bluuebunny/crossref_metadata_2025 huggingface dataset:
INCloud: integrated neuroimaging cloud for data collection, management, analysis and clinical translations
Authors: Li Qingfeng , Jiang Lijuan , Qiao Kaini , Hu Yang , Chen Bing , Zhang Xiaochen , Ding Yue , Yang Zhi , Li Chunbo
Published: December 2021

Background

Neuroimaging techniques provide rich and accurate measures of brain structure and function, and have become one of the most popular methods in mental health and neuroscience research. Rapidly growing neuroimaging research generates massive amounts of data, bringing new challenges in data collection, large-scale data management, efficient computing requirements and data mining and analyses.

Aims

To tackle the challenges and promote the application of neuroimaging technology in clinical practice, we developed an integrated neuroimaging cloud (INCloud). INCloud provides a full-stack solution for the entire process of large-scale neuroimaging data collection, management, analysis and clinical applications.

Methods

INCloud consists of data acquisition systems, a data warehouse, automatic multimodal image quality check and processing systems, a brain feature library, a high-performance computing cluster and computer-aided diagnosis systems (CADS) for mental disorders. A unique design of INCloud is the brain feature library that converts the unit of data management from image to image features such as hippocampal volume. Connecting the CADS to the scientific database, INCloud allows the accumulation of scientific data to continuously improve the accuracy of objective diagnosis of mental disorders.

Results

Users can manage and analyze neuroimaging data on INCloud, without the need to download them to the local device. INCloud users can query, manage, analyze and share image features based on customized criteria. Several examples of 'mega-analyses' based on the brain feature library are shown.

Conclusions

Compared with traditional neuroimaging acquisition and analysis workflow, INCloud features safe and convenient data management and sharing, reduced technical requirements for researchers, high-efficiency computing and data mining, and straightforward translations to clinical service. The design and implementation of the system are also applicable to imaging research platforms in other fields.
Relative environmental and social disadvantage in patients with idiopathic pulmonary fibrosis
Authors: Avitzur Na'ama , Noth Elizabeth M , Lamidi Mubasiru , Nathan Steven D , Collard Harold R , DeDent Alison M , Thakur Neeta , Johannson Kerri A
Published: December 2021

Background

Air pollution exposure is associated with disease severity, progression and mortality in patients with idiopathic pulmonary fibrosis (IPF). Combined impacts of environmental and socioeconomic factors on outcomes in patients with IPF are unknown. The objectives of this study were to characterise the relationships between relative environmental and social disadvantage with clinical outcomes in patients with IPF.

Methods

Patients with IPF were identified from a longitudinal database at University of California, San Francisco. Residential addresses were geocoded and linked to the CalEnviroScreen 3.0 (CES), a tool that quantifies environmental burden in California communities, combining population, environmental and pollution vulnerability into individual and composite scores (higher scores indicating greater disadvantage). Unadjusted and adjusted linear and logistic regression and Fine and Gray proportional hazards models were used.

Results

603 patients were included. Higher CES was associated with lower baseline forced vital capacity ( β =−0.073, 95% CI −0.13 to −0.02; p=0.006) and diffusion capacity of the lung for carbon monoxide ( β =−0.11, 95% CI −0.16 to −0.06; p<0.001). Patients in the highest population vulnerability quartile were less likely to be on antifibrotic therapy (OR=0.33; 95% CI 0.18 to 0.60; p=0.001) at time of enrolment, compared with those in the lowest quartile. An association between CES and mortality was suggested, but sensitivity analyses demonstrated inconsistent results. Relative disadvantage of the study cohort appeared lower compared with the general population.

Conclusions

Higher environmental exposures and vulnerability were associated with lower baseline lung function and lower antifibrotic use, suggesting that relative socioenvironmental disadvantage has meaningful impacts on patients with IPF.
Hadron wave functions in high-energy scattering, form factors and strong decays: The effects of the Lorentz contracted form
Authors: Simonov Yu. A.
Published: December 2021

In this paper, we study the class of the processes, where dynamics depends essentially on the properties of the hadron wave functions involved in the reactions. In this case, the momentum dependence of the form of the wave functions, imposed by the Lorentz invariance and in particular by the Lorentz contraction, can be tested in the experiment and may strongly influence the resulting cross-sections. One example of such observables is given by the hadron form factors in the case when the large [Formula: see text] behavior is mostly frozen, while the Lorentz contraction of the hadron wave functions is taken into account. Another example, considered earlier, is the strong hadron decay with high-energy emission. In this paper, we study the role of the Lorentz contraction in the high-energy hadron–hadron scattering process at large momentum transfer. For the [Formula: see text] and [Formula: see text] scattering at large [Formula: see text], it is shown that at small [Formula: see text], the picture of two exponential slopes in the differential cross-section, explained previously by the author, remains stable, while the backward scattering cross-section is strongly increased by the Lorentz contraction.
Morphological and structural characterization of Pb:ZnS nanostructure films deposited by simple thermal evaporation
Authors: Abdallah Bassam , Kakhia M. , Zetoune W. , Alwazzeh M.
Published: December 2021

ZnS nanostructure films were deposited by a thermal evaporation method. Two films were prepared; the first was zinc sulfide (undoped ZnS) and the second was Pb-doped zinc sulfide (Pb:ZnS). X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectroscopy (EDX) techniques were employed to investigate the element contents of the two films; they were found to be stoichiometric. Surface and growth evolution of films were explored by SEM images and found to have morphological development from spherical forms into nanostructure lookalike Taraxacum erythrospermum (Dandelion) with increasing the Pb from 0 wt.% to 12 wt.%. A structural study was performed using XRD patterns. The films have ZnS hexagonal structure (002) preferential orientation with various Pb percentages. In the doped sample, ZnS nanostructure and pores and nanowires (NWs) were formed with a mean diameter less than 50 nm; the undoped film had dense structure and was thin. This study illustrated the influence of Pb doped on the morphological modification of ZnS films.
A diagrammatic presentation and its characterization of non-split compact surfaces in the 3-sphere
Authors: Matsuzaki Shosaku
Published: December 2021

We give a presentation for a non-split compact surface embedded in the 3-sphere [Formula: see text] by using diagrams of spatial trivalent graphs equipped with signs and we define Reidemeister moves for such signed diagrams. We show that two diagrams of embedded surfaces are related by Reidemeister moves if and only if the surfaces represented by the diagrams are ambient isotopic in [Formula: see text].
On 2-absorbing ideals of commutative semirings
Authors: Sawalmeh Leena , Saleh Mohammed
Published: December 2021

Let S be a commutative semiring with unity different than zero. In this paper, we study the concept of 2-absorbing ideals of S which can be considered as a generalization of prime ideals. Among others, it is shown that the radical of a 2-absorbing ideal is also a 2-absorbing ideal and there are at most 2 prime k-ideals of S that are minimal over a 2-absorbing ideals.
Profiling Digital Printing Technology Adoption in the Fashion Industry: A New Approach to Exploring Innovation Diffusion
Authors: Yu Yanan , Chapman Lisa Parrillo , Moore Marguerite M.
Published: December 2021

Digital printing technology (DPT) represents a core innovation that is currently revolutionizing the global decorated apparel market by automating the printing process, facilitating customization, and reducing energy costs and production lead time. However, the fundamental understanding of the emerging DPT market remains unexplored due to its novelty. This study aims to identify DPT diffusion patterns over the past decade in the U.S. market and establish a predictive user profile employing social media-based analytics along with data mining and traditional statistical modeling. A proxy variable is used to measure likely adoption which reflects an S-shaped diffusion curve consistent with Diffusion of Innovations Theory. Additionally, the outcome profile suggests that likely DPT adopters reside in locations that reflect higher levels of education (bachelor’s degrees or higher), relatively young populations (i.e. between 19–34 years of age), proportionately higher incomes generated from art and design occupations, but with lower household annual incomes.
Remarks on the rightmost critical value of the triple product L-function
Authors: Fukunaga Kengo , Gejima Kohta
Published: December 2021

Let [Formula: see text] be a normalized cuspidal Hecke eigenform. We give explicit formulas for weighted averages of the rightmost critical values of triple product [Formula: see text]-functions [Formula: see text], where [Formula: see text] and [Formula: see text] run over an orthogonal basis of [Formula: see text] consisting of normalized cuspidal Hecke eigenforms. Those explicit formulas provide us an arithmetic expression of the rightmost critical value of the individual triple product [Formula: see text]-functions.
Highly Improved Photocatalytic Activity of Magnetically Separable Ferroferric Oxide@Zinc Oxide/Carbon Nitride Nanocomposites and Interface Mechanism
Authors: Shi D. S. , Hong B. , Mao J. N. , Song S. Y. , Li C. X. , Xu J. C. , Han Y. B. , Jin H. X. , Peng X. L. , Gong J. , Ge H. L. , Wang X. Q.
Published: December 2021

To develop an efficient and recyclable photocatalyst, ternary magnetic Fe3O4@ZnO/g-C3N4 nanocomposites were synthesized for the photodegradation of methylene blue (MB). The microstructures, magnetic response and photocatalytic activity of the as-prepared nanocomposites were characterized by using X-ray diffraction (XRD), transmission electron microscopy (TEM), vibrating sample magnetometer (VSM), N2 adsorption–desorption isotherms and spectrophotometer. All results indicate that ZnO nanoparticles anchor on the surface of Fe3O4 nanoparticles and Fe3O4@ZnO exists on the surface of g-C3N4 to form Fe3O4@ZnO/g-C3N4 nanocomposites. The photocatalytic activity to MB of Fe3O4@ZnO/g-C3N4 nanocomposites is significantly higher than those of pristine g-C3N4 and Fe3O4@ZnO. Owing to the heterojunctions between the interface of g-C3N4 and ZnO, the high separation efficiency of the photogenerated electrons and holes increases the radicals [Formula: see text]OH and [Formula: see text]O[Formula: see text] to photodegrade MB. Fe3O4@ZnO/g-C3N4 (20%) presents the highest MB removal of 93.74% and could be easily separated from solution with magnetic separation method.
Some new semiring structures
Authors: Ahmed Kashif , Pal Saibal , Mohan Radha
Published: December 2021

In this paper, we introduce some new semiring structures by considering the prime factors, sum of divisors and the number of relatively prime positive divisors of a pair of non-negative integers and solve a variety of system of linear equations over one of these semirings. We then discuss the properties of these structures and how they compare with classical algebra. The paper is concluded by providing a key-exchange scheme using one of the newly discovered semirings.
Isometries of the product of composition operators on weighted Bergman space
Authors: Gupta Anuradha , Yadav Geeta
Published: December 2021

In this paper, the necessary and sufficient conditions for the product of composition operators to be isometry are obtained on weighted Bergman space. With the help of a counter example we also proved that unlike on [Formula: see text] and [Formula: see text] the composition operator on [Formula: see text] induced by an analytic self-map on [Formula: see text] with fixed origin need not be of norm one. We have generalized the Schwartz’s [Composition operators on [Formula: see text], thesis, University of Toledo (1969)] well-known result on [Formula: see text] which characterizes the almost multiplicative operator on [Formula: see text]
Artificial intelligence in constructing personalized and accurate feedback systems for students
Authors: Xu Wenzhong , Meng Jun , Raja S. Kanaga Suba , Priya M. Padma , Kiruthiga Devi M.
Published: December 2021

Artificial Intelligence (AI) systems have evolved with digital learning developments to provide thriving soft groups with digital opportunities in response to feedback. When it comes to learning environments, educators’ training feedback is often used as a response recourse. Through the use of final evaluations, students receive feedback that improves their education abilities. To improve academic achievement and explore knowledge in the learning process, this section provides an AI-assisted personalized feedback system (AI-PFS). An individualized feedback system is implemented to learn more about the student’s lack of academic experience interactivity and different collaboration behaviors. According to their benchmark, PFS aims to establish a personalized and reliable feedback process for each class based on their collaborative process and learn analytics modules. It has been proposed to use multi-objective implementations to evaluate students regarding the learning results and teaching methods. With different series of questions sessions for students, AI-PFS has been designed, and the findings showed that it greatly enhances the performance rate of 95.32% with personalized and reasonable predictive.
Barriers and Enablers for Integrating Public Health Cobenefits in Urban Climate Policy
Authors: Negev Maya , Zea-Reyes Leonardo , Caputo Livio , Weinmayr Gudrun , Potter Clive , de Nazelle Audrey
Published: December 2021

Urban climate policy offers a significant opportunity to promote improved public health. The evidence around climate and health cobenefits is growing but has yet to translate into widespread integrated policies. This article presents two systematic reviews: first, looking at quantified cobenefits of urban climate policies, where transportation, land use, and buildings emerge as the most studied sectors; and second, looking at review papers exploring the barriers and enablers for integrating these health cobenefits into urban policies. The latter reveals wide agreement concerning the need to improve the evidence base for cobenefits and consensus about the need for greater political will and leadership on this issue. Systems thinking may offer a way forward to help embrace complexity and integrate health cobenefits into decision making. Knowledge coproduction to bring stakeholders together and advance policy-relevant research for urban health will also be required. Action is needed to bring these two important policy agendas together.
Predictive equations to estimate peak aerobic capacity and peak heart rate in persons with Down syndrome
Authors: Mendonca Goncalo V. , Santos Inês , Fernhall Bo , Baynard Tracy
Published: December 2021

Our data show that it is possible to estimate absolute peak oxygen uptake in persons with Down syndrome using nonexercise variables. Peak heart rate can also be accurately estimated in groups of people with and without Down syndrome. However, because of large limits of agreement, caution is advised if using these equations for individual estimations of peak oxygen uptake or peak heart rate in either population.
Imaging Tool for Predicting Renal Clear Cell Carcinoma Fuhrman Grade: Comparing R.E.N.A.L. Nephrometry Score and CT Texture Analysis
Authors: Sun Ran , Zhao Sheng , Jiang Huijie , Jiang Hao , Dai Yanmei , Zhang Chuzhen , Wang Song
Published: December 2021

Background. Clear cell renal cell carcinoma (ccRCC) is the most common renal malignant tumor. Preoperative imaging boasts advantages in diagnosing and choosing treatment methods for ccRCC. Purpose. This study is aimed at building models based on R.E.N.A.L. nephrometry score (RNS) and CT texture analysis (CTTA) to estimate the Fuhrman grade of ccRCC and comparing the advantages and disadvantages of the two models. Materials and Methods. 143 patients with pathologically confirmed ccRCC were enrolled. All patients were stratified into Fuhrman low‐grade and high‐grade groups with complete CT data and R.E.N.A.L. nephrometry scores. CTTA features were extracted from the ROI delineated at the largest tumor level, and RNS and CTTA features were included in the logistic regression model, respectively. Results. RNS model constructed based on multivariate logistic regression analysis showed that 3 pts for R‐scores, 2 pts for E‐scores, and 3 pts for L‐scores were significant indicators to predict high‐grade ccRCC, the AUC of RNS model was 0.911, and the sensitivity and specificity were 71.11% and 83.67%, respectively. The CTTA‐model confirmed energy, kurtosis, and entropy as independent predictive factors, and the AUC of CTTA model was 0.941, with an optimal sensitivity and specificity of 84.44% and 93.88%. Conclusions. R.E.N.A.L. nephrometry score has a certain provocative effect on the Fuhrman pathological grading of ccRCC. As a potential emerging technology, CTTA is expected to replace R.E.N.A.L. nephrometry score in evaluating patients’ Fuhrman classification, and this approach might become an available method for assisting clinicians in choosing appropriate operation.
Improving Performance of User Pair Using Reconfigurable Intelligent Surfaces
Authors: UmaMaheswari Kaveti , Pogaku Arjun Chakravarthi , Do Dinh-Thuan , Le Anh-Tu , Munochiveyi Munyaradzi
Published: December 2021

With the given scope for new use cases and the demanding needs of future 6th generation (6G) wireless networks, the development of wireless communications looks exciting. The propagation medium has been viewed as a randomly behaving entity between the transmitter and the receiver since traditional wireless technology, degrading the quality of the received signal due to the unpredictable interactions of the broadcast radio waves with the surrounding objects. On the other hand, network operators could now manipulate electromagnetic radiation to remove the negative impacts of natural wireless propagation due to the recent arrival of reconfigurable intelligent surfaces (RIS) in wireless communications. According to recent findings, the RIS mechanism benefits nonorthogonal multiple access (NOMA), which can effectively deliver effective transmissions. For simple design, of RIS‐NOMA system, fixed power allocation scheme for NOMA is required. The main system performance metric, i.e., outage probability, needs to be considered to look at the efficiency and capability of transmission mode relying on RIS and NOMA schemes, motivated by the potential of these developing technologies. As major performance metrics, we derive analytical representations of outage probability, and throughput and an accurate approximation is obtained for the outage probability. Numerical results are conducted to validate the exactness of the theoretical analysis. It is found that increasing the higher number of reflecting elements in the RIS can significantly boost the outage probability performance, and the scenario with only the RIS link is also beneficial. In addition, it is desirable to deploy the RIS‐NOMA since it is indicated that better performance compared with the traditional multiple access technique.
Parallax Barrier for Weakening Vernier Fringe in Naked-Eye LED 3D Display
Authors: Zeng Hong-Ran , Zhao Wu-Xiang
Published: December 2021

In naked-eye LED 3D display, vernier fringe is apparent for a conventional parallax barrier. This paper presents an intended misplaced parallax barrier with discrepant width of Light Translucent Slits (LTSs) to weaken vernier fringe. Because of the wild Black Matrix (BM) of LED display, which causes apparent vernier fringe, we enlarge the width of the LTS and move the slits properly in their periods. This structure increases the periodic difference between the parallax barrier and pixel of the LED display, which can increase the brightness of the diazone of vernier fringe and make it to appear more sparsely. In this way, vernier fringe produced by those two periods is weakened at the condition that no obvious crosstalk of stereoimages is increased. The performances of simulation and experimental display prototype show that the diazone of vernier fringe is faded and obviously sparser in the naked-eye LED 3D display. As a result, vernier fringe of this display is significantly decreased and not visible for viewing.
DFT Calculations for the HONO Elimination Process of CL-20 Conformers
Authors: Liu Lin-Lin , Zhang Tong-Yong , Hu Song-Qi
Published: December 2021

The HONO elimination process is regarded to be an important initial decomposition process of energetic nitramines. Four CL-20 conformers based on the ε-CL-20 were obtained by the optimization at the m062x/cc-pvtz level in this study, and the Transition State (TS) and Intrinsic Reaction Coordinate (IRC) calculations were carried out at the same level. In addition, the rate coefficients and activation energy of the HONO elimination process were evaluated using conventional transition state theory (TST) and canonical variational transition state theory (CVT) with Eckart and small-curvature tunneling (SCT) methods to correct the transmission coefficients for the quantum tunneling effect. The calculation results have shown that the HONO elimination process concerning the nitro groups located on six numbered rings is the hardest to happen, and it seems that the longer distance between nitro groups and the adjacent hydrogen atom would result in the higher barrier energy; the HONO elimination process is most likely to happen for the axial positioning of nitro groups located on five numbered rings and most unlikely to happen for the ones located on six numbered rings; CL-20 II and CL-20 IV conformers are the most unstable one and most stable one concerning the reaction difficulty of the HONO elimination process.
Analytical and Modelling Study on the Spatial Stress Distribution and Failure Process of Disc Specimen in the Brazilian Splitting Test
Authors: Zhang Wei , Guo Wei-yao , Wang Zhi-qi
Published: December 2021

To correctly obtain the spatial stress distribution and failure process of disc specimen in the Brazilian splitting test, an analytical solution of three-dimensional stress is deduced. Then, the effects of height-diameter ratio and clamp radian on the spatial stress distribution and failure process are analyzed and studied combined with numerical modelling. At last, the influence of spatial effect on the tensile strength of disc specimen is discussed. The results show that the cracks firstly generate at the two ends of the specimen in the axial direction and then extend due to the nonuniform distribution of tensile stress. The macrocracks coalescence does not mean the capacity loss of radial bearing. The maximum radial bearing capacity of the disc specimen decreases with the increase of height-diameter ratio due to the spatial effect. The tensile strength obtained by the two-dimensional calculation formula is significantly smaller. Therefore, when the commonly-used height-diameter ratio of 0.5 is used in the Brazilian splitting test, a correction factor

k
=
1.15
−
1.25

is suggested.
MKA: A Scalable Medical Knowledge-Assisted Mechanism for Generative Models on Medical Conversation Tasks
Authors: Liang Ke , Wu Sifan , Gu Jiayi
Published: December 2021

Using natural language processing (NLP) technologies to develop medical chatbots makes the diagnosis of the patient more convenient and efficient, which is a typical application in healthcare AI. Because of its importance, lots of researches have come out. Recently, the neural generative models have shown their impressive ability as the core of chatbot, while it cannot scale well when directly applied to medical conversation due to the lack of medical-specific knowledge. To address the limitation, a scalable medical knowledge-assisted mechanism (MKA) is proposed in this paper. The mechanism is aimed at assisting general neural generative models to achieve better performance on the medical conversation task. The medical-specific knowledge graph is designed within the mechanism, which contains 6 types of medical-related information, including department, drug, check, symptom, disease, and food. Besides, the specific token concatenation policy is defined to effectively inject medical information into the input data. Evaluation of our method is carried out on two typical medical datasets, MedDG and MedDialog-CN. The evaluation results demonstrate that models combined with our mechanism outperform original methods in multiple automatic evaluation metrics. Besides, MKA-BERT-GPT achieves state-of-the-art performance.
Extreme Learning Machine Denoising Algorithm-Based Transvaginal Three-dimensional Ultrasound Imaging on the Diagnosis Efficiency of Intrauterine Adhesions
Authors: He Man , Sun Wan , Sha Naxian
Published: December 2021

The study was intended to eliminate the noise in three-dimensional transvaginal ultrasound (3D-TVS) images and improve the diagnostic accuracy in intrauterine adhesion (IUA). The extreme learning machine (ELM) algorithm was introduced first for statement. One hundred and thirty cases of suspected IUA patients were taken as the research subjects. The denoising effects of ELM algorithm were evaluated in terms of mean square errors (MSE), peak signal-to-noise ratio (PSNR), and running time, and its diagnostic efficiency of IUA was identified from precise, specificity, and sensitivity. Furthermore, the support vector machine (SVM) algorithm was introduced for comparison. It was found that the MSE and PSNR of the ELM algorithm were 0.0021 and 64.5, respectively, and its average operation time was 11.22 ± 0.89s, that the MSE values of SVM algorithm and ELM algorithm were 0.0045 and 0.0021 and the PSNR values were 52.3 and 64.5, respectively, and that the average running time of SVM algorithm was 16.35 ± 1.33s, and the average running time of ELM algorithm was 11.22 ± 0.89s, superior to the SVM algorithm in denoising effects. Moreover, the ELM algorithm showed excellent diagnostic efficiency for patients with various degrees of IUA. In conclusion, ELM can effectively eliminate noise in 3D-TVS images and demonstrates excellent diagnostic efficiency on IUA, which is worthy of clinical application.
Parallel Cleaning Algorithm for Similar Duplicate Chinese Data Based on BERT
Authors: Li Biqiu , Wang Jiabin , Liu Xueli
Published: December 2021

Data is an important source of knowledge discovery, but the existence of similar duplicate data not only increases the redundancy of the database but also affects the subsequent data mining work. Cleaning similar duplicate data is helpful to improve work efficiency. Based on the complexity of the Chinese language and the bottleneck of the single machine system to large-scale data computing performance, this paper proposes a Chinese data cleaning method that combines the BERT model and a k-means clustering algorithm and gives a parallel implementation scheme of the algorithm. In the process of text to vector, the position vector is introduced to obtain the context features of words, and the vector is dynamically adjusted according to the semantics so that the polysemous words can obtain different vector representations in different contexts. At the same time, the parallel implementation of the process is designed based on Hadoop. After that, k-means clustering algorithm is used to cluster similar duplicate data to achieve the purpose of cleaning. Experimental results on a variety of data sets show that the parallel cleaning algorithm proposed in this paper not only has good speedup and scalability but also improves the precision and recall of similar duplicate data cleaning, which will be of great significance for subsequent data mining.
Evaluation of Input-Output Efficiency of Sports Industry Based on SWOT-PEST Model
Authors: Dong Huan , Liu Zhiguo , Kong Kai , Li Tong , Ma Qianli
Published: December 2021

This article takes the macro-environmental sports industry as the research object. Firstly, based on the development and characteristics of the sports industry, the article uses the principle of data envelopment analysis to analyze its input and output efficiency and finds out its current problems. In addition, the SWOT analysis method is used to conduct a comprehensive analysis of the external and internal environment influencing the efficiency of the sports industry and propose corresponding development countermeasures. Through the comparison of efficiency evaluation methods, the data envelopment analysis method is proposed to evaluate SWOT efficiency. Secondly, on the basis of pointing out the guiding ideology and basic principles of the evaluation system, four input indicators and four output indicators are specifically selected to form the SWOT input-output indicator system. Thirdly, we introduce the PEST model, focusing on its extended BCC model and use it to make empirical analysis of the SWOT efficiency of regions and provinces. The analysis results show that the efficiency of regional SWOT is not optimal and that there are problems such as input redundancy and insufficient output. Through further analysis, it is concluded that the main reason for the inefficiency of regional SWOT is the unreasonable SWOT input structure and insufficient output value of high-tech industries. We use the analytical framework of this method to clarify the current macro-competitive environment of the sports industry, and use the constructed index system to determine whether the sports industry environment in a region is good or bad. In the selection of indicators, we must carefully analyze their connotations, so that the evaluation index system can accurately evaluate the region’s sports industry environment.
Optimal Allocation of Shared Manufacturing Resources Based on Bilevel Programming
Authors: Liu Peng , Liu Caiyun , Wei Xiaoling
Published: December 2021

In the shared manufacturing environment, on the basis of in-depth analysis of the shared manufacturing process and the allocation process of manufacturing resources, a bilevel programming model for the optimal allocation of manufacturing resources considering the benefits of the shared manufacturing platform and the rights of consumers is established. In the bilevel programming model, the flexible indicators representing the interests of the platform are the upper-level optimization target of the model and the Quality of Service (QoS) indicators representing the interests of consumers are the lower-level optimization goal. The weights of the upper indicators are determined by Analytic Hierarchy Process (AHP) and Improved Order Relation Analysis (Improved G1) combination weighting method and the bilevel programming model is solved by the Improved Fast Elitist Non-Dominated Sorting Genetic Algorithm (Improved NSGA-II). Finally, the effectiveness of the model is validated by a numerical example.
Improving the Stability of the Passenger Vehicle by Using an Active Stabilizer Bar Controlled by the Fuzzy Method
Authors: Nguyen Tuan Anh
Published: December 2021

The rollover phenomenon is a particularly dangerous problem. This phenomenon occurs when the driver travels at high speed and suddenly steers. Under the influence of centrifugal force, the body vehicle will be tilted and cause the wheels to lift off the road. To solve this problem, the method of using an active stabilizer bar has been proposed. The active stabilizer bar is controlled automatically by a previously designed controller. The performance of the active stabilizer bar depends on the selected control method. Previous research often only used a half‐car dynamics model combined with a linear single‐track dynamics model to simulate the vehicle’s oscillation. In addition, most of the research focuses only on the use of linear control methods for the active stabilizer bar. Therefore, the performance of the stabilizer bar is not guaranteed. This paper focuses on establishing the model of spatial dynamics combined with the nonlinear double‐track dynamics model that fully describes the vehicle’s oscillation most accurately. Besides, the fuzzy control method is proposed to control the operation of the hydraulic stabilizer bar. This is a completely novel model, and it is suitable for the actual traveling conditions of the vehicle. Also, simulations are done based on different scenarios. The results of the paper showed that the values of the roll angle, the difference in the vertical force at the wheels, and the displacement of the unsprung mass were significantly reduced when the vehicle used the active stabilizer bar, which is controlled by an intelligent control method. Therefore, the stability and safety of the vehicle have been guaranteed. This result will be the basis for performing other more complex research in the future.
An Adrift and Homeless Cyst within the Peritoneal Cavity
Authors: Mehri Ali , Ayati afin Aida , Gharib Masoumeh , Etezadpour Mohammad
Published: December 2021

Introduction. Echinococcosis is a zoonotic infection caused by Echinococcus species. Iran is endemic for Echinococcus granulosus. Here, we present a case of hydatidosis in an 85-year-old man, presented with acute, dull, constant, and generalized abdominal pain. A computed tomography scan (CT scan) showed an intact hydatid cyst on the bladder dome and several hydatid cysts in the liver. Open surgery revealed a cyst with hepatic origin, confirmed with histopathological studies. Conclusion. Although a primary abdominal hydatid cyst is very rare, it can be transferred to the abdominal cavity without any rupture as secondary ones. In this case, rupture of the liver wall was the reason for this transfer. As a result, there should be a suspicion of hydatidosis in a patient with a similar presentation.
The Impact of Fintech on Corporate Technology Innovation Based on Driving Effects, Mechanism Identification, and Heterogeneity Analysis
Authors: Li Jianwei , Li Na , Cheng Xiang
Published: December 2021

Fintech relies on emerging technologies such as artificial intelligence and big data to bring a new business model to the financial system; can this new change promote corporate technological innovation? To explore this question, this paper examines the possible impact mechanism of Fintech on enterprise technological innovation based on the examination of the impact of Fintech on enterprise technological innovation through a panel fixed effects model, using A-share listed enterprises in Shanghai and Shenzhen markets in China from 2011–2019, and further explores whether there is heterogeneity in this impact among enterprises with different traits. The results show that Fintech development can significantly promote firm technological innovation and that Fintech can influence firm technological innovation through two mechanisms: alleviating firm financing constraints and providing market opportunities for firms to enhance their profitability. In addition, the driving effect of Fintech on technological innovation is more pronounced in new firms, state-owned enterprises, and nonborrowed and listed firms. Based on the conclusion, it is proposed that the government should enhance certain policy support for Fintech guided by emerging technologies such as artificial intelligence, help Fintech empower the real economy, and at the same time promote the deep integration of Fintech and real enterprises, especially to strengthen the identification of Fintech for new enterprises and encourage state-owned enterprises to implement employee stock ownership system, as well as sound market construction to reduce barriers to listing of high-quality enterprises, so as to improve innovation policy effectiveness and provide a reference for the mitigation of enterprise innovation problems in the new situation.
Analysis of Land Use and Land Cover Changes in Urban Areas Using Remote Sensing: Case of Blantyre City
Authors: Gondwe Jane Ferah , Lin Sun , Munthali Rodger Millar
Published: December 2021

Blantyre City has experienced a wide range of changes in land use and land cover (LULC). This study used Remote Sensing (RS) to detect and quantify LULC changes that occurred in the city throughout a twenty-year study period, using Landsat 7 Enhanced Thematic Mapper (ETM+) images from 1999 and 2010 and Landsat 8 Operational Land Imager (OLI) images from 2019. A supervised classification method using an Artificial Neural Network (ANN) was used to classify and map LULC types. The kappa coefficient and the overall accuracy were used to ascertain the classification accuracy. Using the classified images, a postclassification comparison approach was used to detect LULC changes between 1999 and 2019. The study revealed that built-up land and agricultural land increased in their respective areas by 28.54 km2 (194.81%) and 35.80 km2 (27.16%) with corresponding annual change rates of 1.43 km·year−1 and 1.79 km·year−1. The area of bare land, forest land, herbaceous land, and waterbody, respectively, decreased by 0.05%, 90.52%, 71.67%, and 6.90%. The LULC changes in the study area were attributed to urbanization, population growth, social-economic growth, and climate change. The findings of this study provide information on the changes in LULC and driving factors, which Blantyre City authorities can utilize to develop sustainable development plans.
A Comparative Study of CO2 Emission Forecasting in the Gulf Countries Using Autoregressive Integrated Moving Average, Artificial Neural Network, and Holt-Winters Exponential Smoothing Models
Authors: Alam Teg , AlArjani Ali
Published: December 2021

Forecasting is the process of making predictions based on past and present data, with the most common method being trend analysis. Forecasting models are becoming increasingly crucial in uncovering the intricate linkages between large amounts of imprecise data and uncontrollable variables. The main purpose of this article is to compare CO2 emission forecasts in Gulf countries. In this study, the autoregressive integrated moving average (ARIMA), artificial neural network (ANN), and holt-Winters exponential smoothing (HWES) forecasting models are used to anticipate CO2 emissions in the Gulf countries on an annual basis. This study attempts to predict time series data on CO2 emissions in the Gulf countries using statistical tools. The current analysis relied on secondary data gathered from the United States Energy Information Administration (EIA). The study’s findings show that the ARIMA (1,1,1), Holt-Winters exponential smoothing, ARIMA (1,1,2), and ARIMA (2,1,2) models do not outperform the artificial neural network model in estimating CO2 emissions in the Gulf countries. This study gives information on the current state of CO2 emission forecasts. This study will aid the researcher’s understanding of CO2 emissions forecasts. In addition, government agencies can use the findings of this study to develop strategic plans.
Dynamic Mechanical Characteristics of Impact Rock under the Combined Action of Different Constant Temperatures and Static and Dynamic Loads
Authors: Zhao Enlai , Wang Enyuan , Zang Zesheng , Feng Xiaojun , Shen Rongxi
Published: December 2021

The complex mechanical environment of deep coal and rock masses leads to obvious changes on their dynamic mechanical properties. However, there are few reports on the dynamic mechanical properties of rocks under the combined action of medium temperature (normal temperature ∼100°C) and static and dynamic loads. In this paper, a dynamic load and temperature combined action Hopkinson pressure bar experimental system is used to experimentally study the impact type of a fine sandstone under temperature conditions of 18°C, 40°C, 60°C, 80°C, and 100°C, an axial static load of 3 MPa, a gas chamber pressure of 0.06 MPa, and a constant temperature time of 4 h. The dynamic characteristics of the change law of the fine sandstone and the energy dissipation characteristics of the load process are analyzed, and the characteristic law of the fine sandstone surface response is analyzed using digital image correlation technology. Our results indicate the following. (1) Under conditions in which the other experimental conditions remain unchanged, the dynamic stress‐strain of the fine sandstone presents a bimodal shape with a “rebound” phenomenon. Increasing temperature causes the peak strength of the fine sandstone to increase; however, the relative strength can increase or decrease. The relative increase in the strength is 1.14 MPa (°C) when the temperature increases from 40°C to 60°C, 0.15 MPa (°C) when the temperature increases from 60°C to 80°C, and 0.62 MPa (°C) when the temperature increases from 80°C to 100°C. (2) The digital image correlation results show that, under the action of a dynamic load stress wave, the fine sandstone experiences a displacement vector change on the sample surface; furthermore, under the combined action of the temperature and dynamic and static loads, the fine sandstone experiences macroscopic shear failure. The surface strain in the propagation direction of the stress wave is obviously higher and can even reach values of more than 10 times that of the strain in other directions. (3) From the perspective of energy dissipation, the incident energy, reflected energy, and dissipated energy of the fine sandstone under an impact load have the same change law. After being affected by a dynamic load, the energy rapidly increases to a certain value and then remains relatively stable. The transmitted energy is relatively small and can be approximated as a horizontal line. As the temperature increases, the incident energy, reflected energy, and dissipated energy tend to first decrease and then increase, and most of the incident energy in the fine sandstone is dissipated in the form of reflected waves.
QoE‐Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming
Authors: Liu Jingyao , Feng Guangsheng , Sun Jiayu , Zheng Liying , Wang Huiqiang
Published: December 2021

The popularity of online vehicular video has caused enormous information requests in Internet of vehicles (IoV), which brings huge challenges to cellular networks. To alleviate the pressure of base station (BS), Roadside Units (RSUs) and vehicle peers are introduced to collaboratively provide broadcast services to vehicle requesters where vehicles act as both service providers and service requesters. In this paper, we propose an efficient framework leveraging scalable video coding (SVC) technique to improve quality of experience (QoE) from two perspectives: (1) maximizing the data volume received by all requesters and (2) determining buffer action based on playback fluency and average playback quality. For (1), potential providers cooperate to determine the precached video content and delivery policy with the consideration of vehicular mobility and wireless channel status. If one provider fails, other sources will complement to provide requested content delivery. Therefore, their cooperation can improve the QoE and enhance the service reliability. For (2), according to buffer occupancy status, vehicle requesters manage buffer action whether to buffer new segments or upgrade the enhancement level of unplayed segment. Furthermore, the optimization of the data volume is formulated as an integer nonlinear programming (INLP) problem, which can be converted into some linear integer programming subproblems through McCormick envelope method and Lagrange relaxation. Numerical simulation results show that our algorithm is effective in improving total data throughput and QoE.
Coriolis Force Sliding Mode Control Method for the Rotary Motion of the Central Rigid Body‐Flexible Cantilever Beam System in TBM
Authors: Zhou Chuanlu , Qin Long , Chen Ming , Zhang Jingxiang
Published: December 2021

Beam slab structure is often encountered in a complex tunnel boring machine. Beam slab structure is subject to dynamic load, which is easy to cause fatigue damage and affect its service life. Therefore, it is necessary to control the vibration of this kind of beam slab structure. In this study, the central rigid body‐flexible beam model is established for the rotating beam and plate rotating around the y‐axis. Based on the Hamilton variational principle, the dynamic equation of the central rigid body‐flexible beam system is established, and the dynamic model of the central rigid body‐flexible beam system considering the influence of Coriolis force and centrifugal force is given. The vibration control of the central rigid body‐flexible beam system is studied. The vibration mode of the rotating Euler Bernoulli beam is determined by using the elastic wave and vibration mode theory. The influence of the rotating motion on the beam vibration is analyzed, and the variable structure control law is designed to suppress the beam vibration. Numerical simulation results show that the control method can effectively suppress the first‐order and second‐order vibration of the beam and verify the effectiveness of the control strategy.
Prediction of Acute COPD Exacerbation in the Swiss Multicenter COPD Cohort Study (TOPDOCS) by Clinical Parameters, Medication Use, and Immunological Biomarkers
Authors: Huebner Simona Tabea , Henny Simona , Giezendanner Stéphanie , Brack Thomas , Brutsche Martin , Chhajed Prashant , Clarenbach Christian , Dieterle Thomas , Egli Adrian , Frey Martin , Heijnen Ingmar , Irani Sarosh , Sievi Noriane Andrina , Thurnheer Robert , Trendelenburg Marten , Kohler Malcolm , Leuppi-Taegtmeyer Anne Barbara , Leuppi Joerg Daniel
Published: December 2021

Background and Objective: Whether immunological biomarkers combined with clinical characteristics measured during an exacerbation-free period are predictive of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) frequency and severity is unknown. Method: We measured immunological biomarkers and clinical characteristics in 271 stable chronic obstructive pulmonary disease (COPD) patients (67% male, mean age 63 years) from “The Obstructive Pulmonary Disease Outcomes Cohort of Switzerland” cohort on a single occasion. One-year follow-up data were available for 178 patients. Variables independently associated with AECOPD frequency and severity were identified by multivariable regression analyses. Receiver operating characteristic analysis was used to obtain optimal cutoff levels and measure the area under the curve (AUC) in order to assess if baseline data can be used to predict future AECOPD. Results: Higher number of COPD medications (adjusted incident rate ratio [aIRR] 1.17) and platelet count (aIRR 1.03), and lower FEV1% predicted (aIRR 0.84) and IgG2 (aIRR 0.84) were independently associated with AECOPD frequency in the year before baseline. Optimal cutoff levels for experiencing frequent (>1) AECOPD were ≥3 COPD medications (AUC = 0.72), FEV1 ≤40% predicted (AUC = 0.72), and IgG2 ≤2.6 g/L (AUC = 0.64). The performance of a model using clinical and biomarker parameters to predict future, frequent AECOPD events in the same patients was fair (AUC = 0.78) but not superior to a model using only clinical parameters (AUC = 0.79). The IFN-lambda rs8099917GG-genotype was more prevalent in patients who had severe AECOPD. Conclusions: Clinical and biomarker parameters assessed at a single point in time correlated with the frequency of AECOPD events during the year before and the year after assessment. However, only clinical parameters had fair discriminatory power in identifying patients likely to experience frequent AECOPD.