Tamura et al.
Journal of Physiological Anthropology(2022) 41:16
https://doi.org/10.1186/s40101-022-00289-x

ORIGINAL ARTICLE

Open AccessPhysiological comfort evaluation
under different airflow directions in a heating
environment
Kaori Tamura1,2, Sayaka Matsumoto1, Yu Hsuan Tseng3, Takayuki Kobayashi4, Jun’ichi Miwa5, Ken’ichi Miyazawa4,
Soichiro Matsumoto4, Seiji Hiramatsu4, Hiroyuki Otake4 and Tsuyoshi Okamoto1,3*

Abstract
Background: Indoor airflow and thermal comfort are difficult to assess through subjective evaluations because airflow sensations can differ based on various factors, such as personal characteristics, interests, preferences, and the current state of mind. Thus, subjective evaluations should be combined with objective assessments, such as physiological measurements. This study evaluated airflow and thermal comfort through physiological measurements, including
skin temperature, electroencephalography, respiration, and electrocardiography, in addition to subjective evaluations.
Methods: Twenty participants entered a test room at 30 °C after staying in an acclimation room at 18 °C for 20 min.
They were exposed to indirect and direct airflow toward their faces and performed four tasks under each condition: resting, counting to 10 s following time alerts, counting to 10 s in the mind, and mental calculation. The mean
speed of the air directed to the participants’ faces was 0.123 m/s and 0.225 m/s in the indirect and direct conditions,
respectively.
Results: The gamma and beta bands of electroencephalograms taken at the left-temporal (T3) and left-parietal (P7)sites showed significantly lower amplitudes under the indirect condition (gamma, T3: p = 0.034, P7: p = 0.030; beta,
T3: p = 0.051, P7: p = 0.028). Similarly, the variability of respiration was lower under the indirect condition (p < 0.010).
The amplitudes of gamma and beta waves showed significant correlations with anxiousness levels (gamma, T3: r =0.41; beta, T3: r = 0.35).
Conclusions: Our results suggest that indirect heating airflow causes lower mental stress and fatigue than thoseinduced by direct flow, which is equivalent to more comfort. The results of this study suggest that physiological measurements can be used for the evaluation of unconscious indoor comfort, which cannot be detected by subjective
evaluations alone.
Keywords: Comfort evaluation, Airflow direction, Electroencephalogram, Respiration, Heating environment
Background
Airflow movement and direction can influence the comfort level in a heating environment [1–4]. When evaluating thermal comfort, air movements are regarded as*Correspondence: okamoto@artsci.kyushu-u.ac.jp
1
Faculty of Arts and Science, Kyushu University, 744 Motooka, Nishi‑ku,
Fukuoka 819‑0395, Japan
Full list of author information is available at the end of the article

unwanted because high wind velocities decrease indoor
comfort [1, 3]. Satisfaction with airflow direction will
improve quality of life and promote better productivity inoffices and schools.
Discomfort under different indoor airflow velocities haslong been studied, and satisfaction varies depending on
temperature, climate, and individuals [5]. Some studies
have investigated the preferences for airflow movement© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/. The Creative Commons Public Domain Dedication waiver (http://​creat​iveco​
mmons.​org/​publi​cdoma​in/​zero/1.​0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

through subjective votes, showing that air movement waspreferred when people felt warm or slightly warm [6–8].
On the other hand, a study in subtropical regions showedthat the preferences for air movement largely differed
between the responders [9], and another report from asubtropical region showed that fewer people preferred
air movement when the room temperature in an air-conditioned environment increased [10]. Other studies havealso investigated the effects of the velocity and direction of the heating airflow. They showed that more people favored a lower warm airflow velocity [11] and thatwarm airflow directed to the face induced uncomfortable feelings [12]. Subjective evaluations can vary intraand inter-individually according to physical and mental
states. Thus, objective measurements should be added tothe conventional subjective approach for a more robust
evaluation of thermal comfort.
There are many subjective but few objective approaches
for evaluating feelings of comfort depending on airflow
conditions. This imbalance is one possible reason that
there is no consensus on how airflow affects comfort. For
example, Yamashita et al. found a non-linear relationshipbetween airflow temperature and subjective comfort,
while they found a linear relationship between airflow
temperature and thermal sensation [13]. Cui et al. evaluated the effects of heating airflow on cognitive tasks, butthere was no significant difference in the performancelevels [14]. These reports highlight the difficulty in evaluating the effects of airflow on mental states through subjective assessments and behavioral measurements.
Conventionally, to assess indoor pleasantness, including airflow comfort, subjective assessments or rating
scales are widely used. A common international scalecalled predicted mean vote (PMV) [15] was establishedto summarize and standardize such assessments. However, the validity of PMV is controversial [16, 17], especially in East Asian regions [18]. The reported predictedaccuracy of the actual votes involving several building
types was 34% [17]. Van Hoff summarized the discrepancies between PMV prediction and actual votes of theparticipants, showing that the preferences for the indoor
environmental conditions varied individually and weredissociated from those based on the PMV model in Asianstudies [16]. Differences between PMV and actual mean
vote in cases of naturally ventilated and air-conditioned
spaces have been reported as well. Humphreys and Nicol
reported that there were underlying biases concerning
all variables contributing to the PMV calculation. For
example, when the airflow speed exceeded 0.2 m/s, thedivergence between PMV and actual votes tended to be
wider [19]. PMV is one of the typical scales that aim at
estimating a mean subjective comfort evaluation, which
may provide a misleading prediction of the comfort

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level when considering the effect of airflow direction.
Objective parameters such as physiological measurements should be added to the conventional subjective
evaluation.
Electroencephalography (EEG) is a strong candidate forthe objective evaluation of airflow comfort. Some components of EEG signals are known to directly reflect themental state [20, 21]. For example, a previous study foundthat the beta and gamma wave amplitudes recorded fromthe participants were higher when they were subjected todirect airflow than when airflow was not provided [22],thus demonstrating that these measurements could be
useful indicators to evaluate comfort feelings with different airflow conditions. Such results were also confirmed
in a more recent study that investigated airflow comfort
in a cooling environment using EEG measurements [23],and this same methodology is used in this study to estimate the differential effects of either direct or indirect
heating airflows on comfort feelings [23]. Some conventional studies have shown that direct and fast warm airflow induced uncomfortable feelings [11, 12], but others
reported that air movement was preferred [6–8]. Heating airflow comfort needs to be evaluated through various methods, such as physiological measurements andsubjective assessments, to confirm whether similar EEG
patterns can be observed under cooling and heating airflows. In this study, we hypothesized that indirect heating airflow will induce lower mental stress than direct
airflow, which will be reflected in lower beta and gamma
EEG amplitudes. According to our studies [22, 23], thebeta and gamma amplitudes can be useful to assess airflow comfort under a heating environment as well.
The activity of the central nervous system can bereflected in EEG signals and that of the autonomic nervous system can be related to airflow comfort. In addition to EEG, other physiological measurements, such as
respiration and electrocardiography (ECG), are useful toestimate comfort states. Respiration has previously beenmeasured to evaluate the physiological effects of indoor
environments [24, 25]. It has been suggested that respiration variability increases due to spontaneous sighs undermental stress tasks [26, 27] and that it reflects cognitive
states, including sustained attention, cognitive loads, andmental stress [26–28]. By contrast, ECG parameters can
indicate the activity of the autonomic nervous system.
The heart rate variability calculated from ECG signals is
indicative of parasympathetic and sympathetic activity[29]. The combination of physiological measurements
can enable the evaluation of airflow comfort based on
both the central and autonomic nervous systems.
The main purpose of this study was to evaluate how thedirection of airflow produced by air conditioners influences the feeling of comfort in a heating environment

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

through subjective and objective measurements, including EEG, respiration, ECG, and other behavioral andphysiological responses. These measurements were performed with heated air flowing either directly or indirectly into the participants’ faces. The physiologicalmeasurements were used alongside subjective assessments to evaluate the comfort as a function of airflow
direction.

Materials and methods
Participants

We recruited 20 university students to participate in our
experiments (female, n = 8; male, n = 12). Their meanage, mean body weight, and mean height were 21 ± 2.1
years, 57.9 ± 8.1 kg, and 167.6 ± 8.1 cm, respectively.
None of the participants self-reported having smoking
habits or neurological deficits. Four female participants
self-reported that their basal body temperatures werelower due to the follicular phase during the experiment.
The sample size was determined according to the standard of neurophysiological studies: about 20 participants,though behavioral or questionnaire studies, can provide
a larger sample size of at least 150–250 data points. The
typical sample size of neuroscience studies involving EEG
measurements tends to be low because of the need forconsiderable time, investment, and resources per participant, as well as more complex measurement tools thanrequired for behavioral studies [30, 31]. Moreover, ethicalissues should be considered in neurophysiological studies. Bacchetti et al. suggested that the projected burden
per participant would be more suitable for studies witha smaller sample size [32]. Thus, we recruited 20 participants for this study according to the feasibility and ethical issues of neurophysiological studies.
The experiments were approved by the joint ethics
committee of the Faculty of Arts and Science and theCenter for Health Sciences and Counseling from Kyushu
University (201815, 201815-1). Written informed consent
was obtained from each participant. All methods were
performed per the approved guidelines.
Apparatus and protocol

The experiments were conducted in an experimentroom at Mitsubishi Heavy Industries LTD., Aichi, Japan.
Due to a schedule conflict between participants and theexperiment room, the tests were performed in two periods, from 21 January 2019 to 1 February 2019 and from
10 May 2019 to 23 May 2019. The data collected in thetwo periods were pooled. The environmental parameters,
including room temperature, wind direction, and windvelocity, were monitored and controlled simultaneously
from a control room outside the experiment area.

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The indoor climate condition was controlled using acommercially available air conditioner (Draft Prevention Panel, Mitsubishi Heavy Industries Thermal Systems
LTD., Tokyo, Japan) with special panels to avoid direct
impact of air from the indoor unit. Two different airflow
conditions were set as follows (see Fig. 1): (1) direct airflow, where the current from the air conditioner was set
to hit the participants’ faces, and (2) indirect airflow, inwhich the air was diverted by the control panels (Fig. 1c).
The staff stayed in the control room to monitor the physical parameters measured indoors and to control the air
conditioning unit.
To simulate the outdoor temperature during winterin Japan, each participant was asked to stay for 20 min
in a waiting room with the temperature set to 18 °C.
This acclimation to a cool temperature counteracted
the effect of variations in outside temperature during
the experiment period. After the acclimation, each participant moved to the experiment room, with the room
temperature set to 30 °C. The temperature in the experiment room simulated a heating environment during
winter in Japan. EEG and ECG electrodes were attachedto each participant within approximately 10 min after
entering the experiment room. The participants thenperformed tasks under the different airflow conditions(Fig. 1d). The order of airflow conditions was pseudorandomized across the participants. The air conditioner
was controlled before taking measurements under eachcondition.
The participants performed the same sequence oftasks used by the authors in a previous study [23]. The
sequence consisted of four assignments: rest for 1 min,time counting following alerts (CA) for 1 min, time
counting in mind (CM) for 1 min, and mental calculation(Cal) for 1 min. Each task was performed after a short
instruction whose duration was about 40 s. This sequence
was repeated three times in a random order for both thedirect and indirect airflow conditions (Fig. 1d). Thus, thetotal duration per airflow condition was (1 min + 40 s) ×
4 tasks × 3 times = 20 min. During the rest period, theparticipants were instructed to keep their eyes closed for
1 min. After resting, each participant answered a questionnaire (see the “Subjective assessments” section).
The CA and CM assignments were designed to measure psychological time. In the CA task, each participant
was required to press a button following time alerts every
10 s for 60 s. In the CM task, each participant was asked
to estimate 10 s as exactly as possible, press the button
after counting for 10 s in their mind (without utteringwords), and repeat for six times (~ 60 s).
In the Cal assignment, each participant was asked
to consecutively subtract 13 from a four-digit number (e.g., 1012 − 13 = 999, 999 – 13 =…) as quickly as

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 1 Experimental environment, settings, and procedure. a Experimental environment. b Location of the measuring sensors and PMV meter.
A thermography camera was located in front of the participants. c Airflow settings for the two surveyed conditions: direct and indirect. dExperimental procedure. The left side indicates the task sequence in an experiment, and the right side indicates the sequence of physiologicalmeasurements corresponding to the left tasks. The task and measurement sequences were performed at the same time. The order of airflow
settings was pseudo-randomized across participants. We obtained permission from the participants for printing the images of their faces. This figure
was modified from Tamura et al. [23] licensed under CC BY 4.0 (https://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/)

possible for 60 s. The four-digit number was randomly
assigned, ranging from 1000 to 1012.
Each participant sat in a chair placed at the designated position near an experimenter. An anemometer
was placed close to each participant, and a thermography camera was placed in front of them (Fig. 1a, b;
see details in the “Physical measurement and analysis”
and “Wind velocity measurement” sections). The participants were asked to wear the same type of hospital gown to standardize the clothing. The peripheral
oxygen saturation ­(SpO2) was measured with a finger
pulse oximeter to confirm safe oxygen levels of morethan 95% (Fig. 1b).

Physical measurement and analysis

During the experiments, room temperature, relative
humidity, and wind velocities were measured with a
PMV meter (AM-101, Kyoto Electronics Manufacturing
Co. Ltd., Japan) placed close to the participant. To analyze the changes in time series data, each parameter wassegmented and averaged across the three repetitions ofeach airflow condition. One experiment failed to record
PMV data in the direct airflow condition; therefore, thenumber of PMV data under the direct setting was 19,
while the remaining physical data were recorded 20 times
in each condition. To compare the mean parameters,
paired t-tests were performed on the averaged data. The

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

statistical analyses were conducted using JMP® 14SW
(SAS Institute Inc., Cary, NC, USA).

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velocity data in each direction were averaged for the
1-min measurements.
Thermography measurement and analysis

Wind velocity measurement

To ensure the correct environment for physiological measurements of the participants, wind velocity
and direction were measured using a multi-channel
anemometer (Multi-channel anemometer 1550 series,
KANOMAX JAPAN INC., Osaka, Japan) and probe
cables (MODEL 1504 probe cable velocity channel,
KANOMAX JAPAN INC., Osaka, Japan). The anemometer was set up at nine points (Fig. 2). The minimum distance between the floor and the anemometerwas 17 cm due to the anemometer’s size. The measurement was controlled by an accessory software (MODEL
S620-00 Ver 1.46, KANOMAX JAPAN INC., Osaka,
Japan). The wind velocity and direction were measured
for 1 min at a sampling rate of 10 Hz after waiting a few
minutes for the machine to stabilize. The sampled wind

To assess the skin temperature, thermography data were
measured before and after each airflow setting (InfReC
R550, NIPPON AVIONICS Co. LTD., Tokyo, Japan). In
each thermographic image, the face area (178 × 140 pixels) was determined and cropped. The thermal data inthis area were averaged, and a two-way within-subject
analysis of variance (ANOVA) was conducted (airflow
conditions × before/after measurement).
EEG and ECG

The EEG and ECG data were measured during the fourtasks under both airflow conditions. The measured data
were transformed using a discrete Fourier transform(DFT) to extract EEG amplitudes or heart rate variability.
The extracted parameters were compared between the
airflow directions using ANOVA. The detailed methods

Fig. 2 Measurement settings of wind velocity and direction. White circles indicate the nine locations where the wind velocity and direction were
measured using an anemometer. The nine locations were combinations of three positions in the front-back direction and three heights. The black
circle indicates the location of the PMV meter. AC indicates the location of the air conditioner. This figure was reprinted from Tamura et al. [23]licensed under CC BY 4.0 (https://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/)

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

of measurement, preprocessing, and statistical analysis
are described as follows.
We recorded EEG data across 19 channels, as per theinternational 10–20 system [33], using dry active electrodes, CGX Flex, and Drypad sensors with a wirelessheadset (Quick-20, CGX, San Diego, USA). The reference electrode was placed on A1, as defined by the above
system, and an additional electrode was placed on A2 forfurther analysis. To remove artifacts, electrooculogram(EOG) data were also collected using AIM Generation 2(CGX, San Diego, USA) by adding sensors to the headset.
Bipolar horizontal and vertical EOG data were recordedby two additional electrodes placed below the left eye andlaterally from the outer canthus of the right eye, with areference electrode placed on the forehead. The EEG and
EOG data were amplified and digitalized at a sampling
rate of 500 Hz.
In addition to EEG and EOG channels, we recorded
ECG data using AIM Generation 2 (CGX, San Diego,
USA). ECG data were measured by the bipolar limb lead
method, which used a positive electrode on the left arm
and a negative electrode on the right one with a referenceelectrode. These data were sampled at 500 Hz.
The measured EEG data were re-referenced to the averages of A1 and A2 and separated by task to reduce EOG
artifacts efficiently. The EEG data during each session of
60 s were segmented and filtered with a notch filter of 60
Hz. The data were then transformed using a DFT with arectangular window to obtain the DFT coefficients. DFT
analysis was performed using the “fft.m” function in
MATLAB (MathWorks, Inc., Natick, USA). Amplitudes
in the frequency bin calculated from the DFT coefficients were averaged within the beta (14 ≤ f < 30 Hz) andgamma (30 ≤ f < 50 Hz) frequency bands, where f represents the frequency. The segmented data were rejected
when the EOG or EEG data exceeded ± 100 μV to eliminate eye blinking artifacts. The mean rates of segmentsthat remained after rejection were 77.1 ± 11.7% and 80.0
± 10.0% in direct and indirect airflow conditions, respectively. Changes in each oscillation band of the EEG timeseries were assessed using a two-way ANOVA (airflow
conditions × four tasks) for each electrode. We did notconduct any post hoc testing because our purpose was todetermine the main effect of airflow conditions on EEG.
From the EEG data, gamma and beta frequency bands
were analyzed according to our previous studies [22, 23],
which examined the relationship between indoor airflow comfort and the gamma and beta bands. The results
indicated that the amplitudes of these waves decreased
as airflow comfort improved [23]. In this study, the analytic EEG electrodes were determined by a data-driven
method, as it was not possible to select an analytic region
based on previous research, which has found various

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regions to be the areas related to thermal comfort. For
example, an analysis of functional magnetic resonanceimaging (fMRI) reported that thermal sensation and thermal comfort were processed by different brain networks[34]. Thus, the 19 electrodes herein used were screenedby subtracting the amplitudes measured under the direct
condition from those under the indirect condition. Basedon this criterion, electrodes T3 and P7 were identified as
the analytic electrodes for the betta and gamma waves.
ECG data corresponding to each 60-s session were segmented, detrended, and smoothed by a moving window.
R-R intervals were calculated for all data by finding R
peaks. Then, a DFT was applied to the R-R interval data
to obtain heart rate variability. Using the DFT components from the R-R interval data, we calculated the mean
components for high frequency (HF) (0.15 ≤ f < 0.4 Hz)
and low frequency (LF) (0.04 ≤ f < 0.15 Hz). LF/HF wasused as an indicator of sympathetic modulation, and
HF as an indicator of parasympathetic activity. We conducted a two-way ANOVA to assess HF and LF/HF data
across the two airflow conditions and the four tasks.
Respiratory waveform

We recorded respiration waveforms using AIM Generation 2 and spatial bio-impedance-based respiration sensors (CGX, San Diego, USA) during the four tasks. The
two respiration sensors were placed on the left and rightsides of the participant’s chest. These data were sampled
at 500 Hz.
The respiration waveforms during each 60-s session
were segmented. Then, a DFT was applied to obtain thepeak frequency of the power spectral density. From thepower spectral density, the full width at half maximum(FWHM) of the peak frequency was calculated. A smaller
FWHM indicates a more regular breathing state at thepeak frequency of the respiration wave, while a larger
FWHM indicates more irregular breathing. We used
FWHM as a parameter to estimate the relaxation state,with a low mental load corresponding to regular breathing. A two-way within-subject ANOVA was then conducted (airflow conditions × four tasks).
Subjective assessments

Between resting and CA, each participant answered aquestionnaire rating thermal sensation (1, very cold;
7, very hot), pleasantness (0, very unpleasant, 100, very
pleasant), fatigue (0, best condition and no fatigue; 100,worst condition and extreme fatigue), sleepiness (1, fully
awake; 9, very sleepy), and anxiousness (0, not feelinganxious at all; 100, feeling extremely anxious). The thermal sensation and sleepiness were assessed with 7- and9-step Likert scales, respectively, and the remainingTamura et al. Journal of Physiological Anthropology(2022) 41:16

factors were assessed with horizontal visual analog scales(VAS) with 10-cm lines (Table 1).
The responses to the questionnaire were taken after
resting and were analyzed using a t-test. The subjective
assessments were obtained by computing the correlations with the analytic EEG gamma and beta amplitudes
at T3 and P7, using pairwise Pearson’s coefficients within
participants. The EEG amplitudes were averaged acrossthe four tasks.
Psychological time

Psychological time has been discussed in relation to negative emotions, including boredom [35, 36], which willlead to overestimating the passage of time [37]. In thisstudy, we measured psychological time as an indicator of
psychological stress caused by airflows. The analytic psychological time was defined as the mean response duration in each CA and CM task. A positive psychologicaltime indicates that the individual perceived a slow paceof time. A two-way ANOVA was performed across thetwo airflow conditions and the two assignments (CA andCM).
Correlation between the EEG and subjective assessments

The correlations between EEG amplitudes and subjective assessments were performed by pairwise correlation. The EEG amplitudes were averaged across the fourtasks and sessions in each channel, frequency band, andairflow condition. Then, the EEG and subjective assessment results were used for pairwise correlation analysis
within participants to obtain correlation coefficients andp-values, using the “corrcoeff.m” function in MATLAB(MathWorks, Inc., Natick, USA).

Results
Wind velocity and direction

To ensure the correct experimental environment, wemeasured the wind velocity and direction under direct
and indirect airflow conditions (Figs. 2, 3, 4, 5, and 6). In
front of the participant, at a height of 120 cm from thefloor, the wind velocity under direct current was larger
than that under indirect airflow, especially in the Y- andZ-axis directions (Tables 2 and 3, Figs. 3 and 5: 120 cm

Page 7 of 19

at the front). These data indicate that the wind direction
and velocity differed between the two conditions, especially around the sitting sites of the participants.
Physical measurements and PMV

Room temperature, relative humidity, and air velocities
were measured at locations near the participants (Fig. 7).
The mean ± SEM of the room temperature under direct
and indirect airflows were 34 ± 0.19 °C and 30 ± 0.20 °C,
respectively (t(38) = − 14, p < 0.001, 1−β > 0.99). The
mean relative humidity and wind velocity under direct
and indirect airflows were 22 ± 0.61% and 25 ± 0.90%(t(38) = 3.0, p = 0.0048, 1−β = 0.83), and 0.39 ± 0.042
m/s and 0.017 ± 0.0053 m/s (t(38) = − 8.9, p < 0.001,
1−β > 0.99), respectively.
To compare the thermal pleasantness and sensation in
the experiment room, we compared PMV values, which
predict the mean thermal comfort evaluation of a groupof occupants as the combined result of environmental
variables, metabolic rate, and level of clothing insulation[15]. The mean ± SEM of PMV was 2.4 ± 0.056 under
direct airflow and 1.5 ± 0.043 under indirect current(t(37) = − 12, p < 0.001, 1−β > 0.99).
Skin temperature

Before and after each experiment, facial skin temperature
was measured with a thermographic camera. The twoway within-subject ANOVA performed with the measured face temperatures (airflow conditions × before/after
condition) showed no significant effect of the airflow
condition (F(1, 12) = 4.1, p = 0.065, 1−β = 0.52) and nointeraction effect (F(1, 12) = 1.1, p = 0.32) (Fig. 8).
We confirmed the difference in skin temperatures
between the sexes. The mean ± SEM skin temperature ofmales was 34.0 ± 0.09 °C and that of females was 34.7 ±0.21 °C. The difference in skin temperatures among thesexes was analyzed using Welch’s two-sample t-test, anda significant difference was observed (t(4.1) = − 2.9, p =0.042, 1−β = 0.89). The difference among the sexes did
not show any interaction with the airflow conditions,which was revealed through three-way mixed ANOVA(see Supplementary Information, 3).

Table 1 Questionnaire structure
Category

Scale

Type

Thermal sensation

Very cold ←→ very hot (1–7)

7-step Likert, integer values

Best condition and no fatigue ←→ worst condition and extreme fatigue (0–100)

10-cm VAS

Not feeling anxious at all ←→ feeling extremely anxious (0–100)

10-cm VAS

Pleasantness
FatigueSleepiness
Anxiousness

Very unpleasant ←→ very pleasant (0–100)

10-cm VAS

fully awake ←→ very sleepy (1–9)

9-step Likert, integer values

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 3 Wind velocity and direction in the Y-Z plane under direct airflow. Vy and Vz indicate the mean of the 1-min velocity in the Y and Z directions,
respectively. Dotted lines indicate the vectors of the wind velocities

EEG

In the EEG analysis, we focused on the beta and gamma
frequency bands because we reported a relationshipbetween these bands and airflow sensations in our previous study [22]. The amplitudes were analyzed for each
airflow condition and frequency band.
To show the difference of each frequency activity,
the main effect of the airflow condition on each frequency band during the execution of the four tasks wasanalyzed by a two-way ANOVA (airflow conditions ×
four tasks). In terms of gamma activity at T3 and P7,
the amplitudes under the indirect condition were lower
than those under the direct condition (Fig. 9). These

amplitudes showed a significant main effect from theairflow setting (T3: F(1, 132) = 4.6, p = 0.034, 1−β=0.60; P7: F(1, 132) = 4.8, p = 0.030, 1−β = 0.62). There
was no significant main effect from the tasks and nointeraction (F < 0.49, p > 0.67).
In terms of beta activity at both electrodes, the amplitudes under the indirect condition were lower thanthose under the direct condition (Fig. 10). The effectfrom the airflow condition was found to be significant
on waves at P7 (F(1, 132) = 5.0, p = 0.028, 1−β = 0.63)
but only marginal on those at T3 (F(1, 132) = 3.9, p= 0.051, 1−β = 0.53). Again, there was no significant
main effect from the experiment tasks and no interaction in the case of beta activity (F < 0.43, p > 0.73).

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 4 Wind velocity and direction in the X-Y plane under direct airflow. Vx and Vy indicate the mean of the 1-min velocity in the X and Y directions,
respectively. Dotted lines indicate the vectors of the wind velocities

Respiration

To analyze the effects of the airflow setting on the regularity of respiration, we calculated the FWHM of thespectral density from the respiration waves. The twoway ANOVA (airflow conditions × four tasks) showeda significant main effect from the airflow condition (F(1,132) = 7.4, p = 0.0073, 1−β = 0.81). Regarding the tasks,
there was no significant main effect (F(3, 132) = 0.77, p= 0.51) and no interaction effect (F(3, 132) = 2.3, p =0.084) (Fig. 11). These results indicate that the variability of respiration was lower under the indirect condition
than under the direct one.
ECG

To investigate the effects of the airflow condition on theautonomic nervous system, the ECG parameters wereanalyzed. HF reflects parasympathetic activity [38], andLF/HF infers sympathetic activity [39]. The two-way
ANOVA showed no significant main effect or interaction
effect from HF (F < 1.8, p > 0.18, 1−β < 0.32) or LF/HF (F< 0.17, p > 0.17, 1−β < 0.24).
Subjective assessments and behavioral results

Under each airflow condition, the participants answered
the same questionnaire, involving thermal sensation,pleasantness, fatigue, sleepiness, and anxiety. The meanscores are reported by airflow condition in Fig. 12.
The thermal sensation under indirect airflow was
lower than under direct airflow, and the paired t-test
showed a significant difference (t(19) = − 3.4, p =0.031, 1−β = 0.89). The pleasantness under indirect

airflow was higher than under direct airflow, but thepaired t-test showed a marginal significance (t(19)= 2.0, p = 0.060, 1−β = 0.39). The other subjective
assessments showed no significant difference between
the two airflow conditions (p > 0.13).
From the responses to the CA and CM tasks, basedon the two-way ANOVA (airflow conditions × twotasks), the duration of psychological time showed nosignificant main effect or interaction effect from theairflow conditions (p > 0.53) (Fig. 13). This result indicates that the airflow direction did not affect psychological time.
To evaluate the mental calculation performance during the Cal assignment, the number of correct answers
and the total number of mental calculations completed
by each participant were counted. The total numberof mental calculations was an indicator of the calculation speed. A chi-squared test did not reject the null
hypothesis that the correct rate was higher under one
condition than the other (χ2 = 0.13, p = 0.71). A paired
t-test did not show any significant difference in thenumber of mental calculations performed under eachcondition (t(19) = − 0.21, p = 0.84). These results indicate that the airflow direction did not affect the mental
calculation performance.
The correlation between subjective assessments andEEG amplitudes was analyzed. The gamma amplitudes
at T3 showed significant correlation with fatigue (r =0.34, p = 0.048) and anxiousness (r = 0.41, p = 0.014)(Fig. 14a). The remaining subjective ratings showed nosignificant correlation with gamma at T3 (|r| < 0.29, p> 0.94) (Supplementary Fig. 1). The gamma amplitudes

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

Page 10 of 19

Fig. 5 Wind velocity and direction in the Y-Z plane under indirect airflow. Vy and Vz indicate the mean of the 1-min velocity in the Y and Z
directions, respectively. Dotted lines indicate the vectors of the wind velocities

at P7 showed no significant correlation with any of thesubjective assessments (|r| < 0.28, p > 0.10) (Supplementary Fig. 2).
The amplitudes of beta waves from T3 showed a significant correlation with anxiousness (r = 0.35, p = 0.037)(Fig. 14b). The other subjective ratings showed no significant correlation with beta amplitudes at T3 (|r| < 0.30, p >0.083) (Supplementary Fig. 3). The beta waves at P7 showedno significant correlation with any of the subjective assessments (|r| < 0.28, p > 0.17) (Supplementary Fig. 4).

Discussion
In this study, we assessed the effects of the direction ofheating airflow on indoor comfort through subjective
assessments and physiological measurements, including

skin temperature, EEG, ECG, and respiration. The beta
and gamma EEG amplitudes showed a significant main
effect from the airflow condition, and respiration regularity showed the airflow effects for relaxation states. The
gamma and beta amplitudes at T3 showed a significant
correlation with anxiousness levels, and gamma waves
also showed a meaningful correlation with fatigue. In ourprevious report comparing direct and indirect coolingairflows [23], we observed lower gamma and beta activities under the indirect airflow condition. The correlations
suggest that the differences in physiological indices could
be explained by airflow direction rather than by thermal
sensation. In this study, the physiological and subjective
results indicated that an indirect heating airflow induced
lower mental stress than a direct air current. Some

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 6 Wind velocity and direction in the X-Y plane under indirect airflow. Vx and Vy indicate the mean of the 1-min velocity in the X and Y
directions, respectively. Dotted lines indicate the vectors of the wind velocities

Table 2 Wind velocity under direct airflow
Height [cm]Location

Wind velocity [m/s]/
mean of 1 min: X

Wind velocity [m/s]/
mean of 1 min: Y

120

Front

0.063

0.761

120

Participant− 0.055

0.125

120

Back

60

Front

60

Participant

60

Back

− 0.024− 0.031− 0.248− 0.043− 0.103

0.068
0.155

− 0.113Wind velocity [m/s]/
mean of 1 min: Z− 0.651

0.143

Wind velocity [m/s]/
mean of 1 min:composite
1.017
0.225

0.025

0.124

− 0.0190.177− 0.030

0.019

0.340
0.131

17

Front

0.011

0.011

0.012

0.038

17

Participant

0.050

0.000

0.073

17

Back

0.014

− 0.023

0.012

0.029

0.000

The mean of 1-min wind velocity [m/s] in each direction and composite vector length under the direct condition at each measurement position

Table 3 Wind velocity under indirect airflow
Height [cm]Location

120

Front

120

Participant

Wind velocity [m/s]/
mean of 1 min: X− 0.035− 0.024

120

Back

0.050

60

Front

0.006

60

Participant

0.002

60

Back

0.010

Wind velocity [m/s]/
mean of 1 min: Y

Wind velocity [m/s]/
mean of 1 min: Z

Wind velocity [m/s]/
mean of 1 min:composite

0.031

0.009

0.121

0.068

0.051

0.123

− 0.0020.016− 0.033− 0.034

0.034

0.076

0.023

0.046

0.019

0.061

0.009

0.040

17

Front

0.017

0.003

0.012

0.088

17

Participant

0.021

Back

0.004

− 0.0100.040

17− 0.027− 0.011

0.000

0.018

The mean of 1-min wind velocity [m/s] in each direction and composite vector length under the indirect condition at each measurement position

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 7 Mean and standard errors of physical data and predictive mean votes (PMV). a Room temperature. b Relative humidity. c Wind velocity. d
Predictive mean votes. The combinations of an asterisk and u-shaped line show significant differences (**p < 0.01, ***p < 0.001)

reports have concluded that people do not prefer direct
airflow and high air velocity in heating [11, 12], especiallyin subtropical regions [9, 10]. On the other hand, otherstudies reported that air movement was preferred [6–8].
PMV has been widely used to estimate indoor comfort,
but its equation does not factor airflow direction [15].
Therefore, we suggest that the effect of heating airflow on
the mental state be evaluated through a combination ofsubjective and objective measurements.
We discuss the gamma activity under the different
heating airflow conditions. The gamma amplitudes at
T3 showed a significant correlation with anxiousness
and fatigue. A possible interpretation of this correlation result is that the lower gamma amplitudes underindirect airflow reflected lower fatigue and anxiousness levels, though we could not find a significant correlation at P7. Lower gamma and beta amplitudes havealso been observed under indirect cooling airflow incomparison with direct cooling airflow [23]. Thus, we
can conclude that the EEG differences were caused by
airflow direction, rather than by thermal sensation. We
also observed the decrease in beta and gamma amplitudes in environments with no airflow, both in cooling and heating [22]. Higher gamma amplitudes reflect
awareness and emotional content [40] under airflow
stress [23] and have been associated with unpleasantand aversive emotions [41–43]. Furthermore, gamma
amplitudes in the temporoparietal regions were foundto be higher for people with an anxiety disorder thanfor healthy people [44]. We observed different gamma
activities in the temporoparietal regions under different
airflow conditions, as was also previously reported [22].
The gamma waves at the temporoparietal sites recordedin this study support such a difference in mental stresslevels between direct and indirect heating airflows.

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

Fig. 8 Face temperature measured by thermography images. a An
example of face area selection. b Mean and standard errors of theface temperatures. The Y-axis shows the face temperature, which was
calculated by averaging the thermography data of the face area. The
X-axis shows the two time points, before and after the experiment

Page 13 of 19

The lower beta activity observed under indirect airflow agreed with the findings of our previous study[22], and the beta amplitudes were negatively correlatedwith pleasantness [23]. In this study, we found a significant correlation between anxiousness ratings and beta
amplitudes at T3. We can deduce that the beta waves
reflected anxiousness levels of the participants, withindirect airflow inducing lower anxiousness feelings
than direct airflow. Several studies have reported an
association between beta activity and fatigue levels, but
the sites at which differences in beta amplitudes havebeen recorded—right and middle frontal sites [45], lefttemporal sites [46], and left and right central sites [22]—are inconsistent across these studies. In this study, weobserved significant beta differences at the left temporalsites, which is consistent with a conventional report on
the relationship between beta activity and mental fatigue[46]. Our results showing smaller amplitudes in the beta
frequency band from left temporal areas suggest thatbeta activity could be a biomarker for estimating indoor
comfort state.
Notably, the correlation results were insufficient tomake a conclusion about the relationship between the
EEG findings and mental states, although the results
could provide a possible interpretation as to why the EEG
differed under different airflow conditions. The t-test forthe subjective assessments did not show significant differences, which suggests that the mental states may not
differ under different airflow directions, as we discuss
below.
The results of respiration regularity support ourestimation that indirect heating airflow induces morerelaxed states than direct airflow. The FWHM of therespiration waves was significantly smaller underindirect airflow than under direct airflow. It has beenreported that mental stress could increase the variability in respiration rate, while sustained attention could

Fig. 9 Gamma amplitudes. a Topography of the mean difference between direct and indirect airflow across four tasks. The electrode labels T3 andP7 indicate the analytic electrodes for which the difference between amplitudes under direct and indirect airflow was the largest. b Mean andstandard errors of the amplitudes at T3 and P7. The X-axis shows the four tasks. The Y-axis shows the gamma amplitude, which was calculated byaveraging the amplitudes across the test repetitions. The main effect of the airflow condition was significant in the two-way ANOVA (p < 0.05)

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 10 Beta amplitudes. a Topography of the mean difference between direct and indirect airflow across four tasks. The electrode labels T3 andP7 indicate the analytic electrodes for which the difference between amplitudes under direct and indirect airflow was the largest. b Mean andstandard errors of the amplitudes at T3 and P7. The X-axis shows the four tasks. The Y-axis shows the gamma amplitude, which was calculated byaveraging the amplitudes across the test repetitions. The two-way ANOVA showed a significant main effect from the airflow condition at P7 (p <0.05) and a marginal significant effect at T3 (p < 0.10)

decrease it [27]. A larger variability of respiration rate
reflects in a larger FWHM, which was calculated fromthe spectral density of the respiration waves. Here, we
observed a smaller FWHM under indirect airflow thanunder direct airflow. Thus, the analysis of respiration
records confirms our prediction that indirect heating

Fig. 11 FWHM of the peak power spectral density from respiration
rate. The X-axis shows the four tasks. The Y-axis shows the meanof the FWHM. The error bars are the standard errors. The two-way
ANOVA showed a significant main effect from the airflow condition(p < 0.01)

airflow can induce lower mental stress than direct
airflow.
While EEG and respiratory data indicated less stressand fatigue under indirect airflow, we could not find significant differences in subjective assessments of pleasantness, sleepiness, fatigue, or anxiousness. The subjective
assessment of thermal sensation under different airflow
directions showed a significant difference, and it wasconsistent with relative PMV values. Thus, PMV could
reflect thermal sensation but not mental pleasantness.
This lack of correspondence between variations in mostof the subjective assessments and the airflow setting
may be caused by the difficulty to verbalize indoor comfort feelings, which consequently needs to be evaluatedthrough various aspects. Otherwise, if the evaluation
relied on only subjective ratings and PMV, unconscious
comfort feelings would be overlooked. Related studies have reported that indoor comfort can be reflectedby physiological responses, such as EEG and respiration(e.g., [24, 25, 47–49]). These physiological responses canbe used as objective parameters for the evaluation of
unconscious environmental comfort.
Heart rate variability showed no significant difference
between airflow conditions. Other reports have founddifferences in heart rate variability under different environmental temperatures, such as 28 and 30 °C, 26 and 30°C, and below [25, 49–51]. Zuo et al. reported an increasein heart rate, not heart rate variability, between 30 and 37°C [24] and discussed that this increase may have beencaused by higher heat stress. In this study, the resulting
room temperature was different under the direct andindirect conditions (30 and 34 °C, respectively), but thedifference was smaller than in the report by Zuo et al.,and the higher temperature was not sufficient to induce
heat stress. Heart rate variability may not be influenced

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

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Fig. 12 Mean and standard errors of the subjective assessments. a Thermal sensation. b Pleasantness. c Sleepiness. d Fatigue level. e Anxiousness.
The asterisks indicate significant differences between the airflow conditions (***p < 0.001)

by heating airflow direction at high room temperatures,i.e., 30+ °C. We suggest that evaluating mental state
under a heating indoor environment requires several
physiological measurements to be combined.
Although the environmental parameters changed
depending on the airflow conditions, there was no

significant difference in the participants’ face temperature. As we discussed above, we observed some EEG
differences from one airflow condition to the other, but
we did not observe changes in face temperature. This
result suggests that EEG and respiration changes canbe derived from changes in mental state but not from

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

Fig. 13 Mean and standard errors of the psychological time. The
Y-axis indicates the mean of the psychological time, which is the time
the participants perceived as 10 s. The X-axis indicates the two tasks.
CA, counting following alerts; CM, time counting in mindchanges in skin temperature. We also confirmed theeffects of sex on skin temperature, EEG, and respiration changes, but there was no significant interaction
effect between sex and airflows (see Supplementary
Information), although the mean skin temperature differed between the sexes. The mean temperature under

Page 16 of 19

either direct or indirect flow seemed to be different at
the beginning of the experiments, though the difference
decreased by the end of the tests. The order of airflow
settings was pseudo-randomized across participants sothat the mean temperature before the experiment could
not be affected by acclimation. One possible interpretation is that the face temperature might be affected moreeasily by the local airflow situation before the experiment. The physical parameters were monitored but notcompletely controlled. The local air temperature might
be different due to the airflow direction. Another possible interpretation relates to the relationship between
facial temperature and mental state. It has been reported
that the facial temperature decreases as a result of negative emotions [52]. According to the report, a lower
facial temperature might reflect a negative reaction to
direct airflow. The acceptability of air movement could
increase in time but not significantly [11]. The facial
temperature response to negative emotions may be moderated with time. More investigation is needed on therelationship between facial thermography and indoor
comfort induced by airflow direction.
There were no significant main effects of the airflow
condition on psychological time or mental calculation.
Our previous investigation showed significantly different
scores in the mental counting task in a heating environment under either direct airflow or no airflow [22]. In thisstudy, we only changed airflow direction, and the difference in airflow sensation might not be sufficiently large toaffect behavioral data compared to the previous settings.
Furthermore, the results might be affected by a factor oftask type. Zhang and de Dear reported that simple cognitive tasks are less susceptible to temperature effects thancomplex ones [53]. A meta-analysis and other reports

Fig. 14 Correlation between subjective assessments and EEG amplitudes. The X-axes indicate subjective assessments. The Y-axes indicate a gamma
and b beta amplitudes. Yellow lines show the 95% density ellipse. The amplitudes were averaged across the four tasks performed

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

support there being less effect on cognitive performanceby thermal environment [14, 54]. The differences in room
temperature and other physical parameters had a lesssevere effect on time counting and mental calculation
tasks.
In our procedure, only airflow direction was modulated; however, such settings led to different room temperatures, wind directions, and relative humidity. The
environmental parameters of the experiment room
were monitored by the staff in a control room, but themeasurement point was located nearby the indoor air
conditioning unit, which was separated from the participant’s sitting point. Differences in the PMV scores
resulted from these physical parameters, not only
from the setting of airflow direction. Therefore, we
could not conclude that the observed responses were
induced only by the airflow sensation. Generally, it isvery difficult to maintain the room temperature andother parameters under different airflow directions in
an office or school using a commercial air conditioner.
Our experimental environment was closer to generalscenes. However, more restricted control will be necessary to reveal in further detail the physiological mechanism of airflow sensation.

Conclusions
Using physiological and subjective measurements,
this study investigated how the direction of airflow
produced by air conditioning influences the feelingof comfort in a heating environment. The gamma andbeta of EEG showed low amplitudes under indirect
heating airflow, and these results indicate low emotional stress and fatigue levels. The respiration results
indicate that the variability of the respiration rate was
low under indirect airflow, and the results support theidea of a high relaxation state under the indirect condition. However, subjective assessments and behavioral
performance could not detect the difference in mental state. These results suggest that some physiologicalmeasurements can be useful for estimating the mental
state influenced by airflow direction, which cannot be
determined from subjective measurements. Our procedures and results emphasize the effectiveness of physiological measurements in revealing various aspects ofairflow comfort.
Abbreviations
ANOVA: Analysis of variance; Cal: Mental calculation; CA: Time counting following alerts; CM: Time counting in mind; DFT: Discrete Fourier transform; ECG:
Electrocardiography; EEG: Electroencephalography; EOG: Electrooculogram;
FWHM: Full width at half maximum; fMRI: Functional magnetic resonanceimaging; PMV: Predicted mean vote; SpO2: Peripheral oxygen saturation; HF:
High frequency; LF: Low frequency.

Page 17 of 19

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​022-​00289-x.
Additional file 1: Supplementary Methods. We performed post-power
analysis for the t-test and ANOVA results. To obtain the statistical powerof t-tests, Cohen’s d values were computed as standardized effect size,
using the effsize package in R. Each statistical power (1–β) was calculatedusing the corresponding Cohen’s d, sample size, and significance level (α= 0.05) using the pwr package in R. In terms of ANOVA, Cohen’s f values
were calculated to show effect size of the factors. The post-hoc power
was calculated using effect size, sample size, and significant level (alpha= 0.05) using the pwr2 package in R. Supplementary Table 1. Cohen’sd values for effect size and powers obtained by post-power analysis
for t-test. Supplementary Table 2. Cohen’s d values for effect size andpowers obtained by post-power analysis for subjective assessments.
Supplementary Table 3. Cohen’s f values for effect size and powers forthe main effects obtained by post-power analysis for two-way ANOVA of
EEG. Supplementary Table 4. Cohen’s f values for effect size and powers
for the main effects obtained by post-power analysis for two-way ANOVAof thermography. Supplementary Table 5. Cohen’s f values for effectsize and powers for the main effects obtained by post-power analysis
for two-way ANOVA of respiration. Supplementary Table 6. Cohen’s fvalues for effect size and powers for the main effects obtained by postpower analysis for two-way ANOVA of ECG parameters, HF and LF/HF.
Supplementary Figure 1. Scatter plots of gamma amplitudes at T3 as afunction of each subjective assessment. Yellow lines show the 95% density
ellipse. The amplitudes were averaged across the four tasks performed.
There was no significant correlation (P > 0.05). Supplementary Figure 2.
Scatter plots of gamma amplitudes at P7 as a function of each subjective
assessment. Yellow lines show the 95% density ellipse. The amplitudes
were averaged across the four tasks performed. There was no significant
correlation (P > 0.05). Supplementary Figure 3. Scatter plots of beta
amplitudes at T3 as a function of each subjective assessment. Yellow lines
show the 95% density ellipse. The amplitudes were averaged across thefour tasks performed. There was no significant correlation (P > 0.05). Supplementary Figure 4. Scatter plots of beta amplitudes at P7 as a functionof each subjective assessment. Yellow lines show the 95% density ellipse.
The amplitudes were averaged across the four tasks performed. There wasno significant correlation (P > 0.05).
Acknowledgements
The authors would like to thank Mr. Toyotaka Hirao for the management
support of this research project and Prof. Taeko Ogawa, Assoc. Prof. SachikoKiyokawa, Prof. Hiroshi Ito, and Assoc. Prof. Shinji Yamagata for the assistancewith recruiting the participants.
Authors’ contributions
Conceptualization and experimental design: TO. Data acquisition: SaM, YT, TK,
JM, TO, KT, and SoM. Data analysis: KT, SaM, and TO. Manuscript writing: KT and
TO. Supervision: TO, HO, and SoM. Research management: TO, HO, KM, SH, andSoM. All authors read and approved the final manuscript.
Funding
Not applicable.
Availability of data and materials
The datasets generated and/or analyzed during the current study are not publicly available due to a privacy policy but are available from the corresponding
author on reasonable request.

Declarations
Ethics approval and consent to participate
The experiments were approved by the joint ethics committee of the Facultyof Arts and Science and the Center for Health Sciences and Counseling from
Kyushu University (201815, 201815-1). Written informed consent was obtained
from each participant.

Tamura et al. Journal of Physiological Anthropology(2022) 41:16

Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Faculty of Arts and Science, Kyushu University, 744 Motooka, Nishi‑ku,
Fukuoka 819‑0395, Japan. 2 Department of Information and Systems Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology,
3‑30‑1 Wajiro‑higashi, Higashi‑ku, Fukuoka 811‑0295, Japan. 3 Graduate
School of Systems Life Sciences, Kyushu University, 744 Motooka, Nishi‑ku,
Fukuoka 819‑0395, Japan. 4 Mitsubishi Heavy Industries Thermal Systems
LTD., 3‑1, Asahi, Nishi‑biwajima‑cho, Kiyosu, Aichi 452‑8561, Japan. 5 Researchand Innovation Center, Mitsubishi Heavy Industries Ltd., 1, Aza Kanda,
Iwatsuka‑cho, Nakamura‑ku, Nagoya, Aichi 453‑8515, Japan.
Received: 29 August 2021 Accepted: 7 April 2022

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