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We propose cloud IoT based LMS (Livestock Management System) with three features. i) Animal healthcare monitoring and recording using IoT sensors via wearable collar, ii) Animal livestock identification using UID for animals (smart tag) and owners (smart card), iii) QR code reading, processing and display of the details in mobile via wireless technologies.

The developed animal monitoring device is used to detect animal physiological parameters such as body temperature; physical gestures like sitting, standing, eating and heartbeat, environmental parameters such as air temperature and relative humidity. Also, e-animal husbandry information network management system is the comprehensive web-based animal husbandry software designed for better interaction between veterinary hospital, veterinary doctor, owner, farmer and animal husbandry management.

Animal monitoring device mounted on the neck sense the values and predict the health status of the animal by using cloud IoT analytics platform. The accuracy of the system is 90 per cent and it can be well placed in the livestock environment.

This research is carried out in livestock cows located in Tirunelveli district. The practical difficulty was in placing sensors on the animal. The digital feed from the farmers and the veterinary hospital is input in the animal husbandry management software.

The developed system can be implemented for monitoring the health status of the animal from anywhere using mobile applications. Also, the digitized animal information helps the government to take the right decisions on policies and fund allocation.

The implemented system can be easily scaled up to large environments by using wireless communication and animal husbandry data will be available immediately. UID scheme for animals can uniquely identify the animal and its details.

The proposed work implements novel livestock monitoring and analytics system along with Aadhar (Unique ID) for animal. The proposed UID scheme is innovative and unique.

Aluminium is the most preferred material in engineering structural components because of its excellent properties. Furthermore, the properties of aluminium may be enhanced through metal matrix composites and an in-depth investigation on the evolved properties is needed in view of metallurgical, mechanical and tribological aspects. The purpose of this study is to explore the effect of TiC addition on the tribological behavior of aluminium composites.

Aluminium metal matrix composites at different weight percentage of titanium carbide were produced through powder metallurgy. Produced composites were subjected to sliding wear test under dry condition through Taguchi’s L9 orthogonal design.

Optimal process condition to achieve the minimum wear rate was identified though the main effect plot. Sliding velocity was identified as the most dominating factor in the wear resistance.

The production of components with improved properties is promoted efficiently and economically by synthesizing the composite via powder metallurgy.

Though the investigations on the wear behavior of aluminium composites are analyzed, reinforcement types and the mode of fabrication have their significance in the metallurgical and mechanical properties. Thus, the produced component needs an in-detail study on the property evolution.

In today’s competitive world, the adoption of cloud enterprise resource planning (ERP) has become one of the critical criteria for running a successful business. In this regard, knowing the factors that influence cloud ERP adoption plays a significant role. The purpose of this paper is to rank the factors influencing the cloud ERP adoption in multi-national companies (MNCs) in India. This research would enable the cloud ERP vendors to identify the expectations of MNC customers and develop suitable strategies.

The research is being conducted among the cloud ERP consultants of designation middle managers and above from 29 different ERP consulting firms located in India. Analytical hierarchical processing approach was used to rank the factors influencing the cloud ERP adoption in MNCs located in India. The primary factors considered for this study are usability, assurance, agility, security and privacy, and vendor-related factors.

The three most preferred criteria were usability, assurance and agility. The five most preferred sub-criteria were data accessibility, availability, user friendliness, scalability, and data backup and recovery.

The study is limited to the perspective of cloud ERP consultants in India.

The outcome of this research provides cloud ERP vendors with a better understanding of factors that influence the adoption of cloud ERP in MNCs so that they can develop a focused strategy. From the MNCs point of view, the study provides a guideline on what factors they should consider while going for a cloud ERP implementation.

The research model is developed based on the cloud ERP literature, and the research results and findings would add value to the body of knowledge.

Among various metal oxide nano particles, MgO NPs and ZnO nanoparticles (NPs) in particular are gaining increasing attention due to their multifunctional characteristics, low cost and compatibility with textile materials. Each type of nanoparticle excels over others in certain properties. As such, it is often crucial to carry out comparative studies of NPs to identify the one showing higher efficiency/output for particular applications of textile products.

In the investigation reported in this paper, ZnO NPs and MgO NPs were synthesised via sol-gel technique and characterised. For comparative analysis, the synthesised NPs were evaluated for multiple properties using standard procedures before and after being applied on cotton fabrics by a dip-pad-dry-cure method.

XRD and FTIR analyses confirmed the successful synthesis of ZnO and MgO NPs. Homogeneous formation of desired NPs and their dense and uniform deposition on the cotton fibre surface were observed using SEM. ZnO NPs and MgO NPs coatings on cotton were observed to significantly enhance self-cleaning/stain removal properties achieving Grade 5 and Grade 4 categories, respectively. In terms of ultraviolet (UV) protection, ZnO or MgO NP coated fabrics showed UPF values of greater than 50, i.e. excellent in blocking UV rays. MgO NPs exhibited 20% cleaning efficiency in treating reactive dye wastewater against ZnO NPs which were 4% efficient in the same treatment, so MgO was more suitable for such type of treatments at low cost. Both NPs were able to impart multifunctionality to cotton fabrics as per requirement of the end products. However, ZnO NPs were better for stain removal from the fabrics while MgO NPs were appropriate for UV blocking.

It was therefore clear that multifunctional textile products could be developed by employing a single type of cost effective and efficient nano particles.

Introduction: With many new technologies requiring real-time data processing, cloud computing has become challenging to implement due to high bandwidth and high latency requirements.

Purpose: To overcome this issue, edge computing is used to process data at the network’s edge. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed. It is used to process time-sensitive data.

Methodology: The authors implemented the model using Linux Foundation’s open-source platform EdgeX Foundry to create an edge-computing device. The model involved getting data from an on-board sensor (on-board diagnostics (OBD-II)) and the GPS sensor of a car. The data are then observed and computed to the EdgeX server. The single server will send data to serve three real-life internet of things (IoT) use cases: auto insurance, supporting a smart city, and building a personal driving record.

Findings: The main aim of this model is to illustrate how edge computing can improve both latency and bandwidth usage needed for real-world IoT applications.

This research aims to describe the influence of weight per cent of graphite (Gr), applied load and sliding speed on the wear behavior of aluminum (Al) alloy A356 reinforced with silicon carbide (SiC) (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) particles. The objective is to analyze the effect of the aforementioned parameters on a specific wear rate.

These hybrid composites are obtained by means of the compo-casting process. Tribological analyses were conducted on block-on-disc tribometer at three different loads (10, 20 and 30 N) and three different sliding speeds (0.25, 0.5 and 1 m/s), at the sliding distance of 900 m, in dry sliding wear conditions. Optimization of the tribological behavior was conducted via the Taguchi method, and ANOVA was used for the analysis of the specific wear rate. Confirmation tests are used to foresee and check the experimental results. Examined samples were analyzed via a scanning electron microscope (SEM). Regression models for predicting specific wear rate were developed with Taguchi and ANN (artificial neural network) methods.

The biggest impact on value of specific wear rate has the load (43.006%), while the impact of Wt.% Gr (31.514%) was less. After comparison of the results, i.e. regression models, for predicting the specific wear rate, it was observed that ANN was more efficient than the Taguchi method. The specific wear rate of Al alloy A356 with SiC (10 Wt.%) and Gr (1 Wt.% and 5 Wt.%) decreases with a decrease in the load and weight per cent of Gr-reinforcing material, as well as with a decrease in sliding speed.

The results obtained in this paper using the Taguchi method and the ANN method are useful for improving and further investigating the wear behavior of the SiC- and Gr-reinforced Al alloy A356.

Grey relational analysis (GRA) has already proved itself as an efficient tool for multi-objective optimization of many of the machining processes. In GRA, the distinguishing coefficient (ξ) plays an important role in identifying the optimal parametric combinations of the machining processes and almost all the past researchers have considered its value as 0.5. In this paper, based on past experimental data, the application of GRA is extended to dynamic GRA (DGRA) to optimize two electrochemical machining (ECM) processes.

Instead of a static distinguishing coefficient, this paper considers dynamic distinguishing coefficient for each of the responses for both the ECM processes under consideration. Based on these coefficients, the application of DGRA leads to determination of the dynamic grey relational grade (DGRG) and grey relational standard deviation (GRSD), helping in initial ranking of the alternative experimental trials. Considering the ranks obtained by DGRG and GRSD, a composite rank in terms of rank product score is obtained, aiding in final rankings of the experimental trials for both the ECM processes.

In the first example, the maximum material removal rate (MRR) would be obtained at an optimal combination of ECM parameters as electrolyte concentration = 2 mol/l, voltage = 16V and current = 4A, while another parametric intermix as electrolyte concentration = 2 mol/l, voltage = 14V and current = 2A would result in minimum radial overcut and delamination. For the second example, an optimal combination of ECM parameters as electrode temperature = 30°C, voltage = 12V, duty cycle = 90% and electrolyte concentration = 15 g/l would simultaneously maximize MRR and minimize surface roughness and conicity.

In this paper, two ECM operations are optimized using a newly developed but yet to be popular multi-objective optimization tool in the form of the DGRA technique. For both the examples, the derived rankings of the ECM experiments exactly match with those obtained by the past researchers. Thus, DGRA can be effectively adopted to solve parametric optimization problems in any of the machining processes.

This chapter presents the Smart Irrigation system using the Internet of Things (IoT). IoT Technology is a network of physical objects that are connected with sensors, software, etc. This chapter concludes the project based on the agriculture field that automates the irrigation process and on the agriculture field that automates the irrigation process and solves the challenge of water consumption in those areas. We have developed the system using different sensors like (1) Soil Moisture sensor, which measures the moisture present in the soil, (2) Humidity and Temperature Sensor (DHT11), which traces the temperature change. All these sensors are connected to the Node MCU ESP8266 microcontroller, which is also a Wi-Fi module. It uploads the data to the cloud and displays it in the form of readings detected by the Blynk Application. This sensor's reading values control the pump for emergency purposes, such as stopping the pump for irrigation. Thus, this project can automate the irrigation process by analyzing soil moisture and climatic conditions, covering essential aspects like less labor, power consumption, reliability, and cost.

The research presented in this study replicates and extends the QSport-10 scale proposed by Rial, Varela, Rial and Real in their 2010 study, by incorporating the Programme dimension into the original measurement of service quality in Physical Activity and Sports Centres (PSCs). The objective of this research is to examine the dimensionality of the QSport-10 scale and extend it to capture the additional Programme service quality dimension. Study results confirmed the dimensionality of the service quality measurement offered by the QSport-10 scale and presented strong empirical support for the existence of Programme, Installations and Staff dimensions. Programme dimension was considered the most important factor for the largest consumer segment in the study.

The different performance tests were conducted on diesel engine compression ignition (CI) mode and CRDi engine.

The CI engine was suitably modified to CRDi engine with Toroidal re-entrant combustion chamber (TRCC) and was run in dual-fuel (DF) mode. Hydrogen (H₂) was supplied at different flow rates during the suction stroke, and 0.22 Kg/h of hydrogen fuel flow rate (HFFR) was found to be optimum. Diesel and biodiesel were used as pilot fuels. The CRDi engine with DF mode was run at various injection pressures, and 900 bar was found to be optimum injection pressure (IP) with 10o before top dead center (bTDC) as fuel injection timing (IT).

These operating engine conditions increased formation of oxides of nitrogen (NOx), which were reduced by exhaust gas recycle (EGR). With EGR of 15%, CRDi engine resulted in 12.6% lower brake thermal efficiency (BTE), 5.5% lower hydrocarbon (HC), 7.7% lower carbon monoxide (CO), 26% lower NOx at 80% load as compared to the unmodified diesel engine (CI mode).

The current research is an effort to study and evaluate the performance of CRDi engine in DF mode with diesel-H₂ and BCPO-H₂ fuel combinations with TRCC.