bluuebunny/crossref_metadata_2025 huggingface dataset:
A new windings design for improving single-phase induction motor performance
Authors: Anthony Zuriman , Erhaneli Erhaneli , Warmi Yusreni , Zulkarnaini Zulkarnaini , Anugrah Anggun , Bandri Sepannur
Published: September 2022

Single-phase induction (asynchronous) motors are widely used at home. These motors have two windings and usually operate at a lower performance than 3-phase asynchronous motors which have three windings. For this reason, this study aims to design a new winding of a single-phase asynchronous motor by increasing the number of phases in the motor windings in order to increase the performance of the motor. This research was focused on 36 slot capacitor-start capacitor-run asynchronous motor. The design used 4 non-identical windings in the motor, where three windings acted as auxiliary windings and one winding acted as main winding. The rated current of the designed motor winding was 2.74 A for the main winding and 3.15 A for the auxiliary winding. The performance of the designed motor compared to the traditional single-phase asynchronous motor with the same structure of stator, rotor, and rated current. A traditional single-phase asynchronous motor had data: 1 HP, 220 V, 8.3 A, 1440 RPM, 50 Hz, and 4 poles. The results of this study indicated that the designed motor operated with power factors almost close to unity and had higher output power, torque, and efficiency than the traditional single-phase asynchronous motors.
Simulation and performance analysis of self-powered piezoelectric energy harvesting system for low power applications
Authors: Venugopal Mohankumar , Jayaramaiah Govindanayakanapalya Venkatagiriyappa
Published: September 2022

Energy harvesting is a process of extracting energy from surrounding environments. The extracted energy is stored in the supply power for various applications like wearable, wireless sensor, and internet of thing (IoT) applications. The electricity generation using conventional approaches is very costly and causes more pollution in the environmental surroundings. In this manuscript, an energy-efficient, self-powered battery-less piezoelectric-based energy harvester (PE-EH) system is modeled using maximum power point tracking (MPPT) module. The MPPT is used to track the optimal voltage generated by the piezoelectric (PE) sensor and stored across the capacitor. The proposed PE system is self-operated without additional microarchitecture to harvest the Power. The experimental simulation results for the overall PE-EH systems are analyzed for different frequency ranges with variable input source vibrations. The optimal voltage storage across the storing capacitor varies from 1.12 to 1.6 V. The PE-EH system can harvest power up to 86 µW without using any voltage source and is suitable for low-power applications. The proposed PE-EH module is compared with the existing similar EH system with better improvement in harvested power.
A design of soft-gauge for elevator vibration analysis based on low-cost accelerometer MMA7361L and LabVIEW
Authors: Van Chi Nguyen , Duong Lam Huong , Dao Yen Duy , Phuong Thanh Ngo
Published: September 2022

This paper presents a design of soft-gauge using the low-cost triple-axis accelerometer MMA7361L and LabVIEW software for the purpose of elevator vibration analysis with accuracy according to national standards. The 3-dimensional vibration signals measured and collected respectively by MMA7361L and NI USB6009 are fed into a soft-gauge programmed on LabVIEW to filter, then the fast Fourier transform (FFT) is applied to determine the power spectral density (PSD) and spectrogram of vibrations of filtered vibration signals. The soft-gauge also allows real-time 3-dimensional vibration data to be recorded, this data is used for analyzing later by another professional data software. Practical test results applied for the elevator of the DONGA Plaza building show quite good vibration analysis. Class 1.5 accuracy of the soft-gauge can be obtained by experimental test. This is a fairly cost-effective and inexpensive application that can be made in conditions with limited funds that cannot afford expensive accelerometers in the training of vibration measurement and analysis in high schools and vocational schools in developing countries, like Vietnam.
End-to-end deep auto-encoder for segmenting a moving object with limited training data
Authors: Kebir Abdeldjalil , Taibi Mahmoud
Published: September 2022

Deep learning-based approaches have been widely used in various applications, including segmentation and classification. However, a large amount of data is required to train such techniques. Indeed, in the surveillance video domain, there are few accessible data due to acquisition and experiment complexity. In this paper, we propose an end-to-end deep auto-encoder system for object segmenting from surveillance videos. Our main purpose is to enhance the process of distinguishing the foreground object when only limited data are available. To this end, we propose two approaches based on transfer learning and multi-depth auto-encoders to avoid over-fitting by combining classical data augmentation and principal component analysis (PCA) techniques to improve the quality of training data. Our approach achieves good results outperforming other popular models, which used the same principle of training with limited data. In addition, a detailed explanation of these techniques and some recommendations are provided. Our methodology constitutes a useful strategy for increasing samples in the deep learning domain and can be applied to improve segmentation accuracy. We believe that our strategy has a considerable interest in various applications such as medical and biological fields, especially in the early stages of experiments where there are few samples.
Data augmentation by combining feature selection and color features for image classification
Authors: Meethongjan Kittikhun , Hoang Vinh Truong , Surinwarangkoon Thongchai
Published: September 2022

Image classification is an essential task in computer vision with various applications such as bio-medicine, industrial inspection. In some specific cases, a huge training data is required to have a better model. However, it is true that full label data is costly to obtain. Many basic pre-processing methods are applied for generating new images by translation, rotation, flipping, cropping, and adding noise. This could lead to degrade the performance. In this paper, we propose a method for data augmentation based on color features information combining with feature selection. This combination allows improving the classification accuracy. The proposed approach is evaluated on several texture datasets by using local binary patterns features.
High directivity microstrip antenna with stopband and passband frequency selective surfaces for 6G at low-THz
Authors: Nissanov Uri , Singh Ghanshyam
Published: September 2022

There is still no high-directivity microstrip antenna with directivity beyond 25 dBi, bandwidth (BW) of more than 24%, which can be used for 6G cellular communication at low-THz at a resonance frequency of 144 GHz. So, duo broadband microstrip antennas have been designed at a resonance frequency of 144 GHz with the Taconic TLY-5 laminate in this work. These designs were carried out with the computer simulation technology microwave studio (CST MWS) software. The first antenna simulation results were compared within an Ansys high-frequency structure simulator (HFSS) software, and the obtained simulation results from both software were in fair consent, supporting the proposed designs. The peak directivity, peak gain, total peak efficiency, and BW obtained for the proposed THz microstrip antennas were 27.01 dBi, 25.3 dB, 78.96%, and 34.21 GHz (24.93%), respectively. Therefore, these antennas can be a base for 6G at low-THz.
Radio-frequency circular integrated inductors sizing optimization using bio-inspired techniques
Authors: Hajjami Imad El , Benhala Bachir
Published: September 2022

In this article, a comparative study is accomplished between three of the most used swarm intelligence (SI) techniques; namely artificial bee colony (ABC), ant colony optimization (ACO), and particle swarm optimization (PSO) to carry out the optimal design of radio-frequency (RF) spiral inductors, the three algorithms are applied to the cost function of RF circular inductors for 180 nm beyond 2.50 GHz, the aim is to ensure optimal performance with less error in inductance, and a high-quality factor when compared to electromagnetic simulation. Simulation experiments are achieved and performances regarding convergence velocity, robustness, and computing time are checked. Also, this paper shows an impact study of technological parameters and geometric features on the inductance and the quality factor of the studied integrated inductor. The building method of constraints design with algorithms used has given good results and electromagnetic simulations are of good accuracy with an error of 2.31% and 4.15% on the quality factor and inductance respectively. The simulation shows that ACO provides more accuracy in circuit size and fewer errors than ABC and PSO, while PSO and ABC are better in terms of convergence velocity.
Design of an efficient binary phase-shift keying based IEEE 802.15.4 transceiver architecture and its performance analysis
Authors: Kempanna Vivek Raj , Gowda Dinesha Puttaraje
Published: September 2022

The IEEE 802.15.4 physical layer (PHY) standard is one of the communication standards with wireless features by providing low-power and low-data rates in wireless personal area network (WPAN) applications. In this paper, an efficient IEEE 802.15.4 digital transceiver hardware architecture is designed using the binary phase-shift keying (BPSK) technique. The transceiver mainly has transmitter and receiver modules along with the error calculation unit. The BPSK modulation and demodulation are designed using a digital frequency synthesizer (DFS). The DFS is used to generate the in-phase (I) and quadrature-phase (Q) signals and also provides better system performance than the conventional voltage-controlled oscillator (VCO) and look up table (LUT) based memory methods. The differential encoding-decoding mechanism is incorporated to recover the bits effectively and to reduce the hardware complexity. The simulation results are illustrated and used to find the error bits. The design utilizes less chip area, works at 268.2 MHz, and consumes 108 mW of total power. The IEEE 802.15.4 transceiver provides a latency of 3.5 clock cycles and works with a throughput of 76.62 Mbps. The bit error rate (BER) of 2×10-5 is achieved by the proposed digital transceiver and is suitable for real-time applications. The work is compared with existing similar approaches with better improvement in performance parameters.
Optimized reduction approach of congestion in mobile ad hoc network based on Lagrange multiplier
Authors: Farhan Marwa K. , Croock Muayad S.
Published: September 2022

Over the past decades, computer networks have experienced an outbreak and with that came severe congestion problems. Congestion is a crucial determinant in the delivery of delay-sensitive applications (voice and video) and the quality of the network. in this paper, the Lagrangian optimization rate, delay, packet loss, and congestion approach (LORDPC) are presented. A congestion avoidance routing method for device-to-device (D2D) nodes in an ad hoc network that addresses the traffic intensity problem. The method of Lagrange multipliers is utilized for active route election to dodge heavy traffic links. To demonstrate the effectiveness of our proposed method, we applied extensive simulation that presents path discovery and selection. Results show that LORDPC decreases delay and traffic intensity while maintaining a high bitrate and low packet loss rate and it outperformed the ad hoc on-demand distance vector (AODV) protocol and the Lagrangian optimization rate, delay, and packet loss, approach (LORDP).
Comparative study of optimization algorithms on convolutional network for autonomous driving
Authors: Martinez Fernando , Montiel Holman , Martinez Fredy
Published: September 2022

he last 10 years have been the decade of autonomous vehicles. Advances in intelligent sensors and control schemes have shown the possibility of real applications. Deep learning, and in particular convolutional networks have become a fundamental tool in the solution of problems related to environment identification, path planning, vehicle behavior, and motion control. In this paper, we perform a comparative study of the most used optimization strategies on the convolutional architecture residual neural network (ResNet) for an autonomous driving problem as a previous step to the development of an intelligent sensor. This sensor, part of our research in reactive systems for autonomous vehicles, aims to become a system for direct mapping of sensory information to control actions from real-time images of the environment. The optimization techniques analyzed include stochastic gradient descent (SGD), adaptive gradient (Adagrad), adaptive learning rate (Adadelta), root mean square propagation (RMSProp), Adamax, adaptive moment estimation (Adam), nesterov-accelerated adaptive moment estimation (Nadam), and follow the regularized leader (Ftrl). The training of the deep model is evaluated in terms of convergence, accuracy, recall, and F1-score metrics. Preliminary results show a better performance of the deep network when using the SGD function as an optimizer, while the Ftrl function presents the poorest performances.
An optimized cost-based data allocation model for heterogeneous distributed computing systems
Authors: Tarun Sashi , Kumar Dubey Mithilesh , Batth Ranbir Singh , Kaur Sukhpreet
Published: September 2022

Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation.
Cognitive level classification on information communication technology skills for blog
Authors: Chootong Chalothon , Charoensuk Jirawan
Published: September 2022

Learners can study and update their knowledge continually due to the rapid growth of online content. The Medium blog is a well-known open platform that encourages authors who want to share their experiences to publish content on various topics in multiple languages. Meanwhile, readers can query interesting content by searching for a related topic. However, finding suitable content is still challenging for learners, especially information communication technology (ICT) content in Thai, and needs to be classified into beginner, intermediate, and advanced cognitive levels. Moreover, ICT blog content is usually a mix of Thai language and technical terms in English. To overcome the challenge of content classification, a deep neural network (DNN) classification model was constructed to classify the ICT content from the Medium blog into three levels based on cognition. We examined and compared the classification results with strong baseline models, including logistic regression, multinomial naïve bayes, support vector machine (SVM), and multilayer perceptron (MLP). The experimental results indicate that the proposed DNN model attained the highest accuracy (0.878), precision (0.882), recall (0.878), and F1-score (0.875).  
Evaluating the effectiveness of data quality framework in software engineering
Authors: Rosli Marshima Mohd , Mohamad Yusop Nor Shahida
Published: September 2022

The quality of data is important in research working with data sets because poor data quality may lead to invalid results. Data sets contain measurements that are associated with metrics and entities; however, in some data sets, it is not always clear which entities have been measured and exactly which metrics have been used. This means that measurements could be misinterpreted. In this study, we develop a framework for data quality assessment that determines whether a data set has sufficient information to support the correct interpretation of data for analysis in empirical research. The framework incorporates a dataset metamodel and a quality assessment process to evaluate the data set quality. To evaluate the effectiveness of our framework, we conducted a user study. We used observations, a questionnaire and think aloud approach to provide insights into the framework through participant thought processes while applying the framework. The results of our study provide evidence that most participants successfully applied the definitions of dataset category elements and the formal definitions of data quality issues to the datasets. Further work is needed to reproduce our results with more participants, and to determine whether the data quality framework is generalizable to other types of data sets.
Natural language processing for Albanian: a state-of-the-art survey
Authors: Kastrati Muhamet , Biba Marenglen
Published: September 2022

Due to its wide applicability, natural language processing (NLP) has attracted significant research efforts to the machine learning and deep learning research community. Despite this, research works investigating NLP for the Albanian language are still limited. However, to the best of our knowledge, there is no literature review available, which presents a clear picture of what has been studied, argued, and established in the area. The main objective of this survey is to comprehensively review, analyze and discuss the state-of-the-art in NLP for the Albanian language. Here, we present an extensive study concerning the contribution of several authors that have contributed to the application of NLP to the Albanian language. Also, we present an overview of research carried out in the typical applications of NLP for the Albanian language. Finally, some future challenges and limitations of the area are discussed.
An overview of internet engineering task force mobility management protocols: approaches and its challenges
Authors: Mahenthiran Prabha , Muruganadam Dinakaran
Published: September 2022

In recent years, internet protocol mobility management has become one of the most popular research areas in networking. Mobility management protocols are in charge of preserving continuing communications as a user roam between different networks. All existing internet protocols (IP), like MIPv6, and PMIPv6, rely on a centralized mobility anchor to control mobile node traffic and signaling. The disadvantages of centralized mobility management (CMM) include ineffectiveness in handling massive volumes of traffic, poor scalability, wasteful use of network resources, and packet delay. When CMM is required to handle mobile media, which demands a huge amount of information and frequently needs quality of services (QoS) such as session continuance and reduced latency, these difficulties become apparent. It drives the need for distributed mobility management protocol (DMM) systems to manage the growing amount of mobile data, the overwhelming of this is video communication. DMM approaches could be regarded as an innovative and effective method to deal with mobility. An overview of the CMM protocol and its drawbacks are analyzed. This study examines the various DMM protocol techniques and their performance metrics are compared to highlight similarities and differences. The study reveals the network-based DMM protocol improves overall handoff time and packet loss.
A review on predictive maintenance technique for nuclear reactor cooling system using machine learning and augmented reality
Authors: Mohamad Nor Ahmad Azhari , Kassim Murizah , Minhat Mohd Sabri , Ya'acob Norsuzila
Published: September 2022

Reactor TRIGA PUSPATI (RTP) is the only research nuclear reactor in Malaysia. Maintenance of RTP is crucial which affects its safety and reliability. Currently, RTP maintenance strategies used corrective and preventative which involved many sensors and equipment conditions. The existing preventive maintenance method takes a longer time to complete the entire system’s maintenance inspection. This study has investigated new predictive maintenance techniques for developing RTP predictive maintenance for primary cooling systems using machine learning (ML) and augmented reality (AR). Fifty papers from recent referred publications in the nuclear areas were reviewed and compared. Detailed comparison of ML techniques, parameters involved in the coolant system and AR design techniques were done. Multiclass support vector machines (SVMs), artificial neural network (ANN), long short-term memory (LSTM), feed forward back propagation (FFBP), graph neural networks-feed forward back propagation (GNN-FFBP) and ANN were used for the machine learning techniques for the nuclear reactor. Temperature, water flow, and water pressure were crucial parameters used in monitoring a nuclear reactor. Image marker-based techniques were mainly used by smart glass view and handheld devices. A switch knob with handle switch, pipe valve and machine feature were used for object detection in AR markerless technique. This study is significant and found seven recent papers closely related to the development of predictive maintenance for a research nuclear reactor in Malaysia.
Robusta coffee leaf diseases detection based on MobileNetV2 model
Authors: Aufar Yazid , Kaloka Tesdiq Prigel
Published: September 2022

Indonesia is a major exporter and producer of coffee, and coffee cultivation adds to the nation's economy. Despite this, coffee remains vulnerable to several plant diseases that may result in significant financial losses for the agricultural industry. Traditionally, plant diseases are detected by expert observation with the naked eye. Traditional methods for managing such diseases are arduous, time-consuming, and costly, especially when dealing with expansive territories. Using a model based on transfer learning and deep learning model, we present in this study a technique for classifying Robusta coffee leaf disease photos into healthy and unhealthy classes. The MobileNetV2 network serves as the model since its network design is simple. Therefore, it is likely that the suggested approach will be deployed further on mobile devices. In addition, the transfer learning and experimental learning paradigms. Because it is such a lightweight net, the MobileNetV2 system serves as the foundational model. Results on Robusta coffee leaf disease datasets indicate that the suggested technique can achieve a high level of accuracy, up to 99.93%. The accuracy of other architectures besides MobileNetV2 such as DenseNet169 is 99.74%, ResNet50 architecture is 99.41%, and InceptionResNetV2 architecture is 99.09%.
Smart internet of things kindergarten garbage observation system using Arduino uno
Authors: Aldujaili Ali Abdulameer , Dauwed Mohammed , Meri Ahmed , Abduljabbar Safa Sami
Published: September 2022

Increase the in population and kindergarten number, especially in urban areas made it difficult to properly manage waste. Thus, this paper proposed a system dedicated to kindergartens to manage to dispose of waste, the system can be called smart garbage based on internet of things (SGI). To ensure a healthy environment and an intelligent waste in the kindergarten management system in an integrated manner and supported by the internet of things (IoT), we presented it in detail identification, the SGI system includes details like a display system, an automatic lid system, and a communication system. This system supplied capabilities to monitor the status of waste continuously and on IoT website can show the percentage of waste placed inside the bin. And by using a Wi-Fi communication system, between the system unit and the monitoring body, to collect waste when the trash is full. The smart system proposed in this paper is the most efficient system of traditional waste management systems because it reduces the use of manpower and significantly limits the spread of waste and fully controls it. Additionally, it can be linked via the IoT to the mobile, thus forming an integrated monitoring system.
A unique deep-learning-based model with chest x-ray image for diagnosing COVID-19
Authors: Mahdi Al-khafagy Alyaa , Rafil Hashim Sarah , Ali Enad Rusul
Published: September 2022

Later innovative advancements cleared the way for deep learning-based methods to be used in the therapeutic field due to its exactness for the detection and localization of different illnesses. Recently, the coronavirus widespread has put a parcel of weight on the health framework all around the world. Reverse Transcription- Polymerase Chain Reaction test and medical envisioning are both possible and effective techniques to determine the coronavirus infection. Since coronavirus is highly infection and Reverse Transcription- Polymerase Chain Reaction is time-consuming, determination utilizing a chest X-ray to early diagnosing the infection is considered secure in different situations. A preprocessing step is done first to balance classes inside the dataset and increase the training data. A deep learning-based method is proposed in this study to determine some human lung infections and classify coronavirus from other non-coronavirus diseases accordingly. The proposed model is used for multi-class classification which is more complicated than binary classification especially in the medical image due to the inter classes' large similarity. The proposed procedure effectively classifies four classes that incorporate coronavirus, lung opacity, normal lung, and viral pneumonia with an accuracy of 97.5 %. The proposed strategy appears excellent in terms of accuracy when compared with later strategies.
Assured data deletion in cloud computing: security analysis and requirements
Authors: Qambar Aziz Kawa , Ahmed Mahmood Baban
Published: September 2022

With the rapid development of cloud storage, more data owners store their data on the remote cloud to reduce the heavy local storage overhead. Cloud storage provides clients with a storage space that they may outsource and use on a pay-as-you-go basis. Due to data ownership separation and management, local data owners lose control over their data. Hence, all the operations over the outsourced data such as data transfer, update, and deletion, will be executed by the remote cloud server. As a result of that, various security challenges appear in terms of data privacy and integrity. In addition to data deletion that becomes an important security challenge, once a cloud user intends to delete his data, it must be sure that data is deleted from all cloud storage sources and prevent the cloud server from reserving the data maliciously for economic interests. In this paper, we present and discuss several types of research that use different technologies to solve assured deletion problems and verification the deletion result. The paper also presents a thorough analysis of the surveyed protocols in terms of fine-grained, security, performance, and requirements of remote cloud storage design.
A deep learning based system for accurate diagnosis of brain tumors using T1-w MRI
Authors: Ahmed Mona , Khalifa Fahmi , El-Din Moustafa Hossam , Ahmed Saleh Gehad , AbdElhalim Eman
Published: September 2022

Detection and classification of brain tumors are of formidable importance in neuroscience. Deep learning (DL), specifically convolution neural networks (CNN), has demonstrated breakthroughs
in the field of brain image analysis and brain tumors classification. This work proposes a novel CNN based model for brain tumor classification. Our pipeline starts with prepossessing and data augmentation techniques. Then, a CNN classification step is developed and utilizes ResNet50 architecture as its core. Particularly, our design modified the ResNet50 output with a global average pooling (GAP) layer to avoid over-fitting. The proposed model is trained and tested using different optimization algorithms. The final classification is achieved using a sigmoid layer. We tested the proposed structure on T1 weighted contrast-enhanced magnetic resonance images (T1-w MRI) that are collected from three datasets. A total of 3586 images containing two classes (i.e., bengin, and malignant) were used in our experiments. The proposed model reach highest accuracy 99.8%, and optimal error 0.005 using Adam when compared with other six well-known CNN architectures.
Multi-modal image fusion using contourlet and wavelet transforms: a multi-resolution approach
Authors: V Bhavana , H. K. Krishnappa
Published: September 2022

In recent years, vast improvement and progress has been observed in the field of medical research, especially in digital medical imaging technology. Medical image fusion has been widely used in clinical diagnosis to get valuable information from different modalities of medical images to enhance its quality by fusing images like computed tomography (CT), and magnetic resonance imaging (MRI). MRI gives clear information on delicate tissue while CT gives details about denser tissues. A multi-resolution approach is proposed in this work for fusing medical images using non-sub-sampled contourlet transform (NSCT) and discrete wavelet transform (DWT). In this approach, initially the input images are decomposed using DWT at 4 levels and NSCT at 2 levels which helps to protect the vital data from the source images. This work shows significant enhancement in pixel clarity and preserves the information at the corners and edges of the fused image without any data loss. The proposed methodology with an improved entropy and mutual information helps the doctors in better clinical diagnosis of brain diseases.
Reconfigurable bandstop to allpass filter using defected ground structure in K-band for millimeter-wave communications
Authors: Othman Adib , Azwan Shairi Noor , A. Majid Huda , Zakaria Zahriladha , Ibrahim Imran Mohd , Jamaluddin Mohd Haizal , Osman Anwar Faizd
Published: September 2022

In this paper, a reconfigurable bandstop to allpass filters using Defected Ground Structure (DGS) in K-Band for millimeter-wave communications is proposed. Mathematical analysis of the DGS is discussed in this paper for the bandstop and allpass responses. The reconfigurable filter is performed by using an ideal switch with open circuit and short circuit conditions on DGS at several locations. Hence, two filter designs with different DGS are investigated for their performance in both simulation and measurement. As a result, the ideal switch at location S3 gives the best performance for both DGSs with a narrowband bandstop response and a wideband allpass response with insertion loss of less than 1 dB and return loss of more than 10 dB at the 26 GHz band. Hence, the proposed reconfigurable filter is suitable for millimeter-wave front-end communications.
Healthcare assessment for beauty centers using hybrid sentiment analysis
Authors: Khalid Al-Mashhadany Abeer , T. Sadiq Ahmed , Mazin Ali Sura , Abbas Ahmed Amjed
Published: September 2022

Because of COVID-19, healthcare became the first interesting domain at the world. Here, comes the role of researchers to do what they can to guide people. Nowadays, the most wanted field is beauty industry. It achieved large market. And the estimation is toward the growing. Researchers can give advice to prevent unhealthy causes in this field. They can apply sentiment analysis methods to make decision whether a Beauty center is healthy or unhealthy. This work develops an improved method of sentiment analysis to classify the beauty centers in Iraq into healthy and unhealthy classes. Researchers used comments of beauty centers’ Facebooks to perform the assessment. The methodologies encompass the two approaches lexicon-based and machine-learning-based. Three machine learning mechanisms had been applied; rough set theory, naïve bayes, and k-nearest neighbors. It will be shown that rough set theory is the best compared with the others two. Rough set theory achieved 95.2%, while Naïve Bayes achieved 87.5% and k-nearest neighbors achieved 78%.
Preservation of intangible and tangible cultural heritage using digital technology
Authors: Ningrayati Amali Lanto , Rifai Katili Muhammad , Ismail Wandi
Published: September 2022

There is presently a shortage of preservation of intangible cultural heritage and places for distributing tangible cultural heritage artifacts, regardless of their high value and usability for a nation. Despite efforts to protect cultural heritage, such as mapping and designing information systems to ensure the authenticity of information circulating in the community about intangible traditions and tangible sites obtained from different sources, many historical information places have been converted into new beliefs and buildings. Therefore, this research aims to provide information to promote public awareness about the distribution of tangible sites and intangible information about cultural heritage. A system development method with a prototype model comprising the stages of design and evaluation, system coding, and program testing, alongside system evaluation and usage, was employed. Subsequently, the results showed that the mapping information system increases the effectiveness and efficiency of delivering intangible and tangible cultural heritage information to the public and tourists.
Development and Evaluation of Learning Resource Package in Optics for Grade 10 Science
Authors: Recede Reynald Alfred A. , Gaviola Robert J. , Castro Mark A. , Clemente Shynne Izza F.
Published: September 2022

Learning complicated topics in Physics is one of the most challenging tasks students experi-ence in the classroom. Physics teachers use supplementary materials and laboratory exercises to help students visualize and imagine what they are studying in order to better understand the topics.  These learning aids, however, are sometimes not sufficient. Students would still struggle to learn due to lack of effective teaching materials. Without effective teaching materials, it is impossible to master Physics concepts. The study focused on developing and evaluating a learning resource package in Optics for Grade 10 Science.  The results showed that the resource package will provide an effective and alternative method for students to be exposed to the scientific method and to explore and observe what the beauty of physics does in our lives. These are very helpful for teachers when teaching Physics concepts. Furthermore, because the learning resource package is already complete, the teacher may feel more prepared to teach optics classes. It is recommended that teacher-writers create lessons that are similar to those already used in science classrooms which may directly address the needs of students.
Practices of Teachers and Parents of Learners with Special Educational Needs During Pandemic
Authors: Rosales Janna Marie L. , Ancheta Jr. Rosein A. , Manalastas Rebecca Dc. , De Los Reyes Cecilia Elena P. , Etcuban Jonathan O. , De Los Reyes Niña Rozanne T. , Capuno Reylan G. , Manguilimotan Ramil P. , Pinili Lilibeth C.
Published: September 2022

The research focused on the practices of teachers and parents of learners with special educational needs during pandemic employing descriptive-correlational research design. It specifically sought the demographic profile of the teachers and parent-respondents. Using purposive sampling, the respondents of the study consisted of 22 teachers and 26 parents from Academia De Santiago of Tarlac, and Angel Beats Play and Development Center, Manila, Philippines. The frequency count, percentage weighted mean, and correlation analysis were used to treat the data gathered from the survey-questionnaire. Results revealed that that the teacher-respondents “greatly encountered” behavioral problems and “encountered” cognitive development problems in handling LSENs; parent-respondents “encountered” both behavioral and cognitive development problems. There was a “great extent” of practices of teachers while “little extent” in parents’ practices. There was a significant relationship between the demographic profile of the teacher-respondents and the extent of teachers’ practices. Based from the significant findings drawn, this study recommends to upskill teachers’ competencies in handling learners with special education needs. Finally, implementation of the action plan is hereby recommended to improve the behavioral and cognitive aspects of the learners and to further improve practices of teachers and parents of the home-schooled learners with special educational needs during a pandemic and its aftermath.
Radiography Of The Paranasal Sinus With Maxial Sinusitis In North Sumatra-Medan University Hospital
Authors: Sidauruk Juliana Lasniar , Tarigan Liberti
Published: September 2022

Paranasal sinus radiography is an appropriate method for showing abnormalities in the paranasal sinuses and showing abnormalities in suspected Maxillary Sinusitis. The purpose of this examination is to determine the location and location of sinusitis by obtaining an optimal picture with radiographic examination of the paranasal sinuses. This research was conducted at the Radiology Installation of the University of North Sumatra-Medan Hospital. Using a general X-ray unit with an aircraft capacity of 1100 mA in February 2022 - May 2022, with a qualitative descriptive research type. To show the anatomical description and pathological abnormalities in the paranasal sinuses, especially maxillary sinuses, parietoacanthia (water's method) and lateral projections are used. The results of the examination showed that there was Sinusitis Maxillaris Sinistra and a suspected Sinusitis Ethmoidalis Sinistra.
The Efficacy of Technical Assistance in Crafting and Appraising the En-hanced School Improvement Plan (E-SIP) in East Butuan District III
Authors: Jomonong Jonas H.
Published: September 2022

Today's impact of technical assistance in improving organizational performance, its people, practices and mechanisms, and the organization itself is far more important than before. The study sought to determine the efficacy of technical assistance in crafting and appraising the E-SIP in the district. The study's participants were the school heads in the district holding different positions. Specifically, the participants were profiled in terms of years of experience, the number of leadership training attended, position, and years in school. The appraisal rating of the E-SIP was utilized, and it was found that there was a significant improvement in the ratings after the TA was provided. Further, paired t-test was used to test the significant difference between the appraisal rating before and after the TA was provided and revealed a significant difference characterized by the p-value of 0.000. It was found that when given appropriately and proactively, technical assistance has a long-term impact on school heads, TA providers, policymakers, and program implementers in improving organizational performance.
The The Liquidity Issues and the Profitability Index of Small-Scale Business Entities in Sorsogon Province, Philippines
Authors: Bongalonta Michael B. , Bongalonta Michelle M.
Published: September 2022

This research was conducted to determine the liquidity issues and profitability index of small-scale businesses operating in the Second District of Sorsogon Province, Philippines as well as to establish the relationship of these two variables. This study used mixed design.  A total of 223 small-scale business owners engaged in trading in the Second District of Sorsogon Province, Philippines were the primary respondents of this research. Vertical analyses and stepwise linear regression analyses were utilized.

This study concludes that small-scale business entities in the Province of Sorsogon are highly liquid and profitable depicting their ability to pay short-term maturing obligation. Limited and unavailability of capitalization, Absence of Credit Policy, High Finance cost for debt financing, Inventory obsolescence, Shortage of resources and Lack of markets of measure and scope are the identified liquidity issues by small-scale business owners. To address the issues, the Use equity financing than debt financing, Strengthen the company’s policy of extending loan, Minimize the purchase of inventory or stocks, financial suffering by staying well-informed of your finances and Hiring Bookkeepers are highly recommended by the respondents.
ADVERTISEMENTS AND SEXISM IN THE PHILIPPINES
Authors: Sales April Jane G. , Leuterio Glarissa D. , Lopez Lurie Jane S. , Matinao Claire Dianne G. , Naces Lorna C. , Ramos Ariel L.
Published: September 2022

Advertisements have been an effective tool used to promote a product or service. It can also be considered as a way to shape the mind and the culture of society. Hence, this study determined the linguistic elements and analyzed different types of sexism through language in Philippine television advertisements. The study used qualitative approach to research and utilized a validated self-made research instrument which served as the primary tool of the study. The study identified the linguistic elements present in Philippine television advertisements in terms of its lexical and syntactical feature and analyzed the types of sexism through language in Philippine advertisements. The study revealed that the commonly used lexical feature of advertisements is simple vocabulary; meanwhile for syntactic feature, the most common is ambiguity and the use of imperative. It is also found that advertisements can be used as a sexism platform in the Philippines. Moreover, the way these elements are used or depicted along with the portrayal of men and women in the advertisements may influence the audience’s perception and understanding of what they have seen thus, the audio-video material as output of this study may serve as a tool to liberate the minds of the viewing public.
Academic Self-Efficacy of College Freshmen in Relation to English Language Competency
Authors: Galabasa Nicolas N. , Vibal Jobell Cris T. , Apohen Sharon L.
Published: September 2022

This study is conducted to determine if academic self-efficacy and English language competency are correlated. Survey instruments on academic self-efficacy and English language competencies were administered to 221 college freshmen. Academic self-efficacy and English language competencies composed of vocabulary, grammar, and comprehension were determined using the mean, while the Kruskal Wallis test was used for differences and the Chi-square Test for relationships. Significant differences were evident in the level of academic self-efficacy and the level of English language competencies when grouped according to program enrolled and academic self-efficacy is significantly related to the English language competency of college freshmen. Future research will be conducted.
Impact Assessment of Aqua Silviculture of Milagros and Placer, Masbate
Authors: Velza Jacob Frederick P. , Ibañez Roger Y. , Mahawan Alvin M.
Published: September 2022

With the alarming poverty incidence among fisherfolks communities, the Department of Agriculture – Bureau of Fisheries and Aquatic Resources (DA-BFAR) embarked a project dubbed as Fisheries, Coastal Resources and Livelihood (FishCORAL) funded by United Nations International Fund for Agricultural Development (UN-IFAD) to address the problem by providing livelihood project to the coastal areas of different regions in the country. To assess the impact of the said project in the Bicol region, Bicol University in collaboration with different State Colleges and Universities in the region conducted a project called impact assessment on different project of the FishCORAL livelihood projects. The aim of the study is to assess the key indicators of the project if the goals of the project was met with the implementation of the livelihood project. The study used convenience sampling in the field surveys and focus group discussions to draw conclusion wherein the respondents are the recipient of the aqua silviculture livelihood project. The study reveals that the beneficiaries is under big household category making them hard to financially sustain the basic needs of the family because there is large percentage of dependent individuals. It was also noted that they do not have their own lot where they built their houses and the source of drinking water is unprotected well.  In addition, the livelihood project is not sustainable because of various problems such as no profit from the first production cycle, most of the beneficiaries backed out during the implementation and the association itself is not technically prepared to implement the project to sustainable ends. Conduct of more technical capability training along varied aspects of aqua silviculture shall be caried out. Also, trainings on financial literacy, business management and basic record keeping are very necessary. There is also a need to craft a clearcut guideline and operational protocol to guide the aqua silviculture venture.  Maybe, a root cause analysis of the previous production cycle should be carried-out to highlight lessons and undertake corrective measures. The association must be reorganized, reoriented and retooled.
Philippine English Modules During Covid-19 Pandemic: Its Gender Sensitiv-ity and Responsiveness
Authors: Aguilar-Delavin Elreen
Published: September 2022

The purpose of this research is to evaluate the manifestations of gender bias in DepEd Secondary English Modules. The distribution of characters and the occurrence of gender bias have been determined in self-learning modules (SLMs). In this research, both quantitative and qualitative methods of content analysis were used. These strategies were applied using the methodology given by Brugeilles and Cromer in their paper Analyzing Gender Representations in School Textbooks. The DepEd Region V English Self-Learning Modules for grades seven through ten were used in this research as part of the K-12 curriculum. It was discovered that there was a gender imbalance in secondary English SLMs along with pictures and text; females were underrepresented as men were represented twice in the modules; gender stereotyping was found to have the highest percentage of the four categories of the manifestations of gender bias, which is alarming to women who may feel that they have less value in society. The absence of women in occupations dominated by males demonstrates their inadequacy and dependence on men. The pedagogical implications of gender-responsive education must thus be emphasized, particularly in the new educational standard. It is advised that policies in Philippine schools be reexamined in order to determine their effectiveness.