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This paper is devoted to the investigation of position estimation for a brushless DC machine using only their stator currents. The first application is for a hybrid electric vehicle, where the generator will be used as a motor to start the internal combustion engine (ICE).

This paper describes how to estimate the rotor position of a brushless DC (BLDC). Two different strategies, both based on stator currents, will be used: one for low speeds to start the ICE, and one for normal speeds for future applications in a pure electric vehicle (EV). The first one uses an estimation method based on core saturation and the second one is based on the determination of the current slopes on two of the three phases. The algorithms proposed neither needs to measure any machine parameters, nor the back emf. The methods use the information contained in the current magnitudes and slopes, and the machine mechanical speed. The system was implemented using a Digital Signal Processor (TMS320F241), which controls the phase currents and makes all the calculations required for position estimation. Additionally, the PWM signals are transmitted through a fiber optic link to minimize noise production and error on commutations.

The papers shows how an internal combustion engine can start using this approach in a brushless motor and keep it synchronized.

This work is being applied to a hybrid electric vehicle.

The paper proposes a new way to start the internal combustion engine for hybrid vehicle applications through the estimation of the magnet's position. It also shows a way to estimate the position at other speeds for battery charging of the vehicle.

The aim of this paper is to improve and adapt cascaded multilevel converters for electric vehicles (EVs) to have all the advantages of these converters and to eliminate its limitation in the use of EVs applications. Specifically, the purpose is to use only a single power source (battery pack, fuel cell, etc.) and to generate a higher power‐quality than regular multilevel converters.

This paper is based in a cascaded multilevel converter conformed by two 3‐level inverters connected in series. The voltage sources of the auxiliary inverter were replaced by floating capacitors which work as active filters, reducing the power sources to one. The floating capacitor voltages were controlled by a PI controller that adjusts the modulation index (m) to obtain a zero average power in the auxiliary inverters, and a predictive control selects the optimal redundant state to reduce the error and balance all the capacitor voltages. As the modulation index is determined by the PI controller, the output voltage magnitude must be controlled by a variable voltage source (e.g. buck‐boost chopper). Additionally, the converter works with new optimal voltage asymmetries to obtain higher power quality and capacitor control stability.

The proposed converter uses a topology that conventionally generates 9‐levels of voltage, but with the proposed asymmetry is as generate 11‐levels. Also, the auxiliary power sources were eliminated.

The proposed solution has a limited dynamic response due to the variation rate of the capacitor voltage, which is limited by the load current and the capacitance. However, the dynamic response and control stability is satisfactory for EVs applications.

The paper presents a new control to manage the floating capacitor voltages and uses new voltage asymmetries in cascaded multilevel converters.

The purpose of this paper is to implement a finite set model predictive control algorithm to a shunt (or parallel), multilevel (cascaded H-bridge) active power filter (APF). Specifically, the purpose is to get a controller that could compensate the mains current and, at the same time, to control the voltages of its capacitors. This strategy avoids the use of multiple PWM carriers or another type of special modulator, and requires a relatively low processing power.

This paper is focussed in the application of the predictive controller to a single-phase parallel APF composed for two H-bridges connected in series. The same methodology can be applied to a three-phase APF. In the DC buses of each H-bridge, a floating capacitor was connected, whose voltage is regulated by the predictive controller. The controller is composed by, first, a model for the charge/discharge dynamics for each floating capacitor and a model for the output current of the APF; second, a cost function; and third, an optimization algorithm that is able to control all these variables at the same time, choosing in each sample period the best combination of firing pulses.

The controller can track the voltage references, compensate the current harmonics and compensate reactive power with an algorithm that evaluates only the three nearest voltage levels to the last voltage level applied in the inverter. This strategy decreases the number of calculations required by the predictive algorithm. This controller can be applied to the general case of a single-phase multilevel APF of N-levels and extend it to the three-phase case without major problems.

The implemented controller, when the authors consider a constant sample time, gives a mains current with a Total Harmonic Distortion (THD-I) slightly greater in comparison with the base algorithm (that evaluates all the voltage levels). However, when the authors consider the processing times under the same processor, the implemented algorithm requires less time to get the optimal values, can get lower sampling times and then a best performance in terms of THD-I. To implement the controller in a three-phase APF, a faster Digital Signal Processor would be required.

The implemented solution uses a model for the charge/discharge of the capacitors and for the filter current that enable to operate the cascaded multilevel inverter with asymmetrical voltages while compensates the mains currents, with a predictive algorithm that requires a relatively low amount of calculations.

In today’s digital world, real-time health monitoring is becoming a most important challenge in the field of medical research. Body signals such as electrocardiogram (ECG), electromyogram and electroencephalogram (EEG) are produced in human body. This continuous monitoring generates huge count of data and thus an efficient method is required to shrink the size of the obtained large data. Compressed sensing (CS) is one of the techniques used to compress the data size. This technique is most used in certain applications, where the size of data is huge or the data acquisition process is too expensive to gather data from vast count of samples at Nyquist rate. This paper aims to propose Lion Mutated Crow search Algorithm (LM-CSA), to improve the performance of the LMCSA model.

A new CS algorithm is exploited in this paper, where the compression process undergoes three stages: designing of stable measurement matrix, signal compression and signal reconstruction. Here, the compression process falls under certain working principle, and is as follows: signal transformation, computation of Θ and normalization. As the main contribution, the theta value evaluation is proceeded by a new “Enhanced bi-orthogonal wavelet filter.” The enhancement is given under the scaling coefficients, where they are optimally tuned for processing the compression. However, the way of tuning seems to be the great crisis, and hence this work seeks the strategy of meta-heuristic algorithms. Moreover, a new hybrid algorithm is introduced that solves the above mentioned optimization inconsistency. The proposed algorithm is named as “Lion Mutated Crow search Algorithm (LM-CSA),” which is the hybridization of crow search algorithm (CSA) and lion algorithm (LA) to enhance the performance of the LM-CSA model.

Finally, the proposed LM-CSA model is compared over the traditional models in terms of certain error measures such as mean error percentage (MEP), symmetric mean absolute percentage error (SMAPE), mean absolute scaled error, mean absolute error (MAE), root mean square error, L1-norm and L2-normand infinity-norm. For ECG analysis, under bior 3.1, LM-CSA is 56.6, 62.5 and 81.5% better than bi-orthogonal wavelet in terms of MEP, SMAPE and MAE, respectively. Under bior 3.7 for ECG analysis, LM-CSA is 0.15% better than genetic algorithm (GA), 0.10% superior to particle search optimization (PSO), 0.22% superior to firefly (FF), 0.22% superior to CSA and 0.14% superior to LA, respectively, in terms of L1-norm. Further, for EEG analysis, LM-CSA is 86.9 and 91.2% better than the traditional bi-orthogonal wavelet under bior 3.1. Under bior 3.3, LM-CSA is 91.7 and 73.12% better than the bi-orthogonal wavelet in terms of MAE and MEP, respectively. Under bior 3.5 for EEG, L1-norm of LM-CSA is 0.64% superior to GA, 0.43% superior to PSO, 0.62% superior to FF, 0.84% superior to CSA and 0.60% better than LA, respectively.

This paper presents a novel CS framework using LM-CSA algorithm for EEG and ECG signal compression. To the best of the authors’ knowledge, this is the first work to use LM-CSA with enhanced bi-orthogonal wavelet filter for enhancing the CS capability as well reducing the errors.

The purpose of this paper is to examine, through a microeconomic analysis, the extent to which trip characteristics influence tourism expenditure in 14 emerging urban-cultural cities in Andalusia (Spain).

This analysis was carried out using an ordinary least squares method, which measures influence on tourism expenditure based on the trip characteristics of tourists visiting emerging urban-cultural cities. For this, the authors used 3,030 surveys conducted on tourists who, in 2013, visited 14 emerging urban-cultural cities in Andalusia (Spain).

It was confirmed that certain trip characteristics – type of accommodation, length of stay, trip planning and internet use – determine tourism expenditure in these destinations. The findings provide stakeholders in these destinations with information for the implementation of policies aiming to increase revenue in destinations where tourism development levels are still in their infancy and where, therefore, there are many unexploited opportunities.

First, this study identifies those trip characteristics which influence tourist expenditure in emerging urban tourist destinations. These destinations, to date, had not been previously analysed in expenditure segmentation studies. Second, aside from the factors traditionally analysed in scientific literature, other trip-specific variables were considered; these relate to the means by which tourists familiarise themselves with their destination and the way in which they plan their trip (use of the internet), as the rise of new technologies has radically changed tourism.

The present quantitative-descriptive study aims to measure the level of English language learning autonomy of the pre-service teachers.

A short list questionnaire developed by Dixon (2011) was administered to 61 pre-service English teachers enrolled in Western Mindanao State University in order to measure their English language learning autonomy. Differences on the levels of English language learning autonomy across participants’ gender, number of languages spoken and first language were also explored.

Results revealed that participants are highly autonomous in English language learning. Gender, number of languages spoken and first language were found to have no significant influence on English language learning autonomy.

Results on the level of English language learning autonomy indicate that pre-service teachers are highly autonomous and, therefore, ready for lifelong learning. This implies pedagogical and instructional advantages, as they can learn and explore the language independently.

Since gender, number of languages spoken and first language do not significantly influence the level of English language learning autonomy, it is suggested that English teachers do not need to develop differentiated instructions and activities anymore, which will cater the three profile variables in fostering autonomous learning, as participants already exhibit a level of autonomy in English language learning.

This would allow teachers and students to be aware of the importance of autonomy in language learning. Thus, it will prepare them to be independent and lifelong learners, as they engage themselves in the professional world.

As one of the Asia’s developing countries, the Philippines have captured a small number of studies and contributions to autonomous learning in the field of research (Madrunio, Tarrayo, Tupas and Valdez, 2016 as cited in Iñigo, 2018). Specifically, there is no research study conducted that measures “language learning autonomy” in the Philippine’s higher education much to the knowledge of the researcher. Thus, this study will be the first one to determine the level of autonomy of pre-service teachers in English language learning in the Philippine context. Furthermore, the present study also intended to determine significant differences on the level of language learning autonomy of pre-service teachers across genders, number of languages spoken and their first language.

The literature on manufacturing strategy has focused on analysis of content and process. Limited attention has been given to the concept of manufacturing strategy in taxonomical terms. The purpose of this study is to highlight a taxonomy of manufacturing strategies in Spanish industrial companies.

A cluster analysis was performed. The classification variable used was based on competitive priorities relating to cost, quality, flexibility, delivery, service and environmental protection. The sample group consisted of 353 companies, belonging to different sectors.

Empirical analysis results demonstrate the existence of two manufacturing strategies and significant differences in structure and infrastructure policies but not in performance.

The results of the study are not generalizable, given the nature of the sample. Future studies should consider larger samples, including more countries and contexts.

A taxonomy of manufacturing strategies will serve as a useful mechanism to better understand Spain's manufacturing role and position in European and global economies.

The taxonomy presented contributes to the development and enhancement of operations management theory and demonstrates the current strategic importance of the manufacturing function in Spanish companies.

This paper aims to review state‐of‐the‐art literature on typologies and taxonomies of operations strategy in order to propose generic operations strategies in industrial companies from a theoretical viewpoint.

Typologies and taxonomies are defined and characterised; both concepts delimit the generic configurations model. Then examine the state of the research on configuration analysis relating to operations strategy is examined. For this purpose articles published in 17 important journals in the field of production and operations management from the 1980s to the year 2006 are analysed.

This review allows one to identify three generic operations strategies commonly accepted in the literature.

This research deserves much more attention, both theoretical and empirical, in order to analyse the existing classifications and develop new classifications of operations strategy.

Knowledge of generic operations strategies may help managers to design and implement a strategy that allows their firm to exploit the full potential of its manufacturing resources, and to achieve better results. The literature review contributes to theory development and provides a means of understanding the strategic position of operations.

Despite considerable interest in research on configurations in the study of organisation and business strategy there has not been much effort to examine the current state or even the future development of generic configurations in the operations strategy field. This paper fills this gap.

The theoretical framework guiding these studies draws from the work of Paulo Freire (1989) who argues that transformation in schools and in society is possible when teachers value and utilize the diverse cultural schema of every student. Our work is further informed by critiques of the deficit perspective that dominates many school settings (Bartolomé & Balderrama, 2001; Garcia, 2001) in the United States. According to this perspective, the documented lack of school success of many children of color, of many children whose first language is not English, and of poor children in general is attributed to deficits in the children themselves, their families, and cultures. Most often, parents are cited for not reading to their children, not emphasizing the importance of education, not disciplining their children, not speaking English to their children, and/or for their own “dysfunctions” which interfere with their children’s learning.

Metaverse, that is extended reality (XR)-based technologies such as augmented reality (AR) and virtual reality (VR), are increasingly believed to facilitate fundamental human practice in the future. One of the vanguards of this development has been the consumption domain, where the multi-modal and multi-sensory technology-mediated immersion is expected to enrich consumers' experience. However, it remains unclear whether these expectations have been warranted in reality and whether, rather than enhancing the experience, metaverse technologies inhibit the functioning and experience, such as cognitive functioning and experience.

This study utilizes a 2 (VR: yes vs no) × 2 (AR: yes vs no) between-subjects laboratory experiment. A total of 159 student participants are randomly assigned to one condition — a brick-and-mortar store, a VR store, an AR store and an augmented virtuality (AV) store — to complete a typical shopping task. Four spatial attention indicators — visit shift, duration shift, visit variation and duration variation — are compared based on attention allocation data converted from head movements extracted from recorded videos during the experiments.

This study identifies three essential effects of XR technologies on consumers' spatial attention allocation: the inattention effect, acceleration effect and imbalance effect. Specifically, the inattention effect (the attentional visit shift from showcased products to the environmental periphery) appears when VR or AR technology is applied to virtualize the store and disappears when AR and VR are used together. The acceleration effect (the attentional duration shift from showcased products to the environmental periphery) exists in the VR store. Additionally, AR causes an imbalance effect (the attentional duration variation increases horizontally among the showcased products).

This study provides valuable empirical evidence of how VR and AR influence consumers' spatial bias in attention allocation, filling the research gap on cognitive function in the metaverse. This study also provides practical guidelines for retailers and XR designers and developers.