International Journal of Innovations in Science & Technology
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Comparative Analysis on the Effect of Crack Location and Orientation on Crack Growth in Boiler Tube
No full textIntroduction/Importance of Study:
Safety is the paramount concern in the operations and inspections of pressure vessels, such as water tube boilers. Defects in the boiler tubes can lead to the development of cracks.
Novelty Statement:
The investigation focuses on the effect of crack location and orientation on crack growth under cyclic loading which has been analyzed computationally using Separate Morphing and Re-meshing Technology (SMART) in ANSYS. The effect of location on crack growth is primarily focused which is theoretically investigated as well using Simpson’s Integration of Paris’s Law.
Materials and Method:
The tube in focus is a component of a D-type water-tube industrial boiler, crafted from low-carbon steel (SA 178 A). For the effect of location, semi-elliptical cracks on inner and outer tube surfaces have been studied both theoretically and computationally.
Results and Discussion:
Theoretical investigation revealed that cracks on the inner tube surface exhibit a 30.28% higher accumulative growth rate compared to the outer surface, attributed to hoop stress distribution. For investigating the effect of orientation elliptical embedded cracks at certain orientations have been examined computationally and the critical plane orientation for crack growth is identified as perpendicular to the hoop stress.
Concluding Remarks:
In conclusion, the study underscores that cracks grow faster when located at the inner surface and oriented perpendicular to the hoop stress
Sherpa: Implementing a Hybrid Recommendation System for Next-Gen Tourist Experience
Get PDFIn the digital era, Sherpa revolutionizes personalized tourism with an AI-driven recommendation system, fostering meaningful connections between travelers and local guides. This study explores Sherpa\u27s integration of collaborative and content-based filtering—specifically, singular value decomposition (SVD) and cosine similarity—to tailor travel experiences uniquely. Our methodology includes a detailed examination of Sherpa\u27s algorithm and its implementation within a cross-platform, MERN Stack-powered backend. We assess the system\u27s efficacy in aligning recommendations with individual user preferences, based on quantitative user feedback and engagement metrics. Initial results demonstrate a significant improvement in personalized experience satisfaction. The paper concludes that Sherpa\u27s innovative approach not only enhances the quality of travel recommendations but also sets a new standard for interactive and adaptive tourism platforms. Through continuous algorithmic refinement, Sherpa is positioned to lead a transformative shift in how travelers explore new destinations, offering not just journeys, but transformative experiences
Stress Detection and Prediction Using CNNs from Electrocardiogram Signals
Get PDFStress prediction is a crucial aspect of mental health monitoring, with consequences for both psychological well- being and productivity. This work presents a unique way for stress prediction that uses binary and multiclass classification models. Through extensive experimentations with different durations and frequencies of Electrocardiogram Signal (ECG) signals, we identified a 5-second dataset sampled at 200Hz as the optimal configuration for our model. Moreover, we introduced an innovative feature i.e., the prediction of stress scores ranging from 0 to 100, providing nuanced insights into stress levels, where 0 represents no stress and 100 indicates high stress levels. The model obtains 95.04% accuracy, 95.27% precision, 94.95% F1 score, 86.69% sensitivity, and 99.44% specificity for the binary classification. With "Fun" added to the list of stress categories in addition to "Base" and "TSST," the model continues to perform well in the multiclass classification scenario, with accuracy of 88.10%, precision of 87.60%, F1 score of 87.35%, sensitivity of 95.97%, and specificity of 79.23%. These findings highlight how well this applied strategy predicts stress levels, providing important information for mental health and stress management strategies
SSOCANET SSOCANET - Empowering VANETs with Salp Swarm Optimization-Enhanced Clustering Algorithm
No full textVehicular Ad hoc networks (VANETs) present significant challenges due to the dynamic nature of vehicle movements, leading to a constantly changing vehicular network topology. This instability results in packet loss, network fragmentation, message reliability, and scalability issues. To address these challenges, clustering has emerged as a promising solution to escalate vehicle communication efficiency. However, determining the optimal number of clusters remains a crucial problem. The proposed solution, the Salp Swarm Optimization-Enhanced Clustering Algorithm for VANET (SSOCANET), leverages the foraging behavior of salps to optimize cluster formation based on multiple objectives. SSOCANET achieves an optimal number of clusters by employing carefully designed objective functions, minimizing communication overhead and end-to-end communication latency in a network. The simulation results demonstrate the superior performance of SSOCANET compared to other clustering approaches, offering a robust solution for VANETs
Prediction of Elective Patients and Length of Stay in Hospital
Get PDFThe efficient management of hospital resources and the optimization of patient care are critical tasks in healthcare systems worldwide. One of the key challenges in hospital management is predicting the duration of a patient\u27s stay and accurately determining their location within the hospital, such as whether they are in the Intensive Care Unit (ICU) or the Operating Theater (OT). In this study, we address this problem statement by employing machine learning algorithms to predict both the stay duration and the location of patients within the hospital. The methods applied in this study include Random Forest, Support Vector Machine (SVM), and K-nearest neighbors (KNN) algorithms. These algorithms utilize patient demographic information such as age, weight, and severity of disease as features to predict the stay duration and location. The dataset used for this study consists of a revised dataset containing relevant patient information. Upon applying the machine learning algorithms, we obtained promising results. The Random Forest algorithm achieved the highest accuracy of 88.6% in predicting patient locations, followed by SVM with an accuracy of 60.8% and KNN with an accuracy of 58.1%. Additionally, Random Forest exhibited superior precision, recall, and F1-scores for both ICU and OT classifications compared to SVM and KNN. The results obtained from this study have several practical implications and potential uses. Firstly, accurate predictions of patient stay duration and location can aid hospital administrators in resource allocation and planning, enabling them to efficiently manage bed occupancy and staffing levels. Additionally, healthcare providers can use these predictions to anticipate patient needs and allocate resources accordingly, thereby enhancing patient care and satisfaction. Moreover, the machine learning algorithms utilized in this study can be integrated into hospital information systems to automate the prediction process, providing real-time insights to healthcare professionals. In conclusion, the application of machine learning algorithms in predicting patient stay duration and location within the hospital offers promising results and valuable insights for hospital management. By leveraging patient demographic information and advanced predictive models, healthcare institutions can improve operational efficiency, enhance patient care delivery, and ultimately optimize resource utilization
Impact of Urbanization on Land Use Land Cover and Urban Climate, using Spatio-temporal Techniques: A case study of Islamabad, Pakistan
Get PDFAn increase in urban population has been considered a major challenge over the past few years, especially in developing countries like Pakistan. It reduces vegetation area that directly affects land surface temperature (LST) and thus causes major changes in urban climate. This research mainly focuses on the surface temperature of Islamabad using LULC, LST, and Normalized Difference Vegetative Index (NDVI) as major parameters. This study spans over four years i.e., 2019, 2020, 2021, and 2022. The land use land cover maps are obtained from ESRI Sentinel 2 Land Use Land Cover Explorer. In contrast, LST maps are obtained from Level 2 Sentinel 2B Sea and Land Surface Temperature Radiometer (SLSTR) sensor LST product. NDVI is calculated using Sentinel 2B bands 4 and 8 respectively. The resulting LULC maps show that the vegetation area decreases by 7% and the built-up area increases by 8% from 2019 to 2022. Moreover, the area of dense vegetation decreased from 6.01% to 0.17% from 2019 to 2022 shown by NDVI maps. The study further reveals that the built-up areas exhibit higher LST than other classes. This is validated through Pearson’s Correlation Coefficient between LST and NDVI which shows a negative correlation of -0.41. This research concludes that the built-up area and rangeland are increasing during the studied years while the area of vegetation and bare ground is decreasing in Islamabad. This decrease directly influences LST and consequently the climate of the area. This should be mitigated by adopting such sustainable plans that involves building green alternatives into urban management
Impact of Changing Climate on Floristic Composition And Ecological Characteristics of Sheenghar Range, District Karak, Pakistan
Get PDFIntroduction: The present study assesses the floristic composition and ecological features of plant resources in the Sheenghar range hills, District Karak, Pakistan, highlighting the area\u27s biodiversity and the impacts of climate change.
Novelty Statement: This research uniquely addresses the detailed floristic inventory and ecological classification of plant species in Sheenghar, providing insights into species adaptation to climate stress.
Material and Method: Field surveys were conducted to collect and identify plant species, followed by classification into families and life-forms. The study involved analyzing plant species composition, classification based on habitat, life-form categories and leaf size spectra.
Result and Discussion: The study identified 185 plant species across 49 families, with Asteraceae being the largest family (19 species). Herbs dominated the area (65.40%), followed by shrubs (18.91%), trees (14.59%), and parasites (1.08%). The predominant life-form class was therophytes (47.45%), with microphylls being the most common leaf size (32.43%). The findings indicate significant diversity but also reveal the severe impact of climate change on the flora, necessitating further research to understand species survival under such stress.
Concluding Remarks: The Sheenghar range hills\u27 flora is diverse but vulnerable to climate change, emphasizing the need for continued ecological studies and conservation efforts
The Customer Reviews Analysis Platform by Correlating Sentiment Analysis and Text Clustering
No full textCustomer reviews and feedback are of paramount importance in the improvement cycle of any industry, product, or service. Formerly, product ratings were the basis for performance evaluation and key drivers of improvements. However, ratings were unable to depict the complete picture and were not adequate for an in-depth analysis of any product or service. Hence, customer reviews become the ultimate source of providing feedback for a specific detailed analysis as well as contributing to performance metrics. Although, customer reviews provide a very essential measure for performance evaluation, extracting important features and topics from customer reviews has been challenging due to its unlabeled and variant nature. This paper focuses on extracting topics from customer review data and bringing in use the of implicit knowledge for analytics. To extract topics and clusters from review data, unsupervised machine learning algorithms such as K-Means and Latent Dirichlet Allocation (LDA) are used. These topics are then correlated with sentiment analysis - score of positive or negative feedback - of each customer review. The products or services are then categorized with the help of the topics or domains they belong to alongside the sentiments. This provides a valuable analysis such as the score of positive, neutral, and negative feedback for each customer review input to new customers as well as product managers. This research work aims to use the hotel reviews dataset to categorize and rank hotels based on the different services captured in the text from customer reviews. The research work makes use of the hotel reviews dataset for categorizing and ranking hotels based on the different services discussed in the customer\u27s reviews text. Moreover, this paper also provides a visualization of both text clustering algorithms depicting the topics in each cluster for an insightful analysis
Spatio-Temporal Dynamics of Ground Water Level of Lahore Metropolitan and its Relationship with Urbanization and Rainfall
Get PDFIntroduction/Importance of Study: Lahore, the capital of Punjab Province, has a population of 14.1 million people. The city relies entirely on groundwater to meet its water needs. However, unsustainable water usage has led to a significant decline in groundwater levels.
Novelty Statement: This study aims to investigate the root causes of groundwater depletion in Lahore and propose effective protective measures. The excessive extraction from around 600 tube wells, some reaching depths of 600 to 1000 feet, has resulted in non-functional wells and severe water shortages.
Material and Method: The study employed various tools and techniques, including Google Earth Engine, image classification methods, and R Studio (ordinary Kriging). An overlay analysis assessed the spatial relationship between land use/land cover and groundwater levels. The analysis revealed a significant correlation between urbanization, population growth, and groundwater depletion in Lahore.
Results and Discussion: The rate of groundwater depletion has increased from an average of 2.133 feet per year (0.65 meters per year) between 1980 and 2000 to over 3 feet per year (over 1 meter per year) since 2013. Contributing factors include rapid urbanization, increased water demand due to population growth, and inadequate rainwater recharge. This rapid depletion poses a serious threat to Lahore\u27s groundwater resources, emphasizing the urgent need for sustainable water management practices.
Concluding Remarks: The rapid depletion of groundwater is a critical issue for Lahore, necessitating immediate implementation of sustainable water management practices and groundwater recharge strategies. Effective measures are essential to mitigate the impacts of rapid urbanization and population growth on groundwater resources, ensuring a reliable water supply for the future
Quantifying the Impact of Chashma Right Bank Irrigation Project on the Land use Dynamics and Cropping Pattern of Arid Region, Pakistan
Get PDFThis research is focused on evaluating the impact of Chashma Right Bank Irrigation Project (CRBIP) on the land use dynamics and cropping pattern of arid region in Pakistan. Work on CRB irrigation project (CRBCIP) was started in 1984 and was subsequently completed in three stages during 2003. The CRBC holds 250,000 acres of land in the provinces of Khyber Pakhtunkhwa and Punjab. Its ultimately goal was to enhance agricultural productivity and employment opportunity. Remote sensing and GIS techniques have also been shown to be useful tools for analyzing geographical and temporal changes in land use dynamics. In order to achieve the study objectives, data were collected from both primary and secondary sources. The methodology adopted mostly based on satellite image analysis were obtained for the years 1991 to 2021. Landsat 5 (TM) images for the years 1991, 2011, Landsat 7 (Enhance Thematic Mapper) for 2001 and Landsat 8 (Operational Land Imager/ Thermal Infrared Sensor) for 2021 were acquired from USGS Earth Explorer (open source). However, the crop production data were obtained from the statics wing agriculture department. The result clearly shows that during the last three decades, the vegetation cover has increased 8.8%, built-up area in 15% while a decrease of 24% in barren land, and -0.14 % in water bodies. Significant variation in term of changes in vegetation cover in term of space and time. The found that after the CRBC area under the irrigation has gradually increased. The analysis revealed that advent of CRBC, acreage of both Kharif and Rabi crops have improved considerably. The results of this study can provide detailed information for land-use planners, researchers, policy-decision makers, and municipal authorities