National Institute of Technology Karnataka

Continuous adsorption of Remazol Brilliant Blue (RBB) dye in water onto sulfuric acid activated red mud (CATRM) in a fixed bed column was investigated. Breakthrough curves were obtained experimentally by varying the bed height (Z), influent flow rate (Q), and dye concentration(C0). The adsorption efficiency in the removal of RBB was favored at lower C0, higher Z, and lower Q. The maximum adsorption capacity of the activated red mud bed in the column was obtained at C0 = 70 mg/L, Z = 8 cm, and Q = 5 mL/min and found to be 106 mg/g. Important parameters of column dynamics and design such as mass transfer zone (MTZ) and length of unused bed (LUB) were evaluated from the breakthrough curves. The MTZ and LUB have varied with varying Z, which indicated the existence of nonideal conditions. Thomas model was found to be valid to predict the column dynamics and the model parameters were evaluated. Bed depth service time (BDST) model parameters were evaluated to facilitate the determination of packed bed height for the design of packed bed adsorption column. The bed could be regenerated with NaOH solution with desorption efficiency decreasing from 83.8 to 55.72% from the first to third cycle. A fixed bed of CATRM can be effectively used for continuous dye removal from industrial wastewater.

This study evaluates ARIMA, Facebook Prophet and a new boosting algorithm framework known as ThymeBoost for time series prediction of monthly precipitation of Belagavi district (semi-arid) in Karnataka. The dataset was divided into three periods (1901–2002, 1951- 2002, and 1971 - 2002). The first 70% of the data for each period was applied for training while the rest for testing. Also, the datasets were used in two different forms for both training and testing. In the first set, raw data was used as it is, and the second set of data was used after normalizing the time series using the min-max concept (between 0 and 1). However, the normalized data were de-normalized for each period for performance metrics estimation. ThymeBoost is the best model for the first period of raw data and the second period of normalized data. In contrast, Prophet outperforms all other models for the normalized data in terms of all four measures. For the second period of raw data, no model emerged as the best model in terms of all performance metrics. Therefore, all three models performed similarly for the third period of raw and normalized data.

In the coal and mineral beneficiation industries, screening is one of the crucial physical separation methods carried out to separate the undersized fine particles from the oversize coarse particles. The vibratory screener is a relatively advanced screening technology applied for coal and iron ore beneficiation. This paper deals with the experimental investigation for assessing the efficiency of screening coal and iron ore in the vibratory screener. Furthermore, a comparative study between the test performance of screening coal and iron ore was carried out depending on moisture and density variation. Test results show that the vibratory screener can provide a high recovery of fines and increased efficiency for screening iron ore than coal material. The maximum efficiency of iron ore was attained at a higher angular position, such as 3 and 5 degrees in an upward slope, whereas the maximum efficiency of coal was attained at 1 degree in an upward slope.

This article proposes a nine-level (9 L) inverter with a common leg configuration employing transformers and a single dc source. The suggested inverter uses eight switches and two transformers to produce 9 L output voltage. The suggested circuit minimizes the switches and transformers compared with existing transformer-based multilevel inverters (TMLI). Therefore, the proposed circuit cost, volume and complexity are also reduced. Additionally, a thorough comparison with the various 9 L inverter circuits is conducted to ensure the benefits of the suggested TMLI. A basic logic gate-based pulse width modulation (PWM) is implemented for the suggested 9 L inverter. Simulation and hardware studies verifying the feasibility and proficiency of the suggested inverter are performed.

This paper presents an analytical research study to improve the aerostructural performance of an unmanned medium altitude long endurance aircraft using the adaptive wing concept. Aerodynamic drag and wing root loads are minimized by optimal scheduling of multiple trailing edge flaps located on the wing. A trim optimization process is developed specifically for this purpose. The aeroelastic model is based on finite element formulation for the structure and doublet lattice method for the aerodynamics. A nonlinear numerical lifting line method is used, in combination with airfoil wind tunnel data, to estimate the induced and total drags. Results are presented for the current aircraft configuration and a more flexible proposed configuration, thereby providing an uncommon perspective on the effect of flexibility on the adaptive wing. For example, the benefits of optimal flap deployment turn out to be greater for the flexible aircraft than for the rigid one. It is hoped that this work and its insights will also aid the multidisciplinary design optimization of future aircraft.

Cancer is a disease linked to the untamed and rapid division of cells in the body. Cancer detection through conventional methods like complete blood count is a tedious and time-consuming task prone to human errors. The introduction of image processing techniques and computer-aided diagnostics is beneficial to this field as the results obtained by utilizing these methods are quick and accurate. The proposed method in this paper uses a design Convolutional Leaky RELU with CatBoost and XGBoost (CLR-CXG) to segment the images and extract the important features that help in classification. The binary classification algorithm and gradient boosting algorithm CatBoost (Categorical Boost) and XGBoost (Extreme Gradient Boost) are implemented individually. Moreover, Convolutional Leaky RELU with CatBoost (CLRC) is designed to decrease bias and provide high accuracy, while Convolutional Leaky RELU with XGBoost (CLRXG) is designed for classification or regression prediction problems which will increase the speed of executing the algorithm and improve its performance. Thus the CLR-CXG classifies the test images into Acute Lymphoblastic Leukemia (ALL) or Multiple Myeloma (MM). Finally, the CLRC algorithm achieved 100% accuracy in classifying cancer cells, and the recorded run time is 10s. Moreover, the CLRXG algorithm has gained an accuracy of 97.12% for classifying cancer cells and 12 s for executing the process.

Image processing techniques and algorithms are extensively used for biomedical applications. Convolution Neural Network (CNN) is gaining popularity in fields such as the analysis of complex documents and images, which qualifies the approach to be used in biomedical applications. The key drawback of the CNN application is that it can’t call the previous layer output following the layer’s input. To address this issue, the present research has proposed the novel Modified U-Net architecture with ELU Activation Framework (MU-EAF) to detect and classify cancerous cells in the blood smear images. The system is trained with 880 samples, of which 220 samples were utilized in the validation model, and 31 images were utilized to verify the proposed model. The identified mask output of the segmentation model in the predicted mask fits the classification model to identify the cancer cell occurrence in the collected images. In addition, the segmentation evaluation is done by matching each pixel of the ground truth mask (labels) to the predicted labels from the model. The performance metrics for evaluating the segmentation of images are pixel accuracy, dice coefficient (F1-score), and Jaccard coefficient. Moreover, the model is compared with VGG-16 and simple modified CNN models, which have four blocks, each consisting of a convolutional layer, batch normalization, and activation layer with RELU activation function that are implemented and for assessing the same images used for the proposed model. The proposed model shows higher accuracy in comparison.

Uncertain future and fears about the stock-outs will compel the customers to stock goods at home, resulting in panic buying. Even though it is a frequently observed consumer behaviour, there is scant literature in dual-channel supply chain (DCSC) which address this demand disruption. This study analytically models and analyses the impact of panic buying in a DCSC. For that we consider a two-echelon dual-channel supply chain comprising of a manufacturer, brick and mortar store (r-store), and online store (e-store). The interaction between the upstream and downstream channel members is modelled using a Stackelberg game. Further, we examined two models based on the channel power difference between the r-store and e-store, i.e., (i) r-store leader model and (ii) the e-store leader model. We also used Monte-Carlo simulation to deduce corollaries and managerial insights. We found that the Law of demand doesn’t hold during panic buying disruption, and even essential goods act like Veblen goods during the period. Contrary to the expectation, panic buying was also found to be beneficial for the e-store. Counter-intuitive results with respect to the channel power were also obtained in the sense that it is beneficial for the r-store to operate under the leadership of the e-store and vice versa. The study shows that the manufacturer is better off with panic buying.

Data sharing is common phenomenon between the stakeholders within an organization and it is vital to sustain in the context of application. Internet of Things (IoT) is an umbrella of all applications’ online services like smart city, smart agriculture, smart grid and smart vehicles. In case of autonomous vehicular network (AVN), the data generated by vehicles, sensing units and road side units (RSU) is sensitive in nature so, secure data sharing (SDS) in AVN is the prime importance. In the area of SDS, Blockchain is gaining popularity which is an immutable distributed ledger technology and emerged as one of the prime solutions towards secure data sharing. As an innovative contribution, we proposedBlockchain based hierarchical secure data sharing model for sharing within vehicular network (VN). The proposed architecture is able to share road traffic related information e.g., road conditions, traffic congestion with the nearby vehicles and other stakeholders in the vehicular network. The performance of the proposed model is analyzed by using a simulation study and the efficacy of the simulated results outperforms than that of existing models.

Organic effluents—the gift of industrial development is a critical menace to environs as well as raising a question mark toward the sustainability of humans. In such a scenario, achieving consistent sunlight-driven photocatalytic degradation gains widespread attention due to its eco-friendly low-cost approach with high efficiency. Herein, the CdS-cubic nanoparticles have been synthesized using a simple chemical precipitation technique and structural transition to CdS hexagonal is obtained through annealing. Morphology study has validated the reduction in particle size of CdS cubic and corresponding enhancement in the surface are put forth by BET analysis. The better degradation capability of CdS cubic is demonstrated through the visible-light-driven photocatalysis of MB dye with a degradation rate constant of k = 0.02/min. Meanwhile, CdS hexagonal possesses a rate constant k = 0.005/min. The scavenger study reveals the vital role of conduction band electron and superoxide radical in decolorization. Moreover, lesser carrier recombination with more charge transfer is observed from PL and EIS, respectively, emphasizing the CdS-cubic nanoparticle's catalytic activity.

The present work discusses buckling and vibration characteristics of axially functionally graded (AFG) graphene platelet (GPL) composite beams exposed to axially varying loads (AVLs). Timoshenko beam composition with five different types of axial grading GPLs subjected to six different types of AVLs are studied. The effective elastic properties are obtained using Halpin-Tsai model and the equations of motion are obtained following the Hamilton’s principle. Then the equations are solved for buckling and vibration analysis using the Ritz method. Influences of nature of axial grading of GPLs and load, content of GPL, and structural boundary conditions are investigated through detailed parametric studies. It is found that the grading pattern of GPLs not only influences the buckling load, but also changes buckling mode shapes of the beam at specific type of AVL. Furthermore, results reveal that buckling and vibration characteristics of beam enhanced in case of AFGM-A type for most of the load cases studied. The proposed study will be helpful for the structural engineers to select the nature of graded distribution of GPLs for the given type of AVL and design the structural member.

A significant portion of the hazardous wastes generated by rapid industrialisation and urbanisation end up in landfills. The wastes disposed of in hazardous waste landfills are less biodegradable; thus, the leachate generated due to the physical and chemical changes in the landfill renders high toxicity. If not monitored and handled appropriately, this leachate could lead to contamination affecting human and livestock health and adversely affect the soil and agriculture in the vicinity of the landfill site. A tool to quantify the contamination caused by improper handling of hazardous waste landfill leachate is essential to understand which landfill site would need immediate attention. In the present study, the leachate pollution index is developed based on the predominantly available pollutants in hazardous waste landfill leachate and their toxicity limits. Fuzzy Delphi-Analytic Hierarchy Process has been used to develop the index. These techniques have been used for screening and assigning weights to the pollutants. Further, sub-index curves have been developed considering the available concentration, the toxicity, and the standard concentration limits for each pollutant. The weighted linear sum function has been used to aggregate the weights and sub-index scores. The hazardous waste landfill leachate pollution index developed in this study can serve as a potential tool for quantifying the leachate contamination potential. Furthermore, it can be used as a comparison tool for ranking landfill sites based on the contamination potential.

Conformation of biomolecules like DNA, peptides and amino acids play vital role during nanoparticle growth. Herein, we have experimentally explored the effect of different noncovalent interaction between 5'-amine modified DNA sequence (NH2-C6H12-5'-ACATCAGT-3', PMR) and arginine during the seed mediated growth reaction of gold nanorod (GNR). Amino acid mediated growth reaction of GNR results in snowflake like gold nanoarchitecture. However, in case of Arg, prior incubation of GNR with PMR selectively produces sea urchin like gold suprastructures, via strong hydrogen bonding and cation-π interaction between PMR and Arg. This distinctive structure structure formation strategy has been extended to study the structural modulation caused by two structurally close α-helical RRR (Ac-(AAAAR)3A-NH2) peptide and the lysine mutated KKR (Ac-AAAARAAAARAAAAKA-NH2) peptide with partial helix at the amino terminus. Simulation studies confirm that a greater number of hydrogen bonding and cation-π interaction between the Arg residues and PMR resulted in the gold sea urchin structure for RRR peptide against KKR peptide.

The research paper by Akçay et al. applied innovative polygon trend analysis (IPTA) to derive trend length/volume and trend slope between two consecutive months for rainfall/streamflow in the eastern Black Sea basin, Turkey. Although the trend length/volume equation is correct, the trend slope equation is fundamentally incorrect. A brief discussion is presented to apprise the research community of the correct trend slope equation.

Serverless computing emerges as a new standard to build cloud applications, where developers write compact functions that respond to events in the cloud infrastructure. Several cloud service industries started adopting serverless for deploying their applications. But one key limitation in serverless computing is that it disregards the significance of data. In the age of big data, when applications run around a huge volume, to transfer data from the data side to the computation side to co-allocate the data and code, leads to high latency. All existing serverless architectures are based on the data shipping architecture. In this paper, we present an inter-region code shipping architecture for serverless, that enables the code to flow from computation side to the data side where the size of the code is negligible compared to the data size. We tested our proposed architecture over a real-time cloud platform Amazon Web Services with the integration of the Fission serverless tool. The evaluation of the proposed code shipping architecture shows for a data file size of 64 MB, the latency in the proposed code shipping architecture is 8.36 ms and in existing data shipped architecture is found to be 16.8 ms. Hence, the proposed architecture achieves a speedup of 2x on the round latency for high data sizes in a serverless environment. We define round latency to be the duration to read and write back the data in the storage.

The study addresses the long-term trend in rainfall, minimum and maximum temperature, and the climate indices for the river catchments located in the diverse climate of the Western Ghats of India. The dry sub-humid Chaliyar catchment and humid Kajvi catchment have shown a dramatic change in the decadal rainfall, with the decade 1950–1960 being the point of change. The monsoon rainfall has decreased in the Chaliyar and Netravati catchments and increased insignificantly in the Kajvi catchment. With the increase in mean temperature, the number of rainy days is decreasing, and intense rainfall is increasing in the pre-monsoon. The increase in minimum temperature is more severe in all three catchments, irrespective of the region’s climate. The decline in rainy days is more figurative in the humid and per-humid catchments and has seen a 16–20% decrease in R×1 day, R×3 day, and R×5 day in the past six decades with an insignificant increase in the dry sub-humid catchment. The frightful increase in warm days/nights with a decrease in cool days/nights has been alarming for the extremity of temperature in future years. The significant changes in the forest area in Chaliyar and Kajvi catchment and the increase in a built-up area in Netravati may have a decisive role in the nonseasonal variability in rainfall and temperature along with increasing greenhouse gases. In the case of meteorological drought studied using the Standardized Precipitation Index (SPI), moderate droughts have occurred over the Chaliyar and Kajvi, and extreme droughts over the Netravati catchments with no reduction in the frequency or severity of short-duration extreme rainfall events. The geographical location of the catchment has a greater impact on the characteristics of the rainfall and meteorological drought, and these changes in the hydrological regimes of the catchment have a significant bearing on the water availability in the catchments in the future years.

A microwave hybrid heating technique has been employed to develop NiCr-Mo-SiC composite cladding on titanium alloy (Grade-5/Ti-6Al-4 V/Titan-31). The developed claddings have been characterized for microstructural features, phase analysis, microhardness measurements, and 3D optical profile parameters by employing scanning electron microscopy, x-ray diffraction, Vickers microhardness tester, and 3D optical profilometer, respectively. Microwave clads have been subjected to linear reciprocator ball on plate wear test with static alumina indenter. Wear track parameters and friction coefficients have been studied. A dense microstructure with uniform distribution of hard phases and good metallurgical bonding with no visible pores and cracks has been obtained. Cladding exhibits nearly 2 times higher hardness than the base alloy. Coefficient of friction studies revealed that higher molybdenum content enhances internal lubricity.

Shear induced migration is the diffusion of particles in a direction normal to the flow of suspension. The particles migrate towards the low shear stress region from the higher shear stress region. In the kinetic theory of granular flow, the driving potential for the migration of particles is the difference in granular temperature, defined as the kinetic energy of particle fluctuations. The effect of bulk volume fraction on the particle migration in a dense suspension flow through a rectangular microchannel is analyzed. The multiphase simulation was conducted using the Multi Volume of Fluid (MVOF) and granular temperature model. The results show a blunted velocity profile, reduced peak velocity, and an increase in variation of local volume fraction with an increase in bulk volume fraction.

Herein, we have tried to explore the charge storage properties of mesoporous NiWO4 as an anode in lithium-ion batteries (LIB). A one pot-solvothermal synthesis is used to tweak the properties of mesoporous NiWO4 nanoparticles with reduced graphene oxide (rGO) for the first time and explored the LIB anode applications. Materials are well characterised using structural and morphological characterisations to corroborate the relation between the electrochemical properties and the graphene addition. At 100 mA g−1, the NiWO4@rGO (NWZC) exhibits initial discharge capacity of 1439 mAh g−1, which is more than that of NiWO4 (NWZ). Both NWZ and NWZC display initial coloumbic efficiency of 91.65% and 62.1%. After 500 cycles, the coloumbic efficiency of the NWZ and NWZC is above 99%. The improved lithium-ion storage characteristics of the NWZC may be from the synergetic effect between NiWO4 and r-GO.

As the use of inductor-based topologies demands a large amount of space, capacitor-based topologies have garnered attention. Electric Vehicles (EVs) are usually equipped with two-level inverters, which require separate control strategies for each level and synchronizing the strategies increases the complexity of operation and makes them unreliable. Therefore, a single-stage converter with boost and conversion abilities with better power quality at optimal component count and efficiency is needed. A novel capacitor-based boost multilevel inverter (CB-MLI) topology is proposed in this paper as it is found suitable for EV and HEV applications. It is capable of generating an eleven-level waveform with only eleven switches, three capacitors, and a single isolated source. The self-balancing property of the capacitors makes the topology one of a kind. A constant carrier PWM-based control strategy is utilized to switch the IGBTs. Testing results from hardware setup confirm the proposed capacitor-based CB-MLI topology operating modes and potentiality. Lastly, by highlighting the proposed and existing MLI circuits, the benefits of the recommended configuration are outlined by component count and total cost. Additionally, it is a simplified design that needs fewer footprint areas and space.