Seismic risk assessment for developing countries : Pakistan as a case study

Modern Earthquake Risk Assessment (ERA) methods usually require seismo-tectonic information for Probabilistic Seismic Hazard Assessment (PSHA) that may not be readily available in developing countries. To bypass this drawback, this paper presents a practical event-based PSHA method that uses instrumental seismicity, available historical seismicity, as well as limited information on geology and tectonic setting. Historical seismicity is integrated with instrumental seismicity to determine the long-term hazard. The tectonic setting is included by assigning seismic source zones associated with known major faults. Monte Carlo simulations are used to generate earthquake catalogues with randomized key hazard parameters. A case study region in Pakistan is selected to demonstrate the effectiveness of the method. The results indicate that the proposed method produces seismic hazard maps consistent with previous studies, thus being suitable for generating such maps in regions where limited data are available. The PSHA procedure is developed as an integral part of an ERA framework named EQRAM. The framework is also used to determine seismic risk in terms of annual losses for the study region

مثنوی معنوی کے ابتدائی اشعار میں اختلاف متن کا جائزہ

  Masnavi Manavi is one of the most influential books of world. It has been popular among educated people in every region of the world. About 700 manuscripts of this fantastic book are preserved in libraries all over the world especially in sub-continent. Due to this multiplicity of manuscripts, there has been considerable variation in the text of Masnavi that starts from the very first verse. In this article, the variation in text of the beginning verses of the Masnavi has been reviewed with reference to eighteen manuscripts dating from 677AH to 1248AH.    

Navigating Debt Sustainability: An In-Depth Analysis of Debt Coverage and other Performance Drivers in Pakistani Textile Sector

Our research sought to ascertain the impact of capital structure, particularly debt financing, on firm liquidity using panel data of 91 Pakistani textile companies from 2017 to 2021. Data was collected from balance sheets of the interested firms which are available at web-site of State Bank of Pakistan (SBP). A well-known econometric technique known as 'Panel Corrected Standard Error (PCSE)' has been employed to acquire precise coefficient values of the interested variables. Findings of the current study demonstrate that Debt Coverage Ratio (DCR) has a significant association with Current Ratio, Return on Assets, Financial Leverage, and Business Growth. Though, Tobin’s Q, Firm age and Firm size have shown insignificant association with DCR. So, present study contributes to the understanding of capital structure choices and financial well-being within textile industry sector of Pakistan. Moreover, our study highlights the trade-off between leverage and financial stability; indicating the role of growth with debt management. These findings offer valuable insights for investors, lenders, and corporate decision-makers

Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks

In the field of ophthalmology, diabetic retinopathy (DR) is a major cause of blindness. DR is based on retinal lesions including exudate. Exudates have been found to be one of the signs and serious DR anomalies, so the proper detection of these lesions and the treatment should be done immediately to prevent loss of vision. In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. Recently, deep CNNs were individually applied to solve the specific problems. But, pretrained CNN models with transfer learning can utilize the previous knowledge to solve the other related problems. In the proposed approach, initially data preprocessing is performed for standardization of exudate patches. Furthermore, region of interest (ROI) localization is used to localize the features of exudates, and then transfer learning is performed for feature extraction using pretrained CNN models (Inception-v3, Residual Network-50, and Visual Geometry Group Network-19). Moreover, the fused features from fully connected (FC) layers are fed into the softmax classifier for exudate classification. The performance of proposed framework has been analyzed using two well-known publicly available databases such as e-Ophtha and DIARETDB1. The experimental results demonstrate that the proposed pretrained CNN-based framework outperforms the existing techniques for the detection of exudates

Deep Learning Approach for Automatic Microaneurysms Detection

In diabetic retinopathy (DR), the early signs that may lead the eyesight towards complete vision loss are considered as microaneurysms (MAs). The shape of these MAs is almost circular, and they have a darkish color and are tiny in size, which means they may be missed by manual analysis of ophthalmologists. In this case, accurate early detection of microaneurysms is helpful to cure DR before non-reversible blindness. In the proposed method, early detection of MAs is performed using a hybrid feature embedding approach of pre-trained CNN models, named as VGG-19 and Inception-v3. The performance of the proposed approach was evaluated using publicly available datasets, namely “E-Ophtha” and “DIARETDB1”, and achieved 96% and 94% classification accuracy, respectively. Furthermore, the developed approach outperformed the state-of-the-art approaches in terms of sensitivity and specificity for microaneurysms detection

Organochlorine pesticides (OCPs) contaminants in sediments from Karachi harbour, Pakistan

Mangrove swamps, intertidal mudflats and creeks of backwaters represent main feature of Karachi harbour area. Karachi harbour sediment is under continuous influence of untreated industrial effluents and domestic waste discharged into the Harbour area vra Lyari River Sediment samples from sixteen locations were collected to evaluate the levels of contamination of organochlorine pesticides (OCPs) in Karachi harbour and adjoining areas. It has been observed that residual concentrations of various organochlorine pesticides were considerably higher in the semienclosed area of the upper Harbour in the vicinity of the discharge point of Lyari River. The residue of DDT mainly its metabolites (DDE and DDD) were widely distributed and have been detected in most of the sediment samples in relatively higher concentrations as compared to other OCPs. The higher levels of the DDTs would attribute to low tidal flushing of the area The high proportion of pp'-DDE in the most sediment sampled (41-95%) suggested old inputs of DDTs in the environment. Ratio of SDDT and DDT was in the range of 0,04 -0 24 at all locations which also reflects that the discharges of DDT were negligible in the Harbour area. This may be due to the restrictions being implemented on the use of DDTs and Pakistan has also switched over to natural pest control or using safer formulas. The data obtained during the study showed that concentration levels of other pesticides such as HCHs, HCB and Cyclodienes in the sediment were generally lower than the threshold levels known to harm wildlife by OCPs. The results clearly indicate that elevated concentration of organochlorine pesticides (OCPs) in the marine sediment of Karachi harbour and adjoining area was localized and much lower than the concentrations reported from neighbouring and regional countries which suggests/confirms that the present use of pesticide in Pakistan is environmentally safe

Wastewater driven trace element transfer up the food chain in peri-urban agricultural lands of Lahore, Pakistan

In peri-urban agricultural lands of Lahore city, untreated wastewater from trans-boundary Hudiara drain is widely used for agriculture. The irrigated water may pose a hazard of trace element (TE) contamination in agricultural produce and consequently threat to human health. This study was designed to investigate the quality and transferability of TE contamination in water, soil, fodder and buffalo milk. Samples from the 4 assets and products were collected at upstream, midstream and downstream sites along Hudiara drain. Potentially toxic elements (Cd, Cu, Ni and Zn) were analyzed using standard methods. Physicochemical analysis of water, bioaccumulation factor (BAF) from soil to fodder and Pearson correlation of metal contamination in water, soil, fodder and milk were determined. TE contamination increased from upstream to downstream site. Highest Cd concentration in water, soil, fodder and milk was 0.29 mg/L (downstream), 1.10 mg/kg (midstream), 2.12 mg/kg (downstream) and 0.29 mg/L (downstream), respectively, which surpassed permissible limits for all the 4 mediums. Similar results were found with Cu, Ni and Zn which increased downstream and the concentrations higher than permissible limits in all mediums except soil. Significantly high Hazard Index (HI) values were recorded for irrigation wastewater (23.25–31.75), fodder (7.41–11.13), and milk (11.12–17.85), which were increasing down the stream of drain. All 4 metals have shown strong positive correlation among water, soil, fodder and milk, showing transferability risk up to food chain. Transfer of TE from soil to fodder was highest for Ni (BAF 8.44) and lowest for Zn (BAF 0.41) with the following ascending trend: Zn < Cd < Cu < Ni. The physicochemical parameters of drain water also did not meet the permissible limits for wastewater irrigation. Use of untreated water of Hudiara drain for agriculture in peri-urban areas of Lahore needs to be stopped until appropriate treatment and reuse

PLDPNet: End-to-end hybrid deep learning framework for potato leaf disease prediction

Agricultural productivity plays a vital role in global economic development and growth. When crops are affected by diseases, it adversely impacts a nation’s economic resources and agricultural output. Early detection of crop diseases can minimize losses for farmers and enhance production. In this study, we propose a new hybrid deep learning model, PLDPNet, designed to automatically predict potato leaf diseases. The PLDPNet framework encompasses image collection, pre-processing, segmentation, feature extraction and fusion, and classification. We employ an ensemble approach by combining deep features from two well-established models (VGG19 and Inception-V3) to generate more powerful features. The hybrid approach leverages the concept of vision transformers for final prediction. To train and evaluate PLDPNet, we utilize the public potato leaf dataset: early blight, late blight, and healthy leaves. Utilizing the strength of segmentation and fusion feature, the proposed approach achieves an overall accuracy of 98.66%, and F1-score of 96.33%. A comprehensive validation study is conducted using Apple (4 classes) and tomato (10 classes) datasets achieving impressive accuracies of 96.42% and 94.25%, respectively. These experimental findings confirm that the proposed hybrid framework provides more effective and accurate detection and prediction of potato crop diseases, making it a promising candidate for practical applications