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This study examines how genetic algorithms (GA) can be used to make protective structures for photovoltaic (PV) systems more efficient. We tried to increase the efficiency of solar cell systems in five different construction scenarios with regard to costs, energy savings, and payback time. To optimize this, we used construction parameters such as width, height, depth, insulation thickness, shading, and roof angle. An evolutionary algorithm generated the most efficient individual with parameters of 32.89 m width, 8.83 m height, 1.46 m depth, WWR = 32.52%, insulation = 8.96 cm, shading = 7.94 m², and roof angle = 72.08°. The solar panels had an efficiency of 31.27%. According to the cost analysis, Building 4, which had installation costs of $40,000 and annual maintenance costs of $1,500, provided the greatest energy savings of $7,000 per year and a payback period of five years. The features that distinguished Building 2 were installation cost, $ 25000; the pay back of installation was seven years; annual maintenance cost $ 800 per year. But it also saved less power which was around four thousand dollars for the year consumption. In Building 3 the greatest energy savings were found and pay back period was also five years In Buildings 3 and 5 average cost and performance was observed. According to the practical outcomes of the study, it has been perceived that applying of genetic algorithms can lead to enhancement of the economic efficiency of the solar power plants including energy saving aspects. It is therefore argued here that these skills can be applied to building design such that possible returns on solar panel systems are boosted.

Logic Encryption is a hardware security technique that protects integrated circuit designs that are fabricated at untrusted pure play foundries from being pirated or maliciously modified. In the technique, logic gates are added to the design that are driven by an added key input bus, such that the correct behavior of the circuit is recovered with only the exact correct key input pattern. However, the power, performance, and area (PPA) cost of implementing logic encryption has often been ignored in the literature in favor of increasing the level of security provided. This has proved to be a significant hurdle in transitioning the method to use in commercial-grade designs and a systematic methodology of constraining the cost of logic encryption is needed. In this paper, we propose a generalized Constraint-Directed Logic Encryption (CDLE) methodology. In CDLE, the potential design space of encrypted versions of a circuit is searched to apply logic encryption under PPA constraints. Two example CDLE methods are proposed. The first is a concurrent tree search method which uses commercial tools to sample designs for their PPA cost and determine the optimal encryption strategy. In this method, PPA cost is accurately analyzed at the cost of heavy runtime. The second is a machine learning approach which estimates the PPA cost to predict the optimal encryption strategy. The machine learning model developed in this work is limited, but the results are promising as a direction for study in logic encryption. Detailed experimental results evaluating both methods are presented.

Achieving the goals of information security is a key factor in the decision to outsource information technology and, in particular, to decide on the migration of organizational data, applications, and other resources to the infrastructure, based on cloud computing. And the key issue in the selection of optimal architecture and the subsequent migration of business applications and data to the cloud organization information environment is the question of the total cost of ownership of IT infrastructure. This paper focuses on solving the problem of minimizing the total cost of ownership cloud.