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The purpose of this paper is to suggest a novel current sensor-less drive system for a novel axial flux-switching permanent-magnet motor drive to reduce the costs and avoid problems caused by faults of the current sensors.

Commonly, a conventional controller needs at least two current sensors; in this paper, the current sensors are removed by replacing estimated stator current with the extended Kalman filter.

A prototype of the novel axial flux-switching permanent-magnet motor is fabricated and tested. It is found that the experimental results confirm the proposed method and show that the control has almost the same performance and ability as the conventional control.

The axial flux-switching permanent-magnet motor is one of the most efficient motors, but current sensor-less control of an axial flux-switching permanent-magnet motor with a sandwiched permanent magnet and a unity displacement winding factor has not been specially reported to date. Thus, in this paper, the authors report on current sensor-less control based on the extended Kalman filter for electric vehicles.

The purpose of this paper is to investigate a novel axial flux-switching motor with sandwiched permanent magnet for direct drive electric vehicles (EVs), in which the torque density is increased and the cogging torque is decreased. For reducing the back-electromotive force (EMF) harmonics and cogging torque, a twisted structure is employed. To improve the dynamic performance of the axial field flux-switching sandwiched permanent magnet (AFFSSPM) motor a space vector modulation-direct torque and flux control scheme is proposed.

A multi-objective optimization is performed by means of artificial neural network and non-sorting genetic algorithm II to minimize the cogging torque while preserving the average torque.

A comparative study between two proposed machines and the conventional flux-switching permanent magnet (FSPM) machine is accomplished and the static electromagnetic characteristics are analyzed. It is demonstrated that the proposed model with twisted structure has significantly improved performance over the conventional FSPM machine in back-EMF and efficiency. The proposed controller has a speed loop only and contains neither the current loop nor hysteresis control. The AFFSSPM motor exhibits excellent dynamic performance with this scheme.

The axial flux-switching permanent-magnet machine is one of the most efficient machines but the AFFSSPM with sandwiched permanent magnet has not been specially reported to date. Thus in this paper, the authors report on optimal design of an axial flux-switching sandwiched permanent magnet machine for electric vehicles and investigate its dynamic performance.

The purpose of this paper is to propose a novel axial field flux-switching machine with sandwiched permanent magnets. It is one of the most efficient machines which is appropriate for high-torque and low-speed direct-drive applications. The proposed model is equipped with an advanced phase-group concentrated-coil winding to obtain a unity displacement winding factor. Two configurations of the proposed motors with 6-stator-slot (S)/10-rotor-pole (P) and 12S/19P are investigated. These two structures are compared with optimized a conventional axial-field flux-switching permanent-magnet (CAFFSPM) machine. Unity displacement winding factor increases the back-EMF and electromagnetic torque. Moreover, the prototype 12S/19P motor is built to verify the design.

The torque equation is obtained and the dimensions of the two proposed motors are determined. Some specific design issues, including the stator/rotor pole sandwiching pole angle, the stator tooth angle and the rotor pole angle have been optimized to minimize the cogging torque while maintaining the high torque density by means of response surface methodology (RSM) and 3-D finite element model of the machine.

To improve the performance, especially at high torque density, low cogging torque and high level of fault-tolerant capability, the 12S/19P axial field flux-switching sandwiched permanent-magnet (AFFSSPM) motor is proposed. Based on the optimized design, a prototype of the 12S/19P AFFSSPM motor is fabricated and tested. It is found that the experimental results validate the 3-D finite element method (FEM) simulation results.

The AFFSSPM motor is one of the most efficient motors, but the 12S/19P AFFSSPM motor with sandwiched permanent magnet and unity displacement winding factor has not been specially reported to date. Thus, in this paper, the authors report on optimal design of a novel axial flux-switching sandwiched permanent-magnet machine for electric vehicles and fabricate a prototype of the 12S/19P AFFSSPM motor.

The purpose of this paper is to assess the survival probability among patients with liver trauma injury using the anatomical and psychological scores of conditions, characteristics and treatment modes.

A logistic model is used to estimate 173 patients’ survival probability. Data are taken from patient records. Only emergency room patients admitted to University Hospital of Trauma (former Military Hospital) in Tirana are included. Data are recorded anonymously, preserving the patients’ privacy.

When correctly predicted, the logistic models show that survival probability varies from 70.5 percent up to 95.4 percent. The degree of trauma injury, trauma with liver and other organs, total days the patient was hospitalized, and treatment method (conservative vs intervention) are statistically important in explaining survival probability.

The study gives patients, their relatives and physicians ample and sound information they can use to predict survival chances, the best treatment and resource management.

This study, which has not been done previously, explores survival probability, success probability for conservative and non-conservative treatment, and success probability for single vs multiple injuries from liver trauma.

This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature.

The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction.

Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction.

This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

Improved indigenous chicken is considered a sustainable agricultural practice with social, economic and environmental indicators. Therefore, the analysis of the choice of market channels is of considerable importance to farmers with reference to improved livelihoods and poverty alleviation in developing countries. The purpose of this study is to investigate the factors that influence market channel choices among improved indigenous chicken farmers in Baringo County and to rank the determinants according to their level of importance in influencing farmer's choice of marketing channels.

A multistage sampling technique was employed to collect data from 209 households for the study conducted between April and July 2019, out of which, 198 useful responses were obtained. Multinomial logit regression and neural network models were used to analyze the factors influencing market channel choice based on socioeconomic, demographic and farm characteristics.

It was established that group membership, education, market distance, transport costs, farm size, cost of information and bargain costs were statistically significant in the choice of market channels (wholesaler, brokers, processors and supermarkets). With the direct consumer as the base market choice. The cost of transport had the highest normalized importance in the prediction of a farmer's selection of market channels for both radial basis function (RBF) and multilayer perceptron (MLP) neural networks. However, flock attributes and age of household head had the least normalized importance in MLP and RBF, respectively.

Due to the insufficiency of resources and time, this study only focused on a small part of the country (Baringo County). However, improved indigenous chicken farming is widely practiced in Kenya. Further studies can be carried out in other counties to validate the results of this study.

The outcome can be used in policy implementation involving improved indigenous chicken production in Kenya.

This study suggests the methods aimed at enhancing poultry sector in other counties in Kenya as well as other developing countries.