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More frequent and severe extreme weather events such as heatwaves are among the most serious challenges to society in coping with the changing climate. To evaluate the impacts of the heatwave on large-scale urban areas, a multi-scale weather forecasting system is designed by integrating different resolutions of the Canadian urbanized version of the Global Environmental Multiscale (GEM) Numerical Weather Prediction (NWP) model, cascading from 10km to 2.5 km, and 250 m. The multi-scale model is implemented in Montreal, Canada, for modeling the 2018 heatwave. Simulation results are well-validated against measurement data, including Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and ten weather stations in the city. The Universal Thermal Climate Index (UTCI) map was calculated to identify vulnerable regions in the city against the heatwave. Land-use types in hotspots and coldspots are analyzed to find dominant factors in the formation of hot and cold areas. It is found that natural landscapes such as vegetation, trees, and water bodies are the dominant features of most coldspots. On the other hand, roads, parking lots, less tree covers, and industrial activities are the common land use features in the hotspots. A weak correlation is found between heat-related death locations and the outdoor UTCI map, implying that the assessment of an outdoor heatwave may not address overheated buildings and communities. This paper shows the importance of built environments - their properties and occupants' socio-demographic factors in the study of heat-related mortalities in cities.

Understanding and quantifying the interactions between urban microclimate and urban buildings is essential to improve the urban environment and building performance, especially during heatwaves. Most previous studies used tool or application specific data exchange mechanisms that cannot be generalized for other tools or applications. In this paper, we introduce a new flexible and tool-agnostic data schema to facilitate the exchange of data between urban building energy models and urban microclimate models. The JSON schema was tested using a district of 97 buildings in San Francisco and running simulations with CityBES and CityFFD as the urban building energy and microclimate modeling platforms, respectively. Compared with simulation results using the historical weather data, simulation results considering interactions between two models over a two-day heatwave event showed a 5.3°C higher average peak building facade temperature, an 8.9 °C higher average peak air node temperature, and a 19.5% higher peak cooling energy use.

Machine-to-machine (M2M) communication is one of the critical technologies of the industrial Internet of Things (IoT), which consists of sensors, actuators at the edge, and servers. In order to solve the security and availability problems regarding communication between edge devices with constrained resources and servers in M2M communication, in this study we proposed an authentication and session establishment protocol based on physical unclonable functions (PUFs). The scheme does not require clock synchronization among the devices, and it circumvents the situation where the authentication phase has to use a high computational overhead fuzzy extractor due to PUF noise. The protocol contains two message interactions, which provide strong security and availability while being lightweight. The security modelling is based on CPN Tools, which verifies security attributes and attack resistance in the authentication phase. After considering the design of the fuzzy extractor and scalability, the proposed scheme significantly reduces the computational overhead by more than 93.83% in the authentication phase compared with other schemes using PUFs. Meanwhile, under the guarantee of availability, the communication overhead is maintained at a balanced and reasonable level, at least 19.67% lower than the solution using XOR, hashing, or an elliptic curve.

This paper reviews typical threats and attack vectors leading to unauthorized access to industrial networks via the corporate information system and facilitating subsequent attacks against ...

Airborne transmission of aerosols produced by asymptomatic individuals is a large portion of the SARS-CoV-2 spread indoors. Outdoor air ventilation rate, air filtration, room occupancy, exposure time, and mask-wearing are among the key parameters that affect its airborne transmission in indoor spaces. In this work, we developed a new web-based platform, City Reduced Probability of Infection - CityRPI, to calculate the indoor airborne transmission of COVID-19 in various buildings of a city scale. An archetype library of twenty-nine building types is developed based on several standards and references. Among the mitigation strategies recommended to reduce infection risk, some could result in significant energy impacts on buildings. To study the combined effects of energy consumption and reduced infection probability, we integrated CityRPI with City Building Energy Model. We applied the integrated model to Montreal City and studied the impact of six mitigation measures on the infection risk and peak energy demand in winter. It shows that the same strategy could perform quite differently, depending on building types and properties. In the winter season, increasing the outdoor air ventilation rate may cause massive building energy consumption. All strategies are shown to reduce the infection risk but wearing a mask and reducing exposure time are the most effective strategies in many buildings, with around 60% reduction. Doubling the outdoor air ventilation rate is not as effective as other strategies to reduce the risk with less than 35% reduction. It also significantly increases building peak heating demand with 10-60%.