Mathematical modelling for the drying method and smoothing drying rate using Cubic Spline for seaweed Kappaphycus Striatum variety durian in a solar dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m2 and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R2), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested

Seaweed modeling for drying and the efficiency as heavy metal removal in Kappaphycus Striatum variety Sacol using Solar Dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 92.68% to 32.06% in 4 days at average solar radiation of about 600W/m 2 and mass flow rate about 0.5 kg/s. The drying kinetics was fitted with six published exponential model thin layer drying models. The models were fitted using the coefficient of determination (R 2), and root mean square error (RMSE). The result showed modified page was the best model for describe the drying behavior. In addition, the dried seaweed was used to show biosorptions of cadminium, lead, zinc and copper. Batch mode experiments were performed to determine experimental parameters affecting sorption process such as pH, initial metal ion concentration, shaking rate and biomass dosage. The Pb(II) showed Int. J. Environ. Bioener. 2013, 8(1): 42 highest sorption on pH 4, shaking rate on 250 rpm with 24.18% removal rate; at initial concentration of 100 ppm and adsorbent dosage at 4g/l the removal percentage is 28.30%. Overall, this report indicates that Kappaphycus Striatum Variety Sacol is an effective and economical sorbent for removal of heavy metals from wastewaters

AI-Based Hand Gesture Recognition Through Camera on Robot

This paper presents an innovative approach to real-time hand gesture recognition for robot control using Artificial Intelligence (AI). The core of this project is a machine learning model trained on a custom data set of hand gestures, which was meticulously hand-annotated to ensure accuracy. To enhance the model's performance and generalization, data augmentation techniques were employed. Furthermore, the model leverages the power of transfer learning, with a ResNet backbone serving as the foundation, to efficiently learn from the data set. In addition to the development of the AI model, a custom robot was designed and built using Arduino and Raspberry Pi. This robot is equipped with a camera to capture images of hand gestures, which are then transmitted to the machine learning model for real-time analysis. The hardware of the robot was meticulously optimized to ensure smooth operation and accurate data capture. The resulting system enables real-time hand gesture recognition on the robot, opening up a plethora of applications, from industrial automation to smart home technology. By synergistically combining AI, computer vision, and robotics, this project not only demonstrates the potential for innovative solutions to real-world problems but also significantly enhances the functionality and usability of robots. It paves the way for improved human-computer interaction through the practical implementation of advanced AI and computer vision techniques

AI-Based Hand Gesture Recognition Through Camera on Robot

This paper presents an innovative approach to real-time hand gesture recognition for robot control using Artificial Intelligence (AI). The core of this project is a machine learning model trained on a custom data set of hand gestures, which was meticulously hand-annotated to ensure accuracy. To enhance the model’s performance and generalization, data augmentation techniques were employed. Furthermore, the model leverages the power of transfer learning, with a ResNet backbone serving as the foundation, to efficiently learn from the data set. In addition to the development of the AI model, a custom robot was designed and built using Arduino and Raspberry Pi. This robot is equipped with a camera to capture images of hand gestures, which are then transmitted to the machine learning model for real-time analysis. The hardware of the robot was meticulously optimized to ensure smooth operation and accurate data capture. The resulting system enables real-time hand gesture recognition on the robot, opening up a plethora of applications, from industrial automation to smart home technology. By synergistically combining AI, computer vision, and robotics, this project not only demonstrates the potential for innovative solutions to real-world problems but also significantly enhances the functionality and usability of robots. It paves the way for improved human-computer interaction through the practical implementation of advanced AI and computer vision techniques.<br/

Analytical and numerical approach for the analysis of heat transfer of squeezing flow between two parallel plates

This research offers a thorough examination of thermal transmission within the context of the squeezing flow of a Casson fluid that is confined within two circular plates aligned in parallel. The development of a feasible mathematical framework involves combining the principles of conservation with appropriate similarity transformations. The resulting framework generates a couple of strongly non-linear ordinary differential equations. To tackle these equations, established analytical methods like the (HPM) and its variation, the Least Square Homotopy Perturbation Method (LSHPM), are employed.Additionally, for the validation of the analytical findings, a numerical approach using the BVP5C technique is utilized. A comparative evaluation reveals that the LSHPM consistently provides results of exceptional accuracy compared to the traditional HPM. The investigation delves into observing how the flow performs when experiencing diverse variations in physical attributes, elucidating the complexities through comprehensive visual representations. It is worth noting that the presented problem is subject to specific parameter limitations, which are extensively discussed and taken into account throughout the study. The investigation encompasses a thorough analysis across a range of parameters, including the squeeze number, the Casson fluid parameter, the Prandtl number, the Eckert number and. Notably, an acceleration in the rate of motion is observed concerning the squeeze number and the Casson fluid parameter. In terms of the temperature profile, it is revealed that this profile demonstrates a decreasing trend in relation to both the squeeze number and the Casson fluid parameter. Conversely, it displays an increasing trend with respect to the Prandtl number and the Eckert number

Effects of neurodynamic mobilization on health-related quality of life and cervical deep flexors endurance in patients of cervical radiculopathy : A randomized trial

Purpose: Cervical radiculopathy is disorder of cervical spinal nerve root affecting large number of population. Previously many studies are conducted to design suitable protocol for management of this disorder, but they lack in quality. The purpose of this study was to compare the effects of neural mobilization and cervical isometrics on health-related quality of life and deep flexors endurance in cervical radiculopathy. Methods: A double-blinded randomized clinical trial was conducted at Mayo Hospital, Lahore, Pakistan. Eighty-eight patients within the age range of 35-50 years were included in the study after taking their consent. In the experimental group (n = 44), median nerve mobilization was applied along with cervical isometric exercises. The control group (n = 44) performed cervical isometric exercises alone. Muscle endurance was measured by craniocervical flexion test and quality of life on 36 items short form health survey SF-36 scale. Measurements were taken at baseline, at 2nd week, and at 4th week. For missing data, intention-to-treat analysis was used. Results: Within-group comparison with Friedman test showed a significant difference between pre, mid, and posttreatment scores on craniocervical flexion test and in all domains of SF 36 in both groups. While between-group comparison with Mann-Whitney U test showed all variables were similar at baseline but after 4 weeks there was a statistically significant improvement in craniocervical flexion test scores and all domains of SF 36 in the experimental group. But domain of pain showed mean rank of 49.43 after 4 weeks in the experimental group and 39.57 in the control group with p = 0.065 and d = 0.579, while for all the other 7 domains values were p &lt; .05 and d &gt; 0.25. Conclusion: Neural mobilization combined with cervical isometrics shows significant effects in improving quality of life and deep flexors endurance in patients with cervical radiculopathy than cervical isometrics alone

Comparison of neural mobilization and conservative treatment on pain, range of motion, and disability in cervical radiculopathy : A randomized controlled trial

Objective: The objective of the study was to compare the effectiveness of neural mobilization technique with conservative treatment on pain intensity, cervical range of motion, and disability. Methods: It was a randomized clinical trial; data was collected from Mayo Hospital, Lahore. Eighty-eight patients fulfilling the sample selection criteria were randomly assigned into group 1 (neural mobilization) and group 2 (conventional treatment). Pain intensity was measured on a numeric pain rating scale, range of motion with an inclinometer, and functional status with neck disability index (NDI). Data were analyzed using SPSS, repeated measure ANOVA for cervical ranges and the Friedman test for NPRS and NDI were used for within-group analysis. Independent samples t-test for cervical ranges and Mann-Whitney U test for NPRS and NDI were used for between-group comparisons. Results: There was a significant improvement in pain, disability, and cervical range of motion after the treatment in both groups compared to the pre-treatment status (p &lt; 0.001), and when both groups were compared neural mobilization was more effective than conventional treatment in reducing pain and neck disability (p &lt; 0.001), but there was no significant difference present in the mean score of cervical range of motion between both groups. (p&gt;0.05). Conclusions: The present study concluded that both neural mobilization and conservative treatment were effective as an exercise program for patients with cervical radiculopathy, however, neural mobilization was more effective in reducing pain and neck disability in cervical radiculopathy