bluuebunny/crossref_metadata_2025 huggingface dataset:
Retrospective observational study of the robustness of provider network structures to the systemic shock of COVID-19: a county level analysis of COVID-19 outcomes
Authors: Linde Sebastian , Egede Leonard E
Published: May 2022

Objective

To evaluate whether certain healthcare provider network structures are more robust to systemic shocks such as those presented by the current COVID-19 pandemic.

Design

Using multivariable regression analysis, we measure the effect that provider network structure, derived from Medicare patient sharing data, has on county level COVID-19 outcomes (across mortality and case rates). Our adjusted analysis includes county level socioeconomic and demographic controls, state fixed effects, and uses lagged network measures in order to address concerns of reverse causality.

Setting

US county level COVID-19 population outcomes by 3 September 2020.

Participants

Healthcare provider patient sharing network statistics were measured at the county level (with n=2541–2573 counties, depending on the network measure used).

Primary and secondary outcome measures

COVID-19 mortality rate at the population level, COVID-19 mortality rate at the case level and the COVID-19 positive case rate.

Results

We find that provider network structures where primary care physicians (PCPs) are relatively central, or that have greater betweenness or eigenvector centralisation, are associated with lower county level COVID-19 death rates. For the adjusted analysis, our results show that increasing either the relative centrality of PCPs (p value<0.05), or the network centralisation (p value<0.05 or p value<0.01), by 1 SD is associated with a COVID-19 death reduction of 1.0–1.8 per 100 000 individuals (or a death rate reduction of 2.7%–5.0%). We also find some suggestive evidence of an association between provider network structure and COVID-19 case rates.

Conclusions

Provider network structures with greater relative centrality for PCPs when compared with other providers appear more robust to the systemic shock of COVID-19, as do network structures with greater betweenness and eigenvector centralisation. These findings suggest that how we organise our health systems may affect our ability to respond to systemic shocks such as the COVID-19 pandemic.
A theory of tensor products of modules for 𝒮-local vertex operator algebra M(n,V )
Authors: Du Fang , Wang Hao
Published: May 2022

In this paper, we introduce the notions of intertwining operator and tensor product for [Formula: see text]-modules, where [Formula: see text] is a vertex operator algebra, and prove that the tensor product is commutative and associative.
Automated Fake News Detection by LSTM Enabled with Optimal Feature Selection
Authors: Hannah Nithya S. , Sahayadhas Arun
Published: May 2022

Fake news plays a major role by broadcasting misinformation, which influences people’s knowledge or perceptions and distorts their decision-making and awareness. Online forums and social media have stimulated the broadcast of fake news by embedding it with truthful information. Thus, fake news has evolved into the main challenge of better impact in the information-driven community for intense fakesters. The detection of fake news articles that is generally found by considering the quality of the information in their news feeds under uncertain authenticity calls for automated tools. However, designing such tools is a major problem because of the multiple faces of fakesters. This paper offers a new text-analytics-driven method for detecting fake news to reduce the risks impacted by the consumption of fake news. The methodology for improved fake news detection focusses on four phases: (a) pre-processing, (b) feature extraction, (c) optimal feature selection and (d) classification. The pre-processing of the text data will be initially done by stop word removal, blank space removal and stemming. Further, the feature extraction is performed by term frequency-inverse document frequency, and grammatical analysis is done using mean, Q25, Q50, Q75, Max, Min and standard deviation. Then, the optimal feature selection is developed, which minimises the number of input variables. It is intended to reduce the number of input variables to improve the model’s performance by minimising the computational cost of modelling. An improved meta-heuristic algorithm called successive position-based barnacles mating optimisation is used for optimal feature selection and classification. As the main contribution, the influence of deep learning is employed, which employs optimised long short-term memory. Finally, the result shows the superiority in terms of different significant measures by the proposed model over other methods for fake news detection experimentally done on a publicly available benchmark dataset.
A Unified Model Using Distantly Supervised Data and Cross-Domain Data in NER
Authors: Hu Yun , He Hao , Chen Zhengfei , Zhu Qingmeng , Zheng Changwen
Published: May 2022

Named entity recognition (NER) systems are often realized by supervised methods that require large hand-annotated data. When the hand-annotated data is limited, distantly supervised (DS) data and cross-domain (CD) data are usually used separately to improve the performance. The distantly supervised data can provide in-domain dictionary information, and the hand-annotated cross-domain information can be provided by cross-domain data. These two types of information are complemental. However, there are two problems required to be solved before using directly. First, the distantly supervised data may contain a lot of noise. Second, directly using cross-domain data may degrade performance due to the distribution mismatching problem. In this paper, we propose a unified model named PARE (PArtial learning and REinforcement learning). The PARE model can simultaneously use distantly supervised data and cross-domain data as external data. The model uses the partial learning method with a new label strategy to better handle the noise in distantly supervised data. The reinforcement learning method is used to alleviate the distribution mismatching problem in cross-domain data. Experiments in three datasets show that our model outperforms other baseline models. Besides, our model can be used in the situation where no hand-annotated in-domain data is provided.
Laser-Induced Fluorescence Studies on Some Edible Oils and Aromatic Frankincense Oil Excited by Blue and Violet Diode Lasers at 447 nm and 405 nm
Authors: Abedin Kazi Monowar
Published: May 2022

Laser-induced fluorescence (LIF) of certain edible oils (olive, mustard, sunflower, corn, sesame, peanut, rice bran, and flaxseed) and one unique aromatic oil (frankincense oil) have been studied using compact blue and violet lasers operating at 447 nm and 405 nm, respectively. LIF studies excited at these wavelengths have not been performed before, to our knowledge. The various features of the obtained spectra and their possible molecular origins have been discussed. The presence of vitamin E has been confirmed in corn, rice bran, peanut, sunflower, and frankincense oils, and the possible origin of the double peaks in the red region for olive oil has been explained.
[Retracted] Training a Feedforward Neural Network Using Hybrid Gravitational Search Algorithm with Dynamic Multiswarm Particle Swarm Optimization
Authors: Nagra Arfan Ali , Alyas Tahir , Hamid Muhammad , Tabassum Nadia , Ahmad Aqeel
Published: May 2022

One of the most well‐known methods for solving real‐world and complex optimization problems is the gravitational search algorithm (GSA). The gravitational search technique suffers from a sluggish convergence rate and weak local search capabilities while solving complicated optimization problems. A unique hybrid population‐based strategy is designed to tackle the problem by combining dynamic multiswarm particle swarm optimization with gravitational search algorithm (GSADMSPSO). In this manuscript, GSADMSPSO is used as novel training techniques for Feedforward Neural Networks (FNNs) in order to test the algorithm’s efficiency in decreasing the issues of local minima trapping and existing evolutionary learning methods’ poor convergence rate. A novel method GSADMSPSO distributes the primary population of masses into smaller subswarms, according to the proposed algorithm, and also stabilizes them by offering a new neighborhood plan. At this time, each agent (particle) increases its position and velocity by using the suggested algorithm’s global search capability. The fundamental concept is to combine GSA’s ability with DMSPSO’s to improve the performance of a given algorithm’s exploration and exploitation. The suggested algorithm’s performance on a range of well‐known benchmark test functions, GSA, and its variations is compared. The results of the experiments suggest that the proposed method outperforms the other variants in terms of convergence speed and avoiding local minima; FNNs are being trained.
Research on Sentiment Analysis Model of Short Text Based on Deep Learning
Authors: Zhou Zhou Gui
Published: May 2022

With the wide application of the Internet and the rapid development of network technology, microblogs and online shopping platforms are playing an increasingly important role in people’s daily life, learning, and communication. The length of these information texts is usually relatively short, and the grammatical structure is not standardized, but it contains rich emotional tendencies of users. The features used by custumal machinery schooling methods are too sparse on the vector space model and lack the semantic information of short texts, which cannot well identify the semantic features and potential emotional features of short texts. In response to the above problems, this paper proposes a bidirectional long-term and short-term memory network model based on emotional multichannel, combining the attention mechanism and convolutional neural network features in deep learning and learning the short text by combining shallow learning and deep learning. The semantic information and potential emotional information of the short text can be improved to promote the effective expression of short-text emotional features and improve the short-text emotional classification effect. Finally, this paper compares the above models on multidomain classification data sets such as NLPIR and NLPCC2014. The accuracy and F1 value of the model proposed in this paper have achieved good improvement in the field of short-text sentiment analysis.
Multidecadal Land Use Patterns and Land Surface Temperature Variation in Sri Lanka
Authors: Makumbura Randika K. , Samarasinghe Jayanga , Rathnayake Upaka
Published: May 2022

Agricultural land conversion due to urbanization, industrialization, and many other factors is one of the significant concerns to food production. Therefore, analyzing the temporal and spatial variation of agricultural lands is an emerging topic in the research world. However, an agrarian country like Sri Lanka was given weaker attention to the temporal and spatial variation of the land use, including the agricultural lands. This study presents an extended analysis of temporal and spatial variation of land use patterns in Sri Lanka, specifically looking at the agricultural land conversion and land surface temperature (LST) change. Remote sensing techniques and geographic information system (GIS) were used for the presented work. The satellite images from three Landsat’s were analyzed for 2000, 2010, and 2020 to identify the potential land use conversions. In addition, LSTs were extracted for the same period. Significant and continuous increases can be seen in the agricultural lands from 33.94% (of total area) in 2000 to 43.2% in 2020. In contrast, the forest areas showcase a relative decrease from 38.51% to 33.82% (of total area) during the analyzed period. In addition, the rate of conversion from agriculture to settlements is higher in the latter decade (2010–2020) compared to the earlier decade (2000–2010). Only general conclusions were drafted based on the LSTs results as they were not extracted in the same months of the year due to high cloud cover. Therefore, the results and conclusions of this study can be effectively used to improve the land use policies in Sri Lanka and lead to a sustainable land use culture.
Intuitionistic Fuzzy Hamacher Generalized Shapley Choquet Integral Operators Based Decision-Making Model for Feature Extraction and Automatic Material Classification in Mining Area Using Satellite Data
Authors: Khanum Seema , Gunasekaran M. , Rajiga S. V. , Firos A. , Agbevanu Kafui Tsoeke
Published: May 2022

The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.
Study of Component Composition and Antimicrobial Activity of the Ophthalmic Emulsion Based on the Safflower Flowers (Carthamus tinctorius L.)
Authors: Abuova Zhanar , Turgumbayeva Aknur , Jumagaziyeva Ardak , Rakhimov Kairolla , Jussupkaliyeva Aigul
Published: May 2022

The use of medicinal plants has increased significantly in recent years. More than 80% of the world’s population uses medicinal plants to treat themselves. Many antibacterial and anti-inflammatory synthetic drugs are available in medical practice. However, recent tendency of increasing capability of resistance of bacteria to usage of antibacterial drugs of different groups is taking place. Considering the wide range of pharmacological and antimicrobial activity of safflower flower extracts and available vitamins in their composition, it was decided to create a preparation based on the CO2 extract of safflower (Carthamus tinctorius L.) in the form of an ophthalmic emulsion. The aim of this research is to study the composition and antimicrobial activity of the extract and ophthalmic emulsion drops against test strains of microorganisms. The subject of this study is the ophthalmic emulsions from flowers of Kazakhstan species of “Akmai” safflower, collected in the flowering stage in southern Kazakhstan in August 2021. The component composition was determined using gas chromatography with the Agilent 7890A/5975C mass spectrometry technique. A study of the antimicrobial activity of the ophthalmic emulsion drop extracts was performed with two strains of Gram-positive bacteria, one strain of Gram-negative bacteria, and one culture of fungi. The following biologically active substances were determined from the GC-MS results: tridecane 94%, tricosane 93%, hexacosane 93%, dodecanoic acid 92%, pentacosane 91%, and linoleic acid 63.7%. The investigated emulsion-type eye drop shows bactericidal activity against S. aureus ATCC 6538-P, where the zone of growth suppression under the ophthalmic emulsion action corresponded to 9.0 ± 0.0 mm. The tested ophthalmic emulsion drops show the presumed biological activity against conditionally pathogenic bacteria. The results of chromatographic analysis and antimicrobial activity of the tested samples indicate the prospects for their further study for use as anti-infectious (anti-inflammatory) agents in medicine.
Osteochondritis Lesions of the Ischiopubic Area in Young Adolescents
Authors: Laliotis Nikolaos , Chrysanthou Chrysanthos , Konstandinidis Panagiotis , Giannakopoulou Lambrini , Moumtzouoglou Anestis
Published: May 2022

Osteochondritis of the ischiopubic area is a rare disease of children that presents with hip pain and limping. Careful examination and appropriate investigations are essential to establish a definite diagnosis. We report a case series of four children, ages 10–14-year-old, with osteochondritis of the ischiopubic area. Plain X-ray examination showed an area of diffuse irregular calcification of the ischium in two of the children, while in the other two there was an asymmetrical enlargement of the ischiopubic synchondrosis. MRI investigation was the most helpful examination. Bone edema was found in all four children. A calcified mass separated from the host ischium was found in the first two children. The cortex was normal, without irregular destruction. Bone edema of both the ischium and pubic alongside the synchondrosis was found in the following two children, with intact cortices and asymmetrical enlargement. Osteochondritis lesions of the ischium and the ischiopubic area have radiological findings similar to several severe diseases. Bone edema on MRI investigation in children must be properly evaluated. Appropriate radiological examination enabled us to confirm the diagnosis of the osteochondritis and to avoid unnecessary procedures. We want to draw attention to the rare diagnosis of osteochondritis of the ischiopubic area, and the clinical significance, as a cause of hip pain and limping in children.
Downstream Semantic Segmentation Model for Low-Level Surface Crack Detection
Authors: Siriborvornratanakul Thitirat
Published: May 2022

As surface crack detection is essential for roads and other building structures in most countries, this has been a very popular topic in computer vision for automating structural health monitoring. Recently, many deep learning engineers have attempted to find solutions to the problem. However, to the best of our knowledge, most previous methods were about designing and experimenting with a deep learning model from scratch, which is highly technical and very time-consuming. This study proposes a new approach of using downstream models to accelerate the development of deep learning models for pixel-level crack detection. An off-the-shelf semantic segmentation model named DeepLabV3-ResNet101 is used as a base model and then experimented with different loss functions and training strategies. Our experimental results have revealed that the downstream models trained by the classic cross-entropy loss function cannot provide reasonable results in pixel-level crack detection. The most successful downstream model we found is trained by the focal loss function without using the pretrained weights that are accompanied by the base model. Our selected downstream model is generalized well across different test datasets and yields the optimal dataset scale F-measures of 84.49% on CrackTree260, 80.29% on CRKWH100, 72.55% on CrackLS315, and 75.72% on Stone331.
Ceramic Painting and Traditional Cultural Element Fusion Composition Design Based on Virtual Reality
Authors: Hu Xiaobing , Lai Yuanqian , Zhao Dan , Tong Fang , Hu Yingshuang , Li Yingzhuo
Published: May 2022

Chinese traditional ceramic culture and art are an important foundation for the progress of civilization today, and these cultural foundations are closely related to the daily lives of modern people. Traditional ceramics, as a valuable cultural heritage leftover from ancient times, are inseparable from the various daily lives of modern people and have always played an important leading role in modern people’s daily life. Ceramic painting is also an important embodiment form of traditional culture, and the integration of the two is very conducive to enriching people’s spiritual world. This paper proposes a VRML‐oriented method suitable for establishing a virtual ceramic product space model, that is, a method of building a VRML model with 3DS MAX and other three‐dimensional modeling tools combined with a VRML visual editor. According to this method, based on the LOD node, the method is given the optimization and integration method of the virtual ceramic display space scene and completed the modeling of the outdoor background and building required by the virtual ceramic product display space scene. Subsequently, the interactivity of the virtual ceramic display space in the web application is explained, the realization method of the color switching and placement of the virtual ceramic product based on the built‐in sensor is proposed, and the interaction based on the external data source of the web page is also given. Finally, in this article, the response time of the page can be basically controlled below 4 s/time, and after a long‐term stress test, the system can still maintain the stability of operation, and the probability of serious errors is less than 0.512%.
Evaluate Short-Term Outcomes of abciximab in ST-Segment Elevation Myocardial Infarction Patients Undergoing Percutaneous Coronary Intervention: A Meta-Analysis of Randomized Clinical Trials
Authors: Bai Nan , Niu Ying , Ma Ying , Shang Yao-Sheng , Zhong Peng-Yu , Wang Zhi-Lu
Published: May 2022

Objectives. This meta-analysis was to verify the short-time efficacy and safety of abciximab in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). Background. Abciximab has long-term efficacy in patients with STEMI undergoing PCI, but the short-term efficacy is still controversial. Methods. We conducted a systematic review and meta-analysis compared with or without abciximab in patients with STEMI undergoing PCI. The relevant randomized controlled trials were included by searching PubMed, EMBASE, Cochrane Library, and Web of Science databases and other sources. The relative risk (RR) and 95% confidence intervals (CI) of outcomes were calculated by the fixed-effects model. Results. Ten randomized controlled trials with 5008 patients met inclusion criteria. There were no significant differences in risk of all-cause death at 30-day (RR 0.79, CI 0.55–1.12,

P
=
0.18

), major bleeding (1.37, 0.93–2.03,

P
=
0.11

), and transfusion (1.23, 0.94–1.61,

P
=
0.13

) between the two groups. However, there were significant differences in risk of all-cause death at 6 months (0.57, 0.36–0.90,

P
=
0.02

), recurrent myocardial infarction (0.55, 0.33–0.92,

P
=
0.02

), repeat revascularization (0.58, 0.43–0.78,

P
=
0.0004

), final TIMI flow <3 (0.77, 0.62–0.96,

P
=
0.02

), minor bleeding (1.29, 1.02–1.63,

P
=
0.04

), and thrombocytopenia (2.04, 1.40–2.97,

P
=
0.0002

). Conclusions. The application of abciximab can lead to a lower risk of reinfarction, revascularization, and all-cause death at 6 months, but a higher risk of minor bleeding, and thrombocytopenia.
Translation and Psychometric Testing of the Arabic Version of the Problematic Media Use Measure Short Form for Children
Authors: Dwairej Doa’a Abdullah , Obeidat Hala Mahmoud , Alfarajat Eqbal Mohammad , Dwairej Lubna Abdullah
Published: May 2022

Background. There is growing concerns that excessive use of media among children will become problematic. Research on the management of children’s problematic use of media would be improved if screening tools are widely applied. Problematic Media Use Measure Short Form (PMUM-SF) was developed to screen problematic use of media among 4- to 11-year-old US children. Purpose. The study is aimed at developing and validating a cross-cultural version of the PMUM-SF for use in Arabic-talking parents of children from age 3 to 13 years. Design. Cross-cultural adaptation and cross-sectional psychometric testing study. Methods. Using the World Health Organization (WHO) framework for instrument translation and adaptation, the instrument was translated, back translated, pretested, and reviewed by a committee. The PMUM-SF was tested in 180 parents of children aging from 3 to 13 years. Results. The results demonstrated that the Arabic version of the PMUM-SF had high reliability (Cronbach’s alpha was 0.90); it has good convergent and predictive validity. The factor structure of the Arabic version of PMUM-SF was confirmed through exploratory and confirmatory factor analysis (comparative fit index

CFI

=
0.93

; goodness of fit index

GFI

=
0.90

; incremental fit index

IFI

=
0.93

). Conclusion. Because the Arabic version of the PMUM-SF seems to be reliable and valid in assessing problematic use of media of children in Arabic-speaking parents, the use of this translated version is recommended in future research.
[Retracted] Integrated Learning Model‐Based Assessment of Enteral Nutrition Support in Neurosurgical Intensive Care Patients
Authors: Jiang Suxue , Wang Roushi , Zhang Haiying
Published: May 2022

To observe the clinical efficacy of early enteral nutrition application in critically ill neurosurgical patients, in this paper, we have developed a prediction model for enteral nutrition support in neurosurgical intensive care patients which is primarily based on an integrated learning algorithm. Additionally, we have compared the prediction performance of each model. The patients were divided into control and combined treatment groups according to the random number table method, and 175 patients in each group were treated with a parenteral method and early enteral nutrition support, respectively. A reentry ICU prediction model based on the integrated learning method random forest, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) was developed, and the prediction performance of integrated learning and logistic regression was compared. The average sensitivity, positive predictive value, negative predictive value, false‐positive rate, false‐negative rate, area under the receiver operating characteristic curve (AUROC), and Brier score after fivefold cross‐validation were used to evaluate model effects, and the best performance model based on the top 10 predictor variables in order of importance was given. Among all models, GBDT (AUROC = 0.858) was better than random forest (AUROC = 0.827) and slightly better than AdaBoost (AUROC = 0.851). The GBDT algorithm gave a higher ranking of importance for variables such as mean arterial pressure, systolic blood pressure, diastolic blood pressure, heart rate, urine volume, and blood creatinine and relatively poorer cardiovascular and renal function in neurosurgical intensive care patients.
Metabolites Profiling of Manilkara mabokeensis Aubrév Bark and Investigation of Biological Activities
Authors: Worowounga Xavier , Rahmani Rami , Namkona Armel-Frederic , Cazaux Sylvie , Syssa-Magalé Jean-Laurent , Matondo Hubert , Bouajila Jalloul
Published: May 2022

Manilkara mabokeensis Aubrév is a tree that belongs to the Sapotaceae family, native to the tropical forest in Latin America, Asia, Australia, and Africa. The bark of this species is used as traditional medicine to treat diseases. The present study is the first phytochemical investigation on M. mabokeensis Aubrév bark in terms of phytochemical composition and bioactivity. Among the different extracts, ethyl acetate (EtOAc) exhibited the highest values of total phenolic content (TPC), total flavonoid content (TFC), condensed tannin content (CTC), and reducing sugar content, as well as a high antioxidant activity. Interestingly, gas chromatography-flame ionization detector (GC-FID) and gas chromatography-mass spectrometry (GC-MS) analysis were enabled to identify 23 compounds in the essential oil of M. mabokeensis Aubrév bark, which have not been previously described in the literature. Phytol and 8,14-cedranoxide were the major identified compounds, with area percentages of 27.9 and 18.8%, respectively. For HPLC analysis, 3,4-dihydroxy-benzoic acid methyl ester showed the highest concentration with 61.8 mg/g of dry residue (dr) among other identified molecules. Manilkara mabokeensis Aubrév bark MeOH extract showed a good anti-15-lipoxygenase (anti-15-LOX) and anti-acetylcholinesterase (anti-AChE) activities of 65.8 and 71.0%, respectively, while it exhibited a moderate antixanthine oxidase (anti-XOD) activity (41.5%) at 50 μg/mL. Furthermore, cyclohexane (CYHA) and ethyl acetate induced the highest cytotoxicity against the human ovarian cancer cell lines, OVCAR (49.5%) and IGROV (48.7%), respectively. Taken together, obtained results argue that M. mabokeensis Aubrév bark is an excellent source of natural compounds and justify its use in folk medicine.
A Cooperative Lightweight Translation Algorithm Combined with Sparse-ReLU
Authors: Xu Xintao , Liu Yi , Chen Gang , Ye Junbin , Li Zhigang , Lu Huaxiang
Published: May 2022

In the field of natural language processing (NLP), machine translation algorithm based on Transformer is challenging to deploy on hardware due to a large number of parameters and low parametric sparsity of the network weights. Meanwhile, the accuracy of lightweight machine translation networks also needs to be improved. To solve this problem, we first design a new activation function, Sparse-ReLU, to improve the parametric sparsity of weights and feature maps, which facilitates hardware deployment. Secondly, we design a novel cooperative processing scheme with CNN and Transformer and use Sparse-ReLU to improve the accuracy of the translation algorithm. Experimental results show that our method, which combines Transformer and CNN with the Sparse-ReLU, achieves a 2.32% BLEU improvement in prediction accuracy and reduces the number of parameters of the model by 23%, and the sparsity of the inference model increases by more than 50%.
Study on Preparation and Properties of High-Strength Nano-Ceramic Coating on the Surface of Steel Structure Connectors
Authors: Wang Hui
Published: May 2022

In this paper, the preparation process of high-strength nano-ceramic coating on the surface of steel structure connectors is analyzed. By observing the surface morphology and internal defects of the ceramic coating, this paper analyzes the coating defects in combination with the coating deposition process. Moreover, this paper uses XRD to analyze the phase transition of the ceramic coating before and after spraying and tests the toughness of the coating, the adhesion between the coating and the reinforcement matrix, and the adhesion between the ceramic coating and the concrete. In addition, this paper studies its performance in combination with test research. The test results verify that the high-strength nano-ceramic coating on the surface of the steel structure connector has good performance.
The Start of Combustion Prediction for Methane-Fueled HCCI Engines: Traditional vs. Machine Learning Methods
Authors: Namar Mohammad Mostafa , Jahanian Omid , Koten Hasan
Published: May 2022

In this work, 11 regression models based on machine learning techniques were employed to provide a fast-response and accurate model for the prediction of the start of combustion in homogeneous charge compression ignition engines fueled with methane. These regression models are categorized into linear and nonlinear types. Although the robust random sample consensus (RANSAC) model is a nonlinear type as well as SAM (simple algebraic model), the prediction accuracy is enhanced from 89.3% to 98.4%. Such accuracy is also achieved for the linear models, namely, ordinary least squares, ridge, and Bayesian ridge models. Indeed, due to the linear hypothesis (the correlation for the start of combustion prediction), the presented models have an acceptable response time to be used in real-time control applications like the electronic control units of the engines.
Dynamics of an Antitumour Model with Pulsed Radioimmunotherapy
Authors: Wang Gang , Yi Ming , Tang Sanyi
Published: May 2022

In this paper, an antitumour model for characterising radiotherapy and immunotherapy processes at different fixed times is proposed. The global attractiveness of the positive periodic solution for each corresponding subsystem is proved with the integral inequality technique. Then, based on the differentiability of the solutions with respect to the initial values, the eigenvalues of the Jacobian matrix at a fixed point corresponding to the tumour-free periodic solution are determined, resulting in a sufficient condition for local stability. The solutions to the ordinary differential equations are compared, the threshold condition for the global attractiveness of the tumour-free periodic solution is provided in terms of an indicator

R

0

, and the permanence of a system with at least one tumour-present periodic solution is investigated. Furthermore, the effects of the death rate, effector cell injection dosage, therapeutic period, and effector cell activation rate on indicator

R

0

are determined through numerical simulations, and the results indicate that radioimmunotherapy is more effective than either radiotherapy or immunotherapy alone.
Critical Study on Corrosion Inhibitors in U-Shaped RCC Beams
Authors: Shanmughan Suchithra , Velusamy Sampathkumar , Nallasamy Jothi Lakshmi , Selvakumar Kothai Palanisamy , Thirumoorthy Pradeep , Saravanan M. M. , Sakthivel Haripriya , Periyasamy Mageshkumar , Kasirajan Ramachandran
Published: May 2022

In most structural failures, deterioration of reinforced concrete structure is due to corrosion of reinforcing bars. Ingress of chloride ions and reaction of carbon dioxide with the cement paste due to poor-quality concrete or inadequate cover are the main causes of corrosion. Corrosion can be reduced by using high-quality concrete and providing proper cover to reinforcement. In addition, corrosion inhibitors are used to protect the reinforcement in order to extend the service life of concrete structures. This research work is focused on determining the effectiveness of corrosion inhibitors under cracked and uncracked sections of U-shaped beam. The U-shaped beam concept was used in this study, which helps to develop the crack width within 0.3 mm as per IS: 456 code provision. The two vertical stubs of the U-shaped beam were stressed in order to generate the cracks at the soffit of the beam. Then, the beams are tested for accelerated corrosion along with the half-cell potential meter calculations for the corrosion potential measurement.
Research and Design of Radiofrequency Antenna on LCD
Authors: Zhu Wanshan , Gao Zhe , Meng Zhuo , Wang Chen , Li Yang , Wang Chunmei , Jia Yunwei
Published: May 2022

This work studies the electromagnetic field of a radiofrequency (RF) antenna on 7-inch liquid crystal display (LCD) and presents a new approach where the RF antenna is designed on LCD. This approach takes the magnetic field effect of LCD as an input parameter when building and simulating antenna models using the software Ansoft HFSS. After obtaining the PCB of the antenna, place the antenna on LCD, so that the antenna and LCD overlap up and down and measure the equivalent physical parameters as a whole. Last, use the vector network analyzer and Smith circle graph to tune the antenna system for completing the impedance matching of the whole system. This method breaks through the limitation that the card swiping area and the display area cannot be in the same area due to electromagnetic interference. It has changed the appearance design concept of the mobile point of sale (POS), and the antenna size can be 20%–100% of the POS display area. A contactless card read-write experiment for the proposed method has been carried out. Results showed the return loss is less than −30 dB, and the effective read-write distance of the antenna can reach about 5 cm. This design method provides a broader application prospect for mobile POS.
Optimal Sizing of Hybrid Energy System Using Random Exploratory Search-Centred Harris Hawks Optimizer with Improved Exploitation Capability
Authors: Anand Priyanka , Kamboj Vikram Kumar , Alaraj Muhannad , Rizwan Mohammad , Mwakitalima Isaka J.
Published: May 2022

Due to the depletion of traditional energy resources, emissions of greenhouse gases, climate change, etc., renewable energy resources (RER) based power generation is becoming the main source of the present and future power sector. The major RERs, including solar, wind, and small hydro, may provide reliable and sustainable solutions in the smart grid environment. Solar and wind energy-based power generation is more prevalent but varies in nature and is not even very predictable very efficiently. Therefore, it has become necessary to integrate two or more RER and develop a hybrid energy system (HES). The HESs provide a cost-effective and reliable power supply with reduced and/or almost negligible greenhouse gas emissions as well. Due to economic and power reliability concerns, the optimal sizing of components is necessary for the development of an optimum HES. In recent years, metaheuristic evolutionary algorithms have been widely used for optimal sizing of HES. Harris hawk’s optimizer (HHO) is a recently devised metaheuristics search method that has the ability to discover global minima and maxima. However, due to its weak exploitation capacity, the basic HHO algorithm’s local search is pretty slow and has a slow rate of convergence. Thus, to boost the exploitation phase of HHO, a new approach, random exploratory search centered Harris hawk’s optimizer (hHHO-ES), has been developed in the present work for optimal sizing of HES. The suggested approach is validated and compared to existing optimization approaches for a variety of well-known benchmark functions, including unimodal, multimodal, and fixed dimensions. Following this, it is used to develop HES, which will be capable of providing power to remote areas where grid supply is scarce. The objective function is formulated using net present cost (NPC) as a prime function under a set of constraints such as bounds of system components and reliability. The obtained results are compared with those from harmony search (HS) and particle swarm optimization (PSO) and found to be better.
Conservative Management of Muscle Invasive Bladder Cancer in Kidney-Pancreas Transplant Patient
Authors: Glosser Logan D. , Zakeri Brandon S. , Lombardi Conner V. , Ekwenna Obi O.
Published: May 2022

Introduction. Solid organ transplant increases the risk for muscle-invasive bladder cancer (MIBC). Although a common tumor, urothelial cell carcinoma (UCC) of the bladder in patients with kidney-pancreas transplants is scarcely reported. Case Presentation. A 65-year-old male with history of type 1 diabetes and a 14-year status post deceased donor pancreas-kidney transplant presented with 3 weeks of gross hematuria. CT scan showed multiple bladder masses. Transurethral resection of bladder tumor (TURBT) showed papillary UCC. 5 months later, the patient reported new-onset gross hematuria. TURBT showed MIBC. The patient elected for bladder-preserving TMT. On cystoscopy there was no gross evidence of carcinoma at 3.5 years of follow up. Discussion. Currently, no specific management guidelines target this population with MIBC. The first-line treatment for MIBC is radical cystectomy (RC) with neoadjuvant chemotherapy. For patients that are medically unfit or unwilling to undergo RC, trimodal therapy (TMT) is an alternative. TMT for bladder cancer consists of complete tumor resection with chemotherapy and radiation. This report demonstrates a unique case of a patient with kidney-pancreas transplant diagnosed with MIBC treated with TMT that has no evidence of gross tumorigenesis at 3.5 years after diagnosis. Our findings suggest that trimodal therapy should be considered for treatment of MIBC in patients with kidney-pancreatic transplants to preserve the donated allografts.
Artificial Intelligence-Based Sustainable Development of Smart Heritage Tourism
Authors: Li Dan , Du Pengju , He Haizhen
Published: May 2022

World heritage is a kind of affirmation and high honor given by the international community to the important civilization, historical relics, or natural landscape of a country and nation. This paper studies and analyzes the sustainable development of heritage tourism boosted by smart tourism based on big data artificial intelligence. This paper first analyzes big data and then introduces the concept of smart tourism. Smart tourism is a new future-oriented tourism form that serves the public, enterprises, and governments. It uses the Internet of Things, cloud computing, next-generation communication network, high-performance information processing, intelligent data mining, and other technologies in tourism experience, industrial development, administrative management, and other applications, so that tourism physical resources and information resources have been highly systematically integrated and deeply developed and activated. And then, we analyze and discuss artificial intelligence algorithms. Artificial intelligence is a new technological science that is researched and developed on the basis of computer science as a simulation and extension of human intelligence activities. Finally, a comprehensive analysis is made on the tourism ecological footprint of natural heritage sites and the carrying capacity of tourism ecology in natural heritage sites. The experimental results of this paper show that the tourism development of this natural heritage site is in a sustainable state. The reasons are as follows: First, the average tourist ecological footprint of the place is 0.009466 hm2, the average tourism ecological carrying capacity is 0.032861 hm2, and there is an average ecological surplus of 0.02339 hm2; secondly, the average tourist natural footprint is 18285.93 hm2, the average tourism environmental carrying capacity is 40421.97 hm2, and the average ecological surplus is 22136.04 hm2. To sum up, it shows that its tourism development is in a state of sustainable development.
Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things
Authors: Abd Elaziz Mohamed , Mabrouk Alhassan , Dahou Abdelghani , Chelloug Samia Allaoua
Published: May 2022

Recently, the 6G-enabled Internet of Medical Things (IoMT) has played a key role in the development of functional health systems due to the massive data generated daily from the hospitals. Therefore, the automatic detection and prediction of future risks such as pneumonia and retinal diseases are still under research and study. However, traditional approaches did not yield good results for accurate diagnosis. In this paper, a robust 6G-enabled IoMT framework is proposed for medical image classification with an ensemble learning (EL)-based model. EL is achieved using MobileNet and DenseNet architecture as a feature extraction backbone. In addition, the developed framework uses a modified honey badger algorithm (HBA) based on Levy flight (LFHBA) as a feature selection method that aims to remove the irrelevant features from those extracted features using the EL model. For evaluation of the performance of the proposed framework, the chest X-ray (CXR) dataset and the optical coherence tomography (OCT) dataset were employed. The accuracy of our technique was 87.10% on the CXR dataset and 94.32% on OCT dataset—both very good results. Compared to other current methods, the proposed method is more accurate and efficient than other well-known and popular algorithms.
Abnormal Expression of N6-Methyladenosine RNA Methylation Regulator IGF2BP3 in Colon Cancer Predicts a Poor Prognosis
Authors: Wu Tao , Zhang Xuan , Xing Lu , Pan Dingguo , Liu Ping , Ding Rong , Yang Renfang , Yang Xudong , Li Yunfeng
Published: May 2022

The value of insulin-like growth factor 2 mRNA-binding protein 3 (IGF2BP3), an N6-methyladenosine (m6A) RNA methylation regulatory factor, in the prognosis of colon cancer was still unclear. High levels of IGF2BP3 were expressed in colon adenocarcinoma (COAD) samples and in human colon cancer tissues, which was associated with poorer overall survival (OS). We validated IGF2BP3 as an independent prognostic risk biomarker in COAD patients. Moreover, functional enrichment analysis suggested that differentially expressed genes (DEGs) of groups with high versus low IGF2BP3 expression were related to immune- and cancer-related pathways. Furthermore, the tumor microenvironments of high- versus low-IGF2BP3 expression groups showed significant differences and IGF2BP3 predicted the efficiency of immunotherapy. Finally, protein-protein interaction network analysis suggested that there was a direct or indirect interaction among IGF2BP3, WNT7B, VANGL2, NKD1, AXIN2, RNF43, and CDKN2A. In brief, IGF2BP3 was confirmed as an independent prognostic signature in COAD patients and might be a therapeutic target in this study. Moreover, IGF2BP3 could be used in personalized immunotherapy for COAD.
A Prioritizing Interdiction Surface-Based Vulnerability Remediation Composite Metric for Industrial Control Systems
Authors: Wang Zibo , Zhang Yaofang , Liu Zhiyao , Li Tongtong , Chen Yilu , Yang Chen , Wang Bailing , Liu Zhusong
Published: May 2022

Recently, industrial control system (ICS) has gradually been a primary attack target. The main reason is that increasing vulnerabilities exposed provide opportunities for launching multistep and multihost attacks to breach security policies. To that end, vulnerability remediations are crucial for the ICS. However, there exist three problems to be tackled in a sound way. First of all, it is impractical to remove all vulnerabilities for preventing the multistep and multihost attacks in the consideration of the actual ICS demands. Secondly, ranking vulnerability remediations lacks a guidance. The last problem is that there is a lack of a metric for qualifying the security level after each remediation. In this paper, an ICS-oriented assessment methodology is proposed for the vulnerability remediations. It consists of three phases corresponding to the above problems, including (1) prioritizing Interdiction Surfaces, (2) ranking vulnerability remediations, and (3) calculating composite metrics. The Interdiction Surface describes a minimum set of vulnerabilities of which the complete removal may interdict all discovered attack paths in the system. Particularly, it innovates to take the urgent security demands of the ICS into account. Subsequently, ranking the vulnerability in the optimal Interdiction Surface is conducive to guide the remediations with the priority. A composite metric is ultimately given to assess the security level after vulnerability remediations. The effectiveness of the proposed methodology is validated in an ICS scenario which is similar to the real-world practice. Results show that the entire procedure is suitable for the context of the ICS. Simultaneously, the composite metric enhances both the comprehensiveness and the compatibility in contrast with attack path-based metrics. Hence, it overcomes the shortcomings when they are used in isolation.