**2. Principle and instrumentation**

#### **2.1. Raman spectroscopy**

The Raman effect is due to inelastic interaction between photons and atomic bonds. In this process, the scattered photons lose or gain energy with respect to the incident photons by vibrating or stabilising the atomic bonds of the sample. This effect leads to a shift in energy called the Stokes shift if the photons lose energy and the Anti-Stokes shift if the photons gain energy. At room conditions, the Stokes part of the Raman signal is generally stronger and is, therefore, generally used in Raman analysis. Due to the quantification of the energy and to the activated vibrational modes, photons are only scattered for particular energies and thus for particular wavelengths. Therefore, Raman scattering of a monochromatic light by molecular entities or an ordered solid will give spectra consisting of sharp spectral lines. Most systems today use a CCD camera interfaced with the spectrometer. The collected signal is then represented in a graph showing the number of photons versus wavenumber, i.e., the shift in cm−1 with respect to the incident beam, the laser wavelength corresponding then to 0 cm−1. The spectral resolution is limited by the CCD camera resolution and by the grating used to diffract the light. It also depends on the laser wavelength. Since the Raman effect is sensitive to atomic bonds, a Raman spectrum is associated with only one compound and its intensity is proportional to its concentration. Polymorphic minerals (similar compositions but different crystalline structures) will thus have different spectra. For example, anatase, rutile and brookite have different Raman spectra despite their similar composition (TiO2). It is important to note that different parameters may influence the Raman signal, such as instrumental setup, structural defects, traces elements, internal stresses or temperature [1–3] (**Figure 1**). Although these parameters may sometimes complicate interpretation, some of them can also be used to measure particular properties, for example, the shift related to internal stresses [4].

Finally, the Raman effect is relatively complex to model, and the identification of a compound is generally made by comparison with reference spectra found in the literature or in databases. More information about the Raman effect can be found in this book or others [5–7].

**Figure 1.** Origins of the parameters and modifications of the Raman spectrum [1].

#### **2.2. Instrumentation**

on sample transparency. For technical reasons, the technique began to be widely used only at the beginning of the 1980s with the generalisation of lasers. At the end of the 1990s, Raman spectroscopy underwent a second phase of development with the introduction of Charge Couple Device (CCD) technology, which has widely improved the sensitivity of spectrometers. These technical revolutions allow acquisition of a Raman spectrum in only few milliseconds. This strong decrease in the acquisition time permitted development of new applications, in particular Raman mapping. This technique consists of scanning the sample with the laser while acquiring

Here, we present the principle of the technique and the basis of the associated data processing followed by an overview of the information that can be extracted from Raman mapping to improve mineralogical and petrological analyses. In particular, we discuss how it can be used to study the general composition of rocks, to detect and identify small phases, or to differentiate minerals whose spectra are very close. More exotic uses of the collected signal are also presented, e.g., detection of particular phases using luminescence. Finally, we illustrate the

The Raman effect is due to inelastic interaction between photons and atomic bonds. In this process, the scattered photons lose or gain energy with respect to the incident photons by vibrating or stabilising the atomic bonds of the sample. This effect leads to a shift in energy called the Stokes shift if the photons lose energy and the Anti-Stokes shift if the photons gain energy. At room conditions, the Stokes part of the Raman signal is generally stronger and is, therefore, generally used in Raman analysis. Due to the quantification of the energy and to the activated vibrational modes, photons are only scattered for particular energies and thus for particular wavelengths. Therefore, Raman scattering of a monochromatic light by molecular entities or an ordered solid will give spectra consisting of sharp spectral lines. Most systems today use a CCD camera interfaced with the spectrometer. The collected signal is then represented in a graph showing the number of photons versus wavenumber, i.e., the shift in cm−1 with respect to the incident beam, the laser wavelength corresponding then to 0 cm−1. The spectral resolution is limited by the CCD camera resolution and by the grating used to diffract the light. It also depends on the laser wavelength. Since the Raman effect is sensitive to atomic bonds, a Raman spectrum is associated with only one compound and its intensity is proportional to its concentration. Polymorphic minerals (similar compositions but different crystalline structures) will thus have different spectra. For example, anatase, rutile and brookite have different Raman spectra despite their similar composition (TiO2). It is important to note that different parameters may influence the Raman signal, such as instrumental setup, structural defects, traces elements, internal stresses or temperature [1–3] (**Figure 1**). Although these parameters may sometimes complicate interpretation, some of them can also be used to

measure particular properties, for example, the shift related to internal stresses [4].

spectra so that spatial distribution can be added to structural information.

discussion using a number of different types of rocks and minerals.

**2. Principle and instrumentation**

**2.1. Raman spectroscopy**

164 Raman Spectroscopy and Applications

Raman mapping consists of scanning a sample with the laser beam while acquiring spectra. For the study example, we present here, a WITec Alpha500 RA Raman spectrometer was used. Other systems from Renishaw, Horiba Jobin Yvon, Thermo Fisher or Bruker, for example, may use slightly different methods for mapping, but the general principle of scanning remains the same. The general discussion of this chapter can thus be applied to any other system.

This study was made using a CW green Nd:YAG frequency doubled laser with a wavelength of λ = 532 nm. The laser beam is focused on the sample using optical microscope objectives and optical observations of the analysed area are made with a camera. The spectral resolution of the spectrometer is of 1 and 3 cm−1 using 1800 and 600 g/mm gratings, respectively. Surface scanning is made by moving the sample below the objective along successive lines using motorised and/or piezoelectric scan tables. The instrument used is equipped with two positioning systems: (1) a small-scale system working with piezoelectric ceramics that can analyse an area of 200 × 200 μm in the horizontal plan and 20 μm in the vertical direction and (2) a large-scale motorised system that can move the sample over a distance of 15 × 10 cm horizontally and 2 cm vertically.

Raman mapping systems are generally confocal, i.e. only the signal coming from the near object focal plane is redirected to the spectrometer. This is done by placing a pin hole in the image focal plane of the microscope. The volume used for the analysis is then reduced and the inplane resolution is slightly increased, depending on the size of the pin hole, the objective and the laser wavelength. The main interest of the confocality is to permit 3D imaging using a stacking process by acquiring Raman maps at different depths. It is important to note that the depth of analysis depends on the refractive index of the material; there is a difference between the apparent depth, corresponding to the distance with respect to the sample surface, and the true depth of analysis (**Figure 2**).

**Figure 2.** Apparent depth *D*0 versus true depth *Di* of analysis in optical microscopy when analysing (a) a material with a refractive index equal to *n*1, (b) a material with a refractive index equal to *n*2 with *n*2 > *n*1 and (c) when analysing across *n* different materials of refractive index *ni* and thickness *ei* .

The ratio between the true depth *Di* and the apparent depth *D*0 is given by the following equation:

$$\frac{D\_l}{D\_0} = \frac{\tan i\_0}{\tan i\_l} \tag{1}$$

where *i*0 and *i*1 are the maximal angle with respect to the optical axis in the observation medium (e.g. air, water or oil) and in the analysed material, respectively (see **Figure 2a**). The famous Snell-Descartes law and the definition of the numerical aperture NA give:

$$NA = n\_0.\sin i\_0 = n\_l.\sin i\_l \tag{2}$$

where *n*0 and *ni* are the refractive index of the observation medium and of the analysed material, respectively. Using Eq. (2), and noting that:

$$\tan\left(\arcsin x\right) = \frac{x}{\sqrt{1-x^2}}\tag{3}$$

Eq. (1) finally writes:

$$\frac{D\_l}{D\_0} = \sqrt{\frac{n\_l^2 - NA^2}{n\_0^2 - NA^2}}\tag{4}$$

This ratio is thus all the more important when the difference in refractive index is high (see **Figure 2b**). For example, when analysing quartz (*n*qz = 1.55) in air (*n*air = 1) with an objective having a numerical aperture of *NA* = 0.9, the true depth of analysis is ~2.9 times deeper than the apparent depth corresponding to the mechanical vertical displacement.

In the case where several phases are crossed by the laser (see **Figure 2c**), Eq. (4) can be generalised into:

$$\frac{D\_n}{D\_0} = \sum\_{l=1}^n e\_l \sqrt{\frac{n\_l^2 - NA^2}{n\_0^2 - NA^2}}\tag{5}$$

where *ei* is the thickness of the phase *i*. For 2D maps, the depth of the measurement and the volume analysed are thus different for each phase, and for 3D Raman maps, the volume and the depth of the different phases are associated with different scales in the raw data.

#### **2.3. Raman mapping**

depth of analysis depends on the refractive index of the material; there is a difference between the apparent depth, corresponding to the distance with respect to the sample surface, and the

a refractive index equal to *n*1, (b) a material with a refractive index equal to *n*2 with *n*2 > *n*1 and (c) when analysing

0

where *i*0 and *i*1 are the maximal angle with respect to the optical axis in the observation medium (e.g. air, water or oil) and in the analysed material, respectively (see **Figure 2a**). The famous

.

and thickness *ei*

0 1 tan tan *D i i*

( ) 2

=

1 *x x*

> 2 2 2 2

0 0 *D n NA i i D n NA* *x*

Snell-Descartes law and the definition of the numerical aperture NA give:

tan arcsin

of analysis in optical microscopy when analysing (a) a material with

and the apparent depth *D*0 is given by the following

*D i* = (1)

0 0 .sin .sin *NA n i n i* = = *i i* (2)

are the refractive index of the observation medium and of the analysed material,

true depth of analysis (**Figure 2**).

166 Raman Spectroscopy and Applications

**Figure 2.** Apparent depth *D*0 versus true depth *Di*

across *n* different materials of refractive index *ni*

The ratio between the true depth *Di*

respectively. Using Eq. (2), and noting that:

equation:

where *n*0 and *ni*

Eq. (1) finally writes:

As explained above, Raman mapping consists of scanning the sample with the laser beam while acquiring spectra. The scanned area is then divided into a pixel-assigned array of spectral elements, sometimes called "spexels" [8], as shown in **Figure 3a**.

**Figure 3.** (a) Principles of Raman mapping illustrated using a polished thin section of a metachert from the 3.8 billion years old Isua Greenstone belt, Greenland. The scanned area of the sample is divided into small squares, or spexels, each associated with a Raman spectrum. (b) Raman map of the concentration of quartz in the sample, as identified by the intensity of the main peak at 465 cm−1. (c) Raman map of the composition of the sample obtained by attributing a colour to the main spectral peak of each mineral (yellow for quartz, grey for cumingtonite, pink for apatite and green for graphite). Scan size: 500 × 500 μm2 .

Historically, the first Raman maps were constructed manually by making a map point by point. The sample was then not scanned continuously but was immobile beneath the laser beam during the acquisition of each spectrum (**Figure 4**). These first maps were interesting for the study of homogeneity in materials, but, because of the limited number of spectra associated with this time-consuming method, imaging was not very efficient. With the recent development in fine positioning systems and synchronisation between the positioning system and the CCD detector, automated scanning is now available. This automation allows high resolution mapping point by point as well as by continuous scanning.

**Figure 4.** Principle of Raman mapping. (a) Laser spot size versus area of interest and different ways of scanning, (b) point by point, (c) continuously and horizontally and (d) continuously and vertically. The spectrum associated to each pixel corresponds to the average spectrum of the different associated phases. The concentration map is thus darker where the vermicular structure is analysed. If the spot size is larger than the pixel size (left row), there is oversampling, and the associated maps are the same whatever the method used. However, if the laser spot size is small in comparison with the pixel size (right row), there is undersampling and the associated maps depend on the type of the scan.

In all scanning methods of imaging (e.g. Raman mapping as well as atomic force microscopy, scanning electron microscopy, etc.), the lines are accumulated one by one and the spatial resolution is fixed by the ratio of the width and height of the scanned area over the number of lines and rows, respectively. Then, for a square scan, the time needed to go down a row is very long compared to the time needed to go along a line (for a horizontal scan). The axis parallel to the lines is thus called the fast axis, and the axis perpendicular to the lines is called the slow axis (see **Figure 4**). This notion must be kept in mind while scanning a small area where thermal or mechanical drift may induce deformation in the images (shearing) and/or a loss of focus with the time. During Raman mapping, the spectra are generally accumulated continuously during the scan. The spectrum associated with each point corresponds thus to the average spectrum acquired over a line whose length is fixed by the spatial resolution but whose thickness is equal to the laser spot size. If the spot size is higher than the resolution, the associated map is then under-sampled since not all the area are scanned; a scan obtained over the same area rotated by 90° would give a different result (**Figure 4**). To avoid this effect, the chosen resolution must be at least equal to the spot size. On the other hand, the Rayleigh criterion implies that the resolution cannot be physically higher than half of the spot size as given by the Airy disk equation:

$$S = 1.22 \frac{\lambda}{NA} \tag{6}$$

where *λ* is the laser wavelength, *S* is the laser spot diameter, and *NA* is the numerical aperture of the objective. Using a green laser (*λ* = 532 nm) and the 100× objective (with a numerical aperture of 0.9), the spot size is thus *S* ~ 720 nm in diameter and the maximal physical resolution is thus ~360 nm/pixel. Using a confocal system, this resolution can be slightly decreased by ~10% [6]. However, even if using a higher resolution does not give more information, oversampling could provide a better contrast.

In conclusion, continuous scanning is generally the best choice for imaging due to its rapidity, but point-by-point mapping may be useful when the sample contains fluorescent phases that may drown out the Raman signal of the other phases in the average spectra acquired with the continuous scan method.


**1. Introduction**

Emotions play an essential role in rational decision-making, perception, learning and a variety of other functions that affect both human physiological and psychological status [1]. Therefore, understanding and recognising emotions are very important aspects of human behaviour research. To study human emotions, affective states need to be evoked in laboratory environments, using elicitation methods such as images, audio, videos and, recently, virtual reality (VR). VR has experienced an increase in popularity in recent years in scientific and commercial contexts [2]. Its general applications include gaming, training, education, health and marketing. This increase is based on the development of a new generation of low-cost headsets which has democratised global purchases of head-mounted displays (HMDs) [3]. Nonetheless, VR has been used in research since the 1990s [4]. The scientific interest in VR is due to the fact that it provides simulated experiences that create the sensation of being in the real world [5]. In particular, environmental simulations are representations of physical environments that allow researchers to analyse reactions to common concepts [6]. They are especially important when what they depict cannot be physically represented. VR makes it possible to study these scenarios under controlled laboratory conditions [7]. Moreover, VR allows the time- and cost-effective isolation and modification of variables, unfeasible in real space [8].

#### *1.1. Virtual Reality Set-Ups*

The set-ups that display VR simulations have been progressively integrated into studies as the relevant technologies have evolved. These consist of a combination of three objective features, formats, display devices and user interfaces.

The format describes the structure of the information displayed. The most common are two-dimensional (2D) multimedia and three-dimensional (3D) environments, and the main di fference between them is their levels of interactivity [9]. 2D multimedia, including 360◦ panoramic images and videos, provide non-interactive visual representations. The validity of this format has been extensively explored [10]. Moreover, the latest advances in computer-generated images simulate light, texture and atmospheric conditions to such a degree of photorealism that it is possible to produce a virtual image that is indistinguishable, to the naked eye, from a photograph of a real-world scene [11]. This format allows scientists to test static computer-generated environments, with many variations, cheaply and quickly in a laboratory. On the other hand, 3D environments generate interactive representations which allow changes in the user's point of view, navigation and even interaction with objects and people [12]. Developing realistic 3D environments is more time consuming than developing 360◦ computer-generated photographs, and their level of realism is limited by the power of the hardware. However, the processing potency of GPUs (graphics processing units) is increasing every year, which will enhance the performance of 3D environments. Moreover, the interaction capacity of 3D environments, which facilitates the simulation of real-world tasks, is a key aspect in the application of virtual reality [2].

The display devices are the technological equipment used to visualise the formats. They are classified according to the level of immersion they provide, that is, the sensorimotor contingencies that they support. These are related to the actions that experimental subjects carry out in the perception process, for example, when they bend down and shift the position of their heads, and their gaze direction, to see underneath an object. Therefore, the sensorimotor contingencies supported by a system define a set of valid actions (e.g., turning the head, bending forward) that carry meaning in terms of perception within the virtual environment [13]. Since immersion is objective, one system is more immersive than another if it is superior in at least one characteristic while others remain equal. There are three categories of immersion system, non-immersive, semi-immersive and immersive [2]. Non-immersive systems are simpler devices which use a single screen, such as a desktop PC, to display environments [14]. Semi-immersive systems, such as the cave automatic virtual environment (CAVE), or the powerwall screen, use large projections to display environments on walls, enveloping the viewer [15,16]. These displays typically provide a stereo image of an environment, using a perspective projection linked to the position of the observer's head. Immersive devices, such as HMDs, are fully-immersive systems that isolate the user from external world stimuli [17]. These provide a complete simulated experience, including a stereoscopic view, which responds to the user's head movements. During the last two decades, VR has usually been displayed through desktop PCs or semi-immersive systems, such as CAVEs and powerwalls [18]. However, improvements in the performance and availability of the new generation of HMDs is boosting their use in research [19].

The user interfaces, which are exclusive to 3D environments which allow this level of interaction, are the functional connections between the user and the VR environment which allow him or her to interact with objects and navigate [20]. Regarding interaction with objects, manipulation tasks include: selection, that is, acquiring or identifying an object or subset of objects, positioning, that is, changing an object's 3D position, and rotation, that is, changing an object's 3D orientation. In terms of the navigation metaphors in 3D environments, virtual locomotion has been thoroughly analysed [21], and can be classified as physical or artificial. Regarding the physical, there are room-scale-based metaphors, such as real-walking, which allow the user to walk freely inside a limited physical space. These are normally used with HMDs, and position and orientation are determined by the position of the user's head. They are the most naturalistic of the metaphors, but are highly limited by the physical tracked area [22]. In addition, there are motion-based metaphors, such as walking-in-place or redirected walking. Walking-in-place is a pseudo-naturalistic metaphor where the user performs a virtual locomotion to navigate, for example, by moving his/her hands as if (s)he was walking, or by performing footstep-like movements, while remaining stationary [23]. Redirected walking is a technique where the user perceives (s)he is walking freely but, in fact, is being unknowingly manipulated by the virtual

display: this allows navigation in an environment larger than the actual tracked area [24]. Regarding the artificial, controller-based metaphors allow users to control their movements directly through joysticks or similar devices, such as keyboards and trackballs [25]. In addition, teleportation-based metaphors allow the user to point where (s)he wants to go and teleport him or her there with an instantaneous "jump" [26]. Moreover, recent advancements in the latest generation HMD devices have increased the performance of navigation metaphors. Point-and-click teleport metaphors have become mainstream technologies implemented in all low-cost devices. However, other techniques have also increased in performance: walking-in-place metaphors have become more user-friendly and robust, room-scale-based metaphors now have increased coverage areas, provided by low-cost tracking methods, and controller-based locomotion now addresses virtual sickness through effective, dynamic field-of-view adjustments [27].

#### *1.2. Sense of Presence*

In addition to the objective features of the set-up, the experience of users in virtual environments can be measured by the concept of presence, understood as the subjective feeling of "being-there" [28]. A high degree of presence creates in the user the sensation of physical presence and the illusion of interacting and reacting as if (s)he was in the real world [29]. In the 2000s, the strong illusion of being in a place, in spite of the sure knowledge that one is not actually there, was characterised as "place illusion" (PI), to avoid any confusion that might be caused by the multiple meanings of the word "presence". Moreover, just as PI relates to how the world is perceived, and the correlation of movements and concomitant changes in the images that form perceptions, "plausibility illusion" (PsI) relates to what is perceived, in a correlation of external events not directly caused by the participant [13]. PsI is determined by the extent to which a system produces events that directly relate to the participant, and the overall credibility of the scenario being depicted in comparison with viewer expectations, for example, when an experimental participant is provoked into giving a quick, natural and automatic reply to a question posed by an avatar.

Although presence plays a critical role in VR experiences, there is limited understanding of what factors affect presence in virtual environments. However, there is consensus that exteroception and interoception factors affect presence. It has been shown that exteroception factors, such as higher levels of interactivity and immersion, which are directly related to the experimental set-up, provoke increased presence, especially in virtual environments not designed to induce particular emotions [30–32]. As to the interoception factors, which are defined by the content displayed, participants will perceive higher presence if they feel emotionally affected; for example, previous studies have found a strong correlation between arousal and presence [33]. Recent research has also analysed presence in specific contexts and suggested that, for example, in social environments, it is enhanced when the VR elicits genuine cognitive, emotional and behavioural responses, and when participants create their own narratives about events [34]. On the other hand, presence decreases when users experience physical problems, such as cybersickness [35].

#### *1.3. Virtual Reality in Human Behaviour Research*

VR is, thus, proposed as a powerful tool to simulate complex, real situations and environments, offering researchers unprecedented opportunities to investigate human behaviour in closely controlled designs in controlled laboratory conditions [33]. There are now many researchers in the field, who have published many studies, so a strong, interdisciplinary community exists [2].

Education and training is one field where VR has been much applied. Freina and Ott [36] showed that VR can offer great educational advantages. It can solve time-travel problems, for example, students can experience different historical periods. It can address physical inaccessibility, for example, students can explore the solar system in the first person. It can circumnavigate ethical problems, for example, students can "perform" serious surgery. Surgical training is now one of the most analysed research topics. Interventional surgery lacked satisfactory training methods before the advent of VR, except learning on real patients [37]. Bhagat, Liou and Chang [38] analysed improvements in military training. These authors suggested that cost-e ffective 3D VR significantly improved subjects learning motivation and outcomes and provided a positive impact on their live-firing achievement scores. In addition, besides enhancements in cost-e ffectivity, VR o ffers a safe training environment, as evidenced by the extensive research into driving and flight simulators [39,40]. Moreover, de-Juan-Ripoll et al. [41] proposed that VR is an invaluable tool for assessing risk-taking profiles and to train in related skills, due to its transferability to real-world situations.

Several researchers have also demonstrated the e ffectiveness of VR in therapeutic applications. It o ffers some distinct advantages over standard therapies, including precise control over the degree of exposure to the therapeutic scenario, the possibility of tailoring scenarios to individual patients' needs and even the capacity to provide therapies that might otherwise be impossible [42]. Taking some examples, studies using VR have analysed the improvement in the training in social skills for persons with mental and behavioural disorders, such as phobias [43], schizophrenia [44] and autism [45]. Lloréns, Noé, Colomer and Alcañiz [46] showed that VR-based telerehabilitation interventions promoted the reacquisition of locomotor skills associated with balance, in the same way as in-clinic interventions (both complemented with conventional therapy programmes). Moreover, it has been proposed as a key tool for the diagnosis of neurodevelopmental disorders [47].

In addition, VR has been applied transversally to many fields, such as architecture and marketing. In architecture, VR has been used as a framework within which to test the overall validity of proposed plans and architectural designs, generate alternatives and conceptualise learning, instruction and the design process itself [48]. In marketing, it has been applied in the analysis of consumer behaviour in laboratory-controlled conditions [49] and as a tool to develop emotionally engaging consumer experiences [50].

One of the most important topics in human behaviour research is human emotions, due to the central role that they play in many background processes, such as perception, decision-making, creativity, memory and social interaction [51]. Given the presence that VR provokes in users, it has been suggested as a powerful means of evoking emotions in laboratory environments [8]. In one of the first confirmatory studies into the e fficacy of immersive VR as an a ffective medium, Baños et al. [30] showed that emotion has an impact on presence. Subsequently, many other similar studies showed that VR can evoke emotions, such as anxiety and relaxation [52], positive valence in obese children taking exercise [53], arousal in natural environments, such as parks [54], and di fferent moods in social environments featuring avatars [55].

#### *1.4. The Validity of Virtual Reality*

Finally, it is crucial to point out that the usefulness of simulation in human behaviour research has been analysed through the validity concept, that is, the capacity to evoke a response from the user in a simulated environment similar to one that might be evoked by a physical environment [56]. Thus, there is a need to perform direct comparisons between virtual and real environments. Some comparisons have studied the validity of virtual environments by assessing psychological responses [57] and cognitive performance [58]. However, there have been fewer analyses of physiological and behavioural responses [59,60]. Heydarian et al. analysed user performance in o ffice-related activities, for example, reading texts and identifying objects, and found that the participants performed similarly in an immersive virtual environment setting and in a benchmarked physical environment for all of the measured tasks [61]. Chamilothori, Wienold, and Andersen compared subjective perceptions of daylit spaces, and identified no significant di fferences between the real and virtual environments studied [62]. Kimura et al. analysed orienteering-task performance, where participants in a VR room showed less facility, suggesting that caution must be applied when interpreting the nuances of spatial cue use in virtual environments [63]. Higuera-Trujillo, López-Tarruella, and Llinares analysed psycho-physiological responses, through electrodermal activity (EDA), evoked by real-world and VR scenarios with di fferent immersion levels, and demonstrated correlations in the physiological

dynamics between real-world and 3D environments [64]. Marín-Morales et al. analysed the emotional responses evoked in subjects in a real and a virtual museum, and found no self-assessment differences, but did find differences in brain dynamics [65]. Therefore, further research is needed to understand the validity of VR in terms of physiological responses and behavioural performance.

#### *1.5. Implicit Measures and the Neuroscience Approach*

Traditionally, most theories of human behaviour research have been based on a model of the human mind that assumes that humans can think about and accurately verbalise their attitudes, emotions and behaviours [66]. Therefore, classical psychological evaluations used self-assessment questionnaires and interviews to quantify subjects' responses. However, these explicit measures have been demonstrated to be subjective, as stereotype-based expectations can lead to systematically biased behaviour, given that most individuals are motivated to be, or appear to be, nonbiased [67]. The terms used in questionnaires can also be differentially interpreted by respondents, and the outcomes depend on the subjects possessing a wide knowledge of their dispositions, which is not always the case [68].

Recent advances in neuroscience show that most of the brain processes that regulate our emotions, attitudes and behaviours are not conscious. In contrast to explicit processes, humans cannot verbalise these implicit processes [69]. In recent years, growing interest has developed in "looking" inside the brain to seek solutions to problems that have not traditionally been addressed by neuroscience. Thus, neuroscience offers techniques that can recognise implicit measurements not controlled by conscious processes [70]. These developments have provoked the emergence in the last decades of a new field called neuroeconomics, which blends psychology, neuroscience and economics into models of decision-making, rewards, risks and uncertainties [71]. Neuroeconomics addresses human behaviour research, in particular the brain mechanisms involved in economic decision-making, from the point of view of cognitive neuroscience, using implicit measures.

Several implicit measuring techniques have been proposed in recent years. Some examples of their applications in human behaviour research are: heart rate variability (HRV) has been correlated with arousal changes in vehicle drivers when detecting critical points on a route [72], electrodermal activity (EDA) has been used to measure stress caused by cognitive load in the workplace [73], electroencephalogram (EEG) has been used to assess engagement in audio-visual content [74], functional magnetic resonance imaging (fMRI) has been used to record the brain activity of participants engaged in social vs. mechanical/analytic tasks [75], functional near-infrared spectroscopy (fNIRS) has been used as a direct measure of brain activity related to decision-making processes in approach-avoidance theories [76], eye-tracking (ET) has been used to measure subconscious brain processes that show correlations with information processing in risky decisions [77], facial expression analysis (FEA) has been applied to detect emotional responses in e-learning environments [78] and speech emotion recognition (SER) has been used to detect depressive disorders [79]. Table 1 gives an overview of the implicit measuring techniques that have been used in human behaviour research.

In addition, recent studies have highlighted the potential of virtual reality environments for enhancing ecological validity in the clinical, a ffective and social neurosciences. These studies have usually involved the use of simple, static stimuli which lack many of the potentially important aspects of real-world activities and interactions [90]. Therefore, VR could play an important role in the future of neuroeconomics by providing a more ecological framework within which to develop experimental studies with implicit measures.

#### *1.6. A*ff*ective Computing and Emotion Recognition Systems*

A ffective computing, which analyses human responses using implicit measures, has developed into an important field of study in the last decades. Introduced by Rosalind Picard in 1997, it proposed the automatic quantification and recognition of human emotions as an interdisciplinary field based on psychophysiology, computer science, biomedical engineering and artificial intelligence [1]. The automatic recognition of human emotion statements using implicit measures can be transversally applied to all human behaviour topics and complement classic explicit measures. In particular, it can be applied to neuroeconomic research as they share the same neuroscientific approach of using implicit measures, and due to the important relationship that has been found between emotions and decision-making [71]. Emotion recognition models can be divided into three approaches: emotional modelling, emotion classification and emotion elicitation.

The emotional modelling approach can be divided into the discrete and the dimensional. Discrete models characterise the emotion system as a set of basic emotions, which includes anger, disgust, fear, joy, sadness and surprise, and the complex emotions that result from combining them [91]. On the other hand, dimensional models propose that emotional responses can be modelled in a multidimensional space where each dimension represents a fundamental property common to all emotions. The most commonly used theory is the circumplex model of a ffect (CMA), which proposes a three-dimensional space consisting of: valence, that is, the degree to which an emotion is perceived as positive or negative, arousal, that is, the intensity of the emotion in terms of activation, from low to high, and dominance, which ranges from feelings of total lack of control or influence on events and surroundings to the opposite extreme of feeling influential and in control [92].

A ffective computing uses biometric signals and machine-learning algorithms to classify emotions automatically. Many signals have been used, such as voice, face, neuroimaging and physiological [93]. It is noteworthy that one of the main emotion classification topics uses variables associated with central nervous system (CNS) and autonomic nervous system (ANS) dynamics [93]. First, human emotional processing and perception involve cerebral cortex activity, which allows the automatic classification of emotions using the CNS. EEG is one of the techniques most used in this context [94]. Second, many emotion recognition studies have used the ANS to analyse the changes in cardiovascular dynamics provoked by mood changes, where HRV and EDA are the most used techniques [95]. The combination of physiological features and machine-learning algorithms, such as in support vector machines, linear discriminant analysis, K-nearest neighbour and neural networks, has achieved high levels of accuracy in inferring subjects' emotional states [96].

Finally, emotion elicitation is the ability to reliably and ethically elicit a ffective states. This elicitation is a critical factor in the development of systems that can detect, interpret and adapt to human a ffect [97]. The many methods that elicit emotions in laboratories can be mainly divided into two groups, active and passive. Active methods involve directly influencing subjects, including behavioural manipulation [98], social interaction [99] and dyadic interaction [100]. Passive methods usually present external stimuli, such as images, sound or video. As to the use of images, the International A ffective Picture System (IAPS) is among the databases most used as an elicitation tool in emotion recognition methodologies [95]. This includes over a thousand depictions of people, objects and events, standardised on the basis of valence and arousal [97]. As to audio, the International A ffective Digitalised Sound System (IADS) database is the most commonly applied in studies which use sound to elicit emotions [101]. However, some studies directly use music or narrative to elicit emotions [102]. With respect to

audio-visual stimuli, many studies have used film to induce arousal and valence [103]. These emotion elicitation methods have two important limitations. The set-ups used, mostly screens, are non-immersive devices, which provoke only a low level of presence in subjects [30]. Therefore, the stimuli do not evoke in the subjects a feeling of "being there", which is needed to analyse emotions in simulated real-world situations. In addition, the stimuli are non-interactive, so they do not allow the subjects to intervene in the scene, which would open the possibility to recognise emotional states during interactive tasks. These limitations can be overcome by using immersive VR as a new emotion elicitation method. Since the year 2000, VR has increasingly been used as affective stimulation, however the majority of the studies undertaken have applied classic statistical methods, such as hypotheses testing and correlation, to analyse subjects' physiological responses to different emotions [104]. However, in recent years, some research has started to apply affective computing paradigms with VR as the emotion elicitation method, combining implicit measures with machine-learning methods to develop automatic emotion recognition models [105].

This paper provides a systematic review of the literature on the use of head-mounted displays in implicit measure-based emotion recognition research, and examines the evolution of the research field, the emotions analysed, the implicit techniques, the data analysis, the set-ups and the validations performed.

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


**5. Conclusions**

To solve the problem of mold growth causing damage to the architectural style of an ancient palace in Beijing, a simulation study of indoor heat and humidity transfer characteristics of ancient buildings was carried out via an ANSYS numerical simulation research method. The results of the parameters affecting the indoor humidity and the proposed protective measures were obtained as follows.

**Author Contributions:** All authors contributed to this paper. Conceptualization, F.L. and X.Z.; methodology, writing—original draft preparation, resources, supervision, F.L.; software, J.Z.; validation, X.Z. and J.Z.; formal analysis, Y.L.; writing—original draft preparation, supervision, F.L.; writing—review and editing, G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** No new data were created or analyzed in this study.

**Acknowledgments:** We also would like to thank the anonymous reviewers for their valuable comments and suggestions that lead to a substantially improved manuscript.

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

#### **References**

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**Xidi Jiang \*, Shuheng Zhong and Jialu Liang**

School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China<EMAIL_ADDRESS>(S.Z<EMAIL_ADDRESS>(J.L.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +86-152-1057-2805

**Abstract:** Mine accidents are mostly caused by human unsafe behavior. To reduce the unsafe behavior of mine operation and reduce the accident of mine operation, the main body of unsafe behavior 'people' is analyzed, and 24 attribute factors are selected from five aspects of people's emotions, motivation, ability, personality, and pressure to construct the comprehensive model of human behavior SMAPP (sentiment–motivation–ability–personality–pressure). The program tool for recording, saving, and executing the mutual and interactive influence relationship of 24 attribute factors under different state values and the simulation process framework of SMAPP was constructed by using 1071 rule statements written in Python language. The fuzzy rules are used to simulate different scenarios. The simulation results are consistent with the actual research results, which shows the reliability and scientificity of the model. In addition, additional events are added to the simulation process to make the model more realistic. Through the simulation results, the influence of employees' emotions, motivations, abilities, personalities, pressures, and additional events on the unsafe behavior of mine operations is analyzed and predicted, and the measures to reduce the unsafe behavior of mine operations are further proposed.

**Keywords:** SMAPP model; unsafe behavior; fuzzy rules; simulation framework


**5. Conclusions**

154 Computational Simulations and Applications

particles. In fact, the numerically obtained profiles of particle mass concentration are highly appreciated because of the efficient operation of CFB units. On the contrary, in the flow domain the gradient profiles of mass concentration can cause retard of enhancement of the combustion process. Thus, an additional sand mass fraction brought to the flow domain

> **0 0.25 0.5 0.75 1 Radial distance**

Fig. 6. Distribution of particle mass concentration for ash and sand solid phases in different

Finally, the Fig. 7 shows distribution of turbulent the energy across the flow. All considered results for given three different regimes: mixture1, mixture2 and mixture3 are matched between each other and versus also turbulent energy of single phase flow. As a whole, the trend shows that the particles in all the observed regimes generate the turbulence, which stems from the vortex shedding phenomenon behind the particles which is input to the level of turbulence generated by the flow itself. This effect of turbulence modulation, namely, the turbulence enhancement due to the presence of coarse particles is explained and computed using the four-way coupling model by Crowe (2000). This amount of an additional turbulent energy is proportional to the square of velocity slip between the gaseous and the solid phases following the model by Crowe (2000) and it is substantial because of large velocity slip between the phases owing to high inertia of large particle size. Following to the model of Crowe (2000), this generation term is balanced by the introduced dissipation rate of turbulent energy and calculated via the hybrid turbulence length scale (last term in the right-hand side of Eq. 5). The given four-way coupling model by Crowe (2000) is based on the criteria of turbulence modulation by particles considering the ratio of particle size to the integral turbulence length scale. In accordance with this criterion for the considered cases of two-phase turbulent flow loaded by 500 and 1000µm particles, this scale ratio is far above 0.1 and therefore the particles enhanced the turbulence of the carrier gas-phase flow. In addition, the effect of increase of polydispersity grade, i.e. particle size variation from 500 up to 1000µm occurred for the mixture2 (only with ash particles) is less pronounced than that with increase of mass flow ratio up to 20kg/kg occurred in the case of the mixture3 (cf. bold dashed line in Fig. 7), on forming the shape and magnitude level of turbulent energy.

flow conditions shown in previous Figs. 4 and 5: for mixtures 1 and 3.

may contribute to the improvement of the combustion process in CFB cycles.

**mixture1:ash mixture1:sand mixture3:ash mixture3:sand**

**0.975**

**0.98**

**0.985**

**0.99**

**0.995**

**Particle mass concenration**

**1.005**

**1.01**

**1**

The numerical study of particulate turbulent flow modelled by 2D RANS (Euler/Euler) approach showed importance of addition of second solid fraction, characterized by heavy (sand) particles along with existence of first solid fraction of lighter (ash) particles in the mixing process taken place in freeboard CFB process. The main contribution to the flow formation stems from the inclusion of inter-particle collisions and four-way coupling turbulence modulation due to the presence of polydispersed solid particles with various physical properties. Other forces exerted on the motion of solids are: the gravitation, viscous drag and lift forces. On the basis of the performed calculations one can conclude:

a. variation of solids material properties results in the enhancement of flow turbulence in comparison with the turbulence level of the flow loaded by one particle fraction;

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The obtained results can be implemented for updating/refurbishing of the industrial scale CFB risers using the real sizes distribution of solid particles of ash and sand for the combustion of Estonian oil-shale particles.

## **6. Acknowledgements**

The work was support from target financing of the Project SF0140070s08 (Estonia). The authors are grateful for the technical support of cluster computers from the Universities of Texas at Austin and San Antonio (USA).

## **7. References**

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**1. Introduction**

numerical solutions.

the direction normal to the plane.

two-dimensional steady state case.

Fig. (1).

Vai Kuong Sin and Chon Kit Chio

The full Navier-Stokes equations are difficult or impossible to obtain an exact solution in almost every real situation because of the analytic difficulties associated with the nonlinearity due to convective acceleration. The existence of exact solutions are fundamental not only in their own right as solutions of particular flows, but also are agreeable in accuracy checks for

**Computation of Non-Isothermal Reversed**

**Stagnation-Point Flow over a Flat Plate**

In some simplified cases, such as two-dimensional stagnation point flows, by introducing coordinate variable transformation, the number of independent variables is reduced by one or more. The governing equations can be simplified to the non-linear ordinary differential equations and are analytic solvable. The classic problems of two-dimensional stagnation-point flows can be analyzed exactly by Hiemenz Hiemenz (1911), one of Prandtl's first students. These are exact solutions for flow directed perpendicular to an infinite flat plate. Howarth Howarth (1951) and Davey Davey (1961) extended the two-dimensional and axisymmetric flows to three dimensions, which is based on boundary layer approximation in

The similarity solutions for the temperature field were studied by Eckert Eckert (1942). Case corresponding a step change in wall temperature or in wall heat flux in laminar steady flows at a stagnation point has been also investigated by several authors (see Chao et al. Chao & Jeng (1965), Sano Sano (1981) and Gorla Gorla (1988)). Further, Lok et al. Lok et al. (2006) investigated the mixed convection near non-orthogonal stagnation point flow on a vertical plate with uniform surface heat flux, where the results published are very good with present value of *θ*(0) for the constant wall temperature boundary condition. On the contrary, reversed stagnation-point flow over an infinite flat wall does not have analytic solution in

The aim of this study is to investigate the unsteady viscous reversed non-isothermal stagnation-point flow, which is started impulsively in motion with a constant velocity away from near the stagnation point. A similarity solution of full Navier-Stokes equations and energy equation are solved by applying numerical method. Studies of the reversed stagnation-point flow have been considered during the last few years, as this flow can be applied in different important applications that occur in oil recovery industry, as shown in

*University of Macau Taipa, Macao SAR, China*

**8**


**4. Conclusions**

In this paper, the effects of carbon content on the characteristics, microstructure, hardness, and properties of the bionic unit were studied, and the response of the base material, with different compositions, to laser bionic treatment was obtained. The conclusions are as follows:

**Author Contributions:** G.C. Study design, Data collection, Data analysis, Writing; T.Z. Literature search. H.Z. Study design; P.Z. Study design, Literature Search; S.M. Data analysis; B.Z. Literature search; S.W.: Data analysis. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Project 985-High Performance Materials of Jilin University, Project 985-Bionic Engineering Science and Technology Innovation, and the double first-class project by Jilin Province and Jilin University (SXGJXX2017-14), Natural Science Foundation of Liaoning Province (No.<PHONE_NUMBER>1) and National Natural Science Foundation of China (U1601203).

**Conflicts of Interest:** It is submitted to be considered for publication as an "Original research work" in your journal. This paper is new. Neither the entire paper nor any part of its content has been published or has been accepted elsewhere. It is not being submitted to any other journal. And there is no conflict of interest in this article.


**3.2 Insecticide resistance to neonicotinoid**

Resistance is quickly developed due to the selection of highly effective compounds with kill or mitigate to insect, long residual effect, and is regular use of single biochemical target site. The toxicant is converted into a nontoxicant form in the body of an insect by various enzymes. All these enzymatic changes are carried forward and transmitted through genes. Resistance in B- and Q-type has been noticed in *Bemisia tabaci* to enhanced oxidative detoxification of neonicotinoids due to overexpression of monooxygenases. No evidence for target-site resistance has been found in whiteflies [18, 19]. Biotic and abiotic degradation processes contribute to the environmental persistence of neonicotinoids. The half-life of neonicotinoids varies depending on physiochemical

**Figure 2.**

*Several reported cases of neonicotinoid resistance up to 2014. Only those pests with >10 (fold) reported cases are shown.*

#### **Figure 3.**

*Pesticide resistance can build up in the pest population when a change in the genetic characteristic of the pest population is inherited from one generation to the next.*

conditions (i.e., organic matter content, soil texture, residence time) before undergoing complete degradation. The development of resistance is a complex and dynamic process and depends upon many factors. As per the IRAC, resistance is well-defined as a heritable change in the sensitivity of a pest population, that is reflected in the constant failure of a product to attain the expected level of control when used according to the label endorsement for that pest species [20]. The environmental protection agency (EPA) is divided into two classes of toxicity agents i.e., II and III. Accumulation (increase the level of pesticides) of those pesticides into the soil affects the pollen quality of sprayed plants, especially due to their toxic effects, against pollinators the consequences of the occurrence of these insecticides have been discussed [10, 21]. It is determined that the transfer of vertical gene (a particular gene) from the microorganism, higher plants, animals into a host plant for crop improvement and researches are called transgenic plant or insects, virus, fungus resistance plant. It gives us facts for future research. Mutation (can build up in the pest population when a change in the genetic characteristic of the pest population is inherited from one generation to the next) in nAChR subunits and in most cases, metabolism is also responsible for the development of resistance (**Figure 3**). The brown planthopper, *Nilaparvata lugens*, was selected with imidacloprid treatment at a sublethal dose to obtain resistant mutation. Both *Bemisia tabaci* (sweet potato whitefly) and *Trialeurodes vaporariorum* (greenhouse whitefly) have been shown to have a high possibility for resistance development and characterize some of the main targets for which IRAC specific strategies have been developed [8, 22]. Global resistance management guiding principles were designed by the Neonicotinoid Working Group of the Insecticide Resistance Action Committee and are based on guidelines published and updated earlier [9].

## **4. Mechanism of resistance and factors that influence resistance development**

#### **4.1 Resistance mechanisms**

The various mechanisms that enable insects to resist the action of insecticides can be grouped into several categories:

*Insect Resistance to Neonicotinoids - Current Status, Mechanism and Management Strategies DOI: http://dx.doi.org/10.5772/intechopen.101129*

#### *4.1.1 Single resistance*

Resistance to Dichloro diphenyl trichloroethane (DDT) amounts to resistance to several DDT analogs such as methoxychlor, but not to hexa chloro cyclohexane (HCH). Due to excessive and continuous use of the insecticides.

#### *4.1.2 Cross-resistance*

It occurs when resistance to one insecticide or within a group. Eg: organophosphate insecticides, fungicides etc.

## *4.1.3 Multiple resistance*

It involves multiple, independent resistance mechanisms, which often lead to resistance to chemicals from different families (i.e., organophosphate and carbamate insecticides) [23].

#### *4.1.4 Metabolic resistance*

It plays one of the significant roles, in the development of resistance which helps to change the activity of enzyme systems that all insects possess to help them detoxify naturally occurring foreign materials. Enzymes are classified viz., esterases, monooxygenases, and glutathione S-transferases typically fulfill this function. These enzyme systems are often enhanced in resistant insect strains enabling them to metabolize or degrade insecticides before they can exert a toxic effect. Metabolic resistance appliances have been noticed in whitefly, aphid, and Colorado potato beetle populations for all major classes of insecticides, currently also used for soft body insect control including neonicotinoids insecticide [24].

#### *4.1.5 Target site resistance*

Insecticides generally perform on a specific site within the insect, especially within the nervous system (e.g., OP, carbamate, and pyrethroid insecticides). The site of action can be reformed in resistant strains of insects such that the insecticide no longer binds effectively. This results in the insects being unaffected, or less affected, by the action of insecticide than susceptible insects.

#### *4.1.6 Reduced penetration*

Changes in the insect cuticle or digestive tract linings that avoid or slow the absorption or diffusion of insecticides can be found in some strains of resistant insects. This resistance mechanism can affect a broad range of insecticides. Examples of reduced penetration mechanisms are limited and are often considered a contributing factor to reduced susceptibility.

#### *4.1.7 Behavioral resistance*

This resistance illustrates any adjustment in insect behavior that helps to avoid the lethal effects of insecticides [22]. Insecticide resistance in mosquitoes is not always based on biochemical mechanisms such as metabolic detoxification or target site mutations, but may also be conferred by behavioral changes in response to prolonged exposure to an insecticide.

#### **Table 1.**

*Mechanisms of neonicotinoid resistance and tolerance in insects.*

## *4.1.8 Genetic basis of resistance*

It occurs naturally, genetic mutations allow a small proportion of the population to resist and endure the effects of the insecticide. This occurs due to continually using the same insecticide and horizontal genes. Resistance insects will reproduce and the genetic changes that confer resistance are transferred from parents to offspring so that eventually they become numerous within the population (**Table 1**).


*2.1. Field Description*

The study region is located in Wanglang National Nature Reserve (32◦49–33◦02 N, 103◦55–104◦10 E; 2300–4983 m a.s.l.), Pingwu County, Sichuan, southwestern China, which is distributed on the eastern Qinghai-Tibet Plateau and in the upper reaches of Yangtze River. The area has a semihumid climate typical of the Danba-Songpan region. The annual precipitation is 859.9 mm, and the annual mean temperature is 2.9 ◦C, with maximum and minimum mean temperatures of 12.7 ◦C (in July) and −6.1 ◦C (in January), respectively [35]. The subalpine forest consists of abundant plant species, including mosses, herbs, shrubs, deciduous broadleaved trees and coniferous trees.

#### *2.2. The Method of Selection of Study Plots and Their Characteristics*

Wanglang subalpine coniferous region seriously damaged by long-term the commercial logging of natural forests below 2700 m since the 1950s and natural disturbance such as the 1976 Songpan-Pingwu earthquake [38,39]. Forest logging in the area ceased in 1962. Therefore, the Wanglang Nature Reserve has formed many secondary forests in different stages of succession and primary forests that have never been destroyed [35,38–40]. We considered their distribution based on forest disturbance history and natural recovery process and interviews with local supervisors, We defined the forests of S1 to S5 succession stages by referring to the succession division of Zhang et al. [35], and defined the primary coniferous forests which have never been destroyed to S6 succession stage. According to Zhang et al. [35], the S1 successional stage was dominated by shrubs (e.g., *Salix* spp., *Ulmus pumila* L. and *Hippophae rhamnoides* L.); the S2 successional stage was dominated by the deciduous broadleaved species *Betula* spp. and *Populus* spp.; the S3 successional stage was characterized by mixed forests dominated by deciduous broadleaved species (e.g., *Betula* spp.) and coniferous species (e.g., *Abies faxoniana* Rehd.); the S4 successional stage was dominated by middle-age coniferous species with a diameter at breast height (DBH) less than 20 cm (e.g., *Larix gmelinii* (Rupr.) Kuzen. and *Sabina saltuaria* Rehd.); the S5 successional stage was dominated by mature coniferous plants with a DBH less than 40 cm and more than 20 cm (e.g., *A. faxoniana* Rehd.); and the S6 succession stage was characterized as primary coniferous forest dominated by *Picea purpurea* Mast (Table S1). The traits of the six successional stages, including the coordinates, altitudes, directions, canopy covers, soil names and dominant species are shown in Table S1.

#### *2.3. Soil Sampling Method*

Soils across the subalpine forest successional series were sampled in August 2019. Replicate plots (10 m × 20 m) were established in each successional stage as follows: 12 plots in S1, 9 plots in S2, 9 plots in S3, 6 plots in S4, 6 plots in S5 and 6 plots in S6. After removing the litter layers, nine soil samples in each plot were randomly collected from the top 20 cm of the soil profiles by a stainless steel corer (5-cm diameter) and mixed. Moreover, the topsoil temperature (T) of each plot was measured by a portable soil thermometer (CEM,

DT-131, Shenzhen, China). The roots and stones were removed from the soil samples, which were then divided into two subsamples. One subsample was stored in a bag on ice, immediately transported to a laboratory, and stored at −80 ◦C for high-throughput sequencing for microbial community diversity analysis [31]. The other subsample was transported to the laboratory and air-dried for soil property analysis.

#### *2.4. Soil Agrochemical and Chemical Properties Analyses*

After being passed through a 1-mm sieve, the air-dried soil samples were used for chemical analyses. Soil pH was measured at a soil: water ratio of 1:2.5 ( *m*:*v*) with a digital pH meter (FE20K, Mettler-Toledo, Greifensee, Switzerland). The concentrations of total nitrogen (TN) in the soils were determined from a milled sample by combustion at 950 ◦C using an elemental analyzer (Vario EL Ш, Elementar, Langenselbold, Germany). Soil organic carbon (SOC) was measured by the potassium dichromate oxidation method [41]. The C:N ratio (C:N) was calculated as the mass ratio of SOC and TN [42].

#### *2.5. Soil DNA Extraction, PCR Amplification and High-Throughput Sequencing*

Total soil DNA was extracted from 0.5 g soil samples using the FastDNA Spin Kit for Soil (MP Biomedicals, Santa Ana, CA, USA) according to the instructions of the manufacturer. The V4 hypervariable region of the 16S rDNA [43] and the internal transcribed spacer (ITS1 and ITS2) region of rDNA [44] were selected as sequencing targets for bacteria and fungi, respectively. The library was built using the NEBNext® Ultra ™ DNA Library Prep Kit for Illumina (NEB, Ipswich, MA, USA) at Novogene Company (Beijing, China). Sequencing was performed using a paired\_end sequencing strategy with barcode connectors based on the Illumina MiSeq PE250 platform (Illumina Inc., San Diego, CA, USA). The barcode sequences were spliced with FLASH (v 1.2.7) software to obtain the raw tags [45]. The clean tags were obtained by quality filtering the raw tags using QIIME (v1.9.0) software [46,47]. Chimeric sequences were removed using the UCHIME algorithm [48]. Operational taxonomic unit (OTU) clustering for all the effective tags with 97% identity was performed by UPARSE (v 7.0.1001) software after discarding singletons [49]. The SSUrRNA database of SILVA132 [50] and Mothur software, and the UNIT (v 7.2) database [51] and QIIME (v 1.9.0) [52] software were selected as species annotation analysis for 16S rDNA and ITS, respectively.


*2.5. Sensitivity Study*

Simulation output depends on the mesh configuration, such as the element size and the tilt angle. The element size of 0.01 mm was selected to compromise between the computational load and convergence. A smaller mesh size of 0.005 mm was compared to the 0.01 mm mesh using the 30 pcf case and showed a similar force magnitude and chip formation, but the computation could hardly proceed after a few steps due to a large number of elements and surfaces.

For the tilt angle, although 45◦ is the theoretically preferred cracking path, different angles were also tested at 0◦ (square mesh), 30◦, and 60◦ to study the mesh sensitivity using the 30 pcf case with a scaling factor of 0.02. In the case of square mesh, the chip layer was sheared off without any cutting phenomenon due to the lack of fracture path around the theoretical shear angle. Results of the other cases are shown in Figure 5. Compared to the 45◦ mesh, the chip size is larger at 30◦ and smaller at 60◦. Consequently, the cutting force is a little smaller (about 2%) at the 30◦ mesh and larger (about 20%) at the 60◦ mesh because the work done of cutting force is proportional to the number of fractured surfaces. Therefore, although the 45◦ mesh is recommended based on the shear angle, other mesh angles may also work but would produce different results. In any case, the scaling factor needs to be adjusted to match the chip behavior to the experiment for the best outcome.

**Figure 5.** Material responses to the cutting tool with different tile angles: (**a**) 30 degrees, (**b**) 45 degrees, and (**c**) 60 degrees.