International Journal of Innovations in Science & Technology
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Assessment of Palatability and Grazing Preferences under Changing Climate: A Case Study of Plant Species in District Karak, Pakistan
Get PDFThis research work aims to assess the palatability and grazing preferences under the changing climate of various plant species in District Karak Pakistan. The plants were categorized into different palatability classes, and grazing preferences by different animals, to analyze preferred forms of plant consumption and assess palatable species availability across the study region. It was found that out of 205 plant species, 58 (28.29%) were non-palatable, 82 (40%) palatable, 29 (14.14%) were highly palatable, 10 (4.87%) moderately palatable, 12 (5.85%) less palatable and 13 (6.34%) rarely palatable. Grazing preferences showed that goats grazed on 101 (49.26%) plant species, sheep on 93 (45.36%) and cows on 56 (27.31%) species. Whole plants of 82 (40%) species, leaves of 53 (25.85%) and inflorescence/flowers of 6 (3%) plant species were preferred by the grazing animals. The fresh form of 100 (48.7%) plant species was favored by animals followed by 41 (20.7%) plants in dry form and both fresh and dry forms of 24 (11.7%) plant species were grazed by animals. Plant palatability varied widely, impacting animal husbandry and agriculture. Grazing preferences differ among species and animals, with fresh plants preferred. Conservation efforts are crucial, especially in areas with scarce palatable species, particularly during seasons of low availability
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
Optimized Production of Cellulase using Different Agrowaste Biomass Substrates
Get PDFCellulase is a crucial industrial enzyme, with developing countries expending significant resources on its import for various industrial and scientific applications. A major challenge in cellulase production is the lack of affordable technology and suitable substrates for cultivating enzyme-producing microbes. This study optimized a substrate mixture to enhance cellulase production using solid-state fermentation with the Aspergillus Niger strain. Five agro-industrial substrates—sugarcane bagasse, corn cobs, rice straw, orange peel, and wheat straw—were individually inoculated with A. Niger spore suspension, and their cellulase activity was compared to that of a substrate mixture. The enzyme activity from individual substrates was notably lower compared to the mixture. Response Surface Methodology (RSM) was employed to identify the optimal substrate combination, which consisted of equal amounts of sugarcane bagasse, corn cobs, orange peel, and wheat straw, with rice straw in double the amount of the other substrates. The study also optimized fermentation parameters, including temperature, pH, incubation time, substrate concentration, moisture content, urea, MgSO4, and inoculum size of A. Niger. Maximum cellulase activity was achieved at 50°C, 80% moisture content, pH 4.0, 120 hours incubation, with 6.5 g of the substrate mixture, 2% w/w urea, 0.2% w/w MgSO4, and 4 ml of A. Niger spore suspension. Optimization resulted in cellulase activity of 0.205 IU/ml, significantly higher than the 0.025 IU/ml from individual substrates. Given its key role in industries such as pulp and paper, textiles, food and beverages, detergents, and agriculture, the demand for cellulase is expected to surge, particularly with the rise in biofuel production
Assessing Food Availability Potential in the Drylands of South Punjab
Get PDFIntroduction/Importance of Study: In South Punjab, Pakistan, unpredictable weather patterns and a heavy reliance on rain-fed agriculture pose significant challenges to food security. This study investigates how climatic variability affects food security in the region.
Objective: To evaluate the impact of climate variability on the per capita availability of wheat and rice in the districts of Bahawalpur, Rahim Yar Khan, and Rajanpur in South Punjab, Pakistan, from 1991 to 2021.
Novelty Statement: This study provides a unique analysis of the effects of climatic factors on food security in this under-researched region, offering a novel quantification of per capita wheat and rice availability over a three-decade period.
Material and Method: Temperature and precipitation data were sourced from the CHIRPS and APHRODITE datasets. Data on rice and wheat production were obtained from the Crop Reporting Service. The study assessed per capita availability of wheat and rice and explored correlations between climate data and farmer experiences.
Result and Discussion: From 1991 to 2021, per capita availability of wheat and rice fluctuated across Bahawalpur, Rahim Yar Khan, and Rajanpur districts. Key factors influencing these variations included population growth, water scarcity, extreme weather events, and climate variability. Surveys of farmers revealed the challenges they face in adapting to changing climatic conditions.
Concluding Remarks: Climate variability poses a significant threat to food security in South Punjab. Ensuring long-term food security in the region will require advancements in climate-smart agriculture, improved water management, and the implementation of early warning systems
Cause and Damages Assessment of 2022-Flood in Khyber Pakhtunkhwa, Pakistan
Get PDFFloods are among the most devastating hazards, occurring globally and impacting many regions annually. Pakistan is frequently affected by floods, including the significant floods of 2022 in Khyber Pakhtunkhwa (KP). This study assesses the causes and damages of the 2022 floods in KP using data from NASA Worldview and USGS, complemented by Geographical Information System (GIS) analysis. The study considers the role of climate change and the topography of KP in making it prone to floods. It examines weather patterns, environmental factors, and local vulnerabilities that contributed to the floods, as well as the extent of damage to communities, infrastructure, and the environment. Flood and precipitation data were collected from two satellites and analyzed using ArcGIS. The study identified massive rainfall and increased temperatures as the primary causes of the flood. Significant damage was recorded in District Dera Ismail Khan, followed by Tank and Swat. The floods resulted in approximately 300 fatalities across various districts of KP and caused total economic losses estimated at 201,414 million Pakistani rupees. Public sector losses were estimated at 121,283 million PKR, with house damages amounting to 23,780 million PKR. The peak flooding occurred in August during high rainfall. Understanding the root causes and damages of the 2022 KP flood is crucial for developing effective prevention and mitigation plans, as well as for assessing the impact on communities, infrastructure, and the environment. This study provides critical insights and comprehensive data to inform disaster management and policy-making for future resilience. Its novelty lies in its exclusive focus on the 2022 KP floods, a topic not previously studied in detail. In conclusion, the research effectively analyzes the causes and assesses the damages of the 2022 Khyber Pakhtunkhwa flood, offering essential insights for improving flood management strategies
Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques
Get PDFInfertility in females implies failure by such women to conceive even after having at least one year of intercourse without using any contraceptives. Infertility can be caused by a variety of factors, including ovulation problems, blocked fallopian tubes, hormone imbalances, and abnormalities of the uterus and so on. Infertility can negatively impact people\u27s emotional, psychological, and social well-being. Our proposed study utilizes advanced machine learning techniques to present an innovative and novel method for predicting female infertility. We analyzed a dataset with medical attributes related to reproductive health using logistic regression, Naive Bayes, Support Vector Machines (SVM), and Random Forest algorithms. The Random Forest algorithm achieved an outstanding accuracy rate of 93%, with its exceptional capabilities. The findings show that in the future, this model can be used to diagnose infertility early and provide personalized treatment recommendations. The results of this study have practical implications for reproductive healthcare, as well as providing much-needed support to infertile couples and individuals
Enhanced Brain Tumor Diagnosis with EfficientNetB6: Leveraging Transfer Learning and Edge Detection Techniques
Get PDFCorrect identification of brain tumors is crucial for determining the subsequent steps in patient management and prognosis. This study introduces a novel approach by mimicking three enhanced deep learning models EfficientNetB0, EfficientNetB6, and ResNet50 on a dataset of 7022 MRI instances, each depicting one of four varieties of brain tumors. The research was conducted using advanced neural network architectures, leveraging transfer learning to improve model performance. Results indicated that EfficientNetB6 achieved the highest testing accuracy at 99.39%, outperforming EfficientNetB0 and ResNet50, which recorded test accuracies of 95% and 97% respectively. Evaluation metrics further highlighted the superior performance of EfficientNetB6, with a precision, recall, and F1 score all at 99%. These findings demonstrate the significant potential of deep learning algorithms in enhancing the diagnostic accuracy of brain tumors, suggesting their implementation in clinical settings could lead to better diagnosis and treatment options
The Agroforestry Potential and Analysis of Growth and Yield of different Vegetables Grown Under Olive Orchard to Mitigate Climate Change Effects
No full textOlive is a drought-tolerant plant, making it suitable for cultivation in various dry regions of Pakistan. By applying the principles and regulations of agroforestry, we can increase crop yields, thereby creating a self-sustained farming ecosystem. Agroforestry is a technique that integrates the production of trees, vegetation, and livestock on the same land to achieve financial, environmental, ecological, and cultural benefits. A field experiment was conducted on six winter vegetables—cabbage, Chinese cabbage, kohlrabi, leafy green lettuce, leafy red lettuce, and broccoli—grown under three olive orchards of different ages (10, 20, and 30 years) with varying shading capacities at the Horticultural Research Institute, National Agricultural Research Center, Islamabad. The study focused on intercropping vegetables within olive orchards of different ages. Critical parameters were monitored, and strict plant inspections were carried out during the experimentation period. Plant samples were tested for morphology and chemical composition. It was found that more vigorous olive trees significantly decreased the growth, leaf chlorophyll content, nutrient uptake, and yield of the intercropped vegetables. Maximum shading from the 30-year-old olive orchard severely reduced plant growth and yield. The extent to which growth is limited by intercropping or shade intensity may vary with the genetic makeup of different crops. The results showed that plants grown under optimal light conditions exhibited greater plant height, spread, and stem diameter, attributed to the stimulation of cellular expansion and cell division under adequate sunlight, which increases photosynthetic efficiency. Cabbage and kohlrabi were identified as the most viable crops under the experimental conditions
Real Estate Price Prediction
Get PDFReal estate price predictions are critical for stakeholders, including investors and developers, because they have a considerable impact on investment decisions and market stability. In order to fill in the shortcomings in earlier approaches, this work presents a novel methodology by utilizing deep learning (DL) and machine learning (ML) techniques to improve real estate price forecast accuracy. We used the "House Prices 2023 Dataset" from Kaggle, which contains 168,000 entries of Pakistani property data. Our methodology included extensive data preparation, feature engineering, and the use of various algorithms, including Linear Regression, Gradient Boosting, Random Forest, Convolutional Neural Networks (CNN), and K-Nearest Neighbors (KNN). The models were tested using MSE, RMSE, R-squared, and accuracy. KNN outperformed the other models, with a lower RMSE of 13.79 and a higher R-squared value of 0.85, indicating improved predictive accuracy. RF also produced impressive results, with an accuracy of 80%. Handling complicated feature interactions, guaranteeing model scalability, and controlling hardware resources were all challenges that suggested possibilities for future improvement. As a result, our research offers a solid foundation for raising forecasting accuracy in fluctuations in the market and emphasizes the possibility of utilizing ML approaches for better real estate price prediction
Breaking Down Monoliths: A Graph Based Approach to Microservices Migration
Get PDFIntroduction: The software industry has increasingly transitioned from Monolithic Architecture (MA) to Microservices Architecture (MSA) due to the significant advantages offered by MSA. A crucial first step in this migration process is the identification of suitable microservices.
Novelty Statement: This work aims to introduce an automated method for more effectively identifying potential microservices within monolithic applications.
Materials and Methods: Our approach leverages the source code to construct a frequency-based class dependency graph through graph analysis techniques. A clustering algorithm is then applied to this graph to identify optimal candidate microservices.
Results and Discussion: We evaluate the effectiveness of the proposed approach using several metrics, including the number of microservices, Newman-Girvan Modularity (NGM), and F1-Score. The results demonstrate that the approach accurately identifies candidate microservices, achieving an average F1 score of 0.88 and an average NGM score of 0.526.
Concluding Remarks: The proposed approach proves to be an effective tool for assisting developers in migrating from MA to MSA, facilitating a more streamlined transition process