International Journal of Innovations in Science & Technology
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Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder
Get PDFRelapse to substance abuse after withdrawal is one of the most perplexing and frustrating aspect of addiction. The word relapse defines as collapse/hindrance to the client’s prior substance dependence behavior after treatment due to many psychological, social, and other associated factors. This study is designed to find out the psychological and social factors which determine relapse behavior in individuals with substance use disorder. A cross-sectional study was conducted among 200 relapse patients from November 2020 to January 2021 in different rehabilitations in Wazirabad, Gujranwala, Lahore, Gujrat, and Sialkot cities of Pakistan. Data were collected by using a psychosocial functioning scale. Data analyzed using SPSS 21. A total of 200 individuals were taken ,of which 170 were males and 30 females. The results indicates that the most important predictor was risk-taking 0.148 (100% of normalized importance) followed by social conformity 0.130 (88% of normalized importance), decision making confidence 0.128 (86.2% of normalized importance), childhood problem 0.125 (84.4% of normalized importance), hostility 0.125 (84.0% of normalized importance), depression 0.119 (80.5% of normalized importance), self-esteem 0.114 (76.6% of normalized importance) and anxiety 0.111 (74.7% of normalized importance).Social factors were connected with a high relapse rate. These results may help clinicians to improve treatment and policy guidelines for the prevention of relapse to drug addiction
Soil Classification & Prediction of Crop Status with Supervised Learning Algorithm: Random Forest
Get PDFCrop Management System (CMS) was developed in an Ionic framework with a Real-Time Firebase database for loop backing and decision support. The main two features were; Soil classification where the soil was classified based on temperature, humidity, and soil properties such as soil moisture, soil nutrients, and soil PH level using Random Forest Algorithm. By Bootstrap method using Random Forest, samples from the dataset were selected & then classification trees was generated. The other feature was crop precision where the condition of the crop was and examined using temperature, humidity, soil moisture, soil PH levels, and soil nutrients (N, P, K). IoT device was used to fetch data from the field and then compare with already stored ideal values, suitable for optimal yield, in CMS database then process using the application to suggest the crop for cultivation and to optimize the usage of water and fertilizers. Currently, we classify the soil using Random Forest Algorithm & suggest the suitable crop for the classified type of soil & also measure the soil moisture and soil nutrients of agricultural field Acre based on the reading results we are suggesting the crop to is cultivated and pre-requisite which would be needed in future. The proposed method gives an accuracy of 96.5% as compared to existing methods of Artificial Neural Networks and Support Vector Machines
Numerical Analysis of Impact of Relative Humidity on Crossflow Heat Exchangers with Staggered Configuration at Maximum Operating Temperature
Get PDFHeat exchangers are employed in numerous applications of industry, automotive and air conditioning systems. The efficacy of heat exchangers depends upon various factors e.g., Reynolds number (Re) of the fluids, geometry of heat exchanging surfaces, and the Prandtl number of the cooling air. In this paper, the working of a crossflow heat exchanger with elliptical tubes is simulated numerically for 5000 < Re < 20000 at its maximum operating temperature of 323K. The tubes were arranged in a staggered way. The radical investigations were done at one-of-a-kind relative humidity ranges within the cooling air ranging from 0% to 80%. The relative humidity was modeled in the shape of mass fractions of water vapors in the air. The thermos-physical properties of dry and moist air were employed for the analysis. The impact of this changing of relative humidity on forced convection heat transfer of heat exchangers is examined in the form of percentage change in Nusselt number. With the increase in moisture content in the air, the Nusselt number was observed increased up to 4.5%. The paper provides a tool to analyze the Nusselt number of the elliptical-shaped heat exchanger while operating in moist atmospheric conditions.
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First Report of Genus Parmeliella Müll. (Peltigerales; Lecanoromycetes; Ascomycota) from Pakistan
Get PDFWhen studying lichens in Pakistan, we came across a crustose species with small to moderate squamulose on a thin blackish hypothallus with a dry, rough, gray-brown to the black upper surface. The standard chemical tests integrated with conventional to modern taxonomic tools were used to name the specimen. Consequently, with minor differences in the morphology, and no difference in nucleotides, the lichen species was baptized Parmeliella thriptophylla (Ach.) Müll. Arg. The descriptive taxonomy and n-ITS-based phylogeny of this species with its habitus are presented in this study. No previous record of this species, genus, or family was found in Pakistan.
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Sales Prediction of Cardiac Products by Time Series and Deep Learning
Get PDFMaintaining inventory level to avoid high inventory costs is an issue for Cardiac Product Distribution Companies (CPDCs) because of the shortage of their products which affect their sale and causes loss of the customer. This research aims to provide a method for predicting the upcoming demand of the Balloon and Stents by using time series analysis (Auto Regression Integrated Moving Average) and Deep learning (Long-Short Term Memory). To conduct this research, data was collected from Pakistan’s leading cardiac product distributors to determine the method\u27s performance. The findings were compared using Mean absolute error (MAE) and Root Mean Square Error (RMSE). Resulst conclude that the ARIMA algorithm successfully forecasts cardiac products sale
The Significance of Cognitive Distortions and Risk Factors Due to Android Games Addictions for Adults
Get PDFBackground:
The primary purpose of this study is to find out the Significance of Cognitive Distortions and Risk Factors due to Android Game Addiction for Adults.
Methods:
The research population includes three main parameters Physical Effects, Mental Effects, and, Cognitive Effects each part contains 10 questions. The sample comprises 200 school students (male=150, females=50) between the ages of 8 to 20 years recruited from schools in Lahore, Pakistan. A total of 200 copies of the Questionnaire are distributed among the students. Only 180 copies are received and filled so the total response rate of the data collection was 90%. The SPSS tool was used to analyze the results of the Questionnaire. It aims to investigate the impacts that Android Games have on adults. As most of the researchers have done Quantitative research in this area, so I decided to conduct both Quantitative and Qualitative research designs which can help us get a deeper understanding of Android Game Addiction.
Findings:
By going through the results, it is revealed that the all above-mentioned three main parameters are found positive in adults. According to the range of the frequency, it is concluded that all three factors are in the medium range not more harmful.
There was a strong positive association between the addictions of students to Android mobile games and their physical and mental well-being in terms of physical, mental, and cognitive health
Assessment of Water Stress in Rice Fields Incorporating Environmental Parameters
Get PDFRice is considered as a major crop due to its demand globally. Pakistan is famous throughout the world to produce export quality rice which have healthy contribution in boosting the regional economy. Rice plant require plenty of water for its proper growth and development therefore, water conservation is significant to maintain water reserves for a sustainable future. The main objective of this study was to identify day-to-day availability of water in rice fields from Germination to Ripening (GTR) using Carnegie Ames Stanford Approach (CASA) model. CASA model incorporates real-time parameter e.g., temperature, pressure, extraterrestrial radiations, Leaf Area Index (LAI), vapor pressure and sunshine hours to compute net-shortwave radiations (Rns), net-longwave radiations (Rnl), net-radiations (Rn), actual incoming radiations (Rso), sensible heat flux (H), ground heat flux (Go) and finally the water stress (W). The averaged values of Rn, Rso, Rns, Rnl and H were computed as 206, 319, 178, 34 and 124 (wm-2) respectively for GTR. Total expected sunshine hours were 1584h but we could receive only 874 h during GTR due to “off and on” cloud activity. LAI and Go were observed in inverse relation to each other.
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MODIS-Observed Spatiotemporal Changes in Surface Albedo of Karakoram Glaciers During 2000-2018
Get PDFThe role of albedo is very important in modulating the surface energy balance of glaciers. The main objective of this study is to assess the spatiotemporal variability in surface albedo of the Karakoram glaciers in Pakistan during the summer seasons (June, July and August) for the period from 2000-2018. We used Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate the amount of glacier surface albedo. We combined the MODIS Terra- and Aqua-derived albedo products to reduce the amount of cloud influence and to improve the estimation of glacier surface albedo. Our results indicate that the average annual decrease in albedo is ~0.041% during the summer. The decrease in albedo was relatively high during recent years, with an annual rate of decrease of ~0.45%. The decreasing trend in albedo is towards the north-western part of the Karakoram mountain range. Climate change is the potential cause of albedo variations in the study area. Albedo has a strong negative correlation with temperature (r = -0.811) and a strong positive correlation with precipitation (r = 0.809). The present study concludes that trend in decreasing albedo is higher during the recent years than the last decade and climate change is playing a vital role in it.
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Blind Image Deblurring Using Laplacian of Gaussian (LoG) Based Image Prior
Get PDFBlind image deconvolution, a technique for obtaining restored image as well as the blur kernel from an inexact image. This research uses spatial characteristics to tackle the problem of blind image deconvolution. To work, the proposed method does not necessitate prior information about the blur kernel. Many applications, such as remote sensing, astronomy, and medical X-ray imaging, necessitate blind image deconvolution algorithms. This study used the maximum a posteriori (MAP) paradigm to create a new blind deblurring approach for removing blur from images. In beginning, we employed a Laplacian of Gaussian (LoG)-based image before regularising the gradients of an image. In the second phase, we used an operator known as the Iterative Shrinkage Thresholding Algorithm (ISTA) to cope with the non-convex challenge that develops during the entire deblurring procedure. Finally, we compared our method to several well-known methods in terms of quantitative and qualitative qualities, and we were able to determine which strategy was the most effective. Our findings show that the strategy we propose outperforms the others by a large margin.
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Sentiment Classification Using Multinomial Logistic Regression on Roman Urdu Text
Get PDFSentiment analysis seeks to reveal textual knowledge of literary documents in which people communicate their thoughts and views on shared platforms, such as social blogs. On social blogs, users detail is available as short comments. A question of sentiment analysis has been raised by information across large dimensions published on these blogs. Although, some language libraries are established to address the problem of emotional analysis but limited work is available on Roman Urdu language because most of the comments or opinions available online are published in text-free style. The present study evaluates emotions in the comments of Roman Urdu by using a machine learning technique. This analysis was done in different stages of data collection, labeling, pre-processing, and feature extraction. In the final phase, we used the pipeline method along with Multinomial Logistic Regression for the classification of the dataset into four categories (Politics, Sports, Education and Religion). The whole dataset was divided into training and test sets. We evaluated our test set and achieved results by using Precision, Recall, Accuracy, F1 Score and Confusion Matrix and found the accuracy ranging to 94%.
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