International Journal of Innovations in Science & Technology
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Assessment Of Groundwater Quality Index For Agriculture And Domestic Purpose Of Taluka Sehwan, District Jamshoro
Get PDFIntroduction/Importance of study
Groundwater has become an important source of freshwater around the world, used for a variety of reasons such as home usage, agricultural irrigation, and industrial applications.
Novelty statement
This study provides a novel solution by using the water quality index (WQI) and GIS-based Kriging analysis to comprehensively assess and spatially visualize groundwater quality in Taluka Sehwan, Sindh, Pakistan, addressing the critical issue of contamination from Manchar Lake.
Material and Method
Thirty groundwater samples were collected from Taluka Sehwan, Sindh, Pakistan, and sixteen parameters, including pH, electrical conductivity (EC), and total dissolved salts (TDS), were analyzed in the lab. The water quality index (WQI) and irrigation indices (SAR, SSP, MH, and PI) were calculated, and the results were spatially analyzed using the GIS-based Kriging method.
Result and Discussion
The WQI in the study area ranges from 34.53 to 213.362, with only 13% of the water deemed good, 23% poor, 7% very poor, 30% unsuitable, and 27% unfit. The overall WQI indicates that the groundwater is unsafe and non-potable, except for a few localized pockets (13%) in the northern side. SSP was categorized as unsure (83.33%) or poor (13.33%) for irrigation. SAR values indicated that 10% of the water is excellent, 46.67% good, 33% allowable, and 10% unsuitable for agriculture. MH and PI indices showed 70% of the water as excellent and 30% as safe. The water quality is poor, with moderate to good irrigation indices suitable for 70-75% of the area. Spatial analyses reveal low concentrations in the north and high concentrations in the south, highlighting the area\u27s heterogeneity.
Concluding Remarks
The policy should prioritize monitoring pollution, research on sources, and mitigation methods to prevent irreversible harm to the local ecosystem and communities
Geodemographic assessment of tuberculosis patients using Principal Component Analysis (PCA) in Gujranwala city, Pakistan
Get PDFIntroduction/Importance of the Study: Tuberculosis (TB) is a highly contagious disease caused by the bacterium Mycobacterium tuberculosis. It has persisted for centuries and primarily affects the lungs, spreading through airborne droplets. First identified by Robert Koch in 1882, TB remains a global health challenge. The World Health Organization (WHO) has been actively working to reduce TB incidence worldwide, and their efforts have led to a decline in infection rates over time. TB is closely related to geodemographic factors, which influence its prevalence and distribution.
Objective: This study aims to investigate the risk factors, spatial distribution, and hotspot areas of TB in Gujranwala city.
Material and Methods: Primary data were collected through questionnaire surveys, and secondary data were obtained from TB center records. These data were analyzed using statistical Principal Component Analysis (PCA) and Geographic Information System (GIS) software.
Novelty Statement: This study provides a geographical analysis of TB patients, offering significant insights that could enhance TB treatment strategies.
Results and Discussion: The analysis revealed that socioeconomic status, diet, diagnostic practices, and ecological conditions are key risk factors for TB. High-incidence areas are often characterized by poor ecological and economic conditions, predominantly inhabited by low- to middle-income labor class populations. Specific areas such as Ladhewala Wraich, Chicherwali, Kachi Phatuman, and Loyawala face ongoing environmental and socioeconomic challenges.
Concluding Remarks: Addressing these adverse conditions is crucial for reducing TB spread. Strengthening the immune system is also vital in preventing the disease. The government has a critical role in implementing measures to eradicate TB in Pakistan and improve overall public health
Comprehensive Review on Postoperative Central Nervous System Infections (PCNSI): Causes, Prevention Strategies, and Therapeutic Approaches using Computer Based Electronic Health Record (EHR)
Get PDFThe central nervous system is susceptible to various infections. Over centuries, bacterial infections have proven lethal in various surgical procedures. Infections that occur after craniotomy are often due to the reopening of operating wounds and past contamination of the scalp. Electronic health record (EHR) although provides programs to support surveillance efforts for these infections. But the problem with these tools traditionally used is lack of accuracy. Till now, the EHR systems are giving data to monitor and plan for these infections but this system definitely needs more accuracy. The rate of postoperative infection in craniotomy ranges from 0.8% to 7% in patients who have received preoperative antibiotic prophylaxis. This rate increases significantly to about 10% in patients without antibiotic prophylaxis. Different types of bacteria manifest infections at different intervals after surgery. For instance, Streptococcus pyogenes infections typically appear within one or two days, Staphylococcal infections usually become evident after four to five days post-surgery, while gram-negative bacillary problems may arise within six or seven days. Resistance in bacteria contributes to the prevalence of postoperative infections, with examples such as Vancomycin Resistant Streptococcus aureus (VRSA), Vancomycin Resistant Enterococci (VRE), and Methicillin-Resistant Streptococcus aureus (MRSA). Given the high incidence of postoperative neurosurgical infections, there is a pressing need to manage such infections meticulously to reduce the risk of infections and associated fatalities. Treatment options include antibiotics and surgical practices aimed at minimizing pathogenic infections. Early and prompt recognition of bacterial infections after craniotomy is crucial, necessitating an understanding of both local and general infection symptoms. Additionally, cranioplasty can be considered as a means to address postoperative neurosurgical pathogenic infections
Exploring the Impact of Land Cover Changes on Genesis of Smog in District Lahore, Pakistan
Get PDFIntroduction: Smog is a major global issue, severely impacting Pakistan, particularly Lahore. This problem arises from a mix of natural factors and human activities, notably rapid urbanization, which has intensified fog into smog, affecting human health. In Lahore, urbanization has altered land use patterns, contributing to the urban heat island effect and elevated temperatures. Changes in land cover (LC), combined with pollution sources like industrial emissions and vehicle exhaust, play a significant role in smog formation.
Novelty Statement: This study highlights the long-term impact of LC changes on smog from 2002 to 2022. Water indirectly influences smog through meteorological conditions, while particulate matter (PM) from various sources poses health risks. The primary objective is to investigate how changes in land cover contribute to smog formation.
Material and Methods: ArcGIS was used to process data on land cover images, temperature, and air pollutants (NO₂, SO₂, and CO) within a controlled Geographic Information System (GIS) environment.
Results and Discussion: Land cover images of Lahore from different years were obtained using Google Earth Pro. ArcGIS was employed to analyze temperature data, and the inverse distance weighted (IDW) interpolation technique was used to visualize temperature variations and air pollutant concentrations over time. LC data for 2002, 2012, and 2022 were integrated into ArcGIS to demonstrate how land cover changes contribute to smog formation in Lahore.
Conclusion: The research highlights the need for effective management of urbanization and environmental challenges to address smog-related issues
Flood Risk Assessment Using Geospatial Techniques: A Case Study of River Ravi-Punjab-Pakistan
Get PDFFlooding, an increasingly prevalent environmental hazard, has been worsened by climate change, particularly affecting developing countries. Pakistan is especially vulnerable to hydrological hazards. This study aims to evaluate flood risk using geospatial technology and analyze return periods to assess the impacts of floods on crops. Water is a significant driver of landscape change. Landsat 8 datasets are utilized to examine crop patterns and built-up areas. Return periods of 50, 100, and 250 years are used to define risk zones, with gauges at Jassar, Syphon, and Shahdara considered. Historical images from 1995, 1996, and a recent year are analyzed to track changes in crop and built-up areas. SRTM and Pulsar DEM data are employed to study the watershed. The analysis indicates that over a 150-year period, the probability of a significant flood event is 0.25. This low probability suggests minimal water flow, with only small amounts arriving during the monsoon season, causing minimal disruptions to crops. These probabilities are based on established methodologies. While the probability of a significant flood event over 250 years is very low, it is included for classification purposes. The chance of a flood affecting crop patterns is 0.5, but this may vary if river water levels rise due to other sources. Nonetheless, current data and historical records indicate that the likelihood of floods significantly impacting crop patterns remains very low
Impact of Internal Forces on Employee Behaviors: Role of Situational Factors
Get PDFThe current research investigated the effects of motivation, ability, and role perception (internal forces), also known as drivers on employee behaviors as well as to find out the moderating role of situational factors between drivers and employee behaviors. Data were collected from 800 in-service employees across various organizations and industries in Gujranwala using a convenience sampling technique. Work-related behaviors assessment battery was used to collect data from individuals which consists of 7 scales. Each scale consists of 10 items and the response rate varies from 1= strongly disagree to 5= strongly agree. Analysis indicates that motivation, ability, and role perception have a significant effect on employee behaviors. Moderation analysis results indicate that situational factors significantly moderate the relationship between drivers and behaviors. The current research sheds light on the significance of behaviors depending upon the four driving forces that need to be changed, or modified in regards to an increase in organizational performance
AI-Driven Prediction of Electricity Production and Consumption in Micro-Hydropower Plant
Get PDFMicro hydropower plants must effectively manage demand response to preserve operational firmness and prevent system breakdowns. This research focuses on accomplishing a fine balance while predicting consumption and production, which is significant for upholding system integrity. The study delves into predictive modeling methods to forecast patterns in the production and consumption of electricity over an array of time horizons. We adopted a custom sliding window mechanism, in which actual and predicted values are used to predict the next hour of electricity. We set a baseline to resolve this and examined various algorithms, focusing on RNN-LSTM and CGP-LSTM. The CGP-LSTM forecasting output sequences with different time horizons precisely outperform the RNN-LSTM. The dataset utilized is downloaded from the Kaggle website. 50% of the data is used to train the models, and the rest is used to test the models. This work deals with the complex fluctuations in the demand response system and provides electricity production and consumption predictions. CGP-LSTM model gave a training MAPE of 6.67 (Accuracy of 93.33%) and a testing MAPE of 6.68 (accuracy of 93.32%) for the next three hours; on the other hand, LSTM gave a training MAPE of 6.53 (accuracy of 93.47%) and testing MAPE of 7.46 (accuracy of 92.54%) for the next three hours. The results offer a base for further developments and improvements in the field, drawing attention to more effective and reliable energy management capabilities in micro hydropower plants.
CGP-LSTM model gave a training MAPE of 6.67 (Accuracy of 93.33%) and a testing MAPE of 6.68 (accuracy of 93.32%) for the next three hours; on the other hand, LSTM gave a training MAPE of 6.53 (accuracy of 93.47%) and testing MAPE of 7.46 (accuracy of 92.54%) for the next three hours.
The results offer a base for further developments and improvements in the field, drawing attention to more effective and reliable energy management capabilities in micro hydropower plants
Deep Learning for Viral Detection: Affordable Camera Technology in Public Health
Get PDFViral infections like chickenpox, measles, and monkeypox pose significant global health challenges, affecting millions with varying severity. This study presents a novel deep learning approach using widely available low-cost RGB camera technology to accurately identify these infections based on skin manifestations. Our aim is to enhance diagnostic capabilities and enable timely interventions, thus improving public health outcomes and individual well-being. Using MobileNetV3 for data classification, our model achieved a precision of 95% for positive cases, an overall accuracy of 95.73%, a recall of 88.37%, and an F1-score of 91.56%, indicating balanced performance between precision and recall. Notably, the model demonstrated exceptionally high specificity at 98.34%, effectively identifying negative cases. This deep learning approach holds promise for improving diagnostic accuracy and efficiency, especially in resource-limited settings with limited access to specialized medical expertise. By leveraging low-cost RGB camera technology, our method enables broad deployment, facilitating early detection and treatment of viral infections. We focus on the potential of deep learning in public health by emphasizing the critical role of early detection and intervention in mitigating the impact of viral infections. Our findings contribute to advancing healthcare technology and lay the groundwork for future innovations in disease detection and management
Eco-Mobility in Lahore, Pakistan: Assessing the Role of Electric Vehicles in Air Pollution Mitigation
Get PDFIntroduction/Importance of the Study: This study investigates the potential of electric bikes to replace fossil fuel-powered motorbikes as a solution to reduce air pollution in Lahore. Globally, the transport sector relies heavily on fossil fuels, which are a major contributor to air pollution. In Pakistan, 23% of greenhouse gas (GHG) emissions come from road transport. Lahore alone has 4.2 million petrol-based motorbikes, significantly contributing to deteriorating air quality and posing serious health and environmental risks. Transitioning to electric vehicles, particularly in Punjab\u27s major cities like Lahore, offers a promising approach to reduce harmful air pollutants. However, Pakistan faces various challenges that hinder the rapid adoption of electric vehicles.
Novelty Statement: This research highlights key policies needed for infrastructural and technological advancements to accelerate the adoption of electric bikes. The study is unique in that it focuses on controlling emissions in Lahore by replacing petrol motorbikes with electric bikes, using empirical data specific to the city. The analysis includes a detailed examination of daily and annual emissions from petrol-based motorbikes, focusing on pollutants like carbon monoxide, hydrocarbons, and sulfur and nitrogen oxides.
Materials and Methods: A questionnaire-based survey was conducted to determine the average daily mileage and annual working days of motorbikes in Lahore. Emission data for petrol and electric bikes was sourced from secondary sources, allowing a comparison between vehicles powered by renewable and non-renewable energy. Additionally, emissions from coal-based power plants generating electricity for electric bikes were also analyzed using secondary data.
Results and Discussion: The results indicate that electric bikes powered by renewable energy produce negligible emissions compared to petrol and diesel vehicles. However, when electricity is generated from non-renewable sources, such as coal-fired power plants, the emissions remain high and continue to contribute to air pollution.
Concluding Remarks: The study recommends that policymakers prioritize renewable energy sources for powering electric bikes. It also stresses the need for public-private partnerships, tax exemptions, and cost reductions to promote electric bike schemes for the general public
Machine Learning-Based Estimation of End Effector Position in Three-Dimension Robotic Workspace
Get PDFIntroduction/Importance of Study: The Workspace is the area around the robot where a robot can freely move with possible input variations of different joint angles.
Novelty statement: Conventionally iterative simulation methods are used to find robotic workspace. Which are computationally slow and difficult to model. Our approach utilizes machine-learning algorithms to predict the workspace and position of an end effector.
Material and Method: Multiple Linear Regression (MLR), Decision-Tree Regression, and Artificial Neural Network (ANN) algorithms trained for prediction. The dataset, which is collected and used as train and test data, is further for the validation step.
Result and Discussion: By simulating the robot with the Denavit-Hartenberg (D-H) approach in MATLAB. The results findings show the accuracy of Machine learning algorithms specifically Artificial Neural Networks (ANN) perform better than conventional mathematical methods
Concluding Remarks: Artificial Neural Network (ANN) outperformed other machine learning methods