Comfort Responses Research Articles

Adaptive Thermal Comfort in the Different Buildings of Temperate Climates—Comparison Between High-Latitude Europe and Mountainous Himalayas in India

Thermal comfort in buildings is essential for occupant well-being and energy efficiency, particularly in naturally ventilated environments where indoor conditions are closely influenced by outdoor climates. Current studies have not fully explored how thermal comfort varies across regions with similar climatic classifications but distinct geographic and cultural contexts. Addressing this gap, we analyzed and compared the adaptive thermal comfort responses in different naturally ventilated buildings located in temperate oceanic regions arising due to the high latitude in Europe and the elevated Himalayan region of Darjeeling, India. A mixed-methods approach was used with data from classrooms, offices, and residential buildings with adaptive thermal comfort modeling. The results show that European buildings exhibit narrower thermal comfort ranges compared to Darjeeling, for example, 21.2~24.8 °C versus 16.0~21.6 °C for 80% comfortability in classroom settings, respectively. Statistical analysis revealed significant differences in clothing insulation levels, with occupants in Darjeeling buildings demonstrating higher variability (mean rank 2103.31) compared to their European counterparts (mean rank 1207.30, p < 0.001). Additionally, a stronger correlation between indoor and outdoor air temperature was observed in Darjeeling (R: 0.785, p < 0.001), reflecting limited thermal buffering compared to European buildings (R: 0.372, p < 0.001). The paper advances adaptive thermal comfort models that account for regional differences and links these finding to sustainable building practices. The findings provide actionable insights for energy-efficient, climate-responsive building practices while supporting global sustainable development goals.

THERMAL COMFORT PREDICTION USING SVM AND RANDOM FOREST MODEL

Predicting thermal comfort is crucial for optimizing built environments for human habitation, as it impacts health, productivity, and overall well-being. To address this imperative, interdisciplinary collaboration among architects, engineers, psychologists, and data scientists is needed to develop reliable predictive models that anticipate occupants’ thermal comfort preferences across diverse environmental conditions and architectural designs. Traditional methods rely on human comfort models, which can be subjective and time-consuming. Machine learning algorithms, such as Support Vector Machines (SVM) and Random Forest (RF), have been utilized to predict thermal comfort with high accuracy and efficiency. The Internet of Things (IoT) is revolutionizing the building management systems industry, with adaptive control algorithms and modular architectures exploring the IoT paradigm. This paper discusses the use of SVM and Random Forest algorithms for predicting thermal comfort in buildings, exploring their strengths and weaknesses and comparing their performance in different scenarios. The study analyzed a dataset of thermal comfort data, filtering by quantity and removing outliers. The data was split into 80% for training and 20% for testing. The study used SVM and Random Forest models to capture complex relationships between environmental parameters and thermal comfort responses. The results showed that the IQR method provided 3-4% accuracy, while the reducing label values method offered 20-23% accuracy. The study also tested the parameters of the models, resulting in a 2-4% difference between the two models. The study concluded that Random Forest appears more stable than SVM and plans to add new features to improve accuracy.

Data-efficient comfort modeling: Active transfer learning for predicting personal thermal comfort using limited data

Personal comfort models are used to predict thermal comfort responses at the individual level rather than predicting the average thermal comfort responses for large populations. These models, data-driven in nature, need to be trained on large amounts of occupant comfort feedback and sensor data to achieve accurate predictions. However, collecting such data is often expensive and labor-intensive in reality. To address this, we proposed a data-efficient active transfer learning (ATL) framework to improve the performance of personal comfort models under limited data. To demonstrate the validity of this framework, we developed a base Convolutional Neural Network-Long Short-term Memory (CNNLSTM) model alongside two transfer learning models utilizing feature extraction (TL-CNNLSTM-FE) and fine-tuning (TL-CNNLSTM-FE) approaches, enhanced by a novel active learning strategy. Using these models, three comfort prediction tasks (i.e., thermal preference, thermal acceptability, and air movement preference) were performed by transferring the knowledge from the ASHRAE Global Thermal Comfort Database II to a limited dataset collected in the tropics. Empirical results indicate that the active transfer learning framework proposed was able to consistently outperform the base and transfer learning models using only less than 10% of the training data for all personal comfort tasks, highlighting the effectiveness of this strategy. The implications of this work are especially useful for the research community working on the practical applications of data-efficient machine learning approaches for personal thermal comfort predictions.

Thermal comfort in university classrooms in humid subtropical climate: Field study during all seasons

Ensuring students thermal comfort has complex implications involving student performance and energy consumption costs. Existing models may not be suitable for predicting the thermal comfort of university students. This study aimed to compare the thermal comfort responses of university students in air-conditioned and naturally ventilated classrooms located in a humid Brazilian subtropical climate with the analytical and adaptive models. The study was conducted in 28 classrooms of two universities located in a humid subtropical climate in southern Brazil. Environmental variables were measured using a microclimatic station, while personal variables were obtained through student responses in electronic questionnaires between September 2022 and July 2023. In classrooms operating with natural ventilation, the mean thermal neutrality temperatures were 19.8 °C and 21.4 °C in Joinville and São José, respectively. When operating with air-conditioning, the mean thermal neutrality temperatures were 21.8 °C and 22.1 °C in Joinville and São José, respectively. Even when they answered “neutral” on the thermal sensation questionnaire, students reported that they preferred the cooler environment. Classroom occupant densities less than 1.00 did not influence the students’ thermal sensation. In general, the PMV/PPD model was not able to predict the thermal comfort of university students. The adaptive comfort model proved adequate to predict the thermal comfort of students when classrooms were naturally ventilated.

The Role of Urban Vegetation in Counteracting Overheating in Different Urban Textures

With growing global concerns about climate change, the significance of urban greenery in architecture and urban planning is becoming increasingly apparent. Urban vegetation naturally cools cities, provides comfort and clean air, and has positive social, health, and economic effects. It is essential to ensure passive thermal comfort and safeguard biodiversity. It is widely recognized that urban greenery not only withstands severe outdoor climatic events, but also symbiotically interacts with buildings and citizens. Several studies demonstrated the potential of vegetation to provide outdoor thermal comfort, air purification, noise reduction, and various other ecosystem services. To emphasize the potential of urban green spaces to interact with the local urban morphology in terms of microclimatic aspects, the research examines the dynamic connection between various urban textures and urban green spaces. This study emphasizes how urban green spaces, such as parks, green spaces, and urban greenery, respond to temperature variations in both the present scenario and the projected future. Central to this contribution is the examination of the relationship between urban vegetation and its potential to reduce and counteract urban overheating in both current and projected future scenarios. The aim is to evaluate the effectiveness of urban vegetation compared to dense urban textures. The interaction between urban block morphology, building types, vegetation, and microclimates is presented here for comparative assessment, highlighting the different thermal behaviour and outdoor comfort responses in various urban areas in current and projected scenarios. Using a microclimatic simulation tool, the research will delve deeper into the potential and constraints associated with the role of urban greens in addressing the increasing temperatures in climate change. This paper presents a comparative microclimatic evaluation of two selected green areas in Parma, Italy, within different urban contexts. The evaluation compares the current situation with a projected future scenario (2050) to determine the most effective factors for mitigating overheating phenomena in existing cities.

Thermal comfort assessment of non air-conditioned railway coach in Central India during extreme summer

Assessment of thermal comfort for residential buildings has been in trend for years. However almost all the field surveys carried out in recent years, have solely focused on static occupational environment, such as offices, houses, schools etc. Thus, an attempt here has been made to assess the thermal comfort of a dynamic environment for ventilated moving passenger trains in India. In this context, Passenger’s Comfort Vote (PCV) has been collected from passengers who have travelled in a naturally ventilated railway coach during summer 2022 in India. More than 1500 passengers have participated, and recorded their responses in either paper based or digital questionnaire sheet. The study results could contribute towards the enrichment of global thermal comfort database and further for improvement of passengers’ travelling experience during summer. The PCV value for male passengers is found to be slightly more (1.4) as compared to female passengers (1.3). A moderately strong correlation is found between passengers’ comfort response and operative temperature (R2 = 0.78, p < 0.001). The regression operative temperature at optimum thermal comfort condition is found to be 33.2 °C. Air velocity, which primarily depends upon, windows, fans and seat location in the coach, also seems to cause predominant influence on thermal comfort in a ventilated coach. The upper seat is found to be least comfortable with an average sensation vote (PCV) of 1.7 while passengers at window seats are comparatively more comfortable (PCV 1.33).

Adaptive thermal comfort in naturally ventilated hostels of warm and humid climatic region, Tiruchirappalli, India

• Comfort range for hostel building was found between 26.1 and 32.8 °C. • The desired temperature was found to be 27.8 °C, while neutral temperature was 29.5 °C. • The comfort limit extended by 2.8 °C if air velocity rises from 0.1 to 1.1 m/s. • Sweating Acceptability extended the thermal acceptability limit from 31.8 to 32.4 °C. • At 26.6 °C temperature about 50% of the fans were found switched on. Thermal comfort is an important factor in hostel buildings when the aim is to maximize the productivity of the students. Due to the extreme weather conditions, achieving thermal comfort in a hostel building in a hot and humid climate is even more difficult. Studies conducted in naturally ventilated hostel buildings in warm-humid climates involved the influence of outdoor air temperature only up to 34.4 °C and have been conducted in a specific season. In contrast, the Tiruchirappalli climate is characterized by a higher range of environmental variables. Therefore, to understand the thermal comfort conditions and usage of the environmental controls in naturally ventilated hostel buildings at the higher range of the environmental variables, a thermal comfort field study spread over one year was carried out at the National Institute of Technology, Tiruchirappalli, India, in twenty-seven hostel buildings. This study relies on field observation and thermal comfort responses from 2028 questionnaires collected from the students between September 2019 to August 2020. The analysis revealed a neutral temperature of 29.5 °C and a comfort range from 26.1 °C to 32.8 °C, indicating a wide range of thermal adaptation than suggested by the National Building Code of India and ASHRAE standard 55. The preferred temperature was 27.8 °C, indicating that students preferred a cooler environment. Acceptability with sweating conditions extended the upper limit of thermal acceptability from 31.8 °C to 32.4 °C. The use of a mosquito net can increase the probability of opening a window. Results indicated that overall behavioral adjustment could extend the comfort limits. The study results would be helpful to develop guidelines and designs for naturally ventilated hostel buildings in warm and humid climates that will contribute to reducing energy demand.

The Chinese thermal comfort dataset

Heating and cooling in buildings accounts for over 20% of total energy consumption in China. Therefore, it is essential to understand the thermal requirements of building occupants when establishing building energy codes that would save energy while maintaining occupants’ thermal comfort. This paper introduces the Chinese thermal comfort dataset, established by seven participating institutions under the leadership of Xi’an University of Architecture and Technology. The dataset comprises 41,977 sets of data collected from 49 cities across five climate zones in China over the past two decades. The raw data underwent careful quality control procedure, including systematic organization, to ensure its reliability. Each dataset contains environmental parameters, occupants’ subjective responses, building information, and personal information. The dataset has been instrumental in the development of indoor thermal environment evaluation standards and energy codes in China. It can also have broader applications, such as contributing to the international thermal comfort dataset, modeling thermal comfort and adaptive behaviors, investigating regional differences in indoor thermal conditions, and examining occupants’ thermal comfort responses.

Effect of short-term thermal history on thermal comfort and physiological responses: A pilot study

The purpose of this study is to examine the effect of outdoor short-term thermal history on the thermal comfort and physiological responses of human in the indoor environment. We conducted a field study in 4 split air-conditioned dormitory buildings. A total of 345 valid datasets were collected from 62 participants. We investigated thermal comfort, physiological responses and adaptation behaviours. According to the indoor staying time, we clarified the participants into two groups, namely STN30 (indoor staying time less than 30 min) and ST30 (indoor staying time more than 30 min). We compared the thermal comfort responses and physiological responses between two indoor staying time groups. We determined the relationship between thermal sensation and physiological responses. Subjects had a higher percentage of warmer thermal sensation, thermal discomfort and cooler preference in STN30 than in ST30. The upper extremity skin temperature was highly correlated to operative temperature and was consistent in both ST30 and STN 30 groups under the same operative temperature. Participants with shorter indoor staying time had lower neutral temperature (1.1 °C) and lower preferred temperature. The upper extremity skin temperatures were significantly correlated to thermal sensation.

Internet of agriculture: Analyzing and predicting tractor ride comfort through supervised machine learning

The aim of this study is to improve ride comfort among tractor drivers by utilizing Internet of Things (IoT) technology and Machine Learning (ML) techniques to analyze and predict vibration exposure. The study explores the association between tractor ride characteristics, including average speed, tool depth, and pulling force, and ride comfort during actual rotary soil tillage operations. Ride comfort was evaluated by calculating the Overall Vibration Value (OVV) at three measurement locations (i.e., the floor, seat pan, and seat backrest). The study employed spectral analysis i.e., Fast Fourier Transform (FFT) and Power Spectral Density (PSD) to determine the predominant resonant frequencies. In addition, ML approaches i.e., Linear Regression (LR), Decision Tree Regressor (DTR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) were used to predict ride comfort response. Three hyperparameter optimization techniques, including Grid Search, Random Search, and Bayesian optimization were used. Tractor-tiller system was operated on a real agricultural field by five male tractor drivers. Vibration levels were measured along three translational axes at three measurement locations using a smart monitoring device based on the IoT concept. Average OVV magnitude was computed as 0.84 m/s2 i.e., exceeding the ISO 2631-1 (1997) threshold limits. FFT and PSD analysis revealed a variety of dominant frequencies, including resonance peaks at 2 Hz, 6–7 Hz, and 9–11 Hz. The analysis of variance (ANOVA) indicates that tractor speed and tool depth are significant variables (at the 5% level) in determining ride comfort. Regarding predictive accuracy, the ANN model exhibits the highest coefficient of determination of 0.90, followed by the SVM and GPR models with a coefficient of determination of 0.89. The DTR and LR models demonstrate slightly lower coefficient of determination values of 0.83 and 0.82, respectively. Furthermore, Bayesian optimization proved to be an effective approach in achieving more accurate predictions of tractor ride comfort. In general, the study outcomes can be utilized by tractor designers in numerous ways to optimize tractor design (such as suspension system, cab design, seat design, etc.) to improve tractor ride comfort in real field applications.

Occupant thermal comfort and indoor environmental conditions in Mies van der Rohe’s S. R. Crown Hall

ABSTRACT There is little empirical data in the published literature on occupant thermal comfort in buildings from the Modern Movement in architecture. We present a field survey of occupant thermal comfort and indoor environmental conditions in Mies van der Rohe’s modern S. R. Crown Hall (1956) on the campus of Illinois Institute of Technology in Chicago, IL. Surveys were deployed to 557 student participants on four separate days, including two days in the cooling season and two days in the heating season, to assess their perceptions of thermal comfort throughout the space. Indoor air temperature, relative humidity and mean radiant temperature were measured concurrently in several locations throughout the space. Occupants reported high levels of dissatisfaction with comfort in the space (percent dissatisfied ranging 37%–54%), with somewhat counterintuitive results for this building type. Overcooling was apparent during warm weather and a combination of both over- and under-heating occurred during cold weather, contrary to what was expected in this building with a high thermal transmittance enclosure. There was also high spatial variability in comfort responses and measured indoor environmental conditions. Findings highlight the need to develop contextual approaches to meeting occupant comfort needs in this building while preserving architectural aesthetics/intent.

Maternal and paternal emotion socialization relates to adolescent self-compassion

Parent emotion socialization contributes to youth socioemotional adjustment and is an important consideration for prevention and intervention efforts. Extant research posits that parental emotion socialization influences youth mental health through child-level factors, such as how youth process and manage their emotions. Self-compassion is a protective factor for youth mental health, but the role of parents in youth self-compassion is unclear. Moreover, there is relatively limited research on maternal and paternal emotion socialization in adolescence. The present study examined associations of maternal and paternal emotion socialization with adolescent self-compassion. Adolescents (N = 165, 33% female, M = 14.56 years, SD = 1.34) and their parents completed questionnaires as part of an online survey. Parents’ validation and comfort responses to negative emotions were associated with higher adolescent self-compassion. Parents’ punishment and reciprocation of adolescents’ negative emotions were associated with lower adolescent self-compassion. Similar findings were present for mothers and fathers. Supporting parent-child communication about emotions may be relevant for youth exercising more self-compassion, providing important directions for prevention and intervention efforts.

Machine-learning-based personal thermal comfort modeling for heat recovery using environmental parameters

Thermal comfort modeling based on average response of occupants has been of interest in building performance assessment for many years. Energy consumption optimization was the main concern initially, but recently, it was shown that poor health was significantly associated with thermal discomfort. It is crucial to control hospitals’ thermal comfort requirements, particularly when the patient and the caregiver have to stay in the same room for a long time. These goals may be obtained if personal comfort models, that predict individuals’ comfort responses, are considered instead. The primary goal in this study was to investigate the effects of physiological and environmental factors on individuals’ thermal preferences in indoor environments using datasets collected for the Green Buildings Innovation Cluster (GBIC) project. Hence, several feature sets were created considering various combinations of the physiological, environmental and personal factors and several machine learning classification algorithms were utilized to develop personal comfort models, including Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and an ensemble algorithm in two different approaches: multi classification and binary classification. Models’ Performances were evaluated and compared employing several metrics. The median accuracies of 0.89 and 0.84 were obtained considering the models with the best performances on the cross-validation sets in multi-classification and binary classification approaches, respectively. Furthermore, more complex models such as RF and SVM predicted individuals’ thermal preferences more precisely yet required more computational time than other classifiers. The results in this study indicated that skin temperature is a strong predictor of personal thermal preferences and a combination of several physiological parameters, including metabolic rate and heart rate, with the skin temperature data as inputs for the models would provide higher predicting accuracies.

Maternal Mental Health Needs When the Unexpected Happens: A cross-sectional study among mothers with preterm infants in Western Kenya

Background: Mothers of preterm infants are usually unprepared for the baby’s birth earlier than expected, making them face emotional problems. Preterm births have increased lately with World Health Organization reporting an estimated prevalence of up to 5–18% and Kenya reporting an 18.3% prevalence. Mothers of preterm infants need special support to help them cope with this stressful event. The study determined the mental health needs of mothers with preterm infants in the neonatal care unit.&#x0D; Materials and Methods: It was a hospital-based cross-sectional study among 182 mothers with preterm babies admitted to neonatal care units of two referral hospitals in Western Kenya. A simple random sampling technique was used to select the participants and data was collected using a semi- structured pretested questionnaire and Critical Care Family Needs Inventory scale (CCFNI). Analysis was done using STATA 15 and a significance level set at P≤ 0.05 and 95% confidence interval.&#x0D; Results: The majority of the respondents 67 (36.8%) were aged between 18-22 years and 34 (18.7%) were above 34 years. The majority, 148 (81.3%) of the mothers accepted that the needs indicated on the CCFNI scale were important. By subscales, responses from the information scale dominated the top-ranked "very important" and "important" responses. The items concerning "to have questions answered honestly" from the information scale and “to help with the infant's physical care” from the proximity scale were highly ranked “very important” (Mean, 3.50). Proximity and assurance sub-scales also dominated the top-ranked “very important” and “important” needs while support and comfort responses dominated the needs ranked slightly important. There exists at least a significant difference among the means at a p-value=0.006.&#x0D; Discussion: The results are contrary to those from a study carried out in Saudia and Georgia which ranked needs from the assurance scale as most important, followed by proximity, information, comfort and finally support. However, the two studies are in agreement with the current study that needs from the support and comfort scales were least important. The difference could have been brought about due to the difference in the study population. &#x0D; Conclusion: The most important mental health needs for mothers include proximity, assurance, and information. However, mothers with preterm babies in neonatal units have unmet mental health needs which should be addressed.

Perceptual responses of (sports-)clothing-body interaction simulating pre- and post-purchase experience

The appreciation of textile products highly depends on a satisfactory ‘feel’ in fabric-skin contact. The question arising is whether the haptic interpretation of a garment (by hand) is comparable to a feeling produced when it is donned or used in its intended application. Sports T-shirts made from three different fiber types (CO, PES I, PES II) were studied in a pre- and post-purchase scenario by exposing 20 female participants to a hand, a donning (pre-purchase) and running evaluation (post-purchase) in 22 °C and 50% relative humidity (RH). Objective measurements such as skin temperatures, heart rate, body sweat loss, and sweat absorption of the garments were recorded. Subjective data was collected during the fabric hand and the donning evaluation as well as within the running protocol after 5 min, 20 min, and 5 min of cool down. Perceptual responses to 12 hand-/skin-feel descriptors (e.g., rough, smooth) were rated on a scale from 0 (not at all) to 10 (completely) and a feeling of discomfort was given. No significant differences between a hand and a donning evaluation were found in the rating of the sensations. The hand evaluation provided sufficient information for a comfort response to garment wear. The pre- and post-purchase comparison found a significantly lower perception of the feeling of roughness whilst running with the CO shirt and smoothness during running in PES II. The stickiness and comfort perception increased significantly in the post-purchase wear trial. Hence, moisture on the skin provoked through running influences comfort characteristics as well as the perception on haptic cues in t-shirts. Especially surface related haptic characteristics e.g., roughness and smoothness, are reduced.

Decision-Refillable-Based Two-Material-View Fuzzy Classification for Personal Thermal Comfort

The personal thermal comfort model is used to design and control the thermal environment and predict the thermal comfort responses of individuals rather than reflect the average response of the population. Previous individual thermal comfort models were mainly focused on a single material environment. However, the channels for individual thermal comfort were various in real life. Therefore, a new personal thermal comfort evaluation method is constructed by means of a reliable decision-based fuzzy classification model from two views. In this study, a two-view thermal comfort fuzzy classification model was constructed using the interpretable zero-order Takagi–Sugeno–Kang (TSK) fuzzy classifier as the basic training subblock, and it is the first time an optimized machine learning algorithm to study the interpretable thermal comfort model is used. The relevant information (including basic information, sampling conditions, physiological parameters, physical environment, environmental perception, and self-assessment parameters) was obtained from 157 subjects in experimental chambers with two different materials. This proposed method has the following features: (1) The training samples in the input layer contain the feature data under experimental conditions with two different materials. The training models constructed from the training samples under these two conditions complement and restrict each other and improve the accuracy of the whole model training. (2) In the rule layer of the training unit, interpretable fuzzy rules are designed to solve the existing layers with the design of short rules. The output of the intermediate layer of the fuzzy classifier and the fuzzy rules are difficult to explain, which is problematic. (3) Better decision-making knowledge information is obtained in both the rule layer of the single-view training model and in the two-view fusion model. In addition, the feature mapping space is generated according to the degree of contribution of the decision-making information from the two single training views, which not only preserves the feature information of the source training samples to a large extent but also improves the training accuracy of the model and enhances the generalization performance of the training model. Experimental results indicated that TMV-TSK-FC has better classification performance and generalization performance than several related state-of-the-art non-fuzzy classifiers applied in this study. Significantly, compared with the single view fuzzy classifier, the training accuracies and testing accuracies of TMV-TSK-FC are improved by 3–11% and 2–9%, respectively. In addition, the experimental results also showed good semantic interpretability of TMV-TSK-FC.

Effect of Mothers' Voice Recorded, Breast Milk Odor on Preterm Infants’ Pain and Comfort Response during Peripheral Cannulation

Effect of Mothers' Voice Recorded, Breast Milk Odor on Preterm Infants’ Pain and Comfort Response during Peripheral Cannulation

Land surface temperature and human thermal comfort responses to land use dynamics in Chittagong city of Bangladesh

Intense urbanization alters the microclimate and ecology of cities by converting naturally vegetated and permeable surfaces into impervious built-up surfaces. These artificial impermeable surfaces re-balance the surface energy budget by storing solar heat due to their higher thermal conductivity, and consequently, increase the Land Surface Temperature (LST). The higher LST affects the city dwellers' Human Thermal Comfort (HTC). To address these issues, unlike most prior research, we assess not only the influence of Land Use and Land Cover (LULC) alterations on summer LST but also on winter LST in Chittagong City of Bangladesh between 1993 and 2020 by using Remote Sensing (RS) and Geographic Information Systems (GIS). Additionally, the study evaluates the impact of LULC changes on the HTC during summer as LST substantially affects HTC in summer. The LULC analysis shows an increase in built-up area by 204% from bare lands, vegetated lands, lowlands, and water bodies between 1993 and 2020. In contrast, bare lands were converted from naturally vegetated surfaces, followed by lowlands and water bodies because of anthropogenic activities. The LSTs of Chittagong city, derived from remote sensing data, show a strong upward trend, with summer (winter) ranges of 20.62–34.07 °C (7.50–27.52 °C), 22.82–37.62 °C (14.92–29.32 °C), and 22.32–43.52 °C (17.08–31.83 °C) for 1993, 2007, and 2020, respectively. Between 1993 and 2020, the spatial mean winter and summer LSTs increased by 4.04 °C and 6.45 °C, respectively, or 0.15 °C and 0.24 °C per year. Chittagong had the highest mean LST in built-up areas for all the years. In addition, the study area's HTC gradually shifted to intense heat stress. The summer LST strongly correlated with normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI) and normalized difference water index (NDWI) while winter LST exhibited poor correlation with these indices.

Acute Effects of Wearing Different Surgical Face Masks during High-Intensity, Short-Rest Resistance Exercise on Cardiorespiratory and Pulmonary Function and Perceptual Responses in Weightlifters.

Simple SummaryWearing a face mask can block and reduce the exposure of the oronasopharyngeal region to viruses. It is unclear whether wearing a surgical mask (SM) or a three-dimensional (3D) SM (3DSM) during high-intensity, short-rest resistance exercise could influence the cardiac capacity, pulmonary function, and comfort in weightlifters. Wearing both SM and 3DSM during whole-body, high-intensity, short-rest resistance exercise exerted no detrimental effect on blood pressure (BP) or pulmonary function and promoted postexercise hypotension (PEH). Furthermore, wearing a typical SM during exercise produced higher breathing resistance and tightness than did wearing a 3DSM or no mask.This study investigated the effect of wearing a typical surgical mask (SM) or a three-dimensional (3D) SM (3DSM) during whole-body, high-intensity, short-rest resistance exercise on cardiorespiratory, respiratory, and perceptual comfort responses in weightlifters. Twenty elite weightlifters (6 women and 14 men; age = 24.1 ± 4.9 years; height: 167.45 ± 7.60 cm; body mass = 76.48 ± 19.86 kg) who participated in this study performed 3 resistance exercise sessions in a randomized order: (1) without a mask (NM), (2) while wearing a typical SM, and (3) while wearing a 3DSM. Resistance exercise consisted of a descending pyramid scheme starting at 10 repetitions, with a decrease of one repetition per set for the back squat, bench press, and deadlift, as fast as possible at 75% of the one-repetition maximum. Cardiorespiratory and pulmonary function and comfort were measured. Across all conditions, effective postexercise hypotension (PEH) was noted in terms of decreased systolic blood pressure (−4.64%), diastolic BP (−5.36%), mean arterial pressure (−5.02%), and ankle–brachial index (−6.84%). However, the heart rate (40.34%) and rate of pressure product (33.60%) increased, and no effects on pulmonary function were observed in the three conditions. The participants reported higher breathing resistance and tightness when wearing a typical SM than when wearing a 3DSM or no mask. Therefore, both wearing and not wearing a face mask during whole-body, high-intensity, short-rest resistance exercise promoted PEH and exerted no detrimental effect on pulmonary function. Coaches, trainers, and athletes should consider wearing a 3DSM during resistance exercise.

Experimental study on human comfort responses after simulated summer commutes with double transients of temperature and metabolic rate

In summer, when people arrive indoors after commuting, they will experience transients in metabolic rate and environment temperature. This makes the occupants who just arrive indoors feel too warm and uncomfortable at neutral temperatures. Therefore, it is necessary to study the thermal comfort requirements of human after commuting. Thirty subjects were asked to perform four levels of exercise (sedentary-SE, low speed-LS, medium speed-MS, high speed-HS) for 20 min at 25, 30 and 33 °C to simulate commuting activities, and then entered a temperature adjustable room to sit sedentary for 50 min. The physiological responses, human thermal responses, and environmental parameters corresponding to different experimental cases were studied to obtain the recovery time of the thermal neutral state and the optimal comfort temperature ranges. The results show that the average recovery time of physiological responses and human thermal responses was 38% lower than that in the room with the recommended temperature of 26 °C by subjects adjusting the indoor air temperature. Commuting activities significantly reduced the thermal neutral temperatures of subjects. For example, after exercising at 30 °C, the thermal neutral temperatures of SE, LS, MS, and HS were 24.6, 24.3, 23.8, and 22.8 °C, respectively. Within 50 min after exercising, the acceptable temperature range for 80% of the subjects in the states of SE, LS, MS, and HS was 24.6–27.3 °C, 24.3–27.1 °C, 23.8–26.0 °C, and 22.8–25.8 °C, respectively. This study provides references for enhancing occupants' satisfaction with thermal environments and optimizing the indoor environments.