Ashfaq Ahmed

Effects of Job Stress on Employees Job Performance A Study on Banking Sector of Pakistan

Currently Bankers are under a great transaction of stress and due to many backgrounds of stress such as Excess, Role doubt, Role conflict, Concern for people, Contribution, Lack of feedback, possession up with rapid technologicalchange. Being in an inventive role, Career development, Organizational structure and climate, and recent episodic events. The thing which find out is stress. This study examines the relationship between job stress and job performance on bank employees of banking sector in Pakistan. The study examines the purpose model in relation of job stress and its impact on job performance by using sample of 144 participants. In participant the data ofsenior, graduate employees including customer services officers and managers of well reputed rising bank in Pakistan. The data were obtained through close ended questionnaire. A statistical test of regression, correlation and reliabilities were also confirmed. The results are significant with negative correlation between job stress and job performances and shows that job stress significantly reduces the performance of an individual. The results suggest to the organization that they have sustained a very health, cooperative and friendly environment within the team for better performance. Keywords: Job performance, Job stress, Effects of stress

Study of Magnetothermal Properties of Strontium Doped Lanthanum Manganite Nanoparticles for Hyperthermia Applications

Magnetic nanoparticles of strontium doped lanthanum manganite (LSMO) have been synthesized with Sr concentration x = 0.20 and 0.27 using the citrate gel technique. Magnetic and magnetothermal properties have been investigated with the objective of determining their specific absorption rates (SARs) and comparing them with those obtained using the linear response theory. Adiabatic magnetothermia measurements were carried out in an RF magnetic field of 800 A/m and 214 kHz. Both magnetic and thermomagnetic behaviors of the LSMO nanoparticles are observed to be governed by the strontium content of the samples. The specific absorption rate has been determined using experimental data and good quantitative agreement has been observed between experimentally determined and theoretically derived values. Zero-field-cooled thermal demagnetization measurements were used to obtain the Curie temperature TC, which was found to be 320 K for x = 0.20 and 350 K for x = 0.27. The suitability of these materials for magnetic hyperthermia and self-controlled hyperthermia applications has been demonstrated.

Thermochemical characterisation of Acacia auriculiformis tree parts via proximate, ultimate, TGA, DTG, calorific value and FTIR spectroscopy analyses to evaluate their potential as a biofuel resource

ABSTRACT Continuously increasing energy requirements coupled with environmental pollution have established pressure to utilise lignocellulosic biomass for energy production. Acacia auriculiformis is a fast-growing species capable of accumulating large quantities of biomass without requiring major agricultural inputs. The aim of this research was to investigate the thermochemical properties of its tree parts including phyllodes (leaves), trunk, bark and branches to utilise them as solid fuel to produce bioenergy. Thermogravimetric and derivative thermogravimetric (TGA and DTG ) analyses were performed to study the biomass degradation behaviour, which showed the decomposition of biomass in three major stages corresponding to the decomposition of hemicellulose, cellulose and lignin components. Fourier transform infrared (FTIR) analysis was carried out to determine the functional groups. Proximate analysis showed the weight percentages of moisture contents, volatile matter, fixed carbon and ash contents as 7.25–9.27%, 61.79–73.28%, 16.50–27.92% and 2.13–3.72%, respectively. Ultimate analysis showed the ranges of carbon, hydrogen and oxygen as 44.27–49.41%, 5.3–6.10% and 41.93–49.44% respectively, while lower values of sulphur and nitrogen components were reported which are encouraging from an environmental perspective. Higher heating values (HHV) for the parts were reported to range between 17.85 and 20.93 MJ/kg on a dry basis.

Residual interpolation for division of focal plane polarization image sensors

Division of focal plane (DoFP) polarization image sensors capture polarization properties of light at every imaging frame. However, these imaging sensors capture only partial polarization information, resulting in reduced spatial resolution output and a varying instantaneous field of overview (IFoV). Interpolation methods are used to reduce the drawbacks and recover the missing polarization information. In this paper, we propose residual interpolation as an alternative to normal interpolation for division of focal plane polarization image sensors, where the residual is the difference between an observed and a tentatively estimated pixel value. Our results validate that our proposed algorithm using residual interpolation can give state-of-the-art performance over several previously published interpolation methods, namely bilinear, bicubic, spline and gradient-based interpolation. Visual image evaluation as well as mean square error analysis is applied to test images. For an outdoor polarized image of a car, residual interpolation has less mean square error and better visual evaluation results.

Multiple Power Line Outage Detection in Smart Grids: Probabilistic Bayesian Approach

Efficient power line outage identification is an important step which ensures reliable and smooth operation of smart grids. The problem of multiple line outage detection (MLOD) is formulated as a combinatorial optimization problem and known to be NP-hard. Such a problem is optimally solvable with the help of an exhaustive evaluation of all possible combinations of lines in outage. However, the size of search space is exponential with the number of power lines in the grid, which makes exhaustive search infeasible for practical sized smart grids. A number of published works on MLOD are limited to identify a small, constant number of lines outages, usually known to the algorithm in advanced. This paper applies the Bayesian approach to solve the MLOD problem in linear time. In particular, this paper proposes a low complexity estimation of outage detection algorithm, based on the classical estimation of distribution algorithm. Thanks to an efficient thresholding routine, the proposed solution avoids the premature convergence and is able to identify any arbitrary number (combination) of line outages. The proposed solution is validated against the IEEE-14 and 57 bus systems with several random line outage combinations. Two performance metrics, namely, success generation ratio and percentage improvement have been introduced in this paper, which quantify the accuracy as well as convergence speed of proposed solution. The comparison results demonstrate that the proposed solution is computationally efficient and outperforms a number of classical meta-heuristics.

An insight to the performance of estimation of distribution algorithm for multiple line outage identification

Abstract Realtime information relating to line outages has significant importance to pre-empt against the power system blackouts. Realtime information can be obtained by using phasor measurement units (PMUs) facilitating the realtime synchronized observations of voltage and current phasors at buses being monitored. Different optimization formulations including but not limited to linear, integer, stochastic, mixed integer and NP hard combinatorial optimization have been used to manipulate these phasor measurements for the detection of line outages. Single and double line outages can be addressed using combinatorial optimization but these are infeasible to apply for the detection of multiple line outages as the increased number of lines increases computational complexity. To alleviate the exponentially increased complexities of these combinatorial optimization problems, while investigating for multiple line outage, evolutionary, Estimation of Distribution Algorithm is used. This method gives near optimal solution in which computational complexity and time is reduced efficiently. In this paper we scrutinize the use of phasor angle measurements to detect multiple power line outages. The proposed EDA is compared with binary particle swarm optimization (BPSO) algorithm, adaptive BPSO and genetic algorithm (GA) in terms of line outage detection performance, fitness convergence w.r.t. iterations and time consumption. The simulation results depict that the proposed EDA outperforms the other state of the art algorithms.

Swarm Intelligence Based Resource Management for Cooperative Cognitive Radio Network in Smart Hospitals

Intelligent and efficient wireless sensor devices (IEWSD) can greatly facilitate the working of paramedic staff in next generation health care facilities. The wireless network of IEWSDs is composed of tiny low power sensor devices (TLPSD), personal wireless hubs (PWH) and wireless receivers. High proliferation of these IEWSDs into new generation of health care centers has culminated numerous challenges. The major challenges include spectrum overloading, higher data rate requirements, achieving low power consumption and decreasing the computational complexity. In this paper, we propose a shared band cooperative cognitive radio network to tackle these challenges. To help the TLPSD, we use multiple PWHs to transmit sensed information to the sink node acting as the main controller. The use of multiple PWHs can add reliability, leverage the coverage and efficiency of the IEWSDs network in hospitals, nursing homes and health care facilities. We propose an efficient power allocation and PWH placement strategy to maximize the data rate under the cognitive radio interference constraint. The numerical results depict the efficacy of the proposed algorithm having low complexity.

Resource Management in Energy Harvesting Cooperative IoT Network under QoS Constraints

Cooperative communication with RF energy harvesting relays has emerged as a promising technique to improve the reliability, coverage, longevity and capacity of future IoT networks. An efficient relay assignment with proper power allocation and splitting is required to satisfy the network’s QoS requirements. This work considers the resource management problem in decode and forward relay based cooperative IoT network. A realistic mathematical model is proposed for joint user admission, relay assignment, power allocation and splitting ratio selection problem. The optimization problem is a mixed integer non-linear problem (MINLP) whose objective is to maximize the overall sum rate (bps) while satisfying the practical network constraints. Further, an outer approximation algorithm is adopted which provides epsilon-optimal solution to the problem with guaranteed convergence and reasonable complexity. Simulations of the proposed solution are carried out for various network scenarios. The simulation results demonstrate that cooperative communication with diversity achieves a better admission of IoT users and increases not only their individual data rates but also the overall sum rate of an IoT network.

Residual interpolation for 3-micropolarizer design of division of focal plane polarization image sensors

In this paper, we purpose interpolation technique for a 3-micropolarizer design of division of focal plane (DoFP) image sensor based on polarization residuals. The super pixel of 3-micropolarizer division of focal plane (DoFP) polarization image sensor records the first three (S0, S1, S2) at each frame but each specific pixel within the superpixel has a somewhat different field of view. The missing polarization information creates edges and nonconformities in the polarized images. The micropolarizer consists of a 3-micropolarizer filter array of 0°, 90°, and 135°. The DoFP image sensor output image remains 2D. We will demonstrate the performance of the algorithm on the intensity, degree of linear polarization (DoLP) and angle of linear polarization (AoP) images. We will further demonstrate that our proposed algorithm can estimate suitable missing polarization information for low-resolution images to generate high-resolution images.

Precise calibration of optical fiber sensor for ammonia sensing using multivariate analysis

Detection in chemical sensing which needs to be carried out in a specific controlled environment, becomes complex in multivariate environment. This complication is caused by chemical interference, sensor degradation or drifting of the signals with time. A minute drifting or overlapping of the signals affects the calibration, especially in the detection of sub-ppm level of concentration of any chemical species. The presence of other compounds can well interfere providing false positive readings, deterring calibration of the system in precise quantification of any compound. This problem is known to also happen in our optical fiber sensor for the detection of ammonia. A clad-modified polymer optical fiber sensor for ammonia detection is explored in this work where oxazine 170 per chlorate dye is used as a recognition element to detect ammonia. The sensor was tested in water media and the sensitivity of the sensor we found was 0.0006 ppm-1cm-2. However, the lower sensitivity causes significant overlaps in between signals corresponding to different concentrations. To resolve this problem, multivariate analysis method, such as principal component analysis (PCA) was explored to interpret the datasets for precision of measurement and classification of each concentration. PCA generates unique regression curve which represents each concentration of ammonia considering principle components. The significance of this research lies in its versatility dealing with the existing challenge of calibration of sub-ppm level measurement of any volatile compound, such as ammonia.