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Abstract Rapid urbanization increases psychological stress among pedestrians, potentially heightening mental health disorders. This study examines the role of street walls' visual and textural characteristics in stress recovery, using Qingdao as a case study. Virtual reality is employed to simulate five distinct street walls: yellow mortar, brown stone, red brick, green plant, and white mortar. The stress recovery effectiveness of these walls was evaluated through psychological and physiological indicators from 48 young college students. Results indicated that street walls with warm tones, particularly brown stone, significantly aid stress recovery. Psychologically, Restorative Components Scale was highest for brown stone at 1.13. Physiologically, it was linked with notable reductions in diastolic and pulse pressure (decreases of 2.95 mmHg and 2.27 mmHg, respectively), and enhanced parasympathetic activity, as evidenced by the fastest decrease in low frequency/high frequency ratio (LF/HF), and increases in pNN50 and RR (0.14–2.01% and 1.57–11.81 ms, respectively). For urban design, the incorporation of warm-toned materials and natural elements like stone is recommended for their superior restorative benefits.

Abstract The physical characteristics of classrooms can significantly impact the physical and mental health as well as the learning performance of college students. This study investigates the effects of classroom size and ceiling height on learning performance using virtual reality technology. Four classroom settings were created: two small classrooms (40.5 m2) with ceiling heights of 3.0 m and 3.9 m, and two large classrooms (62.1 m2) with ceiling heights of 3.9 m and 4.8 m. 34 students participated in task tests while their subjective evaluations and physiological data were recorded. Results showed higher subjective ratings in larger classrooms with the same ceiling height. Classroom size did not significantly affect task test scores. However, there is a significant difference in Task test scores for ceilings of different heights with the same size classroom. The task test improved by 17.3% in the Big and High Room (BHR) and by 20.1% in the Small and Low Room (SLR). Physiological data revealed significant effects of ceiling height, with HRV-nLF/nHF and EEG-β power increasing by 26.5% and 53.9% in BHR, and by 10.7% and 22.8% in SLR, respectively. This study concludes that classroom size and ceiling height plays a crucial role in learning performance and provides insights for classroom design. It also establishes a framework for future research on the interplay between heart rate variability, EEG, and learning performance.

To assess the viability and effectiveness of bioretention cell in enhancing rainwater resource utilization within sponge cities, this study employs field monitoring, laboratory testing, and statistical analysis to evaluate the water purification capabilities of bioretention cell. Findings indicate a marked purification impact on surface runoff, with removal efficiencies of 59.81% for suspended solids (SS), 39.01% for chemical oxygen demand (COD), 37.53% for ammonia nitrogen (NH3-N), and 30.49% for total phosphorus (TP). The treated water largely complies with rainwater reuse guidelines and tertiary sewage discharge standards. Notably, while previous research in China has emphasized water volume control in sponge city infrastructures, less attention has been given to the qualitative aspects and field-based evaluations. This research not only fills that gap but also offers valuable insights and practical implications for bioretention cell integration into sponge city development. Moreover, the methodology and outcomes of this study serve as a benchmark for future sponge city project assessments, offering guidance to relevant authorities. HIGHLIGHTS Explored the purification of bioretention cells on rainwater.; Under the same conditions, the bioretention cell has the best purification effect on suspended solids.; The bioretention cell's effluent quality complies with standards for urban recycled water, wastewater discharge, and sponge city assessments.;

To address the slow heating and low efficiency issues of traditional macro-packed phase change material (PCM) radiant floor heating systems, this study innovatively explores the impact of altering the eccentric positions of hot water pipes within the PCM encasement, based on computational fluid dynamics (CFD) numerical simulations. Starting with a concentric case as a baseline, the investigation extends to six different configurations achieved by displacing the hot water pipes vertically (upward and downward) and horizontally. The analysis encompasses the PCM's thermal state, temperature dynamics, spatial distribution, and heat flux. The results indicate that the arrangement of hot water pipes with upward and horizontal displacements demonstrates a more positive impact on improving the thermal performance of the PCM radiant floor heating system, with the effect becoming more pronounced as the displacement distance increases. Cases with upward eccentric movement of r/4 (where r is the encasement radius) and r/2 reached the optimal floor temperature of 297.15 K faster than the concentric case by 2 h and 3.8 h, respectively, during the heating phase. Cases with horizontal eccentric movement advanced by 0.5 h and 2.5 h, respectively. Cases with horizontal displacements of r/4 and r/2 caused smaller temperature fluctuations and provided greater comfort and energy efficiency than those with upward displacements, with average heat flux values being 4.75 % and 11.82 % lower, respectively. These findings offer theoretical support for the optimization and application of macro-packed PCM floor heating systems.

With the rapid development of the national economy, increasing attention has been given to the living environment in rural areas, especially indoor thermal and wind environments. This study conducted on-site measurements and questionnaire surveys during winter in the indoor environment of five villages in northeastern Sichuan. Eighty-nine traditional residences were selected to investigate their fundamental characteristics, indoor thermal environment, humidity conditions, and comfort levels. Questionnaires and comparative experiments were also conducted with natives and locals. This combination of objective data and subjective feedback provides a comprehensive perspective for assessing the indoor thermal environment. The results showed that when expressed as neutral temperature and humidity, the temperature difference was 2.36 °C, with little humidity difference. Residents exhibited higher tolerance towards the local thermal and humidity environment than nonlocal volunteers, who showed greater sensitivity to it. In addition, residents had a Predicted Mean Vote (PMV) value of −0.69 and a heat acceptance value of −1.78, while non-local volunteers had a PMV value of −0.76 and a heat acceptance value of −1.32. A detailed evaluation and analysis of the relationship between the indoor thermal environment and human comfort of local residential houses was carried out, providing technical guidance for energy conservation and thermal insulation of local buildings.

Abstract Along with the further integration of demand management and renewable energy technology, making optimal use of energy storage devices and coordinating operation with other devices are key. The present study takes into account the current situation of power storage equipment. Based on one year of measured data, four cases are designed for a composite energy storage system (ESS). In this paper, a two-tiered optimization model is proposed and is used to optimizing the capacity of power storage devices and the yearly production of the system. Furthermore, this paper performs a comparative analysis of the performance of the four cases from the energy, environmental and economic perspectives. It is concluded that this kind of energy storage equipment can enhance the economics and environment of residential energy systems. The thermal energy storage system (TESS) has the shortest payback period (7.84 years), and the CO2 emissions are the lowest. Coupled with future price volatility and the carbon tax, the electrothermal hybrid energy storage system (HESS) has good development potential. However, the current investment cost is very high, and it will not be possible to recover this cost in 10 years. Finally, it is recommended that the cost of equipment be reduced in combination with subsidies and incentives for further promotion. The research results not only fill a gap in the study area, but also provide some suggestions for further development of industry and research on user-side energy storage.

Soil erosion poses significant ecological and economic challenges, necessitating precise and effective identification methods. Traditional models frequently overlook the intricate relationships between erosion factors and multispectral remote sensing data. To enhance these traditional methods, a novel dual-input gated fusion Convolutional Neural Network (CNN) has been developed, integrating channel and spatial attention mechanisms. This innovative model strengthens the connection between multispectral images and erosion factors, improving the accuracy and generalizability of erosion predictions. The model utilizes data collected from unmanned aerial vehicles (UAVs) equipped with high-precision multispectral sensors. By processing both spectral images and erosion factor data, the model effectively captures complex soil spatial distributions. The dual-input gated fusion mechanism allows the network to extract high-level semantics while suppressing redundant information, ensuring robust performance even in heterogeneous terrains. Experimental results indicate that this framework significantly enhances the performance of traditional models, providing superior predictions for small and medium-sized areas. Experimental results indicate that the presented framework can achieve better accuracy (96.92%) compared with other machine learning approaches, such as Random Forest (89.64%), VGGNET (91.52%), and RESNET (90.18%). Moreover, the proposed method can improve accuracy by 26.59% compared to the traditional RUSLE model. This improvement is critical for applications in ecological restoration and sustainable development. The integration of deep learning techniques with UAV-based data collection offers a powerful tool for environmental monitoring and management.

This work aims to explore reform methods for Japanese classroom teaching modes in an informationized environment. Addressing issues such as monotonous classroom content and low student engagement in traditional teaching, this work introduces deep learning technology and constructs a personalized recommendation model based on the Bidirectional Encoder Representations from Transformer (BERT) fused with the Graph Convolutional Neural Network (GCNN). Furthermore, this model is applied to Japanese flipped classroom teaching, and a personalized recommendation system-supported Japanese flipped classroom mode is designed. The performance of the constructed model is finally verified. The results show that the personalized recommendation model constructed here has achieved good application effects in Japanese teaching, with a recommendation recognition accuracy of 95.96% and an F1 value of 90.12%. The constructed model exceeds the precision of the baseline algorithm Convolutional Neural Network (CNN) by over 4%. Through questionnaire surveys and empirical analysis of controlled experimental designs, it is found that the Japanese flipped classroom mode supported by the personalized recommendation system demonstrates significant advantages in enhancing student learning abilities and satisfaction. The improvement rates of experimental group students in various indicators all exceed 18%. Therefore, the Japanese teaching mode proposed can significantly improve the teaching effectiveness of Japanese classrooms and provide experimental references for educational mode reforms.

Terahertz (THz) communications have naturally promising physical layer security (PLS) performance since narrow beams created by antenna arrays can effectively prevent eavesdroppers outside the beam from overhearing legitimate messages. However, eavesdroppers residing in the THz beam sector are still threatening even with the equipment of traditional multi-antenna techniques. This security challenge is referred to range security, since the range of the legitimate receiver and the eavesdropper differs. To address this problem, in this paper, a fundamental theoretical analysis on multiple-antenna techniques to provide range security is studied. Based on this, the frequency diverse array, as a candidate multiple-antenna technique, is proven ineffective in addressing the range security problem. Moreover, a widely-spaced array and beamforming design for THz range security is demonstrated. A non-constrained optimum approaching (NCOA) algorithm is proposed to achieve sub-optimal performance based on a THz-specific multiple-antenna array structure. Simulation results illustrate that our proposed widely-spaced antenna communication scheme can ensure a 6 bps/Hz secrecy rate when the transmit power is 10 dBm and the propagation distance is 10 m.

Providing continuous bandwidth over several tens of GHz, the Terahertz (THz) band (0.1-10 THz) supports space-air-ground integrated network (SAGIN) in 6G and beyond wireless networks. However, it is still mystery how THz waves interact with the channel medium in SAGIN. In this paper, the absorption and scattering effect in THz space-air-ground channels are modeled as a collision process between THz photons and medium particles, based on which a universal space-air-ground attenuation model is proposed for THz waves. The proposed model incorporates the attenuation effects induced by particles including condensed particles, molecules, and free electrons. The propagation loss of THz waves in the atmosphere and the outer space are numerically assessed. Numerical results indicate that the attenuation effects except free space loss are all negligible at the altitude higher than 50 km while they need to be considered in the atmosphere lower than 50 km.

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Spatial disparities in health issues persist within urban development, particularly in areas undergoing urbanization processes, especially in developing countries. Consequently, it is imperative to address the challenge of establishing self-sufficient neighborhoods with essential living functions where residents can meet their daily needs. This study aims to address this challenge, which is crucial to explore and comprehend these disparities and to inform appropriate planning. The study focuses on spatial disparities and accessibility to urban facilities, with a specific case study conducted in Pathum Thani Province, Thailand, based on spatial network analysis. Subsequently, the study considers differences in access within the context of the urban hierarchy. The results indicate that Pathum Thani province offers a wide distribution of activities and services directly related to health, with most residents having accessibility within a 15-minute radius. However, it is essential to prioritize planning for activities and services that promote physical activity, especially in rural areas, to enhance accessibility.

The COVID-19 pandemic began to spread at the beginning of 2020 in the world and in Japan, which is expected to impact the air conditioning (AC) energy use (EU) in educational buildings when AC and natural ventilation are used together as a new regulation to decrease infectious disease transmission. Furthermore, this regulation of natural ventilation is expected to impact the AC EU when the outside temperature affects indoor comfort causing changes in the regular AC setting temperature and AC operating times. This study aims to evaluate the AC EU, and the indoor thermal environment changes with Private Finance Initiative (PFI) data monitoring in junior high schools in Oita City before and during the COVID-19 pandemic during summer and winter through a sensitivity analysis method. The result found that AC EU significantly increased during the pandemic, 2 times escalation in summer and 1.9 times in winter. Besides, during the pandemic, indoor air temperature comfort was achieved with longer AC operation time, lower AC setting temperature in summer, and higher AC setting temperature in winter. The result of this research can be expected to be beneficial for future studies regarding AC energy saving and indoor thermal comfort-related investigation and valuable for new regulations related to air conditioner settings and operations for the new normal during the pandemic.

Considering the trend of home energy use in the context of global energy scarcity, this study examines the effects of variables such as physical and social factors on household energy. Article summarization and text mining approaches were utilized to create a broad picture of home energy demand with successful predictive models to identify the most significant drivers and potential simulation techniques. Results show that the data-driven method was largely adopted, and hybrid modeling is the cutting-edge option to consider for energy forecasting. The findings demonstrated the importance of occupant-related parameters, which primarily focus on occupant behaviors. The shift in energy demand necessitates increased research efforts in residential areas in Asia-Pacific regions, which call for an energy conservation strategy in these emerging nations. The findings give academics and decision-makers a complete vision of analyzing home energy demand in the early phases and drive the development of effective energy-saving strategies and policies. Our review provides comparative perspectives of energy use drivers and a holistic understanding of various approaches, which can help to close the existing gaps and provide researchers in the same field with a straightforward direction.

Ecological folk houses which are formed under the influence of the local climate, topographic features, and regional resources, are generally environmentally friendly and energy-saving with low-carbon emissions. They conform to local habits and behavior patterns. The study of ecological folk houses provides a valuable reference for the sustainable development of houses. One of the world’s most representative ecological folk houses, the “seaweed house”,was selected as the object of this study. A comprehensive review of research on seaweed houses was carried out in the Jiaodong Peninsula in China regarding their origins, historical development, structure, ecological strategies, and the conservation situation based on a bibliographic survey of more than 100 references over the last thirty years. The findings suggest that: (1) seaweed houses have a very high research value for sustainable development, which is mainly reflected in the ecology, culture, and region; (2) ecological strategies such as the material characteristics of seaweed and granite, roof slope, volume factor, and layout of the seaweed house courtyard are key influencing factors for the warmth of seaweed houses in winter and their coolness in summer; (3) the reduction in seaweed materials, unsustainable roof thatching skills, and the preference of young people for modern houses are the three main reasons for the rapid decline in seaweed houses; and (4) cultural excavation, economic development, and ecological optimization are key to the sustainable development of seaweed houses. Thus, this paper proposes possible future research and application directions.

To address the challenges of indoor mold in southern China, this study designed and constructed an innovative experimental system to investigate mold growth in buildings under the combined influence of multiple factors. Using Fluent simulation (Ansys Fluent 19.0), we designed a suitably sized experimental chamber to realistically replicate the effects of factors such as temperature, humidity, and air velocity on mold growth. After establishing and fine-tuning the experimental system, we conducted two preliminary experiments, successfully validating the feasibility of our setup. Additionally, we observed that in a high-temperature, high-humidity environment of 28 °C and 80% relative humidity, the mold growth rate in the experimental chamber increased with the rise in inlet air velocity. This experimental system will serve as the foundation for future studies on indoor mold growth in building spaces in southern China.

Building operations account for a large amount of energy use and CO2 emissions, and the morphology of buildings in residential clusters strongly impacts energy efficiency performance. However, little research has focused on the morphology and energy electricity usage of high-rise residential clusters in hot summer and cold winter (HSCW) regions. We investigated 96 residential clusters in Hangzhou, China, and established a corresponding morphology database. Additionally, we obtained annual electricity consumption for 16 of these residential clusters. With this database, we performed optimization of morphological parameters upon energy use intensity (EUI) using a genetic algorithm (GA). Specifically, the cooling, heating, and lighting EUIs of high-rise residential clusters were studied. After implementing the optimized morphological parameters, there was a reduction of up to 7.73% in EUI. According to regression analysis, the average aspect ratio was the most significant factor influencing EUI (r = −0.907), followed by floor area ratio (r = −0.755), average orientation (r = 0.502), and average number of floors (r = −0.453). These results indicate that a higher intensity of land development with a greater floor area ratio, average aspect ratio, and average number of floors can reduce total energy consumption. Additionally, we found that an average building orientation of southwest 15° (with respect to south) is optimal. The findings of this study can assist urban planners and designers in developing more sustainable residential clusters, leading to decreased energy costs and CO2 emissions.

Wilderness has proven to provide a wide range of ecosystem services (ES). Wilderness have been shown to play an important role in preserving biodiversity and providing ES, which is indispensable to both nature and people. It is essential to integrate ES quantification into wilderness conservation and management. This study presents a method that combines the wilderness mapping and ecological indicators to measure ES of wilderness. The method was applied in 155,800 km2 of Shandong province in China, across 23 types of land use. First, relative wilderness value (RWV) and ecosystem service capability (ESC) were quantified according to Weighted Linear Combination (WLC). Then, spatial autocorrelation between RWV and ESC were tested and analyzed using GeoDa software. Finally, the Redundancy analysis was used to investigate the primary drivers of RWV and ESC in different landscape types. The findings showed that, there were 27,263 km2 of wilderness in Shandong province, of which 6,294 km2 were high-quality wilderness. The region with the highest RWV was the Yellow River Delta (RWV = 0.82), and the area with the highest ESC was Laoshan Mountain (ESC = 0.76). Spatial autocorrelation analysis showed a positive correlation between RWV and ESC (Pearson’s r = 0.637), both of which had significant spatial relevance (Moran’s I = 0.589, P

Indoor space is the place where human beings live, and how to meet people's indoor thermal comfort needs in the context of energy scarcity is one of the important issues for sustainable development of buildings, and the evaluation of the indoor thermal environment has become the key to it. In the case of Japanese wood-framed detached houses, which contain different types of space forms, it is difficult to accurately apply the existing thermal environment evaluation results. In this study, detached houses in Kitakyushu City, Japan, were selected as the research object, and continuous measurements and subjective questionnaires of the indoor thermal environments were conducted based on three modes of no heating, continuous heating, and intermittent heating, in order to grasp the current information of the thermal comfort in detached houses. The results showed that: 1. The average indoor temperatures in both Washitsu and Yoshitsu were lower than the human thermal comfort range; 2. The thermally neutral temperature of 15.9 °C in Washitsu was lower than that of 17.7 °C in Yoshitsu, and the acceptable temperature zones were (14.3–17.5 °C) and (15.6–18.9 °C), respectively; 3. Develop modified models (PMVe) and adaptive predicted mean vote (aPMV) model suitable for thermal comfort evaluation of detached houses in Japan. The results of this study will help to improve the indoor thermal environment and thermal comfort evaluation system of Japanese detached houses, and provide a useful reference for the study of the renewal and renovation of houses in this area.

Although an interaction between light and thermal physiology has been reported, the combined effects of environmental factors remain unclear. In this study, we experimentally manipulated light and thermal factors to examine the main, crossed and interaction effects on human subjective perception. In this study, we used experimental manipulation of temperature, color temperature, and illuminance levels and repeated measurements to explore the effects on human subjective perceptions in 36 participants.The results showed that temperature significantly influenced visual perception, illuminance and correlated color temperature (CCT) weakly influenced thermal perception. The interaction of temperature and correlated color temperature (CCT) significantly influenced visual perception. In a high-temperature environment, higher CCT enhanced perceived coolness and brightened perceived luminance. However, the interaction of temperature, illuminance and CCT did not influence visual perception or thermal perception. These results seem to confirm that temperature influences human thermal and visual perception in the uncomfortable zone. Along with the actual indoor environment, illuminance and color temperature of the light source at different ambient temperatures should be considered to increase comfort and energy savings.

Households are an important sector in carrying out human development activities, accounting for more than 30% of the total global energy consumption. The continued growth of household energy consumption (HEC) and carbon emissions is threatening economic and environmental sustainability. This review focuses on the research in the field of HEC and conducts a bibliometric analysis of research articles from the Web of Science Core Collection since 2000. The results show that: 1) HEC research has undergone rapid development since 2014, and interdisciplinary fusion and collaborative research have become dominant trends. 2) Keyword co-occurrence analysis clearly identifies the current urgent themes, including energy demand and its determinants, environmental impact factors and assessments, and energy-saving technologies and emission reduction measures. 3) The analysis of citations reveals that economic models, such as input-output models and life cycle assessment, are frequently employed in the field of HEC. Based on a summary of household energy-saving and emissions reduction work, this paper critically discusses the limitations of existing measures such as smart home technology, sustainable energy systems, and behavioral interventions. The main directions for promoting household energy-saving development in the future are identified: including improving the security and customer engagement of smart home technology, focusing on the availability and stability of sustainable energy, and paying more attention to low-income and aging households in behavioral intervention measures. One of the important obstacles facing research is how to reduce energy management efficiency and usage costs through technology and policy.

Outdoor thermal comfort is crucial for creating sustainable and livable urban spaces. Studying the distribution and impact factors of the thermal environment can provide theoretical support for improving the thermal environment and spatial planning. This study investigates the influence of well-defined boundary spaces on outdoor thermal comfort and proposes design measures to improve spatial thermal comfort. High-resolution data obtained from unmanned aerial vehicles (UAVs) is integrated into outdoor microclimate simulations to enhance the accuracy and precision of the models. The Physiological Equivalent Temperature (PET) index is employed as an evaluation indicator, considering the categorization of PET values into different comfort levels. Additionally, the axial evaluation method is introduced to assess thermal comfort more accurately, reflecting the perceived thermal comfort by individuals. Through a case study in a mixed-use and clear boundary area, the research identifies the variables that influence outdoor thermal comfort and provides design guidelines to enhance spatial thermal comfort. The correlations between the spatial morphology index and PET were analyzed by multiple regression. The findings contribute to the understanding of outdoor thermal comfort in complex urban environments and offer valuable insights for the design and planning of comfortable and sustainable outdoor spaces.

Large-scale particle swarm optimization (PSO) has long been a hot topic due to the following reasons: Swarm diversity preservation is still challenging for current PSO variants for large-scale optimization problems, resulting in difficulties for PSO in balancing its exploration and exploitation. Furthermore, current PSO variants for large-scale optimization problems often introduce additional operators to improve their ability in diversity preservation, leading to increased algorithm complexity. To address these issues, this paper proposes a dual-competition-based particle update strategy (DCS), which selects the particles to be updated and corresponding exemplars with two rounds of random pairing competitions, which can straightforwardly benefit swarm diversity preservation. Furthermore, DCS confirms the primary and secondary exemplars based on the fitness sorting operation for exploitation and exploration, respectively, leading to a dual-competition-based swarm optimizer. Thanks to the proposed DCS, on the one hand, the proposed algorithm is able to protect more than half of the particles from being updated to benefit diversity preservation at the swarm level. On the other hand, DCS provides an efficient exploration and exploitation exemplar selection mechanism, which is beneficial for balancing exploration and exploitation at the particle update level. Additionally, this paper analyzes the stability conditions and computational complexity of the proposed algorithm. In the experimental section, based on seven state-of-the-art algorithms and a recently proposed large-scale benchmark suite, this paper verifies the competitiveness of the proposed algorithm in large-scale optimization problems.

A large number of people in China still live in rural villages. The indoor environment of these rural dwellings directly affects the quality of life of the occupants. Nevertheless, constrained by the quality of dwelling construction, rural buildings have poorer indoor environments and, at the same time, have a higher operating energy consumption. However, inadequate attention has been given to the summer thermal environment in cold regions. This work has been carried out around the thermal environment of rural residences in cold regions during summer. Field measurements, questionnaires, and data analysis were used in this study. We recorded the indoor and outdoor thermal environment parameters on a typical summer day in the Linyi rural area. Moreover, the subjective sensations and thermal adaptive behaviors of the participants were recorded in detail with a questionnaire. Linear regression showed that the neutral temperature for residents in summer was 27.52 °C, with acceptable temperatures ranging from 25.14 °C to 29.9 °C. Age and gender differences were found to affect the occupants’ sensation of thermal comfort and humidity, as well as their thermal adaptive behavior. In addition, a thermal adaptive model has been constructed in the study, which will further enrich the thermal adaptive investigation and provide a scientifically sound reference for the renovation and development of the local rural areas.

Since the implementation of Chinese Western Development Strategy, gully regions of the Loess Plateau experience rapid development. Large number of residents living in gullies move to tableland, village combination and extension come to be a common phenomenon. Due to the neglect of ecological carrying capacity evaluation and the lack of scientific methods for human settlement planning, the unreasonable village combination and extension cause severe damage for the natural environment, low quality living environment, large waste of resource and lots of unnecessary investment. In this paper, cultivable land, water resource and infrastructure are figured out as three important limiting factors for the ecological carrying capacity evaluation. Grid computing method is used to draw distribution maps for each factor. After overlaying these distribution maps, the space distribution of ecological carrying capacity can be visualized. Tao Qu Yuan village is chosen as a research object for its high ecological carrying capacity. According to the ecological carrying capacity evaluation, the priority areas for new immigrants can be fixed, natural resource and social resource can be saved, and investment for new construction can also be reduced. Results summarized by this method can be used as guideline for the future village planning.

How to make full use of geographical advantage to improve the indoor thermal environment of passive buildings in plateau area has become a hot topic, which is of great significance for building energy conservation and thermal engineering design of passive buildings. This study took a residential house in Lhasa, Tibetas as the research object, obtained indoor air temperature distribution through field measurement. A multiple regression model was used to explore the influence of outdoor meteorological factors on indoor thermal environment. Results showed that the temperature near the south window was the highest mostly higher than 16 °C, and the vertical temperature gradient was higher than 7.5 °C with the solar radiation and outdoor temperature gradually raised during daytime. Besides, the vertical temperature difference is less than 3 °C as the distance from the south window increases, and the air temperature gradually goes down. The standardized regression coefficient (Beta) illustrated that solar radiation has the greatest influence on the indoor thermal environment compared with the outdoor temperature. Therefore, for direct gain passive buildings in Tibetan areas, the potential of reasonably setting transparent heat generation structures to improve the indoor thermal environment is far greater than that of improving the thermal insulation of non-transparent enclosures.

3D printing, which allows components to be fabricated in layers directly from a digital file, has been increasingly used in the construction industry. However, the deficiencies in mechanical properties of 3D printing concrete (3DPC) confined its wide usage in bearing structures. The 3DPC components have the disadvantages of geometric defects, residual stress, and a high level of anisotropy, resulting in printed components with poor load-bearing capacity. Studies have been conducted to enhance understanding and control of the mechanical properties of 3DPC, aiming at promoting the application of 3DPC in structural components. This paper summarized and analyzed the related research from the perspective of the kinds of structural components, i.e., beams, columns, and walls, and discussed the effects of reinforcement methods and various factors on their mechanical properties. Finally, the current main challenges and directions of future work required to optimize the usage of this technology in 3DPC components have been identified.

Rapidly increasing household energy consumption poses significant challenges to global warming mitigation and the transition to low-carbon economies, particularly in China. This paper addresses this issue by introducing a comprehensive segmentation model which effectively subdivides household energy usage into five end-uses: cooking/hot water, heating, cooling, lighting, and power. The segmentation model uncovers compelling insights into urban end-use energy consumption patterns across China and variations among provinces. We observe a consistent increase in urban household end-use energy consumption and per capita energy consumption levels over the past decade. Heating and cooking/hot water emerge as the dominant contributors to household energy consumption, accounting for 26% and 40% of the total, respectively. Furthermore, it is found that higher levels of urbanization and improved living conditions are positively correlated with increased power energy consumption. The declining number of household members, primarily due to the prevalence of nuclear families, has resulted in higher energy end-use, particularly in both developed and underdeveloped economic areas. This paper serves as a valuable foundation for understanding and quantifying household end-use energy consumption. The findings contribute to a more comprehensive understanding of energy consumption patterns, facilitating a cleaner and more sustainable transformation of energy consumption structures.

Nowadays, evaporatively cooled office buildings commonly observed in dry hot areas in summer of China. However, few dedicated studies to record the local residents' thermal comfort and adaptability in these buildings. The contribution of adaptive comfort theory on thermal perception still remains unclear for optimizing office building design parameters. Hence, to deeper probe the adaptive thermal comfort of the related indoor environment, a field study of office buildings during summer considering evaporative cooling air conditioned (ECA) and naturally ventilated (NV) mode was conducted in Turpan, China. Based on 931 valid datasets collected from questionnaires, we found that the neutral temperature (Tn) of 28.4 °C in ECA group, 0.6 °C lower than NV group (29.0 °C). A lower air temperature (Ta) and higher humidity (RH)/air-velocity (Va) were expected in two modes, and Va has a stronger influence than RH on mean thermal sensation votes (MTSV). Meanwhile, occupants can adapt to current indoor environment through physiological, psychological and behavioral adjustments, while the clothing regulation had limited effect on MTSV unless the outdoor temperature exceeds 38 °C. Whether in ECA or NV mode, the predicted mean votes (PMV) model overestimated actual thermal sensation when operative temperature (Top) beyond 28 °C. Adaptive models were also proved varied from that in current standards, which indicated that they were not suitable for evaluating the studied buildings in Turpan. Above findings could suggest us a better understanding of the occupants' thermal adaptability, thereby providing the reference of design parameters revision and passive strategies for local newly/renovated buildings.

Nowadays, urban cycling activities in China are developing rapidly, and the number of cyclists has also increased dramatically. The improvement of urban cycling environment design in cities has positive significance for improving the quality of living environment. However, the related research about the environmental design theory is relatively scarce in China. This paper tries to apply the Post-Occupancy Evaluation (POE) method to evaluate the cycling environment in a green park. This study investigates the problems of the cycling environment of a park in Qingdao and then proposes some methods for improvement. The evaluation model is established by using the analytic hierarchy process (AHP) to assess the cycling environment. POE has been carried out by collecting data through questionnaires. The low score of POE has shown the shortcomings of the green park cycling design. Furthermore, this study proposes some principles of systematization, people-oriented, refinement and ecological economy to optimize the cycling environment of the green park. This paper creates a new method of designing the cycling environment, namely “POE – behavior analysis – design optimization”, which provides a theoretical reference for cycling environment design of the green park.

Purpose: The purpose of this study was to screen the genes and pathways that are involved in spermatogonia stem cell (SSC) differentiation regulation during the transition from Aundiff to A1. Methods: RNA sequencing was performed to screen differentially expressed genes at 1 d and 2 d after SSC differentiation culture. KEGG pathway enrichment and GO function analysis were performed to reveal the genes and pathways related to the initiation of early SSC differentiation. Results: The GO analysis showed that Rpl21, which regulates cell differentiation initiation, significantly increased after 1 day of SSC differentiation. The expressions of Fn1, Cd9, Fgf2, Itgb1, Epha2, Ctgf, Cttn, Timp2 and Fgfr1, which are related to promoting differentiation, were up-regulated after 2 days of SSC differentiation. The analysis of the KEGG pathway revealed that RNA transport is the most enriched pathway 1 day after SSC differentiation. Hspa2, which promotes the differentiation of male reproductive cells, and Cdkn2a, which participates in the cell cycle, were significantly up-regulated. The p53 pathway and MAPK pathway were the most enriched pathways 2 days after SSC differentiation. Cdkn1a, Hmga2, Thbs1 and Cdkn2a, microRNAs that promote cell differentiation, were also significantly up-regulated. Conclusions: RNA transport, the MAPK pathway and the p53 pathway may play vital roles in early SSC differentiation, and Rpl21, Fn1, Cd9, Fgf2, Itgb1, Epha2, Ctgf, Cttn, Timp2, Fgfr1, Hspa2, Cdkn2a, Cdkn1a, Hmga2 and Thbs1 are involved in the initiation of SSC differentiation. The findings of this study provide a reference for further revelations of the regulatory mechanism of SSC differentiation.

Accurate and detailed solar radiation data play a crucial role in the simulation of building thermal and photovoltaic systems. However, developing a highly precise and dependable solar radiation model using cost-effective data has proven challenging. This work proposes a new attenuation solar radiation model formed by conducting a comprehensive analysis of existing models and gaining new insights into solar radiation's seasonal and stochastic properties. Meanwhile, the model is constructed using easily obtainable surface meteorological parameters. The results demonstrate that the proposed model exhibits good performance in terms of prediction accuracy. Moreover, the majority of existing hourly solar radiation models have been primarily developed for clear-sky conditions. However, there is a growing demand for solar radiation hourly estimations that can uphold a high level of accuracy and reliability even in different weather state. Conversely, the proposed model is developed and validated by more than twenty year's meteorological data encompassing various weather conditions in Japan. It effectively captures the stochastic nature of solar radiation by utilizing turbidity parameters, even on cloudy and rainy days. Additionally, the inclusion of interaction variables significantly enhances its interpretability.

Gaotai dwellings are the most important representative of dwellings in Xinjiang. It is an important evidence for studying the living customs and architectural features of ethnic minorities. The building enclosure type (BET) and building orientation (BO) are important factors that influence the indoor thermal comfort of the courtyard building. In this research, 20 test scenarios are simulated through field investigation and virtual simulation integrated in the software to grasp the influence of BET and BO on indoor thermal environment. The conclusions are as follows: 1. The higher the degree of building enclosure, the higher the number of comfort hours and the better the comfort. 2. Each BET has its suitable orientation. The best orientation of “T” type is east, the best orientation of “I” type and “L” type is north, and the best orientation of “C” type and “O” type is south. 3. When the optimal BET is combined with its corresponding optimal orientation of the main room, the indoor thermal comfort will be greatly increased (comfort hours in winter and summer will increase by 62.9% and 37.1%).The conclusion of this study can provide flexible design ideas for courtyard buildings to improve the thermal comfort in Kashi.

The solar radiation has a great effect on the thermal environment of regional buildings, and reflective coatings can increase the amount of solar reflectance and reduce the solar heat gain. In summer, it can effectively improve the regional thermal environment, but in winter, it may be ineffective, or even counterproductive. This study aimed to experimentally evaluate the thermal performance of retro-reflective coatings (RRC) with unique reflective mechanism in enclosed buildings in both summer and winter, and took high-reflective coatings (HRC) as the comparison. The regional albedos, wall temperatures, and air temperatures were measured, which showed that both in summer and winter, RRC had higher regional albedos, lower wall temperatures and air temperatures than HRC, however, the differences were smaller in winter, which would lead to more thermal benefits in summer caused by RRC than thermal penalties in winter. For the regional albedos, the maximum differences in summer and winter were 4.99% and 2.46%, and the values were 2.74 °C–11.61 °C and 1.39 °C–5.61 °C for the wall temperatures, 2.10 °C and 0.39 °C for the air temperatures in the enclosed space.

The cycling activity features green, low-carbon, energy-saving, and zero-emission characteristics, it is becoming a popular leisure activity in people’s daily lives. Compared with the riding lanes within the city, coastal roads are more attractive to cyclists. However, there is less research regarding the coastal riding environment, and it was important to investigate the attitudes of cyclists toward the existing coastal lanes. This study evaluated and optimized coastal cycling environment in Qingdao city, China. Firstly, a questionnaire survey was carried out, 22 elements related to evaluation of cycling environment were selected, focusing on the basic information of cyclists, and their attitudes to the importance and satisfaction of evaluation elements. Secondly, we applied the Importance Performance Analysis (IPA) to obtain the coastal road environmental elements that needed to be prioritized for improving cycling conditions. Results found that there existed great differences in the lighting conditions of the road section, most of the factors that fell within the interval of high importance-low satisfaction, which was highly related to security, the importance of parking facilities was relatively high. To promote the popularity of bicycle ride along the coastal roads, it is essential to pay attention to improving cycling safety at the design stage.

In this study, we focused on electricity consumption and household characteristics with Dynamic Pricing(DP) experiment in the collective housing complex of a Japanese Smart Community, which had been divided into two groups, Treatment group (110 households) and Control group (65 households). We collected and analyzed a questionnaire survey on the family attributes of residents and all housing electrical appliances to understand the actual conditions of household characteristics. The annual energy consumption by smart meters had been aggregated and analyzed to understand the actual conditions of electricity end-use of the households. The results showed the main factors of household, which affect the electricity consumption, were floor area, family number, income of households, and the number of air conditioner. During dynamic pricing experiment in summer and winter, we found that the implementation of Dynamic Pricing would affect household users' habits of using electrical equipment and had a contribution to energy peak cut. Through the hierarchical cluster analysis, we divided both group into three types of household characteristics respectively. The result has revealed that the family structure would affect the electricity consumption during Dynamic Pricing implement.In this study, we focus on energy consumption and household characteristics with dynamic pricing experiment in the collective housing complex of a Japanese Smart Community. we collected and analyzed a questionnaire survey on the family attributes of residents and the lifestyle of all housing equipment to understand the actual conditions such as the attributes of the resident generation. The annual energy consumption by smart meters will be aggregated and analyzed to understand the actual conditions of electricity consumption of the resident generation. Based on the aggregated results, a database of lifestyle patterns and energy consumption is constructed, and the relationship between each factor and energy consumption, and each lifestyle pattern has been analyzed.

Energy sharing within building complexes depends greatly on the complex load adaptability and policy support. However, most building complex microgrid research concentrates on configuration and operation optimization, rather than energy contributions from different users. The objective of this study is to analyze energy sharing ability and load adaptability for a building complex comprising multiple building types, under transaction liberalization restrictions in different energy markets. The specific research for this study is a smart community in Kitakyushu, Japan, with 49 buildings of different types. The complex's microgrid system in this study comprises photovoltaic, battery, and vehicle to grid services. We obtained six representative typical clusters by reducing the 49 buildings’ dimensionality using principal components and clustering, then Monte Carlo simulation to predict permissible discharge capacities for vehicle to grid (V2G) services. We propose three demand-side liberalization scenarios: self-use, photovoltaic feed-in tariffs (FITs), and free trade; optimizing system configuration under different scenarios using a genetic algorithm. Results confirm that shopping malls achieve best economic benefits with self-use scenario; whereas office building adaptability is more significant for the other scenarios. These results will contribute to user selection and system design for building complex microgrid systems.

Nighttime light (NTL) data and points of interest (POI) data offer precise visual indications of the distributional characteristics of urban spatial structures. This study employed nighttime light data and point of interest data to investigate spatial distribution patterns in Changchun, a selected study area. The built-up area was extracted from the nighttime light data, while kernel density analysis was utilized to examine the distribution of point of interest data. The processing outcomes of both datasets were gridded with spatially resolved resolution. Afterward, the fishnet tool was employed to conduct two-factor integrated mapping and visual analysis, which helped identify shared or divergent spatial coupling relationships. The results indicated a high degree of consistency in the distribution of both NTL and POI across Changchun, with 84.58% of the coupling demonstrating a concordant pattern. The spatial analysis conducted in this study showed that the heterogeneities of the coupling relationship within each administrative borough expanded outward from the center of the borough. POI provided a more accurate depiction of the spatial distribution of urban built-up areas compared to NTL, leading to a more precise representation of spatial patterns of human activity intensity. Changchun has undergone zoning adjustments, resulting in the emergence of multiple urban centers in both the central city and the surrounding administrative districts. These urban centers are gradually merging into each other. The study found that the level of spatial coupling was much higher in the central area compared to the surrounding administrative districts. This has contributed to the formation of multiple urban centers and the gradual expansion of the urban built-up area beyond the main city, indicating a trend towards regional integration and development. This study provides a more detailed and accurate description of the current distribution of urbanization and spatial structural characteristics of Changchun by investigating the spatial coupling between POI and NTL. The findings contribute to a better understanding of the urban development patterns in the region and provide insights for future urban planning and management.

The textile industry is one of the most intensive industries in chemical products whose wastewater contains hazardous dyes, pigments, dissolved/suspended solids, and heavy metals. Hence, it is important to effectively treat the wastewater generated by this industry before releasing it into the environment. Although the field of textile wastewater treatment has made tremendous progress, the developed treatment methods should be improved further to make them economically viable and friendly. In this review, characteristics of textile wastewater are introduced. We have primarily focused on the environmental impact and toxicity of textile wastewater. Toxic and harmful contaminants must be removed from textile wastewater to reduce the extent of environmental pollution caused when textile wastewater is released into rivers or reused. Hence, various techniques that are used to treat textile wastewater are discussed. Finally, the challenges faced, and prospects of the methods have been discussed in detail. HIGHLIGHTS Characteristics of textile wastewater were introduced.; The release of textile wastewater is the main source of environmental contamination.; Diverse treatment methods to remove textile water pollution were summarized.; Achievable combinations of various treatments for textile wastewater were proposed.;

Hydrogen-fuelled buses play an important role in the construction of low-carbon cities as a means of green travel. Beijing, as a pilot city of hydrogen-fuelled buses in China, is very important in the promotion of hydrogen-fuelled buses in China. Unfortunately, the public acceptance of hydrogen-fuelledfuelled buses and their environmental positive externality value have not been studied. In this paper, we investigated the willingness of Beijing households to pay for the promotion of hydrogen-fuelled buses and its influencing factors by means of a web-based questionnaire. The spike model was also used to estimate the willingness to pay (WTP) for hydrogen buses. The results show that the WTP of Beijing households is CNY 3.19 per trip. The value of a positive environmental externality is approximately CNY 29.15 million per trip. Household income level, environmental knowledge, individual environmental ethics, and perceived behavioural control are the main influencing factors of WTP. Therefore, policymakers should strengthen publicity efforts to increase individuals’ environmental awareness and environmental ethics and optimize the layout of hydrogen-fuelled bus schedules and riding experiences to improve individuals’ perceptual and behaviour control. Finally, the positive environmental externality value of hydrogen buses should be valued, which will help increase investor interest.

Electrolytic hydrogen storage technology of renewable energy is considered as one of the important measures to realize the high proportion of renewable energy. However, developing this technology has been slow due to the cost. This study proposes highlighting the environmental advantages of renewable power combined with a carbon tax at the national level to expand the application space of hydrogen storage technology. First, the economy and technical characteristics of hydrogen storage technology and conventional energy-storage technology were compared. Then, Japan's top 10 power companies, which account for 90% of the power supply, were taken as the research objects. The proportion of change in renewable power after introducing a carbon tax was studied, taking the weighted minimum of the feed-in tariffs as the optimization objective. The contribution of hydrogen storage technology to the change in renewable power share was analyzed. Next, a sensitivity analysis was conducted on the fossil energy price and cost of renewable power equipment that affects the research. Finally, the contribution of a carbon tax and hydrogen storage technology to Japan's emission reduction target was calculated and analyzed. The results showed that the characteristics of long-distance transportation of hydrogen energy and making renewable energy have economic advantages under the current carbon tax (5–6 JPY/kg-CO 2 ). The cost of liquified natural gas (LNG) and the investment of photovoltaic (PV) have a significant impact on the weighted minimum feed-in price, which can make the development of hydrogen storage technology no longer rely on the setting of carbon tax. The results obtained in this study provide a theoretical reference for the application of hydrogen storage technology in the high proportion of renewable energy consumption. At the same time, it can also provide data reference for large-scale construction of renewable energy in regions where solar and wind energy resources are not excellent.

Distributed energy resources are important measures to increase energy self-sufficiency and overcome the global carbon reduction problem. However, individual planned renewable energy generation poses a significant threat to the power grid. Therefore, virtual power plant (VPP) is attracting considerable attention as a means of aggregating distributed energy in urban areas. This study proposed a VPP model consisting of updating high-efficiency appliances and photovoltaic and energy storage systems. A comprehensive analysis for assessing the technical, economic, and environmental benefits derived from the VPP was presented, indicating the feasibility of a smart community to achieve power self-sufficiency with the support of the VPP. A smart community in Japan was selected as the research object, with a peak power demand of 57,350 kW. The VPP’s load leveling performance, return on investment (ROI), and CO2 emission reduction were analyzed. In addition, external factors, such as electricity price changes and FiT policies, are considered to assess the impact on the economics of the VPP. The results show that the introduction of the VPP system in the community can effectively stabilize the grid load with a peak shaving rate reaching 42.55% and improve the energy self-sufficiency rate of the region reached 100%, besides providing superior economic and environmental benefits (16.26% CO2 emission reduction) on the demand side. Furthermore, the economic performance of VPP shows a good prospect with the fall in equipment prices and the future trend of carbon tax growth. This study provides important insights into the development of VPP in other countries, especially for low-energy self-sufficiency regions.

In recent years, virtual reality training technology (VRTT) has been considered by many scholars as a new training method instead of traditional training (TT) to reduce unsafe behaviors ascribed to construction workers (CWs) and corporate accident rates. However, in this process, a conflict of interest arises among the government, construction enterprises (CEs), and CWs. Therefore, this study introduces a quantitative research method, the three-party evolutionary game and creatively combining them with the product life cycle (PLC) to solve this problem by analyzing the equilibrium and evolutionarily stable strategies of the system. Finally, collaborative players’ decision-making behaviors and their sensitivity to critical factors are examined. This paper will illustrate these in each stage through numerical simulations. The results of the study indicate that the government plays a dominant role in the VRTT introduction stage. When the government gives CEs appropriate subsidies, CEs will eventually realize the importance of VRTT for CWs. Then the government will gradually reduce the amount of the subsidies in this process. In addition, we also find that the continually high cost will lead to negative policies by the government, which requires the active cooperation and attitude change from CEs and CWs. Ultimately, the government, CEs and CWs adopt the best strategy in the evolutionary process to facilitate the promotion, application and sustainability of VRTT in the construction industry.

This paper used a philosophy method called knowledge archaeology to identify the relations between the Venice Architecture Biennale and the Pritzker Architecture Prize. Two questions were posed: (1) “Who is speaking” in the biennale and prize, respectively? (2) Do the biennale and curating change architectural thinking? Through knowledge archaeology, the domain-specific knowledge graphs of the Venice Architecture Biennale participants and the Pritzker Architecture Prize laureates are compared from three aspects: “persons,” “words,” and “Asia.” Comparing the number, type, and nationality of exhibitors and laureates, the importance of the biennale and its influence on architectural development are confirmed. Results show that the thinking and curating of the Venice Architecture Biennale have influenced and changed who wins the Pritzker Architecture Prize. Moreover, Asian architects played increasingly important roles in the exhibition and prize process. Starting from the architectural exhibition, we can connect the entire architectural world. In other words, we may be able to “curate the whole world.”

With the goal of achieving “carbon peak in 2030 and carbon neutrality in 2060”, as clearly proposed by China, the transportation sector will face long–term pressure on carbon emissions, and the application of hydrogen fuel cell vehicles will usher in a rapid growth period. However, true “zero carbon” emissions cannot be separated from “green hydrogen”. Therefore, it is of practical significance to explore the feasibility of renewable energy hydrogen production in the context of hydrogen refueling stations, especially photovoltaic hydrogen production, which is applied to hydrogen refueling stations (hereinafter referred to “photovoltaic hydrogen refueling stations”). This paper takes a hydrogen refueling station in Shanghai with a supply capacity of 500 kg/day as the research object. Based on a characteristic analysis of the hydrogen demand of the hydrogen refueling station throughout the day, this paper studies and analyzes the system configuration, operation strategy, environmental effects, and economics of the photovoltaic hydrogen refueling station. It is estimated that when the hydrogen price is no less than 6.23 USD, the photovoltaic hydrogen refueling station has good economic benefits. Additionally, compared with the conventional hydrogen refueling station, it can reduce carbon emissions by approximately 1237.28 tons per year, with good environmental benefits.

At present, most developing countries need to improve the quality of the built environment by means of large-scale infrastructure construction, thereby promoting rapid urbanization. The quality of the built environment (QU) and its environmental pressure (PU) have become our primary focus to achieve a globally acknowledged vision of the Sustainable Development Goals (SDGs). In this study, we proposed an overall workflow by combining the proven urban sustainability (SU) assessment tool with the evaluation process and the analysis of the spatiotemporal dimension to investigate the urban characteristics of the 41 cities in the Yangtze River Delta. Our results showed an upward trend of urban sustainability from 2010 to 2018, but there are still 19 cities with unsustainable urbanization processes. The megalopolis is rapidly progressing toward an imbalanced state. Specifically, the urban sustainability of the southern region performs better than the northern region, coastal cities perform better than the inland cities, and the regional peripheral cities perform better than the inner cities. Across the 41 cities in the delta, five different relational trends between QU and PU have been found to predict their future development. The results of this research will help decision-makers to coordinate the future development of regional integration between cities and to target the alleviation of the adverse chain reaction brought about by the situation of imbalance or further improving urban sustainability.

This paper reports an experimental study on college students in a climate chamber, analyzed the scenes of measured skin temperature, EEG activity and task performance of subjects exposed to three temperatures. The concentration performances of the subject task were evaluated by measured neurophysiological parameters. Results shown that there were gender differences in local skin temperatures and EEG-based concentration index at the indoor temperatures to which they were exposed, females’ mean skin temperature is more sensitive to temperature steps. Accuracy divided by response time was proposed as a performance index, the influence of exposed temperature on two indicators depends on the type of cognitive task. The concentration index was more affected by cold in the male subjects. Experimental results strongly confirmed that EEG-based concentration index should be affected by indoor temperatures. Furthermore, a correlational analysis identified the close relationship between concentration index and performance index.

Energy use differences between day and night have been a key point in the efficient use of utilities. The battery energy storage system (BESS) is an attractive solution to level the grid load and has been introduced independently into many communities, although with high costs. Battery sharing presents the possibility of integrating independent energy storage systems to save money and improve energy use. Battery sharing highlights the interactions between a smart grid, smart buildings, and distributed energy storage to produce better energy management practices. In this work, we provide an analysis of battery sharing by establishing a coordinated control model for a distributed battery system. In our case study design, we selected 39 buildings with different capacities of energy storage systems as a battery-sharing community to optimize sharing schedules and the load-leveling performance. The results indicate that battery sharing could achieve a 13.2% reduction in building battery capacity compared with independent operation. We further investigate the impact of a building’s load profile patterns on the battery capacity in a battery-sharing community. It is economical to introduce a larger capacity battery system into buildings with no closing days and higher electricity consumption throughout the year. The optimal BESS capacity in commercial buildings depends on the lowest daytime power consumption. Commercial buildings with closing days have limitations regarding BESS deployment. On closing days, buildings can only use BESS by sharing. Buildings with two closing days a week lose 14.3% more energy than those with one closing day.

The development of distributed renewable energy resources and smart energy management are efficient approaches to decarbonizing building energy systems. Reinforcement learning (RL) is a data-driven control algorithm that trains a large amount of data to learn control policy. However, this learning process generally presents low learning efficiency using real-world stochastic data. To address this challenge, this study proposes a model-based RL approach to optimize the operation of existing zero-energy houses considering PV generation consumption and energy costs. The model-based approach takes advantage of the inner understanding of the system dynamics; this knowledge improves the learning efficiency. A reward function is designed considering the physical constraints of battery storage, photovoltaic (PV) production feed-in profit, and energy cost. Measured data of a zero-energy house are used to train and test the proposed RL agent control, including Q-learning, deep Q network (DQN), and deep deterministic policy gradient (DDPG) agents. The results show that the proposed RL agents can achieve fast convergence during the training process. In comparison with the rule-based strategy, test cases verify the cost-effectiveness performances of proposed RL approaches in scheduling operations of the hybrid energy system under different scenarios. The comparative analysis of test periods shows that the DQN agent presents better energy cost-saving performances than Q-learning while the Q-learning agent presents more flexible action control of the battery with the fluctuation of real-time electricity prices. The DDPG algorithm can achieve the highest PV self-consumption ratio, 49.4%, and the self-sufficiency ratio reaches 36.7%. The DDPG algorithm outperforms rule-based operation by 7.2% for energy cost during test periods.