Using immersive virtual environments (IVEs), researchers explore important research questions related to how occupants perceive and interact with their indoor environment in response to different environmental stimuli (e.g., visual and thermal). Despite an increasing number of applications, the literature lacks review efforts of IVE-based comfort research, leaving the current state of such efforts and their prospects unclear. The current work aims to address this gap by presenting a critical review of scholarly articles using IVEs to study occupant comfort in buildings. It reviews of available IVR technologies and their associated fundamental concepts, followed by a review of studies that employed IVEs to assess single or multiple domains' comfort. A critical analysis is then presented to reflect on the current state-of-the-art, its gaps, and way forward. Results show that despite significant advancements in IVR technology, its application to occupant comfort is limited in studying multi-domain comfort and cross-effects between the domains.
Mangroves represent a crucial component of the global carbon cycle because of their elevated carbon sequestration rates in biomass and sediments. Still, the future capability of Mangroves to act as carbon sinks depends on their ability to endure sea-level rise and its most direct impacts: increasing coastal salinization and altered submersion regimes. Salinization, in particular, is known to limit Mangrove transpiration and productivity through osmotic effects. In contrast, atmospheric CO2 concentration is projected to increase steeply under current emission scenarios (atmospheric carbon enrichment), thus enhancing plant productivity - a process known as atmospheric fertilization. Here we model the interplay of salinity and CO2 enrichment and its impacts on Mangrove transpiration and productivity at the plant scale. Our analysis is central to understand the future of coastal wetlands under climate change.
In this paper, we propose a post-carbon urban district in Dubai that explores the design of such a district in times of climate change and a future without oil. The paper also aims to study the current conditions of sustainability in Dubai and be a case study for the future of sustainable development in the Emirate. The district welcomes residents from all backgrounds and has educational, religious, governmental institutions, recreational and commercial sites, and offers many job opportunities.
Autonomous vehicles (AVs) are expected to improve traffic flow, reduce accidents, reduce social exclusion and improve the utility of time on travel. Regardless, the challenges remain, especially in convincing the consumers to switch to AVs despite their benefits. This paper reviewed recent studies on the implications of autonomous vehicles and the user acceptance of technology according to the level of automation based on different models of acceptance like TAM and UTAUT. The review results have shown that initial trust, perceived safety risk, perceived privacy risk and environmental concerns are the most critical factors in promoting a positive attitude towards AVs. Early AV adopters will likely be young students, more educated, and spend more time in vehicles. These findings provide guidance for designing interventions aimed at improving the public's acceptance of AVs
Streets make up the majority of our urban environment and are detrimental to a city's sustainability. While multiple studies focus on assessing street performance, they often exclude alleyways despite being a ubiquitous walking infrastructure that greatly increases network efficiency. This paper examines uses MCA to assess the impact of alleyways on a neighborhood's accessibility and on network betweenness. Results reveal that alleys increase neighborhood accessibility; however, they have varying impact on betweenness centrality. Further studies are needed to enable the interpretation of betweenness values to wholly understand street network performance.
The co-location of photovoltaic and crop production - often referred to as Agrivoltaic - has been proven to be an economically viable solution to mitigate the competition for land between food and solar energy production in temperate climates. In arid regions, where competition for land has a more marginal role, Agrivoltaic could, in contrast, help reclaim marginal land and enhance agricultural and solar energy production efficiency. When deployed above cropland, PV modules can substantially modify the surface energy budget, reduce sensible heat fluxes, and increase latent heat fluxes (evaporative cooling). However, the successful implementation of Agrivoltaic in arid regions also require the selection of crops able to endure shadowing and the harsh environmental conditions of the region. Here, we present a crop selection methodology for dryland agrivoltaic, based on the hydraulic, salt-tolerance and shadow-tolerance characteristics of different crops. Our results represent a benchmark for future agrivoltaic implementation in arid regions.
Pore network modeling was performed to calculate the absolute permeability of 3D micro-CT rock images. A regression-based renormalization method was applied to calculate the equivalent permeability of four different rock samples. And significant decreases on the relative error between the direct permeability and scaled permeability were observed by comparing the results obtained through original Karim and Krabbenhoft's renormalization method and the regression one, respectively.
This study adopts state-of-art transfer learning, into semantic segmentation of real 2D carbonate rock images. With pre-trained networks such as VGG16 and MobileNet-V2 used as the encoder of proposed semantic segmentation deep neural networks in literature, we obtained comparable results of training, validation, and testing metrics and minimize required computational time with low training samples.
Surfactants are used in oil industry for applications such as chemical enhanced oil recovery and hydraulic fracturing. While it is widely agreed that surfactants alter rock surface wettability towards a more desired wetness state, parameters and mechanisms responsible for such alteration have not been fully understood and require further investigation. This study involves experimental investigation of wettability alteration potential of surfactants as a function of rock minerology. To accomplish this, contact angle, surface properties, adsorption, pore structure and mineralogy of rocks pre and post applying surfactant are measured. Samples include carbonates and shales which are expected to cover a representable range of minerology. Moreover, contact angle determination is carried out at ambient and high-pressure operating conditions to reflect reservoir conditions. Surfactants utilized include cationic CTAB and anionic SDBS. Results are further complemented by image analysis. Results demonstrate a relationship between surfactant adsorption behavior to operating conditions, concentration and surfactant type.
A model for single-phase fluid flow in tight UCRs was previously produced by modifying the flow Forchheimer's equation. The new modification addresses the fluid transport phenomena into three scales incorporating a diffusion term. In this study, a new liner model, numerically solved, has been developed and deployed for a tight gas case study. Ideally, the new model suits fluid flow in tight UCRs. The modified Forchheimer's model presented is solved using the MATLAB numerical method for linear flow. Very simple profiles and flow dynamics of the main flow parameters have been established and a thorough parametric analysis and verifications were performed. It has been observed that the diffusion system becomes more prominent in regulating flow velocity with low permeability of the formation rock and low viscosity of the flowing fluid. The findings indicate a behavioral alignment with a previous hypothesis that matches actual reservoir behavior.
