Indians Diabetes) included in the University of California at Irvine machine learning repository. Furthermore, CRM-PM is run in three different validation modes: cross-validation, training without test data, and training with random splitting. Regarding experimental results, it can be said that the presented method has a promising capability for classification, and it achieves comparative or superior results. [Display omitted] • A novel CRM model based on multiobjective optimization, run fast, and has a high accuracy rate. • A stepwise rule extraction process. • A new flexible structure to determine the optimum ranges of each attribute in multi-class data sets. • A new running model depends on a removing strategy that evolves the classification accuracy. • A fair comparison with 3 different validations: cross-validation, training without test, and random split. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents experimental and numerical results of the first phase of a multi-phase comprehensive joint research program between University of Salerno, Italy, and the University of California, Irvine, USA, on investigating one of the major structural issues that defines the strength limit-state of pultruded fiber-reinforced polymer (PFRP) profiles. Specifically, the strength and stiffness of the web–flange junction (WFJ) of the majority of commercially-produced pultruded composite profiles. A summary of experimental results for 28 full-scale pull-out tests are presented and typical modes failure are identified. Moreover, the influence of the pull-out load distance (d) from the edge of the specimens on the failure strength of the web–flange junction has been investigated and a new definition for an “influence zone” is proposed that is found to be dependent on the loaded length, with a maximum value equal to approximately the PFRP member’s depth. This proposed zone was observed in all laboratory tests and its existence was confirmed by the results of FEM numerical analysis. 3-D finite-element models were also developed to predict the behavior of these specimens. The results from the numerical models were compared to those obtained from the experimental program and found to be satisfactory. [Copyright &y& Elsevier]

Swarm intelligence techniques have a vast range of real world applications.Some applications are in the domain of medical data mining where, main attention is on structure models for the classification and expectation of numerous diseases. These biomedical applications have grabbed the interest of numerous researchers because these are most serious and prevalent causes of death among the human whole world out of which breast cancer is the most serious issue. Mammography is the initial screening assessment of breast cancer. In this study, an enhanced version of Harris Hawks Optimization (HHO) approach has been developed for biomedical databases, known as DLHO. This approach has been introduced by integrating the merits of dimension learning-based hunting (DLH) search strategy with HHO. The main objective of this study is to alleviate the lack of crowd diversity, premature convergence of the HHO and the imbalance amid the exploration and exploitation. DLH search strategy utilizes a dissimilar method to paradigm a neighborhood for each search member in which the neighboring information can be shared amid search agents. This strategy helps in maintaining the diversity and the balance amid global and local search. To evaluate the DLHO lot of experiments have been taken such as (i) the performance of optimizers have analysed by using 29-CEC -2017 test suites, (ii) to demonstrate the effectiveness of the DLHO it has been tested on different biomedical databases out of which we have used two different databases for Breast i.e. MIAS and second database has been taken from the University of California at Irvine (UCI) Machine Learning Repository.Also to test the robustness of the proposed method its been tested on two other databases of such as Balloon and Heart taken from the UCI Machine Learning Repository. All the results are in the favour of the proposed technique. [ABSTRACT FROM AUTHOR]

Background. The radiofrequency impedance measurement is one of the basic parameters monitored during ablation procedures. An abrupt rise in impedance is often observed corresponding to a steam pop. The exact correlation between the occurrence of steam pop and subsequent rise in impedance has not been experimentally described so far. Objectives. To evaluate the relationship between steam pop occurrence and impedance fluctuations observed during radiofrequency ablation (RFA). Materials and methods. Porcine heart tissue specimens were appropriately prepared and placed in an experimental setup connected to electrophysiological equipment with 3D anatomical mapping facilities. The RFA lesions were performed in standardized conditions with the use of contact force measurement-enabled open irrigation ablation catheter (ThermoCool SmartTouch™, 3.5 mm tip, F-J curvature; Biosense Webster, Irvine, USA) in the power-control mode. The RFA delivery was stopped when the steam pop occurred. Time taken for the steam pop to occur and to the subsequent abrupt impedance rise was recorded, along with the impedance fluctuations during an application. Results. In total, 25 experimental radiofrequency (RF) current deliveries ended up with steam pops, which occurred after 30--60 s. The time recorded from the beginning of the application up to the steam pop was shorter if increased power was applied (35 W compared to 30 W: 41.5 ±9.9 s compared to 49.9 ±8.2 s; p = 0.046). During all RF applications, impedance significantly but gradually decreased from 122.9 ±7.9 Ω to 87.5 ±3.6 Ω (p < 0.001) with a mean drop rate of 0.8 ±0.2 Ω/s. During all experiments, the abrupt and significant impedance increase (8.2 ±2.0 Ω, p < 0.001) was observed always after steam pop occurrence (207.4 ±155.9 ms). Conclusions. During RF current delivery which ended up with steam pop, an abrupt impedance increase was always registered after the occurrence of this phenomenon. Therefore, the impedance rise observed during steam popping cannot be used for its prediction. The time to steam pop was shorter for applications with increased power but not with greater contact force. [ABSTRACT FROM AUTHOR]

Using individual level transaction data and a revised difference-in-differences method with nonparametric smoothing, we study the effect of COVID-19 on house prices. The analyses are performed on the areas of Houston, Santa Clara, Honolulu, Irvine, and Des Moines in the US, which vary in the economic features and the implementation of stay home orders. The results show that only Honolulu experienced noticeable house price declines from the outbreak, suggesting that a heavier reliance on service industries might be correlated with higher vulnerabilities. Santa Clara and Irvine lead the house price increase rates, followed by Des Moines and Houston, indicating that stronger housing market fundamentals, better amenities and less dependence on service industries are associated with more positive house price effects. [ABSTRACT FROM AUTHOR]

• We have proposed a Bi-Phased Multi-Objective Genetic Algorithm. • We have done data classification by the proposed algorithm. • It is a cyclic algorithm. • Statistical tests show that the performance of the proposed algorithm is comparable. This paper presents a novel Bi-Phased Multi-Objective Genetic Algorithm (BPMOGA) based classification method. It is a Learning Classifier System (LCS) designed for supervised learning tasks. Here we have used Genetic Algorithms (GA s) to discover optimal classifiers from data sets. The objective of the work is to find out a classifier or Complete Rule (CR) which comprises of several Class Specific Rules (CSR s). Phase-I of BPMOGA extracts optimized CSR s in I F − T H E N form by following Michigan approach, without considering interaction among the rules. Phase-II of BPMOGA builds optimized CR s from CSR s by following Pittsburgh way. It combines the advantages of both approaches. Extracted CR s help to build CSR s for the next run of phase-I. Hence, phase-I and phase-II are cyclically related, which is one of the uniqueness of BPMOGA. With the help of twenty one benchmark data sets from the University of California at Irvine (UCI) machine learning repository we have compared performance of BPMOGA based classifier with fourteen GA and non- GA based classifiers. Statistical test shows that the performance of the proposed classifier is either superior or comparable to other classifiers. [ABSTRACT FROM AUTHOR]

Background: Critically ill patients are at high risk of developing neurological complications. Among all the potential aetiologies, brain hypoperfusion has been advocated as one of the potential mechanisms. Impairment of cerebral autoregulation (CAR) can result in brain hypoperfusion. However, assessment of CAR is difficult at bedside. We aimed to evaluate whether the automated pupillometer might be able to detect impaired CAR in critically ill patients. Methods: We included 92 patients in this retrospective observational study; 52 were septic. CAR was assessed using the Mxa index, which is the correlation index between continuous recording of cerebral blood flow velocities using the transcranial Doppler and invasive arterial blood pressure over 8 ± 2 min. Impaired CAR was defined as an Mxa > 0.3. Automated pupillometer (Neuroptics, Irvine, CA, USA) was used to assess the pupillary light reflex concomitantly to the CAR assessment. Results: The median Mxa was 0.33 in the whole cohort (0.33 in septic patients and 0.31 in the non-septic patients; p = 0.77). A total of 51 (55%) patients showed impaired CAR, 28 (54%) in the septic group and 23 (58%) in the non-septic group. We found a statistically significant although weak correlation between Mxa and the Neurologic Pupil Index (r2 = 0.04; p = 0.048) in the whole cohort as in septic patients (r2 = 0.11; p = 0.026); no correlation was observed in non-septic patients and for other pupillometry-derived variables. Conclusions: Automated pupillometry cannot predict CAR indices such as Mxa in a heterogeneous population of critically ill patients. [ABSTRACT FROM AUTHOR]

We aimed to examine aneurysm hemodynamics with intra-saccular pressure measurement, and compare the effects of coiling, stenting and stent-assisted coiling in proximal segments of intracranial circulation. A cohort of 45 patients underwent elective endovascular coil embolization (with or without stent) for intracranial aneurysm at our department. Arterial pressure transducer was used for all measurements. It was attached to proximal end of the microcatheter. Measurements were taken in the parent artery before and after embolization, at the aneurysm dome before embolization, after stent implantation, and after embolization. Stent-assisted coiling was performed with 4 different stents: LVIS and LVIS Jr (Microvention, Tustin, CA, USA), Leo (Balt, Montmorency, France), Barrel VRD (Medtronic/ Covidien, Irvine, CA, USA). Presence of the stent showed significant reverse correlation with intra-aneurysmal pressure–both systolic and diastolic—after its implantation (r = -0.70 and r = -0.75, respectively), which was further supported by correlations with stent cell size–r = 0.72 and r = 0.71, respectively (P<0.05). Stent implantation resulted in significant decrease in diastolic intra-aneurysmal pressure (p = 0.046). Systolic or mean intra-aneurysmal pressure did not differ significantly. Embolization did not significantly change the intra-aneurysmal pressure in matched pairs, regardless of the use of stent (p>0.05). In conclusion, low-profile braided stents show a potential to divert blood flow, there was significant decrease in diastolic pressure after stent placement. Flow-diverting properties were related to stent porosity. Coiling does not significantly change the intra-aneurysmal pressure, regardless of packing density. [ABSTRACT FROM AUTHOR]

Feature selection is an effective way to reduce the data dimensionality of hyperspectral imagery and obtain a better performance in the subsequent applications, such as classification. The ideal approach is to obtain the optimal tradeoff between two criteria for hyperspectral image feature selection: 1) information preservation and 2) redundancy reduction. However, constructing a hyperspectral feature selection model for the above two criteria is difficult due to the complexity of hyperspectral imagery. Although evolutionary multiobjective optimization methods have been recently presented to simultaneously optimize the above criteria, they cannot control the global exploration versus local exploitation capabilities in the search space for the hyperspectral feature selection problem. Thus, in this article, a novel discrete sine cosine algorithm (SCA)-based multiobjective feature selection (MOSCA_FS) approach is proposed for hyperspectral imagery. In the proposed method, a novel and effective framework of multiobjective hyperspectral feature selection is designed. In the framework, the ratio between the Jeffries–Matusita (JM) distance and mutual information (MI) is modeled to minimize the redundancy and maximize the relevance of the selected feature subset. In addition, another measurement—the variance (Var)—is applied for maximizing the information amount. Furthermore, to resolve the discrete hyperspectral feature selection problem, a novel discrete SCA is first proposed, which enhances the selection of the ideal feature subset. The effectiveness and universality of the proposed method was verified by experiments with ten University of California at Irvine (UCI) data sets, five hyperspectral image data sets, and one spectral data set of typical surface features. [ABSTRACT FROM AUTHOR]

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While surface microstructures of butterfly wings have been extensively studied for their structural coloration or optical properties within the visible spectrum, their properties in infrared wavelengths with potential ties to thermoregulation are relatively unknown. The midinfrared wavelengths of 7.5 to 14 µm are particularly important for radiative heat transfer in the ambient environment, because of the overlap with the atmospheric transmission window. For instance, a high midinfrared emissivity can facilitate surface cooling, whereas a low midinfrared emissivity can minimize heat loss to surroundings. Here we find that the midinfrared emissivity of butterfly wings from warmer climates such as Archaeoprepona demophoon (Oaxaca, Mexico) and Heliconius sara (Pichincha, Ecuador) is up to 2 times higher than that of butterfly wings from cooler climates such as Celastrina echo (Colorado) and Limenitis arthemis (Florida), using Fourier-transform infrared (FTIR) spectroscopy and infrared thermography. Our optical computations using a unit cell approach reproduce the spectroscopy data and explain how periodic microstructures play a critical role in the midinfrared. The emissivity spectrum governs the temperature of butterfly wings, and we demonstrate that C. echo wings heat up to 8 °C more than A. demophoon wings under the same sunlight in the clear sky of Irvine, CA. Furthermore, our thermal computations show that butterfly wings in their respective habitats can maintain a moderate temperature range through a balance of solar absorption and infrared emission. These findings suggest that the surface microstructures of butterfly wings potentially contribute to thermoregulation and provide an insight into butterflies' survival. [ABSTRACT FROM AUTHOR]

Background: This study aims to determine if the intravitreal dexamethasone implant (DEX implant, Ozurdex; Allergan, Inc., Irvine, California) is effective for treating intermediate, posterior, and panuveitis as a monotherapy or adjunctive treatment to systemic immunomodulatory therapies. Methods: A systematic review using MEDLINE, EMBASE, and PubMed database searches was conducted with the Oxford Centre for Evidence-based Medicine Levels of Evidence criteria to select publications. Available background information and patient data from each study was tabulated. Outcomes studied were central retinal thickness (CRT), best corrected visual acuity, intraocular inflammation (anterior chamber cells, vitreous haze), number of patients with prior and concomitant immunomodulatory treatments, intraocular pressure (IOP) elevation (≥ 25 mmHg), and other adverse effects associated with the implant. Results: One hundred ninety-five (61.51%) patients had previous immunomodulatory treatment while 232 (64.8%) were treated with concomitant immunomodulatory therapy with the DEX implant. CRT decreased by an average of 198.65 μm (42.74%). Visual acuity improved to an average of 0.451 (logMAR) or 20/57 (Snellen) which is a 43.11% improvement from baseline. One hundred seventy-three (59%) of eyes were quiescent at the end of the trials, of which 40 (13.7%) previously inflamed eyes became quiescent. Elevated IOP occurred in 91 (20.6%). The most common adverse events were cataract/posterior subcapsular opacities in 47 (11.03%) patients and conjunctival hemorrhage in 24 (5.44%) patients. Conclusions: The DEX implant is an effective medication for the treatment of posterior segment uveitis, uveitic macular edema, and results in improved visual acuity. Development of elevated IOP and cataract should be closely monitored as they are tangible risks associated with the DEX implant. This study was not able to determine whether the DEX implant was more effective as a monotherapy or as an adjunctive therapy to systemic immunomodulatory treatment. [ABSTRACT FROM AUTHOR]

The article reports on the claims of PrimeGen, a biotechnological company based in Irvine, California, that it has used carbon nanotubes to reprogram adult human cells to an embryonic-like state, a breakthrough that removes the elevated risk of cancer that blights other techniques. PrimeGene researchers used the nanotube delivery system to introduce genes into human testicular and retinal cells, and PrimeGene has reported that they were quickly taken up by an impressive 80% of the cells. It is reported that PrimeGene announced an alliance with Unidym, based in Menlo Park, California, which makes the nanotubes.

Significance: Spatial frequency domain imaging (SFDI) is a wide-field diffuse optical imaging technique for separately quantifying tissue reduced scattering (μs ′ ) and absorption (μa) coefficients at multiple wavelengths, providing wide potential utility for clinical applications such as burn wound characterization and cancer detection. However, measured μs ′ and μa can be confounded by absorption from melanin in patients with highly pigmented skin. This issue arises because epidermal melanin is highly absorbing for visible wavelengths and standard homogeneous light–tissue interaction models do not properly account for this complexity. Tristimulus colorimetry (which quantifies pigmentation using the L * "lightness" parameter) can provide a point of comparison between μa, μs ′ , and skin pigmentation. Aim: We systematically compare SFDI and colorimetry parameters to quantify confounding effects of pigmentation on measured skin μs ′ and μa. We assess the correlation between SFDI and colorimetry parameters as a function of wavelength. Approach: μs ′ and μa from the palm and ventral forearm were measured for 15 healthy subjects with a wide range of skin pigmentation levels (Fitzpatrick types I to VI) using a Reflect RS® (Modulim, Inc., Irvine, California) SFDI instrument (eight wavelengths, 471 to 851 nm). L * was measured using a Chroma Meter CR-400 (Konica Minolta Sensing, Inc., Tokyo). Linear correlation coefficients were calculated between L * and μs ′ and between L * and μa at all wavelengths. Results: For the ventral forearm, strong linear correlations between measured L * and μs ′ values were observed at shorter wavelengths (R > 0.92 at ≤659 nm), where absorption from melanin confounded the measured μs ′ . These correlations were weaker for the palm (R < 0.59 at ≤659 nm), which has less melanin than the forearm. Similar relationships were observed between L * and μa. Conclusions: We quantified the effects of epidermal melanin on skin μs ′ and μa measured with SFDI. This information may help characterize and correct pigmentation-related inaccuracies in SFDI skin measurements. [ABSTRACT FROM AUTHOR]

Aposematic passion-vine butterflies from the genus Heliconius form communal roosts on a nightly basis. This behaviour has been hypothesized to be beneficial in terms of information sharing and/or anti-predator defence. To better understand the adaptive value of communal roosting, we tested these two hypotheses in field studies. The information-sharing hypothesis was addressed by examining following behaviour of butterflies departing from natural roosts. We found no evidence of roost mates following one another to resources, thus providing no support for this hypothesis. The anti-predator defence hypothesis was tested using avian-indiscriminable Heliconius erato models placed singly and in aggregations at field sites. A significantly higher number of predation attempts were observed on solitary models versus aggregations of models. This relationship between aggregation size and attack rate suggests that communally roosting butterflies enjoy the benefits of both overall decreased attack frequency as well as a prey dilution effect. Communal roosts probably deter predators through collective aposematism in which aggregations of conspicuous, unpalatable prey communicate a more effective repel signal to predators. On the basis of our results, we propose that predation by birds is a key selective pressure maintaining Heliconius communal roosting behaviour. [ABSTRACT FROM AUTHOR]

A case study is presented wherein the author examines how factors such as immigration and international investments have contributed to increased globalization and a post-industrial knowledge economy in the city of Irvine, California. The author focuses on the economic and cultural impact of Asian American immigrants and businesses on Irvine's globalization, comments on diversity within the city's population, and explores the concept of suburban cosmopolitanism.

This retrospective study analyzed the surgical and refractive outcomes of a XEN Gel Implant (Allergan, Abbvie Company, Irvine, CA, USA) in naïve patients versus those with previous glaucoma surgery. We evaluated the efficacy of XEN implantation in 86 glaucoma patients during a long-term follow-up period. Patients were divided into two groups: naïve patients (Group 1) and patients with previous glaucoma surgery (Group 2). Eyes that received a XEN Gel Stent placement from December 2014 to October 2019 were included. Intraocular pressure (IOP) change, corrected distance visual acuity (CDVA), change in glaucoma medications, frequency of slit lamp revision procedures, and frequency of secondary glaucoma surgeries were the primary outcomes. In Group 1, the mean IOP before surgery was decreased significantly from 25.00 ± 7.52 mmHg to 16.83 ± 5.12 mmHg by the end of the study. In Group 2, the mean IOP decreased significantly from 25.35 ± 7.81 mmHg to 17.54 ± 5.34 mmHg. The mean IOP decrease from baseline was 29% in Group 1 and 27% in Group 2 (p = 0.567). There were no significant differences between the groups in the IOP baseline level, the final level, or the change between preoperative and final levels. The qualified success rate for Group 2 was 68.7% versus 76.5% for Group 1 for the initial procedure and 15.4% vs. 20.2%, respectively, for complete success rate (p > 0.05). However, at the end of the follow-up, more patients achieved an IOP < 18 mmHg in Group 1 than in Group 2. Despite the need for more anti-glaucoma medications, repeat XEN Gel implantation appears to show promising results in patients with previously failed anti-glaucoma procedures, owing to its minimal invasiveness. [ABSTRACT FROM AUTHOR]

The article offers information on a proposed extensive architecture which is free of the existing Web service notification and eventing standards' limitations and may function as a basis for complimenting and extending these existing standards. Moreover, it is noted that this approach is a new, highly extensible and modular platform which emerged from a work conducted on QuickSilver, a computer game company based in Irvine, California. It can function reliably on scalable, reliable, and high-performance eventings.

Trained radial basis function networks are well-suited for use in extracting rules and explanations because they contain a set of locally tuned units. However, for rule extraction to be useful, these networks must first be pruned to eliminate unnecessary weights. The pruning algorithm cannot search the network exhaustively because of the computational effort involved. It is shown that using multiple pruning methods with smart ordering of the pruning candidates, the number of weights in a radial basis function network can be reduced to a small fraction of the original number. The complexity of the pruning algorithm is quadratic (instead of exponential) in the number of network weights. Pruning performance is shown using a variety of benchmark problems from the University of California, Irvine machine learning database. [ABSTRACT FROM AUTHOR]

Describes the institutional structure of the Humanities Core Course (HCC) at the University of California, Irvine, actualizing Gerald Graff's argument that students should be taught the `conflicts' that have turned the humanities into a venue of provocative debate. Three year rotation of topics; Discussion sections; Dialectical organization of material; Writing assignments.

Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number of clusters and the initialization of the clusters, then, it is necessary to apply a validity index to measure the compactness and the separability of the final clusters and run the clustering algorithm several times. We propose a new fuzzy C-means algorithm in which a validity index based on the concepts of maximum fuzzy energy and minimum fuzzy entropy is applied to initialize the cluster centers and to find the optimal number of clusters and initial cluster centers in order to obtain a good clustering quality, without increasing time consumption. We test our algorithm on UCI (University of California at Irvine) machine learning classification datasets comparing the results with the ones obtained by using well-known validity indices and variations of fuzzy C-means by using optimization algorithms in the initialization phase. The comparison results show that our algorithm represents an optimal trade-off between the quality of clustering and the time consumption. [ABSTRACT FROM AUTHOR]