bluuebunny/crossref_metadata_2025 huggingface dataset:
Essential self-adjointness of a weighted 3-simplicial complex Laplacians
Authors: Baalal Azeddine , Hatim Khalid
Published: August 2022

In this paper, we construct a weighted [Formula: see text]-simplicial complex [Formula: see text] on a connected oriented locally finite graph [Formula: see text] by the introduction of the notion of oriented tetrahedrons [Formula: see text], the notion of oriented triangular faces [Formula: see text], a weight on [Formula: see text], a weight on [Formula: see text], a weight on [Formula: see text] and a weight on [Formula: see text]. Next, we create the weighted Gauss–Bonnet operator of [Formula: see text] and we use it to construct the weighted Laplacian associated to [Formula: see text], the weighted Laplacian associated to [Formula: see text], the weighted Laplacian associated to [Formula: see text], the weighted Laplacian associated to [Formula: see text] and the weighted Laplacian associated to [Formula: see text]. After that, we introduce the notion of the [Formula: see text]-completeness of [Formula: see text] and we give necessary conditions for [Formula: see text] to be [Formula: see text]-complete. Finally, we prove that the weighted Gauss–Bonnet operator and the weighted Laplacians are essentially self-adjoint based on the [Formula: see text]-completeness.
Properties of derived signed graphs on Beck’s zero-divisor graph
Authors: Kaur Bableen , Sinha Deepa
Published: August 2022

The concept of associating a graph to a ring was initiated by Beck in 1988. The zero-divisor graph of a commutative ring [Formula: see text] with unity ([Formula: see text]) is defined as a simple graph, denoted by [Formula: see text], where elements of the ring [Formula: see text] represent vertices and two distinct vertices in [Formula: see text] are adjacent if the product of the corresponding elements is zero in [Formula: see text]. Here, we extend the notion of zero-divisor graphs to signed graphs. We introduce four types of signing to the zero-divisor graphs. Further, we characterize rings for which these four types of signed graphs, its lined signed graphs and their negations are balanced, clusterable, sign-compatible, canonically sign-compatible, and canonically consistent.
d-Gaussian Pell polynomials and their matrix representation
Authors: Özkan Engin , Uysal Mine
Published: August 2022

We introduce a new generalization of Gaussian Pell polynomials. We call it [Formula: see text]-Gaussian Pell polynomials. Then we present the sum, generating functions and Binet formulas of these polynomials. We give the matrix representation of [Formula: see text]-Gaussian Pell polynomials. We introduce the matrix as binary representations according to the Riordan matrix representation. Using the Riordan method, we obtain the factorizations of the Pascal matrix involving the polynomial. In addition, we present the inverse of matrices of the polynomial.
Twin prime difference set and its application on a coded mask
Authors: Akarsu Emek Demirci , Günay Tuğba Navdar
Published: August 2022

Difference sets have wide applications in constructing sequences and codes in engineering and cryptography, and in imaging with coded masks in astronomical events and in medical events. In this paper, the relation between difference sets and binary sequences is presented and a coded mask as an application of difference sets is given. First of all, an algorithm for an appropriate difference set is written in C++ by using twin primes and then a coded mask for imaging astronomical events is designed with the aid of this algorithm.
AN EXAMINATION OF MARKET REACTION WHEN NEGATIVE EMOTIONS RUN HIGH AMIDST A TROPICAL CYCLONE
Authors: LEE CHUN-I , TSAO CHUEH-YUNG
Published: August 2022

We find evidence of negative returns, greater volatility, higher turnover, and lower liquidity around a tropical cyclone. Before the land warnings are issued, there is significant under-reaction by investors. Throughout the storm, market volatility increases with negative returns. This leverage effect is similarly present in liquidity before and after the storm. The abnormal returns, volatility, and activities are not related to the characteristics of the storm and exist after the weather effect and various determinants have been accounted for. These findings strongly suggest that underlying all the negative market reaction is the prevalent emotional distress, anxiety, and fear among investors evoked by the destructive and deadly forces of the storm. These negative emotions presumably are stronger when faced with stronger storms and may be managed with better preparedness. This is indeed the case given that we find evidence of more significant market reaction to moderate and severe typhoons and in the early years than in recent years.
PSDF: Privacy-aware IoV Service Deployment with Federated Learning in Cloud-Edge Computing
Authors: Xu Xiaolong , Liu Wentao , Zhang Yulan , Zhang Xuyun , Dou Wanchun , Qi Lianyong , Bhuiyan Md Zakirul Alam
Published: August 2022

Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weights before the model aggregation on the cloud. Besides, a service deployment scheme based on deep deterministic policy gradient is proposed. Eventually, the performance of PSDF is evaluated by massive experiments.
Modeling and Analysis of Explanation for Secure Industrial Control Systems
Authors: Adepu Sridhar , Li Nianyu , Kang Eunsuk , Garlan David
Published: August 2022

Many self-adaptive systems benefit from human involvement and oversight, where a human operator can provide expertise not available to the system and detect problems that the system is unaware of. One way of achieving this synergy is by placing the human operator
on the loop
—i.e., providing supervisory oversight and intervening in the case of questionable adaptation decisions. To make such interaction effective, an
explanation
can play an important role in allowing the human operator to understand why the system is making certain decisions and improve the level of knowledge that the operator has about the system. This, in turn, may improve the operator’s capability to intervene and, if necessary, override the decisions being made by the system. However, explanations may incur costs, in terms of delay in actions and the possibility that a human may make a bad judgment. Hence, it is not always obvious whether an explanation will improve overall utility and, if so, then what kind of explanation should be provided to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of explanation
content
,
effect
, and
cost
. We then present a dynamic system adaptation approach that leverages a probabilistic reasoning technique to determine when an explanation should be used to improve overall system utility. We evaluate our explanation framework in the context of a realistic industrial control system with adaptive behaviors.
Low-Power Feature-Attention Chinese Keyword Spotting Framework with Distillation Learning
Authors: Lei Lei , Yuan Guoshun , Zhang Tianle , Yu Hongjiang
Published: August 2022

In this paper, we propose a novel Low-Power Feature-Attention Chinese Keyword Spotting Framework based on a
depthwise separable convolution neural network (DSCNN)
with distillation learning to recognize speech signals of Chinese wake-up words. The framework consists of a low-power feature-attention acoustic model and its learning methods. Different from the existing model, the proposed acoustic model based on
connectionist temporal classification (CTC)
focuses on the reduction of power consumption by reducing model network parameters and
multiply-accumulate (MAC)
operations through our designed feature-attention network and DSCNN. In particular, the feature-attention network is specially designed to extract effective syllable features from a large number of MFCC features. This could refine MFCC features by selectively focusing on important speech signal features and removing invalid speech signal features to reduce the number of speech signal features, which helps to significantly reduce the parameters and MAC operations of the whole acoustic model. Moreover, DSCNN with fewer parameters and MAC operations compared with traditional convolution neural networks is adopted to extract effective high-dimensional features from syllable features. Furthermore, we apply a distillation learning algorithm to efficiently train the proposed low-power acoustic model by utilizing the knowledge of the trained large acoustic model. Experimental results thoroughly verify the effectiveness of our model and show that the proposed acoustic model still has better accuracy than other acoustic models with the lowest power consumption and smallest latency measured by NVIDIA JETSON TX2. It has only 14.524
KB
parameters and consumes only 0.141
J
energy per query and 17.9
ms
latency on the platform, which is hardware-friendly.
Community Monitoring of Natural Resource Systems and the Environment
Authors: Danielsen Finn , Eicken Hajo , Funder Mikkel , Johnson Noor , Lee Olivia , Theilade Ida , Argyriou Dimitrios , Burgess Neil D.
Published: August 2022

Community monitoring can track environmental phenomena, resource use, and natural resource management processes of concern to community members. It can also contribute to planning and decision-making and empower community members in resource management. While community monitoring that addresses the environmental crisis is growing, it also gathers data on other global challenges: climate change, social welfare, and health. Some environmental community monitoring programs are challenged by limited collective action and community participation, insufficient state responsiveness to data and proposals, and lack of sustainability over time. Additionally, community members monitoring the environment are increasingly harassed and sometimes killed. Community monitoring is more effective with improved data collection, improved data management and sharing, andstronger efforts to meet community information needs, enable conflict resolution, and strengthen self-determination. Other promising areas for development are further incorporating governance issues, embracing integrated approaches at the community level, and establishing stronger links to national and global frameworks.
The Effects of Public and Private Equity Markets on Firm Behavior
Authors: Bernstein Shai
Published: August 2022

In this article, I review the theoretical and empirical literature on the effects of public and private equity markets on firm behavior, emphasizing the consequences that emerge from disclosure requirements, ownership concentration, and degree of firm standardization. While publicly listed firms benefit from a lower cost of capital, enabling increased focus on commercialization and profitability, they are less suited to pursue long-term risky investments. Privately held firms are better equipped to pursue innovative projects but face a higher cost of capital, which limits their growth. Complementarities between public and private equity markets can mitigate their respective limitations. Innovation in private equity markets supplements commercialization efforts of public firms, and demand for innovation by public firms accelerates entrepreneurial activity in private equity markets. I conclude by discussing directions for future research.
CaMKII as a Therapeutic Target in Cardiovascular Disease
Authors: Reyes Gaido Oscar E. , Nkashama Lubika J. , Schole Kate L. , Wang Qinchuan , Umapathi Priya , Mesubi Olurotimi O. , Konstantinidis Klitos , Luczak Elizabeth D. , Anderson Mark E.
Published: August 2022

CaMKII (the multifunctional Ca2+ and calmodulin-dependent protein kinase II) is a highly validated signal for promoting a variety of common diseases, particularly in the cardiovascular system. Despite substantial amounts of convincing preclinical data, CaMKII inhibitors have yet to emerge in clinical practice. Therapeutic inhibition is challenged by the diversity of CaMKII isoforms and splice variants and by physiological CaMKII activity that contributes to learning and memory. Thus, uncoupling the harmful and beneficial aspects of CaMKII will be paramount to developing effective therapies. In the last decade, several targeting strategies have emerged, including small molecules, peptides, and nucleotides, which hold promise in discriminating pathological from physiological CaMKII activity. Here we review the cellular and molecular biology of CaMKII, discuss its role in physiological and pathological signaling, and consider new findings and approaches for developing CaMKII therapeutics.
Impedance Spectroscopy and Structural Characterization of Nanostructured ZnO and Hf-Doped ZnO Ceramics
Authors: Seçkin Ayten , Koralay Haluk , Cavdar Sukru , Turan Neslihan , Tuğluoğlu Nihat
Published: August 2022

In this research, nanostructured zinc oxide (ZnO) and hafnium-doped zinc oxide ceramic samples were prepared by the sol-gel technique. ZnO and at%0.5 hafnium-doped ZnO (HZO) nanostructures and their surface morphologies were studied by XRD and FESEM. AC electrical properties (capacitance, conductance, and complex impedance) of ZnO and HZO were studied by impedance analyzer with the frequency range from 20 Hz to 1.5 MHz and temperature range from 300 K to 500 K. General analysis of AC electrical measurements showed that both samples had different equivalent circuit diagrams. The best-fitted equivalent circuit diagram for ZnO was “R(RC)(RC)” at all temperatures. The circuit diagram for HZO was “R(RCPE)” at 300 K, 350 K, and 400 K temperatures and “R(RCPE)(RCPE)” at 450 K, and 500 K temperatures. Negative temperature coefficient of resistance (NTCR), non-Debye behavior, and multiple relaxation times were observed.
Optoelectronic Properties of ZrO2/Cu/ZrO2 Multilayers Prepared by DC Pulsed Magnetron Sputtering for Electrode and Nano-Filter Applications
Authors: Raaif M. , Abd El-Moula A. A. , El-Hossary F. M. , Aroua W. , Abo El-Kassem M.
Published: August 2022

In this study, ZrO2/Cu/ZrO2 nanostructured multilayers were constructed on glass substrates with diverse Cu interlayer thickness (5–25 nm) employing pulsed DC magnetron sputtering. The optoelectronics and structural characteristics of the multilayer films were reconnoitered. The calculated band gap was reduced from 3.0 to 2.68 eV as the Cu interlayer thickness increased from 0 to 25 nm. The refractive index and coefficient of extinction of ZrO2/Cu/ZrO2 multilayers increased with increasing the Cu interlayer thickness in the visible range. The resistivity recorded a value of 7.29 × 10−3 Ω·cm for ZrO2/Cu (5 nm)/ZrO2 multilayer film while recorded a value of 3.3 × 10−3 Ω·cm for ZrO2/Cu (20 nm)/ZrO2 multilayer film. It was found that the ZrO2/Cu (20 nm)/ZrO2 multilayer film verified the greatest figure of merit value of 3.35 × 10−3 Ω−1 which signifying the best multilayer for transparent conductive film. The ZrO2/Cu/ZrO2 multilayer can be involved as a platform for designing optical nano-filter for molecular detections. For this purpose, the quality factor Q, the FWHM and the optical response of the proposed (ZrO2/Cu/ZrO2)3/Cumid/(ZrO2/Cu/ZrO2)3 optical nano-filter model were calculated theoretically using finite difference time domain technique (FDTD). The quality factor and FWHM of the proposed model recorded values of 5800 and 0.23 nm respectively for Cu mid thickness of 30 nm, which can be potentially engaged as optical nano- filter for molecular detections.
Investigation of Frequency-Selective Surfaces Based on Graphite in the Absorption of Electromagnetic Waves
Authors: Ahmadi Mohamad Taghi , Hesami Mahsa , Rahmani Meisam
Published: August 2022

Today, electromagnetic waves play an important role in our lives. These waves are used for radio and television communications, telecommunication networks and all wireless communications. Therefore, due to the widespread use of electromagnetic waves in the GHz range for mobile phones, national networks, radar systems, etc., it is a serious threat to human health. The presence of different electromagnetic fields and waves in space also causes improper operation or reduced efficiency in electrical and electronic circuits and components. Therefore, the issue of designing appropriate and efficient filters to protect electrical devices and maintain human health is doubly important. In this research, metamaterials and their application as absorbers in frequency-selective surfaces are studied. The design and development process of the frequency-selective surfaces based on graphite are presented in two steps. Finally, the performance of proposed structures with one and two hexagonal loops are discussed. The obtained results demonstrate that the base element consists of a hexagonal loop made of graphite filters the frequency band of 8–12 GHz. However, the base element consists of two hexagonal loops is able to filter the frequency band of 4–12 GHz. In fact, the proposed structure with two hexagonal lopps has filtered a larger frequency band.
Modeling and Simulation of Real Estate Valuation Based on Genetic Neural Network Model
Authors: Shen Shuli
Published: August 2022

China’s real estate market is developing rapidly, but the house price is abnormal. The nonlinear relationship between housing characteristics and real estate value is difficult to calculate, resulting in the difficulty of house price prediction. Based on this, the relationship model between characteristic variables and house prices is constructed by using the machine learning method. At the same time, genetic algorithm is used to screen the specific values. The experimental results show that the optimized model converges in 56 iterations; in the application test, the research model found that in 90% of the test samples, the error between the predicted value and the actual measured value shall not exceed 10%. Experiments show that the genetic algorithm is effective in optimizing the BP house price valuation model and improves the calculation efficiency and valuation accuracy of the valuation model.
Deformation Characteristics of Sandy Soil around a Plate Anchor under Lateral Loading
Authors: Ren Miao , Jiang Tong , Liu Chen , Zhang Junran , Wang Lijin
Published: August 2022

The lateral loading of a plate anchor is a complicated process that involves complex anchor-soil interactions. The deformation characteristics of the soil around an anchor have an important effect on its lateral bearing capacity. In this paper, the noncontact digital image correlation (DIC) technique is used to study the distribution and variation of the soil deformation field under a laterally loaded anchor in sandy soil. The results show that the sand density and embedment ratio significantly affect the distribution and influence range of the active and passive zones around the anchor. The active zone behind the rod gradually decreases with increasing sand density until ultimately disappearing, and the passive zone increases. The maximum influence height occurs in the passive zone behind the rod in dense sand, and the influence range of the passive zone in front of the rod expands with an increasing embedment ratio. Shear bands form during the lateral loading process, which are accompanied by dilatancy in the shear process. The motion path of the rotation center in loose and medium sand is initially rigid translational and then becomes rigid rotational, while the opposite trend occurs in compact dense sand. The results provide important guidance for the development of predictive models for anchor lateral loading and design.
Scutellarin Loaded on Ultradeformable Nanoliposome Scutellarin EDTMP (S-UNL-E) Promotes Osteogenesis in Osteoporotic Rats
Authors: Teng Minhua , Yuan Xiao , Wang Dashan , Gao Hui , Zhang Kaiyue , Wang Wenxue , Zhao Baodong
Published: August 2022

Scutellarin is known as a safe, effective, and low-cost traditional Chinese medicine and has a variety of biological activities. Studies reported that the scutellarin loaded on ultradeformable nanoliposome scutellarin EDTMP (S-UNL-E) could promote osteoblast differentiation and bone formation in vitro. However, its effect on promoting osteogenesis in vivo is still unclear. In this study, pharmacology network and transcriptome sequencing were used to screen the potential targets and pathways of scutellarin in treating osteoporosis. The female Sprague-Dawley (SD) rats were operated on with bilateral oophorectomy and femoral defect to establish an osteoporosis model and then treated separately with bone dust, single scutellarin, 40 mg/kg ultradeformable nanoliposome scutellarin (S-UNL), and the optimal concentration of 40 mg/kg S-UNL-E for a total of 56 d to detect the parameters of trabecular bones. And qRT-PCR and western blot were performed to determine the expression of prostaglandin-endoperoxide synthase 2 (PTGS2), alkaline phosphatase (ALP), transcription factor 4 (TCF4), and β-catenin. Results of microscopic computed tomography (Micro-CT) of trabecular bones showed that single scutellarin, S-UNL, and S-UNL-E all promoted the bone formation of osteoporotic rats, in which S-UNL-E manifested the most remarkable therapeutic effect. And it is found that 40 mg/kg of S-UNL-E increased the expression of PTGS2, ALP, TCF4, and β-catenin, which indicated that S-UNL-E stimulated the secretion of ALP in bone defect areas to promote bone healing, and increased PTGS2 expression thereby enhancing the transcription and translation of key gene β-catenin and TCF4 in the Wnt/β-catenin signaling pathway to treat osteoporotic rats.
Bufei Decoction Improves Lung-Qi Deficiency Syndrome of Chronic Obstructive Pulmonary Disease in Rats by Regulating the Balance of Th17/Treg Cells
Authors: Shen Junxi , Zhu Xing , Chen Yunzhi , Li Wen , Liu Huaiquan , Chu Cancan , Zhang Yu , Xu Changjun , Tong Pingzhen , Yu Xinran , Yang Guangyong , Deng Ying
Published: August 2022

Bufei decoction (BFD) has been applied to treat chronic obstructive pulmonary disease (COPD) for centuries as a recognized traditional Chinese herbal formula. However, mechanisms of BFD on COPD are unclear. This study conducts an inquiry into the underlying mechanisms of the therapeutic effect of BFD on COPD. A COPD rat model with qi deficiency in lungs was established through induction using cigarette and sawdust smoking combined with intratracheal instillation of lipopolysaccharide following BFD treatment for 28 days. Changes in Th17/Treg cells of COPD rats with the syndrome of lung qi deficiency after BFD administration were verified using pulmonary function, ELISA, flow cytometry, histopathology, and Western blotting assays. The findings showed that BFD protected COPD rats from decreased lung function and lung injury. BFD administration reduced proinflammatory cytokines IL-6 and IL-17 secretion, promoted inhibitory cytokines IL-10 and TGF-β secretion, decreased Th17/Treg cell ratio, markedly downregulated the Th17 cell transcription factor ROR-γt expression, and upregulated transcription factor Foxp3 expression in Treg cells. We speculate that lung tonic soup improved pulmonary qi deficiency in rats with COPD by regulating the balance of Th17/Treg cells.
Optical Microscope Rehabilitation Nursing Study of Anterior Cruciate Ligament Injury through Lateral Knee Incision Based on Medical Internet of Things
Authors: Liu Liping , Chang Nan , Li Shihong , Gong Peipei , Wang Junhua
Published: August 2022

With the high development of sports, football has attracted more and more attention from the public. However, the hot competition has made football players undergo high-intensity training, and the risk of injury caused by training is also increasing. Lateral incision knee ACL injury is one of the most common types of injuries in football players, which has a serious impact on the athlete’s physiology and daily training. The comprehensive, multi-level, convenient and fast medical system brought by the medical Internet of Things has become the growing demand of the medical industry. Based on the medical Internet of Things, this paper studies the rehabilitation nursing of football players’ ACL injury by lateral cutting and running combined with optical microscope. In this paper, 36 male and female football players were selected for group experiments, and the landing and peak torque indexes of the experimental group and the control group of male and female athletes were analyzed under the observation of an optical microscope. Group (P <0.05), coxa valgus (P <0.01), internal rotation and knee valgus angles were greater than those in the EM group. And the peak hip flexion angle and knee valgus moment in NF group were lower than those in EF group (P <0.05). After 12 weeks of rehabilitation training, there was a significant difference in the Q/H joint contraction index between the REF group and the CON group (P <0.05), and there was no statistical difference in the other groups. After 12 weeks of rehabilitation training, there was a significant difference in the Q/H joint contraction index between the REF group and the CON group (P <0.05), and there was no statistical difference in the other groups. That is to say, after the systematic rehabilitation training proposed in this paper, the joint contraction performance of the hamstrings/quadriceps in the REF group has been significantly improved. This shows that the rehabilitation nursing under the optical microscope based on the medical Internet of Things has a good effect on the rehabilitation of the football player’s side-cut running knee joint ACL injury.
Toward Backdoor Attacks for Image Captioning Model in Deep Neural Networks
Authors: Kwon Hyun , Lee Sanghyun
Published: August 2022

Deep neural networks perform well in image recognition, speech recognition, and text recognition fields. The image caption model provides captions for images by generating text after image recognition. After extracting features from the original image, this model generates a representation vector and provides captions for the image by generating text through a recursive neural network. However, this image caption model has weaknesses in the backdoor sample. In this paper, we propose a method for generating backdoor samples for image caption models. By adding a specific trigger to the original sample, this proposed method creates a backdoor sample that is misrecognized as a target class by the target model. The MS-COCO dataset was used as the experimental dataset, and Tensorflow was used as the machine learning library. When the trigger size of the backdoor sample is 4%, experimental results show that the average attack success rate of the backdoor sample is 96.67%, and the average error rate of the original sample is 9.65%.
On Some New Common Fixed Point Results for Finite Number of Mappings in Fuzzy Metric Spaces
Authors: Bartwal Ayush , Ahmad Junaid , Dimri R. C. , Prasad Gopi , Bonyah Ebenezer
Published: August 2022

We essentially suggest the concept of mutual sequences and Cauchy mutual sequence and utilize the same to prove the existence and uniqueness of common fixed point results for finite number of self- and non-self-mappings using fuzzy

ℤ

∗

-contractive mappings in fuzzy metric spaces. Our main result was obtained under generalized contractive condition in the fuzzy metric spaces. We provide examples to vindicate the claims and usefulness of such investigations. In this way, the present results generalize and enrich the several existing literature of the fuzzy metric spaces.
Mathematical Methods for Sensitive Information Mining Method of News Communication Platform Based on Big Data IOT Analysis
Authors: Jiang Chao , Yang ZhiXian
Published: August 2022

It is urgent to effectively monitor the public opinion of the news communication platform. The platform designed in this paper takes microblog public opinion as the research goal, uses MongoDB to build a distributed computing platform for sensitive information of news communication platform, establishes a corpus of sensitive event topics, introduces PageRank algorithm to deal with microblog social relations, obtains the characteristics of sensitive information of news communication platform, and carries out information screening, so as to accurately screen and mine the keywords in high impact information. To ensure the practical application effect of sensitive information mining method of news communication platform based on big data analysis. Finally, the experiment proves that the sensitive information mining method of news communication platform based on big data analysis has the advantages of high timeliness and high accuracy, which fully meets the research requirements. This is fully in line with the requirements of the study.
Construction of Automatic Acquisition System for Multichannel Electronic Communication Signals Based on Sorting Optimization Algorithm
Authors: Hu Hao
Published: August 2022

In both the military and the civilian world, multichannel electronic communication signals are widely used. The goal of signal processing in contemporary communication systems is to capture the signal. The traditional multichannel electronic communication signal automatic capture system has a limited range of performance, which slows down the rate of capture. This study used the sorting algorithm to build an automatic acquisition system for multichannel electronic communication signals in order to address this issue. The system processes the communication signal using the quick sorting method in the sorting optimization algorithm, achieving the goal of more precisely obtaining the carrier frequency and pseudo-code phase, and resolving the issue of slow system acquisition caused by the lengthy pseudo-code period. According to the experimental findings, the improved system’s acquisition rate is 6.89% higher than the conventional system’s, ensuring efficiency and reducing processing time in real-world communication applications.
Multi-Cloud Integration Security Framework Using Honeypots
Authors: Alyas Tahir , Alissa Khalid , Alqahtani Mohammed , Faiz Tauqeer , Alsaif Suleiman Ali , Tabassum Nadia , Naqvi Hafiz Hasan
Published: August 2022

This rapidly changing digital world is always sensitive to improving security and resilience to protect the inhabitants of this ecosystem in terms of data, processes, repositories, communication, and functions. The transformation of this digital ecosystem is heavily dependent on cloud computing, as it is becoming the global platform for individuals, corporates, and even governments. Therefore, the concerns related to security are now linked closely with cloud computing. In this paper, a multi-cloud security framework takes a view on the development of security mechanisms to provide a diversion to the attacker. The purpose is to gain more time to analyze the attack and mitigate the intrusion without compromises. This mechanism is designed using the honeypot technology that has been around for some time but has not been used in cloud computing and other technologies. The proposed framework provides modules related to managing the multi-cloud platform, the intrusion detection and prevention system, and honeypots. The results show significant improvement in the accuracy of detecting attacks. These results are generated in a two-phase scenario, and the first phase has been analyzed without the engagement of the honeypot module presented in the framework. The second phase has been executed with same parameters and conditions by engaging the honeypot module. It includes a comparison taxonomy of both results and an in-depth study of existing honeypots, as well as critical design elements for current honeypot research and outstanding concerns for future honeypots in IoT, multi-cloud contexts.
Preoperative Nutritional Risk Assessment for Predicting Complications after Radical Cystectomy plus Urinary Diversion for Bladder Cancer
Authors: Wei Xing , Wang Jia , Liu Haitao , Fan Weizhe , Guo Gang
Published: August 2022

Objective. To investigate the predictive value of preoperative nutritional risk assessment on the occurrence of complications after radical cystectomy plus urinary diversion for bladder cancer. Methods. Retrospective analysis of 178 patients with bladder cancer between July 2010 and March 2022 who underwent elective radical cystectomy plus urinary diversion was conducted. The occurrence of complications within 90 days after surgery was counted for all patients, and the postoperative complication rates of patients with and without nutritional risk were compared and analyzed. Also, logistic regression analysis was used to assess the relative risk coefficients of NRS-2002 and the occurrence of postoperative complications. Results. Comparison of clinicopathological characteristics and surgical conditions between the two groups showed that the proportion of combined diabetes mellitus, operative time, and postoperative hospital stay were higher in the nutritional risk group (NRS ≥3 score) than in the no nutritional risk group (NRS <3 score), while the preoperative blood albumin (ALB) level was lower than that in the no nutritional risk group (NRS <3 score). The results of multifactorial risk regression analysis showed that low preoperative ALB level and high NRS score were independent risk factors for postoperative complications in bladder cancer (

P
<
0.05

). Conclusion. The NRS-2002 nutritional risk score has good predictive value for the incidence of postoperative complications in patients with bladder cancer and provides a scientific basis for perioperative nutritional support.
A Survey of Museum Applied Research Based on Mobile Augmented Reality
Authors: Wang Chong , Zhu Ye
Published: August 2022

Museums are the important places of education for preservation and dissemination of human material and intangible heritage. Nowadays, there are many problems in museums such as single display mode and fixed interaction style of collections, which makes cognitive load higher and visiting experience poorer for tourists. Mobile Augmented Reality is a technology that seamlessly integrates virtual information with real environment based on mobile devices. Museum applications based on Mobile Augmented Reality can improve the display richness and meet new visiting needs of the public in the digital era. Firstly, this article introduces the key technologies of Mobile Augmented Reality and its use in museums. Secondly, according to different application functions, classifiable explanations for Mobile Augmented Reality application examples are given. Thirdly, based on the progress in the application of Mobile Augmented Reality in museums at home and abroad, from three dimensions of content, guide, and interaction, designing thought regarding the application of Mobile Augmented Reality for museums is summarized.
Study on the Segmentation Method of the Improved DeepLabv3+ Algorithm in the Basketball Scene
Authors: Li Kai
Published: August 2022

Aiming at the weak robustness and low segmentation accuracy of traditional basketball scene segmentation methods, this study proposed an improved basketball scene semantic segmentation model based on DeepLabv3+ for the purpose of basketball scene segmentation and accurate positioning of players. In this model, a relatively complex decoder is designed based on the DeepLabv3+ network. Multiple feature fusion is used to restore the semantic information of the image, and the convolutional block attention mechanism is introduced to optimize the channel weight and position information, reduce the computational complexity of the model, and improve the edge sensitivity. Experimental results show that the proposed model is 21.8% better than FCN’s full convolution model and 1.9% better than DeepLabv3+. At the speed of segmentation, it can process 6 pictures per second, greatly improving the accuracy of semantic segmentation for basketball scenes. In the future, real-time detection of sports such as basketball using computer vision methods will become more and more important.
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
Authors: Wang Zhixue , Zhang Yu , Luo Lin , Wang Nan
Published: August 2022

This paper proposed a novel anomaly detection (AD) approach of high-speed train images based on convolutional neural networks and the Vision Transformer. Different from previous AD works, in which anomalies are identified with a single image using classification, segmentation, or object detection methods, the proposed method detects abnormal difference between two images taken at different times of the same region. In other words, we cast anomaly detection problem with a single image into a difference detection problem with two images. The core idea of the proposed method is that the “anomaly” commonly represents an abnormal state instead of a specific object, and this state should be identified by a pair of images. In addition, we introduced a deep feature difference AD network (AnoDFDNet) which sufficiently explored the potential of the Vision Transformer and convolutional neural networks. To verify the effectiveness of the proposed AnoDFDNet, we gathered three datasets, a difference dataset (Diff dataset), a foreign body dataset (FB dataset), and an oil leakage dataset (OL dataset). Experimental results on the above datasets demonstrate the superiority of the proposed method. In terms of the F1-score, the AnoDFDNet obtained 76.24%, 81.04%, and 83.92% on Diff dataset, FB dataset, and OL dataset, respectively.
A Comprehensive Review on Distribution, Pharmacological Properties, and Mechanisms of Action of Sesamin
Authors: Zhang Yao , Liu Fen , Lin Yan , Li Linhai , Chen Mingfeng , Ni Lin
Published: August 2022

Sesamin is a kind of fat-soluble lignan extracted from sesame seeds or other plants. It has attracted more and more attention because of its extensive pharmacological activities. In this study, we systematically summarized the pharmacological activities of sesamin including antioxidant, anti-inflammatory, anticancer, protection of liver and kidney, prevention of diabetes, hypertension, and atherosclerosis. Studies focus on the abilities of sesamin to attenuate oxidative stress by reducing the levels of ROS and MDA, to inhibit the release of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6, etc.), and to induce apoptosis and autophagy in cancer cells through a variety of signaling pathways such as NF-κB, JNK, p38 MAPK, PI3K/AKT, caspase-3, and p53. By inhibiting the production of ROS, sesamin can also enhance the biological activities of NO in blood vessels, improve endothelial dysfunction and hypertension, and change the process of atherosclerotic lesion formation. In line with this, the various pharmacological properties of sesamin have been discussed in this review so that we can have a deeper understanding of the pharmacological activities of sesamin and clear the future development direction of sesamin.
An Investigation of the Dielectric Properties of Barium Oxide: Therm500 Nanofluids at Different Temperatures
Authors: Prakash P. , Catherine Grace John J. , Kingson Solomon Jeevaraj A. , Asfaw Belete Tessema
Published: August 2022

The dielectric characteristics of barium oxide: Therm500 nanofluids were investigated at a static frequency in this study. Barium oxide: Therm500 nanofluids are made by dispersing barium oxide nanoparticles in Therm500 (base fluid) using a sonicator. The static dielectric measurements were made with a frequency domain reflectometry (FDR) sensor. At various temperatures, the values of several dielectric properties, including dipole moment (μ), molar polarization (P), excess dielectric constant (

ε

E

), static dielectric constant (

ε

O

), and excess dipole moment (

μ

E

), have been measured for BaO: Therm500 nanofluids. Kirkwood factor and Bruggman factor were also computed and presented. The fluctuations in dielectric properties concerning six distinct combinations including 0.001 grams, 0.002 grams, 0.003 grams, 0.004 grams, 0.005 grams, and 0.006 g of nanofluid systems and four different temperatures such as 300 K, 303 K, 313 K, and 323 K are investigated in terms of intermolecular interactions. At all temperatures, the abovementioned dielectric properties BaO: Therm500 nanofluids are positive-negative variations across the whole combination range suggesting in nanofluids the existence of molecular interactions. Synchronization in both parallel and antiparallel of the dipoles in the fluid’s mixture are verified by the Kirkwood factor. The Bruggman factor depicts the divergence from the linear relationship in nanofluids, indicating molecular interactions. Dielectric properties of BaO: Therm500 are being studied to understand better their physical, technical, chemical, industrial, laboratory, biological, and pharmaceutical applications.
Application of Cloud Computing Combined with GIS Virtual Reality in Construction Process of Building Steel Structure
Authors: Dong Yugang , Sui Haozhi , Zhu Lei
Published: August 2022

In order to ensure that the steel structure of the building can meet the requirements of the strength of the building structure and the strict requirements in the fields of earthquake resistance, fire protection, energy saving, and environmental protection in the construction process, a method based on virtual reality supply chain cloud computing collaborative management technology is proposed. This method analyzes the main technology of building steel structure and establishes personalized cloud computing collaborative management technology based on virtual reality supply chain. Based on the analysis of industry characteristics and personalized service characteristics, the virtual reality control mechanism of personalized data mining is proposed by considering the service time cost and user experience quality. Based on cloud computing and VIRTUAL reality GIS, a collaborative management and control system architecture is proposed to optimize user personalized needs and solve the impact of differentiation on the supply chain. Experimental results show that compared with the noncollaborative management scheme, the collaborative management algorithm proposed by this method reduces the interference of personalized differences on supply chain management by 25%. The experimental results show that this method can greatly guarantee the satisfaction of steel structure construction process.
[Retracted] Environmental and Geographical (EG) Image Classification Using FLIM and CNN Algorithms
Authors: Ajay P. , Nagaraj B. , Huang Ruihang , Pradeep Raj M. S. , Ananthi P.
Published: August 2022

Intelligent machines have grown in importance in recent years in object recognition in terms of their ability to envision, comprehend, and reach decisions. There are a lot of complicated algorithms that accomplish AI utilities. In addition to their use in the medical industry, these methods of object recognition have a wide range of other fields, most notably industries, in which they can be applied. In contrast to the proposed calculation, the proposed calculation is less complex and more accurate under certain SNR conditions. In the deep nervous tissue fine‐tuning discriminator, phantom highlights and binding highlights are separated as sources; modified direct components are used as neuronal activation abilities; and cross entropy is used as unfortunate abilities. Optimized recognition of profound nervous tissue builds profound and periodic nervous tissue for regulatory confirmation of the corresponding signal.
Predictors of Long-Time Survivors in Nonmetastatic Colorectal Signet Ring Cell Carcinoma: A Large Population-Based Study
Authors: Zhang Wuguang , Gong Wenqian , Wu Changhai , Li Mengting , Tu Xiaolong
Published: August 2022

Background. Colorectal signet ring cell carcinoma (SRCC) is a rare and distinct subtype of colorectal cancer (CRC), with extremely poor prognosis and aggressive tumor biological behavior. In this study, we aimed to analyze the clinicopathological characteristics and to identify the independent predictors of long-time survivors (LTSs) of nonmetastatic colorectal SRCC. Methods. Patients diagnosed with nonmetastatic colorectal SRCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We compared and analyzed the clinicopathological characteristics between LTSs (patients survived over 5 years) and non-LTSs (patients survived of or less than 5 years). Afterwards, multivariate logistic regression analysis was used to identify independent predictors of LTSs, which were further used to construct a nomogram model to predict the probability of being LTSs. Results. We enrolled 2050 patients with nonmetastatic colorectal SRCC, consisting of 1441 non-LTSs and 609 LTSs. Multivariate logistic regression analysis revealed that race, marital status, tumor infiltration, lymph node involvement, and primary tumor treatment were independent predictors of LTSs. In addition, these five parameters were incorporated into a nomogram model to predict the probability of being LTSs. In terms of the model performance, the calibration curve revealed good agreement between observed and predicted probability of LTSs, and receiving operator characteristic curve showed acceptable discriminative capacity in the training and validation cohorts. Conclusion. Collectively, we analyzed and profiled the clinicopathological characteristics of LTSs in patients with nonmetastatic colorectal SRCC. Race, marital status, T stage, N stage, and primary tumor treatment were independent predictors of LTSs.
Research on the Relationship of Coupling Coordination between Digitalization and Agricultural Sustainable Development
Authors: Xiumei Xu , Xianghui Tian
Published: August 2022

This paper constructs a coupling coordination degree model of digitization and agricultural sustainable development and tests coupling and coordination development levels by using the data from Shandong Province. Furthermore, the Tobit model is constructed to deeply analyze the influencing factors of its coordination development. The results show that the comprehensive development level of digitization and agricultural sustainable development in Shandong Province is generally on the rise; the degree of coupling coordination between digitization and agricultural sustainable development in Shandong Province has been steadily rising and entering into the stage of high-quality coordination in 2018; since 2016, digitization in Shandong Province has played the more important role in promoting agricultural sustainable development than economic development, opening-up degree, industrial structure, and talent reserve. Finally, based on the above research conclusions, relevant policy suggestions are put forward.