The sustainability of the hydrological and ecological ecosystems of any region requires continuous monitoring of the water bodies. Recent advancements in satellite-based remote optical sensors, big data analysis and cloud computing have given new dimensions to the field of water body studies including their detection as well as analysis. The present study extends the existing methods to assess the contemporary surface water detection and monitoring techniques via remote sensing. The proposed technique implies an improved hybrid approach for the purpose along with the calculation of the boundary areas. The study has been carried out on the Manchar Lake, the largest natural freshwater lake in Pakistan as well as in South Asia. The proposed hybrid water index along with the different existing water body detection indices and spectral bands have been worked out on the satellite images retrieved from the Google Earth Engine to detect and analyze the area/flow changes in the water body. Based on the 7 years of data, the proposed algorithm calculates the water body area more precisely. With limited availability of metadata about the study area, the results have been validated both qualitatively through national-met data and statistically. These results help to better preserve and improve the quality of the water resource.

The use of experimental relations to approximate the efficient thermophysical properties of a nanofluid (NF) with Cu nanoparticles (NPs) and hybrid nanofluid (HNF) with Cu-SWCNT NPs and subsequently model the two-dimensional pulsatile Casson fluid flow under the impact of the magnetic field and thermal radiation is a novelty of the current study. Heat and mass transfer analysis of the pulsatile flow of non-Newtonian Casson HNF via a Darcy–Forchheimer porous channel with compliant walls is presented. Such a problem offers a prospective model to study the blood flow via stenosed arteries. A finite-difference flow solver is used to numerically solve the system obtained using the vorticity stream function formulation on the time-dependent governing equations. The behavior of Cu-based NF and Cu-SWCNT-based HNF on the wall shear stress (WSS), velocity, temperature, and concentration profiles are analyzed graphically. The influence of the Casson parameter, radiation parameter, Hartmann number, Darcy number, Soret number, Reynolds number, Strouhal number, and Peclet number on the flow profiles are analyzed. Furthermore, the influence of the flow parameters on the non-dimensional numbers such as the skin friction coefficient, Nusselt number, and Sherwood number is also discussed. These quantities escalate as the Reynolds number is enhanced and reduce by escalating the porosity parameter. The Peclet number shows a high impact on the microorganism’s density in a blood NF. The HNF has been shown to have superior thermal properties to the traditional one. These results could help in devising hydraulic treatments for blood flow in highly stenosed arteries, biomechanical system design, and industrial plants in which flow pulsation is essential.

The term big data has been used with almost every problem involving large size, real time and heterogeneous data. However, these data attributes are not enough to identify big data by ignoring the application and platform properties for finding processing thresholds. The equivocated identification of big data can lead to an inefficient use of optimization techniques, resulting into global inefficiency, reduced system performance, requiring greater effort on the part of the programming team, and misallocation of the hardware resources required for the task. In this regard, a structured approach has been presented for identification of big data, which is based on three equations that categorize the Volume, Velocity, and Variety characteristics. In addition to 3Vs identification, the classification of big data into strong, moderate or weak level has been proposed. To evaluate the proposed methods, a set of well-known applications have been experimented and categorized. Moreover, four case studies reported as big data have been analyzed according to the proposed system. Also, the proposed equations reduce the computation and human resources up to 75% of Spark cluster execution, saving the unnecessary investments. Furthermore, the proposed equations can be integrated into different tools for assisting selective offloading of big data.

A variety of methods have been proposed in the existing literature for the estimation of passenger car equivalent (PCE) factors. These methods are based on the comparison of selected attributes of different vehicles. This research, for the first time, utilizes the basic notion of the linear relationship between road area occupancy and density for the estimation of PCE factors for different vehicle types in heterogeneous traffic. Aerial photographs obtained from an unmanned aerial vehicle (UAV) were analyzed to estimate the road area occupancy and the number of vehicles classified in seven selected groups. A linear least-squares regression model was developed between road area occupancy and classified vehicle count. The coefficients of the occupancy-density linear regression model were used to estimate PCE and motorcycle equivalent (MCE) factors. The comparison of the estimated set of PCE values with the values reported in the literature shows that PCE factors estimated using the proposed method are reasonable and produce a better occupancy-density relationship than the other studies. In comparison with the existing methods that rely on lane-based measurements, the proposed method is well suited for traffic with weak/no lane discipline, as it considers the entire road width and the dynamics of lateral movement of different types of vehicles. The proposed method does not need extensive traffic data of speeds, headways, flow rates, and so forth, and is applicable on aerial photographs obtained from other sources, such as satellites.