Prevalence of Cerebrospinal Fluid Leak in Traumatic Head Injury at a Tertiary Care Center

Background: Cerebrospinal fluid circulates around the surface of brain and spinal cord and through the brain’s ventricles. CSF leak is a condition that occurs when the CSF leaks through a defect in the dura or skull and out through the ear or nose. The most common causes of CSF leak include head injury, brain and sinus surgery. The objective of this study was to determine the frequencies of post-traumatic cerebrospinal fluid leak in traumatic head injury. Material and Methods: A descriptive case series was carried out in the Department of Neurosurgery, Hayatabad Medical Complex, Peshawar for a period of 1 year, from 1st February 2016 to 31st January 2017. A total of 422 patients presenting within 48 hours of acute trauma to the head were included in a consecutive manner and followed up till 7th day to determine the CSF leak. Results: The mean age group of our sample was 37.37 + 12.3 years of which 79.6% were male patients and 20.4% female patients. Most of the patients (55.5%) were ≤ 40 years of age. CSF leak was observed in 5.2% of patients, with otorrhea seen in 2.1% patients and rhinorrhea in 3.1% patients, respectively. Conclusion: CSF leak is quite common in our population after acute trauma to the head. The high prevalence may be due to high frequency of accidents in our society with high velocity impact and more commonly seen in the younger age group (≤ 40 years)

Deep learning-based change detection in remote sensing images:a review

Images gathered from different satellites are vastly available these days due to the fast development of remote sensing (RS) technology. These images significantly enhance the data sources of change detection (CD). CD is a technique of recognizing the dissimilarities in the images acquired at distinct intervals and are used for numerous applications, such as urban area development, disaster management, land cover object identification, etc. In recent years, deep learning (DL) techniques have been used tremendously in change detection processes, where it has achieved great success because of their practical applications. Some researchers have even claimed that DL approaches outperform traditional approaches and enhance change detection accuracy. Therefore, this review focuses on deep learning techniques, such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectral, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted. In the end, some significant challenges are discussed to understand the context of improvements in change detection datasets and deep learning models. Overall, this review will be beneficial for the future development of CD methods

Transgenic Bt Cotton: Effects on Target and Non-Target Insect Diversity

Occurrence of diversity in ecosystem sustains particular characteristic of a biological community and also ensures stability of the community. Transgenic crops may affect insect biodiversity by unintended impacts on non-target arthropod population. For example, transgenic GM cotton specific to target lepidopterous pests can change the cotton pest spectrum and may induce the growth of new harmful pest species having no pest status. The change in species composition may influence IPM approach in cotton crop. The results of authors’ research studies as well as global impact indicate that GM cotton is highly specific to target pests and has no unintended impact on non-target insect population. GM cotton provides significant season-long field control of target pests (Helicoverpa armigera, Earias spp. and Pectinophora gossypiella), with no significant control of Spodoptera species. The decreased insecticide use in GM cotton has a positive impact on beneficial insect populations and can increase the stability of rare species. Bt cotton has no resistance against non-target sucking insect pests. As GM cotton has no adverse effects on the non-target insect population and can reduce the use of broad-spectrum insecticides, it can become an important tool of IPM program in cotton agro-ecosystem of Pakistan

Combined Influence of Fly Ash and Recycled Coarse Aggregates on Strength and Economic Performance of Concrete

Recycled coarse aggregates (RCA) and fly ash (FA) are materials with least to very low global warming potential. Considering long term strength and durability, various studies have suggested to use RCA in concrete with FA. This research paper deals with the strength and economic performance of concrete made with individual and combined incorporation of FA and RCA. Nine different mixtures of concrete were prepared by varying the incorporation levels of RCA and FA. 0% RCA, 50% RCA and 100% RCA were used in concrete with three different levels of FA (0%FA, 20%FA, and 40%FA). The compressive strength of each mixture of concrete was determined at the age of 3, 28, 90 and 180 days. To evaluate economic performance cost of 1 m3 of each mixture of concrete was compared to that of the control mixture having 0% RCA and 0% FA. Results showed that RCA was detrimental to the compressive strength of concrete at all ages, whereas, FA reduced early strength but improved the strength at later ages of testing i.e. 90 and 180 days. FA plus RCA mixes also showed lower early age strength but gained higher strength than conventional concrete at the age of 180 days. RCA did not reduce the cost of concrete effectively. FA despite having a very high transportation cost, it reduced the cost of concrete efficiently. FA did not only reduce the cost of binder but also lower the demand of plasticizer by improving workability. Cost to strength ratio (CSR) analysis also indicated that FA significantly improve the combined economic and strength performance of RCA concrete mixes

Mentoring and its Effects on Turnover Intensions in Perspective of Pakistan’s Telecom Sector

In today’s era of towering competition the retention of workforce is highly desirable for long term success of any organization. The employees serve as backbone for any organization and are responsible for attaining the laid down objectives of the organization. The ongoing study investigates the effects of mentoring on turnover intensions of employees working in telecom sector of Pakistan. Researchers retrieved data with the help of questionnaires based on five point likert scale from almost 300 employees working in telecom organizations of Pakistan. Multiple regressions were used to analyze the collected data. Result shows that mentoring mostly is negatively associated with turnover intension because employee was not satisfied with mentoring and commitment in employees is only due to experienced learning which helps to develop additional skills in employee

Multihopping Multilevel Clustering Heterogeneity-Sensitive Optimized Routing Protocol for Wireless Sensor Networks

Effective utilization of energy resources in Wireless Sensor Networks (WSNs) has become challenging under uncertain distributed cluster-formation and single-hop intercluster communication capabilities. So, sensor nodes are forced to operate at expensive full rate transmission power level continuously during whole network operation. These challenging network environments experience unwanted phenomena of drastic energy consumption and packet drop. In this paper, we propose an adaptive immune Multihopping Multilevel Clustering (MHMLC) protocol that executes a Hybrid Clustering Algorithm (HCA) to perform optimal centralized selection of Cluster-Heads (CHs) within radius of centrally located Base Station (BS) and distributed CHs selection in the rest of network area. HCA of MHMLC also produces optimal intermediate CHs for intercluster multihop communications that develop heterogeneity-aware economical links. This hybrid cluster-formation facilitates the sensors to function at short range transmission power level that enhances link quality and avoids packet drop. The simulation environments produce fair comparison among proposed MHMLC and existing state-of-the-art routing protocols. Experimental results give significant evidence of better performance of the proposed model in terms of network lifetime, stability period, and data delivery ratio

A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics

The Impact of Advertisement on Customer Perception: A Case of Telecom Sector of Pakistan

The study was conducted on the topic “The impact of advertising on customer perception: A case of telecom sector of Pakistan”. The objective of this study was to evaluate the advertising of Mobilink and Warid (Jazz Company). The purpose was to observe the correct views of subscribers concerning diverse advertisements of individual companies. The objectives of the current study were obtained by means of primary and secondary data collection that was collected from users of these companies through questionnaires and from internet, the media and earlier accomplished case and research studies on related topics. Convenience sampling technique was used to collect the data from respondents. Data was analyzed with the help of SPSS 22. Frequency distribution and Chi-square were applied to measure the relationship between variables. The finding of results shows that all independent variables Advertising, Customer Service, Sales Promotion, Coverage, Signal Strength, Colour Perception and Quality have significant relationship with dependent variable Customer Perception. It was also observed that Mobilink and Warid ads came through the whole day for several times that was enjoyed by the customers. Mobilink and Warid ads convey a clear and meaningful message. It was also observed that Mobilink and Warid ads motivate the users to purchase their product and remain loyal with Jazz