bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of eyes‐closed resting and eyes‐open conditions on cerebral blood flow measurement using arterial spin labeling magnetic resonance imaging
Authors: Matsutomo Norikazu , Fukami Mitsuha , Kobayashi Kuninori , Endo Yuta , Kuhara Shigehide , Yamamoto Tomoaki
Published: October 2022

Objective

The purpose of this study was to evaluate the impact of eyes‐closed resting and eyes‐open conditions on the measurement of cerebral blood flow (CBF) using arterial spin labeling magnetic resonance imaging (ASL MRI).

Methods

Thirty healthy volunteers underwent 3.0‐T MRI scans under two conditions—eyes‐closed resting and eyes‐open (stationary state)—1 week apart. CBF was measured using an asymmetric inversion slab technique with a three‐dimensional fast field echo sequence, which is a pulsed ASL technique, in the temporal lobe, precentral gyrus, limbic lobe, occipital lobe, and parietal lobe.

Results

CBF was not significantly different under eyes‐closed resting and eyes‐open states in the temporal lobe, precentral gyrus, limbic lobe, and parietal lobe. However, the CBF values of the occipital lobe were significantly lower under the eyes‐closed resting condition than under the eyes‐open condition. The means and standard deviations of CBF in the occipital lobe were 62.3 ± 7.9 ml/100 g/min for eyes‐open and 56.9 ± 7.9 ml/100 g/min for eyes‐closed resting. CBF obtained under eyes‐closed resting and eyes‐open conditions showed good reproducibility. In addition, the reproducibility and CBF measurement error were improved by excluding the occipital lobe.

Conclusions

The eyes‐closed resting condition affects the CBF measurement obtained by ASL MRI, particularly in the occipital lobe. To improve the measurement accuracy of ASL, measurements should be performed under the eyes‐closed resting condition in the clinical setting.
Population‐based cohort study to assess the gingival lesions in 1319 patients with lichen planus
Authors: Arduino Paolo G. , Gambino Alessio , Macciotta Alessandra , El Haddad Giorgia , Conrotto Davide , Carbone Mario , Carrozzo Marco , Broccoletti Roberto
Published: October 2022

Aims

Oral lichen planus (OLP) is a chronic immune disease. In this paper, we evaluated the overall characters, clinical presentation, and outcome of gingival lesions in OLP Italian patients.

Methods and Results

A retrospective cohort study was accomplished: a total of 1319 charts were investigated, of whom 922 were female (69.9%): 617 patients (46.8%) manifested white lesions and 702 red ones (53.2%). While most patients had several oral sites of involvement, the gingiva was the unique location in 103 cases. Symptoms were reported in 480 patients (36.4%): 286 patients with erosive OLP, 103 with atrophic form, and 91 with a white form. Long‐lasting surveillance showed that only 40 patients (3.03%) had a total clinical signs remission. Regarding OLP medical treatment provided, patients attending less frequently a dental office underwent more often a specific therapy.

Conclusions

To the best of our knowledge, this is the biggest collections of patients with gingival OLP ever described; exclusive gingival lesions are, however, rare and unlikely to undergo a malignant transformation. Moreover, gum lesions seemed to anticipate the appearance of oral lesions and a higher rate of OLP therapy was observed in patients with less frequent dental check‐ups and oral hygiene instructions.
SAP Principle Guided Free Hand Technique: A Secret for T1 to S1 Pedicle Screw Placement
Authors: He Jinyue , Luo Fei , Wang Hao , Xu Jianzhong , Zhang ZeHua
Published: October 2022

Objective

Existing freehand techniques of screw placement mainly emphasized on various entry points and complex trajectory reference. The aim of this study is to illustrate a standardized and reliable freehand technique of pedicle screw insertion for open pedicle screw fixation with a universal entry point and a stereoscopic trajectory reference system and report the results from a single surgeon's clinical experience with the technique.

Method

In this study, the author respectively reviewed a total of 200 consecutive patients who had undergone open freehand pedicle screw fixation with Superior Articular Process (SAP) technique from January 2019 to May 2020. For accuracy and safety, all 200 cases had undergone postoperative X‐ray while 33 cases including spinal deformity, infection, and tumor had received additional CT‐scan. Screw accuracy was analyzed via a CT‐based classification system with Student's t test.

Results

A total of 1126 screws had been placed from T1‐S1 with SAP‐guided freehand technique and the majority had been confirmed safe in X‐ray without the need of CT scan. A total of 316 screws in deformity or infectious or tumor cases had undergone additional CT scan with 95.5% (189 of 198 screws) accuracy in thoracic group and 94.9% (112 of 118 screws) in lumbar group. The accuracy had been 90.5% (114 of 126 screws) in deformity group and 95.8% (182 of 190 screws) in non‐deformity group. All perforation cases had been rated Grade B (<2 mm) without significant difference between the medial and the lateral (p < 0.05). No cases had been detected with significant neurological deficiencies. The mean intraoperative X‐ray shots were 0.73 per screw.

Conclusion

SAP‐guidance is a reliable freehand technique for thoracic and lumbar pedicle screw instrument. It allows accurate and safe screw insertion in both non‐deformity and deformity cases with less radiation exposure.
The active Fe chelator proline‐2′‐deoxymugineic acid enhances peanut yield by improving soil Fe availability and plant Fe status
Authors: Wang Tianqi , Wang Nanqi , Lu Qiaofang , Lang Shanshan , Wang Kunguang , Niu Lei , Suzuki Motofumi , Zuo Yuanmei
Published: October 2022

Iron (Fe) deficiency restricts crop yields in calcareous soil. Thus, a novel Fe chelator, proline‐2′‐deoxymugineic acid (PDMA), based on the natural phytosiderophore 2′‐deoxymugineic acid (DMA), was developed to solve the Fe deficiency problem. However, the effects and mechanisms of PDMA relevant to the Fe nutrition and yield of dicots grown under field conditions require further exploration. In this study, pot and field experiments with calcareous soil were conducted to investigate the effects of PDMA on the Fe nutrition and yield of peanuts. The results demonstrated that PDMA could dissolve insoluble Fe in the rhizosphere and up‐regulate the expression of the yellow stripe‐like family gene AhYSL1 to improve the Fe nutrition of peanut plants. Moreover, the chlorosis and growth inhibition caused by Fe deficiency were significantly diminished. Notably, under field conditions, the peanut yield and kernel micronutrient contents were promoted by PDMA application. Our results indicate that PDMA promotes the dissolution of insoluble Fe and a rich supply of Fe in the rhizosphere, increasing yields through integrated improvements in soil‐plant Fe nutrition at the molecular and ecological levels. In conclusion, the efficacy of PDMA for improving the Fe nutrition and yield of peanut indicates its outstanding potential for agricultural applications.
Hampered gamma oscillations induced by sad emotion underlying suicide attempt in major depressive disorder
Authors: Dai Zhongpeng , Zhang Siqi , Wang Huan , Chen Zhilu , Zhang Wei , Hu Xiaowen , Yao Zhijian , Lu Qing
Published: October 2022

Aim

Major depressive disorder (MDD) is associated with high suicidality, especially for those with suicide attempt (SA). Although impaired oscillatory activity has been previously reported in patients with SA, little is known about precise temporal–spatial variability of its neural dynamics. To solve this, the current study probed the spectral power and network interactions underlying SA in MDD.

Methods

The present study recruited 104 subjects including 56 subjects with MDD (30 with SA and 26 without SA) and 48 healthy controls, who performed sad expressions recognition task during magnetoencephalography (MEG) recording. By investigating source‐reconstructed MEG‐data, brain states representing different task stages were estimated from a Hidden Markov model. Spectrum power and network connectivity were compared via Gaussian Mixture Models, and fractional occupancy (FO) of states were compared via an independent F‐test.

Results

Brain states were corresponding to various frequencies (theta/beta/low gamma/ high gamma). In low gamma band (35–45 Hz), the early visual state exhibited increased activation and hyper inter‐network connectivity between visual regions and the limbic system, while the middle fronto‐parietal state exhibited attenuated activation and decreased intra‐network connectivity within fronto‐parietal regions in SA group. Crucially, FO values of these two states were significantly correlated with the suicide risks.

Conclusions

Suicide behavior of patients with MDD was significantly associated with aberrant oscillations in low gamma band. Elevated oscillations in occipital cortices and attenuated oscillations in fronto‐parietal cortices were significantly associated with SA. Manifesting sadness indulging and reckless decision‐making, the hampered temporal characteristics could help explain the neural‐electric basis of SA.
How do I initiate and maintain a mobile apheresis service in the era of cellular therapy
Authors: Karasick Michael H. , Betancourt Carolina , Dormesy Stephanie , Sheehy Jackie , Isaacs Rawlinson J. , Sachais Bruce S. , Shi Patricia A.
Published: October 2022

Background

Mobile delivery of apheresis services is an increasingly important component of health care equity, as patients should not have to transfer care providers or travel far distances to receive critical therapeutic apheresis procedures or cell therapy‐based treatments. Therefore, the availability of such services should be expanded.

Study design and methods

In this “How Do I" article, we provide a detailed overview of the elements necessary to initiate and maintain a successful mobile apheresis service, including challenges and potential solutions.

Results

Safe and efficient operation of a mobile apheresis service must consider acquisition of physical assets, such as apheresis sites, personnel, equipment and supplies, communication devices, and transportation vehicles, and optimize organizational aspects, such as staff responsibilities, service partnerships, logistics management, case scheduling and triage, and billing. In the era of cellular therapy, additional critical considerations include regulatory compliance and facility accreditation.

Discussion

To our knowledge, no previous publication provides the extensive details described herein to set up and maintain a successful mobile apheresis service, and thus will be very helpful to those facilities wishing to initiate or expand mobile apheresis services.
Identifying the temporal dynamics of densification and sparsification in human contact networks
Authors: Ferguson Shaunette T. , Kobayashi Teruyoshi
Published: October 2022

Temporal social networks of human interactions are preponderant in understanding the fundamental patterns of human behavior. In these networks, interactions occur locally between individuals (i.e., nodes) who connect with each other at different times, culminating into a complex system-wide web that has a dynamic composition. Dynamic behavior in networks occurs not only locally but also at the global level, as systems expand or shrink due either to: changes in the size of node population or variations in the chance of a connection between two nodes. Here, we propose a numerical maximum-likelihood method to estimate population size and the probability of two nodes connecting at any given point in time. An advantage of the method is that it relies only on aggregate quantities, which are easy to access and free from privacy issues. Our approach enables us to identify the simultaneous (rather than the asynchronous) contribution of each mechanism in the densification and sparsification of human contacts, providing a better understanding of how humans collectively construct and deconstruct social networks.
As-Is Approximate Computing
Authors: Soni Mitali , Pal Asmita , Miguel Joshua San
Published: October 2022

Although approximate computing promises better performance for applications allowing marginal errors, dearth of hardware support and lack of run-time accuracy guarantees makes it difficult to adopt. We present As-Is, an Anytime Speculative Interruptible System that takes an approximate program and executes it with time-proportional approximations. That is, an approximate version of the program output is generated early and is gradually refined over time, thus providing the run-time guarantee of eventually reaching 100% accuracy. The novelty of our As-Is architecture is in its ability to conceptually marry approximate computing and speculative computing. We show how existing innovations in speculative architectures can be repurposed for anytime, best-effort approximation, facilitating the design efforts and overheads needed for approximate hardware support. As-Is provides a platform for real-time constraints and interactive users to interrupt programs early and accept their current approximate results as is. 100% accuracy is always guaranteed if more time can be spared. Our evaluations demonstrate favorable performance-accuracy tradeoffs for a range of approximate applications.
Domain Adaptation Problem in Sketch Based Image Retrieval
Authors: Yu Hongchuan , Huang Mengqing , Zhang Jian Jun
Published: October 2022

In this article, we present two algorithms that discover the discriminative structures of sketches, given pairs of sketches and photos in sketch-based image retrieval (SBIR) scenarios. Unlike the existing approaches, we aim at the few-shot and domain adaptation (DA) problems, and set up a module with canonical correlation analysis (CCA) technique in our algorithms to improve retrieval performance. For single source domain settings, our first algorithm can effectively transfer a classifier trained on a known dataset to a new one. For multisource settings, our second algorithm sophisticatedly combines multisource domain data to yield a classifier on the target domain. To the best of our knowledge, these two works are the first research in SBIR field. Experiments on the Sketchy and TU-Berlin sketch benchmark datasets demonstrate the effectiveness of our algorithms and compelling performance. Compared with the state-of-the-art methods, our algorithms do not use text based semantic information, but achieve competitive results. Furthermore, experiments also turn out that feature representation by available trained deep networks has distinct advantages and combination with traditional machine learning methods brings substantial improvements against the state-of-the-art methods on image retrievals. The complete source code of the proposed algorithms will be released on gitHub:
[

GitHub- ADAM0912/Domain-Adaptation-Problem-in-Sketch-Based-Image-Retrieval: The code repository for the articleDomain Adaptation Problem in Sketch Based Image Retrieval

]
.
SAT-Reach
: A Bounded Model Checker for Affine Hybrid Systems
Authors: Kundu Atanu , Das Sarthak , Ray Rajarshi
Published: October 2022

Bounded model checking (BMC) is well-known to be undecidable even for simple hybrid systems. Existing work targeted for a wide class of non-linear hybrid systems reduces the BMC problem to the satisfiability problem of an satisfiability modulo theory formula encoding the hybrid system dynamics. Consequently, the satisfiability of the formula is deduced with a δ-decision procedure. However, the encoded formula can be complex for large automaton and for deep exploration causing the decision procedure to be inefficient. Additionally, a generalized decision procedure can be inefficient for hybrid systems with simple dynamics. In this article, we propose a BMC algorithm built upon the foundation of the counter example guided abstraction refinement (CEGAR) technique and targeted for hybrid systems with piecewise affine dynamics, modeled as a hybrid automaton. In particular, our algorithm begins by searching an abstract counterexample in the discrete state-space of the automaton. We check whether a discovered abstract counterexample is spurious or real by a two-tier refinement of the state-space guided by the abstract counterexample. The primary refinement is through symbolic reachability analysis and the following refinement is via a search of a real counterexample by the trajectory splicing method, guided in turn by the outcome of reachability analysis. We show that our algorithm reaps the benefits of the CEGAR technique by directing the exploration in the regions of interest and pruning search space that is irrelevant to the property under consideration. In addition, an optimization by memoizing the computed symbolic states during reachability analysis has been proposed for efficiency. The proposed algorithm is implemented in the tool
SAT-Reach,
and we compare its performance with
dReach
,
XSpeed
,
Flow*
,
SpaceEx,
and a pattern database heuristic-guided search algorithm. Experiments demonstrate the efficacy of our algorithm.
Design Space Exploration of Galois and Fibonacci Configuration Based on Espresso Stream Cipher
Authors: Shi Zhengyuan , Chen Cheng , Yang Gangqiang , Xiong Hailiang , Li Fudong , Hu Honggang , Wan Zhiguo
Published: October 2022

Fibonacci and Galois are two different kinds of configurations in stream ciphers. Although many transformations between two configurations have been proposed, there is no sufficient analysis of their FPGA performance. Espresso stream cipher provides an ideal sample to explore such a problem. The 128-bit secret key Espresso is designed in Galois configuration, and there is a Fibonacci-configured Espresso variant proved with the equivalent security level. To fully leverage the efficiency of two configurations, we explore the hardware optimization approaches toward area and throughput, respectively. In short, the FPGA-implemented Fibonacci cipher is more suitable for extremely resource-constrained or high-throughput applications, while the Galois cipher compromises both area and speed. To the best of our knowledge, this is the first work to systematically compare the FPGA performance of cipher configurations under relatively fair cryptographic security. We hope this work can serve as a reference for the cryptography hardware architecture research community.
Large-Scale Assessment of Soil Physical Quality of Rural Settlements in the Southeast of Goiás, Brazil
Authors: Da Silva Anderson Rodrigo , Vieira Wênio , de Souza Ítalo Rômulo Mendes
Published: October 2022

Land use and soil physical quality are related. In this work, the S index was used to evaluate the physical quality of the soils of three rural settlement units located in the Brazilian Cerrado, with areas ranging from 700 to more than 4000 hectares, based on a novel method. To map the S index, a set of pedotransfer functions for water retention curves was adapted according to a broad range of clay content for Cambisols, in three depth layers. Some rural units presented associations with classes of land use. In general, deeper layers presented higher values of S. In areas with less than 370 g/kg of clay, land uses that provide low soil cover should be avoided to prevent physical degradation.
Lymph Node Metastasis-Related Gene ITGA4 Promotes the Proliferation, Migration, and Invasion of Gastric Cancer Cells by Regulating Tumor Immune Microenvironment
Authors: Fang Tianyi , Yin Xin , Wang Yufei , Wang Hao , Wang Xibo , Xue Yingwei
Published: October 2022

The Integrin Subunit Alpha 4 (ITGA4) plays important roles in cancers pathogenesis. However, the expression and association with clinicopathological and survival probability have not been previously assessed in gastric cancer (GC). Protein expression of ITGA4 was assessed in TMA using immunohistochemistry and correlated with clinicopathological factors and survival. The mRNA expression of ITGA4 was also assessed in the HMU-GC cohort. Bioinformatics function analysis was conducted through GSEA. The “CIBERSORT” package was used for immune infiltration analysis. “SvyNom” package is used to construct prognosis model. ITGA4 knock down using shRNA. The evaluation of cell function was performed by CCK-8 and Transwell invasion and migration experiments. ITGA4 was significantly associated with N classification (

P
=
0.031

), tumor location (

P
=
0.033

), WHO classification (

P
=
0.007

), and poor prognosis in mRNA level. GSEA analysis of the validation cohort suggested that ITGA4 was associated with macrophage infiltration. Immunohistochemistry showed that ITGA4 was associated with poor prognosis. Multivariate Cox regression analysis found that ITGA4 (

P
=
0.045

) and lymph node metastasis rate (

P
=
0.026

) were independent prognostic factors and could construct a prognosis model. ITGA4 knockdown cell line significantly reduced the ability of proliferation, invasion, and metastasis. ITGA4 is associated with patient survival in GC and may be an important prognostic biomarker.
Advance of Mesenchymal Stem Cells in Chronic End-Stage Liver Disease Control
Authors: Gao Yun , Yin Xiushan , Ren Xiaomeng
Published: October 2022

The chronic liver diseases will slowly develop into liver fibrosis, cirrhosis, and even liver cancer if no proper control is performed with high efficiency. Up to now, the most effective treatment for end-stage liver diseases is liver transplantation. However, liver transplantation has the problems of donor deficiency, low matching rate, surgical complications, high cost, and immune rejection. These problems indicate that novel therapeutic strategies are urgently required. Mesenchymal stem cells (MSCs) are somatic stem cells with multidirectional differentiation potential and self-renewal ability. MSCs can secrete a large number of cytokines, chemokines, immunomodulatory molecules, and hepatotrophic factors, as well as produce extracellular vesicles. They alleviate liver diseases by differentiating to hepatocyte-like cells, immunomodulation, homing to the injured site, regulating cell ferroptosis, regulating cell autophagy, paracrine effects, and MSC-mitochondrial transfer. In this review, we focus on the main resources of MSCs, underlying therapeutic mechanisms, clinical applications, and efforts made to improve MSC-based cell therapy efficiency.
[Retracted] Income Uncertainty and Consumer Demand among Rural Residents Analysis Based on Optimisation of Household Consumption Utility
Authors: Cheng Xiejun , Qiao Yang , Feng Qi , Huang Shenghua
Published: October 2022

As China enters a new era, the role of consumption as a driving force for the economy has become increasingly prominent. The consumption of rural residents is not only a matter of high‐quality economic development but also an important part of realizing people’s aspirations for a better life. This study contributes to the literature by establishing an econometric model based on the construction of a theoretical model for optimizing the utility of household consumption, using panel data from China’s 31 provinces, eliminating endogenesis by using instrumental variables, taking  2SLS and dynamic  GMM. The results show that the relationship between income uncertainty and rural residents’ consumption varies in the same direction, stimulating consumption when real income exceeds psychological income and reducing consumption when real income does not reach psychological income. The quantile model shows that income uncertainty has a greater impact on the middle‐high‐consumption groups and a smaller impact on the lower‐income groups.
Identification of Two Novel Immune Subtypes Characterized by Distinct Prognosis and Tumor Microenvironment in Osteosarcoma
Authors: Yao Shunhan , Deng Meiling , Du Xiaojing , Chen Qingfeng , Huang Rongzhi
Published: October 2022

Osteosarcoma is a kind of primary malignant tumor of bone. In recent years, its therapeutic effect and prognostic survival are dissatisfactory. The tumor immune microenvironment (TIME) reflects immune status of patients, but it is little known in osteosarcoma. Therefore, this study attempts to conduct a comprehensive analysis to explore TIME of osteosarcoma and identify TIME-related subtypes for clinical management and treatment. We successfully established two novel tumor immune infiltration clusters (TIIC) which are characterized by difference of microenvironment and immune-related biological processes. High tumor immune infiltration cluster (H-TIIC) subtypes with higher immune infiltration score shows a better overall survival. Further, the two immune subtypes are shown to differ in immunotherapy and chemotherapy. The results would be helpful for clinical decision in osteosarcoma.
New Analytical Solution of the Large Deflection Problem of a Circular Thin Plate with Edge Clamped but Sliding Freely Under a Combined Loading
Authors: Zhao Chu , Yun Yin-shan
Published: October 2022

This paper intends to study the large deflection problem of a circular thin plate with an edge clamped but sliding freely under the combined action of uniformly distributed load and centrally concentrated load by the Adomian decomposition method. If the uniformly distributed load is not decomposed, the parameters obtained by the Adomian decomposition method are the same as the perturbation solution studied by scholars before. If the uniformly distributed load is decomposed into a finite series, a new analysis solution can be obtained through the Adomian decomposition method. From the error comparisons between the new solution in this paper and the perturbation solution, one can see that the result in this paper is better. Finally, the internal stress and deflection are discussed through the graphics based on the new analysis method.
Research on the International Trade Performance Evaluation of Cross-Border e-Commerce Based on the Deep Neural Network Model
Authors: Shen Jianjun
Published: October 2022

The rapid development of e-commerce international trade has driven the rapid growth of the economic system of international trade enterprises. This also means that industry competition is gradually intensifying, which also makes performance evaluation the key to cross-border e-commerce international trade. At present, my country’s research on the performance evaluation of cross-border e-commerce international trade is in a blank state. Therefore, this paper takes the international trade performance evaluation of cross-border e-commerce as the research object and, based on the deep neural network model, develops a cross-border international trade performance evaluation model, changes trade strategies, and improves trade performance. This paper first analyzes various neural network models, such as artificial neural network, “BP” neuron model, and LSTM neural network. This paper summarizes a deep neural network model that is conducive to the development of cross-border e-commerce and points out the problems in the current performance evaluation of cross-border e-commerce international trade: the e-commerce market supervision system is not perfect; the second is the inconsistent evaluation indicators; the third is the evaluation system. There are some differences with the actual. Finally, this paper puts forward relevant suggestions for the performance evaluation of cross-border e-commerce international trade and points out the advantages and disadvantages of various neural networks, as well as their roles in cross-border e-commerce performance evaluation, and compares these neural networks through experiments. Experiments show that among these neural network models, the deep neural network model is the best and has the highest accuracy and stability in e-commerce trade performance evaluation. In the later stage, we will improve the global logistics system, strengthen the application of big data technology, and improve the overall performance of global operations. First, a set of indicators is designed to evaluate the performance of e-commerce systems, using the enterprise key factor model concept. In addition, this evaluation method is different from the commonly used expert evaluation method and physical evaluation method in evaluating the construction quality, cost, education and growth ability, and performance level of the international business system of cross-border e-commerce.
Comprehensive Review on Modelling, Estimation, and Types of Faults in Solar Photovoltaic System
Authors: Prasanna R. , Karthik C. , Chowdhury Subrata , Khan Baseem
Published: October 2022

Solar photovoltaic (SPV) system fault diagnostics is vital in advanced supervision because it can alert users to catastrophic failure or greater risks. To provide green and clean energy using solar, it is mandatory to analyse various faults associated with photovoltaic system which can result in energy deficit and system breakdown and may lead to fire hazards which are often difficult to avoid. Hence, as an endeavour to improve the efficiency level, more study beginning with modelling of SPV system with its parameter estimation and types of SPV faults is aimed in this work.
Urban Landscape Design Based on Virtual Reality Technology
Authors: Li Xiang , Guo Yanan
Published: October 2022

Different from the western classical stone buildings, China has formed the traditional architectural style with wood as the main body since ancient times. With the progress of the times and the collision between Chinese and western cultures, people put forward higher requirements for the current urban planning and landscape design, hoping that the future urban landscape design can follow the style of “Chinese and new.” Traditional urban landscape design usually adopts solid model, rendering renderings, or three-dimensional animation, but these methods all have some defects. With the development of computer technology and the rise of Internet industry, virtual reality technology appears, and because of its excellent multiperception, immersion, interactivity, and autonomy, it is gradually applied to medical, military, entertainment, virtual city, and other fields of human life. Urban landscape design based on virtual reality technology is an important technical way of urban landscape design at present. It can help designers to better perfect the design details, experience it personally in the design process, find the existing problems and defects in the current design in time, and improve the design efficiency. It can also assist urban planners to make corresponding decisions. However, due to the high cost and the shortage of technical personnel, it is only implemented in a small area at present. Take the virtual Los Angeles project in the United States as an example, which is a typical case of the application of virtual reality technology in urban planning and construction and successfully realizes the interactive function between users and virtual environment. With the continuous development and improvement of virtual reality technology, there are more and more corresponding talents, and this technology will be widely used in the future urban planning and construction, forming our own “middle and new” urban landscape style.
[Retracted] Effect of Cold Fluid Compensatory Swallowing Combined with Balloon Dilation on the Treatment of Poststroke Cricopharyngeal Achalasia: A Pilot Randomized Controlled Trial
Authors: Li Xiangwei , Jin Linna , Gu Chengxiao , Zhang Wangyuan , Zhou Xiao , You Xiaoting
Published: October 2022

Objective. This study is aimed at comparing the treatment efficacy between catheter balloon dilation combined with cold fluid compensatory swallowing training and catheter balloon dilation alone on poststroke cricopharyngeal achalasia (CPA). Methods. We conducted a single‐blind, randomized controlled trial (RCT). Poststroke patients with CPA were divided into two groups: the control group (treated with catheter balloon dilation) and the trial group (catheter balloon dilation combined with cold fluid compensatory swallowing). Videofluoroscopic swallowing study (VFSS) was performed, and functional oral intake scale (FOIS) was used to evaluate and compare the swallowing function of patients in the 2 groups before and after intervention. Posttreatment VAS pain scores and recovery time were also measured. Results. VFSS and FOIS scores in the two groups were improved after treatment (P < 0.05). In the trial group, VFSS scores in the pharyngeal phase and aspiration degree were significantly higher compared with the control group (P < 0.05) but not in the oral phase (P > 0.05). The difference in FOIS scores and patients’ recovery time from intervention to eating mushy food between the trial and control groups was significant (P < 0.05), but not the VAS scores (P > 0.05). Conclusion. The catheter balloon dilation combined with cold fluid compensatory swallowing was superior to catheter balloon dilation alone in terms of relieving dysphagia and reducing aspiration in patients with CPA following stroke. Long‐term efficacy should be followed up with more objective and quantitative indicators in future studies.
Analytical Approach for the Approximate Solution of Harry Dym Equation with Caputo Fractional Derivative
Authors: Nadeem Muhammad , Li Zitian , Alsayyad Yahya
Published: October 2022

This paper presents the concept of a new strategy that examines the analytical solution of the Harry Dym equation (HDEq) with fractional derivative in the Caputo sense. This new approach is called the Mohand homotopy perturbation transform scheme (MHPTS) which is constructed on the basis of the Mohand transform (MT) and the homotopy perturbation method (HPM). The implementation of MT produces the recurrence relation without any assumption and hypothesis theory whereas HPM is additionally used to overcome the nonlinearity in differential problems. Our primary focus is to handle the error analysis in the recurrence relation and generates the solution in the order of series. These obtained results yield the exact solution very rapidly due to its fast convergence. Some graphical representations are demonstrated to show the high efficiency and performance of this approach.
Optimal Decision and Coordination of Organic Food Supply Chain from the Perspective of Blockchain
Authors: Ding Junhui , Bai Shizhen
Published: October 2022

Driven by the pain points of the organic food supply chain, which has been plagued by counterfeiting and difficulties in pursuing accountability, this paper investigates a secondary organic food supply chain consisting of suppliers and retailers and establishes two supply chain models under the traditional model and in the blockchain traceability context. In order to effectively solve the problem of unrealized Pareto improvement in organic food supply chain after applying blockchain, a new hybrid contract based on benefit‐sharing and cost‐sharing is designed to coordinate the supply chain and realize Pareto improvement, and this solution is gradually applied to organic food enterprises. Based on the fact that blockchain can improve trust in the supply chain and eliminate counterfeiting of organic food, the relationship between the rate of genuine products and market demand and the cost of blockchain is established, and then the analysis is developed using the Stackelberg game. We compare the traditional model with the model in the blockchain context and analyze the optimal profit of each supply chain entity, comparing the change in optimal profit before and after the blockchain implementation, and clarifying the cost threshold of the blockchain technology input application. We find that: (i) The adoption of blockchain can not only improve the authenticity of products and combat counterfeit and shoddy organic food, but at the same time, the improvement of organic level in the context of blockchain will also attract some consumers to buy organic food, which will increase the main body of the supply chain and the overall profit. (ii) Blockchain‐adopted supply chains are consistently more profitable for all parties and overall than traditional supply chains. The main contribution of this study is that in the organic food supply chain under the application of blockchain technology model, by introducing revenue‐sharing and cost‐sharing contracts, the profit between each member of the organic food supply chain is further improved than the traditional model, and also, all of them are optimized, which further improves the stability of the supply chain and brings the supply chain to a coordinated state. Finally, in this context, the obtained results show the effectiveness and realistic operational efficiency of the proposed approach for companies compared to traditional single revenue‐sharing covenants. A combination of revenue‐sharing and cost‐sharing covenants is the best approach to solve such problems. In conclusion, it should be noted that the analysis presented in this study will help decision makers choose the most appropriate option among the possible solutions according to their criteria. This proposed framework can also be extended in various cases where profits are out of balance in the organic food supply chain, such as safety and value gain.
Mutual-Supervised Federated Learning and Blockchain-Based IoT Data Sharing
Authors: Liu Jianwei , Miao Qinyang , Fan Xinmin , Wang Xiaoding , Lin Hui , Huang Yikun
Published: October 2022

Due to the decentralized, tamper-proof, and auditable properties of blockchain, more and more scholars and researchers are studying the application of blockchain technology in IoT data sharing. Federated learning is an effective way to enable data sharing, but can be compromised by dishonest data owners who may provide malicious models. In addition, dishonest data requesters may also infer private information from model parameters. To solve the above problems, a secure data sharing mechanism based on mutual-supervised federated learning and blockchain, BPCV-FL, is proposed. This mechanism ensures data privacy by adopting gradient descent algorithm with differential privacy protection in local model training and ensures the reliability of shared data through mutual supervision on the blockchain. Experimental results show that the proposed BPCV-FL has high accuracy and security in IoT data sharing.
Prediction Model and Data Simulation of Sports Performance Based on the Artificial Intelligence Algorithm
Authors: Lu Guang
Published: October 2022

There is still a certain deviation between the current artificial intelligence technology and the traditional learning mode, which makes it unable to be effectively applied in teaching and learning. Therefore, an effective method needs to be proposed to use functions to predict data. Function calculation can not only solve the complex problems of data calculation process but also make the data evenly distributed to take full advantage of the capabilities of each system. In this experiment, we mainly use the control function. After substituting the data into the control function, the function will automatically classify the data. In this paper, according to the actual situation of physical education in colleges and universities, from the two aspects of artificial intelligence and comprehensive learning algorithm, to build a system which can collect and analyze the past achievements of college students’ physical education performance simulation can effectively help the design of physical education curriculum. According to the distribution of experimental data, a specific conclusion can be drawn; that is, the test model we choose can calculate and measure the physical fitness level of students, but there are big differences. In contrast, our experimental method using the ensemble computing model can not only predict and analyze the physical fitness level of college students but also reduce errors and shorten the time required for the experiment.
Security Warning Model of Chinese Corporate Bond Defaults Based on Deep Neural Networks
Authors: Bo Lan , Weiwei Zhang  , Chen Chen , Ji Mingkai
Published: October 2022

The Chinese bond market has achieved rapid development over the years. However, since the “rigid payment” in China was broken in 2014, the number and size of bond defaults have climbed up promptly and caused huge volatility in bond and even stock markets. To better manage and control the risk of the Chinese corporate bonds market, deep learning can be used as a helpful tool to predict the corporate default risk. This paper constructs a security warning model based on deep neural networks after a reasonable selection of characteristic indicators. By comparing Multi-Layer Perceptron (MLP) with the logistic regression method, it is found that MLP is more suitable in the security warning model to predict the default risk. And, hyper-parameter analyses and ablation study are conducted to explore the performance and accuracy of the best MLP settings. The experimental results show that the deep learning method accommodating the widely chosen factors in the security warning model is effective in predicting corporate bond defaults in China.
The Therapeutic Roles of Cinnamaldehyde against Cardiovascular Diseases
Authors: Lu Li , Xiong Yuan , Zhou Juan , Wang Guangji , Mi Bobin , Liu Guohui
Published: October 2022

Evidence from epidemiological studies has demonstrated that the incidence and mortality of cardiovascular diseases (CVDs) increase year by year, which pose a great threat on social economy and human health worldwide. Due to limited therapeutic benefits and associated adverse effects of current medications, there is an urgent need to uncover novel agents with favorable safety and efficacy. Cinnamaldehyde (CA) is a bioactive phytochemical isolated from the stem bark of Chinese herbal medicine Cinnamon and has been suggested to possess curative roles against the development of CVDs. This integrated review intends to summarize the physicochemical and pharmacokinetic features of CA and discuss the recent advances in underlying mechanisms and potential targets responsible for anti-CVD properties of CA. The CA-related cardiovascular protective mechanisms could be attributed to the inhibition of inflammation and oxidative stress, improvement of lipid and glucose metabolism, regulation of cell proliferation and apoptosis, suppression of cardiac fibrosis, and platelet aggregation and promotion of vasodilation and angiogenesis. Furthermore, CA is likely to inhibit CVD progression via affecting other possible processes including autophagy and ER stress regulation, gut microbiota and immune homeostasis, ion metabolism, ncRNA expression, and TRPA1 activation. Collectively, experiments reported previously highlight the therapeutic effects of CA and clinical trials are advocated to offer scientific basis for the compound future applied in clinical practice for CVD prophylaxis and treatment.
The Intelligent Selection Method of Distribution Sites Driven by the Intelligent Optimization Algorithm
Authors: Jin Juanli
Published: October 2022

For the internal enterprise, the intelligent selection of logistics distribution sites can optimize the distribution route, which is conducive to reducing the total distribution cost and improving the enterprise revenue. This paper takes urban logistics distribution point selection optimization as the research content and compares and analyses the applicability of different intelligent algorithms to logistics data. The AFSA is selected as the optimization algorithm, and knowledge learning is introduced to optimize the algorithm. The optimized AFSA model is applied to the mathematical model of distribution point selection. An intelligent algorithm-driven logistics distribution point selection model is established, and the optimized AFSA is used to solve the problem. Based on the actual case of CSC Logistics Company, the route of the distribution point in a region of CSC Logistics Company is optimized and the model is validated and solved. The results show that GWO and AFSA search capabilities are significantly better than of other intelligent algorithms, but there is some instability in the GWO algorithm. The AFSA is most suitable for solving logistics-related problems. The optimized AFSA considering knowledge learning has high efficiency and good optimization results. CSC Company uses this intelligent algorithm to select distribution sites intelligently, which shortens the total logistics path and improves the distribution efficiency. The total mileage of the initial route is 115 km. After intelligent algorithm optimization, the total mileage of the distribution changes to 83 km, which reduces by 32 km and 28% compared with the original route. The whole distribution process saved about 1.5 hours, which fully optimized the efficiency of distribution.
Effect of Green Synthesized Iron Oxide Nanoparticles Using Spinach Extract on Triton X-100-Induced Atherosclerosis in Rats
Authors: Obidah Abert Habila , Umaru Aduwamai Hauwa , Shehu Adamu Saminu
Published: October 2022

The effect of iron oxide nanoparticles (FeONPs) synthesized using Spinacia oleracea leaf extract on Triton X-100-induced atherosclerosis in white Wistar rats was determined. FeONPs were characterized to determine their size, structure, composition, and shape. In vitro antioxidant activity of FeONPs against 2, 2-diphenyl-1-picryl-hydrazyl-hydrate (DPPH) was determined. Atherosclerosis was induced by intraperitoneal administration of 5% Triton X-100 (100 mg/kg body weight) for 14 days. Group 1 received standard rat chow and water. Group 2 received 100 mg/kg body weight of Triton X-100 and a standard diet. Group 3 received 100 mg/kg body weight of Triton X-100 followed by 20 mg/kg body weight of atorvastatin for 21 days. Groups 4, 5, and 6 received 100 mg/kg body weight Triton X-100 was followed by variable concentrations of 100, 300, and 500 µg/kg body weight FeONPs, respectively, for 21 days. Blood samples were analyzed for lipid, liver, antioxidant, and cardiovascular markers. Histopathology of the heart was also examined. Characterization revealed the amorphous nature, functional groups, and clustered topography of FeONPs. An upregulated antioxidant activity of FeONPs was observed in a dose-dependent manner. Administration of Triton X-100 showed elevated levels of lipid biomarkers except for high-density lipoprotein (HDL), which decreased in group 2 in comparison to group 1. Liver, antioxidant, and cardiovascular biomarkers all significantly increased. The structural alteration was observed in the heart tissue following histopathology examination. Administration of FeONPs significantly decreased all biomarkers and increased the level of HDL. Also, tissue architecture was restored. Our findings demonstrated that FeONPs were effective in ameliorating Triton X-100-induced atherosclerosis in rats.
Analysis on the Educational Value of Winter Olympic Spirit in Track and Field Teaching Based on Data Mining under Artificial Intelligence
Authors: Yu Ao , Wang Jianxing , Zhou Yixin , Qiao Xiaoyu , Yan Song , Feng Yufang , Marcos Elvan , Jae Lee Seok , Fiaidhi Avera
Published: October 2022

School sports is the basis for the development of mass sports and competitive sports. It goes without saying that the key to the development of school physical education is school physical education, and the teaching of track and field (TAF) courses occupies an irreplaceable position in students’ growth education. The stepping stone for the cultivation of sports awareness plays a pivotal role in the better healthy growth of students and the improvement of the physical quality of all citizens. As a major gold medalist in the Olympic Games, TAF is welcomed, valued, and popularized by many countries. Especially in colleges and universities, TAF can cultivate the will of college students and the spirit of continuous improvement. The Winter Olympics (WO), adhering to the spiritual concept of overcoming challenges and facing difficulties, embodies the strength of unity and collective. Based on the current situation of TAF teaching in a university, this paper uses data mining technology to analyze the students’ favorite TAF events and their mastery of the WO knowledge and then combines the educational value of the WO spirit to conduct research to explore the teaching significance of the WO spirit to the TAF curriculum.