**7. Conclusions**

*Visions of Cardiomyocyte - Fundamental Concepts of Heart Life and Disease*

frequency of the infarction [3]. In the Mediterranean, the highest incidence of myocardial infarction is between midday and midnight, while in the UK the highest incidence is between midnight and midday [62]. Numerous factors might affect the later occurrence of infarction in the Mediterranean, such as the number of sunlight hours, inequality in the prevalence of risk factors for cardiovascular disease, and the habit of afternoon rest or 'siesta' [63]. It has also been noted that the incidence of myocardial infarction is higher in the winter [3]. The specific circadian pattern of infarction symptoms has been observed, and the correlation of the circadian rhythm gene with the infarction investigated. The role of the molecular circadian clock in myocardial activity was initially investigated on animal models. It has been observed that the clock gene mutations of the circadian rhythm affect the heart rate, myocardial contractility, energy metabolism, which altogether leads to ischemia [64, 65]. In contrast, variants of the *Per2* gene in mice reduce the severity of the injury after myocardial infarction because it does not only reduce inflammatory response, but also reduces apoptosis, induces cardiovascular hypertrophy, and thus preserves cardiac function [65].

Different variations of circadian rhythm genes are associated with many risk factors for cardiovascular disease. Thus, CLOCK gene variations are associated with metabolic syndrome in humans, type 2 diabetes, and some with stroke [64, 66–68], while *CRY2* and *PER2* gene variations are associated with myocardial infarction [69]. Expression of *CRY1* and *PER2* genes in fatty tissue is associated with metabolic syndrome in humans [64, 70]. Metabolic syndrome is a significant risk factor for cardiovascular disease and contributes to the common pathophysiological processes leading to the development of diabetes and cardiovascular diseases [48]. Atherosclerotic changes in blood vessels in patients with diabetes are more severe than those with normal glucose concentration [71]. It has been shown that the risk of cardiovascular disease in diabetic patients is two to three times higher than in healthy subjects [72]. Patients with diabetes usually have a higher heart rate in sleep and lower heart rate variability over the day than people without diabetes, which causes unnecessary oxygen consumption in the myocardium, with reduced nutritional blood supply. Biological and epidemiological studies suggest a direct link between lifestyle and metabolic disorders [12], although the genetic and biochemical linkage of human circadian rhythm with metabolic disorders has not been fully explored. Accordingly, the importance of the circadian rhythm in maintaining

A peripheral clock is also found in cardiomyocytes, and the internal molecular mechanism of cardiomyocytes, such as the circadian clock, might contribute to cardiovascular disease [73]. Similar to SCN, cardiomyocytes have a circadian expression of clock genes in response to serum shock or norepinephrine. Several genes are associated with intracellular metabolism or physiological activity that has a circadian expression in cardiomyocytes [74]. After development, cardiomyocytes do not replicate, although they possess a meager and permanent rate of renewal. Cardiomyocytes renew cellular structure with their new proteins and membrane lipids every few weeks [75]. The ischemic precondition is an adaptation of cardiomyocytes to hypoxia, and once the heart has suffered an ischemic insult, cardiomyocytes become more resistant to MI because of PER2 and hypoxia inducible factor (HIF)-1a [74]. Circadian genes regulate a group of genes encoding for cardiac metabolic enzymes, and it is considered that a significant role of circadian genes in the heart is to synchronize cardiomyocyte metabolic activity with the availability of nutrients in the blood (i.e., feeding time) [29]. It is known that *PER2* plays an essential role in carbohydrate metabolism during myocardial ischemia [76].

'energy' homeostasis and metabolism is evident.

**6.1 Cardiomyocyte circadian clock**

**28**

Circadian rhythm adjusts the physiological functions of an individual on a daily basis. Daily variations of physiological parameters in the cardiovascular system maintain cardiovascular function according to the needs of different activities during the day. This information suggests that we need to know not only how, but also when to treat heart disease, and also to treat pathological changes not only symptomatically but to treat non-symptomatic but potentially harmful changes in the circadian rhythm.

Understanding the pathophysiological processes involved in the onset of myocardial infarction requires additional studies to assess the crucial elements of the circadian rhythm. In today's personalized medicine, knowledge of the circadian rhythm (i.e., the genetic background) of an individual can be significant for treatment and should be included as an essential part of the diagnostic process.


**4. Early childhood education and learning for children with disabilities before the pandemic**

Learners with disabilities include those who have sensory impairments, cognitive differences, physical difficulties, emotional and behavioral difficulties,

communication disorders, and those with multiple disabilities [6]. Whereas some learners with disability may follow regular curriculum with adaptation or modification of the pedagogical approaches used, there are those who may need specialized syllabuses and intervention programs to access meaningful education. A majority of learners who follow regular curriculum with modification include children with visual impairment, hearing impairment, physical handicap, mild cerebral palsy, mild learning disabilities, mild autism, mild emotional and behavioral difficulties, communication disorders, and the gifted and talented [7]. They require additional support and care services such as therapy, assistive devices, and technology to access education. Leaners with disabilities who need special syllabus include those with mental handicap, deaf blindness, severe autism, severe cerebral palsy, multiple handicaps, and those with profound disabilities [8]. They learn social, communication, and activities of daily living skills at school [9]. In this chapter, we focus on reviewing literature on the pedagogical methods that were employed to enhance continuity of learning among millions of African children after the schools closed and how they favored or did not favor early childhood education (ECE) learners with disabilities.

Child-centered teaching approaches have been found to effectively enhance learning of children in early childhood education. This is predominantly learning by doing through imitation of teachers and their peers [10]. The approach has also been found to be crucial for learners with disabilities who have various challenges and need extra support [11]. Education for all children in their early years supports their development of social, communication, problem-solving, cognitive, and emotional skills [10]. This also applies to children with disabilities in early childhood education. According to Davis [9], the pedagogical approaches used for effective learning of these foundational skills among children with disabilities include: formulating clear expectations based on learning needs of the child, stimulating learning activities that enhances learners' participation in classroom, use of audio-visual material, real objects, or simulations to make the learning process concrete, break tasks into less complex tasks and build on already acquired skills and knowledge, peer learning, sharing the learning objectives and process with parents so they can help with homework. Other strategies include carrying out every day routines consistently and aiding active learning by use of immediate reward for good behavior, completing work on time, and participating in class activities. In addition, individualized education plans where lessons are modified to meet each learner's needs are also critical for children with disabilities [12]. Much as the approaches to support learning of these children are well articulated, they are often not practiced because of various reasons including lack of teacher skills, resources, and poor attitude of teachers among others [13, 14]. Moreover, children with disabilities are often stigmatized and discriminated against by teachers and pupils, further hindering them from full participation in learning.

Before the era of COVID-19 pandemic, access to education and other services was a major challenge for people with disabilities (PWD) including children in many low- and middle-income settings. There have been increasing efforts toward inclusive education, which recommends that children with special needs are accommodated within the mainstream teaching; however, implementation of this has been slow. Education for people with disability has been characterized by exclusion, stigma, discrimination, non-supportive attitudes of parents and community members, long distance to school, school environments that are not supportive (access, materials, equipment), and limited training for teachers on special needs education, limited involvement in planning and allocation of resources for learners with special needs as well as lack of enforcement and financing of the inclusive education *Perspective Chapter: Pedagogical Approaches and Access to Education among Early Childhood… DOI: http://dx.doi.org/10.5772/intechopen.104921*

policies [5, 15, 16]. As a result, there was a major gap in education access for children with disabilities in many countries in Africa prior to the pandemic. Clearly, as we will lean from subsequent sections, the pandemic and resulting school closures worsened the challenges for ECE learners with disabilities especially those in LMICs where governments were least prepared to deal with these challenges. In the section that follows, we take a critical look at the pedagogical approaches that were used to support continued learning for ECE learners with disabilities in Africa.


3.3.1. Change in Process Conditions in Batch HTC (Cases 1, 4, 6) or VTC Processes (Case 5)

In batch reactors, the solids and liquids are introduced at the beginning of the run and the reactor is sealed before heating starts. The initial *VFo* and *%So* can be easily calculated and are usually reported (Table 3). Feedstock initially submerged in water can unequivocally be called HTC when *VFw* at the holding temperature remains as large or larger than *VFo*. This is true for all batch HTC cases (1, 4, 6) analyzed here. Each case includes results for two feedstocks under slightly differing conditions. In Case 4, with a relatively large amount of initial water (*VFo* = 0.46 and 0.64, for wheat straw and digestate, respectively), the expansion of water at 230 ◦C causes *VFw* to increase by approximately 20%. As only 0.5 to 1.4% (m/m) of the initial liquid water is transferred to the vapor phase, there is little to no change in *%S(T)*. Similarly, very little increase in solid content is observed in Case 6. In contrast, Case 1 at 200 ◦C has a low degree of initial water filling for both feedstocks (i.e., *VFo* = 0.1 and 0.16), and *VFw* is very similar to *VFo*. Between 4 and 7% of the water is transferred to the vapor, causing a corresponding increase in the value of *%S(T)*. The values for *%S(T)* ranged from 1.0 to 19.9% for all batch HTC cases, ensuring adequate contact with liquid water to promote HTC reactions. Despite the loss of liquid water due to vaporization, the filling volume (*VFw*) slightly increases because the volume of water expands with the reactor temperature, guaranteeing that the feedstock is completely submerged in the liquid water throughout the reaction period. Therefore, hydrothermal reactions will take place between the feedstock and liquid water and the process can be called batch HTC in Cases 1, 4, and 6.

If the solids are suspended in the reactor in baskets or on trays so that they are not submerged in water, the process is commonly called VTC (Cases 5). If the feedstock has a high moisture content such as the dried feedstock soaked in water (Case 5, *MC* = 75% or *%So* = *%S(To)* = 25%), or it is made up of intact microorganisms or fresh plant material, the actual *%S(T)* slightly increases compared to *%So* (27.6 or 28.9 vs. 25%) due to the small loss of liquid water in the feedstock to vapor (Table 3).

**Table 3.** Overview of process conditions and water distribution for the seven cases of VTC/HTC.

n.r.—not reported. \* Assumed 50% of reactor volume filled with bark or sugar beet suspended in baskets. # Averaged value for feedstock. @ Assumed the same amount of feedstock as in Case 6.

3.3.2. Change in Process Conditions for Semi-Batch VTC Process with Steam Injection (Cases 2 and 7 with Condensate Removal or Separation, and Case 3 without Condensate Removal)

For reactors in Cases 2 and 3 with a semi-batch mode of operation where saturated steam is introduced over time to first heat the reactor and then to maintain the desired operating temperature, the calculations for how much mass of the water is present as liquid or vapor are not as straightforward. The steam condenses as it heats the feedstock to the targeted operating temperature, and more will condense over the targeted holding time. As steam is introduced, the reactor pressure will remain constant at the saturation pressure if there are no reaction products entering the vapor phase. However, gases are normally produced by the hydrothermal reactions and the pressure rises as the gases, mainly CO2, enter the headspace.

For systems with condensate removal, as in Case 2, or condensate separation, as in Case 7, the mass of bulk liquid water in contact with feedstock comes from moisture already present within the feedstock and water condensation on the surface of feedstock. Wet feedstocks will retain most of their moisture. For dried feedstock, the majority of the water in the system will be in the vapor, with some steam condensing on the feedstock surface, especially in the heating phase. Assuming that the amount of steam condensed on the feedstock surface is negligible, the overall specific volume *vR* is mostly that of the saturated water vapor and the moisture content of the feedstock. In such systems, the process can be labeled VTC without much ambiguity. The amount of liquid water that can react with the feedstock for VTC systems mainly depends on the moisture content of the original feedstock and the condensed water on the feedstock surface. It is very difficult to quantify this amount of water. For these two cases, *%S(T)* was assumed to remain the same as the initial value. The condensed water is sometimes flashed off at the end of the run (e.g., for energy recovery (Revatech, 2012)), so that the solids come out about as wet as they went in. This is helpful in reducing dewatering requirements, but this hinders easily assessing how much water was in contact with the feedstock.

In systems without condensate removal, as in Case 3, the continuous injection of steam will build up the total mass of water in the system, with the majority present in the form of liquid water. The VTC process then approaches the HTC process. In this hybrid VTC–HTC commercial-scale unit, the reaction system is well-mixed and liquid water is mixed into the feedstock, gradually lowering the value of *%S(T)* in the reactor from the initial *%So* value 47% to 34.6%. The volume fraction of vapor water (*xV*) changes somewhat. Starting with 3% of the water present as vapor, it reduces to 1.4% at the end of the run (Table 3). In general, it is important to measure the mass of steam introduced in systems without

condensate removal, so that the mass of accumulated condensed water can be monitored as a safety precaution. Steam injection must stop before *VFw* approaches 1 to avoid rupture of the reactor.

It is interesting to note that the solid content *%S(T)* of feedstock for all seven cases was less than 45% at the reaction temperature. The solid content for HTC systems ranged from 1.0 to 19.9%, while that for VTC systems from 27.6 to 45%. It means that 55% or more of the total water mass was present as liquid water and had direct physical contact with feedstock promoting carbonization reactions. According to Cao et al., the lower the *%S(T)*, the more the product was carbonized. The highest solids content for VTC was 45% in Case 2 because of the water already present within the raw feedstock even though additional water was not supplied. This leads to questions on what will happen if we conduct VTC with completely dried feedstock, such as: Is it possible to carbonize the dried feedstock with steam alone? For such reaction systems, the initial value of *%S(To)* equals one. The value of *%S(T)* will become less than one as some of the steam condenses on the surface of feedstock promoting the HTC reactions. In such a system, the extent of carbonization will be determined by the extent of the wetting of the feedstock by steam. More detailed study is needed to understand the relationship between the degree of wetting by steam and carbonization.


**3. Results**

#### *3.1. Genetic Characterization*

The chosen nuclear microsatellites successfully amplified all the olive cultivars under analysis, with identical genetic profiles among the replicates of each cultivar. With the exception of missing data for only three loci related to the markers DCA09, DCA17 and GAPU101, unique alleles for all the used SSR were assigned to each cultivar, thus allowing an accurate varietal identification (Table 2 and Figure S1).

The Unweighted Neighbor Joining dendrogram allowed us to group the genotypes predominantly on the basis of their regional origin, except one. Indeed, as expected, three main clusters were identified (Figure 1). Cluster I included two of the three analyzed Abruzzo cultivars, i.e., Tortiglione and Dritta. The third Abruzzo cultivar, Gentile dell'Aquila, was included in Cluster II together with all the Sardinian cultivars (Semidana, Sivigliana and Corsicana). Finally, Cluster III encompassed cultivars from the Apulia region, i.e., Bambina, Cima di Melfi and Oliva Rossa.

**Table 2.**

Molecular

fingerprinting

 of the analyzed cultivars by means of 12

microsatellite

 markers (SA, AB and AP,

respectively,

 indicate Sardinian, Abruzzo and

**Figure 1.** Unweighted Neighbor Joining dendrogram illustrating the clusterization of the olive cultivars under study, and a map of Italy with indication of the considered regions and the corresponding drupe sampling sites.


**Abbreviations**

The following abbreviations are used in this manuscript:

### **Appendix A. Scattering Theory, Phase-Shifts and the Friedel Sum Rule**

In this Appendix we review some useful results from scattering theory. In Appendix A.1, we provide the definition of the S- and T-matrix in scattering theory. In Appendix A.2, we derive the Friedel sum rule for a non-interacting electron gas with an elastic impurity. In Appendix A.3, we illustrate these concepts on the simple case of a chiral edge state tunnel coupled to a single resonant level. In Appendix A.4, we derive the phase-shift induced on lead electrons by the scattering potential of the LFL, Equation (8).


**2. Literature**

Previous studies on industrial production efficiency, such as the one conducted by [7], used the SDG-9 index to assess the degree of industrialization of countries, as well as social inclusiveness, less use of natural resources and environmental impact. Ref. [8] using the DEA method, discusses the relationship between the American manufacturing industry

and environmental performance. The unintended output is reported as SOx, NOx, Co, etc. It is found that air pollution is mainly a by-product of manufacturing activities. The share of the manufacturing industry in the total amount of state-owned products and the share of the polluting industry in the total amount of manufacturing activities are two important factors determining the intensity of pollution. Using DEA, Ref. [9] discuss the energy conservation and carbon reduction efficiency of China's industrial production from 2006 to 2010. The input variables are labor, capital and energy consumption, and the output variables are SO2, wastewater and GDP. It is found that the energy conservation and emission reduction efficiency in East China is the best. Ref. [10] using the DEA method, evaluated the environmental efficiency of 46 countries in 2002, 2007 and 2011. The input variables are labor, capital and energy use, and the output variables are GDP, CO2 and NOx. The study found that the energy efficiency of countries rich in oil and natural gas resources is relatively poor. Ref. [11] using the DEA method, discuss the analysis of the energy and environmental efficiency of two petrochemical plants in China from 2012 to 2013, and divide the output into expected output and unexpected output. It was found that by analyzing the energy efficiency and environmental efficiency of the ethylene production process in complex chemical processes, the energy saving and emission reduction potential of ethylene plants can be obtained, and the efficiency performance of DMU can be improved by improving energy efficiency and reducing carbon emission. Research on energy efficiency by [11] evaluated the efficiency of the water, food and energy (WEF) relationship in 30 provinces and municipalities in China from 2005 to 2017. Inputs were labor force, water resource use, energy use, food consumption and other variables, and outputs were social benefits, wastewater discharge and solid discharge. The researchers analyzed the weight of the WEF relationship, and put forward the strategy of sustainable resource management. Ref. [12] discussing the research results of DEA application in the field of energy and environment from the 1980s to 2010, found that the development process will produce various pollutants to air, water and other types of pollutants which are related to health and climate change. Therefore, it is necessary to strike a balance between economic growth and pollution mitigation. Ref. [13] using the DEA method to explore the impact of U.S. economic growth on the environmental efficiency of the power sector, found that there is a stable n-shape relationship between environmental efficiency and regional economic growth, while in the case of local pollutants, there is an inverted n-shape relationship between environmental efficiency and regional economic growth. For policymakers, climate change needs to consider the relationship between economy, environment and society at the same time. On the research related to water use efficiency, Ref. [14] evaluated the efficiency of SDG-6 and a serious water shortage in the Medjerda Basin in Tunisia. Ref. [15] used the DEA method to explore the water use efficiency of 10 cities in the Minjiang River Basin in China in 2018. The research found that the input of social water and economic water are different, and the output of GDP and unintended wastewater are the factors affecting water use efficiency. Ref. [16] using TFP and Tobit models, discuss the water use efficiency of 30 provinces and municipalities in China from 2006 to 2015. The study found that the efficiency of water use in the administrative regions of provinces and cities is low, so we should establish the awareness of water conservation from the investment of education, so as to balance economic development and water use efficiency. Ref. [17] used the DEA method to explore China's regional ecological efficiency from 2003 to 2014. The input variables were labor force, water consumption, energy consumption, etc., and the output variables were GDP, SO2, smoke and dust, industrial wastewater, household waste, etc. The study found that the efficiency and progress rate of the eastern region are better than other regions, and there is still room for improvement in China's overall environmental efficiency. Ref. [18] used DDF to evaluate the water resources and wastewater discharge efficiency of China's industrial sector. The input variables were labor, capital and industrial water consumption, and the output variables were industrial output value, chemical oxygen demand, etc. The study found that the eastern region has made progress in science and technology, and the

pollutants discharged by industrial production in the western region are more serious. Ref. [19] using the DDF model, evaluated the efficiency of administrative water removal in 31 provinces and cities in China from 2011 to 2015. The study found that there were significant differences between the efficiency performance and technology gap in Eastern, Central and Western China. Ref. [20] evaluating the relationship between China's industrial water efficiency and regional differences from 2005 to 2015, found that the industrial water efficiency values of administrative regions in 31 provinces and cities are less than 1, among which the per capita water resources, R&D investment, regulation formulation, GDP and industrial structure will affect the industrial water efficiency. Ref. [5] using the SBM model, studied the economic production and sewage treatment efficiency of 30 provinces and cities in China from 2011 to 2017. The input variables were labor force, domestic and industrial water, investment in sewage treatment projects and the number of sewage treatment plants. The output variables were GDP, chemical oxygen demand of wastewater discharge and heavy metal pollution. The study found that there are great differences in inefficiency in different regions of China. The efficiency in the economic production stage is significantly higher than that in the sewage treatment stage. The sewage treatment efficiency is the main drag factor of the overall efficiency. Ref. [21] assessed the regional differences of China's provincial air pollution efficiency from 2006 to 2015. The study found that there were significant differences in air pollution emission efficiency in various regions. Air pollution emission efficiency was significantly positively correlated with economic development level, industrial structure optimization, technological innovation and foreign direct investment (FDI), and negatively correlated with energy consumption structure. Ref. [22] used DEA and regression analysis to explore China's energy efficiency performance from 2001 to 2013. Input variables included labor, capital and energy use, and output variables were GDP, industrial wastewater, solid waste and air pollutants. The study found that technological innovation has a positive impact on TFEE. The government should pay attention to technological innovation, which will be conducive to the effectiveness of energy conservation and emission reduction and environmental pollution prevention and control. Research on pollution control costs, such as [23], discusses the efficiency of China's iron and steel industry and pollution control. It is found that the production efficiency of China's iron and steel industry is low and causes serious pollution to the environment. Enterprises must improve the overall efficiency by increasing environmental protection investment, introducing foreign advanced technology and strengthening the R&D of pollutant management.

As for the discussion on energy consumption and pollution control technology, for example, in a paper by [24], it is estimated that Beijing, China, will improve its air quality by adjusting its industrial structure, controlling pollutant emissions, controlling vehicle pollution emissions and other measures and regulations due to rapid industrialization, urbanization and motorization, the continuous growth of energy consumption and the resulting emissions of a variety of pollutants. Ref. [25] assessing the impact of foreign investment on greenhouse gas emissions in developing countries, found that foreign investment enabled technology transfer, improved labor, reduced greenhouse gas emissions, improved energy efficiency and achieved sustainable development goals.

As mentioned above, most previous studies focused on industrial production efficiency, energy efficiency, pollutant emission and control. Therefore, this study uses the DDF method to explore the impact of pollution control on the production efficiency of 31 provinces and municipalities in the Yellow River Basin and Non-Yellow River Basin in China from 2013 to 2017, and uses TGR to measure the change in the technology gap. We objectively evaluate the efficiency difference of pollution control in different provinces and cities to provide an effective reference basis for policy formulation and budget control.

The main contributions of this study are as follows:

(1) Different from the previous literature results, this study uses the parallel method and takes the industrial production department and the pollution control department as input variables to objectively evaluate the impact of pollution prevention and control funds on industrial production efficiency in 31 provinces and municipalities in China;

(2) This study compares the changes in industrial production efficiency and the technology gap between the Yellow River Basin and Non-Yellow River Basin, which is conducive to mastering the situation of pollution control and production efficiency in 31 provinces and municipalities in China, and provides objective suggestions as a reference for SDG-6- and SDG-9-related policy making.

## **3. Research Method**

Ref. [26] first put forward the concept of a deterministic nonparametric front in 1957. It is used to measure the production level of a decision-making unit. Then, Ref. [27] proposed the CCR model. Ref. [28] proposed the BCC model. Over time, Ref. [29] (1996) proposed the directional distance function (DDF). In addition, Ref. [30] introduced the VRS super-efficiency Nerlove–Luenberger (N–L) model to solve the unreasonable problem. This method can adjust the input and output levels in the same proportion, and the efficiency value obtained under the VRS super-efficiency of DDF can be used for ranking all DMUs. The directional distance function model under variable return to scale (VRS) and the calculation method of efficiency values used in this study are as follows:


*2.1. Theoretical Perspective of the Analytical Model*

We refer to a region that is composed of *N* subareas (*n* = 1, 2, ... , *N*). Each subarea *n* could comprise water/irrigation districts that incorporate several agricultural producers regulated by individual water district mandates. Each subarea *n* includes *Kn* (*kn* = 1, 2, ... , *Kn*) agricultural producers that are considered nonpoint source polluters of a given pollutant, or of a set of several pollutants (for simplicity we will refer to salinity as the pollutant in question). Each agricultural producer applies water on agricultural crops to produce market products. A byproduct in the form of agricultural pollution is the irrigation return flow that may contain a regulated water quality pollutant, which we will specify to be salinity.

Each of the *k* producers in the *n*-th subarea may have different factors affecting agricultural production conditions (natural and technical) that can lead to different cropping patterns, crop yields, net revenue, and the salt concentration and salt load of the return flow. We define a production function of agricultural yield and return flow for producer *k* as (for simplicity we drop the indexes *k* and *n*):

$$\begin{cases} \begin{array}{c} \mathcal{Y} = f(\mathcal{W}, \mathcal{C}, T | \underline{\mathbf{X}})\\ \mathcal{Q} = \mathcal{g}(\mathcal{W}, \mathcal{C}, \mathcal{T} | \underline{\mathbf{X}})\\ \mathcal{S} = h(\mathcal{W}, \mathcal{C}, \mathcal{T} | \underline{\mathbf{X}}) \end{array} \tag{1} $$

where *Y* is yield per acre of a given crop, *W* is water applied per acre, *C* is salinity level of applied water, *T* is irrigation technology used (expressed in integer values to indicate various irrigation technologies available to each agricultural grower within a designated subarea), *Q* is volume of return flow produced on that farm, *S* is the salt concentration of the return flows, and *X* is a vector of all fixed effects related to the location of that producer. We will discuss later the first and second order conditions of the production function derivatives, namely the shape of these three components of the production function.

Given Equation (1), agricultural producers within a designated subarea maximize their net revenue under constraints imposed by both natural and regulatory conditions:

$$
\pi\_{k\_n} = \sum\_{\mathbb{C} \text{rops}} p \cdot Y \cdot L - w \cdot \mathcal{W} - t \cdot T \tag{2}
$$

*s*.*t*.:

$$\sum\_{Crops} L \le \overline{L} \tag{3}$$

$$\sum\_{Crops} L \cdot \mathcal{W} \le \overline{\mathcal{W}} \tag{4}$$

Additional constraints imposed on each agricultural producer within a designated subarea by subarea management, are summarized in (5), and discussed below

$$\begin{array}{c} \text{Land following constraints} \\ \text{Irrigation technology constraints} \\ \text{Specific crop constraints} \end{array} \tag{5}$$

where for each agricultural producer within a designated subarea, *p* is the price per unit of crop, *L* is the area grown with that crop, *w* is the price of water, *t* is the per-acre cost of the technology, *L* is the total cultivable land of the agricultural producer, and *W* is the water quota imposed by the subarea on the agricultural producer. Net revenue is defined as the revenue from crop sales minus the variable costs of production and payments of fees for exceedance of pollution load.

The solution to (2)–(5) provides for each agricultural grower within a designated subarea: the area under production with each crop selected; the total amount of water applied; the technology selected for each crop; the total profit; the total volume of return flow from the designated subarea; and the salt concentration of the return flow that can be used to compute drainage salt (mass) loads. While we may predict the volume *Q* and salt loading with associated *S* for each subarea, such information is not available to either the subarea management or to the federal regulator.

The subarea managers have access to monitoring data that provides the total volume of *Q* from all agricultural producers and its quality, *S*, that can be used to estimate salt loading. Salt loading is the factor each subarea manager is obligated not to exceed on a monthly and annual basis by the regulator, as defined within the Total Maximum Daily Load (TMDL) allocation for each subarea. TMDLs are the policy vehicles that are used by the US Environmental Protection Agency to limit nonpoint source pollution to levels that do not exceed the assimilative capacity of the receiving water body. TMDLs are keyed into water quality standards or objectives at a compliance monitoring station for the pollutant in the receiving water, and are designed to be protective. The agricultural non-point source pollutant management problem is a typical principal–agent problem under circumstances of asymmetry of information. Hence, we need to introduce several simplifying assumptions. We start by drawing (Figure 1) a schematic regional setting, using four agriculturally dominated subareas located on the valley floor, and a water body in the form of a river (describing the actual situation in the region that we will empirically analyze). The remaining three subareas are tributary river watersheds where water flow is controlled by upstream dams and reservoirs and whose operation is largely independent of agricultural drainage decision making.

**Figure 1.** A schematic representation of the region with subareas, agricultural producers, and a regulated receiving water body (river).


**4.2 Moran tests on residuals of the MTDg models incorporating climatic covariates**

In this section synchronisation among species pairs is tested using Moran's correlation method on the cross-residuals, based on MTDg models which incorporate both climate covariates and lagged effects of previous flowering. This work is based on [16], where MTDg models allowing interactions were fitted to the same four species. We present here only MTDg models with two covariates, namely, mean temperature and rainfall.

Parameters of the MTDg models are shown in **Table 5**. Significant lag effects of previous flowering states (*lag j*, where *j* = 1, ..., 12 months), and of the climatic covariates (*meanT* and *rain*) and their interaction (*meanT\*rain*) are also given in **Table 5**. The estimated parameters for the MTDg models generally show a (positive) 1 month lag effect and 9, 11 and 12 months lag effects of previous flowering status (**Table 5**).

From **Tables 5** and **6** we observe that mean diurnal temperature (*meanT*) has a significant effect on flowering for all species; *rain* impacts significantly only on *E. tricarpa* (Tri) and an interaction effect between *rain* and *meanT* exists for *E. polyanthemos* (Pol). Overall, flowering increases as temperature (*MeanT*)

increases for *E. microcarpa*; and flowering decreases as temperature increases for both *E. leucoxylon* and *E. tricarpa*. Rainfall positively impacts the flowering of

*E. polyanthemos* exhibits increased flowering at low *meanT* when there is contemporaneous below average rainfall and at high *meanT* with above average rainfall

In what follows we denote the species used to estimate the parameters for the

*E. tricarpa* (i.e. flowering increases with more rainfall). Interestingly

*E. polyanthemos* (i.e. (0.88, 0.12, 0.20, 0.96)) in **Table 6**.

*Synchrony relationships among the four eucalypts species.*

**Species Climate**

*E. poly* Inter-

*1*

*2*

*3*

*4*

*ϕ*

**Table 7.**

**Figure 7.**

**65**

**Table 6**.

**effects**

**(temp/ rain)**

action

*Cut point for less rain: rain 40.44 mm.*

*Cut point for more rain: rain >40.45 mm. Note that '-' indicates cells with zero probabilities.*

*Cut point for low temperature states: MeanT 13.83°C.*

*Cut point for high temperature states: MeanT >13.84°C.*

*Transition probabilities of flowering for the meanT and rain MTDg models.*

*A negative and significant correlation indicates an asynchronous species pair.*

*Significant Moran correlations (in brackets) from the MTDg models.*

**Previous flowering**

*DOI: http://dx.doi.org/10.5772/intechopen.88554*

**Off On Low1 High2 Less3 More4 Low/**

*Mixture Transition Distribution Modelling of Multivariate Time Series of Discrete State…*

*E. mic* (+/) 0.00 1.00 0.00 1.00 0.39 0.28 - - - -

*E. leu* (/+) 0.05 1.00 1.00 0.00 0.88 0.94 - - - - *E. tri* (/+) 0.00 1.00 1.00 0.00 0.00 1.00 - - - -

**Model species mic pol leu tri** Synchronous fitted species tri (0.14) leu (0.14) pol (0.16) mic (0.15)

Asynchronous fitted species mic (0.14ϕ

**Temperature Rain Temperature by rain interaction**

**less**

0.01 1.00 0.00 0.34 0.94 0.03 0.88 0.12 0.20 0.96

**Low/ more**

tri (0.11)

)

**High/ less**

**High/ more**

(see the transition probabilities to flowering for the interaction effect of

MTD-based equation as the 'Model species' and the species fitted with these

*ϕ Covariate effects above 0.03 are considered significant.*

*- indicates cells with zero probabilities.*

#### **Table 5.**

*MTDg mixing probabilities of MeanT and rain models.*

*Mixture Transition Distribution Modelling of Multivariate Time Series of Discrete State… DOI: http://dx.doi.org/10.5772/intechopen.88554*

*1 Cut point for low temperature states: MeanT 13.83°C.*

*2 Cut point for high temperature states: MeanT >13.84°C.*

*3 Cut point for less rain: rain 40.44 mm.*

*4 Cut point for more rain: rain >40.45 mm.*

*Note that '-' indicates cells with zero probabilities.*

#### **Table 6**.

• *E. polyanthemos* flowering is synchronous only with *E. leucoxylon*; and

flowering effects and climate covariates (see also **Table 7** and **Figure 7**).

**4.2 Moran tests on residuals of the MTDg models incorporating climatic**

In this section synchronisation among species pairs is tested using Moran's correlation method on the cross-residuals, based on MTDg models which incorporate both climate covariates and lagged effects of previous flowering. This work is based on [16], where MTDg models allowing interactions were fitted to the same four species. We present here only MTDg models with two covariates, namely,

Parameters of the MTDg models are shown in **Table 5**. Significant lag effects of

From **Tables 5** and **6** we observe that mean diurnal temperature (*meanT*) has a significant effect on flowering for all species; *rain* impacts significantly only on *E. tricarpa* (Tri) and an interaction effect between *rain* and *meanT* exists for *E. polyanthemos* (Pol). Overall, flowering increases as temperature (*MeanT*)

**Species lag 1 lag 9 lag 10 lag 11 lag 12 Temp variable Rain Temp rain** *E. mic* 0.534 - - 0.032ϕ 0.275 0.136 - - *E. poly* 0.530 0.060 - 0.160 0.105 0.091 0.009 0.045 *E. leu* 0.611 - - 0.124 0.042 0.202 - - *E. tri* 0.617 0.059 0.009 0.096 - 0.157 0.062 -

previous flowering states (*lag j*, where *j* = 1, ..., 12 months), and of the climatic covariates (*meanT* and *rain*) and their interaction (*meanT\*rain*) are also given in **Table 5**. The estimated parameters for the MTDg models generally show a (positive) 1 month lag effect and 9, 11 and 12 months lag effects of previous flowering

• *E. tricarpa* flowering is synchronous only with that of *E. leucoxylon*; and is asynchronous with *E. polyanthemos* (and has no relationship with

We can view **Figure 5** as the transition signatures from past states, where both species flowering is off or both species flowering is on, for synchronous pairings (LeuTri or LeuPol) and the asynchronous species pairs (PolTri, LeuMic and MicPol). **Figure 6** likewise delineates transition signatures from past states, where only one species of the pair is flowering. These signatures (**Figures 5** and **6**) distinctly differ according to whether a species pair is synchronous or asynchronous. For MicTri the associated sum of the probabilities for transitions A and B (both off/on to one off/on) is 0.591 (see **Table 4**), which is close to the threshold for synchrony of 0.65. Note that the more sophisticated MTDg modelling approach in Section 4.2 which incorporates covariates (mean temperature and rainfall) with interactions, shows that indeed *E. microcarpa* and *E. tricarpa* are synchronous (**Tables 6** and **7**), wherein the MTDg model allows for prior lag 1 to lag 12 month

asynchronous with both *E. microcarpa* and *E. tricarpa*.

*E. microcarpa*).

*Probability, Combinatorics and Control*

**covariates**

status (**Table 5**).

*ϕ*

**64**

**Table 5.**

mean temperature and rainfall.

*Covariate effects above 0.03 are considered significant.*

*MTDg mixing probabilities of MeanT and rain models.*

*- indicates cells with zero probabilities.*

*Transition probabilities of flowering for the meanT and rain MTDg models.*

#### **Table 7.**

*Significant Moran correlations (in brackets) from the MTDg models.*

**Figure 7.**

*Synchrony relationships among the four eucalypts species.*

increases for *E. microcarpa*; and flowering decreases as temperature increases for both *E. leucoxylon* and *E. tricarpa*. Rainfall positively impacts the flowering of *E. tricarpa* (i.e. flowering increases with more rainfall). Interestingly *E. polyanthemos* exhibits increased flowering at low *meanT* when there is contemporaneous below average rainfall and at high *meanT* with above average rainfall (see the transition probabilities to flowering for the interaction effect of *E. polyanthemos* (i.e. (0.88, 0.12, 0.20, 0.96)) in **Table 6**.

In what follows we denote the species used to estimate the parameters for the MTD-based equation as the 'Model species' and the species fitted with these

estimated parameters as the 'Fitted species'. **Table 7** gives the resultant significant Moran correlations based on the residual series from the MTDg-based model and fitted species equations. Significant Moran correlations from both the MTDg (and the EKF models show that (a)synchronous pairings found via the MTD and EKF models in [15–19] generally agree (**Tables 7** and **8**); refer also to **Figure 7**, where a solid line indicates synchronous pairs and a dashed line indicates asynchronous pairs of species.

**Table 7** shows significant positive MTDg-based correlations (*P* < 0.006) for the following (model species: fitted species) pairs—(LeuPol), (PolLeu), (LeuTri), (MicTri) and (TriMic), indicating that *E. leucoxylon* is synchronous with *E. polyanthemos*, in agreement with the rules of synchrony described earlier (**Tables 3** and **4**). *E. leucoxylon* is synchronous with *E. tricarpa*; and that *E. microcarpa* and *E. tricarpa* are synchronous. The synchrony of the latter species pair (MicTri) however, contrasts the results of Moran-based results on raw intensity profiles which indicate that *E. microcarpa* and *E. tricarpa* were neither synchronous or asynchronous (**Table 4**). It is noteworthy however, that for this species pairing, *E. microcarpa and E. tricarpa* (i.e. MicTri or TriMic), the associated sum of the probabilities for transitions D and E (one species off/on to both species off/on) is 0.591 (**Table 4**), which is close to the threshold for synchrony of 0.65 (**Tables 3** and **4**).

**Tables 7** and **8** shows significant negative-based correlations (*P* < 0.001) for the following (model species: fitted species) pairs; (LeuMic), (PolMic) and (MicLeu) indicating that that *E. leucoxylon* is asynchronous with *E. microcarpa* and *E. microcarpa* is asynchronous with *E. polyanthemos* (only via the EKF-based residuals) (**Figure 7** RHS); in agreement with the rule for asynchrony (**Table 4**) and Moranbased AR analysis of the flowering intensities.

The resulting parameters estimated from the MTDg models with and without interaction terms can be compared among all four species using **Figure 8**, which shows that the separation of *E. tricarpa* (/+) and *E. microcarpa* (+/) from other species along the horizontal axis 1, is due to the effect of mean temperature. Although *E. leucoxylon* is affected by the similar lag 1 and 11 month flowering terms as *E. polyanthemos*, *E. leucoxylon* (/+) commences flowering at low temperature and shuts down at high temperatures. *E. microcarpa* begins flowering at high temperature (+/). **Figure 8** also displays the similarity (synchronicity) of *E. leucoxylon*

*Distances in the (λ, Q, d, s) parameters among the 4 species—without interaction terms (left) or with*

*Mixture Transition Distribution Modelling of Multivariate Time Series of Discrete State…*

*DOI: http://dx.doi.org/10.5772/intechopen.88554*

The highest degree of synchrony (via the B-MTD rules of synchrony, the MTD models and Moran AR method) occurs between *E. leucoxylon* and *E. tricarpa*; then followed by *E. polyanthemos* and *E. leucoxylon* which indicates the potential for intense competition for potential pollinators, and therefore the prospect for a high level of hybridization. Both these species pairs were shown to be synchronous by Keatley et al., [20]; with *E. leucoxylon* and *E. tricarpa* having 6 years of no overlap (and a long term mean synchrony value of 0.62); and *E. polyanthemos* and *E. leucoxylon* having 5 years of the 31 years (between 1940 and 1970) with no overlap (long term mean synchrony value of 0.51); as quantified in [20]. The degree of synchrony or overlap of flowering was however determined using the method outlined in [22] which measures the extent of overlapping in the flowering periods

*E. leucoxylon* is the only species to synchronise flowering with *E. tricarpa*, as shown by all three methods, namely the B-MTD rules of synchrony, MTD models and Moran's AR method. Synchrony between *E. leucoxylon* and *E. tricarpa*, may be explained in terms of niche/competition and also facilitation may be a factor, due to their different modes of flower production. This agrees with the findings of [20]. Interestingly the MTD models discussed here (see also [6, 16, 24]) show that the climatic drivers or signature of *E. leucoxylon* and *E. tricarpa* is similar with respect to temperature, in that both exhibit decreased flowering with increased temperature. Likewise *E. leucoxylon* is the only species to synchronise flowering with *E. polyanthemos. E. leucoxylon* and *E. polyanthemos* sometimes occur in the same

and *E. polyanthemos*.

*interaction terms (right).*

**Figure 8.**

**67**

**5. Discussion and conclusion**

among pairs of individuals in a population.

Both the MTDg- and EKF-based models show that *E. tricarpa* is not asynchronous with *E. polyanthemos* (**Tables 7** and **8**). Note that for this species pairing *E. tricarpa* and *E. polyanthemos* (i.e. TriPol and PolTri) the associated sum of the probabilities for transitions P(A) and P(B) (both species off/on to one species off/ on) is equal to 0.802 (**Table 4**), which is just above the to the threshold for asynchrony of 0.80.


3.2.2. Precipitation

Monsoon and Western Disturbances were both sources of precipitation in Pakistan. Pakistan experienced widespread flooding from July through September due to the monsoon season. From October–May, precipitation spread over the whole of Pakistan due to western disturbances, with the heaviest rain falling in Northern Pakistan. However, in June, Western Disturbances sometimes affect the northern areas of Pakistan. On rare occasions, the pre-monsoon season might begin in this month as well. In the first week of

 7

the month, the country was battered by a Monsoonal downpour reminiscent of August's, when an extremely low air pressure system (29") developed over Kashmir and then swept eastward into Northern Pakistan. Devastating rains fell from 1–5 September, bringing death and destruction to many cities. The rivers Chenab, Jhelum, Ravi, Sutlej, and Indus in Pakistan all overflowed their banks during the latter two days of the period, which were particularly rainy. According to the Pakistan Meteorological Department, the region in northern Pakistan received around 200 mm of rain between 1 and 5 September 2014, as shown in Figure 3. A powerful low-pressure region formed in the Bay of Bengal in the latter days of August, after the extreme drought conditions in Sindh throughout the months of July and August. In the first two weeks of September 2012, a low-pressure region came into Sindh, bringing with it torrential rains in Upper Sindh. Jacobabad received 481 mm of rain in only 7 days, and 441 mm of rain in just 36 h, both world records. Larkana set a record with 239 mm, while Sukkur set a record with 206 mm. The worst of the flooding occurred in the Larkana district. In the last week of July 2010, Pakistan saw unprecedented severe monsoon rainfall, leading to flooding in the provinces of Balochistan and Sindh. Over 200 mm of rain occurred in a 24 h period in several locations in Khyber Pakhtunkhwa and Punjab. The previous 24 h rainfall record in Peshawar was 187 mm in April of 2009, but the recent total of 274 mm smashed that mark. The province of Khyber Pakhtunkhwa had rainfall in the cities of Risalpur, Cherat, Saidu Sharif, Mianwali, and Kohat.

**Figure 3.** Precipitation extreme events in Pakistan (**a**) Precipitation in 2010 < 200 mm; (**b**) precipitation ≥ 200 mm; (**c**) heavy precipitation in a single season; (**d**) precipitation >200 and < 400 mm; (**e**) all the regions in Pakistan in which extreme precipitation events occurred.

### 3.2.3. Vulnerability Due to Climate Change

Climate change's impact and the occurrence of extreme events on the Indus Basin's water supply are still impossible to predict. There is a lot of guesswork involved in predicting how melting glaciers, melting snow, glacial retreat, and changes in precipitation will affect specific areas. About 45% of the flow to the basin comes from glacier melt and snow in the Himalayas, making it very susceptible to climate change and glacial melt [6]. Spanning six different countries in Asia—Pakistan, Nepal, India, China, Bhutan, and Bangladesh—is the Hindu Kush Himalaya (HKH) range. There are many major rivers in this area, including the Indus, Brahmaputra, and Ganges. Nearly one billion people rely on these rivers as their primary supply of drinking water. The loss of local glaciers is consistent with global trends, providing more evidence that glacial retreat has increased over the last century. New projections suggest that the pace of glacier melt caused by a rise in global temperature will accelerate in the coming years. This will increase river flow by 40% [3]. In the long term, however, the Indus River's typical flows will decrease by over 60% [2]. In addition, rising temperatures are expected to increase evapotranspiration rates across the irrigated Indus Basin, leading to higher demands for irrigation water and fiercer competition for surface and groundwater supplies among the provinces. Between May and

September, monsoon rainfall, glacier, and snowmelt account for over 85% of the yearly flow in the Indus Basin. The intensity, location, and timing of monsoon activity are all predicted to shift, causing widespread changes to the hydrological system during the next decade or two. One result of this shift in the hydrological system is the devastating flood that hit Pakistan in 2010. The thawing of glaciers had little effect on this.


**4.3 Neutrophil apoptosis and clearance**

Apoptosis is the process of programmed cell death after pathogen phagocytosis and eliminating. Apoptosis facilitates following phagocytosis and elimination of the spent neutrophils from areas of inflammation by macrophages, thus helping resolution of inflammation and avoiding excessive tissue destruction [14, 19]. When phosphatidyl serine is exposed during the apoptotic process, which culminates in caspase activity, the cells are marked for uptake and elimination by macrophages. Fascinatingly, since caspases are thiol-dependent enzymes, ROS generated by active neutrophils can inactivate them. As a result, ascorbic acid may be expected to prevent the oxidant-sensitive caspase-dependent apoptotic process after activation of neutrophils. This idea is supported by in vitro experiments that show ascorbic acid can enhance the amount of *Escherichia coli*-mediated neutrophil apoptosis in human neutrophils. Reduced apoptosis was seen in peritoneal neutrophils obtained from ascorbic acid-deficient Gulo mice, who instead experienced necrotic cell death. These ascorbic acid-deficient neutrophils were not phagocytosed by macrophages in vitro, and remained at inflammatory sites in vivo. In addition, supplementation of ascorbic acid to severe infection animals reduced the levels of neutrophils in the lungs of these animals [14, 20].

Several researches have explained about reduced neutrophil apoptosis in subjects with sepsis compared with healthy individuals. The greater tissue destruction seen in individuals with severe infection is assumed to be associated to the delayed apoptosis, which seems to be linked to disease severity. Additionally, it was shown that immature "band" neutrophils produced during sepsis had extended life spans and were resistant to apoptosis. Apoptosis in healthy neutrophils was shown to be inhibited by plasma from patients with severe infections, indicating that pro-inflammatory cytokines were crucial for the elevating in vivo survival of neutrophils under inflammatory situations [14]. Interestingly, high-dose ascorbic acid supplementation has been exhibited to regulate cytokine concentrations in individuals with carcinoma and, although this has not yet been evaluated in subjects with sepsis, it is conceivable that ascorbic acid may regulate neutrophil function in these subjects through a different mechanism. The role of ascorbic acid administration on neutrophil apoptosis in severe infection subjects has only been described in one study. Ascorbic acid 0.45 g/day administered intravenously to abdominal surgery subjects with severe infections was shown to reduce caspase-3 protein concentrations and thus was thought to have an anti-apoptotic impact on peripheral blood neutrophils. However, caspase activity and apoptosis of the neutrophils after activation was not examined. Additionally, neutrophils in circulation could not accurately represent their activation level at inflammatory tissue areas. Clearly, further researches are required to determine how ascorbic acid affects neutrophil apoptosis and elimination from inflammatory sites [14, 15, 20].


**3. Results and discussion**

#### **3.1 Phenolic content of wine lees, grape pomace and grape stems**

In the present work, the determination of the phenolic composition and the antioxidant capacity of wine lees, grape stems, and grape pomace extracts of two grape (*Vitis vinvifera* L.) varieties (Sousão and Tinta Barroca) were performed. The phenolic content of these samples collected from Douro region (Northern Portugal) was presented in **Table 1**. As it can be observed, in general, grape stems were the WPBs with the highest content of total phenols (168.75 mg GA g−1 DW, on average), *ortho*-diphenols (166.39 mg GA g−1 DW, on average), and flavonoids (152.31 mg CAT g−1 DW, on average), followed by grape pomace and wine lees. Concerning this last winery by-product, it can be stated that the samples from Sousão variety showed the lowest content of these three studied parameters, being significantly different from the other variety and WBPs (p < 0.05). In fact, wine lees and grape stems presented the highest values of phenolic content in Tinta Barroca samples which has not been observed for the grape pomace extracts, which can be explained by the different phenolic compounds present in these WBPs.

Romero et al. obtained similar values in wine lees of total phenols (38–254 mg CAT g−1) and flavonoids (16–146 mg CAT g−1) content from the Tempranillo variety, with these ranges caused by the extraction solvent employed by these authors [16]. However, Pérez-Serradilha et al. [17] obtained higher values in this WBP of total phenols content (364 mg g−1) than those obtained in this work after a microwaveassisted extraction optimized. Our research group have analyzed the phenolic content of grape stem extracts prepared with conventional extraction methods (hydro-methanolic solvents) [3, 4, 11, 18].

The values ranged between 32 and 123 mg GA g−1 DW for total phenols, between 35 and 116 mg GA g−1 DW for *ortho*-diphenols, and from 34 to 106 mg CAT g−1 DW for flavonoids, depending on cultivar, geographical localization, crop season, among other factors [4, 18, 19]. The values of the present work were slightly higher than the values obtained in those studies, maybe due to the new efficient extraction method performed in this work (UAE). Grape pomace has been also analyzed by other authors concerning its phenolic content, obtaining values around 40 mg GA g−1 DW for total phenols and around 14 mg CAT g−1 DW for flavonoids which are significantly lower than those obtained in this work.

**9**

*Winery By-Products as Source of Bioactive Compounds for Pharmaceutical and Cosmetic…*

solvent, with particular advantageous for natural sources [8, 20].

**3.2 Antioxidant capacity of wine lees, grape pomace and grape stems**

In fact, these contents are particularly dependent of several factors, such as the agronomic conditions [3], cultivar [4] and extraction methods employed [6]. In this sense, in order to reduce production costs and optimize processes, new technologies—such as ultrasound-assisted extraction (UAE) or microwave-assisted extraction (MAE)—have been employed to decrease energy consumption and increase the product or process safety/control and quality. These techniques, already used at large scale, emerged as efficient, energy/time-saving and clean extraction methodologies, providing higher recoveries of bioactive compounds using low amounts of

The antioxidant capacity of the WBPs investigated in this work was performed by three methods (ABTS, DPPH, and FRAP), being the results presented in **Table 1**. As expected, due to the correlation of most phenolic compounds with antioxidant capacity, Tinta Barroca samples presented the highest values for all the methods concerning wine lees and grape pomace samples. In contrast, grape pomace samples from Sousão variety showed the highest antioxidant capacity for ABTS (5.54 ± 0.09 mmol Trolox g−1 DW), DPPH (1.64 ± 0.03 mmol Trolox g−1 DW), and FRAP (1.75 ± 0.01 mmol Trolox g−1 DW) methodologies, being significantly different from the Tinta Barroca

Romero et al. [16] also determined the antioxidant capacity of wine lees extracts from Tempranillo variety, obtaining values ranged between 0.46 and 2.197 mmol Trolox g−1. The values obtained in the present work are in the range of those presented by these authors, concerning FRAP method. In literature, grape stem extracts have been also analyzed concerning this biological property, which present antioxidant capacities of 0.35–0.84 mmol Trolox g−1 DW, 0.15–0.76 mmol Trolox g−1 DW, and 0.33–1.03 mmol Trolox g−1 DW for ABTS, DPPH, and FRAP methodologies [4, 18, 19], which were lower than those presented in the present study essentially due to several factors, such as different extraction methods, extraction

The phenolic profile of wine lees, grape pomace and grape stems was performed by RP-HPLC-DAD, being the results presented in **Table 2**. Fifteen compounds were identified, being grape pomace samples the ones with more phenolic compounds identified, including phenolic acids, flavanols, and anthocyanins. In this study, it was possible to observe the same behavior referred above, namely the significant highest content of the phenolic compounds identified in grape pomace extracts from Sousão variety (p < 0.05). Similar compounds were identified in wine lees, namely gallic acid, catechin, epicathechin, and malvidin-3-*O*-gluside, beside others which were found only in this by-product, namely protocatechuic acid (0.337 mg g−1 DW, on average), delphinidin-3-*O*-glucoside (0.190 mg g−1 DW, on average), and petunidin-3-*O*-glucoside (0.268 mg g−1 DW, on average). All these compounds were also determined in higher concentrations in Tinta Barroca samples which is in agreement with the previous results reported above. Grape stem extracts presented three phenolic compounds which were not identified in wine lees and grape pomace, namely isorhammetin-3-*O*-(6-*O*-feruloyl)-glucoside, caftaric acid, and ԑ-viniferin, which were also present in higher concentration in Tinta Barroca

*DOI: http://dx.doi.org/10.5772/intechopen.97881*

samples (p < 0.05).

solvents or protocols, varieties, among others.

**3.3 Phenolic profile of wine lees, grape pomace and grape stems**

samples, being significantly different from Sousão samples (p < 0.05).

Several compounds identified in this work have been also identified by other authors in these WBPs, namely gallic acid, catechin, delphinidin-3-*O*-glucoside,

*Total phenols (mg GA g−1 DW),* ortho*-diphenols (mg GA g−1 DW), and flavonoids (mg CAT g−1 DW) content and antioxidant capacity (mmol Trolox g−1 DW) of WBPs from Sousão and Tinta Barroca varieties.* *Winery By-Products as Source of Bioactive Compounds for Pharmaceutical and Cosmetic… DOI: http://dx.doi.org/10.5772/intechopen.97881*

In fact, these contents are particularly dependent of several factors, such as the agronomic conditions [3], cultivar [4] and extraction methods employed [6]. In this sense, in order to reduce production costs and optimize processes, new technologies—such as ultrasound-assisted extraction (UAE) or microwave-assisted extraction (MAE)—have been employed to decrease energy consumption and increase the product or process safety/control and quality. These techniques, already used at large scale, emerged as efficient, energy/time-saving and clean extraction methodologies, providing higher recoveries of bioactive compounds using low amounts of solvent, with particular advantageous for natural sources [8, 20].

#### **3.2 Antioxidant capacity of wine lees, grape pomace and grape stems**

The antioxidant capacity of the WBPs investigated in this work was performed by three methods (ABTS, DPPH, and FRAP), being the results presented in **Table 1**. As expected, due to the correlation of most phenolic compounds with antioxidant capacity, Tinta Barroca samples presented the highest values for all the methods concerning wine lees and grape pomace samples. In contrast, grape pomace samples from Sousão variety showed the highest antioxidant capacity for ABTS (5.54 ± 0.09 mmol Trolox g−1 DW), DPPH (1.64 ± 0.03 mmol Trolox g−1 DW), and FRAP (1.75 ± 0.01 mmol Trolox g−1 DW) methodologies, being significantly different from the Tinta Barroca samples (p < 0.05).

Romero et al. [16] also determined the antioxidant capacity of wine lees extracts from Tempranillo variety, obtaining values ranged between 0.46 and 2.197 mmol Trolox g−1. The values obtained in the present work are in the range of those presented by these authors, concerning FRAP method. In literature, grape stem extracts have been also analyzed concerning this biological property, which present antioxidant capacities of 0.35–0.84 mmol Trolox g−1 DW, 0.15–0.76 mmol Trolox g−1 DW, and 0.33–1.03 mmol Trolox g−1 DW for ABTS, DPPH, and FRAP methodologies [4, 18, 19], which were lower than those presented in the present study essentially due to several factors, such as different extraction methods, extraction solvents or protocols, varieties, among others.

#### **3.3 Phenolic profile of wine lees, grape pomace and grape stems**

The phenolic profile of wine lees, grape pomace and grape stems was performed by RP-HPLC-DAD, being the results presented in **Table 2**. Fifteen compounds were identified, being grape pomace samples the ones with more phenolic compounds identified, including phenolic acids, flavanols, and anthocyanins. In this study, it was possible to observe the same behavior referred above, namely the significant highest content of the phenolic compounds identified in grape pomace extracts from Sousão variety (p < 0.05). Similar compounds were identified in wine lees, namely gallic acid, catechin, epicathechin, and malvidin-3-*O*-gluside, beside others which were found only in this by-product, namely protocatechuic acid (0.337 mg g−1 DW, on average), delphinidin-3-*O*-glucoside (0.190 mg g−1 DW, on average), and petunidin-3-*O*-glucoside (0.268 mg g−1 DW, on average). All these compounds were also determined in higher concentrations in Tinta Barroca samples which is in agreement with the previous results reported above. Grape stem extracts presented three phenolic compounds which were not identified in wine lees and grape pomace, namely isorhammetin-3-*O*-(6-*O*-feruloyl)-glucoside, caftaric acid, and ԑ-viniferin, which were also present in higher concentration in Tinta Barroca samples, being significantly different from Sousão samples (p < 0.05).

Several compounds identified in this work have been also identified by other authors in these WBPs, namely gallic acid, catechin, delphinidin-3-*O*-glucoside,

*Innovation in the Food Sector Through the Valorization of Food and Agro-Food By-Products*

**8**

**Phenolic content**

*Ortho***-diphenols**

**Flavonoids**

**ABTS**

**Total phenols**

**Sousão**

Wine

Z15.44 ± 1.25a

125.39 ± 1.12b

118.91 ± 0.95a

136.03 ± 1.10b

18.50 ± 0.89a

128.34 ± 1.07b

1.71 ± 0.01a

3.28 ± 0.03b

1.24 ± 0.03a

1.58 ± 0.03c

1.54 ± 0.01a

1.96 ± 0.03b

lees

Grape

153.70 ± 0.53d

135.32 ± 2.76c

151.78 ± 1.89c

138.70 ± 1.42b

144.81 ± 1.75c

129.93 ± 0.93b

5.54 ± 0.09d

4.01 ± 0.03c

1.64 ± 0.03d

1.59 ± 0.01c

1.75 ± 0.01d

1.61 ± 0.02b

pomace

Grape

156.81 ± 1.29e

180.68 ± 2.77f

162.53 ± 1.01d

170.24 ± 1.88e

143.90 ± 2.13c

160.71 ± 1.44d

5.62 ± 0.02d

8.02 ± 0.02e

1.49 ± 0.07e

1.85 ± 0.02c

1.69 ± 0.01c

2.02 ± 0.01e

stems

*P*-value

Y\*\*\* *YLevel of significance: N.s.: not significant ( > 0.05);*

∗*significant at < 0.05;*

∗∗*significant at < 0.01;*

∗∗∗*significant at < 0.001.*

**Table 1.**

*Barroca varieties.*

*Total phenols (mg GA g−1 DW),* ortho*-diphenols (mg GA g−1 DW), and flavonoids (mg CAT g−1 DW) content and antioxidant capacity (mmol Trolox g−1 DW) of WBPs from Sousão and Tinta*

\*\*\*

\*\*\* *ZData presented as Mean (n = 3) ± SD values for the same parameter evaluated followed by different superscript lowercase letters are significantly different at p < 0.001, according to Tukey's test.*

\*\*

\*\*

\*\*

**Tinta**

**Sousão**

**Tinta**

**Sousão**

**Tinta**

**Sousão**

**Tinta**

**Sousão**

**Tinta**

**Sousão**

**Tinta**

**Barroca**

**Barroca**

**Barroca**

**Barroca**

**Barroca**

**Barroca**

**Antioxidant capacity**

**DPPH**

**FRAP**

**Table 2.**

**11**

**Author details**

**Acknowledgements**

Irene Gouvinhas and Ana Barros\*

Agri-Food Production, Vila Real, Portugal

provided the original work is properly cited.

\*Address all correspondence to<EMAIL_ADDRESS>
Centre for the Research and Technology of Agro-Environmental and Biological Sciences, University of Trás-os-Montes and Alto Douro (CITAB-UTAD), Inov4Agro - Institute for Innovation, Capacity Building and Sustainability of

© 2021 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,

*Winery By-Products as Source of Bioactive Compounds for Pharmaceutical and Cosmetic…*

epicatechin, *p*-coumaric acid, petunidin-3-*O*-glucoside, malvidin-3-*O*-glucoside, among others [16, 17, 21, 22]. However, it is well known that the extraction method, the cultivars, the geographical conditions, the growing season, the plant diseases, among others, affect the chemical composition of WBPs, namely the secondary metabolites

Nowadays, it is well established in the scientific community that wine has an important role in the prevention of some cardiovascular diseases, resulting from their content in bioactive phytochemicals with antioxidant capacity. Many of these compounds are derived from the solid parts of the grape cluster (stem, pomace, trimmed vine shoots, and wine lees). However, during the winemaking process, a complete extraction of these compounds to the juice/wine does not occur, and they may remain at high concentrations in certain wastes, such as in the stems. Indeed, the wine industry involves the production of large quantities of by-products, characterized by a valuable composition in phytochemicals with putative health-promoting qualities. Additionally, in light of the biological properties revealed recently, the search for natural bioactive

compounds has paid attention on these materials as promising alternatives.

conventional extraction methods employed by the research group.

industries, giving rise to sustainable agro-industrial activities.

In this work, it was possible to observe the high content of phenolic compounds and the high antioxidant capacities demonstrated by several winery by-products, namely wine lees, grape pomace, and grape stems which were subjected to an ultrasound assisted extraction, obtaining higher values than those obtained by

In this sense, the phenolics present in winery by-products may have an addedvalue to be used as an alternative to synthetic substances employed in distinct

This work is supported by National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UIDB/04033/2020 (CITAB). We also acknowledge Eng. Manuel Henrique from ROZÉS who kindly provided the samples.

*DOI: http://dx.doi.org/10.5772/intechopen.97881*

which are highly present in these matrices.

**4. Conclusions**

∗*significant at < 0.05;*

∗∗*significant at < 0.01;*

∗∗∗*significant at < 0.001. nd: not detected.*

*Content of individual phenolics (mg g−1 dw) of WBPs from Sousão and Tinta Barroca varieties. Statistical treatment notes: Data were subjected to analysis of variance (ANOVA) and multiple range test (Tukey's test) with a significance of p < 0.05.*

*Winery By-Products as Source of Bioactive Compounds for Pharmaceutical and Cosmetic… DOI: http://dx.doi.org/10.5772/intechopen.97881*

epicatechin, *p*-coumaric acid, petunidin-3-*O*-glucoside, malvidin-3-*O*-glucoside, among others [16, 17, 21, 22]. However, it is well known that the extraction method, the cultivars, the geographical conditions, the growing season, the plant diseases, among others, affect the chemical composition of WBPs, namely the secondary metabolites which are highly present in these matrices.


**6. Conclusions**

The results from simulation help to overcome the physical and architectural limitations of computational power that can be achieved with a single-processor system. It is then very convenient for the application to use a more powerful processor, a faster cache, and faster access to the RAM with higher transfer capacity. The results obtained in the simulation of structured mathematical models of aircraft motion and the use of this method appear to be effective and pragmatic according to the requirements of the integration algorithm given in the chapter.

In general, in accordance with the proposed solution, it is also possible to run the program faster due to factors such as the availability of better bandwidth for communication within node. Greater bandwidth provides additional communication with the interface to transfer message between nodes and the higher performance of the graphics processing unit.

The described design of a structured mathematical model of aircraft motion to perform simulation on the GPU, this simulation was chosen as an example of an algorithm confirming the proposed solution. The presented problem of simulation methods of block structure of mathematical models of aircraft motion was solved by predetermined known procedures. The advantages of the methods are a combination of both the features: the efficiency of computer system programming and the scalability of a GPU computational method.

Based on knowledge gained in design of the block structure of mathematical models of aircraft motion, we can say that the graphics card can be used in multiple and parallel simulation, which confirms high speed of computations. The advantage of faster simulation can sometimes be obtained at the earliest defined quality. These facts and accuracies are well suited for use in real-time simulation and a virtual reality imaging system. The results of the simulation of such designed structured mathematical model of aircraft motion show very fast calculation and confirm the effectiveness of such a structure of the mathematical model of aircraft for the proposed system of the simulator, which was the motivation of our research.


*2.7. Optimization Model Implementation*

The multi-objective optimization model was written in MATLAB® and successively implemented in mode FRONTIER® for scenario analysis. The selected parameters for the optimization process are summarized in Table 1. The range of modelled COD concentration was in line with the observed COD values in the analyzed territory, characterized by consistent wastewater dilution, due to mixed sewers and significant infiltrations from the aquifer. A wide range of treated flowrates was considered, to extend the validity of the obtained results to different scale WWTPs. The modelled flowrate range in WWTPs approximately corresponds to plant potentialities of 75,600–225,600 PE, consistent with most of medium scales WWTPs in Friuli-Venezia Giulia region (North-east of Italy). As for compressed air system characteristics, different combinations of pressure and volume were considered, to evaluate the influence of these parameters (both singularly and in combination) on the economic output.

**Table 1.** Selected input parameters considered in the multi-objective optimization model.

Separate simulations were conducted for scenario 1 (CAS integration without AD) and scenario 2 (CAS integration with AD). The relevant output parameters that were considered in the analysis were common economic indexes (PB, net present value (NPV)) and, in the case of scenario 2, meaningful environmental aspects (primary energy saving, PES). Since the main goal of this research was the implementation of a new technology in a WWTP following the DR perspective, the multi-objective optimization functions have been selected among those most representative from a sustainable perspective (Table 2). For the assessment of the economic advantages obtained through this intervention, the NPV maximization objective has been chosen. Regarding the environmental impact, the most meaningful objective was PES maximization.

**Table 2.** Selected output optimization functions considered in the multi-objective optimization model.

According to the stakeholders' interests, a constraint of 10 years has been applied to the PB output value.


**Viktor Bukovszki 1, Ábel Magyari 1, Marina Kristina Braun 1, Kitti Párdi 1 and András Reith 1,2,\***

Received: 16 March 2020; Accepted: 26 April 2020; Published: 5 May 2020

**Abstract:** Mainstreaming energy communities has been one of the main challenges in the low-carbon transition of cities. In this sense, urban building energy modelling (UBEM) has an untapped role in enabling energy communities, as simulations on urban models provide evidence-based decision support to reduce risks, engage, motivate and guide actors, assert wider policy goals and regulatory requirements. This accelerating role and the potential of UBEM is not sufficiently understood, as research into energy community focuses on its barriers and impacts, while the research of UBEM is mainly technologically oriented. This review takes a sociotechnical approach to explore whether UBEM is a technological trigger for energy communities, furthering the conceptual framework of transition management. factors influencing energy community progression in different use-cases and stages of their lifecycle are compiled to assess the affordances of distinct capabilities of prevalent UBEM tools. The study provides a guide for energy community planners to UBEM. It matches different tool capabilities to the various stages of the project lifecycle for the different use-cases, equipping them with the means to accelerate the low-carbon transition of cities from the bottom-up. Finally, the study defines a development trajectory oriented towards application in urban sustainability to a rather new UBEM field.

**Keywords:** energy community; urban building energy modelling; transition management; multi-level perspective; sustainable transition; energy modelling; urban scale energy modelling


**3. The GPS structure**

While each of these systems has unique characteristics, all have major aspects in common. Each has a space segment, control segment, and user segment. What is more, all are based on transmitting radio frequency (RF) signals in a one-way fashion from satellites to receivers on and near the Earth's surface. Using measurements obtained from these signals, a GNSS receiver can find its position, velocity, and time (PVT) solution. Moreover, all GNSS systems use the notion of time-of-arrival (TOA) ranging. This requires measuring the signal transit time and the time interval the signal takes to travel between the satellite and the receiver to calculate the receiver-to-satellite range [1]. The transmitter-to-receiver distance can then be

This chapter provides an overview of Global Positioning System (GPS) and GLONASS and their signals. First, it describes the system architecture in terms of the three main segments: control, space, and user. Then, it addresses the new civilian and military GPS signal characteristics, highlighting their significance. Following that, it briefly discusses the GPS measurement error sources. The chapter also covers essential aspects of the GLONASS system, including GLONASS signal characteristics, the GLONASS modernization program, the GLONASS Radio Frequency (RF) plan, pseudorandom (PR) ranging codes, and the intra-system interference navigation message. Finally, advantages of combining both GPS and GLONASS are

GPS provides three-dimensional positioning and navigation services for both civilian and military users [2]. The GPS receivers use the TOA ranging to generate code pseudorange to determine the user's position. They also monitor changes in signal frequency to produce a rate of change of range measurements to determine velocity [3]. The time between the transmission of the signal and its arrival at the receiver is measured. The transmitter-to-receiver distance can then be obtained by scaling the signal transit time by the speed of light. Using the concept of trilateration, a GPS receiver can determine its position using the measured travel time along with the satellites' locations that are obtained from the navigation message carried by the signal. Though three satellites can be used to determine the user's position, at least four

**Figure 1** illustrates the concept of position fixing by trilateration by using the range to three satellites. Using four satellites to find the position improves the accuracy of the solution by eliminating the receiver clock offset. The first and second user-to-satellite range measurements define two spheres on two different satellites, and the intersection of these two spheres defines a circle of possible receiver positions. A third range measurement, intersecting with the first two, narrows those receiver positions to an ambiguous pair, while the fourth measurement resolves this ambiguity and determines the clock bias. The GPS positioning equations are found in [1–6]. Military GPS signals are more robust against interference and spoofing than civilian signals [3]; hence, the position determined by military signals is more precise

obtained by multiplying the signal transit time by the speed of light.

listed to give the reader insight into the benefits of such integration.

are required owing to an additional estimation of the receiver clock offset.

than the position determined using civilian signals.

**2. Overview of GPS**

120 Multifunctional Operation and Application of GPS

As mentioned earlier, the GPS is composite of three segments [7]: the space segment, a constellation of satellites orbiting the Earth at very high altitudes; the control segment, made up of a group of ground control stations; and the user segment, a user's equipment or simply the variety of military and civilian receivers. **Figure 2** illustrates the three segments, which are discussed in greater detail in this section.

#### **3.1. The space segment**

The GPS space segment is made up of a constellation of satellites that continuously broadcasts RF signals to users. In recent years, the US Air Force has operated 32 GPS satellites, of which 24 are available 95% of the time [4]. GPS satellites travel in medium Earth orbit (MEO) at an altitude of approximately 20,200 km, and each circles the Earth twice a day, meaning that the orbital period is approximately 12 h [7]. These satellites are distributed among six equally-spaced orbital planes, each having a target inclination of 55° [6], a satellite distribution that improves the visibility of satellites to GPS users across the globe, thereby enhancing navigation accuracy. GPS satellites broadcast RF signals containing coded information and navigation data, enabling a receiver to calculate pseudoranges and Doppler measurements to estimate position, velocity, and time.

In June 2011, the US Air Force successfully expanded its GPS constellation, known as the "Expandable 24" configuration [9]. Three of the 24 slots were upgraded, and six satellites were repositioned; thus, three additional satellites were added to the constellation. With a 27-slot constellation, GPS improved satellite visibility across the globe. **Table 1** summarizes the features of the current and future generations of GPS satellites, including Block IIA (second generation, "Advanced"), Block IIR ("Replenishment"), Block IIR (M) ('Modernized"), Block IIF ("Follow-on"), and GPS III [10].

**Figure 2.** The GPS segments [8].

#### **3.2. The control segment**

Made up of a global network of ground facilities that track GPS satellites, the GPS control segment's main tasks are the control and maintenance of the system through monitoring and analyzing signal transmissions and sending commands and data updates to the GPS constellation.

Referring to [7], the current operational control segment includes a Master Control Station (MCS), an alternate master control station, 12 command and control antennas, and 16 monitoring sites. The locations of these facilities are shown in **Figure 3**.

#### **3.3. The user segment**

The user segment is represented by the wide array of types of GPS receivers. These capture and track satellite signals and process signals transmitted by GPS satellites, estimate the user-to-satellite ranges and range rates, and compute a PVT solution [12]. A GPS receiver had cost more than \$100,000 in the mid-1980s; nowadays, an on-chip receiver is available in the market for less than \$20, and it is estimated that more than 1 million receivers have been produced each year since 1997 [1]. As GPS is available at no direct charge to users, they can use receivers at any time and any place across the globe to determine their position [6].

**Legacy satellites**

Block IIA 6 Operational

•

Coarse acquisition (C/A)

• • •

On-board clock monitoring

P(Y) code on L1 and L2

• (L2C)

•

New military M code

• •

Improved accuracy, signal

strength, and quality

• • • •

Begins launching in 2016

GNSSs, Signals, and Receivers

123

http://dx.doi.org/10.5772/intechopen.74677

15-year design lifespan

Satellites 9+: laser reflectors;

search and rescue payload

No Selective Availability

Advanced atomic clocks

•

Enhanced signal reliability,

accuracy, and integrity

signals for enhanced jam

resistance

•

Flexible power levels for

• •

Launched since 2010

12-year design lifespan

military signals

• •

Launched in 2005–2009

7.5-year design lifespan

Second civil signal on L2

•

Third civil signal on L5

frequency (L5)

C/A code on L1

•

All legacy signals

•

All Block IIR (M) signals

• • (L1C)

Fourth civil signal on L1

All Block IIF signals

code on L1 frequency for

civil users

•

Precise P(Y) code on L1 and

L2 frequencies for military

• •

Launched in 1997–2004

7.5-year design lifespan

users

• • **Table 1.**

The features of the current and future generations of GPS satellites [10].

Launched in 1990–1997

7.5-year design lifespan

Block IIR 12 Operational

Block IIR (M) 7 Operational

Block IIF 6 Operational

GPS III

Now in production

**Modernized satellites**

**3.2. The control segment**

**Figure 2.** The GPS segments [

122 Multifunctional Operation and Application of GPS

8].

**3.3. The user segment**

6].

constellation. Referring to [

position [

Made up of a global network of ground facilities that track GPS satellites, the GPS control segment's main tasks are the control and maintenance of the system through monitoring and analyzing signal transmissions and sending commands and data updates to the GPS

(MCS), an alternate master control station, 12 command and control antennas, and 16 moni

The user segment is represented by the wide array of types of GPS receivers. These capture and track satellite signals and process signals transmitted by GPS satellites, estimate the user-to-satellite ranges and range rates, and compute a PVT solution [12]. A GPS receiver had cost more than \$100,000 in the mid-1980s; nowadays, an on-chip receiver is avail

able in the market for less than \$20, and it is estimated that more than 1 million receivers

users, they can use receivers at any time and any place across the globe to determine their

toring sites. The locations of these facilities are shown in **Figure 3**

have been produced each year since 1997 [

7], the current operational control segment includes a Master Control Station

.

1]. As GPS is available at no direct charge to

To prepare the GPS signal for transmission by the satellite, first, an XOR operation is applied to combine the binary navigation message with the code. If the message bit and the code chip are the same, the result is 0; if they are different, the result is 1. Second, the combined signal is merged with the carrier using binary phase shift keying (BPSK) modulation: a "0" bit leaves the carrier signal intact, whereas a "1" bit causes the signal to be multiplied by −1 and shifts

As mentioned above, the PRN code patterns are nearly orthogonal, an important property that makes the satellite identification process much easier [2]. Two codes are orthogonal when the sum of their term products shifted arbitrarily against each other is nearly zero. The cross

Another important property of PRN codes is that each PRN pattern is almost uncorrelated

and *C*(l)

(*i* + *n*) ≈ 0, *for all k* ≠ *l* (1)

(*i* + *n*) ≈ 0, *for all* |*n*| ≥ 1 (2)

, is expressed as

GNSSs, Signals, and Receivers

125

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the carrier by 180°. **Figure 4** illustrates this process.

∑1

∑1

**Figure 4.** GPS signal structure [15].

with itself:

correlation function for satellites m and n, with PRN codes *C*(*k*)

1023 *C*(*k*)

This orthogonality makes the cross satellite interference small [14].

1023 *C*(*k*)

(*i*) ⋅ *C*(*l*)

(*i*) ⋅ *C*(*k*)

**Figure 3.** The locations of the GPS Master Control Station, an alternate Master Control Station, 12 command and control antennas, and 16 monitoring sites [11].