46 research outputs found
Folio
No full textPrincipal's Message; Sajjad Zafar Irfani-Editorial. pp. 1; Amar Bin Adil-Article-A Poet's Search for Values. pp. 2-6; Arif A. Khan-Article-The House of God. pp. 7-10; Sajjad Zafar Irfani-Article-On Education. pp. 11-15; Tanvir Mohsin Khan-Essay-Success. pp. 16-17; Muhammad Asif-Ah! Exam. pp. 18-19; Mohayuddin Abu Bakar-Disillusionment. pp. 20-21; Gohar Majid Sheikh-Good Temper. pp. 22-23; Shahid Imtiaz-Poetry-Aspiration of Freedom. pp. 25; Sports: 96th Annual Athletic Championship, February 15-16, 1983. pp. 26-27; Dr. E. J. Sinclair Passes Away. pp. 30-31; Literacy. pp. 32-33; Folio '83 [Urdu-Punjabi]. 199 p.Quaid-e-Azam. after title; Prof Nasim Zakaria, Principal. after Principal's Message; Editors. before editorial; Dr E. J. Sinclair. before page 31; Prof Mir Muhammad Yaquib. after page 33; Department of Political Science. after page 33; Rana Iftikhar Ahmad, President Student Union. after page 33; Members of Student Union. after page 33; Editors (Urdu). before Urdu content
Fabrication of band gap engineered nanostructured tri-metallic (Mn-Co-Ti) oxide thin films
No full textIntegrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method
No full textThis research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices
Integrating Multiple Datasets in Google Earth Engine for Advanced Hydrological Modeling Using the Soil Conservation Service Curve Number Method
No full textThis research explores the feasibility of using cloud computing and open data sources for hydrological modeling, specifically leveraging Google Earth Engine (GEE) and the Soil Conservation Service Curve Number (SCS CN) method to estimate runoff. The SCS CN approach is commonly applied in simulating rainfall-runoff processes and is effective for estimating water inflow into rivers, lakes, and streams. Google Earth Engine provides a range of functionalities, including algorithms for rapid data manipulation and visualization, and access to extensive global remote sensing and geographic information system (GIS) datasets. The study introduces an algorithm developed in GEE to analyze precipitation data and generate antecedent moisture condition (AMC) maps. This algorithm integrates MODIS land use/land cover (LULC) data with USDA soil texture data to classify hydrological soil groups. Runoff estimation utilizes three datasets: CHIRPS, GPM, and TRMM. A thorough analysis of the rainfall-runoff relationship in the Mangla watershed from 2005 to 2015 is conducted. The study quantifies runoff estimates from each dataset and performs comparative analysis to validate the accuracy and reliability of the hydrological modeling. Over the ten-year period (2005-2015), significant fluctuations in average rainfall and runoff levels are observed, with notable seasonal patterns. The highest average precipitation of 1412.194 mm occurred in 2015, resulting in an average runoff of 215.021 mm. Conversely, 2009 recorded the lowest average precipitation of 672.808 mm and an average runoff of 78.476 mm. The accuracy of the modeled runoff observations is validated using meteorological data from the Pakistan Meteorological Department (PMD), Water and Power Development Authority (WAPDA), and Climate Forecast System Reanalysis (CFSR). In 2008, 2009, and 2010, CHIRPS consistently demonstrated better accuracy compared to GPM and TRMM, with accuracies of 90%, 79%, and 86% respectively. Additionally, a sensitivity analysis of the SCS CN model parameters reveals the effects of initial abstraction and Curve Number values on runoff estimation. In conclusion, this research enhances the understanding of hydrological processes in monsoon-affected regions and offers valuable recommendations for implementing sustainable water resource management practices
Controlled hydrothermal carbonization of wood-derived lignin-rich lignocellulose: Redefining pyrolytic pathways to tailored biochar and hydrogen-enriched syngas
No full textThis research illustrates the efficacy of hydrothermal carbonization (HTC) as a pretreatment method to improve the pyrolytic performance of wood-derived lignin-rich lignocellulosic biomass (LB), supported by thorough characterization of its derived products such as syngas, tar, and biochar. A systematic comparison of non-HTC-treated LB and HTC-treated LB through their respective pyrolytic-derived biochar (NLB, HLB) obtained across temperatures (400-1000°C) revealed their basic structural and reactivity variations. HTC resulted in a new carbonyl peak with a 28 % increase in Cdouble bondO concentration in derived biochar, with partial aromatization evidenced by Cdouble bondC bonds at 1509 cm-¹ . Spectroscopic analysis confirmed that HTC promoted a defective carbon structure in derived biochar while enhancing its crystallinity and maintaining its integrity even at higher temperatures. XPS analysis demonstrated that at 1000°C, HLB-T10 retained active oxygen functionalities, while its associated pyrolytic products H2 and CO boosted from 22.45 % to 40.4 % and 32.3–33.4 %, respectively, with drastically lowered CO₂ emissions from 39.95 % to 11.5 %. Regulated deoxygenation routes cause tar composition to shift toward desirable aromatic chemicals. This comprehensive strategy offers a sustainable valorization technique that increases syngas generation efficiency, lowers emissions, and optimizes biorefinery product selection.This research was sponsored by the Fundamental Research Funds for the Central Universities, along with the FLEXBY (GA-101144144), and (JDC2022-048533-I) Projects.Peer reviewe
A modified truncated distribution for modeling the heavy tail, engineering and environmental sciences data.
No full textTruncated models are imperative to efficiently analyze the finite data that we observe in almost all the real life situations. In this paper, a new truncated distribution having four parameters named Weibull-Truncated Exponential Distribution (W-TEXPD) is developed. The proposed model can be used as an alternative to the Exponential, standard Weibull and shifted Gamma-Weibull and three parameter Weibull distributions. The statistical characteristics including cumulative distribution function, hazard function, cumulative hazard function, central moments, skewness, kurtosis, percentile and entropy of the proposed model are derived. The maximum likelihood estimation method is employed to evaluate the unknown parameters of the W-TEXPD. A simulation study is also carried out to assess the performance of the model parameters. The proposed probability distribution is fitted on five data sets from different fields to demonstrate its vast application. A comparison of the proposed model with some extant models is given to justify the performance of the W-TEXPD
Molding robust S-box design based on linear fractional transformation and multilayer Perceptron: Applications to multimedia security
No full textThis study introduces a novel and refined approach for generating exceptionally efficient S-boxes. The proposed methodology employs a hybrid approach that combines linear fractional transformation (LFT) with a multilayer perceptron (MLP) architecture. This method makes use of a perceptron with three layers: input, hidden, and output. Each layer's neuron count is fine-tuned to conform to the S-box layout. In addition, a threshold nonlinear transformation is utilized to increase nonlinearity, and a novel algorithm for boosting nonlinearity is introduced. The utilization of both LFT and MLP approaches has led to the development of S-boxes that possess nearly ideal average nonlinearity values, surpassing those that have been presented in literature. Notably, one S-box achieved an exceptional nonlinearity score of 114.50. Furthermore, to demonstrate how well the S-box works, this study also employs it in an image encryption application
Optimization of conditions for improved solar energy harvesting application by hydrothermally grown TiO2 nanorods
No full textIn this study, using the optimum annealing temperature and time for hydrothermally grown TiO 2 nanorods, photooxidation of water at different pH values of the electrolyte solution is investigated. The composition, crystallinity and topographic studies of films, sintered at different temperatures of 200–500 °C for 2 h and annealed for 1–4 h at 400 °C, were evaluated by X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy and field emission scanning electron microscopy. The sintering at high temperature and for longer time demonstrates an increase in crystallinity, but at the same time agglomeration of nanorods for prolonged heating and at high temperature leads to cracking at the surface of the films. Further, UV–Vis spectroscopic studies revealed prolonged heating and high-temperature sintering resulted in a red shift in light absorption edge of the films. The chronoamperometric measurement results under regular interrupted chopping revealed that the sample annealed at 400 °C for 2 h gives the best photoelectrochemical response with photocurrent density of 522 µA cm − 2 using 0.5M NaOH. The chronoamperometric response under different pH values of 13.7, 7.2 and 2.5 proved that TiO 2 gives the best response under a basic pH of 13.7 and the least under acidic media. The electrochemical impedance studies provided an insight into the charge transfer mechanism under dark and illumination with R ct value of 1188.8 Ω under dark and 164.5 Ω under light conditions for the film annealed at 400 °C for 2 h. [Figure not available: see fulltext.]. © 2019, Iranian Chemical Society
Stimulation of Employees’ Green Creativity through Green Transformational Leadership and Management Initiatives
No full textDrawing upon the componential theory of creativity and social information processing theory, this research elucidates how and why the synergy of green HR practices and green strategies stimulate green creativity. It also explores the possible mediation effect of green transformational leadership (TFL) on the relationship between organizational interventions and green creativity. Survey questionnaires were used to collect data from managers working in large manufacturing firms. The structural equation modeling technique was applied to test the hypothesized relationships. Findings of research revealed that green management initiatives and green TFL stimulate green creativity. Moreover, the intervening impact of green TFL on the relationship between the aforementioned relationships was also established. Policymakers should devise green strategies and provide support to green HR practices for the stimulation of green creativity, whereas HR managers must ensure the compatibility of HR functions with corresponding organizational green strategies. Employees involved in green creative behaviors should be rewarded and retained. Training must be provided in order to keep employees abreast of the latest practices for environment conservation. Furthermore, managers should exhibit a green TFL style to advance green management initiatives and fuel green creativity among employees. This study highlights the significance of the synergy between green HR practices and the firm’s green strategies to stimulate employees’ green creativity. Furthermore, green management initiatives were also found to be the contextual precursor of green TFL, which enhances our understanding of the green TFL style. Lastly, the mediation effect of green TFL implies that it can serve as a proximal HR outcome to implement the organizational green agenda
Design and optimization of nonlinear component of block cipher: Applications to multimedia security
No full textNowadays, various image encryption schemes based on chaotic systems have been developed, each of them has
its own limitations and strength in terms of security and computational speed. The proposed image encryption
scheme utilizes 2-D maps without disturbing their mathematical structure, characterized by topological features
such as chaotic behavior and fractal properties, namely the Zaslavasky, Bakers, and Henon Maps. This approach
utilizes both confusion and diffusion stages to achieve high levels of security against various attacks. The
confusion stage utilizes chaotic values to muddle the rows and columns of the image, reducing the correlations
between neighboring pixels, while the diffusion step achieves the avalanche effect with 2D Bakers map and
Henon map. The proposed image encryption scheme is analyzed thoroughly to evaluate its security and performance. To evaluate the security and computational efficiency of the proposed image encryption method,
various analysis such as correlation, contrast, entropy, energy, homogeneity, and performance analyses are
conducted. Moreover, the three proposed S-boxes are also tested to evaluate their effectiveness using cryptographic analysis tests such as nonlinearity, strict Avalanche criterion, differential probability, linear probability,
and bit independence criterion, which we also utilized in our proposed image encryption scheme
