*2.4. Summary*

Highly complex interactions between a multitude of cellular and viral components at the level of DNA, chromatin and upstream signalling pathways determine the initiation and magnitude of transcription from the HCMV MIEP. The highly dynamic transcription from the major IE gene is complemented by post-transcriptional processing and translational regulation, ultimately controlling the synthesis of the IE1 and IE2 families of predominantly nuclear proteins. The major IE proteins are subject to post-translational regulation and are thought to activate the viral replicative cycle during both initial infection and reactivating from latency. It is therefore believed that the eventual outcome of HCMV infection depends on the level and timing of IE1 and IE2 expression.

#### **3. Major IE Protein Function**

#### *3.1. Role in Activation and Repression of Transcription*

IE1 and IE2 were initially identified as activators of transcription in reporter assays using transiently transfected plasmids [11,13,14]. In these assays, the IE proteins were shown to activate the HCMV MIEP (positive auto-regulation) and various viral early gene promoters. In addition, IE2 turned out to be a repressor of the MIEP (negative auto-regulation). In fact, IE2 sequence-specifically binds to the crs in the core promoter (Figure 1) to block RNA polymerase II occupancy at the transcription start site. Furthermore, several heterologous viral promoters as well as cellular promoters proved to be responsive to activation by the IE proteins. The impact of IE1 and IE2 on transcription from a broad variety of promoters in transient transfection assays earned them the title "promiscuous transactivators". IE2 usually appeared as the stronger activator compared to IE1 and, depending on the reporter construct, the two proteins often acted in an additive or synergistic manner. Activation by IE1 and IE2 was mapped to both upstream elements as well as core promoter regions including the TATA-box. Accordingly, numerous specific and basic transcription factors or transcription factor complexes were reported to interact with IE1 (e.g., CEBP, E2F1-5, SP1) and IE2 (e.g., AP1, CREB1, EGR1, SP1, TAF4, TBP, TFIIB, TFIID).

Many key findings from transient transfection assays about the impact of IE1 and IE2 on HCMV transcription were later corroborated by studies involving mutant viruses and global transcriptome analyses. These findings confirmed positive autoregulation at the MIEP by IE1 [217], crs-dependent repression of the MIEP by IE2 and activation of viral early genes by IE1 and IE2 [9,218–221]. In contrast, "promiscuous transactivation" by IE1 and IE2 was not replicated in transcriptome analyses of endogenous human genes. Instead of showing broad activation of gene expression from the human genome, the differential transcript profiles from cells individually expressing IE1 or IE2 were rather distinct with little or no overlap to the genes activated by the IE proteins in assays with transfected reporter plasmids. Following expression under conditions closely mimicking the situation during productive infection, IE1 turned out to be as significant a repressor as it is an activator of host gene expression in growth-arrested human fibroblasts [222,223]. Cells induced to express IE1 exhibited global repression of interleukin 6 (IL6)- and oncostatin M-responsive signal transducer and activator of transcription (STAT) 3 target genes. This repression was followed by STAT1-dependent activation of type II IFN-stimulated genes (ISGs), normally induced by IFN-γ, many of which encode immune-stimulatory proteins including proinflammatory chemokines [222–224]. Moreover, in the presence of IFN-α or IFN-β, IE1 was found to inhibit the induction of type I ISGs by the trimeric complex of STAT1, STAT2 and IRF9 known as ISG factor 3 (ISGF3) [225,226]. The effects IE1 exerts on the human transcriptome are thought to result largely from direct physical interactions with STAT2 and STAT3 (see Section 3.4). While transcriptional regulation by IE1 appears to be dominated by pathways depending on proteins of the STAT family, IE2 has been shown to inhibit the induction of IFN and other antiviral cytokine genes via a mechanism involving NF-kB and STING (see Section 3.4). However, the transcriptome profile for IE2 in cycling human fibroblasts was dominated by genes regulating the cell cycle and DNA replication many of which are E2F-responsive [227]. This finding likely reflects the impact of IE2 on the cell cycle. IE2 promotes cell cycle progression from G0/G1 to G1/S and arrests cells at the G1/S interface, inhibiting cellular DNA synthesis, or at the G2/M interface [13,14,228]. Many human genes activated or repressed by isolated expression of IE1 or IE2 were also shown to be differentially regulated during productive HCMV infection.

### *3.2. Role in Chromatin-Based Epigenetic Regulation*

IE2 is known to bind sequence-specifically to DNA, but there is no convincing evidence for direct DNA binding by IE1 [11,108,131]. However, IE1 associates with chromatin via core histones. IE1 exhibits two physically separable histone interacting regions with differential binding specificities for H2A-H2B dimers and H3-H4 dimers or tetramers. The H2A-H2B binding region was mapped to an evolutionarily conserved nucleosome binding motif (amino acids 479–488) within the chromatin tethering domain (CTD) at the C-terminus [229,230]. This motif docks with the acidic patch formed by H2A-H2B on the nucleosome surface [229,230]. The consequences of the IE1-nucleosome interaction have not been fully elucidated, but they appear to include alterations to higher order chromatin structure [230]. Histone binding by IE1 might also be linked to the overall low nucleosome levels and temporal reorganization of nucleosomes across viral genomes observed during productive HCMV infection [9,105]. To our knowledge, transcriptional regulation via the IE1 CTD has not been experimentally addressed. Instead, it has been reported that IE1x4, a small variant form of IE1 expressed from a promoter internal to major IE exon 4, facilitates viral genome maintenance and replication during HCMV latency via a CTD-dependent mechanism. Despite a lack of experimental evidence, the mechanism is predicted to involve HCMV episome tethering to host mitotic chromosomes via nucleosome binding by IE1x4 resulting in nuclear retention and partitioning of viral genomes across latently infected dividing cells [75]. Although IE2 appears to have relatively little affinity for histones, both IE proteins have been shown to be present in complexes with nucleosome modifying host cell proteins. For example, IE1 binds to histone deacetylase (HDAC) 3 [124], while IE2 binds to HDAC1-3 [124,126,231], lysine acetyltransferases CREB binding protein (CBP, KAT3A), p300 (KAT3B) and p300/CBP-associated factor (KAT2B) [232,233], and lysine methyltransferases G9A (euchromatic histone lysine methyltransferase 2, EHMT2) and suppressor of variegation 3-9 homolog 1 (SUV39H1) [126]. Accordingly, transcriptional activation of viral IE and early genes by IE1 correlates with histone acetylation, while transcriptional repression of the MIEP by IE2 involves histone deacetylation and methylation [130,131,234].


**1. Introduction**

Till now the synthesis problem of the optimum control of the observation process has been considered and solved satisfactorily basically for the linear stochastic objects and observers by optimization of the *quadratic* criterion of quality expressed, as a rule, through the a posteriori dispersion matrix [1-4]. At the same time, the statement of the synthesis problem for the optimum observation control in a more general case assumes, first, a nonlinear character of the object and observer, and, second, the application of the non-quadratic criteria of quality, which, basically, can provide the potentially large estimation accuracy[3-6].

In connection with the fact that the solution of the given problem in such a statement generalizing the existing approaches, represents the obvious interest, we formulate it more particularly as follows.


**1.2 Basis of mobile ad hoc networks**

A MANET is one that does not have any base station and fixed substructure that can freely and dynamically self-organize into temporary network topologies. An ad hoc mobile

Energy Consumption Analysis of Routing Protocols in Mobile Ad Hoc Networks 251

As shown in Figure 3, routing protocols in mobile ad hoc networks are classified into two classes: table-driven and on-demand [Misra S et al, 2008]. The table-driven d, or proactive, method is used for alternate updating links and can use both the distance vectors and link statuses used in fixed networks. The word "proactive" means that this method is always active and will also react to a change in the linkage [Norouzi. A and Ustundag B. B, 2011]. A problem arises for this method when movement is low because additional work is done and

In the on-demand method with reactions, other nodes do not update the route and the routes are determined at the origin of the request. Therefore, there is the possibility of using a caching mechanism. The advantage of this method is that both energy and bandwidth are used effectively. In this chapter, the table-driven and the on-demand protocols are explained

In this group of protocols, each node maintains one or more tables that include routing information to other nodes of the network. All nodes update their tables to preserve compatibility and to give upgraded viewpoints of the network. When the topology of the network changes, the nodes distribute update messages across the network [Misra S et al, 2008]. Some identifying aspects of this class of routing protocols include the ways in which information is distributed, the ways the topology is changed and the number of tables necessary for routing. The following sections explain some of these routing protocols

Fig. 3. Categorization of MANET routing protocols

the network tends toward instability [Bai F et al, 2004].

**2.2 Table-driven protocols**

[Boukerche. A et al, 2011].

and then compared with different parameters [Dixit et al, 2005].

**2.1 Mobile ad hoc network routing protocols**

network is an unstable network formed dynamically by a collection of wireless mobile nodes without the use of an existing network substructure [Chlamtac et al, 2003]. Such networks back up calculations at any time and in any place, and their structures can change automatically [Norouzi. A and Ustundag B. B, 2011]. In such networks, each mobile host acts as a router. For this reason, peer-to-peer communication as well as peer-to-remote communication is possible in this kind of network [Camp et al, 2002].

Fig. 2. An example of a Mobile Ad hoc Network (MANET)

Figure 2 shows an example of a MANET. This type of network has been comprised of different kinds of mobile devices such as PDAs, smart signals and mobile hosts. In MANETs, routing is a complex problem and the reason for this is mobility of routes. However, links may change frequently and this subject refers to the fact that communication links should be updated continually and their messages should be sent frequently; hence this control creates traffic [Perkins E, 2008].

There are special devices in different forms, but one of their common specifications is that they use battery energy, and this energy is limited. Wireless transmission, collision, resubmission and conductive radio waves are all effective in energy consumption. As a result, there is strong need for the presence of protocols which use energy efficiently and effectively as well as technology for better management of energy [Norouzi. A and Ustundag B. B, 2011].


*Article* **Credit Valuation Adjustment Compression by Genetic Optimization**

#### **Marc Chataigner and Stéphane Crépey \***

LaMME, Univ Evry, CNRS, Université Paris-Saclay, CEDEX, 91037 Évry, France<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>
Received: 4 August 2019; Accepted: 23 September 2019; Published: 29 September 2019

**Abstract:** Since the 2008–2009 financial crisis, banks have introduced a family of X-valuation adjustments (XVAs) to quantify the cost of counterparty risk and of its capital and funding implications. XVAs represent a switch of paradigm in derivative management, from hedging to balance sheet optimization. They reflect market inefficiencies that should be compressed as much as possible. In this work, we present a genetic algorithm applied to the compression of credit valuation adjustment (CVA), the expected cost of client defaults to a bank. The design of the algorithm is fine-tuned to the hybrid structure, both discrete and continuous parameter, of the corresponding high-dimensional and nonconvex optimization problem. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, we propose an approach that circumvents portfolio revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only. This is illustrated by a CVA compression case study on real swap portfolios.

**Keywords:** counterparty risk; credit valuation adjustment (CVA); XVA (X-valuation adjustments) compression; genetic algorithm

**MSC:** 91B30; 91G20; 91G40; 91G60

**JEL Classification:** D52; D53; G01; G13; G24

#### **1. Introduction**

XVAs, where VA stands for valuation adjustment and X is a catch-all letter to be replaced by C for credit, F for funding, M for margin, or K for capital, have been implemented by banks in reaction to the regulatory changes aroused by 2008 financial turmoils. They monetize counterparty risk and its funding and capital consequences by add-ons to derivative entry prices sourced from clients. According to the cost-of-capital XVA approach of Albanese and Crépey (2019), accounting for the impossibility for a bank to replicate the jump-to-default related cash flows, the final, all-inclusive XVA formula reads

$$\text{CVA} + \text{FVA} + \text{MVA} + \text{KVA}\_{\prime} \tag{1}$$

to be sourced by the bank from clients on an incremental run-off basis at every new deal.

As stated by the Basel Committee on Banking Supervision (2015), major counterparty credit losses on OTC (Over-the-counter) derivative portfolios in 2008 arose from XVA accounting losses rather than from actual client defaults. In particular, a bank incurs a CVA loss when the market perceives a deterioration of the credit risk of a client. This has motivated the creation of XVA desks for dealing with these risks.

In this paper, we deal with CVA compression, i.e., the minimization of the CVA of a client portfolio by the introduction of an incremental trade, subject to the constraint of not altering too much the market risk of the portfolio. In the financial derivative industry, the term compression term is generally applied in the context of "trade compression", i.e., the reduction of the gross notional of positions in the market. Trade compression aims notably at reducing certain capital requirements, the number of transactions, and their amount (see Section 5.3 of (Gregory 2015)). As reflected by the proliferation of related industry presentations1, this kind of balance sheet optimization is very active in top tier banks at the moment.

XVAs reflect market inefficiencies that should be compressed as much as possible. Here, we focus on CVA compression for concreteness and simplicity, but the developed XVA compression methodology is generic. It could and should be applied to further XVA metrics, as soon as these are available with sufficient speed, for computation at the portfolio level, and accuracy, for numerical significance of the results at the incremental trade level (see Section 5 in (Albanese et al. 2018), which emphasizes the XVA compression perspective on the pricing and risk management of financial derivatives in the post-2008–2009 global financial crisis era, and cf. (Kondratyev and Giorgidze 2017), which uses a genetic algorithm for determining an optimal trade-off between MVA compression and transaction costs).

The complexity of XVA compression problems stems, in particular, from the hybrid nature of the state space of the corresponding optimization problems. Indeed, a new trade (financial derivative) is described by a combination of continuous and discrete parameters. This rules out the use of standard convex optimization schemes for such problems. Instead, we are lead to the use of metaheuristic algorithms. In this paper, we show how a genetic algorithm with penalization can efficiently find a CVA offsetting trade, while limiting the impact of the trade on the market exposure profile. The latter is necessary for staying in line with the separation of mandates between the XVA desks, in charge of managing counterparty risk, and the other, dubbed "clean", trading desks of the bank, in charge of hedging the market risk of the bank positions.

The other XVA compression challenge is execution time, with intensive valuation of the involved XVA metrics as a bottleneck. The XVA metrics are primarily defined at the portfolio level: Time-0 XVAs can be formulated as expectations of nonlinear functionals of the bank derivative portfolio exposure, i.e., "clean" valuation (or "mark-to-market" (MtM) ignoring counterparty risk) of the bank portfolio, assessed at randomly sampled times and scenarios. Each new deal gives rise to XVA add-ons computed as the corresponding trade incremental XVA amounts, i.e., the differences between the XVAs of the portfolios including and excluding the new deal. To make intensive trade incremental XVA computations practical in real-time as required for XVA compression purposes, our proposed MtM store-and-reuse approach circumvents clean revaluation at the cost of disk memory, storing the portfolio exposure of the night so that the exposure of the portfolio augmented by a new deal can be obtained at the cost of computing the exposure of the new deal only.

#### *Outline and Contributions*

The paper is outlined as follows. Section 2 formulates the penalized CVA compression problem and introduces the related genetic optimization algorithm. Section 3 is about two key acceleration techniques in this regard. Section 4 presents a numerical case study on real swap portfolios. Section 5 concludes.

The main contributions of the paper are the design of a parallelized genetic algorithm for the CVA compression task, the MtM store-and-reuse acceleration technique for trade incremental XVA computations, and the numerical CVA compression case study on real swap portfolios.

1 See also, e.g., David Bachelier's panel discussion Capital and Margin Optimisation, Quantminds International 2018 conference, Lisbon, 16 May 2018.

More broadly, this paper enriches the literature on the use of genetic (also called evolutionary) optimization algorithms in finance. Cont and Hamida (2005) applied evolutionary algorithms to investigate a set of co-calibrated model parameterizations in order to assess the associated model risk. Kroha and Friedrich (2014) compared different genetic algorithms for automatic trading. Jin et al. (2019) applied evolutionary algorithms to optimal investment and consumption stochastic control problems. For wider reviews of genetic algorithms in finance, we refer the readers to (Chen 2012; Drake and Marks 2002).

We refer the reader to the end of the paper for a list of the main abbreviations.

#### **2. CVA Compression Modeling**

#### *2.1. Credit Valuation Adjustment*

We consider a complete stochastic basis (Ω, F, P), for a reference market filtration (ignoring the default of the bank itself) F = (F*t*)*t*∈R+ , satisfying the usual conditions, and a risk-neutral pricing measure P, calibrated to market quotes of fully collateralized transactions. All the processes of interest are F adapted and all the random times of interest are F stopping times. This holds at least after so-called reduction of all the data to F, starting from a larger filtration G including the default of the bank itself as a stopping time, assuming immersion from F into G for simplicity (see (Albanese and Crépey 2019) for details). The P expectation and (F*t*, P) conditional expectation are denoted by E and E*t*, respectively.

In developed markets, the overnight indexed swap (OIS) rate is together the reference remuneration rate for posted collateral and the best market proxy for a risk-free rate. We denote by *r* = (*rt*)*t*∈R+ an F progressive OIS rate process and we write *β* = *e*− · 0 *rsds* for the corresponding risk-neutral discount factor.

By clean valuation or mark-to-market of a contract (or portfolio), we mean the (trade additive) risk-neutral conditional expectation of its OIS discounted future promised cash flows, ignoring counterparty risk and its capital and funding implications.

We consider a bank engaged in bilateral trading with a single corporate counterparty (client) with default time and recovery rate *τc* and *Rc*, respectively. This setup, which is chosen for simplicity, is consistent with a common situation where credit risk budget is assigned at each counterparty level within the bank. We denote by MtM the corresponding mark-to-market process of the client portfolio to the bank.

The (time 0) CVA of the bank is its expected discounted loss in case of client default, i.e.,

$$\text{CVA} = \mathbb{E}\left[\mathbb{1}\_{\{\tau\_{\mathcal{C}} \leq T\}} \beta\_t^{-1} \beta\_{\tau\_{\mathcal{C}}} (1 - R\_{\mathcal{E}}) \text{MtM}^{+}\_{\tau\_{\mathcal{E}}}\right]. \tag{2}$$

Assuming deterministic interest rates, this can be rewritten as

$$\text{CVA} = (1 - R\_c) \int\_0^T \beta\_l \text{EPE}(t) \mathbb{P}(\tau\_c \in dt),\tag{3}$$

where the expected positive exposure (EPE) is defined as

$$\mathbb{E}\mathrm{PE}(t) = \mathbb{E}(\mathbf{M}\mathbf{t}\mathbf{M}\_s^+|\mathbf{s} = \mathbf{r}\_\mathbf{c})\_{|\mathbf{r}\_\mathbf{c} = t}.\tag{4}$$

Equation (3) is popular with practitioners because it allows obtaining the CVA as the integral of the EPE against the client CDS (Credit default swap) curve. However, it is only really practical in simplistic models where the market and credit sides of the problem are independent, so that EPE(*t*) = E(MtM+ *t* ). However, a key CVA modeling issue is wrong-way risk, i.e., the risk of adverse dependence between market and credit (see (Brigo and Vrins 2018; Crépey and Song 2016 2017; Glasserman and Yang 2018; Hull and White 2012; Iben Taarit 2018; Li and Mercurio 2015; Pykhtin 2012)).

Assuming the client default time endowed with an intensity *γc*, a more flexible formula is

$$\text{CVA} = (1 - R\_c) \mathbb{E} \int\_0^T \beta\_s e^{-\int\_0^s \gamma\_u^\varepsilon du} \gamma\_s^\varepsilon \text{MtM}\_s^+ \, ds. \tag{5}$$

Under a credit support agreement (CSA), MtM should be replaced by (MtM − C) in all equations above, where C is the collateral posted by the counterparty. Obviously, collateral can mitigate the EPE and the CVA considerably. In the data of our case study there is no CSA, i.e., C = 0.

Non-linearity of MtM+ with respect to the portfolio payoff components imposes CVA calculations at the counterparty portfolio (netting set) level.

Similar approaches apply to FVA computations, with analogous comments, whereas the MVA can be computed based on quantile regression for the embedded dynamic initial margin calculations (see (Crépey et al. 2019)). In any case, the numerical bottleneck of XVA computations lies in intensive MtM calculations.

#### *2.2. Fitness Criterion*

By the augmented and initial portfolios, respectively, we mean the portfolio of the bank inclusive and exclusive of a newly considered deal with the client. The aim of an XVA compression problem is to find a new trade that minimizes the corresponding XVA metric of the augmented portfolio. This is equivalent to minimize the incremental CVA, which we denote by

$$\begin{split} \Delta \text{CVA} &= (1 - R\_c) \mathbb{E} \int\_0^T \beta\_s e^{-\int\_0^t \gamma\_u^\varepsilon d\mu} \gamma\_s^\varepsilon (\text{MtM}\_s^{\text{augm}})^+ \, ds - (1 - R\_c) \mathbb{E} \int\_0^T \beta\_s e^{-\int\_0^t \gamma\_u^\varepsilon d\mu} \gamma\_s^\varepsilon (\text{MtM}\_s^{\text{unit}})^+ \, ds \\ &= (1 - R\_c) \mathbb{E} \int\_0^T \beta\_s e^{-\int\_0^t \gamma\_u^\varepsilon d\mu} \gamma\_s^\varepsilon ((\text{MtM}\_s^{\text{augm}})^+ - (\text{MtM}\_s^{\text{unit}})^+) \, ds, \end{split} \tag{6}$$

where the indices *init* and *augm* refer to the initial portfolio and augmented portfolio, respectively. We emphasize that trade incremental CVA computations require two portfolio-wide calculations: one without the new trade and another one including it.

Minimizing an XVA metric is most easily obtained through a significant deformation of the portfolio exposure process (especially in the context of this work of a portfolio with a single counterparty). However, an XVA compression procedure should not affect too much the market risk of the portfolio, because market risk is the mandate of the clean desks of the bank, who, in particular, are subject to trading limits.

This motivates the addition of a penalization to the incremental XVA criterion. In our case study, the incremental deal will consist of an interest rate swap. As such product is mostly sensitive to interest rate moves, a natural penalization is then in terms of its DV01 (dollar value of an 01), i.e., the variation of its mark-to-market (at time 0) under a parallel shift of the yield curve by one basis point (=10−4).

More precisely, an interest rate swap exchanges one leg indexed on a floating interest rate against one leg paying a fixed interest rate, called swap rate. The swap is said to be payer (respectively, receiver) for the party that pays (respectively, receives) the floating payments. A monocurrency swap exchanges both legs in the same currency. It is mainly sensitive to the fluctuations of the corresponding floating interest rate term structure. DV01 measures the associated risk as the difference between the prices of the swap under the baseline (actual market data observed in the real market) and for a bumped yield curve defined as the concatenation of the money market rates, forward rates, and swap rates, on the relevant (successive) time segments. Bumping the yield curve typically means adding 10−4 to each tenor of this curve and updating the other reference curves (zero coupon rates, forward rates, etc.) accordingly.

Focusing on the CVA metric in this paper, we obtain the following fitness minimization problem:

$$\underset{\mathbf{x}\in\mathcal{A}}{\text{minimize}}\quad f(\mathbf{x}) = \Lambda \text{CVA}(\mathbf{x}) + \mathfrak{a}|\text{DV01}(\mathbf{x})|\,\tag{7}$$

where *x* parameterizes a new deal (swap) to be found in a suitable search space A (see Section 4.1), ΔCVA(*x*) is its incremental CVA (cf. Equation (6)), DV01(*x*) is its DV01, and *α* is a penalization parameter controlling the trade-off between CVA reduction and market risk profile preservation. By solving Equation (7), we aim at identifying a new deal which, if added to the current client portfolio of the bank, would diminish its counterparty risk without impacting too much its market risk. Note that, for scaling reasons (with, in particular, market penalization), we address the XVA compression problem in terms of trade incremental (as opposed to augmented portfolio) XVA numbers.

A new deal is determined by a combination of quantitative (e.g., notional, maturity, etc.) and qualitative (e.g., currency, long or short direction, etc.) parameters, so that no gradient or Hessian is available for the fitness function *f* in Equation (7). Moreover, one is interested in exploring a variety of local minima of *f* , to see different trading opportunities emerge from the optimization procedure. Furthermore, we can guess that some (crucial) parameters need be learned first, such as currency or maturity; other parameters, such as notional, can be refined in a second stage. All these features lead us to addressing Equation (7) by means of a genetic optimization algorithm.

#### *2.3. Genetic Optimization Algorithm*

Genetic optimization algorithms belong to the class of derivative-free optimizers, which is surveyed and benchmarked numerically in (Rios and Sahinidis 2013) (including the CMA-ES and DAKOTA/EA genetic algorithms).

The idea of genetic (or evolutionary) optimization algorithms is to evolve a population of individuals through cycles of modification (mutation) and selection in order to improve the performance of its individuals, as measured by a given fitness function. In addition, so-called crossover is used to enhance the search in parameter space. To the best of our knowledge, evolutionary algorithms were first explicitly introduced in (Turing 1950, chp. 7 *Learning Machines*, p. 456). See the classical monographs by the authors of (Back 1996; Goldberg 1989; Holland 1975). They then experienced the general artificial intelligence disgrace and comeback before and after the 2000s. However, they always stayed an active field of research, seen from different perspectives, such as particle filtering, MCMC, or sequential monte carlo methods (see (Del Moral 2004)). Beyond its financial applications reviewed at the end of Section 1, genetic optimization has been used in many different fields, such as mechanics (Verma and Lakshminiarayanan 2006), calibration of neural networks hyperparameters (Young et al. 2015), or operational research (Larranaga et al. 1999).

Genetic optimization offers no theoretically guaranteed rate of convergence, but it is often found the most efficient approach in practice for dealing with hybrid (partly continuous, partly discrete/combinatorial, hence without well defined gradient and Hessian), nonconvex (in the sense of one local minimum, at least, for each set of values of the discrete parameters), and high-dimensional optimization problems such as Equation (7).

At each iteration, the fitness *f*(*x*) is computed for each individual (also named chromosome) *x* of an initial population (a set of chromosomes). The values returned by the objective function are used for selecting chromosomes from the population. Among numerous selection methods (see (Blickle and Thiele 1995)), we can quote fitness proportionate selection, ranking proportionate selection (in order to avoid the overrepresentation of the chromosomes with the highest fitness values), and tournament selection (selection of the best among randomly drawn chromosomes). The common intention of these selection methods is to sample in priority individuals with the best fitness values. A genetic algorithm is dubbed elitist if the selection operator always keeps the chromosome with the best fitness value. Otherwise (as in our case), there is no guarantee that the best visited chromosome is contained in the population corresponding to the final iteration.

The mutation stage is intended to maintain some diversity inside the population, in order to avoid the algorithm being trapped by local minima. A mutation randomly changes one gene, i.e., one component (e.g., in our case, the notional of a new swap) of a chromosome.

Selection and mutation play opposite roles: a focus on fitness leads to a quicker convergence toward a local minimum; conversely, a too heavily mutated population results in a slow random research.

In addition, a crossover operator plays the role of a reproduction inside the algorithm. The principle of crossover is to build two children chromosomes from parent chromosomes. A distribution (often the same as the one used for selection) is chosen for picking chromosomes from a population of the previous iteration and for recombining pairs of selected chromosomes. Children share gene values of their parents but a gene value from one parent cannot be inherited by both children. A crossover mask decides for each gene in which parent a child can copy the gene version. One of the most popular crossover masks is single point crossover (see Appendix A).

The role of the crossover operator is paradoxical, as crossover can be seen as a combination of mutations, which increase the genetic diversity, while crossover also promotes chromosomes with higher fitnesses. Crossover aims at benefiting of a presupposed proximity of best solutions.

The above operators are applied iteratively until a suitable stopping condition is satisfied. The most basic one is a fixed number of iteration, but customized criteria may also be used to limit further the number of iterations. For instance, the algorithm can be interrupted when the minimum (or sometimes even the maximum) fitness value within the population at the beginning of an iteration is below a predefined threshold.

See Algorithm 1 and Figure 1 for the algorithm in pseudo-code and skeleton forms, denoting by *rm* the mutation rate, i.e., the percentage of individuals in a population affected by a mutation, and by *rc* the crossover rate, i.e., the percentage of individuals affected by crossover recombination.

The behavior of a genetic optimization algorithm is essentially determined by the choice of the selection operator, the number of solutions affected by a mutation, and the number of chromosomes affected by a crossover. See the work of Tabassum and Kuruvilla (2014) for a user guide to the main genetic algorithm ingredients and Carvalho et al. (2011) for applications of genetic optimization algorithms to benchmark functions.

mutated chromosomes

**Figure 1.** Skeleton of an optimization genetic algorithm.

#### **3. Acceleration Techniques**

Without suitable acceleration techniques, the above CVA compression approach is not workable in real time on realistic banking portfolios: in the examples of Section 4, a naive (desktop) implementation requires about 20 h of computations. This becomes even more problematic for hyperparameters tuning (such as *α*, crossover rate *rc*, etc.). Hyperparameters are generally chosen with grid search, random search (see (Bergstra et al. 2011)), Bayesian optimization (see (Snoek et al. 2012)) or even evolutionary algorithms again (see (Young et al. 2015)). In any case, their calibration is greedy in terms of overall genetic algorithm execution.

In this section, we deal with the two following acceleration techniques, which may be used simultaneously:

#### *3.1. MtM Store-and-Reuse Approach for Trade Incremental XVA Computations*

Most of the time in portfolio-wide XVA calculations is spent in clean valuation (i.e., mark-to-market MtM) computations: by comparison, simulation of the risk factors or of the collateral is typically negligible.

Our case study is based on the CVA metric. As observed after Equation (6), by lack of trade-additivity of the (portfolio-wide) CVA, trade incremental XVA computations require two portfolio-wide calculations: one without the new trade and another one including it. However, it is possible to store the (including MtM) paths simulated for the initial portfolio and reuse them each time we want to compute a new trade incremental XVA. Then, each trade incremental XVA computation only requires the forward simulation of the mark-to-market process of the new deal.

The corresponding MtM store-and-reuse approach to trade incremental XVA computations circumvents repeated valuations at the cost of disk memory. It exploits the trade additivity of clean valuation by recording the MtM paths of the initial portfolio on a disk. For every new deal, the augmented portfolio exposure is obtained by adding, along the paths of the risk factors, the mark-to-market of the initial portfolio and of the new deal. This augmented portfolio exposure is then plugged into the XVA engine.

An optimally implemented MtM store-and-reuse approach brings down trade incremental XVA computations to the time of generating the clean price process of the trade itself, instead of the one of the augmented portfolio as a whole. Another advantage of this approach is its compliance with desk segregation: As far as clean valuation is concerned, the XVA desks just use the pricers of the clean desks. Hence, the MtM process plugged into the XVA computations is consistent with the one used for producing the market risk hedging sensitivities.

However, such an approach comes at the costs of memory disk (obviously), but also data slippage as, for consistency, it requires anchoring all the trade incremental XVA computations at the market data and parameters corresponding to the generation of the initial portfolio exposure. In practice, an MtM process at the overall portfolio level can only be generated during night runs, between two market sessions.

Moreover, we have to distinguish between first-order (or first-generation) XVAs, which are options on the MtM process, and higher-order (or second-generation) XVAs (see (Crépey et al. 2019)), which can be viewed as compound options of order two or more on the MtM process. Second-generation XVAs may also involve conditional risk measures, e.g., conditional value-at-risk for the dynamic initial margin calculations that are required for MVA2 computations, as opposed to conditional expectations only in the case of first generation XVAs.

A Monte Carlo simulation diffuses risk factors *X* (such as interest rates, credit spreads, etc.) along drivers *Z* (such as Brownian motions, Poisson processes, etc.), according to a model formulated as a Markovian system of stochastic differential equations, starting from some given initial condition *X*0 for all risk factors, suitably discretized in time and space. Modulo calibration, *X*0 can be identified with the time 0 market data. We denote by *Y* a suitable estimate of a process *Y* at all (outer) nodes of a Monte Carlo XVA engine. In particular, MtM is the fully discrete counterpart of the MtM process of the initial portfolio, namely the clean value of the portfolio at future exposure dates in a time grid and for different scenario paths.

At first sight, an MtM store-and-reuse approach is unsuitable for second-order XVAs, such as the MVA and the KVA (but also the CVA in the case of a CSA where the bank receives so-called initial margin), Indeed, in their case, the principle of swapping computations against storage would require to store not one portfolio exposure MtM-, but a whole family of resimulated, future conditional portfolio exposures, (at least, over a certain time horizon), which seems hardly feasible in practice. However, even in the case of second-order XVA metrics, an MtM store and reuse approach can be

2 For details regarding the initial margin and the MVA, see (Crépey et al. 2019, sets. 5.2 and 6.4).

implemented with the help of appropriate regression techniques (at the cost of an additional regression error; see (Crépey et al. 2019)).

Formalizing the above discussion, the conditions for a straightforward and satisfactory application of the MtM store-and-reuse approach to a given XVA metric are as follows, referring by indices *init*, *incr*, and *augm* to the initial portfolio, the new deal, and the augmented portfolio:

These conditions have the following implications:

Figure 2 depicts the embedding of an MtM store-and-reuse approach into the trade incremental XVA engine of a bank.

**Figure 2.** MtM store-and-reuse implementation of a trade incremental XVA engine with drivers *Z*.

#### *3.2. Parallelization of the Genetic Algorithm*

Most of the XVA compression computational time is spent in the evaluation of the incremental XVA metric involved in the fitness criterion visible in Equation (7). The MtM store-and-reuse approach allows reducing the complexity of such trade incremental XVA computations to trade (as opposed to portfolio) size. However, to achieve XVA compression in real time, this is not enough; another key step is the parallelization of the genetic algorithm that is used for solving Equation (7).

The genetic algorithm is a population based method, which implies to maintain a population of individuals (tentative new deals) through each iteration of the algorithm. The calculation of the objective function, for a given individual, does not depend on the fitness value of the other individuals. Therefore we can vectorize the computation of the fitness values within the population. Provided a suitable parallel architecture is available, a perfectly distributed genetic algorithm makes the execution time independent of the population size *P* (see Algorithm 1 and Figure 1).

This makes an important difference with other metaheuristic optimization algorithm, such as simulated annealing or stochastic hill climbing, which only evaluate one or very few solutions per iteration, but need much more iterations to converge toward a good minimum (see (Adler 1993; Janaki Ram et al. 1996)). As discussed in (Pardalos et al. 1995), the above parallelization of the fitness function evaluation, for a given population, should not be confused with a parallel genetic algorithm in the sense of an independent evolution of several smaller populations.

In our context, where individuals only represent incremental trades, a parallelization of population fitness evaluation is compatible with an MtM store-and-reuse approach for the trade incremental XVA computations. Combining the two techniques results in an XVA compression time independent of the sizes of the initial portfolio of the bank and of the population of the genetic algorithm used for the optimization, which represents an XVA compression computation time gain factor of the order of

Number of trades in the initial portfolio × population size.

#### **4. Case Study**

In the remainder of the paper, we present CVA compression results on real swap portfolios3, using an additional swap for the CVA compression. We aim at addressing questions such as:

To ease the implementation of the MtM store-and-reuse approach, we assume no CSA (cf. Section 3.1).

#### *4.1. New Deal Parameterization*

A swap is parameterized by its notional, its maturity, its direction, and its currency. The quantitative parameters are encoded through grids of values:

The qualitative parameters are encoded as enumerations of values:

3 The underlying interest rate and FX models are proprietary and cannot be disclosed in the paper. We use a deterministic credit spread model for the counterparty, calibrated to the CDS term structure of the latter.

Moreover, we impose the additional swap to be at par so that it can be entered at no cost, which is equally desirable from the bank and the client perspectives.

The above parameterization defines a discrete search space A with 100 × 22 × 4 × 2 = 1.76 × 104 elements.

#### *4.2. Design of the Genetic Algorithm*

We address the optimization problem in Equation (7) by a genetic algorithm as per Section 2.3. The new deal space A in Equation (7) is viewed as a space of chromosomes *x*, the genes (deal parameters) of which evolve randomly along the iterations of the algorithm as detailed in Section 2.3.

In the theoretical literature on genetic algorithms, an individual is represented as a bit string. In practice, however, bit string representation of parameters does not give enough control on the mutation distribution. Namely, in bit string representation, mutations affect all bits uniformly, whereas we might want to mutate some parameters more frequently (the quantitative parameters, in particular, as the algorithm tends to quickly identify the relevant values of the qualitative parameters). Hence, we rather model our individuals *x* by a variable string, a choice also made in (Kondratyev and Giorgidze 2017).

We choose rank proportionate selection to avoid fitness scaling issues. More precisely, if we have a population P = {1, . . . , *P*} of *P* individuals and the associated fitnesses (*fi*)*i*∈P, then the probability to select chromosome *i* is

$$p\_i = \frac{2rank(f\_i)}{P(P+1)},$$

where *rank* is a function that ranks chromosomes according to their fitness value (returning one for the highest value, in the context of a minimization problem).

Regarding the crossover operator, we use a uniform crossover mask, i.e., the choice of gene inherited from one parent or another is drawn with a uniform probability.

Regarding mutations, the probability to mutate a gene is proportional to the number of alleles (values) that it can take. The mutation operator then selects uniformly a new gene allele (value).

In our experiments, the notional of the new swap can take 100 different values, its currency 4 values, its position 2 values, and its maturity 22 values. Hence, when a chromosome is selected for mutation, the probability to mutate each of its genes is equal to 100 128 for the notional, 22 128 for the maturity, 4 128 for the currency, and 2 128 for the position. Indeed, a more frequent mutation of notional and maturity parameters are desirable. Diversity for currency and position is ensured at the initialization of the algorithm (with a large population) and maintained across the iterations thanks to the crossover operator. A prerequisite for a successful implementation is a reasonable specification of the search space A.

Hyperparameters strongly impact the behavior of the algorithm. In the case of XVA compression, which is time-consuming, searching good values for the hyperparameters by a grid search method would be too demanding computationally. In our numerics, the mutation rate *rm* is set to 20% and the crossover rate *rc* to 50%. In the genetic algorithms literature, the crossover rate is often close to one, but for problems with few genes (i.e., components of *x*, or parameters, only four in our case), it is recommended to select a smaller value.

With parallelization in mind (see Section 3.2), we prefer to decrease the number of iterations even if it implies exploring more solutions. We set the genetic algorithm population size to *P* = 100 individuals and we limit the number of iterations to 5. Hence, we value the fitness function on 600 tentative new swaps *x*.

#### *4.3. Results in the Case of Payer Portfolio Without Penalization*

First, we consider a portfolio only composed of payer swaps. The expected exposure (EE) and the expected positive and negative exposures (EPE and ENE), i.e., EMtM*t* and EMtM± *t* , are shown as a function of time *t* in Figure 3, which illustrates the asymmetric market risk profile of the portfolio.

**Figure 3.** Market risk profile of the portfolio (payer portfolio without penalization).

Our first point is to verify that the algorithm without penalization, i.e., for *α* = 0 in Equation (7), will select a receiver swap with a maturity comparable to those of the swap of the initial portfolio.

Table 1 reports after each iteration the three best solutions (from top to bottom) ever found since the beginning of the algorithm (in terms of the fitness criterion in Equation (7) with *α* = 0, i.e., ΔCVA). A negative incremental CVA means that the new swap decreases the counterparty risk of the bank. The initial portfolio CVA amounts to 34,929e. We also report the |DV01|s of the augmented portfolios in order to be able to assess the impact of the penalization in our next experiment.

**Table 1.** Evolution of optimal solutions after each iteration (payer portfolio without penalization).

As shown in Figure 4, a stabilization of the algorithm is observed after four iterations, on a new swap leading to a CVA gain of about 27,300e, i.e., about 78% of the initial portfolio CVA. The maturity and the notional are found the two most sensitive genes in the optimization. The maturity of the swap

is chosen by the algorithm so as to reduce the exposure peak: The decrease of the exposure in the first eight years of the portfolio is visible in terms of EPE profile in Figure 5 and of CVA profile4 in Figure 6.

**Figure 5.** Market risk profile of the portfolio before and after optimization (payer portfolio without penalization).

4 Term structure obtained by integrating the EPE profile against the CDS curve of the counterparty from time 0 to an increasing upper bound *t* ≤ *T* (cf. Equation (3)).

**Figure 6.** CVA profile before and after optimization (payer portfolio without penalization).

#### *4.4. Results in the Case of Payer Portfolio With Penalization*

We keep the same initial portfolio but we now penalize our objective function by the |DV01| of the new swap, setting the regularization parameter *α* to one in Equation (7). As shown below, this choice achieves a good balance between the two terms ΔCVA and *α*DV01 in Equation (7).

In the present context of a payer portfolio, |DV01| control and CVA gain are two antagonistic targets. This may explain why the algorithm seems to struggle in finding a stable solution: indeed, the last iteration still decreases the fitness significantly (see Figure 7).

**Figure 7.** Fitness as a function of iteration number (payer portfolio with penalization).

During the execution (see Table 2), the algorithm first optimizes the CVA and then (in iteration 5) reduces the |DV01|. This is due to the difference of order of magnitude between ΔCVA and |DV01| (recalling *α* = 1): ΔCVA is more important, hence the algorithm only takes care of the penalization once ΔCVA has been compressed.

**Table 2.** Evolution of optimal solutions after each iteration (payer portfolio with penalization).

In the end, the gains in CVA are of the same order of magnitude as in the case without penalization (92% of the CVA gain without penalization), but for about 20% of |DV01| less than before. The second and third best solutions also achieve a great CVA gain, while diminishing the |DV01| by a factor three with respect to the nonpenalized case. By comparison with the unpenalized case (cf. Tables 1 and 2), the trades identified by the algorithm have a lower maturity or a smaller notional, hence a smaller |DV01|.

Figures 8 and 9 show the corresponding market risk and CVA profiles before and after the optimization.

**Figure 8.** Market risk profile of portfolio before and after optimization (payer portfolio with penalization).

**Figure 9.** CVA profile before and after optimization (payer portfolio with penalization).

#### *4.5. Results in the Case of a Hybrid Portfolio With Penalization*

Next, we challenge our algorithm with a more balanced initial portfolio, as shown in Figure 10 (to be compared with Figure 3). The initial CVA is now 6410e. We set the regularization parameter *α* in Equation (7) to 0.3, as opposed to 1 in the previous case, in view of the lower CVA of the initial portfolio.

**Figure 10.** Market risk profile of the portfolio (hybrid portfolio with penalization).

As visible in Table 3 and Figure 11, the stabilization of the algorithm occurs after three iterations, showing that, for the hybrid portfolio, |DV01| penalization and ΔCVA play less antagonistic roles. This is obtained by a relatively small notional and a maturity limited to 9 years, versus 11 years in the previous case of a payer portfolio with penalization. The corresponding market risk and CVA profiles, before and after the optimization, are displayed in Figures 12 and 13. Figure 12 explains the choices operated by the algorithm: as we restrict our incremental strategy to one swap, the algorithm limits the EPE until the first positive peak before 2026. A better strategy, but one outside our search space A, would be to add a second swap with entry date in 2028 and end date in 2037.

**Table 3.** Evolution of optimal solutions after each iteration (hybrid portfolio with penalization).

**Figure 11.** Fitness value as a function of iteration number (hybrid portfolio with penalization).

**Figure 12.** Market risk profile of portfolio before and after optimization (hybrid portfolio with penalization).

**Figure 13.** CVA profile before and after optimization (hybrid portfolio with penalization).

#### **5. Conclusions**

There exists a trade-off between CVA compression and DV01 penalization, which have antagonistic influences on the incremental exposure. Provided the search space for incremental trades is adequately chosen and parameterized, genetic optimization can result in significant CVA gains and, under DV01 penalization, this can be achieved without too much impact on the market risk of the bank position.

On the portfolios considered in our case studies, with 10–20 trades, a basic XVA compression run on a standard PC without the acceleration techniques of Section 3 takes about 20 h. The time gain resulting from an MtM store-and-reuse implementation of the trade incremental XVA computations as per Section 3.1 primarily depends on the size of the initial portfolio, but also on the maturity, and complexity more generally (vanilla vs. callable or path-dependent, etc.), of the constituting trades. Likewise, the time gain resulting from a parallel implementation of the genetic algorithm as per Section 3.2 primarily depends on the population size *P*, but it can be deteriorated by grid latency, hardware limitation, or data flow management features. In our simulations, an MtM store-and-reuse implementation of the trade incremental XVA computations reduces the XVA compression time to about 7 h. A further parallel implementation of the genetic optimization algorithm lowers the execution time to about 1 h.

The case study of this paper is only a first step toward more complex optimizations. One could thus enlarge the search space with, e.g., crosscurrency swaps. In this case, the market risk penalization should be revisited to penalize other risk factors, beyond interest rate risk that is already accounted for by |DV01|. The penalization could also be refined with a focus on forward mark-to-market, i.e., market risk in the future (our current |DV01| penalization only controls spot market risk).

CVA compression strategies involving several additional trades could be implemented. A first step toward such a multi-variate, multi-trade, compression would be an iterated application of single-trade XVA compressions, whereby, after each compression, the optimally augmented portfolio becomes the initial portfolio for the next compression. The benefit of such an iterative approach would be the ability to work with a search space A (or a sequence of them) of constant size, as opposed to a global search space A that would need to grow exponentially with the number of new trades in the case of a single multi-trade compression cycle.

Additional XVA metrics, and ultimately the all-inclusive XVA add-on in Equation (1), should be included in the compression (which, in particular, would allow one to identify possible XVA cuts across different netting sets).

**Author Contributions:** Conceptualization, M.C. and S.C.; methodology, M.C.; software, M.C.; validation, M.C.; formal analysis, M.C. investigation, M.C. and S.C.; writing—original draft preparation, S.C.; writing—review and editing, S.C.; supervision, S.C.; project administration, S.C.; and funding acquisition, S.C.

**Funding:** This research was conducted with the support of the Research Initiative "Modélisation des marchés actions, obligations et dérivés" financed by HSBC France under the aegis of the Europlace Institute of Finance. The views and opinions expressed in this presentation are those of the author alone and do not necessarily reflect the views or policies of HSBC Investment Bank, its subsidiaries or affiliates. The PhD thesis of Marc Chataigner is co-funded by a public grant as part of investissement d'avenir project, reference ANR-11-LABX-0056-LLH LabEx LMH.

**Acknowledgments:** The authors would like to thank Hugo Lebrun, Rémi Ligoureau, and Guillaume Macey (HSBC France) for their assistance in the conduct of this study.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **Appendix A. Single Point Crossover**

Let (*p*1, *p*2) be a pair of chromosomes chosen as parents and let (*c*1, *c*2) denote the children. We assume that each chromosome has four genes *A*, *B*, *C*, *D*, that *p*1 has gene versions {*A*1, *B*1, *C*1, *D*1} and *p*2 has gene versions {*A*2, *B*2, *C*2, *D*2}. For a single point crossover, we draw uniformly an integer *i* such the first *i* genes for *c*1 are inherited from *p*1 and the remaining genes are transferred from *p*2 to *c*1, and symmetrically so for *c*2. For instance, if we draw *i* = 2, then *c*1 has gene versions {*A*1, *B*1, *C*2, *D*2}, and *c*2 has gene values {*A*2, *B*2, *C*1, *D*1}.

#### **References**

c 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Author details**

Chiebuka Uzoebo Botany Department, University of Ibadan, Nigeria

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© 2022 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


**3. Daratumumab in relapsed/refractory multiple myeloma**

Approval of daratumumab by regulatory bodies was made possible thanks to clinical trials evaluating its use in RRMM. Patients with RRMM still represent the patients best benefitting from this monoclonal antibody, both as single agent and in combination with other agents (**Table 1a**).

#### **3.1 Daratumumab in relapsed/refractory multiple myeloma as single agent**

GEN501 and SIRIUS are the two main trials who led to approval of monotherapy with daratumumab. Both studies enrolled patients with RRMM: patients in GEN501 had relapsed after or were refractory to ≥2 prior lines of therapy, including inhibitors of proteasome (PIs), immunomodulatory drugs (IMiDs), chemotherapy and autologous stem cell transplantation (ASCT); patients in SIRIUS had relapsed after ≥3 lines of therapy, including a PI and a IMiDs or were double refractory to the most recently received PI and IMiDs. The primary endpoint of GEN501 was evaluation of safety while SIRIUS was designed to first evaluate overall response rate (ORR). Data regarding 148 patients from pooled analysis of the two trials confirmed how daratumumab, at the dosage of 16 mg/kg, is effective and safe in a population of heavily pretreated patients [12]. With a median number of 12 infusions, the ORR was 31.1%. At the time of the analysis, after a median follow-up of 20.7 months, the progression free survival (PFS) was 4 months, with a 12-month PFS rate of 22%. Stratifying the patients by the response according to International Myeloma Working Group, the PFS and the overall survival (OS) went out to be 15 months and not reached respectively for responders, 3 months and 18.5 months for patients with a stable disease or minimal response, 0.9 months and 3.7 months for non-responders. The median duration of response was 7.6 months and it deepened and improved in patients continuing daratumumab.

*Multiple Myeloma*

**Figure 1.**

Daratumumab binds CD38, killing tumour cells via Fc-dependent immune effector

*Mechanism of action of daratumumab. Daratumumab binds CD38, killing myeloma cells via Fc-dependent immune effector mechanisms: CDC, ADCC and ADCP. Daratumumab also inhibits enzymatic activity of*

The anti-tumour activity of Daratumumab does not depend only on the direct action on plasma cells but also on the interaction with other lymphoid and myeloid cells with a weak expression of CD38: NK cells, B and T regulatory cells and CD8+ effector cells. Krejcik et al. have demonstrated that bone marrow and peripheral blood from patients on treatment with Daratumumab present low levels of regulatory cells and high levels of NK and CD8+ effector cells. This monoclonal antibody may interfere with the immunosuppressive microenvironment in the multiple myeloma bone niche, in favour of major susceptibility for the plasma cells to the NK

Daratumumab is usually administered at the dosage of 16 mg/kg weekly for 8 weeks then every 2 weeks for 16 weeks and every 4 weeks thereafter until progression of disease. The administration on a mg/kg basis is due to the observation that distribution and clearance of daratumumab depends on bodyweight. It seems to be not influenced by age, gender, race, mild renal and liver impairment. To our knowledge, the extra-liver metabolism of daratumumab is the reason for the absence of

The efficacy and safety of this schedule have been demonstrated by two studies involving patients with relapsed/refractory multiple myeloma (RRMM) treated with the anti-CD38 monoclonal antibody as single agent: GEN501 and SIRIUS. GEN501 was a phase I/II, open-label, multicenter study. In the dose-escalation part, sequential cohorts of patients received intravenous doses of daratumumab ranging from 0.005 to 24 mg/kg, administered over 6–8 h. In the dose-expansion

mechanisms including complement-dependent cytotoxicity (CDC), antibodydependent cell-mediated cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) [5]. The complement activation seems to be the most effective mechanism used by Daratumumab [6]: the Fc tail of the drug binds the activating factor C1q leading both to ultimate activation of membrane attack complex and to deposition of C3b on the surface of multiple myeloma plasma cells. The activation of membrane attack complex causes osmotic lysis of cells while the deposition of complement factors attracts phagocytic cells. The recruitment of immune effector cells is also boosted by the

release of circulating factors such as C3 and C5a (**Figure 1**).

*CD38, downregulating intracellular Ca2+ trafficking.*

and CD8+ cells toxicity [7].

interactions with other drugs.

**2. Pharmacokinetics**

**68**

*RRMM: Relapsed/Refractory Multiple Myeloma, IV: intravenous, SC: subcutaneous, Rd.: lenalidomidedexamethasone, Vd: bortezomib-dexamethasone, ORR: Overall Response Rate, Maximum CTrough: Maximum Concentration Trough, AEs: Advese Events, PFS: Progression Free Survival, NDMM: Newly Diagnosed Multiple Myeloma, VMP: bortezomib-melphalan-dexamethasone, VTd: bortezomib-thalidomide-dexamethasone, RVd: lenalidomide-botrezomib-dexamethasone, sCR: stringent Complete Response.*

#### **Table 1.**

*Overview of main trials using Daratumumab in (a) RRMM, (b) NDMM.*

#### *3.1.1 Daratumumab in relapsed/refractory multiple myeloma in combination therapies: with IMiDs*

Efficacy of daratumumab seems to be strengthened by other drugs used for multiple myeloma, given the synergic action on the immune system. As said before, the anti-CD38 may stimulate NK and T-cells, restoring "tumor suppressive immunological surveillance". Also IMiDs could increase the amount of regulatory cells in the bone niche, through inhibition of some transcriptional factors (Ikaros and Aialos) and the subsequent production of interleukin 2 [13]. Furthermore, some studies show that the main target of daratumumab is upregulated by action of IMiDs [14]. NCT01615029 was the first trial exploring the applicability of these laboratory observations, investigating efficacy of daratumumab in combination with lenalidomide and dexamethasone (Rd) [15]. It was a phase 1/2 study addressed to patients with relapsed multiple myeloma: phase 1 was a dose-escalation study in which the dose of 16 mg/kg for daratumumab was again determined; phase 2 was a dose-expansion study using the recommended dose of the first part. The three drugs were administered in cycle of 28 days: daratumumab was given according to the standard schedule, lenalidomide at 25 mg/day from days 1 to 21 of each cycle and dexamethasone at 40 mg/week. This combination revealed to be safe and very effective: the 18-months PFS rate was 72% and ORR was 81%, in this case too with an improvement of responses in time. To evaluate the advantage of adding daratumumab to a regimen with lenalidomide and dexamethasone, from 2014 to 2015, a phase III, randomized trial was carried out across Europe, Northern America and Asia [16]. The POLLUX trial enrolled 569 patients with multiple myeloma who had

**71**

*The Modern Age of Monoclonal Antibodies: The Revolution of Daratumumab*

received one or more previous lines of therapy: 286 were assigned to the daratumumab group (daratumumab plus lenalidomide and dexamethasone) and 283 to the control group (lenalidomide and dexamethasone). Also in this trial, each cycle was of 28 days, with daratumumab administered according to the usual schedule, lenalidomide at 25 mg/day from days 1 to 21 of each cycle and dexamethasone at a dose of 40 mg weekly. At 12 months, the PFS rate was 83.2% in the daratumumab group *vs* 60.1% in the control group. In a sub analysis, this extension of PFS in the experimental group went out to be independent from the number of previous lines of therapy and from the previous exposure to lenalidomide, even if the paucity of refractory patients to IMiDs enrolled in this trial may represent a bias. After a follow-up of 13.5 months, progression disease or death occurred in 53 patients in the daratumumab group *vs* 116 patients in the control arm, with a hazard ratio of 0.37 in favour of the first group. Also in this case, an improvement of deepness of molecular response was observed with continuation of therapy with the monoclonal antibody and it translated in a longer survival. Indeed, 22.4% of patients in the experimental group had results below the threshold for minimal residual disease (MRD), compared to 4.6% in the control group. Neutropenia, diarrhea and infusional reactions were the main adverse events reported in the experimental arm with a higher incidence than in the control group but, in spite of that, the rate of grade 3 and grade 4 infections was not so different. In conclusion, POLLUX trial confirmed the efficacy and safety of adding daratumumab to a regimen with IMiDs and high-dose steroid. Furthermore, the excellent results below the threshold for minimal residual disease suggest that minimal residual disease negativity could

*3.1.2 Daratumumab in relapsed/refractory multiple myeloma in combination*

Some *in-vitro* studies have shown that not only IMiDs but also PIs interact with daratumumab in a synergic way, strengthening its effect. An assay performed by the Dutch group [14] evaluated the rate of lysis in samples of bone marrow mononuclear cells from 16 multiple myeloma patients incubated with medium containing either daratumumab, lenalidomide and bortezomib or just one drug. The rate of lysis went out to be higher in the samples with the addition of daratumumab, showing that not only lenalidomide but also bortezomib enhance the effect of this monoclonal antibody by sensitizing the cells to the antibody-mediated lysis. The "lysis effect" was even better in cells from patients who previously showed refractoriness to IMiDs or IPs, suggesting that immunomodulatory effects of daratumumab may restore host susceptibility to anti-myeloma agents. Based on a phase 1b trial in which daratumumab showed encouraging results in combination with PIs-based regimens in naive patients [17], a phase 3 trial randomized patients with relapsed and/or refractory multiple myeloma to a treatment with only bortezomib and dexamethasone or with the addition of daratumumab [18]. Of 498 patients, 251 were assigned to the daratumumab group and 247 to the control group. Each cycle had a duration of 21 days. Daratumumab was administered at the usual dosage of 16 mg/ kg once per week during cycles 1 to 3, once every 3 weeks during cycles 4 to 8 and once every 4 weeks thereafter until toxicity or progression disease. Dexamethasone was given for a total dose of 160 mg per cycle and bortezomib was administered in the subcutaneous formulation at the dosage of 1.3 mg per square meter on days 1, 4, 8 and 11 of cycles 1 to 8. The 12-month rate of PFS was 60.7% in the experimental group and 26.9% in the control group. After a follow up of 7.4 months, progression disease or death occurred in 67 patients in the daratumumab group *vs* 122 in the control group. Given the results of the interim analysis, the trial was unblended

*DOI: http://dx.doi.org/10.5772/intechopen.95406*

represent a goal also for RRMM patients.

*therapies: with PIs*

#### *The Modern Age of Monoclonal Antibodies: The Revolution of Daratumumab DOI: http://dx.doi.org/10.5772/intechopen.95406*

received one or more previous lines of therapy: 286 were assigned to the daratumumab group (daratumumab plus lenalidomide and dexamethasone) and 283 to the control group (lenalidomide and dexamethasone). Also in this trial, each cycle was of 28 days, with daratumumab administered according to the usual schedule, lenalidomide at 25 mg/day from days 1 to 21 of each cycle and dexamethasone at a dose of 40 mg weekly. At 12 months, the PFS rate was 83.2% in the daratumumab group *vs* 60.1% in the control group. In a sub analysis, this extension of PFS in the experimental group went out to be independent from the number of previous lines of therapy and from the previous exposure to lenalidomide, even if the paucity of refractory patients to IMiDs enrolled in this trial may represent a bias. After a follow-up of 13.5 months, progression disease or death occurred in 53 patients in the daratumumab group *vs* 116 patients in the control arm, with a hazard ratio of 0.37 in favour of the first group. Also in this case, an improvement of deepness of molecular response was observed with continuation of therapy with the monoclonal antibody and it translated in a longer survival. Indeed, 22.4% of patients in the experimental group had results below the threshold for minimal residual disease (MRD), compared to 4.6% in the control group. Neutropenia, diarrhea and infusional reactions were the main adverse events reported in the experimental arm with a higher incidence than in the control group but, in spite of that, the rate of grade 3 and grade 4 infections was not so different. In conclusion, POLLUX trial confirmed the efficacy and safety of adding daratumumab to a regimen with IMiDs and high-dose steroid. Furthermore, the excellent results below the threshold for minimal residual disease suggest that minimal residual disease negativity could represent a goal also for RRMM patients.

## *3.1.2 Daratumumab in relapsed/refractory multiple myeloma in combination therapies: with PIs*

Some *in-vitro* studies have shown that not only IMiDs but also PIs interact with daratumumab in a synergic way, strengthening its effect. An assay performed by the Dutch group [14] evaluated the rate of lysis in samples of bone marrow mononuclear cells from 16 multiple myeloma patients incubated with medium containing either daratumumab, lenalidomide and bortezomib or just one drug. The rate of lysis went out to be higher in the samples with the addition of daratumumab, showing that not only lenalidomide but also bortezomib enhance the effect of this monoclonal antibody by sensitizing the cells to the antibody-mediated lysis. The "lysis effect" was even better in cells from patients who previously showed refractoriness to IMiDs or IPs, suggesting that immunomodulatory effects of daratumumab may restore host susceptibility to anti-myeloma agents. Based on a phase 1b trial in which daratumumab showed encouraging results in combination with PIs-based regimens in naive patients [17], a phase 3 trial randomized patients with relapsed and/or refractory multiple myeloma to a treatment with only bortezomib and dexamethasone or with the addition of daratumumab [18]. Of 498 patients, 251 were assigned to the daratumumab group and 247 to the control group. Each cycle had a duration of 21 days. Daratumumab was administered at the usual dosage of 16 mg/ kg once per week during cycles 1 to 3, once every 3 weeks during cycles 4 to 8 and once every 4 weeks thereafter until toxicity or progression disease. Dexamethasone was given for a total dose of 160 mg per cycle and bortezomib was administered in the subcutaneous formulation at the dosage of 1.3 mg per square meter on days 1, 4, 8 and 11 of cycles 1 to 8. The 12-month rate of PFS was 60.7% in the experimental group and 26.9% in the control group. After a follow up of 7.4 months, progression disease or death occurred in 67 patients in the daratumumab group *vs* 122 in the control group. Given the results of the interim analysis, the trial was unblended

*Multiple Myeloma*

**(a) RRMM**

**(b) NDMM**

**Table 1.**

*3.1.1 Daratumumab in relapsed/refractory multiple myeloma in combination*

Efficacy of daratumumab seems to be strengthened by other drugs used for multiple myeloma, given the synergic action on the immune system. As said before, the anti-CD38 may stimulate NK and T-cells, restoring "tumor suppressive immunological surveillance". Also IMiDs could increase the amount of regulatory cells in the bone niche, through inhibition of some transcriptional factors (Ikaros and Aialos) and the subsequent production of interleukin 2 [13]. Furthermore, some studies show that the main target of daratumumab is upregulated by action of IMiDs [14]. NCT01615029 was the first trial exploring the applicability of these laboratory observations, investigating efficacy of daratumumab in combination with lenalidomide and dexamethasone (Rd) [15]. It was a phase 1/2 study addressed to patients with relapsed multiple myeloma: phase 1 was a dose-escalation study in which the dose of 16 mg/kg for daratumumab was again determined; phase 2 was a dose-expansion study using the recommended dose of the first part. The three drugs were administered in cycle of 28 days: daratumumab was given according to the standard schedule, lenalidomide at 25 mg/day from days 1 to 21 of each cycle and dexamethasone at 40 mg/week. This combination revealed to be safe and very effective: the 18-months PFS rate was 72% and ORR was 81%, in this case too with an improvement of responses in time. To evaluate the advantage of adding daratumumab to a regimen with lenalidomide and dexamethasone, from 2014 to 2015, a phase III, randomized trial was carried out across Europe, Northern America and Asia [16]. The POLLUX trial enrolled 569 patients with multiple myeloma who had

**Trial Phase Therapy Primary outcome**

GEN501 1/2 IV daratumumab single agent evaluation of safety SIRIUS 2 IV daratumumab single agent ORR PAVO 1B SC daratumumab Maximum ctrough

NCT01615029 1 / 2 DARA-Rd ORR CASTOR 3 DARA-Vd *vs* Vd PFS POLLUX 3 DARA-Rd *vs* Rd PFS

ALCYONE 3 DARA-VMP *vs* VMP PFS MAIA 3 DARA-Rd *vs* DARA-Rd PFS

PERSEUS 3 DARA-RVd *vs* RVd PFS *RRMM: Relapsed/Refractory Multiple Myeloma, IV: intravenous, SC: subcutaneous, Rd.: lenalidomidedexamethasone, Vd: bortezomib-dexamethasone, ORR: Overall Response Rate, Maximum CTrough: Maximum Concentration Trough, AEs: Advese Events, PFS: Progression Free Survival, NDMM: Newly Diagnosed Multiple Myeloma, VMP: bortezomib-melphalan-dexamethasone, VTd: bortezomib-thalidomide-dexamethasone, RVd:*

CASSIOPEIA 3 DARA-VTd *vs* VTd sCR after consolidation

GRIFFIN 2 DARA-RVd *vs* RVd sCR after consolidation

daratumumab

COLUMBA 3 IV daratumumab *vs* SC

*lenalidomide-botrezomib-dexamethasone, sCR: stringent Complete Response.*

*Overview of main trials using Daratumumab in (a) RRMM, (b) NDMM.*

N° of patients with AEs

ORR Maximum ctrough

PFS

*therapies: with IMiDs*

**70**

#### *Multiple Myeloma*

earlier and patients in the control group with a progression disease were offered daratumumab monotherapy. This may represent a bias in the interpretation of all the long-term results. Nevertheless, this trial showed how daratumumab could give an advantage also in combination with PIs-based regimens. The recorded responses are deep and durable. The main adverse events reported in the daratumumab group were thrombocytopenia and infusion-related reactions but none of them led to a treatment discontinuation higher than in the control group.


Preface

The LabVIEW software environment from National Instruments (NI) is a reference used by engineers and scientists worldwide. It is known for being used as a specific solution and, normally, is coupled with NI-made or third-party hardware or in conjunction with additional software. There is no laboratory or industry that does not adopt LabVIEW as a standard.

Since its inception in 1986, LabVIEW has grown enormously. It provides not only an annual upgrading policy, but also a variety of annexed software products such as supporting software and design tools for measurements, automation, simulation, hardware integration, and data analysis.

LabVIEW is wrongly regarded as a simple tool for acquiring, processing, and displaying data. On the contrary, it is an extremely powerful and complete programming language. The peculiarity of its graphical development interface, implemented in the so-called G-programming language (where G stands for "graphical"), is both an advantage and disadvantage of LabVIEW. Its implicitly simple approach based on graphical diagrams could deceive the user or potential developers, inducing them to think that LabVIEW is a simple or even a trivial way of programming basic or simple tasks. However, in spite of its intuitive interface, LabVIEW needs to be carefully understood and its development techniques must be acquired and well known in order to develop professional applications that are robust, readable, scalable, and maintainable. The knowledge of the language elements is far enough from the ability to develop good and effective applications. With the aim to accomplish that, it is suggested that beginners consistently study and practice using LabVIEW, while advanced users should keep abreast of updates to the software. Courses, webinars, documentation, forums, discussions, experience, and formation, in general, should be considered as a part of the personal patrimony as a professional LabVIEW developer. Moreover, developers should think about good and specific planning before they start to merely "design," in the sense of trying to put elements together to get a result.

To guarantee a complete and effective knowledge of the product, users should take into account the certification program available from NI. This program is a graduation process divided into three levels and based on exams. LabVIEW certification needs to be renewed every three years, making the owner responsible for keeping up with updates in the field. This is particularly important, especially since NI introduced a new platform for LabVIEW called LabVIEW NXG (New Generation), which should take the place of the "old" (but currently still used) traditional LabVIEW platform.

Given all of this, it is clear that LabVIEW has applications in many fields. As such, the chapters in this book cover such topics as didactics, laboratory applications of tests and measurements, and computational and modeling applications.

The book is organized into two sections: "LabVIEW as a Laboratory Tool" and "LabVIEW in Modeling." The first section contains six chapters and the second section contains three chapters.

**II**

**Chapter 8 143**

**Chapter 9 157**

LabVIEW as Power Disturbances Classification Tools *by Ahmad Farid Abidin and Mohd Abdul Talib Mat Yusoh*

Digital System Design *by Janani Rajaraman*

Chapter 1, "Analyzing and Presenting Data with LabVIEW," may appear to fit better in the second section of the book. However, I chose it as an introductory chapter because it contains a detailed summary of the calculation and analysis functions present in LabVIEW, giving the reader a wide overview of the possibilities that the development environment provides, which new users of the system often overlook. The same can be said about the aspects of data presentation made with LabVIEW, which is a peculiarity often delegated to further "external" software, an action that is not always necessary.

Chapter 2, "TeraVision: A LabVIEW Software for THz Hyper-Raman Spectroscopy," is a review of a complete research lab application carried out with LabVIEW. The chapter opens with a premise on terahertz physics, necessary to understand the nature of the application, and then moves on to the description of the measurement implementation using LabVIEW. It is interesting to note the complexity of the application, mainly due to the considerable quantity of instruments and elements to be controlled centrally.

Chapters 3, 4, and 5 deal with the same theme: everyone is looking for, and finding, a way to interface LabVIEW software with lean and affordable hardware like Arduino's Open Source. Although accompanied by respectable hardware from NI, LabVIEW can also be used easily with third-party hardware. This solution can be used where there is no need for stringent and absolute reliability (typical of industrial applications) but one can be satisfied with cheaper solutions. In this field, Arduino represents a frontier that has always been widely considered, given its low cost compared to that of NI hardware. Chapter 3 opens with a discussion of the management, economic and otherwise, of resources for an educational laboratory, with an eye toward the "remote control" of laboratories. Chapter 4 instead points directly to data acquisition techniques, with a focus on the concrete aspects of the electrical type. Chapter 5 deals with the practical aspects of interfacing with Arduino, presenting two different technical approaches to the integration between the parts and focusing on different platforms such as Raspberry.

Chapter 6, "Certain Applications of LabVIEW in the Field of Electronics and Communication", generically deals with the applications of signal treatments that can be carried out in LabVIEW in various fields. These include pattern recognition to use with embedded systems as well as applications in robotics, medicine, and education.

The second section begins with Chapter 7, which presents an algorithm, built entirely in LabVIEW, for determining the structural response of buildings to strong stimuli such as earthquakes. The chapter also presents some historical earthquakes that have been studied and analyzed using LabVIEW, using a discrete-time integration computation model. It concludes with a positive opinion regarding the use of LabVIEW for this application.

Chapter 8, "LabVIEW as Power Disturbances Classification Tools," presents an interesting application of LabVIEW in the field of monitoring the quality of electrical networks for power distribution. The system is based on a hybrid architecture, which uses standard commercial electrotechnical tools placed in the field and ends with an interface to LabVIEW where the data is collected and subsequently analyzed.

Chapter 9, "Digital System Design," uses LabVIEW for modeling digital logic. This represents a useful opportunity in the field of teaching, where potential learners

**V**

can build combinatorial logic based exclusively on gates and elementary logic. This activity represents a strong stimulus to the understanding of the realization of advanced logic networks, such as those built-in small- and medium-scale

LabVIEW offers endless possibilities and, as we said at the beginning, it must be imagined as a programming language in all respects. The use of LabVIEW requires passion, method, and rigor, which are all essential skills to obtain professional results. In particular, it should be pointed out that LabVIEW must be approached methodically, avoiding improvisations and attempts supported by the fact that the use of the G-language is implicitly easy; starting from this assumption would represent a false step. Any professional use of LabVIEW must be supported by adequate preparation, starting with the execution of the appropriate existing basic courses (Core 1, Core 2, and Core 3) and proceeding hand in hand with personal

Remember, the G is not just for graphics; the G combined with the correct programming structures and strategies makes a LabVIEW program worthy of

**Riccardo de Asmundis**

Section of NAPOLI, Italy

Department of Physics,

Napoli, Italy

Università di Napoli "Federico II",

INFN (Istituto Nazionale di fisica nucleare),

integrated circuits starting from basic logic.

experiences, towards any advanced courses.

the name.

can build combinatorial logic based exclusively on gates and elementary logic. This activity represents a strong stimulus to the understanding of the realization of advanced logic networks, such as those built-in small- and medium-scale integrated circuits starting from basic logic.

LabVIEW offers endless possibilities and, as we said at the beginning, it must be imagined as a programming language in all respects. The use of LabVIEW requires passion, method, and rigor, which are all essential skills to obtain professional results. In particular, it should be pointed out that LabVIEW must be approached methodically, avoiding improvisations and attempts supported by the fact that the use of the G-language is implicitly easy; starting from this assumption would represent a false step. Any professional use of LabVIEW must be supported by adequate preparation, starting with the execution of the appropriate existing basic courses (Core 1, Core 2, and Core 3) and proceeding hand in hand with personal experiences, towards any advanced courses.

Remember, the G is not just for graphics; the G combined with the correct programming structures and strategies makes a LabVIEW program worthy of the name.

**Riccardo de Asmundis**

INFN (Istituto Nazionale di fisica nucleare), Section of NAPOLI, Italy

> Università di Napoli "Federico II", Department of Physics, Napoli, Italy

**IV**

education.

LabVIEW for this application.

Chapter 1, "Analyzing and Presenting Data with LabVIEW," may appear to fit better in the second section of the book. However, I chose it as an introductory chapter because it contains a detailed summary of the calculation and analysis functions present in LabVIEW, giving the reader a wide overview of the possibilities that the development environment provides, which new users of the system often overlook. The same can be said about the aspects of data presentation made with LabVIEW, which is a peculiarity often delegated to further "external" software, an action that

Chapter 2, "TeraVision: A LabVIEW Software for THz Hyper-Raman Spectroscopy," is a review of a complete research lab application carried out with LabVIEW. The chapter opens with a premise on terahertz physics, necessary to understand the nature of the application, and then moves on to the description of the measurement implementation using LabVIEW. It is interesting to note the complexity of the application, mainly due to the considerable quantity of instruments and elements

Chapters 3, 4, and 5 deal with the same theme: everyone is looking for, and finding, a way to interface LabVIEW software with lean and affordable hardware like Arduino's Open Source. Although accompanied by respectable hardware from NI, LabVIEW can also be used easily with third-party hardware. This solution can be used where there is no need for stringent and absolute reliability (typical of industrial applications) but one can be satisfied with cheaper solutions. In this field, Arduino represents a frontier that has always been widely considered, given its low cost compared to that of NI hardware. Chapter 3 opens with a discussion of the management, economic and otherwise, of resources for an educational laboratory, with an eye toward the "remote control" of laboratories. Chapter 4 instead points directly to data acquisition techniques, with a focus on the concrete aspects of the electrical type. Chapter 5 deals with the practical aspects of interfacing with Arduino, presenting two different technical approaches to the integration between

the parts and focusing on different platforms such as Raspberry.

Chapter 6, "Certain Applications of LabVIEW in the Field of Electronics and Communication", generically deals with the applications of signal treatments that can be carried out in LabVIEW in various fields. These include pattern recognition to use with embedded systems as well as applications in robotics, medicine, and

The second section begins with Chapter 7, which presents an algorithm, built entirely in LabVIEW, for determining the structural response of buildings to strong stimuli such as earthquakes. The chapter also presents some historical earthquakes that have been studied and analyzed using LabVIEW, using a discrete-time integration computation model. It concludes with a positive opinion regarding the use of

Chapter 8, "LabVIEW as Power Disturbances Classification Tools," presents an interesting application of LabVIEW in the field of monitoring the quality of electrical networks for power distribution. The system is based on a hybrid architecture, which uses standard commercial electrotechnical tools placed in the field and ends with an interface to LabVIEW where the data is collected and subsequently analyzed.

Chapter 9, "Digital System Design," uses LabVIEW for modeling digital logic. This represents a useful opportunity in the field of teaching, where potential learners

is not always necessary.

to be controlled centrally.

Section 1

LabVIEW as a Laboratory

Tool

**1**

Section 1


2.3.2. Altitudinal Belt

We classified species according to the Holdridge's life zone system [70], which divides Costa Rica into five altitudinal belts: lowland, premontane, lower montane, montane, and subalpine. Due to the elevational limits for altitudinal belts being slightly di fferent among regions in Costa Rica, we established the limits of each belt as follows: lowland (0–700 m), premontane (700–1500 m), lower montane (1500–2700 m), montane (2700–3500 m), and subalpine (>3500 m).

## 2.3.3. Foraging-Reproduction Habitat Index

To compare *Bd* infection dynamics across taxonomic groups, we developed a foraging–reproduction habitat index (FRHI). The FRHI was created to classify species with a system of three letters that represented life history traits associated with foraging and reproduction (Table 2). First, we classified species according to their development into indirect- (I) or direct-developing amphibians (D). Second, we classified species according to their foraging habitat into terrestrial (T), arboreal (A), pond/puddle-dwellers (P), stream-dwellers (R), and phytotelma (F). Finally, species were classified according to their reproductive habitat into terrestrial (T), arboreal (A), pond/puddle-breeders (P), stream-breeders (R), and phytotelma (F).

**Table 2.** Categories and taxonomic examples of the foraging–reproduction habitat index (FRHI) that we developed for this study to analyze current prevalence of *Batrachochytrium dendrobatidis* across taxonomic groups. Symbology: First letter represents development: (I) indirect- or (D) direct-developing amphibians. Second letter represents foraging habitat: terrestrial (T), arboreal (A) pond/puddle-dwellers (P), stream-breeders (R), and phytotelma (F). Third letter represents reproductive habitat: terrestrial (T), arboreal (A), pond/puddle-dwellers (P), stream-breeders (R), and phytotelma (F).


**6. Acknowledgements**

The project was supported by the National Natural Science Foundation of China (10802099), Doctoral Fund of Ministry of Education of China<PHONE_NUMBER>20), and Natural Science Foundation of Shandong Province (ZR2009AQ006). The author also acknowledges the support from the Brain Korea 21 program at Seoul National University.

## **7. References**

**3**

*Vietnam*

**Incompressible Non-Newtonian Fluid Flows**

A non-Newtonian fluid is a fluid whose flow properties differ in many ways from those of Newtonian fluids. Most commonly the viscosity of non-Newtonian fluids is not independent of shear rate or shear rate history. In practice, many fluid materials exhibits non-Newtonian fluid behavior such as: salt solutions, molten, ketchup, custard, toothpaste, starch suspensions, paint, blood, and shampoo etc. In a Newtonian fluid, the relation between the shear stress and the shear rate is linear, passing through the origin, the constant of proportionality being the coefficient of viscosity. In a non-Newtonian fluid, the relation between the shear stress and the shear rate is different, and can even be time-dependent. Therefore a constant coefficient of viscosity cannot be defined. In the previous parts of this book, the mechanics of Newtonian fluid have been mentioned. In this chapter, the common rheological models of non-Newtonian fluids are introduced and several approaches concerned with non-Newtonian fluid flows are considered. In addition, the solution of common non-Newtonian fluid flows in a circular pipe,

As above mentioned, a non-Newtonian fluid is one whose flow curve (shear stress versus shear rate) is nonlinear or does not pass through the origin, i.e. where the apparent viscosity, shear stress divided by shear rate, is not constant at a given temperature and pressure but is dependent on flow conditions such as flow geometry, shear rate, etc. and sometimes even on the kinematic history of the fluid element under consideration. Such materials may be

1. fluids for which the rate of shear at any point is determined only by the value of the shear stress at that point at that instant; these fluids are variously known as 'time

3. substances exhibiting characteristics of both ideal fluids and elastic solids and showing partial elastic recovery, after deformation; these are categorized as 'viscoplastic fluids'. Among the three groups, the time independent Non-Newtonian fluids are the most popular and easiest to handle in analysis. In this chapter, only this group of Non-Newtonian fluids

independent' , ' purely viscous' , 'inelastic' or 'generalized Newtonian fluids'); 2. more complex fluids for which the relation between shear stress and shear rate depends, in addition, upon the duration of shearing and their kinematic history; they

**1. Introduction**

are considered.

annular and rectangular duct are presented.

**2. Classification of non-Newtonian fluid**

conveniently grouped into three general classes:

are called 'time-dependent fluids', and finally,

Quoc-Hung Nguyen and Ngoc-Diep Nguyen *Mechanical Faculty, Ho Chi Minh University of Industry,*


**About the Editors**

#### **Mustafa Ucgul**

Dr. Mustafa Ucgul has a Doctor of Philosophy degree (Mechanical Engineering) from the University of South Australia. Before joining Southern Cross University, he worked at the University of South Australia and had a long career as an agricultural engineering researcher. Mustafa has a strong interest in computational mechanics and its applications in machinery design (particularly in agricultural machinery design). With 17 years of research experience in Turkish and Australian contexts, a major component of his research has focused on the discrete element simulations of soil-engaging tools and the design of mechanical systems. His doctorate specialization was in discrete element modelling of soil–sweep tool interactions, and since then, he has undertaken research activities in research and other industry-linked projects.

#### **Chung-Liang Chang**

Chung-Liang Chang received the B.S. degree in Industrial Education from National Taiwan Normal University, Taipei, Taiwan, in 1998. He gained M.S. and Ph.D. degrees in Electrical Engineering from the National Cheng Kung University, Tainan, Taiwan, in 2004 and 2008, respectively. In 2009, he joined the Department of Biomechatronics Engineering at the National Pingtung University of Science and Technology, Pingtung county, Taiwan, where he is now a distinguished Professor. He has been involved in many research projects financed by the National Science and Technology Council (NSC), Council of Agriculture (CIA), etc. He served as the head of Intelligent Agriculture Production Technology Alliance at the Pingtung University of Science and Technology, creating business opportunities for intelligent agricultural technology. Recently, his work has focused on the autonomy of farming systems with agricultural robots and their impact on the growth of native crops or poultry. His main research interests include smart farming machine, autonomous bio-robot designs for agriculture, the greenhouse system, and artificial intelligence of things.


**3. A contemporary approach: soundscape**

The soundscape concept has been introduced to modify and complement the assessment of noise and its effects on humans. From the beginning, soundscape research has questioned the limits of existing acoustic measurements and the cultural dimension which Schafer included in his research for the first time in the 1960s [17].