Search results for: Gaurav Vaidya

Authors: Seema Vaidya

Abstract:

Mining association rule is a key issue in data mining. In any case, the standard models ignore the distinction among the exchanges, and the weighted association rule mining does not transform on databases with just binary attributes. This paper proposes a novel continuous example and executes a tree (FP-tree) structure, which is an increased prefix-tree structure for securing compacted, discriminating data about examples, and makes a fit FP-tree-based mining system, FP enhanced capacity algorithm is used, for mining the complete game plan of examples by illustration incessant development. Here, this paper handles the motivation behind making remarkable and weighted item sets, i.e. rare weighted item set mining issue. The two novel brightness measures are proposed for figuring the infrequent weighted item set mining issue. Also, the algorithm are handled which perform IWI which is more insignificant IWI mining. Moreover we utilized the rare item set for choice based structure. The general issue of the start of reliable definite rules is troublesome for the grounds that hypothetically no inciting technique with no other person can promise the rightness of influenced theories. In this way, this framework expects the disorder with the uncommon signs. Usage study demonstrates that proposed algorithm upgrades the structure which is successful and versatile for mining both long and short diagnostics rules. Structure upgrades aftereffects of foreseeing rare diseases of patient.

Keywords: association rule, data mining, IWI mining, infrequent item set, frequent pattern growth

Procedia PDF Downloads 396

Authors: Gaurav Verma

Abstract:

Organoclay loaded (0-5 weight %) polyester-urethane (PU) coatings were prepared with a branched hydroxyl-bearing polyester and an aliphatic poly-isocyanate. TEM micrographs show partial exfoliation and intercalation of clay platelets in organoclay-polyester dispersions. AFM surface images reveals that the PU hard domains tend to regularise and also self-organise into spherical shapes of sizes 50 nm (0 wt %), 60 nm (2 wt %) and 190 nm (4 wt %) respectively. IR analysis shows that PU chains have increasing tendency to interact with exfoliated clay platelets through hydrogen bonding. This interaction strengthens inter-chain linkages in PU matrix and hence improves anti-ageing properties. 1000 hours of accelerated weathering was evaluated by ATR spectroscopy, while yellowing and overall discoloration was quantified by the Δb* and ΔE* values of the CIELab colour scale. Post-weathering surface properties also showed improvement as the loss of thickness and reduction in gloss in neat PU was 25% and 42%; while it was just 3.5% and 14% respectively for the 2 wt% nanocomposite coating. This work highlights the importance of modifying surface and bulk properties of PU coatings at nanoscale, which led to improved performance in accelerated weathering conditions.

Keywords: coatings, AFM, ageing, spectroscopy

Procedia PDF Downloads 453

Authors: Suresh Sahu, Abhijeet M. Vaidya, Naresh K. Maheshwari

Abstract:

The efforts to understand the heat transfer behavior of supercritical water in supercritical water cooled reactor (SCWR) are ongoing worldwide to fulfill the future energy demand. The higher thermal efficiency of these reactors compared to a conventional nuclear reactor is one of the driving forces for attracting the attention of nuclear scientists. In this work, a solution procedure has been described for solving supercritical fluid flow problems in complex geometries. The solution procedure is based on non-staggered grid. All governing equations are discretized by finite volume method (FVM) in curvilinear coordinate system. Convective terms are discretized by first-order upwind scheme and central difference approximation has been used to discretize the diffusive parts. k-ε turbulence model with standard wall function has been employed. SIMPLE solution procedure has been implemented for the curvilinear coordinate system. Based on this solution method, 3-D Computational Fluid Dynamics (CFD) code has been developed. In order to demonstrate the capability of this CFD code in supercritical fluid flows, heat transfer to supercritical water in circular tubes has been considered as a test problem. Results obtained by code have been compared with experimental results reported in literature.

Keywords: curvilinear coordinate, body-fitted mesh, momentum interpolation, non-staggered grid, supercritical fluids

Procedia PDF Downloads 127

Authors: Suresh Palla, Kalpna Sharma, Gaurav Bhatnagar, S. K. Chaturvedi, B. N. Mohapatra

Abstract:

Determining the content of free lime is especially important to judge reactivity of the raw materials and clinker quality. The free lime limit isn’t the same for all cements; it depends on several factors, especially the temperature reached during the cooking and the grain size distribution in cement after grinding. Estimation of free lime by conventional method is influenced by the presence of portlandite and misleads the actual free lime content in the clinker for quality check up conditions. To ensure the product quality according to the standard specifications in terms of within the quality limits or not, a reliable, precise, and very reproducible method to quantify the relative phase abundances in the Portland Cement clinker and Portland Cements is to use X-ray diffraction (XRD) in combination with the Rietveld method. In the present study, a methodology was proposed using XRD to validate the obtained results of free lime by conventional method. The XRD and TG/DTA results confirm the presence of portlandite in the clinker to take the decision on the obtained free lime results through conventional method.

Keywords: free lime, quantitative phase analysis, conventional method, x ray diffraction

Procedia PDF Downloads 135

Authors: Shipra Shukla, Gaurav Chaudhary, S. K. Tewari, Mahesh Pal, D. K. Upreti

Abstract:

Cassia tora L. is widely distributed in tropical Asian countries, commonly known as sickle pod. Various parts of the plant are reported for their medicinal value due to presence of anthraquinones, phenolic compounds, emodin, β-sitosterol, and chrysophanol. Therefore a sensitive analytical procedure using UHPLC-ESI-MS/MS was developed and validated for simultaneous quantification of five phenolic compounds in leaf, stem and root extracts of Cassia tora. Rapid chromatographic separation of compounds was achieved on Acquity UHPLC BEH C18 column (50 mm×2.1 mm id, 1.7µm) column in 2.5 min. Quantification was carried out using negative electrospray ionization in multiple-reaction monitoring mode. The method was validated as per ICH guidelines and showed good linearity (r2 ≥ 0.9985) over the concentration range of 0.5-200 ng/mL. The intra- and inter-day precisions and accuracy were within RSDs ≤ 1.93% and ≤ 1.90%, respectively. The developed method was applied to investigate variation of five phenolic compounds in the three geographical collections. Results indicated significant variation among analyzed samples collected from different locations in India.

Keywords: Cassia tora, phenolic compounds, quantification, UHPLC-ESI-MS/MS

Procedia PDF Downloads 268

Authors: Sunil Kamboj, Vipin Saini, Suman Bala, Gaurav Sharma

Abstract:

The main aim of the present research was to encapsulate mefenamic acid in niosomes and incorporate the prepared niosomes in the carbopol gel base for sustained therapeutic action. Mefenamic acid loaded niosomes were prepared by thin film hydration technique and evaluated for entrapment efficiency, vesicular size and zeta potential. The entrapment efficiency of the prepared niosomes was found to increase with decreasing the HLB values of surfactants and vesicle size was found to increase with increasing the cholesterol concentration. Niosomal vesicles with good entrapment efficiencies were incorporated in carbopol gel base to form the niosomal gel. The prepared niosomal gel was evaluated for pH, viscosity, spreadability, extrudability and skin permeation study across the rat skin.The results of permeation study revealed that the gel formulated with span 60 niosomes sustained the drug release for 12 h. Further the in vivo study showed the good inhibition of inflammation by the gel prepared with span 60 niosomes.

Keywords: mefenamic acid, niosomal gel, nonionic surfactants, sustained release

Procedia PDF Downloads 407

Authors: Harshada Vaidya-Kannur, Dattatraya Naik

Abstract:

Propolis, one of the beehive products also referred as bee-glue is sticky dark coloured complex mixture of compounds. The volatile oil can be isolated from the propolis by hydrodistillation. The mark that is left behind after the removal of volatile oil is referred as the de-oiled propolis. Antioxidant as well as anti-inflammatory properties of total ethanolic extract of de-oiled propolis (TEEDP) was investigated. Another lot of deoiled propolis was successively exacted with hexane, ethyl acetate and ethanol. Activities of these fractions were also determined. Antioxidant activity was determined by studying ABTS, DPPH and NO radical scavenging. Determination of anti-inflammatory activity was carried out by topical TPA induced mouse ear oedema model. It is noteworthy that ethyl acetate fraction of deoiled propolis (EAFDP) exhibited 49.45 % TEAC activity at the concentration 0.2 mg/ml which is equivalent to the activity of trolox at the concentration 0.2 mg/ml. Its DPPH scavenging activity (72.56%) was closely comparable to that of trolox (75%). However its NO scavenging activity was comparatively low. From IC50 values it could be concluded that the efficiency of scavenging ABTS radicals by the de-oiled propolis was more pronounced as compared to scavenging of other radicals. Studies by TPA induced mouse ear inflammation model indicated that the de-oiled propolis of Indian origin had significant topical anti-inflammatory activity. The EAFDP was found to be the most active fraction for this activity also. The purification of EAFP yielded six pure crystalline compounds. These compounds were identified by their physical data and spectral data.

Keywords: anti-inflammatory activity, anti-oxidant activity, column chromatography, de-oiled propolis

Procedia PDF Downloads 286

Authors: Mahvish Anwaar

Abstract:

Research Question: In this paper, we explore the causal relationship between banking competition, banking stability, and economic growth. Research Findings: The unbalanced panel data starting from 2000 to 2018 is collected to analyze the causality among banking competition, banking stability, and economic growth. The main focus of the study is to check the direction of causality among selected variables. The results of the study support the demand following, supply leading, feedback, and neutrality hypothesis conditional to different measures of banking competition, banking stability, and economic growth. Theoretical Implication: Jayakumar, Pradhan, Dash, Maradana, and Gaurav (2018) proposed a theoretical model of the causal relationship between banking competition, banking stability, and economic growth by using different indicators. So, we empirically test the proposed indicators in our study. This study makes a contribution to the literature by showing the defined relationship between developing and developed countries. Policy Implications: The study covers various policy implications regarding investors to analyze how to properly manage their finances, and government agencies will take help from the present study to find the best and most suitable policies by examining how the economy can grow concerning its finances.

Keywords: competition, stability, economic growth, vector auto-regression, granger causality

Procedia PDF Downloads 62

Authors: Sourabh Jain, Sukhvir Singh Jain, Gaurav V. Jain

Abstract:

Intelligent Transportation System is an integrated application of communication, control and monitoring and display process technologies for developing a user–friendly transportation system for urban areas in developing countries. In fact, the development of a country and the progress of its transportation system are complementary to each other. Urban traffic has been growing vigorously due to population growth as well as escalation of vehicle ownership causing congestion, delays, pollution, accidents, high-energy consumption and low productivity of resources. The development and management of urban transport in developing countries like India however, is at tryout stage with very few accumulations. Under the umbrella of ITS, urban corridor management strategy have proven to be one of the most successful system in accomplishing these objectives. The present study interprets and figures out the performance of the 27.4 km long Urban Bus route having six intersections, five flyovers and 29 bus stops that covers significant area of the city by causality analysis. Performance interpretations incorporate Passenger Boarding and Alighting, Dwell time, Distance between Bus Stops and Total trip time taken by bus on selected urban route.

Keywords: congestion, dwell time, passengers boarding alighting, travel time

Procedia PDF Downloads 334

Authors: Gaurav Datta, Sourav Ghosh, Rahul Roy

Abstract:

The incorporation of silica fume into the normal concrete is a routine one in the present days to produce the tailor made high strength and high performance concrete. The design parameters are increasing with the incorporation of silica fume in conventional concrete and the mix proportioning is becoming complex. The main objective of this paper has been made to investigate the different mechanical properties like compressive strength, permeability, porosity, density, modulus of elasticity, compacting factor, slump of concrete incorporating silica fume. In this present paper 5 (five) mix of concrete incorporating silica fume is cast to perform experiments. These experiments were carried out by replacing cement with different percentages of silica fume at a single constant water-cementitious materials ratio keeping other mix design variables constant. The silica fume was replaced by 0%, 5%, 10%, 15% and 20% for water-cementitious materials (w/cm) ratio for 0.40. For all mixes compressive strengths were determined at 24 hours, 7 and 28 days for 100 mm and 150 mm cubes. Other properties like permeability, porosity, density, modulus of elasticity, compacting factor, and slump were also determined for five mixes of concrete.

Keywords: high performance concrete, high strength concrete, silica fume, strength

Procedia PDF Downloads 287

Authors: Thandar Zaw Win, Cho Win Aung, Gaurav Khandal, Sabyasachi Ghosh

Abstract:

Systematic and comparative research on Lorenz ratios for graphene and nonrelativistic systems has been studied to identify their Wiedemann-Franz law violation domain. Fermi energy and temperature are the main governing parameters for deciding the values of the Lorenz ratio, which is basically thermal conductivity divided by electrical conductivity times temperature times Lorenz number. Metals as three-dimensional nonrelativistic electron gas are located at higher Fermi-energy by temperature domain, where Lorenz ratio remains one. Hence, they obey the Wiedemann-Franz law. By creating higher doping in a two-dimensional graphene system, one can again reach a higher Fermi-energy by temperature domain and get a constant Lorenz ratio. For both graphene and nonrelativistic systems, the Lorenz ratio goes below one if we go lower Fermi-energy by temperature domain, which is possible for the graphene system by decreasing the doping concentration. Experimentally observed greater than one Lorenz ratio in this lower Fermi-energy by temperature domain or Dirac Fluid domain indicates that nonfluid expressions of Lorenz ratio should be replaced by fluidtype expressions. We have noticed a divergent trend of Lorenz ratio in the Dirac Fluid domain using its fluid-type expression, and it matches the trend of experimental data.

Keywords: graphene, Lorentz ratio, specific heat, Wiedeann-Franz law

Procedia PDF Downloads 29

Authors: Manisha Dubey, Gaurav Gupta, Anoop Arya

Abstract:

The demand for power supply increases day by day in developing countries like India henceforth demand of reactive power support in the form of ancillary services provider also has been increased. The multi-line and multi-type Flexible alternating current transmission system (FACTS) controllers are playing a vital role to regulate power flow through the transmission line. Unified power flow controller and interline power flow controller can be utilized to control reactive power flow through the transmission line. In a restructured power system, the demand of such controller is being popular due to their inherent capability. The transmission pricing by using reactive power cost allocation through modified matrix methodology has been proposed. The FACTS technologies have quite costly assembly, so it is very useful to apportion the expenses throughout the restructured electricity industry. Therefore, in this work, after embedding the FACTS devices into load flow, the impact on the costs allocated to users in fraction to the transmission framework utilization has been analyzed. From the obtained results, it is clear that the total cost recovery is enhanced towards the Reactive Power flow through the different transmission line for 5 bus test system. The fair pricing policy towards reactive power can be achieved by the proposed method incorporating FACTS controller towards cost recovery of the transmission network.

Keywords: interline power flow controller, transmission pricing, unified power flow controller, cost allocation

Procedia PDF Downloads 146

Authors: Arnab Adhikari, Adrija Majumdar, Gaurav Gupta, Arnab Bisi

Abstract:

Team formation plays an instrumental role in the sports like cricket. Existing literature reveals that most of the works on player selection focus only on the players’ efficiency and ignore the consistency. It motivates us to design an improved player selection method based on both player’s efficiency and consistency. To measure the players’ efficiency measurement, we employ a modified data envelopment analysis (DEA) technique namely ‘super-efficient DEA model’. We design a modified consistency index based on semi variance approach. Here, we introduce a new parameter called ‘fitness index’ for consistency computation to assess a player’s fitness level. Finally, we devise a single performance score using both efficiency score and consistency score with the help of a linear programming model. To test the robustness of our method, we perform a rigorous numerical analysis to determine the all-time best One Day International (ODI) Cricket XI. Next, we conduct extensive comparative studies regarding efficiency scores, consistency scores, selected team between the existing methods and the proposed method and explain the rationale behind the improvement.

Keywords: decision support systems, sports, super-efficient data envelopment analysis, semi variance approach

Procedia PDF Downloads 397

Authors: Gaurav Kumar Sinha

Abstract:

The volatility of the oil market, influenced by geopolitical, economic, and environmental factors, presents significant challenges for stakeholders in predicting trends and demand. This article explores the application of artificial intelligence (AI) in developing robust forecasting models to anticipate changes in the oil market more accurately. We delve into various AI techniques, including machine learning, deep learning, and time series analysis, that have been adapted to analyze historical data and current market conditions to forecast future trends. The study evaluates the effectiveness of these models in capturing complex patterns and dependencies in market data, which traditional forecasting methods often miss. Additionally, the paper discusses the integration of external variables such as political events, economic policies, and technological advancements that influence oil prices and demand. By leveraging AI, stakeholders can achieve a more nuanced understanding of market dynamics, enabling better strategic planning and risk management. The article concludes with a discussion on the potential of AI-driven models in enhancing the predictive accuracy of oil market forecasts and their implications for global economic planning and strategic resource allocation.

Keywords: AI forecasting, oil market trends, machine learning, deep learning, time series analysis, predictive analytics, economic factors, geopolitical influence, technological advancements, strategic planning

Procedia PDF Downloads 33

Authors: Gaurav Bisht, Rahul Rathnakumar, Ravikumar Duggirala

Abstract:

Predictive simulations are one of the key focus areas in safety-critical industries such as aerospace and high-performance automotive engineering. The stone-chipping study is one such effort taken up by the industry to predict and evaluate the damage caused due to gravel impact on vehicles. This paper describes a finite elements based method that can simulate the ejection of gravel chips from a vehicle tire. The FE simulations were used to obtain the initial ejection velocity of the stones for various driving conditions using a computational contact mechanics approach. To verify the accuracy of the tire model, several parametric studies were conducted. The FE simulations resulted in stone loft velocities ranging from 0–8 m/s, regardless of tire speed. The stress on the tire at the instant of initial contact with the stone increased linearly with vehicle speed. Mesh convergence studies indicated that a highly resolved tire mesh tends to result in better momentum transfer between the tire and the stone. A fine tire mesh also showed a linearly increasing relationship between the tire forward speed and stone lofting speed, which was not observed in coarser meshes. However, it also highlighted a potential challenge, in that the ejection velocity vector of the stone seemed to be sensitive to the mesh, owing to the FE-based contact mechanical formulation of the problem.

Keywords: abaqus, contact mechanics, foreign object debris, stone chipping

Procedia PDF Downloads 261

Authors: Madhukar Kasarla, Sumit Fogla, Kiran Panuganti, Gaurav Jain, Abhijit Ramanujam, Astha Jain, Shashank Kraleti, Sharat Musham, Arun Chaudhury

Abstract:

Patients have often chosen to travel for care — making pilgrimages to academic meccas and state-of-the-art hospitals for sophisticated surgery. This culture is still persistent in the landscape of US healthcare, with hundred thousand of visitors coming to the shores of United States to seek the high quality of medical care. One of the major challenges in this form of medical tourism has been the language barrier. Thus, an Iraqi patient, with immediate needs of communicating the healthcare needs to the treating team in the hospital, may face huge barrier in effective patient-doctor communication, delaying care and even at times reducing the quality. To circumvent these challenges, we are proposing the use of a state-of-the-art tool, Talk2All, which can translate nearly one hundred international languages (and even sign language) in real time. The tool is an easy to download app and highly user friendly. It builds on machine learning principles to decode different languages in real time. We suggest that the use of Talk2All will tremendously enhance communication in the hospital setting, effectively breaking the language barrier. We propose that vigorous incorporation of Talk2All shall overcome practical challenges in international medical and surgical tourism.

Keywords: language translation, communication, machine learning, medical tourism

Procedia PDF Downloads 212

Authors: Gaurav Kumar Sinha

Abstract:

The oil and gas industry, characterized by its complex and global supply chains, faces significant challenges in ensuring security, transparency, and efficiency. Blockchain technology, with its decentralized and immutable ledger, offers a transformative solution to these issues. This paper explores the application of blockchain technology in the oil and gas supply chain, highlighting its potential to enhance data security, improve transparency, and streamline operations. By leveraging smart contracts, blockchain can automate and secure transactions, reducing the risk of fraud and errors. Additionally, the integration of blockchain with IoT devices enables real-time tracking and monitoring of assets, ensuring data accuracy and integrity throughout the supply chain. Case studies and pilot projects within the industry demonstrate the practical benefits and challenges of implementing blockchain solutions. The findings suggest that blockchain technology can significantly improve trust and collaboration among supply chain participants, ultimately leading to more efficient and resilient operations. This study provides valuable insights for industry stakeholders considering the adoption of blockchain technology to address their supply chain management challenges.

Keywords: blockchain technology, oil and gas supply chain, data security, transparency, smart contracts, IoT integration, real-time tracking, asset monitoring, fraud reduction, supply chain efficiency, data integrity, case studies, industry implementation, trust, collaboration.

Procedia PDF Downloads 34

Authors: Soumya Bhattacharjya, Avinandan Sahoo, Gaurav Datta

Abstract:

Wind fragility analysis of chimney is often carried out disregarding temperature effect. However, the combined effect of wind and temperature is the most critical limit state for chimney design. Hence, in the present paper, an efficient fragility analysis for concrete chimney is explored under combined wind and temperature effect. Wind time histories are generated by Davenports Power Spectral Density Function and using Weighed Amplitude Wave Superposition Technique. Fragility analysis is often carried out in full Monte Carlo Simulation framework, which requires extensive computational time. Thus, in the present paper, an efficient adaptive metamodelling technique is adopted to judiciously approximate limit state function, which will be subsequently used in the simulation framework. This will save substantial computational time and make the approach computationally efficient. Uncertainty in wind speed, wind load related parameters, and resistance-related parameters is considered. The results by the full simulation approach, conventional metamodelling approach and proposed adaptive metamodelling approach will be compared. Effect of disregarding temperature in wind fragility analysis will be highlighted.

Keywords: adaptive metamodelling technique, concrete chimney, fragility analysis, stochastic extreme wind load, temperature effect

Procedia PDF Downloads 213

Authors: Poornima Khadanga, Gaurav Pawar, Madhavi Rairikar

Abstract:

Background: The COVID 19 pandemic, with its unparalleled mental health repercussions, has impacted people globally and has catalyzed a Mental Health pandemic among the youth. The detrimental effects on mental health needs to be pondered at the earliest. Aims: To study the behavioral problems among children and adolescents visiting Psychiatry Outpatient Department Tertiary Health Care during COVID pandemic and its correlation with socio-demographic profiles. Methods: A cross sectional study was conducted by interviewing 120 participants between 4 to 17 years of age and their parents, visiting Psychiatry OPD. Behavioral problems were assessed using the Strength and Difficulties Questionnaire and diagnosed by DSM-5. Statistical analysis was done by SPSS-21. Results: Male participants showed significant association with conduct (t=2.36, p=0.02) and hyperactive problems (t=5.07, p<0.05). Increase in screen time showed a positive correlation with conduct problems (r=0.22. p=0.02). Attention Deficit Hyperkinetic Disorder (18.3%) was the most commonly diagnosed psychiatric illness. Total difficulty score was significantly associated with difficult temperament (F=68.69, p<0.05). Conclusion: The study brings to light the pattern of behavioral problems that emerged during recent times of uncertainties among the young ones, including those with special needs. The increase in disruptive behaviors with increase screen time needs to be addressed at the earliest.

Keywords: behavioral problems, pandemic, screen time, temperament

Procedia PDF Downloads 166

Authors: Gaurav Kumar Sinha

Abstract:

The oil and gas industry is a critical component of the global economy, relying heavily on industrial control systems (ICS) to manage and monitor operations. However, these systems are increasingly becoming targets for cyber-attacks, posing significant risks to operational continuity, safety, and environmental integrity. This paper explores comprehensive cybersecurity strategies for protecting oil and gas industrial control systems. It delves into the unique vulnerabilities of ICS in this sector, including outdated legacy systems, integration with IT networks, and the increased connectivity brought by the Industrial Internet of Things (IIoT). We propose a multi-layered defense approach that includes the implementation of robust network security protocols, regular system updates and patch management, advanced threat detection and response mechanisms, and stringent access control measures. We illustrate the effectiveness of these strategies in mitigating cyber risks and ensuring the resilient and secure operation of oil and gas industrial control systems. The findings underscore the necessity for a proactive and adaptive cybersecurity framework to safeguard critical infrastructure in the face of evolving cyber threats.

Keywords: cybersecurity, industrial control systems, oil and gas, cyber-attacks, network security, IoT, threat detection, system updates, patch management, access control, cybersecurity awareness, critical infrastructure, resilience, cyber threats, legacy systems, IT integration, multi-layered defense, operational continuity, safety, environmental integrity

Procedia PDF Downloads 41

Authors: Tanvi P. Medshinge, Prasad Vaidya, Monisha E. Royan

Abstract:

In India, cooling loads in residential sector is a major contributor to its total energy consumption. Due to the increasing cooling need, the market penetration of air-conditioners is further expected to rise. Natural Ventilation (NV), however, possesses great potential to save significant energy consumption especially for residential buildings in moderate climates. As multifamily residential apartment buildings are designed by repetitive use of prototype designs, deriving individual NV based design prototype solutions for a combination of different wind incidence angles and orientations would provide significant opportunity to address the rise in cooling loads by residential sector. This paper presents the results of NV performance of a selected prototype apartment design with a cluster of four units in Pune, India, and an attempt to improve the NV performance through design modifications. The water table apparatus, a physical modelling tool, is used to study the flow patterns and simulate wind-induced NV performance. Quantification of NV performance is done by post processing images captured from video recordings in terms of percentage of area with good and poor access to ventilation. NV performance of the existing design for eight wind incidence angles showed that of the cluster of four units, the windward units showed good access to ventilation for all rooms, and the leeward units had lower access to ventilation with the bedrooms in the leeward units having the least access. The results showed improved performance in all the units for all wind incidence angles to more than 80% good access to ventilation. Some units showed an additional improvement to more than 90% good access to ventilation. This process of design and performance evaluation improved some individual units from 0% to 100% for good access to ventilation. The results demonstrate the ease of use and the power of the water table apparatus for performance-based design to simulate wind induced NV.  

Keywords: fluid dynamics, prototype design, natural ventilation, simulations, water table apparatus, wind incidence angles

Procedia PDF Downloads 226

Authors: Gaurav Kumar Sinha

Abstract:

The oil refining industry, significant in its energy consumption and carbon emissions, faces increasing pressure to reduce its environmental footprint. This article explores the application of energy efficiency and sustainability analytics as crucial tools for reducing carbon emissions in oil refineries. Through a comprehensive review of current practices and technologies, this study highlights innovative analytical approaches that can significantly enhance energy efficiency. We focus on the integration of advanced data analytics, including machine learning and predictive modeling, to optimize process controls and energy use. These technologies are examined for their potential to not only lower energy consumption but also reduce greenhouse gas emissions. Additionally, the article discusses the implementation of sustainability analytics to monitor and improve environmental performance across various operational facets of oil refineries. We explore case studies where predictive analytics have successfully identified opportunities for reducing energy use and emissions, providing a template for industry-wide application. The challenges associated with deploying these analytics, such as data integration and the need for skilled personnel, are also addressed. The paper concludes with strategic recommendations for oil refineries aiming to enhance their sustainability practices through the adoption of targeted analytics. By implementing these measures, refineries can achieve significant reductions in carbon emissions, aligning with global environmental goals and regulatory requirements.

Keywords: energy efficiency, sustainability analytics, carbon emissions, oil refineries, data analytics, machine learning, predictive modeling, process optimization, greenhouse gas reduction, environmental performance

Procedia PDF Downloads 29

Authors: Sandra Mitrovic, Gaurav Singh

Abstract:

In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

Procedia PDF Downloads 314

Authors: Zhichao Song, Swanand Vaidya

Abstract:

Nowadays, most portable (e.g., smartphones) and wearable (e.g., smartwatches and earphones) consumer hardware are designed to be dustproof following IP5 or IP6 ratings to ensure the product is able to handle potentially dusty outdoor environments. On the other hand, the design guideline is relatively vague for indoor devices (e.g., smart displays and speakers). While it is generally believed that the indoor environment is much less dusty, in certain circumstances, dust ingression is still able to cause functional failures, such as microphone frequency response shift and camera black spot, or cosmetic dissatisfaction, mainly the dust build up in visible pockets and gaps which is hard to clean. In this paper, we developed a simulation methodology to analyze dust settlement and ingression into known ports of a device. A closed system is initialized with dust particles whose sizes follow Weibull distribution based on data collected in a user study, and dust particle movement was approximated as a settlement in stationary fluid, which is governed by Stokes’ law. Following this method, we simulated dust ingression into MEMS microphone through the acoustic port and protective mesh. Various design and environmental parameters are evaluated including mesh pore size, acoustic port depth-to-diameter ratio, mass density of dust material and inclined angle of microphone port. Although the dependencies of dust resistance on these parameters are all monotonic, smaller mesh pore size, larger acoustic depth-to-opening ratio and more inclined microphone placement (towards horizontal direction) are preferred for dust resistance; these preferences may represent certain trade-offs in audio performance and compromise in industrial design. The simulation results suggest the quantitative ranges of these parameters, with more pronounced effects in the improvement of dust resistance. Based on the simulation results, we proposed several design guidelines that intend to achieve an overall balanced design from audio performance, dust resistance, and flexibility in industrial design.

Keywords: dust settlement, numerical simulation, microphone design, Weibull distribution, Stoke's equation

Procedia PDF Downloads 106

Authors: Dileep G., Akash Singh, Dalchand Ahirwar, Arkadeep Ghosh, Ashutosh Purohit, Gaurav Guleria, Kshatriya Om Prashant, Pushkar Patel, Saksham Kumar, Vanshaj Nathani, Vikas Dangi, Shubhajit Roy Chowdhury, Varun Dutt

Abstract:

Transcranial direct current stimulation (tDCS) has shown promise as a novel approach to enhance cognitive performance and provide therapeutic benefits for various brain disorders. However, the exact underlying brain mechanisms are not fully understood. We conducted a study to examine the brain's functional changes when subjected to simultaneous tDCS and music (Indian classical raga). During the study, participants in the experimental group underwent a 20-minute session of tDCS at two mA while listening to music (raga) for a duration of seven days. In contrast, the control group received a sham stimulation for two minutes at two mA over the same seven-day period. The objective was to examine whether repetitive tDCS could lead to the formation of additional functional connections between the medial prefrontal cortex (the stimulated area) and the auditory cortex in comparison to a sham stimulation group. In this study, 26 participants (5 female) underwent pre- and post-intervention scans, where changes were compared after one week of either tDCS or sham stimulation in conjunction with music. The study revealed significant effects of tDCS on functional connectivity between the stimulated area and the auditory cortex. The combination of tDCS applied over the mPFC and music resulted in newly formed connections. Based on our findings, it can be inferred that applying anodal tDCS over the mPFC enhances functional connectivity between the stimulated area and the auditory cortex when compared to the effects observed with sham stimulation.

Keywords: fNIRS, tDCS, neuroplasticity, music

Procedia PDF Downloads 70

Authors: Ramkumar R., Gaurav Shinde, Pratik Shroff, Sachin Kumar Jain, Nagesh Walke

Abstract:

Advancements in electric vehicles have significantly reduced the powertrain noise and moving components of vehicles. As a result, in-cab noises have become more noticeable to passengers inside the car. To ensure a comfortable ride for drivers and other passengers, it has become crucial to eliminate undesirable component noises during the development phase. Standard practices are followed to identify the severity of noises based on subjective ratings, but it can be a tedious process to identify the severity of each development sample and make changes to reduce it. Additionally, the severity rating can vary from jury to jury, making it challenging to arrive at a definitive conclusion. To address this, an automotive component was identified to evaluate squeak and rattle noise issue. Physical tests were carried out for random and sine excitation profiles. Aim was to subjectively assess the noise using jury rating method and objectively evaluate the same by measuring the noise. Suitable jury evaluation method was selected for the said activity, and recorded sounds were replayed for jury rating. Objective data sound quality metrics viz., loudness, sharpness, roughness, fluctuation strength and overall Sound Pressure Level (SPL) were measured. Based on this, correlation co-efficients was established to identify the most relevant sound quality metrics that are contributing to particular identified noise issue. Regression analysis was then performed to establish the correlation between subjective and objective data. Mathematical model was prepared using artificial intelligence and machine learning algorithm. The developed model was able to predict the subjective rating with good accuracy.

Keywords: BSR, noise, correlation, regression

Procedia PDF Downloads 78

Authors: A. Vaidya-Soocheta, K. M. Wong-Hon-Lang

Abstract:

Craft and fashion have always been interlinked. The combination of both often gives stunning results. The present study introduces ‘Paper Cutting Craft Techniques’ like the Japanese –Kirigami, Mexican –PapelPicado, German –Scherenschnitte, Polish –Wycinankito in textiles to develop innovative and novel design structures as embellishments and ornamentation. The project studies various ways of using these paper cutting techniques to obtain interesting features and delicate design patterns on fabrics. While paper has its advantages and related uses, it is fragile rigid and thus not appropriate for clothing. Fabric is sturdy, flexible, dimensionally stable and washable. In the present study, the cut out techniques develop creative design motifs and patterns to give an inventive and unique appeal to the fabrics. The beauty and fascination of lace in garments have always given them a nostalgic charm. Laces with their intricate and delicate complexity in combination with other materials add a feminine touch to a garment and give it a romantic, mysterious appeal. Various textured and decorative effects through fabric manipulation are experimented along with the use of paper cutting craft skills as an innovative substitute for developing lace or “Broderie Anglaise” effects on textiles. A number of assorted fabric types with varied textures were selected for the study. Techniques to avoid fraying and unraveling of the design cut fabrics were introduced. Fabrics were further manipulated by use of interesting prints with embossed effects on cut outs. Fabric layering in combination with assorted techniques such as cutting of folded fabric, printing, appliqué, embroidery, crochet, braiding, weaving added a novel exclusivity to the fabrics. The fabrics developed by these innovative methods were then tailored into garments. The study thus tested the feasibility and practicability of using these fabrics by designing a collection of evening wear garments based on the theme ‘Nostalgia’. The prototypes developed were complemented by designing fashion accessories with the crafted fabrics. Prototypes of accessories add interesting features to the study. The adaptation and application of this novel technique of paper cutting craft on textiles can be an innovative start for a new trend in textile and fashion industry. The study anticipates that this technique will open new avenues in the world of fashion to incorporate its use commercially.

Keywords: collection, fabric cutouts, nostalgia, prototypes

Procedia PDF Downloads 356

Authors: Gaurav D. Sonawane, Vikas G. Sargade

Abstract:

The present investigation is a study of the effect of advanced Physical Vapor Deposition (PVD) coatings on cutting temperature residual stresses and surface roughness during Duplex Stainless Steel (DSS) 2205 turning. Austenite stabilizers like nickel, manganese, and molybdenum reduced the cost of DSS. Surface Integrity (SI) plays an important role in determining corrosion resistance and fatigue life. Resistance to various types of corrosion makes DSS suitable for applications with critical environments like Heat exchangers, Desalination plants, Seawater pipes and Marine components. However, lower thermal conductivity, poor chip control and non-uniform tool wear make DSS very difficult to machine. Cemented carbide tools (M grade) were used to turn DSS in a dry environment. AlTiN and AlTiCrN coatings were deposited using advanced PVD High Pulse Impulse Magnetron Sputtering (HiPIMS) technique. Experiments were conducted with cutting speed of 100 m/min, 140 m/min and 180 m/min. A constant feed and depth of cut of 0.18 mm/rev and 0.8 mm were used, respectively. AlTiCrN coated tools followed by AlTiN coated tools outperformed uncoated tools due to properties like lower thermal conductivity, higher adhesion strength and hardness. Residual stresses were found to be compressive for all the tools used for dry turning, increasing the fatigue life of the machined component. Higher cutting temperatures were observed for coated tools due to its lower thermal conductivity, which results in very less tool wear than uncoated tools. Surface roughness with uncoated tools was found to be three times higher than coated tools due to lower coefficient of friction of coating used.

Keywords: cutting temperature, DSS2205, dry turning, HiPIMS, surface integrity

Procedia PDF Downloads 131

Authors: Gaurav Bhanjana, Ganga Ram Chaudhary, Sandeep Kumar, Neeraj Dilbaghi

Abstract:

Main sources of endocrine disrupting compounds in the ecosystem are hormones, pesticides, phthalates, flame retardants, dioxins, personal-care products, coplanar polychlorinated biphenyls (PCBs), bisphenol A, and parabens. These endocrine disrupting compounds are responsible for learning disabilities, brain development problems, deformations of the body, cancer, reproductive abnormalities in females and decreased sperm count in human males. Although discharge of these chemical compounds into the environment cannot be stopped, yet their amount can be retarded through proper evaluation and detection techniques. The available techniques for determination of these endocrine disrupting compounds mainly include high performance liquid chromatography (HPLC), mass spectroscopy (MS) and gas chromatography-mass spectrometry (GC–MS). These techniques are accurate and reliable but have certain limitations like need of skilled personnel, time consuming, interference and requirement of pretreatment steps. Moreover, these techniques are laboratory bound and sample is required in large amount for analysis. In view of above facts, new methods for detection of endocrine disrupting compounds should be devised that promise high specificity, ultra sensitivity, cost effective, efficient and easy-to-operate procedure. Nowadays, electrochemical sensors/biosensors modified with nanomaterials are gaining high attention among researchers. Bioelement present in this system makes the developed sensors selective towards analyte of interest. Nanomaterials provide large surface area, high electron communication feature, enhanced catalytic activity and possibilities of chemical modifications. In most of the cases, nanomaterials also serve as an electron mediator or electrocatalyst for some analytes.

Keywords: electrochemical, endocrine disruptors, microscopy, nanoparticles, sensors

Procedia PDF Downloads 272

Authors: Gaurav Kumar Sinha

Abstract:

In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

Procedia PDF Downloads 63