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THE power output from an engine is roughly proportional to the absolute pressure of the charge mixture in the induction system. If we take a normally aspirated engine, the maximum power output is obtained when the induction pressure is raised to atmospheric pressure by opening the throttle to wide open. The power output may be lowered by reducing the induction pressures by part closing the throttle. The power output could be increased by supplying the induction gases at a pressure higher than atmospheric. This can be done by attaching a supercharger to the engine which, drawing air at atmospheric pressure, will deliver it to the induction system at a pressure higher than atmospheric, and the greater the pressure of delivery, the greater will be the power output.

This paper is devoted to the quasi‐Z‐source (qZS) converter family. Recently, the qZS‐converters have attracted high attention because of their specific properties of voltage boost and buck functions with a single switching stage. As main representatives of the qZS‐converter family, this paper aims to discuss the traditional quasi‐Z‐source inverter as well as two novel extended boost quasi‐Z‐source inverters.

Steady state analysis of the investigated topologies operating in the continuous conduction mode is presented. Input voltage boost properties of converters are compared for an ideal case. Mathematical models of converters considering losses in components are derived. Practical boost properties of converters are compared to idealized ones and the impact of losses on the voltage boost properties of each topology is justified. Finally, the impact of losses in the components on the boost conversion efficiency is analyzed.

To demonstrate the impact of component losses on the overall efficiency of the qZS‐converter, a number of experiments were performed. The impact of inductor winding resistance was compared with the forward voltage drop of qZS‐network diodes. It was found that the forward voltage drop of diodes has the highest effect on the efficiency. If the diodes are replaced with high‐power Schottky rectifiers with a low forward voltage drop (UD=0.6 V), the effective efficiency rise by at least 5 percent could be expected for all three qZS‐converter topologies. For the same operating parameters and component values, the traditional qZS‐converter had the highest efficiency of the qZS‐converter family. The boost converter was compared with the traditional qZS converter in terms of efficiency. It was found that the boost converter has an efficiency 2 percent higher in the boost operation mode and approximately the same efficiency in the non‐boost operation.

The paper provides a good theoretical background for further practical studies. qZS‐converters have voltage boost and buck functions with a single switching stage, which could be especially advantageous in renewable energy applications.

The paper presents a detailed study of the qZS‐converter family. Mathematical models of converters considering losses in components are derived. It is the first time the boost converter is compared with the qZS converter.

Increasing availability of the financial data has opened new opportunities for quantitative modeling. It has also exposed limitations of the existing frameworks, such as low accuracy of the simplified analytical models and insufficient interpretability and stability of the adaptive data-driven algorithms. I make the case that boosting (a novel, ensemble learning technique) can serve as a simple and robust framework for combining the best features of the analytical and data-driven models. Boosting-based frameworks for typical financial and econometric applications are outlined. The implementation of a standard boosting procedure is illustrated in the context of the problem of symbolic volatility forecasting for IBM stock time series. It is shown that the boosted collection of the generalized autoregressive conditional heteroskedastic (GARCH)-type models is systematically more accurate than both the best single model in the collection and the widely used GARCH(1,1) model.

This paper aims to provide a better understanding of the prolonged consumption journey and how they are sustained by service providers’ use of habit-boosting strategies. Existing research is critically evaluated, and a research agenda is provided to inspire and guide future research.

This paper develops a conceptual framework that integrates habit and transformative consumer intervention theories with customer journey literature to explain the role of habit in sustaining prolonged consumption journeys. Habit-boosting strategies are introduced as mechanisms for service providers to facilitate their customers’ prolonged consumption journeys.

This paper argues that habit strength is a limited operant resource that often lacks resource integration efficiency and hinders customers’ abilities to sustain prolonged consumption journeys. Four distinct habit-boosting strategies are identified that provide the potential for service providers to facilitate their customers’ prolonged consumption journeys.

This study presents a typology of habit-boosting strategies and a research agenda that discusses a range of practically relevant and theoretically insightful contributions.

Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost).

Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method.

Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails.

This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study.

In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers.

A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

Control-signal-to-output-voltage transfer function of the conventional boost converter has at least one right-half plane zero (RHPZ) in the continuous conduction mode which can restrict the open-loop bandwidth of the converter. This problem can complicate the control design for the load voltage regulation and conversely, impact on the stability of the closed-loop system. To remove this positive zero and improve the dynamic performance, this paper aims to suggest a novel boost topology with a step-up voltage gain by developing the circuit diagram of a conventional boost converter.

Using a transformer, two different pathways are provided for a classical boost circuit. Hence, the effect of the RHPZ can be easily canceled and the voltage gain can be enhanced which provides conditions for achieving a smaller working duty cycle and reducing the voltage stress of the power switch. Using this technique makes it possible to achieve a good dynamic response compared to the classical boost converter.

The observations show that the phase margin of the proposed boost converter can be adequately improved, its bandwidth is largely increased, due to its minimum-phase structure through RHPZ cancellation. It is suitable for fast dynamic response applications such as micro-inverters and fuel cells.

The introduced method is analytically studied via determining the state-space model and necessary criteria are obtained to achieve a minimum-phase structure. Practical observations of a constructed prototype for the voltage conversion from 24 V to 100 V and various load conditions are shown.

Customer churn prediction is one of the most well-known approaches to manage and improve customer retention. Machine learning techniques, especially classification algorithms, are very popular tools to predict the churners. In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction.

In this paper, three ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. The first classifier, which is called boosted bagging, uses boosting for each bagging sample. In this approach, before concluding the final results in a bagging algorithm, the authors try to improve the prediction by applying a boosting algorithm for each bootstrap sample. The second proposed ensemble classifier, which is called bagged bagging, combines bagging with itself. In the other words, the authors apply bagging for each sample of bagging algorithm. Finally, the third approach uses bagging of neural network with learning based on a genetic algorithm.

To examine the performance of all proposed ensemble classifiers, they are applied to two datasets. Numerical simulations illustrate that the proposed hybrid approaches outperform the simple bagging and boosting algorithms as well as base classifiers. Especially, bagged bagging provides high accuracy and precision results.

In this paper, three novel ensemble classifiers are proposed based on bagging and boosting for customer churn prediction. Not only the proposed approaches can be applied for customer churn prediction but also can be used for any other binary classification algorithms.

To create new hybrid algorithms that combine boosting and support vector machines to outperform other known algorithms in selected contexts of binary classification problems.

Support vector machines (SVM) are known in the literature to be one of the most efficient learning models for tackling classification problems. Boosting algorithms rely on other classification algorithms to produce different weak hypotheses which are later combined into a single strong hypothesis. In this work the authors combine boosting with support vector machines, namely the AdaBoost.M1 and sequential minimal optimization (SMO) algorithms, to create new hybrid algorithms that outperform standard SVMs in selected contexts. This is achieved by integration with different degrees of coupling, where the four algorithms proposed range from simple black‐box integration to modifications and mergers between AdaBoost.M1 and SMO components.

The results show that the proposed algorithms exhibited better performance for most problems experimented. It is possible to identify trends of behavior bound to specific properties of the problems solved, where one may hence apply the proposed algorithms in situations where it is known to succeed.

New strategies for combining boosting and SVMs may be further developed using the principles introduced in this paper, possibly resulting in other algorithms with yet superior performance.

The hybrid algorithms proposed in this paper may be used in classification problems with properties that they are known to handle well, thus possibly offering better results than other known algorithms in the literature.

This paper introduces the concept of merging boosting and SVM training algorithms to obtain hybrid solutions with better performance than standard SVMs.

The purpose of this paper is to present the Switched Inductor Z-Source Inverter (SLZSI) topology for three-phase on-line uninterruptible power supply (UPS) by employing third harmonic injected maximum constant boost pulse width modulation (PWM) control. Conventional UPS consists of step-up transformer or boost chopper along with voltage source inverter (VSI) which reduces the efficiency and increases energy conversion cost. The proposed three-phase UPS by using SLZSI has the voltage boost capability through shoot through zero state which is not available in traditional VSI and current source inverter.

Performance of three-phase on-line UPS based on ZLZSI by using third harmonic injected maximum constant boost PWM control is analyzed and evaluated in MATLAB/Simulink software and the results are compared with Z-source inverter (ZSI) fed UPS. Experimental results are presented for the validation of the simulation and theoretical analysis.

The output voltages, currents, THD values, voltage stress and efficiencies for different loading condition are determined and compared with the theoretical values and UPS with ZSI. The experimental results validate the theoretical and simulation results.

Compared with the traditional ZSI, the SLZSI provides high-voltage boost inversion ability with a very short shoot through zero state. This proposed UPS by using SLZSI increases the efficiency with less number of components, reduces the harmonics, increases the voltage gain and reduces the voltage stress.