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The purpose of this paper is to experimentally and numerically investigate the cooling performance of the air-to-water thermoelectric cooling system under different working conditions.

An air-to-water thermoelectric cooling system was designed and manufactured according to the principle of discrete binary thermoelectric Peltier modules, and the thermal performance, heat transfer rate and average COP values were examined at different cooling water temperatures and voltages applied. Additionally, numerical simulations were performed by computational fluid dynamics approach to investigate the temperature distribution and airflow structure inside the cooling chamber.

Analyses were performed using experimental tests and numerical methods. It was concluded that, by decreasing the cooling water temperature from 20 to 5 °C, the average COP increases about 36%. The voltage analysis showed that the efficiency of the system does not always increase as the voltage rises; more importantly, the optimum voltage is different and depends on whether it is desired to increase COP or increase the cooling rate.

In the studies published in the field of thermoelectric cooling systems, little attention has been paid to the voltage applied and its relationship to other operating conditions. In most cases, the tests are performed at a constant voltage. In this study, several options, including applied voltage and cooling water temperature, were considered simultaneously and their effects on performance have been tested. It was found that under such studies, optimization work should be done to evaluate maximum performance in different working conditions.

This study reviews the extent of application of artificial intelligence (AI) tools in the construction industry.

A thorough literature review (based on 165 articles) was conducted using Elsevier's Scopus due to its simplicity and as it encapsulates an extensive variety of databases to identify the literature related to the scope of the present study.

The following items were extracted: type of AI tools used, the major purpose of application, the geographical location where the study was conducted and the distribution of studies in terms of the journals they are published by. Based on the review results, the disciplines the AI tools have been used for were classified into eight major areas, such as geotechnical engineering, project management, energy, hydrology, environment and transportation, while construction materials and structural engineering. ANN has been a widely used tool, while the researchers have also used other AI tools, which shows efforts of exploring other tools for better modelling abilities. There is also clear evidence of that studies are now growing from applying a single AI tool to applying hybrid ones to create a comparison and showcase which tool provides a better result in an apple-to-apple scenario.

The findings can be used, not only by the researchers interested in the application of AI tools in construction, but also by the industry practitioners, who are keen to further understand and explore the applications of AI tools in the field.

There are no studies to date which serves as the center point to learn about the different AI tools available and their level of application in different fields of AEC. The study sheds light on various studies, which have used AI in hybrid/evolutionary systems to develop effective and accurate predictive models, to offer researchers and model developers more tools to choose from.

Sustainability, especially in terms of development and growth, has been in the agenda of the world community for several decades. However, apparently not all the aspects of sustainability are given equal importance. Ecologic and economic components of sustainability have been in the focal point of many theoretical and practical works, as the social aspect has been mostly left out of emphasis. The purpose of this paper is to examine the social aspects of sustainability and its relation to architecture, with respect to the strong connection between the society and the built environment.

The core of the paper consists of a case study conducted at a design studio course for third-year architecture students whereas the outcomes of the student works on the design problem are evaluated as examples for design approaches to reflect the effects of the built environment on social sustainability. The case study is supported with a literature review and examination of existing approaches to similar subjects regarding social sustainability.

The findings resulted in a better understanding of social sustainability in architectural education which is reflected on the built environment through several architectural strategies.

This paper contributes to the literature by providing a holistic understanding of sustainability including its social aspects and creating an awareness for the importance of social sustainability in architectural education.

Web 2.0 applications enable travelers to evaluate several services and assessment attributes. Constructed websites in several languages trigger a new way of data collections resulting in data streams leading to the accumulation of vast amounts of data, called big data. The need for analysis is in high demand. This study aims to construct a model to investigate which single attribute or interrelated ones having an impact on the performances of hotels.

The total number of 1,137 observations collected from the website HolidayCheck.de are used from the hotels in the Bavaria region in 2016. Bavaria is a region where both domestic and foreign travelers mostly prefer to visit. Fuzzy rule-based systems, which is a combination of fuzzy set theory (FST) and fuzzy logic, are used. Although the FST is used to convert linguistically expressed perceptions by travelers into mathematically usable data, fuzzy logic is used to construct a model between service attributes and price-performance (PP) to attain the set of single and interrelated attributes on the assessment of PP.

No single attribute plays a key role in PP assessment. However, two or more interrelated combinations have different impacts on PP. For example, when “Food—Drink” and “Room” moves together from average to good level, PP reaches the highest level of assessment.

Accessibility to too much data is difficult.

A model can be continuously run so that any changes can be observed during the incoming of data.

As the consumer reviews and ratings are the crucial source of information for other travelers, hoteliers must monitor and respond them on time in order to deal with the complaints.

Travelers’ perceptions or evaluations are treated with a FST that measures the impression of human beings. New modeling enables researchers to observe not only any single attribute but also interrelated ones on the PP.

This article aims to introduce a guide to improving hospital bed setup by combining lean technical practices (LTPs), such as kaizen and value stream mapping (VSM) and lean social practices (LSPs), such as employee empowerment.

Action research approach was employed to analyze the process of reconfiguration of bed setup management in a Brazilian public hospital.

The study introduces three contributions: (1) presents the use of VSM focused specifically on bed setup, while the current literature presents studies mainly focused on patient flow management, (2) combines the use of LSPs and LTPs in the context of bed management, expanding current studies that are focused either on mathematical models or on social and human aspects of work, (3) introduces a practical guide based on six steps that combine LSPs and LSPs to improve bed setup management.

The research focused on the analysis of patient beds. Surgical beds, delivery, emergency care and intensive care unit (ICU) were not considered in this study. In addition, the process indicators analyzed after the implementation of the improvements did not contemplate the moment of the COVID-19 pandemic. Finally, this research focused on the implementation of the improvement in the context of only one Brazilian public hospital.

The combined use of LSPs and LTPs can generate considerable gains in bed setup efficiency and consequently increase the capacity of a hospital to admit new patients, without the ampliation of the physical space and workforce.

The improvement of bed setup has an important social character, whereas it can generate important social benefits such as the improvement of the admission service to patients, reducing the waiting time, reducing hospitalization costs and improving the hospital capacity without additional physical resources. All these results are crucial for populations, their countries and regions.

While the current literature on bed management is more focused on formal models or pure human and social perspectives, this article brings these two perspectives together in a single, holistic framework. As a result, this article points out that the complex bed management problem can be efficiently solved by combining LSPs and LTPs to present theoretical and practical contributions to the important social problem of hospital bed management.