bluuebunny/crossref_metadata_2025 huggingface dataset:
DESIGN AND DEVELOPMENT OF A DEVICE FOR REDUCTION OF DATA LOSS AND TIME TAKEN FOR TRANSFERRING THE DATA INTO AN ARTIFICIAL BIOCOMPOSITE SOCKET PROSTHESIS THROUGH ARDUINO
Authors: SANKARAN SAKTHIVEL , MURUGAN PALLIKONDA RAJASEKARAN
Published: April 2022

In recent years, technical innovations have made artificial limbs much more comfortable, but not patient satisfaction. In this work, an artificial bio-composite prosthesis is designed and developed. Further, the concept of cloning the movement action of the anatomical leg and feeding it to the prosthetic leg is achieved through an accelerometer sensor and microcontroller. Results demonstrate that the efficiency of the proposed device depends on the angle suspension, hinge lubrication and the prosthetic material, which should be sufficient to sustain the development and application of technological solutions. The proposed sensor-based technology plays an important role in rehabilitation field. It has been proven from this work that the data transfer from the normal leg to the prosthetic leg is quick and data loss is reduced. The design of the prototype could mimic various gestures and motions to make things feel much better for amputees. A prototype mimicking the functions of the human leg was successfully designed, bringing out various gestures and motions for the betterment of the amputees. This sensor-based technology gives a great solution for amputees to make life better like a normal person. As the sockets are made up of jute fiber reinforced epoxy biocomposite material, the skin irritation is reduced for the above-knee amputee patients. Through this proposed prosthetic leg, the patients can perform the normal actions of the leg as a healthy person. Also, the proposed mechanism is highly suitable for disabled patients for their better gait movement.
DESIGN OF A ROBOTIC EXO-SUIT FOR KNEE LATERAL SUPPORT WITH AN INTERACTION CONTROL BASED ON RELATIVE MOTION ANGLE
Authors: TANYILDIZI ALPER K. , TAŞAR BEYDA , YAKUT OĞUZ
Published: April 2022

In this study, we focused on the design and control of a single DoF laterally supported knee exoskeleton robot designed to improve the load-carrying and strength capacity of healthy individuals, especially soldiers and workers. First, a nonlinear second-order differential model of the robotic knee orthosis was produced. Then, a single degree of freedom exoskeleton robot was designed and manufactured. An interactive motion control method based on the relative angle measurement principle between the knee joint of the user and the exo-suit knee joint was proposed. The user’s knee joint motion is detected via an IMU sensor, and a controller was provided to allow the exo-suit to track the human knee joint in synchrony. In this study, a conventional PID, SMC, and moving surface SMC controllers were designed, and the controllers’ performance were tested in real-time experiments. The maximum tracking errors in the PID, SMC, and MSMC controllers were 2.631∘, 1.578∘, and 1.289∘, respectively, and the average tracking time errors were 0.10, 0.08, and 0.06 s, respectively. In addition, the designed and produced knee orthosis weighs 2.5 kg, making it one of the lightest and most compact designs for use by healthy individuals. The knee orthosis was made of steel, and thus it is very durable for use in all kinds of terrain conditions (military, industrial, etc.). Experimental findings about the proposed design and control method are analyzed in the results and discussion section.
Predicting Tunneling-Induced Ground Collapse Based on TBM Operational Data and Geological Data
Authors: Zhu Mengqi , Zhu Hehua , Gutierrez Marte , Ju J. Woody , Zhuang Xiaoying , Wu Wei
Published: April 2022

Collapses are the most sensational types of events and frequently the ones that cause the most serious consequences in tunneling operations. It often occurs because of insufficient geological studies and the limitations of experience-based decision-making. To cope with those problems, we proposed a data-driven model based on the tunnel boring machine operational data and the in situ geological information to forecast tunneling-induced ground collapse. In the proposed model, we offered a general data process flow diagram to process engineering data. Three machine learning classifiers, k-nearest neighbors, support vector classifier, and random forests were adopted for collapse prediction. The performance of the three classifiers was verified based on the data from the Yinsong water conveyance tunnel. The results illustrated that the proposed data-driven model was sufficient for the studying task with 90% of the collapsed zones being identified on average. The contributions of this paper are to provide a reliable data process flow diagram to process engineering data and offer an accurate and robust model for identifying collapses.
Sponsorship-Linked Marketing: Sport Consumers' Attitudes and Behaviors
Authors: Koronios Konstantinos , Ntasis Lazaros , Dimitropoulos Panagiotis E.
Published: April 2022

The rapid increase in sport sponsorship expenditure has been accompanied by a significant growth of research studies evaluating sponsorship efficiency in various sport contexts. Nevertheless, the majority of research studies have concentrated on the comprehension of the variables driving to behaviors. A more precise evaluation of sport sponsorship efficiency is of necessity, taking into attitudinal as well as behavioral constructs. This study focused on the exploration of the attitudinal and behavioral constructs in the context of sport sponsorship. More precisely, the aim of this research is to offer some elucidations of the effect of fans’ involvement with sport, the degree of their attachment with their favorite team, their beliefs about sponsorship and the anticipated sincerity of the sponsors on the awareness of and attitude toward real sponsoring firms, as well as its subsequent influence on future purchase intentions concerning sponsoring firms’ merchandise. Data from 2,752 fans were successfully gathered and analyzed utilizing structural equation modeling analysis. The results of a structural equation model provide an exceptional conceptual framework that underlines the significance of comprehending the role of important factors in sponsorship efficiency. The findings highlight the influential role of the examined factors in guiding sport clubs’ supporters to form favorable attitudes and purchase sponsors’ goods and services — an outcome of considerable significance for both the practitioners and academics in the field. Various implications for future research studies as well as strategies to boost the advantages for both the sport clubs and sponsoring firms can be drawn from the suggested model.
Identifying Varying Thermal Diffusivity of Inhomogeneous Materials Based on a Hybrid Physics-Informed Neural Network
Authors: Li Xiang , Chen Haolong , Liu Zhanli , Yan Ziming , Zhuang Zhuo
Published: April 2022

Inhomogeneous materials are characterized by uneven material properties with changing dimensions. Effective and precise identification of nonuniform material properties is essential for studying material inhomogeneity. In this study, we propose a hybrid physics-informed neural network (PINN) to identify the inhomogeneous thermal diffusivity only using temperature data collected in a transient heat conduction problem. A PINN and a fully connected neural network are combined in the proposed model. The PINN model is trained to learn the law of physics governed by the transient heat conduction equation. The identification network is trained to uncover the thermal diffusivity variation with changing dimensions. The proposed model successfully identifies two types of inhomogeneity: temperature-dependent and space-dependent thermal diffusivity, and both two-dimensional and three-dimensional heat conduction problems are investigated. The proposed model is examined to be robust to small datasets and noisy training data. In addition to heat conduction problems, the proposed model can be adopted to identify the material properties of various types of inhomogeneous materials.
Cyclic longest common subsequence
Authors: Naiman Aaron E. , Farber Eliav , Stein Yossi
Published: April 2022

We present an efficient algorithm for finding the longest common subsequence of two sequences in cases where the starting points of the sequences are not known. In addition, we differentiate between cases when there can or cannot be assumptions regarding the clustering of the subsequences.
LCD matrix product codes with an application
Authors: Bhowmick Sanjit , Bagchi Satya , Bandi Ramakrishna
Published: April 2022

Matrix product (MP) codes over a finite field [Formula: see text], where [Formula: see text] a prime power, were first introduced by Blackmore and Norton [MP codes over [Formula: see text], Appl. Algebra Engrg. Comm. Comput. 12 (2001) 477–500]. This paper investigates linear complementary dual (LCD) MP codes over finite commutative Frobenius rings. We derive a necessary and sufficient condition for an MP code to have an LCD. We provide some efficient constructions of LCD MP codes. Finally, we have shown an application on cryptography of LCD codes.
ADAPTATION TO CLIMATE CHANGE IN ARID LANDS: EVIDENCE FROM PASTORAL AREAS OF SENEGAL
Authors: ASSANE BEYE , WAOUNDÉ DIOP
Published: April 2022

This paper analyzes the determinants of adaptation options in pastoral drylands and investigates whether adaptation strategies can be used jointly. We assume that decisions can be made jointly as complements or substitutes and investigates whether herders in Senegal adapt to climate change by pursuing multiple strategies. We use a multinomial probit model with primary data collected from 410 herders of Senegalese drylands to identify adaptation determinants. Results show that 73.7% of the surveyed households rely on at least one adaptation strategy including storage of livestock feed, increased mobility, changes in water management, diversification of activities and changes in herd composition. Moreover, we notice that adaptation decisions of pastoral households can be taken jointly and those with mobility do not pursue other adaptation strategies, while those lacking mobility undertake multiple strategies. The diversity of factors explaining adaptation calls for targeted policies that promote adaptation strategies to strengthen the resilience of pastoralists.
Textflow
: Toward Supporting Screen-free Manipulation of Situation-Relevant Smart Messages
Authors: Karimi Pegah , Plebani Emanuele , Martin-Hammond Aqueasha , Bolchini Davide
Published: April 2022

Texting relies on screen-centric prompts designed for sighted users, still posing significant barriers to people who are blind and visually impaired (BVI). Can we re-imagine texting untethered from a visual display? In an interview study, 20 BVI adults shared situations surrounding their texting practices, recurrent topics of conversations, and challenges. Informed by these insights, we introduce
TextFlow
, a mixed-initiative context-aware system that generates entirely auditory message options relevant to the users’ location, activity, and time of the day. Users can browse and select suggested aural messages using finger-taps supported by an off-the-shelf finger-worn device without having to hold or attend to a mobile screen. In an evaluative study, 10 BVI participants successfully interacted with
TextFlow
to browse and send messages in screen-free mode. The experiential response of the users shed light on the importance of bypassing the phone and accessing rapidly controllable messages at their fingertips while preserving privacy and accuracy with respect to speech or screen-based input. We discuss how non-visual access to proactive, contextual messaging can support the blind in a variety of daily scenarios.
How to Support Users in Understanding Intelligent Systems? An Analysis and Conceptual Framework of User Questions Considering User Mindsets, Involvement, and Knowledge Outcomes
Authors: Buschek Daniel , Eiband Malin , Hussmann Heinrich
Published: April 2022

The opaque nature of many intelligent systems violates established usability principles and thus presents a challenge for human-computer interaction. Research in the field therefore highlights the need for transparency, scrutability, intelligibility, interpretability and explainability, among others. While all of these terms carry a vision of supporting users in understanding intelligent systems, the underlying notions and assumptions about users and their interaction with the system often remain unclear.

We review the literature in HCI through the lens of implied user questions to synthesise a conceptual framework integrating user mindsets, user involvement, and knowledge outcomes to reveal, differentiate and classify current notions in prior work. This framework aims to resolve conceptual ambiguity in the field and enables researchers to clarify their assumptions and become aware of those made in prior work. We further discuss related aspects such as stakeholders and trust, and also provide material to apply our framework in practice (e.g., ideation/design sessions). We thus hope to advance and structure the dialogue on supporting users in understanding intelligent systems.
Trade Policy Uncertainty
Authors: Handley Kyle , Limão Nuno
Published: April 2022

Trade policy uncertainty (TPU) has become an important source of economic uncertainty and research. We review the main sources and measures of TPU. We then provide a conceptual framework for modeling TPU and methods for estimating and quantifying its effects. We analyze its role in trade agreements and discuss open questions for future research.
Recent Advances in Understanding Diffusion in Multiprincipal Element Systems
Authors: Dash Anuj , Paul Aloke , Sen Sandipan , Divinski Sergiy , Kundin Julia , Steinbach Ingo , Grabowski Blazej , Zhang Xi
Published: April 2022

Recent advances in the field of diffusion in multiprincipal element systems are critically reviewed, with an emphasis on experimental as well as theoretical approaches to determining atomic mobilities (tracer diffusion coefficients) in chemically complex multicomponent systems. The newly elaborated and augmented pseudobinary and pseudoternary methods provide a rigorous framework to access tracer, intrinsic, and interdiffusion coefficients in alloys with an arbitrary number of components. Utilization of the novel tracer-interdiffusion couple method allows for a high-throughput determination of composition-dependent tracer diffusion coefficients. A combination of these approaches provides a unique experimental toolbox to access diffusivities of elements that do not have suitable tracers. The pair-exchange diffusion model, which gives a consistent definition of diffusion matrices without specifying a reference element, is highlighted. Density-functional theory–informed calculations of basic diffusion properties—asrequired for the generation of extensive mobility databases for technological applications—are also discussed.
Using Severe Plastic Deformation to Produce Nanostructured Materials with Superior Properties
Authors: Valiev Ruslan Z. , Straumal Boris , Langdon Terence G.
Published: April 2022

The past decade was marked by significant advances in the development of severe plastic deformation (SPD) techniques to achieve new and superior properties in various materials. This review examines the achievements in these areas of study and explores promising trends in further research and development. SPD processing provides strong grain refinement at the nanoscale and produces very high dislocation and point defect densities as well as unusual phase transformations associated with particle dissolution, precipitation, or amorphization. Such SPD-induced nanostructural features strongly influence deformation and transport mechanisms and can substantially enhance the performance of advanced materials. Exploiting this knowledge, we discuss the concept of nanostructural design of metals and alloys for multifunctional properties such as high strength and electrical conductivity, superplasticity, increased radiation, and corrosion tolerance. Special emphasis is placed on advanced metallic biomaterials that promote innovative applications in medicine.
Complex Dynamic Behavior During the Electro-Reduction of Bromate Ions
Authors: Glasco Dalton L. , Sheelam Anjaiah , Bell Jeffrey G.
Published: April 2022

This article describes the electrochemical reduction of bromate ions in strongly alkaline media on platinum electrodes. Bromates’ importance stems from its wide use in a variety of industrial processes, its incorporation into energy storage devices, as well as its known toxicity, which has led to it being designated as a human carcinogen. Interestingly, during the electro-reduction of bromate in alkaline solutions sustained oscillatory behavior was observed in both current and potential under both potentiostatic and galvanostatic control. Mechanistic characterization using electrochemical impedance spectroscopy (EIS) lead to the discovery that the oscillatory behavior is driven by the presence of a negative differential resistance (NDR). The EIS measurements allowed for the classification of the bromate oscillator into the HN-NDR class, which was supported by potentiostatic oscillations emerging once sufficient external resistance was included into the system as well as oscillating under galvanostatic conditions. Through a systematic exploration of experimental conditions, the electrochemical bromate oscillator also displayed complexity in the form of mixed-mode oscillations as well as complex bursting patterns.
Effect of Solvent on Optical Properties and Surface Topography of Nanocomposite Conjugated Polymer Thin Film
Authors: Al-Bati Sameer , Aljboor Hussam , Ibtehaj Khatatbeh , Ooi Poh Choon , Al-Asbahi Bandar Ali , Ahmed Abdullah Ahmed Ali , Albiss Borhan A. , Jumali Mohammad Hafizuddin Hj.
Published: April 2022

The addition of TiO2 nanoparticles (NPs) to a white-emission conjugated polymer (CP) blend of poly(9,9-dioctylfluorene-2,7-diyl) (PFO) and poly(2-methoxy-5(2-ethylhexyl)−1,4-phenylenevinylene (MEH-PPV) enhanced the optical properties. However, the agglomeration of the nanoparticles restricted enhancement. This drawback was successfully overcome in this study. The highly polar solvent chloroform was mixed with toluene and used to prepare thin films of the blends. Solution blending and spin-coating techniques were used to prepare thin films with different TiO2 nanoparticle contents. In addition, pure toluene and chloroform were investigated as solvents for the nanocomposite blend. These three cases were compared by studying the emission spectra, absorption spectra, Commission Internationale d’Eclairage coordinates (CIE), field emission scanning electron microscopy (FE-SEM) images and scanning probe microscope (SPM) images. The average roughness, quenching constant, and energy transfer probability were calculated. The optimum physical properties of the thin film were achieved using nanoparticles at 15wt% and applied to the binary blend with a mixture of equal amounts of toluene and chloroform. Although chloroform is better for nanoparticle distribution, toluene is mandatory for obtaining the highest yield of PFO.
English Text Recognition Deep Learning Framework to Automatically Identify Fake News
Authors: Wu Fei , Luo Xiaoyu
Published: April 2022

Fake news spreading rapidly worldwide is considered one of the most severe problems of modern technology that needs to be addressed immediately. The remarkable increase in the use of social media as a critical source of information combined with the shaking of trust in traditional media, the high speed of digital news dissemination, and the vast amount of information circulating on the Internet have exacerbated the problem of so-called fake news. The present work proves the importance of detecting fake news by taking advantage of the information derived from friendships between users. Specifically, using an innovative deep temporal convolutional network (DTCN) scheme assisted using the tensor factorization non-negative RESCAL method, we take advantage of class-aware rate tables during and not after the factorization process, producing more accurate representations to detect fake news with exceptionally high reliability. In this way, the need to develop automated methods for detecting false information is demonstrated with the primary aim of protecting readers from misinformation.
Dynamic Development Analysis of Complex Network Research: A Bibliometric Analysis
Authors: Zhou Wei , Pan Yuting , Shu Qiu , Meng Sun
Published: April 2022

In recent years, the method of the complex network has been applied to various fields. Dynamics research in complex networks is also an important branch. There are many types of research into dynamic complex network, but few scholars use bibliometrics to study it. Therefore, this paper adopts the method of bibliometrics to analyze the development history and status quo of dynamic complex network, providing a summary description of this research field. We used CiteSpace and Pajek to analyze 2936 relevant papers on the website of Web of Science and further interpreted and summarized the analysis results. To fully show the bibliometric results, we further derive the productive countries, institutions, sources, and authors and some main research directions in the dynamic complex network research. The research results show that the dynamic complex network research method was applied to various fields after the dynamic research method had been integrated into the complex network. According to the subject analysis of the cited papers, we find that the dynamic complex network method has been widely used in various subjects. Through emergent analysis, we found that the latest hot research trends are the study of infectious diseases and applications in neural networks. At the same time, through the main path analysis, we find the essential literature to elaborate on the development context of a dynamic complex network at different time points. This paper gives a comprehensive analysis of dynamic complex network research and provides some enlightenment for the future research direction.
DLSM Simulation Analysis of the Influence of Blasting Construction on Adjacent Tunnels in Rock Mass with Discontinuities
Authors: Li XuHui , Qiao GuoLong , Guan Junwei
Published: April 2022

In order to study the influence of blasting construction of rock mass with discontinuities on adjacent tunnels, a feasible blasting scheme is formulated to ensure the safety of adjacent tunnels during construction. Based on the water resources allocation project in the Pearl River Delta, a tunnel construction model is established by using continuous‐discontinuous medium mechanics calculation model, Distinct Lattice Spring Model (DLSM), to simulate the impact of blasting construction of the left tunnel on the right adjacent tunnel. According to the numerical simulation results, the stress wave transmission process of the left tunnel with blasting time t = 18 ms is analyzed. Furthermore, the effects of the positions of the discontinuity, the width of the discontinuity, and the angle between the discontinuity and the horizontal direction on the adjacent tunnel are studied. The modelling results show that the existence of discontinuities plays a certain role in improving the safety of adjacent tunnels in the blasting stage. The farther the distance between the discontinuity and the adjacent tunnel is, the smaller the impact of blasting on the adjacent tunnel is. In the process of blasting, the larger the width of discontinuity is, the more effective it is to block the influence of blasting stress wave on the adjacent tunnel. The smaller the angle between discontinuity and horizontal direction, the smaller the impact of blasting on adjacent tunnels.
Analyzing Interdisciplinary Research Using Co‐Authorship Networks
Authors: Ullah Mati , Shahid Abdul , Din Irfan ud , Roman Muhammad , Assam Muhammad , Fayaz Muhammad , Ghadi Yazeed , Aljuaid Hanan
Published: April 2022

With the advancement of scientific collaboration in the 20th century, researchers started collaborating in many research areas. Researchers and scientists no longer remain solitary individuals; instead, they collaborate to advance fundamental understandings of research topics. Various bibliometric methods are used to quantify the scientific collaboration among researchers and scientific communities. Among these different bibliometric methods, the co‐authorship method is one of the most verifiable methods to quantify or analyze scientific collaboration. In this research, the initial study has been conducted to analyze interdisciplinary research (IDR) activities in the computer science domain. The ACM has classified the computer science fields. We selected the Journal of Universal Computer Science (J.UCS) for experimentation purposes. The J.UCS is the first Journal of Computer Science that addresses a complete ACM topic. Using J.UCS data, the co‐authorship network of the researcher up to the 2nd level was developed. Then the co‐authorship network was analyzed to find interdisciplinary among scientific communities. Additionally, the results are also visualized to comprehend the interdisciplinary among the ACM categories. A whole working web‐based system has been developed, and a forced directed graph technique has been implemented to understand IDR trends in ACM categories. Finally, the IDR values between the categories are computed to quantify the collaboration trends among the ACM categories. It was found that “Artificial Intelligence” and “Information Storage and Retrieval”, “Natural Language Processing and Information Storage and Retrieval”, and “Human‐Computer Interface” and “Database Applications” were found the most overlapping areas by acquiring an IDR score of 0.879, 0.711, and 0.663, respectively.
Analytical Assessment of MHD Flow of Nanoliquid Subject to Thermal Radiation and Brownian Effect
Authors: Rasheed Haroon Ur , Bonyah Ebenezer , Gouadria Soumaya , Khan Waris , Alshehri Ahmed , Khan Raees
Published: April 2022

This manuscript studies the impact of the heat and mass flow and chemical reaction with electromagnetic field and heat flow of non‐Newtonian Walter‐B nanofluid via the uniform magnetic field. A mathematical model is used to simulate the arisen nonlinear partial differential equations (PDEs). By employing the suitable transformations, the system of PDEs is then transformed to a nonlinear system of ordinary differential equations (ODEs). The impact of the pertinent parameters on the velocity profile, energy, and concentration distribution has been discussed. These nonlinear coupled equations were addressed analytically by implementing an efficient and validated analytical method, where Mathematica 11.0 programming code is established for simulating the flow system. Stability and convergence analysis have been performed in order to improve the accuracy of the flow system. In order to gain physical insight, the effects of dimensionless parameters on flow fields are investigated. In addition, the impression of system parameters on skin‐friction, heat transfer coefficient, and mass flow rate profiles is also debated.
The Network Pharmacology Study of Dahuang Fuzi Decoction for Treating Incomplete Intestinal Obstruction
Authors: Guo Jun-feng , Zhao Yu-ting , Du Quan-yu , Ren Yuan , Wang Yan , Wang Zhen-xing , Jin Wei
Published: April 2022

Objective. To explore the mechanism of Dahuang Fuzi decoction in the treatment of incomplete intestinal obstruction (IIO) based on network pharmacology and molecular docking. Methods. The chemical components of Rhubarb, Aconite, and Asarum were searched by the Traditional Chinese Medicine Systems Pharmacology database, where the possible active components were screened by oral bioavailability and drug likeness as filtering indicators. The relevant targets in the Swiss Target Prediction database were obtained according to the structure of the chemical components confirmed by the PubChem database. Disease targets of IIO were collected using GeneCards and OMIM databases. We obtained the cross‐target using VENNY to capture the common targets. PPI analysis was performed on the intersection genes combined with Cytoscape 3.7.2. Gene Ontology (GO) function enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were carried out by David database. The core targets and active ingredients were molecularly docked through AutoDock Vina software to predict the detailed molecular mechanism of Dahuang Fuzi decoction for treating IIO. Results. There are 45 active components in Dahuang Fuzi decoction, with 709 corresponding targets, 538 IIO targets, and 97 common targets, among which kaempferol, deltoin, and eupatin are the main active ingredients. 10 core targets were obtained by protein‐protein interaction network analysis. Through GO enrichment analysis, it was found that Dahuang Fuzi decoction may be involved in biological processes such as signal transduction, anti‐apoptosis, promotion of gene expression, regulation of cell proliferation, and differentiation. Besides, KEGG pathway analysis revealed that it mainly relates to PI3K‐AKT signal pathway and HIF‐1 signal pathway, etc. Molecular docking results showed that the active ingredients of Dahuang Fuzi decoction possess a good binding activity with the core targets. Conclusion. Dahuang Fuzi decoction may act on target genes such as TNF, IL6, AKT1, VEGFA, SRC, EGFR, and STAT3 through active ingredients such as kaempferol, deltoin, and eupatin to regulate signaling pathways such as PI3K‐AKT and HIF‐1 and reduce the expression of various inflammatory factors such as TNF‐α, IL‐6, iNOS, and COX‐2 to play a role in the treatment of IIO.
Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning
Authors: Xie Wangsong
Published: April 2022

With the gradual integration of global economy and finance, the financial market presents many complex financial phenomena. To increase the prediction accuracy of financial data, a new nonlinear volatility risk prediction algorithm is proposed based on the improved deep learning algorithm. First, the financial data are taken as the research object and the closing price is set as the prediction target. Then, the nonlinear volatility risk prediction model of the financial data is established through the wavelet principal component analysis noise reduction module and the long and short‐term memory network (LSTM) module, and the nonlinear volatility trend is extracted from multiple financial data series to realize the nonlinear volatility risk prediction of the financial data. During the whole experiment, the time of the research method was less than 1.5 minutes. And for 1200 test samples, the average error of data risk prediction of the proposed method is 0.0217%. The average cost of the research method is 114.25 million yuan, which is significantly lower than other existing algorithms. Experimental results show that the research method can effectively predict the risk of financial data and is more suitable for the risk control early warning of Internet financial platform.
Impact Evaluation of Prefabricated Buildings Cost on Game Theory‐Cloud Model
Authors: Li Na , Fan Zhifeng
Published: April 2022

Prefabricated buildings have the advantages of energy‐saving, high efficiency, and high quality, which is energetically promoted in China’s construction industry. But the prefabricated buildings’ high construction cost hinders their rapid development. Around the higher construction cost of prefabricated buildings, this paper starts with the full life cycle of prefabricated buildings and selects four stages: design, component production, transportation, and construction. And constructs the evaluation index system of influencing factors of the prefabricated buildings cost. Based on this, this paper proposes an evaluation framework based on the game theory‐cloud model to evaluate the influencing factors of prefabricated buildings costs. Taking a prefabricated building in Tianjin as the research object, the game theory combinatorial empowerment method and cloud model are used to evaluate the impact effect of prefabricated buildings cost. Game theory is used to optimize the subjective and objective weights determined by the attribute hierarchy model and the entropy weight method and to confirm the comprehensive weight of the evaluation index. The index’s weight is scientific and accurate, and the subjective influence of a single process is avoided or does not conform to the actual situation and so on. The cloud model is used to evaluate the cost impact of prefabricated buildings comprehensively. The experimental results show that the model is feasible; the scientific and accurate evaluation results are improved; and the model is simple to operate and has some reference value.
Sustainable Land Use Dynamic Planning Based on GIS and Symmetric Algorithm
Authors: Chen Shuiying , Guo Qingxia , Li Lina
Published: April 2022

The general land use planning is to arrange all kinds of land use behaviors in an orderly manner, and its own disorder will inevitably affect the scientific and authoritative nature of the general land use planning, which makes the effect of land management unsatisfactory. The reason is that there are differences in the spatial scale of general land use planning, which makes the analysis of land information not comprehensive enough. Therefore, this paper puts forward the dynamic planning of sustainable land use based on GIS and symmetry algorithm. The collected land information is preprocessed by ArcGIS, and the land remote sensing images are classified by decision tree combined with ENVI (Visual Image Environment), using local sparse coding to extract land use features, with enhancement and reconstruction of remote sensing images using symmetric algorithm. On this basis, the factors that limit the spatial expansion of land are analyzed, and the land planning is completed by substituting them into Arc Map software. Experiments show that the average accuracy rate of this method for land planning reaches 91.22%, which can effectively complete land planning.
A Hybrid Approach for Network Rumor Detection Based on Attention Mechanism and Bidirectional GRU Model in Big Data Environment
Authors: Zhang Li , Huang Guan
Published: April 2022

In view of the low accuracy of Internet rumor detection in traditional machine learning detection methods due to the influence of comment directivity and incomplete features caused by too long sequences, an Internet rumor detection method combining attention mechanism and bidirectional gated recurrent unit (GRU) model in big data environment is proposed. First, the continuous bag of words (CBOW) model is modified by defining the context window and introducing the weighting module, and it is used to obtain the word vectors of the text. Then, the word vectors are used as the word embedding layer which is fed into a bidirectional GRU combined with an attention mechanism through the convolutional layer and pooling layer of the convolutional neural network (CNN) to fully extract the features of the text. Finally, the text feature vectors are input into the Softmax classifier for classification so as to realize the detection of Internet rumors. Based on the selected microblog dataset, the experimental results of the proposed model show that when the learning rate is set to 0.015, Accuracy, Precision, Recall, and F1 values are about 95.8%, 93.1%, 92.9% and 93.5%, respectively, which are better than other comparison models.
A Video Classification Method Based on Spatiotemporal Detail Attention and Feature Fusion
Authors: Gong Xuchao , Li Zongmin
Published: April 2022

With the explosive growth of Internet video data, demands for accurate large-scale video classification and management are increasing. In the real-world deployment, the balance between effectiveness and timeliness should be fully considered. Generally, the video classification algorithm equipped with time segment network is used in industrial deployment, and the frame extraction feature is used to classify video actions However, the issue of semantic deviation will be raised due to coarse feature description. In this paper, we propose a novel method, called image dense feature and internal significant detail description, to enhance the generalization and discrimination of feature description. Specifically, the location information layer of space-time geometric relationship is added to effectively engrave the local features of convolution layer. Moreover, the multimodal feature graph network is introduced to effectively improve the generalization ability of feature fusion. Extensive experiments show that the proposed method can effectively improve the results on two commonly used benchmarks (kinetics 400 and kinetics 600).
Characterizations of Pairs of Rings with Few Non‐Dedekind Intermediary Rings
Authors: Al-Kuleab Naseam , Jarboui Noômen
Published: April 2022

We determine the Dedekind domain pairs of rings; that is, pairs of rings R ⊂ S such that each intermediary ring in between R and S is a Dedekind domain. We also establish that if R ⊂ S is an extension of rings having only one non‐Dedekind intermediary ring, then necessarily R is not Dedekind and so R is a maximal non‐Dedekind domain subring of S. Maximal non‐Dedekind domain subrings R of S are identified in the following cases: (1) R is not integrally closed, (2) R is integrally closed and either |Supp(S/R)| < ∞ or |Max(R)| < ∞, (3) S is a field, (4) R is a valuation domain, and (5) R ⊂ S is an integral extension. We also provide some classifications of pairs of rings having exactly two non‐Dedekind domain intermediary rings.
Clinical Efficacy of Gandakang Tablets plus Methylprednisolone in Patients with Systemic Lupus Erythematosus
Authors: Wang Min , Li Guoquan , Wang Shanzhi , Ye Feng , Huang Yanni , Wang Huanan , Guo Feng
Published: April 2022

Objective. To evaluate the clinical efficacy of Gandakang tablets plus methylprednisolone in patients with systemic lupus erythematosus (SLE). Methods. From February 2015 to February 2019, 60 eligible patients with SLE were recruited and assigned via the random number table method at a ratio of 1 : 1 to receive either methylprednisolone (control group) or Gandakang tablets plus methylprednisolone (observation group). The primary endpoint was clinical efficacy, and the secondary endpoints included Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) score, immunoglobulin (Ig), inflammatory factor levels, and adverse events. Results. Gandakang tablets plus methylprednisolone were associated with a significantly higher treatment efficacy versus methylprednisolone alone (

P
<
0.05

). Gandakang tablets plus methylprednisolone resulted in significantly lower SLEDAI scores and lower levels of IgG, IgM, IgA, tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), and interleukin-6 (IL-6) versus single medication of methylprednisolone (

P
<
0.05

). The two groups showed a similar incidence of adverse events (

P
>
0.05

). Patients given Gandakang tablets plus methylprednisolone had higher mental health, emotional role, physical role, social functioning, and bodily pain scores versus those receiving the monotherapy of methylprednisolone (

P
<
0.05

). Conclusion. Gandakang tablets plus methylprednisolone is effective in the treatment of SLE by enhancing the patients’ immunity, mitigating the inflammatory response, eliminating negative emotions, and improving their quality of life.
Double-Lung Transplantation in a Patient with Pulmonary Type B Niemann-Pick Disease: A Valid Treatment Option
Authors: Mora Víctor Manuel , Osorio Joy Selene , Iturbe David , Tello Sandra , Guzmán Yedra , Sánchez Laura , Gómez José Javier , Cifrián José Manuel
Published: April 2022

Niemann-Pick disease is a rare autosomal recessive disease characterized by an abnormal intracellular lipid accumulation. Type B is later in onset and a less severe form of the disease, so affected people may survive in adulthood. Storage of sphingomyelin in pulmonary macrophages can lead to interstitial lung disease. There are very few published cases of lung transplantation in patients with Niemann-Pick disease, all of them described in the last 2 years. We present here one case of a 57-year-old man successfully treated with a double-lung transplant.
Insecticide Resistance in the Disease Vector Culex pipiens in Morocco: Intensity, Mechanisms, and Contribution in Insecticide Resistance Management
Authors: Aboulfadl Souhail , Mellouki Fouad , Ameur Btissam , Faraj Chafika
Published: April 2022

West Nile (WN) virus has been detected in Morocco in 1996, 2003, and 2010. One WN human case and 94 equine cases with 42 deaths were reported in the first outbreak. The second and the last epizootics were in Kénitra and Mohammedia regions, respectively. Culex pipiens mosquitoes were strongly suspected as WN vectors during these epizootics in Morocco. To help propose the first insecticide resistance management strategy in Morocco, we provide baseline data on the resistance status of C. pipiens mosquitoes to insecticides most used in public health in Morocco. We investigated the resistance intensity of C. pipiens and the role of monooxygenases in pyrethroids resistance using the synergist piperonyl butoxide (PBO) in three prefectures of central Morocco: Mohammedia, Benslimane, and Skhirate. Bioassays were carried out using WHO (World Health Organization) diagnostic test kits for adult mosquitoes, with DDT 4%; deltamethrin 0.05%, 0.25%, and 0.5%; bendiocarb 0.1%, 0.5%, and 1%; and malathion 5%. The synergist PBO 4% was used to check for the involvement of detoxification enzymes in pyrethroids resistant populations. Mosquito populations tested displayed high resistance intensity to pyrethroids and carbamates in Mohammedia and moderate resistance intensity in Skhirate. In Benslimane, the resistance intensity of C. pipiens was high to carbamate and moderate to pyrethroids. The pre-exposure to PBO restored full or partial susceptibility to pyrethroids in the different regions. According to our finding, Mohammedia and Benslimane are at risk level 3 of control failure, while Skhirate is at risk level 2 of it. The employment of IRM is therefore imperative in all study sites.
Automated MSCT Analysis for Planning Left Atrial Appendage Occlusion Using Artificial Intelligence
Authors: Michiels Kilian , Heffinck Eva , Astudillo Patricio , Wong Ivan , Mortier Peter , Bavo Alessandra Maria
Published: April 2022

Background. The number of multislice computed tomography (MSCT) analyses performed for planning structural heart interventions is rapidly increasing. Further automation is required to save time, increase standardization, and reduce the learning curve. Objective. The purpose of this study was to investigate the feasibility of a fully automated artificial intelligence (AI)-based MSCT analysis for planning structural heart interventions, focusing on left atrial appendage occlusion (LAAO) as the selected use case. Methods. Different deep learning models were trained, validated, and tested using a cohort of 583 patients for which manually annotated data were available. These models were used independently or in combination to detect the anatomical ostium, the landing zone, the mitral valve annulus, and the fossa ovalis and to segment the left atrium (LA) and left atrial appendage (LAA). The accuracy of the models was evaluated through comparison with the manually annotated data. Results. The automated analysis was performed on 25 randomly selected patients of the test cohort. The results were compared to the manually identified landmarks. The predicted segmentation of the LA(A) was similar to the manual segmentation (dice score of 0.94 ± 0.02). The difference between the automatically predicted and manually measured perimeter-based diameter was −0.8 ± 1.3 mm (anatomical ostium), −1.0 ± 1.5 mm (Amulet landing zone), and −0.1 ± 1.3 mm (Watchman FLX landing zone), which is similar to the operator variability on these measurements. Finally, the detected mitral valve annulus and fossa ovalis were close to the manual detection of these landmarks, as shown by the Hausdorff distance (3.9 ± 1.2 mm and 4.8 ± 1.8 mm, respectively). The average runtime of the complete workflow, including data pre- and postprocessing, was 57.5 ± 34.5 seconds. Conclusions. A fast and accurate AI-based workflow is proposed to automatically analyze MSCT images for planning LAAO. The approach, which can be easily extended toward other structural heart interventions, may help to handle the rapidly increasing volumes of patients.
Comparative Effect of Incorporation of ZrO2, TiO2, and SiO2 Nanoparticles on the Strength and Surface Properties of PMMA Denture Base Material: An In Vitro Study
Authors: Azmy Emad , Al-kholy Mohamed Reda Zaki , Al-Thobity Ahmad M. , Gad Mohammed M. , Helal Mohamed Ahmed
Published: April 2022

Objective. This study aimed to investigate the effects of nanoparticles (zirconium dioxide (ZrO2), titanium dioxide (TiO2), and silicon dioxide (SiO2)) on the flexural strength, impact strength, hardness, and wear resistance of the acrylic resin denture base material. Materials and Methods. Acrylic resin specimens were fabricated in dimensions according to American Dental Association (ADA) specifications per test. Specimens were divided according to nanofiller into four groups; unmodified as control, ZrO2 (Z), TiO2, (T), and SiO2 (S) groups. Each one was subdivided into two subgroups according to nanoparticle concentrations; 3% and 7% (Z3, Z7, T3, T7, S3, and S7). A 3-point bending test, Charpy impact test, and Vickers hardness test were used for flexural strength, impact strength, and hardness measurements, respectively. Wear resistance was measured by the differences in surface roughness of tested specimens before and after the wear test. A scanning electron microscope was used to assess nanoparticle specifications and distributions and for fracture surfaces analysis. ANOVA, Bonferroni’s post hoc test, and the Kruskal–Wallis test were applied for data analysis (α = 0.05). Results. Regarding the flexural and impact strength, there was a statistically remarkable increase for all tested groups compared with the control group, except for the T7 and S7 groups (

P

value <0.001, effect size = 0.893) and (

P

value <0.001, effect size = 0.759), respectively. There was a statistically significant improvement in the hardness of all tested groups compared with the control group (

P

value <0.001, effect size = 0.67) except T3 and S3. Regarding wear, a statistically significant enhancement was noticed in the wear resistance of all tested groups (

P

value <0.001, effect size = 0.685), except for the T7 and S7 groups. Conclusion. The flexural strength, impact strength, and wear resistance improved with both concentrations of ZrO2 and low TiO2 and SiO2 concentrations. The hardness increased with both concentrations of ZrO2 and high TiO2 and SiO2 concentrations.