International Journal of Innovations in Science & Technology
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Bioethanol Production from Waste Banana Peels using Alkaline Textile Industry Wastewater for Delignification Process
Full text linkDepletion of fossil fuel quantity and the higher dependence on it may cause serious problems in the future. Alternative energy sources are required to overcome potential problems. Bioethanol is one of the suitable alternatives to fulfill our energy requirements. Bioethanol can be produced from various sources, including organic waste such as fruit and vegetable waste, which has the potential to produce bioethanol. In this work, bioethanol was produced from banana peels using alkaline textile industry wastewater for the delignification process. The effect of H2SO4 strength, pH of the solution for fermentation, banana peels delignification, and grinding (size reduction) on ethanol production was analyzed. Experimental results show that increasing the sulfuric acid concentration from 2% to 5%, and then to 10%, led to an increase in the refractive index and hence, ethanol production, with maximum ethanol yield observed at 10% H₂SO₄. Increasing the pH of the solution of fermentation from 2 to 14 shows an increase in the refractive index, and maximum ethanol was obtained at pH 6. The delignification and grinding (size reduction of banana peels) also showed a positive effect on the production of ethanol
A Computational Simulation of Fractional Advection-Diffusion Model Using Differential Quadrature and Local Radial Basis Functions
Full text linkThis article presents a local radial basis function-based differential quadrature method for solving the time-fractional advection-diffusion equation. Backward difference formula is utilized to approximate Caputo fractional derivative. Differential quadrature approach is employed to compute the space derivatives by 3-point central scheme in the neighborhood of a node. Two types of radial basis functions are utilized in numerical simulations. Accuracy and computational efficiency of proposed technique is assessed via , error norms, fractional order, time and spatial step sizes, rate of convergence and execution time. Three nonhomogeneous test problems are solved to validate the method, and the results are compared with finite volume method to show its superiority
PsyRA – A Retrieval-Augmented Dialogue System for Mental Health Support
Full text linkMental health support continues to face numerous challenges, including limited access to care, persistent social stigma, and a shortage of trained mental health professionals. In response to these issues, this paper introduces PsyRA, an innovative AI-powered system designed to enhance psychological assessments through a specialized retrieval-augmented generation (RAG) approach. Unlike conventional chatbots that often fail to capture the nuanced context of patient interactions, PsyRA leverages domain-specific psychological knowledge to deliver more accurate and in-depth assessments. It draws from a carefully curated knowledge base that includes psychological research, diagnostic guidelines, therapy exercises, and intervention strategies to inform its responses and suggestions. PsyRA is equipped to understand patient narratives more clearly, provide evidence-based assessments by retrieving relevant psychological information, and offer personalized intervention recommendations tailored to individual needs. Early evaluations indicate that PsyRA is capable of detecting subtle emotional cues within patient conversations and responding in alignment with established psychological practices. The system demonstrates promising potential to broaden access to mental health support, assist professionals in the assessment process, and reduce the barriers that often prevent individuals from seeking treatment. This work contributes to the expanding field of AI-assisted mental health care by illustrating how retrieval-based models can enhance both the depth and quality of psychological assessments, offering improved emotional sensitivity and reliable, evidence-driven guidance
Development of a Generative AI Application for Therapy of Speech-Impaired Patients
Full text linkWith the use of this web application, speech therapy can be provided to individuals with speech disabilities through an easy-to-use and engaging interface, which allows them to receive treatment from anywhere. Conventional treatment often requires the patient\u27s presence, which can be hard due to their location, finances, or even other health conditions. The application helps users practice articulation, fluency, and pronunciation with the help of guided exercises, visual aids, and voice recognition technology. Users are provided with a personalized treatment plan that reviews their progress over time and provides them with feedback, reports, and reminders to help ensure users are motivated and consistent. This new technology will increase the availability of services to patients in underserved regions, which will improve their communication as well as the quality of life
Enhancing Non-Player Characters (NPC) Behaviour in Video Games Using Reinforcement Learning
Full text linkNPCs enrich the immersive experience of a video game, and traditionally exist along purely rule- or script-based paradigms, denying adaptability or intelligent decision-making very often. The research integrates RL into the NPC behaviour to allow for the more realistic, dynamic interactions and responsive behaviour that today\u27s gaming environments require. We will review state-of-the-art RL algorithms and validate improvements implemented in our own RL model within a sandbox game environment into NPC decision-making and player engagement. According to our results, RL makes NPCs adaptive, tactically deep, and realistic while the classical ones fail. The study provides rigorous methodology and analysis to demonstrate the feasibility and advantages of using RL for the design of a new generation of games
Advancements in Automatic Text Summarization using Natural Language Processing
Full text linkWith the rapid expansion of data across various domains, the need for automated text summarization has become increasingly crucial. Given the overwhelming volume of textual and numerical data, effective summarization techniques are required to extract key information while preserving content integrity. Text summarization has been a subject of research for decades, with various approaches developed using natural language processing (NLP) and a combination of different algorithms. This paper is an SLR-type essay presenting the existing text summarization techniques and their evaluation. It covers the basic concepts behind extractive and abstractive summarization and how deep learning models could serve as a boost in the performance of summarization. The study goes on to investigate the present use of text summarization in different areas and looks into the various methodologies applied in this area. A total of twenty-four carefully selected research articles were being analyzed to identify key trends, challenges, and limitations regarding text summarization techniques. The paper further discusses the existing literature and proposes a number of open research challenges with insight concerning possible future directions in text summarization
Understanding the Role Of Emotional Intelligence in Agile Teams in Context of Requirement Change Management
Full text linkRequirement changes are inevitable in Agile software development, wherein flexibility is the key. Although Scrum offers defined change management procedures, but they tend to overlook the emotional aspects involved in Requirement Change Management (RCM) success. This systematic literature survey investigates the application of Emotional Intelligence (EI) in Agile RCM, drawing conclusions from 27 studies. Results emphasize recurring issues like ineffective change planning, uncertain prioritization, inadequate stakeholder involvement, resistance to changes, and affective barriers in the form of fear, lack of trust, and low motivation. The review finds shortcomings in disciplined RCM practices, role-based EI integration, and alignment for performance. To fill these gaps, the research recommends a role-centric RCM framework incorporating EI concepts to enhance communication, trust, and flexibility with Agile adaptability
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
Performance Analysis of a Lifi System Based on VLC Over a LOS Channel with and Without Ambient Light Using Opti System
Full text linkLi-Fi (Light Fidelity) stands as a state-of-the-art wireless technology that sends data through light-based transmissions. Mobile robots require effective indoor location systems because they operate within hospitals, museums, and airport interiors. Researchers have studied the behavior of the On-Off Keying (OOK) modulation technique used in Li-Fi systems by observing the impact of background interference. Our model determines the performance of a 550 nm wavelength white LED transmitter using Opti System software. Evaluation of the system occurs through examinations under two conditions: when ambient light noise exists and when it does not. The research outcomes demonstrate that Li-Fi technology can deliver dependable high-speed indoor localization services for environments experiencing changes in ambient lighting conditions. Simulation findings indicate a Q factor measurement of 6.47 with noise, while the results show 19.18 when noise is not present. The network supports 10 Gbps data transmission at 2.96e-11 Bit Error Rate with ambient noise and 2.3e-82 Bit Error Rate with ambient noise under a 10-meter connection range
Assessing Drought Conditions using SPEI in Bahawalpur Division, Punjab, Pakistan
Full text linkThis research study analyzes drought conditions using the Standardized Precipitation Evapotranspiration Index (SPEI) in Bahawalpur Division, South Punjab, Pakistan. Drought is one of the most complex natural disasters and is difficult to predict due to several involved factors. Among all natural hazards, drought causes significant damage to human lives and other living communities. Nearly 85% of all disasters are related to weather events, and drought is one of the most damaging among them. In Pakistan, drought causes damage in many areas, and Bahawalpur Division is one of those facing severe drought conditions. Temporal data on temperature and rainfall were collected from the Pakistan Meteorological Department for the period 1992 to 2020. The data were analyzed spatially using GIS technology. Precipitation and temperature data were analyzed using SPEI to monitor drought in three selected districts in Bahawalpur Division: Bahawalnagar, Bahawalpur, and Rahim Yar Khan. The study revealed that less rainfall was recorded in all three districts, leading to drought conditions. Moreover, this reduced rainfall severely affected the concerned districts