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The generation and use of energy are significant contributors to CO2 emissions. Globally, approximately 30% to 40% of all energy consumption can be directly or indirectly linked to buildings. Nearly half of energy usage in buildings is linked to maintaining the thermal comfort of the inhabitants. Therefore, finding solutions that are not only technically but also economically feasible is of utmost importance. Though much research has been conducted to address this issue, most solutions are still costly for developing countries to implement practically. This study endeavors to find a less expensive yet straightforward methodology to achieve thermal comfort while conserving energy. This study takes a broader view of multiple habitat-related CO2 emission issues in developing regions and describes a hybrid solution to address them. New technologies and innovative concepts are being globally examined to benefit from the considerable potential of PCMs and their role in thermal energy storage (TES) applications for buildings. The current study numerically investigates the thermal response of a hybrid building envelope consisting of PCM and local organic waste materials for low-cost low-energy buildings. The local organic waste materials used are those whose disposal is usually done by burning, resulting in an immense amount of greenhouse gases. In the first phase, different waste materials are characterized to determine their thermophysical properties. In the second phase, a low-cost, commonly available PCM calcium chloride hexahydrate, CaCl2·6H2O, is integrated with a brick and corn husk wall to enhance the thermal storage in the building envelope to minimize energy consumption. Temperature distribution plots are primarily used for analysis. The results show a marked improvement in thermal comfort by maintaining a maximum indoor temperature of 27 °C when construction is performed with a 6% corn husk composite material embedded with the PCM, while under similar conditions, the standard brick construction maintained a 31 °C indoor temperature. It is concluded that the integration of the PCM layer with the corn husk wall provides an adequate solution for low-cost and low-energy buildings.

The rise in energy requirements and its shortfall in developing countries have affected socioeconomic life. Communities in remote mountainous regions in Asia are among the most affected by energy deprivation. This study presents the feasibility of an alternate strategy of supplying clean energy to the areas consisting of pristine mountains and forest terrain. Southeast Asia has a much-diversified landscape and varied natural resources, including abundant water resources. The current study is motivated by this abundant supply of streams which provides an excellent environment for run-of-river micro vertical axis water turbines. However, to limit the scope of the study, the rivers and streams flowing in northern areas of Pakistan are taken as the reference. The study proposes a comprehensive answer for supplying low-cost sustainable energy solutions for such remote communities. The suggested solution consists of a preliminary hydrodynamic design using Qblade, further analysis using numerical simulations, and finally, experimental testing in a real-world environment. The results of this study show that the use of microturbines is a very feasible option considering that the power generation density of the microturbine comes out to be approximately 2100 kWh/year/m2, with minimal adverse effects on the environment.

We present a new approach to nonlinear adaptive filtering based on Successive Linearization. Our approach provides a simple, modular and unified implementation for a broad class of polynomial filters. We refer to this implementation as the layered structure and note that it offers substantial computational efficiency over previous methods.A new class of Polynomial Autoregressive filters is introduced which can model limit cycle and chaotic dynamics. Existing geometric methods for modeling and characterizing chaotic processes suffer from several drawbacks. They require a huge number of data points to reconstruct the attractor geometry and performance is severely limited by noisy experimental measurements.We present a new method for processing chaotic signals using nonlinear adaptive filters. We demonstrate the modeling, prediction and filtering of these signals. We also show how the prediction error growth rate can be used to estimate the effective Lyapunov exponent of the chaotic map. Our approach requires orders of magnitude fewer data points and is robust to noise in the experimental data. Although reconstruction of the attractor geometry is unnecessary, the adaptive filter contains most of the geometric information.

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