bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding the burden of interstitial lung disease post-COVID-19: the UK Interstitial Lung Disease-Long COVID Study (UKILD-Long COVID)
Authors: Wild Jim M , Porter Joanna C , Molyneaux Philip L , George Peter M , Stewart Iain , Allen Richard James , Aul Raminder , Baillie John Kenneth , Barratt Shaney L , Beirne Paul , Bianchi Stephen M , Blaikley John F , Brooke Jonathan , Chaudhuri Nazia , Collier Guilhem , Denneny Emma K , Docherty Annemarie , Fabbri Laura , Gibbons Michael A , Gleeson Fergus V , Gooptu Bibek , Hall Ian P , Hanley Neil A , Heightman Melissa , Hillman Toby E , Johnson Simon R , Jones Mark G , Khan Fasihul , Lawson Rod , Mehta Puja , Mitchell Jane A , Platé Manuela , Poinasamy Krisnah , Quint Jennifer K , Rivera-Ortega Pilar , Semple Malcolm , Simpson A John , Smith DJF , Spears Mark , Spencer LIsa G , Stanel Stefan C , Thickett David R , Thompson A A Roger , Walsh Simon LF , Weatherley Nicholas D , Weeks Mark Everard , Wootton Dan G , Brightling Chris E , Chambers Rachel C , Ho Ling-Pei , Jacob Joseph , Piper Hanley Karen , Wain Louise V , Jenkins R Gisli
Published: September 2021

Introduction

The COVID-19 pandemic has led to over 100 million cases worldwide. The UK has had over 4 million cases, 400 000 hospital admissions and 100 000 deaths. Many patients with COVID-19 suffer long-term symptoms, predominantly breathlessness and fatigue whether hospitalised or not. Early data suggest potentially severe long-term consequence of COVID-19 is development of long COVID-19-related interstitial lung disease (LC-ILD).

Methods and analysis

The UK Interstitial Lung Disease Consortium (UKILD) will undertake longitudinal observational studies of patients with suspected ILD following COVID-19. The primary objective is to determine ILD prevalence at 12 months following infection and whether clinically severe infection correlates with severity of ILD. Secondary objectives will determine the clinical, genetic, epigenetic and biochemical factors that determine the trajectory of recovery or progression of ILD. Data will be obtained through linkage to the Post-Hospitalisation COVID platform study and community studies. Additional substudies will conduct deep phenotyping. The Xenon MRI investigation of Alveolar dysfunction Substudy will conduct longitudinal xenon alveolar gas transfer and proton perfusion MRI. The POST COVID-19 interstitial lung DiseasE substudy will conduct clinically indicated bronchoalveolar lavage with matched whole blood sampling. Assessments include exploratory single cell RNA and lung microbiomics analysis, gene expression and epigenetic assessment.

Ethics and dissemination

All contributing studies have been granted appropriate ethical approvals. Results from this study will be disseminated through peer-reviewed journals.

Conclusion

This study will ensure the extent and consequences of LC-ILD are established and enable strategies to mitigate progression of LC-ILD.
Midazolam suppresses ischemia/reperfusion-induced cardiomyocyte apoptosis by inhibiting the JNK/p38 MAPK signaling pathway
Authors: Zhou Weixiao , Cai Dongjiang
Published: September 2021

Myocardial ischemia/reperfusion (I/R) injury causes irreversible injury to the heart, thereby causing acute myocardial infarction. Midazolam is a benzodiazepine commonly utilized in anesthesia and intensive care. Research has indicated that midazolam plays a critical role in many diseases; however, the function of midazolam in myocardial injury induced by I/R still needs further investigation. The infarct size and damage to the heart tissues were examined through 2,3,5-triphenyl tetrazolium chloride (TTC) staining and hematoxylin and eosin staining. The creatine kinase-myocardial band isoenzyme, lactate dehydrogenase, and aspartate aminotransferase levels were tested using commercial kits. Cell apoptosis was determined through TUNEL staining or flow cytometry assays. Bax, Bcl-2, cleaved caspase-3, phospho-38 (p-p38), p38, p-JNK, JNK, extracellular signal-regulated kinases (ERK), and p-ERK expression was examined through Western blot. In our study, midazolam was shown to suppress the infarct size and heart tissue damage and reduce myocardial enzyme leakage in I/R rats. Additionally, midazolam was found to retard cardiomyocyte apoptosis in I/R rats. The JNK/p38 MAPK signaling pathway in I/R rats was inhibited by midazolam. Our findings demonstrated that in hypoxia/reoxygenation (H/R) – mediated H9C2 cells, anisomycin abolished the suppressive effects of midazolam on the JNK/p38 MAPK signaling pathway. Next, exploration discovered that anisomycin abolished the cytoprotective effects of midazolam on H/R-treated H9C2 cell apoptosis. In conclusion, this work demonstrated that midazolam retarded I/R-induced cardiomyocyte apoptosis by inhibiting the JNK/p38 MAPK signaling pathway. These results may provide new insight into the treatment of myocardial I/R injury.
Rapid increase in glyphosate resistance and confirmation of dicamba-resistant kochia (Bassia scoparia) in Manitoba
Authors: Geddes Charles M. , Pittman Mattea M. , Gulden Robert H. , Jones Tammy , Leeson Julia Y. , Sharpe Shaun M. , Shirriff Scott W. , Beckie Hugh J.
Published: September 2021

Increased adoption of crops with stacked traits conferring glyphosate and dicamba resistance, and recent confirmation of kochia [Bassia scoparia (L.) A.J. Scott] biotypes resistant to these herbicides in Alberta and Saskatchewan, warrant surveillance of herbicide-resistant kochia in Manitoba. A randomized–stratified survey of 315 sites in Manitoba was conducted in the fall of 2018. Overall, 58% of the kochia populations tested were glyphosate-resistant, while 1% were dicamba-resistant. This survey documents rapid increase in glyphosate-resistant kochia over a five-year time frame, and also confirms the first cases of kochia in Manitoba with dicamba resistance alone and in combination with glyphosate resistance.
Effect of crop rotation and cropping history on net nitrogen mineralization dynamics of a clay loam soil
Authors: Zhang Bin , Li Jingyi , Drury Craig F. , Woodley Alex L. , Yang Xueming
Published: September 2021

Estimating soil nitrogen (N) mineralization is critical to balance fertilizer N requirements and their environmental impacts. In this study, net N mineralization was examined in soils under different crop rotations with each phase of the rotation present every year with biologically based incubations in 2011 and 2015. Net N mineralization was significantly different among treatments when the current crop was soybean, and the effect was dependent upon the previous crop and the cropping sequence. In particular, net increases in inorganic N were greater when the previous crop was winter wheat with or without red clover than if it were corn, and greater for the first year of soybean compared with the second year for rotations with two consecutive years of soybean in the 2011 incubation. However, cropping history did not influence net soil N mineralization when the current crop was corn, winter wheat, or winter wheat with red clover. In 2015, the presence of red clover in the rotation increased net N mineralization in all phases of the rotation. These results suggest both current and previous crops should be considered when estimating the N supplying capacity (net mineralization) of the soil. Net mineralizable N was found to be significantly correlated with total amino sugars (P < 0.001), glucosamine (P < 0.001), and galactosamine (P = 0.003), which suggests that amino sugars could be used as an indicator of the N supplying capacity of soil.
Thermal production of sexaquarks in heavy-ion collisions
Authors: Blaschke David , Bravina Larissa , Bugaev Kyrill , Farrar Glennys R. , Grinyuk Boris , Ivanytskyi Oleksii , Kabana Sonia , Kuleshov Sergey V. , Potashnikova Irina K. , Sagun Violetta , Taranenko Arkadiy , Vitiuk Oleksandr V. , Zabrodin Evgeny , Zhang Xiaoming , Zhou Daicui
Published: September 2021

We present new results on the thermal production yield of a hypothetical state made of six quarks [Formula: see text] assuming its production in heavy-ion collisions at the CERN Large Hadron Collider (LHC). A state with this quark content and mass low enough to be stable against decay in timescales of the order of the age of the Universe has been hypothesized by one of us (G. Farrar) and has been discussed as a possible dark matter candidate. In this work, we address for the first time the thermal production rate that can be expected for this state in heavy-ion collisions at colliders. For this estimate we use a thermal model which has been shown to describe accurately the production of hadrons and nuclei in heavy-ion collisions at LHC energy. This estimate is of great relevance for sexaquark searches at colliders as well as for its consideration as a dark matter candidate and for the composition of neutron stars.
A successive multi-phase transitions in polycrystalline BaFe2As2: Emergence of C4 phase
Authors: Oner Yildirhan , Boyraz Cihat
Published: September 2021

We report magnetization and resistivity studies on polycrystalline BaFe2As2 prepared by solid-state reaction, in the temperature range of 5–350 K, upto the field of 9 T. Low-field susceptibility exhibits multi-phase transitions with two new magnetic phase transitions beside the well-known transition at [Formula: see text] K from paramagnetic/antiferromagnetic-tetragonal/orthorhombic transitions. The phase at [Formula: see text] K is attributed to the phase transition from antiferromagnetic-orthorhombic (C2-phase) to antiferromagnetic-tetragonal phase (C4-phase), while the phase transition at higher temperatures remains unsolved. Making an analogy to the antiferromagnetic nanosized particles, we suggest that BaFe2As2 consists of smaller but similar nanosized clusters. We have analyzed the magnetization data using the modified Langevin function on the basis of thermally activated induced uncompensated spins (thermoinduced moments). The nanosized clustering in this compound is evidenced by the exchange bias and coercivity stemming from the exchange coupling interactions between weak ferromagnetic bulk magnetization in clusters and spin-glass-like phase interface layers surrounding the clusters. We also observe that annealing enhances the superconductivity, similar to the effect of pressure on the superconductivity.

We find that an exponential term well describes the resistivity of this compound due to magnon-assisted interband electron–phonon scattering between the bands with [Formula: see text] and [Formula: see text] orbitals forming two-hole pockets around the zone center and one electron pocket around the zone corner. We have also obtained the Kadowaki–Woods ratio ([Formula: see text] cm (K mol/mJ)[Formula: see text] and the Sommerfeld–Wilson ratio ([Formula: see text]) for BaFe2As2, both ratios are much larger than those ([Formula: see text]/[Formula: see text] cm (K mol/mJ)2, [Formula: see text]) for Kondo lattice systems, indicating the existence of a weak ferromagnetic correlation between Fe moments. It appears that magnon-mediated pairing is responsible for superconductivity.

Finally, we observe zero resistance at [Formula: see text] K in amorphous BaFe2As2, which gives a new insight into the superconductivity under very high pressure.
Decision Support for Portfolio Management by Information System with Black–Litterman Model
Authors: Stoilov Todor , Stoilova Krasimira , Vladimirov Miroslav
Published: September 2021

An algorithm is derived for the development of portfolio decision-support information service. The algorithm allows being automated evaluations for the definition and solution of portfolio problems. Small set of historical data of asset returns with limited set of assets are used for the portfolio, which is the case for no institutional portfolio manager. The algorithm applies analytical relations for decreasing the computational workload for the estimation of the market parameters due to the limited number of assets. The subjective expert views in the Black–Litterman (BL) model are defined from additional assessment of historical data of the asset returns. The algorithm makes comparisons of the results for active portfolio management from the mean variance (MV) model, the BL one and the equal-weighted investment strategy. Benefits of the algorithm are the usage of small set of historical data and limited number of assets, which are proved in investment rolling horizon.
ENTREMPLOYEES AS A TYPE OF HYBRID ENTREPRENEUR: A THEORETICAL EXPLANATION OF HOW THE ENVIRONMENT SHAPES ENTREPRENEURS
Authors: CHAKUZIRA WELLINGTON , SHAMBARE RICHARD
Published: September 2021

This paper describes a growing but under-documented entrepreneurial phenomenon called entremployees. This is a hybrid form of entrepreneurship, whereby an individual simultaneously pursues a dual career in both formal employment and, based on that employment, entrepreneurship. Typically, in the mainstream literature, entrepreneurship and formal employment are understood as being mutually exclusive phenomena — one either gets a job or engages in entrepreneurship. However, Zimbabwe’s economic downturn in the last two decades has catalysed the growth of entremployeeism. The paper first outlines the characteristics of entremployeeism. Motivating factors including the external environment as push factors are discussed. The theories that beckon entremployeeism as a distinct form of entrepreneurship are presented. Finally, implications for theory and future research conclude the paper.
Phase-Structure in Conditional Simulation of Spatially Varying Ground Motion and Possible Influence on Structural Demand
Authors: Rodda Gopala Krishna , Gurjar Narsiram , Basu Dhiman
Published: September 2021

Recorded ground motion is nonstationary in both intensity and frequency contents. Two methodologies were reported by the authors elsewhere for generating spatially varying ground motion (SVGM), namely, (i) auto-spectral density (ASD)-based framework, and (ii) evolutionary power-spectral density (EPSD)-based framework. While the former framework imparts nonstationarity through a uniform modulation (that accounts for nonstationarity only in intensities), the latter framework accounts for nonstationarity in both intensity and frequency contents. Reported EPSD-based framework was modeled through a decay function and a random component and was investigated only in the context of horizontal ground motion. Reported EPSD-based framework made two strong assumptions that need further investigation: (i) spatial variation of the random component was assumed to be frequency independent; and (ii) phase-structure of the ground excitation simulated around the reference station (with seed motion) was assumed to be same as that of the seed motion. This paper investigates the possible impact of these two assumptions on the simulated SVGM through appropriately revising the framework and introducing the phase-structure accordingly. Possible effects of the phase-structure on structural demand are investigated through an idealized long-span bridge. Revised EPSD-based framework is next assessed against the vertical recordings of SMART1 array along with the auto-spectral density (ASD) framework. Though spectral representation is nearly identical in both the frameworks, the acceleration time series simulated using the revised EPSD-based framework matches the recorded data better when compared with the ASD-based framework. Possible effect of spatially varying vertical ground motion on the seismic design is investigated through the same idealized bridge model. Significant increase in the demand of axial force in piers and mid-span moment in the deck are observed. Although these inferences are contingent on the idealized example considered for illustration, the spatially varying vertical ground motion is expected to contribute significantly to the seismic design of long-span bridges.
An in-situ catalytic-insoluble strategy enabled by sulfurized polyacrylonitrile-based composite cathode for potassium–sulfur batteries
Authors: Yuan Xiaomin , Zhu Bo , Feng Jinkui , Wang Chengguo , Cai Xun , Qin Rongman
Published: September 2021

Owing to the insoluble organosulfur mechanism and stable cycling life, sulfurized polyacrylonitrile (SPAN) developed as a promising cathode material for high-energy potassium–sulfur batteries (KSBs). However, it is yet a major challenge to achieve fast catalytic kinetics and high reversible capacity in SPAN-based cathodes. Here, one-step electrospun SPAN nanofibers embedded with Fe[Formula: see text]Nb[Formula: see text]O metal oxide nanoparticles (FeNb@SPAN) have been successfully developed to construct sulfur electrodes with high electrochemical activity, high sulfur utilization, and high cycling stability. The as-prepared freestanding FeNb@SPAN composite cathode, which featuring interwoven nanofibers with Fe[Formula: see text]Nb[Formula: see text]O nanoparticles homogeneously implanted, possesses high storage space for volume expansion and suppresses polysulfide dissolution during potassiation/depotassiation. Benefiting from its unique structure and composition in electrode design, the FeNb@SPAN cathode is endowed with outstanding energy storage performances with a high initial specific capacity of 776 mAh [Formula: see text] g[Formula: see text] under 50 mA [Formula: see text] g[Formula: see text] and an excellent cycling capability of 201 mAh [Formula: see text] g[Formula: see text] after 80 charge/discharge processes. This work heralds a feasible strategy toward SPAN-based sulfur host materials in the structural design of next-generation high-performance cathode materials for KSBs and other metal–sulfur batteries.
Skeletonization via Local Separators
Authors: Bærentzen Andreas , Rotenberg Eva
Published: September 2021

We propose a new algorithm for curve skeleton computation that differs from previous algorithms by being based on the notion of
local separators
. The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data are initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality.
Collaborative Local-Global Learning for Temporal Action Proposal
Authors: Zhu Yisheng , Han Hu , Liu Guangcan , Liu Qingshan
Published: September 2021

Temporal action proposal generation is an essential and challenging task in video understanding, which aims to locate the temporal intervals that likely contain the actions of interest. Although great progress has been made, the problem is still far from being well solved. In particular, prevalent methods can handle well only the local dependencies (i.e., short-term dependencies) among adjacent frames but are generally powerless in dealing with the global dependencies (i.e., long-term dependencies) between distant frames. To tackle this issue, we propose CLGNet, a novel Collaborative Local-Global Learning Network for temporal action proposal. The majority of CLGNet is an integration of Temporal Convolution Network and Bidirectional Long Short-Term Memory, in which Temporal Convolution Network is responsible for local dependencies while Bidirectional Long Short-Term Memory takes charge of handling the global dependencies. Furthermore, an attention mechanism called the
background suppression module
is designed to guide our model to focus more on the actions. Extensive experiments on two benchmark datasets, THUMOS’14 and ActivityNet-1.3, show that the proposed method can outperform state-of-the-art methods, demonstrating the strong capability of modeling the actions with varying temporal durations.
Free-viewpoint Indoor Neural Relighting from Multi-view Stereo
Authors: Philip Julien , Morgenthaler Sébastien , Gharbi Michaël , Drettakis George
Published: September 2021

We introduce a
neural relighting
algorithm for captured indoors scenes, that allows interactive
free-viewpoint
navigation. Our method allows illumination to be changed synthetically, while coherently rendering cast shadows and complex glossy materials. We start with multiple images of the scene and a three-dimensional mesh obtained by multi-view stereo (MVS) reconstruction. We assume that lighting is well explained as the sum of a view-independent diffuse component and a view-dependent glossy term concentrated around the mirror reflection direction. We design a convolutional network around input feature maps that facilitate learning of an implicit representation of scene materials and illumination, enabling both relighting and free-viewpoint navigation. We generate these input maps by exploiting the best elements of both image-based and physically based rendering. We sample the input views to estimate diffuse scene irradiance, and compute the new illumination caused by user-specified light sources using path tracing. To facilitate the network's understanding of materials and synthesize plausible glossy reflections, we reproject the views and compute
mirror images
. We train the network on a synthetic dataset where each scene is also reconstructed with MVS. We show results of our algorithm relighting real indoor scenes and performing free-viewpoint navigation with complex and realistic glossy reflections, which so far remained out of reach for view-synthesis techniques.
Self-Sampling for Neural Point Cloud Consolidation
Authors: Metzer Gal , Hanocka Rana , Giryes Raja , Cohen-Or Daniel
Published: September 2021

We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud. Unlike other point up-sampling methods which analyze shapes via local patches, in this work, we learn from global subsets. We repeatedly
self-sample
the input point cloud with global subsets that are used to train a deep neural network. Specifically, we define source and target subsets according to the desired consolidation criteria (e.g., generating sharp points or points in sparse regions). The network learns a mapping from source to target subsets, and implicitly learns to consolidate the point cloud. During inference, the network is fed with random subsets of points from the input, which it displaces to synthesize a consolidated point set. We leverage the inductive bias of neural networks to eliminate noise and outliers, a notoriously difficult problem in point cloud consolidation. The
shared
weights of the network are optimized over the entire shape, learning non-local statistics and exploiting the recurrence of local-scale geometries. Specifically, the network encodes the distribution of the underlying shape surface within a fixed set of local kernels, which results in the
best
explanation of the underlying shape surface. We demonstrate the ability to consolidate point sets from a variety of shapes, while eliminating outliers and noise.
dhCM: Dynamic and Hierarchical Event Categorization and Discovery for Social Media Stream
Authors: Guo Jinjin , Gong Zhiguo , Cao Longbing
Published: September 2021

The online event discovery in social media based documents is useful, such as for disaster recognition and intervention. However, the diverse events incrementally identified from social media streams remain accumulated, ad hoc, and unstructured. They cannot assist users in digesting the tremendous amount of information and finding their interested events. Further, most of the existing work is challenged by jointly identifying incremental events and dynamically organizing them in an adaptive hierarchy. To address these problems, this article proposes
d
ynamic and
h
ierarchical
C
ategorization
M
odeling (dhCM) for social media stream. Instead of manually dividing the timeframe, a multimodal event miner exploits a density estimation technique to continuously capture the temporal influence between documents and incrementally identify online events in textual, temporal, and spatial spaces. At the same time, an adaptive categorization hierarchy is formed to automatically organize the documents into proper categories at multiple levels of granularities. In a nonparametric manner, dhCM accommodates the increasing complexity of data streams with automatically growing the categorization hierarchy over adaptive growth. A sequential Monte Carlo algorithm is used for the online inference of the dhCM parameters. Extensive experiments show that dhCM outperforms the state-of-the-art models in terms of term coherence, category abstraction and specialization, hierarchical affinity, and event categorization and discovery accuracy.
Vulnerability Exposure Driven Intelligence in Smart, Circular Cities
Authors: Jarvis Paul-David , Damianou Amalia , Ciobanu Cosmin , Katos Vasilis
Published: September 2021

In this article, we study the vulnerability management dimension in smart city initiatives. As many cities across the globe invest a considerable amount of effort, resources and budget to modernise their infrastructure by deploying a series of technologies such as 5G, Software Defined Networks, and IoT, we conduct an empirical analysis of their current exposure to existing vulnerabilities. We use an updated vulnerability dataset that is further enriched by quantitative research data from independent studies evaluating the maturity and accomplishments of cities in their journey to become smart. We particularly focus on cities that aspire to implement a (data-driven) Circular Economy agenda that we consider to potentially yield the highest risk from a vulnerabilities exposure perspective. Findings show that although a smarter city is attributed with a higher vulnerability exposure, investments on technology and human capital moderate this exposure in a way that it can be reduced.
Regulation of Apelin-13 on Bcl-2 and Caspase-3 and Its Effects on Adipocyte Apoptosis
Authors: Li Zhan , Wang Sha , He Yiwei , Li Qiong , Gao Guoying , Tong Guoxiang
Published: September 2021

Objective. The effects of apelin-13 on the expression of Bcl-2 and caspase-3 factors and the apoptosis of adipocytes were studied at the cellular and animal levels. Methods. 3T3-L1 preadipocytes were cultured and grouped. The third-generation cells were added to the control DMSO solvent and amidation-modified apelin-13. The expression of Bcl-2 and caspase-3 were detected. The cell growth viability and cell apoptosis were detected. DOI model rats were established. The effects of apelin-13 on DOI rat biochemical indicators, the expression of Bcl-2, caspase-3, and cell apoptosis were investigated by injecting amidation-modified apelin-13 through the tail vein. Result. In in vitro experiments, amidation-modified apelin-13 can significantly reduce the growth viability of adipocytes and the expression of Bcl-2, increase the expression of caspase-3, and promote the apoptosis of adipocytes. Animal experiments also show that apelin-13 modified by amidation can adjust the abnormal biochemical indicators of DOI rats, decrease the expression of Bcl-2 in adipose tissue, increase the expression of caspase-3, and promote the apoptosis of adipocytes. Conclusion. Amidation of apelin-13 can promote fat cell apoptosis and reduce the incidence of obesity. The mechanism may be accomplished by inhibiting Bcl-2 and caspase-3 factors. This study helps us understand the effect of apelin-13 on fat cell apoptosis and hopes to provide a basis for the development of antiobesity drugs.
Improved COVID‐19 Outcomes following Statin Therapy: An Updated Systematic Review and Meta‐analysis
Authors: Vahedian-Azimi Amir , Mohammadi Seyede Momeneh , Banach Maciej , Beni Farshad Heidari , Guest Paul C. , Al-Rasadi Khalid , Jamialahmadi Tannaz , Sahebkar Amirhossein
Published: September 2021

Background. Although vaccine rollout for COVID‐19 has been effective in some countries, there is still an urgent need to reduce disease transmission and severity. We recently carried out a meta‐analysis and found that pre‐ and in‐hospital use of statins may improve COVID‐19 mortality outcomes. Here, we provide an updated meta‐analysis in an attempt to validate these results and increase the statistical power of these potentially important findings. Methods. The meta‐analysis investigated the effect of observational and randomized clinical studies on intensive care unit (ICU) admission, tracheal intubation, and death outcomes in COVID‐19 cases involving statin treatment, by searching the scientific literature up to April 23, 2021. Statistical analysis and random effect modeling were performed to assess the combined effects of the updated and previous findings on the outcome measures. Findings. The updated literature search led to the identification of 23 additional studies on statin use in COVID‐19 patients. Analysis of the combined studies (n = 47; 3,238,508 subjects) showed no significant effect of statin treatment on ICU admission and all‐cause mortality but a significant reduction in tracheal intubation (OR = 0.73, 95% CI: 0.54‐0.99, p = 0.04, n = 10 studies). The further analysis showed that death outcomes were significantly reduced in the patients who received statins during hospitalization (OR = 0.54, 95% CI: 0.50‐0.58, p < 0.001, n = 7 studies), with no such effect of statin therapy before hospital admission (OR = 1.06, 95% CI = 0.82‐1.37, p = 0.670, n = 29 studies). Conclusion. Taken together, this updated meta‐analysis extends and confirms the findings of our previous study, suggesting that in‐hospital statin use leads to significant reduction of all‐cause mortality in COVID‐19 cases. Considering these results, statin therapy during hospitalization, while indicated, should be recommended.
Evaluating Knowledge, Attitude, and Behavior of Dentists on HIV/AIDS in West Java, Indonesia, in the COVID-19 Era
Authors: Sufiawati Irna , Rafi Muhammad Arib , Putri Fidya Meditia
Published: September 2021

Purpose. HIV/AIDS is still a serious public health problem in Indonesia. It has been concerned that the coronavirus disease 2019 (COVID-19) pandemic has a serious impact on people living with HIV (PLWH). Therefore, dentists remain to have a significant role to play in the overall healthcare delivery to PLWH and reducing new HIV infections. The purpose of this study was to assess the knowledge, attitudes, and behavior of dentists in West Java about HIV/AIDS in the COVID-19 era. Methods. This cross-sectional study was conducted by distributing online questionnaires using a Google form to dentists in West Java. We used the purposive sampling technique to recruit the participants. The questionnaire consists of characteristics of respondents, 13 questions about knowledge, 9 questions about attitudes, and 6 questions about behaviors, which has previously been tested for validity and reliability. Data were analyzed using the Spearman correlation test and the Chi-square test. Results. The Questionnaire was sent to 435 dentists in the West Java region, Indonesia, with the assistance of the Indonesian Dental Association West Java region, to participate in this study. We received completed questionnaires from 209 (48%) respondents. The results of this study showed that 44% of dentists have good knowledge, 53% have a positive attitude, and 53% have positive behavior. No significant relationship was found between knowledge and attitudes (

p
=
0.202

) and behavior (

p
=
0.087

), but there was a significant relationship between attitudes and behavior (

p
=
0.0001

). Conclusion. About half of the dentists in West Java have good knowledge, positive attitudes, and behavior towards HIV/AIDS, but the others lack knowledge, negative attitudes, and behavior. Continuing HIV/AIDS education and training programs for dentists are still needed to keep improving their knowledge and awareness to support disease prevention and control in this COVID-19 era.
Multicriteria Decision‐Making Approach for Aggregation Operators of Pythagorean Fuzzy Hypersoft Sets
Authors: Siddique Imran , Zulqarnain Rana Muhammad , Ali Rifaqat , Jarad Fahd , Iampan Aiyared
Published: September 2021

The Pythagorean fuzzy hypersoft set (PFHSS) is the most advanced extension of the intuitionistic fuzzy hypersoft set (IFHSS) and a suitable extension of the Pythagorean fuzzy soft set. In it, we discuss the parameterized family that contracts with the multi‐subattributes of the parameters. The PFHSS is used to correctly assess insufficiencies, anxiety, and hesitancy in decision‐making (DM). It is the most substantial notion for relating fuzzy data in the DM procedure, which can accommodate more uncertainty compared to available techniques considering membership and nonmembership values of each subattribute of given parameters. In this paper, we will present the operational laws for Pythagorean fuzzy hypersoft numbers (PFHSNs) and also some fundamental properties such as idempotency, boundedness, shift‐invariance, and homogeneity for Pythagorean fuzzy hypersoft weighted average (PFHSWA) and Pythagorean fuzzy hypersoft weighted geometric (PFHSWG) operators. Furthermore, a novel multicriteria decision‐making (MCDM) approach has been established utilizing presented aggregation operators (AOs) to resolve decision‐making complications. To validate the useability and pragmatism of the settled technique, a brief comparative analysis has been conducted with some existing approaches.
Expression Level, Correlation, and Diagnostic Value of Serum miR-127 in Patients with Acute Respiratory Distress Syndrome
Authors: Lin Hui , Jiang Lingxiang , Ren Yiqun , Sheng Fen , Wang Luxi , Zhang Sujiang
Published: September 2021

Objective. To analyze the expression of miR-127 in the serum of patients with acute respiratory distress syndrome (ARDS) and to explore its correlation with the severity of ARDS patients and its value as a molecular marker for diagnosis of ARDS. Methods. 70 patients with ARDS admitted to our hospital from September 2017 to September 2019 were selected as the observation group, and 60 healthy persons with physical examination were collected as the control group. RT-PCR was used to detect the serum miR-127 levels of all subjects, and the serum miR-127 levels of the observation group and control group were compared. The oxygenation index (PaO2/FiO2) of ARDS patients was recorded and divided into three subgroups: mild group, moderate group, and severe group. Serum miR-127 levels of patients in the mild group, moderate group, and severe group were compared. Pearson correlation was used to analyze the relationship between serum miR-127 levels and the severity of ARDS patients. The receiver operating characteristic curve (ROC) was drawn, and the area under the ROC curve (AUC) was used to evaluate the diagnostic value of miR-127 in patients with ARDS. Results. The serum level of miR-127 (10.15 ± 1.03) in the observation group was significantly higher than that in the control group (3.09 ± 0.62). And in the three subgroups of mild, moderate, and severe, the serum miR-127 level in the moderate group (10.43 ± 0.71) and the severe group miR-127 level (11.05 ± 1.26) were significantly higher than those in the mild group level (9.38 ± 1.24). Pearson correlation analysis showed that the serum miR-127 level was negatively correlated with PaO2/FiO2 (r = −0.715,

P
<
0.05

), that is, the serum miR-127 level was positively correlated with the severity of ARDS patients. The area under the curve (AUC) of the diagnostic value of serum miR-127 for ARDS was 0.732 (95% CI 0.607–0.858). When the optimal cutoff value was 0.380, the sensitivity was 59.1% and the specificity was 78.6%, which suggested that miR-127 can be used as a marker for ARDS diagnosis. Conclusion. There is an increase in miR-127 levels in the serum of ARDS patients. The serum miR-127 level is positively correlated with the severity of ARDS. The higher the level of miR-127, the worse the condition of ARDS, which is positively correlated with the severity of the condition. It suggests that the serum miR-127 level is an important indicator for evaluating the severity of ARDS patients. It can be used as a molecular marker for clinical diagnosis of ARDS.
FPGA Realization and Lyapunov–Krasovskii Analysis for a Master-Slave Synchronization Scheme Involving Chaotic Systems and Time-Delay Neural Networks
Authors: Perez-Padron J. , Posadas-Castillo C. , Paz-Perez J. , Zambrano-Serrano E. , Platas-Garza M. A.
Published: September 2021

In this paper, the trajectory tracking control and the field programmable gate array (FPGA) implementation between a recurrent neural network with time delay and a chaotic system are presented. The tracking error is globally asymptotically stabilized by means of a control law generated from the Lyapunov–Krasovskii and Lur’e theory. The applicability of the approach is illustrated by considering two different chaotic systems: Liu chaotic system and Genesio–Tesi chaotic system. The numerical results have shown the effectiveness of obtained theoretical results. Finally, the theoretical results are implemented on an FPGA, confirming the feasibility of the synchronization scheme and showing that it is hardware realizable.
BEI‐TAB: Enabling Secure and Distributed Airport Baggage Tracking with Hybrid Blockchain‐Edge System
Authors: Si Pengbo , Wang Fei , Sun Enchang , Su Yuzhao
Published: September 2021

Global air transport carries about 4.3 billion pieces of baggage each year, and up to 56 percent of travellers prefer obtaining real‐time baggage tracking information throughout their trip. However, the traditional baggage tracking scheme is generally based on optical scanning and centralized storage systems, which suffers from low efficiency and information leakage. In this paper, a blockchain and edge computing‐based Internet of Things (IoT) system for tracking of airport baggage (BEI‐TAB) is proposed. Through the combination of radio frequency identification technology (RFID) and blockchain, real‐time baggage processing information is automatically stored in blockchain. In addition, we deploy Interplanetary File System (IPFS) at edge nodes with ciphertext policy attribute‐based encryption (CP‐ABE) to store basic baggage information. Only hash values returned by the IPFS network are kept in blockchain, enhancing the scalability of the system. Furthermore, a multichannel scheme is designed to realize the physical isolation of data and to rapidly process multiple types of data and business requirements in parallel. To the best of our knowledge, it is the first architecture that integrates RFID, IPFS, and CP‐ABE with blockchain technologies to facilitate secure, decentralized, and real‐time characteristics for storing and sharing data for baggage tracking. We have deployed a testbed with both software and hardware to evaluate the proposed system, considering the performances of transaction processing time and speed. In addition, based on the characteristics of consortium blockchain, we improved the practical Byzantine fault tolerance (PBFT) consensus protocol, which introduced the node credit score mechanism and cooperated with the simplified consistency protocol. Experimental results show that the credit score‐based PBFT consensus (CSPBFT) can shorten transaction delay and improve the long‐term running efficiency of the system.
Fuzzy Plastic Constitutive Model and Its Application to Subgrade
Authors: Wang Xigang , Jin Liling , Xing Yang , Fu Mingfu
Published: September 2021

The subgrade of a road is subjected to cyclic loading and unloading under the action of traffic loads. To study this mechanical response, the plastic membership function was introduced into the modified Cambridge model, and thus, the fuzzy plastic Cambridge constitutive model was obtained. With the continuous evolution of the plastic membership function from 0 to 1, the fuzzy plastic Cambridge constitutive model continuously transitions the plastic properties inside and outside the initial yield surface. The evolution of the plastic membership function can replace the complex hardening law. The reliability of the model was verified using triaxial tests and simple cyclic loading and unloading tests. Using the finite element method, the mechanical response of the subgrade under cyclic loading was calculated. The fuzzy plastic Cambridge model can explain the insignificant yield characteristics of geotechnical media. In the fuzzy plasticity theory, the stress inside and outside the initial yield surface can produce plasticity. Absolute plasticity was not observed; only different degrees of plasticity existed.
Image Classification Algorithm Based on Big Data and Multilabel Learning of Improved Convolutional Neural Network
Authors: Chang Haibin , Cui Ying
Published: September 2021

More and more image materials are used in various industries these days. Therefore, how to collect useful images from a large set has become an urgent priority. Convolutional neural networks (CNN) have achieved good results in certain image classification tasks, but there are still problems such as poor classification ability, low accuracy, and slow convergence speed. This article mainly introduces the image classification algorithm (ICA) research based on the multilabel learning of the improved convolutional neural network and some improvement ideas for the research of the ICA based on the multilabel learning of the convolutional neural network. This paper proposes an ICA research method based on multilabel learning of improved convolutional neural networks, including the image classification process, convolutional network algorithm, and multilabel learning algorithm. The conclusions show that the average maximum classification accuracy of the improved CNN in this paper is 90.63%, and the performance is better, which is beneficial to improving the efficiency of image classification. The improved CNN network structure has reached the highest accuracy rate of 91.47% on the CIFAR‐10 data set, which is much higher than the traditional CNN algorithm.
An Adaptive Parallel Arithmetic Optimization Algorithm for Robot Path Planning
Authors: Wang Ruo-Bin , Wang Wei-Feng , Xu Lin , Pan Jeng-Shyang , Chu Shu-Chuan
Published: September 2021

Path planning is one of the hotspots in the research of automotive engineering. Aiming at the issue of robot path planning with the goal of finding a collision-free optimal motion path in an environment with barriers, this study proposes an adaptive parallel arithmetic optimization algorithm (APAOA) with a novel parallel communication strategy. Comparisons with other popular algorithms on 18 benchmark functions are committed. Experimental results show that the proposed algorithm performs better in terms of solution accuracy and convergence speed, and the proposed strategy can prevent the algorithm from falling into a local optimal solution. Finally, we apply APAOA to solve the problem of robot path planning.
On the Fractional Metric Dimension of Convex Polytopes
Authors: Aslam M. K. , Javaid Muhammad , Zhu Q. , Raheem Abdul
Published: September 2021

In order to identify the basic structural properties of a network such as connectedness, centrality, modularity, accessibility, clustering, vulnerability, and robustness, we need distance-based parameters. A number of tools like these help computer and chemical scientists to resolve the issues of informational and chemical structures. In this way, the related branches of aforementioned sciences are also benefited with these tools as well. In this paper, we are going to study a symmetric class of networks called convex polytopes for the upper and lower bounds of fractional metric dimension (FMD), where FMD is a latest developed mathematical technique depending on the graph-theoretic parameter of distance. Apart from that, we also have improved the lower bound of FMD from unity for all the arbitrary connected networks in its general form.
Structural Dynamic Response of a Shield Tunnel under Aircraft Taxiing Load
Authors: Luo Zhanyou , Xie Kuangqin , Zou Baoping , Jiang Mingyao , Zheng Wei
Published: September 2021

Dynamic load is an important factor affecting the safety and stability of subway tunnel structures. To obtain the variation law of shield tunnel structure dynamic response under aircraft taxiing load, a three‐dimensional numerical simulation of such tunnel under the action of aircraft taxiing load is performed on the basis of a project involving a shield tunnel orthogonal underpass taxiway. The effects of sliding speed and tunnel depth on the structure of the shield tunnel are also analyzed. The results show that the transverse displacement and acceleration response of tunnel segment exhibit evident time‐space effect under the action of aircraft taxiing load. The transverse displacement and arch waist acceleration of the shield segment increase first and then decrease. The transverse displacement of the arch waist reaches its maximum when the aircraft taxis directly above the tunnel. The sliding speed exhibits an evident influence on the dynamic response of shield tunnel structure. The vertical and convergence displacements of tunnel segments increase with the increase in sliding speed. The dynamic response of tunnel structure is significantly affected by the factors of tunnel buried depth. The vertical and convergence displacements of tunnel segments decrease with the increase in tunnel buried depth. Therefore, the safety of the shield tunnel structure can be ensured by controlling the taxiing speed when the aircraft taxis directly above the tunnel. The measures of increasing the buried depth of the tunnel or strengthening the tunnel structure need to be considered when the taxiing speed is large.
A Comparative Study of the Impact of the Stirrer Design in the Stir Casting Route to Produce Metal Matrix Composites
Authors: Muhammad Farooq , Jalal Shawnam
Published: September 2021

Aluminum matrix composites are widely utilized in many sectors, and their popularity is rising due to their ability to combine high mechanical characteristics with their lightweight. Stir casting is typically achieved in a closed crucible with an invisible flow pattern to produce aluminum alloy matrix composites. Researchers employed a hybrid method to optimize the stir casting parameters. The vast number of parameters and their overlap affects the uniform distribution of reinforcement particles. Investigators on their way to the best technique have gotten promising outcomes in their specific situations, but they still need more work to be able to generalize their findings to optimize the stirrer design to get efficient mixing. Due to an experimental technique alone is insufficient for optimizing stir casting parameters, researchers combined theoretical, experimental, statistical, and numerical simulation approaches to get more precise and reliable findings. The design of the experiment (DOE), particularly Taguchi, and other standard statistics such as ANOVA and regression were discovered to be the most often utilized statistical contributions. Recent attempts to simulate stir casting have begun to match the experimental or analog model data by developed numerical software and analytical analysis. Finally, previous study results and suggestions were collected and compared, arranged, revised, and presented simply about the proper stirrer design, stages, and position in that to make the paper unique.