bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Impact of Wood Waste Ash on Physical Mechanical Properties of Concrete
Authors: Vaiciene Marija , Malaiskiene Jurgita 
Published: December 2021

Abstract
In this work is analysing the impact of wood waste bottom ash (WWBA) on the physical mechanical properties of Portland cement concrete (PCC). WWBA is a waste generated in power plants during burning forest residues to produce energy and heat. In 2019, about 19,800 tons of WWBA was generated only in Lithuania. Usually, WWBA is disposed of in landfills, only 26% of WWBA is used in the construction or maintenance of local roads, because of that it is useful to know properties of such WWBA and to analyse possibilities of using it in cement concrete. In the chemical composition of such WWBA type was fixed a big amount ~50% of CO2. It is known, that C retards cement hydration. Due to stabilisation this process, it was used in the same amounts catalyst waste from oil cracking (FCCCw), which could accelerate hydration processes. Oil refineries worldwide generate more than 800,000 tonnes of FCCCw per year, of which around 20% in Europe and it is the big problem to landfill. In the investigation the amount of Portland cement (5-20% by mass) was replaced by mentioned wastes and properties of fresh PCC (density, slump, flow diameter) and physical mechanical properties of hardened PCC (water absorption, capillary water absorption, ultrasound pulse velocity, density, compressive strength after 28 days and 2 years curing, SEM) were established. It was determined, that by increasing amount of waste (till 20%) the workability of concrete decreases, because used wastes had higher water requirement. The best results were obtained, when 5% of cement was replaced by WWBA. Then compressive strength after 28 days curing comparing to control sample decreased 8%, but after 2 years curing it increased 1%, also the capillary water absorption decreased, denser structure was formed. The obtained results of hardened PCC density, ultrasound pulse velocity and water absorption are similar to control samples.
Influence of Slenderness on the Evaluation of Epistemic Uncertainty Related to Non-Linear Numerical Analysis of RC Columns
Authors: Gino Diego , Anerdi Costanza , Bertagnoli Gabriele , Giordano Luca , Marano Giuseppe 
Published: December 2021

Abstract
This investigation is devoted to quantify the epistemic uncertainty related to the nonlinear analysis of reinforced concrete columns characterized by high slenderness using numerical codes. The adoption of refined numerical tools, which are able to consider both mechanical and geometric non linearities, implies to perform assumptions and approximations with respect to reality. Whit reference to reliability analysis, these simplifications lead, inevitably, to additional uncertainties which are of epistemic nature. In fact, these uncertainties may be reduced by the engineers/analysts by increasing the level of refinement of the numerical model and/or increasing knowledge about parameters associated to material models. However, also numerical model established by expert engineers/analysts are affected by this kind of epistemic uncertainty. Accepting that the level of uncertainty associated to the experimental tests set are minimized, the epistemic uncertainty associated to non-linear numerical simulations can be quantified characterizing the model uncertainty random variable comparing the outcomes of numerical results to the associated experimental ones. The present investigation proposes the quantification of the model uncertainty related to non-linear numerical simulations of slender RC columns. A total number of 40 experimental results known from literature are herein selected in coherence with current Eurocodes specifications. The experiments are reproduced adopting non-linear numerical analysis differentiating between several modelling hypotheses (i.e., numerical code; materials models). The comparison between experimental and numerical results is adopted to characterize the most suitable probabilistic model for the model uncertainty random variable associated to non-linear numerical simulations of RC columns subjected to significant slenderness. The outcomes of the research are useful to provide background to the characterization of partial safety factor for model uncertainty in non-linear numerical analysis using the approach of the global resistance format for safety verifications.
Wind-Induced Vibration of an Irregular Pentagon Lamella
Authors: Jendzelovsky Norbert , Antal Roland 
Published: December 2021

Abstract
At present, there are increasingly encountering the use of lamellar structures, for example on the roofs of buildings, which, in addition to their visual function, also fulfil the function of reducing the flow of wind into the roof space. These structures are often designed as long and subtle structures and therefore their very common problem is unwanted vibration. In this article, the main focus is to show the methodology of the determination of the effects of wind on the lamella of the shape of an irregular pentagon. A real-size model made of steel with a total length of 2 m and a weight of 7.4 kg was used. Its size and shape were influenced by several factors which are specified in more detail in the paper. In the wind tunnel experiment, it was very important to ensure the exact position of the model and also to secure both ends of the model against shifting (to replicate fixed ends). Dynamic response of the structure in two directions together with wind speed were measured simultaneously. To investigate the wind effects by numerical analysis, fluid-structure interaction software simulation (FSI) on a full-size model was used. The main pitfall of the software solution was to get as close as possible to the conditions of the wind tunnel. The actual wind speed measured under laboratory conditions was used as the input wind speed for FSI simulation. The material of the model and the shape of the model was set in software simulation to be as close as possible to the real structure. Subsequently, other boundary conditions were set and the solution process was executed. The biggest problem, especially in terms of comparing the results of both approaches which greatly affected the results, was the very high stiffness of the model. Due to the extent and interconnectedness of results, findings are presented in more detail in the conclusions of the paper. The methodology of setting up a relatively complex FSI simulation, its results, as well as new findings that we came up with if the measurement of the dynamic effects of wind is the matter of interest are presented in this paper.
Bahareque as a Sustainable Construction System: Analysis of Unit Prices
Authors: Palacios Andrés , Angumba Pedro 
Published: December 2021

Abstract
The bahareque is a form of vernacular construction that, with the help of modern materials with adequate techniques and knowledge, constitute an alternative to provide housing solutions. Despite this, the lack of knowledge and technical processes in relation to costs, gives rise to a research problem. Thus, this document presents a set of technical strategies based on the Cost-Benefit analysis of using bahareque as a raw material and structuring a unit price analysis tool to guide construction professionals. The study had a qualitative and quantitative approach, surveys were applied to the population and professionals in the construction area that allowed to approach the reality of the construction system in the city of Cuenca-Ecuador. It is evident that people know about the construction system, but show some resistance to its application because they are unaware of the advantages and disadvantages of the vernacular technique. With the cost analyzes carried out, it is concluded that a typical house of 130 m2 has a cost of $ 36,387.72 dollars; which, starts from concrete foundations, which support a reinforced masonry assembly that raises the wooden frames, associated with meshes that erect the walls with mortar and finally covered with roofs with a mixed material of wood and tile fired with clay.
Seismic Vulnerability and Risk of Losses Case Study Center of the City of Azogues
Authors: Castro Carlos Julio Calle , Noboa Juan Sebastián Maldonado , Sánchez Luis Mario Almache 
Published: December 2021

Abstract
Ecuador is located in the Pacific Ring of Fire, a country with high risk and seismic sensitivity, evidenced by the 6.8-degree earthquake in Ambato in 1949, which left approximately 6000 dead, the 7.8-degree earthquake in Manabí and Esmeraldas in the year 2016 with 663 victims and 29672 buildings without the possibility of use. Currently there is a problem about seismic performance in reinforced concrete buildings, since many were built with old regulations; so, it is necessary to assess their vulnerability. Quito, Guayaquil and Cuenca, large cities in Ecuador, have formal studies of seismic vulnerability, mostly carried out by university students and teachers. In contrast, most small cities do not have these studies; or, they need to be updated to validate their results. This is the case of the city of Azogues. The objective of this research is to evaluate the vulnerability of structures using the Hazus methodology, adapted to Ecuador, in the downtown area of the city of Azogues, in structures located around the Central Park, to establish the seismic performance in reinforced concrete buildings. The Hazus methodology, which determines the vulnerability of buildings from fragility curves, which are entered with inputs as the capacity, performance level and drift curves calculated through Ecuadorian models. The capacity curves, depending on various aspects such as: the material, number of floors, spans between columns, among others; they vary from building to building. In this sense, capacity curves were defined for sets of buildings with similar characteristics, coinciding with the Hazus methodology. The performance levels and the displacements were calculated with the ETABS computer package. For fragility curves, the model that most real simulates the response of a structure is the non-linear analysis, because it considers the decrease in stiffness in columns and beams, as well as the deterioration of the properties of the materials. In this sense, there are fragility curves of Ecuadorian buildings for four levels. The earthquake readings enable the construction of a demand spectrum, which, when contrasted with the capacity spectrum, leads to the performance point. Its position sometimes varies per the elastic demand spectrum, which is diminished by its inelastic behavior. As the demand spectrum decreases, the damage will increase. Once the coordinates of the performance point are known, the fragility curves are used; and, the possible damages are defined, quantifying them in percentage.
Materials and technologies to achieve better energy efficiency of buildings
Authors: Łoziczonek Henryk B. 
Published: December 2021

Abstract
The article will discuss the aspects of the possibilities of improving the energy efficiency of buildings. In particular, the author will analyze innovative technologies in the field of improving the thermal insulation of external partitions and improving the efficiency of underfloor heating. The author will present the results of research on the thermal properties of metal and wooden frame structures, as well as innovative veneer floor panels. Frame structures are increasingly used in single-family housing, and veneer floor panels are used in underfloor heating. Innovative solutions of building partitions are to improve the energy efficiency of newly constructed buildings, and thus reduce the emission of greenhouse gases into the atmosphere.
Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network
Authors: Hou Xukun , Hu Pengjie , Du Wenliao , Gong Xiaoyun , Wang Hongchao , Meng Fannian 
Published: December 2021

Abstract
Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.
Modelling dimensional change of machined parts and optimizing replacement time and initial position of cutting tool
Authors: Jiang Renyan 
Published: December 2021

Abstract
In a manufacturing process, a key quality characteristic such as a certain dimension of machined parts can have a systematic shift toward the upper or lower specification limit due to the cutting tool wear. That is, the quality characteristic is a stochastic process with trend. As a result, the process capability and quality loss vary with time and depend on the initial position of the cutting tool. In this setting, decision problems that need to be solved are optimization of replacement time and initial position of the cutting tool. This paper proposes a non-homogenous Wiener process to model the dimensional change of machined parts; and the above-mentioned decision problems are solved through combining this model with a cost model that involves Taguchi quality loss. A real-world example is included to illustrate the appropriateness of the proposed models. The results are useful for both reliability researchers and manufacturing engineers.
Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races
Authors: Luo Wei , Yan Changfeng , Yang Junbao , Liu Yaofeng , Wu Lixiao 
Published: December 2021

Abstract
Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.
Comparative analysis and forecasting of isentropic efficiency of gas turbine compressor with ARIMA, VAR, NARNN and ANFIS approaches
Authors: Yao Jiachi , Liu Chao , Jin Yunfeng , Deng Gaofeng , Guan Yunlong , Hao Jiangang , Huang Haizhou , Jiang Dongxiang 
Published: December 2021

Abstract
It is extremely important to monitor the status of gas turbine to ensure its safe and reliable operation. In this work, the variation trend of isentropic efficiency of compressor is analysed based on the measured data of F-class heavy-duty gas turbine in practical industrial application. The actual measured data of F-class heavy-duty gas turbine includes the data under start-stop and unstable working conditions, which cannot be directly used for calculation and analysis. To solve this problem, the data selection rules are designed and determined according to the operating conditions of gas turbine to select the data under effective working state. The isentropic efficiency of compressor is calculated based on the selected data. Then the forecasting effects of four forecasting methods on the variation trend of isentropic efficiency of compressor are studied. Four indexes, namely, symmetric mean absolute percentage error (SMAPE), mean absolute percentage error (MAPE), root mean square error (RMSE), and similarity (SIM) values are utilized to evaluate the forecasting accuracy. The research results indicate that the Adaptive Neuro-Fuzzy Inference System (ANFIS) method has better forecasting effect than Autoregressive Integrated Moving Average (ARIMA), Vector Autoregression (VAR) and Nonlinear Autoregression Neural Network (NARNN) for this F-class heavy-duty gas turbine. Through the ANFIS method, the SIM up to 96.77%, the SMAPE and MAPE are less than 0.1, and the RMSE is only 0.1157. Therefore, the ANFIS method is suitable for forecasting the isentropic efficiency of this F-class heavy-duty gas turbine compressor.
Adversarial flow-based model for unsupervised fault diagnosis of rolling element bearings
Authors: Dai Jun , Wang Jun , Yao Linquan , Shi Juanjuan , Wang Lei , Wang Guolei 
Published: December 2021

Abstract
Nowadays, numerous supervised deep learning models have been applied to bearing fault diagnosis. However, labelling the health states of the bearing vibration data is a time-consuming work and dependent on expert experience. In order to tackle this problem, a novel unsupervised bearing fault diagnosis method named adversarial flow-based model is explored in this paper. Flow-based model is a type of generative models that is proved to be better than other types in many aspects. This paper introduces the flow-based model into the field of machinery fault diagnosis, and designs an appropriate model architecture so as to train the model in unsupervised and adversarial ways. The proposed model contains an autoencoder (AE), a flow-based model, and a classifier. Firstly, the AE maps the vibration data from signal space to latent vector space. Then, the flow-based model aligns the distributions of the latent vectors of different bearing states with specific prior distributions. Finally, the classifier tries to discriminate the aligned latent vectors from the vectors sampled from the prior distributions. With the help of distinguishable prior distributions and the adversarial training mechanism between the classifier and the flow-based model together with the AE, the bearing data with the same health states are clustered into the same areas. The good clustering performance of the adversarial flow-based model is verified by a dataset with 10 health states from a bearing test rig.
Joint feature enhancement mapping and reservoir computing for improving fault diagnosis performance
Authors: Kong L J , Huang Y W , Yu Q B , Long J Y , Yang S 
Published: December 2021

AbstractComplicated industrial robot structure and harsh working conditions may cause signal features collected in the condition monitoring process to be seriously disturbed. In this paper, a joint feature enhancement mapping and reservoir computing (FEM-RC) method is presented to handle the industrial robot fault diagnosis problem. Firstly, a feature enhancement mapping (FEM) method is proposed to achieve intraclass distance minimization and interclass distance equalization to obtain an enhanced feature matrix. Then, the first reservoir computing (RC) network is adopted to map the original feature matrix to the feature enhancement matrix, and the second RC network is for fault type classification. The results of the experiment carried out on a six-axial industrial robot demonstrate that compared with other peer models, the present FEM-RC has better fault diagnosis performance and robustness.
AMDE: a novel attention-mechanism-based multidimensional feature encoder for drug–drug interaction prediction
Authors: Pang Shanchen , Zhang Ying , Song Tao , Zhang Xudong , Wang Xun , Rodriguez-Patón Alfonso 
Published: December 2021

Abstract
The properties of the drug may be altered by the combination, which may cause unexpected drug–drug interactions (DDIs). Prediction of DDIs provides combination strategies of drugs for systematic and effective treatment. In most of deep learning-based methods for predicting DDI, encoded information about the drugs is insufficient in some extent, which limits the performances of DDIs prediction. In this work, we propose a novel attention-mechanism-based multidimensional feature encoder for DDIs prediction, namely attention-based multidimensional feature encoder (AMDE). Specifically, in AMDE, we encode drug features from multiple dimensions, including information from both Simplified Molecular-Input Line-Entry System sequence and atomic graph of the drug. Data experiments are conducted on DDI data set selected from Drugbank, involving a total of 34 282 DDI relationships with 17 141 positive DDI samples and 17 141 negative samples. Experimental results show that our AMDE performs better than some state-of-the-art baseline methods, including Random Forest, One-Dimension Convolutional Neural Networks, DeepDrug, Long Short-Term Memory, Seq2seq, Deepconv, DeepDDI, Graph Attention Networks and Knowledge Graph Neural Networks. In practice, we select a set of 150 drugs with 3723 DDIs, which are never appeared in training, validation and test sets. AMDE performs well in DDIs prediction task, with AUROC and AUPRC 0.981 and 0.975. As well, we use Torasemide (DB00214) as an example and predict the most likely drug to interact with it. The top 15 scores all have been reported with clear interactions in literatures.
Corticosteroids and mycophenolic acid analogues in immunoglobulin A nephropathy with progressive decline in kidney function
Authors: Huerta Ana , Mérida Eva , Medina Laura , Fernandez Maria , Gutierrez Eduardo , Hernandez Eduardo , López-Sánchez Paula , Sevillano Angel , Portolés Jose , Trimarchi Hernan , Praga Manuel 
Published: December 2021

ABSTRACT

Background
A randomized controlled trial demonstrated a beneficial effect of corticosteroids (CS) + cyclophosphamide followed by azathioprine in progressive immunoglobulin A nephropathy (IgAN). Although treatment with CS and mycophenolic acid analogues (MPAAs) remains controversial in IgAN, there is no information about their effects in progressive IgAN.

Methods
Patients with progressive IgAN, defined by a decrease in estimated glomerular filtration rate (eGFR) of at least 10 mL/min/1.73 m2 in the 12 months prior to the start of treatment, proteinuria ≥0.75 g/24 h despite maximum tolerated doses of renin–angiotensin system blockers, and persistent haematuria who had received treatment with CS + MPAA were included in this retrospective study. The main outcome was the difference between the eGFR slope from the start of treatment with CS + MPAA to the last visit with this treatment with respect to the eGFR slope during the 12 months prior to the start of treatment.

Results
A total of 25 patients were included in the study. The mean duration of CS + MPAA treatment was 24.7 ± 15.2 months. In the 12 months prior to treatment the median rate of kidney function decline was 23 mL/min/1.73 m2/year [interquartile range (IQR) –32 to –16]. After the onset of treatment, the median eGFR slope was 5 mL/min/1.73 m2/year (IQR 3–9; P = 0.001 with respect to the 12 months prior to treatment). Proteinuria decreased from 1.8 g/day (IQR 1.0–2.5) at baseline to 0.6 g/day (IQR 0.3–1.2) at the end of treatment (P = 0.01) and haematuria disappeared in 40% of patients. There were no serious adverse effects requiring treatment discontinuation.

Conclusions
CS + MPAA is an effective treatment in IgAN patients with a sustained decline in kidney function accompanied by persistent proteinuria and haematuria despite optimized conservative treatment. Prospective studies are needed to confirm these results.