Management Paradigm Change in Pak¬- Turk (International Schools & Colleges) After a Failed Military Coup in Turkey: A Case Study

As parents and students wished not to be closed these schools because the direct victim will be the students if any action form government is taken for shutting down the schools. These schools should be handed over to local management. About the issue of closing Pak-Turk Schools, Imran Khan, head of the leading political party, now the Prime Minister of Pakistan, said that Pakistan would respect the Turk government’s decision; however, he suggested an amicable solution of the issue so as to protect the future of the students as well as of the teachers. According to the press statement released from PTI Central Media Department, during the meeting they discussed the issues of mutual importance Now the local administration should develop diversification in vision and mission of the organizatio

Determinants of Voluntary and Involuntary Underemployment in Pakistan

Having to work in a sub optimal capacity is a socio economic problem which is apparently veiled but it is equally detrimental as having no work to do. This study intends to compare the demographic factors of Pakistan which determine underemployment and two sub components such as voluntary underemployment non-voluntary underemployment which lacked focus in past studies conducted in Pakistan. The present study filled this gap by measuring the different dimensions and the determinants of underemployment using the micro data from Labor Force Survey (2010-11). The estimates indicate that females, people living in rural areas and the province Khyber Pakhtunkhwa (KPK) have higher tendency to be voluntarily underemployed, head of households are less likely to be underemployed. Employees are less likely to be voluntary underemployed. Out of underemployed persons, only a small percentage of people have involuntary reasons for working less than 35 hours otherwise a high percentage of employed people have voluntary reasons. This shows the presence of voluntary underemployment at a very large extent in Pakistan

Closed-loop elastic demand control under dynamic pricing program in smart microgrid using super twisting sliding mode controller

Electricity demand is rising due to industrialisation, population growth and economic development. To meet this rising electricity demand, towns are renovated by smart cities, where the internet of things enabled devices, communication technologies, dynamic pricing servers and renewable energy sources are integrated. Internet of things (IoT) refers to scenarios where network connectivity and computing capability is extended to objects, sensors and other items not normally considered computers. IoT allows these devices to generate, exchange and consume data without or with minimum human intervention. This integrated environment of smart cities maintains a balance between demand and supply. In this work, we proposed a closed-loop super twisting sliding mode controller (STSMC) to handle the uncertain and fluctuating load to maintain the balance between demand and supply persistently. Demand-side load management (DSLM) consists of agents-based demand response (DR) programs that are designed to control, change and shift the load usage pattern according to the price of the energy of a smart grid community. In smart grids, evolved DR programs are implemented which facilitate controlling of consumer demand by effective regulation services. The DSLM under price-based DR programs perform load shifting, peak clipping and valley filling to maintain the balance between demand and supply. We demonstrate a theoretical control approach for persistent demand control by dynamic price-based closed-loop STSMC. A renewable energy integrated microgrid scenario is discussed numerically to show that the demand of consumers can be controlled through STSMC, which regulates the electricity price to the DSLM agents of the smart grid community. The overall demand elasticity of the current study is represented by a first-order dynamic price generation model having a piece-wise linear price-based DR program. The simulation environment for this whole scenario is developed in MATLAB/Simulink. The simulations validate that the closed-loop price-based elastic demand control technique can trace down the generation of a renewable energy integrated microgrid

Vacuum assisted closure-utilization as home based therapy in the management of complex diabetic extremity wounds

Objective: Vacuum assisted closure is a reported technique to manage complex wounds. We have utilized this technique by using simple locally available material in the management of our patients on outpatient basis. The objective of this study is to present our experience. Methods: This study was conducted from June 2011 to June 2013 at Dow University Hospital and Aga Khan University Hospital, Karachi. There were 38 patients managed with vacuum assisted closure. Mean age was 56±7.8 years. Twenty three patients presented with necrotizing fasciitis and 15 patients with gangrene. Lower limbs were involved in majority of the patients. Debridement or amputations were done. Vacuum dressing was changed twice weekly in outpatient department. Wounds were closed secondarily if possible or covered with split thickness skin graft in another admission. Results: All the wounds were successfully granulated at the end of vacuum therapy. Mean hospital stay was 7.5 days. Vacuum dressing was applied for a mean of 20 days. There was reduction in the size of the wound. Thirteen patients underwent secondary closure of the wound under local anesthesia, 18 patients required coverage with split thickness skin graft and 7 patients healed with secondary intention. Conclusion: Vacuum assisted closure appeared to be an effective method to manage complex diabetic wounds requiring sterile wound environment

A Novel Control Approach to Hybrid Multilevel Inverter for High-Power Applications

This paper proposes a hybrid control scheme for a newly devised hybrid multilevel inverter (HMLI) topology. The circuit configuration of HMLI is comprised of a cascaded converter module (CCM), connected in series with an H-bridge converter. Initially, a finite set model predictive control (FS-MPC) is adopted as a control scheme, and theoretical analysis is carried out in MATLAB/Simulink. Later, in the real-time implementation of the HMLI topology, a hybrid control scheme which is a variant of the FS-MPC method has been proposed. The proposed control method is computationally efficient and therefore has been employed to the HMLI topology to mitigate the high-frequency switching limitation of the conventional MPC. Moreover, a comparative analysis is carried to illustrate the advantages of the proposed work that includes low switching losses, higher efficiency, and improved total harmonic distortion (THD) in output current. The inverter topology and stability of the proposed control method have been validated through simulation results in MATLAB/Simulink environment. Experimental results via low-voltage laboratory prototype have been added and compared to realize the study in practice.publishedVersio

Maximum Power Extraction from a Standalone Photo Voltaic System via Neuro-Adaptive Arbitrary Order Sliding Mode Control Strategy with High Gain Differentiation

In this work, a photovoltaic (PV) system integrated with a non-inverting DC-DC buck-boost converter to extract maximum power under varying environmental conditions such as irradiance and temperature is considered. In order to extract maximum power (via maximum power transfer theorem), a robust nonlinear arbitrary order sliding mode-based control is designed for tracking the desired reference, which is generated via feed forward neural networks (FFNN). The proposed control law utilizes some states of the system, which are estimated via the use of a high gain differentiator and a famous flatness property of nonlinear systems. This synthetic control strategy is named neuroadaptive arbitrary order sliding mode control (NAAOSMC). The overall closed-loop stability is discussed in detail and simulations are carried out in Simulink environment of MATLAB to endorse effectiveness of the developed synthetic control strategy. Finally, comparison of the developed controller with the backstepping controller is done, which ensures the performance in terms of maximum power extraction, steady-state error and more robustness against sudden variations in atmospheric conditions

A System for True and False Memory Prediction Based on 2D and 3D Educational Contents and EEG Brain Signals

We studied the impact of 2D and 3D educational contents on learning and memory recall using electroencephalography (EEG) brain signals. For this purpose, we adopted a classification approach that predicts true and false memories in case of both short term memory (STM) and long term memory (LTM) and helps to decide whether there is a difference between the impact of 2D and 3D educational contents. In this approach, EEG brain signals are converted into topomaps and then discriminative features are extracted from them and finally support vector machine (SVM) which is employed to predict brain states. For data collection, half of sixty-eight healthy individuals watched the learning material in 2D format whereas the rest watched the same material in 3D format. After learning task, memory recall tasks were performed after 30 minutes (STM) and two months (LTM), and EEG signals were recorded. In case of STM, 97.5% prediction accuracy was achieved for 3D and 96.6% for 2D and, in case of LTM, it was 100% for both 2D and 3D. The statistical analysis of the results suggested that for learning and memory recall both 2D and 3D materials do not have much difference in case of STM and LTM

A sustainable approach for demand side management considering demand response and renewable energy in smart grids

The development of smart grids has revolutionized modern energy markets, enabling users to participate in demand response (DR) programs and maintain a balance between power generation and demand. However, users’ decreased awareness poses a challenge in responding to signals from DR programs. To address this issue, energy management controllers (EMCs) have emerged as automated solutions for energy management problems using DR signals. This study introduces a novel hybrid algorithm called the hybrid genetic bacteria foraging optimization algorithm (HGBFOA), which combines the desirable features of the genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) in its design and implementation. The proposed HGBFOA-based EMC effectively solves energy management problems for four categories of residential loads: time elastic, power elastic, critical, and hybrid. By leveraging the characteristics of GA and BFOA, the HGBFOA algorithm achieves an efficient appliance scheduling mechanism, reduced energy consumption, minimized peak-to-average ratio (PAR), cost optimization, and improved user comfort level. To evaluate the performance of HGBFOA, comparisons were made with other well-known algorithms, including the particle swarm optimization algorithm (PSO), GA, BFOA, and hybrid genetic particle optimization algorithm (HGPO). The results demonstrate that the HGBFOA algorithm outperforms existing algorithms in terms of scheduling, energy consumption, power costs, PAR, and user comfort

Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described