Photovoltaic (PV) systems have been used extensively worldwide over the past few years due to the mitigation of fossils fuels; it is the best source because of its eco-friendly nature. In PV systems, the main research area concerns its performance under partial shading (PS) and complex partial shading (CPS) conditions. PV sources perform perfectly under ideal conditions, but under practical conditions, their performance depends upon many factors, including shading conditions, temperature, irradiance, and the angle of inclination, which can bring a photovoltaic or solar system into a PS or CPS condition. In these conditions, many power peaks appear, and it is hard to find the global peak among many local peaks. The ability to track the maximum power peak and maintain it to avoid fluctuations depends on the maximum power point tracking (MPPT) technique used in a photovoltaic system. This article is based on the implementation of a hybrid algorithm, combining Harris hawk’s optimization (HHO), a new technique which is based on natural inspiration, and a conventional perturb and observe (P&O) technique. The hybrid technique was tested under different weather conditions in MATLAB Simulink and showed less computational time, a fast convergence speed, and zero oscillations after reaching a power point’s maximum limit. A performance comparison of the hybrid technique was made with bio-inspired particle swarm optimization (PSO), adaptive cuckoo search optimization (ACS), the dragonfly algorithm (DFO), and the water cycle algorithm (WCA). The hybrid technique achieves 99.8% efficiency on average and performs very well among the rest of the competing techniques.

Recently, tight sandstone oil has played an increasingly important role in the energy strategies of countries around the world. However, the understanding of a microscopic mechanism is still not clear enough, which has been affecting the improvement of the recovery of tight sandstone oil. In this article, a digital core model was established to simulate the pore network of a physical core with CT scan and difference equations were verified by Fourier counting. Then, a combination of orthogonal tests and cubic digital cores was used to experimentally investigate various parameters including pressure, length, permeability, viscosity, and time. By combining the physical experiments with the digital core methods, it can be observed that the state of the micro-crack affects the conductivity of the core, which may be the decisive reason for changing the pressure gradient. The orthogonal test showed that the sensitivity of the parameters was pressure, length, permeability, time, and viscosity in order. The results of the numerical simulations showed that this method can reveal the seepage mechanism of a tight sandstone reservoir, greatly shortening the experimental time and improving flexibility.

Multi-camera tracking is quite different from single camera tracking, and it faces new technology and system architecture challenges. By analyzing the corresponding characteristics and disadvantages of the existing algorithms, problems in multi-camera tracking are summarized and some new directions for future work are also generalized.

This paper made a short review of Cloud Computing and Big Data, and discussed the portability of general data mining algorithms to Cloud Computing platform. It revealed the Cloud Computing platform based on Map-Reduce cannot solve all the Big Data and data mining problems. Transplanting the general data mining algorithms to the real-time Cloud Computing platform will be one of the research focuses in Cloud Computing and Big Data.