bluuebunny/crossref_metadata_2025 huggingface dataset:
Construction and Analysis of School Moral Education System Based on Big Data Technology
Authors: Du Gang , Sun Yufeng , Zhao Yue
Published: September 2022

Using educational big data to enhance the performance of the moral education system in colleges and universities is important to smart moral education. However, educational big data has an obvious diversity. For this reason, combined with multiple resources, the “adaptive” data processing method with strong dynamic and active adaptability is cited to analyze the big data in colleges and universities to understand and grasp the dynamic of users’ thoughts and behaviors. At the same time, according to the interest bias, the concept of “interest group” is used to classify the interests of a large number of users form different groups for modeling and use it as the core framework to build an intelligent research model of moral and ideological education. Then, on this basis, the experiment was carried out using the WeChat public account data of 20 universities, and the WeChat Communication Index (WCI), WeChat Service Index (WSI), and WeChat Management Index (WMI) were used to evaluate the function of the WeChat public accounts of universities in ideological and moral education. The roles and characteristics in the system are classified into three categories: communication-oriented, service-oriented, and general type. Among them, the communication-oriented type is mainly used to provide and disseminate educational information, while the service type can be the bridge to build direct connection between teachers and students. Therefore, the performance of ideological and moral education system in universities can be enhanced. While the general type is multifunctional, its performance in information dissemination and interaction is modest. Therefore, it is suggested that the WeChat public account should take the dissemination of information and the interaction between teachers and students into account.
Analysis and Application of Crosscultural Knowledge System Structure in English Teaching Based on Hierarchical Correlation Analysis
Authors: Jianhong Zhu
Published: September 2022

With the prosperity of the economy, China’s foreign exchanges are becoming more and more frequent, and English is becoming more and more important as the common language of international communication. For decades, Chinese educators and scholars have achieved great success in teaching English, but there are also some problems, including neglecting the teaching of crosscultural knowledge in English classrooms. And in the crosscultural English teaching based on hierarchical correlation analysis, its specific structure system is not complete, and its application scope is also less. Therefore, this paper analyzes hierarchical correlation analysis, teaching mode, etc., which lays the foundation for subsequent research. In addition, combined with the current research status, this paper analyzes the past crosscultural knowledge system and proposes a new structural model. A large number of experiments have verified the application effect of the improved knowledge architecture model in crosscultural English teaching under the hierarchical correlation analysis. The new model can improve students’ crosscultural awareness and crosscultural communication competence and improve the previous teaching system structure. There is a problem that both hierarchical teaching and overall planning cannot be taken into account. This paper studies the crosscultural issues in English teaching and conducts research from the following four aspects: first, it discusses the cultivation of crosscultural communicative competence of college students in English teaching and proposes some feasible solutions to the problem of neglecting the integration of crosscultural knowledge in English teaching. Sexual suggestions cultivate students’ crosscultural awareness of English learning and improve the efficiency of English learning. The second is to point out that college school students should pay attention to the differences in cultural communication between China and foreign countries and develop the awareness and ability of crosscultural communication. The third is to explore ways to improve students’ crosscultural communicative competence and cultivate all-round English talents for the country and society. The fourth is how to achieve crosscultural communication, so that people with different cultural backgrounds can communicate and learn smoothly. Based on the above problems, this paper adopts the method of case analysis to discuss the problem of imperfect cross-cultural knowledge system in English teaching in China, and the educational structure is incomplete.
lncRNA NEAT1 Promotes Colorectal Cancer Progression by Increasing Inflammation
Authors: Chen Qingmin , Qin Yi , Lin Min , Li Zhao , Tang Weizhong
Published: September 2022

Background. Colorectal cancer is a digestive tract malignant tumor, ranking the second mortality and the third incidence cancer worldwide. The abnormal expression of NEAT1 is related to the occurrence and development of colorectal cancer. However, the specific mechanism of NEAT1 mediated-inflammatory pathway in the progression of colorectal cancer is still unclear. Methods. In this study, expression of NEAT1 in colorectal cancer patients was analyzed by bioinformatics. Clinical samples including peripheral blood and colorectal cancer tissues were collected for qRT-PCR, Western blot, and immunohistochemistry assay. The role of NEAT1 in the colorectal cancer progression was further confirmed by both in-vivo and in-vitro functional experiments. Results. By bioinformatics prediction, it is found that NEAT1 expression level is significantly higher in the peripheral blood of patients with colorectal cancer and is associated with poor prognosis. In-vitro functional studies indicated that NEAT1 knockdown suppressed the proliferation and migration of colorectal cancer cells by mediating inflammatory response. In-vivo tumorigenesis experiments showed that NEAT1 knockdown suppressed tumor growth. Conclusion. Abnormal high expression level of NEAT1 in colorectal cancer tissues and cells leads to poor prognosis. Mechanistically, NEAT1 triggers off the proliferation and migration of colorectal cancer cells through promoting the inflammatory reaction. Clinically, the expression level of NEAT1 in serum may be a marker for diagnosis and prognosis of colorectal cancer.
[Retracted] Auricular Point Pressing Beans + Continuous Intervention on β2‐MG, Curative Effect, and Relationship with Prognosis in Elderly MHD Sufferers
Authors: Zhou Yan , Cai Weiwei , Wang Xiaoxia
Published: September 2022

The effect of auricular point pressing beans + continuous intervention on β2‐microglobulin (β2‐MG), curative effect, and its relationship with prognosis in elderly maintenance hemodialysis (MHD) sufferers are explored. This examination selects 110 elderly MHD sufferers who are treated in our hospital from February 2020 to February 2021 as the examination objects, and they are randomly divided into two sets, 55 cases in the examination set and 55 cases in the contrast set. The sufferers in the contrast set are given routine attendance care, and the sufferers in the examination set are given auricular acupuncture combined with continuous attendance intervention. The experimental results show that for MHD sufferers, auricular acupuncture combined with continuous attendance during the therapy period can effectively enhance the clinical therapy effect, enhance the sufferers’ inflammatory indexes and renal function indexes, reduce the readmission rate, and enhance the prognosis quality of life.
Providing a Mathematical Routing‐Inventory Model for the Drug Supply Chain Considering the Travel Time Dependence and Perishability on Multiple Graphs
Authors: Ali Madad , Ng Martha , Dias Rui , Muda Iskandar , Al-Obaidi Riyadh , Abdullaeva Barno , Sharma Himanshu , Abu Al-Rejal Hussein Mohammed Esmail , Hammid Ali Thaeer
Published: September 2022

High turnover in the pharmaceutical industry, the location of placebos in urban spaces, and the high rate of corruption of products in this industry are the distinguishing features of the drug supply chain. Thus, to survive and maintain competitive advantages in the current business environment, managers are active in this background to implement the theoretical foundations of supply chain management. One of the influential areas in this category is integrated inventory management, inventory control, and vehicle routing. Therefore, this study mainly aims to analyze and define a routing problem, inventory in the drug supply chain with perishable products, and travel time dependence on multiple graphs with travel time dependence. The Box–Jenkins forecasting method has been utilized to meet the study’s aim and deal with demand uncertainty. This method can identify the best pattern governing the data. Finally, the mathematical model is validated, and managerial perspectives are provided. The study results demonstrated the possibility of achieving cost‐saving and reducing product spoilage. Applying the solutions of this model can provide some inherent social and environmental advantages, including reducing traffic load and emissions.
Seroprevalence of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) among Blood Donors from Bahir Dar, Ethiopia
Authors: Assefa Ayenew , Shiferaw Dibekulu , Bishaw Zewdu , Kiros Teklehaimanot
Published: September 2022

Background. Hepatitis B virus (HBV), a highly contagious virus, is a circular partial double-stranded DNA virus. Hepatitis C virus (HCV) is an enveloped, single-stranded RNA virus with a major blood-borne infection worldwide. Infection of HBV and HCV among blood donors is an important public health problem. Objective. To assess the seroprevalence of hepatitis B and C viruses and associated factors among blood donors at Bahir Dar Blood Bank, Northwest Ethiopia. Method. Institutional-based cross-sectional study was conducted at Bahir Dar Blood Bank from May 18, 2020 to July 27, 2020. A systematic random sampling technique was used to select 426 participants. Variables having a

p

value of <0.05 with 95% CI and AOR were considered statistically significant. Results. From 426 proposed participants, 418 (98.1%) of them completed the face-to-face interview. The seroprevalence of HBV and HCV among study participants was 4.07% and 0.48%, respectively. Having multiple sexual partners (AOR = 10.356: 95% CI: (2.277–47.099)), having a family history of hepatitis (AOR = 8.106: 95% CI: (1.278–51.403)), and having sharp materials sharing experience (AOR = 11.313, 95% CI: (1.144–111.864)) have a significant association with HBV infection. No risk factors were found for HCV infectious markers. Conclusion and Recommendations. This study showed that the seroprevalence of HBV and HCV infections confirmed with positive tests among voluntary blood donors in Bahir Dar Blood Bank was 4.07% and 0.48%, respectively. HBV seroprevalence among blood donors in this study has remained stable when compared to a study done in 2013 in the same population. Strict donor selection, safe sex practice, using proper safety precautions when offering care to a family member, and immunization of people at risk could constitute an important package of a prevention program.
Single-Valued and Interval-Valued Neutrosophic Hidden Markov Model
Authors: Nagarajan D. , Kavikumar J.
Published: September 2022

Neutrosophic sets are employed to be handled indeterminacy in a real-life situation. Thus, neutrosophic approaches in the medical domain prove their excellence. The neutrosophic hidden Markov model is an inventiveness domain for uncertainty. The existing hidden Markov models are not able to consider the uncertainty information, but the neutrosophic hidden Markov model effectively finds the optimal path between the states where vagueness exists. The proposed study comprises the idea of single-value and interval-valued neutrosophic sets into the hidden Markov model and decoding the path using the Viterbi algorithm. It has been used to determine the sequence of motility primitives for an afforded time series. The method is to be handled without having a lower membership function for falsity, and because of this advantage, one can save time significantly during computation. The neutrosophic score helps to find the crisp value of the probability. Moreover, the proposed work highlights the main childhood obesity risk in lockdown situations.
Targeted Diagnosis, Therapeutic Monitoring, and Assessment of Atherosclerosis Based on Mesoporous Silica Nanoparticles Coated with cRGD-Platelets
Authors: Zhang Wei , Lv Zheng , Zhang Yupeng , Gopinath Subash C. B. , Yuan Yi , Huang Deyou , Miao Liu
Published: September 2022

Objective. The off-target effects and severe side effects of PPARα and LXRα agonists greatly limit their application in atherosclerosis (AS). Therefore, this study intended to use mesoporous silica nanoparticles as carriers to generate MnO nanoparticles in situ with T1WI-MRI in mesoporous pores and simultaneously load PPARα and LXRα agonists. Afterward, cRGD-chelated platelet membranes can be used for coating to construct a new nanotheranostic agent. Methods. cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles were synthesized by a chemical method. Dynamic light scattering (DLS) was utilized to detect the size distribution and polydispersity index (PDI) of the nanoparticles. The safety of the nanoparticles was detected by CCK8 in vitro and HE staining and kidney function in vivo. Cell apoptosis was detected by flow cytometry detection and TUNEL staining. Oxidative stress responses (ROS, SOD, MDA, and NOX levels) were tested via a DCFH-DA assay and commercial kits. Immunofluorescence and phagocytosis experiments were used to detect the targeting of nanoparticles. Magnetic resonance imaging (MRI) was used to detect the imaging performance of cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles. Using western blotting, the expression changes in LXRα and ABCA1 were identified. Results. cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles were successfully established, with a particle size of approximately 150 nm and PDI less than 0.3, and showed high safety both in vitro and in vivo. cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles showed good targeting properties and better MRI imaging performance in AS. cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles showed better antioxidative capacities, MRI imaging performance, and diagnostic and therapeutic effects on AS by regulating the expression of LXRα and ABCA1. Conclusion. In the present study, cRGD-platelet@MnO/MSN@PPARα/LXRα nanoparticles with high safety and the capacity to target vulnerable plaques of AS were successfully established. They showed better performance on MRI images and treatment effects on AS by promoting cholesterol efflux through the regulation of ABCA1. These findings might address the problems of off-target effects and side effects of nanoparticle-mediated drug delivery, which will enhance the efficiency of AS treatment and provide new ideas for the clinical treatment of AS.
Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network
Authors: Nonita S. , Xalikovich Pardayev Abdunabi , Kumar C. Ramesh , Rakhra Manik , Samori Issah Abubakari , Maquera Yuselino Maquera , Gonzáles José Luis Arias
Published: September 2022

This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When the aggregating node is utilized to transmit the data to the base station, the research attempts to show that the traffic situations of WSN may be modified appropriately by parameter tuning and algorithm modification. IWD is used to generate an optimum data aggregation tree in WSN as one of its applications. IWD assumes that all nodes in the environment are identical, resulting in identical parameter updates for all nodes. In practical scenarios, however, diverse nodes with variable beginning energy, communication range, and sensing range characteristics are deployed. In order to replicate the influence of heterogeneity in the environment, improved IID (IIWD) is offered as an enhancement to the original IID. The suggested enhancement is appropriate for scenarios in which the aggregation node is utilized to transmit data to the base station in heterogeneous configurations. In terms of residual energy, dead nodes, payload, and network lifespan, a series of simulation results demonstrates that the proposed IIWD significantly improves the accuracy and effectiveness of the IWD method in comparison.
Code2tree: A Method for Automatically Generating Code Comments
Authors: Wen Wanzhi , Chu Jiawei , Zhao Tian , Zhang Ruinian , Zhi Bao , Shen Chenqiang
Published: September 2022

Source code comments can improve the efficiency of software development and maintenance. However, due to the heterogeneity of natural language and program language, the quality of code comments is not so high. So, this paper proposes a novel method Code2tree, which is based on the encoder-decoder model to automatically generate Java code comments. Code2tree firstly converts Java source code into abstract syntax tree (AST) sequences, and then the AST sequences are encoded by GRU encoder to solve the long sequence learning dependency problem. Finally, the attention mechanism is introduced in the decoding stage, and the quality of the code comment is improved by increasing the weight of the key information. We use the open dataset java-small to train the model and verify the effectiveness of Code2tree based on common-used indicators BLEU and F1-Score.
An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules
Authors: Li Xiangxia , Li Bin , Yin Hua , Xu Bo
Published: September 2022

Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.
Spider Parental Care and Awe-Inspiring Egg Sac (Cocoon)
Authors: Ewunkem Akamu Jude , Agee Kyle
Published: September 2022

Spiders (Arachnida, Araneae) represent one of the largest groups of organisms on Earth with more than 45,000 recorded species found in nearly all terrestrial communities. In these communities, spiders are obligate predators and generalist consumers regulating the density of pests. Spiders have a stupefying array of prey hunting strategies ranging from ambushing to the use of complex silk nares. Spider silk is incredibly tough and can be used for many applications such as wrapping and immobilization, catching prey, as dragline to connect spiders to the web, as ballooning to aid dispersal of juveniles, as shelters in burrows, for mating, and as egg sacs (or cocoons). Typically, spider egg sacs are multilayered, complex structures that physically protect the eggs and hatchlings against parasitoids, predators as well as changing temperatures. Much research has been undertaken to elucidate the ecological role of spiders and the mechanical characteristics of silks. However, few studies have examined the parental care of spiders and the role of egg sacs. This review goes into great detail about spider parental care and the functions of egg sacs.
Establishment and Optimization of College Students Physical Health Standard Test System Based on Neural Network Reliability Evaluation Model
Authors: Zhang Jianhua , Zhang Shubing , Li Dan
Published: September 2022

At present, the physical condition of college students is declining day by day, and the relevant sports departments pay more and more attention to the daily physical exercise of college students. To deeply understand the physical health status of college students and further adjust the health intervention measures, this paper constructs a neural network reliability evaluation model and carries out the physical health standard test on 50 college students from the sports department of Nanchang University and analyzes the physical health test data by using the neural network error reverse evaluation method; to optimize the physical health test system, the accuracy of physical health test is fitted in parallel. Then, according to the fitting degree of the test results, the direction of health intervention is predicted, and some specific suggestions are made for college students’ health indicators such as running and vital capacity. The research shows that the fitting degree of the neural network reliability evaluation model is 86%, and the accuracy of the neural network model is high. In the 50 college students’ physical health project, the fitting degree of 50 m running and vital capacity is higher than that of long distance running. Therefore, the neural network reliability evaluation model is feasible for college students’ physical health tests and can rank the intervention items, which has great practical significance for the improvement of college students’ physical health.
Downregulation of SIRT3 Aggravates Lung Ischemia Reperfusion Injury by Increasing Mitochondrial Fission and Oxidative Stress through HIF-1α-Dependent Mechanisms
Authors: Liu Chunxia , Pei Shenglin , Dai Huijun , Liu Zhen , Ye Mengling , Liu Hao , He Xiaojing , Wu Siyi , Qin Yi , Lin Fei
Published: September 2022

Lung ischemia-reperfusion injury (LIRI) is a severe multifaceted pathological condition that can lead to poor patient outcome where oxidative stress and the resulting inflammatory response can trigger and exacerbate tissue damage in LIRI patients. Sirtuin3 (SIRT3), a member of the sirtuin family, protects against oxidative stress-related diseases. However, it remains unclear if and how SIRT3 alleviates lung injury induced by ischemia/reperfusion (I/R). Our previous study showed that lung tissue structures were severely damaged at 6 h after lung I/R in mice, however, repair of the injured lung tissue was significant at 24 h. In this study, we found that both SIRT3 mRNA and protein levels were markedly increased at 24 h after lung I/R in vivo. Meanwhile, inhibition of SIRT3 aggravated lung injury and inflammation, augmented mitochondrial fission and oxidative stress and increased Hypoxia-inducible factor-1α (HIF-1α) expression in vivo. The results suggest that SIRT3 may be an upstream regulator of HIF-1α expression. Knockdown of SIRT3 resulted in excessive mitochondrial fission and increased oxidative stress in vitro, and we found that knocking down the expression of HIF-1α alleviated these changes. This suggests that the SIRT3-HIF-1α signaling pathway is involved in regulating mitochondrial function and oxidative stress. Furthermore, inhibition of dynamin-related protein 1 (Drp-1) by the inhibitor of mitophagy, Mdivi-1, blocked mitochondrial fission and alleviated oxidative stress in vitro. Taken together, our results demonstrated that downregulation of SIRT3 aggravates LIRI by increasing mitochondrial fission and oxidative stress. Activation of SIRT3 inhibits mitochondrial fission and this mechanism may serve as a new therapeutic strategy to treat LIRI.
Personalized Recommendation of Online Shopping Products Based on Online Fast Learning through Latent Factor Model
Authors: Shi Meng
Published: September 2022

In order to improve the personalized recommendation effect of online shopping products, this article combines online fast learning through latent factor model to construct a personalized virtual planning recommendation system for online shopping products. Moreover, this article improves on the ONMTF model. In the problem of cross-domain recommendation, this article clusters users and items in each data domain with hidden scoring patterns and learns common scoring patterns that can be shared between different data domains to deal with the data sparse problem that often occurs in recommender systems. The experimental research results show that cross-domain recommendation can indeed use the implicit semantics or topics between domains to share information and knowledge, thereby improving the accuracy of recommendation.
Effects of Dexmedetomidine on Postoperative Pain and Recovery Time in Obese Patients
Authors: Zhang Qian , Zhang Zhaojian , Wang Bing , Zhao Chao , Xu Yuedan
Published: September 2022

Objective. The objective of this study is to investigate the effect of dexmedetomidine on postoperative pain and recovery time in obese patients. Methods. A total of 100 obese patients with body mass index

BMI

≥
30

 kg/m2 who underwent laparoscopic sleeve gastrectomy under general anesthesia in our hospital from January 2019 to December 2021 were included and assigned into DEX group (dexmedetomidine group) and NS group (normal saline group). The bariatric surgery patients who were given normal saline pump were the NS group (

n
=
50

), and the bariatric surgery patients who were given the dexmedetomidine pump were the DEX group (

n
=
50

). The patients in the DEX group were given continuous intravenous infusion of dexmedetomidine before, during, and after induction of anesthesia at a dose of 0.4 μg. kg-1. h-1, 0.4 μg·kg-1. h-1, 0.2 μg·kg-1. h-1, respectively. The NS group was infused with the same volume of normal saline for the same time. The two groups of patients were treated with the same anesthesia induction and maintenance program. By comparing the operation, anesthesia, postoperative extubation, and recovery time of the two groups of patients, the effect of dexmedetomidine on the postoperative recovery time of obese patients was analyzed. Visual analogue scale (VAS) and adverse reactions were compared to analyze the effect of dexmedetomidine on postoperative pain in obese patients. Results. The operation, anesthesia, postoperative extubation, and recovery time of the DEX group were significantly lower than those of the NS group, whereas the VAS and adverse reactions were significantly lower than those in the NS group (

P
<
0.05

). Conclusion. An appropriate dose of dexmedetomidine in bariatric surgery for morbidly obese patients can effectively shorten the recovery time and extubation time of patients, reduce postoperative pain and the incidence of adverse reactions, and is worthy of clinical application. Dexmedetomidine 2 μg/kg has promising anesthesia benefits in bariatric surgery of obese patients, can provide favorable analgesia and quality of recovery, help reduce the degree of stress response of patients, and does not increase the risk of adverse events. However, this study has certain limitations, so physicians should tailor the dosage according to the patient’s physical condition in clinical practice.
A Fabry Disease Patient Who Developed Hypersensitivity Reaction against Agalsidase Beta following COVID-19 Infection
Authors: Sonmez Ozge , Ozcan Seyda Gul , Trabulus Sinan , Seyahi Nurhan
Published: September 2022

Fabry disease (FD) is a rare, X-linked inherited lysosomal storage disorder, characterized by the accumulation of globotriaosylceramide (Gb3) due to the deficiency or absence of alpha-galactosidase A. Due to the accumulation of Gb3, cardiac, renal, neurological, and skin manifestations can be observed. Enzyme replacement therapy (ERT) with agalsidase alfa or agalsidase beta is the cornerstone in the management of FD. Both enzymes are clinically effective and widely used. In this study, we present a 19-year-old male patient with FD who had received ERT for almost two and half years without any complications. In January 2021, he was diagnosed with COVID-19 infection. Later, he developed an infusion reaction during his first ERT infusion following the resolution of COVID-19 infection. The patient experienced shortness of breath, shivering, and rash. Despite decreased infusion rate and premedication in repetitive infusion, his symptoms were not resolved. Subsequently, he developed an IgE antibody against agalsidase beta, and his skin prick test was positive. Since IgG positivity against agalsidase beta was also detected, agalsidase beta was replaced with agalsidase alfa. The patient did not experience any allergic reaction with agalsidase alfa. Moderate to severe allergic reactions during ERT infusion should be alarming for IgE development. Furthermore, COVID-19 should be considered a trigger for allergic reaction against ERT in patients with FD.
Metamaterial and metasurface based emitters for solar thermal photovoltaic applications: analytical review
Authors: Anggraini Lydia , Tarigan Hendra Jaya , Simatupang Joni Welman
Published: September 2022

The motivation behind this paper stemmed from the fact that the world consumes the fast-depleting fossil fuel energy. Before the fossil fuel runs out, new technologies to harvest energy from alternative sources are needed. Sunlight is clean, free and abundant. The market for solar thermal and photovoltaic electricity generation is expanding rapidly. Therefore, an analytical review on the types of emitter for solar thermal photovoltaic (STPV) applications utilizing metamaterials and metasurfaces is presented in this research study. STPV is still important in the development of an emitter technology. STPV classifications based on the types of materials, compositions, dimensions, geometries, and long-term temperature stability are considered. The ability to engineer STPV by controlling one or more of the foregoing physical parameters are useful for researchers. Different types of design and simulation tools are considered. The near future plans are to optimize the efficiency of the emitter and investigate how various layers and different combinations of metamaterials affect such an efficiency by employing a simulation tool such as finite-difference time-domain (FDTD, Lumerical).
Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning
Authors: Munsarif Muhammad , Sam’an Muhammad , Safuan Safuan
Published: September 2022

Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
Smart grid application in the Iraqi power system: current and future challenges
Authors: Ismaeel Khalel Saraa , Hikmat Aziz Nagham , Abdulrhman Al-Flaiyeh Maha
Published: September 2022

A smart grid could generate and distribute electricity effectively economically, securely and sustainably. It offers customers more information and choice, including the export of energy to the grid, demand-side participation and energy efficiency. However, to implement a smart grid into the Iraqi power system, various challenges should be faced, especially concerns related to understanding the contents and features of this network compared with the traditional Iraqi network. As well as the challenges and risks of implementing the smart grid itself in the modern work environment, especially with the tremendous progress in communication technologies, which has brought serious problems to the operation of the network such as cyberattacks. Also, the traditional Iraqi network suffers from various problems, including the large deficit in the generated power-to-load demand ratio, which reaches about more than a third, and the great destruction that the network has been subjected to because of the wars that the country has been exposed to during the past three decades. In this study, a clear vision was presented to researchers and engineers who are interested in applying the smart grid in Iraq on this vital topic, which will greatly help in applying this essential matter to develop the work of the Iraqi power system and improve the efficiency and services it provides.
Hybrid load-balancing algorithm for distributed fog computing in internet of things environment
Authors: Kadhim Abrar Saad , Manaa Mehdi Ebady
Published: September 2022

Fog computing is a novel idea created by Cisco that provides the same capabilities as cloud computing but close to objects to improve performance, such as by minimizing latency and reaction time. Packet failure can happen on a single fog server across a large number of messages from internet of things (IoT) sensors due to several variables, including inadequate bandwidth and server queue capacity. In this paper, a fog-to-server architecture based on the IoT is proposed to solve the problem of packet loss in fog and servers using hybrid load balancing and a distributed environment. The proposed methodology is based on hybrid load balancing with least connection and weighted round robin algorithms combined together in fog nodes to take into consideration the load and time to distribute requests to the active servers. The results show the proposed system improved network evaluation parameters such as total response time of 131.48 ms, total packet loss rate of 15.670%, average total channel idle of 99.55%, total channel utilization of 77.44%, average file transfer protocol (FTP) file transfer speed (256 KB to 15 MB files) of 260.77 KB/sec, and average time (256 KB to 15 MB) of 19.27 sec.
A comprehensive overview of the ADALINE method applied to rapid voltage sags detection in multi-motors drive systems
Authors: Bensaid Mounir , Ba-Razzouk Abdellfattah , Elharoussi Mustapha
Published: September 2022

Several strategies have been developed for identifying power quality issues, monitoring them, and compensating for relevant disturbances. In this field, online estimate of amplitudes and phase angles of network voltages and currents is commonly used. The adaptive linear neuron (ADALINE)-based voltage sag detection algorithm with least mean square (LMS) adaptation allows for rapid convergence of estimate techniques based on artificial neural networks (ANN). This approach has the advantage of being straightforward to implement on hardware and based on simple calculations (essentially multiply and accumulate "MAC"). This paper gives a comparison of the performance of two ADALINE approaches ("with" and "without" error supervision) for detecting and estimating voltage dips. The described techniques and models of a two-coupled motor system were implemented in MATLAB/Simulink/SimPowerSystems to run simulations under various fault scenarios in order to create the three-phase voltage sag alarm signal. The simulation outcomes are presented and debated.
Machine learning approaches in the diagnosis of infectious diseases: a review
Authors: Mishra Smriti , Kumar Ranjan , Tiwari Sanjay Kumar , Ranjan Priya
Published: September 2022

Infectious diseases are a group of medical conditions caused by infectious agents such as parasites, bacteria, viruses, or fungus. Patients who are undiagnosed may unwittingly spread the disease to others. Because of the transmission of these agents, epidemics, if not pandemics, are possible. Early detection can help to prevent the spread of an outbreak or put an end to it. Infectious disease prevention, early identification, and management can be aided by machine learning (ML) methods. The implementation of ML algorithms such as logistic regression, support vector machine, Naive Bayes, decision tree, random forest, K-nearest neighbor, artificial neural network, convolutional neural network, and ensemble techniques to automate the process of infectious disease diagnosis is investigated in this study. We examined a number of ML models for tuberculosis (TB), influenza, human immunodeficiency virus (HIV), dengue fever, COVID-19, cystitis, and nonspecific urethritis. Existing models have constraints in data handling concerns such data types, amount, quality, temporality, and availability. Based on the research, ensemble approaches, rather than a typical ML classifier, can be used to improve the overall performance of diagnosis. We highlight the need of having enough diverse data in the database to create a model or representation that closely mimics reality.
Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods
Authors: Aljuboori Ali Mohsin , Abed Mohammed Hamzah
Published: September 2022

Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.
Analysis of a new voltage stability pointer for line contingency ranking in a power network
Authors: Badrudeen Tayo Uthman , Ariyo Funso Kehinde , Salau Ayodeji Olalekan , Braide Sepiribo Lucky
Published: September 2022

Improper management of reactive power in a power network could lead to voltage instability. This paper presents a well-detailed study on voltage instability due to violation of power equilibrium in a power network and introduces a new voltage stability pointer (NVSP). The proposed NVSP is developed from a reduced 2-bus interconnected network to predict the sensistivity of voltage stability to reactive power variation. The simulation results from MATLAB were evaluated on IEEE 14-bus test system. The contingency ranking was achieved by varying the reactive power on the load buses to its maximum loading limit. The maximum reactive power point was taken at each load bus and the critical lines were ranked according to their vulnerability to voltage collapse. The results were compared with other notable voltage stability indices. The results prove that the NVSP is an essential tool in predicting voltage collapse.
Transfer learning for detecting COVID-19 on x-ray using deep residual network
Authors: Imaduddin Helmi , Hermansyah Brian Aditya
Published: September 2022

Coronavirus 2019 (COVID-19), caused by the SARS-CoV-2 virus, has been a disaster for humanity, especially in the health sector. Covid-19 is a serious disease, a large number of people lose their lives every day. This disease not only affects one country, but the whole world suffers from this viral disease. In the fight against COVID-19 immediate and accurate screening of infected patients is essential, one of the most widely used screening approaches is chest X-Ray (CXR) which is rated faster and cheaper. This study aims to detect patients suffering from COVID-19 through chest X-Ray using a transfer learning approach, the method used is with several deep residual network architectures such as ResNet50, RexNet100, SSL ResNet50, semi-weakly supervised learning (SWSL) ResNet50, Wide ResNet50, SK ResNet34, ECA ResNet50d, Inception ResNet V2, CSP ResNet50, and ResNest50d. Then the results will be compared with previous studies. The study was conducted ten times using different pre-training and got the best results on the SWSL ResNet50 architecture with an accuracy value of 99.28%, this value increased 6.98% from previous studies, 99.51% F1-Score, 99.41% Precision, 99.61% Sensitivity, and 98.33% Specificity, that means this study obtained better results than previous studies.
A review on techniques for improving power quality: research gaps and emerging trends
Authors: Manoj Vasupalli , Khampariya Prabodh , Pilla Ramana
Published: September 2022

Many issues have arisen for the electricity sector because of the widespread use of gadgets that rely on electrical power. India's energy demands are rising rapidly due to the country's rapid economic development. These days, almost every industry makes extensive use of electrical machinery and equipment. Electronic gadgets add a non-linear demand to the power grid, which can cause power quality (PQ) problems such as voltage fluctuations that can damage electrical equipment and even knock the grid down. As the use of DG units becomes more commonplace, complications related to power conversion interfaces, load switching, and other factors emerge as potential bottlenecks in service. Power filters were first created several decades ago to address problems with PQ and to dampen harmonics caused by nonlinear loads. This document summarises previous studies that have investigated the effectiveness of power filters and other techniques for enhancing power quality. There are also descriptions of research that is still going on and where it stands, as well as a look ahead to what its possible future benefits might be.
A hybrid lightweight security approach in internet of things for healthcare application
Authors: Kadhim Ameer Saad , Alazam Ali Haider , Sahib Noor Fahem
Published: September 2022

The internet of things (IoT) is a rapidly developing area that consists of a globally linked network architecture based on the Internet. The internet of healthcare things (IoHT) is a subset of IoT that comprises of smart healthcare devices that are critical in monitoring, processing, storing, and transferring sensitive data. It is confronted with new issues in terms of data privacy protection. To safeguard healthcare information, this work proposes hybrid lightweight ciphers (PRESENT and TEA) that leverage elliptic curve cryptography (ECC) in the key generation phase. The proposed system evaluated using the main network evaluation parameters as throughput in Kbps, delay in ms, packet loss rate (%). The proposed approach provides secure data transmission of IoT devices based on the used lightweight security algorithms, in addition it provides conserving network performance, improving channel resource usage, network latency is increased due to the security level added by PRESENT and TEA with ECC, and decrease number of loss packets compared without security case study.
Acute Kidney Injury After Transcatheter Aortic Valve Replacement Mediates the Effect of Chronic Kidney Disease
Authors: Crimi Gabriele , De Marzo Vincenzo , De Marco Federico , Conrotto Federico , Oreglia Jacopo , D'Ascenzo Fabrizio , Testa Luca , Gorla Riccardo , Esposito Giuseppe , Sorrentino Sabato , Spaccarotella Carmen , Soriano Francesco , Bruno Francesco , Vercellino Matteo , Balbi Manrico , Morici Nuccia , Indolfi Ciro , De Ferrari Gaetano Maria , Bedogni Francesco , Porto Italo
Published: September 2022

Background

Acute kidney injury (AKI) after transcatheter aortic valve replacement (TAVR) is associated with increased mortality. However, it is controversial whether AKI affects prognosis per se, being linked to baseline chronic kidney disease (CKD) and bleeding complications. The aim of this study was to disentangle, applying mediation analysis, the association between AKI and clinical outcome, considering CKD and bleedings.

Methods and Results

Consecutive patients undergoing TAVR were prospectively enrolled at 5 high‐volume centers in Italy. AKI was defined according to Valve Academic Research Consortium‐3 consensus, whereas bleeding with Bleeding Academic Research Consortium. Primary outcome was all‐cause mortality after 1‐year follow‐up. Among 2621 patients undergoing TAVR, AKI occurrence was associated with 1‐year mortality. This association of AKI with the primary end points remained significant after adjusting for baseline risk estimators, either Society of Thoracic Surgeons score (hazard ratio [HR], 2.78 [95% CI, 1.95–3.80],
P
<0.001) or EuroSCORE‐II (HR, 1.85 [95% CI, 1.35–2.56],
P
<0.001). Both AKI and CKD significantly and independently affected primary outcome (HR, 3.06 [95% CI, 2.01–4.64],
P
<0.001 and HR, 1.82 [95% CI 1.27–2.65],
P
<0.01, respectively). The estimated proportion of the total effect of CKD mediated via AKI was, on average, 15%, 95% CI, 4%–29%,
P
<0.001. The significant effect of Bleeding Academic Research Consortium 2–5 bleedings on the primary outcome was not mediated by AKI.

Conclusions

AKI occurs in 1 out of 6 patients and significantly mediates one fifth of the effect of baseline CKD on all‐cause mortality after TAVR. Our analysis supports a systematic effort to prevent AKI during TAVR, which may potentially translate into improved patients' 1‐year survival.