bluuebunny/crossref_metadata_2025 huggingface dataset:
A Single Mutation of VP2 is Responsible for the Lethality and Antigenicity Differences between Novel Variant and Very Virulent IBDV Strains
Authors: Jiang Nan , Wang Guodong , Zhang Wenying , Wang Yulong , Niu Xinxin , Huang Mengmeng , Gao Li , Li Kai , Cui Hongyu , Liu Changjun , Zhang Yanping , Bao Keyan , Wang Suyan , Chen Yuntong , Wang Xiaomei , Gao Yulong , Qi Xiaole
Published: November 2023

Infectious bursal disease is an acute, immunosuppressive infectious disease in chickens caused by the infectious bursal disease virus (IBDV), which causes huge economic losses to the global poultry industry. Persistently circulating very virulent IBDV (vvIBDV) and newly emerging novel variant IBDV (nVarIBDV) are the two dominant epidemic strains of IBDV in East Asian countries such as China. Compared to lethal vvIBDV, nonlethal nVarIBDV has more insidious pathogenicity and can partially escape the immune protection of the existing vvIBDV vaccine, suggesting its potential adaptive survival strategy. However, the underlying molecular mechanism remains unknown. The viral capsid protein VP2 is closely related to cell tropism, virulence, and antigenic variation of IBDV. In this study, for the first time, we demonstrated that residue 279 of VP2 is responsible for the difference in pathogenicity between nVarIBDV and vvIBDV and that the D279N substitution reduces the lethality of vvIBDV from 70% to 0%. Moreover, a significant reduction in the viral load and inflammatory factor levels in the immune organs and blood of infected chickens may be important mechanisms for reducing the lethality of IBDV. Additionally, residue 279 was an important molecular basis for the antigenic differences between nVarIBDV and vvIBDV. D279N substitution reduced the neutralizing ability of vvIBDV antiserum against nVarIBDV by affecting the binding ability of antibodies and antigens. Our results indicate that nVarIBDV has an infection transmission strategy that facilitates its survival by hiding viral pathogenicity and escaping antiserum neutralization, which not only has significant implications for the systemic cognition of viral genetic evolution and pathogenesis but also provides new ideas for the comprehensive prevention and control of IBDV.
Employing Atrous Pyramid Convolutional Deep Learning Approach for Detection to Diagnose Breast Cancer Tumors
Authors: Sadeghi Pour Ehsan , Esmaeili Mahdi , Romoozi Morteza
Published: November 2023

Breast cancer is among the most common diseases and one of the most common causes of death in the female population worldwide. Early identification of breast cancer improves survival. Therefore, radiologists will be able to make more accurate diagnoses if a computerized system is developed to detect breast cancer. Computer‐aided design techniques have the potential to help medical professionals to determine the specific location of breast tumors and better manage this disease more rapidly and accurately. MIAS datasets were used in this study. The aim of this study is to evaluate a noise reduction for mammographic pictures and to identify salt and pepper, Gaussian, and Poisson so that precise mass detection operations can be estimated. As a result, it provides a method for noise reduction known as quantum wavelet transform (QWT) filtering and an image morphology operator for precise mass segmentation in mammographic images by utilizing an Atrous pyramid convolutional neural network as the deep learning model for classification of mammographic images. The hybrid methodology dubbed QWT‐APCNN is compared to earlier methods in terms of peak signal‐to‐noise ratio (PSNR) and mean square error (MSE) in noise reduction and detection accuracy for mass area recognition. Compared to state‐of‐the‐art approaches, the proposed method performed better at noise reduction and segmentation according to different evaluation criteria such as an accuracy rate of 98.57%, 92% sensitivity, 88% specificity, 90% DSS, and ROC and AUC rate of 88.77.
Mathematical Analysis of the Impact of French Standardization Strategy and Electric Vehicle Development on the Breakthrough Development of EVs in China
Authors: Wu Linfeng , Liu Junhui , Liu Peng , Cong Dan
Published: November 2023

The problems of operating range and costs are the two most critical bottlenecks restricting the extensive application of electric vehicles (EVs) in China and some other countries. There are also some prominent problems in China’s EVs, which lead to poor competitiveness of EVs compared with traditional internal combustion engine vehicles. This paper analyzes the key mathematical factors restricting the development and popularization of EVs in China from the aspects of strategic policy, sales situation, and self‐problems. For the best‐selling family cars in China, select the representative models with the same body structure, and make statistics on various parameters most concerned by consumers, establish a database through data statistics and analysis software. Through summarizing France’s standardization development strategy and relevant issues reflected by the development data of its EVs, this paper puts forward the hypothesis leading the development of EVs through standardization to enhance their competitiveness, gives the specific suggestions, and briefly analyzes the feasibility from the aspects of product situation. The research content of this paper provides a certain basis and ideas for the future research work.
Evaluating the Utility of REVEL and CADD for Interpreting Variants in Amyotrophic Lateral Sclerosis Genes
Authors: Fiorini Michael R. , Dilliott Allison A. , Farhan Sali M. K.
Published: November 2023

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease affecting approximately two per 100,000 individuals globally. While there are many benefits to offering early genetic testing to people with ALS, this has also led to an increase in the yield of novel variants of uncertain significance in ALS-associated genes. Computational (in silico) predictors, including REVEL and CADD, are widely employed to provide supporting evidence of pathogenicity for variants in conjunction with clinical, molecular, and other genetic evidence. However, in silico predictors are developed to be broadly applied across the human genome; thus, their ability to evaluate the consequences of variation in ALS-associated genes remains unclear. To resolve this ambiguity, we surveyed 20 definitive and moderate ClinGen-defined ALS-associated genes from two large, open-access ALS sequencing datasets (total people withALS=8,230;controls=9,671) to investigate REVEL and CADD’s ability to predict which variants are most likely to be disease-causing in ALS. While our results indicate a predetermined pathogenicity threshold for REVEL that could be of clinical value for classifying variants in ALS-associated genes, an accurate threshold was not evident for CADD, and both in silico predictors were of limited value for resolving which variants of uncertain significance (VUS) may be likely pathogenic in ALS. Our findings allow us to provide important recommendations for the use of REVEL and CADD scores for variants and indicate that both tools should be used with caution when attempting to evaluate the pathogenicity of VUSs in ALS genetic testing.
Multiantenna Clustering Collaboration for WPCNs Based on Nonlinear EH
Authors: Yuan Lina , Chen Huajun , Zhou Anran , Wang Rong , Wen Xianli
Published: November 2023

This article considers a wireless-powered communication network (WPCN) composed of a multiantenna hybrid access point (HAP) based on nonlinear energy harvesting (EH). To improve some distant WDs’ throughput performance, one of them is allowed to be selected as a cluster head (CH) to help transfer information from other cluster members (CMs). Nevertheless, the proposed clustering collaboration’s performance is essentially restricted by the CH’s energy-intensive consumption (EC), which requires to transfer every WDs’ information, covering its own. In order to figure out the question, the HAP’s energy beamforming (EB) capability with multiple antennas is utilized that can concentrate greater transmission power into the CH to equilibrate its EC to assist other WDs. To be specific, each WD’s throughput performance is firstly derived under the proposed approach. A high-efficiency optimization algorithm for addressing cooperative optimization problem is put forward. In addition, the simulations are carried out in the actual network environment, and the results demonstrate that our proposed clustering collaboration with multiple antennas can validly enhance the WPCN’s throughput fairness based on nonlinear EH.
Synchronous Primary Lung Cancers Containing Discrete Driver Mutations in a Never-Smoker: Case Report
Authors: Davis Andrew , Jarrar Sophia , Ciunci Christine
Published: November 2023

Although most lung cancer patients present with one primary cancer, some present with multiple lung cancers of different clonal origin. Timely recognition of synchronous multifocal primary lung cancer (MPLC) enables distinct treatment regimens that reflect the unique genotypic makeup and location of each cancer. However, recognition of synchronous MPLCs is challenging given the prevalence of multifocal disease. Here, we report a case of a patient diagnosed with anaplastic lymphoma kinase, termed ALK, positive metastatic lung adenocarcinoma whose follow-up computerized tomography (CT) imaging identified one lesion, present since the patient’s initial presentation, with a distinctly different response to treatment than other lesions. Biopsy results showed a distinct MPLC, an epidermal growth factor receptor (EGFR)-positive adenocarcinoma with no evidence of an ALK mutation. The EGFR lesion was treated with curative intent via surgical resection while the ALK disease was managed with palliative intent via targeted therapy. To our knowledge, there have been no other reports of two synchronous MPLCs of an adenocarcinoma subtype with completely distinct EGFR and ALK driver mutations. This case highlights the importance of serial follow-up imaging, combined with biopsy of lesions with atypical treatment responses, as a method for identifying synchronous MPLCs and adjusting treatment to optimize patient outcomes.
Tumor Lysis Syndrome Is Associated with Worse Outcomes in Adult Patients with Acute Lymphoblastic Leukemia
Authors: Rios-Olais Fausto A. , Gil-Lopez Fernando , Mora-Cañas Analy , Demichelis-Gómez Roberta
Published: November 2023

Introduction: Tumor lysis syndrome (TLS) occurs frequently during induction therapy for acute lymphoblastic leukemia (ALL). Patients are categorized into intermediate- or high-risk based on the lactate dehydrogenase (LDH) value and white blood cell (WBC) count, according to an expert panel, although no effort has been made to analyze TLS in ALL and its potential consequences. Methods: We retrospectively analyzed TLS, variables associated with its occurrence, and its impact on overall survival (OS) and mortality during induction in a cohort of ALL patients in their first induction regimen. Results: A total of 138 patients were included, 52.9% were male and the median age at diagnosis was 34 years. Most of them were treated with hyper-CVAD (39.1%) or a modified CALGB 10403 regimen (37.7%). TLS was identified in 42 patients (30.4%), and half of them fulfilled criteria for clinical TLS (C-TLS). Median OS was the lowest in C-TLS patients. An LDH 3 times greater than its upper laboratory normal (ULN) value and a WBC count equal to or greater than 50×109/L were associated with TLS development, and being male, hyperuricemia and an LDH 3 times greater than its ULN value were associated with C-TLS development. C-TLS and acute kidney injury were associated with excess mortality during induction. Conclusion: TLS was identified in almost one-third of ALL patients during induction therapy. Different thresholds for LDH value and WBC count as well as other variables could identify patients at risk of developing this complication, which is associated with shorter OS. C-TLS confers a higher risk for mortality during induction.
Natural Compounds with Antimicrobial Activities in Oral Candida Infections during Head and Neck Radiotherapy
Authors: Imperia Elena , Bonincontro Graziana , Altomare Annamaria , Simonetti Giovanna , Gherardi Giovanni , Brasili Elisa , Badiali Camilla , Bove Manuela , Pasqua Gabriella , De Gara Laura , Cicala Michele , Guarino Michele Pier Luca
Published: November 2023

Background: Oral colonization and infections are frequently observed in patients during and soon after radiation therapy. Infective mucositis is a common side effect associated with cancer therapy, characterized by an inflammation of the oral mucous membranes with histological mucosal and submucosal changes. Ulcerative mucositis is responsible for significant pain, impairing the patient’s nutritional intake and leading to local or systemic infections promoting mycosis due to several species of the genus Candida. According to international guidelines, treatment of candidiasis depends on the infection site and patient’s condition. Summary: Recently, several studies have shown the protective role of natural compounds counteracting the activity of Candida biofilms. The aim of this review was to discuss the antimicrobial activities of natural compounds in fungal infections, especially Candida spp., during and soon after radiotherapy. Indeed new molecules are being discovered and assessed for their capacity to control Candida spp. growth and, probably in the future, will be used to treat oral candidiasis, overall, during radiotherapy. This review reports several preliminary data about preclinical and clinical evidence of their efficacy in the prevention and/or treatment of mucositis due to radiotherapy with a brief description of the natural compounds with anti-Candida activities. Key Messages: The increase in the resistance to the available antifungal drugs related to Candida spp. infections increased as well as drug interactions, urging the development of innovative and more effective agents with antifungal action. Recent preclinical and clinical studies are identifying natural substances with anti-inflammatory and antifungal activity that could be tested in the prevention of candidiasis in patients undergoing radiotherapy. Further studies are needed to confirm these preliminary data.
Reduced Intra- and Inter-Network Functional Connectivity Identified in Patients with Tinnitus with and without Hearing Loss
Authors: Du Haoliang , Chen Jie , Qian Xiaoyun , Ding Xiaoqiong , Zhang Jian , Liu Bin , Yu Chenjie , Li Ao , Gao Xia , Feng Xu
Published: November 2023

Introduction: The aim of the study was to investigate differences in the intra- and inter-network functional connectivity (FC) of the brain using resting-state functional magnetic resonance imaging (rs-fMRI) in patients with tinnitus, with (T + H) or without hearing loss (T). Methods: We performed rs-fMRI on 82 participants (21 T, 32 T + H, and 29 healthy controls). An independent component analysis (ICA) was performed to obtain the resting-state networks (RSNs) and calculate the differences in FC. Moreover, we investigated the relationships between networks using functional network connectivity analysis. Results: We identified nine major RSNs, including the auditory network; default mode network; executive control network (ECN), including the right frontoparietal network and left frontoparietal network (LFPN); somatomotor network (SMN); dorsal attention network; ventral attention network; salience network (SN); and visual network (VN). These RSNs were extracted in all groups using ICA. Compared with that in the control group, we observed reduced FC between the LFPN and VN in the T group and between the LFPN and SN in the T + H group. The inter-network connectivity analysis revealed decreased network interactions in the SMN (IC 22)-ECN (IC 2), SMN (IC 22)-VN (IC 8), and VN (IC 14)-SN (IC 3) connections in the T + H group, compared with the healthy control group. Furthermore, we observed significantly decreased network interactions in the SMN (IC 22)-VN (IC 8) in the T group. Conclusions: Our results indicated abnormalities within the brain networks of the T and T + H groups, including the SMN, ECN, and VN, compared with the control group. Furthermore, both T and T + H groups demonstrated reduced FC between the LFPN, VN, and SMN. There were no significant differences between the T and the T + H groups. Furthermore, we observed reduced FC between the right olfactory cortex and the orbital part of the right middle frontal gyrus, right precentral gyrus, left dorsolateral superior frontal gyrus, and right triangular part of the inferior frontal gyrus within the T and T + H groups. Thus, disruptions in brain regions responsible for attention, stimulus monitoring, and auditory orientation contribute to tinnitus generation.
Endocavitary Right Ventricular Cardiac Metastasis of a Lung Adenocarcinoma Treated by Surgery: A Case Report About a Novel Multimodal Therapeutic Approach
Authors: Borne Serge , Benoit Nicolas , Douadi Youcef , Carre Olivier , Thielen-Bethembos Stéphanie , Carette De Franssu Hortense , Touati Gilles , Colta Anna-Laura , Patrao David , Delattre Emilie , Monconduit Julien , Piriou-Garoute Camille , Chauffert Bruno , Dayen Charles
Published: November 2023

Introduction: Cardiac metastasis (CM) is a rare lung cancer location. It often remains clinically silent but may cause life-threatening complications. Better survival rates thanks to the immunotherapy revolution and the improving performance of imaging lead to an increasing number of CM diagnosis. Case Presentation: We report a case of a 54-year-old woman who was diagnosed with a stage IIIa non-small cell lung cancer. She developed a right ventricular CM without symptoms during treatment by immunotherapy after concurrent chemoradiotherapy. Cardiac magnetic resonance imaging confirmed the presence of an endocavitary lesion in the right ventricle apex. Complete surgical resection through a right ventriculotomy was performed. Conclusion: The diagnosis of similar cases has become more frequent due to immunotherapy and more advanced imaging technology. Our case report also highlights the fact that CM surgery has to be considered as a successful therapeutic option in those oligo-progression situations. Guidelines on the management and treatment of lung cancer CM are needed as well as larger studies to evaluate the survival benefit from surgical treatment.
Real-time mask-wearing detection in video streams using deep convolutional neural networks for face recognition
Authors: Suhirman Suhirman , Saifullah Shoffan , Hidayat Ahmad Tri , Kusuma M. Apriandi , Drezewski Rafał
Published: November 2023

This research aims to develop a real-time mask-wearing detection system using deep convolutional neural networks (CNNs). This is crucial in the coronavirus disease 2019 (COVID-19) pandemic to alert individuals who are not wearing masks early on, thereby reducing the spread of the virus. Since COVID-19 primarily spreads through respiratory droplets and mask-wearing is recommended, our proposed study utilizes computer vision techniques, specifically image processing, to detect masked and unmasked faces. We employ a customized CNN architecture consisting of five convolutional layers, followed by max-pooling layers and fully connected (FC) layers. The final output layer utilizes softmax activation for classification. The model is updated with optimized layer configurations and parameter values. We are developing an application that uses a digital camera as an input device. The application utilizes a dataset comprising 11,792 image samples, which are used for training and testing purposes with the 80:20 ratio. Real-time testing is conducted using human subjects captured by the camera. The experimental results demonstrate that the CNN method achieves a classification accuracy of 99% on the training data and 98.83% during real-time video testing. These findings suggest that the real-time mask detection system using CNN performs effectively.
Smart city: an advanced framework for analyzing public sentiment orientation toward recycled water
Authors: Bahra Mohamed , Fennan Abdelhadi
Published: November 2023

The coronavirus pandemic of the past several years has had a profound impact on all aspects of life, including resource utilization. One notable example is the increased demand for freshwater, a lifeblood of our planet, on the other hand, the smart city vision aims to attain a smart water management goal by investing in innovative solutions such as recycled water systems. However, the problem lies in the public’s sentiment and willingness to use this new resource which discourages investors and hinders the development of this field. Therefore, in our work, we applied sentiment analysis using an extended version of the fuzzy logic and neural network model from our previous work, to find out the general public opinion regarding recycled water and to assess the effects of sentiments on the public’s readiness to use this resource. Our analysis was based on a dataset of over 1 million text content from 2013 to 2022. The results show, from spatio-temporal perspectives, that sentiment orientation and acceptance-behavior towards using recycled water have increased positively. Additionally, the public is more concerned in areas driven by the smart city vision than in areas of medium and low economic development, where investment in sensibilization campaigns is needed.
Deep learning with filtering for defect characterization in pulsed thermography based non-destructive testing
Authors: Selvan Sethu Selvi , Delanthabettu Sharath , Murugesan Menaka , Balasubramaniam Venkatraman
Published: November 2023

Pulsed thermography is widely used for non-destructive testing of various materials. The temperature profile obtained after pulse heating is used to characterize the underlying defects in an object. In this paper, the automation of the process of defect visualization and depth quantification in pulsed thermography through various deep learning algorithms is reported. Stainless steel plate with artificial defects is considered for analysis. The raw temperature data is smoothed using moving average, Savitzky-Golay and quadratic regression filters to reduce noise. Thermal signal reconstruction, the conventional method to eliminate noise, is also used for generating filtered datasets. Defect visualization refers to identifying and locating the defects in an image sample and Mask region convolutional neural network (Mask R-CNN) is considered for not just detecting the defects but also locating them on the image. The located defects are utilized for depth estimation using the following networks-multi-layer perceptron (MLP), long short-term memory (LSTM) and gated recurrent units (GRU). The input to the networks is the temperature contrast characteristics which symbolizes the difference in temperature over defective and non-defective areas measured over 250 time points and output of the networks is the estimated depth. The study shows that LSTM based approach provides the least percentage error of 5.5% and is a very suitable approach for automation of defect characterization in pulsed thermography.
Efficient network management and security in 5G enabled internet of things using deep learning algorithms
Authors: Poojari Thippeswamy Sowmya Naik , Raghavan Ambika Padinjareveedu , Rajgopal Manjunath , Sujith Annie
Published: November 2023

The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model.
Rapid detection of diabetic retinopathy in retinal images: a new approach using transfer learning and synthetic minority over-sampling technique
Authors: Mustafa Hiri , Mohamed Chrayah , Nabil Ourdani , Noura Aknin
Published: November 2023

The challenge of early detection of diabetic retinopathy (DR), a leading cause of vision loss in working-age individuals in developed nations, was addressed in this study. Current manual analysis of digital color fundus photographs by clinicians, although thorough, suffers from slow result turnaround, delaying necessary treatment. To expedite detection and improve treatment timeliness, a novel automated detection system for DR was developed. This system utilized convolutional neural networks. Visual geometry group 16-layer network (VGG16), a pre-trained deep learning model, for feature extraction from retinal images and the synthetic minority over-sampling technique (SMOTE) to handle class imbalance in the dataset. The system was designed to classify images into five categories: normal, mild DR, moderate DR, severe DR, and proliferative DR (PDR). Assessment of the system using the Kaggle diabetic retinopathy dataset resulted in a promising 93.94% accuracy during the training phase and 88.19% during validation. These results highlight the system's potential to enhance DR diagnosis speed and efficiency, leading to improved patient outcomes. The study concluded that automation and artificial intelligence (AI) could play a significant role in timely and efficient disease detection and management.
Enhancing healthcare services through cloud service: a systematic review
Authors: Guo Bo , Shukor Nur Syufiza Ahmad , Ishak Irny Suzila
Published: November 2023

Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.
Driver-centered pervasive application for heart rate measurement
Authors: Abdul Razak Siti Fatimah , Jun Tong Yong , Yogarayan Sumendra , Sayed Ismail Sharifah Noor Masidayu , Chia Sui Ong
Published: November 2023

People spend a significant amount of time daily in the driving seat and some health complexity is possible to happen like heart-related problems, and stroke. Driver’s health conditions may also be attributed to fatigue, drowsiness, or stress levels when driving on the road. Drivers’ health is important to make sure that they are vigilant when they are driving on the road. A driver-centered pervasive application is proposed to monitor a driver’s heart rate while driving. The input will be acquired from the interaction between the driver and embedded sensors at the steering wheel, which is tied to a Bluetooth link with an Android smartphone. The driver can view his historical data easily in tabular or graph form with selected filters using the application since the sensor data are transferred to a real-time database for storage and analysis. The application is coupled with the tool to demonstrate an opportunity as an aftermarket service for vehicles that are not equipped with this technology.
Energy demand forecasting of remote areas using linear regression and inverse matrix analysis
Authors: Sarker Md. Tanjil , Jaber Alam Mohammed , Ramasamy Gobbi , Nasir Uddin Mohammed
Published: November 2023

Efficient energy demand forecasting is pivotal for addressing energy challenges in remote areas of Bangladesh, where reliable access to energy resources remains a concern. This study proposes an innovative approach that combines linear regression analysis (LRA) and inverse matrix calculation (IMC) to forecast energy demand accurately in these underserved regions. By leveraging historical energy consumption data and pertinent predictors, such as meteorological conditions, population dynamics, economic indicators, and seasonal patterns, the model provides reliable forecasts. The application of the proposed methodology is demonstrated through a case study focused on remote regions of Bangladesh. The results showcase the approach's effectiveness in capturing the intricate dynamics of energy demand and its potential to inform sustainable energy management strategies in these remote areas. This research contributes to the advancement of energy planning and resource allocation in regions facing energy scarcity, fostering a path towards improved energy efficiency and development. These techniques can be applied to estimate short-term electricity demand for any rural or isolated region worldwide.
Passive magnetic coil design for electromagnetic interference evaluation of axle counters
Authors: Yoppy Yoppy , Yudhistira Yudhistira , Nugroho Hutomo Wahyu , Trivida Elvina , Wahyu Wijanarko Tyas Ari , Bakti Aditia Nur , Mandaris Dwi
Published: November 2023

Measurement of magnetic fields near the railway tracks is crucial to ensure compatibility with the operation of axle counters. According to EN 50592 standard, the magnetic field is detected with a passive magnetic coil and an oscilloscope. From previous studies, in general, there has been no in-depth analysis of how the choice of coil winding parameters could affect the coil output voltage, which then affect the measurement sensitivity, in particular the coil design based on the standard and it is applicability for electromagnetic interference (EMI) evaluation of axle counters. Therefore, this paper will explore the design of a passive magnetic coil to obtain the optimum coil output voltage within the frequency range. Simulations showed that for 10-100 kHz and 100 kHz–1.3 MHz range, the optimum number of turns happened at 60-100 and 15-60 turns, respectively. Based on that, two example coils had been built. Simulations and measurements of their frequency response were in good agreement, with a deviation less than 1.0 dB.
A novel improved elephant herding optimization for path planning of a mobile robot
Authors: Oultiligh Ahmed , Ayad Hassan , El Kari Abdeljalil , Mjahed Mostafa , El Gmili Nada
Published: November 2023

Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.
Investigation of auto-oscilational regimes of the system by dynamic nonlinearities
Authors: Siddikov Isamidin , Khalmatov Davronbek , Alimova Gulchekhra , Khujanazarov Ulugbek , Feruzaxon Sadikova , Usanov Mustafaqul
Published: November 2023

The paper proposes a method for the analysis and synthesis of self-oscillations in the form of a finite, predetermined number of terms of the Fourier series in systems reduced to single-loop, with one element having a nonlinear static characteristic of an arbitrary shape and a dynamic part, which is the sum of the products of coordinates and their derivatives. In this case, the nonlinearity is divided into two parts: static and dynamic nonlinearity. The solution to the problem under consideration consists of two parts. First, the parameters of self-oscillations are determined, and then the parameters of the nonlinear dynamic part of the system are synthesized. When implementing this procedure, the calculation time depends on the number of harmonics considered in the first approximation, so it is recommended to choose the minimum number of them in calculations. An algorithm for determining the self-oscillating mode of a control system with elements that have dynamic nonlinearity is proposed. The developed method for calculating self-oscillations is suitable for solving various synthesis problems. The generated system of equations can be used to synthesize the parameters of both linear and nonlinear parts. The advantage is its versatility.
Design of a prototype for sending fire notifications in homes using fuzzy logic and internet of things
Authors: Huaman Castañeda Johan , Tamara Perez Pablo Cesar , Paiva-Peredo Ernesto , Zarate-Segura Guillermo
Published: November 2023

This paper highlights the need to address fire monitoring in densely populated urban areas using innovative technology, in particular, the internet of things (IoT). The proposed methodology combines data collection through sensors with instant notifications via text messages and images through the user’s email. This strategy allows a fast and efficient response, with message delivery times varying from 1 to 4 seconds on Internet connections. It was observed that the time to send notifications on 3G networks is three times longer compared to Wi-Fi networks, and in some 3G tests, the connection was interrupted. Therefore, the use of Wi-Fi is recommended to avoid significant delays and possible bandwidth issues. The implementation of fuzzy logic in the ESP32 microcontroller facilitates the identification of critical parameters to classify notifications of possible fires and the sending of evidence through images via email. This approach successfully validated the results of the algorithm by providing end users with detailed emails containing information on temperature, humidity, gas presence and a corresponding image as evidence. Taken together, these findings support the effectiveness and potential of this innovative solution for fire monitoring and prevention in densely populated urban areas.
Statistical analysis of range of motion and surface electromyography data for a knee rehabilitation device
Authors: Sengchuai Kiattisak , Sittiruk Thantip , Booranawong Apidet , Jindapetch Nattha
Published: November 2023

This work introduces a statistical analysis of knee range of motion (ROM) and surface electromyography (EMG) data gathered from a knee extension rehabilitation device. Real-time ROM and EMG signals of rehabilitation users are measured using a single angle sensor and a two-channel EMG device (for the vastus lateralis and vastus medialis muscles). These signals are collected by the NI-myRIO embedded device in accordance with the designed rehabilitation program. The main contribution and novelty of this study is that real-time signals are automatically processed and transformed into statistical data for use by users and medical experts. A solution for extracting raw signals is proposed, in which several statistical functions such as range, mean, standard deviation, skewness, percentiles, interquartile range, and total knee holding times above the threshold level, are implemented and applied. The proposed solution is tested using data acquired from healthy people, which includes gender, age, body size, knee side, exercise behavior, and surgical experience. Results indicated that real-time signals and related statistical data on the knee’s performance can be efficiently monitored. With this solution, rehabilitation users can practice and learn about their knee performance, while medical experts can evaluate the data and design the best rehabilitation program for users.
Robust individual pig tracking
Authors: Jaoukaew Aggaluck , Suwansantisuk Watcharapan , Kumhom Pinit
Published: November 2023

The locations of pigs in the group housing enable activity monitoring and improve animal welfare. Vision-based methods for tracking individual pigs are noninvasive but have low tracking accuracy owing to long-term pig occlusion. In this study, we developed a vision-based method that accurately tracked individual pigs in group housing. We prepared and labeled datasets taken from an actual pig farm, trained a faster region-based convolutional neural network to recognize pigs’ bodies and heads, and tracked individual pigs across video frames. To quantify the tracking performance, we compared the proposed method with the global optimization (GO) method with the cost function and the simple online and real-time tracking (SORT) method on four additional test datasets that we prepared, labeled, and made publicly available. The predictive model detects pigs’ bodies accurately, with F1-scores of 0.75 to 1.00, on the four test datasets. The proposed method achieves the largest multi-object tracking accuracy (MOTA) values at 0.75, 0.98, and 1.00 for three test datasets. In the remaining dataset, the proposed method has the second-highest MOTA of 0.73. The proposed tracking method is robust to long-term occlusion, outperforms the competitive baselines in most datasets, and has practical utility in helping to track individual pigs accurately.
Land scene classification from remote sensing images using improved artificial bee colony optimization algorithm
Authors: Gowda Ganashree Kamenahalli Chandre , Hemavathy Ramakrishna , Ramakrishna Anala Maddur
Published: November 2023

The images obtained from remote sensing consist of background complexities and similarities among the objects that act as challenge during the classification of land scenes. Land scenes are utilized in various fields such as agriculture, urbanization, and disaster management, to detect the condition of land surfaces and help to identify the suitability of the land surfaces for planting crops, and building construction. The existing methods help in the classification of land scenes through the images obtained from remote sensing technology, but the background complexities and presence of similar objects act as a barricade against providing better results. To overcome these issues, an improved artificial bee colony optimization algorithm with convolutional neural network (IABC-CNN) model is proposed to achieve better results in classifying the land scenes. The images are collected from aerial image dataset (AID), Northwestern Polytechnical University-Remote Sensing Image Scene 45 (NWPU-RESIS45), and University of California Merced (UCM) datasets. IABC effectively selects the best features from the extracted features using visual geometry group-16 (VGG-16). The selected features from the IABC are provided for the classification process using multiclass-support vector machine (MSVM). Results obtained from the proposed IABC-CNN achieves a better classification accuracy of 96.40% with an error rate 3.6%.
Effectiveness of Filtering Methods in Enhancing Pulmonary Carcinoma Image Quality: A Comparative Analysis
Authors: Elavarasu Moulieswaran , Govindaraju Kalpana
Published: November 2023

In recent years, information technology has vastly improved. The quality of the image has been degraded by noise, which defeats the purpose of the noisy images. The major purpose of this paper is to find out which filters provide a better outcome while preprocessing medical images using computer tomography scans. The purpose of this paper is to remove noise from any images, whether they are real-time datasets or online datasets. To enhance an image for preprocessing, I have compared various filters; these filters are already available, but the major purpose is to identify the best filter. I compared the different parameters to find the best and finally found that the modified bilateral filtering provided a better result. The noise has been removed by using a bilateral filter, and the image clarity has not changed when using this filter. We have discussed the advantages and drawbacks of each approach. The effectiveness of these filters is compared using the peak signal-to-noise ratio, structural similarity index, signal-to-noise ratio, and mean square error. An enhanced image processing analysis using different filtering techniques can improve the accuracy of diagnosis, facilitating timely treatment and ultimately improving patient outcomes.
Improvement of misalignment tolerance in free-space optical interconnects
Authors: Al-Ababneh Nedal , Aldiabat Hasan
Published: November 2023

In this paper, the use of micro lenses with non-uniform transmittance apertures as an alternative to those with uniform transmittance apertures in optical communication systems is proposed. In particular, we consider the use of micro lenses with tapered Gaussian transmittance profiles to improve the misalignment tolerance in optical interconnects. We study the effects of utilizing Gaussian transmittance profiles on the propagation of light beams and the signal to crosstalk ratio of misaligned optical systems. Moreover, we consider the use of uniform transmittance profiles in optical systems for the sake of comparison. To this end, the crosstalk optical noise is modeled at the plane of the detectors array considering the two scenarios of uniform and Gaussian apertures. This was possible after finding the optical field for both scenarios at the of the detectors array. Numerical results clearly demonstrate the significant improvement in decreasing the crosstalk and increasing the signal to crosstalk ratio in the considered optical systems upon utilizing the Gaussian profiles.
A 5G beam-steering microstrip array antenna using both-sided microwave integrated circuit technology
Authors: Hossain Md. Farhad , Das Debprosad , Hossain Md. Azad
Published: November 2023

In this paper a beam steering 2×2 microstrip array antenna is proposed and simulated for the 5G sub-6 GHz frequency band. The array antenna is designed at the resonant frequency of 3.5 GHz. The antenna has four patches excited by two microstrip lines. Microstrip lines on top of teflon substrate of 0.8 mm height and slot line in the ground plane makes a hybrid junction. The design uses both sided microwave integrated circuit (MIC) to feed signal to the patch elements. This designed array antenna has the beam steering capability of maximum -17º to +17º while keeping the side lobe gain below 10 dB. The simulation results show that the array antenna is designed through good input impedance matching. The antenna has a return loss of -43 dB at center frequency 3.5 GHz. The results also show that the array antenna has a high gain of 12.57 dBi and directivity of 25.11 dB. The maximum gain of this antenna is 24.1 dB at -17º and +17º. The proposed work is simulated on keysight technologies advanced designed system (ADS).
Enhancing cryptographic protection, authentication, and authorization in cellular networks: a comprehensive research study
Authors: Moldamurat Khuralay , Seitkulov Yerzhan , Atanov Sabyrzhan , Bakyt Makhabbat , Yergaliyeva Banu
Published: November 2023

This research article provides an extensive analysis of novel methods of cryptographic protection as well as advancements in authentication and authorization techniques within cellular networks. The aim is to explore recent literature and identify effective authentication and authorization methods, including high-speed data encryption. The significance of this study lies in the growing need for enhanced data security in scientific research. Therefore, the focus is on identifying suitable authentication and authorization schemes, including blockchain-based approaches for distributed mobile cloud computing. The research methodology includes observation, comparison, and abstraction, allowing for a comprehensive examination of advanced encryption schemes and algorithms. Topics covered in this article include multi-factor authentication, continuous authentication, identity-based cryptography for vehicle-to-vehicle (V2V) communication, secure blockchain-based authentication for fog computing, internet of things (IoT) device mutual authentication, authentication for wireless sensor networks based on blockchain, new secure authentication schemes for standard wireless telecommunications networks, and the security aspects of 4G and 5G cellular networks. Additionally, in the paper a differentiated authentication mechanism for heterogeneous 6G networks blockchain-based is discussed. The findings presented in this article hold practical value for organizations involved in scientific research and information security, particularly in encryption and protection of sensitive data.
Best sum-throughput evaluation of cooperative downlink transmission nonorthogonal multiple access system
Authors: Albdairat Ahmad , Wanis Zaki Fayez , Ashour Mohammed Mahmoud
Published: November 2023

In cooperative simultaneous wireless information and power transfer (SWIPT) nonorthogonal multiple access (NOMA) downlink situations, the current research investigates the total throughput of users in center and edge of cell. We focus on creating ways to solve these problems because the fair transmission rate of users located in cell edge and outage performance are significant hurdles at NOMA schemes. To enhance the functionality of cell-edge users, we examine a two-user NOMA scheme whereby the cell-center user functions as a SWIPT relay using power splitting (PS) with a multiple-input single-output. We calculated the probability of an outage for both center and edge cell users, using closed-form approximation formulas and evaluate the system efficacy. The usability of cell edge users is maximized by downlink transmission NOMA (CDT-NOMA) employing a SWIPT relay that employs PS. The suggested approach calculates the ideal value of the PS coefficient to optimize the sum throughput. Compared to the noncooperative and single-input single-output NOMA systems, the best SWIPT-NOMA system provides the cell-edge user with a significant throughput gain. Applying SWIPT-based relaying transmission has no impact on the framework’s overall throughput.