bluuebunny/crossref_metadata_2025 huggingface dataset:
Multifunctional microrobot with real-time visualization and magnetic resonance imaging for chemoembolization therapy of liver cancer
Authors: Go Gwangjun , Yoo Ami , Nguyen Kim Tien , Nan Minghui , Darmawan Bobby Aditya , Zheng Shirong , Kang Byungjeon , Kim Chang-Sei , Bang Doyeon , Lee Seonmin , Kim Kyu-Pyo , Kang Seong Soo , Shim Kyung Mi , Kim Se Eun , Bang Seungmin , Kim Deok-Ho , Park Jong-Oh , Choi Eunpyo
Published: November 2022

Microrobots that can be precisely guided to target lesions have been studied for in vivo medical applications. However, existing microrobots have challenges in vivo such as biocompatibility, biodegradability, actuation module, and intra- and postoperative imaging. This study reports microrobots visualized with real-time x-ray and magnetic resonance imaging (MRI) that can be magnetically guided to tumor feeding vessels for transcatheter liver chemoembolization in vivo. The microrobots, composed of a hydrogel-enveloped porous structure and magnetic nanoparticles, enable targeted delivery of therapeutic and imaging agents via magnetic guidance from the actuation module under real-time x-ray imaging. In addition, the microrobots can be tracked using MRI as postoperative imaging and then slowly degrade over time. The in vivo validation of microrobot system–mediated chemoembolization was demonstrated in a rat liver with a tumor model. The proposed microrobot provides an advanced medical robotic platform that can overcome the limitations of existing microrobots and current liver chemoembolization.
Snapping for high-speed and high-efficient butterfly stroke–like soft swimmer
Authors: Chi Yinding , Hong Yaoye , Zhao Yao , Li Yanbin , Yin Jie
Published: November 2022

Natural selection has tuned many flying and swimming animals to share the same narrow design space for high power efficiency, e.g., their dimensionless Strouhal numbers St that relate flapping frequency and amplitude and forward speed fall within the range of 0.2 < St < 0.4 for peak propulsive efficiency. It is rather challenging to achieve both comparably fast-speed and high-efficient soft swimmers to marine animals due to the naturally selected narrow design space and soft body compliance. Here, bioinspired by the flapping motion in swimming animals, we report leveraging snapping instabilities for soft flapping-wing swimmers with comparable high performance to biological counterparts. The lightweight, butterfly stroke–like soft swimmer (2.8 g) demonstrates a record-high speed of 3.74 body length/s (4.8 times faster than the reported fastest flapping soft swimmer), high power efficiency (0.2 < St = 0.25 < 0.4), low energy consumption cost, and high maneuverability (a high turning speed of 157°/s).
GTF3A
mutations predispose to herpes simplex encephalitis by disrupting biogenesis of the host-derived RIG-I ligand
RNA5SP141
Authors: Naesens Leslie , Muppala Santoshi , Acharya Dhiraj , Nemegeer Josephine , Bogaert Delfien , Lee Jung-Hyun , Staes Katrien , Debacker Veronique , De Bleser Pieter , De Bruyne Marieke , De Baere Elfride , van Gent Michiel , Liu GuanQun , Lambrecht Bart N. , Staal Jens , Kerre Tessa , Beyaert Rudi , Maelfait Jonathan , Tavernier Simon J. , Gack Michaela U. , Haerynck Filomeen
Published: November 2022

Herpes simplex virus 1 (HSV-1) infects several billion people worldwide and can cause life-threatening herpes simplex encephalitis (HSE) in some patients. Monogenic defects in components of the type I interferon system have been identified in patients with HSE, emphasizing the role of inborn errors of immunity underlying HSE pathogenesis. Here, we identify compound heterozygous loss-of-function mutations in the gene
GTF3A
encoding for transcription factor IIIA (TFIIIA), a component of the RNA polymerase III complex, in a patient with common variable immunodeficiency and HSE. Patient fibroblasts and
GTF3A
gene–edited cells displayed impaired HSV-1–induced innate immune responses and enhanced HSV-1 replication. Chromatin immunoprecipitation sequencing analysis identified the 5
S
ribosomal RNA pseudogene 141 (
RNA5SP141
), an endogenous ligand of the RNA sensor RIG-I, as a transcriptional target of TFIIIA.
GTF3A
mutant cells exhibited diminished
RNA5SP141
expression and abrogated RIG-I activation upon HSV-1 infection. Our work unveils a crucial role for TFIIIA in transcriptional regulation of a cellular RIG-I agonist and shows that
GTF3A
genetic defects lead to impaired cell-intrinsic anti–HSV-1 responses and can predispose to HSE.
Supplemental Material: Oceanic intraplate faulting as a pathway for deep hydration of the lithosphere: Perspectives from the Caribbean
Authors: Bishop B.T. , al. et
Published: November 2022

PDF contains a map with labeled seismic station locations, plots of results obtained using alternate velocity models, an illustration of ray path differences induced by misestimated Mohorovičić Discontinuity depths, and an Hκ-plot for seismic station PRV. Excel file (Table S3) contains a catalog of relocated hypocenters obtained using the preferred velocity model.
Strain partitioning in the Moine Nappe, northernmost Scotland
Authors: Southern Sarah Collier , Mosher Sharon , Orlandini Omero Felipe
Published: November 2022

Extreme strain in the form of flattening or constriction during noncoaxial shear in ductile shear zones provides a record of ductile thrust system dynamics and the overall tectonic evolution. Within the Moine Nappe in northern Scotland, between the Ben Hope and Moine thrusts, the Strathan Conglomerate displays apparent strain partitioning with extreme flattening (e.g., laterally extensive sheets of deformed pebbles with aspect ratios of 134:113:1 and 88–92% estimated thinning) adjacent to the overlying Ben Hope Thrust and extreme constriction (e.g., rods with aspect ratios of 21:4:1 and estimated extension of 1000%) lower in the nappe package. We demonstrate that partitioning of strain is between its intensity and how deformation is manifested. Field, microstructural, and crystallographic orientation data from this study indicate that both areas were deformed by WNW-directed noncoaxial shear and coaxial flattening under amphibolite-facies conditions. Adjacent to the Ben Hope thrust, flattening was pervasive during nonco-axial shear, whereas beneath and within the Moine Nappe package, polyphase folding dominated. There, early, large-scale folds (F2) rotated into the transport direction. Subsequent transport-parallel (F3) folds and tubular sheath folds formed on the F2 limbs and were dismembered to form rods. No evidence of constriction is observed; instead, pervasive noncoaxial shear was accompanied by minor flattening under decreasing temperature conditions. Thus, these S-tectonites in the Moine Nappe are the result of concentrated flattening of pebbles into sheets during WNW-directed shear, whereas the L-tectonites result from heterogeneously distributed shear and folding, coupled with minor flattening, which produced rods without constriction.
Illuminating geology in areas of limited exposure using texture shading of lidar digital terrain models
Authors: Allmendinger Richard W. , Karabinos Paul
Published: November 2022

Regions of sparse exposure challenge geologic mappers because of limited information available on the underlying structure and continuity of the map units. We introduce here a little-known technique for post-processing bare earth digital terrain models (DTMs) that can dramatically improve knowledge of the underlying structure in covered areas. Texture shading enhances changes in slope and does not suffer from limitations introduced by artificial illumination required in hillshade or shaded relief images. When this technique is applied to lidar DTMs, layers of rock units with variable resistance to erosion can be clearly imaged, even in areas with limited outcrop. This technique enables one to collect comprehensive orientation data in areas of deformed sedimentary strata, assess the continuity of metamorphic and igneous rock units, and depict basement fracture sets. We demonstrate the use of texture shading in the Valley and Ridge of northern Pennsylvania, metamorphic rocks in the Berkshire Hills of western Massachusetts and Green Mountains of Vermont, and glacial deposits in the Finger Lakes region of upstate New York (northeastern United States).
Nonlinear modelling parameters and acceptance criteria in ASCE/SEI 41: a critical review and applicability to Canada
Authors: Fazileh Farokh , Fathi-Fazl Reza , Cai Zhen , Bérubé Antoine
Published: November 2022

The National Research Council Canada is currently developing seismic evaluation and upgrading guidelines (SEG) for existing buildings in Canada. The SEG consist of both linear and nonlinear analysis procedures to evaluate the seismic adequacy of existing buildings. Where nonlinear analysis procedures are selected, nonlinear modelling parameters (NMP) and acceptance criteria (AC) are to be used. Due to the lack of Canadian guidelines for nonlinear analyses, the state of the practice in Canada often refers to ASCE/SEI 41 for guidance. Given the differences in seismic design and construction practices in the United States and Canada, ASCE/SEI 41 should be used with caution. This technical note presents a critical review of NMP and AC in ASCE/SEI 41 and recommends key steps for the investigation of the applicability of NMP and AC in ASCE/SEI 41 to the Canadian context. An example is included to demonstrate the recommended steps and the importance of such investigation.
Detection and phylogenetic assessment of PRV-1 via sampling of biological materials released from salmon farms in British Columbia
Authors: Mordecai Gideon , Beardslee Kurt , Glambeck Bonny , Frazer Neil , Routledge Richard , Morton Alexandra
Published: November 2022

The growth of the net pen salmon farming industry and the concurrent decline of many wild salmon populations have generated an ongoing call for research into the potential role of infectious agents associated with salmon farming. Here, we describe a method to screen for the presence of viral nucleic acids by sampling biological waste in the vicinity of farms. We collected biological samples adjacent to 56 marine net pens from five different companies, and two farm salmon processing plants ( n = 230), 70% of samples were positive for Piscine orthoreovirus-1 (PRV-1). Viral genome segments S1 ( n = 68) and M2 ( n = 39) were sequenced and subjected to phylogenetic analysis. Sequences were all monophyletic to the clade of PRV-1 routinely found in the region. Consistent with previous studies, samples collected near Atlantic salmon ( Salmo salar) farms were related to a lineage of PRV-1a commonly detected in both farmed and wild salmon in British Columbia, while a related lineage of PRV-1a was detected near Chinook salmon ( Oncorhynchus tshawytscha) farms. Similarity in sequence within companies suggests that Atlantic salmon freshwater hatcheries are a probable source of this virus to the marine environment, concordant with recent detections of PRV-1 within those hatcheries.
Origin and destination attachment: study of cultural integration on Twitter
Authors: Kim Jisu , Sîrbu Alina , Giannotti Fosca , Rossetti Giulio , Rapoport Hillel
Published: November 2022

The cultural integration of immigrants conditions their overall socio-economic integration as well as natives’ attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration—or assimilation—can be detrimental in that it implies forfeiting one’s ties to the origin country and eventually translates into a loss of diversity (from the viewpoint of host countries) and of global connections (from the viewpoint of both host and home countries). Cultural integration can be described using two dimensions: the preservation of links to the origin country and culture, which we call origin attachment, and the creation of new links together with the adoption of cultural traits from the new residence country, which we call destination attachment. In this paper we introduce a means to quantify these two aspects based on Twitter data. We build origin and destination attachment indices and analyse their possible determinants (e.g., language proximity, distance between countries), also in relation to Hofstede’s cultural dimension scores. The results stress the importance of language: a common language between origin and destination countries favours origin attachment, as does low proficiency in the host language. Common geographical borders seem to favour both origin and destination attachment. Regarding cultural dimensions, larger differences among origin and destination countries in terms of Individualism, Masculinity and Uncertainty appear to favour destination attachment and lower origin attachment.
SPILLOVER EFFECTS OF ASIAN BANKING SECTOR ON SYSTEMIC RISK IN THE INSURANCE SECTOR: BASED ON A DOUBLE-CoVaR MODEL
Authors: WANG LIZHEN , HAO YILIN , JING ZHONGBO , ZHANG JIANDI
Published: November 2022

This study explored the spillover effect of the banking sector on the systemic risk of the insurance sector. We selected 27 listed insurers from China, South Korea, Japan and Singapore, and built a double-CoVaR model based on monthly data for 2012–2021. We explored the impact mechanism by decomposing the spillover effect and conducted regression analysis to determine factors influencing net spillover. First, the banking sector has positive spillover on insurance systemic risk. Second, the banking sector affects insurance systemic risk mainly through direct risk spillover. Third, the size, asset similarity and leverage of insurers significantly impact net spillover. This paper contributes to the previous literature and regulation by providing a methodology for predicting risk spillovers using a new model and an up-to-date data.
For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability
Authors: Rangamani Akshay , Rosasco Lorenzo , Poggio Tomaso
Published: November 2022

In this paper, we study kernel ridge-less regression, including the case of interpolating solutions. We prove that maximizing the leave-one-out ([Formula: see text]) stability minimizes the expected error. Further, we also prove that the minimum norm solution — to which gradient algorithms are known to converge — is the most stable solution. More precisely, we show that the minimum norm interpolating solution minimizes a bound on [Formula: see text] stability, which in turn is controlled by the smallest singular value, hence the condition number, of the empirical kernel matrix. These quantities can be characterized in the asymptotic regime where both the dimension ([Formula: see text]) and cardinality ([Formula: see text]) of the data go to infinity (with [Formula: see text] as [Formula: see text]). Our results suggest that the property of [Formula: see text] stability of the learning algorithm with respect to perturbations of the training set may provide a more general framework than the classical theory of Empirical Risk Minimization (ERM). While ERM was developed to deal with the classical regime in which the architecture of the learning network is fixed and [Formula: see text], the modern regime focuses on interpolating regressors and overparameterized models, when both [Formula: see text] and [Formula: see text] go to infinity. Since the stability framework is known to be equivalent to the classical theory in the classical regime, our results here suggest that it may be interesting to extend it beyond kernel regression to other overparameterized algorithms such as deep networks.
On some skew codes over ℤq + uℤq
Authors: Hebbache Zineb , Kaya Abidin , Aydin Nuh , Guenda Kenza
Published: November 2022

In this paper, we investigate the structure and properties of skew negacyclic codes and skew quasi-negacyclic codes over the ring [Formula: see text] Some structural properties of [Formula: see text] are discussed, where [Formula: see text] is an automorphism of [Formula: see text] A skew quasi-negacyclic code of length [Formula: see text] with index [Formula: see text] over [Formula: see text] is viewed both as in the conventional row circulant form and also as an [Formula: see text]-submodule of [Formula: see text], where [Formula: see text] is the Galois extension ring of degree [Formula: see text] over [Formula: see text] and [Formula: see text] is an automorphism of [Formula: see text] A sufficient condition for one generator skew quasi-negacyclic codes to be free is determined. Some distance bounds for free one generator skew quasi-negacyclic codes are discussed. Furthermore, given the decomposition of a skew quasi-negacyclic code, we provide the decomposition of its dual code. As a result, a characterization of self-dual skew quasi-negacyclic codes over [Formula: see text] is provided. By using computer search we obtained a number of new linear codes over [Formula: see text] from skew negacyclic and skew quasi-negacyclic codes over [Formula: see text].
Order types and natural price change: Model and empirical study of the Chinese market
Authors: Liu Siyu , Zhao Chaoyi , Wu Lan
Published: November 2022

Order type plays an important role in algorithmic trading and is a key factor of price impact. In this paper, we propose a new framework for studying the discrete price change process, which focuses on the impacts of aggressive orders (market orders and aggressive limit orders) and cancelations. The price change process is driven by states and events of best quotes, and we define the event-based price change as the “natural price change” (NPC). Under the framework, we propose a heteroscedastic linear econometric model for the NPC to explore the impact of different types of orders on the price dynamics. To verify the usability of our model and explore the driving factors of price dynamics, we conduct a thorough empirical analysis for 786 large-tick stocks traded on the Shenzhen Stock Exchange. Empirical results statistically demonstrate that aggressive orders can introduce stronger impact on the NPC than cancelations. Meanwhile, splitting a big order into several small orders can lead to a larger NPC. Our framework can also be applied for the prediction of price change.
The Ordovician of Central Asia (Kyrgyzstan, Uzbekistan and Tajikistan)
Authors: Pour Mansoureh Ghobadi , Popov Leonid E. , Kim Aleksey I. , Abduazimova Zoja M. , Mikolaichuk Alexander V. , Kim Irina A. , Ospanova Narima , Erina Maya V. , Salimova Firuza A. , Klishevich Inna , Saidov Mirzo S. , Davlatov Nodirzhon H. , Abdiyev Nuriddin H. , Koldybekov Oybek Ya , Gordeev Dmitriy , Rustamov Shohmuhammad T. , Yangiboev Shokhrukh B.
Published: November 2022

The region of Kyrgyzstan, Tajikistan and Uzbekistan includes five first-order tectonic units with an Early Paleozsoic sedimentary record, comprising North Tien Shan, Karatau Naryn, Turkestan–Alai, Zeravshan–Hissar and the Central Pamirs. Available palaeobiogeographical and palaeomagnetic data suggest that these were widely dispersed in the Ordovician. North Tien Shan, Karatau Naryn, Turkestan–Alai were separate microcontinents located in the low southern latitudes throughout the Ordovician in relative proximity to the western Gondwana margin. Zeravshan–Hissar and the Central Pamirs were also parts of the Gondwana supercontinent but were located in temperate latitudes. The geological literature on the Ordovician of the region is assessed to provide an updated palaeontological record, outline of lithostratigraphy and biostratigraphic correlation based on the International Chronostratigraphic Chart. The Ordovician biostratigraphy of Central Asia is mainly graptolite-based; however, that record is discontinuous, and the absence of detailed faunal logs and lack of monographic studies causes difficulty in precisely locating system and stage boundaries. Although an extensive faunal record has been documented, often it is based on preliminary taxonomical identifications which are not reliable for high-resolution biostratigraphy and tracing biodiversity patterns.
CAD Support for Security and Robustness Analysis of Safety-critical Automotive Software
Authors: Koley Ipsita , Dey Soumyajit , Mukhopadhyay Debdeep , Singh Sachin , Lokesh Lavanya , Ghotgalkar Shantaram Vishwanath
Published: November 2022

Modern vehicles contain a multitude of electronic control units that implement software features controlling most of the operational, entertainment, connectivity, and safety aspects of the vehicle. However, with security requirements often being an afterthought in automotive software development, incorporation of such software features with intra- and inter-vehicular connectivity requirements often opens up new attack surfaces. Demonstrations of such security vulnerabilities in past reports and literature bring in the necessity to formally analyze how secure automotive control systems really are against adversarial attacks. Modern vehicles often incorporate onboard monitoring systems that test the sanctity of data samples communicated among controllers and detect possible attack/noise insertion scenarios. The performance of such monitors against security threats also needs to be verified.

In this work, we outline a rigorous methodology for estimating the vulnerability of automotive CPSs. We provide a computer-aided design framework that considers the model-based representation of safety-critical automotive controllers and monitoring systems working in a closed loop with vehicle dynamics and verifies their safety and robustness w.r.t.
false data injection
attacks. Symbolically exploring all possible combinations of attack points of the input automotive CPS, the proposed framework tries to find out which sensor and/or actuation signal is vulnerable by generating
stealthy and successful
attacks using a formal method-based counter-example guided abstract refinement process. We also validate the efficacy of the proposed framework using a case study performed in an industry-scale simulator.
CANOA: CAN Origin Authentication through Power Side-channel Monitoring
Authors: Thakur Shailja , Moreno Carlos , Fischmeister Sebastian
Published: November 2022

The lack of any sender authentication mechanism in place makes Controller Area Network (CAN) vulnerable to security threats. For instance, an attacker can impersonate an Electronic Control Unit (ECU) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address this problem, we propose a novel source authentication technique that uses power consumption measurements of the ECU to authenticate the source of a message. A transmission of an ECU affects the power consumption and a characteristic pattern will appear. Our technique exploits the power consumption of each ECU during the transmission of a message to determine whether the message actually originated from the purported sender. We evaluate our approach in both a lab setup and a real vehicle. We also evaluate our approach against factors that can impact the power consumption measurement of the ECUs. The results of the evaluation show that the proposed technique is applicable in a broad range of operating conditions with reasonable computational power requirements and attaining good accuracy.
Geo-Tile2Vec: A Multi-Modal and Multi-Stage Embedding Framework for Urban Analytics
Authors: Luo Yan , Leong Chak-Tou , Jiao Shuhai , Chung Fu-Lai , Li Wenjie , Liu Guoping
Published: November 2022

Cities are very complex systems. Representing urban regions are essential for exploring, understanding, and predicting properties and features of cities. The enrichment of multi-modal urban big data has provided opportunities for researchers to enhance urban region embedding. However, existing works failed to develop an integrated pipeline that fully utilizes effective and informative data sources within geographic units. In this article, we regard a geo-tile as a geographic unit and propose a multi-modal and multi-stage representation learning framework, namely Geo-Tile2Vec, for urban analytics, especially for urban region properties identification. Specifically, in the early stage, geo-tile embeddings are firstly inferred through dynamic mobility events which are combinations of point-of-interest (POI) data and trajectory data by a Word2Vec-like model and metric learning. Then, in the latter stage, we use static street-level imagery to further enrich the embedding information by metric learning. Lastly, the framework learns distributed geo-tile embeddings for the given multi-modal data. We conduct experiments on real-world urban datasets. Four downstream tasks, i.e., main POI category classification task, main land use category classification task, restaurant average price regression task, and firm number regression task, are adopted for validating the effectiveness of the proposed framework in representing geo-tiles. Our proposed framework can significantly improve the performances of all downstream tasks. In addition, we also demonstrate that geo-tiles with similar urban region properties are geometrically closer in the vector space.
On the Timescales of Embodied Intelligence for Autonomous Adaptive Systems
Authors: Iida Fumiya , Giardina Fabio
Published: November 2022

Embodiment is a crucial concept for the autonomy and adaptivity of systems working in the physical world with high degrees of uncertainty and complexity. The physical bodies of autonomous adaptive systems heavily influence the information flow from the environment to the central processing (and vice versa), requiring us to consider the full triad of brain, body, and environment to investigate intelligent behavior. This article provides a structured review of embodied intelligence with a special emphasis on the concept of timescales and their role in self-organization and the emergence of complex behavior. We classify embodied interactions into three types—cross-timescale matching, separation, and nontemporal sequences—and discuss how these interactions were studied in the past as well as how they can contribute to the systematic investigation of complex autonomous and adaptive systems in both biological and artificial entities.
Retrieval-Based Factorization Machines for Human Click Behavior Prediction
Authors: Tang Yu , Wang Sheng , Huang Yuancai , Zhao Xiaokai , Zhao Weinan , Duan Yitao , Wang Xu
Published: November 2022

Human click behavior prediction is crucial for recommendation scenarios such as online commodity or advertisement recommendation, as it is helpful to improve the quality and user satisfaction of services. In recommender systems, the concept of click-through rate (CTR) is used to estimate the probability that a user will click on a recommended candidate. Many methods have been proposed to predict CTR and achieved good results. However, they usually optimize the parameters through a global objective function such as minimizing logloss or root mean square error (RMSE) for all training samples. Obviously, they intend to capture global knowledge of user click behavior but ignore local information. In this work, we propose a novel approach of retrieval-based factorization machines (RFM) for CTR prediction, which can effectively predict CTR by combining global knowledge which is learned from the FM method with the neighbor-based local information. We also leverage the clustering technique to partition the large training set into multiple small regions for efficient retrieval of neighbors. We evaluate our RFM model on three public datasets. The experimental results show that RFM performs better than other models in metrics of RMSE, area under ROC (AUC), and accuracy. Moreover, it is efficient because of the small number of model parameters.
Network Architecture for Optimizing Deep Deterministic Policy Gradient Algorithms
Authors: Zhang Haifei , Xu Jian , Zhang Jian , Liu Quan
Published: November 2022

The traditional Deep Deterministic Policy Gradient (DDPG) algorithm has been widely used in continuous action spaces, but it still suffers from the problems of easily falling into local optima and large error fluctuations. Aiming at these deficiencies, this paper proposes a dual-actor-dual-critic DDPG algorithm (DN-DDPG). First, on the basis of the original actor-critic network architecture of the algorithm, a critic network is added to assist the training, and the smallest Q value of the two critic networks is taken as the estimated value of the action in each update. Reduce the probability of local optimal phenomenon; then, introduce the idea of dual-actor network to alleviate the underestimation of value generated by dual-evaluator network, and select the action with the greatest value in the two-actor networks to update to stabilize the training of the algorithm process. Finally, the improved method is validated on four continuous action tasks provided by MuJoCo, and the results show that the improved method can reduce the fluctuation range of error and improve the cumulative return compared with the classical algorithm.
Investigation of Blockchain Technology by Using the Innovative Concepts of Complex Pythagorean Fuzzy Soft Information
Authors: Jan Naeem , Gwak Jeonghwan , Jeon Younghoon , Akram Bushra
Published: November 2022

A blockchain is a valuable and proficient type of digital ledger technology that involves of expanding list of records, called blocks, that are strongly connected simultaneously using cryptography. Further, complex Pythagorean fuzzy sets (CPFSs) are the generalized form of the intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PyFSs), and complex intuitionistic fuzzy sets (CIFSs), used for evaluating the awkward and unreliable information in genuine life problems. In this analysis, we aim to diagnose the innovative idea of complex Pythagorean fuzzy soft relations (CPyFSRs) by using the Cartesian product (CP) of two complex Pythagorean fuzzy soft sets (CPyFSSs), which are computed with the help of two different ideas, called complex Pythagorean fuzzy relation and soft sets. Additionally, using the presented approaches, we examined different kinds of relations and also justified them with the help of some suitable examples. The CPyFSRs has a comprehensive structure because it is discussing both degrees of membership and non‐membership with multidimensional variable. Further, includes the CPyFSR‐based modeling techniques that use the score function to choose the best blockchain technology (BCT) to enhance the worth of the evaluated information. Using a good BCT, the transaction may be simply transferred record between users. Finally, the benefit of this proposed framework is demonstrated by comparing it to other frameworks to show the supremacy and feasibility of the diagnosed approaches.
Noncoding RNA in the Regulation of Acute Aortic Dissection: From Profile to Mechanism
Authors: Wei Ruibin , Feng Yingqing
Published: November 2022

Aortic dissection is a life-threatening condition caused by a tear in the intimal layer of the aorta or bleeding within the aortic wall, resulting in the separation of the layers of the aortic wall. As Nienaber reported, aortic dissection is most common in people 65–75 years old and has an incidence of 35 cases per 100,000 people per year in this population. Many pathogenic factors are involved in aortic dissection, including hypertension, dyslipidemia, and abnormality of the aortic intima caused by genetic variation. However, with the development of gene sequencing and transgenic technology, genetic methods are being used for the diagnosis and treatment of diseases, including acute aortic dissection. Genetic research on acute aortic dissection began around 2006. Recently, research on acute aortic dissection has mainly focused on microRNA (miRNA). Studies have found that miRNA plays a critical regulatory role in the occurrence and development of acute aortic dissection. By regulating miRNA expression, acute aortic dissection can be prevented and treated.
Identifying Common Genes and Pathways Associated with Periodontitis and Aging by Bioinformatics Analysis
Authors: Xie Qi , Lv Hongyu , Wang Tianqi , Sun Jingxuan , Li Yuekun , Niu Yumei , Xie Weili
Published: November 2022

Background. This work used bioinformatic analysis to identify the relationship between periodontitis (PD) and aging, which could lead to new treatments for periodontal disease in the elderly. Method. Four microarray datasets were obtained from the Gene Expression Omnibus (GEO) database and analyzed in R language to identify differentially expressed genes (DEGs). The common DEGs of PD and aging were evaluated as key genes in this investigation by a Venn diagram. These common DEGs were analyzed through additional experiments and analysis, such as pathway analysis and enrichment analysis, and a network of protein-protein interactions (PPIs) was constructed. Cytoscape was used to visualize hub genes and critical modules based on the PPI network. Interaction of TF-genes and miRNAs with hub genes is identified. Result. 84 common DEGs were found between PD and aging. Cytohubba was performed on the PPI network obtained from STRING tool, and the top 10 genes (MMP2, PDGFRB, CTGF, CD34, CXCL12, VIM, IL2RG, ACTA2, COL4A2, and TAGLN) were selected as hub genes. VIM may be a potential biomarker in the analysis of linked hub gene regulatory networks, and hsa-mir-21 and hsa-mir-125b are predicted to be associated in PD and aging. Conclusion. This study investigated the key genes and pathways interactions between PD and aging, which may help reveal the correlation between PD and aging. The current research results are obtained by prediction, and follow-up biological experiments are required for further verification.
Computational Intelligence Based Recurrent Neural Network for Identification Deceptive Review in the E-Commerce Domain
Authors: Alsubari Saleh Nagi , Aldhyani Theyazn H. H. , Deshmukh Sachin N. , Maashi Mashael , Alharbi Sadeen , Al-Baity Heyam H.
Published: November 2022

Most consumers depend on online reviews posted on e-commerce websites when determining whether or not to buy a service or a product. Moreover, due to the presence of fraudulent (deceptive) reviews, the fundamental problem in such reviews is not fully addressed. Thus, deceptive reviews present wrong and misguiding opinions that are harmful to consumers and e-commerce. People called fraudsters who intentionally write deceptive reviews to target and deceive potential consumers, as they target businesses that have a well-built reputation or fame for their personal promotion, create such reviews. Therefore, developing a deceptive review detection system is essential for identifying and classifying online product reviews as truthful or fake/deceptive reviews. The main objective of this research work is to analyze and identify online deceptive reviews in electronic product reviews in the Amazon and Yelp domains. For this purpose, two experiments were conducted individually. The first was executed on standard Yelp product reviews. The second was performed on Amazon product review datasets. For this dataset, we created and labeled it using a deceptiveness score calculated based on features extracted from the review text using the linguistic inquiry and word count (LIWC) tool. These features were authenticity, negative words, comparing words negation words, analytical thinking, and positive words as well as the given rating value by a user. The recurrent neural network, bidirectional long short-term memory (RNN-BLSTM) model, was used to both datasets in order to conduct the evaluation. The application of this model was contingent upon the learning of words embedding of the review text. Finally, we evaluated the RNN-BLSTM model’s performance using the Yelp and Amazon datasets and compared the results. The results were 89.6% regarding testing accuracy for both datasets. From our experimental results, we observed that the LIWC feature with word embedding in the review text provided better accuracy performance compared with other existing methods.
Variability and Performance Evaluation of Released Linseed Varieties for Yield and Related Traits in South Gondar Zone, Ethiopia
Authors: Amare Kassahun , Abebe Getaneh
Published: November 2022

Even though numerous improved linseed varieties have been released in Ethiopia, the mean seed yield per unit area of the crop remains low due to poor access to improved varieties. To improve the crop’s production and productivity, testing the performance of improved linseed varieties in potential agroecologies where the varieties have never been grown before is an essential activity. Additionally, understanding the amount of genetic variation that is available within the linseed germplasm using genetic traits is also critical to the successful and sustainable improvement program of linseed. The main objective of this research was to evaluate the performance of improved linseed varieties and their genetic variability for seed yield and related traits in the Lay Gayint district, South Gondar zone, Ethiopia. The experiment was conducted following a randomized complete block design with three replications, and nine released varieties and one local linseed variety were assessed in the study. Seed yield and yield-related traits were collected and subjected to a combined analysis of variance. The result shows that there was a significant difference between the tested linseed varieties for all examined traits, indicating the existence of genotypic variation between the experimental linseed varieties. The varieties Berene, CI-1525, Yadeno, and Furtu produced relatively higher seed yields, with an average of 894.28, 879.36, 823.28, and 820.85 kg·ha−1, respectively. Approximately 70% of the evaluated varieties produced higher seed yields than the local variety. The analysis of variance also resulted in a significant variety by year interaction effect for height of the plant, number of tillers, capsule number/plant, and seed yield, indicating the inconsistency of the varieties across the two growing seasons. Higher genotypic coefficient of variation (GCV) and phenotypic coefficients of variation (PCV) (>20%) coupled with high heritability estimated values (>80%) were observed for seed yield per hectare, the number of capsules per plant, and tiller number. Seed yield and the number of capsules per plant provided higher genetic advance estimations. To make the selection more successful, breeders should concentrate on traits with high genotypic variance and heritability estimates, and promising varieties should be introduced and dispersed within the research area.
Fuzzy Neural Network for the Online Course Quality Assessment System
Authors: Bai Xue , Bai Yongguo
Published: November 2022

Under the influence of COVID-19, online office and online education has ushered in a golden period of development. The teaching quality of online education has been a controversial issue. Our study takes online course teaching quality assessment as the starting point, explores the influencing factors of online course quality assessment with online courses as the research object, and analyzes the latest research proposal for an online course quality index. To make the online course quality assessment more intelligent, we propose an online course quality assessment method based on a fuzzy neural network. The method uses fuzzy rules as the baseline and adds a TSK perception mechanism to expand the perception domain of the fuzzy neural network and improve the course quality index prediction accuracy. At the input side of the fuzzy neural network, we preclassify the online course data into four parts, and each part of the data represents a different assessment domain. Due to the large data cost, we expanded the collective amount of data using data augmentation methods. In addition, we parse the structure of the fuzzy neural network hierarchy and introduce the construction and role of the TSK perception mechanism in the fuzzy rules. An optimal learning strategy is proposed in the fuzzy neural network training. Finally, in the experimental session, we verify the effectiveness of data augmentation and explore the distribution of course quality assessment weights. In the comparison of the model prediction results with the actual assessment results, our method achieves an excellent matching rate, which proves the high efficiency of our method in the online course quality assessment system.
Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society
Authors: Wang Di , Xu Weijing , Jia Xuesong
Published: November 2022

With the growth of national economic strength, urban modernization has become an indispensable part. Among them, urban road traffic congestion has become the main problem affecting work and traffic. Traffic pressure promotes the massive use of Internet of Things and other technologies in the transportation system. The integration of computer, Internet of Things and transportation system forms a new intelligent transportation concept. With the advent of intelligent information society, the application of intelligent transportation is imminent. In view of the shortcomings of urban intelligent transportation, this paper uses Internet of Things technology and big data technology to study it. Firstly, it analyzes the function and value of IoT intelligent transportation, analyzes the system components from the construction principle, and makes full use of intelligent technology to improve the transportation system. Secondly, the optimal path algorithm is proposed in the IoT urban intelligent transportation system model. The advantages and disadvantages of genetic algorithm and heuristic algorithm are analyzed, and ant colony algorithm is used for further optimization. In order to verify the feasibility of the intelligent transportation system, technologies such as big data edge computing are also used to comprehensively evaluate and study the use of the system. Finally, we analyze the results of the practical application of the intelligent transportation system under the Internet of Things technology. The research results show that the urban intelligent transportation system, supported by the Internet of Things technology, can realize the dynamic planning of the optimal route, help the people to improve their driving experience in the high safety and intellectualization, and greatly avoid a series of problems caused by congestion.
Development of Machine Learning Tools for Predicting Coronary Artery Disease in the Chinese Population
Authors: Zhang Tiexu , Huang Shengming , Xie Pengfei , Li Xiaoming , Pan Yingxia , Xu Yue , Han Peng , Ding Feifei , Zhao Jiangman , Tang Hui
Published: November 2022

Purpose. Coronary artery disease (CAD) is one of the major cardiovascular diseases and the leading cause of death globally. Blood lipid profile is associated with CAD early risk. Therefore, we aim to establish machine learning models utilizing blood lipid profile to predict CAD risk. Methods. In this study, 193 non-CAD controls and 2001 newly-diagnosed CAD patients (1647 CAD patients who received lipid-lowering therapy and 354 who did not) were recruited. Clinical data and the result of routine blood lipids tests were collected. Moreover, low-density lipoprotein cholesterol (LDL-C) subfractions (LDLC-1 to LDLC-7) were classified and quantified using the Lipoprint system. Six predictive models (k-nearest neighbor classifier (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost)) were established and evaluated by the confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), recall (sensitivity), accuracy, precision, and F1 score. The selected features were analyzed and ranked. Results. While predicting the CAD development risk of the CAD patients without lipid-lowering therapy in the test set, all models obtained AUC values above 0.94, and the accuracy, precision, recall, and F1 score were above 0.84, 0.85, 0.92, and 0.88, respectively. While predicting the CAD development risk of all CAD patients in the test set, all models obtained AUC values above 0.91, and the accuracy, precision, recall, and F1 score were above 0.87, 0.94, 0.87, and 0.92, respectively. Importantly, small dense LDL-C (sdLDL-C) and LDLC-4 play pivotal roles in predicting CAD risk. Conclusions. In the present study, machine learning tools combining both clinical data and blood lipid profile showed excellent overall predictive power. It suggests that machine learning tools are suitable for predicting the risk of CAD development in the near future.
The Traditional Chinese Medicine Gedan Jiangya Decoction Alleviates Left Ventricular Hypertrophy via Suppressing the Ras/ERK1/2 Signaling Pathway
Authors: Mohammed Shadi A. D. , Liu Hanxing , Baldi Salem , Chen Pingping , Wang Yu , Lu Fang , Liu Shumin
Published: November 2022

Gedan Jiangya Decoction (GJD), a Chinese herbal medicine composed of six botanical medicines, was designed to treat hypertension (patent published number (CN114246896A)). The overexpression of the ERK (extracellular signal-regulated kinase) signaling pathway is essential in developing left ventricular hypertrophy (LVH). This study aimed to evaluate GJD’s effects on LVH in spontaneously hypertensive rats (SHRs) and examine its potential mechanisms on Ras/ERK1/2 pathway regulation. Thirty-five ten-week-old SHRs were randomly assigned to one of five groups: GJD low dosage, medium dose, high dose, model, and captopril. Wistar–Kyoto (WKY) rats served as the control group. All rats received a 6-week treatment. The following parameters were measured: systolic (SBP) and diastolic blood pressure (DBP), left ventricular mass index (LVMI), and serum TGF-beta1. The pathologic structure was determined by H & E staining and Masson. TGF-beta1, Ras, ERK1/2, and C-Fos levels were determined using western blotting and real-time qPCR. SBP, DBP, and LVMI were reduced significantly in the GJD group compared with the model group. GJD inhibited TGF-beta1, Ras, ERK1/2, and C-Fos expression in LVH. In conclusion, GJD reduced the Ras/ERK1/2 pathway expression, which decreased hypertension-induced heart hypertrophy. GJD may protect hypertension-induced myocardial hypertrophy by altering gene expression patterns in the heart.
A Study on the Influence of Interactivity on Behavior Intention in Platform B2C Model
Authors: Sun Hao
Published: November 2022

The progress of information technology and the popularity of the Internet have led to the rapid development of the network economy, and as a new shopping method, online shopping is gradually accepted and respected by consumers. With the development and maturity of online shopping market, the concept of consumers has also been changing, gradually changing from “panning for cheap” and “panning for convenience” to “panning for quality,” paying more attention to the quality of goods and services. B2C is more trusted by consumers than C2C in terms of reputation and quality assurance, so it has gradually become a hot spot in online shopping. With the popularization of online shopping, the development of e-commerce is increasingly characterized by the platform, and it is more and more difficult for shopping websites to attract online consumers with rich product information and functions alone. Using college students with online shopping experience in T-mall as the research subjects, descriptive analysis, exploratory factor analysis, correlation analysis, and regression analysis were used to study the influence of interactivity on customers’ behavioral intention under the platform-based model from a perception perspective.