For decades, photovoltaic-thermal hybrid solar systems (PVT) have been presented in a single unit to combine PV cells and solar thermal absorbers to increase solar utilization and reduce the relative cost per unit installation area. The building-integrated photovoltaic-thermal configuration (BIPV/T) has exploited the envelope or roof of buildings with PVT assemblies to produce both heat and electricity. Consequently, the BIPV/T system provides a viable way for reducing energy consumption and achieving low-energy building requirements. This study provides an up-to-date review of the current developments in the individual and combined BIPV/T systems. It focuses on the multiple numerical and experimental investigations undertaken to evaluate the design and performance of the various wall- and roof-mounted BIPV/T configurations, such as the BIPV/T air-cooled systems, BIPV/T water-cooled systems, BIPV/T systems with concentrators, and PCM-based BIPV/T systems. The effects of several explored parameters on the building performance, energy, exergy, energy savings, etc. have been analyzed. The systems designs, their benefits and drawbacks, performance indicators, developments, and limitations have been analyzed. The literature research revealed that BIPV/T air systems could achieve optimal performance if the ideally designed characteristics, such as tilt angles, configuration arrangements, and fluid flow rate, were chosen correctly. On contrary, the BIPV/T water-cooled systems have demonstrated better thermal performance, but with additional manufacturing costs. Additionally, with the integration of the BIPV/T systems with other systems, such as HVAC and heat recovery systems, the benefits to utilization and techno-economic performance were maximized. Under the same testing settings, hybrid BIPV/T-PCM and BIPV/T with concentrators have produced superior results compared to air- and water-cooled BIPV/T systems. The review provides several conclusions and highlights challenges with recommendations for future research topics that should be followed to sustain the use of BIPV/T systems.

Recently, super-resolution (SR) techniques based on deep learning have taken more and more attention, aiming to improve the images and videos resolutions. Most of the SR methods are related to other fields of computer vision such as image classification, image segmentation, and object detection. Based on the success of the image SR task, many image SR surveys are introduced to summarize the recent work in the image SR domains. However, there is no survey to summarize the SR models for the lightweight image SR domain. In this paper, we present a comprehensive survey of the state-of-the-art lightweight SR models based on deep learning. The SR techniques are grouped into six major categories: include convolution, residual, dense, distillation, attention, and extremely lightweight based models. Also, we cover some other issues related to the SR task, such as benchmark datasets and metrics for performance evaluation. Finally, we discuss some future directions and open problems, that may help other community researchers in the future.

This study investigates the potential of two bitumen modifiers, high-density polyethylene (HDPE) and nano clay (NC), to enhance the rutting resistance of asphalt mixture. Four HDPE asphalt binders were prepared by mixing the HDPE at percentages of 2%, 4%, 6%, and 8% with the virgin binder, while four NC asphalt binders were produced by mixing the NC at percentages of 1%, 2%, 3%, and 4%. The consistency and flow of virgin binder, HDPE binders, and NC binders were evaluated by penetration, softening point, and viscosity tests. The results show a gradual increment in the binder stiffness by increasing the percentage of both modifiers. The static creep test was conducted at a temperature of 40 ◦C to evaluate the rutting resistance. The results confirm that both modifiers can greatly improve the rutting resistance of the asphalt mixture, where 8% HDPE and 3% NC modifications reduce the strains provoked in the asphalt mixture under loading by about 50%. According to the correlation analysis, the mixture rutting performance is highly attributed to the binder stiffness, where the lower the penetration value of the asphalt binder, the lower the strains in the asphalt mixture and the higher the stiffness modulus of the asphalt mixture.

Traffic accidents are usually unique events with unpredictable geographical and temporal dimensions; thus, accident injury severity level (INJ-SL) analysis presents formidable categorization and data stability problems. Classical statistical models are limited in their ability to correctly model INJ-SL, whilst sophisticated machine learning approaches do not appear to have any equations to prioritize/analyze multiple contributing factors to forecast accidents accompanying INJ-SLs. In addition, the intercorrelations between the input variables may render the conclusions of a formal sensitivity analysis incorrectly. Rear-end collisions are the most common form of traffic accidents; consequently, their linked INJ-SL requires more research. This paper provides a complex technique based on a deep learning paradigm paired with different indicators of Global Sensitivity Analysis to address all of these concerns. Unlike existing neural network designs, this technique presents a deep residual neural network structure that employs residual shortcuts (i.e., connections). The connections enable the DRNNs to sidestep a few levels of the deep network architecture, evading the regular training with high accuracy issues. Using the trained DRNNs model, a Latin Hypercube sampling simulation was undertaken to determine each explanatory component's influence on the resulting INJ-SL. The latest available data from 2011 to 2018 is used to assess all rear-end collisions in North Carolina. A comparison was made between the performance of two different schemes of data categorization using a set of global sensitivity metrics. It was determined that the devised technique overcame the data heterogeneity problems to achieve an accuracy of 87%. In addition, the proposed sensitivity analysis identified the most relevant factors associated with INJ-SL rear-end collisions.