bluuebunny/crossref_metadata_2025 huggingface dataset:
Outage performance analysis of NOMA under fading channels in presence of imperfect SIC
Authors: Nguyen Nhat-Tien , Nguyen Hong-Nhu , Voznak Miroslav , Bao Le-Chi , Duc Nguyen Nhan
Published: July 2022

The introduction of 5th and 6th-generation wireless networks has elevated the demand for huge device connectivity, spectral efficacy, and improved signal quality. The non-orthogonal multiple access technique (NOMA) has been demonstrated to be a candidate to address these requirements. NOMA can assist many users using the same resource block by varying the assigned power levels fairness to the users. To perform this, the NOMA technique superimposes the signals from both the users and transmits them to the receiver. On the receiver side, it performs successive interference cancellation (SIC) techniques to separate the respective signals. Meanwhile, the fading channels also play a major role in deciding the quality of the signal that is being transmitted. In our paper, a NOMA system is considered in presence of two users having  fading channels. The closed-form expressions are derived for outage probability and throughput of the system in presence of perfect SIC and imperfect SIC. The expressions are numerically analyzed by varying various parameters such as fading channels, power level coefficients, and the number of antennas at the receivers. The obtained results demonstrate that each parameter plays a major role in enhancing the quality of each user's signal and the outage performance of the system.
Spatial interpolation method comparison for physico-chemical parameters of river water in Klang River using MATLAB
Authors: Jaffar Azhar , M. Thamrin Norashikin , Amin Megat Ali Megat Syahirul , Farid Misnan Mohamad , Mohd Yassin Ahmad Ihsan , Mohamed Zan Noorolpadzilah
Published: July 2022

Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.
A modified artificial bee colony based fuzzy motion tracking scheme for mobile robot
Authors: Abdalla Abdulkareem Younis , Abdalla Turki Y.
Published: July 2022

In this study a new modified artificial bee colony algorithm for the optimization of the fuzzy control scheme for motion tracking of mobile robot is developed. The modification is based on using some features from the particle swarm optimization algorithm to improve solution quality. The modified artificialbee colony (MABC) balance the exploration and exploitation of the original one. This balancing results in going through the global search space and increases the convergence speed and solution accuracy. MABC is then used for the design of an efficient fuzzy system that perform motion tracking for mobile robot more accurate through minimizing a suitable selected objective function. Results illustrate the high quality of the proposed method.
RGB-D and corrupted images in assistive blind systems in smart cities
Authors: Yehia Amany , Taie Shereen A.
Published: July 2022

Assistive blind systems or assistive systems for visually impaired in smart cities help visually impaired to perform their daily tasks faced two problems when using you only look once version 3 (YOLOv3) object detection. Object recognition is a significant technique used to recognize objects with different technologies, algorithms, and structures. Object detection is a computer vision technique that identifies and locates instances of objects in images or videos. YOLOv3 is the most recent object detection technique that introduces promising results. YOLOv3 object detection task is to determine all objects, their location, and their type of objects in the scene at once so it is faster than another object detection technique. This paper solved these two problems red green blue depth (RGB-D) and corrupted images. This paper introduces two novel ways in object detection that improves YOLOv3 technique to deal with corrupted images and RGB-D images. The first phase introduces a new prepossessing model for automatically handling RGB-D on YOLOv3 with an accuracy of 61.50% in detection and 57.02% in recognition. The second phase presents a preprocessing phase to handle corrupted images to use YOLOv3 architecture with high accuracy 77.39% in detection and 71.96% in recognition.
Comparison between convolutional neural network and K-nearest neighbours object detection for autonomous drone
Authors: Istiqomah Arrahmah Annisa , Rahmania Rissa , Eka Saputra Dany
Published: July 2022

In autonomous drones, the drone’s ability to move depends on the drone’s capacity to know its position, either in relative or absolute position. The Pinhole model is one of the methods to calculate a drone’s relative position based on the triangle similarity concept using a single camera. This method utilizes bounding box information generated from an object detection algorithm. Thus, accuracy of the generated bounding box is crucial, and selection of object detection algorithm is necessary. This paper compares and evaluates machine learning and deep learning object detection methods to determine which method is suitable for distance measurement using a single camera for autonomous drone’s controller based on pinhole model. A novel K-nearest neighbours-based (KNN-based) object detection is constructed to represent the machine learning method while you only look once version 5 (YOLOv5) convolutional neural network (CNN) architecture is selected to represent the deep learning method. A dataset consists of two different classes, with a total of 1520 images, collected from the unmanned aerial vehicle (UAV) camera for training and evaluation purposes. Confusion matrix and intersection over union (IoU)/generalized intersection of union (GIoU) matrix are used to evaluate the performance of both methods. The result of this paper shows the performance of each system and concludes the suitable type of object detection algorithm for the autonomous UAV purpose.
Crowd evacuation navigation for evasive maneuver of brownian based dynamic obstacles using reciprocal velocity obstacles
Authors: Juniastuti Susi , Fachri Moch , Nugroho Fresy , Nugroho Supeno Mardi Susiki , Hariadi Mochamad
Published: July 2022

This paper presents an approach for evasive maneuver against dynamic obstacles in multi-agent navigation in a crowd evacuation scenario. Our proposed approach is based on reciprocal velocity obstacles (RVO) with a different manner to treat the obstacles. We treat all possible hindrances in velocity space reciprocally thus all collision cones generated by other agents and obstacles are treated in the same RVO manner with the key difference in the effort of avoidance. Our approach assumes that dynamic obstacles bear no awareness of navigation space unlike agents thus the avoidance effort lies on behalf of the mobile agents, creating unmutual effort in an evasive maneuver. We display our approach in an evacuation scenario where a crowd of agents must navigate through an evacuation area trespassing zone filled with dynamic obstacles. These dynamic obstacles consist of random motion built based on Brownian motion thus posses an immense challenge for the mobile agent in order to overcome this hindrance and safely navigate to their evacuation area. Our experimentation shows that 51.1% fewer collisions occurred which is denote safer navigation for agents in approaching their evacuation point.
Provably curb man-in-the-middle attack-based ARP spoofing in a local network
Authors: Nasser Hiba Imad , Hussain Mohammed Abdulridha
Published: July 2022

Even today, internet users’ data security remains a significant concern. One problem is ARP poisoning, otherwise referred to as ARP spoofing. Such attacks are intended to exploit the identified ARP protocol vulnerability. Despite no straightforward remedy for ARP spoofing being apparent, certain actions may be taken to maintain one’s safety. The most basic and common defence against a poisoning attack is manually adding MAC and IP addresses to the static ARP cache table. However, this solution is ineffective for large networks where static entries require considerable time and effort to maintain, whether by human input or via special tools and settings for the static entries of network devices. Accordingly, this paper aimed to monitor network packet information and detect the behaviour of ARP poison attacks on operating systems, for instance Windows and Linux. The discovery and defence policy systematically and periodically check the MAC addresses in the ARP table, enabling alerts to be issued if a duplicate entry is detected. This enables the poison-IP address to be blocked before a reply is sent. Finally, the results showed that the superiority was successfully achieved in the detection, prevention and reporting mechanisms in the real-world environment.
Multilevel inverter application for railway traction motor control
Authors: Thanh Ha Vo , Thi Giang Pham , Vu Phuong
Published: July 2022

This paper will present why choosing a 7-level reverse voltage source fed to three-phase induction motors to the railway traction motor. In addition, this paper shows the implementation of space vector pulse width modulation (SVPWM) and the math model of induction motor, stator currents, and speed controller design of electric traction drive system based on field-oriented control (FOC). By MATLAB/Simulink method, this multi-level inverter in FOC structure reduces total harmonic distortion (THD) more than other multi-level inverters such as 3 and 5-level inverter. Furthermore, this FOC control structure combined with 7-level inverter improved speed and torque responses required for railway traction motor load.
SiO2@LaOF:Eu3+ in white light emitting diodes optic efficiency enhancement
Authors: Dang Huu Phuc , Feng Luo Guo , Quang Minh Pham
Published: July 2022

The earliest intense red hue compound of SiO2@LaOF:Eu3+ core-shell nanostructures (NS) was created utilizing a basic solvothermal technique and heat processing. The produced core-shell particles are spherical, non-agglomerated, and have restricted size dispersion. Photoluminescence (PL) radiation spectra exhibit sharp maximums in 593, 611, and 650 nm, corresponding with 5D0 -- 7FJ (J=0, 1, 2) Eu3+ conversions. The Judd-Ofelt (J-O) hypothesis helps determine the spectrum strength indices and Eu-O ligand activities. The CIE coordinates are x=0.63, y=0.36, nearly equal the NTSC coordinates which are x=0.67, y=0.33. Because of the CCT level of 3475 K, which is lower than 5000 K, this phosphor is appropriate for warm light-emitting diodes. To visualize latent fingermarks both porous and non-porous substrates, the fluorescent labeling marker adapted core-cover SiO2 (coat III)@LaOF:Eu3+ (5 mol%) was utilized. With no background influence, the fingermarks obtained are exceedingly sensible and exclusive, permitting for fingerprint ridge features ranging from level-I to level-III. The findings indicate the significant enhancement in the illumination of corecover NS as a responsive operational nanoparticle for increased forensics and firm status illuminating implementations.
Ca7Si2P2O16:Eu2+ green phosphor for optic enhancement of the WLEDs dual-layer remote structure
Authors: Liem Bui Van , Feng Luo Guo , Nguyen Kieu Tam
Published: July 2022

To enhance the dual-layer remote phosphorus configuration’s color standard help spread its application in the LED devices, the new green-emitting phosphor of Ca7Si2P2O16:Eu2+ is proposed. The sol-gel method is used to dope the Eu2+ ions with Ca7Si2P2O16. Increasing the ion Eu2+ concentration can lead to high thermal stability, color-tunable ability, stronger green emission band, and higher photoluminescence extraction. The dual-layer structure’s color standards, as well as the luminous flux, are examined with different concentrations of Ca7Si2P2O16:Eu2+ in the phosphor layer. Owing to the improved features, the green phosphor Ca7Si2P2O16:Eu2+ has enhanced the emission intensity in the blue and green wavelengths, resulting in better color mixing and distribution. The luminescence shows the enhancement when increasing the concentration of Ca7Si2P2O16:Eu2+. However, the color rendering feature can present a reduction with more than 10% wt. green phosphor within the double-layer phosphorus remote configuration, due to color balance’s loss.
A cloud GIS-based framework implementation in developing countries
Authors: Nur Wawan Hendriawan , Narulita Ida , Kumoro Yugo , Susilowati Yuliana , Yuliana Yuliana , Fajary Faiz Rohman , Wulandari Sekar Nur
Published: July 2022

Cloud computing technology has successful cost savings, business effectiveness, and higher scalability in various fields, including the government field. The pandemic Covid-19 era has been accelerating the adoption of cloud technology; the enterprises have instructed the employees to work from home to reduce transmission. The cloud-based framework for government in developing countries was developed. However, it was arduous to apply in Indonesia as a developing country, especially in areas where technology infrastructure, human resources, and funding are insufficient. Thus, the study of the cloud implementation framework in developing countries is essential. This paper used the enterprise architecture planning method for designing a cloud GIS-based framework. The developed framework successfully implemented the cloud-based GIS technology in Indonesia with limited ownership and infrastructure of technology, resources, and funding.
Single phase grid interconnected high gain boost converter with soft switching capability
Authors: Sirisha Bogimi , Saieni Akhilesh
Published: July 2022

The grid interconnected application systems require high voltage levels which leads to an efficiency reduction. To overcome this, a new high gain boost converter in association with buffer capacitor, passive clamp recovery circuit to restore leakage energy in coupled inductor is presented. The magnetic field of the linked inductor stores the energy obtained from the supply. The energy is transmitted to the connected load for consumption in further operating modes. A passive clamp network recovers the energy that is stored in the leakage inductance, enhances the gain of voltage, and improves overall system efficiency. The essential feature of this converter is that, high duty ratios are not required to achieve higher voltage gain, hence the reverse recovery problem of the diode is prevented. Moreover, a passive clamp network decreases the voltage stress of the switch, thus a minimum rating switch is used, as a result, the system's total efficiency improves. This converter output is fed as input to a single-phase full-bridge inverter and also synchronized to a single-phase grid. The performance and powers injected are analyzed by connecting a resistive load. 
Chaotic dynamics model based on field-programmable analog array technology
Authors: Salih Thair Ali
Published: July 2022

Engineering applications in secure communications and encryption based on chaotic signals have attracted a lot of attention in the past decade. This paper presents an extensive study of the chaotic dynamics model of the Van Der Pol oscillator via MATLAB/Simulink simulation and its practical implementation using field-programmable analog matrix techniques. The obtained results show the ease of changing the frequency of the oscillator signal once it is retrieved, creating the required changes, and loading it again according to the required changes. An unlimited number of signals can be generated, allowing them to be used as a chaotic signal oscillator used in many transceiver systems, which require the generation of an unlimited number of such a signal. These research results confirmed that the AN231E04 field-programmable analog array (FPAA) device could be an interesting choice for analog circuit designers, it has the advantage of the ease of use in introducing design changes and testing many critical design solutions.
Indonesian load prediction estimation using long short term memory
Authors: Yuniarti Erliza , Nurmaini Siti , Suprapto Bhakti Yudho
Published: July 2022

Prediction of electrical load is important because it relates to the source of power generation, cost-effective generation, system security, and policy on continuity of service to consumers. This paper uses Indonesian primary data compiled based on data log sheet per hour of transmission operators. In preprocessing data, detrending technique is used to eliminate outlier data in the time series dataset. The prediction used in this research is a long-short-term memory algorithm with stacking and time-step techniques. In order to get the optimal one-day forecasting results, the inputs are arranged in the previous three periods with 1, 2, 3 layers, 512 and 1024 nodes. Forecasting results obtained long short-term memory (LSTM) with three layers and 1024 nodes got mean average percentage error (MAPE) of 8.63 better than other models.
Binary spider monkey algorithm approach for optimal siting of the phasor measurement for power system state estimation
Authors: Palepu Suresh Babu , Reddy Manubolu Damodar
Published: July 2022

The phasor measurement unit (PMU) is an essential measuring device in current power systems. The advantage seems to be that the measuring system could simultaneously give voltages and currents phasor readings from widely dispersed locations in the electric power grid for state estimation and fault detection. Simulations and field experiences recommend that PMUs can reform the manner power systems are monitored and controlled. However, it is felt that expenses will limit the number of PMUs that will be put into any power system. Here, PMU placement is done using a binary spider monkey optimization (BSMO) technique that uses BSMO by simulating spider monkeys’ foraging behavior. Spider monkeys have been classified as animals with a fission-fusion social structure. Animals that follow fission-fusion social systems divide into big and tiny groups, and vice versa, in response to food shortage or availability. The method under development produced the optimum placement of PMUs while keeping the network fully observable under various contingencies. In the study published in IEEE14, IEEE24, IEEE30, IEEE39, IEEE57, and IEEE118, the proposed technique was found to reduce the number of PMUs needed.
Machine learning modeling of power delivery networks with varying decoupling capacitors
Authors: Liew Yeong Kang , Ahmad Nur Syazreen , Aziz Azniza Abd , Goh Patrick
Published: July 2022

This paper presents modeling of power delivery network (PDN) impedance with varying decoupling capacitor placements using machine learning techniques. The use of multilayer perceptron artificial neural networks (ANN) and gaussian process regression (GPR) techniques are explored, and the effects of the hyperparameters such as the number of hidden neurons in the ANN, and the choice of kernel functions in the GPR are investigated. The best performing networks in each case are selected and compared in terms of accuracy using test data consisting of PDN impedance responses that were never encountered during training. Results show that the GPR models were significantly more accurate than the ANN models, with an average mean absolute error of 5.23 mΩ compared to 11.33 mΩ for the ANN.
Semi-supervised approach for detecting distributed denial of service in SD-honeypot network environment
Authors: Setiawan Sumadi Fauzi Dwi , Kusuma Aditya Christian Sri , Maulana Ahmad Akbar , Syaifuddin Syaifuddin , Suryani Vera
Published: July 2022

Distributed Denial of Service (DDoS) attacks is the most common type of cyber-attack. Therefore, an appropriate mechanism is needed to overcome those problems. This paper proposed an integration method between the honeypot sensor and software defined network (SDN) (SD-honeypot network). In terms of the attack detection process, the honeypot server utilized the Semi-supervised learning method in the attack classification process by combining the Pseudo-labelling model (support vector machine (SVM) algorithm) and the subsequent classification with the Adaptive Boosting method. The dataset used in this paper is monitoring data taken by the Suricata sensor. The research experiment was conducted by examining several variables, namely the accuracy, precision, and recall pointed at 99%, 66%, and 66%, respectively. The central processing unit (CPU) usage during classification was relatively small, which was around 14%. The average time of flow rule mitigation installation was 40s. In addition, the packet/prediction loss occurred during the attack, which caused several packets in the attack not to be classified was pointed at 43%.
Machine learning of tax avoidance detection based on hybrid metaheuristics algorithms
Authors: Masrom Suraya , Rahman Rahayu Abdul , Mohamad Masurah , Abd Rahman Abdullah Sani , Baharun Norhayati
Published: July 2022

This paper addresses the performances of machine learning classification models for the detection of tax avoidance problems. The machine learning models employed automated features selection with hybrid two metaheuristics algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA). Dealing with a real dataset on the tax avoidance cases among companies in Malaysia, has created a stumbling block for the conventional machine learning models to achieve higher accuracy in the detection process as the associations among all of the features in the datasets are extremely low. This paper presents a hybrid meta-heuristic between PSO and adaptive GA operators for the optimization of features selection in the machine learning models. The hybrid PSO-GA has been designed to employ three adaptive GA operators hence three groups of features selection will be generated. The three groups of features selection were used in random forest (RF), k-nearest neighbor (k-NN), and support vector machine (SVM). The results showed that most models that used PSO-GA hybrids have achieved better accuracy than the conventional approach (using all features from the dataset). The most accurate machine learning model was SVM, which used a PSO-GA hybrid with adaptive GA mutation.
Multi-objective optimization path planning with moving target
Authors: Abed Baraa M. , Jasim Wesam M.
Published: July 2022

Path planning or finding a collision-free path for mobile robots between starting position and its destination is a critical problem in robotics. This study is concerned with the multi objective optimization path planning problem of autonomous mobile robots with moving targets in dynamic environment, with three objectives considered: path security, length and smoothness. Three modules are presented in the study. The first module is to combine particle swarm optimization algorithm (PSO) with bat algorithm (BA). The purpose of PSO is to optimize two important parameters of BA algorithm to minimize distance and smooth the path. The second module is to convert the generated infeasible points into feasible ones using a new local search algorithm (LS). The third module obstacle detection and avoidance (ODA) algorithm is proposed to complete the path, which is triggered when the mobile robot detects obstacles in its field of vision. ODA algorithm based on simulating human walking in a dark room. Several simulations with varying scenarios are run to test the validity of the proposed solution. The results show that the mobile robots are able to travel clearly and completely safe with short path, proving the effectiveness of this method.  
Integrating singular spectrum analysis and nonlinear autoregressive neural network for stock price forecasting
Authors: Fathi Asmaa Y. , El-Khodary Ihab A. , Saafan Muhammad
Published: July 2022

The main objective of stock market investors is to maximize their gains. As a result, stock price forecasting has not lost interest in recent decades. Nevertheless, stock prices are influenced by news, rumor, and various economic factors. Moreover, the characteristics of specific stock markets can differ significantly between countries and regions, based on size, liquidity, and regulations. Accordingly, it is difficult to predict stock prices that are volatile and noisy. This paper presents a hybrid model combining singular spectrum analysis (SSA) and nonlinear autoregressive neural network (NARNN) to forecast close prices of stocks. The model starts by applying the SSA to decompose the price series into various components. Each component is then used to train a NARNN for future price forecasting. In comparison to the autoregressive integrated moving average (ARIMA) and NARNN models, the SSA-NARNN model performs better, demonstrating the effectiveness of SSA in extracting hidden information and reducing the noise of price series.
Troop camouflage detection based on deep action learning
Authors: Muslikhin Muslikhin , Nasuha Aris , Arifin Fatchul , Suprapto Suprapto , Winursito Anggun
Published: July 2022

Detecting troop camouflage on the battlefield is crucial to beat or decide in critical situations to survive. This paper proposed a hybrid model based on deep action learning for camouflage recognition and detection. To involve deep action learning in this proposed system, deep learning based on you only look once (YOLOv3) with SquezeeNet and the fourth steps on action learning were engaged. Following the successful formulation of the learning cycle, an instrument examines the environment and performance in action learning with qualitative weightings; specific target detection experiments with view angle, target localization, and the firing point procedure were performed. For each deep action learning cycle, the complete process is divided into planning, acting, observing, and reflecting. If the results do not meet the minimal passing grade after the first cycle, the cycle will be repeated until the system succeeds in the firing point. Furthermore, this study found that deep action learning could enhance intelligence over earlier camouflage detection methods, while maintaining acceptable error rates. As a result, deep action learning could be used in armament systems if the environment is properly identified.
Abusive comment identification on Indonesian social media data using hybrid deep learning
Authors: Sari Tiara Intana , Ardilla Zalfa Natania , Hayatin Nur , Maskat Ruhaila
Published: July 2022

Half of the entire social media users in Indonesia has experienced cyberbullying. Cyberbullying is one of the treatments received as an attack with abusive words. An abusive word is a word or phrase that contained harassment and is expressed be it spoken or in the form of text. This is a serious problem that must be controlled because the act has an impact on the victim's psychology and causes trauma resulting in depression. This study proposed to identify abusive comments from social media in Indonesian language using a deep learning approach. The architecture used is a hybrid model, a combination between recurrent neural network (RNN) and long short-term memory (LSTM). RNN can map the input sequences to fixed-size vectors on hidden vector components and LSTM implemented to overcome gradient vector growth components that have the potential to exist in RNN. The steps carried out include preprocessing, modelling, implementation, and evaluation. The dataset used is indonesian abusive and hate speech from Twitter data. The evaluation result showed that the model proposed produced an f-measure value of 94% with an increase in accuracy of 23%.
Return on investment framework for profitable crop recommendation system by using optimized multilayer perceptron regressor
Authors: Janrao Surekha , Shah Deven
Published: July 2022

Return on investment (ROI) plays very important role as a financial dimension in the agriculture sector. Many government agencies like Indian space research organization (ISRO), Indian council of agricultural research (ICAR), and Nitiayog are working on different agriculture projects to improve profitability and sustainability. This paper presents ROI framework to recommend more profitable crop to the farmers as per the current market price and demand which is missing in the existing crop recommendation system. Crop price prediction (CPP) and crop yield prediction (CYP) system are integrated in the ROI framework to predict more demandable crop to yield. This framework is designed by applying data analysis to provide regression statistics which further helps for model selection and improve the performance also. Optimized multilayer perceptron regressor algorithm has been evaluated through experimental results and it has been observed that it gives better performance as compared to other existing regression techniques.
Pipe leakage detection system with artificial neural network
Authors: Rezzwan Radzman Muhammad Iqmmal , Mahamad Abd Kadir , Mohd Muji Siti Zarina , Saon Sharifah , Ahmadon Mohd Anuaruddin , Yamaguchi Shingo , Setiawan Muhammad Ikhsan
Published: July 2022

This project aims to develop a system that can monitor to detect leaks in water distribution networks. It has been projected that leakage from pipelines may lead to significant economic losses and environmental damage. The loss of water from leaks in pipeline systems accounts for a large portion of the water supply. Pipelines are maintained throughout their lives span; however, it is difficult to avoid a leak occurring at some point. A tremendous amount of water could be saved globally if automated leakage detection systems were introduced. An embedded system that monitors water leaks can efficiently aid in water conservation. This project focuses on developing a real-time water leakage detection system using a few types of sensors: water flow rate sensor, vibration sensor, and water pressure sensor. The data from the sensors is uploaded and stored by the microcontroller (NodeMCU V3) to the database cloud (Google Sheets). The data that is stored in the database is analyzed by artificial neural network (ANN) by using Matlab software. An application is developed based on results from ANN training to detect the leakage event. Implementing the proposed system can increase operations efficiency, reduce delay times, and reduce maintenance costs after leaks are detected.
Impact of COVID-19 pandemic on education: Moving towards e-learning paradigm
Authors: Odeh Ammar , Keshta Ismail
Published: July 2022

Besides the economic impact and loss of jobs and revenues, COVID-19 have a great impact on the education sector, with several learning institutions across the world remaining shut down for months. This study evaluated the impact of COVID-19 pandemic on education. The study employed a cross-sectional study design in which data was collected using qualitative methods from various education stakeholders as well as secondary literature. The result showed that learning has severely been affected by the strict protocols adopted by various governments in response to COVID-19 pandemic. The major responses to COVID-19 like closing up schools have left most learners hopeless as they cannot afford the recommended online learning. The major responses to COVID-19 at the few operating schools include: wearing masks, hand sanitization, regular hand washing, constant temperature check for both staff and learners, and lastly change on the sitting arrangements as students are required to maintain a social distance of at least 1.5 meters. Moreover, closure of schools has also had a severe impact on the co-curricular activities that are always undertaken within the academic institutions like athletics, drama and ball games. Measures should be put in place by various governments to ensure that all learners have access to equitable, quality and inclusive forms of education during the present COVID-19 pandemic.
Effectiveness of blended learning in the new normal era
Authors: Sukirman Sukirman , Masduki Yusron , Suyono Suyono , Hidayati Dian , Kistoro Hanif Cahyo Adi , Ru’iya Sutipyo
Published: July 2022

The effects of the pandemic in the world of education have as many influences as the adjustment of learning models. This study aimed to determine the effectiveness of blended learning as an alternative to online education in the new normal era. This was an experimental study with a one-shot case study design conducted in several public high schools in the Special Region of Yogyakarta, Indonesia. The respondents involved in this study were 70 teachers who were determined using a simple random sampling technique. The data was collected by using a questionnaire. Multiple regression analysis was used to analyze the effect of the effectiveness of the application of the blended learning model during the COVID-19 pandemic. The results showed that the effectiveness of educational programs after using blended learning is 95.05%, much higher than the effectiveness of educational programs before using blended learning with a score of 73.24%. The blended learning model affects the effectiveness of learning during the COVID-19 pandemic by 85.20%. Learning during the COVID-19 pandemic that implements the blended learning model will run more effectively and more successfully. Schools need to apply the blended learning model because the use of this method is effective, efficient, and more successful in the new normal era.
Cooperative problem-based learning experience and coaching strategies of engineering course
Authors: Alaloul Wesam Salah , Qureshi Abdul Hannan
Published: July 2022

The problem-based learning (PBL) methodologies are considered adequate for core engineering courses. The integration between cooperative learning and PBL methodologies establishes an encouraging environment for the students. However, for effective implementation of cooperative problem-based learning (CPBL) environment, close supervision of students’ experiences is vital, and deficient areas are to be improved, as PBL is a dynamic process. A study was conducted for the first-year undergraduate engineering class taught under the PBL environment. The objective was to evaluate the course by the preview of students, for highlighting weak domains in the teaching methodology for future improvements. A course experience questionnaire was designed considering PBL implications, with 35 question items, and 31 responses were collected by the end of the semester. Three different analyses were performed on the collected data, i.e., descriptive statistics and Cronbach’s alpha, Student's t-test, and Pearson Chi-square test. The achieved results supported the effective adoption of the PBL system by the students. However, few areas were highlighted requiring special consideration, such as PBL workload, pressure due to extra course content, and assessment opportunities under the PBL system. It was proved that maximum students considered PBL methodologies convenient and effective for learning than the traditional learning approach.
Tarannum Smart Learning Application: Embracing the beauty of tarranum through multimedia technology
Authors: Awang Hapini , Zakaria Mohd Zaini , Al-Mashhadani Abdulrazak F. Shahatha , Mustapha Ramlan , Mohd Yaakob Mohd Faiz , Mohamed Yusof Adnan , Ramli Nusairah , Rasdi Mohd Nur Adzam , Jafar Mohd Fairuz , Nawi Aliff , Zain Farah Muhamad , Kasim Marini
Published: July 2022

This article discusses the development procedures of the Tarannum Smart Learning Application (Tarannum-SLA). It is a teaching and learning aid that has been developed for those who are interested in the tarannum subject. The tarannum is a technique to beautify and curve the tone of voice, in a certain tempo and rhythm when reading the Quran. The main aim of Tarannum-SLA is to facilitate the process of learning tarannum. Also, this courseware could create awareness among Muslims on the importance of the Quran. The Tarannum-SLA was developed not only to attract Muslims but also for non-Muslims who are interested to know about Islam, especially in terms of the arts of Quran recitation. Furthermore, in this courseware, we provide three language options, which are Malay, English, and Arabic for the convenience of our diverse prospective users. There are seven styles of tarannum, including Bayyati, Nahwand, Hijaz, Rast, Soba, Jiharkah, and Sika that are included in the Tarannum-SLA. Furthermore, to examine the ability of Tarannum-SLA to tackle the users’ needs, we have conducted the usability analysis based on the diffusion of innovation model. The result indicated that most of the constructs are perceived at a high level by respondents. Thus, this evidenced the high usability of Tarannum-SLA in promoting Quranic education. Although the development of this courseware has reached the implementation stage, it still needs some future improvements, for example, to amend the verses and reciters.
In a long-winded road: Understanding the lived experiences of faculty members on module writing in the new normal
Authors: Soriano Roselle Matammu , Blando Carmela Gandia , Apolonio Kristine Bernadette M.
Published: July 2022

The COVID-19 pandemic has caused unprecedented difficulties and obstacles for educational sectors, with no one knowing when it will end. The number of illnesses continued to rise while countries worldwide established plans and processes to limit the virus. To maintain and deliver a high-quality education notwithstanding community quarantine, the new normal should be incorporated into the formulation and implementation of the "new normal educational policy." Thus, this qualitative study sought to develop a deeper understanding of teachers' experiences in module writing. Specifically, it explored the challenges they experienced and identified the coping mechanisms they employed. A total of 15 participants were purposely selected who took part in an individual in-depth and semi-structured interview. The themes emerged as the challenges of the faculty members, namely: Insufficient training on module writing, lack of books & other resources, subjects are not in line with the field of specialization, overloaded number of subject preparations, and inadequate office supplies/equipment for reproduction of modules. Further, the themes on the coping mechanisms include collaboration with colleagues, self-motivation, proper time management, and initiative and resourcefulness. Hence, the faculty member's best practices and coping mechanisms to improve their teaching through module writing were indicators that they want to grow and embrace professional development. On the other hand, to address the identified problems, a proposed enhancement program will be initiated. The platform focused on Strengthening the “Campus’ Instructional Materials Development Program” and enhancing campus activities on developing instructional materials.
Predicting the sharing and reception of COVID-19-related information on social media
Authors: Ratu Aurelius , Susilowati Endang , Moerad Sukriyah Kustanti
Published: July 2022

In a health crisis, social media is one of the primary means of disseminating and receiving information. Nevertheless, questions remain regarding what facilitates individual engagement in sharing or receiving information in the context of COVID-19, specifically for the Indonesian context. Analysis of a questionnaire survey among 255 from the random population of Indonesian showed that sharing information is predicted by gender and education. There was no significant relationship between social-demographic and receiving information. For psychosocial characteristics, personal risk perception predicted receiving information, while sharing information was predicted by societal risk perception. Societal risk perception and self-efficacy interacted with each other to predict the sharing of COVID-19-related information. One of the findings indicated a socio-cultural feature through which is considered to play an important role in personal and societal risk perception when sharing or expressing information. Given the benefits of social media communication during this pandemic, this study suggested that governments and policymakers should pay closer attention to a local community that is more effective in keeping everyone safe. Unfortunately, on another side, social media provides less information about narratives, storytelling, and empathy to maintain public trust.
Planning of single-used mask waste containers as personal protective equipment: a case study of Jakarta City station
Authors: Sari Mega Mutiara , Yosafaat Michael , Nastiti Annisa Kamilia , Septiariva Iva Yenis , Aryanto Reifaldy Tsany Betta , Priutama Yesaya Emeraldy , Suryawan I Wayan Koko , Suhardono Sapta
Published: July 2022

During the COVID-19 pandemic, the use of masks is an obligation for everyone to maintain the transmission of the virus to fellow humans. Public places such as stations are one of the locations that may produce single-use masks as personal protective equipment waste. This study aimed to plan the most appropriate type of infectious waste container for disposable masks. Storage with the addition of ultraviolet lamp technology is one way to increase virus removal efficiency compared with no further processing. In this study, it was estimated that the most appropriate container volume is with a capacity of 50 L per station. The collection must be done every 24 hours; the estimated cost for one container can reach IDR 1,988,900. This planning can reduce the amount of mask waste used for the environment and reduce the burden applied to these places at the end.
Awareness and knowledge of uterine fibroid among women in Kerala, India
Authors: Venugopal Neelima , Jacob Nissy , Nallasamy Venkateswaramurthy , Ramanathan Sambathkumar
Published: July 2022

Uterine fibroid is a common pelvic tumor seen in women of reproductive age causing complications and affecting the quality of life of women. Despite the fact that fibroids are ubiquitous and have a major impact, public awareness of uterine fibroids is exceedingly low. Hence, the study aims to determine the amount of awareness present about uterine fibroid among women in Kerala. In order to assess the knowledge, an online based survey was carried out among women (n=417) for a period of six months. For that we had prepared a questionnaire which was divided into 2 parts, part 1 contains sociodemographic details of the participants, while the next part consisted of 21 questions which evaluates the knowledge of women towards uterine fibroid. The contact numbers or mail ID of women of different age groups were collected from certain women’s social groups and the prepared questionnaire has been sent to women through social medias such as WhatsApp, Facebook, Gmail and the responses were collected. This study concluded that, approximately 66.4% of women are aware of uterine fibroid, while the rest have various misconceptions, and thus awareness must be spread about it. Various measures such as educational programs and camps must be conducted in rural areas to improve the awareness of uterine fibroid among women which help in early management and thereby preventing the complications of the disease.
Youth perspective on pulmonary tuberculosis parent’s care
Authors: Lisum Kristina , Waluyo Agung , Nursasi Astuti Yuni , Pasaribu Jesika
Published: July 2022

When parents have pulmonary tuberculosis (TB), youth can provide attentiveness to parent’s physical need and TB treatment. The purpose of this study was to determine the response of youth whose parents suffer from pulmonary TB. This research used descriptive qualitative design. Semi-structured interviews were conducted online and/or face-to-face in November 2020 to June 2021. There were 17 participants contributed to this study consisting of seven youth and seven parents or older siblings with pulmonary TB at Bogor regency. The data obtained were further analyzed using thematic analysis. Four thematics were produced from this research. These themes include knowledge of youth and parents with pulmonary TB: the transmission, administration duration, and side effects of TB medication; negative responses of youth to parents with pulmonary TB; youth poor skills in taking care for families; and cooperation between family, and health care workers in supporting the healing of pulmonary TB. Future research could develop and strengthen youth capacity to increase their skills providing care to parents with pulmonary TB.
Impact of VUCA world on children’s emotional development during online learning
Authors: Ghani Afiq Azri Mohd , Pek Lim Seong , Mee Rita Wong Mee , Ismail Md Rosli , Tazli Uzzairah Nabila Ahmad , Shahdan Tengku Shahrom Tengku , Yob Fatin Syamilah Che
Published: July 2022

Living in a world of unstable and fluctuating economy has put children’s development at risk particularly children from low-income families. Hence their development should be on alert. The concern regarding online learning is crucial towards children’s emotional development as it can positively or negatively affect them. In the volatility, uncertainty, complexity, and ambiguity (VUCA), no other performance is relevant due to high-speed change. Children as young generations today have more disruptive behaviours causing adults to be fearful in dealing with their unruly behaviour. This study discovered the impact of the current environmental situation of uncertainty and parent-child relationship on children’s emotional development. In view of the COVID-19 pandemic restrictions and preventive measures, the study was conducted with parents’ consent using an online survey tool administered using Google Form. The quantitative survey comprised general population-CORE (GP-CORE) and perceived stress scale (PSS) questionnaires from 108 respondents studying in primary schools around urban areas in Selangor, Malaysia. The findings were analysed and described descriptively. Findings showed that children are greatly affected by parents’ job loss and low-income households’ instability, causing emotional stress when learning from home. Therefore, the study can be the mechanism to aid the educational system in emphasising emotional learning in school.