*Article* **Listeners Sensitivity to Di**ff**erent Locations of Di**ff**usive Surfaces in Performance Spaces: The Case of a Shoebox Concert Hall** †

#### **Louena Shtrepi \*, Sonja Di Blasio and Arianna Astolfi**

Department of Energy, Politecnico di Torino, 10129 Turin, Italy<EMAIL_ADDRESS>(S.D.B<EMAIL_ADDRESS>(A.A.)

**\*** Correspondence<EMAIL_ADDRESS>Tel.: +39-011-090-4545

† Part of this work was presented at EURONOISE 2018, Crete, Greece.

Received: 27 May 2020; Accepted: 23 June 2020; Published: 25 June 2020

**Abstract:** Diffusive surfaces are considered as one of the most challenging aspects to deal with in the acoustic design of concert halls. However, the acoustic effects that these surface locations have on the objective acoustic parameters and on sound perception have not yet been fully understood. Therefore, the effects of these surfaces on the acoustic design parameters have been investigated in a real shoebox concert hall with variable acoustics (Espace de Projection, IRCAM, Paris, France). Acoustic measurements have been carried out in six hall configurations by varying the location of the diffusive surfaces over the front, mid, and rear part of the lateral walls, while the other surfaces have been maintained absorptive or reflective. Moreover, two reference conditions, that is, fully absorptive and reflective boundaries of the hall have been tested. Measurements have been carried out at different positions in the hall, using an artificial head and an array of omnidirectional microphones. Conventional ISO 3382 objective acoustic parameters have been evaluated in all conditions. The results showed that the values of these parameters do not vary significantly with the diffusive surface location. Moreover, a subjective investigation performed by using the ABX method with auralizations at two listening positions revealed that listeners are not sensitive to the diffusive surface location variations even when front-rear asymmetric conditions are compared. However, some of them reported perceived differences relying on reverberance, coloration, and spaciousness.

**Keywords:** shoebox concert hall; diffusive surfaces; diffusers location; acoustical parameters; variable acoustics; subjective investigation; auralization

#### **1. Introduction**

The definition of materials for absorptive and diffusive surfaces is the main design issue once the shape and the volume of an auditoria have been determined. These surfaces can be used by acousticians and architects to reach the desired sound field and achieve a trade-off with the aesthetical architectural aspects [1]. In performance spaces, the absorptive surfaces are usually hidden by layers of perforated panels or textiles. Conversely, the diffusive surfaces are commonly visible and become an important part of the design of the interior space. Their effects have been intensively investigated in the last decade and are usually related to corrections of the acoustic glare, echoes, focusing of sound, and enhancement of the uniformity of the sound field [1–3]. Depending on the combination with the absorptive surfaces, they can also generate negative effects, such as the reduction of sound level and reverberation time [4]. Diffusive surfaces are considered one of the most critical aspects in the acoustic design and renovation of concert halls since there is a lack of knowledge on how their effects on the sound field are related to practical design choices, that is, their location and extension. Thus, this experimental study aims to give more insight on the former aspect, by investigating the effects of

diffusive surface location on the objective acoustic parameters used in the design process. Moreover, the sensitivity of listeners to variations in the diffusive surfaces location is investigated.

It has been highlighted that the direct relation between the diffusive surfaces and any objective acoustic parameter is not as immediate as the absorptive surfaces related to the reverberation time [5]. Therefore, more adequate diffuser design and evaluation tools for acousticians and architects are needed since the preliminary phases of the design process to promote the use of sound diffusers. In order to better understand the diffusive surfaces effects, several case studies have been used for objective and subjective investigations through measurements in real halls [4,6–8], physical-scale models [4,8–12], and simulations of performance spaces [12–16].

Different investigations have focused on the ISO 3382-1 [17] parameters since these are used as design parameters at a larger scale. Ryu and Jeon [4] found that hemispherical and polygonal diffusers installed on the sidewalls close to the proscenium arch, the sidewalls of stalls, and balcony fronts of a shoebox-horseshoe plan hall decrease sound pressure level (*SPL*), reverberation time (*RT*) and early decay time (*EDT*) at most seats, compared to reflective surfaces. Furthermore, these surfaces affect clarity (*C*80) and the interaural cross-correlation coefficient (1-*IACC*E) by increasing and decreasing their values at the front and the rear seats, respectively. Other investigations on the effects of hemispherical diffusers applied to 1:50 scaled rectangular and fan-shaped hall surfaces confirmed the decreasing effects of diffusers on *RT* and *SPL* [9]. In this study, the halves of the lateral walls closest to the stage have been judged as the most effective areas for diffuser installation since they reduce the spatial deviation of the acoustic parameters and minimize the decrease of *RT* and listening level (*LL*). This was mainly valid for shoebox halls rather than fan-shaped halls. Moreover, large and sparse diffuser profiles resulted as more effective on the acoustic results. Jeon et al. [18] made measurements in real reverse fan-shaped and rectangular halls and found that saw-tooth and cubic shaped diffusers installed on lateral walls do not have any significant effect on the acoustic parameters. However, their presence improves the spatial uniformity of the sound energy. Based on simulations in a fan-shaped hall with two different hall volumes (3600 m3 and 7300 m3), Shtrepi et al. [16] showed that the ISO 3382 objective parameters are mostly affected when the diffusive surfaces with a scattering coefficient higher than 0.70 are located on the ceiling, lateral walls and rear wall simultaneously. These effects are more evident in the smaller volume and are reduced when the rear wall only is treated independently of the volume. Jeon et al. [19] have suggested the use of another objective parameter, namely the number of reflection peaks (*N*p) in an impulse response, which describes the spatial and temporal variation of the sound field. They considered a scaled model of a shoebox hall with polygon- and hemisphere-type diffusive surfaces applied to the lateral walls and ceiling, as well as a real reverse fan-shaped recital hall with diffusive front halves lateral walls closest to the stage. Their measurements showed an increase in the Np at higher frequency bands and no significant differences for the other ISO 3382 parameters. In addition, Jeon et al. [12] showed differences below the just noticeable difference (JND) for the ISO acoustic parameters through simulations in 12 performance halls of various shapes (shoebox, fan-shape, and other complex shapes) and with increasing scattering coefficient of the walls and ceiling. In a second part of the study based on measurements in a scale model of a vineyard-shape hall, they noticed that the periodic diffusers installed over the sidewalls and balcony decrease *RT* and *G* (strength), while increase *C*80. However, this was mainly attributed to the absorption added by the diffusers.

Besides the objective investigations, also the perceptual differences between different surface treatments have been the object of continuous research. Torres et al. [20] showed that changes in diffusion characteristics of the surfaces are audible in a wide frequency region and depend on the input signals, i.e., sustained signals make the perception of the differences easier than impulsive signals. Takahashi and Takahashi [21] and Shtrepi et al. [7] showed that perceptual differences between reflective and diffusive surfaces are related to the listening distance from the surface itself. Moreover, they are related to the difference of scattering coefficient between the compared surfaces [13,15]. Singh et al. [22] found that the perceived diffuseness is related to the interaural cross-correlation coefficient (*IACC*), which is an important parameter in the design process. Furthermore, Jeon et al. [19] showed that the

perceived diffuseness could be quantified in terms of the number of reflected peaks (*N*p), which is correlated to the listener preference. In another study, Ryu and Jeon [4] showed that the preference of the diffusive surface presence highly correlates with the perceived loudness (*SPL*) and reverberance (*EDT*). Other studies reported that changes in diffusive surfaces characteristics are mainly perceived in terms of coloration and spaciousness variations [7,20,21,23]. Jeon et al. [12] showed that despite small changes in the objective parameters, the presence of the diffusers made a clear and positive contribution to the overall impression of the listeners, which was mainly related to intimacy and envelopment.

Although these results highlight the importance of the location of the diffusive surfaces and their configuration combined to the size and shape of the hall, there is still need for clear and generalized guidelines useful for acousticians and practitioners alike. Since the scattering properties of these surfaces can be easily assessed by using the ISO 17497-1, -2 [24,25], the application of diffusive surfaces based on scientific investigations, and not only on the architectural and design preferences, should be a common practice for modern concert hall designers. Moreover, the subjective data, i.e., the listeners' sensitivity, would help to determine the measurement accuracy needed for the characterization of these surfaces [26].

However, very little research on this aspect has been carried out in real concert halls due to both technical and economic issues. Therefore, the present study attempts to clarify the influence of diffusive surface location on the objective and subjective aspects by means of both in-situ measurements and perceptual listening tests. Since both technical and economic issues would limit the research, a flexible environment—the hall Espace de Projection at IRCAM (Paris)—has been involved. Six configurations have been created by varying the location of the diffusive surfaces over the front, mid and rear part of the lateral walls, while the other surfaces have been maintained absorptive or reflective. Moreover, two reference conditions, that is, fully absorptive and reflective boundaries of the hall have been tested. The ISO 3382 objective acoustic parameters, such as reverberation time (*T*30), early decay time (*EDT*), clarity (*C*80), definition (*D*50), center time (*T*s), and interaural cross-correlation (*IACC*) have been estimated from the measured impulse responses. Furthermore, subjective investigations have been performed in order to identify the detectable differences between different locations of the diffusive surfaces.

#### **2. Method**

#### *2.1. Objective Measurements*

#### 2.1.1. Hall Description

A variable-acoustic environment, the Espace de Projection (ESPRO) at IRCAM in Paris (Figure 1), has been used for in-field measurements in order to investigate how the location of diffusive surfaces can influence the generated sound field. Table 1 provides the architectural and acoustical details of the variability of ESPRO based on Peutz [27,28]. The hall characteristics have been extensively described in Shtrepi et al. [7,13,14] and here only a brief overview is given in order to help the reader understand the context of the experiment.

The ESPRO is a modern facility with variable passive acoustics, which is achieved through the variation of room geometry and surface acoustic properties: the former is reached by moving the ceiling height from 3.5 m up to 10 m, while the latter is controlled by acting on independently pivoting prisms. The prisms are grouped in panels of three and have three faces with different acoustic properties that are reflective, diffusive, and absorptive (Figure 1). The frequency-dependent absorptive and scattering properties of the surfaces have been shown in [7], while diffusion polar distributions have been presented in [13,14]. Based on these references, the data at 500–1000 Hz for the absorptive surfaces present a mean absorption coefficient of a = 0.80, while the diffusive surfaces are characterized by a mean scattering coefficient of s = 0.75 and a diffusion coefficient of d45◦ = 0.52. The rotation is automated and managed from a control room. Only the eye-level panels, i.e., the first row from

the floor level, are controlled manually and could be set in either absorptive or reflective conditions. The floor is a hard-reflective surface.

**Figure 1.** Surface acoustic conditions absorptive, reflective, and diffusive (A, R, and D). Interior view and simplified models of the eight configurations of the hall. Six configurations tested with three different locations of the diffusive surfaces (Df-A, Dm-A, Dr-A, Df-R, Dm-R, Dr-R) and two reference conditions (All-A and All-R).

**Table 1.** Architectural and acoustical details of the ESPRO based on Peutz [27,28].

#### 2.1.2. Hall Acoustic Conditions and Measurements

Six hall configurations have been considered in this study by varying the location of the diffusive surfaces over the lateral walls within two different main acoustic conditions of the overall surfaces of the hall: absorptive (-A) and reflective (-R) (Figure 1). Three conditions of the diffusive surfaces (Figures 1 and 2) have been tested by shifting their location over the front, mid, and rear part of each lateral wall (hereafter labeled Df, Dm, Dr, respectively). Moreover, two reference conditions, that is, all variable surfaces set in the absorptive (All-A) and reflective (All-R) mode have been considered in order to investigate the overall absolute effect of the presence of a diffusive surface.

The absorptive condition was chosen for the eye-level fixed panels in all the measurements in order to avoid the strong reflections from the lower parts of the walls. The ceiling was set at the maximum operative height of 10 m, i.e., leading to a room volume of 3720 m3.

ISO 3382-1 [17] objective parameters have been measured in the unoccupied room conditions. A detailed description of the measurement set-up is given in [7], while here a brief overview is given in order to help the reader understand the main elements. Measurements have been carried out using the ITA-Toolbox, an open-source toolbox for Matlab [29]. Monaural and binaural measurements have been performed with twenty-four omnidirectional microphones (Sennheiser KE-4) and two artificial heads (ITA Head), respectively (Figure 2). The microphones have been set at a height of 3.7 m in a crossed array

that extended to one of the two halves of the audience area (Figures 1 and 2). This height was chosen in order to reach the center of the first level of variable panels. Additionally, the artificial heads (Head 1 and Head 2) have been placed in the middle of the microphone array in order to be representative of the largest number of receiver positions and adjusted at an ear height of 3.7 m from the floor level as the omnidirectional microphones. Head 1 was located close to the central symmetry axis of the room and Head 2 at the midway between the axis and the lateral wall. The impulse responses at these positions have been used for the auralization introduced in the listening test session. Two omnidirectional sound sources have been positioned at the front part of the room. Each source consisted of a three-way system of low, medium, and high-frequency sources, which were positioned at different heights, that is, at 0.40, 3.70, and 3.90 m, respectively [7]. The excitation signal was an exponential sine sweep with a sampling rate of 44.1 kHz, a length of 16.8 s, and a frequency range separated for each speaker of the sources. Two repetitions have been performed for each configuration; however, given the high S/N ratio no averaging was applied [30]. Three Octamic II by RME (Haimhausen, Germany) have been used as microphone preamps and an ADA8000 Ultragain Pro-8 by Behringer (Willich, Germany) served as DA-converter. Loudspeaker, artificial head, and amplifier were custom made devices by the Institute of Technical Acoustics, Aachen, Germany.

**Figure 2.** (**a**) Measurement positions (source, microphones, and artificial head) and main dimensions of the room (metric scale). (**b**) Schematic plan view of the diffusers location with respect to the source and artificial head positions.

#### 2.1.3. Objective Analyses

The ISO 3382-1 [17] parameters, that is, reverberation time (*T*30), early decay time (*EDT*), clarity (*C*80), definition (*D*50), center time (*T*s), interaural cross-correlation (*IACC*) have been assessed by using the functions of ITA-Toolbox. Specifically, these parameters have been considered as a measure of reverberance and liveness (*T*30 and *EDT*), clarity and balance between early and late energy, or the balance between clarity and reverberance (*C*80, *D*50, and *T*s), and perceived spaciousness (*IACC*). This last parameter has been evaluated only for the binaural measurements at the head locations.

Averaged values, as suggested in ISO 3382-1 [17], have been calculated over the 500 Hz and 1000 Hz octave bands, while the *IACC* values were averaged over 500 Hz, 1000 Hz and 2000 Hz octave band results since these frequencies concern the subjectively most important range. Besides the *IACC* for the full length of the impulse responses, the early-arriving (0–80 ms) and late-arriving (80 ms-inf) sound have been considered separately in the evaluation of *IACC*E and *IACC*L, respectively. The JND values of each parameter have been used to compare the results for different configurations (Table 2).

**Table 2.** Objective acoustic parameters and respective JND values.

#### *2.2. Subjective Investigation*

An auditory experiment has been conducted to investigate the listener's ability to perceive variations of the diffusive surfaces location by using the ABX method [31]. The test also allowed to evaluate the effects of different source and listener positions and type of music/signal passages (Figure 3).

**Figure 3.** (**a**) Listening test anechoic room set-up, (**b**) user-interface in Italian, and (**c**) listening test scheme.

#### 2.2.1. Test Subjects and Experimental Environment

A group of twenty-four professors, research assistants, and students aged between 25 to 50 years old with normal hearing ability have been involved in the test. All the listeners were volunteers interested in acoustic topics and no one of them could be considered as an expert listener, based on their musical experience. All of them provided written consent for the anonymized use of their test results. The normal hearing ability of each listener was tested by using the app "Loud Clear Hearing Test," developed by JPSB Software [32] and the same headphones (Sennheiser 600 HD) subsequently used in the listening test. This procedure is helpful for a more accurate screening compared to just self-reported hearing ability, which is often used in acoustic investigations.

The listening test sessions have been conducted in the anechoic room at Politecnico di Torino (Figure 3a), which has a background noise of LAeq = 17.3 dB. During the two days test, the room conditions, as well as the set-up, have been kept unvaried. The equipment consisted of one computer, a sound card (Tascam US-144 MKII), and headphones (Sennheiser 600 HD). The environment was made comfortable for the listeners and they were familiarized with the test procedure by an illustrated written and verbal explanation.

#### 2.2.2. ABX Method

The ABX methodology [31] is a standard psychoacoustic test for the determination of audible differences between two signals. In this procedure, three stimuli are presented to the listener: stimulus "A" and stimulus "B," which have a known difference, and stimulus "X", which regards the task of the listener who has to identify whether it is the same as "A" or the same as "B." If there is no audible difference between the two signals, the listener's responses should be binomially distributed such that the probability of replying "X = A" is equal to the probability of replying "X = B," i.e., 50%. This score is interpreted as indicating no perceptual difference between A and B. The minimum number of correct answers needed to indicate a perceptual difference can be given by the inverse cumulative probability of a binomial distribution, based on the number of trials, confidence level and probability of correct answer.

For the sake of this investigation, an ad-hoc routine in Matlab 2018b (MathWorks, Natick, MA, USA) with an intuitive user interface in Italian language has been implemented to present the test to each participant (Figure 3b).

#### 2.2.3. Test Procedure

The listening test consisted of signals recorded in the same "head" position (Figure 1), i.e., Head 1 and Head 2 for the front-rear asymmetric configurations (Df-A, Dr-A, Df-R, and Dr-R). Figure 3c depicts the test structure. A pair of two different configurations are compared in each experiment (Df-A vs. Dr-A or Df-R vs. Dr-R), while the sources, the artificial head, and the music/signal passage remain unvaried within each pair of samples.

The auditory tests consisted of 48 stimuli (24 pairs), which were created by convolving the binaural impulse responses obtained from in-situ measurements with three anechoic music passages. The three music/signal passages were chosen based on different style, tempo, and spectral contents: an orchestra track ("Water Music Suite"—Handel/Harty, Osaka Philarmonic Orchestra, Anechoic Orchestral Music Recordings, Denon, Kawasaki, Japan), a solo instrument trumpet (MAHLER\_tr1\_21.wav, Mahler, Odeon anechoic signals database) and pink noise. The temporal and spectral contents of the first two samples are shown in Figure 4. The pink noise was included in the test for its objective and perceptual acoustic properties, although it is not a realistic signal for concert halls. Pink noise has a well-known spectral density that decreases at a rate of 6 dB per octave which leads, on average, to the same amount of power for every octave band. From a perceptual point of view, the signal sounds flatter to the ear. The orchestra and trumpet signals present some differences below 400 Hz, where the trumpet sample has less energy (Figure 4a). Figure 4b,c shows the temporal development and the characteristics of the transients in the signals. The trumpet sample is constituted by abrupt onsets and reasonably damped offsets, while the orchestra sample is a more sustained signal that has ramped onsets and damped offsets. The listening test samples are made available in an open-access repository [33].

A sample length of 5 s was chosen to be long enough in order to give the listener the necessary time to assess the full extent of their acoustic perception and, at the same time, short enough to avoid excessive fatigue. Given the comparative structure of the test, no equalization has been applied for the sound level between the conditions in each pair.

The test was structured as a double-blind test, i.e., the administrator did not know the answers either, in order to avoid any accidental cues to the listeners. Moreover, the test was based on a fully randomized order of presentation of A and B pairs, as well as a random distribution of the correct answers, i.e., X could be randomly A or B. After listening to A and B, the listeners were asked to answer to the question "Which one is X?" by choosing between one of three options, that is, "sample A" and "sample B."

Compulsorily, the listeners had to listen to all of the three samples (A, B, and X) in order to continue to the next step of the test. However, they could freely choose the listening order of the three samples (A, B, and X) and repeat the samples as many times as they judged necessary.

**Figure 4.** Orchestra and trumpet anechoic stimuli: (**a**) spectral content, (**b**) waveform of the orchestra sample, and (**c**) waveform of the trumpet sample.

The listeners did not receive any instructions on which features of the sound samples they should concentrate on. This aspect was investigated (Figure 3b) by asking them to give more details on their answers related to:

The authors explained the case study and the purpose of the experiment at the end of the individual test. The listeners could not take breaks during the test, which lasted about 30 min. After the test, the listener's impressions and opinion were collected. Further information was gathered on their experience with previous listening tests, on their music skills, as well as on their age and general health conditions.

#### **3. Results**

#### *3.1. Objective Results*

Figures 5–9 show the results of each objective room acoustic parameter in all the considered hall conditions. Each parameter is given with respect to the source-to-receiver distance (S1 and S2). Moreover, the figures provide the objective acoustic parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr for an easier direct comparison to the JND values for the absorptive (-A) and reflective (-R) conditions, respectively. Differences within ±1 JND of the parameters are highlighted through a gray area. A summary of these differences has been given numerically in the tables in Appendix A.

The results of *EDT* (Figure 5) do not show a strong dependence on the source-to-receiver distance for both S1 and S2 in both the reflective (-R) and absorptive (-A) conditions. *EDT* values of the reflective conditions result higher for source location S2 compared to S1 for source-to-receiver distances between 8–12 m. The Δ*EDT* graph shows that there are a few significant differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions, i.e., >1 JND. These differences result higher for source location S2 and occur at a larger number of receivers in the absorptive (-A) conditions. However, no significant trend could be observed with respect to the source-to-receiver distance.

**Figure 5.** *EDT* parameter averaged over 500 Hz and 1000 Hz for S1 and S2 source-to-receiver distance. Δ*EDT* represents the parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. Differences equal ±1 JND of the parameters are highlighted through a gray area.

The results of *T*30 (Figure 6) show a decrease at the farthest positions for both S1 and S2 in the reflective conditions Df-R, Dm-R, Dr-R, and All-R. Conversely, there is no decreasing trend in the absorptive conditions Df-A, Dm-A, Dr-A, and All-A. *T*30 values of the reflective conditions result higher for source location S2 compared to S1 for the nearest receivers. Very few receiver locations seem to present differences (Δ*T*30) higher than the JND between the different diffuser locations Df-Dm,

Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. However, no significant trend can be detected considering the overall receivers and the source-to-receiver distance.

**Figure 6.** *T*30 parameter averaged over 500 Hz and 1000 Hz for S1 and S2 source-to-receiver distance. Δ*T*30 represent the parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. Differences equal±1 JND of the parameters are highlighted through a gray area.

The results of *C*80 (Figure 7) present different trends for S1 and S2 with respect to the source-to-receiver distance in both the reflective (-R) and absorptive (-A) conditions. Generally, it can be noticed that Δ*C*80 values present a few differences higher than the JND between the different diffuser locations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. However, it not possible to detect a significant general trend of differences due to the diffuser location when a comparison is made overall the source-to-receiver distances.

The results of *D*50 (Figure 8) show a decrease at the farthest positions both for S1 and S2 in the reflective (-R) and absorptive (-A) conditions. It can be noticed that *D*50 values present a higher variability in the absorptive conditions at each receiver position for both sources. Generally, it can be noticed that Δ*D*50 values present a few differences higher than the JND between the different diffuser locations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. However, no significant trend can be detected considering the overall receivers.

The results of *T*s (Figure 9) show an increase at the most distant positions both for S1 and S2 in the reflective (-R) and absorptive (-A) conditions. Only a very few receiver locations seem to present differences higher than the JND between the different diffuser locations. This is observed mainly for the reflective (-R) conditions.

**Figure 7.** *C*80 parameter averaged over 500 Hz and 1000 Hz for S1 and S2 source-to-receiver distance. Δ*C*80 represents the parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. Differences equal ±1 JND of the parameters are highlighted through a gray area.

**Figure 8.** *D*50 parameter averaged over 500 Hz and 1000 Hz for S1 and S2 source-to-receiver distance. Δ*D*50 represents the parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. Differences equal ±1 JND of the parameters are highlighted through a gray area.

**Figure 9.** *T*s averaged over 500 Hz and 1000 Hz for S1 and S2 source-to-receiver distance. Δ*T*s represent the parameter differences between the configurations Df-Dm, Df-Dr, Dm-Dr in the reflective (-R) and absorptive (-A) conditions. Differences equal ±1 JND of the parameters are highlighted through a gray area.

A statistical analysis has been performed on the data shown in Appendix A to investigate the main factor (that is the absorptive/reflective conditions, source S1 and S2, source-to-receiver distance) effects on the variability of the objective acoustic parameters in the comparisons between the tested configurations (Df-Dm, Df-Dr, Dm-Dr). To this aim, only differences above the JND have been considered since it is not meaningful from an acoustic point of view to investigate data lower than the perceived ones. Thus, *EDT*, which resulted in the most affected parameter, was retained suitable for a statistical analysis given the relatively high number of receiver locations that showed differences above the JND. However, it was not possible to apply an ANOVA analysis since the assumptions of normality of data distribution and homogeneity of variance are violated. Given this result, the Kruskal–Wallis (KW) test, which is a non-parametric test and an extension of the Mann–Whitney U Test for more than two groups, has been applied [34]. The Kruskal–Wallis test did not show a statistically significant result (*p* > 0.05) for the differences due to the diffusers location variations.

Table 3 shows the differences in the spatial mean values of each parameter obtained in the conditions with the three different locations of the diffusive surfaces (Df, Dm, and Dr) with respect to the absorptive (All-A) and reflective (All-R) conditions. It can be noticed that the overall results show significant differences for the *EDT* in all the configurations (Df, Dm, and Dr) and also for T30 in the Df and Dm configurations with respect to All-A. However, this might be due to the variation of the equivalent absorption area, which decreases when one part of the lateral absorptive walls is set into a diffusive condition. This effect is not evident with respect to the reflective condition (All-R).

A more detailed analysis of the objective parameters has been performed at the head positions. Table 4 gathers the differences of the objective parameters between each compared pair for source position S1 and S2 in the subjective test. The objective parameters at the head position have been evaluated as the values of the parameters obtained at the nearest microphone positions, i.e., microphone position 18 for head 1 and average values of microphone positions 14 and 15 for head 2. The conditions Df and Dr, i.e., the subjectively compared conditions, that lead to differences between the objective parameters above the JND are highlighted in bold.

The combination of the listening position head 1 and source location S1 presents a greater number of parameters (*EDT, D*50, and *IACC*L) that reveal differences above the JND in the comparison of Df-A towards Dr-A. Conversely, in the reflective condition (-R), significant differences (>JND) are present only for *T*s values. No significant differences can be observed for the combination of the listening position head 2 and source location S1 in both the reflective (-R) and absorptive (-A) conditions.

The combination of the listening position head 2 and source location S2 presents significant differences (>JND) for *EDT* only in both conditions (-R and -A). No significant differences can be observed for the combination of the listening position head 1 and source location S2 in both reflective (-R) and absorptive (-A) conditions.

**Table 3.** Spatial mean values and overall standard deviation of reverberation time (*T*30), early decay time (*EDT*), clarity (*C*80), definition (*D*50), center time (*T*s) in the eight conditions. Differences (Δ = All − D) with respect to the reference configurations All-A and All-R are given in brackets for each configuration (Df, Dm, and Dr). The differences above the JND have been highlighted in bold.

\* *EDT* and *T*30 differences (Δ = (All − D) × 100/All) are given in [%].

**Table 4.** Objective acoustic parameters obtained at the head position for the compared pairs of configurations. The differences above the JND between Df and Dr values in the reflective (-R) and absorptive (-A) conditions have been highlighted in bold.

#### *3.2. Subjective Results*

The subjective data gathered from the listening tests have been analyzed based on binomial distribution [35] in order to determine the statistical significance of the test results. The inverse cumulative probability is used to evaluate the minimum number of correct answers that are needed to indicate a perceptual difference. The inverse cumulative probability is given as a function of the trials (corresponding to the thirty-one listeners), probability of correct answers (50%), and confidence level (95%). Therefore, the minimum number of correct answers necessary to indicate a significant difference between pairs at a 95% confidence level was found to be 15, i.e., correct answers should result equal or higher than 15.

Figure 10 shows the correct answers for each music/signal passage, listening (head), and source position. The dashed horizontal line indicates the minimum number of correct answers necessary to detect a significant perceptual difference between configurations compared in one pair. No significant variations of the location of diffusive surfaces were significantly perceived in any of the compared pairs. Some of the listeners could still indicate a few differences relying on different attributes as presented in Figure 11, which shows the occurrences of each attribute given in the correct answers. Further, according to the feedback of the listeners, for each signal (trumpet, orchestra, pink noise), more than 75% of the correct answers were given by relying on two or more attributes (reverberance, coloration, and spaciousness). Among them, reverberance is the main attribute when the orchestra and pink noise samples are compared in the reflective condition.

Finally, given the small perceived differences, it was not possible to collect reliable results regarding the preference indicated by the listeners.

**Figure 10.** Listening test results. The dashed horizontal line indicates the minimum number of correct answers necessary to indicate a significant perceptual difference between configurations compared in one pair. *x*-axis indicates the pairs compared in each trial.

**Figure 11.** Listening test results. The listeners' subjective evaluations on the perceived differences between front and rear location of the diffusive surfaces in the absorptive and reflective conditions (Df-A vs. Dr-A and Df-R vs. Dr-R). The *y*-axis depicts the occurrences of each attribute given in the correct answers. The *x*-axis reports the attributes Co—coloration, Sp—spaciousness, Cl—clarity, Re—reverberance, Lo—loudness.

#### **4. Discussion**

This work aims to give more insight into the design aspects of concert halls related to the effects of diffusive surfaces location. Based on the results presented above, a few practically relevant comments can be made in order to achieve a more mindful design of concert halls and intervene in those areas that could lead to the required objective and perceived acoustic quality.

The objective analyses presented in Figures 5–9 and Appendix A showed that the objective parameters are not significantly influenced by the diffusive surface location. These results confirm the findings of previous investigations Jeon et al. [18] and Jeon et al. [12], i.e., the diffusers installation on lateral walls do not have any significant effect on the overall acoustic parameters. However, a few significant differences could be observed at single receiver positions. Generally, no clear trend can be observed for *T*30, *C*80, *D*50, and *T*s variations in the different configurations in both the absorptive (-A) and reflective (-R) conditions. *EDT* was shown to be the most affected parameter. The differences over the configurations show that this is more evident for source location S2 and occurs at a larger number of receivers in the absorptive (-A) conditions. However, no significant trend

could be observed with respect to the source-to-receiver distance and the statistical analysis did not show a statistically significant difference between the different diffusers locations. Generally, when the different configurations have been compared to the reference conditions (Table 3), no significant differences resulted in the reflective condition while in the absorptive conditions *EDT* and *T*30 resulted in the most affected.

It was shown that in the absorptive conditions (-A), the combination of the listening position head 1 and source location S1 presented a greater number of parameters (*EDT, D*50, and *IACC*L) that reveal differences above 1 JND in comparison to Df-A with Dr-A (Table 4). Conversely, the listening position head 2 and source location S2 presented significant differences (>1 JND) for *EDT* only in both the reflective (-R) and absorptive (-A) conditions. Given these differences, as in previous studies [7,12], it was not possible to correlate the objective parameters differences in these two positions to the perceived differences.

The subjective test did not show significant perceived differences between the configurations Df-A and Dr-A or Df-R and Dr-R, i.e., front-rear asymmetric conditions of the diffusive surface location with respect to the listener position. Some of the listeners could still indicate a few differences by relying on different attributes as presented in Figure 10, which shows the occurrences of each attribute given in the correct answers. However, it was not possible to identify the preferred location of the diffusers due to the small perceived differences. It was observed that for each signal (trumpet, orchestra, pink noise), more than 75% of the correct answers were given relying on two or more attributes (reverberance, coloration, and spaciousness). These attributes have been also highlighted as the most affected in previous studies [4,7,20,21,23]. Reverberance seems to be the main attribute when the orchestra and pink noise samples are compared in the reflective condition. However, despite the differences between the samples typologies it was not possible to determine a significant difference between them, which is in line with the findings in [12–16].

The objective and subjective results highlight the need for further investigations on new parameters. More systematic investigations might focus on the number of peaks (*N*p) proposed by [19], which correlates to the listener preference or the 'effective duration' of the autocorrelation function (τe), which correlates to the intimacy and reverberance [36] and has been proposed as key factor to 'preferred' values of several room criteria in relation to different kind of music signals [37].

It should be highlighted that this study focuses on perceptual differences within a shoebox hall only. Different results might be expected for different hall's shapes and volumes [12,16].

Further research could be performed, as indicated in Kim et al. [9] and Jeon et al. [19], also by taking into account the diffuser shape, size, and directivity of the polar distributions of diffuse reflections. In the ESPRO hall, the diffusers are alternatively vertically or horizontally oriented, i.e., a uniform directivity might be approximated. As it was shown in [9] large and sparse diffuser profiles might result in more effectivity over the acoustic parameters. Moreover, the extension over other surfaces might lead to more significant differences [16]. From the designers' perspective become more interesting the configurations that do not lead to any significant variation on the objective parameters and on the subjective perception. In this way, there might be more freedom on the aesthetical choices that can be applied to the design of a concert hall once that the acoustic optimal conditions have been obtained.

#### *Limitations of the Study*

Given the conditions studied in this paper, it should be noted that the receiver's area could be extended also at closer or further locations from the source positions. However, given the small spatial variability of the measured objective parameters, we would expect a limited effect also in the very rear part of the hall. The overall number of measurements in this project was made in an automatized way: the surfaces of the room were varied from a control room and the overall set-up of sources and microphone positions were set in the most representative locations in order to avoid entering the room with the risk of variation of its conditions. Therefore, in the attempt to reach the right tradeoff between

the gathered acoustic information, room configurations, number of microphones and sources, and time limitations on the use of the room itself, it was concluded that the presented protocol was the most suitable one.

The results of this study have highlighted some important issues related to the relevance of the diffuser's location in a performance hall. However, it should be underlined that only two listening positions have been used in this investigation. Given the differences that might occur due to source-receiver locations, it might be useful to increase the number of listening positions in order to have clearer evidence of the diffusive surface effects on the overall sound field perception. It might be useful to investigate also more representative positions of the front and rear rows of listeners. However, given the time limitations of the use of the ESPRO for this project, it was not possible to extend the number of dummy head positions. It should be considered that the simplification introduced by an ensemble generated from a single source location on the stage might have influenced the spatial impression for the orchestra sample. When technical and budget availability may cover important experimental costs, multiple sources might be a more accurate representation for this case as shown for the orchestra of loudspeakers in [38,39]. Moreover, in each receiver position, a multi-microphone technique could be used to enable multichannel 3D sound reproduction. Therefore, a spatial sound reproduction could have led to a more realistic listening condition. It might have been easier to identify differences when head movements are allowed since they are naturally used when attending concerts [40].

One of the limitations of this study is related to the use of non-individual HRTF, which could have affected the performance of the subject by diminishing the effects of the different surface locations. Research on the use of individual HRTF data sets has shown that their use would allow for better performance of the subjects in localization tasks and lower front-back confusions [41]. It was not possible to apply individual HRTFs due to the amount of technical effort that should be put to measure these data sets [42]. However, since the same dummy head was used in all the measurements here, this could not have any influence on the relative differences between the compared conditions.

The reverberation time characteristics of around 1 s in the absorptive conditions might have influenced the perception and preference of music samples, which are usually played in rooms with longer reverberation times for optimal listening. However, since the test was based on relative comparisons the influence on the distinction of the differences. Based on the JND definition, it measures the sensitivity of the listeners to a change in a given parameter and is focused on acoustic conditions typically found in concert halls or auditoria [1]. In very large or very small rooms the relations between the different parameters may change and consequently, the perceived differences may also be affected [43]. Therefore, the effects investigated in this research should be considered valid for the room volume of the case study and related ranges of reverberation time.

These aspects remain open to future research where also investigations with experts might lead to a more detailed description of other attributes related to the acoustic quality [44]. Although, previous studies on diffusive-to-reflective surface discrimination have shown compatible results between experts and non-expert listeners [7]. Moreover, also the effects of the diffuser location over the stage area and musicians' perception could be investigated with specific protocols as in [8]. The effect of diffusers on a different type of performances and related preference remains a crucial point to be further investigated given the importance of the specific effects recreated by the artists' work [45].

Finally, this study is by no means comprehensive, many other diffusive surface locations strategies exist, and further investigations of additional strategies will be useful to refine and expand the findings presented here over a larger number of hall's shapes and volumes.

#### **5. Conclusions**

In situ measurements and perceptual listening tests have been used to investigate the influence of diffusive surface location on the acoustic parameters used in the design process of concert halls and on the perceived acoustic sound field. The case study involved a real concert hall with variable acoustics (ESPRO, IRCAM, Paris, France), where eight hall configurations have been generated by modifying the characteristics of the lateral walls. The objective evaluation has been carried out by analyzing the variation of the ISO 3382-1 [17] acoustic parameters *T*30, *EDT*, *C*80, *D*50, and *IACC* in each configuration, while the perceptual tests have been performed using the ABX method in order to determine whether listeners are sensitive to variations of diffusive surface location. This study gives further insight into the importance of the quantification of the trade-off between the design effort and objective and subjective efficacy of the diffusers application in shoebox halls.

The main conclusions can be summarized as follows:

Future work should include different hall shapes and volumes in order to have also a more generalized overview of the interaction between room shape and effects of diffusive surfaces. More effort should be put into the investigation of differently shaped surfaces, i.e., different diffusion patterns and scattering values, and different degrees of diffusive surface extensions. More adequate sound sources and reproduction systems might be used in order to have more accurate results although the technical and economical effort for these improvements seems to be important.

The findings of this study should be seen as a milestone based on in situ results towards the redaction of reliable guidelines, which could enable an easier design process for architects and practitioners alike. The limited effects of the diffusive surfaces give space to a broad field of design alternatives from the designers' perspective. In this way, there might be more freedom on the aesthetical choices that can be applied to the design of a concert hall once that the acoustic optimal conditions have been obtained. It might be useful to investigate the boundaries of this filed within which the dialog between designers and acousticians would promote further aspects related to creativity.

**Author Contributions:** L.S. and A.A. conceived and designed the data collection campaigns; L.S. collected data on site; L.S. and S.D.B. performed data analysis; L.S. drafted and curated the first version of the manuscript. All the authors revised the paper. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded through a Ph.D. scholarship awarded to the first author by the Politecnico di Torino (Turin, Italy).

**Acknowledgments:** The authors would like to thank all the participants in the listening test sessions for the collaboration. They would also like to express their gratitude to Michael Vorländer, Sönke Pelzer, Renzo Vitale, and Fabian Knauber from the Institute of Technical Acoustic in Aachen, and Olivier Warusfel and Markus Noisternig from IRCAM in Paris for their technical support and availability during the measurements.

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

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


2.3.1. Image Preparation

The images were assembled using Agisoft Metashape to obtain orthomosaics for each acquisition year. These were georeferenced with ArcGIS 10.8.1 using ground control points (geospatial reference: tefenua.gov.pf (accessed on 15 January 2021)) with either adjust or 2nd polynomial transformations. Fixed geomorphological features such as lagoon pinnacles were used along with anthropogenic features as ground control points (similarly to [17]).

#### 2.3.2. Shoreline Tracing

The shoreline was manually traced based on the sea-side edge of vegetation (including the sea-side edge of mangroves and grass/reed areas), as per previous publications (similarly to the stability line used in [17]), with the Editor tool on ArcGIS. When sparse coastal vegetation (i.e., spaced-out trees) had dense canopies, the middle of the canopy, in the alignment of non-vegetated areas with a clear shoreline position, was used to position the shoreline. For sandy beaches, the line of first vegetation was used and not the sea-sand boundary for two reasons:

Clouds were present over a few tens of meters of the coastline in 2019 in the urban area of Vaitape, in a zone with embankments and no natural shoreline. A ground survey confirmed the lack of further constructions or changes compared to the previous image of 2016. The shoreline was, hence, traced in the same position as for 2016. No other clouds were present over the coastline at any date.

The total error (*Etot*) of the shoreline positioning is taken as the square root of the sum of the squares (cf. [18]) of the three sources of error identified for this study: the spatial resolution (*Eres*), the georeferencing error (provided by the forward error of the ground control points on ArcGIS, *Egeo*) and the shoreline tracing inaccuracies (*Etra*) (Equation (1)).

$$E\_{\rm tot} \ (\dot{m} \ m) = \sqrt{E\_{\rm rcs}^2 + E\_{\rm \%}^2 + E\_{\rm tra}^2} \tag{1}$$

The shoreline error (total and yearly) is provided in Table 1 for each date.


*3.2.2 SARS-CoV-2 variants in domestic and wild animals*

The virus has been detected in domestic cats and dogs [14]. Therefore, the transmission from humans to domestic animals is plausible. Specifically, the B.1.1.7 (Alpha) variant has been identified in domestic cats as well as in domestic dogs [19]. In contrast it appears that cattle, goats, and sheep are not infected by the virus [20].

Among animals kept in zoos the virus has been detected in gorillas, tigers, pumas, cougars, Asian small-clawed otters, and snow leopards. The genetic variability of SARS-CoV-2 is evident from the 9 genomes identified in tigers, lions, and their keepers [21]. The B.1.1.7 (Alpha) variant has been detected in gorillas, lions, leopards, and tigers [20, 22]. In another study the B.1.617.2 (Delta) variant was reported in Asiatic lions from India [23]. Farmed wild animals have been diagnosed with SARS-CoV-2. Specifically, SARS-CoV-2 has been identified in American minks (*Neovison vison*) and in ferrets (*Musteal furo*) [20].

Therefore, this possibility of emergence of new variants is ever present due to SARS-CoV-2 spread to different ecological environments and newer animal reservoirs resulting in a subsequent risk for spillover into humans and other species.

### **3.3 Immunological determinants**

### *3.3.1 Herd immunity*

Lately, the phrase "Herd Immunity" is constantly brought up in news outlets, in commentaries, opinion pieces, and peer-reviewed articles. First introduced almost 100 years ago, it only recently gained popularity [24]. Although Herd Immunity is now a widely accepted concept, it may take on multiple meanings, each slightly different than the next. Some researchers consider Herd Immunity a threshold of the proportion of immune individuals that leads to a decline in infections or outbreaks [25, 26]. While others may use it to describe the proportion immune to a specific infection among a population or refer to it as a protective immunity pattern [25]. Herd Immunity is most referred to as the reduction of risk, of an infection, to susceptible individuals by the proximity and presence of immune individuals [25]. Herd Immunity may be used interchangeably as "indirect protection" or "herd effect". Regardless of the definition variations, Herd Immunity leads to one outcome – the reduction of infection incidence. This concept, in conjunction with vaccines, has contributed to some of the most important public health achievements in the 20th and 21st century such as the smallpox eradication, polio elimination, and other vaccine-preventable diseases. This section explores the concepts behind Herd Immunity and current and future implications during the COVID-19 pandemic.

### *<IP_ADDRESS> Theories which constructed herd immunity*

Topley and Wilson (1923) were the first to coin the term "Herd Immunity" and specifically look at host resistance in comparison with mass infection. After first mention of Herd Immunity, the term and overall concept started appearing and developing soon after [27–29]. Dudley [27] explored the idea of a "herd" or community and how it could be defined. He defined the idea of "infection pressure" (i.e., fundamental parasite factor) which may be determined by the infectious agent distribution frequency rates which is in the members of the herd [27]. He claimed, infectious pressure reacts with Herd Immunity, the increase of one increases the other and then decreases it to zero. This introduced the idea of needing a minimum amount of Herd Immunity, a threshold, in order to keep the infectious pressure at zero. Furthermore, he mentioned those two factors contributed to the type, quantity, infection speed (i.e., now known as R0) and the frequency and distribution of cases and their severity [27].

Yet Herd Immunity had one large limitation—to provide protection, a high proportion of the population must be immune to the pathogen. Before immunizations individuals had to survive and pass the pathogen to become immune; depending on the pathogen, likelihood of survival and being left with life-altering morbidities varied. However, as concepts behind Herd Immunity were evolving, vaccinations were becoming a staple of public health practice, allowing a large proportion of the population to be safely immunized against specific pathogens. Vaccination allowed for the fulfillment of Herd Immunity at a much faster rate and safer manner. This allowed for the concepts to be turned into mathematical possibilities.

Before vaccination and Herd Immunity there were two main hypotheses as to why outbreaks would end even though not all susceptible were affected: (1) the agent naturally loses virulence (2) the dynamics between susceptible, infected,

and immune [26]. The later hypothesis, prevailed with its mathematical idea of "mass action principle" (MAP) [26]. This principle was based on a simple logical argument in favor of indirect protection given by Herd Immunity and became an epidemiological theoretical cornerstone. Eventually three theories converged into one general theory driving Herd Immunity: MAP, case reproduction rates (later called base reproductive rates [BRR]), and the Reed-Frost heterogenous population simulation approach [26]. The current formula used for Herd Immunity is H = 1–1/ R0 = (R0–1)/R0, where R0 is the BRR. H is the Herd Immunity threshold, the proportion of immunes needed in order to reduce incidence and R0 is derived from the duration of contagiousness of an infected individual, the likelihood of infection per contact between a susceptible person and an infectious person or vector, and the contact rate [30]. The BRR serves as an indicator of the contagiousness of an infectious agent—the higher the R0, the more transmissible. An R0 > 1 indicates an outbreak will continue, while a R0 < 1 indicates the end of an outbreak, if R0 = 1 then the outbreak is stable [30]. In novel outbreaks, where everyone is susceptible the R0 defines the infectiousness of a pathogen. However, as individuals pass the infection or become immunized, the number of susceptible decreases and immune increases, and although this does not technically reduce the BRR, because the definition of R0 assumes a completely susceptible population, one can use the effective reproduction number (R) in lieu, which is similar to R0 but does not assume complete population susceptibility and, thus, can be estimated with populations with immune members [30]. Efforts aimed at reducing the number of susceptible persons through vaccination would result in a reduction of the R value, rather than R0 value.


**4. Studies about bryophilic mushrooms**

Bryophilic mushrooms have been known for a long time, their habitats are well known (swamps, moss-covered tree trunks and mounds). In the 1980's the works about bryophilous fungi around the world include optical microscopy observations, in both Ascomycota and Basidiomycota. In 1981, the fungal species *Lyophyllum palustre* (Peck) Singer was found in mosses and it was constantly associated with necrotic areas of the species, and relationships with bryophytes discussed. Apparently less aggressive species of associated mushrooms were obtained from pure cultures made of *Sphagnum capillaceurn* (Weiss) Schrank, isolating *Lyophyllum palustre* and *Galerina paludosa* (Fr.) Kühner [26]. Research was carried out in 1987 on bryophilic fungi found in samples from the main herbaria in central and northern Europe, identifying ascomycetes associated with the bryophyte *Polytrichum sexangulare*: *Bryochiton heliotropicus* Döbb., *Bryochiton perpusillus* Döbb., *Lizonia sexangularis* Döbb. & Poelt., *Protothelenella polytrichi* Döbb. & Mayrh., *Gloeopeziza interlamellaris* Döbb. and *Hymenoscyphus norvegularis* Döbb [27].

Bryophytes are involved in a variety of competitive, parasitic, symbiotic, mutualistic and not yet specified interactions with fungi [28]. Some of Bryophilous species have very specific substrates such as *Galerina paludosa* found only in *Sphagnum* swamps, *Cyphellostereum laeve* was found on polytrichoid mosses in coastal regions, in addition to *Rickenella fibula* (Bull.) Raithelh. and *Rickenella setipes* which descriptions reported that a mound formed by *Alnus glutinosa* roots, covered by bryophytes served as substrate for the development of the species [29, 30]. In the 1990s, the main genera of bryophytic fungi were well known: *Galerina*, *Omphalina*, *Rickenella*, *Hypholoma*, *Mycenella* and *Psilocybe*, whose collection area was more widespread, knowing that their nature is more delicate they must be collected with a considerable amount of their substrate of origin [30]. Some islands were targets of ecological studies, such as South Georgia (Southern South America) and Iceland where the bryophyte substrate used by some Agaricomycetes was reported. Collections focused further south of the island of South Georgia were found to have species of the genus *Galerina*, *Gerronema*, *Phaeogalera* and *Hypholoma* [31]. Experiments about growth symbiosis using *Laccaria* spp. on different substrates, including *Sphagnum*, showed that when *Sphagnum* was mixed with vermiculite it was beneficial for the development of the *Laccaria* species and its symbiotic effects [32]. Another research reports that *Sphagnum* and other bryophytes can increase the presence of macrofungi in the substrate [33]. Studies of capture and evasion of nitrogen in soils with suspension of mosses verified that rhizomes of bryophytes have covering links with hyphae of Basidiomycetes fungi, forming a sheath around the rhizoids. This is important since the soil alone cannot handle nitrogen excess, nor accumulating without moss [34].

Research related to Agaricomycetes fungi and mosses among the years 2000 to 2010 cover mainly themes involved in the taxonomy, phylogeny, distribution, diversity, and classification of these organisms. Based on morphological characteristics and phylogenetic analysis, it was proposed that the *Omphalina giovanellae* Bres. systematic position is better maintained in another genus, making the mushroom that grows between mosses and low grass belonging to the genus *Clitopilus* (Fr. ex Rabenh.) P. Kumm [35]. *Multiclavula ichthyiformis* Nelsen, Lücking, L. Umaña, Trest & Will-Wolf, was identified as a new basidiolichen from Costa Rica with terricolous habits that grows with bryophytes [36]. *Psathyrella laurentiana* A.H. Sm and *Omphalina philonotis* (Lasch) Quél. (currently classified as *Arrhenia philonotis* (Lasch) Redhead,

Lutzoni, Moncalvo & Vilgalys) had their occurrence linked to bryophytes and/or peatlands, suggesting a more specialized niche for some basidiomycetes [37]. The occurrence of decomposing species of the genus *Galerina* Earle and *Coprinus* Pers. in bryophyte swamps including *Polytrichum alpestre* Hoppe in South Georgia Island [38], previously reported [32] can suggest a niche specialization in some species of these genera. *Chromocyphella muscicola* (Fr.) Donk was reported for the first time in Turkey [39], a species that grows near or on mosses, or even parasitizes them while alive [40]. The occurrence in association with mosses is used as identifying character for *Maireina callostoma* (Pilát) W.B. Cooke when using an identification key of this genus [41], showing that associations between Bryophytes and Fungi is an important character also to the taxonomy of Fungi.

Phylogenetic analysis helped to understand the bryophilic habits and high concentration of basidiolichens in Hygrophoraceae (Basidiomycota, Agaricales), suggesting a predisposition of these fungi to change their mutualist nutrition associated with photobionts to saprotrophic [42]. The occurrence of *Marasmius epidryas* Kühner, currently classified as *Rhizomarasmius epidryas* (Kühner ex A. Ronikier) A. Ronikier & Ronikier, among mosses was recorded in cold areas of Canada, Denmark and Russia highlights the occurrence of this relationships in cold and harshest areas [43].

Among 2015–2022 most analyses focused on more complex analyses about bryophilic mushrooms. Association among *Sphagnum* mosses and 26 species of Agarics and Boleti (Agaricomycetes, Basidiomycota) were found in Ukraine, whereas *Galerina cerina*, *G. paludosa*, *G. sphagnicola*, *Hypholoma elongatum*, *H. udum,* and *Tephrocybe palustris* could be considered as closely associated by substrate links [44]. In 2018, it was demonstrated that many bryophilous Hymenochaetales have values of stable isotope indicating ectomycorrhizal habits or a biotrophic cluster indicative of parasitism or an endophytic lifestyle [18].

The mycorrhizal-like associations, diversity and distribution of fungal associations in bryophytes, as between liverworts of the Jungermaniidae and Marchantiidae with Basidiomycota clades were explored by bibliographic review [4]. Although several works have been published reporting the occurrence of Agaricomycetes fungi growing among mosses, the importance of more specialized studies on the subject is highlighted. Part of these works did not perform microscopic and phylogenetic analysis in order to prove the relationship between Fungi and Bryophytes, but only reported the growth of certain species among mosses. The use of microscopic and phylogenetic analysis will contribute not only to prove the association, but also to understand the phylogeny and evolution of these organisms.


*Edited by Viduranga Y. Waisundara*

The palm oil industry is one of the oldest and most established commercial industries in the world. The product is hailed as an economically viable crop as compared with other oils used and consumed globally. This book provides an overview of the palm oil industry and the management of the crop itself. There has always been a growing interest in the scientific community to better understand this crop as well as the related industrial processes for environmental and nutritive purposes. As such, this book presents useful information on the palm oil industry from multiple perspectives.

ISBN 978-1-83768-065-8

Palm Oil - Current Status and Updates

Published in London, UK © 2023 IntechOpen © slpu9945 / iStock


*2.14. Drug Release Profile*

MTX-loaded bi-HNTs (20 mg) were mixed with 2 mL PBS in a tube and incubated at room temperature on a rocking shaker. After incubated for different time periods (0.5, 1, 2, 4, 6, 8, 14, 24, 32, 48, 56, 64, 72, 80, 88, and 96 h) tube was centrifuged and 1 mL of supernatant was collected, and 1 mL fresh PBS refilled to tube. The collected samples were stored at −20 ◦C until all the time periods samples were collected and measured by MTX Elisa kit as above.


**7.2 Pedestrian bridge failure: Florida**

A concrete pedestrian bridge under construction at the International University in Miami suddenly collapsed in March 2018. The bridge, which had not yet opened, killed five people. The 174 foot concrete structure, **Figure 8**, weighing 930 tons was non-redundant and fracture critical. Seven days before opening numerous wide and deep structural cracks developed, with cracks lengthening daily. Before the collapse the EOR stated there were no safety concerns.

*Failure analysis:* Root cause was a failure in a single structural element. Among the contributing factors was a failure by the Engineer of Record (EOR) and the Construction Engineer and Inspector (CEI) to recognize the severity of the developing cracks. In addition there was a lack of a clear line responsibility for enforcing safety and structural issues.


**7. Transcranial Doppler - TCD**

Transcranial Doppler (TCD) was developed in Switzerland in 1982 by Aaslid et al. [28]. A low frequency transducer (≤2 MHz) emits and receives ultrasound waves able to pass through skull bone and allow hemodynamic brain evaluation noninvasively2, through the observation of arterial blood flow systolic and diastolic velocities (**Figure 4**). With the introduction of TCD in Neurology, Neurosurgery and Intensive Care, new frontiers were opened to the understanding of the physiopathology of the various diseases associated with the dynamics of brain blood flow. TCD is performed at the bedside, has low cost and can be repeated whenever necessary without the need for patient transport, allowing the diagnosis and evolutionary follow-up of cerebrovascular diseases.

The main applications of TCD for brain hemodynamic monitoring in adults and children are:

**105**

*Management of Patients with Brain Injury Using Noninvasive Methods*

• evaluation in ischemic cerebrovascular disease with and without arterial

• ischemia mechanisms determination, whether arterial–arterial embolism, cardioembolic, arterial–venous shunting or hemodynamic [45, 46]

• measurement of hemodynamic repercussion in systemic diseases (sepsis and

**7.1 Hemodynamic indices of transcranial Doppler and functional evaluation**

Lindegaard Index (LI), Soustiel Index (SI) and breath-holding index (BHI). Mv is the central parameter of brain blood flow velocity spectrum analysis and is defined by the following formula: Mv = Sv + (Dv × 2)/3 [51] .Mv is a variable influenced by different physiological factors and its interpretation cannot be performed in isolation. Changes in Mv are due to age, sex, temperature, partial CO2 pressure (PaCO2), mean arterial pressure (MAP), hematocrit, pregnancy, presence of hypermetabolic states, and administration of anesthetic/sedative drugs. In general, there is an increase of the Mv from 6 to 10 years of age, then, there is a

The indexes calculated from the spectra of blood flow velocities obtained by TCD allow the characterization of brain circulatory patterns (**Table 1**). Thus, the following variables are analyzed: mean velocity (Mv), systolic velocity (Sv), diastolic velocity (Dv), Gosling Pulsatility Index (PI), Pourcelot Resistance Index (RI),

PI is the relationship between systole and diastole of the cerebral blood flow velocity spectrum. In situations where there are no cardiovascular pathologies and where there is no change in the diameter of the studied vessel, this index can be used to indirectly assess the integrity of the distal vascular bed and provide information on the microvascular brain resistance. It is calculated by the formula: Sv-Dv/Mv; its acceptable value ranges from 0.6 to 1.19 [53]. In stenosis or proximal occlusions, there may be a reduction in PI due to downstream arteriolar vasodilation. On the other hand, critical stenosis or distal occlusions, as well as microvascular vasoconstriction may be associated with PI elevation in proximal arterial segments. The PI below 0.5 may indicate the presence of intracranial arteriovenous malformation,

diseases, of intra- and extracranial arteries [43, 44],

*Spectral wave graph that has a peak systolic velocity (A) and final diastolic velocity (B).*

• risk of stroke evaluation and follow-up in sickle cell anemia16,

• complementary diagnosis of brain death [49, 50]

*DOI: http://dx.doi.org/10.5772/intechopen.94143*

liver failure) [47, 48]

**Figure 4.**

lifetime reduction [52].

*Management of Patients with Brain Injury Using Noninvasive Methods DOI: http://dx.doi.org/10.5772/intechopen.94143*

#### **Figure 4.**

*Advancement and New Understanding in Brain Injury*

the oxygenation status of the tissue.

cated signal interpretation.

**7. Transcranial Doppler - TCD**

pressure, etc.) [40, 41].

evolutionary follow-up of cerebrovascular diseases.

The basis of NIRS relies upon two principles:

1.that tissue is relatively transparent to near-infrared light and

2.that there are compounds in tissue in which absorption of light is dependent on

The propagation of light in tissue depends on the combination of absorption, scattering, and reflection properties of photons. Absorption and scatter in tissue is dependent on the wavelength. Scatter decreases with increasing wavelengths; thereby favoring the transmission of near-infrared light compared to visible light. NIRS, like most technology, has various limitations. The most important of those limitations are as follows: interference from non-targeted chromophores; indefinite differential path-length; unknown scattering loss factor; and compli-

Considering the pending technical challenges, the limited number of patients studied, and the conflicting results and opinions on this subject, we believe that this non-invasive method of predicting ICP should be restricted to research centers. Cerebral injury due to hypoxic/ischemic and hyperperfusion are common issues associated with clinical and surgical practice. Monitoring of cerebral oxygenation during surgery, e.g.; cardiac and cerebral endarterectomy, has been shown to

improve patient outcomes and reduce the risk of negative surgical outcomes. In addition to surgical monitoring, NIRS technology provides useful insight into cerebral hemodynamics when used in combination with other cerebral monitoring systems. NIRS monitoring and comparisons have been made with transcranial Doppler (TCD) and electroencephalography (EEG) in its ability to accurately predict cerebral ischemia and hyperperfusion. In addition to perioperative monitoring in clinical settings, many researchers utilize the various NIRS systems to reflect on the cerebral tissue oxygenation status during environmental and exercise interventions

Transcranial Doppler (TCD) was developed in Switzerland in 1982 by Aaslid et al. [28]. A low frequency transducer (≤2 MHz) emits and receives ultrasound waves able to pass through skull bone and allow hemodynamic brain evaluation noninvasively2, through the observation of arterial blood flow systolic and diastolic velocities (**Figure 4**). With the introduction of TCD in Neurology, Neurosurgery and Intensive Care, new frontiers were opened to the understanding of the physiopathology of the various diseases associated with the dynamics of brain blood flow. TCD is performed at the bedside, has low cost and can be repeated whenever necessary without the need for patient transport, allowing the diagnosis and

The main applications of TCD for brain hemodynamic monitoring in adults and

• functional evaluation of intracranial circulation by estimating cerebral perfusion pressure and reactivity tests at different stimuli (CO2, arterial

• subarachnoid hemorrhage (HSA)6, head trauma and other diseases that may occur with intracranial hypertension and segmental vessel stenosis [42]

despite strong evidence and proper analytical techniques [36, 39].

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children are:

*Spectral wave graph that has a peak systolic velocity (A) and final diastolic velocity (B).*

#### **7.1 Hemodynamic indices of transcranial Doppler and functional evaluation**

The indexes calculated from the spectra of blood flow velocities obtained by TCD allow the characterization of brain circulatory patterns (**Table 1**). Thus, the following variables are analyzed: mean velocity (Mv), systolic velocity (Sv), diastolic velocity (Dv), Gosling Pulsatility Index (PI), Pourcelot Resistance Index (RI), Lindegaard Index (LI), Soustiel Index (SI) and breath-holding index (BHI).

Mv is the central parameter of brain blood flow velocity spectrum analysis and is defined by the following formula: Mv = Sv + (Dv × 2)/3 [51] .Mv is a variable influenced by different physiological factors and its interpretation cannot be performed in isolation. Changes in Mv are due to age, sex, temperature, partial CO2 pressure (PaCO2), mean arterial pressure (MAP), hematocrit, pregnancy, presence of hypermetabolic states, and administration of anesthetic/sedative drugs. In general, there is an increase of the Mv from 6 to 10 years of age, then, there is a lifetime reduction [52].

PI is the relationship between systole and diastole of the cerebral blood flow velocity spectrum. In situations where there are no cardiovascular pathologies and where there is no change in the diameter of the studied vessel, this index can be used to indirectly assess the integrity of the distal vascular bed and provide information on the microvascular brain resistance. It is calculated by the formula: Sv-Dv/Mv; its acceptable value ranges from 0.6 to 1.19 [53]. In stenosis or proximal occlusions, there may be a reduction in PI due to downstream arteriolar vasodilation. On the other hand, critical stenosis or distal occlusions, as well as microvascular vasoconstriction may be associated with PI elevation in proximal arterial segments. The PI below 0.5 may indicate the presence of intracranial arteriovenous malformation,

**Table 1.** *Brain hemodynamic indexes.*

since the resistance in the proximal vessels is reduced due to the absence of brain tissue between arterioles and venules. PI can correlate positively with intracranial pressure (ICP); changes of 2.4% in PI may reflect a variation of 1 mmHg in PIC. The RI is calculated by the following formula: Sv-Dv/Sv. In practice, it has the same function of PI and values greater than 0.8 indicate an increase downstream of resistance to blood flow [54].

LI is defined as the relationship between the Mv of the middle cerebral artery and the Vm of the ipsilateral extracranial internal carotid artery. In the condition of significant increase of Mv in the middle cerebral arteries, this index allows the differentiation between hyperdynamic blood flow and vasospasm [55]. A LI lower than 3 may suggest hyperdynamic blood flow and an LI greater than 3 may suggest narrowing of an artery segment as occurs in vasospasm. SI consists of the relationship between the Mv of the basilar artery and the extracranial vertebral artery. This index is used for the diagnosis of vasospasm in the posterior brain circulation. These indices together with Mv in the studied arteries are also used to classify the degree/severity of vasospasm, as shown in **Table 2**.

#### *7.1.1 Reactivity test*

BHI or voluntary apnea Index evaluates CO2 reactivity and is given by the following formula: (Mv after apnea - baseline Mv) /baseline Mv) × 100/30, in which 30 represents time in seconds of voluntary apnea performed by the patient. This index evaluates brain circulatory reactivity to hypercapnia (CVR), that is, the vasodilator capacity of brain circulation during elevation of carbon dioxide induced by apnea. BHI > 0.6 indicates preserved CVR, between 0.21 and 0.60 indicates compromised reactivity, and ≤ 0.20 reserves significantly compromised. Impairment of CVR may be related to a higher risk of cerebral ischemia caused by hemodynamic mechanism [56].

#### *7.1.2 Noninvasive estimation of cerebral perfusion pressure*

Several studies have shown that the measurement of blood flow velocities in the middle cerebral arteries by TCD allows an alternative noninvasive method of estimating cerebral perfusion pressure (eCPP) with high positive predictive value and low negative predictive value. The estimation of eCPP by TCD uses a method that involves Fourier's analysis of the first harmonic of the waveforms of both systemic blood pressure and the velocity of blood flow in the middle cerebral artery [57].

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Several studies have demonstrated an adequate correlation between TCD to estimate eCPP and invasive measurement through the ICP catheter. Therefore, it has been proposed as a safe technique with the potential benefit of allowing intermittent or continuous analysis through monitoring. It can be used in situations where invasive measurement cannot be performed or when eCPP does not appear to be real or questionable. It is a robust, noninvasive method and allows qualitative analysis of CBF and tissue perfusion. Therefore, it can be used as an important guide for clinical

**Vasospasm severity (MCA) Mv (cm/s) LI** Take 120–130 3rd-3.9th Moderate 131–180 4–6 Serious >180 >6 **Vasospasm Severity (AB) Mv (cm/s) SI** Take 70–85 2–2.49 Moderate >85 2.5–2.99 Serious >85 >3 *MCA - middle cerebral artery; BA - basilar artery; Mv - mean velocity; LI - Lindegaard Index; SI - Soustiel Index.*

Patients with SAH may experience cerebral blood flow and metabolic changes that may culminate in increased intracranial pressure and ischemia. Three hemodynamic stages can be identified in this context: hyperemia, oliguemia and vasospasm. With TCD recognition of hemodynamic stages, physicians can be guided for

In general, in the first 24 hours, there is an overall decrease in cerebral blood flow (CBF) which may be due to two mechanisms: increased intracranial pressure associated with reduced cerebral perfusion pressure and intense microvascular constriction associated with low concentrations of nitric oxide (NO). These phenomena can trigger tissue hypoperfusion, decreased supply of tissue O2 with

TCD in the hyper-acute phase of SAH may demonstrate cerebral oliguemia status. Thus, it helps decision-making in clinical conduct to be adopted, such as: 1) management of mean arterial pressure (MAP) more appropriate; 2) avoid hyperventilation, which in turn will cause hypocapnia and further reduction of CBF; and 3) avoid states that increase brain tissue metabolic demand (e.g. fever, seizure, etc.).

Brain microcirculatory vasodilation causes overall elevation of CBF. States of brain hyperemia may signal neurovascular decoupling and autoregulation impairment due to brain or systemic tissue acidosis and, in general, occurs 24 hours after

management of patients who are victims of acute brain injury [58].

**7.2 Subarachnoid hemorrhage (SAH)**

*Diagnostic criteria for vasospasm by CTD.*

optimal patient treatment [59].

*7.2.1 Oliguemia stage*

**Table 2.**

consequent ischemia.

*7.2.2 Hyperemia stage*

the state of oliguemia.

*DOI: http://dx.doi.org/10.5772/intechopen.94143*

*Management of Patients with Brain Injury Using Noninvasive Methods DOI: http://dx.doi.org/10.5772/intechopen.94143*

#### **Table 2.**

*Advancement and New Understanding in Brain Injury*

**Index Formula**

Average speed (Mv) Mv = Sv + (Dv × 2)/3 Pulsatility Index (PI) IP = Sv - Dv / Mv Resistance Index (RI) IR = Sv – Dv / Sv

Soustiel Index (SI) IS = BA Mv / VA Mv

resistance to blood flow [54].

*Dv - diastolic velocity.*

*Brain hemodynamic indexes.*

**Table 1.**

*7.1.1 Reactivity test*

hemodynamic mechanism [56].

degree/severity of vasospasm, as shown in **Table 2**.

*7.1.2 Noninvasive estimation of cerebral perfusion pressure*

since the resistance in the proximal vessels is reduced due to the absence of brain tissue between arterioles and venules. PI can correlate positively with intracranial pressure (ICP); changes of 2.4% in PI may reflect a variation of 1 mmHg in PIC. The RI is calculated by the following formula: Sv-Dv/Sv. In practice, it has the same function of PI and values greater than 0.8 indicate an increase downstream of

Breath-holding index (BHI) BHI = (Mv after apnea - Baseline Mv) / Baseline Mv) × 100/30 *MCA - middle cerebral artery; BA - basilar artery; VA - vertebral artery; Mv – mean velocity; Sv - systolic velocity;*

Lindegaard Index (LI) IL = MCA Mv / extracranial ipsilateral ICA Mv

LI is defined as the relationship between the Mv of the middle cerebral artery and the Vm of the ipsilateral extracranial internal carotid artery. In the condition of significant increase of Mv in the middle cerebral arteries, this index allows the differentiation between hyperdynamic blood flow and vasospasm [55]. A LI lower than 3 may suggest hyperdynamic blood flow and an LI greater than 3 may suggest narrowing of an artery segment as occurs in vasospasm. SI consists of the relationship between the Mv of the basilar artery and the extracranial vertebral artery. This index is used for the diagnosis of vasospasm in the posterior brain circulation. These indices together with Mv in the studied arteries are also used to classify the

BHI or voluntary apnea Index evaluates CO2 reactivity and is given by the following formula: (Mv after apnea - baseline Mv) /baseline Mv) × 100/30, in which 30 represents time in seconds of voluntary apnea performed by the patient. This index evaluates brain circulatory reactivity to hypercapnia (CVR), that is, the vasodilator capacity of brain circulation during elevation of carbon dioxide induced by apnea. BHI > 0.6 indicates preserved CVR, between 0.21 and 0.60 indicates compromised reactivity, and ≤ 0.20 reserves significantly compromised. Impairment of CVR may be related to a higher risk of cerebral ischemia caused by

Several studies have shown that the measurement of blood flow velocities in the middle cerebral arteries by TCD allows an alternative noninvasive method of estimating cerebral perfusion pressure (eCPP) with high positive predictive value and low negative predictive value. The estimation of eCPP by TCD uses a method that involves Fourier's analysis of the first harmonic of the waveforms of both systemic blood pressure and the velocity of blood flow in the middle cerebral artery [57].

**106**

*Diagnostic criteria for vasospasm by CTD.*

Several studies have demonstrated an adequate correlation between TCD to estimate eCPP and invasive measurement through the ICP catheter. Therefore, it has been proposed as a safe technique with the potential benefit of allowing intermittent or continuous analysis through monitoring. It can be used in situations where invasive measurement cannot be performed or when eCPP does not appear to be real or questionable. It is a robust, noninvasive method and allows qualitative analysis of CBF and tissue perfusion. Therefore, it can be used as an important guide for clinical management of patients who are victims of acute brain injury [58].

### **7.2 Subarachnoid hemorrhage (SAH)**

Patients with SAH may experience cerebral blood flow and metabolic changes that may culminate in increased intracranial pressure and ischemia. Three hemodynamic stages can be identified in this context: hyperemia, oliguemia and vasospasm. With TCD recognition of hemodynamic stages, physicians can be guided for optimal patient treatment [59].

#### *7.2.1 Oliguemia stage*

In general, in the first 24 hours, there is an overall decrease in cerebral blood flow (CBF) which may be due to two mechanisms: increased intracranial pressure associated with reduced cerebral perfusion pressure and intense microvascular constriction associated with low concentrations of nitric oxide (NO). These phenomena can trigger tissue hypoperfusion, decreased supply of tissue O2 with consequent ischemia.

TCD in the hyper-acute phase of SAH may demonstrate cerebral oliguemia status. Thus, it helps decision-making in clinical conduct to be adopted, such as: 1) management of mean arterial pressure (MAP) more appropriate; 2) avoid hyperventilation, which in turn will cause hypocapnia and further reduction of CBF; and 3) avoid states that increase brain tissue metabolic demand (e.g. fever, seizure, etc.).

#### *7.2.2 Hyperemia stage*

Brain microcirculatory vasodilation causes overall elevation of CBF. States of brain hyperemia may signal neurovascular decoupling and autoregulation impairment due to brain or systemic tissue acidosis and, in general, occurs 24 hours after the state of oliguemia.

TCD is able to identify the state of cerebral circulatory hyperdynamia and, consequently, guide the management of the hemodynamic condition of patients in order to avoid brain swelling associated with this condition. At this stage, situations that worsen the condition of brain hyperemia, such as hypercapnia, systemic arterial hypertension, anemia, and hypermetabolic brain conditions (e.g., seizure) should be avoided. In the study of cerebral autoregulation (CAR) the ability of the brain to maintain constant blood flow dynamics regardless of variations in systemic blood pressure is evaluated. SAH is one of the pathologies in which ra is impaired, which requires adequate systemic blood pressure levels to prevent hyperemia or oliguemia. TCD can identify CAR impairment through the relationship between flow velocity oscillations in the face of MAP changes (spontaneous or provoked); and this analysis is performed through modeling used in signals analysis, requiring the use of specific software for this purpose. Thus, TCD can help identify the most appropriate blood pressure range in impaired states.

#### *7.2.3 Vasospasm stage*

Vasospasm in SAH is one of the main causes of late cerebral ischemia. Therefore, its early recognition is mandatory in the clinical management of neurocritical patients. Before symptoms arise, vasospasm can be detected by TCD. Thus, clinical treatment of vasospasm can be instituted early, before the installation of neurological deficits.

There are several reasons that determine late cerebral ischemia in SAH-related vasospasm: 1) vasospasm intensity; 2) occurrence in multiple arteries or sequential vasospasm in "Tanden"; 3) presence or absence of activated collateral circulation; 4) early onset of vasospasm; 5) fast vasospasm progress (elevation of >25 cm/s/day); 6) associated tissue hypermetabolism; 7) mitochondrial tissue dysfunction; 8) presence of intracranial hypertension; 9) associated circulatory oliguemia; 10) impaired brain microcirculatory reserve; 11) preexistence of intracranial stenosis [60].

TCD is capable of detecting vasospasm in the middle and basilar cerebral arteries with high sensitivity and specificity [60]. Classically, vasospasm can occur between 4 and 14 days after the day of bleeding, and in some cases (13% of patients) can be detected early in the first 48 hours or late after 17 days. The possibility of monitoring vasospasm intensity may allow the optimization of clinical management. In severe vasospasm, the conjunction of other hemodynamic factors also observed by TCD determines the indication of, in addition to clinical measures, such as the use of vasoactive drugs and/or endovascular interventional treatment. The opportunity for the evolutionary follow-up of the response obtained to the treatment adopted is also an important benefit of TCD at this stage. **Table 2** shows the diagnostic and classification criteria of vasospasm severity by TCD using Mv and LI.

#### **7.3 Traumatic brain injury**

Intracranial circulatory abnormalities occur frequently in patients with TBI. Ischemic brain lesions can be identified in about 90% of patients who die after severe TBI [61], suggesting that changes in systemic and/or brain blood flow dynamics are frequent causes of ischemia and unfavorable outcomes. Studies of blood flow and brain metabolism suggest that hyperemic brain phenomena are the most frequently found in comatose patients after severe TBI [62].

#### *7.3.1 Brain hemodynamic phases after severe TBI*

As in SAH, there is a definition of 3 hemodynamic stages after severe TBI. The oliguemia stage occurs on the day of TBI (day 0) and is characterized by a reduction

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*Management of Patients with Brain Injury Using Noninvasive Methods*

in CBF. The hyperemia stage usually occurs on days one through three and is characterized by increased CBF. The vasospasm stage usually occurs from days 2 to 6 after

Cerebral changes in the acute stage of moderate or severe TBI, characterized by reduced blood flow velocity and increased PI in intracranial arteries, can be revealed by TCD, including during the first three hours after TBI occurrence. At this stage, TCD should be used early in order to guide therapeutic approaches. When oliguemia has been demonstrated, the possibilities of systemic blood pressure insufficiency of maintaining CBF dynamics (MAP below the autoregulation range), hyperventilation with reduction of partial arterial CO2 pressure, resulting in cerebral microvasculature vasoconstriction, posttraumatic thrombosis of the carotid arteries, and intracranial hypertension (especially if associated with increased PI) should be considered. The reduction in blood flow velocity in cerebral arteries may also be due to brain hypometabolism that may be associated with severe brain lesions. Presence of oliguemia may be associated with a higher risk of brain ischemia and an unfavor-

Cerebral hemodynamic patterns indicative of hyperemia can be detected by TCD in about 30% of patients during the first two weeks after severe TBI. The occurrence of this pattern is associated with worsening brain swelling and increased intracranial pressure. TCD can identify patients with posttraumatic brain hyperemia prior to the development of brain swelling, which allows the establishment of therapies aimed at minimizing neural tissue lesions secondary to ICH, such as the determination of the best mean arterial blood pressure range or the determination of the best PCO2 for a patient on mechanical ventilation. Persistence of hyperemia status may be associated with poor neurological

Studies with TCD in TBI estimate the occurrence of vasospasm in 50% of patients. There is an important association between vasospasm with severe hemodynamic repercussion and unfavorable neurological prognosis, although this repercussion is lower than in cases of spontaneous SAH. It is important to highlight that posttraumatic vasospasm of the basilar artery doubles the possibility of unfavorable prognosis, compared to patients without spasm of this artery. The duration of vasospasm in patients with TBI tends to be shorter due to the non-inflammatory nature as a cause, unlike subarachnoid hemorrhage. Possibly the origin of traumatic vasospasm is associated with stretching of the arteries during trauma and peak intensity, in many cases, occurs between the fifth and seventh day after trauma, although a duration similar to SAH is

Among other applications of TCD in severe TBI, it is worth mentioning: 1) to detect brain circulatory changes resulting from ICH; 2) to evaluate the degree of autoregulation and cerebrovascular reactivity impairment, enabling the prediction of prognosis; 3) to provide evidence of posttraumatic dissection or thrombosis of the arteries that irrigate the brain, allowing early investigation and adoption of

*DOI: http://dx.doi.org/10.5772/intechopen.94143*

TBI and there may be a reduction in CBF.

*<IP_ADDRESS> Oliguemia stage*

able prognosis [63].

prognosis [64].

*<IP_ADDRESS> Vasospasm phase*

observed in some cases [65].

*<IP_ADDRESS> Hyperemia phase*

in CBF. The hyperemia stage usually occurs on days one through three and is characterized by increased CBF. The vasospasm stage usually occurs from days 2 to 6 after TBI and there may be a reduction in CBF.