International Journal of Advances in Scientific Research and Engineering (IJASRE), ISSN:2454-8006, DOI: 10.31695/IJASRE
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Performance Evaluation of a Solar Domestic Hot Water Thermo syphon System for Gembu Taraba State, Nigeria
Full text linkThe performance and economic feasibility of a solar water heating system in Gembu, Taraba state, Nigeria for the provision of hot water demand for domestic application was investigated. The study aimed to determine the effectiveness of the system performance under different weather conditions, and the economic value. Here, the monthly average hot water loads required for domestic use at the location was first estimated as 60 liters per day at 55°C. Based on this demand, the required collector area was calculated using a flat plate collector efficiency of 40%. A TRNSYS model of the thermosyphon system was then developed to simulate thermosyphon system to assess its monthly average performance, considering variations between sunny and cloudy days. The performance of the model was validated through experimental data collected from January to April, with the accuracy measured using the mean average percentage error (MAPE). The findings revealed that 2.04 m² of collector aperture was suitable for most months. The system delivered hot water on the average at 40.15°C in August and 51.91°C in April, falling short of the desired 55°C which necessitated auxiliary heating in some months. The simulation model showed good agreement with the experimental data, with MAPE values ranging from 6.1% to 7.8%, indicating reliability. The system demonstrated a high annual solar fraction of 0.81, reflecting a good savings. The overall annual efficiency of the system was 34%. Economically, the system indicated an annual net saving of approximately 62% after accounting for auxiliary energy costs. The payback period for the system was found to be 7.6 years, with a positive net present value (NPV) of N1,439,319 after 20 years. The system offers significant economic benefits and contributes to environmental sustainability by reducing reliance on conventional energy sources. However, its performance varid with seasonal changes in solar radiation, leading to occasional shortfalls in meeting the desired temperature. Future work could focus on optimizing collector area design, improving auxiliary heating solutions, exploring energy storage options, conducting extended field test, and assessing the impact of technological advancements and fluctuating energy costs on the system's economic and environmental benefits. These steps could potentially enhance the system's reliability, efficiency, and overall feasibility, promoting wider adoption in different regions
Study and Implementation Of API and Hashing Methods in Document Management System
Full text linkDocument Processing Information System, better known as Document Management System, is an information system that utilizes information technology to assist and facilitate the processing of documents in an agency. Currently, the Document Management System is only used as a stand-alone information system and is only limited to managing documents for the system's needs. In its storage, the Document Management System still uses hard disk storage media or computer hard drives in the form of original documents. This makes it difficult for users to provide document backup data and has the risk of document damage and document access from outside the Document Management System. This study aims to connect Document Management System with other information systems that run outside DMS using API and hashing methods. The API method is used as a communication path or bridge so that DMS is a stand-alone information system and can support other information systems in document processing. This study also uses the hashing method to change the form of the original document into plaintext so that the document can be stored in the database storage media so that users can create document backup data, and documents can no longer be accessed from outside the Document Management System
Design and Development of AI Driven Mental Health Support System for Teenagers
Full text linkMental health issues among teenagers are on the rise due to academic stress, social pressures, and the challenges of adolescence. Access to timely and effective mental health support is crucial, yet traditional methods of seeking help may feel intimidating or inaccessible to many young people. This project focuses on developing an AI-driven mental health support app tailored for teenagers, aimed at providing a safe, accessible, and personalized platform for emotional well-being. The app leverages artificial intelligence to offer empathetic, real-time support through an AI-powered Chabot that can engage users in meaningful conversations, detect emotional cues, and provide relevant coping strategies. Key features include a mood-tracking system to help users log and identify emotional patterns, personalized self-care plans, and access to a library of mental health resources. Additionally, the app integrates crisis intervention features, enabling immediate connection to professional support or helplines when high-risk behaviors are detected. Data privacy and security are prioritized, with advanced encryption and authentication mechanisms ensuring sensitive user data remains confidential. The app's user-friendly interface and scalable design make it adaptable to diverse educational and social settings. Regular updates and user feedback loops drive continuous improvements, ensuring the app remains relevant and effective in addressing the mental health needs of teenagers. This innovative AI-driven solution aims to enhance the accessibility, efficiency, and effectiveness of mental health support for teenagers, empowering them to manage their well-being with confidence and fostering a proactive approach to mental health care
A contradiction-based Approach for Air Temperature Choice in Thin-layer Drying of Cassava Roots
Full text linkSetting the drying temperature of cassava slices remains up to now a matter of personal decision from authors, varying from an author to another and for the same author, varying from an experiment to another one. The primary aim of this manuscript is to introduce a decision-making instrument for determining the optimal temperature setting during the thin-layer drying process of cassava slices in general, the thin-layer drying process of bitter cassava roots slices in particular. The thermal conditions for drying should be higher than 37 °C to permit the hydrolysis of cyanogenic compounds into hydrogen cyanide (HCN) and it should not be higher than 30 °C to favor the elimination of HCN upon that of water and prevent the denaturation of thermosensitive nutrients in cassava slices. The TRIZ separation in time method provided a set of three steps for solving that problem. First step, in a preliminary action, cassava slices were introduced in water set at 37 °C for 6 h. The HCN was produced and a large quantity eliminated by solubilization. Second step, during that period, a cassava moisture prior compensation was also achieved: cassava slices uptook additional water, favoring the elimination of the residual HCN at a unique drying temperature, without any particular care. Third and last step, cassava slices were then dried at 35 °C, 40 °C and 45 °C, values of the residual HCN content were 7.23 ± 1.30 mg HCN/kg, 6.42 ± 2.4 mg HCN/kg and 8.06 ± 1.1 mg HCN/kg, respectively. All those values are less than 10 mg HCN/kg, the maximum admissible value for human consumption, set by the World Health Organization.
Keywords: Thin-layer drying, Temperature, Physical contradiction, Cassava cyanide, TRI
Artificial Intelligence in Materials Characterization: Methods, Applications, and Future Perspectives
Full text linkMaterials characterization relies on techniques such as electron microscopy, diffraction, and spectroscopy to extract structural, chemical, and physical information from materials. While these traditional methods provide essential insights, their manual interpretation is time-consuming, operator-dependent, and difficult to scale for high-throughput analysis. In recent years, artificial intelligence (AI) has emerged as a transformative approach, enabling automation in image and pattern recognition, improving reproducibility, and accelerating property inference from experimental data. This paper reviews the current applications of AI in materials characterization, including microscopy, spectroscopy, and mechanical testing, and explains how algorithms assist in segmentation, phase identification, peak analysis, and predictive modelling. The review also discusses challenges such as data quality, model interpretability, and standardization, along with future opportunities like physics-informed learning, multimodal data fusion, and self-driving laboratories. Overall, AI is shown not only to enhance existing characterization workflows but also to redefine how materials data are generated, interpreted, and utilized in scientific decision-making. 
Design and Development of a Web Based Application Pharmacy Finder
Full text linkThis research focuses on the design and development of a web-based application aimed at helping users efficiently locate nearby pharmacies. The application seeks to address challenges individuals face in identifying accessible and convenient pharmacy services, especially in situations where time and location are critical factors. These challenges may include lack of information about nearby pharmacies, inability to find specific services offered by pharmacies, or difficulty accessing such services during emergencies or in unfamiliar areas
Synthesis of Rhodamine based turn-on Fluorescent Sensors for the Detection of Chromium Ions
Full text linkSelective and sensitive rhodamine-based fluorescent Cr3+ sensor, A. ESI mass spectrometry, NMR, and elemental analysis were used to analyze Sensor A, which has been synthesized in high yield. Changes in fluorescence and absorption were used to assess its binding with different metal ions (Ca2+, Na+, Mn2+, Mg2+, Fe2+, Al3+, Ni2+, Fe3+, Co2+, Cu2+, Pb2+, Cr3+, Cd2+, Zn2+, Sn2+, and Hg2+). Compared to other metal ions, A exhibits good sensitivity and high selectivity for Cr3+. The selectivity regarding A is confirmed by its high binding constant value with Cr3+. According to 1H NMR peak broadening tests, Cr3+ binds to the sensor's hydroxyl groups and the imine group's nitrogen. Cr3+ binding to sensor A's carbonyl oxygen is confirmed by the bright pink color that appears following Cr3+ addition. The fluorescence enhancement as dosed the new absorption beyond 400 nm is further confirmed by the quantum yield values for the Cr3+-bound form and the ring-opened form of sensor A. According to the findings, the formation regarding the ring-opened form of sensor A upon Cr3+ binding is the mechanism responsible for the fluorescence enhancement and new absorption. It was found that A's sensitivity limit to Cr3+ was 568 nM. With excess EDTA, the pink color goes away and the binding of Cr3+ is reversible. As a result, this sensor could be employed as a reversible fluorescent sensor for Cr3+ that is visible to the human eye
Analysis of Surface Morphology of CdTe Thin Film Using Digital Image Processing
Full text link This study aims to analyze the scanning electron microscope (SEM) images of cadmium telluride (CdTe) thin films. Image processing techniques were applied to the SEM images to measure the effect of film thickness and annealing process on the porosity, average and pore radius deviation distribution of the film. In addition, edge detection technique was used to identify grain boundaries
A Comparison between Two Elevator Control Algorithms: Normal Algorithm V/S Smart Algorithm with Occupancy Sensing Device
Full text linkElevator systems are an essential component in the operation of multi-story buildings, particularly in environments with high population density and frequent vertical transportation demands. Ensuring efficient elevator performance is critical to minimizing passenger delays and enhancing user satisfaction. This research presents a comparative analysis between two distinct elevator control algorithms: the conventional or “normal” algorithm and a smart algorithm that incorporates an occupancy sensing device. The conventional algorithm processes all floor requests without consideration of the elevator's current load, often resulting in unnecessary stops, increased passenger waiting times, and inefficient operation. Conversely, the smart algorithm utilizes a real-time sensing mechanism within the elevator cabin to detect available space and determines whether to stop for additional passengers based on current capacity.
To assess the performance of both algorithms, a simulation was conducted using twelve different elevator scenarios within a multi-floor environment. The evaluation focused on several key performance metrics, including passenger waiting time, passenger travel time, number of elevator stops, and the total operational time of the elevator system. The data collected for each scenario was analyzed to compare the effectiveness of the two algorithms under varying load conditions and call patterns. The results clearly demonstrate that the smart algorithm reduces unnecessary stops and improves overall efficiency by shortening both waiting and travel times. Additionally, fewer stops contribute to reduced energy consumption and less mechanical wear. These findings indicate that the integration of sensing technology into elevator control systems has the potential to significantly enhance performance, reduce operational costs, and provide a more streamlined and responsive transportation experience in modern buildings
Lambdacism Detection in L2 English Speech Using Spectrogram Features and Machine Learning Techniques
Full text linkThis study investigates the use of machine learning and feature extraction through spectrograms to identify pronunciation errors, particularly lambdacism, in the word “Alive” by native Igbo speakers. Lambdacism leads to an “r” substitution for the “l” sound, and with words like “arrive”, “alive” becoming prevalent in speech, it diminishes clarity and understanding. This work processes audio samples of right and wrong phonemes using DSP and phoneme transcription. Essential features are extracted through STFT, MFCCs, Gammatone, and cochleagram spectrograms. These features are further reduced for visualization and processed using a One-Class Support Vector Machine (SVM) that trains on the distribution of correctly pronounced words and detects mispronunciations as outliers. Validation of mispronunciation using the correct pronunciation is further strengthened by DTW distance calculations. Spectrogram analysis shows that the correct samples have clearer frequency structures, while the incorrect samples are filled with noise and energy incoherence. One-Class SVM outperformed other models of anomaly detection, like Isolation Forest and Local Outlier Factor, by demonstrating high sensitivity in detecting minute errors at the phoneme level. An evaluation based on the F1 score has shown that the model has an equilibrium of precision and recall. Not only does this approach demonstrate the phonetic effects of lambdacism, but it also shows a strong system for the automated detection of phonetic errors in pronunciation. The results provide support for further development as components of a speech training system that is interactive and adaptable, that caters to users with phonological disorders and difficulties with second language pronunciation