Room Size Research Articles

Assessing building thermal resilience in response to heatwaves through integrating a social vulnerability lens

Major cities worldwide are increasingly experiencing intense heatwaves, which disproportionately affect vulnerable populations. This suggests the urgent need for focused attention and mitigation strategies to protect those most at risk from the impacts of heatwaves. The presence of vulnerable groups in buildings with varying thermal conditions can result in different outcomes during extreme heat events, making it crucial to evaluate the thermal resilience of buildings in conjunction with social vulnerability. This research advances the understanding of thermal vulnerability by integrating building thermal characteristics with social vulnerability. While previous studies have predominantly focused on socio-ethnic and economic dimensions of vulnerability, this study specifically investigates the relationship between social vulnerability and thermal resilience, with a focus on building thermal parameters. Datasets from Zillow and ResStock are used to capture the thermal properties of buildings, while social vulnerability data is obtained from the Center for Disease Control and Prevention. A Gaussian Naïve Bayes (GNB) model is applied to enhance building envelope attributes from Zillow, leveraging detailed information from ResStock data. Additionally, a Kolmogorov-Smirnov (KS) test is conducted to analyze diverse thermal attributes, such as age of buildings, room size, number of stories, building area, envelope characteristics, systems, infiltration, across differing levels of social vulnerability (high and low). Specifically, Philadelphia is selected as the analysis focus. The analysis reveals significant associations between thermal resilience and social vulnerability. Groups with higher social vulnerability are more likely to be exposed to elevated levels of thermal vulnerability compared to less vulnerable groups. This highlights the necessity for tailored interventions aimed at promoting the development of more resilient and equitable buildings, capable of reducing the impacts of heatwaves on vulnerable communities.

Performance Comparison of Random Forest (RF) and Classification and Regression Trees (CART) for Hotel Star Rating Prediction

Purpose: This study proposes to evaluate the effectiveness of Random Forest (RF) compared to Classification and Regression Trees (CART) in prediction of hotel star ratings. The objective is to identify the algorithm that provides the most reliable and accurate classification outcomes based on diverse hotel attributes in accordance with the standard categorization of star hotel categories. This is necessary due to the important role of accurate star ratings in guiding consumer choices and enhancing competitive positioning in the hospitality industry. Method: This study conducted a comprehensive dataset about Hotel in Banyumas Regency, including location, facilities, the size of rooms, type of rooms, price of rooms, and customer reviews, subjected to training through both RF and CART algorithms. Both algorithms are evaluated using accuracy, precision, recall, and F1 score. Additionally, both algorithms due to in the same preprocessing while performing hyperparameter tuning improve the efficacy of each model. Result: The results showed that RF achieved the best overall accuracy and robustness than CART across all tests conducted. Furthermore, RF also outperformed CART in classification effectiveness among classes, including enhanced precision and recall scores across multiple stars rating categories, signifying increased generalization and consistency in classification tasks. RF classifier consistently surpassed the CART classifier in terms of both accuracy and F1-score throughout all random states and test sizes, with a highest score of 0.9932 at a random state of 100 and a test size of 0.4. The most reliable results were obtained using RF with 42 random states and a test size of 0.2, resulting in an accuracy of 0.9909, precision of 1.0, recall of 1.0, and F1 score of 1.0. Simultaneously, CART shows values of 0.9818, 1.0, 1.0, and 1.0, respectively, while maintaining the same variation. This consistent performance, regardless of fluctuations, illustrates the robustness and suitability of RF for classification tasks compared to CART. Novelty: This study offered new insights about the implementation of machine learning about hotel star rating predictions using RF and CART algorithms. Also, the novelty of the collected hotel dataset used in this study. A detailed comparative analysis was also provided, contributing to the existing literature by showing the effectiveness of RF over CART for this specific application. Future studies could explore the integration of additional machine learning methods to further enhance prediction accuracy and operational efficiency in the hospitality industry.

Popularity on the roommate diversity problem

A recently introduced restricted variant of the multidimensional stable roommates problem is the roommate diversity problem: each agent belongs to one of two types (e.g., red and blue), and the agents' preferences over the rooms solely depend on the fraction of agents of their own type among their roommates. We study this variant with the notion of popularity.We show that in the roommate diversity problem with the room size fixed to 2, the problem becomes tractable. Particularly, a popular partitioning of agents is guaranteed to exist and can be computed in polynomial time. Additionally, a mixed popular partitioning of agents is always guaranteed to exist in any roommate diversity game. By contrast, when there are no restrictions on the room size of a roommate diversity game, a popular partitioning may fail to exist and the problem becomes intractable.

Hand Surgical Operating Room Size Allocation: A Comparative Space Utilisation Study.

Background: This study evaluated operating room (OR) space required for various hand surgical procedures. We analysed the size requirements for hand surgical cases divided into four settings: (1) large OR setting requiring fluoroscopy and microsurgical equipment, (2) medium-sized OR setting for cases requiring fluoroscopy, (3) smaller OR setting and (4) minor procedural room without anaesthesia with the aim to describe room size requirements for hand surgery practices. Methods: A variety of hand surgical cases were selected: large cases (microvascular digit replantation), medium-sized cases (closed reduction percutaneous pinning [CRPP] of phalangeal fractures) and smaller cases (carpal tunnel release [CTR]) with and without anaesthesia. Space requirements were compared to general surgery cases (laparoscopic appendectomy) and general orthopaedic surgery cases (cephalomedullary nail [CMN]). Necessary operative equipment was measured (ft2) to calculate requirements for each procedure. Results: Large hand cases such as digit replantation necessitated the most OR space (125 ft2), followed by general orthopaedic cases (CMN; 118 ft2), medium-sized hand cases (CRPP phalanx; 107 ft2), general surgery laparoscopic appendectomy (68 ft2), small hand cases (CTR; 85 ft2) and minor procedures (49 ft2). Conclusions: Hand procedures can be divided into major procedures requiring significant OR space (125 ft2), medium procedures in standard OR suites (107 ft2), procedures in small ORs with anaesthesia (81 ft2) or office-based setting without anaesthesia (49 ft2). These findings help define space utilisation for hand procedures and may have practical implications related to efficiency, cost and patient safety in the hospital and outpatient setting. Level of Evidence: Level IV (Economic and Decision Analyses).

Do all complain the same? Examining the role of luxury hotels reviewer attributes using text mining

Purpose Although extant studies found the determinants of customer complaints in luxury hotels, comparison of complaint severity using reviewer characteristics (nationality, reviewer type and expertise) is limited. This study aims to identify the variation in customers’ complaint attributes among different customer categories. Design/methodology/approach This study tried to address this gap by using structural topic modelling and text mining techniques to find the sentiment score, word count, complaints attributes and severity score. The research sample covers luxury hotels in India, and online review text and their associated data has been collected and analysed. Findings The authors found that leisure customers wrote lengthier reviews and complained about the facility-related aspects (valet parking, entertainment and amenities) and food-related aspects (food variety, service and menu) while the business customers’ complaint severity score is more on salient aspects (room size, bathroom and staff related services). The international customers wrote lengthier reviews and complained more about the facility-related aspects (amenities, pick-up and entertainment) and core service offerings (stay experience, bathroom, dirtiness and complaint resolution). Whereas domestic customers complained more about stay-related aspects (food variety, quality, services, staff behaviour and stay experience). Also, this study used correlation analysis to find the dependencies between topics and uncovered underlying patterns, trends and associations, aiding in a deeper understanding and interpretation of the complaints. This study can help service providers tailor their services based on customer attributes to ensure maximum customer satisfaction. Originality/value The findings of this study can enable hotel service providers to customise their services based on customer attributes to reduce dissatisfaction among customers and promote customer loyalty. The findings of this study can also be used to devise differential promotional strategies for different types of customers.

Evaluation of Patient Safety Measures in the ICCU at Pune: A Comparative Study Against ISCCM Guidelines

Introduction: This cross-sectional descriptive study evaluated patient safety measures in the Intensive Coronary Care Unit (ICCU) of a tertiary care hospital in Pune, India, over 90 days. Methodology: A validated checklist from the Indian Society of Critical Care Medicine (ISCCM) was used to assess compliance with established guidelines. Result: The ICCU demonstrated high adherence to infrastructure standards and environmental controls. However, gaps were identified, including the absence of a portable CT scanner and inadequate counselling room size, emphasizing the need for further improvements to align with global best practices. Recommendations include investing in a portable CT scanner and expanding the counselling room. This study underscores the importance of adhering to standardized safety protocols in critical care settings to enhance patient outcomes and reduce adverse events.

Functions for noise reduction in acoustic standards: evaluating reverberation time, mean absorption coefficient, and a novel approach

Contemporary European acoustic standards utilize dimensioning functions based on either reverberation time or mean absorption coefficient. For spaces with the primary goal of noise reduction, achieving consistent overall sound pressure level (SPL) reduction, regardless of room sizes and shapes is evidently desirable. The conditions under which reverberation time and mean absorption coefficient approaches align with the objective of consistent SPL reduction are analyzed. Generally, both approaches fall short of achieving consistency in case of varying ground surface or room shape, i.e. length, width and height relations. Additionally, in large rooms, constant or room height-dependent reverberation time functions may lead to extensive absorption areas, complicating the installation of acoustical treatments and degrading their economic efficiency. A novel dimensioning function, providing consistent SPL reduction independently of room size and shape, is derived based on diffuse field theory. The novel function is multiplied by a linear function of room height and length, to alleviate accuracy limitations of diffuse field theory in case of varying room shapes. Finally, simulations systematically varying room sizes and shapes demonstrate that the novel dimensioning function significantly outperforms existing approaches in achieving consistent SPL reduction.

Optimization of speech intelligibility in street-facing rooms of adult care home based on acoustic simulation

Due to the limited availability of urban land in China, many adult care facilities have resorted to constructing roadside living spaces. However, this has led to a significant issue of traffic noise disturbance. Besides, many rooms lack of control over indoor reverberation. As a result, the communication efficiency of the residents might be greatly impacted. This study aims to investigate the impact of traffic noise level, room size, and surface sound absorption on speech transmission index (STI), based on a typical room layout and conducted using acoustic simulation. The simulation results indicated that in order to achieve the required STI over 0.79 for elderly residents, it is imperative to control the road noise interference below 40 dBA and ensure that the room reverberation time under 0.42 s. To achieve this, it is recommended that for a road with a noise emission level of 72 dBA, the distance between the roadside room and the road should not less than 10 m. Additionally, windows with Rw+Ctr over 28 dB without road sound barriers (over 20 dB with sound barriers) should be installed. Besides, the room area needs to be controlled within 20 m2 and an acoustic ceiling with NRC>0.65 should be installed.

Towards situation dependent radiation efficiency

Designing buildings to protect inhabitants against ground-borne noise from railway lines or other outdoor ground vibration sources has become a subject of importance. Prediction methods to tackle such a problem are under discussion within a European task group (CEN/TC126/WG2/TG1). From velocity response of floors and walls, the structure-borne sound level has to be evaluated; the linking quantity is the radiation efficiency. In the frequency range of interest, i.e. covering the one-third octave bands from 16 to 250 Hz, rooms modes exist for standard size living spaces in building. A parametric investigation is carried out to evaluate the effect of the room modes on the radiation efficiency. In a first step, a modal approach is used to estimate the radiation efficiency for different room sizes, heavy floor and light wall types. Randomly positioned forces are considered as excitation on these structural elements. From these results, the situation dependent radiation efficiencies are investigated and discussed.

Factors Influencing the Location of Hotel Businesses Catering to International Travelers in Bangkok

Abstract: This study investigates what makes international travelers pick hotel locations in Bangkok Thailand. The research tries to answer questions about location factors for international hotels and how these factors shape their decisions when choosing spots to serve international guests. The survey targeted select three to five-star hotels in Thailand's capital gathering just over 250 valid responses for analysis. Seven key factors came to light for international guests staying in Bangkok hotels: nearness to shopping centers, eateries, and tourist spots; ease of access; area wealth and appeal; rival hotels; hotel room size; and market focus. This study's results give us a deeper grasp of hotel placement in Bangkok. Looking at where top-tier hotels are, this paper points out some services that hotels in city centers should offer.

Method of cooling server rooms

Objective. In today's world, where the amount of data is growing every day, the importance of reliable and efficient data centers (DPCs) is becoming increasingly clear. One of the key aspects of maintaining data center availability and reliability is ensuring adequate cooling of server rooms. This scientific work is devoted to the study and analysis of various cooling techniques used in server rooms. Method. The study is based on thermodynamic analysis methods to optimize cooling systems to improve their efficiency and reduce energy consumption. Result. The work examines both traditional cooling systems and modern innovative solutions that improve cooling efficiency while reducing energy consumption. The authors compare different cooling methods, evaluate their advantages and disadvantages, and analyze the potential economic and environmental benefits of introducing advanced technologies into server room management practices. Conclusion. The choice of the optimal cooling method should take into account the specifics of the server equipment, thermal loads, room size and other factors. It is also important to pay attention to maintenance and selection of the optimal cooling system in accordance with the needs and characteristics of the server equipment.

Evidence-based strategies for optimizing long-term temperature monitoring in offices

Long-term monitoring of the thermal environment in office buildings has become increasingly relevant with the rise of wireless sensor networks. However, there is a notable absence of explicit guidelines for implementing monitoring strategies in such contexts. This lack of direction can lead to inconsistent deployment of sensor networks, resulting in higher maintenance costs and inaccurate long-term assessments of thermal conditions. Based on data analyses of high-accuracy, high-frequency field measurements conducted over a year or longer across multiple offices in Sydney and Shanghai, this study proposes a strategy for long-term temperature monitoring. The strategy advises practitioners to prioritize considerations such as air-conditioning type, room size, and space function when selecting "representative" sensor locations. Typically, sampling every 30minutes is deemed adequate for shared offices where an error margin of ±0.5°C is acceptable. For environments with stable indoor temperatures, less frequent sampling intervals suffice. A power regression model tailored for offices equipped with central AC and no operable windows was developed to predict the maximum allowable sampling interval based on several days of indoor temperature monitoring in winter. Regarding monitoring duration, the study advocates a preferred sampling period of one year to comprehensively capture seasonal variations. Alternatively, a minimum monitoring period of four to six months commencing in late spring or early summer is identified as potentially sufficient. These findings offer valuable insights for optimizing long-term thermal monitoring practices in offices and may contribute to expanding the scope of thermal comfort standards.

245 Optimizing Room Size and Accessibility Improves Emergency Department Operational Metrics

245 Optimizing Room Size and Accessibility Improves Emergency Department Operational Metrics

Optimal cost predictive BMS considering greywater recycling, responsive HVAC, and energy storage

A crucial aspect of a sustainable city is ensuring that energy and water supplies can adequately meet urban demand. With the scarcity of natural resources and growing electricity and water demand, it becomes increasingly important for consumers to manage their resource usage more efficiently. This paper proposes a novel perspective of demand-side management coordination strategy for a building's water-energy nexus to enhance the resilience and efficiency of the overall electricity-water-heating system. The model is formulated to optimally coordinates the onsite greywater recycling system, heating, ventilation, air conditioning (HVAC) loads, and distributed energy generation systems with a bidirectional grid connection in a residential building. All subsystems are controlled by a model predictive controller (MPC) receiving real-time time of use (ToU) pricing from electricity and water utilities. The presented mixed integer linear programming model is verified to meet the customers' demands while reducing the operational costs. Results are compared with benchmark systems lacking the water recycling or energy storage system showing 8.3 % operational cost reduction while reducing potable water consumption by 21.5 %. The effect of increased MPC control horizon is also studied showing reduction in cost with increased horizon. Detailed analysis of the proposed framework computational burden and effect of prediction errors is performed to prove the MPC adaptability and robustness. Testing under increased room size and different user preferences further validate the efficacy of the proposed scheme in reducing the operational costs.

Analyze Factors Affecting Room Pricing of Accommodation in Luang Prabang Province

The study of “Analyze Factors Affecting Room Pricing of Accommodation in Luang Prabang Province”. The objectives of the study are the overview Luang Prabang tourism, hotel and guesthouse statistics, in particular to investigate the way which room rates are determined. Data was collected from Agoda website by survey in May 2108. The sample size is 204 hotel and guesthouse that consists of 1,326 observations. Based on Hedonic Pricing Model for estimation and data analyzing for Ordinary Least Square (OLS) by Stata 13. Our result of finding included that there were 643,319 tourists (2016) from domestic and foreign countries. The total number of hotels and guesthouses accounted for 415 (2016) which includes 65% and 35% of domestic and foreigner firms respectively. The room rates of hotel and guesthouse are between $ 2 and $ 400 per night. The results illustrate that the factors determined the hotel and guesthouse room rates in Luang Prabang are 17 variables; for instance, colonial style, deluxe room, river view, room size, number of rooms, guest limited, bed king, wooden parquet floor, free cancellation, car park, front desk 24 hours, swimming pool, and massage. Which these variables are most statistically significant. Furthermore, the symbol of variables is similar on our prediction; expecting some variables are the heritage site area, Wi-Fi and 24 hours security which is not statistically significant. In addition, Free airport transfer service is statistically significant; apart from, the symbol of variable is not the same on our prediction.

A Study on the Spatial Layout of Newly Built Townhouses in Kaohsiung City

Due to their independent structure, piping, and access, townhouses offer great flexibility in floor plan changes and high spatial autonomy, making them the mainstream housing type in Taiwan. This study focuses on row houses and examines 2022 completion cases in Kaohsiung City. It collects floor plans from 14 newly constructed buildings, totaling 227 units, and analyzes spatial dimensions including the facade width, depth, habitable room, staircases, bathrooms, and total floor area for each case. The objective of this study is to examine the spatial layout of row houses in Kaohsiung City, with the aim of providing a reference for the future planning and design of such structures. The study results showed that 81.8% of the row houses analyzed have a total floor area per unit ranging from 136 to 192 m2, a facade width between 4.1 and 6.38 m, and a building depth from 7.67 to 12.68 m. In addition, they showed a low negative correlation between the facade width and total floor area, a high positive correlation between the building depth and total floor area, and a moderate negative correlation between the facade width and building depth. The spatial distribution within these houses includes 78% allocated to habitable room, 13.1% to staircases, and 8.7% to bathrooms. The total area of habitable rooms and bathrooms increases with the total floor area of the building. However, the total area of staircases remains almost constant as the area of habitable rooms increases. Furthermore, the most common location for staircase planning is the rear-right (RB) position, while the least common is the rear-left (LB) position. Among various staircase types, the C-shaped staircase has the largest average area per floor and the highest average proportion. Conversely, the I-shaped staircase has the smallest average area and the lowest average proportion. The U-shaped staircase is the most frequently planned, whereas the C-shaped staircase is the least frequently used. This means that planning of staircase location and type significantly affects the size and layout of habitable rooms and bathrooms in townhouses.

Prediction of flashover time in a compartment fire by CNN-LSTM based deep neural network considering wearable data collection equipment

Real-time prediction of flashover in a compartment fire is of great significance for rescue. This paper investigated a CNN-LSTM neural network to predict the flashover based on the temperature and radiative heat flux in 24 compartment fire cases simulated with FDS. Multiple conditions affecting compartment fires have been taken into account, like room size, room height, fire source location, opening size, number of vents, and fire load. Radiative heat flux meters and thermocouples were positioned at specific heights, to simulate sensors worn by firefighters entering a fire scene. The proposed model consisted of a convolutional layer and three LSTM layers. After training, the deep learning model effectively identified the flashover in compartment fires, with an average relative error of 4.1 % for temperature prediction and 11.2 % for radiative heat flux prediction. In addition, the model was validated on other simulated fire cases and full-scale experimental data, and the results showed good performance. In this paper, the sensitivity of the model to lead time was analyzed, and the application range of the model was determined. The model performed well within the lead time of 25s. In view of the strong performance of the proposed deep learning model in predicting flashover based on wearable sensors (temperature and radiative heat flux), it is believed that this model holds potential for application in a broader range of fire scenarios. This demonstrates the feasibility of using deep learning artificial intelligence models to predict compartment fire development, providing scientific guidance for smart firefighting technology.

Physical space of thirty pediatric intensive care units in the United States of America: a national survey.

The design of Pediatric Intensive Care Unit (PICU) rooms significantly impacts patient care and satisfaction. The aims were first, to describe the current physical space across PICUs in the USA, and second, to identify what proportion of PICUs are compliant with current guidelines. A descriptive cross-sectional survey was conducted, targeting division chiefs and medical directors of PICUs nationwide. The survey collected data on unit type, construction and renovation dates, room sizes, and available amenities. According to the Guidelines for Design and Construction of Hospitals, PICU rooms are recommended to be single rooms, at least 200 sq ft, have a window and a private bathroom. Data were anonymized and reported as median and interquartile ranges or frequencies and percentages. Thirty units responded. Among the respondents, 26 had general PICUs, 9 had cardiac ICUs, and 3 had intermediate care units, with some units containing multiple types of ICUs. The median annual admissions were 1,125, with a median occupancy rate of 78%. Twenty-three percent of units had at least one double room, and 3% had triple or quadruple rooms. The median room size was 265 sq ft (IQR 230; 304), the smallest room size was 220 sq ft (IQR 179; 275), and the largest single room size was 312 sq ft (IQR 273; 330). Thirty-seven percent of units had bathrooms in every room, while 80% had windows in every room. Additionally, 46% of units had dialysis capabilities in every room, and 7% had negative pressure capabilities in every room. The median building year was 2008 (IQR 2001;2014), with 36% of units having undergone at least one renovation. Larger rooms were associated with more recent build dates (p = 0.01). Only 30% of the PICUs met the guidelines for physical space. These compliant units were built at a median of 4 years ago (IQR 1; 8). This study highlights the variability in PICU room design and amenities across healthcare facilities. Many units still fall short of meeting the guidelines for room size, windows, and private bathrooms. Future research should investigate the relationship between room characteristics and patient outcomes to inform better design practices, with a goal of improving patient experiences and clinical outcomes.

Implementation and validation of optimal start control strategy for air conditioners and heat pumps

Commercial buildings are responsible for approximately 20 % of the total energy consumption and greenhouse gas emissions in the United States. Over 85 % of these buildings lack building automation systems, and many are small (<50,000 square feet), underserved, and use rooftop units (RTUs) for heating, ventilation, and air-conditioning needs. Because these buildings lack proper energy management systems, several operational deficiencies lead to excess energy consumption. Studies have shown that managing the RTUs’ heating and cooling set points, schedules, setbacks, and optimal start can result in a 20 % to 25 % reduction in electricity consumption in small commercial buildings. These buildings typically use fixed schedules to start the RTUs 60 to 120 min before occupancy begins, which results in excess energy consumption. This paper presents research that demonstrates and evaluates the performance of four optimal start methods, which utilize data-based modeling as a key element in facilitating adaptive control in response to time-varying inputs while requiring minimal sensor inputs. The evaluation found energy savings in two commercial buildings equipped with RTUs by periodically alternating four different optimal start models during the cooling and heating season. The resulting energy savings are positive for all models and range from 2 to 5 kWh/day/unit. The units on the east side of the building showed higher savings, while interior units showed greater variability in savings due to the differences in capacities and room sizes. Savings were considerably greater during the heating season compared to the cooling season. The performance of all four models on Mondays was poor; models suggested a shorter optimal start time, which resulted in relatively larger errors. The future work will look at using a different model for the days after weekends and holidays.

Computational design of residential units’ floor layout: A heuristic algorithm

Computer-aided intelligent design can improve the quality and efficiency of residential building design. However, the design conditions are diverse, and numerous combinations of spatial elements exist. We propose a heuristic algorithm—the combined Monte Carlo tree search (MCTS) and particle swarm optimization (PSO) algorithm (PSO-MCTS)—for the automated design of residential floor layouts. The MCTS algorithm deals with discrete variables (room locations). It considers architectural design experience and design standards during node pruning to compress the search space and simplify the computation. The PSO algorithm deals with continuous variables (room sizes) and accelerates the computing speed due to parallel processing. The PSO-MCTS algorithm achieves a good balance between computational efficiency and accuracy. It is suitable for various design conditions and requirements. The input parameters include the daylight façade, boundary scope, entrance location, room type and size, and relationships with adjacent rooms. The PSO-MCTS provides 100 %, 80 %, and 75 % better results than heuristic algorithms, designs by architects, and a state-of-the-art deep learning model, respectively.