International Journal of Innovations in Science & Technology
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Current-Injected Mode Control for Coupled-Inductor (Ci) Based Boost Converter
Full text linkWith the increasing demand for electrical energy, there is a need to replace conventional energy resources with renewable energy resources. To properly implement renewable resources at a larger scale, DC/DC converters play a major role. Owing to the variable and unreliable nature of renewable energy resources like PV systems there is a requirement for converters that can regulate the voltage at the output side. High-gain DC/DC converters are preferred for the integration of the solar system in smart grids or microgrids. In this context, a high-gain boost converter utilizing a coupled inductor is a preferable choice. High gain is achieved by the proper selection of the turn’s ratio of coupled inductors in such converters. Whereas to obtain voltage regulation there is a need to employ an effective control scheme. In this paper current-injected control topology has been utilized for coupled inductor-based boost converter. The proposed converter with an appropriate control scheme aims to achieve high voltage gain, reduced switching losses, minimization of current ripple, and less conduction losses while increasing the efficiency of the overall system. A small signal model based on the state space averaging technique is used to derive control to output transfer function for the proposed converter. A hardware prototype has been implemented for the validation of theoretical work. The overall efficiency of the converter is calculated to be around 96% at specific load conditions
Investigate the Operating Temperature Effect on Fast Pyrolysis Products of Food Waste with Hydrogen
Full text linkEnergy crises and environmental pollution are the main issues of concern all over the world and the disposal of wastes by converting into gaseous products can reduce this to a level. Investigating how operating temperature affects the yield and makeup of bio-oil, bio-char, and bio-gas during the pyrolysis process in the presence of hydrogen is the goal of this study. By offering a novel method for enhancing the quality and yield of gaseous products through controlled thermal decomposition in a hydrogen-enriched environment, the findings improve sustainable technologies. In this research, the fast pyrolysis of food waste carried out by using a lab scale fixed bed reactor in the presence of different composition of Nitrogen and Hydrogen to investigate the effect of operating parameters high pyrolysis temperature 600, 650, 700, 750 and 800 °C and hydrogen gas 0 %, 10 % and 20 % with Nitrogen as a carrier gas. The gaseous products maximum yield i.e. 45.68 comes out at 750 °C temperature in the presence of 10 % hydrogen. The results indicate that increasing the pyrolysis temperature boosts decomposition reactions, encouraging the formation of gaseous products. Hydrogen plays a crucial role by facilitating cracking and stabilizing the reaction intermediates, minimizing the formation of heavier components. The results demonstrate that the fast pyrolysis of food waste give residue at high temperature and in the presence of hydrogen up to 10 % achieved a maximum the bio gas yield. Energy crises and environmental pollution are major global concerns. Converting waste into gaseous products can help address these issues. This study examines how operating temperature influences the yield and composition of bio-oil, bio-char, and bio-gas during pyrolysis in a hydrogen-rich environment. By introducing a novel approach to enhance the quality and yield of gaseous products through controlled thermal decomposition, the findings contribute to sustainable technologies. The research involves fast pyrolysis of food waste using a lab-scale fixed-bed reactor, with varying nitrogen and hydrogen compositions. The effects of different operating parameters were analyzed, including high pyrolysis temperatures (600, 650, 700, 750, and 800 °C) and hydrogen concentrations (0%, 10%, and 20%), with nitrogen as the carrier gas.
The highest gas yield (45.68%) was achieved at 750 °C in the presence of 10% hydrogen. The results show that increasing pyrolysis temperature enhances decomposition reactions, leading to higher gas production. Hydrogen plays a key role by promoting cracking reactions and stabilizing reaction intermediates, reducing the formation of heavier byproducts. The study demonstrates that fast pyrolysis of food waste at high temperatures, with up to 10% hydrogen, results in the highest bio-gas yield
Enhancing Quranic Ethics and Morality: An NLP- Based Semantic Search Model for Urdu Translation
Full text linkThe Quran offers unparalleled guidance on ethics and morality, but extracting relevant teachings from its Urdu translations remains a challenge due to conventional keyword-based search methods that lack contextual understanding. This research proposes a Natural Language Processing (NLP)--based query model designed to improve the retrieval of Quranic verses related to ethics and morality in Urdu translations. By integrating Sentence Transformers for semantic search and a custom synonym expansion module, the model enhances accuracy and relevance in retrieving verses. The dataset widely accepted Urdu translation of the Quran, and the system is evaluated using precision, recall, and relevance scoring metrics to ensure effectiveness. The study demonstrates how NLP techniques can bridge the gap between traditional Quranic studies and modern computational methods, providing scholars, educators, and researchers with an advanced tool for exploring Quranic ethics. The proposed system achieves high precision and recall, offering a more effective approach to Quranic verse retrieval compared to conventional keyword-based searches. The research also highlights future opportunities for expanding the model to support multiple languages and broader thematic searches, further enhancing accessibility to Quranic knowledge
Framework for Modeling Risk Factors in Green Agile Software Development for GSD Vendors
Full text linkIn the last decades, agile methodologies are commonly employed to develop and deliver valuable software, with high user satisfaction at a comparatively low cost. However in recent years, the emergence of Green Software Engineering has necessitated that software developers prioritize the development of Green And Sustainable Software (GSS). Green software development is about developing and utilizing software with restricted energy and computing resources. In recent years, as the application of Global Software Development (GSD), software engineers have applied agile methods for fast, interactive, and green software development. However, such adoption of agile methods poses certain risks. The contribution of this study is two-fold. First, it identifies 8 Risk Factors (RFs), through a Systematic Literature Review (SLR), in which 42 relevant papers are identified and reviewed. The identified RFs need to be avoided by the GSD vendors while using agile methods to deliver GSS. Second, the findings of the SLR study are empirically validated through a questionnaire survey from 106 GSD experts belonging to 25 disparate countries. The results of the SLR and survey were compared and analyzed through a two-proportion Z test using R, which shows some significant variation for some RFs. Lastly, a framework for modeling structural association among RFs was established using an interpretive structural modeling approach. Research results illustrate that the outcomes of our industrial survey are mostly coherent with the SLR findings. Future, research should focus on developing predictive models using Artificial Intelligence (AI) and Machine Learning (ML) to analyze project data in real-time, promoting proactive decision-making for GSS development
Transformers as the Foundation of Large Language Models: A Comprehensive Review
Full text linkThe transformation of Transformer architecture has led the way into a new era for NLP, as it broke the traditional RNNs, LSTMs, Seq2Seq models, etc. As their main feature, the Revolution of Transformers was the hybridization of self-attention and multiheaded attention, which allowed the models to learn dependencies across time spans of any length through positioning methods. This resulted in a quick and efficient process for training large-scale Language Models (LLMs) that could handle the data very well and simultaneously learn the long-term dependencies. This paper is titled "Transformers as the Foundation of Large Language Models: A Comprehensive Review", and it not only reflects but also presents a critically reviewed path taken by LLMs from BERT to GPT-4 and beyond, along with the better reasoning, arithmetic, and instruction following attributed to the scaling up of architecture. The review further indicates and discusses the current concerns regarding efficiency, bias, interpretability, and domain specialization, and warns that settling these issues might dictate the fate of T-bases improvements. The authors aim through this project to provide an exhaustive comprehension of the setting in which Transformers enabled LLMs and actively directed the development of contemporary AI research
A Framework for the Prediction of Parkinson’s Disease Using Agentic Artificial Intelligence
Full text linkParkinson’s disease (PD) is a progressive neurodegenerative disorder that is difficult to diagnose, particularly in its early stages. Subtle, slowly evolving symptoms often delay confirmation, reducing opportunities for timely intervention that could improve outcomes and quality of life. Conventional diagnosis relies largely on clinical observation, which can be subjective and insufficiently sensitive for early detection. This thesis proposes an Agentic Artificial Intelligence (AAI) framework for early PD detection and severity assessment using voice-based biomarkers. Biomedical voice parameters are leveraged because vocal changes can reflect early neurological impairment. Two publicly available Kaggle datasets containing voice recordings from individuals with PD and healthy controls are used to train and evaluate the models. For detection, an XGBoost classifier achieves 94.68% accuracy with strong discriminative performance. For severity estimation, XGBoost regression models predict motor and total Unified Parkinson’s Disease Rating Scale (UPDRS) scores with high agreement to clinically reported measurements. A key contribution is an agentic decision-making layer that autonomously interprets model outputs, performs disease staging, and generates stage-dependent monitoring and treatment recommendations. Unlike conventional predictive pipelines that stop at numerical outputs, the proposed system translates predictions into actionable clinical insights to support structured decision-making. Experimental results indicate that the framework can detect PD and estimate severity effectively from non-invasive voice data, highlighting the potential of AAI for earlier diagnosis, personalized monitoring, and intelligent clinical decision support in healthcare. The multi-layer design supports modular updates to models and agent policies, enabling telehealth deployment and longitudinal tracking as additional voice samples become available over time
Growth and Characterization of Multilayer NiO/Ag and NiO/Al Structures for Energy Saving Heat Mirrors
Full text linkOptically Transparent multilayer NiO/Ag and NiO/Al heat mirrors were prepared which allow the visible radiation to pass through it and reflect the Infrared radiations. NiO thin film were deposited by sol-gel spin coating technique, Ag and Al thin films were prepared by thermal evaporation. XRD analysis showed that the formation of cubic structure of NiO. The chemical analysis reveals that Ni2O3 phase also present along with the NiO phase. The transmittance spectra of NiO/Ag and NiO/Al coatings showed good transmittance in visible region while highly reflective in infrared region.XRD analysis of the film showed the formation of cubic structure of NiO. The chemical analysis showed that some peaks are belongs to other phase of the NiO. The transmittance spectra of multilayer of NiO/Ag and NiO/Al films are good transparent in visible region and reflect well in infrared region
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
Impact of Urbanization on Land Use and Land Cover: A Geospatial investigation of Taluka Khairpur (2000-2020)
Full text link Urbanization is a major driver of land use and land cover (LULC) changes, profoundly affecting agricultural lands and promoting urban expansion. Recent studies indicate that urban development often occurs on the most fertile and productive lands, contributing significantly to the reduction of arable land in the outskirts of cities. This research study analyzes the LULC changes in Taluka Khairpur using GIS and remote sensing techniques. It provides a detailed 20-year (2000-2020) analysis that has not been previously addressed. Satellite images for the years 2000, 2005, 2010, 2015, and 2020 were downloaded from the United States Geological Survey (USGS). ArcGIS was utilized for supervised classification and LULC calculation, including categories like built-up areas, agricultural land, barren land, desert, and waterbodies. The study revealed significant changes in LULC over 20 years. The built-up area in Taluka Khairpur increased by 131.59 km² (221%), during the study period which resulted in transformations in other LULC categories. Such as, agricultural land decreased by 34.40 km² (47.25%), barren land by 80.89 km² (34.74%), desert area decreased by 6.74 km² (2.56%), and waterbodies by 9.57 km² (3.64%). This study highlights the significant urban expansion and reduction in agricultural and natural land cover in Taluka Khairpur, underscoring the need for sustainable urban planning and environmental conservation
Spatial Analysis of Land Use and Land Cover of Gujranwala District Using Remotely Sensed Data
Full text linkLand use and land cover change a major problem in most metropolitan areas in the world, where a natural land surface is changed by commercial land. Gujranwala is the 5th most populous city of Pakistan. The present population is 2,290,000. This study is an effort to assess the land use changes in Gujranwala District from the years 1990 to 2020. Land use Land cover (LULC) is the spatial change in land use and land cover from 1990 to 2020. The whole research is categorized into four classes (i.e., Vegetation, Uncultivable Land, Built-up Area, and Waterbody). The objectives revolve around the detection and assessment of Land use and Land cover in the district. The land cover is directly proportional to the expansion of the population of the district. The reasons for the changes are the development of residential and commercial buildings. Two types of analysis are being used in the methodology. The temporal analysis is done using Spatial techniques, including Geographic Information System (GIS) and Remote Sensing. Furthermore, the statistical analysis was also performed using the statistical data of the built-up area. The findings depicted that the alterations in land cover were due to an increase in built-up area and population in the city