International Journal of Innovations in Science & Technology
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Exploring Agile Testing Methodologies: A Perspective from the Software Industry
Get PDFAgile testing is a fast-paced testing method that adheres to the principles outlined in the Agile Manifesto. This research paper explores the adoption of Agile testing methodologies in the context of software houses in Pakistan. The study focuses on identifying the prevalent Agile testing techniques preferred by Software Quality Assurance (SQA) teams and the factors influencing their selection. A survey was conducted to gather insights from professionals in the industry, including SQA experts, developers, and project managers. The findings provided valuable information on the most widely used Agile testing methodologies and the reasons behind their popularity. The core objective of this research is to provide the knowledge related to implemented methodologies, reasons behind the selection of these methodologies, factors that influence the selection of testing tools and techniques, satisfaction level of their selected tools, and how effective their selected tools or techniques are in terms of reducing the number of bugs. The study\u27s contribution lies in offering guidance to software houses in Pakistan by facilitating the adoption of effective Agile testing techniques. The research concludes with recommendations for improving testing practices and enhancing the overall quality of software products in the industry.
Hybrid Approach to Solve Thermal Power Plants Fuel Cost Optimization Using Ant Lion Optimizer with Newton-Based Local Search Technique
Get PDFIntroduction/Importance of Study: The optimization of the power system is a complicated problem that is extremely non-convex, nonlinear, and important for reducing the cost of production.
Novelty Statement: Despite the fact that several metaheuristic algorithms are proposed for solving power system optimization problems, the strength of hybridized global search-based techniques has not commonly been applied to power system optimization.
Material and Method: Deterministic power system optimization strategies are unable to yield global optimal outcomes because of the entrapment in local optimum zones. Stochastic approaches like those in which Ant-Lion Optimizer is used and hybridization algorithms with local search methods SQP, IPA, and active set give better results.
Result and Discussion: Hybridized global search-based techniques have been successfully applied to power system optimization with economic load dispatch in particular. Results from findings hybridized-ALO outperforms modern optimization methods.
Concluding Remarks: Results from findings show 3 and 13 generator systems that hybridized-ALO outperforms modern optimization methods
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
Deep Faces: Advancing Age and Gender Classification using Facial Images with Deep Features
Get PDFIn the realm of identity recognition and social interactions, human facial features play a pivotal role. Accurate age estimation and gender classification from facial images have practical implications across various fields, including biometrics, surveillance, and personalized services. This study presents a novel approach that harnesses deep features extracted by the VGG-19 architecture for age and gender prediction, employing a custom convolutional neural network (CNN) for classification. Leveraging the UTKFace dataset, encompassing a diverse collection of facial images with annotated age and gender labels spanning various ages, ethnicities, and gender representations, provides a robust foundation for model training and evaluation. Deep features extracted from the VGG-19 architecture serve as rich representations of facial patterns, enabling our model to discern discriminative cues for age and gender. These deep features are input to CNN model, which is fine-tuned specifically for age and gender classification. The model comprises input layer, Dense layers, incorporating dropout and batch normalization to mitigate overfitting, and Activation Functions Sigmoid for gender classification and SoftMax for Age group classification. The dataset is divided into training and validation sets (70% and 30%, respectively), enabling the model to learn to map VGG-19 features to age and gender labels. To evaluate the performance of the model, metrics like accuracy, precision, recall, and F1-score are employed. The proposed model achieves an impressive 78.67% accuracy in predicting age and 97.02% accuracy in gender classification on the UTKFace dataset, outperforming traditional methods despite challenges posed by variations in lighting, pose, and expression. The robustness of our approach is evidenced by its capability to handle diverse gender representations
Assessment of Soil Erosion and Neotectonics Geomorphology of Bannu Basin using RS and GIS Techniques
Get PDFSoil erosion presents a significant environmental challenge in Bannu District, adversely impacting agricultural productivity and land sustainability. This research article offers a comprehensive approach to assessing and mitigating soil erosion risk in the region by utilizing the Revised Universal Soil Loss Equation (RUSLE) model in conjunction with hypsometric analysis. The study integrates various geospatial datasets, including mean annual rainfall, digital elevation models, soil maps, land use/land cover classifications, and satellite imagery. These datasets are essential for mapping the five key factors of the RUSLE model: Rainfall Erosivity (R), Soil Erodibility (K), Slope Length and Steepness (LS), Land Cover Management (C), and Support Practice (P). By mapping each factor individually and then integrating them, the study estimates soil erosion rates in Bannu District. Soil erosion risk is categorized into five levels, ranging from very low to excessive, to facilitate practical assessment. This classification assists in identifying areas that require immediate attention and intervention for sustainable land management and agricultural practices. The study highlights the benefits of combining Remote Sensing (RS) and Geographic Information System (GIS) technologies with the RUSLE model. This integration enables policymakers and land managers to evaluate and address soil erosion issues on a broader scale. Additionally, the study examines the role of hypsometry in understanding topography and erosion dynamics, incorporating topographic elements into the RUSLE model to explain soil erosion trends in Bannu District. Overall, this article provides a scientifically rigorous and practical soil erosion risk assessment for Bannu District. By leveraging the RUSLE model and GIS data with hypsometric analysis, the study offers valuable insights for addressing soil erosion and promoting sustainable land use in the region. The findings are intended to assist policymakers and stakeholders in safeguarding agricultural productivity and enhancing land sustainability in Bannu District
Evaluating and Predicting the Land Use Land Cover Changes and its Impact on Land Surface Temperature using CA-Markov model: A study of District Mardan, Pakistan
Get PDFThe Rapid population growth is a global phenomenon that reshapes landscapes and impacts environmental conditions. This study aims to analyze the effects of urbanization on Land Use Land Cover (LULC) changes and their impact on Land Surface Temperature (LST) in District Mardan from 2002 to 2022, while also predicting future LULC and LST changes for the year 2042. Utilizing remotely sensed data and Geographic Information Systems (GIS), the study evaluates the correlation between the conversion of natural landscapes to built-up areas and the resulting changes in LST. The primary objectives are to investigate LULC changes over the past two decades, examine how these changes influence LST, and forecast future LULC and LST trends using the CA-Markov model in IDRISI SILVA software for 2042. The analysis of LULC changes from 2002 to 2022 reveals a significant increase in built-up areas and a decrease in vegetation. Built-up land expanded from 10.10% in 2002 to 16.28% in 2022, representing a 6% increase, while vegetation cover decreased by nearly 10% of the total land cover. Concurrently, LST data show that areas experiencing high temperatures have increased since 2002. In 2002, 37% of the total area had temperatures below 30°C, whereas this figure dropped to 28% by 2022. Correlation between LULC and LST indicates that barren surfaces and built-up regions experience higher temperatures, while areas with vegetation and water exhibit lower and more moderate temperatures. The CA-Markov model forecasts that built-up land will increase by 19% by 2042, continuing the current trend, while vegetation areas are expected to decrease by an additional 4% from their 2022 levels. The LST analysis suggests a further increase in high-temperature areas, with a predicted 3% decrease in low-temperature regions. This research highlights the historical trajectory of urbanization and its thermal effects in District Mardan, providing critical insights for sustainable land-use planning and strategies to mitigate urban heat island effects in the coming decades
Identification of the Potential Areas/Sites for Rain Water Harvesting and Agriculture Development Using GIS and Remote Sensing in District Dera Ismail Khan (DIK) Khyber Pakhtunkhwa
No full textWater resources are rapidly depleting in both rural and urban areas of Pakistan due to increasing demands from agriculture and domestic use. This study aims to identify potential rainwater harvesting sites and evaluate the surface runoff potential for sustainable water resource management in the Dera Ismail Khan district of Khyber Pakhtunkhwa province, utilizing GIS and remote sensing (RS) techniques. The research involves both laboratory and field work. Results were validated through a field survey using handheld GPS, while laboratory analysis was performed using ARCGIS software with the Multi-Influencing Factor (MIF) Model. This model incorporates soil classes, slope, geology, and drainage density, analyzed through detailed maps and scales. Geospatial modeling techniques, combined with ground data, led to the identification of several potential rainwater harvesting sites, primarily in the northern and northwestern parts of the district. A total of 26 sites were selected for rainwater harvesting interventions, located on areas ranging from flat to gentle slopes with elevations below 300 meters. The findings of this study can assist the Soil and Water Conservation Department of KP, which is responsible for rainwater harvesting initiatives in the region. The maps produced using the MIF Approach are valuable tools for engineers, planners, and decision-makers in locating and developing dams, storage ponds, and check dams, and for integrating rainwater harvesting into national water policies
A Spatio-Temporal Assessment Of Land use Land Cover Change on Agriculture Productivity in Punjab, Pakistan
Get PDFIntroduction/Importance of Study: The agricultural sector is crucial to the development of any nation, particularly where food security is a concern. In Punjab province, urban settlements are increasingly encroaching on established agricultural lands, posing a significant threat to agriculture in the region. This issue is compounded by the continuous urban expansion and encroachment on fertile lands. The primary aim of this study is to assess the impact of Land Use and Land Cover (LULC) changes on agricultural productivity in Punjab. Utilizing the Earth Engine, this research performs LULC classification and estimates wheat crop yields in the province.
Novelty Statement: This study presents an innovative application of Earth Engine analytics to monitor and analyze the effects of LULC changes on agricultural productivity in Punjab province.
Material and Method: The research employs 20 years of Land Use and Land Cover data from the MODIS dataset, accessed via Google Earth Engine (GEE). In addition, wheat crop production is estimated using the capabilities of GEE.
Result and Discussion: The findings indicate a substantial shift in land cover in Punjab, which has significantly affected wheat crop production. The study emphasizes the importance of public awareness campaigns and the adoption of advanced agricultural technologies. Continuous monitoring of LULC changes using GEE can enable timely interventions to mitigate negative impacts.
Concluding Remarks: By integrating urban growth management strategies with the preservation of agricultural lands, long-term agricultural sustainability and development can be achieved. This research highlights the urgent need for comprehensive policies and collaborative efforts to counteract the adverse effects of urban expansion on agricultural productivity
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
The Evaluating Chlorine Dosage for Effective Disinfection and Antimicrobial Resistance Profiling in Drinking Water Under Climate Change Influences
No full textIntroduction/Importance of Study: Climate patterns, such as heavy rainfall and flooding, can introduce contaminants into water sources, leading to increased microbial loads. Chlorine disinfection is essential in mitigating these risks by effectively destroying pathogens.
Novelty Statement: This study investigates the effectiveness of different chlorine disinfectant dosages in eliminating disease-causing microorganisms and assessing antimicrobial resistance (AMR) in drinking water.
Material and Method: A biofilm annular reactor (BAR) setup was utilized to assess the impact of chlorination on pathogenic microorganisms. Three chlorine doses were tested: 0.5 mg/L, 1 mg/L, and 1.5 mg/L. Samples were collected and analyzed for AMR. Five selective bacterial strains were isolated using the membrane filtration method, and antibiotic sensitivity was evaluated using the standardized Kirby-Bauer disc diffusion test.
Result and Discussion: The study isolated five gram-negative bacteria on selective agar: E. coli, Salmonella, Shigella, Pseudomonas, and Vibrio cholerae. Their antimicrobial resistance to five antibiotics (amoxicillin, AML 5 µg; ampicillin, AMP 10 µg; Azithromycin, AZM 15 µg; ceftriaxone, CRO 30 µg; and imipenem, IPM 10 µg) was tested on Mueller-Hinton (MH) media. Azithromycin demonstrated the highest activity against all isolates. The optimal chlorine concentration for removing these bacteria from water was 1.5 mg/L, due to chlorine’s high reactivity.
Concluding Remarks: The study concludes that a chlorine concentration of 1.5 mg/L is optimal for pathogen removal from water, and Azithromycin exhibited exceptional effectiveness against all resistant gram-negative bacterial isolates