bluuebunny/crossref_metadata_2025 huggingface dataset:
An Approach to the Utilization of Design Thinking in Artificial Intelligence Education
Authors: Kim Seong-Won , Go HakNeung , Hong Seung-Ju , Lee Youngjun
Published: January 2024

As 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.
Surface Water Wave Detector for Floating Devices with Capacitive Sensor
Authors: Lui Poh Wei , Yeo Boon Chin , Lim Way Soong
Published: January 2024

Water wave monitoring is essential in collecting marine parameters for oceanography studies and early warning systems on security and safety, such as drowning detection, weather detection, and gas leakage from underwater pipeline detection, because these activities create different water wave patterns that can be further analyzed. The current wave detection methods, such as underwater pressure and resistive sensors, have lower durability as they require direct contact with the water. Monocular camera wave detection can detect the line where water waves propagate. However, a static platform is required to perform monitoring operations. This research aims to develop a continuous capacitive sensor system that a buoy can implement for contactless water surface wave detection and to develop a water wave direction detection algorithm by Principal Component Analysis (PCA) calculation. Capacitive sensors arranged in a circular shape and a compass module are implemented inside the prototype buoy. Each capacitive sensor detects the real-time wave height change by changing the capacitance value. The capacitance values from all the capacitive sensors and the North of the compass sensor are sent to the embedded server for further computations. Processes carried out in the embedded server are initial calibration, centroid calculation, PCA calculations for water wave detection, and data visualization on the webpage. The prototype buoy with a capacitive sensor system and compass sensor developed can detect the four positions tested in the experiment with a mean square error of 38.42Â° and a mean absolute error of 5.85Â°.
Analysis of 5.8 GHz Network for Line of Sight (LOS) and Non-Line of Sight (NLOS) in Suburban Environment
Authors: Md Idris Ikha Fadzila , Geok Tan Kim , Rahman Noor Ziela Abd , Md Idris Mohd Haffizzi
Published: January 2024

This paper presents the findings of radio wave characterization based on the measurement data at 5.8 GHz. The measurement data were collected by a testbed channel, which links with the following scenarios: a single tree, a row of trees, a row of trees and a road, a row of trees, a road, and a building. These experiments were conducted at University Teknologi Malaysia (UTM) Skudai, Johor to represent the suburban environment. The links consist of pairs of transmitting and receiving antennas that deploy the path of a line of sight (LOS) and non-line of sight (NLOS) radio propagation wave networks. Based on the measurement data analysis, the general issue concerning the statistical probability distribution and the characteristics of LOS and NLOS are examined and discussed. Note that 5.8 GHz technology can be used in both LOS and NLOS scenarios, but its performance varies based on the presence of obstacles and signal propagation characteristics. Other prominent experimental analysis methods, such as hypothesis testing and goodness of fit tests, are implemented to consolidate the findings. The analysis found that the empirical probability density function of LOS and NLOS channels follows Gaussian, Rayleigh, and Rician distribution. Predicting specific future technological developments, such as the availability of 5.8 GHz technology, is challenging because it depends on various factors, including research and development efforts, regulatory decisions, market demand, and technological advancements.
A Healthcare Recommender System Framework
Authors: Ooi Kher-Ning , Haw Su-Cheng , Ng Kok-Why
Published: January 2024

After the pandemic hit every part of the world, healthcare awareness is slowly rising among every human being, especially leaders of each country. Due to a shortage of manpower in the healthcare industry, patients tend to search the internet for some self diagnoses. This way is extremely dangerous as patients might end up using the wrong treatment such as taking the wrong medication to treat their sickness since there are so many different remedies posted on the internet without valid recognition from the healthcare professionals. To aid in overcoming this problem, this research will be building a Healthcare Recommender System. The goal of a Healthcare Recommender System (HRS) aims to supply its user with medical information that is meant to be highly relevant and tailored to an individuals need. Hence, this paper gives an overview of various recommender systems, datasets employed, and evaluation metrics used in the healthcare system. In addition, we propose the framework for the HRS to capture user input on their condition and recommend the next course of action. The steps involved in our recommender system includes choosing the dataset and techniques, data cleaning and preprocessing, building the recommender system, training the recommender engine, and finally performing the prediction. We generate the accuracy of prediction and analyze with some results. From the experimental results, Cosine Similarity has the highest accuracy compared to Jaccard Similarity and Euclidean Distance.
Human Age Group Estimation Using Gait Features
Authors: Soo Qian Fu , Connie Tee , Ong Goh Michael Kah
Published: January 2024

In many practical applications, identifying the target age group is essential for marketing products and services. For instance, gaming and entertainment companies need to understand which age groups are most likely to purchase their services. This knowledge allows them to optimize their products and services to better cater to their target audience. This study proposes an age group prediction system using gait features. Gait, in this context, pertains to an individual's unique walking style. A diverse dataset containing subjects from 3 to 70 years old is collected. The age group is classified into three categories: child, adult, and senior. The critical aspect of this research lies in the preprocessing techniques applied to the gait patterns. The gait patterns are extracted from landmark human joint positions' key point values and preprocessed using smoothening techniques. Additionally, dimension reduction techniques enhance computational efficiency and accuracy before feeding the features into a deep learning-based classifier. These preprocessing steps play a pivotal role in the success of the deep learning-based classifier. A promising accuracy of up to 95% is reported for correctly recognizing the human age groups. The outcomes of this investigation underscore the tremendous potential of leveraging machine learning techniques to refine marketing strategies and boost customer satisfaction. The proposed approach can aid companies in aligning their products and services with the preferences and needs of distinct age groups, thereby enhancing their market presence and resonance with their target audience.
Comparison of Crossflow Turbine Performance through Nozzle Position Variations Using ANSYS Simulation
Authors: Rantererung Corvis L , Buku Atus
Published: January 2024

The performance comparison of Crossflow turbines is greatly influenced by the position of the nozzle in the conversion of water energy into mechanical energy that occurs through the blades, runners, and shafts of Crossflow turbines. The study aims to directly examine the visualization of water fluid dynamics across the turbine runner blade and enhance the performance of the Crossflow turbine by varying the nozzle position. This study intends to investigate the impact of water flow dynamics and emission on the performance of Crossflow turbines with a combined horizontal-vertical nozzle position, specifically focusing on the magnitude of the number of turbine blades driven and the size of the runner blade area. The objective of investigating nozzle position variations in Crossflow turbines is to determine the specific nozzle position at which the turbine blade may efficiently extract maximum energy from the water flow, hence optimizing turbine performance. The research method using models made using CAD software is AutoCAD by exporting to IGES or IGS format to be compatible with ANSYS. The simulation of this research is with post-processing. There are three, namely making animations, making contours, and taking data to compare cross-turbine performance using variations in nozzle position. Crossflow turbine performance with horizontal nozzle position torque and turbine power is lower, and there is an increase in a vertical position. Then, the horizontal and vertical nozzle position is very good because the nozzle is more effective with maximum turbine performance, namely 13.811-watt turbine power 1,099 turbine torque at 120 rpm.
Mental Health State Classification Using Facial Emotion Recognition and Detection
Authors: Al-zanam Adel Aref Ali , Hussein Alhomery Omer Hussein Abdou Elsayed , Tan Choo Peng
Published: January 2024

Analyzing and understanding emotion can help in various aspects, such as realizing oneâ€™s attitude, behavior, etc. By understanding oneâ€™s emotions, one's mental health state can be calculated, which can help in the medical field by classifying whether one is mentally stable or not. Facial Recognition is one of the many fields of computer vision that utilizes convolutional networks or Conv Nets to perform, train, and learn. Conv Nets and other machine learning algorithms have evolved to adapt better to larger datasets. One of the advancements in Conv Nets and machines is the introduction of various Conv architectures like VGGNet. Thus, this study will present a mental health state classification approach based on facial emotion recognition. The methodology comprises several interconnected components, including preprocessing, feature extraction using Principal Component Analysis (PCA) and VGGNet, and classification using Support Vector Machines (SVM) and Multilayer Perceptron (MLP). The FER2013 dataset tests multiple modelsâ€™ performances, and the best model is employed in the mental health state classification. The best model, which combines Visual Geometry Group Network (VGGNet) feature extraction with SVM classification, achieved an accuracy of 66%, demonstrating the effectiveness of the proposed methodology. By leveraging facial emotion recognition and machine learning techniques, the study aims to develop an effective method.
Increasing Viability of Bacillus subtilis BR610 through Inulin-Loaded Synbiotic Microcapsules
Authors: Tampangallo Bunga Rante , Anshary Hilal , Sriwulan - , Syah Rachman
Published: January 2024

This study aims to determine the growth pattern of Bacillus subtilis BR610 isolated from the intestines of Rabbitfish on broth + inulin nutrient media, the diameter of beads, encapsulation rate, absorption efficiency, and probiotic viability in synbiotic microcapsules when exposed to simulated bile and high temperature. The study was designed with a completely randomized plan. The treatments tested were inulin and alginate concentration, viability in 10% bile, temperatures of 70oC and 90oC. An overview of granular synbiotic microcapsules is presented in the form of images, while the quantitative data obtained was processed using ANOVA with the help of the SPSS application. BR610 synbiotic microcapsule beads are round to oval in shape, transparent white in color, and the granules' elasticity increases with increasing alginate concentration. Statistical test results showed that 1% inulin significantly increased the population of B. subtilis BR610, a diameter of beads 0.9-3 mm, viability of probiotics in beads was 7.776 Â± 0.06 log CFU/mL. The highest rate and efficiency of encapsulation and survival of probiotics on simulated were obtained from beads with 2% alginate and 1% inulin concentration. Synbiotic microcapsules can protect probiotics from environmental stress. This research serves as a scholarly resource on the utilization of probiotics in diverse domains, including fish feed production. It is well-established that the temperature within the feed molding apparatus can exceed 70oC during the fabrication of fish feed pellets. However, this is no longer a hindrance due to the implementation of alginate coating on probiotics.
Business Category Classification via Indistinctive Satellite Image Analysis Using Deep Learning
Authors: Suvon Injamul Haque , Loh Yuen Peng , Hashim Noramiza , Mohd-Isa Wan Noorshahida , Ting Choo-Yee , Ghauth Khairil Imran , Bhattacharijee Arpita , Matsah Wan Razali
Published: January 2024

Satellite image analysis has numerous useful applications in various domains. Extracting their visual information has been made easier using remote sensing and deep learning technologies that intelligently interpret clear visual cues. However, satellite information has the potential for more complex tasks, such as recommending business locations and categories based on the implicit patterns and structures of the regions of interest. Nonetheless, this task is significantly more challenging due to the absence of obvious visual cues and the highly similar appearance of each location. This study aims to analyze satellite image similarity between business class categories and investigate the capabilities of state-of-the-art deep learning models for learning non-obvious visual cues. Specifically, a satellite image dataset is constructed using business locations and annotated with the business categories for image structural similarity analysis, followed by business category classification via fine-tuning of deep learning classifiers. The models are then analyzed by visualizing the features learned to determine if they could capture hidden information for such a task. Experiments show that business locations have significantly high SSIM regardless of categories, and deep learning models only recorded a top accuracy of 60%. However, feature visualization using Grad-CAM shows that the models learn biased features and disregard highly informative details such as roads. It is concluded that typical learning models and strategies are insufficient to effectively solve this complex visual problem; thus, further research should be done to formulate solutions for such non-obvious classifications with the potential to support business recommendation applications.
In Silico Docking to Explore the Coronavirus-2 ACE2 Inhibitor Potential in Brown Seaweed Padina sp. from Morotai Island, North Maluku, Indonesia
Authors: Sundari - , Khadijah - , Syazili Aras , Hapsari Lia , Mas'ud Abdu
Published: January 2024

Efforts to explore new sources of antivirals for coronavirus-2 from abundant marine natural materials are highly encouraged. The study aimed to explore the potential compounds of brown seaweed Padina sp. from Morotai Island extracted using three solvents, i.e., n-hexane, ethyl acetate, and acetone, as an antiviral against coronavirus-2 through an entry inhibitor mechanism using bioinformatics tools. The target protein was Angiotensin-Converting Enzyme-related carboxypeptidase (ACE2) receptor. Protein structure was downloaded from PDB and prepared using Chimera. The interaction of compounds to ACE2 was predicted using AutoDock4 and AutoDockTools. MLN-4760 was used as a standard compound. Results showed that 15 selected compounds were potential as ACE2 inhibitors, resulting in negative binding energies, low inhibition constant, and varying binding modes. The conformation structure of all compounds was occupied on the ACE-2 active site. Four compounds were highly potential as ACE2 inhibitors with binding energy lower than a standard compound, comprised of Neophytadiene (diterpene); 6,9,12,15-Docosatetraenoic acid, methyl ester (fatty acid); N-Dimethylaminomethyl-tert-butyl-isopropylphosphine (alkaloid) and 8,11-Octadecadienoic acid, methyl ester (fatty acid). Ethyl acetate and acetone are suggested to be used as solvents for the extraction to produce compounds as ACE2 inhibitors, but ethyl acetate was found to be the most effective. Brown seaweed of Padina sp. is recommended to be developed as a pharmaceutical and nutraceutical preparation for COVID-19. Further in vivo and in vitro studies are suggested to confirm this study's results and provide stronger evidence.
Activity of Natural Compound Pothos tener Wall on Aeromonas hydrophila Infection to Prevent of Antibiotics
Authors: Nugraha Media Fitri Isma , Kurniati Dwi Asih , Rukmini Sri , Taukhid Imam , Novita Hessy , Slamet Bejo , Khasani Ikhsan , Trisnoharjono Dwi Budiyanto , Rajamuddin Muh. Alias L , Elya Berna
Published: January 2024

This study aims to discover how influential Photos tener Wall is as an anti-bacterial treatment for Aeromonas hydrophila. Methods: Cyprinus Carpio were reared for 28 days, and on the 29th and 30th days before Aeromonas hydrophila infection, the fish were adequately fasted. On the 31st day, they were intramuscularly challenged with A. hydrophila (105 CFU/mL) (the first day in A. hydrophila infection). The treatments given were (I) immersed trial with fresh (live) P tener Wall: (H1) Immersed with 15 g of P. tener Wall plant, (H2) Immersed with 30 g of P. tener Wall plant, and (H3) Immersed with 60 g of P. tener; (II) feeding trials in which the treatments given were (P1) 2% of P. tener Wall powder mixed with 1 kg commercial diet and (P2) 4% of P. tener Wall powder mixed with 1 kg commercial diet; Experiment III combined the best results from experiment I (H2) and experiment II (P2) and Oxytetracycline 5 g/kg feed as a control antibiotic. The result obtained was that the treatment of 30 g of fresh P. tener Wall or adding 4% simplicial P. tener Wall in the diet could increase koi fish's immune response and resistance to A. Hydrophila has a survival rate that reaches 100%. This treatment has the same effect as using antibiotic Oxytetracycline 5 g/kg of feed. An important aspect for further research is that P. tener wall can be tested on other fish diseases caused by bacteria or fungi.
Cross Flow Microfiltration System in Separating Fermented Nixtamal Corn for Preparation of Natural Folic Acid
Authors: Susilowati Agustine , Aspiyanto - , Melanie Hakiki , Maryati Yati , Lotulung Puspa D.
Published: January 2024

A purification process of biomass of nixtamal yellow corn fermented by Rhizopus oligosporus strain C1 (FNC-A) and Rhizopus sp (FNC-B) was performed using a microfiltration (MF) membrane with pore size 0.15 Âµm installed in a crossflow microfiltration (CFMF) system to obtain retentate and permeate fractions as natural folic acid preparations. CFMF process was conducted at room temperature with a pump motor frequency (PMF) of 10 Hz (flow rate of 3.5 L/min) and a transmembrane pressure (TMP) of 4 bar for 0, 15, 30, 45, 60, 75, and 90 minutes. Based on optimum folic acid, the results showed that the best treatments of FNC-B and FNC-B were achieved at 90 min, resulting in a folic acid increase in retentate of FNC-A and FNC- B of 72.61 and 95.26%, reducing sugar of 20.41 and 170.93% (1.7-folds), total sugars of 155.51% (1.55-folds) and 426.76% (4.27-folds), and dissolved protein of 55.33 and 39.20%, and decrease in total solids of 88.91 and 91.64%, respectively, compared to initial biomass of FNC-A and FNC-B. The MFCF system effectively separated folic acid in retentates of FNC-A (33.77%) and FNC-B (95.27%) at the optimal condition. Folic acid monomers predominated the characteristics of FNC-A and FNC-B in optimum conditions with molecular weights of 442.10 and 442.18 Dalton, the average particle size of 38.31 Î¼m and 37.97 Î¼m, and distribution of particles at 10, 50, and 90% from the particle size 10.31, 26.32 and 81.55 Î¼m, and 10.37, 28.04 and 76.09 Î¼m, respectively.
Influence of ZnO on Antibacterial Properties of Portland Cement
Authors: Zainul Rahadian , Trafino Randy , Krismadinata - , Lapisa Remon , Azhari Putri , Lubis Amalia Putri , Muhardi -
Published: January 2024

This research project focuses on evaluating the antimicrobial properties of a composite material composed of Portland Cement and Zinc Oxide (ZnO). The study assesses the antibacterial activity of this composite by using Escherichia coli (E. coli) as the test microorganism. Bacterial growth assessment is carried out through the Total Plate Count (TPC) method. The investigation involved varying the concentration of ZnO within the Portland Cement composite, specifically at levels of 0%, 1%, 3%, and 5%. The study primarily centers on generating reactive oxygen species (ROS) by ZnO, and this evaluation was conducted under both UV light exposure and without it. This dual approach allows for a comprehensive examination of ROS activity. Furthermore, the research project involves material characterization using X-ray Diffraction (XRD) to determine the nanoparticle size of ZnO and identify the crystal structures present within the composite material. Additionally, morphological analysis is performed using Scanning Electron Microscopy (SEM) to visualize the structural properties of ZnO embedded within the Portland Cement. SEM analysis is conducted at various magnifications, including 1000x, 2500x, 5000x, and 10,000x, to provide a detailed view of the ZnO's structural properties. In summary, this research project explores the antimicrobial potential of a composite material incorporating Portland Cement and ZnO, focusing on ROS generation and the composite material's structural properties. The findings will contribute to our understanding of the material's suitability for applications in antimicrobial environments.
Investigating an Enhanced Approach for Greenhouse Climate Control: Optimising Cooling and Heating Systems
Authors: Haq Endi Sailul , Setiadevi Shinta , Rini Eka Mistiko , Yusuf Dianni , Aji Ardito Atmaka
Published: January 2024

This study aimed to enhance greenhouse climate regulation by optimizing the efficiency of existing cooling and heating systems while considering external temperature and humidity conditions. We introduced an automated system capable of regulating the internal greenhouse environment, which underwent testing across 30 days in September 2021, with temperatures ranging from 26Â°C to 31Â°C and humidity from 65% to 70%. The system consistently monitored and adjusted the microclimate, with sensors capturing temperature and humidity data at 30-second intervals, amassing over 83,000 data entries for enhanced control accuracy. The automated regulation effectively maintained desired humidity, significantly reducing nighttime levels by 80% while carefully increasing daytime humidity to counteract external heat. Temperature control was largely successful, sustaining daytime levels around 32Â°C, but faced challenges in maintaining the target of 26Â°C during cooler nights. Energy consumption was optimized, with the automation leading to a significant 4-33% energy saving for cooling and an 8% saving in heating compared to traditional methods. Additionally, the system was accessible via a web interface, allowing for real-time climate tracking and prompt anomaly identification. In conclusion, the developed greenhouse automation system exhibited efficiency in equipment usage and improved temperature and humidity control. Further enhancements are required for lamp-based heating. This research contributes to the efficiency and reliability of greenhouse automation systems, mitigating risks associated with external environmental factors and enhancing stability, productivity, and disease and pest prevention.
Road Maintenance Management Based on Geographic Information System (GIS)
Authors: Rizky Ajeng Meiliana , Zahra Ananda Amatory , Astor Yackob , Septian Ridho , Munawar Ghifari , Sihombing Atmy Verani , Fauzi Cholid
Published: January 2024

This research implements GIS in transportation, specifically road maintenance. The system is built by utilizing 2D/3D models from aerial photographs using UAV as a base map. Attribute data such as the type and dimensions of road damage can be obtained by interpreting high-resolution 2D/3D models, which display each road damage, making it easier to measure the dimensions of road damage. The assessment of road conditions is done using the PCI method, which indicates that 51% of the roads fall under the category of people with low incomes to severely damaged category. These roads are prioritized on a map based on their area and cost of maintenance. The projection calculation of the amount of damage is analyzed with one do-nothing scenario, where the roads have not been maintained for ten years. The progression of the damage is observed each year, and the reactive maintenance cost is calculated from 2023 to 2032. The cost and duration are analyzed using three do-something scenarios: optimistic, moderate, and pessimistic. The research results show that the moderate scenario has the lowest cost among the other scenarios and is the most effective scenario, as it produces road conditions with an International Roughness Index (IRI) value of less than 6. This research can assist the government in making informed decisions regarding road maintenance.
Application of Artificial Intelligence in Predicting Oil Production Based on Water Injection Rate
Authors: Rosiani Diyah , Gibral Walay Muhamad , Rahalintar Pradini , Candra Arya Dwi , Sofyan Akhmad , Arison Haratua Yesaya
Published: January 2024

The 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.
Dynamic Sign Language Recognition Using Mediapipe Library and Modified LSTM Method
Authors: Ridwang - , Ilham Amil Ahmad , Nurtanio Ingrid , Syafaruddin -
Published: January 2024

Hand gesture recognition (HGR) is a primary mode of communication and human involvement. While HGR can be used to enhance user interaction in human-computer interaction (HCI), it can also be used to overcome language barriers. For example, HGR could be used to recognize sign language, which is a visual language expressed by hand movements, poses, and faces, and used as a basic communication mode by deaf people around the world. This research aims to create a new method to detect dynamic hand movements, poses, and faces in sign language translation systems. The Long Short-Term Memory Modification (LSTM) approach and the Mediapipe library are used to recognize dynamic hand movements. In this study, twenty dynamic movements that match the context were designed to solve the challenge of identifying dynamic signal movements. Sequences and image processing data are collected using MediaPipe Holistic, processed, and trained using the LSTM Modification method. This model is practiced using training and validation data and a test set to evaluate it. The training evaluation results using the confusion matrix achieved an average accuracy of twenty words trained, which was 99.4% with epoch 150. The results of experiments per word showed detection accurateness of 85%, while experiments using sentences only reached 80%. The research carried out is a significant step forward in advancing the accuracy and practice of the dynamic sign language recognition system, promising better communication and accessibility for deaf people.
Dynamic QoS: Automatically Modifying QoS Queue's Maximum Bandwidth Rate-Limit of Network Devices for Network Improvement
Authors: Osman Muhammad Fendi , Isa Mohd Rizal Mohd , Khairuddin Mohammad Adib , Shukran Mohd Afizi Mohd , Razali Noor Afiza Mat , Kamarudin Nur Diyana , Suharjono Amin
Published: January 2024

The 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.
Deployment of 5G NR Outdoor-to-Indoor at Midband and mmWave Frequency Implementation in Indonesia's Industrial Area
Authors: Hikmaturokhman Alfin , Fahira Ghina , Esa Ray Nur , Asri Asri Wulandari , Wen Goh Khang
Published: January 2024

In the world of telecommunications, there have been significant advancements in broadband access, especially with the introduction of fifth-generation cellular technology, or 5G NR. The presence of 5G may have an impact on performance. This research compares 5G NR network deployment in mid-band at the 3.5 GHz frequency and high-band at 28 GHz frequency in a 5 km2 Pulogadung industrial area. To provide reliable service, link budget calculations were conducted using the downlink outdoor-to-indoor (O2I) and uplink outdoor-to-indoor (O2I) scenarios based online of sight (LOS). The Urban Micro (UMa) propagation model was used for the 3.5 GHz frequency, while the Urban Micro (UMi) model was used for the 28 GHz frequency, both standardized by 3GPP TR 38.901. The calculation results were simulated using the Automatic Site Placement (ASP) feature in Mentum Planet Tools version 7.2.1, which provided recommendations for the new site locations. The simulations showed that the Downlink O2I-LOS scenario, which requires more sites, resulted in stronger signal strength than the Uplink O2I-LOS scenario. The highest signal strength was achieved by the downlink O2I-LOS scenario at the 3.5 GHz frequency, as indicated by an average SS-RSRP value of -91.88 dBm. On the other hand, the lowest signal strength was obtained by the uplink O2I-LOS scenario at the 28 GHz frequency, with an average SS-RSRP value of -98.11 dBm. The difference in predicted 5G SS-RSRP values is influenced by the variation in standard parameter values in the link budget for each frequency.
Position and Temperature Detector for Autism Spectrum Disorder Children based on Sensor and Using IoT System
Authors: Yunidar Yunidar , Melinda Melinda
Published: January 2024

Children with Autism Spectrum Disorder (ASD) have characteristics where one cannot control emotions, which can cause tantrums that can impact behavior and body temperature. Based on this, they should be supervised by parents/relatives. To reduce the effects of these circumstances, this study seeks to design a technology system that can measure body temperature and detect the position of ASD children who can later monitor the activities they do. This system applies the ESP32 microcontroller and utilizes the GPS module to read the position of objects detected by the system and the MLX90614 temperature sensor, which can detect the body temperature of ASD children. Then, to facilitate checking, the control system is designed with an IoT system through the Blynk application to make it easier for users to supervise ASD children and can be accessed via smartphones in real time. In this study, detection testing was carried out on 3 ASD child subjects by grouping three conditions: namely, the child exits the location when the child is outside the predetermined location; then the child exits the body temperature when the child's body temperature is abnormal, and the child exits the location and body temperature outside normal. The results obtained show that the detector test results provide notifications to application users in the form of "child out of location," "child out of body temperature," and "child out of location and body temperature outside normal".
Evaluation and Optimization Based on Exergy in Kamojang Geothermal Power Plant Unit 3
Authors: Rudiyanto Bayu , Wicaksono Arief , Hijriawan Miftah
Published: January 2024

The quality of production well from the Kamojang geothermal power plant unit 3 diminishes annually, whereas there has been a substantial rise in the demand for electrical energy in the region. This research focuses on optimizing the vacuum pressure in the main condenser by employing exergy analysis, a methodology grounded in the principles of the second law of thermodynamics. Exergy analysis offers insights into each system component's exergy efficiency and irreversibility. Furthermore, an energy assessment is conducted to offer insights into each component's energy consumption or utilization. Energy and exergy rates are computed for every state and component within the power plant, encompassing the steam receiving header, separator, demister, turbine, main condenser, inter condenser, after condenser, and cooling tower. The exergy analysis findings reveal that the exergy rate derived from the production well amounts to 95327 kW, generating 52882 kW of electricity and producing a system exergy efficiency of 55.47%. The turbine experiences the highest irreversibility, totaling 12874 kW. Adjustments are made to the main condenser vacuum pressure to optimize the system, aiming to identify the optimal setting that maximizes both exergy efficiency and power output. The optimization outcomes indicate that reducing the vacuum pressure in the main condenser leads to enhanced exergy efficiency and increased power output. The optimal vacuum pressure obtained is 0.1 bar, resulting in the highest exergy efficiency and output power of 57.42% and 54738 kW, respectively, with the lowest irreversibility of 32751.07 kW.
Teacher Challenges in Designing the Learning after Curriculum Change: An Analysis of Learning Management System
Authors: Marmoah Sri , Sukmawati Fatma , Poerwanti Jenny I S , Supianto - , Yantoro - , Duca Diana Sinziana
Published: January 2024

Curriculum change is an essential educational milestone, which often aims to align the education system with the needs of continuously developing society and global trends. This research aims to identify teachers' challenges in designing learning after curriculum changes. The research design uses quantitative description. The respondents were 214 elementary school teachers in six Central Java, Indonesia cities. An online questionnaire form created using Google Forms is used to collect data. Data was taken using a survey and analyzed descriptively. This instrument consists of 3 (three) parts: (1) teacher challenges regarding curriculum changes, (2) the teacher's ability to design learning, and (3) knowledge and competence about and in using the LMS. The study results show that the biggest challenge for teachers lies in management, experience, and references in implementing the new curriculum, even though their readiness is high. Teachers need supporting facilities like a learning management system (LMS) to help them design lessons and develop learning modules. The majority of teachers are familiar with using LMS, so LMS can be a solution to adapt to teacher challenges. Using LMS to create teaching modules can increase the effectiveness and efficiency of all teacher activities at every level of education. It is hoped that the findings of this research can serve as a guide for educators, researchers, educational technology creators, and governments in exploring and developing new ways to support teachers' work, from planning to assessing learning tools. They can easily upload materials, create assignments and tests, and provide feedback to students.
Transmutation of Plutonium and Minor Actinide in PWR Thorium-Transuranic Fuel Assembly
Authors: Zuhair - , Luthfi Wahid , Dwijayanto R. Andika Putra , Sriyono - , Suwoto -
Published: January 2024

Long-lived radioactive waste is often considered a concerning issue on utilizing nuclear power. This waste is in the form of plutonium and minor actinides (MA), formed due to successive neutron capture of 238U. Both are not particularly hazardous radiologically, but their long half-life caused an issue in the public acceptance of radioactive waste disposal. Thereby, this issue must be resolved, either politically or technically. One of the technical solutions to address the issue of long-lived radioactive waste is the incineration of transuranic (TRU) elements in a pressurized water reactor (PWR) fuel assembly. Mixing TRU with thorium in a PWR fuel assembly can theoretically reduce TRU stockpile more effectively than uranium. This paper discusses plutonium and MA transmutation in a Thorium-Transuranic (Th-TRU) PWR fuel assembly using MCNP6 code and ENDF/B-VII library. A Westinghouse 17Ã—17 PWR fuel assembly was chosen to determine the feasibility of TRU incineration. Three assemblies of 50:50, 55:45, and 60:40 mixture of UO2 and Th-TRU fuel rods are compared to one reference UO2 assembly from the criticality and burn-up point of view. From the calculations, the plutonium incineration rate was observed to be 9.47 %, 10.91 %, and 12.2 %, while MA incineration rates were found to be 11.5 %, 11.93 %, and 12.3 % for Th50-TRU50, Th55-TRU45, and Th60-TRU40, respectively. This observation indicates that a higher thorium fraction in the fuel assembly can increase the TRU transmutation rate. Therefore, a high thorium fraction is recommended to increase the transmutation rate of the TRU.
Research in Electronic Multi-Sensor Accuracy in the Implementation of Soil Fertility Monitoring System Using LoRA
Authors: Tjiptoyuda Wahyu Pamungkas , Afandi Mas Aly , Astiti Sarah , Enrico I Ketut Agung , Zukhrufi Anis Sirwan , Putra Rahmat Hardian , Rohmat -
Published: January 2024

The use of electronic sensors to track the nutrients in the soil is an interesting tool for farmers. This has led to the sale of many different kinds of electronic sensors with different levels of accuracy. The accuracy of this electronic sensor was figured out by comparing the results of the sensor's measurements with the results of lab tests done in different ways. This study compares the accuracy of electronic devices used to measure soil nutrients like nitrogen, phosphorus, potassium, electrical conductivity, water pH, and humidity to measurements made in the lab using the ICP-OES (Inductively coupled plasma-optical emission spectroscopy) method. We used three electronic sensors and a transmission system based on LoRA (Long Range) to measure the nutrients in the soil and put the results on our website. The similarities between electronic sensors and laboratory test parameters include the standard deviation, accuracy value, and correlation test between sensors and from the sensors to laboratory test results. The standard deviation parameter test showed a big value between the electronic sensor and the lab test results. However, none of the three used electronic sensors had a standard deviation number that differed greatly from the others. Except for the pH value of the soil, the electronic sensor's accuracy tests for the other five parameters were not very good compared to the lab tests. Also, the sensor correlation test showed a high correlation, while the correlation test between sensor data and lab test results showed a low correlation.
Performance Study of Multipath Effect in 5G Millimeter-Wave Channel
Authors: Chung Gwo Chin , A/L Sivakumar Pravin Kumar , Tiang Jun Jiat , Pang Wai Leong , Lee Chu Liang
Published: January 2024

5G has been essentially a buzzword for several years, but according to the experts, from 2022 onward, there will be an inflection point between network maturity and the availability of 5G. To make 5G a reality, we must minimize all propagation losses. One of the possible factors that reduces the performance of 5G transmission is the multipath effect. In this paper, we investigate the severity of the multipath effect in the 5G millimeter-wave (mmWave) channel and mitigate the multipath effect using adaptive equalization based on the least mean square (LMS) algorithm to improve the performance of 5G wireless signal transmission. A mmWave channel simulator, NYUSIM, provides complete data for all resolvable multipaths in a specific channel configuration. An analysis of bit-error-rate (BER) based on the minimum BER (MBER) and minimum mean square error (MMSE) optimization criterion is performed to measure the improved performance of a 5G data channel simulated under line-of-sight (LOS) and non-LOS (NLOS) paths. A good overall performance of BER based on the MBER and MMSE criteria is attained using the LMS equalization method in a micro-urban area at a maximum data rate of 50 Mbps. For both LOS and NLOS conditions, the increase in data rate to 55.56 Mbps and 62.5 Mbps causes a significant decrease in BER performance. In conclusion, the primary factor affecting the BER performance is the data rate, not the frequency or transmitter-to-receiver distance.
Addition of Cd2+ Metal Ions to Conway Culture Medium on Phytoplankton Growth of Chaetoceros calcitrans
Authors: Makkasau Andi , Sahabuddin Erma Suryani
Published: January 2024

The main objective of this study is to examine the impact of Cd2+ metal ions on the growth of marine phytoplankton, specifically focusing on their potential as agents for phytoremediation in marine settings affected by heavy metal pollution. In this study, the behavior of Cd2+ metal ions added to the culture medium in phytoplankton type of Chaetoceros Calcitrans In the culture medium, an investigation was conducted on a series of Chaetoceros Calcitrans cultures, comparing those including and not including the Cd2+ metal ionsâ€™ addition. Observations were made on the growth pattern of Chaetoceros Calcitrans. To assess the impact of introducing Cd2+ metal ions into the Conway culture media, various metrics such as definite growth rate, growth inhibition percentage, and test of toxicity were employed. The findings indicated that the growth trajectory of Chaetoceros Calcitrans in the Conway medium, in the absence of Cd2+ metal ions as a control group, exhibited the most substantial growth curve. The growth patterns observed in the culture medium upon the addition of Cd2+ metal ions at a concentration of 0.1 mg/L were found to be comparable to those observed in the samples of control group. Adding Cd2+ metal ions at concentrations exceeding 0.1 mg/L has decreased the inhibited growth rate of Chaetoceros Calcitrans. The concurrent increase in PGI costs further exacerbates this effect. The findings from the statistical analysis of difference tests conducted on blanks investigating the impact of introducing Cd2+ metal ions to Chaetoceros Calcitrans suggest that concentrations ranging from 0.01 to 0.10 ppm of Cd2+ metal ions have no discernible effect on the growth of Chaetoceros Calcitrans. Furthermore, the highest concentration of Cd2+ metal ions that Chaetoceros Calcitrans can withstand is 0.10 ppm, with an EC50 value of 6.13 ppm.
Modelo de Cultura Organizacional, determinante en los procesos de cambio e innovación institucional en la Fuerza Aeroespacial Colombiana
Authors: Martínez Lobo Alicia del Pilar , Munar Casa Luz Giovanna , Rodríguez Gallejo Lina María , Rodriguez Daza Andrés Felipe
Published: January 2024

El siguiente es un artículo resultado de una investigación científica, desarrollado en el marco del proyecto “Modelo de gestión estratégica del talento humano de la Fuerza Aeroespacial Colombiana - una visión sistémica (fase 2) “Ingenium 2030”, código 79761 Minciencias, el cual apoya el plan estratégico 2022-2042 de la Fuerza Aeroespacial Colombiana.
Helamiento en aeronaves
Authors: Avella Sarmiento Richard Giovanni , Lee Cruz Zharik Nicole , Méndez Rozo Santiago José , Riascos Hurtado Joel Sebastián , Gutierréz Mora Juan Pablo , Arango Guzman Ana Maria
Published: January 2024

El helamiento en aeronaves, conocido como aircraft icing, es un fenómeno complejo y potencialmente peligroso que ocurre cuando la superficie de una aeronave se cubre de hielo durante el vuelo en condiciones meteorológicas adversas. Este proceso puede tener graves implicaciones en la seguridad y el rendimiento de las aeronaves, afectando la aerodinámica, la estabilidad y el control.
Evaluación de las celdas de hidrógeno como alternativa al queroseno en la aviación
Authors: Mónico Muñoz Luisa Fernanda , Ríos Esparza Sofía Guadalupe
Published: January 2024

Este estudio se enfoca en analizar en detalle el uso de celdas de hidrógeno como una posible alternativa al queroseno, el combustible tradicionalmente empleado en la aviación, con el propósito de enfrentar los desafíos relacionados con la sostenibilidad energética en el ámbito aeronáutico. La investigación se centra en evaluar tanto el impacto ambiental como la viabilidad a largo plazo al adoptar celdas de hidrógeno en la industria de la aviación.