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Coronavirus Disease 2019 (COVID-19) has affected global economies due to lockdowns, business closures, and travel and other restrictions. To control the spread of the virus, several countries, including Australia, imposed strict border restrictions and lockdown measures. Accordingly, international borders have been closed, and all incoming international passengers are mandated to a 14-day hotel quarantine. Residents’ movements and businesses have been limited to essential services only. Employees have been directed to work from home while businesses moved to a remote working model. Due to such stringent measures, small and medium businesses such as cafes, restaurants, hotels, childcare centers, and tourism-based institutions incurred heavy losses, pushing a considerable portion of such small businesses to close. The airlines, education, tourism, and hospitality sector were the worst impacted among all. Due to such closures and associated effects of COVID-19, the unemployment rates are assumed to be significantly increased in countries like Australia. However, a study investigating this unemployment and reporting its status does not exist for Australia. Therefore, in this study, we investigated the effects of COVID-19 control measures such as travel restriction and lockdown on Australia’s employment status and labor markets. The data for the local transport network, unemployment rates and impacts on the tourism industry in Australia were extracted from the public data sources to assess the unemployment rates at both national and state-wide levels. Further, we also looked into the rehabilitation measures by the Australian government, such as the Job Keeper and Job Seeker programs in March 2020, that aim to provide support to people who are unable to run their businesses or have lost their jobs due to the pandemic. Overall, we observed that despite the global crisis, the Australian unemployment rate has reduced in the last year.

Building Information Modeling (BIM) is recognized as one of the most significant technological breakthroughs in the Architecture, Engineering, and Construction (AEC) industry. The pace of implementation of BIM in AEC has increased during the past decade with an enhanced focus on sustainable construction. However, BIM implementation lags its potential because of several factors such as readiness issues, lack of previous experience in BIM, and lack of market demand for BIM. To evaluate and solve these issues, understanding the current BIM implementation in construction organizations is required. Motivated by this need, the main objective of this study is to propose a tool for the measurement of BIM implementation levels within an organization. Various sets of indexes are developed based on their pertinent Critical Success Factors (CSFs). A detailed literature review followed by a questionnaire survey involving 99 respondents is conducted, and results are analyzed to formulate a BIMp-Chart to calculate and visualize the BIM implementation level of an organization. Subsequently, the applicability of the BIMp-Chart is assessed by comparing and analyzing datasets of four organizations from different regions, including Qatar, Portugal, and Egypt, and a multinational organization to develop a global measurement tool. Through measuring and comparing BIM implementation levels, the BIMp-Chart can help the practitioners identify the implementation areas in an organization for proper BIM implementation. This study helps understand the fundamental elements of BIM implementation and provides a decision support system for construction organizations to devise proper strategies for the effectual management of the BIM implementation process.

The present spreading out of the Internet of Things (IoT) originated the realization of millions of IoT devices connected to the Internet. With the increase of allied devices, the gigantic multimedia big data (MMBD) vision is also gaining eminence and has been broadly acknowledged. MMBD management offers computation, exploration, storage, and control to resolve the QoS issues for multimedia data communications. However, it becomes challenging for multimedia systems to tackle the diverse multimedia-enabled IoT settings including healthcare, traffic videos, automation, society parking images, and surveillance that produce a massive amount of big multimedia data to be processed and analyzed efficiently. There are several challenges in the existing structural design of the IoT-enabled data management systems to handle MMBD including high-volume storage and processing of data, data heterogeneity due to various multimedia sources, and intelligent decision-making. In this article, an architecture is proposed to process and store MMBD efficiently in an IoT-enabled environment. The proposed architecture is a layered architecture integrated with a parallel and distributed module to accomplish big data analytics for multimedia data. A preprocessing module is also integrated with the proposed architecture to prepare the MMBD and speed up the processing mechanism. The proposed system is realized and experimentally tested using real-time multimedia big data sets from athentic sources that discloses the effectiveness of the proposed architecture.

The aim of this paper is to examine students’ performance in a computation-based course by evaluating the effects of key factors including sketching, visualization resources provided to them during the lectures, their attendance and tutors’ experience. A systematic review was conducted including 192 articles published during January 2010 to December 2019. Further, a case study has been conducted in which 633 students from non-engineering backgrounds were taught a core course of construction over three-yearly sessions from 2017 to 2019. The performance has been assessed through two quizzes of 10% weight each, assignment of 40% weight and a final exam with 30% weight in 2017-18 and 40% weight in 2019 were utilized with an attendance criterion of below 75% as low attendance. The statistical result highlights that a clear difference of 14% overall marks exist between the students with less than 75% attendance and the ones with 75% and above in 2017 and a 10% gap in 2018. Students with high marks in sketching secured higher overall marks as compared to others highlighting that the sketching skill is useful to construction students. The findings contribute to the body of education knowledge by evaluating key influential factors and provide a useful benchmark to other educators in the field.

Digital tools and marketing have been widely adopted in various industries throughout the world. These tools have enabled companies to obtain real-time customer insights and create and communicate value to customers more effectively. This study aims at understanding the principles and practices of sustainable digital marketing in the Malaysian property development industry by investigating the extent to which digital marketing has been adopted, the impediments to its adoption, and the strategies to improve digital capabilities for the local context. Digital marketing theories, practices, and models from other industries are adopted and applied to the local property development industry to lay the foundation for making it smart and sustainable. This paper proposes a marketing technology acceptance model (MTAM) for digital marketing strategy and capability development. The key factors used in the model are ease of use, perceived usefulness, perceived cost, higher return, efficiency, digital service quality, digital information quality, digital system quality, attitude towards use, and actual use. The model and hypothetical relationships of critical factors are tested using structural modeling, reliability, and validity techniques using a sample of 279 Malaysian property development sector representatives. A quantitative approach is adopted, using an online questionnaire tool to investigate the behavior of respondents on the current digital marketing practices and capabilities of Malaysian property development companies. The results show that the sample property development companies are driven by the benefit of easily obtaining real-time customer information for creating and communicating value to customers more effectively through the company brand. Further strategies, such as creating real-time interactions, creating key performance indicators to measure digital marketing, personalization, and encouraging innovation in digital marketing are most preferred by local p

Rising demand and limited production of electricity are instrumental in spreading the awareness of cautious energy use, leading to the global demand for energy-efficient buildings. This compels the construction industry to smartly design and effectively construct these buildings to ensure energy performance as per design expectations. However, the research tells a different tale: energy-efficient buildings have performance issues. Among several reasons behind the energy performance gap, occupant behavior is critical. The occupant behavior is dynamic and changes over time under formal and informal influences, but the traditional energy simulation programs assume it as static throughout the occupancy. Effective behavioral interventions can lead to optimized energy use. To find out the energy-saving potential based on simulated modified behavior, this study gathers primary building and occupant data from three energy-efficient office buildings in major cities of Pakistan and categorizes the occupants into high, medium, and low energy consumers. Additionally, agent-based modeling simulates the change in occupant behavior under the direct and indirect interventions over a three-year period. Finally, energy savings are quantified to highlight a 25.4% potential over the simulation period. This is a unique attempt at quantifying the potential impact on energy usage due to behavior modification which will help facility managers to plan and execute necessary interventions and software experts to develop effective tools to model the dynamic usage behavior. This will also help policymakers in devising subtle but effective behavior training strategies to reduce energy usage. Such behavioral retrofitting comes at a much lower cost than the physical or technological retrofit options to achieve the same purpose and this study establishes the foundation for it.

The real estate sector is receiving mix responses throughout the world, with some countries like USA receiving lesser and European and Asia Pacific markets receiving more transactions in recent years. Among the concerning factors, post-purchase regrets by the real estate owners or renters are on the rise, which have never been assessed to date through scholarly research. These regrets can further increase in the time of lockdowns and bans on inspections due to Corona Virus Disease 2019 (COVID-19) and social distancing rules enforced by various countries such as Australia. The current study aims at investigating the key post-purchase regret factors of real estate and property owners and renters over the last decade using published literature and online threads. Based on pertinent literature, 118 systematically identified and text-mined articles, and four online threads with 135 responses, the current study develops system dynamics models to assess and predict the increase in consumers' regrets over the last decade. Further, a user-generated thread with 23 responses involving seven real estate managers and five agents with more than 20 years of experience, 10 buyers with at least three successful rentals or purchases, and a photographer with more than 10 years of experience, is initiated on five online discussion platforms whereby the respondents are involved in a detailed discussion to highlight the regret reasons specific to real estate purchases based on online information. General architecture for text mining (GATE) software has been utilised to mine the text from both types of threads: Published and user generated. Overall, the articles and threads published over the last decade are studied under two periods: P1 (2010-2014) and P2 (2015-2019) to highlight the post-purchase or rent-related regret reasons. The results show that regret levels of the real estate consumers based on published post-purchase data are at an alarmingly high level of 88%, which compared to

Online Platforms (OP) are increasingly being used for e-business in different sectors. With the advances in the internet-based business, OPs are increasingly being used for increasing global outreach and attracting more users in different industries. Real Estate industry, following the global trends, has developed multiple OPs that are currently being used for property-related business and purchase decisions. Although increasing used in the industry, Real Estate Online Platforms (REOPs) have never been investigated from theuser’sperspective to document their experience of online property information search. The current study explores the REOP based information from the users’ perspective in Sydney Australia. Based on 108 responses collected on a20 questions survey, three hypotheses pertinent to REOPs are formulated and investigated. The results show that most of the users are aware of REOPs and are using it easily. Further, the OP design and context is giving a good impression to the users. However, when it comes to decision making based on the OPs, the users are not necessarily making better decisions. This is giving rise to post-purchase regrets among the real estate users. This anomaly is linked to a lack of information provided on the REOPs such as the property photos, neighborhood insights, and real estate agencies’ delayed response. The results are expected to lay the foundation for OP based technology acceptance in Real Estate.

Floods are natural disasters and pose a threat to the lives, property, and infrastructure of an urban area. Though their risk cannot be fully eliminated, several methods can be used to manage floods, once they occur. This includes identification of flood-prone areas, timely detection of the affected areas, mapping rescue routes and arranging logistics to carry out the rescue as soon as possible. The use of advanced innovative technologies for flood management such as image detection and machine learning can assist in effective flood management. This paper presents a novel approach through the integration of image processing and machine learning to detect flood-affected areas using a set of images. The three-step approach proposed in this study is based on landmark detection from images, training of a machine learning algorithm and classifying images from an area as flooded or non-flooded. The results based show an accuracy level of 90% depicting the significance of the proposed model for image-based flood detection.

Digital disruptive technologies are an integral component of the modern world. These technologies are transforming the globalindustries from traditional to more innovative and adaptive. However, the state of global real estate is yet to improve and is currently lagging the technology curve. Because of this lag, useful information is either not made available to the end-users or is shared too late that is raising concerns among the online real estate platform users. This results in larger vacancy rates and post-occupancy regrets among the service consumers. The current study based on the concepts of Technology Acceptance Models (TAM), presents a conceptual Real Estate Stakeholders Technology Acceptance Model (RESTAM) for addressing the key needs of the four important stakeholders of the real estate industry including the end-users or consumers, government & regulatory authorities, agents & agencies and complementary industries. Based on comprehensive literature review of 213 articles, the needs of these stakeholders are assessed and addressed through the Big9 technologies namely drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR & AR), and artificial intelligence and robotics. The resulting RESTAM framework with a specific focus on the online platform based real estate users are expected to lay the foundation for introducing the missing technology acceptance model for real estate stakeholders whereby these Big9 disruptive technologies are implemented in real estate industry to uplift it from traditional to smart real estate. This will reduce the post-occupancy regrets of the real estate service users and improve the relations between various real estate stakeholders.

Virtual and Augmented Realities (VR & AR) are increasingly utilised in constructionand Real Estate industries. Due to the absence of immersive visualisation mechanismsthat records the construction process and its defects, the clients can only discover anydefects after their purchases. Such discoveries portray a negative image of the real estateagencies as some information may deliberately be held to attract customers who laterupon the discoveries of such defects regret their purchase decision. To address theseissues, this paper aims at developing and idealizing an immersive visualisationmechanism for buildings using VR based technologies to help post construction sales.The in-construction 3D videos, models and visualisations, made available to the buyers,will help them see the construction quality, workmanship and defects in real time toconnect to their buildings and increase their purchase confidence. This paper based onfield experimentation using 360 cameras, mobile laser measurement and VR gadgets,collects data from a selected building. Overall, the effects of visualisations onconstruction site management and processes are explored and a concept of using thedata for post-construction sales is presented. The discussions may open avenues for newideas generations, using the in-construction data to enhance real estate and propertysales and address post purchase decision regrets by providing more and detailedinformation to the customers to make informed decisions. The customers can knowabout the defects and construction quality beforehand whereas sellers can controlconstruction workmanship thus creating a win-win scenario and promoting trust basedmutualistic relation.

Real estate needs to improve its adoption of disruptive technologies to move from traditional to smart real estate (SRE). This study reviews the adoption of disruptive technologies in real estate. It covers the applications of nine such technologies, hereby referred to as the Big9. These are: drones, the internet of things (IoT), clouds, software as a service (SaaS), big data, 3D scanning, wearable technologies, virtual and augmented realities (VR and AR), and artificial intelligence (AI) and robotics. The Big9 are examined in terms of their application to real estate and how they can furnish consumers with the kind of information that can avert regrets. The review is based on 213 published articles. The compiled results show the state of each technology’s practice and usage in real estate. This review also surveys dissemination mechanisms, including smartphone technology, websites and social media-based online platforms, as well as the core components of SRE: sustainability, innovative technology and user centredness. It identifies four key real estate stakeholders—consumers, agents and associations, government and regulatory authorities, and complementary industries—and their needs, such as buying or selling property, profits, taxes, business and/or other factors. Interactions between these stakeholders are highlighted, and the specific needs that various technologies address are tabulated in the form of a what, who and how analysis to highlight the impact that the technologies have on key stakeholders. Finally, stakeholder needs as identified in the previous steps are matched theoretically with six extensions of the traditionally accepted technology adoption model (TAM), paving the way for a smoother transition to technology-based benefits for consumers. The findings pertinent to the Big9 technologies in the form of opportunities, potential losses and exploitation levels (OPLEL) analyses highlight the potential utilisation of each technology for addressing consum

Recently, digital technology has made its way into the property market, but the applications of new interactive technologies such as Virtual and augmented realities are yet to be explored in real estate sector. The literature in this area is scarce and thus provides an impetus for thorough exploration. This paper systematically reviews the state of the art technologies in the real estate websites of the US and Australia. It presents a SWOT score matrix including the advantages and functionalities of the Real Estate Technology (RET) such as Virtual and Augmented reality, 3D laser scanners, 360 cameras and walk-throughs along with technology adoption capacity of the websites. Based on the scored matrix, the paper discusses future trend in real estate industry taking user’s perspectives into account and highlights the need of Technology Adoption Model (TAM) for real estate property management. A total of 10 websites: Top 5 visited websites each from Australia and the US were selected for a systematic analysis based on the SWOT based Rubric of focused disruptive RET use, neighbourhood insights, nearby facilities, virtual tours, total results and minimum price. These assessments are made by comparing search results of Mascot Sydney and Laurel Maryland. The results, based on comparative analyses and novel SWOT scores matrix, are expected to pave the way for developing TAMs in real estate management that so far, is non-existent considering available literature. In future, the matrix can be expanded to include customers and websites from both developed and developing countries and a holistic TAM can be proposed to add both theoretical and practical value to real estate body of knowledge

BACKGROUND: The recently held Critical Assessment of Function Annotation challenge (CAFA2) required its participants to submit predictions for a large number of target proteins regardless of whether they have previous annotations or not. This is in contrast to the original CAFA challenge in which participants were asked to submit predictions for proteins with no existing annotations. The CAFA2 task is more realistic, in that it more closely mimics the accumulation of annotations over time. In this study we compare these tasks in terms of their difficulty, and determine whether cross-validation provides a good estimate of performance. RESULTS: The CAFA2 task is a combination of two subtasks: making predictions on annotated proteins and making predictions on previously unannotated proteins. In this study we analyze the performance of several function prediction methods in these two scenarios. Our results show that several methods (structured support vector machine, binary support vector machines and guilt-by-association methods) do not usually achieve the same level of accuracy on these two tasks as that achieved by cross-validation, and that predicting novel annotations for previously annotated proteins is a harder problem than predicting annotations for uncharacterized proteins. We also find that different methods have different performance characteristics in these tasks, and that cross-validation is not adequate at estimating performance and ranking methods. CONCLUSIONS: These results have implications for the design of computational experiments in the area of automated function prediction and can provide useful insight for the understanding and design of future CAFA competitions.