bluuebunny/crossref_metadata_2025 huggingface dataset:
The ontology, origin, and impact of divisive public sector rules: A meta‐narrative review of the red tape and administrative burden literatures
Authors: Campbell Jesse W. , Pandey Sanjay K. , Arnesen Lars
Published: May 2022

A rule is divisive when its legitimacy is contested and divisive rules are an enduring theme of public administration research. For over three decades, this research has been shaped by red tape theory, which conceptualizes divisive rules as those which consume an organization's resources but fail to advance its goals. Recently, however, the administrative burden framework, which prioritizes the impact of divisive rules on citizens and links their origins to political motives, has grown in popularity. We take stock of the last decade of research on red tape and administrative burden using the meta‐narrative review methodology. We identify five narratives within the two research traditions and discuss their distinct research questions, theoretical mechanisms, privileged actors, and rule assumptions, as well as their strengths, limitations, and practical implications. These insights are leveraged to analyze the origins, impact, and ontology of divisive public sector rules. We also raise research questions with cross‐cutting relevance to the red tape and administrative burden research traditions.
Comparing Online and Offline Political Support
Authors: Brandenberger Laurence , Casiraghi Giona , Andres Georges , Schweighofer Simon , Schweitzer Frank
Published: May 2022

Do politicians carry on their interpersonal relations online? We examine how online political support compares to offline support. We overlay two data sets on political support among members of the Swiss National Council to estimate the explanatory power of online endorsements on offline cosponsorship signatures among members, using a gHypEG network regression model. Whereas offline support behavior is driven by a broad range of factors, such as network and homophily effects, endorsement is less diverse. Our findings show that online endorsement is predominantly driven by partisanship and does not mirror the richness found in offline support behavior.
Microinjections with hyaluronic acid in combination with glycerol: How do they influence biophysical viscoelastic skin properties?
Authors: Kleine‐Börger Linda , Hofmann Matthias , Kerscher Martina
Published: May 2022

Background

Skin quality improvement with hyaluronic acid microinjections is increasing as a clinical treatment indication and as a scientific issue. This present study assessed changes in biomechanical viscoelastic skin properties after microinjections with the skin quality booster CPM‐HA20G (Belotero Revive).

Materials and methods

Fifteen subjects have been randomized in a 2:1 ratio to receive either three treatments (total 3 ml per side) or a single‐dose treatment (total 1.5 ml per side) with CPM‐HA20G at dermal level into the lower cheeks via microinjections. Treatments were provided 4 weeks apart. Biophysical measurements were performed describing the viscoelastic skin properties and the underlying skin structure. The measurements were performed before injection (week 0) and on follow‐up visits 4, 8, 16, 24, and 36 weeks after the last injection treatment.

Results

One (p = 0.028) as well as three (p = 0.003) consecutive treatments with CPM‐HA20G improved statistically significant skin firmness (R0). For the multiple‐treatment group improved significant differences were observed for skin fatigue (R3; p = 0.007) and skin density (p = 0.017) with stable skin thickness levels (p > 0.05), too. There were zero‐to‐weak correlations between skin thickness and biomechanical skin properties (R0, rs = 0.084; R3, rs = 0.093).

Conclusion

Overall, microinjections with CPM‐HA20G improved biomechanical viscoelastic skin properties with a stronger and more pronounced effect in the multiple‐treatment group. The observed changes may explain some of the skin quality improvements observed after treatment with CPM‐HA20G.
Identifying the neural basis for rosacea using positron emission tomography‐computed tomography cerebral functional imaging analysis: A cross‐sectional study
Authors: Liu Yunyi , Xu Yingna , Guo Zhe , Wang Xiaoyan , Xu Yang , Tang Lijun
Published: May 2022

Background

The neural basis of rosacea is not well understood. This study aimed to determine whether cerebral glucose metabolism (CGM) changes on 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET)/computed tomography (CT) scans can detect functional network changes in specific brain areas in patients with rosacea.

Materials and methods

Eight adults with rosacea and 10 age/sex‐matched healthy adults (controls) were enrolled in the study. 18F‐FDG PET/CT brain images for all eight patients and whole‐body images for two of the patients were analyzed qualitatively and semi‐quantitatively. Differences between the study groups were examined using Fischer's exact test and a Student's t‐test. A voxel‐based analysis using statistical parametric mapping was performed to compare the brain metabolism of the patients with that of the controls.

Results

Compared with the controls, the patients with rosacea showed extensive changes in the CGM signals in the cerebral cortex and limbic system, with less CGM shown in the right superior parietal lobule, right postcentral gyrus, right parahippocampal gyrus, left superior frontal gyrus, and lateral posterior thalamic nucleus and more CGM in the right precentral gyrus, left inferior frontal gyrus, and cerebellar tonsil. No dysmetabolic lesions were found in the whole‐body 18F‐FDG PET/CT images.

Conclusion

Specific neural functional changes occur in patients with rosacea that may explain its pathogenesis.
A quarterly survey of antibiotic prescribing in small animal and equine practices—Minnesota and North Dakota, 2020
Authors: Bollig Emma R. , Granick Jennifer L. , Webb Teckla L. , Ward Christie , Beaudoin Amanda L.
Published: May 2022

Antimicrobials are critical for medicine, but the problem of antimicrobial resistance (AMR) threatens the effectiveness of these valuable drugs. In USA, there are no national‐ or state‐level programs or policies in place to track antibiotic use (AU) in dogs, cats, and horses, despite acknowledgement of this sector's importance to both the AMR problem and its solution. AU measurement is a key part of antibiotic stewardship and AMR prevention. This study aimed to fill existing gaps in the veterinary professions' knowledge of antibiotic prescribing in small animals and horses. To address this aim, medical record data were collected on a single day per quarter for 1 year from 19 Minnesota and North Dakota small animal and equine practices, totaling 1,899 veterinarian consults of dogs, cats, and horses. Overall, 25.8% of all canine, feline, and equine consults involved an antibiotic prescription. Third‐generation cephalosporins were the most commonly prescribed systemic antibiotic drug class, and the long‐acting injectable drug, cefovecin, was the most commonly prescribed antibiotic for cats (34.5%). Topical antibiotic preparations were prescribed frequently, especially in dogs (42.5% of canine prescriptions), though systemic antibiotics were often prescribed concurrently. Common general indications, based on problem or diagnosis recorded in the medical record, for antibiotics in all species combined were skin conditions (24.4%), otitis (22.1%), ophthalmic (9.4%), gastrointestinal (8.3%), respiratory (8.3%), and urinary tract (7.6%) diseases. While 44.2% of patients for which antibiotics were prescribed had cytology performed, only 3.9% had bacterial culture and susceptibility performed. In a pre‐study survey, veterinarians' recommendations for AU differed from actual prescribing, suggesting collection of AU data provides more accurate assessments of veterinary prescribing behaviour than surveys. This study shows feasibility of AU measurement in small animals and horses. The data collection tool and standard operating procedures described prove suitable for national AU data collection.
A Nonlinear Dynamic Model of Flywheel Energy Storage Systems Based on Alternative Concept of Back Propagation Neural Networks
Authors: He Haiting , Liu Yibing , Ba Liming
Published: May 2022

The flywheel energy storage system (FESS) is a closely coupled electric-magnetic-mechanical multiphysics system. It has complex nonlinear characteristics, which is difficult to be described in conventional models of the permanent magnet synchronous motor (PMSM) and active magnetic bearings (AMB). A novel nonlinear dynamic model is developed based on the alternative concept. Using back propagation (BP) neural network as a bridge, alternative mapping functions can be built from parametric calculation data of the finite element method (FEM) models. These functions are implemented in a system level simulation of the FESS. As a serial of linear equations, the alternative mapping function can precisely reproduce the electric-magnetic-mechanical characteristics in a satisfied speed and robust. Study of the cogging torque in the PMSM shows a good coincidence with the theory prediction. The current and displacement stiffness coefficients of the AMB are not constants as conventional linear models but change in different winding current and rotor positions. The influence parameters to the critical speed frequency and vibration amplitude are comprehensively studied, including the rotor mass, moment of inertial, eccentric distance, and the mass centroid offset. An operation boundary of the FESS is summarized to describe the feasible power load in different rotor rotation speed and PMSM winding current.
Characterization of the Finite Element Computational Fluid Dynamics Capabilities in the Multiphysics Object Oriented Simulation Environment
Authors: Weiss Abdullah G. , Zaidan Laith J. , Bani Ahmad Mohammad T. H. , Abdoelatef M. Gomaa , Peterson John W. , Lindsay Alexander D. , Kong Fande , Ahmed Karim , Kimber Mark L.
Published: May 2022

The multiphysics object-oriented simulation environment (moose) is a code package that couples a variety of physics modules, allowing for highly accessible multiphysics simulations. The physics modules include a finite element Navier–Stokes (N–S) module that is designed to solve laminar fluid dynamics problems. The usage of this module in multiple recent studies coupled with the growing interest in moose for usage in nonlight water reactor safety studies by the Nuclear Regulatory Commission (NRC) prompted the authors to investigate the computational fluid dynamics capabilities of moose. A two-dimensional laminar flow past a circular cylinder scenario is simulated in the moose framework to investigate the effectiveness of the N–S module. Simulations assumed an unsteady laminar flow with a Reynolds number of 200. To verify the results from moose, similar simulations were conducted using the well-utilized simulation of turbulent flow in arbitrary regions—computational continuum mechanics C++ (star-ccm+) finite volume code. Results from both codes are also compared to some results from literature. Velocity and pressure profiles of both transient simulations were compared. The numerical and input errors in moose are also visualized with contour plots to qualitatively understand the evolution of the errors across time and space. The comparisons between moose and star-ccm+ showed nearly perfect agreement between the codes for velocity and pressure, especially after the development of the vortex street in later time-steps. The force coefficients showed excellent agreement after the development of the vortex street, but demonstrated notable discrepancies prior to the vortex street development, which is likely due to how each code simulated the approach to the vortex street in earlier time-steps.
Numerical Study of Horizontally Bent Buried Steel Pipelines Subjected to Oblique Faulting
Authors: Nair Gautam S. , Dash Suresh R. , Mondal Goutam
Published: May 2022

Buried pipelines may be subjected to different seismic hazards along their path, among which faulting can severely affect their integrity by causing large strains in the pipeline confined to a local zone during an earthquake. Historically, the most catastrophic pipeline damages are the ones resulting from oblique faulting due to the severity of ground distortion and induction of compression in the pipeline. Moreover, in the areas of potential ground rupture, the presence of bends near the fault crossing may lead to large strains in the pipeline. Most design guidelines recommend laying the pipelines without field bends, elbows, and flanges near to fault zone. However, bends are sometimes inevitable in the construction of pipelines, and a situation may arise when the pipeline crosses the fault line at/near the bends. Past studies on the response of bent buried pipelines subjected to oblique faulting are very limited. Therefore, the present numerical investigation aims to examine the response of buried continuous steel pipelines with field bends crossing an oblique fault by performing an extensive parametric study. The results obtained are reviewed and presented in the paper. Suitable relationships in terms of modification factors over the response of a straight pipeline as a function of a few critical parameters are determined using regression analyses. It is concluded that providing bends to the pipeline can significantly affect its structural response when subjected to oblique faulting, especially when it is operating at its design internal pressure.
Management and Monitoring of Multi-Behavior Recommendation Systems Using Graph Convolutional Neural Networks
Authors: Liu Changwei , Wang Kexin , Wu Aman
Published: May 2022

Different recommendation algorithms, which often use only a single type of user-item engagement, are plagued by imbalanced datasets and cold start problems. Multi-behavior recommendations, which takes advantage of a variety of customer interaction including click and favorites, can be a good option. Early attempts at multi-behavior suggestion tried to consider the varying levels of effect each behavior has on the target behavior. They also disregard the meanings of behaviors, which are implicit in multi-behavior information. Because of these two flaws, the information isn’t being completely utilized to improve suggestion performance on the specific behavior. In this paper, we take a novel response to the situation by creating a unified network to capture multi-behavior information and displaying the MBGCNNN model (Multi-Behavior Graph Convolutional Neural Network). MBGCNN may effectively overcome the constraints of prior studies by learning behavior intensity via the user-item dissemination level and collecting behavior interpretation via the items dissemination level. Practical derives from various data sets back up our model’s order to leverage multi-behavior data. On real methods, our approach beats the average background by 25.02 percent and 6.51 percent, respectively. Additional research on cold-start consumers supports the viability of our suggested approach.
Finite Approximations and Similarity of Languages
Authors: Rovan Branislav , Varga András
Published: May 2022

A new framework to measure distances (similarity) between formal languages and between grammars based on distances between words is introduced. It is based on approximating languages by their finite subsets and using monotone sequences of such finite approximations to define an infinite language in the limit. Distances between finite languages are defined and extended to distances between monotone sequences of finite languages leading to distances between infinite languages. The framework captures several distances studied in the literature.

Context-free grammars with energy are introduced to enable finite approximations emphasizing “syntactically important” parts of words. Grammars with energy are also used to extend distances between monotone sequences of finite languages to distances between context-free grammars.

A basic toolkit for monotone sequences of finite languages and distances between languages resp. grammars is provided. As part of this toolkit a non-symmetric version of distances is defined, providing additional characterisation of distances in general. Additional properties of distances between grammars are derived by restricting the“energy use” of grammars with energy.

Some methods of estimating the distances are presented to be used in cases where the distance is not computable or difficult to compute.
The Component (Edge) Connectivity of Round Matching Composition Networks
Authors: Liu Xiaoqing , Zhou Shuming , Zhang Hong , Niu Baohua
Published: May 2022

The vertex (edge) connectivity has been regularly used to measure the fault tolerance and reliability of interconnection networks, while it has defects in the assumption that all neighbors of one node will fail concurrently. To overcome this deficiency, some new generalizations of traditional connectivity have been suggested to quantize the size or the number of the connected components of the survival graph. The [Formula: see text]-component (edge) connectivity, one generalization of vertex (edge) connectivity, has been proposed to characterize the vulnerability of multiprocessor systems based on the number of components of the survival graph. In this paper, we determine the [Formula: see text]-component (edge) connectivity of a family of networks, called the round matching composition networks [Formula: see text], which are a class of networks composed of [Formula: see text] ([Formula: see text]) clusters with the same order, linked by [Formula: see text] perfect matchings. By exploring the combinatorial properties and fault-tolerance of [Formula: see text], we establish the [Formula: see text]-component (edge) connectivity [Formula: see text] for [Formula: see text] and [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text].
Fault-Tolerant Strong Menger (Edge) Connectivity of DCC Linear Congruential Graphs
Authors: Yu Zhengqin , Zhou Shuming , Zhang Hong
Published: May 2022

With the rapid development and advances of very large scale integration technology and wafer-scale integration technology, multiprocessor systems, taking interconnection networks as underlying topologies, have been widely designed and used in big data era. The topology of an interconnection network is usually represented as a graph. If any two distinct vertices [Formula: see text] in a connected graph [Formula: see text] are connected by min[Formula: see text] vertex (edge)-disjoint paths, then [Formula: see text] is called strongly Menger (edge) connected. In 1996, Opatrny et al. [16] introduced the DCC (Disjoint Consecutive Cycle) linear congruential graph, which consists of [Formula: see text] nodes and is generated by a set of linear functions [Formula: see text] with special properties. In this work, we investigate the strong Menger connectivity of the DCC linear congruential graph [Formula: see text] with faulty vertices or edges, where [Formula: see text], [Formula: see text], gcd[Formula: see text] and [Formula: see text] is a multiple of [Formula: see text]. In detail, we show that [Formula: see text] is strongly Menger connected if [Formula: see text] for any [Formula: see text]. Moreover, we determine that [Formula: see text] is strongly Menger edge connected if [Formula: see text] for any [Formula: see text]. Furthermore, we prove that, under the restricted condition [Formula: see text], [Formula: see text] is strongly Menger edge connected if [Formula: see text] and [Formula: see text] for any [Formula: see text]. In addition, we present some empirical examples to show that the bounds are all optimal in the sense of the maximum number of tolerable edge faults.
Feasibility of crystal-assisted collimation in the CERN accelerator complex
Authors: Scandale W. , Arduini G. , Assmann R. , Bracco C. , Butcher M. , Cerutti F. , D’Andrea M. , Esposito L. S. , Garattini M. , Gilardoni S. , Laface E. , Lari L. , Losito R. , Masi A. , Metral E. , Mirarchi D. , Montesano S. , Petrucci S. , Previtali V. , Redaelli S. , Rossi R. , Schoofs P. , Silari M. , Tlustos L. , Burmistrov L. , Natochii A. , Dubos S. , Puill V. , Stocchi A. , Bagli E. , Bandiera L. , Baricordi E. , Dalpiaz P. , Fiorini M. , Guidi V. , Mazzolari A. , Vincenzi D. , Addesa F. , Cavoto G. , Iacoangeli F. , Ludovici L. , Santacesaria R. , Valente P. , Galluccio F. , Vallazza E. , Bolognini D. , Foggetta L. , Hasan S. , Lietti D. , Mascagna V. , Mattera A. , Prest M. , Ambrosi G. , Azzarello P. , Bertucci B. , Ionica M. , Battiston R. , Zuccon P. , Burger W. J. , Carnera A. , Della Mea G. , Lombardi A. , De Salvador D. , Milan R. , Vomiero A. , Claps G. , Dabagov S. , Murtas F. , Kovalenko A. D. , Taratin A. M. , Uzhinskiy V. V. , Smirnov G. I. , Denisov A. S. , Gavrikov Yu. A. , Ivanov Yu. M. , Lapina L. P. , Malyarenko L. G. , Skorobogatov V. V. , Suvorov V. M. , Vavilov S. A. , Afonin A. G. , Chesnokov Yu. A. , Durum A. A. , Maisheev V. A. , Sandomirskij Yu. E. , Yanovich A. A. , Yazynin I. A. , Markiewicz T. , Oriunno M. , Wienands U. , Mokhov N. , Still D. , Auzinger G. , Borg J. , Ferguson W. , Fulcher J. , James T. , Hall G. , Pesaresi M. , Raymond M. , Rose A. , Ryan M. , Zorba O. ,   (The UA9 Collaboration)
Published: May 2022

Bent silicon crystals mounted on high-accuracy angular actuators were installed in the CERN Super Proton Synchrotron (SPS) and extensively tested to assess the feasibility of crystal-assisted collimation in circular hadron colliders. The adopted layout was exploited and regularly upgraded for about a decade by the UA9 Collaboration. The investigations provided the compelling evidence of a strong reduction of beam losses induced by nuclear inelastic interactions in the aligned crystals in comparison with amorphous orientation. A conceptually similar device, installed in the betatron cleaning insertion of CERN Large Hadron Collider (LHC), was operated through the complete acceleration and storage cycle and demonstrated a large reduction of the background leaking from the collimation region and radiated into the cold sections of the accelerator and the experimental detectors. The implemented layout and the relevant results of the beam tests performed in the SPS and in the LHC with stored proton and ion beams are extensively discussed.
Thermoelectric performance of porphyrin molecular junction with graphene nanoribbon electrodes
Authors: Ma Qingshan , Rong Yi , Zhang Bei , Ouyang Fangping , Li Haoran , Liu Yiwei , Zhang Jun
Published: May 2022

Thermoelectric performance of porphyrin molecular junction is investigated by using first-principles calculation combined with nonequilibrium Green function (NEGF) method. It is found that porphyrin molecule rotation (90[Formula: see text] rotated) has no obvious effect on its thermoelectric conversion efficiency due to the conjugate structure of central molecule. Through the functions of energy and thermal conductivity, it is found that phonon thermal conductivity accounts for the main part, which provides a potential for further optimizing thermoelectric performance. Moreover, it can be seen from phonon transmission spectrum that porphyrin molecule has a certain filtering effect on high-frequency phonon mode. For power factor and figure of merit (ZT), wave functions for two cases highly coincide. The maximum ZT values of por1 and por2 are 0.38 and 0.34 near Fermi level (FL), respectively. Porphyrin molecular junction could have a good application prospect in nanoscale thermoelectric devices.
Curative Effect of Prebiotics/Probiotics-Assisted Ketogenic Diet on Children with Refractory Epilepsy
Authors: Su Lingying , Li Sai , Sun Bo
Published: May 2022

Objective. The aim is to study the curative effect of prebiotics/probiotics-assisted ketogenic diet (KD) on children with refractory epilepsy. Methods. A retrospective analysis was performed on the clinical data of 80 children with refractory epilepsy treated in the hospital between December 2018 and December 2020. According to different treatment methods, they were divided into the KD group (36 cases, KD) and combination group (44 cases, prebiotics/probiotics assisted KD). All were followed up for 1 year. The curative effect, electroencephalogram findings, levels of neurotransmitters, quality of life scores, cognitive function (verbal intelligence quotient (VIQ), performance intelligence quotient (PIQ)), and incidence of adverse reactions were compared between the two groups. Results. At the last follow-up, the effective rate of the combination group was higher than that of the KD group (95.45% vs 80.56%) (

P
<
0.05

). After 1 year of treatment, video electroencephalogram findings in both groups were improved, and the response rate of the combination group was higher than that of the KD group (97.73% vs 83.33%) (

P
<
0.05

). After 1 year of treatment, levels of VIQ and PIQ in both groups were increased, which were higher in the combination group than the KD group (

P
<
0.05

). After 1 year of treatment, the level of 5-hydroxytryptamine (5-HT) in both groups was increased, which was higher in the combination group than the KD group (

P
<
0.05

). After 1 year of treatment, quality of life scores in both groups were increased, which was higher in the combination group than the KD group (

P
<
0.05

). The incidence of adverse reactions in the combination group was lower than that in the KD group (13.64% vs 36.11%) (

P
<
0.05

). Conclusion. The curative effect of prebiotics/probiotics-assisted KD is better on children with refractory epilepsy, which can effectively improve electroencephalogram and quality of life, increase neurotransmitters and cognitive levels, with good safety.
Blockchain and K-Means Algorithm for Edge AI Computing
Authors: Qiu Xiaotian , Yao Dengfeng , Kang Xinchen , Abulizi Abudukelimu
Published: May 2022

The current development of blockchain, technically speaking, still faces many key problems such as efficiency and scalability issues, and any distributed system faces the problem of how to balance consistency, availability, and fault tolerance need to be solved urgently. The advantage of blockchain is decentralization, and the most important thing in a decentralized system is how to make nodes reach a consensus quickly. This research mainly discusses the blockchain and K-means algorithm for edge AI computing. The natural pan-central distributed trustworthiness of blockchain provides new ideas for designing the framework and paradigm of edge AI computing. In edge AI computing, multiple devices running AI algorithms are scattered across the edge network. When it comes to decentralized management, blockchain is the underlying technology of the Bitcoin system. Due to its characteristics of immutability, traceability, and consensus mechanism of transaction data storage, it has recently received extensive attention. Blockchain technology is essentially a public ledger. This is done by recording data related to trust management to this ledger. To collaboratively complete artificial intelligence computing tasks or jointly make intelligent group decisions, frequent communication is required between these devices. By integrating idle computing resources in an area, a distributed edge computing platform is formed. Users obtain benefits by sharing their computing resources, and nodes in need complete computing tasks through the shared platform. In view of the identity security problems faced in the sharing process, this article introduces blockchain technology to realize the trust between users. All participants must register a secure identity in the blockchain network and conduct transactions in this security system. A K-means algorithm suitable for edge environments is proposed to identify different degradation stages of equipment operation reflected by multiple types of data. Based on the prediction of the fault state for a single type of data, the algorithm uses the historical data of multiple types of data together with the prediction data to predict the fault stage. During the research process, the average optimization energy consumption of K-means algorithm is 14.6% lower than that of GA. On the basis of designing a resource allocation scheme based on blockchain, the problem of how the participants can realize reliable resource use according to the recorded data on the chain is studied. The article implements the verification of the legality of the use of blockchain resources. In addition, a control node is introduced to master the global real-time information of the network to provide data support for the user’s choice.
Computed Tomography Imaging under Artificial Intelligence Reconstruction Algorithm Used in Recovery of Sports Injury of the Knee Anterior Cruciate Ligament
Authors: Zhang Heng , Zheng Haiming , Deng Ren , Luo Kaiwen , Duan Shukai
Published: May 2022

This study aimed to analyze the influence of artificial intelligence (AI) reconstruction algorithm on computed tomography (CT) images and the application of CT image analysis in the recovery of knee anterior cruciate ligament (ACL) sports injuries. A total of 90 patients with knee trauma were selected for enhanced CT scanning and randomly divided into three groups. Group A used the filtered back projection (FBP) reconstruction algorithm, and the tube voltage was set to 120 kV during CT scanning. Group B used the iDose4 reconstruction algorithm, and the tube voltage was set to 120 kV during CT scanning. In group C, the iDose4 reconstruction algorithm was used, and the tube voltage was set to 100 kV during CT scanning. The noise, signal‐to‐noise ratio (SNR), carrier‐to‐noise ratio (CNR), CT dose index volume (CTDI), dose length product (DLP), and effective radiation dose (ED) of the three groups of CT images were compared. The results showed that the noise of groups B and C was smaller than that of group A (P < 0.05), and the SNR and CNR of groups B and C were higher than those of group A. The images of patients in group A with the FBP reconstruction algorithm were noisy, and the boundaries were not clear. The noise of the images obtained by the iDose4 reconstruction algorithm in groups B and C was improved, and the image resolution was also higher. The agreement between arthroscopy and CT scan results was 96%. Therefore, the iterative reconstruction algorithm of iDose4 can improve the image quality. It was of important value in the diagnosis of knee ACL sports injury.
AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics
Authors: Feng Jian , Yu Lang , Ma Rui
Published: May 2022

Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. AGCN-T consists of three modules. In the spatial dependency extraction module, according to the similarity of historical traffic flow sequences of different loop detectors, an adjacency matrix for the road network is constructed based on a sequence similarity calculation method, Predictive Power Score (PPS), to express latent spatial dependency; and then GCN is used on the adjacency matrix to capture the global spatial correlation and Transformer is used to capture dynamic spatial dependency from the most recently flow sequences. And then, the dynamic spatial dependency is merged with the global spatial correlation to obtain the overall spatial dependency pattern. In the temporal dependency extraction module, the temporal dependency pattern of each traffic flow sequence is learned by the temporal Transformer. The prediction module integrates both patterns to form spatial-temporal dependency patterns and performs multistep traffic flow prediction. Four sets of experiments are performed on three actual traffic datasets to show that AGCN-T can effectively capture the dynamic spatial-temporal dependency of the traffic network, and its prediction performance and efficiency are better than existing baselines. AGCN-T can effectively capture the dynamics in traffic flow. In addition to traffic flow prediction, it can also be applied to other spatial-temporal prediction tasks, such as passenger demand prediction and crowd flow prediction.
Internet of Things System of Spatial Structure Sports Events Health Monitoring Based on Cloud Computing
Authors: Feng Rui , Chang Ning
Published: May 2022

At present, the health monitoring system of world sports events uses the system's on-site information or periodic test information to evaluate the health of the system. In order to make the data more accurate, the application of cloud computing is necessary. This article is based on the cloud computing spatial structure sports event health monitoring Internet of Things system, aiming to analyze the data from the structure's resilience, surface load and other aspects, combined with cloud computing means to explore this monitoring system. This research uses literature retrieval methods, cloud computing particle swarm optimization and interpolation fitting methods to process data. Aiming at the data characteristics of spatial structure health monitoring, the overall framework of the Internet of Things system for spatial structure health monitoring is proposed. Taking advantage of cloud computing in processing computing-intensive tasks, an algorithm for data processing at the application layer is established, and the cloud data for spatial structure monitoring is completed. Management system design. Among them, the Internet of Things system, structural health monitoring system and other models are mainly combined with the various levels of the model to carry out targeted analysis; the factors involved are the arrangement of sensors and the installation of sensors at the junctions, for example, magnetoelectric acceleration sensors and optical fiber optical shed sensors for data recording of large stadiums with different structures. The experimental results show that when extreme temperatures exist, the stress of the rods as a whole will decrease to a certain extent. The maximum difference between the test data considering extreme weather and not considering extreme weather is 226.
Multiparty Strict Coin-Tossing Protocols Based on the Eigenvalue
Authors: YanShuo Zhang , ZeHao Wang , Le Zhang , Chao Xia
Published: May 2022

The coin-tossing protocol is an important research area in cryptography. It generates a random bit with uniform distribution even if some participants might fraud. However, traditional coin-tossing protocol could not solve the situation of multiparty. It only divides participants into two parts. In this paper, a new kind of multiparty strict coin-tossing protocol based on the eigenvalue of matrix was proposed. First, matrix tampering attacks can be resisted. On the other hand, collusion attack which was caused by the addition of the Lagrange interpolation formula could be overcome. The analysis shows that the correctness and security of both protocols was guaranteed. Based on the above statements, comparing with the classic coin-tossing protocols, the proposed scheme has the advantage of resisting parties aborting, low complexity, and practicability.
Optimizing WEDM Parameters on Nano-SiC-Gr Reinforced Aluminum Composites Using RSM
Authors: Saravanan R. , Anbuchezhiyan G. , Mamidi Vamsi Krishna , Kumaran Palani
Published: May 2022

Nanocomposites are preferred for performance enhancement over micron composites because of their better performance in enhancing the desired properties of the parent material. Nano-silicon carbide and nano-graphite are considered together for reinforcement of Al7075 in this piece of research. Here, the purpose of reinforcement of graphite considered is to improve machinability. The stir casting was employed for fabrication of nanocomposite. The composite matrix includes Al7075 matrix material, reinforcement material of nano-SiC (5 wt.%), and nano-graphite (5 wt.%). Wire cut Electrical Discharge Machining (WEDM) of the synthesized nanocomposite SiC/Gr/Al7075 is considered for investigating the machinability. The objectives of the study are to synthesize the novel nanocomposite and optimize the process parameters to minimize the kerf width during WEDM. The independent variables like choice of wire electrode including the uncoated brass wire, diffused annealed wire, and zinc coated brass wire were considered along with operating parameters like gap voltage, pulse-off time (Toff), and pulse-on time (Ton). The response surface methodology (RSM) is used for designing experiments and analyzing and optimizing independent parameters of WEDM for minimizing the kerf width. Experimental results reveal that the machinability performance of the novel nanocomposite in WEDM with zinc coated brass wire was the best and produced minimum kerf width with the optimized inputs of 117 μs pulse-on time, 60 μs pulse-off time, 160A input current, and 10 volt gap voltage.
Black Seed Oil, Bentonite Clay, and Probiotics: A Comprehensive Holistic Cure for Clostridium difficile Infection in a 2-Year-Old Female Child
Authors: Littman Emily , Winningham Nicole , Carson Tana B. , Hidalgo Ivette M.
Published: May 2022

There has been a rise in antibiotic resistance in secondary conditions such as Clostridium difficile (C. difficile) due to overuse of antibiotics. Oral antibiotics are used to treat C. difficile, which further disrupts the intestinal flora resulting in unwanted side effects. Naturopathic treatments often have fewer side effects and lower secondary infection risk than pharmaceutical interventions making them ideal for pediatric use. This case report describes the effective treatment of a pediatric clinical case of C. difficile using naturopathic and complementary alternative medicines (CAMs) including black seed oil (Nigella sativa), bentonite clay, and probiotics. A healthy two-year-old patient presented to a pediatrician with symptoms of, and subsequently confirmed, C. difficile after having been recently hospitalized and treated for a gluteal abscess and cellulitis using clindamycin, vancomycin, and piperacillin/tazobactam. Through a shared decision-making process, the patient’s mother and providers developed a treatment plan for the C. difficile infection (CDI), which included black seed oil, bentonite clay, and Lactobacillus probiotics. No C. difficile was detected via stool immunoassay after 4 days of combined CAM therapy. Our results underscore the need for additional research regarding the effectiveness of naturopathic CAMs including black seed oil, bentonite clay, and probiotics as alternatives to antibiotic treatment of CDI in children.
Lithography-Free Technology for the Preparation of Digital Microfluidic (DMF) Lab-Chips with Droplet Actuation by Optoelectrowetting (OEW)
Authors: Doering Christoph , Strassner Johannes , Fouckhardt Henning
Published: May 2022

Electrically conducting liquid droplets can be activated and moved by electrowetting-on-dielectric (EWOD) and optoelectrowetting (OEW). An important application is droplet manipulation in digital microfluidics (DMF, lab-on-a-chip 2.0) as a chip-sized chemical laboratory. For spectroscopic analyses of chemical reactions, it is often necessary to prepare or examine the reagent droplets before, during, and after the reaction. With OEW, single droplets with volumes of 50–250 nl can be moved, analyzed, and merged in one pipetting step. To ensure analysis sensitivity in many applications, lab-chips should only be used once due to contamination of the surface and chemical modification of the layers by the droplets. Single-use chip preparation without a lithographic step, e.g., for the definition of the spacer layer, reduces efforts and costs. Here, exemplarily, we demonstrate the OEW-driven movement and mixing of chemical reagents in a simple color change reaction analyzed by absorption spectroscopy. Stripes made from the insulating tape serve as spacers between sub and superstrate, and any lithographic step can be avoided.
Innovative Research on College English Teaching Mode Based on Strategy Reasoning Mechanism
Authors: Huang Jingjing
Published: May 2022

In the era of artificial intelligence, the traditional English teaching methods cannot meet the increasing demand of course learning at this stage. Therefore, in this paper, a brand-new English teaching model based on strategic reasoning mechanism is designed by analyzing the problems faced by construction of network curriculum in universities. By introducing the idea of artificial intelligence, the student model and teacher model are designed, and the separation of teaching content and teaching strategy is realized. The development of the system adopts B/S structure and relies on lightweight J2EE multitier framework. At the same time, the overall architecture, functions, and processes of the teaching system are analyzed and designed. The system is convenient to transplant and expand, which has certain practical value and can provide reference for the design of various teaching systems and the innovation of teaching modes.
An Empirical Study on the Relationship between Economy and Finance in Underdeveloped Areas Based on the VAR Model
Authors: Zhang Han , Jiang Wenbing
Published: May 2022

In recent years, the level of local economic, social, and financial development had been greatly improved. However, due to the large gap between regions, the economic and financial industries in underdeveloped regions restricted the further development of the whole national economy to a great extent. It was known from the existing research that economic development was inseparable from the support of the financial industry, but the role of the financial industry in the process of regional economic development had not attracted attention. Therefore, from the perspective of economic development in underdeveloped areas, this paper put forward relevant assumptions by analyzing the relationship between economic growth and financial factors. In view of the development of financial industry in underdeveloped areas, this paper mainly analyzed from three aspects: banking, securities, and insurance, and selected corresponding indicators to observe the changes of banking, security, market and insurance market. Vector autoregressive model and vector error correction model were used to carry out cointegration test and Granger causality test for financial and economic-related indicators in underdeveloped areas. According to the impulse response function and variance decomposition results, this paper analyzed the dynamic relationship between financial development and economic growth, as well as the disturbance and duration of financial factors on economic development. The empirical results showed that the VAR model can better analyze the relationship between financial growth and economic growth, as well as the role of financial factors in the process of economic development. This study can provide reference for formulating financial development policies suitable for economic development.
Research on the Intrusion Detection Model of Underwater Sensor Networks
Authors: Huang Haijie , Liu Na , Chen Dandan , Yang Qiuling , Huang Xiangdang
Published: May 2022

The characteristics of underwater nodes, the harshness of the underwater environment, and the openness of the deployment area make underwater sensor networks vulnerable to various network attacks; in order to defend against potential threats and attacks, intrusion detection, as an active defense technology, can detect different attacks before they occur; currently, the technology is less used in underwater sensor networks and has a low detection rate for some types of attacks and cannot effectively detect the problem of multiple attack types. To address this problem, this paper proposes an intrusion detection model for underwater sensor networks for multiple types of attacks. Firstly, cluster head nodes use neighborhood rough sets for feature extraction, and the reduced dimensional data is transmitted to sink nodes to reduce the node computation. Further, the synthetic minority oversampling technique (SMOTE) is used to balance the data set, increase the number of minority class samples, and improve the detection rate of minority class attacks. Finally, determine whether a node is trusted according to the trust value of the cluster head node, and train the classifier using the random forest algorithm to detect the type of attack; it suffers to achieve intrusion detection of multiple types of attacks. Simulation results show that the model can not only improve the performance of intrusion detection of multitype attacks but also achieve an accuracy of over 99% for the detection of imbalance classes.
Metabolic Profile of Sow Blood Serum after Weaning
Authors: Bogolyubova Nadezhda Vladimirovna , Rykov Roman Anatolievich , Zaitsev Sergei Yurevich
Published: May 2022

The aim of our research was to determine the content of protein, carbohydrate, lipid, and mineral metabolites, as well as an antioxidant status of the sow’s blood after weaning and to calculate the correlation between these parameters. The experiment was carried out on twenty clinically healthy crossbred sows (Yorkshire × Landrace). Twenty sows were allocated to one of two groups: (1) 1 day after weaning (group 1, n = 10) and (2) 8 days after weaning (group 2, n = 10). The basis of the sow diet was SK-1 compound feed, balanced in terms of nutrients and energy in accordance with modern standards and the recommended feeding regimen. Sows blood samples were taken and analyzed for the metabolites of nitrogenous, carbohydrate-lipid, and mineral metabolism and indicators of antioxidant status. The results showed that, in group 2, the total protein content was 89.07 g/l, which is 10.2% higher than that in group 1 (

p
<
0.05

); it was mainly achieved due to the globulin fraction. The urea increased by 19.1% (

p
<
0.05

), but the concentrations of magnesium and chlorides decreased by 20.2% (

p
<
0.01

) and 5.43% (

p
<
0.05

), as well as the alkaline phosphatase and ALT activities decreased by 42.5% (

p
<
0.05

). Strong positive correlations of the ceruloplasmin with total protein (0.672) and very strong with globulins (0.780) were observed. There was a strong negative correlation between the AST activity and the TBA-AP content, as well as the values of phospholipids and TAWSA. There are moderate negative correlations of the TBA-AP with magnesium, TAWSA and ALT activity, and moderate positive correlations of the TBA-AP with total protein, albumin, triglycerides, and cholesterol. The revealed tendencies and dependencies will serve as the theoretical basis for the development of practical methods for regulating the level of free-radical reactions.