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In mobile ad hoc network (MANET) cluster head selection is considered a gigantic challenge. In wireless sensor network LEACH protocol can be used to select cluster head on the bases of energy, but it is still a dispute in mobil ad hoc networks and especially when nodes are itinerant. In this paper we proposed an efficient cluster head selection algorithm (ECHSA), for selection of the cluster head efficiently in Mobile ad hoc networks. We evaluate our proposed algorithm through simulation in OMNet++ as well as on test bed; we experience the result according to our assumption. For further evaluation we also compare our proposed protocol with several other protocols like LEACH-C and consequences show perfection.

Earthquakes are natural disasters which may result in heavy losses. Accurate prediction of the time and intensity of future earthquakes can lead to minimizing losses due to earthquakes. A number of earthquake predictions have been proposed based on mathematical and statistical models. In this paper, we present an earthquake prediction technique using Bat Algorithm (BA) and Feed Forward Neural Network (FFNN). The BA is used to train the weights of the FFNN to predict future earthquakes on the basis of past input data. Experimental results show that our proposed approach is highly comparable and more stable than Back Propagation Neural Network (BPNN) with respect to accuracy.

Wind energy is a vital part of renewable energy sector that is increasingly becoming popular to reduce the adverse effect of traditional power production methods in increasing the global temperature. As the demand for wind energy increases, the sizes of the blades of wind turbines are also increasing with the availability of novel materials and manufacturing techniques. On the other hand, these very large wind turbines might be susceptible to design challenges and instability problems because of their sheer size which typically are not concerns for relatively smaller turbines. This has motivated the development of models to predict the unstable behavior of very large vertical axis wind turbines (VAWTs) and horizontal axis wind turbines (HAWTs). This work presents modeling method of rotor-platform system for offshore floating vertical axis wind turbines. Effect of structural design parameters on flutter instability of 2-bladed and 3-bladed VAWTs are studied. An analysis is presented on the effect of floating platform on flutter behavior of rigid body and flexible modes of vibration of the coupled system. A fundamental understanding of how the floating system impacts the resonance and flutter properties of VAWT is sought and presented.Further study has been performed on the impact of aerodynamic modeling assumptions that are conventionally implemented to predict flutter of wind turbines. The shortcomings of simplifying assumptions of standard aerodynamic theory have been demonstrated, and new aerodynamic model is developed to address those shortcomings. Then, this new model is applied to both horizontal axis wind turbines as well as vertical axis wind turbines. Comparative analysis is done of the effect of standard and new aerodynamic model in terms their predictive capability of flutter for both land-based and floating vertical axis wind turbines. Large number of horizontal axis wind turbines with varying sizes and geometry are studied for flutter and edgewise instability with the newly developed aerodynamic model. Similarly, vertical axis wind turbines are examined with the newly developed aerodynamic model.This study also aims at validating numerical models with experimental results. To achieve that goal, a subscale floating VAWT system is manufactured, and experimental test is performed on it to extract modal dynamic properties. The measured structural properties are used to calibrate the rotor model, and free decay test results are used to generate a floating platform model. Finally, the rotor and platform model are coupled and modal analysis (frequency analysis) is performed and the model is further refined by comparing the test results and model predictions.Key findings of this dissertation confirm that moving a VAWT from land-based to floating configuration has the potential to alleviate both resonance and flutter concerns. Developed new aerodynamic model shows higher flutter prediction of tower, propeller and edgewise modes of land-based and floating VAWT compared to the prediction by standard aerodynamic model. For large HAWT blades, the new aerodynamic model has more impact on 3-bladed case than on 2- bladed case in terms of flutter and edgewise instability RPM prediction. Validation study on modal dynamics of floating VAWT confirm reasonably accurate modeling of coupled rotor-platform floating model.

This paper presents a novel denoising approach based on smoothing linear and nonlinear filters combined with an optimization algorithm. The optimization algorithm used was cuckoo search algorithm and is employed to determine the optimal sequence of filters for each kind of noise. Noises that would be eliminated form images using the proposed approach including Gaussian, speckle, and salt and pepper noise. The denoising behaviour of nonlinear filters and wavelet shrinkage threshold methods have also been analysed and compared with the proposed approach. Results show the robustness of the proposed filter when compared with the state-of-the-art methods in terms of peak signal-to-noise ratio and image quality index. Furthermore, a comparative analysis is provided between the said optimization algorithm and the genetic algorithm.

Wireless Mesh Networks (WMN) is a key backhaul technology used in 802.11 networks to provide ubiquitous coverage to isolated areas that require high-speed connectivity. The multi-radio feature of WMN has enabled the mesh routers to derive the full benefits of multiple channels for providing parallel transmissions in a single collision domain. However, co-channel interfering links badly affect the channel capacity and force the mesh routers to switch the radio interface to other less interfering channel. In dynamic channel assignment, if the channel switches occur frequently, the traffic disruptions lead to excessive packet delays and drops. These problems are mostly observed in specific dense areas, where traffic saturation occurs. The existing schemes lack in properly identifying the bandwidth starved links. Therefore, the focus of this paper is to enhance the throughput and minimize the packet drops by critically identifying the bottleneck links and prioritize them for better channel assignments. The proposed metric exploits the statistical inference on dropped packets to determine the effect of interference on the achievable capacity of the links. The traffic load and the effective capacity are collectively used to identify the saturated links. The proposed metric has been evaluated through extensive simulations. The results demonstrate the validation of proposed metric with a considerable increase in performance.

This paper presents the design and manufacturing of two novel small floating Darrieus vertical axis wind turbines (VAWT) developed for a wind-wave basin test campaign. As with typical designs, the rotor design needed to satisfy the traditional structural safety requirements (such as strain, deflection, resonant-free dynamics) from design standards along with other manufacturing and assembly constraints. In addition, for this particular design, some special conditions are present as the facility (the wind-wave basin itself) and use of existing Floating Offshore-wind and Controls Advanced Laboratory (FOCAL) semi-submersible floating platform (originally designed for HAWT test) imposed an additional set of design requirements including wind speed and size constraints, and specific, target overturning moments and rotor mass. Addressing all these constraints (facility, existing hull, structural safety, and manufacturing) presented a challenging design task in this case, thus the focus of this paper is presenting the design approach and results leading to final designs satisfying all these competing requirements. Using the presented design process, two Darrieus troposkein-shaped vertical axis wind turbines (two-bladed and three-bladed versions) were designed and manufactured, after ensuring compliance with all the design requirements. The presented study can aid researchers interested in developing similar floating turbine test campaigns.

Coke production is expensive and requires stringent environmental controls. Pulverized coal injection (PCI) is effective in reducing blast furnace coke consumption however when injected at high rates it can lead to the accumulation of unburnt char. This study consisted of an investigation of four different North American coals, Fording, Thacker, Chisholm, and Pinnacle together with Japanese Cypress wood chips charred at various temperatures for different times in order to establish a fundamental understanding of the relationships between char structure and its influence on char reactivity. It was found that as char crystallinity increased, its reactivity decreased. Fording char was found to be 67% more reactive than Pinnacle char, and 47% more reactive than Thacker char. Graphite was the least reactive of the samples tested. On the other hand, Japanese Cypress contained a small amount of hydrogen and considerable oxygen, and formed nongraphitizing char structures that were highly reactive.

Transthyretin amyloid cardiomyopathy, an often unrecognized cause of heart failure, is now treatable with a transthyretin stabilizer. It is therefore important to identify at-risk patients who can undergo targeted testing for earlier diagnosis and treatment, prior to the development of irreversible heart failure. Here we show that a random forest machine learning model can identify potential wild-type transthyretin amyloid cardiomyopathy using medical claims data. We derive a machine learning model in 1071 cases and 1071 non-amyloid heart failure controls and validate the model in three nationally representative cohorts (9412 cases, 9412 matched controls), and a large, single-center electronic health record-based cohort (261 cases, 39393 controls). We show that the machine learning model performs well in identifying patients with cardiac amyloidosis in the derivation cohort and all four validation cohorts, thereby providing a systematic framework to increase the suspicion of transthyretin cardiac amyloidosis in patients with heart failure. Transthyretin amyloid cardiomyopathy is a treatable but often unrecognized cause of heart failure. We derived and validated a machine learning model based on medical diagnostic codes that identifies heart failure patients at risk for wild-type transthyretin amyloid cardiomyopathy.

This paper presents an improved structure of an asymmetrical single-phase multilevel inverter topology with reduced device count. The proposed topology achieves 19 voltage levels at the output using only 12 power switches and 3 DC sources. The topology can be easily extended, resulting in a modular topology with more voltage levels at higher voltages. Moreover, the reliability analysis of the proposed converter results in a higher mean time to fault. The simulation is performed in MATLAB/Simulink, and a hardware prototype is developed to validate the circuit’s performance. A low-frequency Nearest Level Control PWM technique is implemented to generate switching signals and achieves 4.30% THD in output voltage. The PLECS software is used for power loss and efficiency analysis, resulting in a maximum efficiency of 99.08%. The proposed converter has been compared with other MLI topologies to demonstrate its superiority. The results indicate that the proposed topology has proven superior and outperformed other topologies in various parameters, making it suitable for renewable energy applications.

The recent energy shortfall in Pakistan has prompted the need for the development of hydropower projects to cope with the energy and monetary crisis. Hydropower in the northern areas is available yet has not been explored too much. Focusing on the sustainable development goal (SDG) “Ensure access to affordable, reliable, sustainable and modern energy”, thirteen proposed sites were selected from upstream to downstream of the Hunza River for analysis. The head on all the proposed sites was determined by taking the elevation difference between the proposed turbine and the intake at all sites. The discharge on all proposed ungauged sites was determined using ArcGIS for watershed delineation and the area ratio method along with Soil Conservation Service–Curve Number (SCS-CN) by using gauged data of Hunza River provided by Water and Power Development Authority (WAPDA) Pakistan at Daniyor bridge Gilgit, Shimshal and the Passo tributaries of Hunza River. The Location Search Algorithm (LSA) approach used a multi-criteria decision-making tool (MDM) to make a decision matrix considering the location and constraint criteria and then normalizing the decision matrix using benefit and cost criteria, the relative weights were assigned to all criteria using a rank sum weighted method and the sites were ranked on the basis of the final score. The results revealed that Hunza River has a significant hydropower potential and based on the final score in the LSA approach, proposed site 13, site 4 and site 9 were concluded as the most promising sites among proposed alternatives. The proposed methodology could be an encouraging step for decision makers for future hydropower development in Pakistan.