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With recent and unprecedented attention toward the implementation of Energy efficient building which aids in advocating sustainable construction, numerous attempts have made in establishing flexible approaches to which these practices will assist in savings cost of construction, improve the quality of indoor air as well as lowering the GHG emissions. However, the system is lacking proper and standardized methodology of implementations. Therefore, this, study dwelt in examining several factors capable of influencing the application of energy conservation practices in buildings, along with the advantages and barriers for implementation based on existing literatures, which focuses on raising awareness, providing information, implementing proper policies, and creating incentives to provide alternative solutions for building owners. As presented, study's outcomes offer a blueprint for comprehending the nuanced dynamics of building energy efficiency and lay out a clear course for further investigation. The study also, identifies significant factors which majorly influences building energy efficiency which include government supervision, design standards, construction quality, and energy-efficient materials. The study recommends utilizing cutting-edge innovations, building automation systems, and IoT devices to improve energy monitoring and conservation. [ABSTRACT FROM AUTHOR]

This study explores the influence of different metal gate combinations on the performance of dual-metal gate TFETs (DMG-TFET). The ON-state current in a TFET depends on band-to-band tunneling (BTBT) across the junction between the source and channel regions. Therefore, it is crucial to select a tunnel gate material with an appropriate work function to optimize the devices' overall performance. The present work explores the effect of dual metal gates having different work functions on tunnel barrier width of TFET. In this work, three distinct Double Gate TFET structures; Double Gate (DG) TFET, Dual Metal Gate (DMG) TFET, and Dual Metal Gate Hetero-Dielectric (DMG-HD) TFET are designed using TCAD device simulator. The proposed device (DMG-HD) TFET is benchmarked by considering various performance metrics such as ON-state current ION, OFF-state current IOFF, ON-to-OFF current ratio ION/IOFF, and sub-threshold slope (SS). This study also highlights the key finding of optimizing the length of dual metals and the work functions of metal gates (M-1) and (M-2) in TFETs. The results show that the proposed device (DMG-HD TFET) gives improved performance compared to the other two devices. The proposed TFET structure improves the ON-current by two orders (10-5 to 10-3 (A/μm)), ION/IOFF ratio by three orders (109 to 1012 (A/μm)) and sub-threshold swing (SS) by 23.82% compared to the conventional TFET device. [ABSTRACT FROM AUTHOR]

• Hydropower is a renewable source of energy with low energy generation tariff and higher energy conversion efficiency. • Performance of a Pelton turbines are affected by silt erosion. • The correlations are developed based on the historical operation parameters which can be used to accurately predict the performance of a machine. • Artificial Neural Network (ANN) based correlations accurately predict the plant load (i.e., generated power). • Correlations can be utilized to prepare an effective O&M strategy for the real-time condition monitoring of the machine. Hydropower is a renewable and reliable energy source that can be utilized to fulfil energy demands and promote energy sustainability. In hydropower plants, Pelton turbines are installed at high-head and low-discharge sites. In Pelton turbines, needle, nozzle, buckets, and splitter are most exposed to silt erosion. An eroded turbine takes more discharge than rated for generating the same amount of power. Therefore, to minimize the losses due to silt erosion, real-time condition-based monitoring of a hydropower plant is necessary. In this paper, correlations are developed using the historical data collected from the Urgam hydropower plant to predict the generated power. The artificial Neural Network (ANN) technique has been used to develop correlation, and the performance of this model has been compared with the model developed using the Curve Fitting technique. Based on the comparison, it has been found that the ANN model performs better than the curve-fitting model in forecasting the generated power with an R2-value of 0.9522 and Mean Absolute Percentage Error (MAPE) of 2.40% at 3.1515% Root Mean Square Percentage Error (RMSPE). The developed model can be utilized to plan a maintenance schedule of machines based on real-time conditions. It can also help to decide which machine should be taken in maintenance first. [ABSTRACT FROM AUTHOR]

This article, for the first time, presents an energy harvesting system (EHS) for harvesting the energy from the continuous time-varying energy harvesters using the fractional open-circuit-voltage (FOCV) based dynamic and adaptive maximum power point tracking (MPPT). The proposed EHS includes an energy harvester, a start-up circuit, a DC-DC boost converter, a DC-DC buck converter, and a control circuit. In the FOCV based MPPT, a fraction of the open-circuit-voltage (OCV) of the energy harvester, is maintained at the output of the energy harvester (input of a DC-DC boost converter). In the proposed work, OCV of the energy harvester is dynamically and adaptively sampled depending on the ambient conditions and the maximum available power from the energy harvester. This helps in improving the tracking efficiency of the proposed MPPT circuit for the time-varying energy harvester by more than 10 %. The boost converter switching time period instants are utilized for sampling the OCV of the energy harvester. A 12-stage Dickson based charge pump along with a 5-stage ring oscillator with a modified clock distribution is used for the start-up. Post-layout simulated results of the proposed EHS are presented in the 180 nm mixed-mode CMOS technology. The tracking efficiency and the boost converter efficiency for a wide range of the available input power (30 nW to 4 mW) compatible with many time-varying ambient energy harvesters in both indoor and outdoor places are presented. For a time-varying energy harvester, the tracking efficiency is more than 96%. For a source resistance of 100 kΩ , the boost converter's efficiency is 60.09% and 90.77% at available power of 56 nW and 1.6 μ W , respectively. [ABSTRACT FROM AUTHOR]

Discusses the paper "Estimating Vehicle Fuel Consumption and Emissions Based on Instantaneous Speed and Acceleration Levels," by Kyoung Ahn, Hesham Rakha, Antonio Trani, and Michel Van Aerde. Points for consideration in the methodology.