bluuebunny/crossref_metadata_2025 huggingface dataset:
SM4 Chaotic Masking Scheme Against Power Analysis Based on FPGA
Authors: Jiang Zijing , Yan Wenhao , Ding Wei , Yue Linlin , Ding Qun
Published: June 2022

SCA is one of the biggest threats to the security of encryption chip. It can crack the unprotected encryption chip at lower cost and faster speed, which weakens the security of the SM4 encryption algorithm circuit without any protection. In this paper, the SM4 encryption algorithm is implemented on FPGA. The pseudo-random sequence generated by discrete chaotic system is used to randomly mask the intermediate value in the SM4 encryption process. This will disrupt the power consumption in the encryption process, thus preventing power analysis. Experimental results show that the proposed chaotic mask scheme can effectively prevent intermediate value leakage and protect the SM4 encryption system from power analysis attack.
Limit Cycles Bifurcating from a Periodic Annulus in Discontinuous Planar Piecewise Linear Hamiltonian Differential System with Three Zones
Authors: Pessoa Claudio , Ribeiro Ronisio
Published: June 2022

In this paper, we study the number of limit cycles that can bifurcate from a periodic annulus in a discontinuous planar piecewise linear Hamiltonian differential system with three zones separated by two parallel straight lines. We prove that if the central subsystem, i.e. the system defined between the two parallel lines, has a real center and the other subsystems have centers or saddles, then we have at least three limit cycles that appear after perturbations of the periodic annulus. For this, we study the number of zeros of a Melnikov function for piecewise Hamiltonian system and present a normal form for this system in order to simplify the computations.
An Initially-Controlled Double-Scroll Hyperchaotic Map
Authors: Li Yongxin , Li Chunbiao , Liu Sicong , Lei Tengfei , Jiang Yicheng
Published: June 2022

Initial condition-dominated offset boosting provides a special channel for coexisting orbits. Due to the nonlinearity and inherent periodicity, sinusoidal function is often introduced into a dynamical system for multistability design. Typically, the distance between two attractors or two petals of an attractor is fixed. Moreover, any chaotic signal and sequence need to be modified with amplitude and offset for a real application. In this paper, an initially-controlled double-scroll hyperchaotic map is constructed based on two sine functions. Four patterns of the double-scroll hyperchaotic orbits are found as 0-degree, 90-degree, 45-degree and 135-degree. Consequently, different modes for attractor growing are demonstrated. In this case, all the coexisting attractors are arranged in phase space in a direction defined by the initial value and the distance between two petals of any double-scroll orbit is adjusted. Finally, hardware experiments based on STM32 are carried out to verify the theoretical analysis and numerical simulation.
Pseudo-Bautin Bifurcation in 3D Filippov Systems
Authors: Castillo Juan , Verduzco Fernando
Published: June 2022

Consider a piecewise linear dynamical system in [Formula: see text], divided by a switching plane, with the Teixeira Singularity (TS). Under the antiparallelism hypothesis, it is known that the system undergoes the so-called pseudo-Hopf bifurcation (pH). In this paper, we consider a particular position of the antiparallel vectors to yield a two-parametric unfolding of the TS. The bifurcation analysis gives us, besides the curve of pH bifurcation points, a curve of saddle-node bifurcation points for crossing limit cycles. We call this phenomenon the pseudo-Bautin bifurcation, since it exhibits the exact same behavior as the Bautin bifurcation for smooth dynamical systems.
Query Execution Time Analysis Using Apache Spark Framework for Big Data: A CRM Approach
Authors: Yadav Madan Lal
Published: June 2022

Customer Relationship Management (CRM) is a systematic way of working with current and prospective customers to manage long-term relationships and interactions between the company and customers. Recently, Big Data has become a buzzword. It consists of huge data repositories, having information collected from online and offline resources, and it is hard to process such datasets with traditional data processing tools and techniques. The presented research work tries to explore the potential of Big Data to create, optimise and transform an insightful customer relationship management system by analysing large amount of datasets for enhancing customer life cycle profitability. In this research work, a dataset, “Book Crossing” is used for Big Data processing and execution time analysis for simple and complex SQL queries. This research tries to analyse the impact of data size on the query execution time for one of the majorly used Big Data frameworks, namely Apache Spark. It is a recently developed in-memory Big Data processing framework with a SPARK SQL module for efficient SQL query execution. It has been found that Apache-Spark gives better results with large size datasets compare to small size datasets and fares better as compared to Hadoop, one of the majorly used Big Data Frameworks (based on qualitative analysis).
Functional optical coherence tomography at altitude: retinal microvascular perfusion and retinal thickness at 3,800 meters
Authors: Baker Jacquie , Safarzadeh Mohammad A. , Incognito Anthony V. , Jendzjowsky Nicholas G. , Foster Glen E. , Bird Jordan D. , Raj Satish R. , Day Trevor A. , Rickards Caroline A. , Zubieta-DeUrioste Natalia , Alim Usman , Wilson Richard J. A.
Published: June 2022

Measuring the retinal microvasculature at high altitude provides a surrogate for cerebral microvasculature, offering potential insight into consequence of cerebral hypoxia in critical illness. This study demonstrates dynamic regulation of the retina during rapid ascent and prolonged exposure to high altitude and is the first to demonstrate sex-specific differences in retinal microvasculature at high altitude. The ability to dynamically assess intact microvasculature contiguous with the brain has widespread research and clinical applications.
Comparisons between skeletal muscle imaging techniques and histology in tracking midthigh hypertrophic adaptations following 10 wk of resistance training
Authors: Ruple Bradley A. , Smith Morgan A. , Osburn Shelby C. , Sexton Casey L. , Godwin Joshua S. , Edison Joseph L. , Poole Christopher N. , Stock Matt S. , Fruge Andrew D. , Young Kaelin C. , Roberts Michael D.
Published: June 2022

This is the first study to comprehensively examine how different midthigh muscle imaging techniques and histology compare with one another in participants that performed 10 weeks of resistance training. Our study suggests that histology results show poor agreement with results yielded from other common muscle imaging techniques, and researchers should be aware of this limitation.
Neuromuscular and perceptual mechanisms of fatigue accompanying task failure in response to moderate-, heavy-, severe-, and extreme-intensity cycling
Authors: Iannetta Danilo , Zhang Jenny , Murias Juan M. , Aboodarda Saied Jalal
Published: June 2022

Together with other physiological responses, the neuromuscular fatigue mechanisms, and related perceptual responses, accompanying task failure are suggested to be dependent on the intensity domain within which exercise is sustained. Here, we show that peripheral fatigue demonstrates a high domain specificity, whereas such specificity is absent for central fatigue. Sensations of fatigue, pain, and breathlessness demonstrated intensity domain specificity and might have contributed to reaching maximal levels of RPE and, thus, task failure.
Early Change in Platelet Count and MPV Levels of Patients Who Received Hemodialysis for the First Time: Mogadishu Somalia Experience
Authors: Sari Oznur , Bashir Ahmed Muhammad
Published: June 2022

Introduction. Mean platelet volume (MPV) is a marker used to assess the platelet’ size and is also an indicator of platelet reactivity and prothrombotic status. Objective. In this study, we aimed to determine the relationship between MPV and biochemical parameters in patients who had received hemodialysis (HD) for the first time and then in respect of those same patients after their fourth HD. Method. 151 HD patients were enrolled in this study. Patients were eligible for inclusion if they had received their first HD session during this study protocol. Prehemodialysis blood samples were taken. Most laboratory values, including mean platelet volume (MPV) level and platelets (PLT) count, were measured before the first HD and after the fourth HD session for each patient. Results. Among the patients in our study, the mean age profile of the male patients (n = 103; 68.2%) was found to be higher than that of the female patients (n = 48; 31.8%) (53.62 ± 18.19 vs. 46.17 ± 17.9 years) (

p

 = 0.019).In the patients’ laboratory results after the fourth HD session, MPV, MPV/Plt, and Na values had increased to those after the first HD session (

p

 < 0.001). When age and gender status were taken into account, the level of weak positive correlation with white blood cell count (WBC), neutrophil, and red cell distribution width (RDW) was found, while the weak negative correlation with platelet to lymphocyte ratio (PLR) was found (

p

 < 0.001). Conclusions. In our study, we found that increase in MPV and MPV/PLT levels was significant in the fourth HD session of patients with CKD. It is also debatable that there are findings indicating an increase in platelet reactivity in the first weeks of the onset of HD. This could be an early indicator of the early prevention of cardiovascular diseases.
Study on the Correlation between Urinary Sodium and Potassium Excretion and Blood Pressure in Adult Hypertensive Inpatients of Different Sexes
Authors: Duan Li Qin , Li Xiao Yang , Li Qiong , Zhao Jin Fang , Zhao Li , Zhang Jun , Wang Ze Hui , Han Qing Hua
Published: June 2022

Objective. This study aims to understand the difference in the influence of urinary sodium and potassium excretion on blood pressure in patients of different sexes with hypertension by analyzing the relationship between urinary sodium and potassium excretion and blood pressure. Methods. In this cross-sectional study, 606 hospitalized patients with essential hypertension were recruited from 16 hospitals in the Shanxi Province between June 2018 and December 2019. These patients were grouped by sex, with 368 males and 238 females. Basic information and relevant serum biochemical indexes of patients in the two groups were recorded. The 24-hour urinary sodium and potassium excretion were measured, and 24-hour ambulatory blood pressure monitoring was performed simultaneously. This was done to analyze and compare the relationship between urinary sodium and urinary potassium excretion and blood pressure in adult hospitalized patients of different sexes with hypertension. Results. The 24-hour urinary sodium excretion in male patients with hypertension was significantly higher than that in female patients (

P
<
0.001

). There was no significant difference in 24-hour urinary potassium excretion between male patients with hypertension and female patients. Spearman correlation analysis showed that 24-hour urinary sodium excretion was positively correlated with 24-hour SBP and nSBP in male patients (

P
<
0.05

), while 24-hour urinary potassium excretion was negatively correlated with 24-hour SBP and nSBP in male patients (

P
<
0.05

). The 24-hour urinary sodium in female patients was significantly positively correlated with 24-hour SBP, 24-hour DBP, SBP, dDBP, nSBP, and nDBP (

P
<
0.01

). The 24-hour urinary potassium was significantly negatively correlated with nSBP (

P
<
0.05

). Multiple stepwise linear regression showed that 24-hour urinary sodium excretion was still significantly positively correlated with 24-hour SBP and nSBP in male patients with hypertension after adjusting for various confounding factors. Conclusion. High urinary sodium and low urinary potassium excretion are closely related to elevated blood pressure in adult patients with hypertension, and there are sex differences.
Construction of Selection and Evaluation Algorithm for High-Level Tennis Students in Colleges and Universities Based on Random Matrix Model
Authors: Kang Huayang
Published: June 2022

The evaluation method of tennis students’ ability has an important influence on optimizing the selection and training of high-level tennis students. Based on the random matrix model theory, this paper constructs an evaluation algorithm for high-level tennis students in colleges and universities. Based on the literature and expert opinions, the model selects professional technical ability, knowledge learning ability, comprehensive development ability, and sustainable development ability as weighting factors and constructs the ability evaluation index system for high-level tennis students in colleges and universities. During the simulation process, the visualization system uses springboot, mybatis, and shiro as the back-end development framework to realize the modules of matrix management, role management, resource management, grade management, course management, student management, model management, etc. It adopts the MATLAB software platform to construct that the tennis learning evaluation includes 6 first-level indicators and 24 second-level indicators. The experimental results show that the final weights of the first-level indicators of tennis learning evaluation are as follows: tennis specific quality is 0.15, tennis sports skill is 0.3, tennis theoretical knowledge is 0.1, progress score is 0.15, attitude to learning tennis is 0.2, emotional performance and the spirit of cooperation is 0.1, and the results are visualized, which effectively improves the effectiveness of the random matrix algorithm in the field of selection evaluation.
Identification and Validation of Prognosis-Related Necroptosis Genes for Prognostic Prediction in Hepatocellular Carcinoma
Authors: Gao Xin , Huang Di , Li Shu-Guang , Wang Wen-Xin , Sun Dong-Lei , Qian Jia-Ming , Zhang Xiao-Lan
Published: June 2022

Background. The prediction of hepatocellular carcinoma (HCC) survival is challenging because of its rapid progression. In recent years, necroptosis was found to be involved in the progression of multiple cancer types. However, the role of necroptosis in HCC remains unclear. Methods. Clinicopathological parameters and transcriptomic data of 370 HCC patients were obtained from TCGA-LIHC dataset. Prognosis-related necroptosis genes (PRNGs) were identified and utilized to construct a LASSO risk model. The GEO cohorts (GSE54236 and GSE14520) were used for external validation. We evaluated the distribution of HCC patients, the difference in prognosis, and the accuracy of the prognostic prediction of the LASSO risk model. The immune microenvironment and functional enrichment of different risk groups were further clarified. Finally, we performed a drug sensitivity analysis on the PRNGs that constructed the LASSO model and verified their mRNA expression levels in vitro. Results: A total of 48 differentially expressed genes were identified, 23 of which were PRNGs. We constructed the LASSO risk model using nine genes: SQSTM1, FLT3, HAT1, PLK1, MYCN, KLF9, HSP90AA1, TARDBP, and TNFRSF21. The outcomes of low-risk patients were considerably better than those of high-risk patients in both the training and validation cohorts. In addition, stronger bile acid metabolism, xenobiotic metabolism, and more active immune cells and immune functions were observed in low-risk patients, and high expressions of TARDBP, PLK1, and FLT3 were associated with greater drug sensitivity. With the exception of FLT3, the mRNA expression of the other eight genes was verified in Huh7 and 97H cells. Conclusions. The PRNG signature provides a novel and effective method for predicting the outcome of HCC as well as potential targets for further research.
Preoperative Prediction Value of Pelvic Lymph Node Metastasis of Endometrial Cancer: Combining of ADC Value and Radiomics Features of the Primary Lesion and Clinical Parameters
Authors: Bo Juan , Jia Haodong , Zhang Yu , Fu Baoyue , Jiang Xueyan , Chen Yulan , Shi Bin , Fang Xin , Dong Jiangning
Published: June 2022

Objective. To investigate the value of apparent diffusion coefficient (ADC) value of endometrial cancer (EC) primary lesion and magnetic resonance imaging (MRI) three-dimensional (3D) radiomics features combined with clinical parameters for preoperative prediction of pelvic lymph node metastasis (PLNM). Methods. A total of 136 patients with EC confirmed by postoperative pathology were retrospectively reviewed and analyzed. Patients were randomly divided into training set (n = 95) and test set (n = 41) at a ratio of 7 : 3. Radiomics features based on T2WI, DWI, and contrast-enhanced T1WI (CE-T1WI) sequence were extracted and screened, and then radiomics score (Rads-score) was calculated. Clinical parameters and ADC value of EC primary lesion were measured and collected, and their correlation with PLNM was analyzed. Receiver operating characteristic (ROC) curve was plotted to assess the diagnostic efficacy of the model. A nomogram for PLNM was created based on the multivariate logistic regression model. Results. The ADC value of the EC primary lesion showed inverse correlation with PLNM, while CA125 and Rads-score were positively associated with PLNM. A predictive model was proposed based on ADC value, Rads-score, CA125, and MR-reported pelvic lymph node status (PLNS) for PLNM in EC. The area under the curve (AUC) of the model is 0.940; the sensitivity and specificity (87.1% and 90.6%) of the model were significantly higher than that of the MRI morphological signs. Conclusion. A combination of ADC value, MRI 3D radiomics features of the EC primary lesion, and clinical parameters generated a prediction model for PLNM in EC and had a good diagnostic performance; it was a useful supplement to MR-reported PLNS based on MRI morphological signs.
An Improved Knowledge Graph Question Answering System for English Teaching
Authors: Wang Li
Published: June 2022

Knowledge graph, as a structured semantic knowledge base, has become an essential foundation for artificial intelligence applications with its flexible composition structure and rich semantic representation capability. This paper combines the knowledge graph embedding scoring algorithm with the link scoring algorithm to effectively solve the problem of missing answers in the current knowledge graph embedding question and answer method. This method constructs a query link while searching for the best answer and gives the answer set through the query, which effectively alleviates the omission of answers in the existing methods. The experimental results show that the F1 score of the English teaching test system on the data set is 86.85%, where the answer selection method weakly relies on a priori information such as predicates in the test data and can be trained on a test pair data set without human intervention, with good generalization performance.
Circ_0110940 Exerts an Antiapoptotic and Pro-Proliferative Effect in Gastric Cancer Cells via the miR-1178-3p/SLC38A6 Axis
Authors: Miao Xiaonan , Zou Haiou , Pan Lijuan , Cheng Jing , Wu Youshan , Chen Rui , Su Yong , Du Hongwei
Published: June 2022

Circular RNAs (circRNAs) are essential regulators in human cancers, including gastric cancer, by the miRNA/mRNA axis. A previous study identified the upregulation of circ_0110940 in human gastric cancer tissues. The present study performed in vitro assays to reveal the functions of circ_0110940 and its downstream miRNA/mRNA axis in gastric cancer cells. Traditional proliferation and apoptosis assays including colony formation, EdU staining, and Annexin V-PI staining assays were conducted. A luciferase reporter assay was performed to assess the binding between miR-1178-3p and circ_0110940 or SLC38A. We found the significant upregulation of circ_0110940 in human gastric cancer cells AGS and MKN45. Circ_0110940 was a stable circRNA and exerted an antiproliferative and proapoptotic effect in AGS and MKN45. Circ_0110940 binded with miR-1178-3p, which further targeted SLC38A6 3′UTR. Circ_0110940 degraded miR-1178-3p, and miR-1178-3p degraded SLC38A6. Thus, circ_0110940 has a positive effect on SLC38A6 expression. Furthermore, SLC38A6 rescued the effects of circ_0110940 knockdown on gastric cancer cell proliferation and apoptosis. In conclusion, circ_0110940 exerted an antiapoptotic and pro-proliferative effect in gastric cancer cells via the miR-1178-3p/SLC38A6 axis, which may provide basis for the targeted therapy of gastric cancer.
3D Digital Model of Folk Dance Based on Few-Shot Learning and Gesture Recognition
Authors: Zhang Ning
Published: June 2022

Folk dance is a very unique local culture in China, and dances in different regions have different characteristics. With the development of 3D digital technology and human gesture recognition technology, how to apply it in folk dance is a question worth thinking about. So, this paper recognizes and collects dance movements through human body detection and tracking technology in human gesture recognition technology. Then, this paper writes the data into the AAM model for 3D digital modeling and retains the information by integrating the manifold ordering. Finally, this paper designs a folk dance learning method combined with the Few-Shot learning method. This paper also designs a data set test experiment, an algorithm data set comparison experiment, and a target matching algorithm comparison experiment to optimize the learning method designed in this paper. The final results show that the Few-Shot learning method based on gesture recognition 3D digital modeling of folk dances designed in this paper reduces the learning time by 17% compared with the traditional folk dance learning methods. And the Few-Shot learning method designed in this paper improves the dance action score by 14% compared with the traditional learning method.
Exploring the Development of Chinese Digital Resources under Lightweight Deep Learning
Authors: Song Bai
Published: June 2022

From 2019, countries worldwide have been negatively affected by the corona virus disease 2019 (COVID-19) in all aspects of social life. The high-tech digital industry represented by emerging digital technologies is still vigorous, and correspondingly, the digital economy has become an important force to promote the stable recovery and re-prosperity of the national economy. The digital economy plays a memorable role in preventing and controlling COVID-19, the resumption of work and production, and the creation of new business formats and models. Urban big data (UBD) involves a wide range of dynamic and static data with high dimensions, but there are no mature and clear data classification and grading standards. Currently, it is urgent to strengthen the security protection of high-value datasets. Therefore, a UBD classification and grading method is proposed based on the lightweight (LWT) deep learning (DL) clustering algorithm. It uses a semi-intelligent path based on partial artificial to form data classification (DC) and hierarchical thesaurus, corpus, rule base, and model base. Subsequently, a big data analysis system is built for unstructured and structured data association analysis based on deep learning, spatiotemporal correlation, and big data technology to improve data value and adapt to multiscenario applications. Meanwhile, with the help of data and graphics processing tool Tableau, the present work analyzes the development status and existing problems of digital resources in China. The results show that although China’s digital infrastructure is the top in the world, the trading infrastructure is still only 41.65 percentage points. This shows that China’s digital economy still has a lot of room for growth in distribution and trading. The analysis of the ownership of data resources indicates that the scores of China’s digital economy in accounting, privacy, and security are very low, only 2.4 points, 5.1 points, and 11 points, respectively. This study has solved the problems of distribution and trade in China’s digital economy through research and put forward corresponding suggestions for the current development of China’s digital economy market. Hence, a preliminary summary and suggestions are made on the development of China’s data resources, to promote the open sharing of data, strengthen the management of data quality, activate the data resource market, strengthen data security, and enhance the vitality of the market economy.
Orthotopic Kidney Transplant as a Fifth Intra-Abdominal Organ after Two Previous Kidney and Two Previous Pancreas Transplants
Authors: Al-Adwan Yazan , Singh Navdeep , Rajab Amer , Alebrahim Musab , Subramanian Jayanthan , Washburn W. Kenneth , Siddiqui Farjad , Limkemann Ashley , Chotai Pranit N. , Schenk Austin
Published: June 2022

Background. Patients with more than two prior kidney transplant procedures pose unique surgical challenges. Once both the right and left retroperitoneal spaces have been dissected, intra-abdominal implantation is usually necessary. If the external iliac arteries have been used previously, it is sometimes necessary to use the aorta and vena cava for implantation. Gaining safe exposure in these cases can be complicated by history of prior laparotomy, adhesive disease, and other surgical histories. Case Presentation. A 58-year-old female with type 1 diabetes and end-stage renal disease presented for surgical evaluation for kidney transplant. Surgical history was notable for prior simultaneous kidney-pancreas transplant followed by both a living donor kidney transplant and a pancreas after kidney transplant. She had undergone both an allograft nephrectomy and an allograft pancreatectomy and currently had a nonfunctioning kidney in the left retroperitoneal position and a nonfunctioning pancreatic allograft on the right common iliac artery. The entire distal aortoiliac system was surgically inaccessible. She was listed for transplantation, and a cadaveric graft was allocated. Intraoperatively, severe lower abdominal and pelvic adhesions prevented any use of the iliac system. A left native nephrectomy was performed, and the allograft was implanted in the left orthotopic position. The native left renal vein was used for outflow, the donor renal artery was joined end-to-side to the infrarenal aorta, and a uretero-ureterostomy was created. The operation was uneventful. The allograft functioned without delay, and almost one year later, the GFR is approximately 50 mg/dL. Conclusion. The left orthotopic position can be a good choice for kidney transplant candidates with histories of prior complex lower abdominal surgery.
Immune Infiltration and N(6)-Methyladenosine ncRNA Isoform Detection in Acute Lung Injury
Authors: Gu Chenzheng , Li Caiyun , Wang Weiwei , Yan Wenhui , Yao Yiwen , Shi Meng , Han Fei , Shang Anquan
Published: June 2022

Acute lung injury (ALI) is a severe form of sepsis that is associated with a high rate of morbidity and death in critically ill individuals. The emergence of ALI is the result of several factors at work. Case mortality rates might range from 40% to 70%. Researchers have discovered that epigenetic alterations are important in the pathophysiology of ALI and that using epigenetic inhibitors may help reduce symptoms. In embryonic development, circadian rhythm, the cell cycle, and cancer, methylation of m6A seems to be relevant all along the way. According to recent research, posttranscriptional methylation is a key player in the development of alveolar lymphoma. In this study, we clustered ALI based on m6A-related factors, analyzed different classes of immune cell enrichment and inflammatory cytokine expression, screened clustered differential genes for ALI to construct coexpression networks, screened key ALI genes potentially regulated by m6A modifications, and then typed the disease based on key genes to compare the consistency of different clustering results. Our findings have revealed a hitherto undiscovered prognostic sign and a therapeutic target for ALI therapy in m6A and immune invading cells, respectively.
Fabrication of Silver Nanoparticles Using Fimbristylis miliacea: A Cheap and Effective Tool against Invasive Mosquito Vector, Aedes albopictus
Authors: Baranitharan Mathalaimuthu , Kandeel Mahmoud , Shanmugavel Govindan , Kaliyaperumal Kumaravel , Subramanian Kumaran , Elumalai Kuppusamy , Gokulakrishnan Jayapal , Irrusappan Hari , Rethinam Senthil , Velmurugan S.
Published: June 2022

Mosquitoes are the most critical group of insects in the context of public health, since they transmit key parasites and pathogens, causing millions of deaths annually. Aedes albopictus is an important invasive mosquito vector of dengue fever across urban and semiurban areas of India. In this study, we biofabricated silver nanoparticles (AgNPs) using the Fimbristylis miliacea aqueous leaf extract (Fm‐ALE) as reducing and stabilizing agent. The synthesis of Fm‐AgNPs was confirmed by the excitation of surface plasmon resonance and orange‐brown color using ultraviolet‐visible (UV‐vis) spectrophotometry. High‐resolution scanning electron microscopic (HR‐SEM) and transmission electron microscopic (TEM) showed the clustered (size 0.5 μm) and quasi‐spherical structures of Fm‐AgNPs. The formation of AgNPs has been characterized by X‐ray diffraction (XRD) spectroscopy. Fourier transform infrared (FTIR) spectroscopy investigated the identity of secondary metabolites, which may act as Fm‐AgNP capping agents. These results propose that AgNPs synthesized provided from those Fm‐ALE have the high sources to be improved into the most suitable materials useful for protecting and killing the invasive mosquito vector, Ae. albopictus populations. The acute toxicity of Fm‐ALE synthesized Ag NPs, and a combined treatment testing blends of mosquito vector was evaluated against I, II, III, and IV instar larva’s (ILs) of Ae. albopictus. The LC50 values of Fm‐ALE (174.39 ppm I‐ILs, 214.40 ppm II‐ILs, 232.38 ppm III‐ILs, and 251.62 ppm IV‐ILs) and Fm‐AgNPs synthesized were 23.78 ppm I‐ILs; 27.88 ppm II‐ILs; 31.47 ppm III‐ILs; 36.68 ppm IV‐ILs, respectively. Likewise, Fm‐AgNP synthesis was more toxic than ALE in the invasive mosquito vector and recorded from UV‐vis spectrum, FTIR, TEM, and XRD analysis. These results propose that AgNPs synthesized provided from those Fm‐ALE have the high sources to be improved into the most suitable materials useful for protecting and killing the invasive mosquito vector, Ae. albopictus populations.
The Application of Intuitionistic Fuzzy Set-TOPSIS Model on the Level Assessment of the Surrounding Rocks
Authors: Gu Xin-bao , Ma Ying , Wu Qi-hong , Liu Yang-bing
Published: June 2022

The level assessment of surrounding rocks in underground tunnels has essential significance for the stability of surrounding rocks. Its review is affected by many factors. The intuitionistic fuzzy set-TOPSIS model is first introduced to estimate the quality of surrounding rocks accurately. Secondly, the decisive matrix of the intuitionistic fuzzy sets is established. Then, the weighed decisive matrix is obtained. Finally, the degree of membership at different levels regarding the surrounding rock quality is determined. The quality level corresponding to the maximum degree of membership is the final assessment level. The conclusions are drawn that estimating the quality level of surrounding rocks using the method is feasible compared to the actual investigations and other methods. This method can reflect the quality level of surrounding rocks accurately, providing a new route for the quality assessment of surrounding rocks in the future.
Implementation of Computer-Aided Piano Music Automatic Notation Algorithm in Psychological Detoxification
Authors: Zhang Xinmei
Published: June 2022

This paper analyzes the modeling of a computer-aided piano music automatic notation algorithm, combines the influence of music on psychological detachment, and designs the piano music automatic notation algorithm in psychological detachment model construction. This paper investigates the multiresolution time-frequency representation constant Q-transform (CQT), which is common in music signal analysis, and finds that although CQT has higher frequency resolution at low frequencies, it also leads to lower temporal resolution. The variable Q-transform is introduced as a tool for multibasic frequency estimation of the time-frequency representation of music signals, which has better temporal resolution than CQT at the exact frequency resolution and efficient coefficient calculation. The short-time Fourier transform and constant Q-transform time-frequency analysis methods are implemented, respectively, and note onset detection and multibasic tone detection are implemented based on CNN models. The network structure, training method, and postprocessing method of CNN are optimized. This paper proposes a temporal structure model for maintaining music coherence to avoid manual input and ensure interdependence between tracks in music generation. This paper also investigates and implements a method for generating discrete music events based on multiple channels, including a multitrack correlation model and a discretization process. In this paper, the automatic piano music notation algorithm can play an influential role in significantly enhancing the actual effect of psychological detoxification.
Optimal Number and Locations of Smart RMUs for Self-Healing Distribution Networks
Authors: Abdalla Omar H. , Mostafa Azza
Published: June 2022

Smart grids with self-healing (SH) capability provide an important intelligent feature to help in fast correction actions in case of network faults. SH architecture consists of modern communication systems, smart equipment, and intelligent sensors. With the high cost of SH components (especially smart ring main unit (SRMU)), optimization is required to achieve optimum performance with minimum cost. This study presents a proposed methodology to determine the optimum number and locations of SRMUs in electricity distribution networks considering various cost issues. The disconnection cost of on-grid photovoltaic (PV) plants is taken into consideration as an important factor in determining the locations of the SRMUs. The nonlinear programming (NLP) optimization technique is used to determine the required number of SRMUs, considering the cost/benefit analysis (cost of upgrading MRMUs to SRMUs/benefit due to interruption time reduction), which is the most important factor from DISCOs’ perspective. The mixed integer linear programming (MILP) optimization technique is employed for selecting the optimal locations of the SRMUs considering the cost of losses, energy not supplied (ENS), and PV disconnection, which improves network operation cost. The methodology takes into consideration the cable failure rate and the interest rate. Moreover, the study introduces the Egyptian electrical distribution network and a pilot project for control centre development using SRMUs. The methodology is applied to a modified IEEE 37-node test feeder and a part of a specific district network in South Cairo consisting of 158 nodes; both systems include a number of PV distributed generation plants. Simulation results are presented to show the effectiveness of the proposed method.
Pure Laparoscopic Left Hepatectomy for Regrowth of Mucinous Cystic Neoplasm of the Liver after Laparoscopic Deroofing
Authors: Kumagai Hideki , Umemura Akira , Nitta Hiroyuki , Katagiri Hirokatsu , Kanno Shoji , Takeda Daiki , Amano Satoshi , Kikuchi Koji , Takashimizu Kiyoharu , Nishiya Masao , Uesugi Noriyuki , Sugai Tamotsu , Sasaki Akira
Published: June 2022

Background. Hepatic cystic lesions are common entities, most of which are simple hepatic cysts (SHCs). Mucinous cystic neoplasm of the liver (MCN-L) is a rare tumor characterized by ovarian-like stroma and accounts for <5% of all hepatic cysts. Distinguishing between SHCs and MCN-L is challenging because of the similarity in their imaging findings. Herein, we report a rare regrowth case of MCN-L after laparoscopic deroofing, treated with pure laparoscopic left hepatectomy. Case Presentation. A 63-year-old woman with a large hepatic cystic lesion and abdominal pain was referred to our hospital for surgical treatment. Computed tomography (CT) showed cystic lesions with septations arising from macrolobulations in the left medial segment. She underwent laparoscopic deroofing based on the diagnosis of SHCs; however, the final histopathological diagnosis was MCN-L. She chose observational follow-up, and MCN-L regrowth was detected on follow-up CT 6 months after the laparoscopic deroofing. We performed pure laparoscopic left hepatectomy for complete resection of the MCN-L. She had an uneventful postoperative course and no recurrence at the 5-year follow-up after the radical resection of the MCN-L. Conclusion. MCN-L is a rare entity, and accurate diagnosis with imaging modalities is greatly challenging. Laparoscopic hepatectomy for MCN-L should be considered as a strong alternative to secure safety and curability.
Clustering and Markov Model of Plug-In Electric Vehicle Charging Profiles for Peak Shaving Applications
Authors: Li Dongdong , Zhang Kai , Yao Yin , Lin Shunfu
Published: June 2022

The charging profiles of plug-in electric vehicles (PEVs) have large volatility. It has brought great challenges for aggregator to accurately complete load identification and aggregated configuration. Therefore, an analysis and configuration method of responsive capacity based on clustering and the Markov model are proposed in this paper. Firstly, the adaptive two-scale clustering algorithm based on spectral clustering (ATCSC) is applied to the clustering of charging piles. The offset compensation of the extreme points is used to form the distance measurement in the clustering process. Then, the responsive aggregated power can be obtained after the change control of suitable charging piles. Finally, the variation characteristics of the actual charging profiles based on the Markov model are introduced to the reliability evaluation in the load curtailment service. Simulation results reveal the following. (1) The robustness of the proposed method is better especially for the PEV charging profiles with strong volatility. (2) The validity of the aggregated configuration is verified. Additionally, the sum of power deviation is 0.0707 kW when the change interval of control strategy is 15 min. (3) The maximum shortage of configuration is −98.0875 kW as the entropy of the volatility is 37.027.
Estimation for Akshaya Failure Model with Competing Risks under Progressive Censoring Scheme with Analyzing of Thymic Lymphoma of Mice Application
Authors: Abushal Tahani A. , Kumar Jitendra , Muse Abdisalam Hassan , Tolba Ahlam H.
Published: June 2022

In several experiments of survival analysis, the cause of death or failure of any subject may be characterized by more than one cause. Since the cause of failure may be dependent or independent, in this work, we discuss the competing risk lifetime model under progressive type‐II censored where the removal follows a binomial distribution. We consider the Akshaya lifetime failure model under independent causes and the number of subjects removed at every failure time when the removal follows the binomial distribution with known parameters. The classical and Bayesian approaches are used to account for the point and interval estimation procedures for parameters and parametric functions. The Bayes estimate is obtained by using the Markov Chain Monte Carlo (MCMC) method under symmetric and asymmetric loss functions. We apply the Metropolis–Hasting algorithm to generate MCMC samples from the posterior density function. A simulated data set is applied to diagnose the performance of the two techniques applied here. The data represented the survival times of mice kept in a conventional germ‐free environment, all of which were exposed to a fixed dose of radiation at the age of 5 to 6 weeks, which was used as a practice for the model discussed. There are 3 causes of death. In group 1, we considered thymic lymphoma to be the first cause and other causes to be the second. On the base of mice data, the survival mean time (cumulative incidence function) of mice of the second cause is higher than the first cause.
Machine Learning Approaches for Developing Land Cover Mapping
Authors: Alzahrani Ali , Kanan Awos
Published: June 2022

In remote sensing data processing, cover classification on decimeter-level data is a well-studied but tough subject that has been well-documented. The majority of currently existent works make use of orthographic photographs or orthophotos and digital surface models that go with them (DSMs). Urban land cover classification plays a significant role in the field of remote sensing to enhance the quality of different applications including environment protection, sustainable development, and resource management and planning. Novelty of the research done in this area is focused on extracting features from high-resolution satellite images to be used in the classification process. However, it is well known in machine learning literature that some of the extracted features are irrelevant to the classification process with a negative or no effect on its accuracy. In this work, a genetic algorithm-based feature selection approach is used to enhance the performance of urban land cover classification. Neural networks (NNs) and random forest (RF) classifiers were used to evaluate the proposed approach on a recent urban land cover dataset of nine different classes. Experimental results show that the proposed approach achieved better performance with RF classifier using only 27% of the features. The random forest tree has achieved highest accuracy 84.27%; it is concluded that the RF algorithm is an appropriate algorithm for classifying cover land.
A Deep Neural Network‐Based Approach for Sentiment Analysis of Movie Reviews
Authors: Ullah Kifayat , Rashad Anwar , Khan Muzammil , Ghadi Yazeed , Aljuaid Hanan , Nawaz Zubair
Published: June 2022

The number of comments/reviews for movies is enormous and cannot be processed manually. Therefore, machine learning techniques are used to efficiently process the user’s opinion. This research work proposes a deep neural network with seven layers for movie reviews’ sentiment analysis. The model consists of an input layer called the embedding layer, which represents the dataset as a sequence of numbers called vectors, and two consecutive layers of 1D‐CNN (one‐dimensional convolutional neural network) for extracting features. A global max‐pooling layer is used to reduce dimensions. A dense layer for classification and a dropout layer are also used to reduce overfitting and improve generalization error in the neural network. A fully connected layer is the last layer to predict between two classes. Two movie review datasets are used and widely accepted by the research community. The first dataset contains 25,000 samples, half positive and half negative, whereas the second dataset contains 50,000 specimens of movie reviews. Our neural network model performs sentiment classification among positive and negative movie reviews called binary classification. The model achieves 92% accuracy on both datasets, which is more efficient than traditional machine learning models.
Diffusion‐Weighted Imaging Image Combined with Transcranial Doppler Ultrasound in the Diagnosis of Patients with Cerebral Infarction and Vertigo
Authors: Lv Ying , Zhang Yijie , Wu Jun
Published: June 2022

This study aimed at exploring the application value of diffusion‐weighted imaging (DWI) combined with transcranial Doppler (TCD) in the diagnosis of patients with cerebral infarction and vertigo (CI + V). In this article, using a retrospective case‐control study, 100 CI + V patients (CI + V group) were examined by DWI combined with TCD. Seventy cases of noncerebral infarction with vertigo (control group) who were hospitalized at the same time were collected for clinical data analysis and comprehensive evaluation of each index. The results showed that in patients with CI + V, the abnormal rate of blood vessels was proportional to the size of the lesion, and the abnormal rate of blood vessels in the large‐area infarction group (97%) was much higher than that of the small‐area infarct group (62%) and the lacunar infarction group (51%). The overall abnormal rate of blood vessels in the CI + V group (71%) was greatly higher than that in the control group (15%), showing a statistically and extremely great difference (P < 0.01). In short, DWI can effectively extract lesion‐related data, and combined with TCD examination, the clinical diagnosis of CI + V can be more accurately performed, which had a positive impact on the clinical work of CI + V. This work provided some reference for the clinical effective diagnosis method of CI + V.
Spatial-Temporal Data Integration Modeling and Dynamic Simulation of Coal-Rock Dynamic Disasters
Authors: Kong Yulei , Luo Zhengshan , Wang Xiaomin , Wang Yuchen
Published: June 2022

To solve the problem of accurate identification of coal-rock dynamic disaster precursors, a spatiotemporal data integrated monitoring, and early warning system was proposed. The system consists of a spatiotemporal data integration model, a time series and visual monitoring, and an early warning platform. It takes the comprehensive mining face of a deep coal mine as the monitoring object. It uses structured light 3D scanning and Brillouin optical time domain reflectometry to collect physical entity data in the monitoring area, reconstructs data and processes data redundancy through edge microprocessors, and decomposes spatiotemporal objects into elements to construct a data integration model for data integration. Inner relationship and space-time unity. Using the time series database as the data integration model carrier, the processed physical entity data is mapped to the visual monitoring and early warning platform for dynamic simulation display, which provides data support for accurate early warning of coal-rock dynamic disasters. Finally, a prototype system is developed to verify the generality and feasibility of the system.
Comparative Study to Analyze MEMS Based Microrobot Using Fuzzy TOPSIS Approach
Authors: Oad Ammar , Tayyaba Shahzadi , Ashraf Muhammad Waseem , Feng Dong , Luo Gaofeng , Akhlaq Maham
Published: June 2022

With the emergence in the field of developing small-scale systems, microrobots are gaining enormous attention due to their small size, wide number of application, and negligible effects on the surrounding. MEMS and microrobots both have electrical and mechanical parts which are situated on a single device and chip. This makes MEMS based microrobots and an excellent device due to its ease of fabrication and small size. MEMS-based microrobots have a wide number of applications in the field of bio-medical special drug delivery and invasive surgery. These tiny robots can be sent into the human body to send any medicine inside the human body. Various types of MEMS based microrobot can be used in drug delivery applications including magnetic nanoparticles based microrobots, magnetized spirulina microrobot, and electro-magnetic microrobots. However, the suitability of all these robots for drug delivery applications depends on the locomotion, navigation controlling, shape transformation, actuation requirement, and amount of drug wasted before moving to the destination. In this work, fuzzy rules based system is performed to confirm the dependence of the parameters including the locomotion, navigation controlling, shape transformation, actuation requirement, and amount of drug wasted on the working of microrobots in drug delivery applications. Similarly, fuzzy TOPSIS based study is performed to compare and analyze the most suitable microrobot for drug delivery application. Piezo-electric and electro-magnetic microrobots are considered the most suitable option with relative closeness to the ideal solution of 0.71 and 0.66, respectively, owing to their better shape transformation and movement.