International Journal of Innovations in Science & Technology
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Eco-Friendly Nano Catalyst Preparation for Biodiesel Production from Melia azedarach Seeds: A Step Toward Climate Mitigation
Full text linkBiodiesel is a renewable and sustainable alternative to fossil fuels, offering a cleaner source of energy with significantly reduced greenhouse gas emissions. This study explores the production of biodiesel from non-edible Melia azedarach seed oil using green synthesis through TiO₂ nanocatalysts. Oil was extracted using n-hexane, and transesterification was performed under different conditions of the molar ratio of oil-to-methanol (1:3, 1:6, 1:9, 1:12, and 1:15), temperatures (70, 80, 90, 120, and 140 °C), concentration of TiO₂ catalyst (0.3, 0.5, 0.7, 0.9, and 1 g), and reaction times (1, 2, 3, 4, and 5 hours). Maximum biodiesel yield (93%) was achieved when the molar ratio was 1:12, the temperature was 80 °C, the weight of the TiO₂ catalyst was 0.7 g, and the reaction time was 3 hours. Fourier Transform Infrared Spectroscopy, X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) were used to characterize the TiO₂ nanocatalyst and verified its catalytic activity and structure. The FTIR characterization of the produced biodiesel verified the presence of methyl esters. The use of non-edible feedstock like Melia azedarach is eco-friendly since it is not in food-vs-fuel competition and can be grown on marginal lands. Secondly, the method combats global climate change by minimizing the use of fossil fuels and carbon emissions. Through this research, it is proven that biodiesel synthesis using non-edible feedstock (Melia azedarach seed oil) is a sustainable method of climate-resilient large-scale biodiesel production in accordance with renewable energy and climate resilience criteria
A Hybrid Transformer and CNN-Based Approach for Classifying Mental Health Disorders from Social Media Data
Full text linkMental health disorders are a significant global concern, with increasing prevalence on social media platforms where individuals often share their experiences and emotions. This research presents a novel approach for classifying mental health disorders, specifically depression, anxiety, borderline personality disorder (BPD), and post-traumatic stress disorder (PTSD), using social media text. We propose a hybrid architecture that combines domain-specific transformer models, such as PsychBERT and MetaBERT, with Convolutional Neural Networks (CNNs) to enhance the model’s ability to understand mental health-related language and metaphorical expressions. The transformer models, pretrained on mental health and symbolic data, generate embeddings that capture the unique linguistic features in social media posts. These embeddings are processed through cascaded CNN layers to extract deep features, which are then concatenated and classified into mental illness categories. The model was evaluated using a balanced dataset comprising 40,000 social media posts, achieving an overall accuracy of 96% and an F1-score of 0.96. The proposed model outperforms existing state-of-the-art methods, including fine-tuned BERT and RoBERTa models, demonstrating superior performance in accurately classifying mental health disorders. The results highlight the effectiveness of leveraging domain-specific language models and CNNs for enhanced classification of mental health conditions in social media text. This study underscores the potential of advanced deep learning techniques in addressing mental health issues and facilitating early detection in real-world applications
Security Issues and Research Opportunities in Wireless Body Area Networks
Full text linkWireless Sensor Networks (WSNs) have found application in diverse fields, one of is Wireless Body Area Networks (WBANs). WBANs are essential networks for fitness diagnostics, observation, and flexible actuators, which rely on data gathered from numerous wireless sensors installed in or above the human body. Due to the Ad hoc nature of WBANs, there are security concerns, which can affect the confidentiality, authenticity, and integrity of data. Security and privacy play a critical role in ensuring secure communication by helping networks prevent unauthorized access and avoid fraudulent activities. Despite its significance, no survey has been conducted in WBANs in terms of computation and communication overheads, Man in the Middle attack (MIMA), Denial of Service (DoS), and Spoofing attacks. This paper helps the new researcher in WBANs security to better understand the area and the need for designing new schemes that focus on the aforementioned parameter
Assessment of Soil Fertility in Jhelum, Punjab, Pakistan, using Geospatial Technologies
Full text linkSoil fertility is a key factor influencing agricultural productivity and sustainability. This study evaluates the spatial distribution of essential soil chemical properties-pH, electrical conductivity (EC), available phosphorus (P), available potassium (K), organic matter (OM), and saturation percentage in Tehsil Jhelum, Pakistan. A total of 160 topsoil samples (0–15 cm depth) were collected using random sampling, with GPS coordinates recorded for each site. Laboratory analysis was conducted to assess the nutrient status of the soils, and Ordinary Kriging interpolation was used within a GIS framework to generate spatial distribution maps. The findings revealed notable variability across the region. Soil pH ranged from 4.3 to 7.8 (mean: 7.44), indicating mostly neutral to slightly alkaline conditions. EC values ranged from 0.49 to 1.40 dS/m, suggesting low to moderate salinity. Available phosphorus varied between 1.2 and 7.8 mg/kg, while available potassium ranged from 60 to 180 mg/kg, showing moderate fertility levels. Organic matter content was uniformly low (0.20–0.66%), with a mean of 0.42%, highlighting poor organic inputs. Saturation levels varied from 22% to 72%, displaying a layered spatial pattern. The spatial heterogeneity observed in soil nutrients underscores the need for site-specific nutrient management and precision agriculture practices. The generated maps serve as valuable tools for farmers, agronomists, and policymakers to make informed decisions aimed at improving crop productivity and maintaining soil health in the region
Remote Sensing and Machine Learning-Driven Flood Inundation Mapping of September 2025 Ravi Watershed Using Sentinel-1 SAR
Full text linkFloods is among the most devastating natural hazards in South Asia. The September 2025 flood in the Ravi Basin was triggered by heavy monsoon rainfall and the release of water from cross-border dams. This study utilized Sentinel-1 SAR data, including both ascending and descending passes in VH polarization, to map flood inundation across the basin using a Random Forest classifier. Pre-flood and post-flood composites were prepared for April-May and 27 August to 5 September, respectively. The predictors feature includes VH_pre, VH_post, VH_diff, and VH_ratio. Terrain correction using the NASA DEM and landcover filtering with ESA WorldCover at 10m improved classification accuracy. Results showed that 1,885 km² of land was inundated, representing 5% of the total basin area. Approximately 260 settlements were impacted, including Dera Baba Nanak, Kartarpur, and the low-lying regions of Lahore. Croplands were the most affected class, with 1,610 km² flooded, followed by grasslands (90 km²) and sparse vegetation (62 km²). Built-up areas accounted for 0.7 km² of inundation, though the socio-economic impact was disproportionately high. Precipitation analysis from NOAA CPC confirmed rainfall clustering in the Sialkot and Narowal corridor. The peaks exceeding 800 mm/day cause this region as the epicenter of the flood. News reports corroborated satellite findings, noting that over 2.5 million displaced and more than 100 lives were lost. The study highlights how tributary floods involving the Ravi, Sutlej, and Chenab are emerging as severe hazards for Punjab. Findings underline the need for improved monitoring, resilient agricultural strategies, and disaster preparedness to mitigate future economic and food security risks
Socially Shared Metacognition of Students in Computer-Supported Programming Tasks and Their Stance on the Difficulty of the Task
Full text linkThe internet has brought much emphasis to online collaborative learning, where learning is connected to co-constructing understanding and knowledge about subjects and tasks through collaboration and conversation. This research centers on several groups of students undertaking a programming project in a Zoom-based environment” or “via Zoom meetings. The paper proposes that socially shared metacognition is most effective in group-based problem-solving. It is a process in which one member of the group helps regulate the whole group’s process of solving a problem and elicits other members’ reactions to this proposal. The feeling of difficulty in performing the task helps ascertain and display the role of group interaction in individual learning. The paper also proposes that the increase in socially shared metacognition decreases the level of difficulty of a problem and thus alleviates individuals’ feelings of task difficulty
Bridging the Divide of Formal and Informal Transit in Urban Areas - Considering Multidimensional Aspects of Sustainability
Full text linkPublic transport in cities across the developing world is fundamentally shaped by the dualism of formal and informal services. Informal transport modes, including minibusses, shared taxis, and auto-rickshaws, are not merely supplementary but are essential components of the urban mobility ecosystem, providing critical connectivity for marginalized communities. Contemporary scholarship advocates for a multifaceted evaluation of these systems to capture their full socio-economic, environmental, and operational impact. This paper conducts a systematic literature review to synthesize existing assessment frameworks for public transport. The findings reveal a significant gap: current methodologies often fail to integrate the core dimensions of sustainability—social, economic, and environmental—with emerging imperatives like climate resilience and comprehensive regulatory and technological considerations. By mapping the state of the art, this review underscores the necessity for a more holistic evaluation paradigm, focusing on frameworks that move beyond a simple formal-informal divide to foster comprehensive understanding and strategic integration
Performance Analysis of Task Distribution Mechanism in Multi-User’ Collaborative Assembly Task\u27 In 3d Virtual Environment
Full text linkCVEs are real-time, computer-simulated environments where two or more actors can mutually complete a task using synthetic objects. User performance is one of the major problems that arise due to coordination problems, a good mechanism to divide tasks, or less understanding or interaction among users collaborating. The impact of multi-user collaboration on using the task distribution mechanism remains unexplored. In this study, the impact of TDM on multi-users’ collaborative virtual environment is investigated. The TDM model assigns the task to collaborating users in CVEs on a static or dynamic manner. In static distribution, there exists weak coupling, and the amount of communication during the actual execution of a task is low, while in dynamic distribution, users are tightly coupled and hence need to communicate more. To study the effect of static and dynamic task distribution strategies on user’s performance in CVEs on multi users, a CVE prototype was developed using C++ and OpenGL, simulating an assembly task with distinct roles for multiple users, where twenty (20) group (each consists of two users) perform a task in collaboration under both strategies (static and dynamic) on two users and three users using arrow-casting and audio aids. The result shows that static with arrows-casting for two users takes an average time of 331.15 sec, and for three users, 321.45sec, and for audio (342.73sec and 326.34sec, respectively. Similarly, the dynamic with arrow casting for two users takes 347.76 sec, and for three users, 333.24 sec, and for audio, 350.12 sec and 344.4 sec, respectively. The findings provide valuable insights into how multi-user collaboration, task distribution methods, and cognitive aids can influence task efficiency and teamwork. However, when the number of users increased to three users, there is a chance that the performance will be degraded because, from the experimental data, a lower improvement was observed for three users than for two users. This research contributes to improving task management and collaboration in CVEs, with potential applications in training, education, and remote teamwork
Generative AI’s Impact on Industry: Unveiling Transformative Applications, Opportunities and Challenges
Full text linkWith the advancement of Artificial Intelligence, a new branch of AI has emerged which is known as Generative AI. It has gained a lot of popularity in a very short time because of its human-like computational capabilities. It has the potential to automate hectic work with its efficient processing capabilities. Overall, it can help in solving complex problems and optimizing mundane and redundant tasks. The main objective of this paper is to conduct a thorough analysis of the impacts of Generative AI on the industry and its benefits, advantages, and application. This will help future generations in opting for Generative AI to automate mundane tasks. The methodology includes in-depth secondary research on research papers related to Generative AI and its applications. The findings show that the recent generative AI applications have emerged as the fastest-growing user base. However, there are lots of limitations to incorporating AI models in the industry. Therefore, it will be beneficial to use Generative AI applications but relying on them can be threatening to employment opportunities and may lead to misleading and falsifying information. It is necessary to have human evaluation, considering specific constraints to accomplish desired results
Smart Farming with AI: Comparative Evaluation of CNN Models for Tomato Leaf Disease Classification
Full text linkTomato is a major agricultural crop cultivated worldwide; however, its production is severely threatened by a wide range of plant diseases, necessitating accurate and timely detection methods. In recent years, neural network–based computer software and mobile applications have emerged as effective tools for plant disease detection. In this study, three advanced convolutional neural network (CNN) architectures—ResNet-50, DenseNet-121, and InceptionV3—are comparatively analyzed to evaluate their effectiveness in identifying and classifying tomato diseases using the PlantVillage dataset. To enhance model robustness against real-world variability, comprehensive image preprocessing and data augmentation techniques were employed, including rotation, horizontal and vertical flipping, rescaling, shear transformation, and zooming. A systematic hyperparameter tuning strategy was adopted by experimenting with various combinations of learning rates, batch sizes, and optimizers to optimize training performance. Experimental results demonstrate that hyperparameter optimization significantly improves classification accuracy, with the ResNet-50 model achieving the highest accuracy of 98.2%, along with superior F1-score, precision, and recall values. DenseNet-121 and InceptionV3 also exhibited strong performance, although their results were comparatively lower than those obtained with ResNet-50. These findings underscore the effectiveness of transfer learning and fine-tuning strategies in the development of automated systems for plant disease detection and classification. The study highlights the strong potential of CNN-based architectures for scalable and accurate disease detection, offering valuable support to farmers for early diagnosis and improved crop management. Furthermore, the study identifies future research directions, including deployment under real field conditions and the exploration of additional deep learning architectures