bluuebunny/crossref_metadata_2025_split huggingface dataset:
Possibility the use of UAV platform for building’s crack length measurement
Authors: Rahman Khairulanwar Ab , Tahar Khairul Nizam 
Published: July 2022

Abstract
This study uses Unmanned Aerial Vehicle (UAV) as a platform to inspect and monitor a building. It focuses on the inspection of defects in buildings, especially cracks. The aim of this study is to investigate cracks on a building by using UAV photogrammetry. The building was used as an object for this study because there are demands from the building maintenance team to assess cracks on the building in an effective way, where it can help to provide reliable crack information. The selected building for this study was an old apartment building with cracks, making it appropriate for this study. There were several steps and procedure used to capture the image of cracks on the building. From the image captured by using UAV, measurement of the cracks can be identified by using a software. There were also different approaches for the output. First is the 3D model of the building and second is the measurement of cracks. The 3D model of the building was created by using Agisoft Photoscan software for the purpose of getting the overview of building dimension. The measurement of cracks was processed by using PhotoModeler software. The accuracy achieved for this study was ±0.8cm. The result showed that UAV photogrammetry can help the current surveying work, especially on building maintenance. Besides, it can capture the crack’s images from certain altitude. The authors used UAV for smart building monitoring assessment, which was less considered in the past.
Implications of Automated Height Extrusion and the Selection of Height Reference for LoD 1 SmartKADASTER City Model
Authors: Abdul Halim Nur Zurairah , Abdullah Nazirah , Karim Hairi 
Published: July 2022

Abstract
This paper discusses the lessons learnt from the SmartKADASTER Phase II city model development project, specifically on the reconstruction of the LoD 1 CityGML models. The LoD 1 models were reconstructed using automated height extrusions, either by creating categorised point clouds or by employing a raster-based equation such as CHM=DSM-DTM. The methods for reconstructing the LoD1 are further elaborated in this study. However, due to the particular nature of Malaysian buildings and inaccurate point cloud classifications, automated height extrusion alone was found to be insufficient to achieve the typical recommended average rooftop height as the LoD1 height reference. Additionally, it was determined that the recommended height reference is also unsuitable for cadastre-based analysis and other beyond cadastre purposes in Malaysia. As a result, this paper will discuss the selection of the LoD1 height reference and suggest the approach to ensure accurate height extrusion of the LoD1 model can be met. Finally, it is hoped that this work will contribute to the body of knowledge by appropriately referencing their 3D models for analysis purposes and raising readers’ awareness of the SmartKADASTER application system.
Allocating Of New Potential Bus Stop Location Using Analytical Hierarchy Process (AHP) technique
Authors: Rahman A A A , Majid N A , Ramlan M H , Hakim M A 
Published: July 2022

Abstract
Public bus service is an essential transport in major cities. This service makes it easier for passengers to move from one place to another place. The aim of this research is to allocate new potential bus stop locations in Shah Alam. The purpose is to use the AHP calculation method to find the average priorities weightage value of the criteria and sub criteria used in this research. The applying of GIS to process the data calculated from the AHP will identify which criteria is preferred over each other to locate new bus stop locations. The methodology in this research is divided into four phases which is preliminary work, data acquisition, data processing, and data analysis. Software used in this research for data processing is ArcGIS Pro. How strategic is the locations of the bus stop according to the population? The rising of urbanization has led to increasing in population density; hence the location, functionality, safety, and visual appearance of bus stop are crucial. Therefore, this research is focused on allocating of new potential bus stop locations that has been identified through the GIS geoprocessing tools.
Evaluation of the Performance of Spline Interpolation Method in Mapping and Estimating the Total Suspended Solids over the Coastal Water of Pulau Tuba, Kedah
Authors: Kamaruddin S A , Hashim A R , Zainol Z E , Ahmad A , Abd.Aziz K A , Roslani M A , Shuhaime N , Tajam J , Hamid H A , Mat Nazir E N 
Published: July 2022

Abstract
The purpose of this paper is to evaluate the performance of the spline interpolation method in predicting and mapping the concentration of Total Suspended Solids (TSS) in the surface water of Pulau Tuba, Kedah. Thirty sampling points were set up and geolocated using the Geographic Positioning System (GPS). Gravimetric analyses were used to determine the TSS level. Fifty percent of the total sampling points were randomly chosen for developing spatial models using regularised and tension spline methods. The research found that the tension spline methods outperform the regularised spline method. The Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Error Percentage (MAPE) were reported at 351.641, 18.752, 15.81, and 21.51%, respectively. This study’s findings are critical in the domains of spatial statistics and interpolation for creating a precise map of water properties.
Application of GIS and MCDM on Natural Environment Effect toward the Durability of Water pipe: A Conceptual Study
Authors: Rashid Firdaus Abd , Othman Ainon Nisa , Sulaiman Saiful Aman , Naharudin Nabilah 
Published: July 2022

Abstract
Utility is considered as one of the most important systems in all countries. A structured network is defined as a network comprising specific components and subsystems. Leakage of water pipes is a common problem around the world. It is understood that water is a valuable resource to all living things. The aim of this study is to estimate the durability of water pipe based on natural environment factor in Selangor, Malaysia. There are several proposed objectives listed such as to identify the parameters that affect the durability of the pipe, to develop GIS modelling to predict the durability of water pipelines and to generate durability of pipeline prediction maps to the study area. The criteria such as temperature, soil moisture, soil type, acidity, resistivity, landuse and slope were selected from the expert opinion and also from the previous study. The method used is Analytical Hierarchy Process (AHP) and GIS. AHP technique were used to calculate the weightage for each of the criteria. In this study, map of the mildsteel waterpipe durability estimation can be produced using ArcGIS.
Monitoring of Rice Growth Phases Using Multi-Temporal Sentinel-2 Satellite Image
Authors: Badrul Hisham Nurul Hasniati , Hashim Nurhanisah , Saraf Noraain Mohamed , Talib Noorfatekah 
Published: July 2022

Abstract
Rice is the primary source of nutrition food of more than half of the world’s population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency’s (ESA) launched Sentinel-2 A + B twin platform’s which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.
Oil Palm Plantation Land Cover and Age Mapping Using Sentinel-2 Satellite Imagery and Machine Learning Algorithms
Authors: Jarayee A N , Shafri H Z M , Ang Y , Lee Y P , Bakar S A , Abidin H , Lim H S , Abdullah R 
Published: July 2022

Abstract
Nowadays, there are various techniques and methods used in land cover classification using remote sensing data especially in oil palm monitoring. This study discussed the oil palm mapping using satellite imagery (Sentinel-2) and classification of land cover features using machine learning algorithms such as linear support vector classifier (LSVC), random forests (RF) and deep neural network (DNN). A total 13218 sampling points (80% of the total sampling points used as training samples and 20% applied as testing samples) were randomly selected in the study area which were then classified into six land cover features; water, bare soil, forest, immature oil palm (the age of 2-8 year), mature oil palm (age >8 year) and built-up area. These data were validated by using spectral reflectance, Google Earth Pro and ground checking. The accuracy assessment was conducted by a confusion matrix method. The results showed that classification of land features using DNN with batch size 32 and epoch 100 has the highest accuracy which is 99.35% for overall accuracy and 98.49% kappa accuracy. This study demonstrated various machine learning algorithms that may be used to detect and classify the maturity of oil palm trees, which is vital to record in tree inventories for effective plantation management.
Evaluating Oil Palm Cultivation using Geospatial Approach in Kerdau, Temerloh District
Authors: Abdul Rahim Haris , Bidin Verinus 
Published: July 2022

Abstract
Oil palm is one of the cultivation that synonyms in Malaysia. Malaysia become one of the biggest palm oil producer globally after Indonesia. In order to achieve successful yield per year, oil palm need constant effort and labor to monitor them accordingly. Manual method in monitoring the palm oil consumes large amount of time and energy. Palm oil comes from the fleshy fruit of oil palms. Unrefined palm oil is sometimes referred to as red palm oil because of its reddish-orange color. Remote sensing technique utilizes usage of satellite imageries to analyzes healthiness and canopy features of palm oil plantation. There are several advantage in determining palm oil condition through multispectral and texture analysis in ERDAS Imagine and Envi. Utilizing Landsat-8 imagery, monitoring palm oil cultivation and yield can be effectively implemented in Malaysia. In this study, we will use three vegetation indices which are Normalized Differential Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Ratio Vegetation Index (RVI). This study will demonstrate that selected satellite-derived vegetation indices can be used to estimate oil palm yields with reliable accuracy. In this work, the ability of selected vegetation indices, derived from a single-date archived high resolution satellite imagery, to estimate oil palm yields at the management block scale was demonstrated. This technique applied to determine the condition of the palm oil tree. Using remote sensing technique, the value of the vegetation indices will be determined and analyzed. Result from this process, palm oil condition can be evaluated. This study provides an important benchmark for applying remote sensing technology in the management of plantation-scale oil palm. Oil palm yield estimation based on empirical models, as described in this work, can be computerized using a simple spreadsheet interface so as to facilitate optimal agronomic intervention, particularly with regard to crop harvesting, crop stress alleviation and input application. However, it’s important to note that palm oil should not be confused with palm kernel oil. While both originate from the same plant, palm kernel oil is extracted from the seed of the fruit. It provides different health benefits.
Detection of in-Car-Abandoned Children via Deep Learning
Authors: Norman M. , Pauzi MFM. , Ismail MH. , Mohamad Z. , Rahim A. , Mohd FA. , Shafri HZM 
Published: July 2022

Abstract
Cases of children died in vehicle have been increased each year. Such incident sometimes may happen incidentally especially when children are seated at the rear seats and the problem occurs due to lacking of existing system in detecting children image in a car. Consequently, this study aims to detect the existence of "in-car-abandoned children" using deep learning algorithm. A set of children images model will be classified into two (2) classes; children and no-children via Convolutional Neural Network (CNN) classifier by integrating with programming language, namely TensorFlow. Interestingly, the proposed method can automatically learn pattern features and reduce the incompleteness caused by artificial design features. As a result, a model of sensor that can detect the whole children’s body in various poses with automatic tagging to the children’s image is designed. Accordingly, this study can assist to improve current vehicle systems and create awareness among parents regarding the importance of children’s safety.
A phantom-based study and clinical implementation of brainlab’s treatment planning system for radiosurgical treatments of arteriovenous malformations
Authors: Shrestha Nishan , Narayanasamy Ganesh , Kalantari Faraz , Sabouri Pouya , Xia Fen , Zhong Su 
Published: July 2022

Abstract
Purpose. Development of a simple, phantom-based methodology allowing for pilot applications for the Elements TPS cranio-vascular module and clinical implementation prior to AVM treatments. Methods. A customized phantom was developed to be visible in MRI and CT images. High resolution digital subtraction angiograms (DSAs) and CT images of the phantom were acquired and imported into the Brainlab Elements treatment planning system. A clinical treatment plan with 5 arcs was generated in cranial vascular planning module and delivered to the phantom using a Varian TrueBeam STx Linac equipped with HD-MLCs and Brainlab ExacTrac imaging system for non-coplanar setup verification. The delivered dose was verified using a calibrated ionization chamber placed in the phantom. Upon verification of the TPS workflow, three patients with AVM who have been treated to date at our center using the Brainlab’s cranial vascular module for AVM are presented here for retrospective review. Results. The difference between the planed and measured dose by the ionization chamber was found to be less than 1%. Following a successful dose verification study, a clinical workflow was created. Currently, three AVM patients have been treated successfully. Clinical aspects of imaging and treatment planning consideration are presented in retrospective setting. Conclusions. Dose verification of the Brainlab Elements cranial vascular planning module for intracranial SRS treatments of AVM on Varian TrueBeam was successfully implemented using a custom-made phantom with <1% discrepancy. The Brainlab Elements’ cranial vascular module was successfully implemented in clinical workflow to treat patients with AVM. This manuscript provides a guideline for clinical implementation of frameless Linac-based AVM treatment using the Brainlab Elements TPS.
Evolution of edge turbulent transport induced by L-mode detachment in the HL-2A tokamak
Authors: WU Ting , NIE Lin , YU Yi , GAO Jinming , LI Junyan , MA Huicong , WEN Jie , KE Rui , WU Na , HUANG Zhihui , LIU Liang , ZHENG Dianlin , YI Kaiyang , GAO Xiaoyan , WANG Weice , CHENG Jun , YAN Longwen , CAI Laizhong , WANG Zhanhui , XU Min 
Published: July 2022

Abstract
This paper presents the characteristics of L-mode detachment, together with the behavior of edge turbulent transport and plasma confinement on the HL-2A tokamak. Partially detached and pronounced detached states have been achieved in L-mode plasma. Stored energy was maintained before and after detachment. Edge turbulence and its transport have increased obviously in the partially detached state. In the pronounced detached state, redistribution of the density and temperature profiles due to detachment leads to low amplitude of electron temperature and pressure, as well as very weak edge turbulence and transport. Despite strong plasma radiation in the pronounced detached state, reduced edge turbulent transport contributes to maintaining stored energy in detached L-mode plasma in HL-2A. Different detachment states play an important role in the redistribution of density and temperature profiles, which requires further study.
Numerical study of viscosity and heat flux role in heavy species dynamics in Hall thruster discharge
Authors: SHASHKOV Andrey , LOVTSOV Alexander , TOMILIN Dmitri , KRAVCHENKO Dmitrii 
Published: July 2022

Abstract
A two- and three-dimensional velocity space axisymmetric hybrid-PIC model of Hall thruster discharge called Hybrid2D has been developed. The particle-in-cell (PIC) method was used for neutrals and ions (heavy species), and fluid dynamics on a magnetic field-aligned (MFA) mesh was used for electrons. A time-saving method for heavy species moment interpolation on a MFA mesh was developed. The method comprises using regular rectangle and irregular triangle meshes, connected to each other on a pre-processing stage. The electron fluid model takes into account neither inertia terms nor viscous terms and includes an electron temperature equation with a heat flux term. The developed model was used to calculate all heavy species moments up to the third one in a stationary case. The analysis of the viscosity and the heat flux impact on the force and energy balance has shown that for the calculated geometry of the Hall thruster, the viscosity and the heat flux terms have the same magnitude as the other terms and could not be omitted. Also, it was shown that the heat flux is not proportional to the temperature gradient and, consequently, the highest moments should be calculated to close the neutral fluid equation system. At the same time, ions can only be modeled as a cold non-viscous fluid when the sole aim of modeling is the calculation of the operating parameters or distribution of the local parameters along the centerline of the discharge channel. This is because the magnitude of the viscosity and the temperature gradient terms are negligible at the centerline. However, when a simulation’s focus is either on the radial divergence of the plume or on magnetic pole erosion, three components of the ion temperature should be taken into consideration. The non-diagonal terms of ion pressure tensor have a lower impact than the diagonal terms. According to the study, a zero heat flux condition could be used to close the ion equation system in calculated geometry.
Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer
Authors: Liang Jing , Li Zong-Wei , Yue Cai-Tong , Hu Zhuo , Cheng Han , Liu Ze-Xian , Guo Wei-Feng 
Published: July 2022

Abstract
Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.
High-dimension to high-dimension screening for detecting genome-wide epigenetic and noncoding RNA regulators of gene expression
Authors: Ke Hongjie , Ren Zhao , Qi Jianfei , Chen Shuo , Tseng George C , Ye Zhenyao , Ma Tianzhou 
Published: July 2022

AbstractMotivationThe advancement of high-throughput technology characterizes a wide variety of epigenetic modifications and noncoding RNAs across the genome involved in disease pathogenesis via regulating gene expression. The high dimensionality of both epigenetic/noncoding RNA and gene expression data make it challenging to identify the important regulators of genes. Conducting univariate test for each possible regulator–gene pair is subject to serious multiple comparison burden, and direct application of regularization methods to select regulator–gene pairs is computationally infeasible. Applying fast screening to reduce dimension first before regularization is more efficient and stable than applying regularization methods alone.ResultsWe propose a novel screening method based on robust partial correlation to detect epigenetic and noncoding RNA regulators of gene expression over the whole genome, a problem that includes both high-dimensional predictors and high-dimensional responses. Compared to existing screening methods, our method is conceptually innovative that it reduces the dimension of both predictor and response, and screens at both node (regulators or genes) and edge (regulator–gene pairs) levels. We develop data-driven procedures to determine the conditional sets and the optimal screening threshold, and implement a fast iterative algorithm. Simulations and applications to long noncoding RNA and microRNA regulation in Kidney cancer and DNA methylation regulation in Glioblastoma Multiforme illustrate the validity and advantage of our method.Availability and implementationThe R package, related source codes and real datasets used in this article are provided at https://github.com/kehongjie/rPCor.Supplementary informationSupplementary data are available at Bioinformatics online.