*2.1. Fuzzy Theory*

Fuzzy theory is a subject of science that deals with the study and processing of fuzzy phenomena. It was proposed in 1965 by L.A. Zadeh, an expert in control theory at the University of California, Berkeley, USA. It has been a subject for more than 40 years and has been used for processing imprecise data with vagueness. With rigorous mathematical methods for calculation, the strategic problems under a fuzzy environment can be resolved by this approach [14]. During design assessment, there are many targets, such as aesthetics (overall e ffect, shape, color, decoration, etc.), amenity, safety, machinability, etc. These targets are di fficult to determine by conventional quantitative analysis. Therefore, linguistic variables must be introduced for description in order to acquire a solution. After that, the fuzzy mathematical approach can be used to numerate the fuzzy information for quantitative evaluation.

Hsiao utilized fuzzy theory and hierarchical analysis to carry out strategic decisions [25] and utilized fuzzy theory to evaluate monochromatic color schemes during the car appearance design phase [27,28]. In addition, he also applied fuzzy semantics to the strategic decision of automobile shape designs [21,22]. Moreover, the semantic transformation from adjectives was applied to the design of product forms in order to carry out computer-aided industrial design and assess form images [29,30].

In addition, a number of scholars have also proposed di fferent computer-aided systems to assist designers and engineers in reducing the time for research and development and document processing. In 1997, Moskowitz and Kim proposed a decision support system with optimal product designs [31]. Temponi et al. proposed a deduction framework in 1999 in order to deduce the relationship between customer requirements (CRs) and design requirements (DRs) [32]. However, the knowledge and experience of experts are required to construct the rules when developing this type of system. At the same time, designers also encounter problems whether a system is in good operation or not. According to fuzzy set theory, Kim et al. utilized data, such as the analysis of the competitiveness of companies, to construct a correlation function between CRs and DRs. They proposed a fuzzy multi-objective model. However, it is di fficult to build this correlation function. This is especially true when developing a brand-new product without any data from competitors for analysis [33]. Other scholars have applied fuzzy sets, fuzzy algorithms, or defuzzification techniques to process complex and imprecise quality function deployment problems. However, these methods do not take into account the relationships between engineering design demands. Other scholars have emphasized that the quality function deployment needs to not only determine the performance based on customer satisfaction but also consider organizational criteria such as cost factors and technical di fficulties so as to determine the optimal decision that is economical and satisfactory for customers.

Among various targets of a design, some required concepts are definite while others are vague. It is also required to consider several targets and comprehensively consider various relevant factors and carry out comprehensive assessments. This is the so-called fuzzy comprehensive assessment. The process of implementing fuzzy assessment includes constructing the a ffecting factor set, determining the factor weight set, determining the parameter evaluation set, creating the single-factor assessment matrix, and conducting the fuzzy evaluation. These procedures are described as follows:

#### 2.1.1. Constructing the A ffecting Factor Set

When conducting a fuzzy evaluation, first confirm the factors a ffecting the values of the evaluation parameters. If it is known that the a ffecting factors are *u*1, *u*2, ... , *u*m, the factor set that is composed of these parameters is *U* = {*u*1, *u*2, ... , *u*m}. This factor set is a common set.

#### 2.1.2. Determining the Factor Weight Set

The degree of influence and importance of each parameter for the factors are di fferent; that is, the weight of each parameter is di fferent for each factor. The factor weight set is the set that is composed of the degree of influence of each parameter for each factor and can be expressed as *A* = {*a1, a2,* ... *, an*}. If ai indicates the *i*th factor weight, the weights of all of the factors need to satisfy Equation (1). A weight set is a fuzzy subset of the factor set and can be expressed as Equation (2):

$$\sum\_{i=1}^{n} a\_i = 1, \ a\_i \ge 0 \ (\mathbf{i} = 1, 2, 3, \dots, a\_{\text{ll}}),\tag{1}$$

$$\begin{array}{rcl} A &=& \frac{a\_1}{u\_1} + \frac{a\_2}{u\_2} + \frac{a\_3}{u\_3} + \dots + \frac{a\_n}{u\_n} \\ &=& \{a\_1, a\_2, a\_3, \dots, a\_n\} \end{array} \tag{2}$$

The weight of each factor can be determined by the weight coefficient method, analytic hierarchical process, paired comparison method, Likert scale, or any subjective determination according to the practical problem. It does not matter which approach is used, and human factors are involved, with the only difference being in credibility. Here, A is the fuzzy set of each factor weight. For the same factor, if different data are taken, the assessment result is different. The method of the determinant table is to carry out pairwise comparison of the importance of these evaluation targets. Scores are given after further calculation and are entered into the table. The equation for calculating *ai* is as follows:

$$a\_i = k\_i / \sum\_{i=1}^{n} k\_i \tag{3}$$

where *ki* is the total score of each evaluation target and n is the number of evaluation targets.

$$\sum\_{i=1}^{n} k\_i = \frac{n^2 - n}{2} \times 4 = 2(n^2 - n). \tag{4}$$

#### 2.1.3. Determining the Parameter Evaluation Set

The evaluation set is composed of the various evaluation results that an assessor might obtain for the evaluation target. It is represented as *V*, and *V* = {*v*1, *v*2, ... , *v*n} and *vi* (*i* = 1, 2, 3, ... , *n*) represent the possible total evaluation results. The purpose of fuzzy evaluation is to comprehensively consider all of the affecting factors and obtain an optimal evaluation result from the evaluation set. The relationship between *vi* and *V* is also a common set relationship. Therefore, the evaluation set is also a common set. For the evaluation in this study, the evaluation set is *V* = {*very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, very dissatisfied*}.

#### 2.1.4. Creating the Single-Factor Assessment Matrix

A single-factor fuzzy evaluation is conducted to judge one factor separately and confirm the degree of membership for the target of evaluation toward evaluation-set elements. The evaluation target is carried out by the *i*th factor Ui and the membership grade of the *j*th element *Vi* in the evaluation set is *rij*. Therefore, the evaluation result of the *i*th factor *Ui* can be determined as follows:

$$R\_i = \frac{r\_{i1}}{V\_1} + \frac{r\_{i2}}{V\_2} + \dots + \frac{r\_{in}}{V\_n} \tag{5}$$

where *Ri* is called the single factor evaluation set, which is the fuzzy subset of the evaluation set. It can be expressed as *Ri* = (ri1, ri2, ..., rin). Similarly, the single factor evaluation set of each factor can be determined as follows:

$$\begin{array}{l} \mathcal{R}\_1 = (r\_{11}, r\_{12}, \dots, r\_{1n})\\ \mathcal{R}\_2 = (r\_{21}, r\_{22}, \dots, r\_{2n})\\ \vdots\\ \mathcal{R}\_{\text{ill}} = (r\_{m1}, r\_{m2}, \dots, r\_{\text{min}}) \end{array} \tag{6}$$

The fuzzy matrix that is composed of the membership grade of each single factor evaluation set is called the single-factor assessment matrix, *R*, as shown in Equation (7). R is a fuzzy matrix and can also be viewed as the fuzzy relational matrix from U to V, or so-called fuzzy mapping.

$$R = \begin{bmatrix} R\_1 \\ R\_2 \\ \vdots \\ R\_i \\ \vdots \\ R\_n \end{bmatrix} = \begin{bmatrix} r\_{11} & r\_{12} & \dots & r\_{1j} & \dots & r\_{1m} \\ r\_{21} & r\_{22} & \dots & r\_{2j} & \dots & r\_{2m} \\ \vdots & \vdots & \ddots & \vdots & \ddots & \vdots \\ r\_{i1} & r\_{i2} & \dots & r\_{ij} & \dots & r\_{im} \\ \vdots & \vdots & \ddots & \vdots & \ddots & \vdots \\ r\_{n1} & r\_{n2} & \dots & r\_{nj} & \dots & r\_{nm} \end{bmatrix} \tag{7}$$

However, in this study, there are numerous factors to be considered and each factor can have di fferent levels. It is di fficult to resolve a problem by using single-factor fuzzy evaluation. It is also di fficult to obtain reasonable assessment results. As a result, this study is based on fuzzy comprehensive evaluation, and then the fuzzy comprehensive evaluation is implemented. This is because there are too many factors to consider when making a selection from complicated schemes. There are levels between factors, therefore we must adopt the multifactor assessment matrix. The procedure is to divide the factor set into several levels according to its characteristics, carry out comprehensive assessment on each level, and then conduct in-depth combined evaluations on the evaluation results.

#### 2.1.5. Conducting the Fuzzy Evaluation

If the fuzzy evaluation matrix of a certain scheme on the evaluation target is the R in Equation (7), the weighted comprehensive fuzzy evaluation and the product of fuzzy matrices to be considered is as follows:

$$B = A \bullet R = \left[ b\_1, b\_2, \dots, b\_{j\_1}, \dots, b\_m \right] \tag{8}$$

where • indicates the fuzzy synthetic operation.

There are several synthetic approaches of fuzzy weight matrix *A* and factor judgment matrix *R*. In this study, four synthetic approaches were implemented for the analysis and comparison of the evaluation results. These four models are described as follows:

Model 1: *M* (∧,∨) synthetic operation, in which

$$b\_j = \bigvee\_{i=1}^{m} (a\_i \wedge r\_{ij}); j = 1, 2, \dots, n,\tag{9}$$

where ∨ and ∧ indicate taking the maximum or minimum. When taking the minimum, the *rij* of all *rij* > *wij* is not taken into consideration. Therefore, when there are various factors, the values are bound to be large after the weighted coe fficients are normalized. As a result, a lot of the single-factor evaluation information will be lost. When there are fewer factors, *wij* might be larger and therefore the *wij* of all *wij* > *rij* will not be taken into consideration. The influence of the main factors might be lost.

Model 2: *M* (•,∨) synthetic operation, in which

$$b\_j = \bigvee\_{i=1}^{m} (a\_i r\_{i\bar{j}}); j = 1, 2, \dots, n. \tag{10}$$

The feature of this model deals with no loss of any useful information. However, the operation of taking the maximum ∨ could possibly lose a lot of useful information. It can still reflect the single-factor evaluation result and the degree of importance of each factor.

Model 3: *M* (∧, +◦) synthetic operation, in which

$$b\_{\vec{j}} = \min\{1, \sum\_{i=1}^{m} (a\_i \wedge r\_{\vec{i}\vec{j}})\}; \vec{j} = 1, 2, \dots, n,\tag{11}$$

where +◦ takes the summation with an upper limit of 1. The feature of this model deals with the loss of a lot of valuable information during the operation of taking the minimum. Therefore, it might not achieve the expected evaluation result. When the values of *wi* and *rij* are larger, the resulting *bj* could

be equal to the upper limit of 1. When the values of *wi* and *rij* are smaller, the resulting *bj* could be equal to the summation of all *wi* As a result, it could be more difficult to obtain the expected evaluation result.

Model 4: *M* (•, +) synthetic operation, in which

$$b\_{\vec{j}} = \min\{1, \sum\_{i=1}^{m} a\_i r\_{i\vec{j}}\}; j = 1, 2, \dots, n. \tag{12}$$

This model is also called the weighted-average model and it deals with the situation when *wi* is equipped with normalization, i.e., m i=1 *ai* = 1, m i=1 *airij* << 1.This model can be restructured as *M* (•, +), in which

$$b\_j = \sum\_{i=1}^{m} a\_i r\_{ij}; j = 1, 2, \dots, n; \sum\_{i=1}^{m} a\_i = 1,\tag{13}$$

where *m i*=1 *ai* = 1. This model not only considers the influence of all factors, but also keeps all the information of the single-factor evaluation. During its operation, it does not apply the upper limit to *ai* and *rij* (*i* = 1, 2, ··· , m; *j* = 1, 2, ··· , *n*). However, *ai* should be normalized. These are the significant characteristics and advantages of this model. When carrying out the fuzzy comprehensive evaluation and fuzzy optimization design on engineering design parameters, this model is implemented, since typically it can obtain better effects. This model not only considers the influence of all factors, but also keeps all information of the single-factor evaluation. During the operation, there is no upper limit on *wi* and *rij*. It is only required to carry out normalization on *wi*. This is the distinguishing feature and the main advantage of this model.

The goal of Models 1–3 is to obtain individual evaluation results based on certain limitations and by taking the extreme value. Therefore, a lot of useful information could be lost to various degrees during the evaluation process. As a result, these three models are applied to the scenarios that only care about the extreme values of objects in order to highlight certain main factors. Based on this, Model 4 is used for the synthetic operation in this study.

#### 2.1.6. Processing the Evaluation Indices

After the evaluation indices *bj* (*j* = 1, 2, ... , *n*) are determined, the results of the evaluation target can be determined by the methods of maximum degree of membership and weighted average, as follows.

#### (a) Maximum degree of membership

Based on the principle of maximum degree of membership, the evaluation element *vi* corresponding to the maximum evaluation index *bj* is selected. This approach considers only the contribution of the maximum evaluation index; information supplied by other indices is neglected. Moreover, when there are more than one maximum evaluation indices, it will be difficult for maximum degree of membership to determine a concrete result. In this case, weighted average is usually used.


**7. Summary and future recommendations**

Over 50 years ago renowned psychiatrist Carl Jung predicted a conceptual integration of psychology and physics. He wrote [95]:

*Sooner or later, nuclear physics and the psychology of the unconscious will draw closer together as both of them, independently of one another, and from opposite directions, push forward … If research could only advance far enough, therefore, we should arrive at an ultimate agreement between physical and psychological concepts (p. 261).*

The era that Jung foresaw has arrived. Research has now identified common themes in disciplines as diverse as psychology and physics. Unfortunately, these discoveries have impacted the material world more than the subjective realm of well-being. For example, discoveries in quantum mechanics enabled a technological revolution that has totally transformed society over the past 100 years; however, these discoveries have not yet led to a significant shift in human beliefs or behaviors. This chapter attempts to do so by showing the relationship of these quantum breakthroughs to other paradigm-breaking research in positive psychology and neuroscience and integrating these findings into a set of quantum skills.

The research suggests that as humans master these seven quantum skills, they will learn to more fully utilize both of the brain's hemispheres and greatly expand the capacity of the brain's highest processing center, the neocortex. In so doing, they will be able to transcend the basic survival programming of our ancient reptilian brains (the R-complex) and the pain/pleasure mechanisms of the limbic system. Wade's research [60] suggests that as people learn to fully utilize the capacity of the neocortex, conscious awareness increases; thus, giving increased ability to make conscious choice regarding thoughts and actions that support well-being.

The authors hypothesize that using these quantum skills will result in increased happiness as measured by a subjective sense of well-being. Research is needed to test the efficacy of these skills to accomplish this hypothesized result. Such a project would need to be based on a culturally inclusive definition of happiness that aligns with the research reviewed in this chapter. Future opportunities for disseminating these quantum skills include the design of a comprehensive, cross-cultural curriculum for teaching these skills at a variety of age levels. This chapter is a call for the academic community to test and teach these skills, thus reducing the current knowing/doing gap and helping to create global flourishing.

*Identifying the Gaps between Biopsychosocial Research and Human Behavior… DOI: http://dx.doi.org/10.5772/intechopen.107500*


**Recent Trends in Coatings and Thin Film–Modeling and Application**

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*4.5. Possible H2 Bubbling at Depth*

Once produced by fluid–rock interaction processes (oxidation) at depth, H2 can migrate as a dissolved component. The solubility of H2 in aqueous solutions is rather low and drops when T and P decrease when approaching the surface (Figure 7d). The possible mechanism of H2 discharge, concentrating, and transport upward to the lower T–P where H2 is less reactive is solution boiling, i.e., formation of the vapor phase coexisting with the liquid phase. The concentrations of H2 in vapors are many orders of magnitude higher than that in the liquid (Bazarkina et al., 2020). At the same time, rock permeability is much higher for gas-rich vapors than for salt-rich liquids. Bubble formation is a function of T, P, total salinity, and gas saturation. Thus, during fluid ascent upward to the lower T–P, gas bubble formation is favored (Figure 7d). Periodicity of H2 emission at the surface reported by Prinzhofer et al. (2019) could be related to the kinetics of fluid–rock interaction at depth, further time-dependent bubble accumulation, and the final periodical ejections similar to those described in geysers.

**Figure 6.** Heat flow map in the São Francisco Basin within the studied zone (modified from Alexandrino and Hamza, 2012 [52]).

### **5. Draining Fault System in the São Francisco Basin**

Deep faults serve as significant channels for deep fluids to ascend into and through the crust and the *3He*/*4He* ratio can be used to estimate the flow rate of mantle fluids through the fault zones (Kennedy et al., 1997 [53]). Since the *3He*/*4He* signature seems to be of crustal origin in the São Francisco basin (Flude et al., 2019 [17]), the migration path followed by the *H2* mostly crosses the sedimentary cover without major changes of the ratio value. This weak interaction with the Bambuí sequences could be due to a high flow rate value along the faults. This could be possible if the faults form direct drains from the basement to the surface assuming a sufficiently high value of permeability.

Several interpretations of the available seismic data have been proposed for the fault systems (Figure 3) (e.g., Romeiro-Silva and Zalán, 2005 [24]; Coelho et al., 2008 [39] and Alkmim and Martins-Neto, 2012 [40]). Nevertheless, the São Francisco basin seems to encompass different tectonic elements such as the Proterozoic rift structures, Neoproterozoic foreland f–t-belts and Cretaceous rift structures (Reis and Alkmim, 2015 [25]). The rift structure that cuts across the central portion of the basin is characterized by a system of major NW–SE faults. One could expect that the deep-rooted faults in the graben structures (Precambrian sequence), which have been reactivated during the Neoproterozoic Macaúbas basin-cycle, could cross most of the sedimentary formation. As a major fault system, they may control the drainage at all depths and delineate some morphological features observed on satellite imagery and digital elevation models (Reis et al., 2017b [42]).

If these faults cross different geological layers, mainly shales, sandstones and limestones, their permeability values can range from 10−19 to 10−13 m2 (Donzé et al., 2020 [54]). In terms of hydraulic conductivity for the water carrying the gas and neglecting the contribution of temperature, this could correspond to a value as high as 10−6 m/s. This means that in the fastest scenario, the fluid could take less than 100 years to migrate across the Bambuí sedimentary layer through a fault system.

### **6. Possible Temporary Shallow Zones of H2 Accumulation**

The pressure variation observed at 1 m depth in the São Francisco basin (Prinzhofer et al., 2019 [15]), with a momentary increase in H2 pressure, could indicate that the H2 systems are active. There is only a small temperature window where H2 may remain stable over a long time. This window corresponds to a T range where abiotic redox reactions such as thermochemical sulfate reduction or carbonate reduction (e.g., Fisher Tropsh type reaction) are slow (Truche et al., 2009 [55]), and where bacteria are inactive. Such a T range can be roughly approximated to be 100–200 ◦C. Since these temperature conditions are not met at shallow depth, H2 will probably not survive to long residence time. Despite this fact, previous studies of H2 seepages often indicate that the hydrogen systems are active, and transient accumulations of hydrogen at relatively shallow depth can be observed (Prinzhofer et al., 2018 [14], Goebel et al., 1984 [28]; Guélard et al., 2017 [26]). These observations may suggest a constant recharge of the aquifers by H2 flowing from deeper levels of the basin.

The circular depression where H2 seepage is observed (Figure 2b) could be related to a sinkhole structure resulting from a chemical dissolution process at depth. If so, these depressions will contain standing water connected with a ground-water reservoir contained in karst (De Carvalho et al., 2014 [56]). The presence of resistive carbonate and calcareous rocks was inferred from ~320 m to ~480 m followed by a layer of intercalated shales and sandstones (Solon et al., 2015 [23]). This carbonate layer, which corresponds to the Lagoa do Jacaré carbonate layer, exhibits a potential karst system according to the outcrops located East of the São Francisco Basin (Dos Santos et al., 2018 [57]).

Assuming a karst system at depth, this could imply a high level of porosity favorable for a massive storage volume of an aquifer. Since karst features are controlled by structural heterogeneities, such as faults and fractures, which influence fluid flow, they can provide preferential pathways for geofluids with the development of secondary porosity. This could agree with the fact that the circular depressions where H2 is venting are aligned along a major fault (Cathles and Prinzhofer, 2020 [16]).

### **7. Putting It All Together: A Potential H2 System within the São Francisco Basin**

The first key point is related to potential source areas, e.g., the presence of both ultramafic and U, Th and K-rich rocks. The presence of Archean greenstone belts containing ultramafic rocks, TTG, migmatites and K-rich granitic plutons represent excellent H2-producing zones either via serpentinization, or water radiolysis. Magnetic (Figure 4) and Bouguer (Figure 5) anomalies are compatible with the presence of ultramafic rock producing H2. Temperature conditions also seem favorable for the serpentinization process: with a temperature gradient of 25 ◦C/km (Alexandrino et al., 2008 [48]), the optimum range of temperature would be expected at a depth of 10 km, with possible lower rate processes at a shallower depth.

The second key point is the structural/tectonic context and the presence of faults deeply rooted in the basements capable of draining a potential deep and scattered source. All interpretations of the seismic profile of the zone of interest suggest the presence of deep faults following the graben structures (Figure 3): they can be able to drain hydrogen produced at depth where the Pressure–Temperature conditions are optimal. Some interpretations suggest that some of these faults could cross the entire sedimentary sequence (Coelho et al., 2008 [39]), producing gas seepages directly at the surface. Some others predict that these faults could reach some potential shallow carbonated reservoirs (Romeiro-Silva and Zalán, 2005 [24]). These deep faults could also only reach the unconformity zone which composes the boundary between the sedimentary basin and basement (Alkmim and Martins-Neto, 2012 [40]).

The third key point concerns the storage areas (i.e., reservoirs) of H2 at depth. As mentioned previously, the interface between the basement rocks and the sedimentary layers could represent a potential zone of accumulation. The interface is composed of the Macaúbas and the Espinhaço formations. The Macaúbas sequence is made up of sandstones, pelites, diamictites, carbonates, basic volcanic rocks, and metamorphosed banded iron formations (Alkmim et al., 2012 [40]), whereas the Espinhaço formation is a quartz–arenite dominated package. The presence of the Paranoá–Upper Espinhaço quartzite, which is tectonically uplifted, can facilitate the occurrence of sandstone reservoirs with appreciable permeability and porosity. Thus, potential reservoir rocks could be found among siliciclastics of the Macaúbas–Paranoá Megasequence (Solon et al., 2015 [23]).

An interesting characteristic of the deep topography is that the seepage zone H2G is located near the apex of the basement rock in the central part of the basin (see Figure 3). As the H2 charged fluid reaches the Macaúbas/Espinhaço formations, it migrates along the unconformity toward this highest point before escaping to the surface in the green seepage zone (Figure 2). On its way to the surface, H2 can also be temporarily trapped in the Sete Lagoas formation and at a shallower depth, inside the Lagoa do Jacaré formation. The permeability value of the Sete Lagoas Larst aquifer formation is estimated to range between 10−14 m2 and 10−9 m2 (Galvão et al., 2015 [58]). As for the Lagoa do Jacaré formation, very low permeability and porosity values were found in the Petrobras well 1-RF-1-MG. The presence of faults, possibly connecting all these reservoirs with the surface could explain the apparent structural control of the distribution of the known gas seepages (Curto et al., 2012 [22]). Nevertheless, the presence of sinkholes in the H2G seepage area suggests the existence of a shallow local karst formation, which could constitute a temporary reservoir for H2.

**Figure 7.** Conecputal model of the H2 cycle in the Sao Francisco Basin. (**a**) Interpretated seismic section (Martins-Neto, 2009). (**b**) Zoom of the upper part of the Bambui sequence. (**c**) Possible presence of a karst structure according to the presence of sinkholes (Figure 2). (**d**) Calculated solubility of H2 in H2O vs. depth (Bazarkina et al., 2020 [59]).

Thus, surface seepages may be either in connection with the source rock or with intermediate leaking reservoirs since these two configurations are present in this area. A summary of H2 migration from sources to seeps in the São Francisco basin is presented in Figure 7.

### **8. Discussing the H2 Production from Radiolysis and Hydration Reactions in the São Francisco Basin**

Combining the H2 production rate from water radiolysis and hydration reactions assessed in the previous sections, we obtain an estimate of 1.01 to 6.43 × 108 mol·yr−1 H2 production, i.e., ~200 to 1300 tons·yr−1 (Table 3). Cathles and Prinzhofer (2020 [23]) considered the local flux rate in the H2G seepage zone (Figure 2b) to range from 7000 to 178,000 m3 per day. At a temperature of 21 ◦C and a pressure of 1 atm, these values correspond to 0.105 to 3.68 × 109 mol·yr−1, i.e., 213 to 5400 tons·yr−1 (Table 3). Since the expulsion rate of H2 is almost certainly not steady, the episodic rate measured in the H2G vent might be overestimated.

**Table 3.** Estimated H2flow rate production (tons·yr−1).

In comparison, on the Mid-Atlantic Ridge (MAR), the total H2 discharge at the Rainbow hydrothermal field is estimated to be ~108 moles H2 per year, i.e., ~200 tons·yr−1 (Charlou et al., 2010 [60]) (Table 3). At a larger scale, the H2 flux from all high-temperature basaltic vents along the MAR has been estimated at ~109–1010 mol·yr−1, whereas the H2 flux from high-temperature ultramafic vents along the Mid-Oceanic Ridge (MOR) has been estimated at ~1010–1011 mol·yr−1 (Table 3) (Keir, 2010 [61]; Cannat et al., 2010 [62]), i.e., 20,000 to 200,000 ton.yr−1.

According to our calculations, which are based on the model developed by Sherwood Lollar et al. (2014 [29]) for the Precambrian continental lithosphere, the maximum H2 production rate from the basement rocks of the São Francisco Basin is within the same order of magnitude as the H2 flux of one sinkhole of 500 m in diameter (H2G zone). This latter H2 venting site would also represent from 0.047% to 7.5% of the global estimated H2 production from the Precambrian continental Lithosphere. This large discrepancy in the results leads us to conclude that there is a need to increase the accuracy of hydrogen flux estimates through long term monitoring of soil gas migration according to different methodologies and/or to revisit the global models.


*<IP_ADDRESS> Winter dry and summer rainfall with relative humidity 40–45% (wy)*

Kach-Sibi areas fall in this region. It has hot long moist summers and warm short dry winters. The mean annual total rainfall of the area varies between 5 and 10 inches, with average day relative humidity of 40–45%. The mean annual evapotranspiration of the region is 4 mm or 0.16 inches, with mean sunshine duration of 8.2 h/day, number of rainy days 12.1, and wind speed of 2.9 knots. The mean temperature of the area is 25°C–30°C, with 30°C–35°C maxima and 10°C–15°C minima. The mean daily range of temperature varies between 15 and 20°C, with 35–40°C daily maximum and 15°C–20°C daily minimum. In Sibi, more than 50°C maximum temperature has been recorded for three times in June, making it the hottest region of the country. The lowest minimum temperature of the area is −3.3°C, recorded in January, the coldest month of the region.

Aridity prevail the whole region, with less fertile sandy soil. Kachhi plain is a desert, with no vegetation. However, in the irrigated areas, the natural vegetation in general is xerophytes with shrubs and grasslands, with medium size trees. The area is mostly used for grazing, where there are karezes and there are some agriculture patches of land.

#### *<IP_ADDRESS> Winter dry and summer rainfall with relative humidity of 50–55% (wx)*

The region consists of Khanpur and the upper irrigated Sind. The annual total rainfall of the region is less than 5 inches with annual day relative humidity of 55–60%. The summers are long hot and moist, while the winters are warm short and dry. The annual evapotranspiration is 6.7 mm or 0.27 inches (Jacobabad),

and sunshine is 8.7 h/day. The number of rainy days is 5–10, with a wind speed of 2–4 knots. The mean monthly temperature varies between 25°C and 30°C, with 35°C–40°C maxima and 10°C–20°C minima. The mean daily range is 10–15°C, with 30–35°C daily maximum and 15–20°C daily minimum temperature. The highest maximum temperature of the region is 45°C–52°C recorded in May and June, while the lowest minimum temperature is 0°C to −5°C, recorded in January and December.

The soil of the region used to be subjected to annual flooding before the river embankments were constructed, and because of this, the soils are rich loamy and clayey. High water tables have caused water logging and salinization problems, and the land is unproductive for agriculture growth.

Agriculture is the main activity in this zone, but there are also areas of rough grazing, riverine forest, and tracts of flooded backswamps. Wood resources come from riverine forests and trees on farmlands, much of which is used for firewood or send to Baluchistan as a mining timber.


**6. Implications for future HR research**

HRM research in the African context has adopted either a content approach [15, 57] or a contextual approach [22, 57] to analyse the effects of HR practices on organisational outcomes. However, not much is known about the process approach to HRM in the African context. Process approach connotes how HR is transmitted to the employees and how employee's perceptions and cognitions of those HR practices influence uniform understanding (the HR strength) and execution of HR goals [48]. Scholars have reported that the employee HR process is crucial in determining the effectiveness of HR practices to predict positive outcomes [60, 61]. Specifically, employees' perception and interpretation of implemented HR practices are more salient to positive employee attitudes, behaviour, and organisational outcomes than intended HR practices (e.g. content HR approach) [9]. This is because employees, as the final 'consumer' of practices, are responsible for translating HR strategy, policies, and practices into tangible outcomes.

As such, the first implication of this review for future HRM research in Africa is adopting process theories such as attribution theories to examine the employee's cognition of HR practices. Pursuing such research will contribute to the theory development of HRM in the African context as well as uncover the roles of employees' cognitive processes in the evaluation of Western-developed HRM in the African context. For example, scholars might gain particularistic cues (e.g. employees) needed for designing contextualised HRM policies and practices capable of yielding positive organisational outcomes in a disparate national context. To achieve this, I suggest integrating the three main attribution theories to capture broader cognitive complexities that influence employees' HR attribution process. Moreover, future longitudinal HRM research could investigate whether and how employee HR attributions may change over time in the African context. This may be due to a change in HR strategy and policies, government regulations, social exchange at work, line manager's leadership behaviour, etc. Conducting this research is likely to help gain a better understanding of the HR attribution process as an unbounded cognitive process based on the informational resources at the employee's disposal at a time [43].

Secondly, there is a lack of research on the role of line managers' leadership behaviour in the HR implementation process in the African context. As already stated, the concept of leadership in Africa is rooted in paternalism [57], a dynamic mix of benevolent and authoritarian leadership styles. Based on the ethnic, social, and religious diversity of Nigeria context, the line manager can display differentiated paternalistic leadership behaviour in the HR implementation process. As such, future research might benefit from this review by investigating the possibility of differentiated paternalism in the HRM process in the Nigerian context. One way to conduct this research is to examine the links between differentiated paternalistic leadership to employee perception of fairness in the HR implementation process. There is also an opportunity to examine whether differentiated paternalism accounted for differences in leader-member exchange within a team while accounting for the variances in ethnicity etc. Conducting this research is likely to contribute to leadership development programs with a focus on the enactment of HR practice at the operational level.

Third, there is a lack of research on employees' HR attribution across the private and public sector organisations. Public sector organisations possess certain defining features, such as standardised employment practices, collectivised labour unions, etc. that differentiate them from the private sector organisations - the latter is characterised by profit maximisation, selective HR practices, and private ownership. Based on Kelly's covariation model, future research might benefit from this review by investigating the type of information pattern (distinctiveness, consensus, and consistency) used by employees in making casual attribution about HR practices in both public and private sector organisations. Such the examination is likely to help detect the factors (e.g. organisational structure) that influences employee HR attribution in both organisational settings.


*3.1. Identification of the Albucidin Biosynthetic Gene Cluster*

The aim of this study was to identify the biosynthetic genes leading to the production of the nucleoside phytotoxin albucidin. For this purpose, the genome sequence of the producer strain of *Streptomyces albus* subsp. *chlorinus* NRRL B-24108 was analysed by genome-mining software [17]. This analysis led to the identification of several putative nucleoside clusters. To prove the involvement of these candidate clusters in albucidin production, they were heterologously expressed in a genetically engineered cluster-free strain *Streptomyces albus* Del14 [19] and in *Streptomyces lividans* TK24 [20]. No albucidin production was detected in the extracts of the obtained strains, indicating that either the expressed clusters were not involved in the biosynthesis of albucidin or they were not expressed in the heterologous host environment. The inactivation of the candidate clusters in the natural albucidin producer was not feasible because the strain is refractory to genetic manipulation. Considering the difficulties in identifying the albucidin gene cluster using conventional methods, an alternative approach using chemical mutagenesis was chosen.

For chemical mutagenesis of the albucidin-producing strain *S. albus* subsp. *chlorinus* NRRL B-24108, 1-methyl-3-nitro-1-nitrosoguanidine (NTG) was used. The strain in the exponential growth stage was treated with various NTG concentrations (800 μg/mL, 600 μg/mL and 300 μg/mL) for 30 min. After mutagenesis, the cells were washed with 5% thiosulfate solution and plated in dilutions on MS-agar medium for segregation of mutations. The spores of the obtained mutant populations were washed and plated on MS-agar plates in dilutions to obtain single colonies. Altogether, 4000 individual mutants were analysed for albucidin production. The mutants were cultivated on individual plates with the production medium SG agar. The metabolites were extracted with butanol, and albucidin production was assayed by HPLC-MS. Eight mutants that lost the ability to produce albucidin were identified in the course of this screening: 6-238, 6-260, 6-389, 6-444, 6-612, 6-892, 8-610 and 8-639. The genomic DNA of the obtained zero mutants was sequenced using Illumina technology. The point mutations in the genomes of the mutants were detected by mapping the sequencing reads to the reference genome of the wild type albucidin producer. Up to 100 transition mutations were identified in the genomes of the mutant strains. By comparing the mutation patterns of the separate mutants, a short genomic region was identified that was affected by point mutations in all analysed zero mutants, implying its potential involvement in albucidin production (Figure S2). The identified region contains two genes, *SACHL2\_05525* and *SACHL2\_05524*, which encode putative radical SAM proteins and were named *albA* and *albB* (Table 1, Figure 1b). The genes constitute a putative operon with the third gene *SACHL2\_05523*, which was named *albC*. The *albC* gene encodes a putative ribonucleoside-triphosphate reductase and was not affected by point mutations in the analysed zero mutants of *S. albus* subsp. *chlorinus* NRRL B-24108. The identified genes *albA* and *albB* were not a part of the nucleoside gene clusters previously identified by genome mining and analysed in this study. Interestingly, these genes were located within the DNA fragment annotated by genome mining software as a putative NRPS gene cluster.

**Table 1.** Genes encoded within the chromosomal fragment cloned in BAC 1K1.

 The locus tags refer to the genome sequence of *S. albus* subsp. *chlorinus* NRRL B-24108 available under GenBank accession number VJOK00000000.

**Figure 1.** Chromosomal fragment of *S. albus* subsp. *chlorinus* NRRL B-24108 with the albucidin biosynthetic genes. (**a**) Schematic representations of DNA fragments cloned in BACs 1K1 and 2D4; (**b**) The genes encoded within the fragment cloned in BAC 1K1. The *albA–C* operon is marked in red; and (**c**) Overview of the performed deletions within BAC 1K1.

To determine whether the identified genes *albA* and *albB* encode albucidin biosynthetic enzymes, a BAC 1K1 containing the abovementioned genes (Figure 1a) was selected from the genomic library of *S. albus* subsp. *chlorinus* NRRL B-24108. BAC 1K1 was transferred into the heterologous host strains *S. albus* Del14 and *S. lividans* TK24 by conjugation, and the production profile of the obtained strains *S. albus* 1K1 and *S. lividans* 1K1 was analysed by HPLC-MS. The production of the compound with a high-resolution mass corresponding to albucidin could be detected in the extracts of *S. albus* 1K1 (Figure S3). No production could be detected in *S. lividans* 1K1.

Due to the lack of an albucidin standard, we set out to purify the compound identified in the extracts of *S. albus* 1K1 for structure elucidation studies by NMR spectroscopy. The *S. albus* 1K1 strain was cultivated in 10 L of SG medium for 7 days. The culture supernatant was extracted with equal amount of butanol, and the obtained extract was concentrated under vacuum. Four milligrams of the compound was purified using size exclusion and reverse phase chromatography and used for subsequent NMR studies. Analysis of the recorded NMR spectra of the purified compound unequivocally demonstrated its identity as albucidin (Figure S1 and Figure 2a).

**Figure 2.** The structures of (**a**) albucidin and (**b**) oxetanocin A.

The production of albucidin by *S. albus* 1K1 gives evidence that the genes *albA* and *albB* identified by chemical mutagenesis encode albucidin biosynthetic genes. The lack of albucidin production by *S. lividans* 1K1 can be explained by differences in regulatory networks of the *S. albus* Del14 and *S. lividans* TK24 strains.

#### *3.2. Identification of the Minimal Set of Albucidin Biosynthetic Genes*

BAC 1K1, which leads to the production of albucidin under expression in the heterologous host *S. albus* Del14, contains a 32 kb chromosomal fragment from the natural albucidin producer *S. albus* subsp. *chlorinus* NRRL B-24108. Twenty-nine open reading frames were annotated in this 32 kb region (Table 1, Figure 1b). Of these genes, only two, *albA* and *albB*, were affected by point mutations in albucidin zero mutants identified in the course of the chemical mutagenesis studies. These two genes constitute a putative operon with the gene *albC,* implying that either only the genes *albA* and *albB* are necessary for albucidin production or that all three gene within the operon are required. To experimentally determine the minimal set of albucidin biosynthetic genes, a series of gene deletions was performed within the cloned region of 1K1 BAC.

The genes *albA–C* are located in the middle part of the chromosomal fragment cloned in 1K1 BAC. The *alb* operon is preceded by the genes *SACHL2\_05539–SACHL2\_05526*, followed by the genes *SACHL2\_05522–SACHL2\_05511* (Table 1). For the sake of simplicity, the 29 genes *SACHL2\_05539–SACHL2\_05511* cloned in the BAC 1K1 will be designated in the text according to their sequence number (1 to 29). (Figure 1b) To determine which genes within the 1K1 cloned fragment are essential for albucidin production, five deletions (LS, KO14, KO15, KO16 and RS) were performed in the 1K1 BAC yielding the BACs 1K1\_LS, 1K1\_KO14, 1K1\_KO15, 1K1\_KO16 and 1K1\_RS (Figure 1c). In BAC 1K1\_LS, the left shoulder encompassing genes 1–13 was substituted by an ampicillin resistance marker (Figure 1c). The genes 14, *albA* (gene 15) and *albB* (gene 16) were substituted with the ampicillin resistance gene in BACs 1K1\_KO14, 1K1\_KO15 and 1K1\_KO16, respectively (Figure 1c). In the BAC 1K1\_RS, the right shoulder encompassing genes *albC* (gene 17)-28 was substituted by the hygromycin resistance marker (Figure 1c). The constructed BACs were transferred separately into the *S. albus* Del14 strain by conjugation, and the albucidin production of the resulting strains was assayed by HPLC-MS.

The deletion of gene 14 did not affect albucidin production in *S. albus* 1K1\_KO14 (Figure S4B). As expected from the results of chemical mutagenesis, inactivation of the genes *albA* (gene 15) and *albB* (gene 16) completely abolished albucidin production in the strains *S. albus* 1K1\_KO15 and *S. albus* 1K1\_KO16 (Figure S4C,D). This unambiguously demonstrates the essential role of the genes *albA* and *albB* in albucidin biosynthesis.

Deletion of the genes *albC* (gene 17)-28 did not affect albucidin production by the *S. albus* RS strain (Figure S4F). It was expected from the gene annotation and results of chemical mutagenesis that the genes 18–28 do not participate in albucidin biosynthesis. However, the dispensability of the gene *albC* (gene 17) is surprising since it belongs to the same operon as the essential genes *albA* (gene 15) and *albB* (gene 16). The deletion of the *albC* gene (gene 17) might be cross-complemented by an unidentified gene in the genome of the host strain *S. albus* Del14.

Albucidin production was heavily abolished in the strain *S. albus* 1K1\_LS (Figure S4E), implying that at least one of the genes 1–13 that were deleted in the BAC 1K1\_LS might be essential for albucidin biosynthesis. No genes encoding regulatory proteins or structural enzymes that might participate in nucleoside biosynthesis were identified in close proximity to the *albA–C* operon. To identify the genes within the deleted LS region that influence albucidin production, a BAC 2D4 was isolated from the genomic library of *S. albus* subsp. *chlorinus* NRRL B-24108. The chromosomal fragment cloned in BAC 2D4 overlaps with the fragment cloned in BAC 1K1 and covers the *albA–C* operon (Figure 1a). In contrast to 1K1, 2D4 BAC lacks genes 1–6, which are present in the deleted LS region. BAC 2D4 was transferred into *S. albus* Del14. Albucidin production could be detected in the extracts of the obtained strain *S. albus* 2D4 by HPLC-MS (Figure S5). This indicates that the genes 1–6 within the LS region are not involved in albucidin production and that one of the genes among 7–13 is responsible for the abolishment of albucidin production in *S. albus* 1K1\_LS.

To identify which of the genes 7–13 is involved in albucidin biosynthesis, each of them was individually substituted by an ampicillin resistance marker in 1K1 BAC yielding 1K1\_KO7, 1K1\_KO8, 1K1\_KO9, 1K1\_KO10, 1K1\_KO11, 1K1\_KO12 and 1K1\_KO13 (Figure 1b,c). The constructed BACs were transferred into the *S. albus* Del14 strain, and the albucidin production was analysed. The albucidin

production levels of all obtained strains, except *S. albus* 1K1\_KO12, were in the range of *S. albus* 1K1 harbouring the unmodified BAC (Figure S6). Albucidin production was abolished in *S. albus* 1K1\_KO12 (Figure S6G), indicating that gene 12 is responsible for the detrimental effect of the LS deletion on albucidin biosynthesis. No enzymatic activity could be assigned to the peptide product of gene 12 using blast analysis. The product also did not show homology to any known regulatory protein. Considering this, it was proposed that only the genes *albA* and *albB* encode structural enzymes essential for albucidin production in the heterologous host *S. albus* Del14 and that the product of the gene 12 elicits a regulatory effect on transcription of the *albA–C* operon through a mechanism that is not understood. To prove this, a BAC 1K1\_alb\_act was constructed containing only *albA–C* genes under the control of a strong promoter. The genes downstream of the *albA–C* operon (genes 18–28) were substituted with the ampicillin resistance gene and the genes upstream of the operon (genes 1–14) were substituted with the hygromycin resistance gene (Figure 1c). The hygromycin resistance gene used was under the control of the strong synthetic promoter TS81 and did not contain a terminator at its 3'-end [21]. The insertion of the hygromycin marker in front of the *albA–C* genes was performed in the orientation, which enabled their read-through from the TS81 promoter and their transcriptional activation. The constructed BAC 1K1\_alb\_act was transferred into the heterologous host strain *S. albus* Del14. The production of albucidin was detected in the extracts of the obtained strain *S. albus* 1K1\_alb\_act by HPLC-MS (Figure S7). Three times increase of albucidin production was observed in the strain *S. albus* 1K1\_alb\_act compared to *S. albus* 1K1 containing non-modified albucidin cluster (Figure S7). Taking into account that the total recovered albucidin yield from the *S. albus* 1K1 strain was approximately 0.4 mg/L, the calculated albucidin production by *S. albus* 1K1\_alb\_act corresponded to 1.2 mg/L. The albucidin production rate of 2 mg/L was reported for the original producer *Streptomyces albus* subsp. *chlorinus* NRRL B-24108 [10].

Albucidin production by the strain *S. albus* 1K1\_alb\_act clearly demonstrates that the genes *albA* and *albB* constitute the minimal set of the genes required for albucidin biosynthesis in heterologous host *S. albus* Del14. The role of the gene *albC* in albucidin biosynthesis is not completely understood. Because *albC* constitutes a single operon with *albA* and *albB* and its product shows homology to nucleotide biosynthetic enzymes, it cannot be completely excluded that the *albC* is involved in albucidin production in the natural producer. However, the deletion of *albC* has no effect on albucidin production in heterologous host.

The identification of the minimal set of albucidin biosynthetic genes allows its expression in various heterologous chassis strains as well as rational construction of albucidin overproducers. The engineering of the albucidin biosynthetic genes can be performed in *E. coli* and the obtained constructs can be heterologously expressed in *Streptomyces* hosts. In contrast to the genetically intractable original albucidin producer *Streptomyces albus* subsp. *chlorinus* NRRL B-24108, commonly used heterologous strains possess a well-established toolkit for their genetic manipulation. This opens the possibility to engineer their metabolic network to increase the intracellular levels of biosynthetic precursors and therefore to increase the production yields. The heterologous strains are often characterized by the simplified metabolic background which provides better detection limits for heterologously expressed compounds than the original producers, higher product yields and simplified downstream processing. Construction of the albucidin overproducers based on heterologous expression hosts is not necessarily limited to a rational approach. The chassis strains expressing heterologous cluster may be also subjected to classical mutagenesis and screening for overproducing clones.