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The optimisation of a tubular linear motor with interior permanent magnets is described. For a rapid design the whole process is divided in three parts: an analytical approach for the a preliminary investigation, a parametric analysis by means of a finite element method and an optimisation. The obtained results show that the adopted optimisation process is efficient for rapid and effective optimisation of the tubular linear motor.

The purpose of this chapter is to describe the rationale for and structure of organizational networks in support of traffic safety programming. It outlines the operational considerations and approaches important to both understanding network-based partnerships and improving their functionality. The chapter draws on conceptual and empirical studies of organizational networks in order to enhance the effectiveness of networks and integrate network-based approaches with the cultural orientation already present in traffic safety research and practice.

This chapter proceeds from the premise that, increasingly, efforts to impact traffic safety behaviors will be interconnected with other concerns, and that traffic safety initiatives will require engagement with organizations focused on concerns other than traffic safety. The implication of the ideas examined in this chapter is that traffic safety agencies will need to focus not just on traffic-related behaviors, but also on the strategic and operational coordination with other organizations. Doing so has the potential to create synergies that would be unachievable if agencies operation in isolation.

Fractional slot permanent magnet (PM) brushless machines having concentrated non‐overlapping windings have been the subject of research over last few years. They have already been employed in the commercial hybrid electric vehicles (HEVs) due to high‐torque density, high efficiency, low‐torque ripple, good flux‐weakening and fault‐tolerance performance. The purpose of this paper is to overview recent development and research challenges in such machines in terms of various structural and design features for electric vehicle (EV)/HEV applications.

In the paper, fractional slot PM brushless machines are overviewed according to the following main and sub‐topics: first, machine topologies: slot and pole number combinations, all and alternate teeth wound (double‐ and single‐layer windings), unequal tooth structure, modular stator, interior magnet rotor; second, machine parameters and control performance: winding inductances, flux‐weakening capability, fault‐tolerant performance; and third, parasitic effects: cogging torque, iron loss, rotor eddy current loss, unbalanced magnetic force, acoustic noise and vibration.

Many fractional slot PM machine topologies exist. Owing to rich mmf harmonics, fractional slot PM brushless machines exhibit relatively high rotor eddy current loss, potentially high unbalanced magnetic force and acoustic noise and vibration, while the reluctance torque component is relatively low or even negligible when an interior PM rotor is employed.

This is the first overview paper which systematically reviews the recent development and research challenges in fractional‐slot PM machines. It summarizes their various structural and design features for EV/HEV applications.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.

Past studies suggest that full-time maternal employment may be negatively related to children’s cognitive development. Most studies measure maternal employment at one time point, while mothers’ work hours may not be stable during early childrearing years. Using data from the 2001 Early Childhood Longitudinal Study – Birth Cohort (N ≈ 6,500), the authors examine stability in mothers’ work hours across four waves when children are 9 and 24 months old, in preschool, and in kindergarten, mothers’ background characteristics associated to it, and its link to child cognitive development. Results show that the majority of mothers change work hours across the four waves. Analysis using multinomial logistic regression models suggests that mothers’ older age, fewer children, and higher household income are related to working full time at all four waves compared to varying work hours across the waves; more children and less than high school completion are related to staying home at all four waves; and mothers’ older age, being White, no change in partnership status, and holding a college degree are related to working part time at all four waves. Compared to mothers’ changing work hours, mothers’ stable work hours, full time or part time, at all four waves is related to children’s better reading, math, and cognitive scores in kindergarten, whereas mothers’ staying home at all four waves is negatively related to these scores. These associations disappear when background characteristics are controlled for in ordinary least squares regression models. These findings underscore the role of background characteristics in shaping both mothers’ stable employment and children’s cognitive development.

The purpose of this paper is to give an overview of the design issues of permanent magnet machines for the hybrid electric and plug‐in electric vehicles, including railway traction and naval propulsion.

Focus is given on both synchronous permanent magnet and reluctance machines. An overview of the design rules are provided, covering the topics of: fractional‐slot windings, fault‐tolerant configurations, flux‐weakening capability, and torque quality.

The peculiarities of these machines and the advanced design considerations to fit the automotive requirements are analyzed.

The paper includes a wide description of innovative electrical machines for electric vehicles, including not only the traction capability, but also analysis of features as weight reduction, torque ripple reduction, increase of fault tolerance, and so on.

Rotor shape optimization is crucial in designing synchronous reluctance machines (SynRMs) because the machine performance is directly proportional to the rotor’s magnetic saliency ratio. The rotor geometry in synchronous reluctance machines is complex, and many geometrical parameters must be optimized. When fluid flux-barrier geometry is desirable, using analytic equations to prepare the rotor geometry for finite element analysis could be tedious. This paper aims to provide a robust numerical procedure to draw the fluid flux-barrier geometry in transversally laminated radial flux inner and outer rotor SynRMs by directly solving the magnetic vector potential equation using the finite difference method..

In this paper, the goal is to have a robust procedure for drawing the rotor geometry for an arbitrary number of slots (Ns), poles (p) and flux-barrier layers (Nfb). Therefore, this paper targeted several combinations to investigate the performance of the proposed algorithm. The MATLAB software is used to implement the proposed algorithm. The ANSYS Maxwell software is used for counterpart finite element simulation to check the correctness of the results derived by the proposed method.

Several inner and outer rotor SynRMs considering a different number of poles and a different number of flux-barrier layers per pole are studied to investigate the performance of the proposed algorithm. Results corresponding to each case are presented, and it is shown that the method is robust, flexible and fast enough, which could be used for the generation of the rotor geometry for the finite element analysis effectively.

The value of the proposed algorithm is its simplicity and straightforwardness in its implementation for the preparation of the rotor geometry with the desired fluid flux-barrier layer curvature resolution suitable for the finite element analysis. The procedure presented in this paper is based on the ideal magnetic loading concept, and in future works, a similar idea could be used for linear and axial flux SynRMs.

The purpose of this paper is to provide a model for prediction of respiratory symptoms in the progression of COVID-19, social distancing, frequent hand washes, wearing of face mask in public are some of the potential measures of preventing the disease from further spreading. In spite of the effects and efforts taken by governments, the pandemic is still uncontrolled in major cities of the world. The proposed technique in this paper introduces a non-intrusive and major screening of vital symptoms and changes in the respiratory organs.

The novel coronavirus or Covid-19 has become a serious threat to social and economic growth of many nations worldwide. The pace of progression was significantly higher in the past two months. Identified by severe respiratory illness, fever and coughs, the disease has been threatening the lives of human society. Early detection and prognosis is absolutely necessary to isolate the potential spreaders of the disease and to control the rate of progression.

Recent studies have highlighted the changes observed in breathing characteristics of infected patients. Respiratory pattern of Covid-19 patients can be differentiated from the respiratory pattern of normal cold/flu affected patients. Tachypnoea is one among the vital signs identified to be distinguishing feature of Covid-19. The proposed respiratory data capture will commence with facial recognition, use of infrared sensors and machine-learning approaches to classify the respiratory patterns, which finally narrows down as a symptom of Covid-19.

Proposed system produced outcome of 94% accuracy, precision, recall and a F1-measure as an average in the conducted experiments. This method also proves to be a fruitful solution for large-scale monitoring and categorisation of people based on the symptoms.

The purpose of this paper is performance enhancement of ferrite-assisted synchronous reluctance (FASR) motor using multi-objective differential evolution (MODE) algorithm, considering the significant geometric design parameters.

This work illustrates the optimization of FASR motor using MODE algorithm to enhance the performance of the motor considering barrier angular positions, magnet height, magnet axial length, flux barrier angles of the rotor and air gap length. In the optimization routine to determine the performance parameters, generalized regression neural network-based interpolation is used. The results of MODE are validated with multi-objective particle swarm optimization algorithm and multi-objective genetic algorithm.

The design optimization procedure developed in this work for FASR motor aims at achieving multiple objectives, namely, average torque, torque ripple and efficiency. With multiple objectives, it is essential to give the designer the tradeoff between different objectives so as to arrive at the best design suitable for the application. The results obtained in this work justify the application of the MODE approach for FASR motor to determine the various feasible solutions within the bounds of the design.

Analysis, design and optimization of synchronous reluctance motor has been explored in detail to establish its potential for variable speed applications. In recent years, the focus is toward the electromagnetic design of hybrid configurations such as FASR motor. It is in this preview this work aims to achieve optimal design of FASR motor using multi-objective optimization approach.

The results of this work will supplement and encourage the application of FASR motor as a viable alternate for variable speed drive applications. In addition, the application of MODE to arrive at better design solutions is demonstrated.

The approach presented in this work focuses on obtaining enhanced design of FASR motor considering average torque, torque ripple and efficiency as performance measures. The posteriori analysis of optimization provides an insight into the choice of parameters involved and their effects on the design of FASR motor. The efficacy of the optimization routine is justified in comparison with other multi-objective algorithms.

Considering a micro performance perspective, the purpose of this paper is to analyze whether and to what extent the adoption of better performance management systems could improve the performance levels of a public university.

With reference to a period of four years (2011-2014), the quality of performance management systems of 29 Italian universities (response rate: 48 percent) was examined and the possible effects on performance levels of these institutions were analyzed by means of statistical methodologies (multiple regression analysis). Outcome indicators were considered.

The findings indicate the need to go further “measurement,” and to take care of performance “management,” especially in complex organizations as universities, where academicians identify themselves more with their professions than with the organization and where technicians and administrative employees might look at the performance-based reform with “bureaucratic eyes.” A fruitful cooperation between the professional soul and the bureaucratic one is paramount.

Studies which analyze organizational factors that could affect the adoption and implementation of performance management systems are rare, and use in prevalence qualitative methods or refer to machine bureaucracies, not many to professional ones as public universities. Moreover, the performance management literature in a public university context deepens the topic of the selection of KPIs and the focus is mainly on macro performance or on management tools for gathering and analyzing performance measures.