An Empirical Analysis of Efficiency in Accommodation Industry in Australian Tourism Regions

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

The Association of Bedside Nurse Staffing on Patient Outcomes and Throughput in a Pediatric Cardiac Intensive Care Unit

Health care throughput is the progression of patients from admission to discharge, limited by bed occupancy and hospital capacity. This study examines heart center throughput, cascading effects of limited beds, transfer delays, and nursing staffing on outcomes utilizing elective surgery cancellation during the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic wave. This study was a retrospective single-center study of staffing, adverse events, and transfers. The study period was January 1, 2018 to December 31, 2020 with the SARS-CoV-2 period March to May 2020. There were 2,589 patients, median age 5 months (6 days-4 years), 1,543 (60%) surgical and 1,046 (40%) medical. Mortality was 3.9% ( n = 101), median stay 5 days (3-11 days), median 1:1 nurse staffing 40% (33-48%), median occupancy 54% (43-65%) for step-down unit, and 81% (74-85%) for cardiac intensive care unit. Every 10% increase in step-down unit occupancy had a 0.5-day increase in cardiac intensive care unit stay ( p = 0.044), 2.1% increase in 2-day readmission ( p = 0.023), and 2.6% mortality increase ( p < 0.001). Every 10% increase in cardiac intensive care unit occupancy had 3.4% increase in surgical delay ( p = 0.016), 6.5% increase in transfer delay ( p = 0.020), and a 15% increase in total reported adverse events ( p < 0.01). Elective surgery cancellation is associated with reduced high occupancy days (23-10%, p < 0.001), increased 1:1 nursing (34-55%, p < 0.001), decreased transfer delays (19-4%, p = 0.008), and decreased mortality (3.7-1.5%, p = 0.044). In conclusion, Elective surgery cancellation was associated with increased 1:1 nursing and decreased mortality. Increased cardiac step-down unit occupancy was associated with longer cardiac intensive care unit stay, increased transfer, and surgical delays.

Understanding occupancy pattern of university libraries in the post-pandemic era

Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Investigation of Employee Related Airport Ground Access Strategies from a Post-COVID Perspective

Until recently, addressing the environmental externalities associated with the use of the private car and single occupancy vehicles has been the focus of the airport ground access policies worldwide. However, with the emerging unprecedented challenges of the COVID-19 pandemic, which have already changed the way we live, work, and travel, encouraging a change in commuter behavior has become even more important. This has necessitated that existing strategies be reconsidered in favor of adapting to a highly uncertain ‘‘COVID-19 world.'' Historically, there has been a dearth of literature relating to airport employees' ground access even though as a group employees represent an important segment of airport users with complex access requirements. This paper therefore focuses on airport employee related airport ground access strategies considering an emerging understanding of the future impacts of COVID-19 on global air travel. Pre-COVID strategies are investigated by conducting a documentary analysis of the most recent ground access strategies of 27 UK airports. The findings reveal that airport ground access strategies were mainly focused on setting targets and producing policy measures in favor of reducing car use and increasing the use of more sustainable transport modes including public transport, car sharing, and active travel (walking, cycling). However, measures encouraging public transport and car sharing will be more difficult to implement because of social distancing and fear of proximity to others. Instead, initiatives encouraging remote working, active travel, and improved staff awareness will be at the forefront of the future ground access strategy development. © National Academy of Sciences: Transportation Research Board 2021.

Digital twin for healthy indoor environment: A vision for the post-pandemic era

Indoor environment has significant impacts on human health as people spend 90% of their time indoors. The COVID-19 pandemic and the increased public health awareness have further elevated the urgency for cultivating and maintaining a healthy indoor environment. The advancement in emerging digital twin technologies including building information modeling (BIM), Internet of Things (IoT), data analytics, and smart control have led to new opportunities for building design and operation. Despite the numerous studies on developing methods for creating digital twins and enabling new functionalities and services in smart building management, very few have focused on the health of indoor environment. There is a critical need for understanding and envisaging how digital twin paradigms can be geared towards healthy indoor environment. Therefore, this study reviews the techniques for developing digital twins and discusses how the techniques can be customized to contribute to public health. Specifically, the current applications of BIM, IoT sensing, data analytics, and smart building control technologies for building digital twins are reviewed, and the knowledge gaps and limitations are discussed to guide future research for improving environmental and occupant health. Moreover, this paper elaborates a vision for future research on integrated digital twins for a healthy indoor environment with special considerations of the above four emerging techniques and issues. This review contributes to the body of knowledge by advocating for the consideration of health in digital twin modeling and smart building services and presenting the research roadmap for digital twin-enabled healthy indoor environment.

Hospital bed capacity across in Tunisia hospital during the first four waves of the COVID-19 pandemic: A descriptive analysis

Background Background: In March 2020, the WHO declared COVID-19 as a pandemic, and Tunisia implemented a containment and targeted screening strategy. The country's public health policy has since focused on managing hospital beds. Methods The study analyzed the bed occupancy rates in public hospitals in Tunisia during the pandemic. The evolution of daily cases and NPI actions undertaken by the Tunisian Government were also analyzed. The study used three indices to assess bed flexibility: Ramp Duration Until the Peak (RDUP), Ramp Growth Until the Peak (RGUP), and Ramp Rate Until the Peak (RRUP). The study also calculated the time shift at the start and peak of each wave to evaluate the government's response efficacy. Results The study found that the evolution of the epidemic in Tunisia had two phases. The first phase saw the pandemic being controlled due to strong NPI actions, while the second phase saw a relaxation of measures and an increase in wave intensity. ICU bed availability followed the demand for beds, but ICU bed occupancy remained high, with a maximum of 97%. The government's response in terms of bed distribution and reallocation was slow. The study found that the most deadly wave by ICU occupied bed was the third wave due to a historical variant, while the fifth wave due to the delta variant was the most deadly in terms of cumulative death. Conclusions The study concluded that decision-makers could use its findings to assess their response capabilities in the current pandemic and future ones. The study highlighted the importance of flexible and responsive healthcare systems in managing pandemics.

Graphical-mathematical model to estimate CO2 concentration level as an indicator of SARS-CoV-2 transmission

The first documented cases of Coronavirus disease of 2019 (COVID-19) in Wuhan city (Hubei province, China) were caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), studies mention viral transmission is more common in indoor and poorly ventilated environments compared to outdoor environments or environments with abundant airflow. In this context, this research aimed to estimate, through a mathematical model by a Box-Behnken design, the time and occupancy required in a physical space to reach CO2 levels that exceed the risk level established as a safer condition of 700 ppm for SARS-CoV-2 transmission. According to the proposed mathematical model, it is possible to predict safe conditions. With this, it was found that natural ventilation is the best option to reduce CO2 concentration, considering the occupancy/m3 and time, allowing a constant airflow;the use of air conditioners to control the temperature in rooms without natural ventilation is suggested;notwithstanding, these types of equipment are not designed to reduce CO2 concentration. Thus, their use in rooms with open windows and doors leads to a shorter equipment lifetime, for this reason, its operation in special conditions, such as in rooms without natural ventilation should be considered

The Need for a Paradigm Shift toward an Occupant-Centered Environmental Control Model

Office-based environmental control systems are centralized and designed to control entire spaces, ignoring use dynamics and requirements, and despite being regulated by standardized comfort models, they fail to satisfy real occupants, mainly due to their varied individual characteristics. This research is field-based with a quantitative approach and correlational design. Its objective is to empirically demonstrate that open-plan design, where different users share the same space and generalized environmental conditions, lacks a holistic view of IEQ criteria and the integration of other factors that affect health and well-being. Four buildings are chosen in different Chilean cities, measuring temperatures and CO2 levels at different desks, and applying a survey, which was designed as part of the research to analyze the estimation of relationships between variables and to reveal the factors that cause differences among occupants. The results show that people's satisfaction is multivariable and depends on other factors that positively or negatively stimulate their sensations and perceptions, such as, for example, the option to personally control their environmental conditions. Likewise, it is evident that to achieve comfort, health is being affected while in the building.

Accelerometer-Based Bed Occupancy Detection for Automatic, Non-Invasive Long-Term Cough Monitoring

We present a new machine learning based bed occupancy detection system that uses only the accelerometer signal captured by a bed-attached consumer smartphone. Automatic bed occupancy detection is necessary for automatic long-term cough monitoring since the time that the monitored patient occupies the bed is required to accurately calculate a cough rate. Accelerometer measurements are more cost-effective and less intrusive than alternatives such as video monitoring or pressure sensors. A 249-hour dataset of manually-labelled acceleration signals gathered from seven patients undergoing treatment for tuberculosis (TB) was compiled for experimentation. These signals are characterised by brief activity bursts interspersed with long periods of little or no activity, even when the bed is occupied. To process them effectively, we propose an architecture consisting of three interconnected components. An occupancy-change detector locates instances at which bed occupancy is likely to have changed, an occupancy-interval detector classifies periods between detected occupancy changes and an occupancy-state detector corrects falsely-identified occupancy changes. Using long short-term memory (LSTM) networks, this architecture achieved an AUC of 0.94. To demonstrate the application of this bed occupancy detection system to a complete cough monitoring system, the daily cough rates along with the corresponding laboratory indicators of a patient undergoing TB treatment were estimated over a period of 14 days. This provides a preliminary indication that automatic cough monitoring based on bed-mounted accelerometer measurements may present a non-invasive, non-intrusive and cost-effective means of monitoring the long-term recovery of patients suffering from respiratory diseases such as TB and COVID-19. © 2013 IEEE.

Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization

Before 2020, the way occupants utilized the built environment had been changing slowly towards scenarios in which occupants have more choice and flexibility in where and how they work. The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and hybrid work arrangements. Many occupants and employers are considering keeping some of these flexibility-based strategies due to their benefits and cost impacts. This paper explores how demand-driven control strategies in the built environment might support the transition to increased workplace flexibility by simulating various scenarios related to the operational technologies and policies of a real-world campus using a district-scale City Energy Analyst (CEA) model that is calibrated with measured energy demand data and occupancy profiles extracted from WiFi data. These scenarios demonstrate the energy impact of ramping building operations up and down more rapidly and effectively to the flex-based work strategies that may solidify. The scenarios show a 5–15% decrease in space cooling demand due to occupant absenteeism of 25–75% if centralized building system operation is in place, but as high as 17–63% if occupancy-driven building controls are implemented. The paper discusses technologies and strategies that are important in this paradigm shift of operations. © 2023 The Author(s)

People Counting in the Times of Covid-19

Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Covid-19 and the Performance of Hotels and Lodges in Windhoek, Namibia

This study assessed the effect of the COVID-19 pandemic on the performance of hotels and lodges in Windhoek, Namibia. The study was motivated by the poor and uninspiring performance of hotels and lodges following the pronouncement of successive COVID-19 induced lockdowns. The study adopted the positivism philosophy and quantitative approach to enable the researcher to make use of scientific evidence, including statistics that show how COVID-19 affected the performance of hotels and lodges. The results revealed that rising new COVID-19 cases and deaths as well as restrictions on domestic, regional and international travel led to cancellation of bookings and low occupancy rates. COVID-19 negatively impacted on average daily rate, revenue per available room and profitability of hotels and lodges. The study recommended that hotels and lodges should institute new business models, advocate for a rescue package and target domestic tourism with limited travel restrictions to improve their performance. © 2023 AJHTL /Author(s)

Dynamic ventilation certificate for smart universities using artificial intelligence techniques.

The issue of room ventilation has recently gained momentum due to the COVID-19 pandemic. Ventilation is in fact of particular relevance in educational environments. Smart University platforms, today widespread, are a good starting point to offer control services of different relevant indicators in universities. This study advances a Ventilation Quality Certificate (VQC) for Smart Universities. The certificate informs the university community of the ventilation status of its buildings and premises. It also supports senior management's decision-making, because it allows assessing preventive measures and actions taken. The VQC algorithm models the adequacy of classroom ventilation according to the number of persons present. The input used is the organisation's existing data relating to CO2 concentration and number of room occupants. AI techniques, specifically Artificial Neural Networks (ANN), were employed to determine the relationship between the different data sources included. A prototype of value-added services was developed for the Smart University platform of the University of Alicante, which allowed to implement the resulting models, together with the VQC. The prototype is currently being replicated in other universities. The case study allowed us to validate the VQC, demonstrating both its usefulness and the advantage of using pre-existing university services and resources.

Occupancy-aided ventilation for airborne infection risk control: Continuously or intermittently reduced occupancies?

Ventilation is an important engineering measure to control the airborne infection risk of acute respiratory diseases, e.g., Corona Virus Disease 2019 (COVID-19). Occupancy-aided ventilation methods can effectively improve the airborne infection risk control performance with a sacrifice of decreasing working productivity because of the reduced occupancy. This study evaluates the effectiveness of two occupancy-aided ventilation methods, i.e., the continuously reduced occupancy method and the intermittently reduced occupancy method. The continuously reduced occupancy method is determined by the steady equation of the mass conservation law of the indoor contaminant, and the intermittently reduced occupancy method is determined by a genetic algorithm-based optimization. A two-scenarios-based evaluation framework is developed, i.e., one with targeted airborne infection risk control performance (indicated by the mean rebreathed fraction) and the other with targeted working productivity (indicated by the accumulated occupancy). The results show that the improvement in the airborne infection risk control performance linearly and quadratically increases with the reduction in the working productivity for the continuously reduced occupancy method and the intermittently reduced occupancy method respectively. At a given targeted airborne infection risk control performance, the intermittently reduced occupancy method outperforms the continuously reduced occupancy method by improving the working productivity by up to 92%. At a given targeted working productivity, the intermittently reduced occupancy method outperforms the continuously reduced occupancy method by improving the airborne infection risk control performance by up to 38%.

Hotels' occupancy rates and convergence: Empirical evidence from the first pandemic wave

The scope of this study is to unravel possible convergence clubs regarding the occupancy rates of the hotel industry in the US states in the aftermath of the first COVID-19 pandemic wave. For this reason, the underlying paper resorts to the application of the generic convergence algorithm developed in Phillips and Sul (2007) for a sample of 50 US states over the daily period ranging from 01.12.2019 to 26.07.2020. The empirical analysis supports the identification of two primary convergence clubs consisting of an equally distributed number of regions (states). However, the two clubs can be merged into one after the implementation of the Phillips and Sul (2009) methodology, revealing that the first pandemic wave has eliminated any distinct (economic) characteristic between the different US states. JEL codes: L10, L80, R10 .

Smart Health Care Monitoring and Indoor Occupancy Control System using Arduino

The recent COVID-19 pandemic has necessitated the need to develop effective COVID-19 pandemic control strategies. One of the crucial steps for individual protection is to stop the virus spread by the wearing face masks. The proposed method is developed to monitor the infected people in the crowded public areas like shopping centers, wedding hall, workplace, school or college. The abnormal temperature is detected by using sensor and the obtained signal will then be sent to the Arduino device connected to the controller. In order to stop the spread of COVID 19 viruses, this study intends to design and develop a novel system to automatically limit the room capacity based on temperature. The proposed Atmega328 microcontroller-based body temperature detection and a room capacity measuring device is connected with the android smart phone of the user. © 2022 IEEE.

Measurement and rapid assessment of indoor air quality at mass gathering events to assess ventilation performance and reduce aerosol transmission of SARS-CoV-2

To assess risk factors for COVID-19 transmission and address the closure of mass gathering events since March 2020, the UK Government ran the Events Research Programme (ERP), following which it reopened live events in sports, music, and culture in July 2021. We report the rapid post-occupancy evaluation of Indoor Air Quality (IAQ) and associated long-range airborne transmission risk conducted in the Environmental Study of the ERP. Ten large venues around the UK were monitored with CO2 sensors at a high spatial and temporal resolution during 90 events. An IAQ Index based on CO2 concentration was developed, and all monitored spaces were classified in bands from A to G based on their average and maximum CO2 concentrations from all events. High resolution monitoring and the IAQ Index depicted the overall state of ventilation at live events, and allowed identification of issues with ventilation effectiveness and distribution, and of spaces with poor ventilation and the settings in which long-range airborne transmission risk may be increased. In numerous settings, CO2 concentrations were found to follow patterns relating to event management and specific occupancy of spaces around the venues. Good ventilation was observed in 90% of spaces monitored for given occupancies. Practical applications: High-resolution monitoring of indoor CO2 concentrations is necessary to detect the spatial variation of indoor air quality (IAQ) in large mass gathering event venues. The paper summarises COVID-19 ventilation guidance for buildings and defines a methodology for measurement and rapid assessment of IAQ during occupancy at live events that can be implemented by venue managers. Comparisons of the CO2 concentrations measured during the events identified the spaces at high risk of long-range transmission of airborne pathogens. Building operators should be mindful of the ventilation strategies used relative to the total occupancy in different spaces and the occupant's activities.

People Counting in the Times of Covid-19

Estimating the number of people within a public building with multiple entrances is an interesting problem, especially when limitations on building occupancy hold as during the Covid-19 pandemic. In this article, we illustrate the design, prototyping and assessment of an open-source distributed Cloud-IoT service that performs such a task and detects crowd formation via EdgeAI, also accounting for privacy and security concerns. The service is deployed and thoroughly assessed over a low-cost Fog infrastructure, showing an average accuracy of 94%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Congruence Mapping of the Activity Flows Allocated in Built Environments: A Pilot Application of Under-Development Software in an Emergency-Care Service

Featured ApplicationThis paper presents the application of a software program that is currently under development that provides feedback for the mapping activities that are carried out in built environments and analyses the congruence in the relationship between the flow of activities and their environments. Exemplary results were obtained in the case study of a healthcare emergency facility, although it is possible to apply this software in other types of complex environments. The obtained data allow facility managers to prioritize and reallocate activities when a change is required. It also shows unmapped relationships. It is important to investigate these data because they can indicate failures in the mapping process and can provide an opportunity to obtain a more complete understanding of the allocation and flow of activities. These data can also help us to identify points of conflict or opportunities for adjustment in the allocation of activities in order to improve the flow of activities.Due to the large number of activities that must be carried out by emergency-care services (ESs), the tasks of facility managers and architects are challenging and complex. Several strategies, guides, and diagnoses have already been developed in order to improve ESs. Part of the solution to this problem depends on obtaining a normative and universal understanding of the problem, and another part depends on conducting a specific and relational analysis between the environment and the flow of activities that are allocated within it. This paper presents the results of a study that was conducted using a software program that is currently under development for mapping the congruence relationship between activities and environments. Here, we present a discussion of the first results that were obtained with the instrument, which was applied to a single case. For this purpose, the fundamentals of the instrument, as well as the environment and the flows of an ES at a university hospital, are described. The forms of analysis, benefits, and limitations of the instrument were investigated, with a view towards its use in supporting the management and the design of large and complex environments, such as emergency departments. In this program, the relationships that are hidden from the managers, the designers, and the researchers due to the aforementioned complexity are revealed through the use of matrices. This mapping can supplement the decision making of the managers and the designers. The application showed advantages in modeling with fewer inputs, mainly in pre-design evaluations.