2.1.1. Chemical Composition and Material Processing

Table 1 shows the chemical composition of the analyzed materials using spark emission spectrometry (SPECTROMAXx LMF08, SPECTRO, Kleve, Germany).


**Acknowledgements**

We are thankful to Professor S. Manguin (Montpellier, France) for inviting us to contribute this article for this compilation, and the Directorate of National Vector Borne Disease Control Programme, Directorate of Health Services, Government of India for grant of permission to data access.


**Shaowen Yuan 1, Yuntong Wang 2,3, Chaopu Zhang 1,2, Hanzi He 1,\* and Sibin Yu 1,2,\***

Received: 14 January 2020; Accepted: 14 February 2020; Published: 17 February 2020

**Abstract:** Timing of germination determines whether a new plant life cycle can be initiated; therefore, appropriate dormancy and rapid germination under diverse environmental conditions are the most important features for a seed. However, the genetic architecture of seed dormancy and germination behavior remains largely elusive. In the present study, a linkage analysis for seed dormancy and germination behavior was conducted using a set of 146 chromosome segment substitution lines (CSSLs), of which each carries a single or a few chromosomal segments of Nipponbare (NIP) in the background of Zhenshan 97 (ZS97). A total of 36 quantitative trait loci (QTLs) for six germination parameters were identified. Among them, *qDOM3.1* was validated as a major QTL for seed dormancy in a segregation population derived from the *qDOM3.1* near-isogenic line, and further delimited into a genomic region of 90 kb on chromosome 3. Based on genetic analysis and gene expression profiles, the candidate genes were restricted to eight genes, of which four were responsive to the addition of abscisic acid (ABA). Among them, LOC\_Os03g01540 was involved in the ABA signaling pathway to regulate seed dormancy. The results will facilitate cloning the major QTLs and understanding the genetic architecture for seed dormancy and germination in rice and other crops.

**Keywords:** seed dormancy; quantitative trait locus; ABA; seed germination; chromosome segment substitution lines; linkage mapping


**4.3 Wettability study**

46 Textile Dyeing

Polar functional groups are incorporated onto the polymer film during the plasma treatment. These polar groups are readily detected by ESCA (XPS), but are often missed by ATR-FTIR spectroscopy (Wu S, 1982). XPS is the best technique to study such modified surfaces. Several studies showed that air, N2, O2, NH3, etc. plasma incorporates hydrophilic functional groups onto the polymer surfaces thereby increasing wettability and surface energy (Bhat et al., 2011; Navaneetha et al., 2010; P´chal and Klenko, 2009). It has been reported that the treatment carried out in inert gases like Ar introduces oxygen moieties onto the polymer surface because of post plasma exposure of samples to atmosphere (Deshmukh and Bhat, 2003(b); Gupta et al., 2000). Therefore, IR spectra of N2 and O2 plasma treated nylon samples is not given here. The IR spectra of plasma polymerized acrylic acid

**4000 3500 3000 2500 2000 1500 1000 500**

**Wavenumber (cm-1)**

The FTIR spectrum of a PPAA film prepared using the technique of plasma polymerization was very similar to the spectrum of Poly (acrylic acid) prepared by conventional polymerization techniques and it shows all the characteristic bands. In particular, the FTIR spectrum shows that the film contains a high density of C(O)OH groups. The absorption peaks assignment is given in table below (Alaa et al., 2011; Cho et al., 1990; Chilcoti and Ratner, 1993; Eun-Young Choi and Seung-Hyeon Moon, 2007; Jafari et al., 2006; Mirzadeh et

Fig. 3. FTIR spectra of PPAA (A) 2min. deposition, (B) 4min. deposition.

**(B)**

**(A)**

**4.2 FTIR study**

(PPAA) is given in Fig. 3 below.

**% T**

al., 2002).

The contact angle of untreated nylon fabric was observed to be 830 (±2). Its surface energy comes to be 33.6 mJ/m2. Fig. 4 shows the photograph of water droplet taken on untreated and 4 minute oxygen plasma treated nylon fabric.

However, we could not measure contact angle for any plasma processed samples. It shows that the surface energy of all the samples increases rapidly after the plasma processing. Therefore we have measured wetting time of the samples as given in Table 2.

Pretreatments of Textiles Prior to Dyeing: Plasma Processing 49

Fig. 6. K/S values of nylon fabric subjected to RF plasma treatment.

Control Nylon

oxygen plasma is also more as shown in Fig. 2 (e and f).

The increase in the dyeing in the initial stage could be due to the fact that gaseous plasma etches out the surface of the nylon fibers, creating a rougher surface with irregularities as discussed earlier. The effective surface area increases after the plasma treatment. Thus the interaction and diffusion of the dye molecules is facilitated. The etching of the surface has been confirmed by other researchers (Karahan et al., 2009; Ozdogan et al., 2009). The chemical changes in the nylon fiber surface can lead to the possibility of the formation of free radicals on the nylon chains and the subsequent formation of hydroxyl, carbonyl and carboxyl groups. It is also important to mention that the dyeing process mainly occurs through the amorphous regions. The etching away of the amorphous regions during the plasma treatment (particularly for longer treatment time) can lead to lowering of the dye uptake. Therefore the optimum treatment time needs to be found. Yoon et al. and Thomas et al., have found that longer plasma treatment decreases dye uptake (Thomas et al., 1998; Yoon et al., 1996). It may be noted that the oxygen is more reactive than nitrogen and hence the dye uptake is slightly more for oxygen treated samples. The etching caused due to

N2 4 min Plasma treated

O2 4 min Plasma treated

PPAA 4 min deposited

Plasma polymerization of acrylic acid (PPAA) onto the nylon fabric incorporates good amount of hydroxyl, carbonyl and carboxylic acid groups onto the surface as evident from FTIR studies. These functional groups are responsible for wettability and hence dye uptake. The dyebath exhaustion is slightly more in case of PPAA deposited nylon fabric as compared to that of gaseous plasma treated samples, because of the functional groups which are incorporated during the deposition of PPAA. Deposition causes reduction in the capillary action therefore; we have observed slightly more wetting time for PPAA samples as shown in Table 2. However, when dyeing is carried out for 15 minutes, which is sufficient time for the penetration of dye into the fabric, a major role of functional groups is observed

Table 2. Wetting time of untreated and plasma processed nylon samples.

It may be noted from Table 2 that the wetting time of the untreated nylon fabrics was 112 seconds, which dramatically decreases after N2, O2 and acrylic acid plasma treatment. The decrease is more significant in case of O2 plasma treated samples as etching action and oxidation reaction is predominant. On the other hand, the decrease in the wetting time of PPAA treated sample is rather slow. It may be also be noted from the SEM micrographs that the formation of etch-pits and voids is very predominant for O2 plasma treated nylon fabrics, whereas PPAA treated samples reveal coating on the surface due to formation of PPAA. Due to such thin film formation on the surface the water drop may not easily reach the fabric and as a result the capillary action is reduced.

#### **4.4 Dyeing studies**

Plasma processed samples were dyed using acid dye-blue. The dye absorption was determined spectroscopically by measuring the absorption band maximum of the dye-bath solution before starting and after exhaustion. In addition, the amount of dye uptake was also determined from the color matching instrument. The color values are expressed in terms of K/S as the ratio of reflectivity to absorptivity. For each kind of plasma processing, the dye uptake of plasma-treated sample was compared with the untreated sample of the same variety.

Fig. 5. Relation between the dye bath exhaustion and plasma processing time.

0 112 112 112 0.5 18 12 20 1 15 9 16 2 8 6 14 4 4 3 19

It may be noted from Table 2 that the wetting time of the untreated nylon fabrics was 112 seconds, which dramatically decreases after N2, O2 and acrylic acid plasma treatment. The decrease is more significant in case of O2 plasma treated samples as etching action and oxidation reaction is predominant. On the other hand, the decrease in the wetting time of PPAA treated sample is rather slow. It may be also be noted from the SEM micrographs that the formation of etch-pits and voids is very predominant for O2 plasma treated nylon fabrics, whereas PPAA treated samples reveal coating on the surface due to formation of PPAA. Due to such thin film formation on the surface the water drop may not easily reach

Plasma processed samples were dyed using acid dye-blue. The dye absorption was determined spectroscopically by measuring the absorption band maximum of the dye-bath solution before starting and after exhaustion. In addition, the amount of dye uptake was also determined from the color matching instrument. The color values are expressed in terms of K/S as the ratio of reflectivity to absorptivity. For each kind of plasma processing, the dye uptake of plasma-treated sample was compared with the untreated sample of the

Fig. 5. Relation between the dye bath exhaustion and plasma processing time.

Table 2. Wetting time of untreated and plasma processed nylon samples.

the fabric and as a result the capillary action is reduced.

Time to absorb water droplet on the Plasma Processed Nylon Fabrics (in Sec.) N2 Plasma O2 Plasma PPAA

Treatment Time (min.)

**4.4 Dyeing studies**

same variety.

Fig. 6. K/S values of nylon fabric subjected to RF plasma treatment.

The increase in the dyeing in the initial stage could be due to the fact that gaseous plasma etches out the surface of the nylon fibers, creating a rougher surface with irregularities as discussed earlier. The effective surface area increases after the plasma treatment. Thus the interaction and diffusion of the dye molecules is facilitated. The etching of the surface has been confirmed by other researchers (Karahan et al., 2009; Ozdogan et al., 2009). The chemical changes in the nylon fiber surface can lead to the possibility of the formation of free radicals on the nylon chains and the subsequent formation of hydroxyl, carbonyl and carboxyl groups. It is also important to mention that the dyeing process mainly occurs through the amorphous regions. The etching away of the amorphous regions during the plasma treatment (particularly for longer treatment time) can lead to lowering of the dye uptake. Therefore the optimum treatment time needs to be found. Yoon et al. and Thomas et al., have found that longer plasma treatment decreases dye uptake (Thomas et al., 1998; Yoon et al., 1996). It may be noted that the oxygen is more reactive than nitrogen and hence the dye uptake is slightly more for oxygen treated samples. The etching caused due to oxygen plasma is also more as shown in Fig. 2 (e and f).

Plasma polymerization of acrylic acid (PPAA) onto the nylon fabric incorporates good amount of hydroxyl, carbonyl and carboxylic acid groups onto the surface as evident from FTIR studies. These functional groups are responsible for wettability and hence dye uptake. The dyebath exhaustion is slightly more in case of PPAA deposited nylon fabric as compared to that of gaseous plasma treated samples, because of the functional groups which are incorporated during the deposition of PPAA. Deposition causes reduction in the capillary action therefore; we have observed slightly more wetting time for PPAA samples as shown in Table 2. However, when dyeing is carried out for 15 minutes, which is sufficient time for the penetration of dye into the fabric, a major role of functional groups is observed

Pretreatments of Textiles Prior to Dyeing: Plasma Processing 51

dye used was Red-M and natural dye was Thar. When dyeing was carried out and measurements were done for the dyed samples, it was noticed that, in the case of direct dye, the colour strength after plasma treatment were found to have decreased by 2.1 %. However, in the case of reactive dye, there was an increase in colour strength by 3.2%. The natural dye Thar showed a marginal decrease by 1%. It was surprising that, when the plasma treatment, enhances the wettability of cotton fabrics to a great extent, the increase in the dye uptake was not similar. This makes us believe that, the interaction of dye with the polymer (fibre) is important for improved dye uptake. The tremendous work carried out for wool fibres has revealed that while the plasma treatment, invariably leads to increase in the dye uptake, it is not so for other types of fibres (polyester, cotton, nylon etc.). There are several conflicting results of increase at times and decrease for other cases. Our analysis shows that, in addition to the type of gaseous plasma, the structure and morphology of the fibre is important. If a proper group of dye is used, the interaction of the dye with the fibre becomes compatible leading to increase in the dye uptake. Our conflicting results in case of

In case of direct dye, the dyeing phenomena is supposed to be due to diffusion of the dye molecule into the fibre, whereas reactive dye reacts with the OH group of the cellulose chain. The natural dye Thar has a very long chain with many CH groups and possibly cannot diffuse into the fibre. The very short (few seconds) plasma treatment of cotton fabrics leads to removal of wax and other impurities from the surface of the fibre. Further treatment with plasma (up to 5 min) leads to removal of the amorphous portions of the material, thereby decreasing the possibility of interaction with the OH groups of the cellulose chain. There also occurs cross linking on the surface, which would hinder the diffusion of the dye molecules. The plasma interaction etches the surface and the effective area is enhanced and also oxygen moieties are incorporated and will contribute to synergistic effect leading to enhanced dye uptake. However, there is a competitive process of cross-linking and as a

Similarly in another experiment, we thought interesting to see the effect of DC plasma on polyester fabric. Oxygen was used as precursor gas. Plasma treatment of polyester fabrics was carried for different durations of time. The morphological changes in the fiber structure were assessed by SEM which revealed the formation of rough surface together with voids and cracks. The loss of weight increased with the treatment time while the tenacity was affected to a small extent. Moisture regain and wettability was found to be improved with increasing treatment time. The fabrics developed yellowness after the plasma treatment that increased with time. Such plasma treated fabrics were dyed with disperse dye in HTHP machine. The dye uptake was found to have increased initially up to 10 minutes and slightly decreases at higher time of treatment. The increase in the dye uptake was due to combined effect of the production of polar group on the surface, etching of the surface creating higher effective area and the creation of voids and cracks, which can facilitate entry of dye molecules into the interior of the fibre. The maximum increase was found to be 2% . Such minor rise in the dye uptake is probably due to other competitive process of cross linking reaction on the surface which do not allow penetration of the dye into the interior. Therefore when the time of treatment was increased beyond 10 min there appears to be slight reduction in the dye uptake. This

cotton fabric just described above may be explained as follows:

result the net dyeing may not always show increase.

**4.5.3 DC plasma effect on polyester fabric**

behavior is shown in Fig. 8 below.

on the dyeing behavior rather than wetting time. The deposition is evident from SEM micrographs as shown in Fig. 2 (g and h).

The increase in the dye uptake due to plasma treatment is also evident from the measurement of K/S values shown in the Fig. 6. The bar-graph shows K/S values for control and plasma treated samples for four minutes in different gases. The highest gain is seen to be for PPAA deposited nylon fabrics followed by oxygen treated sample. This trend is similar to that observed for the data of dye bath exhaustion.

#### **4.5 Few results of case studies**

#### **4.5.1 Dyeing behavior of nylon fabric treated in APGD**

Similarly, nylon fabric was treated in atmospheric pressure glow discharge, for various durations of time, it was found that there is increase in the dye uptake with time as shown in Fig. 7. The dye used in these studies was acid dye-red. It may be seen that when the dyeing time was 15 minutes, the differences in the K/S values of the control and the atmospheric pressure plasma (He +air) treated is quite noticeable. The increase in dye is significant. However, as the dyeing time is increased to 60 minutes the equilibrium dye uptake is reached for control as well as plasma treated samples and the difference reduces. This proves that the plasma pretreatment can reduce the time of dyeing and as a result power can be saved.

Fig. 7. K/S values of Nylon fabrics dyed with reactive dye after subjecting the fabrics to atmospheric pressure plasma for different duration of time.

#### **4.5.2 Dyeing behavior of cotton fabric treated in air plasma**

In another recent study (just results are summarized), cotton fabrics (desized, scoured and bleached) were treated with air plasma (RF), for 4 minutes. In order to understand the effect of such treatment on dyeing behavior, it was decided to use three different dyes namely, direct dye, reactive dye and natural dye. The direct dye used was Congo red, the reactive

on the dyeing behavior rather than wetting time. The deposition is evident from SEM

The increase in the dye uptake due to plasma treatment is also evident from the measurement of K/S values shown in the Fig. 6. The bar-graph shows K/S values for control and plasma treated samples for four minutes in different gases. The highest gain is seen to be for PPAA deposited nylon fabrics followed by oxygen treated sample. This trend

Similarly, nylon fabric was treated in atmospheric pressure glow discharge, for various durations of time, it was found that there is increase in the dye uptake with time as shown in Fig. 7. The dye used in these studies was acid dye-red. It may be seen that when the dyeing time was 15 minutes, the differences in the K/S values of the control and the atmospheric pressure plasma (He +air) treated is quite noticeable. The increase in dye is significant. However, as the dyeing time is increased to 60 minutes the equilibrium dye uptake is reached for control as well as plasma treated samples and the difference reduces. This proves that the plasma pretreatment can reduce the time of dyeing and as a result

Fig. 7. K/S values of Nylon fabrics dyed with reactive dye after subjecting the fabrics to

In another recent study (just results are summarized), cotton fabrics (desized, scoured and bleached) were treated with air plasma (RF), for 4 minutes. In order to understand the effect of such treatment on dyeing behavior, it was decided to use three different dyes namely, direct dye, reactive dye and natural dye. The direct dye used was Congo red, the reactive

atmospheric pressure plasma for different duration of time.

**4.5.2 Dyeing behavior of cotton fabric treated in air plasma**

micrographs as shown in Fig. 2 (g and h).

**4.5 Few results of case studies**

power can be saved.

is similar to that observed for the data of dye bath exhaustion.

**4.5.1 Dyeing behavior of nylon fabric treated in APGD**

dye used was Red-M and natural dye was Thar. When dyeing was carried out and measurements were done for the dyed samples, it was noticed that, in the case of direct dye, the colour strength after plasma treatment were found to have decreased by 2.1 %. However, in the case of reactive dye, there was an increase in colour strength by 3.2%. The natural dye Thar showed a marginal decrease by 1%. It was surprising that, when the plasma treatment, enhances the wettability of cotton fabrics to a great extent, the increase in the dye uptake was not similar. This makes us believe that, the interaction of dye with the polymer (fibre) is important for improved dye uptake. The tremendous work carried out for wool fibres has revealed that while the plasma treatment, invariably leads to increase in the dye uptake, it is not so for other types of fibres (polyester, cotton, nylon etc.). There are several conflicting results of increase at times and decrease for other cases. Our analysis shows that, in addition to the type of gaseous plasma, the structure and morphology of the fibre is important. If a proper group of dye is used, the interaction of the dye with the fibre becomes compatible leading to increase in the dye uptake. Our conflicting results in case of cotton fabric just described above may be explained as follows:

In case of direct dye, the dyeing phenomena is supposed to be due to diffusion of the dye molecule into the fibre, whereas reactive dye reacts with the OH group of the cellulose chain. The natural dye Thar has a very long chain with many CH groups and possibly cannot diffuse into the fibre. The very short (few seconds) plasma treatment of cotton fabrics leads to removal of wax and other impurities from the surface of the fibre. Further treatment with plasma (up to 5 min) leads to removal of the amorphous portions of the material, thereby decreasing the possibility of interaction with the OH groups of the cellulose chain. There also occurs cross linking on the surface, which would hinder the diffusion of the dye molecules. The plasma interaction etches the surface and the effective area is enhanced and also oxygen moieties are incorporated and will contribute to synergistic effect leading to enhanced dye uptake. However, there is a competitive process of cross-linking and as a result the net dyeing may not always show increase.

#### **4.5.3 DC plasma effect on polyester fabric**

Similarly in another experiment, we thought interesting to see the effect of DC plasma on polyester fabric. Oxygen was used as precursor gas. Plasma treatment of polyester fabrics was carried for different durations of time. The morphological changes in the fiber structure were assessed by SEM which revealed the formation of rough surface together with voids and cracks. The loss of weight increased with the treatment time while the tenacity was affected to a small extent. Moisture regain and wettability was found to be improved with increasing treatment time. The fabrics developed yellowness after the plasma treatment that increased with time. Such plasma treated fabrics were dyed with disperse dye in HTHP machine. The dye uptake was found to have increased initially up to 10 minutes and slightly decreases at higher time of treatment. The increase in the dye uptake was due to combined effect of the production of polar group on the surface, etching of the surface creating higher effective area and the creation of voids and cracks, which can facilitate entry of dye molecules into the interior of the fibre. The maximum increase was found to be 2% . Such minor rise in the dye uptake is probably due to other competitive process of cross linking reaction on the surface which do not allow penetration of the dye into the interior. Therefore when the time of treatment was increased beyond 10 min there appears to be slight reduction in the dye uptake. This behavior is shown in Fig. 8 below.

Pretreatments of Textiles Prior to Dyeing: Plasma Processing 53

Even if the increase is small, if the process can lead to sufficient dye uptake at a reduced temperature or time, it would serve a good purpose. Advantages of the process in terms of eco-friendliness, water saving and energy saving be explored and emphasized so that the

Further work needs to be carried out by using pulsed plasma as it may avoid the degradation of fabrics when subjected to longer durations of treatments. In-situ cooling of the samples is also desirable to maintain the temperature of substrate constant. It is also necessary to find the electron and ion densities or rather the number of ions hitting the

We thank Miss. G. A. Arolkar and Mr. Kiran Kale for helping us to treat samples in low

Abidi N. and Hequet E., Cotton Graft Copolymerization Using Microwave Plasma. I.

Abidi N. and Hequet E., Cotton Graft Copolymerization Using Microwave Plasma. II.

Abidi N, in Surface modification of textiles, Q. Wei (Ed), Woodhead publishing in Textiles,

Alaa Fahmy, Renate Mix, Andreas Scho¨nhals, Jo¨rg F. Friedrich., Structure of Plasma-Deposited Poly(acrylic acid) Films, Plasma Process. Polym., 8, 147–159, (2011). Araujo R., Casal M., and Cavaco-Paulo A., Applications of Enzymes for textile fibre

Bhat N.V. and Nadiger G.S., Effect of nitrogen plasma on the morphology and allied textile properties of tassar silk fibres and fabrics. Textile Res J; 48: 685–691, (1978) Bhat N.V. and Benjamin Y.N., Surface Resistivity Behaviour of Plasma Treated and Plasma Grafted Cotton and Polyester Fabrics, Textile Res J., 69(1), 38-42, (1999). Bhat N.V. and Deshmukh R.R., X-ray crystallographic studies of polymeric materials

Bhat N. V., Upadhyay D. J., Deshmukh R. R., and Gupta S. K., Investigation of Plasma-

Bhat N.V., Netravali A.N., Gore A.V., Sathianarayanan M.P., Arolkar G.A. and Deshmukh

Bhattacharyya D., Xu H., Deshmukh R.R., Timmons R.B. and Kytai T.N., Surface chemistry and

Cao G., Nanostructures and Nanomaterials-Synthesis, Properties and Applications, Imperial

Induced Photochemical Reaction on a Polypropylene Surface, J. Phys. Chem. B, 107*,*

R.R., Surface modification of cotton fabrics using plasma technology, Textile Res J.,

polymer film thickness effects on endothelial cell adhesion and proliferation, Journal of Biomedical Materials Research Part A, Volume 94A, Issue 2, pages 640–648, (2010). Bozzi A., Yuranova T. and Kiwi J., Self Cleaning of Wool, Polyamide and Polyester Textile

by TiO2 Rutile Modification Under Daylight Irradiation at Ambient Temperature, J.

Indian Journal of Pure & Applied Physics, 40 (5) 361-366, (2002).

Universal ATR-FTIR Study, J. Appl Polym Sci., 93, 145-154, (2004).

Physical Properties, J. Appl Polym Sci., 98, 896-902, (2005).

processing. Biocat.Biotrans. 26(5), 332-349, (2008).

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technology is acceptable to all.

**7. Acknowledgement**

UK (2009).

4550-4559, (2003).

81, 1014-1026, (2011).

College Press, London. (2004).

**8. References**

sample to calculate the energy deposited during processing.

pressure and in atmospheric pressure plasma respectively.

Fig. 8. Dye uptake of DC plasma treated polyester fabric


**1. Introduction**

#### *1.1. The Motivation for this Research*

Crises loom large in finance and macroeconomics. Defining transitions between bull and bear markets, or between recessions and expansions, helps identify distinctive financial or economic regimes. Commodity markets, especially those related to petroleum, undergo their own fluctuations. Indeed, abrupt and abnormal movements within these notoriously turbulent markets often signal trouble in other sectors of the broader economy. Oil price volatility, in particular, experiences structural shifts. The intense financialization of commodities, including crude oil and refined fuels, heightens the importance of identifying shifts and disruptions in volatility across time.

This article proposes a novel method for identifying critical moments in commodity markets, ranging from structural shifts to abrupt disruptions. It places special emphasis on

**Citation:** Chen, J.M.; Rehman, M.U. A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities. *Energies* **2021**, *14*, 6099. https://doi.org/10.3390/en14196099

Academic Editor: Periklis Gogas

Received: 31 July 2021 Accepted: 15 September 2021 Published: 24 September 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

markets for crude oil and refined fuels. Unsupervised machine learning can distinguish crises from normal conditions. It can identify anomalies within an economic time series and set those trading days apart for closer examination, as opposed to finding time time-varying effects through conventional analysis.

Recent work by the authors has demonstrated the use of clustering and manifold learning to arrange commodities into discrete markets for fuels, precious metals, base metals, and agricultural commodities by climate [1]. In an extension of that work, this article focuses more closely on the *temporal* domain of these markets. A suite of clustering can identify critical periods affecting all commodity markets, such as the 2008–2009 global financial crisis and the COVID-19 pandemic. These critical periods also affect markets specific to oil and refined fuels. Even closer examination reveals additional periods of special interest to energy-related markets. Most of those periods are shorter, acute supply disruptions through extreme weather or acts of war.

As between the clustering of commodities and trading days, temporal clustering poses the greater technical challenge and offers the greater practical reward. Discrete commodity markets number in the dozens. A comprehensive span of financial history can cover thousands of trading days. The configuration of commodities in metaphysical financial space need not observe a particular order. By contrast, cogent, temporally defined market regimes must represent contiguous or nearly contiguous blocs of trading days.

Certain branches of finance and macroeconomics seek to define cyclical peaks and troughs. Many conventional definitions of bull and bear markets or recessions and expansions within the broader economy, however, rely upon arbitrary benchmarks or even subjective judgment. If stock prices fall more than 20 percent from a recent peak, for instance, many analysts are prepared to declare the onset of a bear market. A 10 percent decline, by contrast, is labeled a "correction."

Relative to these arbitrary, categorical distinctions, a mathematically informed treatment of conditional volatility forecasts may identify contiguous or nearly contiguous clusters of trading days. Although this article does not immediately pursue the possibility, the methods that it applies may ultimately enable new ways to identify distinctive regimes in financial markets or the broader economy. Though bull-and-bear market indicators and peak-and-trough definitions of the business cycle will undoubtedly persist, data-driven alternatives or complements may arise from unsupervised machine learning and related forms of artificial intelligence.

Unsupervised machine learning also obviates disputes over the definition of local maxima and minima across potentially expansive spans of financial history. These methods serve as an extended metaphor for one of the greatest challenges in machine learning and artificial intelligence: determining whether a model has been globally optimized, or whether an optimization algorithm has converged locally.

By the same token, reliance on unsupervised machine learning presents challenges unique to this set of methods. Unlike conventional regression-based methods or their equivalents within predictive applications of supervised machine learning, unsupervised methods such as clustering and manifold learning are not typically used to validate research hypotheses. They struggle to perform either of the traditional tasks in economics. Other methods outperform unsupervised machine learning in forecasting values and in enabling causal inference. What unsupervised machine learning does excel in doing, however, is revealing patterns within data itself, without reliance on labels, values, or research hypotheses formulated by human analysts.

Mindful of the potential of unsupervised machine learning, as well as its limits, this article targets questions that routinely arise in traditional research on commodities, broader financial markets, and the real economy. This article answers those questions in the narrower, more specific context of energy-related commodities. There is intense interest in comovement and connectedness among commodities trading, financial markets, and macroeconomic phenomena. These relationships are known to vary across time. At its most

intriguing, time-varying conditional volatility supports hypotheses regarding cyclicality and structural shifts in many branches of economics.

This article asks whether raw data consisting of nothing more than logarithmic returns or conditional volatility forecasts can distinguish among ordinary trading days, acute crises that bend the arc of energy commodities trading sharply but only temporarily, and more enduring turning points that can credibly be described as turning points or structural shifts. If unsupervised learning succeeds in this task on a limited slice of the economic universe, then this article may support new approaches that can complement traditional peak-to-trough methods of defining cyclicality in financial markets and the broader business cycle.

#### *1.2. A Section-by-Section Summary*

Section 2 of this article reviews the literature on comovement and volatility spillovers in commodity markets, particularly those involving energy. Section 2 also reviews the literature on rules-based definitions of bull and bear markets and economic recessions. This extended review of the relevant economic literature provides complete background on volatility in crude oil and refined fuel markets. Section 2 ultimately explains why connections between commodities trading, financial markets, and the broader economy motivate efforts to describe cyclicality and other manifestations of variability in the volatility of energy-related markets over time.

Section 3 presents data sources and describes the unsupervised machine-learning methods underlying this article. Conditional volatility forecasts based on a GJR-GARCH(1, 1, 1) process for 22 commodity markets from 2000 through 2020 constitute the primary data source. The subarray containing volatility forecasts for four oil and fuel markets provides the central focus. Logarithmic returns, for all commodities and the energy-specific subset, constitute an additional source of data.

Section 4 aggregates results from five clustering methods—affinity propagation, meanshift, spectral, *k*-means, and hierarchical agglomerative clustering—as applied to a comprehensive market basket of 22 commodities and to a more focused basket of four energyrelated commodities: Brent, West Texas intermediate, gasoil, and gasoline. *t*-distributed stochastic neighbor embedding, or *t*-SNE, helps visualize all clustering results.

Meaningful temporal clusters for broader commodity markets delineate the global financial crisis and the COVID-19 pandemic. Focused clustering in energy-related markets identifies several additional critical periods for crude oil and refined fuel markets. Section 5 presents and distinguishes those two sets of results.

Section 6 discusses the implications of this article's findings for managers, investors, and policymakers. Critical periods in energy-related markets demand a different approach to hedging and risk management, not merely for commodity investors, but also for investors using commodities to neutralize other sources of risk. The role of energy-related crises in macroeconomic policymaking also warrants careful consideration.

The identification of temporal regimes in commodity markets through clustering suggests the generalizability of unsupervised machine learning to other markets and to macroeconomic data. The second half of Section 6 describes these and other possible paths for future research.

#### **2. Literature Review**

The economic literature germane to this article spans four distinct subjects:

This section addresses each body of literature in turn. A review of the relevant literature on unsupervised machine learning is deferred until Section 3s presentation of materials and methods.

#### *2.1. Price Volatility in Crude Oil and Refined Fuels* 2.1.1.OilPriceVolatility

Commodity markets figure prominently in developmental economics and international trade. Representing a quarter of global trade in goods, commodities provide the most important source of income for some of the world's poorest countries [2,3]. Because advanced economies rely so heavily on petroleum-based fuels for transportation and many industrial processes, the wealth of developed nations also hinges on oil-based commodities [4].

The pervasive financialization of commodities raises the premium on proper understanding of the price and volatility dynamics in these markets [5]. This is particularly true of crude oil and fuels refined from it [6–9]. Producers and industrial customers have the greatest stake, since oil price volatility directly affects investments in oil inventories, production and transportation facilities, and physical capital based on oil consumption [10]. These sunk investments demonstrate why "costly reversibility" is a prime mover in the economics of market structure and industrial organization [11–14].

Because of their intrinsic volatility and their dependence on global supply chains, energy markets are especially sensitive to external shocks. The diverse factors affecting oil prices include sociopolitical disturbances, shifts in the global supply and demand, and technological and regulatory changes promoting demand for renewable energy [15]. Discrete events, "such as wars, the release of OPEC production quota decisions, oil stock fluctuations and extreme weather," also affect oil prices [16] (p. 256).

Chronic or acute, these factors are never stable. Structural breaks punctuate the timevarying conditional heteroskedasticity of oil price volatility [17]. Although conventional tools for forecasting oil prices and volatility abound [18,19], models that ignore structural breaks and other sources of temporal variability in volatility "will have very low power" [17] (p. 555). This is yet another instance in which accurate forecasting relies upon the more realistic assumption that volatility does not remain constant [20].

#### 2.1.2. Refined Fuels: Gasoline and Gasoil (Diesel)

Because gasoline and gasoil are refined petroleum products, their price and volatility dynamics depend heavily upon the economics of oil. These markets are nevertheless subject to forces befitting their proximity to retail consumers. Gasoline and gasoil are affected by time-varying consumer income [21] and the price elasticity of demand for petroleum-based fuels among other retail-level energy sources [22]. Demand for gasoline may be less elastic than typically assumed, especially in the short run [23].

Perhaps the most distinctive trait of the price behavior of refined fuels, particularly gasoline, is its asymmetry [24–27]. The "rockets and feathers" hypothesis posits that increases in crude oil prices are transmitted much more quickly to gasoline than decreases [28–30]. Data across the United States showed that retail gasoline prices increased 0.52 percent within the first week of an anticipated 1 percent increase in oil prices, but fell 0.24 percent within the first week of a 1 percent decrease [31].

Other sources describe asymmetry in gasoline pricing according to Edgeworth price cycles, characterized by sawtooth-shaped time series consisting of many price decreases punctuated by occasional upward jumps [32,33]. Straightforward measurements of gasoline demand have shown that elasticity decreases as volatility rises [34,35]. Both the "rockets and feathers" hypothesis and Edgeworth price cycles are consistent with this account of volatility.

Other sources contest the presence of asymmetry in the relationship between oil and refined fuel markets [36]. Asymmetry, if present for gasoline and gasoil in Europe, is fleeting and appears over very short time horizons [37]. Asymmetry appears in Spain and Italy, but not in Greece, the United Kingdom, or the United States [38]. Time-varying effects such as volatility clustering and structural breaks affect the degree of asymmetry in the transmission of volatility from oil to gasoline [39]. Findings of asymmetry may depend on the frequency at which volatility data is sampled [40].

One study reaches an intriguing conclusion: The "rockets and feathers" hypothesis tells the dominant story of oil–gasoline asymmetry, but not the exclusive story [28]. When oil prices are falling, on average, gasoline prices follow a contrary "boulders and balloons" dynamic by which gasoline more swiftly tracks oil price declines than increases. The reversal in the polarity of oil–gasoline asymmetry strongly suggests that volatility transmission between crude oil and refined fuels varies over time. Indeed, the presence of opposite tendencies, based on the timing of the broader business cycle, suggests that asymmetry, persistence, and cyclicality in volatility must be understood in the context of other capital markets and the macroeconomy [41,42].

Though literature on the price dynamics of gasoil is relatively sparse and inconclusive, national fuel mix policies appear to account for some of this fuel's differences relative to gasoline [43]. The European Union [44] and the United Kingdom [45] both nudge their transportation sectors to favor gasoil over gasoline. With mixed success, the United States has maintained a heating oil reserve to stabilize prices for this variant of gasoil, widely used to heat homes in the northeastern region of that country [46]. Home heating can be expected to be one of the least elastic sources of demand for gasoil, at least over short time horizons, for households that depend on this fuel.

#### *2.2. Comovement and Volatility Spillovers within Commodity Markets*

#### 2.2.1. The Financialization of Commodities and Hedging Strategies

As a prime outgrowth of the coordination of commodity markets with other aspects of global finance [5], comovement and volatility spillovers among commodities warrant careful evaluation [47]. Commodity futures have become popular tools for diversification [48,49]. Tools for managing financial risk in other capital markets apply directly to energy-related commodity markets [50]. Commodities as safe havens can offset turbulence from other asset classes, from equities to currencies [51]. The "universe of financial assets," spanning diverse "investment strategies," heightens the importance of "risk transfer between oil" and markets for other "global, large and liquid" assets [52] (p. 56).

Unstable energy prices often induce investors to hold other assets alongside energy commodities. Hedging strategies and portfolio rebalancing enable investors to manage comovement [53]. At a minimum, oil price shocks affect non-energy commodities [54–57]. A study of volatility in oil and refined fuel should therefore consider comovement and volatility spillovers linking energy with other commodity classes, especially metals and agricultural products.

#### 2.2.2. Precious Metals

The traditional role of precious metals as hedges against inflation and economic turbulence [58] casts those commodities in sharp relief against crude oil and refined fuels [59–61]. Markets for oil are more volatile than markets for gold and silver [62]. Precious metals exhibit hedging and safe haven properties *vis-à-vis* energy [49,59,63,64]. Connections between gold and oil extend to other financial instruments [60,65].

Financial risk may not run equally between two markets. Among instances of volatility spillover in commodity markets [66–68], the propensity of oil to transmit volatility to precious metals poses the greatest challenge to investors in energy-related commodities [69–72]. As the global financial crisis of 2008–2009 demonstrated, precious metal returns may be more sensitive to disaggregated structural oil shocks [72].

#### 2.2.3. Base Metals

Because oil prices heavily affect input costs for industrial processes using base metals, connections between energy markets and metals extend beyond gold, silver, platinum,

and palladium [73,74]. Although one study identified platinum, gold, and silver as net transmitters of volatility to oil [60], such spillover may not persist across all periods and market states. Indeed, traditional distinctions between precious and base metals may not hold across all financial conditions. Tin, gold, nickel, lead, and aluminum transmit return and volatility to oil markets. Copper, zinc, and platinum are net receivers—but only "at certain specific moments" [75] (p. 12). Time-varying fluctuations became especially pronounced during the global financial crisis [60] and the COVID-19 pandemic [75].

#### 2.2.4. Agricultural Commodities

Energy markets also transmit volatility to agricultural commodities [3,71,76–79]. The dependence of agricultural commodity markets on energy prices varies over time [80]. A structural break appears to have shifted the relationship between oil and agricultural commodities after 2006 [81]. Sources differ in attributing the disruption to a change in biofuels policy [76] or to a broader crisis in food crops [78].

The relationship may vary more subtly over time [80]. During periods such as the financial crisis of 2008–2009, oil and agricultural commodity markets crash simultaneously. Connectedness likewise strengthened during the COVID-19 pandemic [82]. Under normal economic conditions, however, these markets move in opposite directions. This pattern implies that hedging will fail in the very conditions when hedges would prove most valuable. The counterbalancing effect also denies investors the opportunity to realize excess profits in both markets.

These conclusions are neither universal nor inevitable. A different study focusing on common crisis periods such as the global financial crisis and the pandemic rejects two key conclusions of other studies [83]. Oil and crops have a bidirectional relationship in which each class of commodities transmits volatility to the other with roughly equal probability over long time horizons. As a surprising consequence, oil and agricultural prices remained relatively stable throughout the pandemic.

Certain crops (particularly corn and soybeans) either compete directly against crude oil as a renewable substitute or serve as a complementary product [84]. A third crop, sugarcane, affects these markets because of its substitutability for corn [85]. Conventional wisdom holds that high oil prices invite competition from corn-based ethanol and soybeanbased biodiesel [86].

This relationship, like many others, appears to depend on the state of the market: Spillovers from oil to agricultural and biofuel markets are stronger when oil prices are higher [87]. Conversely, concerns over the diversion of common-pool resources used in agriculture from food to fuel production reach their peak during economic crises [88].

Closer scrutiny of the impact of biofuel policies on oil and gasoline price variability [89] has not found conclusive evidence that energy markets spur volatility in corn [90] or that policy-stimulated demand for biofuels has elevated prices or volatility in agricultural markets [91]. The answer to the conundrum may lie in the limited economic impact of biofuel policies. If such policies were abolished around the world, biofuel demand would implode without materially affecting overall demand for agricultural commodities [92].

#### 2.2.5. The Geopolitics of Energy-Related and Agricultural Commodities

The prominence of oil and export crops in many developing economies heightens the economic, political, and diplomatic sensitivity of volatility spillovers involving those markets [3]. Connectedness between these commodities bridge distant geographic markets, such as Chinese crops and crude oil, whether around the world [93] or specifically in the United States [94]. As a rule, however, research on the impact of oil price volatility on developing countries that import rather than export petroleum remains limited [95].

A global study spanning 157 countries at different stages of development attributed 40 percent of income volatility to oil price fluctuation [96]. Though "the adverse effects of [price] instability" are often "much more severe" in developing countries, those governments can rarely afford "the extensive support programs that typify the agricultural

sectors of the developed world" [97] (p. 1729). Dependence on natural resource extraction is so often associated with stunted economic development that this paradox is known as the "resource curse" [98–101].

Geopolitical tension from oil divides importing and exporting countries [102,103]. Importing countries must rely on insecure foreign sources of an economic lifeblood [104], while global trade and politics drive fiscal policy and economic cycles in exporting countries [105]. The rapid emergence of China portends the revival of a Great Game among global superpowers in central Asia and other oil-rich regions [106].

Again, however, the economic effects are asymmetrical. Economic reactions to energy price shocks in exporting countries are greater and more persistent than in importing countries [107]. In the long run, both oil-importing and -exporting countries stand to lose. At least among OECD countries, oil price volatility stunts economic growth in net importers, while oil price uncertainty hurts net exporters [108]. Furthermore, to the extent that oil price volatility suppresses international trade and globalization, the ensuing reduction of global welfare harms all countries [109].

#### *2.3. Broader Financial and Macroeconomic Effects of Oil and Fuel Price Volatility* 2.3.1.FinancialMarketsbeyondCommodities

Oil markets transmit volatility to other capital markets, including equity markets [110,111]. Although one study concludes that the American stock market is neither a net transmitter nor a net receiver of volatility relative to oil or precious metals [60], others have found spillover effects in smaller economies such as Iran [112] and South Korea [113].

Stock returns and stock market volatility in oil-exporting countries such as Qatar, Saudi Arabia, and Venezuela are assuredly affected by oil prices [114]. These effects follow a regime-switching framework based on the cyclical state of these countries' equity markets— specifically, whether stocks in oil-producing countries are in a bull or bear market [114]. Some sources advise investors in oil-exporting countries to increase their allocation to oil [115,116].

The relationship between oil price volatility and the equity market may depend on the cyclicality of both markets. The "relationship between oil prices and US equities could depend on both the nature of oil price shocks and how well the US stock market is performing" [117] (p. 6). Complete understanding of the mutual dependence of oil prices and broader capital markets requires not only some understanding of cyclicality in commodity and equity markets, but also a principled way of identifying critical periods within financial history.

To like effect, structural heterogeneities in foreign exchange markets coincide with geopolitical and economic impacts [118]. In conjunction with broader macroeconomic phenomena, oil markets exert dynamic influence on trade in currencies [118]. Portfolio management and other forms of risk management therefore hinge on the relationship between oil prices, exchange rates, and the business cycle.

#### 2.3.2. Macroeconomic Effects

Oil price volatility impairs economic growth [119]. Like many other phenomena connected to oil and fuel markets, the macroeconomic effects of disruptions in energyrelated markets are asymmetrical. Oil price increases stunt economic growth more deeply than corresponding decreases in price spur economic activity [120,121]. Even sharp price drops may reduce aggregate output in oil-importing countries by raising uncertainty or inducing inefficient reallocation of resources [122].

Macroeconomic uncertainty spurred by oil price volatility varies over time. Volatility typically peaks during financial crises and recessions [123]. Nonlinear measures capture the overall economic effects of oil price shocks [124]. Oil price volatility in the wake of economic, geopolitical, and natural disturbances often combines short-term perturbations with longer-term macroeconomic factors [125].

A useful trichotomy summarizes the macroeconomic component of oil price volatility [126]. First, "most commodity prices are endogenous with respect to the global business cycle" [127] (p. 313). Second, demand shocks cause slow but sustained changes in price. Third, and in stark contrast, supply shocks have immediate but small and ultimately evanescent price impacts. In oil-related markets, crises and recessions generally reduce demand over a sustained period, while geopolitical events and natural disasters tend to disrupt supplies on an acute basis.

This rigidly logical approach to evaluating the macroeconomic effects of oil and fuel price volatility does leave room for potentially exogenous factors to affect uncertainty. Oil "price uncertainty," conditioned "on macroeconomic uncertainty," might be a more complete and "suitable measure of uncertainty" than purely volatility-based measures [127] (p. 325). As a matter of broad theory, if not empirical precision, uncertainty may depend more heavily on the predictability of energy-related markets than on their volatility [127].

#### *2.4. Identifying Cyclicality and Critical Periods in Energy Markets, Finance, and the Real Economy*

Comprehensive financialization strengthens the connections linking commodities, capital markets, and the broader economy. These relationships reinforce other centrifugal tendencies throughout economics. For instance, asset pricing models should account for tangible assets and human capital as well as financial instruments [128]. The behavior of a firm is likewise influenced by that of its upstream suppliers, downstream purchasers, and competitors in geographically and technologically adjacent markets [129].

Appropriately enough, efforts to track economic cyclicality span stock markets and macroeconomic policymaking. These two domains, neither more than a single degree removed from commodity markets, have invited many efforts to define critical periods. Even though this article applies unsupervised machine language rather than conventional econometric methods, it is motivated by the same desire to trace economic cyclicality in commodity markets, particularly for crude oil and refined fuels.

Stock markets provide the narrower and methodologically simpler basis for comparison. Technical stock analysis typically defines bull and bear markets, respectively, as a market-wide price increase of at least 20 percent since the previous trough or a market-wide decrease of at least 20 percent since the previous peak [130–132]. A 10 percent decline is typically described as a "correction" [133]. Designations of bull and bear cycles within market trends can be made only in retrospect, and there is no justification for these arbitrary 10 and 20 percent thresholds beyond the conventions of technical analysis and financial journalism.

For its part, the Business Cycle Dating Committee of the National Bureau for Economic Research (NBER) tracks recessions and recoveries in the United States [134–137]. The NBER's methodology relies on a dynamic-factor, Markov-switching model that examines non-farm payroll employment, the index of industrial production, real personal income, and real manufacturing and trade sales [134,136].

Figure 1 describes the NBER's announcements regarding the arrival and departure of recessions in the United States [138,139]. It depicts smoothed recession probabilities as they rise and ebb. Notably, only two periods from 2000 through 2020 have exceeded 50 percent according to the NBER: the financial crisis of 2008–2009 and the COVID-19 pandemic. The "dot-com" crisis of 2001 approached but did not exceed a 50 percent probability of recession. As is evident in the shaded areas of Figure 1, however, the NBER did define March through November 2001 as a recession.

**Figure 1.** Smoothed U.S. recession probabilities [RECPROUSM156N], retrieved from FRED, Federal Reserve Bank of St. Louis [138].

One can also frame this problem as the mirror image of an event study [140,141]. An event study traces abnormal effects to determine the duration of a suspected market disturbance. Event studies of oil price shocks [142,143], for instance, have evaluated OPEC announcements [144,145] and storms [146]. Conversely, temporal clustering uses economic anomalies to extract events for further examination amid the flow of financial history.

The timing of recession announcements presents an economically significant issue in its own right [147]. By the NBER's own admission, its business cycle dating committee's "approach to determining the dates of turning points is retrospective" [148]. Before definitively identifying a peak, "the committee tends to wait to identify a peak until a number of months after it has actually occurred" [148]. Likewise, the committee does not immediately announce a trough. Rather, the committee "waits until it is confident that an expansion is underway" [148].

Under this methodology, announcements of recessions and recoveries are not aligned in time with actual economic activity [149]. In the three decades from 1980 to 2010, "the lag between the determined start of [a] recession" and the NBER's "peak announcement" has averaged 9 months [150] (p. 645). At a bit more than 15 months, the lag between a trough and its announcement is longer still [150].

The lag between actual macroeconomic phenomena and their announcements creates an opportunity for machine learning, artificial intelligence, and other automated methods for evaluating economic data. For instance, the United States publishes its official Consumer Price Index on a monthly basis, with a delay of several weeks between the gathering of price data by. the Bureau of Labor Statistics and the announcement of each new CPI reading [151]. By contrast, the Massachusetts Institute of Technology's Billion Prices Project reports a comparable price index on a daily basis [151].

This article develops a methodology for identifying critical periods in energy-related commodity markets. The literature on oil and fuel markets emphasizes volatility and the connectedness of oil and oil-based fuels with other commodities, other financial markets, and the macroeconomy. Instead of defining cycles akin to bull and bear markets or macroeconomic expansions and recessions, this article will try to distinguish between critical and normal periods of trading within markets for petroleum-related commodities. In seeking a crisis-based approach to understanding temporal shifts in these markets, this article aims at an intermediate level of mathematical rigor between the extremes represented by technical definitions of bull and bear markets and the NBER's recessionand-recovery methodology.

Qualitative distinctions between peaks and troughs, expansionary and recessionary cycles, and critical periods dissolve upon closer mathematical inspection. Critical points in calculus identify points within the domain of a function where the derivative or gradient is zero (assuming that the function is differentiable at those points). Peaks and troughs as maxima and minima constitute critical points in a univariate function. In a multidimensional space representing returns on more than one asset, critical points also include saddle points, where all slopes in orthogonal directions are zero, but no local extremum is attained. In this mathematically informed sense, the methods described and applied in this article cast a wider net than methods dedicated of finding peaks and troughs within a single time series.

The second derivative of logarithmic returns on a financial asset is related to volatility through the Taylor series expansion [152,153]. Points within the domain of a function where the second derivative is zero indicate inflection or undulation. Methods focusing on financial volatility may therefore find inflection and undulation points as well as critical points. These observations are not meant to suggest that this article consciously seeks to find all critical and inflection points in a strictly mathematical sense. Rather, this analogy simply offers a conceptually helpful way of understanding similarities as well as meaningful differences between traditional peak-and-trough approaches and this article's clustering methods.

As with stock markets and the broader economy, cyclicality in commodity prices has drawn scholarly attention [154]. Efforts to sharpen forecasting and the understanding of the dependence structure in oil and adjacent markets have highlighted differences between normal trading and economic turmoil [155]. The question is whether existing and novel "econometric tools" can generate reliable volatility forecasts when "periods of heightened volatility in crude oil markets are recurrent" [156] (p. 622).

Conventional econometric tools include unit root tests [157,158]. Those tests aided the discovery of structural breaks in 1990 and 2008, coinciding with the first Gulf War and the global financial crisis [17]. Technical analysis inspired by conventional methods for identifying bull and bear cycles in equity markets [159] has aided the search for cyclical effects in oil-based markets, at higher [4] as well as lower frequencies [160].

Computational tools abound amid economic "big data" [151]. Although some sources have mined linguistic [161] and Internet search data [16,162] in search of novel insights, this article uses machine learning and artificial intelligence to answer a more fundamental question: Whether financial economics can detect oil price fluctuation and its impact on the relationship between risk and return [163].

This article applies unsupervised machine learning to conditional volatility in commodity markets over two decades. An ensemble of clustering methods can identify episodes in commodity markets (especially those related to energy) warranting closer examination. Some episodes, particularly the global financial crisis and the COVID-19 pandemic, reflect a broader, more durable demand shock. Other episodes may last mere days. Such acute events should be expected more often within a confined subset of commodities, such as crude oil and refined fuels. These acute events typically involve geopolitical or natural calamities that disrupt supplies of oil and its downstream derivatives.

#### **3. Materials and Methods**


**12.5 2-D resistivity structure along West-East direction**

The 2D resistivity structure (**Figure 3**) reveals four geologic/geoelectric subsurface layers separated by geologic boundaries; namely topsoil marked by A (generally blue except few points with yellow and green colour), weathered layer marked B (generally blue except few points with yellow and green colour), weathered/fractured

basement marked C (green and yellow colour) and fresh basement marked D (generally red except few points with yellow colour). The topsoil is generally thin but subsume into the weathered layer in many places due to its thickness, the topsoil is characterised mainly by clay with few portions of laterite and sandy clay. The weathered layer is characterised mainly by clay (lowest resistivity represented by blue colour) between stations 5 and 8 (distance at 50 m and 80 m), but the resistivity values increases from the above stations towards the western and the eastern flank signifying the reduction of clay to sand ratio.

A linear feature is noticed between stations 5 and 7 (distance at 50 m and 70 m) which has significant depth extent. The low resistivity zone is between stations 5 and 7 (50 m–70 m) which could be diagnostic of a suspected fault zone with a width of approximately 19 m. The suspected fault zone is flanked on both sides by regions of higher resistivity. The extremely low resistivity value (blue colour) that was noticed at the lower part of the fault zone signifies the effect of high saturation and diagnostic effect of wet clay. The resistive parts are seen at the lower part of the section which is the fresh basement.

**Figure 4.** *VLF profile and inverted 2D model obtained along traverse one (S-N).*

*Geophysical Investigations for Design Parameters Related to Geotechnical Engineering DOI: http://dx.doi.org/10.5772/intechopen.108712*

#### **12.6 VLF profile along South-North and West-East direction**

On the traverse one which is trending South-North, the VLF-EM profile (**Figure 4**) identified peak positive filtered real values at distances 2 m, 40 m, 83 m, 110 m, 138 m, 168 m and 200 m. The amplitude of the peak positive filtered real values are very low, the inverted model shows that most of the peaks manifest as nonanomalous conductive zones, except for the peak positive filtered real noticed at 200 m. The VLF-EM profile and the inverted model are shown on the profile.

On the traverse trending North-South, the VLF-EM profile (**Figure 5)** identified peak positive filtered real values at distances 18 m, 43 m and 58 m. The amplitude of the peak positive filtered real values are very low, the inverted model shows that most of the peaks manifest as non-anomalous conductive zones, except for the peak positive filtered real noticed at 18 m.

On the baseline which is trending West-East direction, the VLF-EM profile and the inverted model are shown in **Figure 6**, the VLF-EM profile identified peak positive filtered real values at distances 18 m, 32 m, 46 m, 60 m, 82 m, 112 m and 138 m, the

**Figure 5.** *VLF profile and inverted 2D model obtained along traverse three (South-North).*

**Figure 6.** *VLF profile and inverted 2D model obtained along baseline (West-East).*

observation agree with conductive zones delineated by the inverted model at distance 20–60 m, 80–100 m and 122–138 m. The conductive zone between distance 122–138 m and 20–60 m are typical of a linear feature (fracture) because of its depth and it is dipping to the west.

On the traverse which is trending West-East, the VLF-EM profile and the inverted model are shown in **Figure 7**. The VLF-EM profile identified peak positive filtered real values at distance 78 m, 102 m, 118 m and 138 m, the observations agree with the conductive zones delineated at distances 3–20 m, 110–140 m, the conductive zones between 110 m and 140 m is typical of a linear feature (fracture) and it is dipping to the east.

### **13. Conclusion**

In this chapter geophysical investigations for design parameters related to geotechnical engineering was explored.

The strength of the geophysical methods lies in the ability of the method to image the subsurface in a faster and cost effective approach when compared with geotechnical investigations. Geophysical methods provides adequate guidance to civil engineers before embarking in boring and drilling operations, this will avoid excessive

*Geophysical Investigations for Design Parameters Related to Geotechnical Engineering DOI: http://dx.doi.org/10.5772/intechopen.108712*

**Figure 7.** *VLF profile and inverted 2D model obtained along traverse two (West-East).*

damage of subsurface materials which result in environmental risks such as flooding activities.

Seismic refraction, electrical methods, electromagnetic method and magnetic method have been proven to be useful for evaluating design parameters/geologic subsurface deductions needed before the construction of engineering structures such as buildings, rail ways, dams, bridges etc. Case studies involving the application of electrical method and Very Low Frequency Electromagnetic method at Study area within Igarra, South-western Nigeria was explained to reveal the importance of geophysical methods in geotechnical engineering. Geologic features such as faults and fractures were delineated from the Electrical Resistivity Image of the subsurface, VLF profile and the Inverted 2D Model, the compositions (sandy clay, clay, clayey sand and laterite) of the subsurface/geoelectric layers were also classified based on their competence.

In conclusion, the importance of geophysical investigations for evaluation of the design parameters related to geotechnical engineering cannot be neglected in the pre-construction phase.


**1. Introduction**

Throughout their lifetime, wild fish face many challenges of the aquatic environment that can impose considerable stress and reduce their welfare [1]. These challenges can be either natural or have an anthropogenic origin, and, depending on the magnitude and duration, they can cause acute or chronic stress responses [2,3]. Acute stress responses, such as those triggered by a predator attack or certain unpredictable weather conditions, can facilitate survival [4], whereas long-term stressors, like exposure to environmental pollution, are associated with a wide range of maladaptive effects [5] that may, ultimately, lead to loss of biodiversity [6,7]. Accordingly, understanding the causes and effects of environmental disturbances on fish physiology may help developing conservation strategies to enhance restoration and protect freshwater ecosystems [5,8].

An economical and practical option that can give a substantial amount of information about the overall health status of individuals is the peripheral blood test [9,10]. The analysis of red blood cells (RBCs) allows the detection of DNA damage and alterations by the assessment of erythrocytic nuclear abnormalities (ENA), circulating micronuclei (MN), and senescent (SE) and immature (IE) erythrocytes [11,12]. The detection of RBC abnormalities has actually been widely used as an indicator of exposure to genotoxic and mutagenic contaminants [13–15]. In parallel, relative white blood cell (WBC) count can be obtained, which offers a very common measure of stress and innate immune response [16]. In particular, the relative proportion of neutrophils to lymphocytes has been successfully applied as a measure of prolonged pollutant exposure [9,17,18]. Other uses of blood samples in ecotoxicology include the quantification of glucocorticoid (GC) hormones, such as cortisol, to assess the stress response [19,20]. Cortisol is the main GC in teleost fish secreted after the activation of the hypothalamic–pituitary–interrenal (HPI) axis in response to acute and chronic stress [2,21]. Analyses of cortisol levels in blood and, more recently, in whole-body and the surrounding water have been effectively used to monitor environmental stress responses [22–24]. Blood, whole-body, and the surrounding water sampling, however, present clear limitations when being applied in wild population studies. First, blood collection is an invasive technique that the process by itself may provoke further stress and thus it can potentially compromise the animal's welfare. Similarly, whole-body cortisol analysis involves sacrifice of the specimens [22]. And finally, collection of the holding water requires fish restriction in a bucket, which can cause additional stress. Moreover, this technique is difficult to apply in the wild [24]. Fish scales can also accumulate cortisol [25,26]; however, their potential as biomarkers of habitat quality deserves further investigation [27]. Cortisol analysis in fish skin mucus has recently gained considerable attraction, especially because the sampling method is much less invasive compared to the aforementioned techniques [28–30]. Skin mucus cortisol levels have been shown to reflect circulating concentrations in several species of farm fish [26,30–32], but there is yet no evidence of such a relationship in free-ranging species. In addition, this method has, to date, only been applied in strictly controlled environments [30,33], hence its applicability in uncontrolled, natural environments remains to be explored. Therefore, the present study aimed to examine whether skin mucus cortisol concentrations (MCC) from the freshwater fish Catalan chub, *Squalius laietanus* [34], are affected by the habitat quality to further develop non-invasive biomarkers in free-living fish. Catalan chub was chosen, as this species has demonstrated to be a good candidate for freshwater biomonitoring using blood tests [11]. It is well known that understanding changes in cortisol levels is not a simple process, especially when measuring cortisol in wild animals by using alternative samples other than blood [6,9]. Given that the measurement of cortisol in skin mucus is a novel method, other physiological endpoints of the effects of pollution in fish were assessed to better interpret cortisol fluctuations in this matrix. Several hematological parameters (RBC anomalies and altered WBC

counts) were measured in parallel, since, as previously mentioned, they have successfully been used as indicators of health condition in the Catalan chub [11], as well as in many other species (reviewed above).

This study was carried out in a populated and industrialized urban river, where efforts are being made to minimize the environmental impacts and recover the aquatic fauna throughout the performance of constructed wetlands. Catalan chub were sampled from a non-impacted upstream site and two downstream polluted sites, located within the constructed wetland system, in order to compare fish residing environments of different habitat quality. Initially designed specifically for wastewater treatment, constructed wetlands are nowadays an important component of urban ecosystems since they play a crucial role in environmental pollution control [35–37]. Constructed wetlands are macrophyte-based systems that remove pollutants through a combination of physical, chemical, and biological processes [36,38]. Wetlands' performances, though, need to be periodically monitored [39]. The described methodologies for wetland monitoring include physical and chemical techniques that provide information about the amount of pollutants present in the water. Nevertheless, these tools do not give insight into how living organisms cope with water contaminants [39]. On this basis, the present study was carried out in a constructed wetland system to highlight the need to apply techniques that provide information about how animals perceive and adapt to their environment.

#### **2. Materials and Methods**

#### *2.1. Study Area and Field Sampling*

In order to study the influence of the habitat quality on skin mucus cortisol concentrations, individuals were sampled from two sites within a wetland system (Besòs River Park, NE Spain), each of which represents a different stage of biodegradation of water pollutants (P1 and P2), and a reference non-impacted upstream site located outside the wetlands (Figure 1). The reference site was set in a small tributary (Riera d'Avencó), 49.6 km distant from the site P1. The sampling site P1 was placed at the beginning of the constructed wetland, 2.9 km distant from P2, which was located at the end of the overall wetland system and 3.6 km to the river mouth. The Besòs River is an urban river adjacent to the City of Barcelona (Catalonia, NE Spain). During the 1970s and 1980s this river was declared the most polluted river in Europe. Fish populations are slowly recovering, making it easy to find an abundance of differences between nearby sites.

**Figure 1.** Map showing the location of the three sites sampled in the present study (Reference, P1 and P2) within the Besòs River, in north-east Spain.

Sampling areas were determined following the protocols from the European Committee for Standardization (CEN prEN 14011:2002). Fish were sampled by electrofishing using a portable unit which generated up to 200 V and 3 A pulsed DC in an upstream direction. This capture method was

employed as it is considered an easy and safe method, very popular for studying stream fish [2,40,41]. To minimize the potential influence of the sampling technique on the results, fish were all handled identically by the same experienced operator.

At the start of the study (17 May 2017), several physico-chemical parameters and contaminants of emerging concern (CEC) were measured as a basis of the water quality from each sampling site (Table 1). Results of the water analyses provided evidence that the sampling sites classified as polluted presented features typically identified in disturbed environments [11,42,43].

To avoid the circadian rhythm being a source of variability, samplings were performed in the morning (10:00–12:00) from 17 May to 15 June 2017, once the constructed wetland system had been operative for 3 months (23 May, P1; 17 May, P2; 15 June, reference site). Unequal sample size was collected within the polluted sites probably due to sub-optimal habitat conditions in P1 (n = 6) compared to P2 (n = 17) or the reference site (n = 22). All procedures followed the national and institutional regulations of the Spanish Council for Scientific Research (CSIC) and the European Directive 2010/63/EU.

**Table 1.** Occurrence of contaminants of emerging concern (CEC) and physico-chemical data from river water samples collected within the wetland system (P1 and P2) and the reference non-impacted site located outside the system (Reference) in May 2017, when fish were sampled.

Concentrations of compounds under the instrumental detection limit (LOD, Limit of detection) are not included. 1 TOC, total organic carbon; 2 COD, chemical oxygen demand; 3 SS, suspended solids; 4 NTU, nephelometer turbidity units.