PFC Topologies for AC to DC Converters in DC Micro-Grid

With increasing dominance of renewable energy resources and DC household appliances, the novelty of DC micro grid is attracting significant attention. The key interface between the main supply grid and DC micro grid is AC to DC converter. The conventional AC to DC converter with large output capacitor introduces undesirable power quality problems in the main supply current. It reduces system efficiency due to low power factor and high harmonic distortion. Power Factor Correction (PFC) circuits are used to make supply currents sinusoidal and in-phase with supply voltages. This paper presents different PFC topologies for single phase AC to DC converters which are analyzed for power factor (PF), total harmonic distortion (THD) and system efficiency by varying output power. Two-quadrant shunt active filter topology attains a power factor of 0.999, 3.03% THD and 98% system efficiency. Output voltage regulation of the presented active PFC topologies is simulated by applying a step load. Two-quadrant shunt active filter achieves better output voltage regulation compared to other topologies and can be used as grid interface

Peak to average power ratio based spatial spectrum sensing for cognitive radio systems

The recent convergence of wireless standards for incorporation of spatial dimension in wireless systems has made spatial spectrum sensing based on Peak to Average Power Ratio (PAPR) of the received signal, a promising approach. This added dimension is principally exploited for stream multiplexing, user multiplexing and spatial diversity. Considering such a wireless environment for primary users, we propose an algorithm for spectrum sensing by secondary users which are also equipped with multiple antennas. The proposed spatial spectrum sensing algorithm is based on the PAPR of the spatially received signals. Simulation results show the improved performance once the information regarding spatial diversity of the primary users is incorporated in the proposed algorithm. Moreover, through simulations a better performance is achieved by using different diversity schemes and different parameters like sensing time and scanning interval

Comparison of Hygienic Behavior of Exotic Honey Bee Apis mellifera L. and Indigenous Honey Bee Apis cerana of Pakistan

Indigenous and exotic honey bee species were evaluated for their hygienic behavior in the climatic condition of Peshawar Khyber Pakhtunkhwa, Pakistan. Colonies of equal strength from indigenous (Apis cerana) and exotic (Apis mellifera) species were selected for the study. The same colonies were tested in two seasons. Sealed brood were killed with different methods i.e pin killed and freeze killed. The uncapping of cells and brood removal was recorded at different intervals. Significant differences were recorded between hygienic behavior of both species of honey bees. Apis cerana showed significantly superior hygienic behavior than Apis mellifera in both seasons. At different intervals in both species significant differences were recorded. A significant difference was recorded after 12 and 24 hours between the species in both seasons. No significant differences were recorded after 48hours in both species. From the study it is concluded that indigenous honey bee species has superior hygienic behavior than exotic species

The impact of capital requirement and ownership structure on risk-taking by banks in Pakistan: Mediating role of profitability

This study examined the impact of capital regulation and ownership structure on risktaking by commercial banks in Pakistan while concentrating on the mediating role of profitability. Bank risk-taking was measured by the standard deviations of return on assets, return on equity and Z-SCORE. Capital regulation was measured by capital adequacy requirement, while ownership structure included private ownership, government ownership, and foreign ownership. Profitability was measured by return on assets (ROA). Data were collected from 21 commercial banks operating in Pakistan, comprising of nineteen domestic and two foreign banks over the period of 2005-2016. Thus, the sample size of the study was 252 observations (21 banks x 12 years). Two regression tests were used to examine the relationships between the variables that were under investigation. Multiple regression test with Driscoll-Kraay standard error method was used to examine the relationships of capital regulation and ownership structure with bank risk-taking, while the multiple regression model with bootstrapping procedure was used to test the mediating effect of profitability. The results revealed that capital regulation had a significant negative impact on variations in bank returns (standard deviations of return on equity and assets) but had a significant positive impact on insolvency risk (Z-SCORE). Furthermore, all ownership structures had a significant negative impact on Z-SCORE, but had a significant positive impact on the standard deviation of return on assets and standard deviation on return on equity except for foreign ownership structure. In addition, the results of the bootstrapping procedure revealed that profitability significantly mediated the relationship of ownership structure (private and foreign) and bank risk-taking. In contrast, no mediation of profitability was found between government ownership structure and capital regulation with bank risk-taking. This study facilitates regulators in the implementation of capital regulations with respect to owners’ behaviors regarding risk-taking and profitability

Cost Effective Bidirectional Power Transactions for Queueing Energy Requests in Smart Micro-Grids

This paper investigates real time problem of cost efficient energy distribution within smart micro-grids (SMG). The aggregator announces a day-ahead price of electricity, and is most often not fully aware of on spot availability of renewable resources. Sometimes, users also encounter estimation errors in their day-ahead energy procurement. In both situations an extra cost is incurred to aggregator or the users to fulfill their needs. This cost could be minimized by intelligently balancing the real time renewable generations with users load demands. The problem is more complex when there are a number of users communicating with each other and with the aggregator at the same time through a Digital Energy Management System (DEMS) for their demand requirements. It is very challenging for DEMS to ensure comfort level for its consumers while providing low cost electricity. Hence, we establish an optimization problem of curtailing the time average cost of electricity, under certain bounds of consumers satisfactions. We introduce load scheduling and energy transaction (LSET) control policy based on Lyapunov optimization theory to develop our proposed solution

Lisfranc’s dislocation and fracture in the Charcot Foot

AbstractDiabetic patients may present with Lisfranc’s Fracture Dislocation which may be confused with osteomyelitis. Rapid diagnosis and early intervention can prevent deformity. We suggest that the diagnosis of Charcot’s foot should be considered in any diabetic patient with unilateral swelling of lower extremity and/or foot

Novel QoS-aware proactive spectrum access techniques for cognitive radio using machine learning

Traditional cognitive radio (CR) spectrum access techniques have been primitive and inefficient due to being blind to the occupancy conditions of the spectrum bands to be sensed. In addition, current spectrum access techniques are also unable to detect network changes or even consider the requirements of unlicensed users, leading to a poorer quality of service (QoS) and excessive latency. As user-specific approaches will play a key role in future wireless communication networks, the conventional CR spectrum access should also be updated in order to be more effective and agile. In this paper, a comprehensive and novel solution is proposed to decrease the sensing latency and to make the CR networks (CRNs) aware of unlicensed user requirements. As such, a proactive process with a novel QoS-based optimization phase is proposed, consisting of two different decision strategies. Initially, future traffic loads of the different radio access technologies (RATs), occupying different bands of the spectrum, are predicted using the artificial neural networks (ANNs). Based on these predictions, two strategies are proposed. In the first one, which solely focuses on latency, a virtual wideband (WB) sensing approach is developed, where predicted relative traffic loads in WB are exploited to enable narrowband (NB) sensing. The second one, based on Q -learning, focuses not only on minimizing the sensing latency but also on satisfying other user requirements. The results reveal that the first strategy manages to significantly reduce the sensing latency of the random selection process by 59.6%, while the Q -learning assisted second strategy enhanced the full-satisfaction by up to 95.7%

Intelligent IoT Framework for Indoor Healthcare Monitoring of Parkinson's Disease Patient

Parkinson’s disease is associated with high treatment costs, primarily attributed to the needs of hospitalization and frequent care services. A study reveals annual per-person healthcare costs for Parkinson’s patients to be 21,482,withanadditional29,695 burden to society. Due to the high stakes and rapidly rising Parkinson’s patients’ count, it is imperative to introduce intelligent monitoring and analysis systems. In this paper, an Internet of Things (IoT) based framework is proposed to enable remote monitoring, administration, and analysis of patient’s conditions in a typical indoor environment. The proposed infrastructure offers both static and dynamic routing, along with delay analysis and priority enabled communications. The scheme also introduces machine learning techniques to detect the progression of Parkinson’s over six months using auditory inputs. The proposed IoT infrastructure and machine learning algorithm are thoroughly evaluated and a detailed analysis is performed. The results show that the proposed scheme offers efficient communication scheduling, facilitating a high number of users with low latency. The proposed machine learning scheme also outperforms state-of-the-art techniques in accurately predicting the Parkinson’s progression