Comfort Satisfaction Research Articles

Occupant comfort and behavior: High-resolution data from a 6-month field study of personal comfort systems with 37 real office workers

Personal Comfort Systems (PCS) provide individual occupants local heating and cooling to meet their comfort needs without affecting others in the same space. It saves energy by relaxing ambient temperature requirements for the HVAC system. Aside from these benefits, PCS offers a wealth of data that can describe how individuals interact with heating/cooling devices in their own environment. Recently developed Internet-connected PCS chairs have unlocked this opportunity by generating continuous streams of heating and cooling usage data, along with occupancy status and environmental measurements via embedded sensors. The data allow individuals' comfort and behavior to be learned, and can inform centralized systems to provide ‘just the right’ amount of conditioning to meet occupant needs. In summer 2016, we carried out a study with PCS chairs involving 37 occupants in an office building in California. During the study period, we collected >5 million chair usage data-points and 4500 occupant survey responses, as well as continuous measurements of environmental and HVAC system conditions. The data analysis shows that (1) local temperatures experienced by individual occupants vary quite widely across different parts of the building, even within the same thermal zone; (2) occupants often have different thermal preferences even under the same thermal conditions; (3) PCS control behavior can dynamically describe individuals' thermal preferences; (4) PCS chairs produce much higher comfort satisfaction (96%) than typically achieved in buildings. We conclude that PCS not only provide personalized comfort solutions but also offer individualized feedback that can improve comfort analytics and control decisions in buildings.

Experimental Evaluation of Radiant Heating Ceiling Systems Based on Thermal Comfort Criteria

Low-temperature radiant heating systems can be considered as suitable candidates for the refurbishment of old heating systems. These systems are proven to save energy, however, their drawback is their impact on the creation of radiant temperature asymmetry and local thermal discomfort, especially in old buildings where the temperatures of surfaces (for example external walls with a low level of insulation and large windows) are low. This study aims to evaluate the potential application of low-temperature radiant ceiling heating systems (28–38 °C) in old and energy-renovated buildings, based on subjective experiments and thermal comfort criteria such as thermal sensation, comfort, satisfaction, and sensation asymmetry votes. Later, in the Discussion section, the guideline for the radiant temperature asymmetry for the warm ceiling presented in ASHRAE Standard-55 is corrected for relatively low air temperatures and different surface temperatures corresponding to “about neutral” conditions for winter clothing. Findings of this research show that the radiant ceiling heating system operating at low temperatures (33–38 °C) can provide fairly neutral thermal sensation and satisfactory comfort at the majority of body-parts, if the building envelope satisfies advanced building energy-efficiency regulations. Additionally, the experimental analyses imply that limitation of 5% suggested by ASHRAE-55 for the percentage of dissatisfied occupants feeling uncomfortable due to overhead radiation can be elevated to 10%.

Event-Based HVAC Control—A Complexity-Based Approach

The optimal control of the heating, ventilation, and air-conditioning (HVAC) system in buildings has a significant energy saving potential and therefore is of a great practical interest. An event-based HVAC control adjusts control actions when certain events occur, which may be faster and more scalable than state-based or time-driven control methods. However, events may capture either local or global changes in the rooms. The choice of events is a tradeoff between the computational efficiency and the control performance. This challenging problem remains open. We consider this as an important problem in this paper and make three major contributions. First, we define local and global events for the HVAC control problem. The complexity of these event-based control policies is defined. Second, based on hypothesis testing, we develop a method to select events that capture a sufficient state information and with a relatively small event space. Third, we demonstrate the performance of this method on two groups of examples, including one group of small-scale problems for the proof of concept and the other group of large-scale problems in the HVAC control. It is shown that our method outperforms the Levin search, which is a traditional complexity-based search method and finds event-based HVAC control policies with a good performance. Note to Practitioners —When there are multiple rooms in a building, the HVAC control may achieve a significant energy saving and an indoor comfort satisfaction in the same time through exploring the coupling among the rooms. By appropriately defining the events, the size of the event space is usually much smaller than the state space. Therefore, an event-based control is more scalable and preferred in practice. Local events capture the state changes of rooms in a small neighborhood, which leads to a small event space but limited information. Global events capture the state changes of rooms in a large neighborhood, which leads to more information but a large event space. It remains open how to select events in large-scale HVAC control problems, especially when the computing budget is limited. In this paper, we define the complexity of an event-based control policy by the number of neighboring rooms considered. We develop a method based on hypothesis testing to select events with a proper complexity in order to achieve a good system performance. The performance of this method is demonstrated on an HVAC control problem.

ITCM: Toward Learning-Based Thermal Comfort Modeling via Pervasive Sensing for Smart Buildings

For decades, ASHRAE Standard 55 has been using the Fanger’s predicted mean vote (PMV) model to evaluate the indoor thermal comfort satisfaction. However, this canonical model has drawbacks in both data inadequacy and lack of inputs from test subjects. In this paper, we propose a learning-based solution for thermal comfort modeling via the emerging machine learning techniques and Internet of Things-based pervasive sensing technologies. First, we build an intelligent thermal comfort management (iTCM) system. It adopts the wireless sensor network to collect environmental data and utilizes the wearable device for vital sign monitoring. In addition, a cloud-based back-end system, with cost-efficient deployment fees, is developed for data management and analysis. Second, we implement a black-box neural network (NN), namely the intelligent thermal comfort NN (ITCNN). To evaluate the performance of ITCNN, we compare it with the PMV model, three traditional white-box machine learning approaches and three classical black-box machine learning methods. Our preliminary results show that four black-box methods achieve better performance than the PMV model and the three white-box approaches. The ITCNN achieves the best performance and outperforms the PMV model by on average 13.1% and up to 17.8%. Third, with the iTCM system, we demonstrate a novel deep reinforcement learning-based application by encouraging human behavioral changes to form energy-saving habits for greener, smarter, and healthier building. Finally, we discuss the limitations of this paper and present the plan for our future research.

Introduction of a spatio-temporal mapping based POE method for outdoor spaces: Suburban university campus as a case study

Outdoor spaces are important to sustainable cities because they establish a common identity for social life by improving quality of urban living. The relation between outdoor spaces and building groups, competency, use period, and interaction of micro-climatic factors are needed to be investigated from a holistic approach. Unfortunately, the limited and narrow scoped POE studies on outdoor spaces make an overall assessment without causality relation. Other existing studies in outdoor spaces are mostly grouped under the headings such as; user satisfaction, space syntax and behavioral mapping, and biometeorological assessments. The intention of this paper is to introduce a new post-occupancy evaluation (POE) method integrates these studies focusing on various problems in outdoor spaces using spatio-temporal mapping. The comprehensive methodology applied in this research attempted to overcome some of the shortcomings of related studies by conducting a longitudinal study (during a year, as opposed to a few days) and also by objectively analyzing the associations of user behavior and physical attributes as well as the configurational properties of the campus layout. With this method, outdoor spaces can be evaluated in the context of the interaction between the physical environment and its users' behavior and activities, level of satisfaction and perceptions of comfort. The method has been applied on a suburban university campus in İstanbul, Turkey. The main courtyard of the campus has been subjected for map creation and result discussions.

Secondary school classrooms daylighting evaluation in Negeri Sembilan, Malaysia

Natural daylight is a renewable energy source that is important in photobiology study which looks into the effect of light to living organism. Students’ visual comfort satisfaction due to efficient daylight in a classroom are highlighted in the physical of the building and learning space such as windows and openings. One of the factor that influences the design of classrooms environmental condition. Environmental condition consideration includes the daylighting requirement for classrooms. Various standards and guidelines on daylighting consideration have been developed in Malaysia including the recommendation on Window-to-Floor Ratio (WFR) and illuminance level. However, the recommended WFR may not achieve the recommended illuminance level. This paper evaluates whether the recommended WFR design achieve the recommended acceptable illuminance level. Two classrooms in Kolej Permata Insan, Nilai, Negeri Sembilan with different orientation were selected for the study. Integrated Environmental Solution (IESVE) simulation software is used for daylight evaluation. The WFR of the two selected classrooms were set to the recommended 20%. Simulation results shows that the illuminance level for both classrooms are higher than recommended. The simulation also shows that the daylight distribution in the classrooms are not uniform, thus creating inefficient daylight condition. Therefore, the WFR recommends further studies with additional of daylighting design strategy considerations.

Determination of human thermal comfort due to moisture permeability of clothes

PurposeThe purpose of this paper is to present the determination of human thermal comfort with wearing clothes, with different water vapor permeability. Currently, the predicted mean vote (PMV) equation is widely used to determine thermal sensation scales of human comfort. However, moisture permeability of clothes has been not taken in account where the heat is lost from a human body due to water vapor diffusion through clothes.Design/methodology/approachIn this study, the heat loss is derived based on the real structure of textiles, causing water vapor pressure difference between air on skin and ambient air. The PMV equation is modified to differentiate a thermal sensation scale of comfort although patterns of clothes are the same. Interview tests are investigated with wearing clothes from three types of textiles: knitted polyester, coated nylon–spandex, and polyurethane, under various air conditions.FindingsThe moisture permeabilities of knitted polyester, coated nylon–spandex and polyurethane are 16.57×10−9 kg/m2 s•kPa, 9.15×10−9 kg/m2•s•kPa and 2.99×10−9 kg/m2•s•kPa, respectively. The interviews reveal that most people wearing knitted-polyester clothes have the greatest cold sensations under various air conditions since moisture permeability is the highest, compared to coated nylon–spandex, and polyurethane leather. Correspondingly, the predicted results of the modified PMV equation are close to the actual mean votes of interviewees with a coefficient of determination R2=0.83. On the other hand, the coefficient of determination from the predicted results of the conventional PMV equation is significantly lower than unity, with R2=0.42.Practical implicationsIn practice, this quantitative determination on human thermal comfort gives some concrete recommendations on textile selection of clothes for acceptable satisfaction of thermal comfort under various surrounding conditions of usage.Originality/valueThe modified PMV equation effectively determines human comfort on a thermal sensation scale due to the moisture permeability of clothes. To make generic conclusion, experimental results of additional three textiles, such as plain weave/lining polyester, knitted spandex, and open structure polyester, are reported. They confirm that the modified PMV equation effectively determines human comfort on a thermal sensation scale due to the moisture permeability of clothes.

Application of IoT and Machine Learning techniques for the assessment of thermal comfort perception.

Thermal comfort is traditionally assessed by using the PMV index defined according to the EN ISO 7730:2005 where the user passively interacts with the surrounding environment considering a physic-based model built on a steady-state thermal energy balance equation. The thermal comfort satisfaction is a holistic concept comprising behavioral, physiological and psychological aspects. This article describes a workflow for the assessment of the thermal conditions of users through the analysis of their specific psychophysical conditions overcoming the limitation of the physic-based model in order to investigate and consider other possible relations between the subjective and objective variables.

The use of total contact orthoses in patients with foot problems in foot clinic, Siriraj hospital

To study the use rate, result and concomitant factors of the use of total contact orthoses in Foot Clinic, Siriraj Hospital which has not been studied before. Studying from patient records and interviews with patients who had foot problems without impairment of foot sensation and received TCO from Foot Clinic between July, 2015 and April, 2016. First interview, patients were asked information before use and latter about the latest TCO after use in first 1 month. One hundred and fifteen participants were recruited but eight lost to follow up. The majority had chronic plantar fasciitis (26.2%), posterior tibial tendon dysfunction (25.2%), hallux valgus (21.5%) and metatarsalgia (21.5%). The use of TCO defined as the participants had to use for more than 3 days/week and for more or equal to 50% of daily walking and standing duration was 67.3%. Using TCO can provide walking stability ( P -value = 0.008). For patients with metatarsalgia, using TCO can significantly reduce pain ( P -value = 0.002). Many factors found associated with the use of TCO including receiving TCO for more than first pair (Odds ratio = 4.09, 95% CI 1.51–11.05), ability to find proper shoes that fit with TCO (Odds ratio = 0.36, 95% CI 0.15–0.89), level of convenience of putting on/taking off shoes with TCO ≥ 9 (Odds ratio = 2.66, 95% CI 1.16–6.12), level of comfort satisfaction during TCO use ≥ 9 (Odds ratio = 3.61, 95% CI 1.55–8.40). The latter two factors are found to be associated with the use of TCO from stepwise logistic regression analysis (adjusted Odds ratio = 3.39, 95% CI 1.18–9.71, 3.02, 95% CI 1.07–8.47 respectively). The use of TCO in Foot Clinic was 67.3%. Using TCO could result in walking stability. Patients who received more than first pair of TCO and had comfort satisfaction level ≥ 9, were more likely to use TCO.

Enhanced Model-Based Predictive Control System Based on Fuzzy Logic for Maintaining Thermal Comfort in IoT Smart Space

Researchers have reached a consensus on the thermal discomfort known as the major cause of sick building syndrome, which hurts people’s health and working efficiency greatly. As a result, the thermal environment satisfaction is important and thus many studies have been dedicated to thermal comfort over the past few decades. Predicted Mean Vote (PMV) is one of the globally used standards to express users’ comfort satisfaction with the given thermal moderate environments. It has been widely used in most of the Heating, Ventilation and Air Conditioning (HVAC) systems to maintain this standard of thermal comfort for occupants of buildings. However, the PMV model is developed on indoor experimental data without taking into account conditions of outdoor space, which greatly affects the performance of the existing HVAC systems and varies with the seasons. In this paper, an enhanced Model-based Predictive Control practical system for maintaining the indoor thermal comfort is demonstrated, including a multiple linear regression predictive model and an innovative fuzzy controller considering both the PMV index and the outdoor environment conditions. To verify the usability of the designed system, an Internet of Things (IoT) smart space prototype was chosen and experimentally tested in a building in Jeju, Korea. Moreover, thermal comfort regulation performances using the proposed approach have been compared with the existing one. The results of our work indicate that the proposed solution is capable of optimizing the thermal comfort condition according to seasonality and outperforms the conventional approaches in different performance indexes.

Influence of Iwans on the Thermal Comfort of Talar Rooms in the Traditional Houses: A Study in Shiraz, Iran

In traditional buildings many climatic strategies have been used to provide indoor thermal comfort in south and central parts of Iran. A common element is called an iwan. This study investigates the indoor thermal comfort of a room adjoined to a talar in a traditional house which has an iwan, in Shiraz, Iran. The data related to the temperature and relative humidity of the room are used to calculate the thermal comfort index of PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied) by considering the following two cases: a talar room with an iwan and one without an iwan, by means of DesignBuilder software. For the purpose of validation, the air temperature and relative humidity of the talar room with an iwan were measured over 10 days and compared to the results of the simulation. Having a valid simulation, computation was conducted for the selected house in various cases for an annual passive operation of calculating PMV and PPD. The numerical results revealed that the talar room adjoined to an iwan located in the south front of the courtyard had relatively good conditions during the hot months, including June and July. In addition, it could provide fully satisfactory comfort conditions during August and September. According to the results, it is found that the iwan makes a talar room 62% more desirable for the hot months in comparison with a talar room without an iwan.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

The pre-operation of the cesarean of the patients with the load of carbohydrate effect on the insulin resistance and the blood glucose level of the newborn

Objective To evaluate the comfort satisfaction and the insulin resistance of patients with cesarean section and the effects of the postoperative blood sugar on the newborns during the operation. Methods Forty patients who were got elective C-section were selected and divided randomly into two groups(n=20): oral carbohydrate group(CHO group), oral placebo group(P group). Both groups used epidural anesthesia, and a routine preoperative fasting 300 ml carbohydrate drinks(it contains 14.1 g of carbohydrates per 100 ml of drink) was administrated orally 2 h before anesthesia in oral CHO group. Patients in oral placebo group were given an equal amount distilled water instead of carbohydrate. The levels of blood glucose, plasma insulin and glucagons were tested after 2 h and after fetus delivery. According to levels of blood glucose and insulin, homeostasis model assessment(HOME) was applied to calculate the index of insulin resistance(HOMA-IR) and insulin sensitivity index(HOMA-ISI). After fetus delivery, neonates blood glucose was tested immediately. Meanwhile, the subjective comfort satisfaction of the two groups patients were also tested. Results After fetus delivery, blood glucose levels of two groups were significantly elevated than the levels of 2 h after loading(P<0.05). But there is no difference of blood glucose levels between two groups. Compare with levels of 2 h after loading, patients in P group had higher insulin levels, elevated HOMA-IR levels and lower HOME-ISI levels than levels of patients in CHO group (P<0.05). Patients in P group had higher glucagon levels that patients in CHO group. Patients in CHO group have less sense of hunger. Patients of two groups have no difference on sense of vomiting. The neonates blood glucose levels in the CHO group was higher than those in P group(P<0.05). Apgar scores of newborns in two groups are not different. Conclusions C-section preoperative oral intake of carbohydrate can safely reduce the insulin resistance and relieve the feelings of thirst, and help to raise blood glucose levels in newborns. Key words: Csection; Carbohydrate; Insulin resistance; Newborn; Blood glucose

4D Flow imaging in real life: A look back on a year of practice

4D Flow imaging is an innovative magnetic resonance imaging modality for qualitative and quantitative analysis of cardiovascular pathologies. Its applications are multiple and find their interest in contentious situations by providing a complete and innovative analysis. We present here the experience of one year of practice in our center (100 examinations) on adult and pediatric populations (from 6 weeks to 43 years, from 4.6 kg to 85 kg). This modality has been applied as a remedial imaging in ambiguous situations (68% of congenital pathology, 23% of valvulopathy, 9% of large vessel pathology) where conventional imaging modalities have proved non-contributory. The feasibility of this examination was excellent, since only 3 examinations were considered uninterpretable due to a lack of perfusion of gadolinium. All the pediatric population examinations could be performed in a satisfactory comfort (maximum duration of 8 minutes against 40 minutes for a conventional 2D MRI/absence of recourse to general anesthesia). The results were compared with those obtained by conventional methods and were useful in the management of our patients by providing a complete understanding of the pathologies. Used in contentious situations, MRI by 4D Flow has been illustrated as a recourse imaging. Based on our experience, 4D Flow has proved its feasibility in various situations and for various pathologies. It remains to this modality to demonstrate its reproducibility as well as its relevance.

Comparing the indoor environmental quality of a displacement ventilation and passive chilled beam application to conventional air-conditioning in the Tropics

The satisfaction of occupants with an indoor environment supplied by a conventional central VAV air-conditioning system was compared to a novel system represented by decentralized ventilation units and a network of passive chilled beams. In parallel with occupant survey measurement of indoor air quality (IAQ) parameters was conducted with the developed wireless IAQ sensing kits. The same occupant cohort was surveyed for both systems, as occupants of the conventionally-conditioning office, known as the ‘Previous Office’ later moved into the office conditioned by the novel system, known as the ‘3for2 Office’. We observed that the perceived thermal comfort and air quality satisfaction of the occupants were markedly higher in the 3for2 Office than in the Previous Office. While occupants of the 3for2 Office could raise or lower indoor air temperatures, they appeared to be consistently satisfied with higher indoor air temperatures than that set by the building management of the Previous Office. The primarily Singaporean occupants of the 3for2 Office also felt thermally comfortable even when indoor air velocities in the office were measured to be below 0.1 m/s recommended by Singaporean Standards. The 3for2 Office's ventilation system was designed to supply outdoor air beyond minimal required ventilation rates, which lead to relatively low levels of CO2 and PM2.5 concentrations in the 3for2 office. We believe that higher ventilation rates resulting in lower pollutant concentrations are responsible for a higher-than-average survey score on air quality satisfaction. We also believe that enabling occupants to set air temperature in the space resulted in higher-than-average thermal comfort satisfaction.

An information provision system to promote energy conservation and maintain indoor comfort in smart homes using sensed data by IoT sensors

Smart cities are often discussed in the context of promoting sustainability. A smart city operates using large numbers of networked sensors to collect, store, and analyze massive amounts of data in near-real time; these data may also have secondary applications. This study proposes one such use of the data gathered by home energy management systems (HEMS). Current HEMS focus mainly on energy efficiency, but residents typically value indoor comfort as well. HEMS collect various types of data that can be used to create an index for short-term indoor comfort called the predicted mean vote (PMV). This study implements a system to identify information within the stored data that is relevant to reducing electricity consumption while maintaining indoor comfort according to each resident’s PMV preference. The system was tested in three households in Japan for 12 days in winter. The system reduced electricity consumption by 5.15% and increased the comfort satisfaction expressed as PMV by 42.3%. Qualitative assessments of indoor comfort increased by 16.4%. Providing users with information selected according to their PMV preferences was more effective in reducing electricity consumption and increasing indoor comfort than providing them with random information.

Mixed Reality in Visceral Surgery: Development of a Suitable Workflow and Evaluation of Intraoperative Use-cases.

The paper evaluates the application of a mixed reality (MR) headmounted display (HMD) for the visualization of anatomical structures in complex visceral-surgical interventions. A workflow was developed and technical feasibility was evaluated. Medical images are still not seamlessly integrated into surgical interventions and, thus, remain separated from the surgical procedure.Surgeons need to cognitively relate 2-dimensional sectional images to the 3-dimensional (3D) during the actual intervention. MR applications simulate 3D images and reduce the offset between working space and visualization allowing for improved spatial-visual approximation of patient and image. The surgeon's field of vision was superimposed with a 3D-model of the patient's relevant liver structures displayed on a MR-HMD. This set-up was evaluated during open hepatic surgery. A suitable workflow for segmenting image masks and texture mapping of tumors, hepatic artery, portal vein, and the hepatic veins was developed. The 3D model was positioned above the surgical site. Anatomical reassurance was possible simply by looking up. Positioning in the room was stable without drift and minimal jittering. Users reported satisfactory comfort wearing the device without significant impairment of movement. MR technology has a high potential to improve the surgeon's action and perception in open visceral surgery by displaying 3D anatomical models close to the surgical site. Superimposing anatomical structures directly onto the organs within the surgical site remains challenging, as the abdominal organs undergo major deformations due to manipulation, respiratory motion, and the interaction with the surgical instruments during the intervention. A further application scenario would be intraoperative ultrasound examination displaying the image directly next to the transducer. Displays and sensor-technologies as well as biomechanical modeling and object-recognition algorithms will facilitate the application of MR-HMD in surgery in the near future.

A post-occupancy evaluation of the influence of wood on environmental comfort

Emerging research on the impact of interior finishes, more specifically wood, is beginning to shed light on informed design opportunities. As a natural building material with unique features, wood creates warm and pleasant atmospheres and has the potential of enhancing the well-being of occupants. This research attempted to better understand how occupants perceive wood in built environments and whether its indoor use influenced the satisfaction of occupants. The comfort of occupants may have been influenced by factors that were unrelated to the indoor environmental quality, which caused difficulties in comparing interior finishes in existing environments and limited research in this field. An exploratory comparative study, within a post-occupancy evaluation framework, investigated the subjective perception of occupants in relation to physical comfort factors. Thirty-six occupants completed a questionnaire to examine comfort satisfaction in a multifunctional room with extensive wooden interior finishes in comparison with a similar space without wood surfaces. The results indicated that occupants were more satisfied in the extensive wood surfaced room in terms of lighting, noise, and temperature, despite the similar environmental conditions in both spaces. Adjectives often used to describe the wood room included bright, pleasant, modern, and warm. Architects should consider the subjective qualities of wood when designing comfortable buildings.

Influences of the cognitive behavior therapy on anxiety for postoperative patients with oral and maxillofacial malignant cancer in surgery intensive care unit

Objective To investigate the effects of the cognitive behavior therapy on anxiety for postoperative patients with oral and maxillofacial malignant cancer in surgery intensive care unit. Methods A total of 82 patients with oral and maxillofacial malignant tumor who accept surgery from January 2014 to September 2016 were chosen and averagely divided into intervention group with cognitive behavior therapy based on control group and control group taking routine nursing and general psychological care by random number table in SICU. The application of self-evaluation of anxiety scale (SAS) evaluation of anxiety level before and after the intervention in patients with and without improvement, and compare the two groups of stay in SICU patients satisfaction evaluation intervention is effective. Results Compared with control group, the cognitive behavioral therapy, after intervention group patients with SAS scoring[(50.56±6.52)]points SAS scores than the control group [(57.51 ± 6.11)] points decrease, the difference was statistically significant (P < 0. 01); intervention group check in SICU satisfaction (97.6%,40/41) also better than the control group (90.2%, 37/41), the difference was statistically significant (χ2=7.527, P < 0.05). Conclusion cognitive behavioral therapy can effectively reduce the anxiety of postoperative patients with oral and maxillofacial malignant cancer in SICU, and effectively improve the comfort level and satisfaction in SICU. Key words: Oral and maxillofacial surgery; Malignant cancer; Surgery intensive care unit nursing; Cognitive behavior therapy; Anxiety

A bi-level optimization for an HVAC system

Improving the control strategy of an heating, ventilation, and air-conditioning (HVAC) system can result in substantial energy saving. In this paper, we formulate the whole HVAC system to a bi-level optimization problem to minimize the energy consumption of the HVAC system and maximize the satisfaction of indoor human comfort. The hierarchical evolutionary algorithm with preliminary feasibility conditions and crude energy index is proposed to find the good-quality control strategy of the HVAC system. Numerical results demonstrate the efficiency and effectiveness of the proposed method and show the performance of the obtained control strategy.