International Journal of Innovations in Science & Technology
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Experimental Design-Based Optimization of Football Manufacturing: A Case Study of Anwar Khawaja Industries
Full text linkThis study aims to investigate the critical factors influencing the weight and quality of football bladders during the manufacturing process, with a focus on optimizing production at Anwar Khwaja Industries (Pvt) Limited, Sialkot. This research employs the Definitive Screening Design (DSD) to identify and quantify the impact of key variables, including material composition and process parameters, on the final product’s performance. Among the factors analyzed, Calcium Carbonate (CaCO3) emerged as the most significant factor, demonstrating a strong effect on the response variable. Additionally, interactions between Sulphur–CaCO3, Zinc Oxide–BHT, and CaCO3–BHT were found to be critical in determining football quality, durability, and cost–efficiency. Statistical analysis, including regression modeling and ANOVA, underscores these relationships but also reveals model limitations. This study also addresses model accuracy concerns, reporting an R–R-squared value of 52.2%, while the low adjusted R2 (19.4%) and predicative R2 (0.0%) indicate limited generalizability. To address multicollinearity concerns, the factor reduction technique was applied, improving the reliability of experimental findings. The study emphasizes the role of advanced statistical techniques in optimizing manufacturing processes to maintain Pakistan’s global leadership in football production
Challenges Faced by Stakeholders during the Requirement Engineering Phase: An Exploratory Study
Full text linkStakeholders are the backbone of any organization and play a vital role in the completion of any product. Different stakeholders with different roles, skills, natures, and experiences are involved throughout the Software Development Life Cycle (SDLC). Unlike other phases of SDLC, Requirement Engineering (RE) requires more stakeholders, active participation, focus, and collaboration. However, stakeholder involvement makes the RE phase more difficult and impacts other phases of Software Development. The inherent complexity of the RE phase is due to numerous factors, including diverse skill sets, language disparities, comprehension issues, and lack of interest, thereby rendering it particularly challenging for stakeholders. Literature also highlights some practices to resolve these issues, like enhancing communication and building trust among team members to overcome these challenges, but still, all these challenges affect software development in one way or another, and lead projects toward failure
Design And Implementation Of Black Box For Automobiles Using Esp 32
Full text linkA black box system in vehicles acts as an important tool that records important information to make vehicles safer, investigate accidents, and even improve the overall performance of the vehicles. This study will present the Black Box System that has been developed using ESP32 microcontrollers for cars to enhance data collection and analysis in automotive fields using technology. The Black Box System or Event Data Recorder (EDR) is an important tool in the enhancement of road safety, investigation of accidents, and evaluation of the performance of a vehicle. The system utilizes ESP32 as the main microcontroller since it is cost-effective, efficient, and can be programmed in multiple ways. It comprises several sensors and data acquisition modules to collect key parameters including speed, acceleration, geographical location, engine, and various diagnostic information about the vehicle. This paper also presents a detailed overview and integration of the system into Hardware and software parts of the automobile. A user-friendly interface facilitates data retrieval and analysis, supporting applications in fleet management, driver behavior monitoring, and accident investigations. The study focuses on the responsibility and protection of personal data, as well as ways of protecting personal data from misuse and violation of the law. Therefore, using ESP32 technology in the vehicle’s Black Box System is a great improvement towards road safety and vehicle monitoring. By ensuring data security and privacy, this system provides the users with a complete data set to support a decision-making process for self-employed drivers and other organizations
Research Article Hydrothermal Synthesis and Characterization of Zinc Oxide (ZnO) Nanoparticles for Glucose Sensor
No full textZinc oxide (ZnO) nanoparticles have gained notable attention for their multifunctional role in biomedical applications, particularly in non-enzymatic glucose sensing. In this work, the hydrothermal synthesis of highly crystalline ZnO nanoparticles with controlled morphology and size is achieved under optimized reaction parameters. Comprehensive physicochemical characterizations were performed using X-ray diffraction (XRD), and UV-Vis spectroscopy, confirming the formation of phase-pure hexagonal wurtzite ZnO with nanoscale dimensions and high surface purity. The optical analysis revealed a direct bandgap energy of ~3.3 eV, supporting efficient electron transfer kinetics. Electrochemical investigations demonstrated excellent glucose sensing performance, including long-term stability, high sensitivity, rapid response, high sensitivity, low detection limit, rapid response time, and long-term stability, attributed to the enhanced surface reactivity and electron transport of the nanostructures. These findings not only advance the understanding of ZnO nanostructures in glucose biosensing but also position hydrothermally synthesized ZnO nanoparticles as a cost-effective and scalable candidate for integration into next-generation biomedical diagnostic devices
Synthesis and Characterization of Silver Nanoparticles Conjugated with Folate and Curcumin for Their Anti-Cancer Activity
Full text linkNanoparticles are small particles with sizes ranging from 1 to 100 nanometers. Silver nanoparticles, composed of silver at the nanoscale, have been widely used in various fields including medicine, healthcare, food, and commercial industries. While silver nanoparticles can be harmful to normal cells depending on their concentration and exposure time, they are highly effective for wound healing and antibacterial applications. Historically, silver was used as a natural antibiotic. In this study, silver nanoparticles were conjugated with curcumin and folic acid using the glutaraldehyde method due to their anti-cancer properties. Curcumin is known for its ability to kill cancer cells, while folic acid—an organic form of vitamin B9—helps in the creation and preservation of healthy cells. The silver nanoparticles were first modified with polyethylene glycol (PEG), then conjugated with curcumin and folic acid. Curcumin was attached through the NH2 group, and folic acid was linked via the carbonyl group, both through PEG. The average crystalline size was calculated using X-ray diffraction (XRD), and functional groups were identified using Fourier-transform infrared spectroscopy (FTIR). These silver nanoparticles are considered to be more beneficial and less harmful than traditional chemotherapy or radiotherapy for targeting and destroying tumor cells
Hybrid Deep Learning Approach for EEG-based Epilepsy Detection
Full text linkEpilepsy is a chronic neurological disorder characterized by continuous relentless seizures resulting from abnormal activity in the brain. Early and accurate diagnosis is very critical. The usual methods can take a lot of time for diagnosis and it can also often vary from one specialist to another. There have been many approaches implemented for detecting seizures with varying success. Electroencephalogram (EEG) analysis is a critical tool for diagnosing neurological conditions like epilepsy. A key focus in medical technology has been automating the detection of epilepsy but it has been challenging due to its complexity and large amount of data. Although the results of some studies have been encouraging, the use of these approaches has not been practical due to various issues i.e. imbalanced data signal variability to name a few. This research presents a new approach to improve performance and accuracy. A Hybrid Deep Learning model combines a number of paradigms of neural networks to leverage the best of multiple models in processing complex data like EEG signals. EEG. As EEG has both temporal and spatial data this hybrid approach is quite practical in handling different EEG components. In addition, a multimodal method is explored to enhance prediction performance. This involves enhancing EEG data with complementary data, such as clinical history and other biomarkers. Through integrating data from multiple sources, the model gains a broader context for epileptic activity detection. Which helps in bypassing the inefficiencies inherent in EEG signals. This combined approach can potentially provide stronger and clinically informative outcomes, hence enabling advancements in the early diagnosis of epilepsy
UAV-Based Flood Mapping and Damage Assessment in Harnai Khawar, Swat, Khyber Pakhtunkhwa, Pakistan
Full text linkFloods is among the most destructive hydrological hazards in Pakistan, particularly across the steep, data‑sparse basins of Khyber Pakhtunkhwa (KPK). The 2022 monsoon produced catastrophic damage in the Swat Valley, disrupting transport, irrigation, and housing. This article demonstrates an Unmanned Aerial Vehicle (UAV) workflow for rapid, high‑resolution flood mapping, damage quantification, and risk zonation in the Barwai Khwar sub‑watershed of the Swat River Basin. Pre‑event context was assembled from Google Earth Pro imagery (12 June 2022), and post‑event aerial surveys were flown using a DJI Phantom 4 Pro (v2.0) with GNSS‑supported Ground Control Points (GCPs). Imagery was processed in Agi soft Meta shape to generate Ortho mosaics and surface products, then analyzed in a GIS to delineate inundation, channel widening, structural damage, and agricultural losses. The floodplain width locally expanded from approximately 7 m to 76 m; damaged linear infrastructure includes ~2.18 km of retaining walls and 39 m of bridges. Surface impacts include ~6,271 m² of residential area and ~22.56 ha of cropland affected. The approach provided near‑centimeter spatial detail, enabling precise polygonal accounting for recovery planning and identification of high‑risk margins where unprotected construction coincides with steep banks and tight meanders. Findings confirm the value of UAV photogrammetry as a fast, replicable, and cost‑effective complement to satellite‑based disaster assessment in Pakistan’s mountain valleys, supporting preparedness, reconstruction, and resilient land‑use decisions
A Computational Analysis of Nonlinear Fractional Partial Integro-differential Equation Using Meshfree Multiquadric Radial Basis Function Method
Full text linkFractional partial integro-differential equations play an important role in describing physical and engineering systems that exhibit memory and nonlocal effects. Their nonlinear structure and the presence of weakly singular kernels make analytical solutions difficult to obtain, which highlights the need for accurate and flexible numerical strategies. This study develops a meshfree computational method based on multiquadric radial basis functions for solving a nonlinear fractional partial integro-differential equation involving the Caputo derivative. The temporal discretization is carried out using a backward difference formula, and the spatial operators are approximated through radial basis function interpolation. The resulting scheme avoids mesh generation and is suitable for irregular or scattered spatial nodes. Numerical experiments are presented to illustrate the accuracy, reliability, and efficiency of the method for representative test problems. The results indicate that the proposed meshfree approach provides a robust tool for nonlinear fractional models with weakly singular kernels
Synergizing Human Behavior and Cybersecurity using Psychometric Scale
Full text linkCybersecurity threats are increasingly shaped by human actions, making it crucial to comprehend the psychological elements that lead to vulnerabilities. This article examines the interplay between human behavior and cybersecurity through the use of psychometric scales to evaluate risk perception, decision-making, and adherence to security protocols. A quantitative research design was employed, using validated psychometric tools like the Human Aspects of Information Security Questionnaire (HAIS-Q) and the Cybersecurity Risk Perception Scale (CRPS). Data was gathered from 200 individuals in different organizational positions and examined using statistical techniques, such as correlation and regression analysis. Findings demonstrated a noteworthy association between psychological characteristics (e.g., risk tolerance, conscientiousness) and cybersecurity practices. People with a greater awareness of risks showed improved compliance with security policies, whereas individuals with lower levels of conscientiousness were more likely to engage in risky online activities. The results indicate that incorporating psychometric evaluations into cybersecurity training can improve threat management by customizing strategies according to personal behavior patterns. This article adds to the expanding research on human-centered cybersecurity strategies, offering empirical data on the impact of psychometrics in enhancing security awareness and compliance. Future studies ought to investigate long-term impacts and cross-cultural assessments of psychometric scales within cybersecurity settings
Reuse of Ablution Water for Landscaping in Hayatabad Peshawar - A Step Towards Climate Change Adaptation
Full text linkRapid urbanization and climate change have intensified water scarcity challenges in Pakistan, particularly in cities like Peshawar. This study assesses the feasibility of reusing mosque (masjid) ablution water to irrigate nearby green belts in Hayatabad, Peshawar, as a cost-effective and sustainable alternative to conventional tubewell irrigation. Spatial analysis showed that most green belts are located within a 450-meter radius of mosques, enabling the use of low-energy pumping systems. Economic analysis indicated that reusing ablution water could reduce daily transport and pumping costs by more than thirteenfold, significantly decreasing fuel consumption and greenhouse gas emissions. Water quality tests found that ablution water had BOD levels of 4.6–6 mg/L and COD of 10–12 mg/L, remaining within acceptable limits for non-potable irrigation use. Overall, the results demonstrate that the reuse of ablution water is technically feasible, environmentally beneficial, and aligns with Sustainable Development Goals (SDG 6 and SDG 13). This approach offers a scalable model to improve urban water resilience and reduce pressure on groundwater resources in water-stressed region