bluuebunny/crossref_metadata_2025 huggingface dataset:
A parametric study on the cost optimization of a reinforced concrete abutment using a genetic algorithm
Authors: Kumari T. Geetha , Srilatha N. , Prasad M. Charan , Ram G. Phani , Vineeth M. Krishna
Published: August 2022

This paper presents the cost optimization of a reinforced concrete abutment of a cantilever type using a genetic algorithm (GA). During the optimization process, six design variables, including two geometrical design variables and four cross-sectional design variables, were considered. The objective function consists of the cost of steel, concrete, and labor. Computation programs have been developed in JAVA to find an economical design adhering to Indian Road Congress (IRC) standards. To get an optimal solution in reasonable computational time, an attempt is carried out to evaluate the optimal GA parameters for the abutment model. A parametric study was conducted to understand the effect of the angle of friction, grade of concrete, and height on the cost optimization of the abutment. From the parametric study, it is observed that optimum cost of the abutment is obtained with a higher value of the angle of friction and concrete with a lower compressive strength. The results of the optimization are further discussed in detail.
The critical role of core in understanding hydraulic fracturing
Authors: Gale Julia Fiona Wells , Elliott Sara J. , Rysak Bethany G. , Laubach Stephen E.
Published: August 2022

Improved understanding of hydraulic fractures is needed to optimize petroleum well drilling and completion strategies. Yet direct observations of hydraulic fractures are rarely made, and reliance is placed on indirect methods such as microseismic monitoring, interference tests, fibre optic detection of fracturing in adjacent wells and numerical modelling. While these techniques provide useful insights, verification of such studies is commonly lacking; core taken through stimulated intervals offers a robust option for verification. We make the case that core can provide complementary information at a different scale from other data types.

Core from a slant well adjacent to two stimulated wells at the Hydraulic Fracture Test Site (HFTS1) in West Texas revealed 375 hydraulic fractures, striking 090°±20°, subparallel to present-day
S
Hmax
. There are more hydraulic fractures than expected, and clustering across a range of scales from approximately 1 cm to 50 m. The largest cluster correlates with high microseismic event density. Diversion, bifurcation, and segmentation structures may account for the large number of fractures observed and the orientation spread. Reactivation of sealed natural fractures and bedding planes is relatively uncommon. Proppant sand packs and patches occur in a few locations, particularly where hydraulic fractures are complex.
Using legacy core material to assess subsurface carbon storage reservoir potentiality
Authors: Payton Ryan L. , Chiarella Domenico , Kingdon Andrew
Published: August 2022

The growing importance of subsurface carbon storage for tackling carbon emissions requires an accurate characterization of potential reservoirs to understand their capabilities. In this context, the use of legacy data originally acquired in the last fifty years for scientific projects and petroleum exploration and production activities would represent a suitable cost-effective solution and help to maximize the value of this extended national asset. Core material represents the only direct observation of subsurface deposits and must be preserved from the current disposal process related to the decommissioning of hydrocarbon fields. In this contribution, a suite of samples from core material stored at national (i.e. British Geological Survey) and local (i.e. Department of Earth Sciences, Royal Holloway, University of London) core repositories, previously characterized at the micro scale using X-ray micro-computed tomographic (μCT) imaging are discussed. Using this technique, it has been possible to investigate how pore and grain geometries control crucial features of a suitable reservoir such as porosity and permeability. The aim of this contribution is to describe the methodology behind digital image analysis (DIA) following μCT imaging applied to core material. We show how DIA can be used to provide significant measures of reservoir suitability when making initial assessments of storage reservoirs, without the need for expensive and time-consuming analyses.
Identification of an Exosome-Related Signature for Predicting Prognosis, Immunotherapy Efficacy, and Tumor Microenvironment in Lung Adenocarcinoma
Authors: Lin Tao , Wang Hong , He Xi
Published: August 2022

Accumulating evidence suggests that exosomes can affect lung adenocarcinoma (LUAD) progression. However, there is still a lack of understanding of the global influence of exosome-related genes (ERGs) on prognostic relevance, tumor microenvironment features, and immunotherapy responsiveness in patients with LUAD. In the TCGA dataset, differential analysis of 490 LUAD samples and 59 normal samples yielded 30 ERGs with differential expression. We have created a predictive signature based on 10 overall survival (OS)-related ERGs and confirmed it in two external cohorts (GSE72094 and GSE68465) via the least absolute shrinkage and selection operator (LASSO) and Cox regression analysis in the TCGA dataset. The new signature revealed superior robustness and prognostic capacity for overall patient survival. Univariate and multivariate Cox regression analyses indicated that this signature was an independent risk factor for survival in patients with LUAD. In addition, for predicting the 1-year, 3-year, and 5-year OS of LUAD patients, we developed a nomogram and confirmed its predictive ability via the C-index and calibration curve. In addition, patients categorized by risk score exhibited distinct immunological states, stemness index, immune subtypes, and immunotherapy response. In conclusion, we created a risk signature for LUAD that was tightly associated with the immune landscape and therapeutic response. Also, such a risk signature effectively promotes the ability of the clinicians in making more precise and individualized treatment recommendations for patients with LUAD.
Green Finance and Corporate Green Innovation: Based on China’s Green Finance Reform and Innovation Pilot Policy
Authors: Han Shuyu , Zhang Zuoqian , Yang Siying
Published: August 2022

Using data on China’s A‐share listed enterprises from 2012 to 2019, we investigate the impact of China’s green finance reform and innovation pilot (GFRIP) policy on green innovation by the difference‐in‐difference (DID) method. The results show that the GFRIP policy has a significant role in promoting enterprises’ green innovation. Heterogeneity analysis shows that the positive effect of the GFRIP policy on green innovation is only significant for heavily polluting enterprises, large enterprises, state‐owned enterprises, and enterprises headquartered in regions with a low level of marketization. Debt financing is an important mechanism for the GFRIP policy to promote corporate green innovation; that is, the GFRIP policy alleviates corporate debt financing constraints and then promotes corporate green innovation. Our study provides theoretical and practical enlightenment for developing countries such as China to deepen reform of the green financial system and promote green innovation.
GIS-Based Medical Resource Evaluation Method
Authors: Yu Xiao
Published: August 2022

As one of the public service resources, medical and health care has a great guarantee for people’s healthy life and life safety and is an important part of ensuring the normal and orderly operation of urban functions. This paper takes the spatial pattern of urban medical resources in Hohhot as the research object and collects three types of urban medical resources, including clinics (health centers), specialized hospitals, and general hospitals, a total of 1300 sample points, based on GIS spatial analysis methods, using the average nearest neighbor, kernel density estimation method, standard deviation ellipse method, and accessibility measure to analyze the spatial distribution of medical resources. The results show that the medical resources in the urban area of Hohhot are all in a state of agglomeration, but due to the different functions of different types of medical resources, their distribution ranges and distribution directions also show different characteristics. Saihan District, Yuquan District in the southwest, has fewer medical facilities resources, and the distribution of the accessibility of the three types of medical facilities is quite different. Therefore, the spatial balance of comprehensive medical resources in the urban area of Hohhot needs to be further improved. The results of this paper have implications for the layout of medical facilities and planning has important reference significance.
Influence of Planetary Ball Mill Parameters on Powder Flowability of AlSi10Mg with Niobium Carbide Using Central Composite Design (CCD)
Authors: R Raj Mohan , R Venkatraman , S Raghuraman , P Manoj Kumar , Sharma Rajneesh , Ankit  , Sarojwal Atul , S Rajkumar
Published: August 2022

The hypoeutectic aluminum alloy (AlSi10Mg) is a well-known candidate material used predominantly for its processability and inherent characteristics in metal-based additive manufacturing. Besides, transition metal carbide, such as niobium carbide (NbC), is added to the AlSi10Mg, enhancing its mechanical properties and preferably its wear resistance to the matrix. However, in additive manufacturing, the mixed powder’s flowability is a prerequisite for determining the final properties of the specimens. In this study, mixed powder flowability analysis was carried out through the regular mixing of AlSi10Mg with a varying weight percentage of NbC conducted in the planetary ball mill with different time, speed, and NbC composition following the Central Composite Design (CCD), with a total of 20 experiments. Here, regular mixing was preferred to retain the morphology of the AlSi10Mg (spherical shape) instead of ball milling, which contributes to the degradation of the powder’s shape and size. Finally, based on the combined analysis of apparent density (AD), tapped density (TD), and static angle of repose (SAoR), the flowability characteristics of the mixed powders (AlSi10Mg  +  X % NbC) were evaluated. The optimum combination (AlSi10Mg + X% NbC) was attained based on the composite desirability criteria.
Hepatic Steatosis Index and the Risk of Type 2 Diabetes Mellitus in China: Insights from a General Population-Based Cohort Study
Authors: Cai Xintian , Gao Jing , Liu Shasha , Wang Mengru , Hu Junli , Hong Jing , Zhu Qing , Tuerxun Guzailinuer , Dang Yujie , Li Nanfang
Published: August 2022

Purpose. In the Chinese population, we looked at the relationship between the hepatic steatosis index (HSI) and the risk of type 2 diabetes mellitus (T2DM). Methods. To evaluate the association between HSI and the risk of T2DM, Cox regression models were employed. Hazard ratios (HR) and 95 percent confidence intervals (CI) were computed. A stratified analysis with interaction testing was also carried out. Additionally, we evaluated the incremental predictive value of the HSI over the established risk factors using the C-statistic, the IDI, and the NRI. Results. During a median follow-up period of 2.97 years, 433 (1.97%) participants developed new-onset T2DM. The smoothing curve fit plot showed a positive correlation between HSI and the risk of T2DM. After adjusting for all noncollinear variables, the risk of T2DM increased by 62% for every 1 standard deviation (SD) increase in HSI. Subgroup analysis indicated that higher HSI levels were associated with a higher risk of T2DM in those

aged
<
40

years. The addition of HSI enhanced the reclassification and discrimination of established risk factors, with an IDI of 0.027 and an NRI of 0.348 (both

P
<
0.001

). Conclusion. Our findings suggest that an elevated HSI is substantially associated with a greater risk of T2DM in the Chinese population. HSI has the potential to be an available and supplementary monitoring method for the management of T2DM risk stratification in the Chinese population.
Characterization Techniques Application on Pesticide Adsorption Mechanism Research of Corn Straw Biochar Based on KOH Thermal Activation
Authors: Wang Minghua , Cai Honglan , Qiao Qingan , Zhang Jiang
Published: August 2022

In this paper, research on pesticide adsorption mechanism of corn straw biochar based on KOH thermal activation is carried out. In this paper, corn stalks and pine needles are used as raw materials for preparing biomass charcoal, and biochar is prepared at temperatures of 200°C, 400°C, and 600°C. This paper uses thermogravimetric analysis, elemental analysis, electron microscopy scanning, aperture measurement, infrared spectroscopy, and other characterization techniques to analyze the structure and properties of biochar in detail. Moreover, this paper uses batch method to select different organic pesticides diuron and carbaryl to test their adsorption performance. This article discusses the relationship between the adsorption mechanism and structural characteristics of biochar for organic pollutants. In addition, this paper studies the adsorption mechanism of diuron and carbaryl in the loess by adding exogenous biochar to the loess. Finally, this article analyzes the impact of the addition of biochar on the environmental behavior of diesel-contaminated loess adsorption of organic pollutants. This paper studies the role of KOH thermally activated corn stover biochar in agricultural adsorption, and verifies the reliability of the method proposed in this paper through experimental studies.
Fine-Grained Software Defect Prediction Based on the Method-Call Sequence
Authors: Yang Fengyu , Huang Yaxuan , Xu Haoming , Xiao Peng , Zheng Wei
Published: August 2022

Currently, software defect-prediction technology is being extensively researched in the design of metrics. However, the research objects are mainly limited to coarse-grained entities such as classes, files, and packages, and there is a wide range of defects that are difficult to predict in actual situations. To further explore the information between sequences of method calls and to learn the code semantics and syntactic structure between methods, we generated a method-call sequence that retains the code context structure information and the token sequence representing semantic information. We embedded the token sequence into the method-call sequence and encoded it into a fixed-length real-valued vector. We then built a defect-prediction model based on the transformer, which maps the code-vector representation containing the method-call sequences to a low-dimensional vector space to generate semantic features and syntactic structure features and also predicts the defect density of the method-call sequence. We conducted experiments on 10 open-source projects using the ELFF dataset. The experimental results show that the method-call sequence-level prediction effect is better than the class-level effect, and the prediction results are more stable than those of the method level. The mean absolute error (MAE) value of our approach was 8% lower than that of the other deep-learning methods.
Noninvasive Mechanical Ventilation with Average Volume-Assured Pressure Support versus BiPAP S/T in De Novo Hypoxemic Respiratory Failure
Authors: Briones-Claudett Killen H. , Briones-Claudett Mónica H. , Baños Mariuxi del Pilar Cabrera , Briones Zamora Killen H. , Briones Marquez Diana C. , Zimmermann Luc J. I. , Gavilanes Antonio W. D. , Grunauer Michelle
Published: August 2022

Background. Bilevel positive airway pressure in spontaneous/time and average volume-assured pressure support (BiPAP·S/T–AVAPS) could maintain an adequate tidal volume by reducing the patient’s inspiratory effort; however, this ventilatory strategy has not been compared with other ventilatory modes, especially the conventional BiPAP S/T mode, when noninvasive mechanical ventilation (NIMV) is used. The primary objective of this study was to determine the rate of success and failure of the use of BiPAP·S/T-AVAPS versus BiPAP·S/T alone in patients with mild-to-moderate “de novo” hypoxemic respiratory failure. Methods. This was a matched-cohort study. Subjects with mild-to-moderate de novo hypoxemic respiratory failure were divided into two groups according to the ventilatory strategy used. The subjects in the BiPAP·S/T group were paired with those in the BiPAP·S/T-AVAPS group. Results. A total of 58 subjects were studied. Twenty-nine subjects in the BiPAP·S/T group were paired with 29 subjects in the BiPAP·S/T-AVAPS group. Twenty patients (34.5%) presented with “failure of NIMV,” while 38 (65.5%) patients did not. In addition, 13 (22.4%) patients died, while 45 (77.6%) recovered. No differences were found in the percentage of intubation (

P
=
0.44

) and mortality (

P
=
0.1

). Conclusion. The BiPAP S/T-AVAPS ventilator mode was not superior to the BiPAP·S/T mode. A high mortality rate was observed in patients with NIMV failure in both modes. This trial is registered with https://doi.org/10.1186/ISRCTN17904857.
Exchange Rate, COVID‐19, and Stock Returns in Africa: Insights from Time‐Frequency Domain
Authors: Agyei Samuel Kwaku , Bossman Ahmed , Asafo−Adjei Emmanuel , Asiamah Oliver , Adela Vincent , Adorm−Takyi Cornelius
Published: August 2022

We examine the co‐movement between exchange rate (EXR) returns and stock (STK) returns in Africa amid COVID‐19 in a time and frequency domain. Therefore, we employ the bi‐ and partial wavelet and the wavelet multiple correlation techniques using daily data from 13 February 2013 to 6 May 2021. Our findings divulge that COVID‐19’s effect does not increase the intensity of the relationship between EXR and STK returns in Africa but causes a significant difference in the lead‐lag relationship between the two assets. We find a strong likelihood for a high market integration between African markets in the long run, regardless of market conditions. Under general market conditions, South African (Namibian) equities have the lead/lag potential in the short and long run (intermediate term). Namibian stocks are the first to respond to shocks before all other remaining variables in the intermediate term, while in the long term, the South African EXR market is the last variable to experience shocks. Owing to the recently intensified alliances between African markets, investors should be wary of the specific African equities they include in their portfolios in the periods ahead. Policymakers are required to have an in‐depth understanding of the nature of the co‐movement between the variables to ensure timely and operative policy responses are rolled out to minimise adverse fluctuations in stocks and the local currencies.
Change in Ocular Surface Staining during Eyelid Warming Is Related to Tear Cytokine Levels
Authors: Landsend Erlend C. S. , Olafsson Jonatan , Lai Xiaoran , Aass Hans C. D. , Utheim Tor P.
Published: August 2022

Purpose. To investigate the changes in the tear cytokine profile of patients with meibomian gland dysfunction (MGD) treated with eyelid warming and to correlate these changes with clinical parameters for dry eye disease (DED). Methods. Seventy patients with MGD were included and treated with the warming of eyelids. Of these, 61 still used the treatment three months after baseline, while 48 completed the whole treatment period of six months. The concentrations of 39 cytokines in the tear fluid were measured at baseline and after three and six months of treatment. All participants were examined with tests for DED, including tear film break-up time (TBUT), ocular surface staining (OSS), and the self-reporting Ocular Surface Disease Index (OSDI). Changes in cytokine concentrations were assessed from baseline to three months, from three to six months, and from baseline to six months. Correlation analyses were performed between changes in the cytokine concentrations and changes in TBUT, OSS, and OSDI during the same time intervals. Results. No significant changes were found in the concentrations of the 39 cytokines during any of the three treatment intervals. However, several correlations were detected between changes in the level of cytokines and OSS from baseline to three months of treatment. Decreasing concentrations of granulocyte chemotactic protein 2 (GCP-2/CXCL6, mean effect 2.36,

p
=
0.042

), interleukin 10 (IL-10, mean effect 1.04,

p
=
0.045

), and IL-16 (mean effect 1.36,

p
=
0.035

) were associated with decreasing OSS. Decreasing concentrations of granulocyte macrophage colony-stimulating factor (GM-CSF, mean effect −2.98,

p
=
0.024

), IL-8 (IL-8/CXCL8, mean effect −1.35,

p
=
0.026

), and macrophage migration inhibitory factor (MIF, mean effect −2.44,

p
=
0.033

) were related to increasing OSS. Conclusions. Warming of eyelids did not change the concentration of cytokines in the tear fluid of patients with MGD significantly. However, alterations in the level of several cytokines were associated with changes in the OSS. This finding indicates a close connection between tear cytokines and OSS in MGD patients treated with eyelid warming.
Study on the Application of the Concept of Childlike Interest with Refined Nursing Intervention in the Treatment of Children with Severe Pneumonia
Authors: Wang Yuwei , Qi Shuping
Published: August 2022

Objectives. To analyze the application of childlike concept with refined nursing intervention in the treatment of children with severe pneumonia. Methods. 100 cases of children with severe pneumonia admitted to our hospital from June 2019 to June 2021 were selected and divided into the control group and the observation group, in which the control group was given refined nursing care and the observation group was given the childlike concept with refined nursing intervention to compare the treatment effects of patients in both groups. The clinical indexes such as recovery time of body temperature, disappearance time of pulmonary rales, recovery time of heart rate, disappearance time of cough, and hospitalization time, were compared between the two groups. Results. Compared with the control group, the time to recovery of body temperature, time to disappearance of pulmonary rales, time to recovery of heart rate, time to disappearance of cough, and hospitalization time were significantly lower in the observation group, and the difference was statistically significant (

P
<
0.05

). Compared with the control group, the compliance rate of patients in the observation group was significantly lower, and the difference was statistically significant (

P
<
0.05

). Compared with the control group, the total effective rate of patients in the observation group was significantly higher, and the difference was statistically significant (

P
<
0.05

). Compared with the control group, the overall satisfaction rate of patients in the observation group was significantly higher, and the difference was statistically significant (

P
<
0.05

). The results of univariate analysis showed that the differences between the two groups were statistically significant (

P
<
0.05

) in terms of economic conditions, payment method, child’s condition, child’s age, comorbid underlying diseases, and nursing care method, and the results of logistic multifactor regression analysis showed that the payment method of self-payment, child’s critical condition, and comorbid underlying diseases were the most important factors affecting the anxiety of the family members of children with severe pneumonia. risk factors for the occurrence of anxiety in the families of children with severe pneumonia, and the method of care was a protective factor (

P
<
0.05

). Conclusion. The concept of childlike fun and refined nursing interventions can effectively improve the treatment effect of children with severe pneumonia, improve patients’ condition, increase patients’ compliance and satisfaction, and reduce the risk of anxiety and other negative emotions of the child’s family members.
Allocation and Optimization of Public Sports Facilities Resources in International Tourist Cities Based on Fuzzy Multicriteria Decision-Making Algorithm
Authors: Peng Shengping , Zhou Yi
Published: August 2022

With the improvement of the economic level and the continuous improvement of people living standards, international tourism has become a major boom. International tourism can not only improve the local economic income but also improve the construction of local facilities. Not only does the development of a country tourism depend on the local tourism attraction, but also the economic situation of country is more critical for tourism. The arrangement of sports facilities is also key to the popularity of international tourist cities and to increase tourism, because public sports facilities can not only enhance the physical exercise of tourists but also display local characteristics in the form of sports facilities. The decisions on public sports facilities are based on local government policies and other factors such as travelers preferences, which requires reference to the layout of public sports facilities in successful international tourist cities. This research uses fuzzy multicriteria decision-making algorithm and neural network technology to conduct decision-making and prediction research on the relevant factors of public sports resource allocation in international tourist cities. The research results show that the fuzzy multicriteria algorithm has high accuracy for decision-making and prediction of public sports resource setting. The maximum error of multicriteria algorithm and neural network method in predicting the relevant factors of public sports setting is only 2.45%, and this part of the error comes from the needs of tourists. The smallest error is only 1.34%. The fuzzy multicriteria algorithm has an accuracy of more than 95% for decision-making and prediction of public sports facilities, which is beneficial to decision makers of sports settings in international tourist cities.
Electrospun Networks of ZnO‐SnO2 Composite Nanowires as Electron Transport Materials for Perovskite Solar Cells
Authors: Irfan Muhammad , Ünlü Feray , Lê Khan , Fischer Thomas , Ullah Hameed , Mathur Sanjay
Published: August 2022

Here, we report on the fabrication of one‐dimensional (1D) zinc oxide‐tin oxide (ZnO‐SnO2, ZTO) hollow nanostructures by coaxial electrospinning followed by investigations of their electron transport properties in regular perovskite solar cells (PSCs). The as‐electrospun nanowires (NWs) were obtained as core‐shell nanostructures comprised of polymeric core and metal oxide precursors‐polymer shell. Thermal analysis studies of the as‐electrospun NWs revealed the optimum calcination temperature for complete removal of the polymer and formation of phase pure ZTO. The obtained nanostructured ZTO materials revealed a porous morphology with tubular nanostructures, i.e., NTs. The porous structure of nanoparticles, i.e., NTs in this case, is of particular interest due to the following reasons: (a) structure, particularly 1D, has a profound influence on the electron transport properties, and (b) suitable porosity helps in effective infiltration of perovskite material and hence supports better charge transport at the ZTO‐perovskite interface. The nanomaterials were characterized by Fourier transform infrared (FTIR), diffuse reflectance spectroscopy (DRS), and energy dispersive X‐ray spectroscopy (EDX) to confirm the presence/absence of functional groups, establish band gap energies (Eg), and determine the elemental compositions, respectively. The ZTO NTs were used as electron transport media in the fabrication of perovskite solar cells (PSCs) and established the structure‐property (electron transport) relationships. The highest solar to power conversion efficiency (PCE) of 13.0% (average: 11.90%) was measured for the PSCs based on ZTO NTs obtained by calcination of as‐electrospun NWs at 800°C. It indicates the fact that the calcination temperature influenced the structure which as a result influenced the electron transport property of the material used as ETL in PSCs.
Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning
Authors: Zhou Yongfu
Published: August 2022

Stroke is a common cerebrovascular disease that threatens human health, and the search for therapeutic drugs is the key to treatment. New drug discovery was driven by many accidental factors in the early stage. With the deepening of research, disease-related target discovery and computer-aided drug design constitute a more rational drug discovery process. The deep learning model was constructed by using recurrent neural network, and then, the classification and prediction of compound-protein interactions were studied. In this study, the network pharmacological prediction of stroke based on deep learning is obtained. (1) In the case of discrete time, a distributed optimization algorithm with finite time convergence is applied. A distributed exact first-order algorithm for the case where the objective function is smooth. On the basis of the DGD algorithm, an additional cumulative correction term is added to correct the error caused by the fixed step size of DGD. Solve multiple optimization problems with equality constraints by using Lagrangian functions. Alternately update the original variable and the dual variable to get the solution of a large global problem. It converges to the optimal solution in an asymptotic or exponential way; that is, the node can reach the optimal solution more accurately when the time tends to infinity. (2) Deep learning, also sometimes called representation learning, has a set of algorithms that can automatically discover the desired classification or detection by feeding it into a machine using raw datasets. Multiple levels of abstraction are abstracted through the use of nonlinear models. This simplifies finding solutions to complex and nonlinear functions. Based on the automatic learning function, it provides the functions of modularization and transfer learning. Deep architectures, which usually contain hidden layers, differ from traditional machine learning, which requires a large amount of data to train the network. There are many levels of modules that are nonlinear and transform the information present on the first level into higher levels which are more abstract in nature and are basically used for feature extraction and transformation. (3) The accuracy rate of the framework based on the multitask deep learning algorithm is 91.73%, and the recall rate reaches 96.13%. The final model was predicted and analyzed using real sample data. In the inference problem, it has the advantages of fast training and low cost; in the generation problem, it also has the advantages of fast training, high stability, high diversity, and high quality of image reconstruction.
Nanosilica‐Based Teff Straw as an Eco‐Friendly Substitute for Special Concrete
Authors: Biratu Minda Kebede , Thanappan Subash , Hailu Hunde , Swamy Nadh Vandanapu , Mahesh Kumar C. L.
Published: August 2022

No construction activity can be conceived in the current context without concrete. A popular method is to manufacture concrete from a mixture of three ingredients: aggregates, cement, and water. Because of poor construction materials, many structures deflect prematurely and excessively. Another major worry in the building business is the cost of materials required to make concrete. As a result, adding other suitable components (known as additives) in a specific proportion to boost concrete strength is a regular requirement. Teff agriculture is more prevalent in the study region (Ambo Town), as Enjera is a common Ethiopian delicacy made from Teff. Nanofiber‐based Teff Straw production from Teff agricultural fields is in excess, and it was not being used for anything other than feeding cattle, donkeys, and other animals. As a result, farmers use the unfavorable habit of burning surplus Nanofiber‐based Teff Straw, resulting in environmental pollution issues such as carbon footprint. Furthermore, the natural Nanofiber‐based Teff Straw is extremely strong, used to blend nanoparticles, and it may be useful in overcoming general structural problems while also being cost‐effective for local building businesses. In light of this, the current research focuses on an experimental assessment of the applicability of Nanofiber‐based Teff Straw as an extra concrete material in concrete mixes. The typical mix for C25 concrete has been designed to achieve a target average strength of 28 MPa with a liquid (water)‐cement ratio (l‐c ratio) of 0.50 and a slump range of 20‐50 mm. All Nanofiber‐based Teff Straw reinforced concrete beam samples failed due to pure flexural failure, whereas plane concrete beams failed due to beam crushing. With the addition of Nanofiber‐based Teff Straw to concrete, the mean flexural strength increased by 19.38 percent, 4.19 percent, and 0.66 percent, respectively, with M1, M2, and M3 adding up this particular ingredient by the weight of concrete. As a result, adding Nanofiber‐based Teff Straw to concrete increased its bending strength when compared to ordinary concrete. Slump reduction effects of 20.00 percent, 40.00 percent, and 50.00 percent were seen for mix designs M1, M2, and M3 when Nanofiber‐based Teff Straw was added to the concrete weight. Finally, due to volume addition of fresh concrete with Nanofiber‐based Teff Straw, fresh concrete densities were reduced by 2.00 percent, 2.32 percent, and 2.84 percent, respectively.
Performance Analysis of an Optimized ANN Model to Predict the Stability of Smart Grid
Authors: Chahal Ayushi , Gulia Preeti , Gill Nasib Singh , Chatterjee Jyotir Moy
Published: August 2022

The stability of the power grid is concernment due to the high demand and supply to smart cities, homes, factories, and so on. Different machine learning (ML) and deep learning (DL) models can be used to tackle the problem of stability prediction for the energy grid. This study elaborates on the necessity of IoT technology to make energy grid networks smart. Different prediction models, namely, logistic regression, naïve Bayes, decision tree, support vector machine, random forest, XGBoost, k‐nearest neighbor, and optimized artificial neural network (ANN), have been applied on openly available smart energy grid datasets to predict their stability. The present article uses metrics such as accuracy, precision, recall, f1‐score, and ROC curve to compare different predictive models. Data augmentation and feature scaling have been applied to the dataset to get better results. The augmented dataset provides better results as compared with the normal dataset. This study concludes that the deep learning predictive model ANN optimized with Adam optimizer provides better results than other predictive models. The ANN model provides 97.27% accuracy, 96.79% precision, 95.67% recall, and 96.22% F1 score.
Eosinophil Levels, Neutrophil-Lymphocyte Ratio, and Platelet-Lymphocyte Ratio in the Cytokine Storm Period of Patients with COVID-19
Authors: Koc Ibrahim , Unalli Ozmen Sevda
Published: August 2022

Background. In the early stages of the COVID-19 pandemic, elevated inflammatory cytokine levels, particularly interleukin-6 (IL-6), were detected in patients with cytokine storm (CS). Aims. This study aimed to investigate levels, diagnostic usefulness, and optimal cutoff values of monocyte, eosinophil, neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR) in CS of patients with COVID-19 and also to identify risk factors for mortality. Methods. Seventy-six patients with COVID-19 who developed CS and randomly chosen 150 COVID-19 patients who had no CS during their stay in the hospital were included in the study. Results. Lymphocytes and eosinophil levels remained lower in the CS group. Patients with low lymphocyte levels had a higher risk for mortality (OR: 1.92). Neutrophil, D-dimer, ferritin, IL-6, NLR, and PLR were higher in the CS group. High levels of neutrophil, ferritin, D-dimer, and NLR and a history of coronary artery disease (CAD) and diabetes mellitus (DM) were identified as independent risk factors for mortality. Conclusion. In the light of the obtained results, COVID-19 patients with a decrease in lymphocyte levels and an increase in NLR and D-dimer levels and a history of CAD and DM have a higher risk of cytokine storm and mortality.
Optimization of English Learning Mode under the Influence of Artificial Intelligence Translation
Authors: Shen Gang , Feng Tao , Cheng Yu
Published: August 2022

With the development of social science and technology, artificial intelligence has been applied to many fields, and artificial intelligence translation has provided great help for language learners. This paper analyzes the necessity of artificial intelligence translation for English learning, explores the influence of artificial intelligence translation on English learning, and proposes optimized English learning modes which provide help to the people involved.
Sensor Optimization for Variation Diagnosis in Multistation Assembly Processes
Authors: He Kang , Li Xiaobiao , Sun Fei , Yang Quan , Wu Bo , Meng Chao
Published: August 2022

Appropriate sensor deployment is the key to the efficient diagnosis of product variation. Yet, optimizing sensor placement in complex manufacturing systems remains challenging. We propose a variation propagation analysis (VPA)-based sensor deployment strategy for variation diagnosis in multistation assembly processes. A state-space model is employed to analyze the influences of fixture faults and workpiece dimensional deviations on assembly variation. Based on matrix transformation, the assembly variation propagation characteristics are quantified and a VPN-based causal graph is constructed to represent the causality between assembly variation and sensor measurement. To ensure the diagnosability of over-tolerance of assembly variation (OAV) and the economics of the sensor system, an optimal sensor deployment scheme is presented. It uses the enhanced shuffled frog-leaping algorithm to minimize the OAV unobservability per unit cost and the sensor cost under the constraint of detectability. Finally, the effectiveness of the proposed approach is illustrated by a case study of sensor deployment for variation diagnosis in a multistation automobile differential assembly process.
Polymer Filament Process Parameter Optimization with Mechanical Test and Morphology Analysis
Authors: Subramani Raja , Kaliappan S. , Sekar S. , Patil Pravin P. , Usha R. , Manasa Narapareddi , Esakkiraj E. S.
Published: August 2022

3D printing is one of the emerging technologies in the manufacturing sector, and polymer materials play a vital role in the raw material of the additive manufacturing sector. This research explores reducing the production time by testing and analyzing the microstructure of the different polylactic acid (PLA) filament polymer samples. For this purpose, 15 pieces of ASTM (American society for testing and materials) D638 tensile samples with polylactic acid (PLA) filaments have been used exclusively with five different sets of modified process parameters in slicing software of 3D printing technology. The results of this research reveal the best PLA filament FDM production method in terms of time, mechanical strength, and FESEM analysis comparing all the results.
An Improved Image Spam Classification Model Based on Deep Learning Techniques
Authors: Singh A. Buboo , Singh Kh Manglem , Chanu Y. Jina , Thongam Khelchandra , Singh Kh Johnson
Published: August 2022

Image Spam is a type of spam that has embedded text in an image. Classification of Image Spam is done using various machine learning approaches based on a broad set of features extracted from the image. For its remarkable results, the convolutional neural networks (CNN) are widely used in image classification as well as feature extraction tasks. In this research, we analyze image spam using a CNN model based on deep learning techniques. The proposed model is fine-tuned and optimized for both feature extraction as well as for classification tasks. We also compared our proposed model to different “Improved” and “Challenge” image spam datasets, which were developed for increasing the difficulty of the classification task. Our model significantly improves the accuracy of the classification task as compared to other approaches on the same datasets.
Kidney Autotransplantation and Orthotopic Kidney Transplantation: Two Different Approaches for Complex Cases
Authors: Artiles Medina Alberto , Gómez Dos Santos Victoria , Díez Nicolás Víctor , Hevia Palacios Vital , Ruiz Hernández Mercedes , Laso García Inés , Mata Alcaraz Marina , Galeano Álvarez Cristina , Jiménez Cidre Miguel Ángel , Arias Fúnez Fernando , Fernández Lucas Milagros , Burgos Revilla Francisco Javier
Published: August 2022

Introduction. Transplantation surgery teams often have to face complex cases. In certain circumstances, such as occlusion of the iliac vessels or prior pelvic surgery, heterotopic kidney transplantation may not be feasible and orthotopic kidney transplantation (OKT) could be a good alternative. Kidney autotransplantation (KAT) has been described as a potential treatment for complex renovascular, ureteral, or neoplastic conditions. There are scarce data regarding the complications and outcomes of these procedures; therefore, we present our experience. Materials and Methods. We retrospectively analysed the medical records of both 21 patients who had received OKT and 19 patients who underwent KAT between 1993 and 2020. We collected demographic features and data regarding surgical technique, complications, and graft outcomes. Kidney graft survival was calculated using Kaplan–Meier survival analysis. Results. Regarding OKT, in 15 (71.43%) cases, it was the first kidney transplantation. The most common indication was the unsuitable iliac region due to vascular abnormalities (57.14%). The early postoperative complication rate was high (66.67%), with 23.81% of Clavien grade 3b complications. During the follow-up period (mean 5.76 -SD 6.15- years), we detected 9 (42.85%) graft losses. At 1 year, the survival rate was 84.9%. Concerning KAT, the most frequent indication was ureteral pathology (52.63%), followed by vascular lesions (42.11%). The overall early complication rate was 42.11%. During the follow-up period (mean of 4.47 years), 4 (15.79%) graft losses were reported. Conclusions. Although OKT and KAT have high complication rates, these techniques can be considered as two valuable approaches for complex cases, in the absence of other therapeutic options.
The Risk Model of Traffic Engineering Investment and Financing by Artificial Intelligence
Authors: Wang Shangen , Zhang Wei
Published: August 2022

This study aims to analyze the influencing factors and mechanisms of investment and financing risks in transportation projects so that regions do not restrict the transportation investment and financing risk models in all areas to achieve intelligent transportation financial risk assessment. Firstly, the investment and financing modes are studied and analyzed. According to the analysis of intellectual investment and the financing report of traffic engineering infrastructure, a traffic engineering investment and a financing model based on intelligent computing is established, which is based on artificial intelligence (AI) big data analysis technology. Secondly, the investment and the financing risk model of traffic engineering is established based on multimodal learning. Finally, the urban traffic engineering of Xi’an is taken as the research object. Based on its investment and financing data in the construction of urban roads, the risk assessment is carried out. Combined with risk influencing factors, the accuracy of the intelligent calculation in the risk assessment model is calculated. Different grades of urban transportation projects have different risks in the investment and financing of transportation projects. The results show that different levels of urban transport projects have different risks in the investment and financing (IAF) performance of transport projects. Among them, the risk index of the first-class project is the highest, reaching 0.55. The risk index of the second-class project is 0.49. The results before and after using the flow engineering IAF risk model are compared. In the test results of traffic engineering risk, all target risks did not increase after the AI-based traffic engineering IAF is tested. The model test results for credit risk and financial risk are the highest at 70 and 60, respectively. Combined with the actual urban development situation, this study can provide investment and financing risk models for urban transportation projects in different regions and provide a reference for the resource control of transportation projects. This study uses AI to learn and analyze traffic engineering investment and financing data and more accurately provide data references for traffic engineering investment and financing risk models.
Image Self-Coding Algorithm Based on IoT Perception Layer
Authors: Wu Hao
Published: August 2022

In fact, with the quick growth of IoT-related industries in recent years, multimedia contents such as digital image videos have also shown explosive growth. In the sensing layer of the three-layer IoT architecture, sensors are the most critical part, which mainly sense the state of the environment. In this paper, an image self-coding algorithm based on the IoT perception layer is proposed. There is no specific encoding algorithm for the pictures collected by the current Internet of Things network perception layer. This results in poor search results for the network images collected by the sensor at the perception layer. A deep convolutional neural network image self-coding algorithm based on the IoT perception layer combines prior knowledge with deep involutional aural networks to increase the discriminative ability of images while preserving the message of the images themselves and improving the goal of image search accuracy. The experimental results show that the block search algorithm is used for image registration to reduce energy consumption, the absolute difference sum algorithm is improved to improve the accuracy of image registration, and the progressively out weighted average algorithm is used to stitch the images. After image stitching, the communication volume with the base station is reduced, which can effectively reduce the network load by 60%.
Addressing Common Misuses and Pitfalls of P values in Biomedical Research
Authors: Wang Ming , Long Qi
Published: August 2022

In recent years, there has been a growing recognition that P values, albeit useful in reporting data analysis results, have often been misused or misinterpreted in biomedical research. The emergence of big health data such as genomics data and electronic health records, sometimes combined with inadequate experimental design, has exacerbated this problem, which has become a major cause of the ongoing crisis in reproducibility in biomedical research. We aim to shed light and raise awareness of common misuses and pitfalls of P values and discuss potential mitigation strategies that leverage state-of-the-art statistical methods. The best practices always start with a sound study design including a robust data collection strategy to minimize data bias and a carefully thought-out analysis plan that can address potential misuses and pitfalls of P values. We highly encourage biomedical researchers to engage and involve statisticians from the very beginning of their studies.