Effect of pretreatment and drying method on drying kinetics of ackee (blighia sapida) arils

Ackee (Blighia sapida) is a tropical fruit known for its nutritional content and health benefits, but its high perishable nature limits its shelf life. The research investigated the effect of different pretreatments and different drying methods on drying kinetics of ackee fruit arils. Freshly harvested ackee aril were portioned and subjected to pretreatments of blanching at 85 ? for 3 min, dipping in salt (NaCl) solution of 1, 2 and 3% w/v for 5 min and untreated samples served as control. The aril samples were dried at different temperatures (50, 60, 70 ?, solar and sun drying) monitored at intervals, until constant weight was obtained, and they were subsequently analyzed for drying kinetics, effective moisture diffusivity (Deff), activation energy (Ea), using standard methods. The drying was observed to take place in a falling rate period. The moisture content of the ackee aril before drying was found to be 62.847% wet basis and at the end of the drying experiment, the moisture content reduce to less than 3% in the dried samples. The moisture loss was at a fast rate in aril dried at 70 ? compared to 60 and 50 ? which could be due to an increase in the energy of water molecules with increased temperature resulting in quick evaporation of water from the sample. The effective moisture diffusivity (Deff) increased with increase in drying temperature from 50 to 70 ?. The overall highest effective moisture diffusivity was found to be 2.07 ×10-4 m2/s at oven drying temperature of 60 ?, 1% salt solution pretreatment, while the lowest was found to be 4.23 × 10-6 m2/s at oven drying temperature of 70 ?, 3% salt solution pretreatment. The activation energy obtained falls within the range of (106.10 – 125.29 kJmol-1) which indicates that the ackee aril processed is highly sensitive to temperature. The obtained drying data were fitted into five different thin layer drying mathematical models using coefficient of determination (R2), least values of Chi Square (?2), Root Mean Square Error (RMSE) and Mean Biased Error (MBE). Pretreatment had significant effect (p?0.05) on the drying of ackee arils. Midilli and Kucuk model described the drying behaviour of ackee aril pretreated with 1% salt (NaCl) solution and oven-dried at 70 ? satisfactorily having the R2 values of 0.999

Development of a Coati-Optimized Convolutional Neural Network for infected citrus fruit detection and classification system

Pest and disease management plays a significant role in minimizing losses to crops, particularly in citrus fruit production. Traditional methods for detecting and classifying infected citrus fruits are complex and tasking, while Convolutional Neural Networks (CNNs) offer promising solutions but still face challenges such as high computational requirements and data dependency. Therefore, this study developed an improved convolution neural network for infected citrus fruit detection and classification system using Coati Optimization Algorithm (COA). A dataset of 1,790 citrus images, containing samples of black spot, greening spot, citrus canker, and healthy fruits, was acquired from www.kaggle.com. The images underwent preprocessing involving cropping to remove unwanted elements, conversion to grayscale to simplify processing, normalization to enhance data consistency and reduce redundancy, and filtering to minimize noise. An optimized CNN model was formulated using COA to tune the hyperparameters (weight and learning rate) of CNN to produce Coati Optimization Algorithm–based Convolutional Neural Network (COA-CNN). The preprocessed images serve as input to the COA-CNN model. The COA-CNN was used for the extraction of edges, corners, texture, patterns and shapes, and classification of citrus fruits as infected or healthy. The developed system was implemented using MATLAB R(2023a). The system’s performance was evaluated using accuracy, false positive rate, sensitivity, specificity, and recognition time. A comparative analysis of CNN and COA-CNN was also carried out. The accuracy, false positive rate, sensitivity, specificity, and recognition time for CNN were 95.83%, 6.02%, 96.63%, 93.98% and 202.17 s, respectively, while the corresponding values for COA-CNN were 96.92%, 4.22%, 97.41%, 95.78% and 136.86 s. This research showed that COA-CNN performed better and is recommended for citrus disease detection and classification systems

A comparative analysis of zebra optimization algorithm and chaotic sinusoidal zebra optimization algorithm for video forgery detection system

This study presents a comparative evaluation of two metaheuristic optimization strategies: Zebra Optimization Algorithm (ZOA) and Chaotic Sinusoidal Zebra Optimization Algorithm (CSZOA) for enhancing the performance of Convolutional Neural Networks (CNNs) in video forgery detection. A dataset comprising 270 videos with deletion, duplication, and insertion forgeries was used to train and evaluate CNN models optimized with ZOA and CSZOA. The experimental results indicate that the CSZOA-CNN model consistently outperforms both the baseline CNN and ZOA-CNN models across all evaluation metrics, achieving an accuracy of 99.51%, a false positive rate of 0.32%, and a detection time of 39.86 seconds. These findings highlight the effectiveness of integrating chaotic sinusoidal dynamics into optimization processes to enhance CNN training efficiency and detection robustness in video forgery applications

Development of a smart plastic collection system with iot remote cloud payment platform

Plastic bottle littering has undoubtedly become an alarming global crisis and a major source of concern that poses potential hazards to landfills, habitats, water bodies, and the ozone layer. A number of these plastic bottles significantly find their way into the drainage systems leading to total drainage blockage resulting in flooding which has rendered many lives dead and homeless. Given the severity of plastic bottle pollution, immediate action is required to lessen its negative consequences. Consequently, while the existing work deployed calibrated sensors that impaired sensing accuracy, and also no consideration for incentives to encourage depositors. This project employs Autodesk Inventor software for robust mechanical modeling, incorporates sensor-based components for precise sensing, implements a control mechanism, and utilizes IoT remote cloud for payment systems to foster local involvement and social accountability aimed at eradicating plastic bottle littering. This initiative outlines the development of a smart plastic collection system with a view to not only curtailing plastic bottle littering but also motivating people with a reward. The smart plastic collection system accepts plastic bottles and all other objects such as glass, metal, and can

Development of an Internet Of Things-Based Fingerprint Biometric Attendance System

This research explores the design and implementation of an Internet of Things (IoT) based fingerprint biometric attendance system. The traditional methods of attendance tracking often suffer from inaccuracies, time fraud, and significant administrative burdens. In response to these challenges, biometric systems have gained popularity for their ability to uniquely identify individuals through their physical or behavioural characteristics, offering a more reliable and secure approach to attendance management. The system proposed in this research utilizes fingerprint recognition, one of the most widely adopted biometric modalities, due to its high accuracy, ease of use, and cost-effectiveness. Integrating this system with the Internet of Things (IoT) expands its capabilities. The system comprises an ESP32 microcontroller, a fingerprint module, an OLED display, and a locally hosted Hypertext preprocessor (PHP)-based web interface. The OLED display serves as an immediate feedback mechanism for users, confirming whether their attendance has been successfully recorded by displaying the appropriate message. The web interface is designed for administrative use, allowing for the management of attendance records, user enrollment, and data exportation for further analysis. The results of this research demonstrate that the proposed IoT-based fingerprint biometric attendance system is a feasible and efficient solution. It offers a user-friendly interface for both students and administrators, significantly improving the accuracy and security of attendance tracking, verifying identities quickly under 1-2 seconds, with high accuracy. The system’s modular design and scalability also allow for future enhancements and adaptations to meet specific needs

Unsteady aerodynamic performance of spiked and unspiked aloe vera-inspired aerofoils at low reynolds number

The increasing demand for Micro Aerial Vehicles (MAVs) and other low-speed aerial platforms has intensified the focus on optimizing aerofoil designs for low Reynolds number applications. This study presents a comparative analysis of the unsteady aerodynamic performance of aloe vera-inspired aerofoils, both spiked and non-spiked, at Reynolds number 1.5 × 105. By employing the Unsteady Reynolds-Averaged Navier-Stokes (URANS) equations alongside the Transition SST (four-equation) turbulence model, the research accurately characterizes the flow field and aerodynamic loads under dynamic conditions. The results indicate that the novel spiked aerofoil exhibits superior performance at all angles of attack, demonstrating delayed flow separation and enhanced lift coefficient and lift-to-drag ratios compared to the non-spiked aloe vera aerofoil

Synthesis and Characterization of Laggera Aurita-Derived Acetic Acid-Activated Carbon (LAAC) and it’s Potential for Toxic Element (TE) Metal Removal from Water

The increasing contamination of water resources by toxic heavy metals necessitates the development of cost-effective and sustainable adsorbents. This study investigates the synthesis, characterization, and adsorption potential of Laggera aurita-derived activated carbon (LAAC) for the removal of Pb(II), Cd(II), and Cu(II) from aqueous solutions. LAAC was prepared via acetic acid activation followed by pyrolysis at 500°C and characterized using FTIR, SEM, EDXRF, BET and XRD surface area analysis. FTIR confirmed the presence of functional groups (–OH, –COOH, –C?C–, and S–H) that facilitate toxic element (TE) adsorption through hydrogen bonding, ?-electron interactions, ion exchange, and chelation. SEM revealed a nanostructured surface (nanotubes and nanospheres) with high affinity for Pb²?, Cd²?, and Cr(VI) due to increased active sites. BET analysis indicated a microporous structure (334.6 m²/g), enhancing TE retention via ion trapping and complexation with –COOH/–OH groups. Horvath-Kawazoe (HK) analysis further demonstrated an ultramicropore volume (0.5939 cc/g), enabling molecular sieving and Pb²? capture through dehydration mechanisms. EDXRF revealed CaO, 5.334%, SiO?, 4.836%, CeO?, 5.009%, P?O?, 1.902%, and SO?, 2.2966%. CaO provides alkaline sites that enhance cation exchange for metals like Pb²?, Cd²?, and Cu²?. XRD confirmed the nanocrystalline nature (3.82 nm crystallite size), contributing to high surface reactivity. These findings highlight LAAC as a promising, sustainable adsorbent for heavy metal removal, with future research needed to optimize activation parameters and assess real-world applicability

Nutritional And Antinutritional Evaluation of Gluten-Free Pasta from Cocoyam Starch and Lima Beans Flour

Pasta is a wheat-based food product which has gained universal popularity in recent years due to its versatility, low cost, ease of preparation and nutritional quality. It is traditionally produced from wheat that is largely imported in Nigeria, placing a heavy burden on the dwindling financial resources of the nation. Wheat also contains gluten, which triggers a multi-system autoimmune disorder called celiac disease in genetically predisposed individuals. Treatment for celiac disease is complete exclusion of dietary gluten. Efforts are currently being made to develop gluten-free pasta from indigenous crops in order to reduce wheat importation and provide alternatives to consumers affected by celiac disease. Cocoyam and lima beans are gluten-free, underutilised indigenous tropical crops with a rich nutritional profile, which can be used to replace wheat flour in pasta production. This study, therefore, aims to develop gluten-free pasta from lima bean flour and cocoyam starch. Composite blends of pregelatinized cocoyam starch, germinated lima beans flour and xanthum gum (binder) were formulated in the following ratios: (100:0, 87.5:12:5, 75:25, 62.5:37.5 and 50:50) and used to produce pasta. The protein, moisture and carbohydrate contents of the gluten-free pasta ranged from 6.54 -18.82%, 9.06-10.56% and 59.29-74.30% respectively. Mineral content of gluten-free pasta ranged from 61.54-108.20 mg/100g for calcium and 0.94-6.09 mg/100g for iron. Lysine values increased from 2.63 (100% Wheat Flour Pasta) to 3.85 g/100 g protein (50% PCS, 50% GLBF & xanthum gum) while total amino acids ranged from 53.66-83.11 g/100 g protein. The antinutritional factors cyanide, oxalate and phytic acid ranged from 0.191-1.199mg/100g, 10.251-85.064 mg/100g and 0.01-1.66% respectively. The study concluded that pregelatinized cocoyam starch and germinated lima beans flour blends are effective in developing nutritious gluten-free pasta

Optimization of H2SO4-modification of ITU bentonitic clay under box Behnken design

Bentonite clay from Itu, Akwa-Ibom State, Nigeria was modified using sulfuric acid (H2SO4). The chemical compositions of the raw (RI) and H2SO4 modified (HI) Itu clay was determined using X-ray fluorescence (XRF) technique. Box Behnken Design (BBD) was used to optimize the H2SO4 and clay modification process using wet acidification method. The process parameters considered for the optimization were H2SO4 concentration (0.1-6.0 M), activation temperature (60-100 oC) and activation time (5-10 minutes). Optimum catalyst yield of 6.12 g was obtained in 7.5 min and at 60 oC when 6 M H2SO4 concentration was used for clay modification. The predicted value of the catalyst yield was found to be in agreement with its observed values (R2 = 0.9681 and Adj R2 = 0.9271). These results revealed that the process parameters had significant influence on the clay modification process. The XRF analysis of the samples also revealed that the RI and HI are calcium montmorillonite with SiO2/Al2O3 ratio values of 3.20 and 5.48 respectively

Wireless body fat measurement machine with a smartphone interface

Scientific literature and the World Health Organization (WHO) predict that in the year 2025, there will be about 2.3 billion adults globally who are overweight and 700 million who will be obese. This persistent metabolic disease has not been effectively addressed due to the use of inappropriate methods for measuring body fat and the unique characteristics of human physical and cellular structure. To ameliorate this condition, a new mechanism for weight measurement is proposed. The proposed mechanism operates with a minute alternating current (bioelectrical impedance technique) of 0.2– 0.8 mA via a two-lead finger. The device was designed with five components: 5 5-Volt Li-Po battery, a HC-05 Bluetooth module, a Dry electrode sensor, a Galvanic skin response sensor, and a programmable microcontroller (Arduino Shield) that is programmed to compute the voltage value for the body fat composition. The study was conducted within the University of Nigeria, Nsukka community, with adolescents aged 5 – 19 years for both genders and adults aged 20 – 38 years. The results revealed that as age (in years) and weight (in kg) increase, body fat percentage tends to increase. Weight in (kg) is a key parameter in the BMI method and was considered in the study, with consistent results as shown in the tables. To use the bioelectrical impedance technique, the approximated internal body resistance of 300 ? – 1 k?, age (in years), and weight (in kg) are necessary to obtain a precise body fat percentag