International Journal on Advanced Science, Engineering and Information Technology
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2006 research outputs found
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The Improvement of the Rheological Model of Leather
Full text linkFibrous capillary-porous materials, such as woven materials and leather, used in the light industry for clothing and footwear, differ sharply from metallic materials. These differences manifest themselves in a complex relationship between stress and strain, which depends on the strain rate and loading time. A method for determining the identified new rheological parameter of the inert resistance of a capillary-porous material (for example, leather) of accelerated deformation is presented in the article; it consists in determining the indentation force of a conical indenter at a constant speed and the coefficient that considers shear zones, and the angle formed by the boundaries of the material deformation zone and application of force value. An equation for an improved rheological model of leather is derived; it consists of the Kelvin model, the Bingham-Shvedov model, and the Khusanov model, expressed in terms of the rheological parameter in the form of a deformation inertness coefficient. The inclusion of the model of deformation inertness in the form of a coefficient into the rheological model of leather will allow the mathematical description of the rheological parameters of leather and the development of engineering methods for their calculation. The implementation of the developed method will allow obtaining numerical values of the rheological parameter of the inert resistance of the capillary-porous material under its accelerated deformation, namely, a new property of the capillary-porous material, which must be taken into account in various technological processing operations, for example, when treating moisture-saturated leather
The Effect of Ratio of Methanol and Concentration of Methanol in Corn Silk Extracts with Ultrasonic-assisted Extraction
Full text linkCornsilk refers to stigmas of female maize flowers. Corn silk contains abundant flavonoids. Flavonoids show a variety of biological activities, such as antioxidants. Several methods, such as microwave, ultrasonic, supercritical fluid extraction, and multiple extraction technologies, were used to extract flavonoids from corn silk. The ultrasonic-assisted extraction method (UAE) seems to be very reliable among these methods. UAE has the advantage of simplicity and can be easily implemented with other extraction techniques. Hence, the current study aimed to determine the effect of the ratio (material: methanol) and concentration of methanol corn silk extracts in UAE. The experimental design used was a factorial Randomized Completely Block Design (RCBD) with two factors and three replications for each treatment. The two treatments were: A1=methanol (1:4) (w/v), A2=methanol (1:6) (w/v), A3=methanol (1:8) (w/v) and methanol concentration (60, 70 and 80%). Results showed that the ratio of methanol and concentration had a significant effect (
Facial and Body Gesture Recognition for Determining Student Concentration Level
Full text linkOnline learning has gained immense popularity, especially since the COVID-19 pandemic. However, it has also brought its own set of challenges. One of the critical challenges in online learning is the ability to evaluate students' concentration levels during virtual classes. Unlike traditional brick-and-mortar classrooms, teachers do not have the advantage of observing students' body language and facial expressions to determine whether they are paying attention. To address this challenge, this study proposes utilizing facial and body gestures to evaluate students' concentration levels. Common gestures such as yawning, playing with fingers or objects, and looking away from the screen indicate a lack of focus. A dataset containing images of students performing various actions and gestures representing different concentration levels is collected. We propose an enhanced model based on a vision transformer (RViT) to classify the concentration levels. This model incorporates a majority voting feature to maintain real-time prediction accuracy. This feature classifies multiple frames, and the final prediction is based on the majority class. The proposed method yields a promising 92% accuracy while maintaining efficient computational performance. The system provides an unbiased measure for assessing students' concentration levels, which can be useful in educational settings to improve learning outcomes. It enables educators to foster a more engaging and productive virtual classroom environment
Enhancing the Sausage Quality of Indonesian Local Lamb Meat with Microbial Transglutaminase Enzyme: Physicochemical, Textural, and Microstructure Properties
Full text linkIndonesia Batur local lamb meat has emerged as a promising meat source for the production of emulsion-type sausages. However, the manufacturing process of this sausage typically requires high-fat content to achieve the desired quality characteristics. To address this issue, this study investigates utilizing microbial transglutaminase (MTGase) enzyme to improve local lamb meat sausage's physicochemical, textural, and microstructure features. This research aimed to develop emulsion sausages using local lamb meat by incorporating the MTGase enzyme. The experimental design encompassed various treatments, including a control group, the addition of 10% tapioca, and incremental amounts of MTGase (ranging from 0.2% to 1.0%). The sausages were evaluated comprehensively: pH value, color, tenderness, texture, and microstructure. The statistical analysis, employing ANOVA, demonstrated a significant improvement in pH, firmness, toughness, cohesiveness, and gumminess with the addition of MTGase, while also influencing the color of the sausages (
Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate
Full text linkThe utilization of artificial intelligence (AI) has become imperative across various domains, including the oil and gas industry, which covers several fields, including reservoirs, drilling, and production. In oil and gas production, conventional methods, such as reservoir simulation, are used to predict the oil production rate. This simulation requires comprehensive data, so each process step takes a long time and is expensive. AI is urgently needed and can be a solution in this case. This research aims to apply AI techniques to forecast oil production rates based on water injection rates from two injection wells. Three wells are connected with a direct line drive pattern. Three different AI methods were applied, including multiple linear polynomial regression (PR), multiple linear regression (MLR), and artificial neural networks (ANN) in constructing oil production rate prediction models. Actual field data of 1180 data are used, including water injection rate data from two injection wells and oil production history data from one production well. The dataset has been split randomly into 80% training and 20% allocated for testing subsets. The training data is used to build predictive models, while the testing data is used to validate model performance. Comparative analysis selects the model with the lowest root mean square error (RMSE) and the highest R^2 test value. Results demonstrate that the ANN model achieves the smallest Root Mean Square Error (RMSE) of 0.142 and the highest R^2 test value of 16.2%, outperforming the PR and MLR methods. The ANN prediction model provides a rapid and efficient approach to estimating oil production rates
Development of Mobile Learning Based on Digital Entrepreneurs Using Raspberry Pi on TVET
Full text linkMobile learning is a method of instruction that uses portable electronics like laptops, smartphones, mobile phones, and PCs so that students may access lesson materials, instructions, and apps whenever and wherever they are without being constrained by time or place. This is an innovation regarding a systematic and structured application-based learning system as an interactive medium for students, especially at TVET. This article aims to develop mobile learning using a mini server Raspberry Pi 3 Model B+ based on digital entrepreneurs (digipreneur) that runs on the Moodle LMS as a source of learning content so that learning can be carried out interactively and flexibly, without having to be connected to the internet, or classrooms that are not effective even in areas with no internet access. This activity goes through 2 stages of system design known as client-server. In designing the server, the Raspberry Pi mini server model B+ configuration is carried out as a source of digital learning resources by using several supporting applications such as MySQL server, SSH, PHPMyAdmin, and Apache in designing the client using the Moodle LMS application, which contains digipreneur-based digital learning materials and resources, all stored in the database server. Three primary users are built into the Mobile Learning Database System: Administrators, Lecturers, and Students. This application is expected to be innovative and the right solution in terms of learning and become an alternative problem-solving tool in education
Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement
Full text linkThe heterogeneous data traffic of today's network is a huge challenge to existing best-effort network technology, particularly in the context of large Ethernet, which handles hundreds to thousands of users. The existing conventional best-effort network technology is no longer efficient to handle the diversity of traffic types in the network and requires network management equipment such as Quality of Service (QOS). Usually, QOS is implemented on the gateway router. However, for better network performance and management, to guarantee high priority for sensitive traffic like video conferencing, Voice over Internet Protocol (VoIP), and streaming media within an internal network, it is nice to have QoS implemented on each router in the LAN network, starting from the access router to the gateway router. This paper is to demonstrate the effectiveness of the proposed dynamic QoS that has been developed and deployed in the LAN, purposely to provide adequate bandwidth for sensitive traffic when the network utilization is high and congested, by automatically modifying the QoS Queue's Maximum Bandwidth Rate-Limit of the best-effort traffic queue of the related router. The performance of the proposed developed dynamic QoS was evaluated via a comparison study before and after the dynamic QoS was presented in the network simulation environment that was built using Mininet. Results from the testing show that the developed dynamic QoS can improve the network's performance by automatically giving the appropriate bandwidth for sensitive traffic on the fly while needed/on demand
An Approach to the Utilization of Design Thinking in Artificial Intelligence Education
Full text linkAs artificial intelligence (AI) continues its rapid and relentless progression, the necessity for a comprehensive AI education has become increasingly evident. While South Korea has initiated various policies related to AI education, recent research has underscored the potential adverse repercussions of current instructional approaches on learners. In response to this pressing concern, the present study delves into integrating design thinking principles into AI education and meticulously assesses its impact on learning outcomes. To achieve this objective, we seamlessly amalgamated design thinking principles with AI problem-solving techniques, developing a tailor-made AI education curriculum explicitly crafted for middle school students. Subsequently, this innovative curriculum was implemented among middle school students, and their Computational Thinking (CT) competence was rigorously evaluated. The findings unequivocally establish that the infusion of design thinking into AI education significantly augmented the CT skills of the participating students. In comparison to the control group, it was discerned that middle school students who underwent AI education integrated with design thinking exhibited a statistically substantial enhancement in their Computational Thinking (CT) proficiencies. This study furnishes compelling empirical evidence that unequivocally endorses design thinking as a potent instructional approach within the domain of AI education, particularly for middle school students. Furthermore, it underscores the necessity of embracing innovative pedagogical methodologies in AI education to equip the younger generation with the indispensable skills to adeptly navigate the perpetually evolving landscape of an AI-driven future
Effect of Calcination on the Ash from Lokon Volcano and Its Potentially Sustainable Binder Material
Full text linkThe need for cement as a housing construction material has continued to increase due to the growing population. This high demand increases carbon dioxide emissions. Hence, it is necessary to optimize the use of natural pozzolan material. Volcanic ash is a natural pozzolan material in North Sulawesi, but its use could be more optimal. This study aimed to determine the effect of calcination on the physical properties of volcanic ash originating from the eruption of Mount Lokon. The calcination was carried out to determine the potential of Lokon ash at different temperatures to assess the structural characteristics, mineral phases, metal oxide composition, functional group bonding, morphology, and its potential as a binder for concrete mixtures. The ash material used comes from sand taken from the Pasahapen River and filtered through a 325-mesh sieve. Lokon ash was calcined at temperatures of 800, 900, and 1000oC to determine the structural and morphological characteristics. At the same time, the effects were examined using an X-ray diffractometer (XRD), Raman spectroscopy, X-ray fluorescence (XRF), Fourier Transform InfraRed (FTIR), and Scanning Electron Microscopy (SEM). The results showed that calcination triggered the formation of hematite in the ash, which will increase its reactivity as a pozzolan material. This process causes the crystallinity of ash minerals to increase, but the ash material produced is predominantly amorphous. Hence, it has excellent potential as a binder material in concrete mixtures
Mechanical Performance of Light Weight 3D Printed Interlocked Assemblies
Full text linkTopologically interlocked assemblies have traditionally been explored to study properties such as strength, toughness and fatigue when applied axial compressive load. The interlocked assemblies are used in real-life applications ranging from the aerospace industry to the construction sector. This concept is generally applied to segmented blocks to study what parameters affect failure and how the failure occurs. The Segmented interlocked assemblies are investigated to optimize performance and weight compared to a monolithic design with poor strength characteristics. Strength to weight ratio is beneficial to applications where the efficiency of the product relies heavily on the weight. This study is performed on the 3D printed cubes with interlocking geometric slots, and the segmented cubes were investigated for failure through a three-point bending test under different conditions. The study used parameters such as speed of testing, lubrication between blocks, and the shape of the slots. This paper makes use of the Taguchi design of experiments in combination with grey relational analysis to optimize the parameters. Different lubrications were used to create a variation in friction between the blocks simply. After processing the data, it was concluded that the best condition was a circle shape, with lubrication #1 at an indenter speed of 10mm/min, followed by a circle and dry at 5mm/min. Triangle shape with lubrication #1 at a speed of 5mm/min was third in terms of the overall rank. According to the results, the worst-performing condition was a circle shape with lubrication #2 at a 1mm/min speed