Connected Regulators: number of connected regulators.

NFAT and CERK are master regulators that were identified in female SPTB placentas (Table 8). Nuclear factors of activated T cells (NFAT) is a family of transcription factors that activate cytokine production and also positively regulate placental FLT-1 and sFlt-1 e15a, the secretion of sFlt-1, and the inflammatory cytokine expression [89]. NFAT is thought to be involved in the pathophysiology of preeclampsia. In addition, NFAT may act as a regulator for the parturition and induction of labor [90]. NFAT was predicted as an activated master regulator and interacted with 23 upstream regulators to regulate the gene expression. Ceramide kinase (CERK) converts ceramide to ceramide-1-phosphate. CERK was predicted as an inhibited master regulator in female SPTB placentas and interacting with 13 upstream regulators to regulate the gene expression.

**Table 8.** Top master regulators altered in female SPTB placentas.


**Acknowledgements**

The authors are thankful to National Oceanic and Atmospheric Administration (NOAA) for providing respective high-resolution SST data in the Public domain. Authors are immensely grateful to Dr. Baban Ingole for his support and encouragement for the study. We also thankful to Sambhaji Mote for his help during field observation in Malvan. The authors are thankful to the GEER Foundation, CSIR-NIO, and SAC-ISRO for supporting the field observations. The authors acknowledge the authority of Marine National Park, Jamnagar, for permitting to conduct field observations at different sites.


**3.3 Deep learning for classification of OCT images**

OCT is also often used to detect BBC at the dermis-epidermal junction due to its ability to sub-epidermally visualize the skin structure and any underlying lesions [86]. Li et al. [87] used an image-based approach to identify the skin surface and normalized the skin image by surface flattening, then used pre-trained AlexNet, VGG-16, VGG-19 and GoogLeNet for deep feature extraction, and finally used SVM for BCC Classification. The experimental results show that the system based on the VGG-16 image descriptor is the best with a sensitivity of 0.935.

In the current research, image-based deep learning is mainly used in medical image noise reduction and reconstruction processing. Disease diagnosis mainly focuses on tumor detection, brain nervous system disease classification, and cardiovascular disease detection. There are few related studies in the field of skin [88–93]. For example, Kermany D et al. applied deep learning to a dataset of optical coherence tomography images to form a diagnostic tool for screening patients with common treatable blind retinal diseases. The trial results showed that the diagnostic tool was as accurate as hospital specialists in classifying age-related macular degeneration and diabetic macular edema [94]. Abdolmanafi et al. also used intra-coronary tomography images provided by intravascular optical coherence tomography (IV-OCT) as a deep learning library, and extracted the features of convolutional neural network features and fully convolutional network. The accuracy of the diagnostic models can be as high as 90% [95].


*<IP_ADDRESS> Determination of Production process secondary KPI indicators Consideration no. 3*

The previous section deals with initial KPI indicator generation and determination of primary KPI indicators for strategic management level. In that section, we shall discuss the KPI indicator decomposition for tactic level, which is based on the following consideration. The outgoing linguistic sets and KPI indicators for KPI decomposition related to tactic level are quantified via formulas (26, 27, 28, 29 and 30). With respect to the above-mentioned issues the following clause might be postulated:

*At the strategic management level, the Production main process might be quantified via {[PROD (i, j1)]} linguistic set and two KPI indicators could be postulated, which indicate that process functionality (performance) (see also formulas (28) and (29) and (30).*

However, the similar sequence of steps (algorithm) might be applied when quantifying and generating KPI indicators for further main processes, sales and distribution, HR, technological and financial management at the strategic management level.

The KPI indicators postulated for strategic management level are represented by formulas (25, 26, (28, 29) and (30) create basis for their further decomposition related to tactic management level.

Let us select the [Mark\_res\_assets\_mat \_ires(0)] subset from {[Tbex (i, j)]} and assign it to market required output products quantified via {[MROP (i, j)]}, while formulas (28 and 29) might be postulated:

$$[\text{Mark\\_res\\_assets\\_mat\\_ires}(\mathbf{0})] \subseteq \{ [\text{Tbx} \ (\mathbf{i}, \mathbf{j})] \} \tag{49}$$

$$[\text{Mark\\_res\\_assets\\_mat\\_ires}(\mathbf{0})] = \{[\text{MROP}\ (\mathbf{i}, \mathbf{j})]\},\tag{50}$$

The {[MROP (i, j)]} contains subsets applied for quantification market required output products classes, e.g. utility glass article classes – bowls, bottles, vases, etc.

$$\{ [\text{MROP} \ (\mathbf{i}, \mathbf{j})] \} = \{ [\text{MROP} \ (\mathbf{i}, \mathbf{1})], [\text{MROP} \ (\mathbf{i}, \mathbf{2})] \dots \{ [\text{MROP} \ (\mathbf{i}, \mathbf{m}\_1)] \} \tag{51}$$

Where.

Index m1 is a number of article classes.

Furthermore, let us create a selected linguistic set {[MROPsel (i, j)]}, a content of which is created by selected classes of [MROPsel (i, j)], [MROP (i, 1)], [MROP (i, 2)], [MROP (i,3)], as for instance (see also formula (53)).

$$\{ [\text{MROP}\_{\text{sel}}(\mathbf{i}, \mathbf{j})] \}, = \{ [\text{MROP}\,(\mathbf{i}, \mathbf{1})], [\text{MROP}\,(\mathbf{i}, \mathbf{2})], [\text{MROP}\,(\mathbf{i}, \mathbf{3})] \} \tag{52}$$

*Business Process Linguistic Modeling: Theory and Practice Part I: BPLM Strategy Creator DOI: http://dx.doi.org/10.5772/intechopen.95096*

and let us postulate the {CUST (i, j2)]} linguistic set, the content of which create data concerned with the customers.

$$\{\left[\text{CUST}\left(\mathbf{i},\mathbf{j}\_2\right)\right]\} = \{\left[\text{CUST}\left(\mathbf{i},\mathbf{1}\right)\right], \left[\text{CUST}\left(\mathbf{i},\mathbf{2}\right)\right], \dots, \left[\text{CUST}\left(\mathbf{i},\mathbf{m}\_{2\parallel}\right)\right] \}\tag{53}$$

where Index m2 means a number of customers.

In the next step, an appropriate {[MROPsel (i, j)]} set for each customer will be assigned, while formula (19) might be postulated.

$$\forall \{ \left[ \text{CUST } (\mathbf{i}, \mathbf{j}\_2) \right] \} \,\exists \, \{ \left[ \text{MROP}\_{\text{sel}} (\mathbf{i}, \mathbf{j}) \right] \} \Rightarrow \{ \left[ \text{CUST } (\mathbf{i}, \mathbf{j}\_2) \right] \} \,\Leftrightarrow \{ \left[ \text{MROP}\_{\text{sel}} (\mathbf{i}, \mathbf{j}) \right] \} \quad \{ \mathbf{54} \}$$

In the next step we shall assign to each {[MROPsel (i, j)]} set a {[MROPselfinass (i, j3)]} and {[MROPselcosts (i, j4)]}, where {[MROPselfinass (i, j)]} set quantifies the financial assets related to selected class of any market required output products.

{[MROPselfincosts (i, j)]} set quantifies the material costs related to selected class of any market required output products.

$$\begin{array}{l} \{ \left[ \text{CUST} \left( \mathbf{i}, \mathbf{j}\_2 \right) \right] \} \otimes \{ \left[ \text{MROP}\_{\text{sel}} \left( \mathbf{i}, \mathbf{j} \mathfrak{Z} \right) \right] \} \otimes \{ \left[ \text{MROP}\_{\text{sel} \text{fmaxs}} \left( \mathbf{i}, \mathbf{j} \mathfrak{A} \right) \right] \} \otimes \{ \left[ \text{MROP}\_{\text{sel} \text{fmaxs}} \left( \mathbf{i}, \mathbf{j} \mathfrak{Z} \right) \right] \} \\\ = \{ \left[ \text{Thexc} \left( \mathbf{i}, \mathbf{j} \right] \right] \} \otimes \{ \left[ \text{Return} \left( \mathbf{i}, \mathbf{j} \right] \right] \} \end{array}$$

(55)

(58)

$$\{\left[\text{TDecx}\left(\mathbf{i},\mathbf{j}\right)\right]\} = \{\left[\text{CUST }\left(\mathbf{i},\mathbf{j}\_2\right)\right], \{\left[\text{MROP}\_{\text{sel}}\left(\mathbf{i},\mathbf{j}\mathbf{2}\right)\right]\}\tag{56}$$

$$\{\left[\text{CONTRACTB}\left(\mathbf{i}\right)\right]\} = \Pi\left\{\left[\text{CUST}\left(\mathbf{i}, \mathbf{j}\_2\right)\right], \left\{\left[\text{MROP}\_{\text{sel}}\left(\mathbf{i}, \mathbf{j}\right)\right]\right\}\tag{57}$$

j=1 …m3

appropriate decomposition of primary KPI indicators related to performance of those processes to should be done. However, the KPI indicator decomposition for tactic level will be explained based on Consideration 3 and the KPI indicator decomposition for operational level will be explained based on Consideration 4 as well, while the Consideration no. 3 results the **secondary KPI indicators** and the Consideration no. 4 results the **tertiary KPI indicators** and both considerations are

*<IP_ADDRESS> Determination of Production process secondary KPI indicators*

The previous section deals with initial KPI indicator generation and determination of primary KPI indicators for strategic management level. In that section, we shall discuss the KPI indicator decomposition for tactic level, which is based on the following consideration. The outgoing linguistic sets and KPI indicators for KPI decomposition related to tactic level are quantified via formulas (26, 27, 28, 29 and 30). With respect to the above-mentioned issues the following clause might be

*At the strategic management level, the Production main process might be quantified via {[PROD (i, j1)]} linguistic set and two KPI indicators could be postulated, which indicate that process functionality (performance) (see also formulas (28) and (29)*

However, the similar sequence of steps (algorithm) might be applied when quantifying and generating KPI indicators for further main processes, sales and distribution, HR, technological and financial management at the strategic manage-

formulas (25, 26, (28, 29) and (30) create basis for their further decomposition

The KPI indicators postulated for strategic management level are represented by

Let us select the [Mark\_res\_assets\_mat \_ires(0)] subset from {[Tbex (i, j)]} and assign it to market required output products quantified via {[MROP (i, j)]}, while

The {[MROP (i, j)]} contains subsets applied for quantification market required output products classes, e.g. utility glass article classes – bowls, bottles, vases, etc.

f g ½ � MROP ið Þ *;* j ¼ f½ � MROP ið Þ *;* 1 , MROP i ½ � ð Þ *;* 2 … *:*f g ½ � MROP ið Þ *;* m1 (51)

Furthermore, let us create a selected linguistic set {[MROPsel (i, j)]}, a content of which is created by selected classes of [MROPsel (i, j)], [MROP (i, 1)], [MROP

MROPsel f g ½ � ð Þ i*;* j , ¼ f g ½ � MROP ið Þ *;* 1 *;* ½ � MROP ið Þ *;* 2 *;* ½ � MROP ið Þ *;* 3 (52)

½ � Mark\_res\_assets\_mat\_ires 0ð Þ ⊆f g ½ � Tbex ið Þ *;* j (49) ½Mark\_res\_assets\_mat\_ires 0ð Þ� ¼ f g ½ � MROP ið Þ *;* j , (50)

described within subsequent sections.

*Operations Management - Emerging Trend in the Digital Era*

*5.2.1 KPI decomposition related to tactic level*

**5.2 KPI indicator decomposition**

*Consideration no. 3*

related to tactic management level.

formulas (28 and 29) might be postulated:

Index m1 is a number of article classes.

(i, 2)], [MROP (i,3)], as for instance (see also formula (53)).

postulated:

*and (30).*

ment level.

Where.

**174**

j2 = 1 …m2

The {[CONTRACTB (i)]} linguistic set quantifies the basic contract, which indicates relations among customers and selected market required output products, incl. Financial costs and financial assets.

$$\{\{\text{Rotor}\ (\mathbf{i}, \mathbf{j})\}\} = \{\{\text{CUST (i, j\_2)}\}, [\text{MROP}\_{\text{sel}}\ (\mathbf{i}, \mathbf{j})], [\text{MROP}\_{\text{selfinstas}}\ (\mathbf{i}, \mathbf{j})], [\text{MROP}\_{\text{selfinstas}}\ (\mathbf{i}, \mathbf{j})]\}$$

$$\{\{\text{CNOT}\text{RACTC}(\mathbf{i})\}\} == \Pi\left\{\{\text{CUST (i, j\_2)}\}, [\text{MROP}\_{\text{sel}}\ (\mathbf{i}, \mathbf{j}\_3)], [\text{MROP}\_{\text{selfinstas}}\ (\mathbf{i}, \mathbf{j}\_4)]\right\},$$

$$\{\text{MROP}\_{\text{selfinstos}}\ (\mathbf{i}, \mathbf{j}\mathbf{5})\}$$

j=1 …m3 j3 = 1 …m3 j4 = 1 …m4 j5 = 1 …m5 j2 = 1 …m2

Before, we make the final step we have to assign an appropriate group of business processes to each group of selected market required output products, while formula (70) might be postulated.

$$\forall \{ \text{[MROP}\_{\text{sel}} \,(\text{i}, \text{j})] \} \,\exists \left\{ \begin{bmatrix} \text{BP (I}, \text{j}\_6) \end{bmatrix} \right\} \Rightarrow \{ \text{[MROP}\_{\text{sel}} \,(\text{i}, \text{j})] \,, \Leftrightarrow \{ \text{[} \text{[BP (I}, \text{j}\_6)] \} \} \qquad \text{(59)}$$

$$\{\left[\text{MROP}\_{\text{sel}}\left(\mathbf{i},\mathbf{j}\right)\right], \left[\text{BP}\left(\mathbf{I},\mathbf{j}\_{\text{6}}\right)\right]\} = \{\left[\text{Tberdp}\left(\mathbf{i},\mathbf{j}\right)\right]\} \otimes \{\left[\text{Retxbp}\left(\mathbf{i},\mathbf{j}\right)\right]\}\tag{60}$$

{[Tbexbp (i, j)]} = {[[BP (I, j6)]} – list of BP groups needed for production of market required output products quantified via {[MROPsel (i, j)],

$$\{ [\text{Rectbp}\ (\text{i}, \text{j})] \} = \{ [\text{MROP}\_{\text{sel}}\ (\text{i}, \text{j})], \{ [[\text{BP}\ (\text{I}, \text{j}\_6)] \ ] \} \tag{61}$$

Formula (24) indicates a list of relations among BP groups and market required output product group.

$$\{\left[\text{CONTRACTD}\left(\text{i}\right)\right]\}=\Pi\left\{\left[\text{CUST}\left(\text{i},\text{j}\_2\right)\right],\left\{\left[\text{MROP}\_{\text{sel}}\left(\text{i},\text{j}\_3\right)\right],\left[\text{MROP}\_{\text{sel}\text{fmass}}\left(\text{i},\text{j}\_4\right)\right]\right\},$$

$$\left[\text{BP}\left(\text{I},\text{j}\_6\right)\right]\}$$

j=1 …m3, j2 = 1 …..m2, j3 = 1 ….m3, j4 = 1 ….m4, j5 = 1 ….m5, j6 = 1 ….m6, Finally, adequate KPI indicators will be defined.

$$\text{KPI}\left(\mathbf{i}, \mathbf{1}\right) = \{ \left[ \text{CONTRACTB}\left(\mathbf{i}\right) \right] \}\tag{63}$$

(62)

Where the linguistic [[MROP1selfincosts (i, j)] subset quantifies financial costs and

3 (69)

(70)

(71)

(73)

(74)

(75)

f½ MROP1selfincosts ½ � ð Þ i*;* j , MROPselfinass ½ � ð Þ i*;* j

9

the [MROP2selmatcosts (i, j)] subset quantifies material costs and create basis for preparation that subset which contains material data needed for production of the

*Business Process Linguistic Modeling: Theory and Practice Part I: BPLM Strategy Creator*

3 ⊆ MROPsel\_bp i*;* j

3

ality and performance might be derived, while the modified PBPL Equation is

8 ⊗ Retx i*;* j

When applying the PBPL Equation, KPI indicator for the selected BP function-

f MROP2selmatcosts ½ � ð Þ i*;* j , MROP1selfincosts ½ � ð Þ i*;* j , MROPselfinass ½ � ð Þ i*;* j , MROPselmatass ½ ð Þ i*;* j �g

¼ MROPselmatass ½ � ð Þ i*;* j *;* MROP2selmatcosts ð ½ � ð Þ i*;* j g (72)

g¼f MROP2selmatcosts ½ � ð Þ i*;* j , MROP1selfincosts ½ � ð Þ i*;* j , MROPselfinass ½ � ð Þ i*;* j ,

When dealing with BP External metrics KPI Indicators, so called basic and

MROPselfinass ½ � ð Þ i*;* j MROPselmatass ½ ð Þ i*;* j �g

The similar algorithm might be applied, when deriving BP Internal metrics KPI

¼ MROPselmatass ½ � ð Þ i*;* j *;* MROP2selmatcosts ð ½ � ð Þ i*;* j g

¼ f MROP2selmatcosts ½ � ð Þ i*;* j , MROP1selfincosts ½ � ð Þ i*;* j ,

KPIem ¼ KPIemb ið Þ *;* 3 ⊗ KPIemb ið Þ *;* 3 (76)

MROP1sel\_bp i*;* j

above-mentioned products

*Step 4*

*Step 5*

f Retx i*;* j

*Step 6*

*Step 7*

Indicators.

**177**

⊗ MROPsel\_bp i*;* j

MROP2selmatcosts ½ � ð Þ i*;* j MROPsel\_bp i*;* j

*DOI: http://dx.doi.org/10.5772/intechopen.95096*

*<IP_ADDRESS> Applying of PBPL equation solutions*

postulated with respect to formula (61).

3 ¼ Tbex i*;* j

*<IP_ADDRESS> PBPL equation solution results*

8

*<IP_ADDRESS> BP external metrics KPI indicators*

external KPI indicators will be defined.

*<IP_ADDRESS> BP Internal metrics KPI Indicators*

KPIemb ið Þ¼ *;* 3 Tbex i*;* j

KPIemext ið Þ¼ *;* 3 Retx i*;* j

MROPselmatass ½ ð Þ i*;* j �g

8

8

Tbex i*;* j

8

$$\text{KPI} \left( \mathbf{i}, \mathbf{2} \right) = \{ \left[ \mathbf{CONTRACTC} \left( \mathbf{i} \right) \right] \}\tag{64}$$

$$\text{KPI}\left(\mathbf{i}, \mathbf{3}\right) = \{ \left[ \text{CONTRACTD}\left(\mathbf{i}\right) \right] \}\tag{65}$$

KPI (i, 3) indicator creates basis for decomposition related to operational level.


**5. Conclusions**

A Mediterranean-like hypocaloric diet helped to decrease the body weight, and associated hyperglycemia/hyperlipidemia and blood pressure in class-I obese patients intervened with PENS. However, the addition of *Lactobacillus plantarum LP115*, *Bifidobacterium brevis B3*, and *Lactobacillus acidophilus LA14*) promoted a positive influence on anti-obesogenic gut bacteria by increasing muconutritive (*Akkermansia muciniphila*) and immunomodulatory (*Bifidobacterium spp*) microbiota, and Bacteroidetes phylum (*Prevotella spp*). Consequently, the Firmicutes/Bacteroidetes ratio was further reduced, and these changes could be mediating, at least in part, the further improvement of the body weight and plasma A1C, triglycerides, and HDL-cholesterol. Therefore, a combined strategy of a hypocaloric diet, PENS and probiotics administration may promote summative e ffects against obesity and related comorbidities (i.e., cardiovascular diseases). Nevertheless, larger studies are needed to analyze the direct actions and interactions of these strategies on gut microbiota.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/1660-4601/17/19/7239/s1, Figure S1. Gut bacteria in control subjects. Fecal samples from one hundred age- and sex-matched non-obese, normoglycemic, and normolipidemic voluntaries without known cardiovascular, malignant and digestive diseases were analyzed to estimate the control ranges for each bacteria. NGC/g: number of gene copies per gram of feces.

**Author Contributions:** M.C.-Y. and T.H. processed and analyzed the fecal samples. M.G.-A. acquired and interpreted the microbiota data. A.M.H. and C.L. provided the probiotics and diet recommendations to patients. J.L.-C. performed the statistics analysis. O.L. and J.R.-T. designed the study and wrote the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. L.-C.J. is a predoctoral fellow of Universidad Autónoma Madrid (FPI contract).

**Acknowledgments:** Authors want to thank Bromatech for the provision of probiotics Adomelle ® and for dealing with the costs of microbiota analysis.

**Conflicts of Interest:** The authors declare no conflict of interest.


**2. Methods**

134 Multivariate Analysis in Management, Engineering and the Sciences

applications, e.g. humification in the course of composting.

their preservation to conditions that prevented or delayed deterioration.

time under different environmental conditions.

incineration ash and slag leads to carbonation [7, 8]. With respect to organic matter humic substances are built up resulting in a stable organic fraction with low turnover rates. These natural processes that come along with material ageing were adopted for technical

On the one hand natural processes serve as a model for anthropogenic activities with regard to the closed loop of material recycling, especially in the field of organic substances [9]. On the other hand every endeavour is made to prevent or retard the natural ageing, deterioration and degradation process of materials and to maintain a constant quality of products by adequate measures. There are several options to achieve this objective: modification of biogenic materials, treatment of the surface and application of chemical substances against microbial deterioration and ageing by abiotic factors. Although abiotic factors play a relevant role for ageing and deterioration of organic materials, biological processes dominate. Inorganic materials are primarily affected by chemical and physical attacks, but some specialised microbial communities are capable of promoting the ageing process of inorganic components. The environmental milieu plays a crucial role as it determines both biological and chemical reactions. Historical and archaeological finds owe

This study reports on natural ageing and degradation processes of organic matter and ageing of inorganic materials over weeks, years, decades and centuries. The questions to be answered focus on two main aspects: the environmental impact by ageing and deterioration of organic and inorganic matter and the proof of resistance of organic materials against biological degradation which is a main concern in material sciences to maintain the quality of products [10-12]. Ageing and deterioration can be described by many parameters. They focus on chemical and physical changes of the material by which the process is paralleled. In some cases, especially for product control, a single parameter might be sufficient to verify the ageing of materials [13]. For an overall characterisation of the state of deterioration those analytical methods are advantageous that provide a "fingerprint" of the material. FT-IR spectroscopy and STA were applied to reveal material characteristics and their changes over

FT-IR spectroscopy is based on the interaction of infrared radiation with matter. Infrared radiation provides the energy for molecule vibrations that become visible as absorption bands. The plot of wavenumbers (energy) within a defined range vs. band intensities results in the spectrum. Infrared spectra describe materials by the unique pattern and provide information on material chemistry. Band intensities depend on the concentration of the compound and the molar decadic absorption coefficient that is reflected in the spectrum and on individual properties of the functional group. Most molecules are infrared active and represented by diverse bands due to different types of molecule vibrations. They are characterised by a typical energy level and are therefore found at defined wavenumbers. The molecule skeleton and other functional groups influence the band position and can cause a band shift. Whereas pure substances show distinct bands that can be attributed to functional groups, complex materials feature broad and overlapping bands that are often not assignable. However, the material shows a "fingerprint". The information of underlying

#### **2.1. FT-IR spectroscopy and simultaneous thermal analysis**

FT-IR spectra of landfill samples were recorded by a Bruker Alpha® (Bruker, Germany) instrument in the mid infrared area (4000 cm-1 to 400 cm-1) in the attenuated total reflection mode (ATR). For the milled lignocellulosic materials the ATR-FT-IR spectra were collected by a Bruker Vertex® with a Pike MIRacle™ ATR device in the wavenumber range from 4000 cm-1 to 600 cm-1, at 4 cm-1 resolution averaging 32 scans. The milled sample was homogenised and directly applied on the ATR reflection module with a diamond crystal providing a measuring area of approximately 4 mm² and a pressure applicator. Twenty four scans per spectrum were collected at a resolution of 4 cm-1 and corrected against ambient air as background. The average of four spectra (maximum deviation of the four spectra from the average spectrum < 5%) was vector normalised prior to multivariate data analysis. Spectra treatment and data evaluation were carried out using the OPUS software.

Thermal analyses were carried out with a STA 409 CD Skimmer instrument (Netzsch GmbH) in an Al2O3 pan with the following combustion parameters: temperature range 30 – 950 °C, heating rate 10 °C·min-1, gas flow 150 ml·min-1 (80% He and 20% O2), sample amount 16.00 mg. The pyrolysis of wood powder was carried out at different temperatures (200, 300, 350, and 600 °C) under oxygen-free conditions (100% He) with a heating rate of 10 °C·min-1 and isothermal treatment for 20 min. After oxidative combustion of the pyrolysed wood

powder the enthalpy was calculated by integration of the area below the heat flow curve and a horizontal baseline from 30 to 650 °C, starting at 30 °C.

Ageing and Deterioration of Materials in the Environment – Application of Multivariate Data Analysis 137

samples outside the limits do not belong to the model. They are located in the quadrant "neither - nor". The 5% significance level means that 95% of the samples in the corresponding quadrants truly belong to the defined classes. New samples in these

Partial Least Squares-Discriminant Analysis (PLS-DA) is based on PLS regression to model the differences between classes. For the separation of two classes the PLS algorithm is used

Methods of the "calibration" group allow models to be developed for parameter prediction if the parameter is adequately reflected by the collected data, in this study by the spectral and thermal patterns. Contrary to classification models by which class assignment according to defined properties is performed, the prediction model provides distinct values of the parameter in question. Prediction models focus on the determination of dependent Yvariables for new samples that were characterised by independent X-variables. Based on an established validated X-Y model the Y-variable can be derived from X-variables. Due to this relationship only X-measurements are necessary. This procedure can be advantageous if expensive and time-consuming methods for the determination of Y-variables are replaced

Degradation of organic matter is a natural necessary process in the environment. On the one hand degradability is an inherent property of materials that depends on both chemistry and structure and on the other hand it is mainly influenced by environmental conditions that determine the velocity of this process. The balance between synthesis, transformation and degradation is a criterion of sustainability. Organic matter in mixed waste consists of native biomolecules, modified biomolecules or organic substances that are exclusively based on chemical syntheses. Modification means chemical and physical diversification. Modification of biomolecules, e.g. wood or cellulose is necessary to enhance the stability against microbial attacks. For biological degradation chemical or physical modification represents a barrier to some degree. Wood modification for instance such as acetylation and thermal treatment can increase the lifetime of a product, but pose a problem for degradation [22-24]. Microbial degradation of synthetics hardly takes place. Ageing by oxygen and UV exposure in the forefront can contribute to a certain bioavailability of molecule moieties. Due to the heterogeneity of materials and conditions in old landfills and dumps the turnover and the emissions are hardly predictable. This fact and the long aftercare phase led to the European "multi-barrier" concept that provides the pre-treatment of municipal solid waste prior to landfilling. This procedure ensures extensive degradation within a limited period of time under controlled aerobic conditions. Nevertheless, landfill sites from the past are still a current topic. Investigations of the solid waste focus on the assessment of the current stage, and the potential of future emissions can consequently be derived. Ageing of the landfill also comprises the attenuation of hazardous substances in the leachate that are released due

quadrants are therefore identified as members of the classes.

**3. Application of multivariate data analysis**

**3.1. Deterioration of organic matter in dumps and landfills**

by superior methods.

with the dummy variable (e.g. -1/+1) to distinguish the two defined groups.

For data evaluation the heat flow profiles and the temperature resolved curve of the CO2 ion current extracted from the mass spectrum were used. For data evaluation the integrated software PROTEUS was used.

The sample sets that were subjected to multivariate data analysis are mentioned in the text. Depending on the material and the question to be answered different multivariate evaluation methods were applied using The Unscrambler® 9.2 and 10.0 respectively (Camo®).