*2.3. Report the Review*

The majority of the studies were journal articles and high-quality conference papers, although some book chapters were also analyzed. A clear depiction of the types of publications identified is presented in Figure 2, while their distribution within the time range of the review is presented in Figure 3. Moreover, their publishers are also presented in Figure 4. From the primary studies (i.e., cluster of 106), the most interesting and relevant ones were selected (excluding the documents in the form of a survey or literature review, which were also considered separately) to highlight the main current research trends and the gaps that have yet to be filled. We selected studies only considering if the paper's argument was built on an appropriate base of theory and concepts, and if the evaluation results were clearly presented and appropriately analyzed. The selected studies were 20 papers and are presented in Appendix A.

**Figure 2.** Distribution of the identified studies based on their type.

The following features were highlighted: problem statement, proposed solution/objectives and outcomes/limitations.

Evaluating the selected studies in terms of research knowledge, it appears that current studies partially tackle DRM designed for IoV. There were studies that were separately studied and solved some of the IoV issues, although none succeed in tackling all of them holistically, providing a systematic literature review or focusing on how blockchain could be incorporated into DR management schemes designed for IoV. The aforementioned statement is supported by Table 3, where it can be seen that no work was identified to employ blockchain-based privacy, DRM, V2V/V2G energy trading, charging scheduling, incentivization schemes and EV profiles. Table 3 summarizes and compares the main features observed in the literature review.

**Figure 3.** Distribution of the identified studies within the review's time range.

**Figure 4.** Publishers for the identified primary studies.

### **3. Current Perspectives and Research Efforts on Blockchain-Enabled IoV**

This section summarizes the research findings derived from the systematic literature review, focusing on the perspectives and research efforts around the demand response management in the IoV, taking into consideration the application of blockchain technology.

### *3.1. P2P Trading and Management in Energy Blockchain*

There are several studies that propose the use of blockchain technology in P2P energy trading [57]. For instance, in [44], a game-theoretic approach for a demand side management model that incorporates a localized PBFT-CB was proposed. The study incorporated the interaction between sellers and buyers, considering the Stackelberg game and noncooperative static games. Additionally, Brooklyn microgrid was one of the first applied engineering programs of energy blockchain [58]. The project is based on blockchain P2P energy trading without the intermediation of a third-party energy supplier. The Brooklyn microgrid demonstrates that blockchain may be utilized in real-world P2P energy transactions. Moreover, Q. Duan [59] proposed an optimal scheduling and management smart city scheme, within the safe framework of blockchain. To do so, Q. Duan presented an enhanced directed acyclic graph strategy to increase the security of data transactions inside a smart city, as well as a security layer based on blockchain to prevent cyber hacking. Additionally, the LO3 Energy company introduced an energy supply scheme to the closest neighbors based on P2P trading [60]. Lastly, the current literature dictates future distributed ledger implementations and mechanisms and revealed that blockchain is an important part of P2P energy trading [52,61–63].

### *3.2. Blockchain-Based Demand Response Programs and Optimization Models*

Demand response has been recognized as an important tool for managing supply and demand in electrical grids [64]. When there is an electrical deficit, DR becomes an effective alternative for absorbing the energy gap and managing power utilization [47]. Significant initiatives in blockchain-based demand response programs and optimization models are presented below.

To handle demand response in a V2G context, a P2P energy trading mechanism between EVs and network operators was proposed by S. Aggarwal [39], in an attempt to overcome smart grid imbalances and to control the ever-growing energy demands from EVs. Moreover, Z. Guo [47] presented a blockchain-enabled DR scheme with an incentive pricing model. First, the authors proposed a blockchain-enabled DR framework to promote the secure implementation of DR, while then they also designed a dual-incentive mechanism, based on the Stackelberg game model, to successfully implement blockchain demand response management. Furthermore, in the area of IoV, there are also several studies that address the DR problem and propose optimization solutions. For instance, Z. Zhou [54] proposed a consortium blockchain-enabled secure energy trading framework for EVs with a moderate cost, using a contract theory-based incentive mechanism to incentivize more EVs to participate in DR. The proposed optimization scheme falls into the category of difference of convex programing and is solved by using the iterative convex– concave procedure algorithm. Likewise, T. Zhang [41] incorporated a blockchain-based cryptocurrency component, with which the system can incentivize users with monetary and non-monetary means in a flat-rate manner.

In recent years, the implementation of DR programs in smart grids has drawn a lot of academic attention. A taxonomy of these research endeavors is depicted in Figure 5, which was generated from the outcomes of the current SLR. This categorization is based on the DR procedure's control mechanism, customer motives to lower or move their expectations and the DR decision variable.

DR systems have two types of control mechanisms: centralized and distributed. In the centralized mode, consumers connect directly with the electricity network without engaging with one another. In the distributed mode, user interactions feed the network with information about overall usage [65].

**Figure 5.** Demand response program taxonomy.

The motivations offered to producers and consumers to decrease their energy usage are classified in the second category of DR schemes. These motivations are divided into two categories:

The main feature of task scheduling DR is control over the desired load's activation time, which may be moved to peak-demand periods [71,72]. The energy-managementbased DR solutions accomplish different power usage during peak-demand hours by decreasing the power consumption of certain loads [73,74].

### *3.3. Electric Vehicles Charging Scheduling Using Blockchain*

To study the consequences of increased EV load and charging mechanisms, the accurate modeling of EV charging profiles is necessary [75]. The size and topology of the energy grid, the number and size of EVs, the mode, time and location of charging as well as the daily driving distance influence the above-mentioned charging profiles. As a consequence, the charging profiles of the drivers are increasingly coupled with charging schedules.

N. Guo et al. [76] proposed a centralized control architecture to handle the modeling and management of EV charging by reducing peak demand and increasing the number of EVs charged concurrently. A common finding in numerous studies on EV battery chargers is that EV battery workloads are commonly thought of as a static, with the actual system behavior of the batteries throughout the charging process being overlooked. In order to tackle the latter, Y. Wu [77] emphasized that a bi-directional energy flow is conceivable. EV batteries may be utilized in the grid in the manner of any other energy storage device, with the additional perk of mobility. The owners of EVs would be able to participate in energy market trading, recharge batteries when energy is cheap and discharge if the smart grid rewards them for their excess energy. This type of energy exchange and negotiation can allow the network to regulate demand (e.g., peak shaving) or offer additional storage in the case of excess renewable energy generation. Consumers will be able to pick where, when and which EV to charge, reducing the strain of the grid.

Using the adaptable EV charging flow, C. Lazaroiu [42] developed a model for smart charging of EVs, in which a software agent selects whether it should load a unit, in what sequence or whether it is better to sell energy to the market. The concept is based on blockchain technology, which makes interactions reliable and traceable, with the goal of decreasing or eliminating intermediaries in energy trade and lowering anxiety.

Furthermore, given the growing popularity of EVs and their unpredictable dynamic nature in terms of charging and route patterns, EV load might be difficult for energy distribution operators and utilities to manage [41]. Thus, T. Zhang proposed SMERCOIN, which is a real-time solution that integrates the concepts of priority and cryptocurrencies to encourage EV owners to charge on a renewable energy-friendly timeframe. Customers with a longer history of utilizing renewable energy are given priority in the system, which uses a rating system. By including cryptocurrency, the system may encourage users using both monetary and non-monetary techniques in a flat-rate manner. Similarly, Z. Zhou [54] developed a distributed, privacy-preserved and incentive-compatible DR mechanism for IoV. In more detail, the authors suggested a low-cost consortium blockchain-enabled secure energy trade platform EVs, as well as an incentive system based on contract theory in order to encourage more EVs to join the DR program.

Furthermore, a dependable solution is required to meet the future energy demands of urban and industrial customers, while also supporting the charging and discharging needs of EVs. Therefore, some research articles have been presented, studying the energy trading in the IoV for demand response management. For example, S. Aggarwal [39] proposed a blockchain-based secure energy trading scheme for demand response management between EVs and the service providers, while a double auction mechanism is proposed between EVs and SPs to maximizes social welfare with privacy preservation. Similar examples can also be found in [12,46], in which the authors analyzed the energy in IoVassisted smart cities, employing blockchain capabilities in order to select the most suitable charging station without sharing private information, and to balance the spatio-temporal dynamic demands of computing resource.


**Abstract**

Nowadays, the value of paediatric nuclear diagnostic medical imaging has been well established within the medical community. Despite decades of nuclear medicine practice, studies in nuclear medicine to achieve the lowest possible radiation dose to the patient while ensuring the optimized image quality have to be continued. Numerous studies highlighted a long list of objectives, in order to obtain the minimum possible absorbed dose, achieve short scan times and generate images with a high signal to noise ratio (SNR) and spatial/temporal resolution. For the development of guidelines, it is necessary to study the handling of radiopharmaceuticals, the dose splitting processes, the quality control protocols, the plan design of infrastructures, the availability of optimized dose calibrators for the corresponding radiopharmaceuticals, the development of new more sensitive radiopharmaceuticals, and optimized protocols for diagnostic or therapeutical examination of the patient. Anthropomorphic phantoms are used to model paediatric patients, but anatomical models and their pharmacokinetic data are not applied directly to any specific patient. There is a need for the development of personalized dosimetry in children. Factors regarding age, weight and biological and molecular background of the pathology must be included in paediatric personalized dosimetry. The developmental process of the child, as shape, mass, volume, anatomy, physiological indices (metabolism, heart rate, etc.) and variations due to pathologies should be taken under consideration. Corrections of radiation time of the target organ, in relation to neighbouring tissues, blood supply, estimation of residual activity/time and clearance rate are parameters in the calculations of paediatric dosimetry in nuclear medicine. In hybrid imaging examinations with computed tomography modality, the contribution of absorbed dose from CT to the paediatric patient must also be calculated.

**Keywords:** paediatric dose phantoms, PDRL, effective dose, image gently, paediatric radiosensitivity, individualize dosimetry


*Edited by Giuseppe Lo Giudice*

This book focuses on the current approaches in the medical and surgical treatment of the most diffuse and important ocular diseases. It reports the latest developments in surgery (anterior segment surgery, posterior pole surgery, etc.) for various types of ocular disease, including glaucoma, corneal diseases, and vitreoretinal disease. the latest developments in the surgical field of ocular disease from the anterior segment to the posterior pole going through the main ocular disease (cataract surgery in normal and/or complicated conditions, glaucoma, corneal diseases, and vitreoretinal disease), with the latest developments in the treatment of ocular disease that enable improved surgical outcomes and fewer complications.

ISBN 978-1-83969-908-5

Vision Correction and Eye Surgery

Published in London, UK © 2022 IntechOpen © a-fitz / iStock


*2.4. Plasmid Replicon Typing*

Identification of replicon types of the 18 major plasmid incompatibility (Inc) groups present in *Enterobacteriaceae* was performed by multiplex PCR.

Standard PCR protocols and conditions were used in the following way: initial denaturation at 94 ◦C for 5 min; 30 cycles of 94 ◦C for 1 min, 60 ◦C for 30 s and 72 ◦C for 1 min; and a final incubation for 5 min at 72 ◦C. We used Taq DNA polymerase and dNTPs from QIAGEN (Hilden, Germany), and a T3000 Biometra thermocycler (Biometra, Gottingen, Germany).

The protocol allows detection of the following Inc groups: Hl1, Hl2, I1-Iγ, X, L/M, N, FIA, FIB, W, Y, P, FIC, A/C, T, FIIs, F, K, B/O [27].

#### *2.5. Transformation by Electroporation*

Preparation of Plasmid-DNA was performed with the QIAprep Spin Miniprep Kit (250) (QIAGEN).

Plasmid-DNA was desalted before electroporation, and therefore 2–3 μL of plasmid-DNA were transferred on a MFTM Membrane Filter (0.025 μm VSWP, Merck), which was placed on the surface of double distilled water. Dialysis was performed for about 15 min.

Competent cells were made with two overnight cultures (each 50 mL, OD of 0.4), which were incubated on ice for 25 min, therefore reaction tubes were cooled in advance, followed by centrifugation for 10 min at 4 ◦C and 4.000 rpm (Eppendorf, Centrifuge 5810R). After decantation, pellets were re-suspended in 100 mL ice-cold glycerine solution (10%). After repeating this step, an additional washing step was performed, and the two pellets together were re-suspended in 10 mL glycerine solution (10%). A last washing step and resuspension were performed with 1 mL glycerine solution (10%). Aliquots of 50 μL were prepared and stored at −20 ◦C.

Electroporation was performed with 2 μL Plasmid-DNA and 40 μL of competent cells. Reaction tubes were cooled in advance and the DNA-cell suspension was incubated on ice for 5 min. Subsequently, the cell suspension was transferred into a sterile electro-cuvette, and transformation was performed at 2500 V using the electroporator (Eppendorf Eporator®). After the transformation, 400 μL of fresh LB liquid media were added and the cell suspension was re-transferred into the reaction tube. Incubation was performed for 40 min at 37 ◦C. Afterwards 100 μL of the cell suspension were plated on selection LB (lysogeny broth) plates (tetracycline 3 μg/mL or tigecycline 1 μg/mL) and a final incubation was performed over night at 37 ◦C.


7. Conclusions

This chapter presented the most important aspects related to the FPGA implementation of a turbo decoder for WiMAX and LTE systems. The serial turbo decoder architectures for the two systems have been developed and efficiently implemented, important results being obtained especially for the proposed architectures of the interleaver/deinterleaver. For LTE systems, the interleaver memory ILM has been introduced. In this manner, the interleaver process effectively works only outside the decoding process itself.

The ILM has been written together with the input data, while the previous block was still under decoding. It should be outlined that this solution allows the transition from the serial to the parallel decoder in an efficient manner, involving only values that are concatenated at same memory locations. The parallel approach requires the same storing capacity (the number of BRAMs) and a single interleaver, thus adding only an even-odd merge sorting network. This unique interleaver has been implemented in an efficient configuration that uses only comparators and subtractors and no multipliers and dividers

The parallel decoding performances have been compared with the serial ones. In this context, certain degradation has been observed. In order to eliminate this degradation, a small overhead is accepted by the overlapping split that is applied to the parallel data blocks.


**3.4 Tertiary treatment**

Tertiary treatment is employed for the removal of specific effluents which cannot be completely removed by secondary method. During this process, around 99% of all contaminants are eliminated. This process removes inorganic substances such as nitrogen and phosphorous and recovers wastewater quality which can be reused for irrigation and drinking and have no harmful effect when discharged to the environment [25].


Section 3 Face Recognition

Chapter 6

Abstract

1. Introduction

113

Face Recognition

possible applications are presented in this chapter.

transform, histogram of oriented gradients, local binary patterns

Spatial Domain Representation for

Toshanlal Meenpal, Aarti Goyal and Moumita Mukherjee

Spatial domain representation for face recognition characterizes extracted spatial facial features for face recognition. This chapter provides a complete understanding of well-known and some recently explored spatial domain representations for face recognition. Over last two decades, scale-invariant feature transform (SIFT), histogram of oriented gradients (HOG) and local binary patterns (LBP) have emerged as promising spatial feature extraction techniques for face recognition. SIFT and HOG are effective techniques for face recognition dealing with different scales, rotation, and illumination. LBP is texture based analysis effective for extracting texture information of face. Other relevant spatial domain representations are spatial pyramid learning (SPLE), linear phase quantization (LPQ), variants of LBP such as improved local binary pattern (ILBP), compound local binary pattern (CLBP), local ternary pattern (LTP), three-patch local binary patterns (TPLBP), four-patch local binary patterns (FPLBP). These representations are improved versions of SIFT and LBP and have improved results for face recognition.  It is widely accepted over other biometric systems like, finger print, iris or speech recognition for security, surveillance, and commercial applications. Face recognition system is generally a procedure of multiple major stages: face detection, preprocessing, feature extraction and verification. A complete structure of face recognition system is shown in Figure 1. Face detection detects a single face or number of faces present in a given image. Viola-Jones face detection algorithms using Haar features [1], faster R-CNN face detector [2], and face detection based on Histograms of Oriented Gradient [3] are popular methods for detecting faces in an image.  Preprocessing mainly aims to reduce noise effect, difference of illumination, color intensity, background, and orientation. The correct recognition of image depends upon quality of captured image, lighting condition etc. [4]. Recognition rate can be improved by performing pre-processing on the captured image. Various pre-processing

## Chapter 6


**10. Rd-HMMer limitations**

232 Bioinformatics

scores, statistical significance and the alignment may guide your template selection. However, if the Rd.HMM of a template candidate gives a negative score, and still you decide to use it, do not trust the Rd.HMM alignment without further improvement

g. Finally, if you use the ROSETTA suite or the ROBETTA server to produce your models, these structures are expected to have a ROSETTA-like bias, *i.e.* their Rd.HMM scores will increase and a good model with this bias is expected to have a ratio of HMMer score to sequence length close to one. While in models produced with other software a Rd.HMM score ratio of 0.3 is acceptable, in a ROSETTA produced model this score is low and may reflect important flaws. Look at the alignment carefully, as recommended

**Figure 5.** Comparison of the yeast α-glucosidase model produced included in the publication by Brindis et al (Brindis et al., 2011), with the X-ray solved structure of its homologue, the yeast isomaltase (Yamamoto et al., 2010). The isomaltase is shown as blue cartoons and the α-glucosidase cartoons are

As an example of the advantages of Rd.HMM, we refer to two cases of recent success. Brindis and coworkers (Brindis et al., 2011) analyzed the effects of a natural product on αglucosidase. This work reports a model for the budding yeast α-glucosidase used to analyze the molecular grounds for the (Z)-3-butylidenephthalide inhibitory action. In the preparation of the model, Rd.HMM allowed to detect a threading problem (insertion/deletion pair) in one β-strand in the core of the model. While the sheet was slid only a few Å from its position, the contact with neighboring strands completely distorted

colored according to the amino acid rmsd from isomaltase, ranging from very low (blue) to

using other tools, as it may be seriously flawed.

in the previous section.

intermediate (white) to high (red).

Since most Rd.HMM limitations have been mentioned. We only summarize them here: