bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimization of Bolted Connection in Steel Corbel Attach to RC Column of MRT Viaduct
Authors: Hamid Yazmin Sahol , Mansor Hazrina , Razak Amir Atif Abdul , Bukhory Haikal Ajmal Bin , Ahmad Nursafarina 
Published: November 2021

Abstract
MRT is one of the backbones of a city’s public transportation system, capable of carrying large crowds. The use of a portal frame in the design of an MRT train station has raised significant concerns about how the portal frame’s load will be supported by the extended structure element known as a corbel. A corbel is a protruding structural element that supports weights like primary beams and girders. Engineers must then decide how to properly bolt the steel corbel structure to the concrete pier segment or columns. Generally, most corbel structure designs were constructed of concrete; however, in this study corbel design was made of steel so that the steel portal frame could rest on the corbel structure, allowing for more usable area on the platform, such as kiosks and other amenities. Optimization of end plate thickness and beam web thickness is carried out. Manual calculations are used in addition to FEA modeling to examine the bolt’s deflection, shear, bearing, tension, slip, and block tearing resistance. When using Eurocode, all three loadings, transverse force, vertical force, and transverse moment, produce values that are 10% lower than the British Standard. As a result, designers can optimize their designs using Eurocode.
Flexural Capacity of RC Beams with Opening Strengthened using CFRP Sheet
Authors: Jusoh M M , Ali N , Abdul Hamid N A , Salleh N , Abdullah S R , Shahidan S 
Published: November 2021

Abstract
The use of Carbon Fibre Reinforced Polymer (CFRP) in strengthening has found to be an effective material which comprises of characteristic that comply to the requirement of structural component. CFRP was selected as strengthening material because of the capability to resist the corrosion and could regain the loss capacity due to presence of opening. The opening in structural member was essential in order to provide the route for the utility pipe, air conditioning, water supply and electrical conduit. However, the presence of opening has contributed to the reduction of stiffness, increase of deflection and extension of cracking of the beams. Therefore, this research was conducted to overcome the problem where the flexural capacity and the load deflection behavior of RC beam with opening strengthened by using CFRP sheet was analyzed. A total of five beam have been casted and tested. The specimens consist of beams with different type of opening which are rectangular and circular. The size of all specimen was 200 mm width, 250 mm height and 2000 mm for total length. The size of circular opening was 150 mm in diameter while rectangular opening was 150 x 200 mm. Bi-directional CFRP sheet were applied at the opening area as strengthening material and all beams were tested until failure. All of specimen were produced with the designed using 30 mm concrete cover, 6 mm link size and 10mm main bar size. The testing of specimens comprises of cube compressive test and four-point load for beam testing in order the determine the flexural strength of RC beam. The result from this research indicated that strengthened beam with circular opening which is SBOC-BI exhibit the highest ultimate load of 71.5 kN with flexural failure as the mode of failure.
Numerical Analysis of Crack Propagation in Tubular Glass Column
Authors: Ahmed-Abdullamohamed A W , Kamarudin M K , Yussof M M. 
Published: November 2021

Abstract
The demand for transparency has increased in the construction industry and contemporary architecture over the last decade. The prior researchers focused on glass columns because their uniqueness and transparent characteristics generate an impressive visual feature. Past studies on structural glass entailed numerous experimental investigations, but FEA was applied in a few investigation exercises. This study aims to validate the experimental data and analyse the crack in the tubular glass column and determine the effectiveness of different slenderness ratios of the glass column. This study investigated the column structural behaviour under compression with different geometrical dimensions of hollow section laminated glass columns to determine their load-carrying capacity, buckling performance, and failure mechanism. Finite element analysis using the explicit method was performed by using ABAQUS. The study found that the failure mechanisms depend on the slenderness ratio classified into two failure modes, either buckling or crushing. The glass column failed due to buckling when the slenderness ratio is more than 40, while it failed due to crushing when the slenderness ratio is less than 40. The finite element analysis did not correlate perfectly with the experimental data since the FEA underestimating the glass performance.
Readiness of Malaysian Small and Medium Enterprises Construction Companies for Building Information Modelling Implementation
Authors: Arif N K , Hasmori M F , Deraman R , Yasin M N , Mohd Yassin M A 
Published: November 2021

Abstract
Building Information Modelling (BIM) has shown its efficiency in helping the construction industry players. The BIM helped to achieve better integration of project information and to enhance collaboration between stakeholders and the industry players from the early phase of projects until the end. Small and medium-sized companies (SMEs) have been under-represented in studies on BIM adoption. Although the technology to implement BIM is readily available and rapidly maturing, the adoption of BIM is still slow. Malaysian government support the adoption of BIM-oriented Integrated Design (ID) approaches in their national markets, however, the implementation of BIM software support systems among Small and Medium Enterprises (SMEs) in Malaysia is considered inadequate. In terms of the implementation of information technology, the construction sector in Malaysia still lags behind other sectors. To have a better knowledge of BIM in SMEs, it is important to first identify the major barriers to BIM adoption in SMEs and then examine potential solutions. Hence, the main motivation of this study is to investigate the readiness of Malaysian SMEs construction companies for BIM implementation, to determine the challenges of BIM implementation, and to develop a strategy to overcome the challenges. An online questionnaire survey was conducted and as many as 115 responses were collected. A quantitative approach was used for this research. This study uses a descriptive analysis method which comprised of structured questionnaires developed from reviews of past researches. The finding of this study indicate that BIM adoption in Malaysia is still relatively low. Many actions are needed to promote BIM adoption among construction SMEs in Malaysia.
Key Design Issues in the Construction Project: Conceptual and Detailed Design Review Phases
Authors: Sarbini N N , Abdul Aziz A N , Mazlan A N , Abdul Shukor Lim N H , Rahman Mohd Fairuz Ab 
Published: November 2021

Abstract
The Malaysian construction industry is among the highest ranked industries that contributes to accident and fatality rates. The Guidelines on Occupational Safety and Health in Construction Industry (Management) (OSHCIM) has introduced guidelines for design risk reduction in the construction industry. It involves determining root causes and mitigating the risk by control of the causation of accidents. The objective of this study is to identify the key design issues during the conceptual and detailed design review phases in the construction project. Although some of the design considerations have already been proposed by the OSCHIM guidelines, however, further implementation need to be diverse and versatile by considering the current design practices that are already being practiced by the industry practitioners. The methodology of this research involves document reviews, and a questionnaire survey that was used to collect answers from respondents in the construction industry. According to the results, the key design issues in the conceptual phase involve the site condition, environmental influences, ground or ground water condition, existing structure, demolition, services, adjacent to major infrastructure, traffic disruption, access for works, adjacent to other properties and projects, and site restriction. Moreover, those found in the detailed phase involve the mechanized construction system, installation of prefabrication components, ease of process, structural opening, edge line, fall hazard, accident prevention, layout optimization, ease of activities, permanent safety features, provision of access, and fall hazard. However, it is the duty of the construction industry practitioners to make sure that all other design issues are also included into their construction project risk analysis. As proposed by the OSHCIM guidelines, the review of design risks in the construction project should consider safety in all the stages: during construction, operation, maintenance, modification, and demolition. Construction Industry Development Board (CIDB)
Triaxial Testing on Geogrid-reinforced Granular Soils
Authors: Mindiastiwi Tigo , Wu Po-Kai , Siswanto Agus Bambang , Salim Mukhamad Afif 
Published: November 2021

AbstractLaboratory triaxial compression tests were carried out to investigate the mechanical behavior of dense sand and geogrid-reinforced granular soils. The tested sand having its mean particle size (D50) equal to 0.6 mm was adopted. Three geogrids with different longitudinal and transverse nominal strengths were used. The dimensions of the cylindrical soil specimen were 70 mm (diameter) × 160 mm (height). The relative density was equal to 70% for all tests. The reinforced sand specimens with one or two geogrid layers were sheared under effective confining pressures (σ′3) equal to 50 kPa. The test results of unreinforced sand indicate the general stress-strain behavior of dense sand when sheared, whereas the deviatoric stress reaches its peak value, after which it gradually decreases to ultimate value (σ1- σ3)ult.The difference of effective confining pressure indicates that the peak of deviatoric stress Δσd= (σ1- σ3) increases with the increase in effective confining pressure (σ′3), while the peak principal stress ratio (σ′1/σ′3) decreases with the increase (σ′3). The friction angle (ϕ′)and cohesion (c′), defined by analytical and graphical methods for unreinforced sand. Geogrid as reinforcement increasing peak shear strength. The increasing peak shear strength is more pronounced with a higher number of geogrid and the geogrid with higher stiffness. Increased in confining stress inside reinforced soil mass (Δσ3R) can be interpreted by cohesive reinforced soil (CR).
A review on fluorescence spectroscopic analysis of water and wastewater
Authors: Khan Muhammad Farooq Saleem , Akbar Mona , Wu Jing , Xu Zhou 
Published: November 2021

Abstract
In recent years, the application of fluorescence spectroscopy has been widely recognized in water environment studies. The sensitiveness, simplicity, and efficiency of fluorescence spectroscopy are proved to be a promising tool for effective monitoring of water and wastewater. The fluorescence excitation-emission matrix (EEMs) and synchronous fluorescence spectra have been widely used analysis techniques of fluorescence measurement. The presence of organic matter in water and wastewater defines the degree and type of pollution in water. The application of fluorescence spectroscopy to characterize dissolved organic matter (DOM) has made the water quality assessment simple and easy. With the recent advances in this technology, components of DOM are identified by employing parallel factor analysis (PARAFAC), a mathematical trilinear data modeling with EEMs. The majority of wastewater studies indicated that the fluorescence peak of EX/EM at 275 nm/340 nm is referred to tryptophan region (Peak T1). However, some researchers identified another fluorescence peak in the region of EX/EM at 225–237 nm/340–381 nm, which described the tryptophan region and labeled it as Peak T2. Generally, peak T is a protein-like component in the water sample, where T1 and T2 signals were derived from the <0.20 μm fraction of pollution. Therefore, a more advanced approach, such as an online fluorescence spectrofluorometer, can be used for the online monitoring of water. The results of various waters studied by fluorescence spectroscopy indicate that changes in peak T intensity could be used for real-time wastewater quality assessment and process control of wastewater treatment works. Finally, due to its effective use in water quality assessment, the fluorescence technique is proved to be a surrogate online monitoring tool and early warning equipment.
Optimization of the thermophysical properties of the thermal barrier coating materials based on GA-SVR machine learning method: illustrated with ZrO2doped DyTaO4system
Authors: Zhu Wenjie , Gan Mengdi , Ye Bo , Xiong Xin , Feng Jing , Chong Xiaoyu 
Published: November 2021

AbstractIt is a critical issue to reduce the thermal conductivity and increase the thermal expansion coefficient of ceramic thermal barrier coating (TBC) materials in the course of their utilization. To synthesize samples with different composition and measure their thermal conductivity by the traditional experimental approaches is time-consuming and expensive. Most classic and empirical models work inefficiently and inaccurately when researchers attempt to predict the thermophysical properties of TBC materials. In this research project, we tentatively exploit a Genetic Algorithm-Support Vector Regression (GA-SVR) machine learning model to study the thermophysical properties, illustrated with the potential TBC materials ZrO2doped DyTaO4, which has resulted in the lowest thermal conductivity in rare earth tantalates RETaO4system. Meanwhile, we employ statistical parameters of correlation coefficient (R2) and mean square error (MSE) to evaluate the accuracy and reliability of the model. The results reveal that this model has brought about high correlation coefficients of thermal conductivity and thermal expansion coefficient (99.8% and 99.9%, respectively), while the MSE values are 0.00052 and 0.00019, respectively. The doping concentration of ZrO2was optimized to reach as low as 0.085–0.095, so as to reduce their thermal conductivity further and increase their thermal expansion. This model provides an accurate and reliable option for researchers to design ceramic thermal barrier coating materials.
Mode structure symmetry breaking of reversed shear Alfvén eigenmodes and its impact on the generation of parallel velocity asymmetries in energetic particle distribution
Authors: MENG Guo , LAUBER Philipp , WANG Xin , LU Zhixin 
Published: November 2021

Abstract
In this work, the gyrokinetic eigenvalue code LIGKA, the drift-kinetic/MHD hybrid code HMGC and the gyrokinetic full-f code TRIMEG-GKX are employed to study the mode structure details of reversed shear Alfvén eigenmodes (RSAEs). Using the parameters from an ASDEX-Upgrade plasma, a benchmark with the three different physical models for RSAE without and with energetic particles (EPs) is carried out. Reasonable agreement has been found for the mode frequency and the growth rate. Mode structure symmetry breaking (MSSB) is observed when EPs are included, due to the EPs’ non-perturbative effects. It is found that the MSSB properties are featured by a finite radial wave phase velocity, and the linear mode structure can be well described by an analytical complex Gaussian expression

Φ
(
s
)
=

e

−
σ

s
−

s

0

2

with complex parameters σ and s
0, where s is the normalized radial coordinate. The mode structure is distorted in opposite manners when the EP drive shifted from one side of

q

min

to the other side, and specifically, a non-zero average radial wave number 〈k

s
〉 with opposite signs is generated. The initial EP density profiles and the corresponding mode structures have been used as the input of HAGIS code to study the EP transport. The parallel velocity of EPs is generated in opposite directions, due to different values of the average radial wave number 〈k

s
〉, corresponding to different initial EP density profiles with EP drive shifted away from the

q

min

.
Distant metastasis identification based on optimized graph representation of gene interaction patterns
Authors: Su Ran , Zhu Yingying , Zou Quan , Wei Leyi 
Published: November 2021

AbstractMetastasis is a major cause of cancer morbidity and mortality, and most cancer deaths are caused by cancer metastasis rather than by the primary tumor. The prediction of metastasis based on computational methods has not been explored much in the previous research. In this study, we proposed a graph convolutional network embedded with a graph learning (GL) module, named glmGCN, to predict the distant metastasis of cancer. Both the mRNA and lncRNA expressions were used to provide more genetic information than using the mRNA alone and we used them to construct gene interaction graph representation to consider the effect of genetic interaction. Then, the prediction of the cancer metastasis was performed under a GCN framework, which extracted informative and advanced features from the built non-regular graph structures. Particularly, a GL module was embedded in the proposed glmGCN to learn an optimal graph representation of the gene interaction. We firstly constructed the protein–protein interaction network to represent the initial gene(node) relationship graph. Then, through the GL module, a new graph representation was built which optimally learned the gene interaction strength. Finally, the GCN was adopted to identify the distant metastasis cases. It is worth mentioning that the proposed method pays more attentions on the gene–gene relation than the previous GCN-based method, so more accurate prediction performance can be obtained. The glmGCN was trained based on two types of cancer and was further validated using two other cancer types. A series of experiments have shown that the effectiveness of the proposed method. The implementation for the proposed method is available at https://github.com/RanSuLab/Metastasis-glmGCN.
PhosPiR: an automated phosphoproteomic pipeline in R
Authors: Hong Ye , Flinkman Dani , Suomi Tomi , Pietilä Sami , James Peter , Coffey Eleanor , Elo Laura L 
Published: November 2021

Abstract
Large-scale phosphoproteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from these data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from large-scale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phosphosite annotation and translation across species, multilevel enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.
Predicting miRNA-disease associations using an ensemble learning framework with resampling method
Authors: Dai Qiguo , Wang Zhaowei , Liu Ziqiang , Duan Xiaodong , Song Jinmiao , Guo Maozu 
Published: November 2021

Abstract
Motivation: Accumulating evidences have indicated that microRNA (miRNA) plays a crucial role in the pathogenesis and progression of various complex diseases. Inferring disease-associated miRNAs is significant to explore the etiology, diagnosis and treatment of human diseases. As the biological experiments are time-consuming and labor-intensive, developing effective computational methods has become indispensable to identify associations between miRNAs and diseases. Results: We present an Ensemble learning framework with Resampling method for MiRNA-Disease Association (ERMDA) prediction to discover potential disease-related miRNAs. Firstly, the resampling strategy is proposed for building multiple different balanced training subsets to address the challenge of sample imbalance within the database. Then, ERMDA extracts miRNA and disease feature representations by integrating miRNA–miRNA similarities, disease–disease similarities and experimentally verified miRNA-disease association information. Next, the feature selection approach is applied to reduce the redundant information and increase the diversity among these subsets. Lastly, ERMDA constructs an individual learner on each subset to yield primitive outcomes, and the soft voting method is introduced for making the final decision based on the prediction results of individual learners. A series of experimental results demonstrates that ERMDA outperforms other state-of-the-art methods on both balanced and unbalanced testing sets. Besides, case studies conducted on the three human diseases further confirm the ERMDA’s prediction capability for identifying potential disease-related miRNAs. In conclusion, these experimental results demonstrate that our method can serve as an effective and reliable tool for researchers to explore the regulatory role of miRNAs in complex diseases.
Characterization of adductomic totality of NNK, (R)-NNAL and (S)-NNAL in A/J mice, and their correlations with distinct lung carcinogenicity
Authors: Hu Qi , Upadhyaya Pramod , Hecht Stephen S , Aly F Zahra , Huo Zhiguang , Xing Chengguo 
Published: November 2021

Abstract
Lung cancer is the leading cause of cancer-related deaths. While tobacco use is the main cause, only 10–20% of smokers eventually develop clinical lung cancer. Thus, the ability of lung cancer risk prediction among smokers could transform lung cancer management with early preventive interventions. Given that DNA damage by tobacco carcinogens is the potential root cause of lung carcinogenesis, we characterized the adductomic totality of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (a potent lung carcinogen in tobacco, commonly known as NNK) in the target lung tissues, the liver tissues and the peripheral serum samples in a single-dose NNK-induced lung carcinogenesis A/J mouse model. We also characterized these adductomic totalities from the two enantiomers of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL, the major in vivo metabolite of NNK) given their distinct carcinogenicity in A/J mice. With these adductomic data, we demonstrated that tissue protein adductomics have the highest abundance. We also identified that the adductomic levels at the 8 h time point after carcinogen exposure were among the highest. More importantly, the relationships among these adductomics were characterized with overall strong positive linear correlations, demonstrating the potential of using peripheral serum protein adductomics to reflect DNA adductomics in the target lung tissues. Lastly, we explored the relationships of these adductomics with lung tumor status in A/J mice, providing preliminary but promising evidence of the feasibility of lung cancer risk prediction using peripheral adductomic profiling.