*Edited by Sadia Ameen,*

*M. Shaheer Akhtar and Hyung-Shik Shin*

The field of nanoporous materials has advanced significantly over the last two decades with new concepts and applications emerging all the time. This book is a comprehensive and easy-to-understand source of information on the latest developments in nanopore research. It is a collection of contributions from leading specialists in the subject that address topics such as synthetic methodologies, characterization techniques, and applications of nanopores. This book will appeal to a wide spectrum of readers, including students, professors, and professionals.

ISBN 978-1-83880-209-7

Nanopores

Published in London, UK © 2021 IntechOpen

© Girolamo Sferrazza Papa / iStock


*2.2. MTPA Equations*

As it could be found from the torque Equation (4), the total torque produced by machine includes magnetic component proportional to the rotor magnetic flux and reluctance component proportional to the difference between direct and quadrature inductances. The magnetic and reluctance torques depend on the phase of the stator current as sine and sine of doubled angle, respectively, therefore the resulting torque of a machine also has a maximum which has to be defined and used for efficient control.

In order to do this, the magnitude of the stator current vector is fixed at *Is* and the relation between the current components, which corresponds to the maximum can be found. The quadrature component of the stator current is:

$$i\_q = \sqrt{l\_s^2 - i\_d^2} \tag{5}$$

Combining (5) and (4) with the following differentiation, with respect to *id*, results in the expression, which is used for the calculation of the maximum of torque [16]:

$$i\_d = -\frac{\Psi\_m}{4\left(L\_d - L\_q\right)} - \sqrt{\frac{\Psi\_m^2}{16\left(L\_d - L\_q\right)^2} + \frac{I\_s^2}{2}}\tag{6}$$

Equation (6) can be rewritten in terms of the current components:

$$i\_d = -\frac{\Psi\_m}{2\left(L\_d - L\_q\right)} - \sqrt{\frac{\Psi\_m^2}{4\left(L\_d - L\_q\right)^2} + i\_q^2} \tag{7}$$

The last two equations define the MTPA condition of synchronous machines with constant parameters. They are used for the implementation of the conventional MTPA control and move stator current vector along the trajectory, the typical shape is depicted in Figure 2. Good examples of MTPA implementation according to this approach can be found in [40–42].

**Figure 2.** MTPA trajectory in *idiq* plane.

Since Equations (6) and (7) are calculation intensive, the MTPA trajectory is frequently written in terms of the stator current and its phase, which is a function of the stator current amplitude. In this case, the MTPA trajectory is described by the following equations:

$$\begin{aligned} \dot{a}\_d &= -I\_s \sin(\gamma), \\ \dot{a}\_q &= I\_s \cos(\gamma); \end{aligned} \quad \begin{aligned} \text{g} &= \\ \text{g}, \end{aligned} \tag{8}$$

$$\gamma(I\_s) = \arccos\left(\frac{-\Psi\_m + \sqrt{\Psi\_m^2 + 8\left(L\_d - L\_q\right)^2 I\_s^2}}{4\left(L\_d - L\_q\right)I\_s}\right). \tag{9}$$

Despite the MTPA angle being hard for calculation, it is a smooth function, which can be easily approximated with a first- or second-order polynomial, which can be easily seen from Figure 3. Good examples of MTPA implementation according to this approach can be found in [43–45].

**Figure 3.** Dependence of the MTPA angle γ on the stator current amplitude.

#### **3. Impact of the Motor Parameters Variation on the MTPA Trajectories**

In order to design constraints properly, the impact of motor parameters on the MTPA trajectory must be analyzed. The analysis was performed for the motor used in the experimental verification. The rated parameters are demonstrated in Table 1.

**Table 1.** Motor rated parameters.


Good Performance

Asynchronism is taken as a natural way of avoiding waiting times to make a decision as well as decreasing the number of robots that are simultaneously making a decision. Since the task allocation computation strongly depends on the number of robots under consideration, asynchronism also makes optimal decisions can be linearly computable most of the time. As a consequence, robots can compute optimal tasks-to-robots distributions in a short time, achieving high levels of dispersion efficiently. Besides, regarding reconnections, the proposal consists of a rendezvous policy where the locations of the selected tasks become the meeting points themselves, avoiding deviations from the planned paths. Compared with others, the proposed approaches are capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.


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Gordon,D. and Chang,A.E. (1993) Direct gene transfer with DNA-liposome complexes in melanoma: expression, biologic activity, and lack of toxicity in

[Development of gene delivery system into skeletal muscles by bubble liposomes

Pedroso de Lima,M.C. (2009) Transferrin lipoplex-mediated suicide gene therapy of oral squamous cell carcinoma in an immunocompetent murine model and mechanisms involved in the antitumoral response. *Cancer Gene Ther.* 16, 91-101. Nihira,S. (2003) Development of HER2-specific humanized antibody Herceptin

trans-tracheal injection of adenoviral-mediated p53 versus docetaxel alone in patients with previously treated non-small-cell lung cancer. *Cancer Gene Ther.*

TP53 database: new online mutation analysis and recommendations to users. *Hum.*

immune responses following adenovirus-directed enzyme prodrug therapy for the

luminescent porous silicon nanoparticles for in vivo applications. *Nat. Mater.* 8, 331-

Fluorescence imaging and whole-body biodistribution of near-infrared-emitting quantum dots after subcutaneous injection for regional lymph node mapping in

**23**

*Iran*

**Toxicogenomics of Nonviral Cationic**

*Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz*

To date, both viral and nonviral vectors have been exploited for delivery of gene-based therapies to target cells/tissues. Despite high efficiency of the viral vectors (e.g., retroviruses and adenoviruses), these vectors appear to be immunogenic and potentially harmful when used in clinical gene therapy protocols (Ferber, 2001b). Besides, the preparation and purification of the viral vectors appear to be laborious, cost-prohibitive and not amenable to industrial-scale manufacture. Nonviral vectors such as cationic lipids (CLs) and cationic polymers (CPs) have been categorized as advanced materials and their low immunogenicity, lack of pathogenicity, and ease of pharmacologic production continue to make them attractive alternatives to viral vectors (Medina-Kauwe et al., 2005). However, these vectors may also suffer from relatively low levels of gene transfer compared to viruses. Thus, the drive to advance these vectors continues resulting in considerable progresses in improved transfection efficiency. Nonviral vectors (in particular cationic gene delivery systems) are able to bind and enter the target cells, however they yield low gene expression. No substantial information is available on interactions of these vectors with cellular biomolecules. Since these medicaments tend to act at genomic levels, thus understanding the genomic impacts of the nonviral vectors may help develop more efficient gene delivery systems. Nonetheless, this needs

To date, exploitation of the "omics" concepts (e.g., genomics, proteomics and metabolomics) is going to change the face of pharmacotherapy towards significantly more advanced and efficient pharmaceuticals (e.g., gene based nanomedicines) with minimal adverse consequences (Aardema & MacGregor, 2002). Enormous efforts have also been devoted for application of the global gene expression profiling in pharmacologic and toxicological investigations. The gene expression profiling technology has been primarily exploited for identification of underlying mechanisms for toxicity of pharmaceuticals and their genomic signatures, by which the safety liabilities can be determined and manifestations of undesired genotoxicity can be prohibited (Suter et al.,

This methodology can be successfully used for the discovery and development of any chemicals and pharmaceuticals including gene delivery nanosystems. The main focus of the current book chapter is to provide some useful information about "genocompatibility" and

recruitment of high throughput screening methodologies.

**1. Introduction**

2004; Yang et al., 2004).

**Gene Delivery Nanosystems**

Yadollah Omidi, Vala Kafil and Jaleh Barar *Research Center for Pharmaceutical Nanotechnology*