Добірка наукової літератури з теми "Personal comfort model"

Thermal comfort control for indoor environment has become an important issue in smart cities since it is beneficial for people’s health and helps to maximize their working productivity and to provide a livable environment. In this article, we present an Internet of things–based personal thermal comfort model with automatic regulation. This model employs some environment sensors such as temperature sensor and humidity sensor to continuously obtain the general environmental measurements. Specially, video cameras are also integrated into the Internet of things network of sensors to capture the individual’s activity and clothing condition, which are important factors affecting one’s thermal sensation. The individual’s condition image can be mapped into different metabolic rates and different clothing insulations by machine learning classification algorithm. Then, all the captured or converted data are fed into a predicted mean vote model to learn the individual’s thermal comfort level. In the prediction stage, we introduce the cuckoo search algorithm, which converges rapidly, to solve the air temperature and air velocity with the learnt thermal comfort level. Our experiments demonstrate that the metabolic rates and clothing insulation have great effect on personal thermal comfort, and our model with video capture helps to obtain the variant values regularly, thus maintains the individual’s thermal comfort balance in spite of the variations in individual’s activity or clothing.

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.

One of the prevalent models to account for thermal comfort in HVAC design is the Predicted Mean Vote (PMV). However, the model is based on parameters difficult to estimate in real applications and it focuses on mean votes of large groups of people. Personal Comfort Models (PCM) is a data-driven approach to model thermal comfort at an individual level. It takes advantage of concepts such as machine learning and Internet of Things (IoT), combining feedback from occupants and local thermal environment measurements. The framework presented in this paper evaluates the performance of PCM and PMV regarding the prediction of personal thermal preferences. Air temperature and relative humidity measurements were combined with thermal preference votes obtained from a field study. This data was used to train three machine learning methods focused on PCM: Artificial Neural Network (ANN), Naive-Bayes (NB) and Fuzzy Logic (FL); comparing them with a PMV-based algorithm. The results showed that all methods had a better overall performance than guessing randomly the thermal preferences votes. In addition, there was not a difference between the performance of the PCM and PMV-based algorithms. Finally, the PMV-based method predicted well thermal preferences of individuals, having a 70% probability of correct guessing.

Passenger comfort is becoming an important issue with the recent increase in air travel. A common cause of passenger discomfort and distress is the invasion of the passenger’s personal space. This paper presents the results of two studies addressing the environmental psychological characteristics of passengers during personal space invasion (PSI) and how PSI affects cabin comfort design. In study 1, our survey shows that PSI has different effects on the comfort of passengers with different genders, ages, education levels, and interpersonal relationships. From these survey data, we extracted 14 factors of PSI. In study 2, a Decision-Making Trial and Evaluation Laboratory (DEMATEL) model was established, with passenger comfort as the target layer, to determine the interrelation between 14 PSI factors. The causal relationships between the 14 factors were visualized by a causal diagram. We established a priority ranking of the 14 aircraft interior design indexes based on the corresponding relationships between the indexes and PSI factors. The findings of this study contribute to the understanding of how PSI impacts passenger comfort and offer strategies to improve the comfort design of aircraft cabins.

Thermal comfort is closely related to the evaluation of heating, ventilation, and air conditioning systems. It can be seen as the result of the perception of the occupants of a given environment, and it is the product of the interaction of a number of personal and environmental factors. Otherwise, comfort issues still do not play an important role in the daily operation of commercial buildings. However, in the workplace, local quality effects, in addition to the health, the productivity that has a significant impact on the performance of the activities. In this regard, researchers have conducted, for decades, investigations related to thermal comfort and indoor environments, which includes developing models and indices through experimentations to establish standards to evaluate comfort and factors and set-up parameters for heating, ventilation, and air conditioning systems. However, to our best knowledge, most of the research work reported in the literature deals only with parameters that are not dynamically tracked. This work aims to propose a prototype for comfort measuring through a wireless sensor network and then presenting a model for thermal comfort prediction. The developed model can be used to set up a heating, ventilation, and air conditioning system to meet the expected comfort level. In particular, the obtained results show that there is a strong correlation between users’ comfort and variables such as age, gender, and body mass index as a function of height and weight.

Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the ideal conditions for each person in a room can conflict with each other. An ideal way to resolve these kinds of conflicts is an intelligent air conditioning system that can independently control air temperature and flow at different areas in a room and then produce thermal comfort for multiple users, which we define as the personal preference of air flow and temperature. In this paper, we propose Personal Atmosphere, a machine learning based method to obtain parameters of air conditioners which generate non-uniform distributions of air temperature and flow in a room. In this method, two dimensional air-temperature and -flow distributions in a room are used as input to a machine learning model. These inputs can be considered a summary of each user’s preference. Then the model outputs a parameter set for air conditioners in a given room. We utilized ResNet-50 as the model and generated a data set of air temperature and flow distributions using computational fluid dynamics (CFD) software. We then conducted evaluations with two rooms that have two and four air conditioners under the ceiling. We then confirmed that the estimated parameters of the air conditioners can generate air temperature and flow distributions close to those required in simulation. We also evaluated the performance of a ResNet-50 with fine tuning. This result shows that its learning time is significantly decreased, but performance is also decreased.

It is crucial to evaluate indoor personal thermal comfort for a comfortable and green thermal environment. At present, the research on individual thermal comfort does not consider its implementation mode. Moreover, the improvement of energy saving efficiency under the premise of increasing human comfort is an urgent problem that needs to be solved. In this paper, we proposed a Building Information Model (BIM) and Artificial Neural Network (ANN) based system to solve this problem. The system consists of two parts including an ANN predictive model considering the Predicted Mean Vote (PMV) index, the persons’ position, and an innovative plugin of BIM to realize dynamic evaluation and energy efficient design. The ANN model has three layers, considering three environment parameters (air temperature, air humidity, and wind speed around the person), three human state parameters (human metabolism rate, clothing thermal resistance, and the body position) and four body parameters (gender, age, height, and weight) as inputs. The plugin provides two functions. One is to provide corresponding personal thermal comfort evaluation results with dynamic changes of parameters returned by Wireless Sensor Networks (WSN). The other one is to provide energy saving optimization suggestions for interior space design by simulating the energy consumption index of different design schemes. In the data test, the Mean Squared Error (MSE) of the established ANN model was about 0.39, while the MSE of traditional PMV model was about 2.1. The system realized the integration of thermal information and a building model, thereby providing guidance for the creation of a comfortable and green indoor environment.

The use of an air conditioner (AC) becomes essential, particularly in a hot and humid climate, to provide a comfortable environment for human activities. The setpoint is the agreed temperature that the building will meet, and the use of the lowest setpoint temperature to accelerate the cooling of indoor spaces should be avoided. A comprehensive field study was conducted under various cooling temperature settings in two student activity rooms in a university building in Malaysia, so as to understand respondents’ characteristics and behavior toward AC usage, to estimate the comfort at various indoor temperatures, to develop an adaptive model of thermal comfort in AC spaces, and to compare the comfort temperature with related local and international indoor thermal environmental standards. The findings indicated that water intake and clothing insulation affected personal thermal comfort. Moreover, the mean comfort temperature for respondents was 24.3 °C, which is within an indoor thermal comfort zone of 23–27 °C. The findings suggest that the preference of occupants living in a hot and humid region for lower temperatures means that setting temperatures lower than 24 °C might underestimate the indoor comfort temperature. Additionally, an adaptive relationship can be derived to estimate the indoor comfort temperature from the prevailing outdoor temperature.

Advances in Information and Communication Technology (ICT) paved the way for decentralized Heating, Ventilation, and Air-Conditioning (HVAC) HVAC operations. It has been envisioned that development of personal thermal comfort profiles leads to accurate predictions of each occupant's thermal comfort state and such information is employed in context-aware HVAC operations for energy efficiency. This dissertation has three key contributions in realizing this envisioned HVAC operation. First, it presents a systematic review of research trends and developments in context-aware HVAC operations. Second, it contributes to expanding the feasibility of the envisioned HVAC operation by introducing novel sensing technologies. Third, it contributes to shedding light on viability and potentials of comfort-aware operations (i.e., integrating personal thermal comfort models into HVAC control logic) through a comprehensive assessment of energy efficiency implications. In the first contribution, by developing a taxonomy, two major modalities – occupancy-driven and comfort-aware operations – in Human-In-The-Loop (HITL) HVAC operations were identified and reviewed quantitatively and qualitatively. The synthesis of previous studies has indicated that field evaluations of occupancy-driven operations showed lower potentials in energy saving, compared to the ones with comfort-aware operations. However, the results in comfort-aware operations could be biased given the small number of explorations. Moreover, required data representation schema have been presented to foster constructive performance assessments across different research efforts. In the end, the current state of research and future directions of HITL HVAC operations were discussed to shed light on future research need. As the second contribution, moving toward expanding the feasibility of comfort-aware operations, novel and smart sensing solutions have been introduced. It has been noted that, in order to have high accuracy in predicting individual's thermal comfort state (≥90%), user physiological response data play a key part. However, the limited number of applicable sensing technologies (e.g., infrared cameras) has impeded the potentials of implementation. After defining required characteristics in physiological sensing solutions in context of comfort-aware operations (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, Doppler radar sensors, and heat flux sensors were evaluated. RGB cameras, available in many smart computing devices, could be a ubiquitous solution in quantifying thermoregulation states. Leveraging the mechanism of skin blood perfusion, two thermoregulation state quantification methods have been developed. Then, applicability and sensitivity were checked with two experimental studies. In the first experimental study aiming to see applicability (distinguishing between 20 and 30C with fully acclimated human bodies), for 16 out of 18 human subjects, an increase in their blood perfusion was observed. In the second experimental study aiming to evaluate sensitivity (distinguishing responses to a continuous variation of air temperature from 20 to 30C), 10 out of 15 subjects showed a positive correlation between blood perfusion and thermal sensations. Also, the superiority of heat flux data, compared to skin temperature data, has been demonstrated in predicting personal thermal comfort states through the developments of machine-learning-based prediction models with feature engineering. Specifically, with random forest classifier, the median value of prediction accuracy was improved by 3.8%. Lastly, Doppler radar sensors were evaluated for their capability of quantifying user thermoregulation states leveraging the periodic movement of the chest/abdomen area induced by respiration. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). On the other hand, in a transient temperature without acclimation time, it was shown that, some of the human subjects (38.9%) used respiration as an active means of heat exchange for thermoregulation. Lastly, a comprehensive evaluation of comfort-aware operations' performance was carried out with a diverse set of contextual and operational factors. First, a novel comfort-aware operation strategy was introduced to leverage personal sensitivity to thermal comfort (i.e., different responses to temperature changes; e.g., sensitive to being cold) in optimization. By developing an agent-based simulation framework and thorough diverse scenarios with different numbers and combinations of occupants (i.e., human agents in the simulation), it was shown that this approach is superior in generating collectively satisfying environments against other approaches focusing on individual preferred temperatures in selection of optimized setpoints. The energy implications of comfort-aware operations were also evaluated to understand the impact from a wide range of factors (e.g., human and building factors) and their combinatorial effect given the uncertainty of multioccupancy scenarios. The results demonstrated that characteristics of occupants' thermal comfort profiles are dominant in impacting the energy use patterns, followed by the number of occupants, and the operational strategies. In addition, when it comes to energy efficiency, more occupants in a thermal zone/building result in reducing the efficacy of comfort-driven operation (i.e., the integration of personal thermal comfort profiles). Hence, this study provided a better understanding of true viability of comfort-driven HVAC operations and provided the probabilistic bounds of energy saving potentials. These series of studies have been presented as seven journal articles and they are included in this dissertation.
Doctor of Philosophy
With vision of a smart built environment, capable of understanding the contextual dynamics of built environment and adaptively adjusting its operation, this dissertation contributes to context-aware/decentralized HVAC operations. Three key contributions in realization of this goal include: (1) a systematic review of research trends and developments in the last decade, (2) enhancing the feasibility of quantifying personal thermal comfort by presenting novel sensing solutions, and (3) a comprehensive assessment of energy efficiency implications from comfort-aware HVAC operations with the use of personal comfort models. Starting from identifying two major modalities of context-aware HVAC operations, occupancy-driven and comfort-aware, the first part of this dissertation presents a quantitative and qualitative review and synthesis of the developments, trends, and remaining research questions in each modality. Field evaluation studies using occupancy-driven operations have shown median energy savings between 6% and 15% depending on the control approach. On the other hand, the comfort-aware HVAC operations have shown 20% energy savings, which were mainly derived from small-scale test beds in similar climate regions. From a qualitative technology development standpoint, the maturity of occupancy-driven technologies for field deployment could be interpreted to be higher than comfort-aware technologies while the latter has shown higher potentials. Moreover, by learning from the need for comparing different methods of operations, required data schemas have been proposed to foster better benchmarking and effective performance assessment across studies. The second part of this dissertation contributes to the cornerstone of comfort-aware operations by introducing novel physiological sensing solutions. Previous studies demonstrated that, in predicting individual's thermal comfort states, using physiological data in model development plays a key role in increasing accuracy (>90%). However, available sensing technologies in this context have been limited. Hence, after identifying essential characteristics for sensing solutions (applicability, sensitivity, ubiquity, and non-intrusiveness), the potentials of RGB cameras, heat flux sensors, and Doppler radar sensors were evaluated. RGB cameras, available in many smart devices, could be programmed to measure the level of blood flow to skin, regulated by the human thermoregulation mechanism. Accordingly, two thermoregulation states' quantification methods by using RGB video images have been developed and assessed under two experimental studies: (i) capturing subjects' facial videos in two opposite temperatures with sufficient acclimation time (20 and 30C), and (ii) capturing facial videos when subjects changed their thermal sensations in a continuous variation of air temperature from 20 to 30C. Promising results were observed in both situations. The first study had subjects and 16 of them showed an increasing trend in blood flow to skin. In the second study, posing more challenges due to insufficient acclimation time, 10 subjects had a positive correlation between the level of blood flow to skin with thermal sensation. With the assumption that heat flux sensing will be a better reflection of thermoregulation sates, a machine learning framework was developed and tested. The use of heat flux sensing showed an accuracy of 97% with an almost 4% improvement compared to skin temperature. Lastly, Doppler radar sensors were evaluated for their capability of quantifying thermoregulation states by detecting changes in breathing patterns. In an experimental study, the results showed that, with sufficient acclimation time, the DRS-based approach could show distinction between respiration states for two distinct air temperatures (20 and 30C). However, using a transient temperature was proven to be more challenging. It was noted that for some of the human subjects (38.9%), respiration was detected as an active means of heat exchange. It was concluded that specialized artifact removal algorithms might help improve the detection rate. The third component of the dissertation contributed by studying the performance of comfort-driven operations (i.e., using personal comfort preferences for HVAC operations) under a diverse set of contextual and operational factors. Diverse scenarios for interaction between occupants and building systems were evaluated by using different numbers and combinations of occupants, and it was demonstrated that an approach of addressing individual's thermal comfort sensitivity (personal thermal-comfort-related responses to temperature changes) outperforms other approaches solely focusing on individual preferred temperatures. The energy efficiency implications of comfort-driven operations were then evaluated by accounting for the impact of human and building factors (e.g., number of thermal zones) and their combinations. The results showed that characteristics of occupants' thermal comfort profiles are dominant in driving the energy use patterns, followed by the number of occupants, and operational strategies. As one of the main outcomes of this study, the energy saving and efficiency (energy use for comfort improvement) potentials and probabilistic bounds of comfort-driven operations were identified. It was shown that keeping the number of occupants low (under 6) in a thermal zone/building, boosts the energy saving potentials of comfort-driven operations. These series of studies have been presented as seven journal articles, included in this dissertation.

The proportion of older people (i.e., those aged 65 or over) in the world’s population is increasing due to historically low fertility rates combined with increased life expectancy. In order to respond to these demographic trends, a growing body of policy and research over the last decades has accepted that ageing-in-place is most beneficial in the interests of older people’s independence, health and wellbeing, as well as to reduce the economic burden on governments and society for the provision of aged care facilities. While there are several guidelines that provide information about designing dwellings to suit ageing-in-place, information to aid older people’s thermal comfort and related wellbeing is not always considered. This thesis addresses the current knowledge on thermal comfort of older people in order to provide environments that meet their individual requirements and help improve their overall wellbeing. Traditionally, thermal comfort standards adopt aggregate modelling approaches as the bases on which to establish the requirements for human occupancy in the built environment. Aggregate models explain thermal comfort at a population level, which can result in limitations in real scenarios as individual thermal perceptions can vary significantly. In recent years, a growing number of studies have been conducted to address these limitations by developing ‘personal comfort models’. Instead of an average response from a large population, personalised models predict individuals’ thermal comfort by using a single person’s direct feedback. Nonetheless, up until the research presented in this thesis, studies on personal comfort models have focused on younger adults, generally in office environments. This presents a critical research gap because intergroup heterogeneity in personal capabilities and needs tends to be greater among older people, causing the use of aggregate models for older adults to result in even more frequent exposure to unacceptable thermal environments. These, in turn, can interact with multiple comorbidities, leading to adverse health outcomes and possibly premature institutional care. Thus, personalising models hold the promise of a more accurate way to predict older people’s thermal comfort and to manage their thermal environments better. Considering the issues and opportunity presented above, the goal of this research is to advance the current knowledge on the use of personal thermal comfort models by focusing on older people in their home environments. The research aims to achieve this goal by: (1) reviewing the present understandings of personal comfort models, (2) investigating older people’s’ thermal environment, behaviours and preferences; (3) developing personal comfort models for older people and comparing the results with the predictions by established aggregate models; and (4) investigating the application of personal comfort models in managing the thermal environment of older people. Two indoor environmental monitoring field studies and related point-in-time thermal comfort surveys were conducted to collect datasets for the analyses. The first dataset was collected from 71 older adults in 57 households located in South Australia across 9 months. This was followed by the application of deep learning (i.e., a class of machine learning) to develop personal comfort models for 28 out of these 71 participants using different combinations of the collected series of indoor environmental measurements, along with behavioural and health/wellbeing survey answers. The second dataset was collected during shorter 2-week periods involving 11 of the original 71 participants, during which, in addition to measuring the indoor environmental parameters and collecting behavioural and health/wellbeing survey answers, the participants’ hand skin temperatures were measured. The development of personal models for 4 of these participants was then conducted, including skin temperatures as an additional modelling input. Several performance indicators, including average accuracy, Cohen’s Kappa Coefficient and Area Under the Receiver Operating Characteristic Curve (AUC) were employed to assess the skill of the developed individual models. All models’ performance indicators were then compared with a ‘version’ of the Predicted Mean Vote (PMV) model, termed, in this thesis, the PMVc. The results showed that the 28 personal thermal comfort models for older adults that used environmental, behavioural and health/wellbeing perception as input variables presented an average accuracy of 74%, an average Cohen’s Kappa Coefficient of 61% and an average (AUC) of 0.83. This represented a significant improvement in predictive performance when compared with the generalised PMVc model, which presented an average accuracy of 50%, an average Cohen’s Kappa Coefficient of 24%, and an average AUC of 0.62. Similarly, the exploration with the 4 personal comfort models adding skin temperature measurements to the abovementioned input variables, and excluding health/wellbeing perception − which yielded slightly lower performance when included −, resulted in an average accuracy of 67%, an average Cohen’s Kappa Coefficient of 50% and an average AUC of 0.77. This also represented a superior predictive performance of the individualised models when compared with the PMVc model. In order to investigate the applications of the personal comfort models in operation, two participants were selected as case studies and their respective personal models were tested for their ability to estimate personal heating and cooling temperature set points, using calibrated building performance simulation models. The simulated energy loads derived from the use of personal set points were compared with simulated energy loads using 21°C as the heating set point and 24°C as the cooling set point, which represented the common averaged set points used in building simulation studies. The results show that, using the personal set points, good agreement between the actual and simulated heating and cooling energy loads was achieved. When comparing the error ratios with the ones resulting from simulations assuming a 21°C set point for heating and a 24°C for cooling, the study also showed that the personal set points significantly outperformed these traditional assumptions. Finally, as a secondary application exploration, one selected participant’s personal model was converted to a smart phone Application (App) format to examine the opportunity to use the model as a web-based smart phone tool to aid designers and caregivers to manage the thermal environments of older people by considering individual requirements. This conversion also proved to be successful, allowing the automatic calculation of thermal preference for the selected participant, thereby demonstrating its potential to aid designers and caregivers. The novelty and therefore the contributions of this research lay in different areas. Whilst the literature on personal comfort models has focussed solely on younger adults in office environments, this research has explored a methodology for predicting thermal comfort of older people in their dwellings. Additionally, it has introduced health/wellbeing perception as a predictor of thermal preference – a variable often overlooked in architectural sciences and building engineering. Finally, the research indicates that, compared with aggregated models, personal models provide superior utility in predicting an individual’s preferred thermal environment, which therefore offers the potential for more accurate tools to design and improve older people’s living environments so that wellbeing is optimised, healthy ageing is fostered and autonomy while ageing is prolonged. The research recommends a range of topics for future investigation, such as the models’ misclassification costs and the integration among wearable sensors, predictors and actuators in the context of older people. In addition, the development of standard protocols necessary for the models’ deployment in real scenarios is prescribed. In conclusion, the research demonstrates that, as a concept, personal comfort models have the ability to absorb people’s diversity in the context of their environmental conditions, and may therefore represent an important step towards providing knowledge aimed at enhancing wellbeing and improving the overall resilience of the built environment.
Thesis (Ph.D.) -- University of Adelaide, School of Architecture and Built Environment, 2022

Online dating is a big business, allowing people from the comfort of their own home to view and read about potential mates all around the world. Different dating sites offer different services. However, it is not yet commonplace for Web sites dedicated to dating to use the social networking tools used by popular online communities, such as those that use the personal homepage and message board genres. The ecological cognition framework (ECF) provides a theoretical model regarding online dating communities’ behavior and relationship development. A model based on the ECF is proposed and provides a basis for developing online dating services that effectively support relationship development. Two investigations are presented in this chapter, one that uses a case study approach to identify and describe online dating services from the perspective of a specific case and another that assess the effectiveness of existing online dating services based on the guidelines developed from the case study. The case study provides a useful insight into the nature of social networking from the perspective of a specific case, which led to guidelines for developing e-dating systems that when evaluated showed that the most popular social networking services also score well against the criteria proposed in those guidelines.

Online dating is a big business, allowing people from the comfort of their own home to view and read about potential mates all around the world. Different dating sites offer different services. However, it is not yet commonplace for Web sites dedicated to dating to use the social networking tools used by popular online communities, such as those that use the personal homepage and message board genres. The ecological cognition framework (ECF) provides a theoretical model regarding online dating communities’ behavior and relationship development. A model based on the ECF is proposed and provides a basis for developing online dating services that effectively support relationship development. Two investigations are presented in this chapter, one that uses a case study approach to identify and describe online dating services from the perspective of a specific case and another that assess the effectiveness of existing online dating services based on the guidelines developed from the case study. The case study provides a useful insight into the nature of social networking from the perspective of a specific case, which led to guidelines for developing e-dating systems that when evaluated showed that the most popular social networking services also score well against the criteria proposed in those guidelines.

This chapter explains the lessons learned when an English professor and a physical therapist decided to work together. Patients in a clinic and students in a classroom share the need for positive role models to teach them effective strategies to enhance their learning. The official research journey focuses on the connections among writing, positive outlook, and healing. The unofficial journey focuses on the lessons learned from the authors teaching each other about their fields of expertise. They encourage readers to accomplish two tasks. First, think about how to get out of your personal comfort zone and change your outlook about the amount of stress in your life. Second, think about how to get out of your professional comfort zone and change your outlook about working with colleagues in other disciplines. By sharing their experiences, the authors provide ideas on how to participate in interdisciplinary collaborations with colleagues in school and community.

With a looming future shortage of fossil fuels, how can consumers be convinced to purchase more fuel-efficient vehicles? To begin to address this basic question, it is necessary to examine consumer attitudes toward the environment, consumer decision-making models, consumer willingness to trade luxury or personal comfort in order to buy smaller and more Fuel-Efficient Vehicles (FEVs), and price sensitivity with regard to purchasing hybrid vehicles or other Alternate Fuel Vehicles (AFVs). By understanding the consumer dynamics behind the purchase of FEVs and AFVs, an effective strategy for social marketing can be developed.

One of the striking characters of civil engineering is its emphasis to the development of hands-on practical skills, innovation, and creativity. Civil engineering's unique epistemological feature is heavily geared towards equipping individuals with relevant skills for occupational safety.. The purpose of this study was to identify employability skills that civil engineering teachers use to prepare students for 4IR. This study used a mixed method approach, where questionnaire and interviews were used to collect data. This study was guided by EASTA's “employability skills for TVET graduates.” This study found that most civil engineering teachers have a challenge in equipping their students with various employability skills. This is as a result of a PAT that only focuses on generic skills than core skills and personal traits. Therefore, this study recommends that the approach for civil engineering course to Fourth Industrial Revolution (4IR) should be viewed through an employability skills lens and calling for teachers to challenge their comfort zone in preparing their students with skills that are pertinent to the 4IR needs.

Nowadays, the human error is usually identified as the conclusive cause of investigations in road accidents. The human although is the person in control of vehicle until the moment of crash but it has to be understood that the human is under continued impact by various factors including road environment, vehicle and human's state, abilities and conduct. The current advances in design of vehicle and roads have been intended to provide drivers with extra comfort with less physical and mental efforts, whereas the fatigue imposed on driver is just being transformed from over-load fatigue to under-load fatigue and boredom. A representational model to illustrate the relationships between design and condition of vehicle and road as well as driver's condition and state on fatigue and the human error leading to accidents has been developed. Thereafter, the stochastic mathematical models based on time-dependent failure rates were developed to make prediction on the road transportation reliability and failure probabilities due to each cause (vehicle, road environment, human due to fatigue, and human due to non fatigue factors). Furthermore, the supportive assessment methodology and models to assess and predict the failure rates of driver due to each category of causes were developed and proposed.

Restrictions on creativity are numerous and operate at ideological, psychological, educational, cultural, societal, and economic levels. This chapter examines approaches that musicians from different cultures found to be effective for overcoming inhibitors and enhancing creative agency. Key factors in formal courses, community music, and music therapy programs are safety, emotional support, compositional scaffolding, improvisation, experimentalism, multiple modes of expression, exposure, high expectations, validation, and the transgression of comfort zones. The personal journeys of several individual musicians are shared. Their transformative experiences involved switching to a new instrument, new idiom, new teacher, new institution, and/or new location; taking up other art forms; undergoing therapy and/or bodywork; and finding mentors and/or supportive collaborators. There is no single formula for increasing creativity; however, a better understanding of what creativity is and what its principal enablers and inhibitors are can help indicate which strategies might be most effective in individual circumstances.

This chapter addresses key elements of effective communication and common communication challenges encountered in the emergency department setting with families and concerned persons. It discusses the principles of effective communication, including AIDET and ask-tell-ask styles; demonstrates language that acknowledges and respects diversity; and provides methods to enhance staff training in these areas. Additionally, the chapter provides models for sharing difficult news, including notification of death, and pertinent legal and ethical principles to guide disclosure of patient information. Specific communication methods are shared to manage common challenges such as conflict, disagreement, and unrealistic patient or family expectations. These situations are commonly encountered in emergency settings and are linked to provider burnout. The chapter includes a primer on “therapeutic language,” including the use of suggestions for patient and family comfort during procedures to lessen anxiety and discomfort.

Thermal comfort of drivers and passengers inside cars compartments is a subject bouncing back to the spotlight with the electrification of vehicles. In fact, air conditioning and heating systems can reduce the battery autonomy of electric vehicles by up to 50% under certain conditions. On the other hand, although some researchers attempted to consider the individualization of thermal sensation and comfort models, the most used thermal sensation and comfort models nowadays are still those that consider a standard average person. Many studies showed the limitations of these models in predicting thermal comfort for different populations in complex environments. Therefore, if a personal thermal comfort at minimum vehicle energy consumption is required, a deep consideration should be given to the understanding of the individualization of the thermophysiological model and to identifying key parameters that have the most influence on thermal comfort. In order to evaluate the impact of different parameters on thermal sensation and comfort, a literature review was undertaken followed by a sensitivity analysis of some potentially influential parameters such as the basal metabolic rate, body weight, cardiac output, body fat content and clothing by considering the influence of their variations on thermal neutrality status and thermal sensation and comfort.

Personal thermal comfort is a crucial yet often over-simplified factor in building climate control. Traditional comfort models lack the adaptability to fit individuals’ demand. Recent advances of machine learning and ubiquitous sensor networks enable the data-driven approach of thermal comfort. In this paper, we built a platform that can simulate occupants with different thermal sensations and used it to examine the performance of support vector machine (SVM) and compared with several other popular machine learning algorithms on thermal comfort prediction. We also proposed a hybrid SVM-LDA thermal comfort classifier that can improve the efficiency of model training.

The study of human thermal comfort requires detailed information about distributions of air velocity, air temperature and relative humidity in an occupied zone. Air quality is related to the contaminant distributions and contaminant removal effectiveness in indoor environment. This paper presents an evaluation of thermal comfort and contaminant removal for an office setting with underfloor air distribution system by the use of computational fluid dynamics modeling. A typical single cubicle in a large office floor in steady state condition of airflow as well as heat and mass transfer is investigated for both cooling and heating scenarios. The model includes a typical cubicle in a large office floor with a chair, a desk with a personal computer on its top, and heat sources such as seated people, computer monitor and CPU, and lights. Air enters the occupied zone through an inlet located at the floor level supplying a vertical upward inflow. Five different locations of the inlet diffuser, three different inlet air speeds, and four different loads of heat loss through the floor slab in heating case scenario were considered. Distributions of velocity, temperature, relative humidity, and contaminant concentration in such cases were computed. The results were compared among various simulation cases and showed reasonable agreement with experimental data taken from related literature.

For autonomous vehicles to gain widespread customer acceptance, safety and reliability are not nearly enough. Comfort and familiarity of the ride is also of essential importance. Because these are highly subjective factors, autonomous vehicles must be able to adopt personal driving styles to meet individual preference. The adaptive cruise control (ACC) system is a critical function performed by the autonomous vehicle and much research effort has been devoted to the development of a system that acts as a human driver. However, studies which investigate ACC models capable of learning a driving style are limited. In this paper, we propose a method to extract quantifiable parameters which represent a drivers’ driving style and apply these parameters to personalize the longitudinal control of an autonomous vehicle. We then develop a longitudinal driver model that integrates those parameters to enable the ACC system to mimic the driving style of the driver. Finally, the effectiveness of the extraction method and the driver model are obtained through simulation.

Abstract Low demand response (DR) participation and high program drop-out rates continue to impede DR goals that could save up to $13 billion in annual grid expansion and electricity demand costs. Yet, the literature lacks a thorough understanding of how different residential customer segments enrolled in DR programs respond to utility signals in view of occupant comfort considerations. The objective of this study is to gain a clear understanding of the effects of four different customer personas on residential DR. Given current data limitations, this work developed an array of hypothetical personas with varied priorities, activity levels, and comfort thresholds based on demographic variables that have been found in previous studies to influence energy consumption. A BEopt™ DR model for a reference residential single-family building located in Colorado was built to isolate the effect of differences in buildings or climate. The results provide useful evidence on how persona-comfort differences lead to significant deviations in DR goals (especially peak demand reduction), ranging from 0.1% to 20%. This work presents a novel framework representing comfort preferences in DR models. The data generated, albeit synthetic, and the results could inform DR program design considerations of how different people respond to different comfort priorities.

Objective: The purpose of this study was to assess the feasibility of administering the Automated Multiple-Pass Method (AMPM), a widely used tool for collecting 24-hour dietary recalls, in participants’ homes by field interviewers. Design: The design included computer-assisted personal interviews led by either a nutritionist (standard) or field interviewer. Portion estimators tested were a set of three-dimensional food models (standard), a two-dimensional food model booklet, or a tablet with digital images rendered via augmented reality. Setting: Residences in central North Carolina. Participants: English-speaking adults. Pregnant women and individuals who were fasting were excluded. Results: Among 133 interviews, most took place in living rooms (52%) or kitchens (22%). Mean interview time was 40 minutes (range 13–90), with no difference by interviewer type or portion estimator, although timing for nutritionist-led interviews declined significantly over the study period. Forty-five percent of participants referenced items from their homes to facilitate recall and portion estimation. Data entry and post-interview coding was evaluated and determined to be consistent with requirements for the National Health and Nutrition Examination Survey. Values for the number of food items consumed, food groups, energy intake (average of 3,011 kcal for men and 2,105 kcal for women), and key nutrients were determined to be plausible and within reasonably expected ranges regardless of interviewer type or portion estimator used. Conclusions: AMPM dietary recall interviews conducted in the home are feasible and may be preferable to clinical administration because of comfort and the opportunity for participants to access home items for recall. AMPMs administered by field interviewers using the food model booklet produced credible nutrition data that was comparable to AMPMs administered by nutritionists. Training field interviewers in dietary recall and conducting home interviews may be sensible choices for nutrition studies when response rates and cost are concerns.