bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of PET bottle pieces and waste wrapper fibers on concrete compressive strength
Authors: Mistry Maulik , Rane Gaurav 
Published: May 2021

Abstract
In this study, the main goal was to look into the effect on concrete’s compressive strength by the addition of used PET bottle pieces & wrapper strips collected from waste generated on railway stations. To achieve the target compressive strength for M50 grade concrete, strips were cut from waste wrappers and shredded polyethylene terephthalate (PET) pieces from PET bottles were used. Waste wrapper strips were cut down in three different aspect ratios and four different proportions, along with differing proportions of shredded PET bottle pieces. After 28 days of curing, compressive strength was determined and it is observed that the compressive strength of concrete was decreased by the inclusion of wrapper strips &PET bottle pieces in different ways. Mix ID M4 i.e. 5 × 30 mm, 7.5 gm wrapper &50 gm PET bottle pieces offer better performance relative to the other Mix IDs as compared to the control mix at 28 days. Therefore, 5 X 30 mm size strips of wrappers along with 50gm of PET bottle pieces are the best mix for the compressive strength of concrete compared with other mixes. In the case of proportioning different sizes of shredded wrappers used along with PET bottle pieces, improved results are obtained relative to all other Mix IDs as relative with the 28- day control mix. Multiple size strip of (PET) wrapper with a mixture of (5 × 20 mm, 2.5 gm), (5 × 30 mm, 3 gm), (5 × 40 mm, 2 gm), and 25 gm of broken bits of PET bottles is the better combination for concrete compressive strength than any other combination.
A comparative study of different kinematics - based methods to predict the time of slope failure
Authors: Philip Shimol , Rakendu R. , Philip Joe G. 
Published: May 2021

Abstract
Landslide forecasting is considered one of the key components of early warning systems. Predicting landslide failure at the slope-scale is a major scientific challenge, but on the other hand, it can mitigate the consequences of slope failures in terms of both human lives and economic losses. Therefore, landslide forecasting is a subject worthy of further research efforts due to its social implications. Landslide forecasting consists of the prediction of a slope failure in spatial and/or temporal terms. Temporal prediction can be performed at a regional/global scale or on a slope-scale. This paper focuses on the different methods for temporal prediction of landslides on a slope scale based on kinematic parameters such as displacement and its derivatives (velocity and acceleration). These kinematic parameters are directly related to the stability conditions of the moving mass. This paper accurately explains the correlation between the kinematics and the collapse time of a slope. Also, the present study systematically explains and compares the methods such as the empirical and semi-empirical methods for forecasting the failure time of slopes with their corresponding advantages and limitations. Finally, a detailed outline of the future technology and risks in this area are also introduced in this paper.
Use of C&D Waste in Road Construction: A critical review
Authors: Ray Arpan , Nanda Radhikesh Prasad , Roy Pronab 
Published: May 2021

Abstract
Sustainable development pickup its importance as population of world is increasing day by day. Demand and supply of natural resource in construction of road is facing great challenge for world pavement industry. Parallel to this, there was big challenge to dispose construction and demolition(C&D) waste according to environmental and economic growth of society. This paper focused on review of literature in use of C&D waste in road construction and on suitable alternative materials, better performance of road as well as the gap in the existing knowledge in construction. Some practical problem, suitability of C&D waste as road aggregate is also discussed.
Development of geopolymer concrete mixes with ambient air curing
Authors: Ahmed Adil , Kumar S Sundar , Nanda R P 
Published: May 2021

Abstract
Mix design for geopolymer concrete is yet to evolve. Though geopolymer concrete has made the much required transition from laboratory to the field, due to inherent difficulties such as huge variation in the chemical composition of the raw material, the sensitivity of this composition and the chemicals involved in the fresh and hardened properties have led to most of the mix development through trials with the actual materials. The aim of the work reported in this paper was to design an optimum mix proportion for geopolymer concrete using fly ash and ground granulated blast furnace slag (GGBS) as an aluminosilicate source, which has good workability, slump retention, and compressive strength without special curing requirements. A total of 10 mixes were prepared with varying fly ash to GGBS ratios. Ambient curing for 28 days was adopted for all mixes; however, heat curing at 85°C for 24 hours, followed by ambient curing, was adopted for one mix alone. It was clear that with the increase in GGBS content, there was a reduction in setting time, and the mix set faster. However, with more GGBS, greater strength was achieved at a relatively milder alkali activator solution. When the GGBS content increased beyond a certain level, the slump retention time reduced to levels where the mixes were unfit for practical applications. However, it can be concluded that with a judicious choice of fly ash and GGBS proportions, a strength of as high as 60MPa can be achieved in geopolymer concrete without any special curing methods.
Condition assessment of the structural elements of a reinforced concrete structure using non-destructive techniques
Authors: Gupta Nakul , Gupta Ankur 
Published: May 2021

Abstract
Assessment of the strength and durability properties of structural elements of a reinforced concrete structure can be of great importance for either tracing potential damaged areas or for carrying out the reliability analysis. Composite materials, these days have become an integral part of any construction. Most of the structures are built using concrete as the construction material. Inspite of the strong and durable nature of the concrete as a material, the RCC structures still face common structural issues that includes cracking, spalling, rusting of reinforcement, dampness, acid attack, carbonation etc. If these issues are left unattended during the initial stages then they may cause serious distress in the structures. This paper reports the condition assessment of the structural elements of a residential building using non-destructive techniques. The hardness and the strength of the cover concrete has been assessed using rebound hammer test, structural integrity and homogeneity by ultrasonic pulse velocity test, Additionally, carbonation test and resistivity test were also conducted to assess the extent of corrosion. The results of the assessment have shown a noteworthy decay in the strength and the durability properties of the structural elements.
Role of Spatial Design in Green Buildings-A Critical Review of Green Building Rating Systems
Authors: Katiyar Manoj , Sahu Ashok Kumar , Agarwal Sanjay , Tiwari Pravesh 
Published: May 2021

Abstract
Green Building is a movement today and given the resources, all buildings aspire to be green certified. While making green building, the task is to achieve efficiency of site, energy, water and waste. The effort is rated and ranked through different green building rating systems prevalent across the world and certification label is issued based upon the credits achieved. This research paper carries out a comparison of all rating systems currently available. The credits are studied and classified under various themes (Architecture, MEP, Materials and Practices). There are further two categories under each theme-products/technology and design. Each credit is put under one or the other category and theme. Categorization of credits revealed some astonishing facts and one of the most astonishing facts is that spatial design/architectural design hardly figures in green building rating systems across the world. Most of the credits can be earned through incorporation of more efficient technology/products. There is very little importance given to design input. While credits can be awarded for selecting higher efficiency chiller, there is no means of comparing efficiency of one type of building design to another simply because base case is different for each building in terms of design. The study reveals that potential of architectural design in achieving efficiency has totally been ignored through credits awarding system. The study concludes that some more credits need to be added to the existing system to take into account the impact of architectural design in performance evaluation of green buildings which would make not just the buildings but architecture of these buildings also green.
Assessment of indoor air suspended particulate matter in GLA University campus, Mathura
Authors: Sharma Vivek , Islam Raisul , Raghvendra Singh Kunwar 
Published: May 2021

Abstract
Indoor air quality represents the quality of air inside room. It affects the health and comfort of the residents. Hence it is very essential to measure the quality of air inside buildings and rooms. In this study, air quality of computer lab of GLA University has been assessed. Particulate matter (PM) is a common air pollutant, including of solid and liquid particles suspended in the air. Normally used indicators PM that are related to human well-being refer to the concentration of particles with a thickness of less than 10 μm. Therefore, for assessment of indoor air quality PM10, PM2.5 and PM1 has been monitored for one week. All observed diurnal data-sets have been represented by box plot. This research is very useful for policymakers of GLA University. Based on indoor air quality, classrooms and laboratories can be designed for good health of students
Study of traffic flow and associated noise pollution near GLA University, Mathura (India)
Authors: Dubey Payai , Singh K. R. , Kumar Goyal Sudhir 
Published: May 2021

Abstract
Urbanization is increasing at a very fast rate in our country; road length and its conditions are also improving. Therefore, the number of vehicles also increasing this has led to traffic congestion on roads and noise pollution. The transportation sector is one of the major contributors to noise in an urban area. The main objective for this research was to characterize the traffic noise on the GLA University, Mathura, India. The research work of this paper is divided in two sections: (i) Traffic Study on NH-19 (ii) Traffic noise measurement at peak hours. The study includes the campus area at GLA University. The study is conducted using Sound Level Meter SLM 100 type 2. The results were compared with WHO guidelines, which states that noise level should not exceed 55 dB. Moreover, because of continuous traffic noise on NH-19 is causing increase in health issues. The impact can be harmful GLA University staff, students and people living in campus area. Hence, it can cause disease such as concentration difficulty, irritation, annoyance and are most prominent disorders due to continuous traffic noise.
Comparison of Mechanical properties of flyash-GGBS based GPC and flyash-alccofine based GPC with different concentrations of alkaline activators
Authors: Ramesh Vemundla , Srikanth Koniki 
Published: May 2021

Abstract
Geo-polymer concrete (GPC) is the perfect solution to replace the conventional concrete making use of unwanted materials such as fly ash, GGBS, Alccofine etc.,. It is noted that many researchers have worked on geopolymer concrete and a tremendous work is going on too, but unlike conventional concrete there is no proper methodology available to follow. GPC has preferable mechanical properties when differentiate to conventional concrete. Replacing GGBS and alccofine with fly ash upto some extend can eliminate oven curing and it becomes suitable to use ambient curing conditions effectively. and This study deals with different proportion of various source materials and concludes at which proportion and concentration the compressive strength and workability is higher. For Alccofine combination with fly ash gives better results than GGBS combination with fly ash but if economy is the criteria alccofine is not preferable as it is costlier than that of GGBS.
Hardware Implementation of Prepaid Power Consumption Using Coin Box System
Authors: Kumar Vikash , Kumar Raghuwanshi Sanjeev 
Published: May 2021

Abstract
For enhancement of technology our implemented system is helpful for the human beings for in this world. Basically in guest house, lodges, hotels and rest houses the power consumption is not measurable by the customer and the owner. So that it is important to control the consumption of power proportional to the payment otherwise the owner pays the higher bills and faces the loss in running the hotel. For we develop the pre-paid system for power consumption by using some simple discreet components and microcontroller programmed for desired output. Using highly advanced ICs and with the help of growing technology the proposed hardware has been successfully implemented but by using pre-paid card in place of token/coin we can make a more advanced system. Moreover, this system, for enhancing the requirement the technology the system can be replaced with GSM (Global System for Mobile Communication) Modems, it will help the tracking of the consumers load on a regular basis is possible, it help in the maximum demand for tracking, more over instead of recharging the chip has be detected through online theft, the readily used in cell phones available recharge cards (smart cards can be introduced. Using with given mechanisms, consumers much user friendly for recharging their meters and at their convenience.
A study on firms’ communication based on artificial intelligence and its influence on customers’ complaint behavior in Social media environment
Authors: Agnihotri D , Kulshreshtha K , Tripathi V 
Published: May 2021

AbstractThe purpose of the study is to understand actual behavior of customers and firms by analyzing the real time interaction between firm and customers on social media platform. The study also pursues to assess the way firms respond to the customer’s complaint on different social media platforms through autobots based on artificial intelligence. The study identifies official Facebook and Twitter pages of top online shopping portals. The number of complaints and responses posted on these pages are documented and analyzed. Netnography method is used for data collection. Connotation key words are used for selection of comments and tweets. The study concludes that organizations respond to the most of the complaints publically but they further ask for the personal interaction with the complainer to resolve the complaint through pre-defined statements. The study has also revealed that pre-defined statements stated by autobots based on artificial intelligence seem insufficient to resolve customer complaints. The limitation of the study is associated with the netnography technique, which has restricted the exploration to only those consumers who have posted comments on Twitter or Facebook. Hence, other physical factors i.e. customer responses through numerous offline modes were absconded. The study is limited to Facebook and Twitter only. This study is limited to four major online shopping portals; it leaves a lot of scope to analyze other industries such as banking and insurance, hospitality, aviation etc. The output of the study suggests that the firms need to be conscious enough to provide customized and adaptive solutions to the customers’ complaints instead of pre-defined responses through artificial intelligence as it lacks emotions to empathize with customers’ issues. As per the literature of review, methods chosen in previous researches by researchers were having a methodological gap, as netnography in social media environment remained unused earlier, which has reinforced to analyze original behavior of customers and responsiveness of organization.
A Survey on Image Captioning datasets and Evaluation Metrics
Authors: Sharma Himanshu 
Published: May 2021

Abstract
In the task of image captioning, a natural language explanation is generated for a given image. It uses the subfields of artificial intelligence: computer vision and language generation. Convolutional Neural Network (CNN) is generally applied to capture image features and language processing models such as Recurrent Neural Network for sentence generation. In this paper, various datasets and evaluation metrics which are useful for image captioning task are discussed. Also, the datasets and evaluation metrics applied by the state-of-the-art image captioning models is summarized.
Signal Processing and Machine Learning Approaches and Evaluation for Indian Classical Music
Authors: Shastry Rajashekar , Bai Anita 
Published: May 2021

Abstract
In the field of signal processing, a considerable amount of work has been done in the analysis and implementation of various techniques related to speech signal processing. When it comes to the Music signal processing, the techniques that are originally developed for speech signal processing have been applied to music signal with good results. In comparison with Indian classical music, lot of research work done in Western classical music on music audio analysis like information retrieval, singer/instrument identification, genre detection, etc.,. This paper provides an overview of some signal processing, and machine learning techniques that specifically applied to the Indian classical music for analyzing and identifying features such as tonic identification, genre classification, raga recognition, music transcription, rhythm, and timbre. We will look at how particular features of Indian Classical music signals impact and how these features are extracted.
Comparison of two Deep Learning Methods for Classification of Dataset of Breast Ultrasound Images
Authors: Saxena Aditya 
Published: May 2021

Abstract
Breast cancer is the prominent cancer types which account to high mortality. Early detection could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique which is used for differentiating benign and malignant tumors. Recently a dataset of breast ultrasound image has been released which gives opportunity to apply machine learning methods over it and enable one to automatically classify them in the respective tumor class. In the present study, I compared the performance of two deep learning methods: AlexNet, and MobileNet for the classification of breast ultrasound images using an augmented data set of 12000 images (6000 each of benign, and malignant) and received an F1-score of 0.914 and 0.974 respectively so herewith concluded that the later deep learning approach performs better then the former in classifying the ultrasound images of breast tissues in benign and malignant cancer types.
Solving N-Queen Problem by Genetic Algorithm using Novel Mutation Operator
Authors: Sharma Sachin , Jain Vinod 
Published: May 2021

Abstract
N-Queen is a well-known NP-Hard problem. In N-Queen problem, the challenge is to place n queens on n*n chess board such that no two queens can attack each other. The problem can-not be solved using traditional algorithms. Genetic algorithm is a well-known optimization technique. The problem has to be represented in genetic form to solve it using genetic algorithm. In this paper a new mutation operator is proposed to solve N-Queen problem. The proposed algorithm is applied on some instances of N-Queen problem and it is observed that the proposed performs better as compared to other existing algorithms.
Morphology-based identification and classification of Pediastrum through AlexNet Convolution Neural Network
Authors: Gaur Ashish , Yadav D.P. , Pant Gaurav 
Published: May 2021

Abstract
In general solutions and outcomes to an issue in image processing, a ton of trail and examination with an enormous assortment of test photographs is incorporated. Pediastrum, which comprises of pigments like carotene, xanthophyll, chlorophylls, and other nourishing elements like complex sugars, major and minor minerals, and protein, enzymes, fiber, is a minute coccal shaped colony forming green algae. In this research, an attempt was made to solve the problem of identifying and classifying different species of Pediastrum with the aid of CNN based deep learning model. The informational collection comprising of 12,000 algal pictures was being utilized by, AlexNet CNN (Convolution Neural Network) model for the training and validating purposes. The features like the presence of sporopollenin in the cell wall, the structures and the functions of the cells, and the structural properties of coenobia make the basis for automated classification by AlexNet deep learning model. The efficacy of the proposed approach is demonstrated by an experimental outcome of 99.54 percent classification accuracy with precision and Fl-score more than 0.98.
Innovative service recovery of customers by food aggregators using sentiment analysis
Authors: Chauhan Sanjeev , Upadhyay Yogesh , Singh S K 
Published: May 2021

AbstractIn the global era, technology plays a vital role in determining the strong market position for the modern-day organizations. Food aggregators make efforts to identify the reasons for service failure in case of customers not in direct communication with them. The ascending online food ordering market has augmented the involvement of peers’ day-to-day interaction on social media resources. Food ordering brands like Swiggy, Food panda, Zomato who have garnered quite a reputation by tapping their audience online via enticing them with the idea of getting quality restaurant meals right at their doorstep with just a click are slowly being victimised by their web of problems. Online marketing had made daily food ordering easy for the users but, the excessive demand of orders at a time can lead to delay in deliveries, loss in quality etc. henceforth, targeting brand’s esteem and loss of valuable consumers. These food ordering websites collaborate with a wide network of restaurants/hotels in a city and provide quick delivery of food products to their customers using aggregator business model. They act as middlemen who deliver the food from restaurants to user’s home maintaining a brands name. The current study focuses on tracking the sentiments of the customers through which these brands can identify the issues which leads to unsatisfactory responses or reviews on various online and social sites. Further study pave ways for innovative service recovery mechanism to frame a sustainable business practice for gaining competitive advantage. In this study, sentiment analysis tools like IBM Watson, social mention and tone analysers were used to observe the reactions of customers and used disruptive technology to analyse how brands could improve their service recovery behaviour to secure and reach more customers by figuring out the reasons of service failure.