One-dimensional lazy quantum walks and occupancy rate

In this paper, we discuss the properties of lazy quantum walks. Our analysis shows that the lazy quantum walks have O（tn） order of the n-th moment of the corresponding probability distribution, which is the same as that for normal quantum walks. The lazy quantum walk with a discrete Fourier transform （DFT） coin operator has a similar probability distribution concentrated interval to that of the normal Hadamard quantum walk. Most importantly, we introduce the concepts of occupancy number and occupancy rate to measure the extent to which the walk has a （relatively） high probability at every position in its range. We conclude that the lazy quantum walks have a higher occupancy rate than other walks such as normal quantum walks, classical walks, and lazy classical walks.

Prediction of office building electricity demand using artificial neural network by splitting the time horizon for different occupancy rates

Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.

Impact of Operating Conditions on the Productivity of Star-Rated Hotels in China's Mainland: Verification from Spatial Metrology

Room occupancy rate is a key indicator of star-rated hotel management quality. This paper takes Cobb-Douglas production function as the theoretical framework. Spatial Moran index of autocorrelation, Spatial Lag Model(SLM) and Spatial Error Model(SEM) are used to analyze the star-rated hotels labor productivity of 31 provincial regions in China's Mainland based on the star-rated hotels statistical data of year 2016. The spatial correlation and spatial difference of the star-rated hotels labor productivity is discussed. This paper studies the impact of three factors on spatial characteristics of star-rated hotels labor productivity in China's Mainland. The econometric estimation results show that:(1) Star-rated hotels labor productivity present significant spatial dependence and spatial difference in China's Mainland.(2) The estimation results of Ordinary least Squares(OLS) are reliable.(3) The reliability of the results obtained by the Spatial Error Model(SEM) analysis is the highest, and has a stronger explanatory power to the spatial relationship of star-rated hotels labor productivity in China's Mainland. The average room occupancy rate has more influence on the labor productivity of the provincial star-rated hotels than the impact of capital and labor.

An automatic identification algorithm for freeway bottleneck based on loop detector data

A bottleneck automatic identification algorithm based on loop detector data is proposed. The proposed algorithm selects the critical flow rate as the trigger variable of the algorithm which is calculated by the road conditions the level of service and the proportion of trucks.The process of identification includes two parts. One is to identify the upstream of the bottleneck by comparing the distance between the current occupancy rate and the mean value of the occupancy rate and the variance of the occupancy rate.The other process is to identify the downstream of the bottleneck by calculating the difference of the upstream occupancy rate with that of the downstream.In addition the algorithm evaluation standards which are based on the time interval of the data the detection rate and the false alarm rate are discussed.The proposed algorithm is applied to detect the bottleneck locations in the Shanghai Inner Ring Viaduct Dabaishu-Guangzhong road section.The proposed method has a good performance in improving the accuracy and efficiency of bottleneck identification.

Matching of Residential and Commercial Space in Shrinking Cities from the Perspective of Supply and Demand:A Case Study of Yichun City,China

Urban shrinkage is becoming an increasingly common phenomenon in China.The research focus has been the identification,origin,and pattern of shrinking cities.Nevertheless,attention has also been paid to the problems associated with urban shrinkage.The present study examines one typical shrinking city in China,specifically the Yichun District in Yichun City.To explore the matching relationship between residential and commercial spaces,this study analyzes supply and demand data,electricity consumption data and multi-source points of interest(POI)of residents.The results showed that the occupancy rate is not reduced in the context of urban shrinkage,and that the supply level of various commercial facilities is not in decline.Apart from leisure and entertainment facilities,the supply levels of catering,shopping and supporting facilities for life were noted to have improved.In reference to urban shrinkage,the matching relationship between residential and commercial space in the 5-min,10-min,and 15-min living circles mainly shifted to a highlevel equilibrium.The matching relationships between residential space and different types of commercial spaces change in both direction and magnitude.From the perspective of supply and demand,the spatial and temporal changes in the relationship relate to multiple factors,such as the level of economic development,the buiding age pattern,public transportation accessibility,aging,and residents’willingness to move.This study provides relevant data for managing urban shrinkage.It also helps improve the relationships between residential and commercial spaces and works to optimize the layout and structure of functional urban spaces.

Aspects of Railway Capacity and Occupation Time Estimation

The priority of the EU transport policy in railway sector is to open up the railway market. The objective is to provide railway undertakings with access to the railway network on equal terms. The main problem is determining the infrastructure capacity. A variety of methodologies are used across Europe for the capacity estimation of railway infrastructure. This diversity has forced railway infrastructure managers to seek a new, common methodology. The UIC methodology is an easy way to calculate the capacity consumption. However, there the possibility to expound this methodology in different ways, which can result in different capacity consumptions. There isan advantage to improve this methodology and to set a clear and unified method of occupation time estimation. The fundamental improvement to UIC methodology is the definition of the occupation time by the trains. This paper gives a description of Slovak and UIC methodologies as a basis for a newly developed approach. The new way of estimation of the capacity consumption （occupation time） is based on a graphic approach. The new methodology concerns the estimation of the infrastructure occupation time and is a conceptual framework developed by the authors for an easier evaluation of occupation time in train traffic diagrams. The new methodology makes the UIC methodology more usable and enables more exact results to be obtained from infrastructure capacity examination.

Comparative Analysis between Community and Occupants’ Rating Systems for Sustainable Urban Communities (SUC)

Overpopulation globally is an addressed issue impacting human lives, marine lives, and the surrounding ecosystem;it is adding pressure on the available resources that should be optimized to suit the needs. Yet with improper management of resources and monitoring of daily activities, the environment will be further negatively impacted. With overpopulation higher urbanization rates are noticed with the demand of seeking better health facilities, better education, better jobs and better well-being;this progression is driving more demand into the infrastructure sector to be able to accommodate the growth rates. Hence, the need to having sustainable communities aiming at optimizing the resources used, working towards more feasible, environmentally friendly and cost-effective communities with a better occupant’s experience is in action. Sustainable development goals (SDG) are vital goals developed by the United Nations Development Program (UNDP) in 2015 to address and guide through 17 interconnected global goals serving the previously mentioned trend. Out of the 17 goals, Sustainable Cities and Communities (goal #11) and Good Health and Well-Being (goal #3) are the focus of this paper directed towards holding a comparative analysis between the community scale commonly known and mostly used rating system Leadership of Energy and Environmental Design (LEED-Cities and Communities) (USA) versus similar rating systems like Tarsheed-Communities (Egypt) and Estidama-Pearl (UAE) rating systems meeting sustainable development goal #11. Conjointly, another complimenting comparative review of the occupant’s health and wellbeing rating systems, such as Fitwel (USA) and Well (USA) are studied under sustainable development goal #3;however, they are focused on a building scale assessment. Living Community Challenge (LCC, USA) rating system linking community rating system with health & wellbeing credits was first issued in 2006, yet is it not cost effective neither easy to apply acting as a primary step while being affordable, accessible, and easy to implement. The objective of this paper is to highlight the pros and gaps under both categories of studies of community rating system and occupants’ health & wellbeing rating systems based on scientific content and commercial acceptance and do-ability. This comparison is done via comparing credits and sections within each rating system type;this will support in addressing the focal points needed for an integrated rating system between both categories that will serve in meeting SDG Sustainable Cities and Communities (goal #11) and Good Health and Well-Being (goal #3).

Review on occupancy detection and prediction in building simulation

Energy simulation results for buildings have significantly deviated from actual consumption because of the uncertainty and randomness of occupant behavior.Such differences are mainly caused by the inaccurate estimation of occupancy in buildings.Therefore,the error between reality and prediction could be largely reduced by improving the accuracy level of occupancy prediction.Although various studies on occupancy have been conducted,there are still many differences in the approaches to detection,prediction,and validation.Reports published within this domain are reviewed in this article to discover the advantages and limitations of previous studies,and gaps in the research are identified for future investigation.Six methods of monitoring and their combinations are analyzed to provide effective guidance in choosing and applying a method.The advantages of deterministic schedules,stochastic schedules,and machine-learning methods for occupancy prediction are summarized and discussed to improve prediction accuracy in future work.Moreover,three applications of occupancy models—improving building simulation software,facilitating building operation control,and managing building energy use—are examined.This review provides theoretical guidance for building design and makes contributions to building energy conservation and thermal comfort through the implementation of intelligent control strategies based on occupancy monitoring and prediction.

Building energy efficiency and COVID-19 infection risk:Lessons from office room management

To prevent COVID-19 outbreaks,many indoor environments are increasing the volume of fresh air and running air conditioning systems at maximum power.However,it is essential to consider the comfort of indoor occupants and energy consumption simultaneously when controlling the spread of infection.In this study,we simulated the energy consumption of a three-storey office building for postgraduate students and teachers at a university in Beijing.Based on an improved Wells-Riley model,we established an infection risk-energy consumption model considering non-pharmaceutical interventions and human comfort.The infection risk and building energy efficiency under different room occupancy rates on weekdays and at weekends,during different seasons were then evaluated.Energy consumption,based on the real hourly room occupancy rate during weekdays was 43%–55%lower than energy consumption when dynamic room occupancy rate was not considered.If all people wear masks indoors,the total energy consumption could be reduced by 32%–45%and the proportion of energy used for ventilation for epidemic prevention and control could be reduced by 22%–36%during all seasons.When only graduate students wear masks in rooms with a high occupancy,total energy consumption can be reduced by 15%–25%.After optimization,compared with the strict epidemic prevention and control strategy(the effective reproductive number Rt=1 in all rooms),energy consumption during weekdays(weekends)in winter,summer and transition seasons,can be reduced by 45%(74%),43%(69%),and 55%(78%),respectively.The results of this study provide a scientific basis for policies on epidemic prevention and control,carbon emission peak and neutrality,and Healthy China 2030.

Study of local environment of Cu atoms in Fe_(73.5)Cu_1Nb_3Si_(13.5)B_9 alloy with different annealing temperature

The local environment of Cu atoms in Fe73.5Cu1Nb3Si13.5B9 alloy was investigated by extended X-ray absorption fine structure(EXAFS).Cu clusters began to order when the annealing temperature was around 733 K from the results of the Fourier transform curves.The fitting results showed that the first shell of the near fcc(face-centered cubic)Cu clusters only contained Cu atoms.The coordination number increased with the annealing temperature.Subsequently,the occupancy rate increased from 33.3%(annealed at 733 K)to 100% (annealed at 853 K).This local structural change of Cu atoms could probably affect the distribution of the bcc(body-centered cubic)α-Fe in Fe73.5Cu1Nb3Si13.5B9 alloy.