bluuebunny/crossref_metadata_2025 huggingface dataset:
Expression of PD-L1 and YWHAZ in Patients with Diffuse Large B Cell Lymphoma: A Possible Association with the Prognosis of Lymphoma
Authors: Yan Shufang , Shang Qinyu , Fan Zhaopu , Yang Yuping , Liu Yanping , Gao Hongliang , Chen Ken , Liang Fei , Li Xinxia , Zhang Qian , Yan Huifang
Published: September 2022

Diffuse large B cell lymphoma (DLBCL) is the most common pathological subtype of non-Hodgkin lymphoma (NHL) and is the most common type of adult lymphoma. Due to the poor prognosis of relapsed/refractory DLBCL, new drug targets and therapeutic methods are urgently needed. We investigated the expression of programmed death ligand 1 (PD-L1) and 3-monooxygenase/tryptophan 5-monooxygenase activating protein zeta (14-3-3ζ or YWHAZ) in patients with DLBCL. The purpose was to verify the expression levels of YWHAZ and PD-L1 and their relationships with the prognosis of DLBCL and to lay a foundation for further study on the role of YWHAZ and PD-L1 in DLBCL. Immunohistochemistry was used in 140 patients with DLBCL to test protein expression levels of YWHAZ and PD-L1. All patients were followed up in the hospital or by telephone or via WeChat. The positive expression rate of YWHAZ was 62.14% (87/140). The expression was negatively correlated with the positive expression of BAD (

r
=
−
0.177

,

P
=
0.036

) and positively correlated with the positive expression of BCL-2 (

r
=
0.180

,

P
=
0.033

). When the cut-off value for PD-L1 was established at 5%, 10%, 15%, and 20%, the corresponding positive expression rates of PD-L1 were 79.66% (94/118), 51.69% (61/118), 40.68% (48/118), and 36.44% (43/118). YWHAZ significantly affected the OS of DLBCL (

P
≤
0.001

). The prognosis of the patients was related to the positive expression of PD-L1 when the cut-off value of PD-L1 was 5% (

P
=
0.033

). However, positive expression of PD-L1 was not associated with the prognosis when the cut-off values of PD-L1 were 10% (

P
=
0.404

), 15% (

P
=
0.208

), and 20% (

P
=
0.408

). The positive expression of YWHAZ (hazard ratio 6.215; 95% confidence interval 3.214-12.017;

P
<
0.05

) was an independent adverse prognostic factor for OS. YWHAZ may be an important oncogene in the occurrence and development of DLBCL and may be used as a therapeutic target. PD-L1 may be an oncogene or tumor suppressor gene in the occurrence and development of DLBCL. Different cut-off values of PD-L1 may affect the prognosis of DLBCL.
An Effective Node-To-Edge Interdependent Network and Vulnerability Analysis for Digital Coupled Power Grids
Authors: Li Yifan , Wang Bo , Wang Hongxia , Ma Fuqi , Ma Hengrui , Zhang Jiaxin , Zhang Yingchen , Mohamed Mohamed A.
Published: September 2022

With the deep coupling between the cyber side and the physical side of power systems, the failure of any link of both sides may lead to power outages, so it is necessary to analyze their vulnerability and vulnerable links for targeted improvement of systems. By dynamically attacking the coupled network nodes, this paper proposes a multilevel model and node-to-edge cyber-physical power system and the corresponding indexes system to analyze the vulnerability of the coupled power grid and its key components. The results showed that in the order of the indexes proposed in this paper, attacking surviving power nodes and cyber nodes results in a network crash rate of 25.0% and 66.7% faster than that in the order of “betweenness” and that attacking surviving cyber nodes results in a network crash rate of 89.4% faster than that in the order of “degree.” In terms of attacking power nodes, the index proposed in this paper has the same rate as “degree.” Therefore, the proposed model can better describe the vulnerability of the power grid to withstand attacks.
Tourism Destination Recommendation and Marketing Model Analysis Based on Collaborative Filtering Algorithm
Authors: Niu Tianjiao , Song Mei , Wang Xiaoyi , Wang Linna
Published: September 2022

The Internet has penetrated into all fields. As the most dynamic “sunrise industry,” tourism has also been swept into such a wave of Internet. In such an era of “information overload,” how to find one’s favorite attractions among the massive tourist attractions has become a difficult problem. In order to solve this problem, personalized recommendation technology is applied, among which collaborative filtering recommendation technology is one of the core technologies while the collaborative filtering algorithm still has problems. The research and analysis of the algorithm, this paper improves the technology for the problems of low recommendation accuracy that considers user interest changes. It for attribute scoring. It uses the multiattribute score of the item to calculate the user’s overall evaluation score of each attribute of the item; for the change of user interest, a time function based on the Ebbinghaus forgetting law is introduced to calculate the user similarity. It is given a certain weight, that is, a time function, to ultimately ensure the accuracy of the recommendation. Exploring the tourism destination recommendation and marketing model based on the collaborative filtering algorithm can enrich the relevant theories of it on the one hand, and on the other hand, it can lay the foundation for building a real tourism recommendation website.
Research on Embedded Ball Screw Diagnosis System for Mechanical Fault Based on Bispectral Image
Authors: Lv Da , Zhang Chao , Zhao Wentao , Wang Shuai
Published: September 2022

Ball screw is one of the main basic transmission components in CNC machine tool feed system because of its high transmission efficiency, positioning accuracy, and rigidity. It plays an irreplaceable role in the process of machine tool torque transmission and positioning. However, ball screw is also one of the parts which are easy to break down in the feed system. If it breaks down, it will affect the processing accuracy of the whole machine tool and even cause shutdown. Numerical control means that digital control is a high-efficiency technology that uses digital information to control mechanical movement and processing processes. Therefore, to increase the high-speed and high-precision operation ability of CNC machine tools, research and development of efficient and reliable predictive maintenance and issue identification technology to guide maintenance of CNC machine tools are critical. Throughout this study, a bispectrum image-based embedded defect fight against the disease for cylindrical gears is provided. Firstly, the bispectrum of fault signal is generated by Wavelet Packet Threshold denoising, wavelet packet reconstruction, and bispectrum analysis, and the characteristics of bispectrum are represented by the first kind of gray moments. These feature statistics are used as input feature vectors of BP neural network pattern recognition algorithm to classify these feature sets and identify the corresponding gear fault types. Experiments show that under the condition of noise, after 14.5 hours of testing, the NGI has increased significantly and can be used to diagnose faults in the gear system, and the method achieves relatively ideal recognition rate and verifies the feasibility of gear box fault diagnosis method based on image recognition. The code compiled by OpenCV library is easy to transplant to embedded system, with high development efficiency and reliable program operation. OpenCV has the characteristics of powerful matrix computing capability and convenient and flexible interface, and it is a general algorithm that can realize real-time image processing.
Thermogravimetric Analysis of Marine Macroalgae Waste Biomass as Bio-Renewable Fuel
Authors: Khan Nida , Sudhakar K. , Mamat R.
Published: September 2022

Macroalgae are considered as the 3rd generation of biofuels and a future feedstock for biorefinery. This research aims to provide simple and dependable analytical techniques for measuring the thermal characteristics of dried seaweed. The main objective was to investigate the thermal characteristics of four seaweed species utilizing a thermogravimetric analyzer. The seaweeds Gracilaria fisheri, Caulerpa lentillifera, Ceramium rubrum, and Eucheuma cottonii were collected from the Pahang state of Peninsular Malaysia. The calorific value of the samples was revealed by using a calorimeter. Ceramium rubrum showed the highest calorific value, while Gracilaria fisheri had the most negligible calorific value among the selected samples. The thermogravimetric analysis (TGA) data revealed that the most significant weight loss for this biomass occurred between 160 and 300° for the selected species. Gracilaria fisheri has shown the highest decomposition with the minor residue at 30.26%, whereas Caulerpa lentillifera has a slow weight loss rate in the mentioned range. SEM analysis has been used to perform the morphology of samples, which shows differences in the concentration of epiphytic diatoms with different structural shapes. Based on the results, macroalgae is a promising sustainable biomass feedstock for biofuel application.
[Retracted] Structural, Optical, Antibacterial, and Anticancer Properties of Cerium Oxide Nanoparticles Prepared by Green Synthesis Using Morinda citrifolia Leaves Extract
Authors: Elderdery Abozer Y. , Alzahrani Badr , Alabdulsalam Abdulrahim A. , Hamza Siddiqa M. A. , Elkhalifa Ahmed M. E. , Alhamidi Abdulaziz H. , Alanazi Fehaid , Mohamedain A. , Subbiah Suresh Kumar , Ling Mok Pooi
Published: September 2022

Currently, new advancements in the area of nanotechnology opened up new prospects in the field of medicine that could provide us with a solution for numerous medical complications. Although a several varieties of nanoparticles is being explored to be used as nanomedicines, cerium oxide nanoparticles (CeO2 NPs) are the most attractive due to their biocompatibility and their switchable oxidation state (+3 and +4) or in other words the ability to act as prooxidant and antioxidant depending on the pH condition. Green synthesis of nanoparticles is preferred to make it more economical, eco‐friendly, and less toxic. The aim of our study here is to formulate the CeO2 NPs (CeO2 NPs) using Morinda citrifolia (Noni) leaf extract and study its optical, structural, antibacterial, and anticancer abilities. Their optical and structural characterization was accomplished by employing X‐ray diffractography (XRD), TEM, EDAX, FTIR, UV‐vis, and photoluminescence assays. Our CeO2 NPs expressed strong antibacterial effects against Gram‐positive S. aureus and S. pneumonia in addition to Gram‐negative E. coli and K. pneumonia when compared with amoxicillin. The anticancer properties of the green synthesized CeO2 NPs against human acute lymphoblastic leukemia (ALL) MOLT‐4 cells were further explored by the meticulous study of their ability to diminish cancer cell viability (cytotoxicity), accelerate apoptosis, escalate intracellular reactive oxygen species (ROS) accumulation, decline the mitochondria membrane potential (MMP) level, modify the cell adhesion, and shoot up the activation of proapoptotic markers, caspase−3, −8, and −9, in the tumor cells. Altogether, the outcomes demonstrated that our green synthesized CeO2 NPs are an excellent candidate for alternative cancer therapy.
A Risk-Assessing Signature Based on Hypoxia- and Immune-Related Genes for Prognosis of Lung Adenocarcinoma Patients
Authors: Wang Yue , Feng Jian , Liu Yang , Han Tingyue
Published: September 2022

Lung Adenocarcinoma (LUAD) drastically influences human health. Tumor hypoxia and immunity impact hugely on the immunotherapeutic effect of LUAD patients. This study is aimed at exploring the prognostic markers associated with hypoxia and immunity in LUAD patients and evaluates their reliability. The relationship between hypoxia and immune-related genes and prognoses of LUAD patients was investigated by the univariate regression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods were used to reveal the enriched pathways and biological processes of prognosis-related genes. Univariate, LASSO, and multivariate Cox regression analyses were used to construct a prognostic signature and verify its independence. The reliability of the signature was evaluated by the Principal Component Analysis (PCA), the Kaplan-Meier (K-M) curve, and the receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA), tumor mutational burden (TMB), and single-sample GSEA (ssGSEA) further verified the performance of the signature. Finally, a prognostic signature for LUAD was constructed based on 7 hypoxia- and immune-related genes. According to riskScores acquired from the signature, the test set was divided into groups, where the prognosis of high-risk patients was poor. The feature genes had good reliability, and the riskScore could be used as an independent prognostic factor for LUAD patients. Meanwhile, high TMB scores and low immune scores were found in high-risk patients, and feature genes were enriched in signaling pathways such as cell cycle and p53 signaling pathway. In sum, a prognostic signature based on 7 hypoxia- and immune-related genes was constructed.
Prognostic Value of an Integrin-Based Signature in Hepatocellular Carcinoma and the Identification of Immunological Role of LIMS2
Authors: Ye Fengning , Le Hao , He Fan , Tu Hao , Peng Dengfa , Ruan Sini
Published: September 2022

Objective. Evidence proves that integrins affect almost every step of hepatocellular carcinoma (HCC) progression. The current study aimed at constructing an integrin-based signature for prognostic prediction of HCC. Methods. TCGA-LIHC and ICGC-LIRI-JP cohorts were retrospectively analyzed. Integrin genes were analyzed via univariate Cox regression, followed by generation of a prognostic signature with LASSO approach. Independent factors were input into the nomogram. WGCNA was adopted to select this signature-specific genes. Gene Ontology (GO) enrichment together with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to explore the function of the dysregulated genes. The abundance of tumor microenvironment components was estimated with diverse popular computational methods. The relative importance of genes from this signature was estimated through random-forest method. Results. Eight integrin genes (ADAM15, CDC42, DAB2, ITGB1BP1, ITGB5, KIF14, LIMS2, and SELP) were adopted to define an integrin-based signature. Each patient was assigned the riskScore. High-riskScore subpopulation exhibited worse overall survival, with satisfying prediction efficacy. Also, the integrin-based signature was independent of routine clinicopathological parameters. The nomogram (comprising integrin-based signature, and stage) accurately inferred prognostic outcome, with the excellent net benefit. Genes with the strongest positive interaction to low-riskScore were primarily linked to biosynthetic, metabolic, and catabolic processes and immune pathways; those with the strongest association with high-riskScore were principally associated with diverse tumorigenic signaling. The integrin-based signature was strongly linked with tumor microenvironment components. Among the genes from this signature, LIMS2 possessed the highest importance, and its expression was proven through immunohistochemical staining. Conclusion. Altogether, our study defined a quantitative integrin-based signature that reliably assessed HCC prognosis and tumor microenvironment features, which possessed the potential as a tool for prognostic prediction.
Mesenteric Ischemia and Its Need for Timely Recognition and Management
Authors: Shrestha Anish Kumar , Shrestha Anisha , Ghimire Bikal , Ghimire Prajwal Ram , Kunwar Simin , Luitel Prajjwol , Adhikari Niranjan
Published: September 2022

Mesenteric ischemia is a fatal vascular emergency of the small intestine which, if not diagnosed and treated in time, has a very high mortality rate. Presenting with nonspecific symptoms such as abdominal pain, nausea, constipation, tachycardia, and gastrointestinal bleeds, it can masquerade as other causes of acute abdomen, particularly bowel obstruction. Ideal laboratory tests and markers are still lacking due to complexity in bowel’s anatomy, physiology, blood supply, and drainage. We report 10 cases of mesenteric ischemia presented in our center with their demography, laboratory findings, approach to diagnosis, and treatment along with their outcomes at discharge. Out of the ten cases, six cases presented with signs and symptoms of acute bowel obstruction without passage of stool and one with passage of black stool. These seven patients underwent laparotomy, while the remaining three cases who either presented with milder symptoms or refused surgical interventions were managed conservatively. All patients were diagnosed with either acute or chronic mesenteric ischemia based on their operative and/or radiographic findings.
Broken Heart: Privacy Leakage Analysis on ECG-Based Authentication Schemes
Authors: Noh Seungil , Kim Jaehan , Lee Seokmin , Kang Youngshin , Park Cheolsoo , Shin Youngjoo
Published: September 2022

Authentications using biometrics, such as fingerprint recognition and electrocardiogram (ECG), have been actively used in various applications. Unlike traditional authentication methods, such as passwords or PINs, biometric-based authentication has an advantage in terms of security owing to its capability of liveness detection. Among the various types of biometrics, ECG-based authentication is widely utilized in many fields. Because of the inherent characteristics of ECG, however, the incautious design of ECG-based authentication may result in serious leakage of personal private information. In this paper, we extensively investigate ECG-based authentication schemes previously proposed in the literature and analyze possible privacy leakages by employing machine learning and deep learning techniques. We found that most schemes suffer from vulnerabilities that lead to the leakage of personal information, such as gender, age, and even diseases. We also identified some privacy-insensitive ECG fiducial points by utilizing feature selection algorithms. Based on these features, we present a privacy-preserving ECG-based authentication scheme.
PF-ViT: Parallel and Fast Vision Transformer for Offline Handwritten Chinese Character Recognition
Authors: Dan Yongping , Zhu Zongnan , Jin Weishou , Li Zhuo
Published: September 2022

Recently, Vision Transformer (ViT) has been widely used in the field of image recognition. Unfortunately, the ViT model repeatedly stacks 12-layer encoders, resulting in a large number of model computations, many parameters, and slow training speed, making it difficult to deploy on mobile devices. In order to reduce the computational complexity of the model and improve the training speed, a parallel and fast Vision Transformer method for offline handwritten Chinese character recognition is proposed. The method adds parallel branches of the encoder module to the structure of the Vision Transformer model. Parallel modes include two-way parallel, four-way parallel, and seven-way parallel. The original picture is fed to the encoder module after flattening and linear embedding processing operations. The core step in the encoder is the multihead attention mechanism. Multihead self-attention can learn the interdependence between image sequence blocks. In addition, the use of data expansion strategies increases the diversity of data. In the two-way parallel experiment, when the model is 98.1% accurate on the dataset, the number of parameters and the number of FLOPs are 43.11 million and 4.32 G, respectively. Compared with the ViT model, whose parameters and FLOPs are 86 million and 16.8 G, respectively, the two-way parallel model has a 50.1% decrease in parameters and a 34.6% decrease in FLOPs. This method has been demonstrated to effectively reduce the computational complexity of the model while indirectly improving image recognition speed.
Risk Estimation in Exchange Rate Markets Based on Stochastic Copula Approach
Authors: Terzi Erol , Yildirim Emre , Saribacak Bünyamin , Cengiz Mehmet Ali
Published: September 2022

Risk estimation is of great importance in financial risk management. In this study, the risk estimation of the exchange rate portfolio is performed via the stochastic copula approach. This model‐based latent process has a parameter that changes over time and thus can model the dependency structure between variables in a comprehensive and dynamic way. First, the marginals of the returns are handled with ARMA‐GARCH‐type models. Then, the dependency between variables is modeled via the stochastic copula approach. Finally, risk estimates are carried out at 95% and 99% confidence level for the foreign exchange portfolios. It is found that the proposed risk estimation model based on the stochastic copula approach outperforms both classical methods and static copula models.
[Retracted] MRI Imaging Omics and Risk Factors Analysis of PWMD in Premature Infants Based on Fuzzy Clustering Algorithm
Authors: Wang Xiaofei , Hao Yuewen , Sun Huan , Chen Chao
Published: September 2022

The magnetic resonance imaging (MRI) characteristics of periventricular white matter damage (PWMD) in premature infants using the fuzzy c‐means clustering algorithm (FCM) is explored, and the influencing factors are further clarified. A total of 100 premature infants admitted to the neonatal department of our hospital from February 2020 to February 2022 are selected for in‐depth investigation. According to the occurrence of PWMD, they are divided into the PWMD group and the simple premature delivery group, with 50 cases in each group. All preterm infants are examined by MRI and the changes in image characteristics and apparent diffusion coefficient (ADC) values are analyzed. Clinical information of the subjects is collected and the influencing factors of PWMD in preterm infants are analyzed by multivariate regression analysis. In the first magnetic resonance imaging (MRI) examination, the cases of punctured, clustered, and linear lesions are 28 cases, 12 cases, and 10 cases, respectively. The experimental results showed that PWMD of preterm infants presented punctate, clustered, and high linear T1 signal MRI manifestations, which caused a downward trend of ADC value, and caused respiratory distress, low birth weight, premature rupture of membranes, respiratory tract infection, and other risk symptoms.
An Innovative Solution for Battery Draining in 5G Devices Using Alternate Routing Model
Authors: Sivasubramanian M. , Vignesh J. , Senthil kumar V. , Sumathi S. , Sathish R. , Parasuraman Kumar , Kumar B Santhosh , Kathirvelu M , Bojaraj Leena , K Srihari , Sbhat Negasi Tsegay
Published: September 2022

When the electrolyte is layered, excessively large and prolonged charging and exhaust currents lead to the random tendency of reactions in different parts of the electrodes. This was leading to mechanical stresses and the warming of the plates of the battery. The presence of nitric and acetic acid contaminants in the electrolyte improves the oxidation of the deeper layers of positive electrodes. Because lead dioxide has a larger volume than lead, electrodes expand and curve. Positive electrodes are subject to war and growth. The negative electrode curve is mainly caused by the nearby distracted positive people. The adjustment of the distracted electrodes can only be carried out by removing the battery. The lack of sulfate and fully charged electrodes are subject to correction because they are soft and easy to adjust. The main contribution of this proposed work is to provide an innovative solution to resolve the battery draining issues in 5G devices with the help of an alternate routing model. The proposed model will provide an idea that is used to slice distracted electrodes washed with water and placed between soft, hard boards. Generally, on the top board, a load is installed, which increases the edges of the electrodes. Electrodes are banned directly or through the amplitude of a top or hammer to avoid the destruction of the active layer; hence, the draining of the battery was reduced.
Cultivation of Innovative Ability of College Physical Education Students Based on the Internet of Things Multimedia Environment
Authors: Cai Bin
Published: September 2022

Innovation is the source of social progress, and all walks of life need to cultivate innovative talents. Therefore, it is also very important to cultivate innovative ability in physical education students in colleges and universities. The purpose of this paper is to study the cultivation of students’ innovative ability of physical education majors in colleges and universities based on the multimedia environment of the Internet of things and to open up a new path for the cultivation of college students’ innovative ability. In this paper, a terminal collaborative indoor positioning algorithm based on RSSI fingerprint optimization was proposed to study the innovation ability of physical education students in colleges and universities. Through experimental analysis, college A was taken as the research object, and 1500 students were investigated. Innovation had a very important position in the hearts of 76.21% of students, and 3.83% of students believed that innovation was not important. In the minds of most people, innovation was still very important, which showed that schools still had a certain effect on the education of students’ innovative consciousness. The experimental results obtained showed that the Internet of things technology played an important role in the research on the innovation ability of students majoring in physical education in colleges and universities.
An End-to-End Cardiac Arrhythmia Recognition Method with an Effective DenseNet Model on Imbalanced Datasets Using ECG Signal
Authors: Ullah Hadaate , Bin Heyat Md Belal , Akhtar Faijan , Sumbul  , Muaad Abdullah Y. , Islam Md. Sajjatul , Abbas Zia , Pan Taisong , Gao Min , Lin Yuan , Lai Dakun
Published: September 2022

Electrocardiography (ECG) is a well-known noninvasive technique in medical science that provides information about the heart’s rhythm and current conditions. Automatic ECG arrhythmia diagnosis relieves doctors’ workload and improves diagnosis effectiveness and efficiency. This study proposes an automatic end-to-end 2D CNN (two-dimensional convolution neural networks) deep learning method with an effective DenseNet model for addressing arrhythmias recognition. To begin, the proposed model is trained and evaluated on the 97720 and 141404 beat images extracted from the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia and St. Petersburg Institute of Cardiological Technics (INCART) datasets (both are imbalanced class datasets) using a stratified 5-fold evaluation strategy. The data is classified into four groups: N (normal), V (ventricular ectopic), S (supraventricular ectopic), and F (fusion), based on the Association for the Advancement of Medical Instrumentation® (AAMI). The experimental results show that the proposed model outperforms state-of-the-art models for recognizing arrhythmias, with the accuracy of 99.80% and 99.63%, precision of 98.34% and 98.94%, and F1-score of 98.91% and 98.91% on the MIT-BIH arrhythmia and INCART datasets, respectively. Using a transfer learning mechanism, the proposed model is also evaluated with only five individuals of supraventricular MIT-BIH arrhythmia and five individuals of European ST-T datasets (both of which are also class imbalanced) and achieved satisfactory results. So, the proposed model is more generalized and could be a prosperous solution for arrhythmias recognition from class imbalance datasets in real-life applications.
Personality Development and Discipline Qualities among the National Cadets Corps in Southern India
Authors: Anish M. , Jayaprakash V. , Ganesh S. , Chandrasekar P. , Joseline X. , Balaji B.
Published: September 2022

The students from the Madras Group (Zone) in Tamil Nadu, India are the primary focus of this investigation. The study was carried out in nine different districts of the state of Tamil Nadu in India, and it received responses from 18,324 college students residing in these diverse areas. The t-test and analysis of variance (ANOVA) methodologies were used in the course of an investigation of the responses provided by students. In accordance with the objectives of the study, the data were gathered, compiled, and arranged into categories before being subjected to statistical and objective analysis. Students who had participated in NCC training in the Madras Group of Tamil Nadu were found to have higher levels of personality development and stronger levels of discipline than students who had participated in NCC training in other parts of Tamil Nadu. These results are presented in a manner that is consistent with the conclusions obtained from the research that was carried out. These findings should be accepted given that they are in agreement with the results of the investigation.
The Current Burden of Oropharyngeal Cancer: A Global Assessment Based on GLOBOCAN 2020
Authors: Lorenzoni Valentina , Chaturvedi Anil K. , Vignat Jerome , Laversanne Mathieu , Bray Freddie , Vaccarella Salvatore
Published: September 2022

Background:

Oropharyngeal cancer (OPC) is a complex disease whose etiologies, either related to risk factors such as smoking or alcohol, or linked to HPV infection, are believed to be responsible for wide gender and geographical variability. This study depicts the current burden of OPC worldwide.

Methods:

Estimated OPC new cases, deaths, age-standardized rates (ASR) for both incidence and mortality in 2020 were obtained from the GLOBOCAN database for each country and across 20 UN-defined world regions by sex. The incidence-to-mortality ratio (IMR) was also estimated from ASR.

Results:

Worldwide, 98,400 new cases and 48,100 OPC deaths were estimated in 2020, with ASR of 1.1 and 0.51 per 100,000 for incidence and mortality, respectively. ASR for both incidence and mortality were approximately four times higher in men and varied greatly across geographical regions and countries within the same region. Higher incidence was estimated in Europe, North-America, Australia, and New Zealand. Mortality was the highest in Central-East Europe, Western Europe, Melanesia, South-Central Asia, and the Caribbean. South-Central Asia, most African areas, and Central America exhibited the lowest IMR values, whereas North-America, Australia, New Zealand, and North-Europe had the highest.

Conclusions:

The marked geographical and gender variability in OPC incidence and mortality is likely to reflect the distribution of risk factors and the diverse prevalence of HPV-negative and HPV-positive cases.

Impact:

Findings are likely to drive future research, support the development of targeted strategies to counteract disease burden, establish priorities for prevention and treatment programs, and address inequality in access to services.
Optic disc localization using graph traversal algorithm along blood vessel in polar retinal image
Authors: Rodtook Annupan , Chucherd Sirikan
Published: September 2022

The optic disc (OD) is an important landmark of the retina and its location is essential for computer-aided diagnosis of diabetic retinopathy and glaucoma. This paper presented a new method that applies the proposed graph traversal algorithm for vertical vascular infrastructure to detect root nodes representing the location of optical discs. The proposed algorithms were designed to process the terrific view of blood vessels from a polar image without depending on the prominent OD characteristic of circular/elliptical shape and brightness. Before constructing the vascular infrastructure, the new multi-scale and multi-orientation top-hat filters were proposed to properly approximate blood vessels of different sizes and directions. The proposed method provided efficiency and certainty for complex retinal images such as low contrast OD with low illumination and distorted OD shape due to complex vascular structure. The algorithms evaluation was performed on five public fundus image databases for retinal image analysis. Experimental results revealed that the proposed algorithm identified valid optical discs with high accuracy as 91.21%, 95.09%, 88.45%, 91.66%, and 95.01% for the five corresponding data sets and outperformed comparison methods.
Plasmonic high gain graphene-based antenna array design for ultra wide band terahertz applications
Authors: Mahdi Rasha H. , Abdulnabi Hussein A. , Alsudani Ahlam
Published: September 2022

Graphene, which consists of one atomic slide made from the Carbon, and it utilized in various of the implementations like the chemical, the thermal, the mechanical, and the electrical. In this research article, a high gain plasmonic antenna array is proposed for the utilizations of the terahertz (THz) regime on the basis of the graphene material. Also, the surface plasmon polariton (SPP) waves phenomena which emerged at THz in graphene are demonstrated the suggested antenna is composed of (4*6) graphene patches and a graphene Nano ribbon feeder to excite the patches deposed on the lumina layer. The simulated antenna was designed and investigated via the utilizing of the CST software. The obtained outcomes show that the antenna operation frequency bands covered a large band at the 2.6-10, 2.28-2.5, 1.87-2.14, and 1.4-1.6 THz with an S11 ≤ -10 dB and an expedient antenna gain in the operating frequency ranges.
Modified ant colony optimization with selecting and elimination customer and re-initialization for VRPTW
Authors: Kosolsombat Somkiat , Ratanavilisagul Chiabwoot
Published: September 2022

Vehicle routing problem with time windows (VRPTW) is a special kind of vehicle routing with adding time windows constraints and has a variety of applications in logistics. Many researchers have attacked the VRPTW by approximate solutions. Ant colony optimization (ACO) is a classical method to solve the VRPTW problem but the constraints of VRPTW are not used to consider customer selection. Most ACO-based optimization algorithms can suffer from the complexity of the VRPTW such as trapping in local optimum. In this paper, we present a novel ACO-based optimization method for VRPTW by using customer selection in order to decrease or solve the inefficiency of the customer selection of the ACO process. Moreover, we enhance performance searching of ACO in order to eliminate these small routes from the ACO process. Finally, we proposed the re-initialization technique in order to decrease or solve trapping in local optimum. Experiments conducted on fifty-six maps dataset have shown that the proposed method achieves encouraging performance compared to other ACOs.
Financial information security using hybrid encryption technique on multi-cloud architecture
Authors: Rashid Mustafa Noori , Abed Leith Hamid , Awad Waleed Kareem
Published: September 2022

One of the most severe issues in the cloud is security. In comparison to financial data, so it is extremely sensitive and must be safeguarded against unwanted access. We have developed a proposed system based on three different keys. We divided the data into insensitive, sensitive, and highly sensitive data. The data will be saved on a separate cloud server. The proposed system used different keys for encryption and decryption purposes. The elliptic curve cryptography (ECC) based distributed cloud-based secure data storage (DDSPE) approach was proposed to provide secure large data based data protection across the different clouds. With DDSPE technology, the ECC scheme has been used for encryption and decryption purposes. The cloud is used for simulation. The results of the tests reveal that the suggested DDSPE system is safe and saves time regarding data retention and retrieval. To analyze performance, we compared the DDSPE method with advance encryption standard (AES), blowfish, rivest shamir adleman (RSA), security-aware efficient distributed storage (SA-EDS), and attribute based encryption (ABE) based secure distributed storage (ASDSS). In terms of information retention and recovery, our methodology is quite effective because it requires less amount of time as compared to other strategies.
Enhanced backstepping control for disturbances rejection in quadrotors
Authors: Saibi Ali , Belaidi Hadjira , Boushaki Razika , Eddine Recham Zine , Hafid Amrouche
Published: September 2022

This work studies the issue of quadrotor trajectory tracking control in presence of disturbances and model uncertainties. The paper starts by extracting the kinematics and dynamics models of the quadrotor. This results in the motion equations, which eventually serve as a blueprint for creating the suggested smart flight control scheme. Secondly, an enhanced backstepping controller (BSC) is developed and tested to keep the quadrotor tracking the desired trajectory both in steady state and in presence of disturbances. Finally, BSC beside two other controllers: sliding mode controller (SMC) and proportional derivative controller (PDC) are implemented in MATLAB/Simulink and the obtained results are compared and conclusions are extracted. Therefore, it is established that PDC is not robust to disturbances as noise will be amplified due to the derivative term. Whereas, although SMC is robust to parameter variations and disturbances; however, it is not continuous which may affect the actuators due to the increased gains which may saturate them. In contrast, BSC requires too many tuning parameters; however, it ensures Lyapunov Stability and does not depend on the system as it does not involve cancelling system nonlinearity. Moreover, BSC results are 1017 better than the results of the two other controllers.
A hybrid recommender system based on customer behavior and transaction data using generalized sequential pattern algorithm
Authors: Somya Ramos , Winarko Edi , Priyanta Sigit
Published: September 2022

In the future, the quality of product suggestions in online retailers will influence client purchasing decisions. Unqualified product suggestions can result in two sorts of errors: false negatives and false positives. Customers may not return to the online store as a result of this. By merging sales transaction data and consumer behavior data in clickstream data format, this work offers a hybrid recommender system in an online store utilizing sequential pattern mining (SPM). Based on the clickstream data components, the product data whose status is only observed by consumers is assessed using the simple additive weighting (SAW) approach. Products with the two highest-ranking values are then coupled with product data that has been purchased and examined in the SPM using the generalized sequential pattern (GSP) method. The GSP algorithm produces rules in a sequence pattern, which are then utilized to construct product suggestions. According to the test results, product suggestions derived from a mix of sales transaction data and consumer behavior data outperform product recommendations generated just from sales transaction data. Precision, recall, and F-measure metrics values rose by 185.46, 170.83, and 178.43%, respectively.
Dynamic voltage restorer based on particle swarm optimization algorithm and adaptive neuro-fuzzy inference system
Authors: Salman Saddam Subhi , Humod Abdulrahim Thiab , Hasan Fadhil A.
Published: September 2022

This article uses a dynamic voltage restorer to tackle a wide range of power quality issues, such as voltage drooping and swelling, spikes, distortions, and so on. The proportional controller, integrated controller (PI), and adaptive neuro-fuzzy inference system (ANFIS) are proposed dynamic voltage restorer (DVR) controllers. The control strategy's goal is to employ an injection transformer to mitigate for the needed voltage and keep the load voltage fixed. The settings of the PI controller are fine-tuned using two methods: trial and error and intelligent optimum. Particle swarm optimization (PSO) is now the most effective method. In terms of settling time, overshoot, undershoot, and disturbances around the final value, the PSO-tuned PI controller outperforms the trial-and-error PI controller. The ANFIS controller is used to regulate the DVR's responsiveness through the PI-PSO controller. The PI-PSO data is used as training data by the ANFIS controller. The results show that a DVR with an ANFIS controller outperforms a PI-PSO controller in terms of overshoot, undershoot spike voltage, steady state time, and settling time. In the case of a failure voltage, the DVR with an ANFIS controller has a 27% undershoot spike voltage while the PI-PSO controller has a 30% undershoot spike voltage.
Physical layer security of reconfigurable intelligent surface empowered wireless network with cooperative jammer
Authors: Odeyemi Kehinde Oluwasesan , Owolawi Pius Adewale , Olufemi Olakanmi Oladayo
Published: September 2022

This paper evaluates the physical layer security performance of a reconfigurable intelligent surface (RIS) enabled wireless network in the presence of a passive eavesdropper. To secure the information transmission, a cooperative jammer is proposed to generate interference signal that degrade the performance of the eavesdropper. The source-to-RIS and RIS-to-destination links are subjected to Rician and Rayliegh fading distributions with phase errors, respectively, while other transmission links in the network follow the Nakagami-m fading distributions. The system phase error of the RIS is estimated by the von Mises distribution. To quantify the secrecy performance of the concerned system, the exact closed-form expressions in terms of connection outage probability (COP), security outage probability (SOP), and secrecy throughput (ST) are derived. In addition, the asymptotic expression of the system COP is obtained at high signal-to-noise ratio (SNR), providing more insight about the system performance. The accuracy of the derived expression is justified by Monte Carlo simulation. Also, the results clarify the analysis of the security performance, taking into account the impact of system and channel parameters on the system.
Energy management strategy with smart building control system to reduction electrical load using ANN
Authors: Jalal Bareq Musaab , Hamid Al-Rubayi Rashid
Published: September 2022

Buildings have long been large energy consumers, and inadequate control of heating, ventilation, and air conditioning kinds of variable refrigeration flow (HVAC-VRF) and lighting systems. To reduce energy consumption by using a smart building control system (SBCS) in a building was created using occupant control, daylight sensors, weather condition variations, load consumed, and changes in solar power. The model was tested using MATLAB/Simulink, and it was then utilized to investigate the impact of an integrated system on energy usage based on two scenarios. The first scenario was tested in a simulation of building occupant behavior, meteorological variables, daylight sensors, temperature, and load control. This resulted in energy savings for the HVAC system (23% on summer days and 16% on winter days), and lighting system energy savings (22% on summer days and 15% on winter days). In the second scenario, the building was tested to integrate PV system power with load consumption by using the artificial neural network (ANN) algorithm to manage building load consumption by PV, grid, and diesel generator. As a result, the energy savings were 56% on a summer day and 65% on a winter day of the combined energy utilized by the HVAC and lights.
Grasshopper optimization algorithm based path planning for autonomous mobile robot
Authors: Shareef Asmaa , Al-Darraji Salah
Published: September 2022

Autonomous mobile robots have become very popular and essential in our life, especially in industry. One of the crucial activities of the robot is planning the path from a start point to a target point, avoiding obstacles in the environment. Recently, path planning received more attention, and many methodologies have been proposed. Path planning studies have shown the effectiveness of swarm intelligence in complex and known or unknown environments. This paper presents a global path planning method based on grasshopper optimization algorithm (GOA) in a known static environment. This algorithm is improved using the bias factor to increase the efficiency and improve the resulting path. The resulting path from this algorithm is further enhanced using an improved version multinomial logistic regression algorithm (MLR). The algorithms were evaluated using three different large environments of varying complexities. The GOA algorithm has been compared with the ant colony optimization algorithm (ACO) using the same environments. The experiments have shown the superiority of our algorithm in terms of time convergence and cost.
Reliability analysis of single phase quazi Z source inverter for standalone photovoltaic system
Authors: Rao Bharathi , Kumar M. Satyendra
Published: September 2022

Quazi Z source inverter has an advantage that it can boost or buck voltage to be given to an inverter. Operation of a QZSI is similar to a Z source inverter (ZSI). A conventional photovoltaic application uses a two-stage topology of boost converter and an inverter. A QZSI can be replaced for a two-stage topology to serve the purpose. A 3 KW single phase QZSI is designed and simulated for a standalone photovoltaic system in this paper. The results for total harmonic distortion (THD) of output voltage and mean time between failure (MTBF) of the overall system is compared with a conventional ZSI and two-stage topology for the same rating. MTBF of the overall system is computed using reliability block diagram method. Reliability curve is also plotted for all the three considered topologies.
An maximum power point tracking interface circuit for low-voltage DC-type energy harvesting sources
Authors: Yun Eun Jeong , Park Jong Tae , Yu Chong Gun
Published: September 2022

This paper presents a maximum power point tracking (MPPT) interface circuit for low-voltage DC-type energy harvesting sources such as light and thermal energy. Most energy harvesting systems used in miniature-sized sensor systems require start-up circuits because the output voltages of small-sized energy transducers are very low and not enough to directly power electronic systems. The proposed interface circuit is driven directly by the low output voltages of small size energy transducers, eliminating the need for complex start-up circuitry. A simple MPPT controller with the fractional open-circuit voltage (FOCV) method is designed and fabricated in a 65-nm complementary metal oxide semiconductor (CMOS) process. Measurement results show that the designed circuit can track the MPP voltage even in the presence of the open-circuit voltage fluctuations and can operate properly at operating voltages as low as 0.3 V. The interface circuit achieves a peak power efficiency of 97.1% and an MPPT accuracy of over 98.3%.
Distributed brain tumor diagnosis using a federated learning environment
Authors: Mahlool Dhurgham Hassan , Abed Mohamed Hamzah
Published: September 2022

In the last few years, a very huge development has occurred in medical techniques using artificial intelligence tools, especially in the diagnosis field. One of the essential things is brain tumor (BT) detection and diagnosis. This kind of disease needs an expert physician to decide on the treatment or surgical operation based on magnetic resonance imaging (MRI) images; therefore, the researchers focus on such kind of medical images analysis and understanding to help the specialist to make a decision. in this work, a new environment has been investigated based on the deep learning method and distributed federated learning (FL) algorithm. The proposed model has been evaluated based on cross-validation techniques using two different standard datasets, BT-small-2c, and BT-large-3c. The achieved classification accuracy was 0.82 and 0.96 consecutively. The proposed classification model provides an active and effective system for assessing BT classification with high reliability and accurate clinical findings.