MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Engineering and Technology

In India, the core of the logistic sector is the trucking industry. The manufacturing business ensures that their product reaches the end customer quickly and thus they outsource their logistical operations to logistic service providers. More than 60% of goods are transported by road. Due to globalization and digitalization, the demand for logistics has increased. To meet the demand most truck owners are recklessly permitting overloading to increase margins, to save time by risking the consequences of in-transit issues as well as its negative impact on the infrastructure. This paper addresses one such problem that many drivers and their customers face regularly. To reduce the time required for weighing and getting instant results a payload measurement system is developed which is an external attachment to the vehicle. The results obtained by this model are quick and accurate. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

World University root node

School of Mechanical Engineering

Lecture Notes in Mechanical Engineering

Payload Measurement Using Suspension System in Goods Carrier

Environmental pollution has been determined to be the root cause of a large number of deaths and illnesses around the world. Heavy metals in wastewater have been a major problem in recent years due to their hazardous effects. Human health, living resources, and ecological systems are all at risk because of the increasing levels of hazardous metals that are being released into the environment as industrial wastes. Heavy metals in wastewater are strictly regulated by environmental organizations and authorities due to their harmful health impacts and toxicological properties. As a result, adequate safeguards must be taken, and technologies for detecting, quantifying, and removing heavy metals from effluent waterways must be developed. The most prevalent method for removing heavy metals from wastewater is adsorption. As a result of recent advances in adsorption technology, it's a potential wastewater treatment method. In recent years, the utilization of agricultural waste materials has grown in popularity. Due to their physicochemical and nontoxic properties, agricultural wastes have the potential to be employed as adsorbents. A low-cost adsorbent made from agricultural waste is one of the most common applications for agricultural waste products. This review provides an overview of applications for various agricultural waste materials added to their ability to absorb heavy metals. The different properties of agricultural waste materials are also summarized. The review article also covered the treatment procedure's effectiveness, difficulties, effects on the environment and public health, and other elements. The process parameters, the adsorption isotherm, and the adsorption kinetics are all thoroughly discussed. In this review, it has been shown that several heavy metals can be effectively removed from wastewater using inexpensive adsorbents. © 2022

School of Chemical Engineering

School of Petroleum Engineering

Materials Today: Proceedings

A review on application of low-cost adsorbents for heavy metals removal from wastewater

The nano materials improve the mechanical properties of a cement concrete is a remarkable achievement in the field of nano composites. Matrix cement has a relatively loosely bonded structure which can be filled with Nano alumina which significantly improves the mechanical properties of cement. The present study discusses the development, manufacture and properties of the concrete made by nano materials. In the present study, the sulphate resistance of concrete is improved by the addition of Nano Alumina by partial replacement of cement. The study is carried out by partial replacement of cement by colloidal nano alumina- Al2O3 (CNA) particles with varying percentage (0%, 1%, 2%, 3%, 4%, and 5%) in the M55 grade of concrete. The study also evaluates an optimum dose of nano alumina at which maximum compressive strength of concrete is achieved with minimal sulphate attack observed in form of mass reduction in the concrete. It is observed that by an addition of 2.3% of CNA, the maximum sulphate resistance of concrete is achieved after 28 days. It was also observed that by an addition of 2% to 2.5% of Nano Alumina the compressive strength of concrete increases with a decrease in trend observed at 3% addition of Nano Alumina. © 2022

School of Civil Engineering

Resistance against sulphate attack in concrete by addition of nano alumina

Grape is considered as a cash-crop throughout the world. As compared to other fruits, shape of every grape cluster is different from each other. The change in region of grape cluster with respect to image size is sparse in nature and hence involves lot of errors. So it's a bit challenging to find shape and estimate weight of grape cluster using modern algorithms as well. In this paper, we proposed a deep learning regression model with combination of basic structures of U-net, VGG-16 and attention modules. The sequence combinations of layers such as convolution layers, max-pooling layers and average pooling layers along with concatenation operations are the main characteristics of these models. This model is capable of predicting weight of grape clusters present in images with a reduced error. © 2022 IEEE.

School of Electronics & Communication Engineering

2022 International Conference for Advancement in Technology, ICONAT 2022

Grape Yield Prediction using Deep Learning Regression Model

There has been a pressing need to consider lifelong building costs and spread awareness about the life cycle cost of a building in the construction sector. Before 1970s, most of the clients', contractors' and developers' decisions were solely based on the initial capital cost with no consideration of the entire life cycle cost of the building. If the cost of a building is estimated considering its entire life, approximately 70% portion is required for operational aspects. This approach of focusing only on the initial cost has failed to give the best value for money to the client in the long term. In this research, questionnaire-survey method is utilized to understand the awareness level and importance of different tangible and intangible costs of the construction over its useful life amongst the construction community. List of critical factors affecting life cycle cost of a building was extracted through an extensive literature survey. The questionnaire survey was prepared by incorporating the most significant parameters and circulated amongst the stakeholders consisting of respondents working at various hierarchical positions in the construction industry. The factors were ranked based on weightages obtained through Relative Importance Index (RII) methodology. The data analysis showed that the top 5 costs which should definitely be considered in life cycle costing are construction cost, Land Acquisition and associated cost, Design team fees and associated cost, Operation & Maintenance Cost and Taxes. Respondents also agreed that the life cycle costing methodology would provide a better and comprehensive estimate of the building cost as a whole and would be crucial in decision making. Incorporating life cycle costing in the design and planning phase would significantly help engineers as well as contractors to focus on the most important cost parameters. Furthermore, the analysis indicated that a lower emphasis is still given to cost factors related to environment. It needs to be emphasized that a comprehensive cost estimate for a project can only be obtained by incorporating sustainability aspects. Thus, a LCC framework considering all important cost parameters would further improve the process of selection of the most cost-effective alternative, as well as significantly increase sustainability in construction projects. © Published under licence by IOP Publishing Ltd.

IOP Conference Series: Earth and Environmental Science

Life Cycle Costing Methodology for Sustainable Construction

Accidents and road crashes occurring due to dissociation of driver from his consciousness is one of the hot topics researchers have been contemplating to curtail, from the past few years. This dissociation wanes the driver's ability to concentrate on the road while driving and promotes occurrence of mishaps. There has been a challenge to non-intrusively detect drowsiness and fatigue-level of drivers in real-time environments and concomitantly make it computationally faster and less complex. The paper proposes a faster real-time driver inactiveness detection system capable of alerting drivers of their drowsiness by inspecting and scrutinizing the state of eyes and mouth and follows a non-obtrusive approach. The system utilizes eyes to monitor the drooping nature of eyes and examines the mouth's current state to detect any yawning activity performed. Initially, it uses Haar transform to extract facial features and strong classifiers for detecting faces by incorporating Adaboost algorithm. Next, shape predictors are used to locate the key points for obtaining position of mouth and eyes within the face area. States of the mouth and eyes are continuously monitored with the help of the metrics viz. Eye Aspect Ratio (EAR) and Mouth Yawn Width respectively and are used to alert the driver through voice-enabled commands followed by a warning alarm sound to trigger them thereby aiding in regaining their consciousness back and focusing on their driving. The algorithm used in this system is tested under different lighting conditions and against different possible situations and accurate results in terms of successful detection rates are obtained. © 2022 IEEE.

2022 6th International Conference on Computing, Communication, Control and Automation, ICCUBEA 2022

Real-Time Voice Enabled Drivers' Safety Monitoring System Using Image Processing

Drought is a frequent hydrometeorological phenomenon that affects every individual, including animals. It causes significant economic and human losses. Various traditional methods are used to assess droughts in the early stages. However, conventional ways are time-consuming, expensive, and laborious. This has led to exploring remote sensing with recent methods for drought assessment. Therefore, the current paper gives a comprehensive overview of the recent studies on agricultural drought impact and its assessment using remote sensing datasets and various spectral index methods. The available satellite data sources have been provided with their sensors and spectral, spatial, and temporal information. Furthermore, we have also provided the general schema for agricultural drought assessment, essential indices, and drought severity methods with their importance and limitations. The normalized difference vegetation index (NDVI), standardized soil moisture index (SSMI), soil moisture percentile (SMP), normalized soil moisture (NSM), standardized precipitation index (SPI), vegetation health index (VHI), and soil moisture deficit index (SMDI) are the most used indices by researchers for drought detection and its assessment. Lastly, this review examines the challenges that need to be handled for the early detection of agricultural drought episodes. It is concluded that a long-term historical record of satellite images and meteorological data is required to calculate drought severity levels and identify drought-prone areas. This review can detect the hidden risks of agricultural drought and offer a theoretical foundation for decision-makers and mitigation agencies. © 2022, The Author(s), under exclusive licence to Società Italiana di Fotogrammetria e Topografia (SIFET).

School of Management UG

Management

School of Computer Engineering & Technology

Applied Geomatics

A review of agricultural drought assessment with remote sensing data: methods, issues, challenges and opportunities

Roads are valuable assets worldwide that must be kept in good condition with minimum maintenance to safeguard against accident. From the literature review and the field observations, it is quite evident that geo-fields are one of the parameters which is responsible for damaging the road surface and hence further increasing the possibility of accidents. Present study is an attempt to provide a detailed insight into the performance of the road segment when subjected to geo-fields. For the same accident data of the Mumbai-Pune expressway, over six years from 2016 to 2021 has been used. Based on this data 46 accident black spots were segregated for investigation. An automated method is used to measure the pavement roughness index (PRI). On these spots, the quality of pavement is determined using the non- destructive test of Ultra Sonic Pulse Velocity (UPV). Geo-fields are measured in terms of electric and magnetic fields at these black spots. Data has been analyzed using Karl Pearson's correlation coefficient and linear regression models are developed for the average number of road accidents (Ā) with respect to PRI, UPV and Geo-fields. The mathematical models developed may provide a useful link between road accidents, geo-fields, and pavement surface conditions. It will also help transport authorities not only to predict the number of accidents at particular spots envisaged on existing expressway but will also enable them to design pavements appropriately for the detrimental effects of weak electric and magnetic fields. © 2022 Elsevier Ltd. All rights reserved.

Investigation for effect of weak electric and magnetic fields at black spots on concrete pavement

For deaf persons it is very difficult to establish a communication, by using Hearing Aid System it becomes possible. But the Hearing Aid which are available in the market are having many issues like heavy weight, more power consumption, voice with less clarity, delay in receiving the signal, cost, stigma associated with wearing the hearing aid system etc. To overcome mentioned problems and to help the deaf people we have designed the filter bank with variable bandwidth (VBF) which can be used in hearing aid system to improve the performance by reducing the hardware, by removing the noise, also by improving the speech quality and reducing the delay. The developed filter bank is implemented using MATLAB, audiograms of different patients and achieved better performance by reducing unwanted noise and increasing the speech intelligibility finally the attained performance of developed filter is contrast with the subsist filter banks in the parameters like consumption of power, matching error, and delay in processing, to check the performance of developed filter © 2022 IEEE.

Audiogram Study in Filter Bank Used for Hearing Aid System to Enhance the Performance

Pavement management systems play a vital role in the development of a country as it is a very important part of the economy. Maintaining a good quality of the road is the key duty of the road authorities. They require methods for pavement-related data collection and analysis to evaluate its condition. Machine learning (ML) methods can be utilized for defect classification from an image, defect recognition and segmentation in the assessment of pavement distress. This paper presents an overview of the machine learning techniques used to analyze pavement condition data. Moreover, information collection methods and pavement condition indices are also studied from the point of view of ML algorithms. Future research directions are also presented by highlighting the limitations of using ML techniques for the assessment of pavement conditions. © 2022 IEEE.