bluuebunny/crossref_metadata_2025_split huggingface dataset:
Emerging Applications of Artificial Intelligence in Structural Engineering and Construction Industry
Authors: Hooda Yaman , Kuhar Preeti , Sharma Kaushal , Verma Neeraj Kumar 
Published: August 2021

Abstract
With the beaming concept of Construction 4.0, the disciplines of structural engineering and construction industry needs an advancement for the data collection, interpretation and analysis. Augmented Intelligence and its various disciplines find an impressive application in the field of civil engineering for data requisition, management and performance. This study focuses on how AI and its various principles can be blended with the emerging areas of structural engineering, and how it is shaping the construction industry by employing in the major areas of monitoring of structural health, assessment of damages and construction management.
Artificial Intelligence Based Breath Analysis System for the Diagnosis of lung cancer
Authors: Binson V A , Subramoniam M 
Published: August 2021

Abstract
Breath analysis has become a promising tool for the detection of pulmonary diseases in recent years. This paper describes the fabrication of an artificial intelligence (AI) based e-nose system for discriminating lung cancer patients from healthy controls. Breath volatile organic compounds (VOC) can be easily analyzed using an electronic nose (e-nose) made of various gas sensor arrays. Metal oxide semiconductor (MOS)-based e-noses are getting popular in the VOC analysis of exhaled breath from humans. Here we have developed the e-nose system with a sensor array system of five MOS gas sensors and incorporated the controller and machine learning algorithms to process the data. The sensor array was designed with MOS sensors developed by Figaro USA and the data acquisition was carried out with the help of the Arduino Uno developer board. 40 healthy control samples and 24 lung cancer patient samples were analyzed using the developed e-nose system. The data analysis was done by two supervised classification algorithms random forest and logistic regression. Among this, random forest with 5-fold cross-validation gave better results with 85.38 % classification accuracy and 0.87 of AUC. This system can be further extended to the diagnosis of various other pulmonary diseases.
A Stacking Model for Aviation High Risk Event Decision Making
Authors: Monika  , Verma Seema , Kumar Pardeep , Kakran Mansi 
Published: August 2021

Abstract
Aviation accidents are one of the most causes of severe injuries and death worldwide. With the increase in inter-countries movement of the peoples around the world, air transportation is increasing at a much faster rate. As result possibilities of air accidents have also increased which may be due to inter-air collision, technical fault in airplane systems and pilot faults, etc. Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts, the research community to look into the factors behind these accidents by applying data analysis techniques based on an advanced machine learning algorithm. This analysis would be beneficial in forecasting the high-risk events based on the available historical accident data. So, in this paper, we classify all aviation abnormal events into three groups with regards to their associated risk: low-risk events, medium-risk events, high-risk events and then deployed ML algorithms like Random Forest, Support Vector Machine (SVM), XGBoost, Logistic Regression, etc., to make forecasts about high-risk events. After analyzing the results, the performance of the hybrid model that is formed by stacking of best-performing algorithms is found to best in quantifying the high-risk events.
A Framework for Analyzing Road Accidents Using Machine Learning Paradigms
Authors: Shweta  , Yadav J , Batra K , Goel A K 
Published: August 2021

Abstract
Road Safety is a matter of great concern throughout the world. As number of casualties is increasing more than 4% annually in all age groups. It has been predicted that due to road accidents causality rate will grow around 8% till 2030. It’s entirely admissible and saddening to let citizens get killed in road accidents. As a result, to handle this sort of situation, an in-depth analysis is required. The Data of Road accidents are very heterogeneous in nature so analysis of such type of data is tricky. Segmentation is the main task for analyzing such data. So, K-means clustering method is mainly used for it as proposed in the research work. Second task of this model is to extract the data, images and hidden patterns by using Supervised Machine Learning algorithm that will help to form the policies for the prevention from road accidents. The combination of segmentation machine learning algorithm produces meaning full information.
Protection and Strengthening of Corroded Prestressing Tendons
Authors: Sharma Kaushal , Hooda Yaman , Kuhar Preeti , Verma Neeraj Kumar 
Published: August 2021

AbstractAs of late, structural deterioration of RC structures affected by corrosion is widely reported. A new evolution in maintenance of RC structures is by cathodic protection by the use of carbon fiber reinforced polymers (CFRP). Using steel bar as a cathode and wrapping of CFRP as anode, which prevents removal of concrete cover and blocks further corrosion of steel. This article presents the utilization of CFRP for the protection of the strand. Series of laboratory trials using cathodic protection systems were performed on pre-stressed concrete structures in order to set up reliable cathodic protection systems. To initiate initial corrosion in the specimens, pre-stressing tendon used as the cathode and CFRP used as the anode while the in the active protection, the impressed charge was passed through the strand. Then, CFRP sheets were bonded by using conductive adhesive to the block samples. Extensively corrosive environment was imposed on the samples for the particular period of time and it was observed that the active protection method is very effectual in slowing down the corrosion of the strand.
Assembling of Robots in Presence of Line Obstacles with Direction-only Axes Agreement
Authors: Sadhu A , Mukhopadhyaya S 
Published: August 2021

Abstract
This paper addresses the problem of assembling semi-synchronous oblivious robots in presence of horizontal line obstacles under direction only axes agreement model. In this article, a distributed algorithm is proposed for a swarm of autonomous mobile robots that are required to assemble over the boundaries of a rectangular bounded region within a finite amount of time. The robots are initially deployed randomly within the region and assumed to support the non-rigid motion. All the line-shaped obstacles are randomly scattered within the region and are assumed to be positioned horizontally, parallel to the top and bottom boundaries of the rectangular region. Though the robots are assumed to have unlimited visibility, these opaque obstacles restrict their vision. The existing solution to this problem assumes full axes agreement among the robots, whereas, the proposed solution assumes direction only axis agreement, a much weaker model. Our proposed algorithm is fault-tolerant and supports the collision-free movement of the robots. It successfully assembles robots on the left and right boundaries of the region.
Implementation of GPSR protocol with various mobility models in VANET Scenario
Authors: Raheja K , Mahajan M , Goel A 
Published: August 2021

Abstract
In Vehicular Ad hoc Networks (VANET), topology varies very frequently because vehicles move in high speed. VANET is deployed on road. In GPSR Protocol, as the data packets are forwarded depending upon on the beacon information sent by neighboring nodes, which contain the location of neighbors. Hence nodes move in mobile nature as they change their positions. Mobile pattern of node is reflected by Mobility Models, as they characterize the movement of users who are mobile on the road, with respect to their direction, velocity and location over a period of time. Mobility models are practiced in implementation of protocols and the pattern by which the mobility models reflect real world practices of vehicles on the roads. Using simple pattern randomly, graph constrained mobility models are common practices while doing research. These models donot describe mobility of vehicles in realistic manner. For instance while accelerating and decelerating in the presence of nearby vehicles, these situations greatly affect the Network performance. Selecting the mobility model which is realistic one is the main focus of this paper. To address the challenges such as high mobility of vehicle nodes on the road and random topology, VANET needs a suitable mobility model to obtain improved Packet Delivery Ratio, Throughput, End to End Delay etc. This paper first implements the GPSR Protocol and then GPSR is analyzed by applying different mobility models such as Random Way Point, Gauss Markov, Manhattan Grid, Reference Point Group and Random direction. Results are analyzed by taking the following parameters: Routing overhead, Throughput, PDR and End to End Delivery. The implementation is carried out using NS—2.35 and Bonmotion is used to create mobility models.
Quality-of-Service Prediction Techniques for Wireless Sensor Networks
Authors: Pundir Meena , Sandhu Jasminder Kaur , Kumar Ajay 
Published: August 2021

Abstract
The rapid development of smart sensor nodes generates great interest in Wireless Sensor Network (WSN). The distinct applications of these networks have evolved in the field of border surveillance, Internet of Things, agriculture, healthcare, and disaster monitoring. It is a complex task to satisfy the need for Quality-of-Service (QoS) in a real-world environment, due to the size and dynamic nature of the mobile sensor nodes. This paper addresses the QoS metric based on the layered architecture of WSN. There are two types of layered architectures, namely classic-layered architecture and cross-layered architecture. In a classic-layered architecture, QoS is achieved based on the individual layer. Due to the interconnected nature of these layers, it is difficult to achieve the overall QoS in a classic-layered architecture. This challenge is resolved with the help of cross-layered architecture. Further, the proposed statistical analysis illustrated the fact that the QoS metric enhances the performance of the network in terms of latency, throughput, energy, and reliability. Finally, the machine learning approaches are discussed in the light of QoS metrics to enhance the overall performance of the network.
Parametric optimization in WEDM with H-13 hot die-hard steel using Taguchi Method
Authors: Deshwal Monika , Deshwal Dinesh , Kumar Pardeep , Kannojiya Vikas 
Published: August 2021

Abstract
Good surface quality and the processing of complex shapes are the essential requirements in various manufacturing industries today. To achieve this, scientists are switching from a variety of conventional machining methods to non-traditional machining processes. Wire EDM (WEDM) is one of the most common non-traditional machining processes. In terms of quality, surface roughness (SR) is an important performance parameter in machining processes. The article summarizes the parameters that affect surface roughness.
Data Acquisition System for Preventive Maintenance of Equipment in the Urban Tunnels
Authors: Kumari Sumit , Siwach Vikas , Singh Yudhvir 
Published: August 2021

Abstract
Urban Tunnel construction for supporting the Under-ground Transit Solutions (UTS) namely Metros and Subways in highly densely populated locations involves huge power requirements. This power requirement is normally met by the power supplied by the power agencies for the over the ground requirements. However, in the cases of underground work at the drilling site or de-watering sites we need to use the smaller portable generator sets. These generator sets generate a lot of harmful gases and CO2 and in-cases of over loading lead to short circuits and also fire. In the cases of prolonged tunneling times these generators remain inside the tunnel and are not serviced on time leading to gen-sets often going out of order leading to loss of work and may also cause accidents. Regular maintenance enhances the lifespan of the generators. This paper takes the approach to provide a solution to the preventive generator maintenance problems which would take care of reliability issues. In the proposed design we monitor the vital parameters of the gen-sets like fuel levels, temperatures, loads (current and voltage), running time, last servicing time, average fuel consumption, last serviced dates, battery voltages, Air filter life etc. This information is then collected by an on-board Data Acquisition System (DAS) which is then passed on to Cloud via a Wi-Fi connection. Once this information is made available on the cloud, we can apply the Machine Learning Algorithms to predict the failure rates of the generators and calculate the RUL - Remaining Useful Life.
Optical Band Gap Determination of Ni-Al Doped Polyaniline at Room Temperature and Different Annealing Temperatures
Authors: Joshi G P , Tyagi Ankit , Meena SL , Chourasia Shubhangi , Saxena N S , Kumar Ajay 
Published: August 2021

Abstract
Spectroscopic techniques are very useful for characterizing semiconducting and conducting materials. The optical properties (specially reflection spectra) of polyaniline mix with metal Ni and Al with different composition in bulk form were studied. The reflection spectra of these materials are recorded by a Hitachi spectro-photometer model (330), at room temperature, in the wavelength range 300-600 nm. From the analysis of reflection spectra, polyaniline mix with metal Ni and Al have been found to have energy bandgap which decreases with increase of concentration of Al. an effort has also been made to study the structure formation using XRD techniques.
Reflective Practice of Pre-Service Mathematics Teacher on Online Learning
Authors: Abidin Z , Budayasa I K , Khabibah S 
Published: August 2021

Abstract
One important part of the teaching and learning process is reflection. The deeper a teacher’s reflection, the better it is for the improvement of subsequent learning. This study aims to determine the depth of reflective thinking of pre-service mathematics teachers on their teaching practices. This type of research is descriptive research with a qualitative approach. The learning was conducted online for two months by ten students of pre-service mathematics teacher at five different schools. There are four types of reflection depth used, namely general type, description type, argumentation type, and contribution type. The method used was observation and in-depth interviews which were also conducted online. The instruments used were observation sheets, interview sheets, questionnaire sheets and portfolios. The results showed that the depth of reflective thinking of pre-service mathematics teacher increases from argumentation type to contribution type. This has a positive impact on improving the quality of learning.
Development of Pop-up Book Mathematics Learning Media on Polyhedron Topics
Authors: Fazira S K , Qohar A 
Published: August 2021

Abstract
One of the difficulties of students studying the polyhedron topics is the visualization of the objects so that a learning media that can help and motivate students in learning polyhedron is needed. This research aims to develop a mathematics learning media in the form of a pop-up book to help students learn the material of polyhedron. This research is a development research that uses the Borge & Gall method, which consisted of several procedures as follows: (1) analysis of the to-be-developed product; (2) first product development; (3) validation and revision; (4) large-scale field tests; and (5) the final product. The subjects of this research were 30 students of 8th grade Sunan Kalijaga Malang Junior High School. The pop-up book media developed was declared as valid and received an excellent response from students. Teachers are advised to use a pop-up book media in polyhedron learning.
The impact of interactive online learning by Pear Deck during COVID-19 pandemic era
Authors: Haryani F , Ayuningtyas N 
Published: August 2021

Abstract
The COVID-19 pandemic surprises the world, switches every learning activity to online learning and increases online education platforms’ demand. However, teachers must be careful in choosing online education platforms as some online learning applications may look exciting but not give meaningful learning for students. This study uses the Pear Deck to build meaningful learning by establishing the interaction in a mindful activity. As an add-on application of PowerPoint or Google slides, Pear Deck is one solution for teachers to engage students in active and meaningful learning even though the learning mode is changed online. The participants of this study are 20 students from Linear Algebra class. After the class is over, the researcher distributes the questionnaire adopted from the Triple E Framework to assess online learning effectiveness. More than 75% of students responded that teaching and learning by using Pear Deck not only built the interaction as well as the engagement between teachers and students but also allowed them to enhance their knowledge and extend their knowledge through the learning activities. Finally, this research suggests that using the Pear Deck should be followed by the flexible type problem to enhance the higher-order thinking skill.
Promoting Prezi-PowerPoint presentation in mathematics learning: the development of interactive multimedia by using ADDIE model
Authors: Rosmiati U , Siregar N 
Published: August 2021

Abstract
Prezi-PowerPoint presentation results from developing learning media that combines PowerPoints with zoom-in and zoom-out effects, which are the hallmarks of Prezi. Prezi-PowerPoint presentation is a new learning medium for students designed to create an interactive and fun learning environment. The development of interactive learning media Prezi-PowerPoint presentations in mathematics learning aims to help students improve learning efficiency to achieve learning objectives. The research method used is research and development using the ADDIE model. The research implementation is limited to two media experts, one material expert, and three students. This is because the research was conducted at the time of the COVID-19 pandemic. The students response to the development of interactive learning media in the Prezi-Powerpoint presentation was good. It could happen because Prezi-PowerPoint presentation is a learning media that is new and attractive to students. Based on the feasibility test of learning media, it is obtained that the average percentage of the feasibility of developing learning media Prezi-PowerPoint presentation is suitable to use with good interpretation. Thus, Prezi-PowerPoint presentations are also relevant to use as an interactive learning media for students.
Promoting the middle school students’ engineering skills and conceptual understanding through STEM-based learning
Authors: Rusmana A N , Widodo A , Surakusumah W 
Published: August 2021

Abstract
Incorporating engineering in STEM education equips the students with the 21st-century skills to design solutions for real-world problems, thus, STEM-based learning has been widely studied. This study aims to examine whether STEM-based learning can promote students’ engineering skills and conceptual understanding. The participants were 68 seventh-grade middle school students, divided into 35 STEM and 33 non-STEM students. This study was conducted in environmental pollution topic where students design and make prototype to clean polluted air and water. The students’ engineering skills during the project were measured by rubric while their conceptual understanding was examined by multiple-choice question. The independent t-test was employed for data analysis. The result showed that engineering skills of STEM and non-STEM students significantly improved (p <.001) from pre to post-test and had a large effect size (d
stem=5.87, d
non-stem=3.19). The N-gain value of STEM students (M=0.62, SD=0.14) was higher than non-STEM students (M=0.20, SD=0.06). However, although concept test scores of STEM students increased significantly (p<.001), STEM students’ N-gain value (M=0.19, SD=0.30) was lower than non-STEM students (M=0.23, SD=0.20), indicating that the improvement is solely a learning effect, not because of STEM-based learning. To conclude, STEM-based learning successfully promotes the student’s engineering skills, but not conceptual understanding.
Trend of environmental science project: A reflection of Thailand GLOBE student research competition
Authors: Laplai Y 
Published: August 2021

Abstract
The Global Learning and Observations to Benefit the Environment (GLOBE) is an international science and education program that promote teaching and learning of science, enhance environmental literacy and awareness. The Institute for the Promotion of Teaching Science and Technology (IPST) acted as a country coordinator. IPST has implemented GLOBE throughout the country. One of the important approach is “GLOBE Student research competition (GLOBE SRC)”, this project aim to promote students to learn and share Earth System Science (ESS) research. The research focused on environmental science by using GLOBE measurement protocols. This study reflected outcome of the project such as the amount of ESS research, type of environmental science research and learning outcome of the project in 2009 - 2020. A total of 451 researches were analyzed in term of title and abstract. The result indicated that Survey research is 69%, Experimental research is 26%, and Invention research is 5%. The trend of the Experimental research increased and Invention research slightly increased in the last six years. The result indicated that this approach enabled students to improve their scientific inquiry, scientific thinking and scientific attitude. They received learning experiences that build 21st century competency to assist them in the future.
Embedding STEM in the mathematics classroom: A case study of vocational high school teacher’s practice
Authors: Marfuah M 
Published: August 2021

Abstract
Mathematics teachers often question the practice of Science, Technology, Engineering, and Mathematics (STEM) education without diminishing mathematics’s potential as a conceptual tool to solve problems. This qualitative case study research explores the STEM teaching practices carried out by two mathematics vocational high school teachers. Further exploration seeks to investigate how is mathematics playing a role in the STEM project they are carrying. Data were collected through documents and media analysis (lesson plans, students’ work, photos, and video) and interview with teachers. The findings showed that both teachers chose to implement the embedded-STEM approach in their first time STEM teaching experience. They used mathematics to facilitate students’ problem-solving, mathematics modelling, and reasoning in the STEM project. The teachers had different practices and perspectives in designing, implementing, and assessing embedded-STEM mathematics classrooms. The contribution of this research is to provide practical cases of embedded-STEM mathematics teaching, which may be relevant as a solution for countries that do not offer a specific STEM curriculum.