bluuebunny/crossref_metadata_2025_split huggingface dataset:
Role of the Rheological Parameters in Debris Flow Modelling
Authors: Khan M A , Mustaffa Z , Balogun A L B , Al-Bared M A M , Ahmad A 
Published: March 2021

Abstract
Nowadays, the debris flow model has become an essential part of risk analysis and impact engineering. Coupled with field observations and historical records, these models have proven powerful tools to understand the behaviour of debris flow in complex terrain. However, their application poses several new challenges to scholars and engineers. A detailed understanding of the debris flow phenomena requires a sound knowledge of the shallow water equation and rheological model used to simulate the debris flow hazard. In this study, important rheological models used to analyse the debris flow process and their limitations have been highlighted. Furthermore, the suitability of the Voellmy-Salm rheological model has been studied for 2D pyroclastic flow taking different combinations of the coefficient of friction namely coulomb friction coefficient \i,and turbulent coefficient of friction £ using IMEX SFLOW 2D dynamic continuum model. It was found that velocity and runout distances are significantly influenced by the variation of the coefficient of the turbulent friction © at a large scale. It is then concluded that the identification of a suitable rheological model is necessary to simulate the precise behaviour of complex and heterogeneous debris flow
Effect of fermentation period on bacterial cellulose production from oil palm frond (OPF) juice
Authors: Supian N N I , Zakaria J , Amin K N M , Mohamad S , Mohamad S F S 
Published: March 2021

Abstract
In this study, bacterial cellulose (BC) was produced by Acetobacter xylinum using oil palm frond (OPF) juice as a carbon source, where the effect of cultivation time in the range of 2-14 days on BC production was investigated. The highest BC production of 0.3365 g/L was obtained at cultivation time of 9 days. Structural analysis by FTIR showed that the properties of BC to be similar regardless of cultivation time. FESEM analysis revealed that the BC produced was composed of randomly packed and loosely fine cellulose fibers. The present study showed the feasibility of using OPF juice as an alternative in the production of BC which could reduce the production cost of BC.
Investigation of Adsorption behaviour of Acetone Vapour towards a Surface Plasmon Resonance Sensing Layer using Adsorption Isotherm Models
Authors: Usman Fahad , Ojur Dennis John , Meriaudeau Fabrice , Cheng Seong Khe , Yousif Ahmed Abdelaziz , Abubakar Abdulkadir Bashir , Abbas Adam Abdullahi , Saidu Aminu 
Published: March 2021

Abstract
Surface plasmon resonance (SPR) sensors are widely explored due their ultra-sensitivity to even a minute alteration of refractive index. Knowledge of adsorption processes could be exploited to explain the performance and interaction mechanism of an SPR sensor. Here in, we report the fitting of the experimental SPR sensing data during the detection of low concentrations of acetone vapour (0.5-5 ppm) using the linearized and non-linearized format of the Langmuir and the Freundlich isotherm models. The sensing layer is made from a ternary composite material of doped polyaniline, reduced graphene oxide and chitosan. The objective is to find the best model, understand the interaction mechanism and investigate the performance of the sensing layer. Correlation factors and error values were used to determine the best fit. The results showed that the Freundlich model could fit the data better than the two formats of the Langmuir model. Also, the interaction mechanism was predicted to be the physical one due to the heterogeneity parameter value, n<1. In addition, the selectivity of the sensing toward acetone compared to water, methanol, ethanol and propanol vapours was explained in terms of proximity of solubility parameters. Moreover, the ternary based sensor was found to be reversible and stable.
Balancing of the moments of the VR-5 type engine taking into account the desaxial and the cylinder camber angle
Authors: Souri Kambiz , Vallejo Maldonado P. R. , Chainov N. D. 
Published: March 2021

Abstract
Recently, V-engines with a small angle of camber are more widely used as power units for passenger cars, especially because of the reduced engine length, and the transverse dimensions are almost the same as in-line ones. All this leads to an increase in overall engine power. However, due to the small camber angle between the parallel rows of cylinders, the intersection of the cylinders in their lower part may occur. To prevent the latter, significant multidirectional desaxial is used. Moreover, the calculation of the dynamics of similar engines introduces a number of features, including balancing the inertia forces and their moments from the reciprocating moving and rotating masses. The article discusses the balancing of 5-cylinder four-stroke VR type engines with desaxial crank mechanism and uniform flash alternation. The formulas allowing to find and analyze moments of the inertia forces of the reciprocating and rotating masses arising in VR-5 engines are given by the given value of the cylinder camber angle, the ratio of the crank radius to the connecting rod length and the relative displacement of the cylinder axis. A method of balancing the moments of inertia forces of the reciprocating and rotating masses is proposed and the results of the presented dynamic study of a VR-5 engine using analytical, software and graph-analytical methods.
Gasification Performance and Tar Generation Comparison From Fluidized Bed Gasification of Raw and Torrefied Empty Fruit Bunch
Authors: Nadiah Sidek Farah , Saleh Suriyati , Asma Fazli Abdul Samad Noor 
Published: March 2021

Abstract
This paper presents the effects of gasification temperature and steam to biomass ratio on gasification performance and tar generation. Fluidized bed gasifier is used for gasification process with steam employs as gasifying agent. Two types of feedstock were used namely raw and torrefied empty fruit bunch (EFB). Experimental results show the synthesis gas yield, lower heating value (LHV), cold gas efficiency (CGE) and tar generation are steadily increased for both feedstock when gasification temperature is increased from 600 and 1000°C. Meanwhile only yield of synthesis gas is increased when steam to biomass ratio is increased from 0.51 to 1.51. LHV, CGE and tar initially show increment trends but start to decrease beyond steam to biomass ratio of 1.02. Torrefied EFB shows superior performance in terms of producing more synthesis gas, better energy and efficiency compare to raw EFB. However, tar generated from torrefied EFB is higher than raw EFB. Based on tar component analysis, it has been found that 6 tertiary tar components are detected which consists of naphthalene, acenaphthylene, fluorene, phenanthrene, anthracene and pyrene for both feedstock. In overall, torrefied EFB shows potential as fuel feedstock for gasification process based on high synthesis gas yield with considerable amount of tar generation.
Experimental evaluation of tertiary blends of water in diesel with butyl alcohol using compression ignition engine
Authors: Akasyah M. K. , Yusri I. M. , Mamat R. , Jamlos M. F. , Abdul Majeed A. P.P. 
Published: March 2021

Abstract
Alcohols are important alternative fuel resources for diesel engines. Prominent fuels of the three types include water in diesel reduce engine temperature and NOx and alcohols with a high number of carbons. There is potential to use tertiary blends of diesel fuel, water and higher alcohols, such as butanol, in diesel engines for the purpose of increasing the use of alternative fuel and decreasing fossil fuel consumption. In this study, diesel fuel (D) was mixed with water (5%), and butanol (5% - 10%). Test fuel blends of W5DBu5 (5% of water, 5% of butanol), W5DBu10 (5% of water, 10% of butanol) and W5DBu15 (5% of water, 15% of butanol) were prepared using ultrasonic emulsifier and tested in a diesel engine. It carried engine performance and exhaust emission tests of the blends out on a four-cylinder, four-cycle diesel engine generator at a fixed load of 50% and various engine speeds from 1000 rpm to 3000 rpm. According to engine test results, brake specific fuel consumptions (BSFC) of Blended fuels decrease compared to diesel at all engine speed. As compared to diesel, W5DBu15 presented the best oxides of nitrogen (NOx) at a speed of 2000 rpm with a reduction of 34.7%.
Exploration of Risky Riding Behaviour Among Young Motorcyclist
Authors: Zun Nurain binti Mohd Ali Siti , Suhana binti Mohd Razelan Intan 
Published: March 2021

Abstract
Road traffic crashes appears to be an endless problem (Kareem A 2003). Nowadays, statistics by Department of Road Transport showed that the road traffic crashes are significantly increasing in Malaysia every year. According to Royal Malaysian Police (PDRM) report, motorcyclists are more prone to crash when they are on the road compared to drivers of other vehicles. There are many factors that contribute to the road traffic crashes in young motorcyclists such an inexperience, lack of riding skill and risk riding behaviour that have been related to the collisions in which motorcycles have been the most popular choice of vehicles among young motorcyclist. This research evaluates the correlation between the set of risky riding behaviour among secondary school students based on their background. To prove that risk behaviours majorly contributed to the number of motorcycle crashes, determination of risky riding behaviour within secondary school student at schools’ compound were conducted. Data collection also were collected through questionnaire distributed through google form for school students and young motorcyclist to assess their level of contribution in risky riding behaviour. The findings proved that, breaking the speed limit are the most popular risky riding behaviour among the school children. Meanwhile, riding while impaired and performing stunting acts are the behaviours considered to be less contributed to the number of motorcycle crashes. Correlation result shows that neglection of speed limit was highly contributed to the motorcycle crashes among the secondary school student. This result is very useful for the enforcement bodies to strategize their effort in curbing the crash issues involving young riders.
Assessment of Forest Fires Risk in U Minh Ha National Park-Vietnam and Proposed Adaptation Measures
Authors: Van T T Ho , Trong K H , Hai A T N , Phan Phuong H V 
Published: March 2021

Abstract
Forest ecosystems are part of the vital and indispensable resources for human survival and social development. Forests not only provide materials such as wood and firewood for some industries, but also maintain and protect the environment, such as climate regulation, erosion control and sedimentation, water regulation and flood mitigation. However, due to some uncontrolled activities of people and abnormal natural conditions, forest fires occur more frequently and severely. U Minh Ha National Park (UMHNP) is one of forest which has the highest risk of forest fires in the Mekong Delta. This study was conducted to assess the status of forest fires and the impacts of climate change on forest fire risk in UMHNP then proposed appropriate measures to reduce the risk of forest fire to minimize damage to the environment, humans, biodiversity, economics as well as people’s livelihoods protection in the Mekong Delta. By the method of data collection and rapid assessment, the topic has statistics of fires within 5 years as well as causes, key areas and its impacts on forest ecosystems. At the same time, it was pointed out that UMHNP is facing an increasing risk of forest fire due to climate change. Forest fire prevention and fighting in UMHNP is being well implemented but still faces some difficulties. Therefore, it is necessary to take appropriate measures such as strengthening communication, completing mechanisms and policies, supplementing fire protection equipment and facilities to improve environmental quality and local’s people life.
Impacts of Climate Change to Eleocharis Habitat in Tram Chim National Park, Dong Thap province, Vietnam
Authors: Trong K H , Thi H V , Thi Phuong C N , Thi Thanh V H 
Published: March 2021

Abstract
Combining the current remote sensing platform combined and field survey is one of the effective methods to assess the impact of climate change on the distribution of plant cover as well as their structure. This study combined field surveys in April 2019 and image analysis from google earth pro in real-time in May 2006, 2009, 2013 to assess the change of Eleocharis group in Tram Chim national park. The results of the study showed that Eleocharis group, which admirably adapts to the acidic soil conditions (pH <4.5), had significant changes. Although water storage has created an extremely safe environment to avoid the impacts of wildfires, temperature rising at midday made Eleocharis less alive. It could be seen that climate change had affected the physiological characteristics of the tropical grass group, more specifically, made their ecological limits change and narrowed the distribution area of the most developed group in the national park.
Impacts of Climate Change to the Growth and Development of The Dragonflies of Tram Chim National Park, Tam Nong – Dong Thap, Vietnam
Authors: Trong K H , Thi N D , Thi Nhu Y N , Thi H V , Thi Thanh Ho Van 
Published: March 2021

Abstract
Dragonflies are insects that have an association with wetland ecosystems and sensitive to changes in environmental factors. Therefore, under the increasingly strong impacts of climate change, they exhibit quite pronounced responses. Assessing the growth and development process of dragonflies can contribute positively to assessing and making strategies to cope with the impacts of climate change which is becoming increasingly complicated. The results of the current study showed that the Dragonfly species composition in Tram Chim National Park was remarkably diverse, including 11 species (2008) and 12 species (2019) of larvae belonging to 6 families in 2 subsets Zygoptera and Anisoptera. Based on information from field surveys, the results showed that the presence of dragonflies tended to be earlier than those recorded in the past, and it was often difficult to find them along the survey route, especially nearer to noon. Range, flight speed, and altitude were somewhat reduced, especially for Zygoptera. These environmental factors fluctuated greatly, which might cause pressing impacts on the growth and development of Dragonflies in the Tram Chim National Park area.
Optimization of operating conditions of Lemon (Citrus aurantifolia) essential oil extraction by Hydro-distillation Process using Response Surface Methodology
Authors: Tran T H , Nguyen T N Q , Le X T , Phong H X , Long T B 
Published: March 2021

Abstract
In this study, extraction of essential oils from the peels of lemon (Citrus aurantifolia) was conducted and optimized. Response Surface Methodology (RSM) was adopted to optimize four process parameters including the ratio of water and lemon peel (2:1 to 4:1 mL/g), extraction time (1 to 2 h) and extraction temperature (110 to 130 °C) to achieve maximal oil yield. The results showed a ratio of 3:1 (mL/g), extraction time of 1.6 hours, a temperature of 120 ºC produced an oil yield of 2.097%. In addition, GC-MS results revealed a total of 15 identified compounds in lemon essential oil with the major compound being Limonene, accounting for 66% of total content. The study applied surface response methodology to optimize the hydrodistillation conditions for extracting lemon essential oil. Application of this computer software is particularly advantageous in reducing the experiment number while producing reliable results.
RETRACTED: Prediction of Presence of Breast Cancer Disease in the Patient using Machine Learning Algorithms and SFS
Authors: Chaurasia V , Pandey MK , Pal S 
Published: March 2021

Abstract
Breast cancer disease is recognized as the common extensive malignant tumor in between women. Identification of the initial stage of malignant growth may treatment of this disease. Early treatment helps to alleviate the disease and helps anticipate its recurrence in women. Experts have used some fact checks and different medical methods or equipment to improve the accuracy of conclusions in clinical medical service management. In this article, it extensively discussed the implementation of data mining strategies to detection as well as prediction of breast malignant tumors, including random forest (RF), support vector classifier (SVC), k-nearest neighbors (KNN), linear discriminant analysis (LDA), Gradient Boosting Classifier (GBC), Decision Tree (DT) In addition, principal component analysis (PCA) to underline changes and show strong patterns in the informational index. The connection framework is likewise used to show the level of close relationship between attributes. The sequential feature selection (SFS) method is used for comparing the accuracy of a data set with all features and the accuracy of a classifier with selected features. The results show that RF_sfs, KNN_sfs, SVC_rbf and SVC_sfs have the highest and equal accuracy, which is 97.66%. They perform well and can predict the growth of harmful malignant tumors.
Effect of Dimensionality Reduction on Prediction Accuracy of Effort of Agile Projects Using Principal Component Analysis
Authors: Manju Vyas Ms. , Naveen Hemrajani Dr. 
Published: March 2021

Abstract
Agile framework for software development has received a lot of recognition in software industry in previous years as it focuses on rapid incremental delivery, lower risk and customer satisfaction. At early stages of development, the effort must be predicted so that the project is completed successfully within the time and cost deadlines. In recent years, various researchers have done study in this area and it is observed that the prediction of effort faces a problem of large dimension of features. Hence the prediction accuracy may be increased by reducing the dimensions of the features. In this paper, PCA has been used for reduction of feature dimensions for effort estimation. PCA identifies the key attributes by reducing the dimensions of the attribute which are those having highest correlation with the effort. The methodology shows the effect of PCA on the original dataset and the results are observed by applying various machine learning techniques pre and post PCA. The comparison metrics used are Mean Magnitude relative Error (MMRE), Root Mean Square Error (RMSE), and Prediction Accuracy (PRED (25)). The decreased values of errors and increased value of accuracy shows the better model accuracy when PCA is applied on the dataset. All the computations and implementations in this paper are done using Python on Scikit-learn library.
Safety and Compliance Management System Using Computer Vision and Deep Learning
Authors: Nain Megha , Sharma Shilpa , Chaurasia Sandeep 
Published: March 2021

Abstract
With the advancement in the Deep Learning and Computer Vision-based methodologies, the safety and productivity analysis of the construction entities (workers, equipment, and structures) on-site can potentially be improved. Construction industry is majorly the most dangerous sectors with respect to occupational safety and health because of the chaotic and dynamic environment at construction sites. The study of safety and compliance monitoring measures at construction by using the computer vision and deep learning-based approaches is the key focus of this paper. Safety and compliance are acclamatory to each other and are very essential for managing the safe conditions at construction. Manual monitoring of the compliant and unsafe conditions at the site is a challenging job and thus, a smart framework for automated monitoring is necessary for the assurance of a safe and healthy environment. A framework for safety and compliance management system is presented in this paper for enhancing the safer work culture along with the measures. This paper examines the prior literature on computer vision as per the safety and compliance constraints for (I) Understanding the existing SOTA (state-of-the-art) methods and their respective outcomes (II) Finding the challenges and limitations in the currently employed approaches and (III) Providing the potential directions for a future line of work. The purpose is to build a culture of safety using Computer Vision and Deep Learning.
Fog Computing in Healthcare: A Review
Authors: Pareek Kamini , Tiwari Pradeep Kumar , Bhatnagar Vaibhav 
Published: March 2021

Abstract
The Internet of things (IoT) connects multiple devices worldwide. It is a growing field in the healthcare system such as health monitoring and tracking, fitness program, and remote medical assistance. With the advent of IoT based technologies in healthcare, it can alleviate the pressure on healthcare systems and can reduce the healthcare cost, and increase the computing and processing speed. Cloud computing was introduced to manage larger and complex healthcare data in the IoT environment. Cloud computing uses centralized cloud data centers. The central server manages the data for all the IoT devices. The integration of IoT with the cloud has some major issues such as latency, bandwidth overuse, real-time response delays, protection, and privacy. So the concept of edge computing and fog computing came into existence to overcome these issues. This paper review the IoT-Fog-based system model architectures, similar paradigm, issues, and difficulties in the area of cloud computing and finally, the performance of some of these proposed systems is assessed using the iFogSim simulator.
Adverse Effects of 5th Generation Mobile Technology on Flora and Fauna: Review Study
Authors: Kumar Rajesh , Geleta Rabira , Pandey Amit , Sinwar Deepak 
Published: March 2021

Abstract
Technology plays a very vital roles in the growth of the economy of any nation. Hence, information communication channel needs to be very strong for timely delivery of information and growth of any country. Mobile technology is the backbone of communication channel in any country who has incorporated it. Since 1980 mobile communication is very popular mode of communication and researches are going on in this area since that time. Starting from the first generation mobile network to fifth generation mobile network, every nation wants to enhance their information communication technology infrastructures in aspect of communication. The 5G mobile technology is subject of debate now a day. Still, most of the countries are in the race for adopting this technology and are ignoring its adverse effects on human health and environments. 5G mobile technology uses millimetre waves and higher frequency band 6 GHz to 100 GHz for communication. Initially, there was appeal made in United Nation Council and later in European Union against the launch of 5G, which was signed by more than three hundred scientists and doctors, stating that the 5G mobile technology is not good for environment. Various research has been conducted regarding the adverse effects of RF-EMF waves, which are generated by cell towers, on human health and environment. 5G uses very dense infrastructure and there is evidence that the RF-EMF radiation level is very strong in fifth generation mobile technology as compare to previous mobile technologies. Hence, the current study is focused on reviewing the impact of 5G mobile technology on flora and fauna Kingdome.