bluuebunny/crossref_metadata_2025 huggingface dataset:
Accuracy and Reproducibility of Cytology Triage in a HPV-Based Primary Screening Setting: A Revision of 384 Pap Tests
Authors: Cannistrà Stefania , Carozzi Francesca , Di Stefano Chiara , Matucci Marzia , Pompeo Giampaolo , Gorini Giuseppe , Puliti Donella , Zappa Marco , Sani Cristina , Confortini Massimo
Published: October 2023

Introduction: After the transition toward the HPV-based screening protocol, which has led to an increase in sensitivity, and in order to bring the specificity back to acceptable values, cytology underwent a change of approach, becoming a triage test. For these reasons, in the Tuscany region (after the recommendations of the GISCi document), it was decided to reduce, as much as possible, the use of ASC-US category in cytology triage, classifying these morphological cases as negative for intraepithelial lesion or malignancies (NILM) or LSIL, basing on the grade of nuclear atypia. So, in Italy, in a cytology triage context (HPV primary screening), a modified Bethesda system (TBS) is currently used. The aim of this study was to evaluate the performance of the review activity of 384 cytology triage cases and of the cervical cancer screening indicators (sensitivity and specificity for CIN2+ lesions) using the TBS 2014 or the modified TBS. Materials and Methods: 384 HPV positive cases at one-year recall (192 with a cytology result of NILM both at baseline and at one-year recall; 192 with a cytology result of NILM at baseline but abnormal at one-year recall), all with a histologically confirmed result (128 CIN2+, 256 ≤ CIN1), were selected, and their baseline Pap tests were reviewed in blind mode by 5 expert cytologists. Results: The cytological results of NILM were confirmed for 92.5% and 83.8% of cases using TBS 2014 or modified TBS, respectively. 20/128 CIN2+ cases could have been reported at the baseline cytology triage, causing an anticipatory effect and an improvement in sensitivity of the screening protocol at baseline (+15.6%). Using TBS 2014, the number of false positives more than tripled with respect to the modified TBS 2014, with a significant increase in unnecessary colposcopies (+11.4%). Conclusion: This work demonstrated that a greater expertise of cytologists, acquired during the following 3 years of experience with cytological triage, and a strong IQC system could lead to the identification of a significant number of lesions reported to baseline rather than at one-year recall (diagnostic anticipation).
Serial Quality of Life Assessment around Screening for Familial Intracranial Aneurysms: A Prospective Cohort Study
Authors: Mensing Liselore A. , Atash Keneshka , Rinkel Gabriel J.E. , Ruigrok Ynte M.
Published: October 2023

Introduction: Screening for intracranial aneurysms (IAs) is cost-effective in first-degree relatives of aneurysmal subarachnoid haemorrhage patients, but its psychosocial impact is largely unknown. Patients and Methods: A consecutive series of persons aged 20–70 years visiting the University Medical Centre Utrecht for first screening for familial IA was approached between 2017 and 2020. E-questionnaires were administered at six time points, consisting of the EQ-5D for health-related quality of life (QoL), HADS for emotional functioning, and USER-P for social participation. QoL outcomes were compared with the general population and between participants with a positive and negative screening for IA. Predictors of QoL outcomes were assessed with linear mixed effects models. Results: 105 participants from 75 families were included; in 10 (10%), an IA was found. During the first year after screening, we found no negative effect on QoL, except for a temporary decrease in QoL 6 months after screening in participants with a positive screen (EQ-5D −11.3 [95% CI: −21.7 to −0.8]). Factors associated with worse QoL were psychiatric disease (EQ-5D −10.3 [95% CI: −15.1 to −5.6]), physical complaints affecting mood (EQ-5D −8.1 [95% CI: −11.7 to −4.4]), and a passive coping style (EQ-5D decrease per point increase on the Utrecht Coping List −1.1 [95% CI: −1.5 to −0.6]). Discussion and Conclusion: We did not find a lasting negative effect on QoL during the first year after screening for familial IA. Predictors for a worse QoL were psychiatric disease, physical complaints affecting mood, and a passive coping style. This information can be used in counselling about familial IA screening.
Retinal Capillary Nonperfusion in Preclinical Diabetic Retinopathy
Authors: Santos Torcato , Santos Ana Rita , Almeida Ana Catarina , Rocha Ana Cláudia , Reste-Ferreira Débora , Marques Inês Pereira , Cunha-Vaz Martinho António , Mendes Luís , Foote Katharina , Cunha-Vaz José
Published: October 2023

Introduction: The aim of the study was to identify retinal microvascular changes using optical coherence tomography angiography (OCTA) in type 2 diabetes (T2D) patients with preclinical retinopathy identified by ultra-widefield fundus photography (UWF-FP). Methods: This is a cross-sectional observational study. All patients underwent UWF-FP 200° examinations with OPTOS California (Optos, Dunfermline, UK) and Cirrus AngioPlex® spectral-domain (SD)-OCTA 3 × 3 mm acquisitions (ZEISS, Dublin, CA, USA). The absence of visible lesions was identified using UWF-FP. Results: One hundred and ninety three eyes of individuals with T2D with no visible lesions in the fundus and identified in a screening setting were included in the study. Skeletonized vessel density (SVD), perfusion density (PD), and areas of capillary nonperfusion (CNP) values on SD-OCTA were significantly decreased when compared with healthy population (p < 0.001). SVD and CNP values of the superficial capillary plexus (SCP) were more frequently decreased (35% and 45%, respectively) than SVD values of the deep capillary plexus (DCP) (9% and 15%, respectively), demonstrating that diabetic microvascular changes occur earlier in the SCP than in the DCP. The ischemic phenotype, identified by a definite decrease in SVD or CNP in the SCP may, therefore, be identified in the preclinical stage of diabetic retinal disease. Conclusions: Retinal capillary nonperfusion detected by OCTA metrics of SVD and CNP can be identified in the central retina in eyes with T2D before development of visible lesions in the retina. Our findings confirm the relevance of OCTA to identify macular microvascular changes in the initial stages of diabetic retinopathy, allowing the identification of its ischemic phenotype very early in the disease process.
Clustering of students admission data using k-means, hierarchical, and DBSCAN algorithms
Authors: Cahapin Erwin Lanceta , Malabag Beverly Ambagan , Santiago Jr. Cereneo Sailog , Reyes Jocelyn L. , Legaspi Gemma S. , Adrales Karl Louise
Published: October 2023

Admissions in the university undergo procedures and requirements before a student can be officially enrolled. The senior high school grades remain the most significant in college admission decisions. This paper presents the use of data mining to cluster students based on admission datasets. The admission dataset for 2019-2020 was obtained from the office of student affairs and services. This dataset contains 2,114 observations with 11 attributes. Data preparation and data standardization were performed to ensure that the dataset is ready for processing and implemented in R programming language. The optimal number of clusters (k) was identified using the silhouette method. This method gave an optimal number of k=2 which was used in the actual clustering using the k-means and hierarchical clustering algorithms. Both algorithms were able to cluster students into two: cluster 1-social sciences or board courses and cluster 2-management or non-board courses. Further, density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm was also used on the same dataset and it yielded a single cluster. This study can be replicated by using at least a 5-year dataset of students’ admission data employing other algorithms that would suggest students’ retention and turn over to board examinations.
Develop a new handling method for selfish nodes in mobile ad-hoc networks
Authors: Al-Shakarchi Sanaa Jafaar Hassan , Alubady Raaid
Published: October 2023

Mobile ad-hoc networks (MANETs) have been a crucial element of next-generation wireless networking technologies during the last decade. Because they allow users to access information and communicate with each other without infrastructure. Selfishness is one of the numerous undesirable behaviors that MANET network nodes may exhibit since this selfish node attempts to safeguard its own resources while accessing the services of other nodes and consuming their resources. Hence, a potential that the network's overall performance may degrade. This study developed a new method named detection, reintroduced, and collaborative of selfish node (DRCSN) that proposed detecting selfish nodes based on two factors: energy and the communication ratio (CR) and handling the rate of selfish nodes. Thus, selfish nodes were exploited to the maximum degree and significantly improve network performance. DRCSN was implemented inside ad-hoc on-demand distance vector (AODV) protocol. The test scenarios were implemented using the network simulator-2 (NS-2); many scenarios were created according to two important network parameters: the number of nodes and movement nodes. The proposed method improved the MANET's performance by increasing both the throughput and packet delivery ratio in the network in addition to that it reduced retransmission rate, delay, and power consumption compared to the related methods.
Adaptation of stochasticity into activation function of deep learning for stock price forecasting
Authors: Patrick Vincent Assunta Malar , Salleh Hassilah
Published: October 2023

Stock market is an example of a stochastic environment in the real world. multilayer perceptron (MLP) is often applied to forecast stock price. However, it is widely used to approximate the input-output mapping deterministically. Hence, this study aims to adapt stochasticity into MLP by introducing the Gaussian process into the sigmoid activation function. In addition, the adapted activation function incorporates Roger-Satchell and Yang-Zhang volatity estimators. Besides, the stochastic activation function was considered as a hyperparameter by applying it either only in training time or in both testing and training time. The stochastic multilayer perceptron (S-MLP) is then applied to forecast one day's highest stock price of eight constituents in FTSE Bursa Malaysia KLCI (FBMKLCI). The result shows that the proposed network is inferior in comparison to MLP except for several constituents. In addition, S-MLP with stochastic activation function during both the training and testing time performs better compared to the presence of stochastic activation function in S-MLP during training time only.
Performance enhancement of 5G uplink MIMO over fading-shadowing, path losses, and ISI using LZF equalize
Authors: Q. Hameed Ashwaq , H. Numan Ali
Published: October 2023

The challenges of the 5G mobile wireless communication systems transmission channels have been changed continuously because of the signal transfer path characteristics such as real-time applications, path loss, multiple users, reflected rays, channel Rayleigh-fading, shadowing, noise, and inter symbol interference (ISI). These challenges have been treated to reach the minimum bit error rate (BER) value of the received data and maximum data rate of high-speed wireless mobile communication systems. In this paper, the linear zero-forcing (LZF) equalizer with uplink multi-input multi-output (MIMO) system has been proposed to enhancement the BER, which is caused by the ISI, Rayleigh fading, shadowing, path losses, and additive white gaussian noise (AWGN). Furthermore, the proposed approach is applied to a different number of antennas constellation to show the effect of increasing the number of antennas on the BER. The results show that the proposed LZF equalizer-MIMO system model has reached lower BER values, and high performance with an increase in the receiver to 8×14, 8×16 and 8×18 antennas constellation at 8, 10, 12, 14, 16, and 18 dB signal to noise ratio (SNR).
Energy efficient clustering-based routing algorithm for internet of things
Authors: Saad Aya , Hegazy Islam , M. El-Horbaty El Sayed
Published: October 2023

Routing process is one of the most critical processes in wireless sensor network (WSN). Due to WSN is mainly used in many applications in internet of things (IoT), routing algorithm can affect the performance of these applications. Thus, the usage of inefficient routing algorithm may lead to losing the data collected by sensors. Moreover, it will cause the sensors to waste energy. This paper proposes an energy efficient clustering-based routing algorithm that is based on tunicate swarm algorithm (TSA). TSAbased clustering algorithm selects the optimal cluster head by calculating the remaining energy, the distance to the base station (BS), the distance to each cluster member and balancing the load between the created clusters. TSAbased routing algorithm is used to create paths from cluster heads to the BS using relay nodes. The TSA-based routing algorithm creates the paths based on the path length, the count of relay nodes in the path, and the number of cluster members of each relay node. The result shows that the proposed algorithm is promising in respect of extending the lifetime of the network and conserving the energy.
Long range technology for internet of things: review, challenges, and future directions
Authors: A. Al-Shareeda Mahmood , Abdullah Alsadhan Abeer , H. Qasim Hamzah , Manickam Selvakumar
Published: October 2023

New networking issues are presented by the increasing need for a wide variety of applications, which has spurred the creation of a new internet of things (IoT) paradigm, such as long range (LoRa). The LoRa protocol uses a patented kind of spread spectrum modulation to provide low-power, long-range communication. In this paper, we provide a comprehensive review of LoRa-IoT in terms of IoT applications, LoRa class, security and privacy requirements, and the evolution of LoRa technology. This review analysis and compares long range wide area network (LoRaWAN) to wireless technology (e.g., Bluetooth, LoRa, 5G, Sigfox, long term evolution-M (LTE-M), Wi-Fi, Z-wave, Zigbee) and provides a list of environment simulators (e.g., OMNeT++, MATLAB, ns-3, SimPy) to carry out experiment for LoRa-IoT. Finally, this review does not only review literature recently studied for LoRa-IoT but also discusses challenges and future directions.
Behavioral drowsiness detection system execution based on digital camera and MTCNN deep learning
Authors: Majeed Ali Hassan , Hussein Ali Adnan , A. Al-Hilali Aqeel , Sameer Jabbar Mohanad , Ghasemi Safiye , G. S. Al-Safi Mohammed
Published: October 2023

Drowsy driving is a major cause of road accidents worldwide, necessitating the development of effective drowsiness detection systems. Each year, there are more accidents and fatalities than ever before for a variety of causes. For instance, there were 22,952 fatalities and 79,545 injuries as a result of nearly 66,500 vehicle accidents in the last 10 years. In this paper, we propose a novel approach for detecting drowsiness based on behavioral cues captured by a digital camera and utilizing the multi-task cascaded convolutional neural network (MTCNN) deep learning algorithm. A high-resolution camera records visual indications like closed or open eye movement to base the technique on the driver's behavior. In order to measure a car user's weariness in the present frame of reference, eyes landmarks are evaluated, which results in the identification of a fresh constraint known as "eyes aspect ratio." A picture with a frame rate of 60 frames per second (f/s) and a resolution of 4,320 eyeballs was used. The accuracy of sleepiness detection was more than 99.9% in excellent lighting and higher than 99.8% in poor lighting, according to testing data. The current study did better in terms of sleepiness detection accuracy than a lot of earlier investigations.
Fake account detection in social media using machine learning methods: literature review
Authors: Kerrysa Nalia Graciella , Utami Ika Qutsiati
Published: October 2023

With the rapid development of emerging technologies in the industrial revolution 4.0 or 5.0, social media has become one of the social environments to carry out social activities, both socializing and advertising. However, since it is an open platform by nature, cybercrime occurrence in social media is inevitable. Currently, more than a million fake accounts are existing on Instagram, Twitter, and Facebook, intending to increase followers, spread hoaxes, and spam. On one hand, it is difficult to manually eliminate these accounts on social media platforms. On the other hand, research on automatic fake account detection has been carried out for more than a decade. This study provides literature reviews aiming to deliver information about several methods and machine learning algorithms with the performances measured in identifying fake accounts on three well-known social media platforms: Twitter, Instagram, and Facebook.
Indoor light energy harvesting technique to energize a heat sensor using polycrystalline solar panel
Authors: Syazana Izzati Razali Nur , Hajar Yusoff Siti , Liza Tumeran Nor , Sharir Fathullah Mohd Yunus Muhammad
Published: October 2023

This paper presents the effect of using different illumination types between the polycrystalline solar panel and the light sources on energy harvesting performance for indoor low-power applications such as heat sensors. The main advantage of indoor energy harvesting is it makes good use of ambient energy from the environment and converts it directly to electricity for small power devices. In this paper, the maximum power of polycrystalline solar panels for four different light illuminations has been investigated under different distances of light sources from the polycrystalline solar panel. Implementation and test results of the effect of varying the distance and the power produced for different light illuminations are presented which highlights the practical issues and limitations of the system.
A novel automated feature selection based approach to recognize cauliflower disease
Authors: Shakil Rashiduzzaman , Akter Bonna , Javed Mehedi Shamrat F M , Haider Noori Sheak Rashed
Published: October 2023

Cauliflower disease is a primary cause of reduced cauliflower yield. Preventing cauliflower disease requires early diagnosis. In the scope of this study, we suggested an agro-medical expert system that would make it easier to diagnose cauliflower disease. In this method, a digital image must be taken off the phone or handled device to diagnose cauliflower sickness. A data augmentation technique was initially used to construct a vast data set. The disease-affected parts of the cauliflower were then segmented using k-means clustering. Following that, ten statistical and gray-level co-occurrence matrix (GLCM) features were retrieved from the segmented pictures. After choosing the top n features (N ranged from 5 to 10), the synthetic minority oversampling technique (SMOTE) approach was used to handle training datasets with different amounts of each feature. After that, we utilized five machine learning (ML) algorithms and evaluated their performance using seven performance evaluation matrices for both augmented and non-augmented datasets. The same procedure was performed on both datasets. Then, we use both datasets to test how well the classifier works. Logistic regression (LR) is the most accurate method for the top nine features in the augmented dataset (90.77%).
Complexity prediction model: a model for multi-object complexity in consideration to business uncertainty problems
Authors: Syah Rahmad B. Y. , Satria Habib , Elveny Marischa , K. M. Nasution Mahyuddin
Published: October 2023

In a competitive environment, the ability to rapidly and successfully scale up new business models is critical. However, research shows that many new business models fail. This research looks at hybrid methods for minimizing constraints and maximizing opportunities in large data sets by examining the multivariable that arise in user behavior. E-metric data is being used as assessment material. The analytical hierarchy process (AHP) is used in the multi-criteria decision making (MCDM) approach to identify problems, compile references, evaluate alternatives, and determine the best alternative. The multi-objectives genetic algorithm (MOGA) role analyzes and predicts data. The method is being implemented to expand the information base of the strategic planning process. This research examines business sustainability along two critical dimensions. First, consider the importance of economic, environmental, and social evaluation metrics. Second, the difficulty of gathering information will be used as a predictor for making long-term business decisions. The results show that by incorporating the complexity features of input optimization, uncertainty optimization, and output value optimization, the complexity prediction model (MPK) achieves an accuracy of 89%. So that it can be used to forecast future business needs by taking into account aspects of change and adaptive behavior toward the economy, environment, and social factors.
Backstepping control with radial basis function neural network for web transport systems
Authors: Ly Tong Thi , Loc Vu Gia , Thang Dao Duc , Hung Pham Van , Huong Trinh Thi Thu , Duc Duong Minh
Published: October 2023

Web transport system (WTS) is commonly used in the production and handling of web materials such as paper, fabric, corrugated iron, steel, and printing operations. These materials are easily damaged if the process performance is poor. Therefore, high technology in mechanics and precise control techniques are required in this system. In addition, because of parameter variation, strong nonlinearity, and many external noises, there are many challenges in controlling this process. This paper proposes a backstepping technique-based algorithm to control the web’s tension and velocity. To solve the parameter variation, a radial basis function (RBF) neural network-based adaptation algorithm is developed to approximate the varied components in the control algorithm. The system stability is guaranteed using the Lyapunov stability theorem. Simulations in MATLAB/Simulink have been done and the effectiveness of the proposed control algorithm is verified. Tension and velocity tracking can be obtained with parameter variations.
Detection of Indonesian wildlife sales and promotion through social media using machine learning approach
Authors: Lestarini Dinda , Rusdy Taufiqurrahman , Iriyani Silfi , Raflesia Sarifah Putri
Published: October 2023

Social media is one of the communication media that is widely used in the digital era as it is today. The use of social media allows people who are far apart to communicate and exchange media, both voice, video, and images quickly and even in real-time. In the past, the sale of protected animals was mostly done on the black market, usually involving a supply chain between sellers that usually existed in traditional markets or certain communities. With the existence of social media, the trend in conducting transactions and promoting wild animals has shifted from traditional to modern thanks to the support of existing technology. Protected wild animals are of concern to the local government or the global world to protect their existence. Therefore, this research proposes a machine learning (ML) based approach to detect the promotion and sale of wild animals on social media. The implementation of Naïve Bayes classifier (NBC) has a high accuracy in detecting trade in wild animals on social media with an accuracy value of 86. The implementation of ML-based approach is expected to produce new technology that allows authorities to know and monitor social media in order to reduce the sale and promotion of protected wildlife.
Enhancement of medical images diagnosis using fuzzy convolutional neural network
Authors: Mahdi Huda Ali , Shujaa Mohamed Ibrahim , Zghair Entidhar Mhawes
Published: October 2023

Brain diseases are primarily brought on by abnormal brain cell growth, which can harm the structure of the brain and eventually result in malignant brain cancer. Major challenges exist when using a computer aided diagnosis (CAD) system for an early diagnosis that enables decisive treatment, particularly when it comes to the accurate detection of various diseases in the pictures for magnetic resonance imaging (MRI). In this study, the fuzzy convolutional neural networks (FCNN) were proposed for accurate diagnosis of brain tumors (glioma, meningioma, pituitary and non-tumor) which is implemented using Keras and TensorFlow. This approach follows three steps, training, testing, and evaluation. In training process, it builds a smart model and the structure consists of seven blocks (convolution, rectified linear unit (ReLU), batch normalization, and max pooling) then use flatten, fuzzy inferences layer, and dense layer with dropout. An international dataset with 7,022 brain tumor MRI images, was tested. The evaluation model attained a high performance with training accuracy of 99.84% and validation accuracy is 98.63% with low complexity and time is 58 s per epoch. The suggested approach performs better than the other known algorithms and may be quickly and accurately used for medical picture diagnosis.
Realization of fractional order lowpass filter using different approximation techniques
Authors: Krishna Battula Tirumala , Janarthanan Midhunchakkaravarthy
Published: October 2023

Many research groups are starting to pay serious attention to the problem of fractional-order circuits. In this paper, a new approach to designing fractional order low-pass filter (FOLPF) is presented. Finding a rational approximation of the fractional Laplace operator sα is a crucial step in the design of fractional order filters. A comparative study of the most widely used approximation techniques named continued fraction expansion (CFE) method and Biquadratic Approximation (RE) method is performed. Then the transfer function of the proposed FOLPF is calculated. Using operational amplifier, the proposed filter is synthesized. The proposed circuit is simulated using Texas instruments TINA software. The results obtained outperform the existing methods.
Classification and keyword extraction of online harassment text in Thai social network
Authors: Hemtanon Siranuch , Phetkrachang Ketsara , Yangyuen Wachira
Published: October 2023

Online harassment in social network services (SNS) is a type of cyberbullying issue that needs to be addressed and required preventive measures. In this paper, we develop a detection of cyberbullying regarding harassment textual posts in Thai on the Facebook SNS. We collect public posts and ask experts to label the post as positive or negative regarding harassment posts or not. The annotated data are trained for binary classification considering words in the centre as features to predict malicious intent to insult and threaten other users. The information gain score obtained in generating a prediction model is ranked for the top 20 words with the highest score as significant words involving online harassment. From experiments, the results show that the detection performance obtained a 0.78 f1 score on average. The result analysis indicated that the word surface approach helps detect insulting post decently, but some posts with metaphor to tone down the malicious intent may not be detected as harmful semantic intent are hidden behind word form. Top-20 significant words for bullying showed that bullying posts were body-shaming and lower social status.
Session to session transfer learning using regularized four parameters common spatial pattern method
Authors: Alhakeem Zaineb M. , Ali Ramzy S.
Published: October 2023

Brain computer interface (BCI) has many useful applications to help disabled people that have an active brain with difficulties in movements and speaking. One of these applications is the wheelchair, this device is operated always by just one user no sharing or borrowing the device. One-user applications need features extraction methods with high classification accuracy and small training datasets, the variability of the subjects’ mood during the recorded sessions and the tiredness during the long sessions are serious problems that affect the classification accuracy in these applications. Transfer learning can solve the problem, by recording short and separated sessions for the same subject in different training times or days. The proposed method in this paper uses motor imagery (MI) signals from different recorded sessions by one user to build an acceptable size training dataset. To regularize different recording sessions, four tuning parameters that are independent from each other are generated using a loop, these parameters are used to find the ratios of the covariance matrices. The suggested method gives very good performance using a different number of training samples compared with six different common spatial patterns (CSP) methods using only two channels.
On-off attack detection in trust model using intra-daily variability for the IoT
Authors: Kannan Sornalakshmi , Venkataraman Revathi , Ramachandran Gowri Sankar
Published: October 2023

The growth of the internet of things (IoT) increases the need to develop the trust computational model for heterogeneous networks with various IoT devices. Trust models are considered as an effective tool to mitigate insider attacks induced by IoT devices. However, trust models are exposed to on-off attacks, in which devices randomly exhibit good and bad behaviors to avoid being categorized as low-trust devices. The objective of this work is to recognize the malicious devices executing on-off attacks in IoT applications. This paper introduces an on-off attack detection strategy for the trust computational model based on the non-parametric index named intra-daily variability (IV). IV indicates trust fragmentation which depends on the frequency and the transitions between periods of low and high trust values of a device. The higher value of IV indicates the occurrence of fragmented trust values and the lower value of IV indicates the occurrence of non-fragmented trust values. Experimental results show that the proposed model outperforms the baseline methods by increasing the on-off attack detection rate.
Tel-MPLS: a new method for maximizing the utilization of IP telephony over MPLS networks
Authors: M. Abualhaj Mosleh , M. Al-Zyoud Mahran , Abu-Shareha Ahmad Adel , O. Hiari Mohammad , Y. Shambour Qusai
Published: October 2023

Currently, the multiprotocol label switching (MPLS) standard is extremely prevalent. By exploiting the features provided by MPLS technology, a range of services, including IP telephony, have enhanced their overall performance. However, due to the size of the packet header, the IP telephony service consumes a significant portion of the MPLS network's available bandwidth. For instance, in IP telephony over MPLS networks, the packet header might account for as much as 80% of lost time and bandwidth. Designers working on IP telephony are making substantial efforts to address this issue. This study contributes to current efforts by proposing a novel approach called Tel-MPLS, which involves IP telephony over MPLS. TelMPLS approach uses the superfluous fields in the IP telephony packet's header to retain the packet data, therefore lowering or zeroing the IP telephony packet's payload. Tel-MPLS is an approach that significantly reduces the bandwidth of IP telephony MPLS networks. According to the findings, the Tel-MPLS approach is capable of reducing the amount of bandwidth that is lost by 12% when using the G.729 codec.
Two-way differential strategy for wireless sensor networks
Authors: Alabed Samer , Alsaraira Amer , Mostafa Nour , Al-Rabayah Mohammad , Shdefat Ahmed , Zaki Chamseddine , A. Saraereh Omar , Al-Arnaout Zakwan
Published: October 2023

In this paper, a novel optimal two-way amplify and forward (AF) differential beamforming method for wireless sensor network is proposed. The proposed method is an advanced signal processing technique used to enhance the performance and reliability of the communication link by exploiting the diversity provided by multiple antennas. Unlike current state-of-the-art methods which require the knowledge of channel state information (CSI) at both transmitting and receiving antennas or at least at the receiving antennas, the suggested method does not need CSI at any transmitting or receiving antenna. Moreover, the proposed method enjoys high error performance with high diversity and coding gain and has a very low encoding and decoding complexity. Through our simulations, the proposed method is proved to outperform the best known non-coherent multi-antenna methods.
Evaluation of three consumers' contribution to the current and voltage distortions at the point of common coupling
Authors: Shklyarskiy Yaroslav , Dobush Iuliia , Sutikno Tole , Hatta Jopri Mohd
Published: October 2023

The article presents the method to evaluate share responsibility for the generation of harmonic currents between utility and consumer that are connected to the same point of common coupling (PCC). For these purposes, an indicator that evaluates the consumer's contribution to the distortion of current and voltage at the PCC is proposed, based on the previously carried out mathematical modeling of the power supply system (PSS) with two consumers. An evaluation of the distortion caused by three different non-linear loads on the consumers’ side is carried out in this study. The approbation of the proposed method was carried out on a laboratory bench, where various operation modes of linear and nonlinear loads were studied. Thus, the results proved that the proposed method could quantify the contribution of voltage and current distortion caused by three and more non-linear loads at the PCC of the consumer’s side. Besides, this proposed method can also be used to draw up recommendations for the mitigation of the harmonic currents compensation.
A highly scalable CF recommendation system using ontology and SVD-based incremental approach
Authors: Mhammedi Sajida , Gherabi Noreddine , El Massari Hakim , Sabouri Zineb , Amnai Mohamed
Published: October 2023

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.
Implementation of the Lamikro Application in the Preparation of J'Ramy Farm Financial Statements in accordance with SAK EMKM
Authors: Pratiwi Ratih , Dahliani Lili , Febrinda Andi Early , Merdekawati Eka
Published: October 2023

J'Rami Farm is a dairy farm located in Bogor. In financial management, the owner manually records sales transactions of milk, dairy products as well as expenditures for the purchase of feed, tofu dregs and employee salaries. Meanwhile, expense transactions for overhead costs such as electricity costs, fuel costs, depreciation costs and trade receivables have not been recorded routinely and completely. This causes the financial condition is not known with certainty, because every month the owner has not made a financial report. The purpose of this study was to analyze the implementation of the Lamikro application (micro business accounting reports) in the presentation of financial statements based on SAK EMKM. The Lamikro application is an application created by the Ministry of Cooperatives and SMEs which is expected to assist business people in making financial reports independently and according to SAK EMKM standards. The research methodology used in this study is a qualitative method through interviews and observations. The results showed that the use of the Lamikro application was able to help J'Rami Farm to present bankable financial reports and in accordance with Accounting Standards. The obstacle faced by the owner in using the application is the lack of understanding of accounting, while the limitation of the Lamikro application is that the user cannot edit and add accounts that match the business character of a business.
ПОПЕРЕДНІ РЕЗУЛЬТАТИ ЗАСТОСУВАННЯ КОМП'ЮТЕРИЗОВАНОГО УПРАВЛІННЯ ДИХАННЯМ ЯК ДОПОМІЖНОГО МЕТОДУ ПСИХОЛОГІЧНОЇ РЕАБІЛІТАЦІЇ
Authors: Шарипанов А.В. , Вишневський В.В. , Чайковський І.А. , Сивак О.В. , Пукавська Л.А.
Published: October 2023

Актуальність проведення досліджень такого типу обумовлена великою кількістю людей, які потребують психологічної реабілітації у наслідок пандемії COVID-19 та війни в Україні. Метою роботи ставилося проведення перевірки ефективності застосування автоматизованої технології управління диханням під час консультацій психологів із клієнтами, які мають високий рівень тривожності.
Дихальні техніки, у тому числі й йогівські, вже давно відомі як такі, що не мають протипоказань для фізично здорової людини. Дієвість дихальних формул, що застосовуються під час дихальних тренувань, наприклад, для заспокоєння та розслаблення, перевірено часом. Ефективність йогівських дихальних технік пов'язана зі здатністю йогів «чути власний пульс», що досягається протягом багаторічних тренувань. Корисність прив'язки такої активності до внутрішніх ритмів конкретної людини пояснюється науковими напрямами «хронобіологія» та «хрономедицина». Однак більшість людей, які б могли отримувати максимальну користь від дихальних тренувань, не мають час на подібне навчання, бо потребують негайної допомоги. У своїй роботі психологи навчають клієнтів навикам стабілізації та саморегуляції, а також їх практичного застосування у повсякденному житті для швидкого покращення свого психоемоційного стану завдяки власним фізіологічним особливостям і не залучаючи при цьому додаткові ресурси. Клієнтам пропонується застосовувати техніку абдомінального дихання по квадрату або 4-7-8. При цьому інтервали часу вимірюються в абсолютних одиницях, як то секунди, кроки тощо. Широке розповсюдження мобільних пристроїв і недорогих бездротових кардіореєстраторів дозволило реалізувати автоматизовану технологію управління диханням, у якій дихальні формули персоналізуються ударами пульсу, що визначаються в режимі реального часу. Представлено попередні результати моніторингового дослідження в якому комп'ютеризоване управління диханням було впроваджено як додатковий допоміжний метод під час психологічної реабілітації.
Abstract 011: Cluster Of Differentiation 14 (CD14) Inhibits NADPH Oxidase 2 (NOX2)-Derived Oxidative Stress In Hematopoietic Cells And Modulates Dahl Salt-Sensitive Hypertension And Renal Damage
Authors: Burns Emily C , Dasinger John H , Shaw Mary , Walton Samuel D , Abais-Battad Justine M , Mattson David L
Published: October 2023

We recently discovered that genetic deletion of CD14 in Dahl SS rats (CD14-/-) amplifies the development of salt-sensitive hypertension and renal damage in females. Interestingly, the expression of CD14 in leukocytes and the level of plasma CD14 protein are increased in cardiovascular disease and hypertension in humans. An examination of macrophages from CD14-/- females revealed enhanced production of superoxide from NOX2 compared to Dahl SS. Furthermore, a double knockout Dahl SS strain lacking both CD14 and functional NOX2 (DKO) exhibits significantly reduced salt-induced hypertension and associated renal damage, indicating that free radical production from NOX2 is likely mediating the amplification of disease following CD14 deletion. The present study examined the specific effects of CD14 and NOX2 deletion in hematopoietic cells via a total body irradiation/bone marrow transfer (TBI/BMT) approach. Female Dahl SS recipients were irradiated (11Gy) then received either CD14 or DKO bone marrow transplantation via tail vein injection. Rats that received DKO hematopoietic cells had blunted mean arterial pressure (147±4 mmHg, n=3, p=0.052) versus those that received CD14-/- (179±19mmHg, n=3) in response to a 3-week high salt challenge (HS, 4.0% NaCl, AIN-76A). As an index of renal damage, albuminuria was significantly reduced in the DKO (110.3±7 mg/day, n=3, p<0.01) compared to the CD14-/- (280.9±42 mg/day, n=3) at HS day 21. Flow cytometric analysis of isolated renal immune cells indicated a reduction in renal inflammation in the DKO compared to the CD14-/-. There were fewer CD45+ total leukocytes (2.8x10
6
vs 3.8x10
6
cells/kidney, p<0.05) and CD11b/c+ monocytes/macrophages (2.4x10
6
vs 3.3x10
6
cells/kidney, n=3, p<0.05) in DKO versus CD14-/- kidneys. Finally, to document the TBI/BMT, macrophages from CD14-/- rats produced 7-fold greater superoxide from NOX2 when stimulated with 12-phorbol myristate 13-acetate compared to DKO macrophages. Together, these data suggest a regulatory role of CD14 in hematopoietic cells to reduce oxidative stress from NOX2 in the kidney of female Dahl SS rats during salt-sensitive hypertension and associated renal disease.
Abstract 019: Perimenopause Exacerbates Hypertension In Doca-salt Rats
Authors: Pestana-Oliveira Nayara , Ruiz Lauar Mariana , Almutlaq Rawan , de la Cruz-Lynch Arthur T , Ross Jaryd , Van Helden Dusty A , Dayton Alex , Evans Louise C , Keller-Ross Manda L , Osborn John W
Published: October 2023

Blood pressure (BP) is lower in premenopausal women than in age-matched men, but after menopause BP rates and the incidence of hypertension (HTN) show opposite tendencies. The mechanisms by which fluctuating ovarian hormone environment during that period impacts the onset and development of HTN are poorly understood, and strategies to reduce HTN risk in peri- and postmenopausal females are lacking. An ovary-intact rat model of perimenopause using the chemical, 4-vinylcyclohexene diepoxide (VCD), has been developed to accelerate the natural process of slow follicular loss. Daily dosing with VCD for 15 days selectively destroys primordial and primary follicles in ovaries of mice and rats by accelerating atresia processes, thus inducing precocious perimenopause/menopause.

Hypothesis:
The induction of perimenopause by VCD will increase susceptibility to the development of HTN in DOCA-salt rats. Female Sprague-Dawley rats at post-natal day 28 received subcutaneous injection of VCD (160mg/kg) or corn oil (control) for 15 days. 57 days after the first injections, nephrectomy and telemetric pressure transducer implantation were performed. After 2 weeks of recovery, a 100 mg/kg DOCA silicone or placebo pellet was implanted subcutaneously. The rats that received DOCA were provided 0.9% saline during the 21 days of DOCA protocol and mean arterial pressure (MAP) was continuously evaluated. Rats were housed weekly in metabolic cages for 24 hours to measure saline intake and urine volume. After protocol completion the animals were decapitated, and blood was collected for the measurement of anti-Mullerian hormone (AMH) and estradiol (E2) by ELISA. The AMH plasma levels were reduced in the periestropause rats (VCD + DOCA, n=9) compared to oil (Oil + DOCA, n=6) confirming the VCD-induced follicular depletion. The steady state increase in MAP (day 21 of DOCA-salt) was higher in periestropause (±45.7mmHg) compared to oil (±28.6 mmHg). Urine volume was higher in periestropause rats compared to oil. No differences were observed in the saline intake between the groups. Our data demonstrate that periestropause rats showed greater susceptibility to the development of HTN in a DOCA-salt rat model.
Abstract 022: Il-33 Supplementation Improves Vascular Function And Maternal Hypertension In Response To Placental Ischemia
Authors: Wang Xi , Shields Corbin , Tardo Geilda , Peacock Gregory , Williams Jan M , Cornelius Denise C
Published: October 2023

Preeclampsia (PE), a leading cause of maternal/fetal morbidity and mortality, is a hypertensive pregnancy disorder with end-organ damage that manifests after 20 weeks of gestation. PE is characterized by chronic immune activation and endothelial dysfunction. Clinical studies report reduced IL-33 signaling in PE. We use the
R
educed
U
terine
P
erfusion
P
ressure (RUPP) rat model, which mimics many PE characteristics including reduced IL-33, to identify mechanisms mediating PE pathophysiology. We hypothesized IL-33 supplementation would improve blood pressure (BP), inflammation, and oxidative stress (ROS) during placental ischemia. We implanted i.p. mini-osmotic pumps infusing recombinant rat IL-33 (1 μg/kg/day) into normal pregnant (NP) and RUPP rats from gestation day 14-19. We assessed maternal BP, cytolytic NK (cNK) and T
H
17 cells, cNK cell activity, ROS, preproendothelin-1 (PPET) expression, and uterine artery resistance index (UARI). MAP (mmHg) was 104±4 in NP vs 124±7 in RUPP (p<0.05 vs NP) and lowered to 108±10 in RUPP+IL-33 (p<0.05 vs RUPP, n=8/group). Circulating cNKs (% gated) were 0.9±0.2% in NP vs 3.9±1.1% in RUPP (p<0.05 vs NP) and decreased to 1.1±0.6% in RUPP+IL-33 (p<0.05 vs RUPP, n=8/group). Circulating T
H
17s (%CD4
+
T cells) were 1.0±0.6% in NP vs 7.2±0.5% in RUPP (p<0.05 vs NP) and 0.8±0.4% in RUPP+IL-33 (p<0.05 vs RUPP, n=8/group). Renal T
H
17s, placenta cNKs and T
H
17s followed similar trends. cNK cell activity (fold change) was 3.92±0.6 fold higher in RUPP vs NP (p<0.05 vs NP) and decreased to -0.24±0.1 in RUPP+IL-33 (p<0.05 vs RUPP, n=3/group). Renal ROS (relative light units/min/mg protein) was 452±33 in NP vs 676±44 in RUPP (p<0.05) and decreased to 258±50 in RUPP+IL-33 (p<0.05 vs RUPP, n=5/group). Placental PPET (fold change) was 2.36±0.4 fold higher in RUPP vs NP (p<0.05) and decreased to 1.22±0.2 in RUPP+IL-33 (p=0.05 vs RUPP, n=6-7/group). UARI (arbitrary units) was 0.71±0.01 in RUPP vs 0.54±0.01 in NP (p<0.05) and was lowered to 0.57±0.02 in RUPP+IL-33 (p<0.05 vs RUPP, n=6/group). These findings demonstrate a role for IL-33 in controlling vascular function and maternal BP during pregnancy by decreasing inflammation, ROS, and PPET expression. These data suggest IL-33 may have therapeutic potential in managing PE.