Entertainment has been recognized as a critical element in mall retailing. Mall management marketing strategies combine functional and emotional elements to draw shoppers to the mall. The current study analyzed the influence of hedonic and utilitarian shopping values on consumers’ attitudes toward the entertainment, pleasure, and fun dimensions of mall events and their impact on Indian consumers’ attitudes toward malls. The study employed a mall-intercept technique for data collection in fifteen malls across India. The findings categorized shopping values as a recreational, adventure, and utilitarian; while, mall events were conceptualized using Babin et al. s’ (1994) shopping motives typology (viz., escape, exploration, social, epistemic, and flow). Mall events encompassed product promotions, celebrity shows, and product launch activities. The hedonic shopping value (recreational and adventure), and utilitarian shopping values influenced attitudes toward mall events and consequently, mall commitment. Mall managers should organize mall activities that combine both hedonic and utilitarian aspects of shopping. Mall events should emphasize the recreational, fun, curiosity, social, and utilitarian elements in mall shopping to improve mall commitment.
X-type samarium-cadmium co-substituted hexaferrite with compositions Ba2-xSmxCo2CdyFe28-yO46 (0.00 ≤ x ≤ 0.08, and 0 ≤ y ≤ 0.4) were prepared at 1340 °C using a simple heat treatment technique. All heated samples were characterized using FTIR, XRD, SEM, VSM, Mӧssbauer, and low-frequency dielectric measurements. XRD analysis of prepared samples shows the formation of X as a major phase along with hematite. The MS value varied from 67.01 Am²/kg to 50.43 Am²/kg; whereas the Hc value changed from 2.95 kA/m to 6.17 kA/m, A high value of MS (67.01 Am²/kg) is observed in the pure sample, and a very low value of Hc (2.95 kA/m) is observed for x = 0.06, y = 0.3 compositions, but Mr/Ms < 0.5 confirm the multi-domain nature of prepared hexaferrites. Hysteresis loops of all samples are narrow and confirmed that formed samples belong to magnetically soft. Mössbauer spectra of the three samples (S1, S3, and S5) show the existence of doublets. Significantly low values of coercivity, retentivity and loss tangent in Sm–Cd substituted samples signified those prepared materials can be used to design electromagnets, transformer cores, electric motors and maybe a potential candidate for lossless low-frequency applications.
The Micro-electromechanical devices operate under the stable operating range. The demarcating Voltage and the displacement separating the stable and unstable operating range are called Pull-in parameters. The accurate determination of these parameters is one of the essential step in the MEMS design. In this paper the Static Analysis of electrostatically actuated micro-cantilever beam is carried out to obtain Pull-in Voltage and displacement considering fringing field effects using Galerkin Method. The fringing field effects are incorporated by considering three different fringing field Models and the results are compared.
The present research work attempted to improve the oral bioavailability of the antiviral drug Efavirenz (EFV) using a pharmaceutical cocrystallization technique. EFV comes under BCS-II and has extremely low water solubility, and results in low oral bioavailability. EFV and nicotinamide (NICO) were selected in a (1:1) stoichiometric ratio and efavirenz nicotinamide cocrystal (ENCOC) was prepared through the liquid-assisted grinding method (LAG). The confirmation of the formation of a new solid phase was done through spectroscopic techniques like Fourier transmission infrared (FTIR), Raman, and 13C solid-state nuclear magnetic resonance (13C ssNMR). Thermal techniques like differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and hot stage microscopy (HSM) illustrated the thermal behavior and melting patterns of ENCOC, EFV, and NICO. The X-ray powder diffraction (XRPD) confirms the formation of a new crystalline phase in ENCOC. The Morphology was determined through scanning electron microscopy (FESEM). The results of saturated solubility studies and in vitro drug release studies exhibited 8.9-fold enhancement in solubility and 2.56-fold enhancement in percentage cumulative drug release. The percentage drug content of ENCOC was found higher than 97% and cocrystal exhibits excellent accelerated stability. The oral bioavailability of EFV (Cmax, 799.08 ng/mL) exhibits significant enhancement after cocrystallization (Cmax, 5597.09 ng/mL) than EFV and Efcure®-200 tablet (2896.21 ng/mL). The current work investigates the scalable and cost-effective method for enhancement of physicochemical stability, solubility, and oral bioavailability of an antiviral agent EFV.
Sustainable agricultural growth and management reduces over-utilization of farm resources and squeezes risk of negative impacts on environment. Monitoring continuous crop growth and health under various conditions at different spatio- temporal resolutions is a key to assess yield stability, crop diversity, adaptability, mitigation for stress and response. The quantification of crop biophysical and biochemical variables spatially from remotely sensed data with help of spectroscopic methods provide a reliable discerning information in the context of crop foliar condition like leaf greenness, senescence, canopy density, crop growth, stress and eco- physiological processes. Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) airborne hyperspectral data offers high spatial and spectral resolution giving a unique advantage and opportunity to test retrievals of crop biophysical (BP)-biochemical (BC) variables under varying conditions over different types of crops. A hybrid inversion of leaf-canopy Radiative Transfer model – PROSAIL- D complemented with use of data driven nonlinear non-parametric methods which offer simplicity, fastness, reliability and competency is a powerful method to retrieve crop biophysical and biochemical variables. The Hyperspectral band (feature) selection is a computationally cost efficient method to overcome data redundancy in high dimensional correlated input spectral bands. The determination of optimum subset or combination of hyperspectral bands specific to retrieval of vegetation properties (including canopy effects) determined using feature selection algorithm for regression- based retrievals are active research topic unlike classification problems in which it is more common. A band selection algorithm based on Gaussian Processes Regression was used to choose most sensitive bands from in-situ biophysical-biochemical measurements and crop spectral signatures collected for analysis in two different agricultural regions: Raichur (Karnataka) and Anand (Gujarat) districts of India representing diverse landscapes, heterogeneous crop canopies and agro climatic settings. The retrieval algorithm for AVIRIS-NG image employed a decision tree ensemble algorithm Canonical Correlation Forests using the optimum subset of bands for retrieval of targeted crop variable. Validation of retrieved crop variables were done using in-situ ground observations collected over heterogeneous diverse crop landscape. The results showed chlorophyll (RMSE = 6.61 µg cm −2 ), equivalent water thickness (RMSE=0.002 cm), leaf area index (RMSE= 0.35 m 2 m -2 ) and dry matter (RMSE= 0.003 g cm −2 ), carotenoid (RMSE= 14.3 µg cm −2 ), anthocyanin (RMSE= 12.92 µg cm −2 ). The results obtained in context of feature (band) selection approach for Radiative Transfer model inversion show overlapping of sensitive spectral bands of chlorophyll-ab, carotenoid and anthocyanin especially between narrow spectral range 480 to 560 nm as predominant reason for decreased accuracies of anthocyanin and carotenoid compared to other variable. This poses a limitation as well as opportunity for further research in signal separation in context of feature selection approach especially in context of broader spectral bandwidths. It also sets ground for further development of feature selection algorithms that use hybrid regression methods customized for crop specific traits.
The loop heat pipe (LHP) is a two-phase heat transfer device used for thermal management in applications like space, mobile, laptop, electronic cooling. In the present study, modeling and parametric analysis of LHP was done by applying energy balance approach at different sections using Dev-C++ software. The effects of various parameters like sink temperature (5 °C, 10 °C, 22 °C), condenser effectiveness (0.4–0.9), vapor line diameter (0.0043 m, 0.0053 m, 0.0063 m), wick thickness (0.0073 m, 0.0048 m) and wick porosity (40%, 60%, 80%) were studied on the performance of LHP. The results obtained were validated with the results published in the literature. With increase in condenser effectiveness from 0.40 to 0.90, steady state operating temperature got decreased by 4.5 °C at a heat load of 600 W. The studies on wick porosity revealed that it has significant effect on the heat leak from evaporator to compensation chamber and the lower porosity leads to higher heat leak. From the analysis, an optimum performance of LHP was observed with 60% wick porosity, 0.0073 m wick thickness, 5 °C sink temperature, vapor line diameter of 0.0053 m and condenser effectiveness as 0.9 among the various parameters under investigations.
Crop classification plays a vital role in felicitating agriculture statistics to the state and national government in decision-making. In recent years, due to advancements in remote sensing, high-resolution hyperspectral images (HSIs) are available for land cover classification. HSIs can classify the different crop categories precisely due to their narrow and continuous spectral band reflection. With improvements in computing power and evolution in deep learning technology, Deep learning is rapidly being used for HSIs classification. However, to train deep neural networks, many labeled samples are needed. The labeling of HSIs is time-consuming and costly. A transfer learning approach is used in many applications where a labeled dataset is challenging. This paper opts for the heterogeneous transfer learning models on benchmark HSIs datasets to discuss the performance accuracy of well-defined deep learning models—VGG16, VGG19, ResNet, and DenseNet for crop classification. Also, it discusses the performance accuracy of customized 2-dimensional Convolutional neural network (2DCNN) and 3-dimensional Convolutional neural network (3DCNN) deep learning models using homogeneous transfer learning models on benchmark HSIs datasets for crop classification. The results show that although HSIs datasets contain few samples, the transfer learning models perform better with limited labeled samples. The results achieved 99% of accuracy for the Indian Pines and Pavia University dataset with 15% of labeled training samples with heterogeneous transfer learning. As per the overall accuracy, homogeneous transfer learning with 2DCNN and 3DCNN models pre-trained on the Indian Pines dataset and adjusted on the Salinas scene dataset performs far better than heterogeneous transfer learning.
Nickel-based super alloys exhibit high strength, oxidation and corrosion resistance; however, the machining of these alloys is a challenge that can be overcome with effective cooling/lubrication techniques. The use of a minimum quantity lubrication (MQL) technique is limited to lower cutting parameters due to the tremendous heat produced during the machining of Inconel 718. Sustainable and eco-friendly machining of Inconel 718 can be attained using MQL and lubricants based on nanofluids because of their improved heat transfer capabilities. For that purpose, the performance of hybrid nanofluid-MQL is examined. In this novel study, graphene and hexagonal boron nitride (hBN) nanoparticles are reinforced with palm oil and delivered to the machining interface using an MQL setup. The machining experiments are performed under the conditions of dry, wet, MQL and MQL with graphene/hBN deposited in palm oil. The machining performance under selected cutting conditions is assessed by analyzing the surface roughness, tool wear, chip morphology and surface quality of the machined workpiece. A comparison of results showcased the effectiveness of hybrid nanofluid-MQL with improvement in surface finish, reduction in tool wear and favorable chip forms concerning all other machining conditions.
- N. Ramaiya
- R. Manchanda
- Malay Bikas Chowdhuri
- J. Ghosh
Spectroscopy in vacuum ultraviolet (VUV) and visible ranges plays an important role in the investigation and diagnosis of tokamak plasmas. However, under harsh environmental conditions of fusion grade devices, such as ITER, VUV–visible systems encounter many issues due to the degradation of optical components used in such systems. Here, near-infrared (NIR) spectroscopy has become an effective tool in understanding the edge plasma dynamics. Considering its importance, a NIR spectroscopic diagnostic has been developed and installed on the ADITYA-U tokamak. The system consists of a 0.5 m spectrometer having three gratings with different groove densities, and it is coupled with a linear InGaAs photodiode array. Radiation from the ADITYA-U edge plasma has been collected using a collimating lens and optical fiber combination and transported to the spectrometer. The spectrum in the NIR range from the ADITYA-U plasma has been recorded using this system, in which Pa β and Pa γ along with many spectral lines from neutral and singly ionized impurities have been observed. The influxes of H and C have been estimated from measurements. The H influx value is found to be 2.8 × 10 ¹⁶ and 1.9 × 10 ¹⁶ particles cm ⁻² s ⁻¹ from neutral hydrogen lines H α and Pa β , respectively, and the C influx value is found to be 3.5 × 10 ¹⁵ and 2.9 × 10 ¹⁵ particles cm ⁻² s ⁻¹ from the neutral carbon and singly ionized carbon, respectively. A good agreement is seen between these results and the results obtained by using a routine photomultiplier tube based diagnostic.
One of the essential requirements for intelligent manufacturing is the low cost and reliable predictions of the tool life during machining. It is crucial to monitor the condition of the cutting tool to achieve cost-effective and high-quality machining. Tool conditioning monitoring (TCM) is essential to determining the remaining useful tool life to assure uninterrupted machining to achieve intelligent manufacturing. The same can be done by direct and indirect tool wear measurement and prediction techniques. In indirect methods, the data is acquired from the sensors resulting in some ambiguity, such as noise, reliability, and complexity. However, in direct methods, the data is available in images resulting in significantly less chances of ambiguity with the proper data acquisition system. The direct methods, which provide higher accuracy than indirect methods, involve collecting images of worn tools at different stages of the machining process to predict the tool life. In this context, a novel tool wear prediction system is proposed to examine the progressive tool wear utilizing the artificial neural network (ANN). Experiments were performed on AISI 4140 steel material under dry cutting conditions with carbide inserts. The cutting speed, feed, depth of cut, and white pixel counts are considered as input parameters for the proposed model, and the flank wear along with remaining tool life is predicted as the output. The worn tool images were captured using an industrial camera during the turning operation at regular intervals. The ANN training set predicts the remaining useful tool life, especially the sigmoid function and rectified linear unit (ReLU) activation function of ANN. The sigmoid function showed an accuracy of 86.5%, and the ReLU function resulted in 93.3% accuracy in predicting tool life. The proposed model’s maximum and minimum root mean square error (RMSE) is 1.437 and 0.871 min. The outcomes showcased the ability of image processing and ANN modeling as the potential approach for developing a low-cost industrial tool condition monitoring system that can measure tool wear and predict tool life in turning operations.
This paper proposed a reliable semi‐analytical approach, namely, the reduced differential transform method, to evaluate the exact solution of the Newell–Whitehead–Segel equation. It models chemical reactions, Rayleigh–Bernard convection, and Faraday instability. The existing analytical approaches in the literature do not deal effectively with nonlinear terms and boundary condition. To show the effectiveness and reliability of the proposed method, this paper discusses three cases. The obtained results are more accurate than the other existing methods available in the literature.The accuracy of solution obtained is of 10⁻¹² order approximately in each of the three cases. The analytical solution is also compared with an exact solution, which shows an excellent agreement. The paper also discusses error analysis for the obtained series solution with the straightforward applicability, with less cost and computational effort.
In multimedia devices such as mobile phones, surveillance cameras, and web cameras, image sensors have limited spatial resolution. As a result, the image captured from these devices misses high-frequency content and exhibits visual artifacts. Image super-resolution (SR) algorithms can minimize these artifacts by reconstructing missing high-frequency textures. Image SR algorithm estimates a high resolution (HR) image from a given low-resolution (LR) image. Given a single LR image, reconstructing an HR image makes SR be an extremely ill-posed problem. Over the past decade, dictionary learning-based methods have shown promising results in SR reconstruction. These methods extract numerous patches from external images for training dictionaries via sparse representation. However, these methods do not involve any patch selection mechanism that enhances the learning process. This paper proposes a dictionary learning-based SR algorithm that extracts selective patches from an input LR image based on the iScore criterion. Results show that patch selection criteria keep only 36% of all extracted patches for training while improving the peak signal-to-noise ratio (PSNR). Furthermore, we have proposed a method to initialize dictionaries to achieve better convergence that enhances PSNR.
Purpose Asiatic acid (AA) is reported for its neuroprotective potential in Alzheimer’s disease (AD). This present work aimed to develop AA loaded nanostructured lipid carriers (AAN) for targeting the delivery of AA into the brain and ameliorating the cognitive deficits in AD rats. Methods AAN was optimized using the Box-Behnken design, considering 3 factors (soya lecithin, tween 80, and high pressure homogenizer (HPH) pressure) as independent variables while particle size (PS), zeta potential (ZP) and entrapment efficiency (EE) were dependent variables. Cytotoxicity assay and internalization studies of AAN were evaluated in SH-SY5Y cells and further neuroprotective efficiency on intracellular amyloid beta (Aβ) aggregation was evaluated in Aβ 1–42 treated cells with thioflavin T (ThT). The behavioral acquisition effects were evaluated in Aβ 1–42 (5 µg/ 5 µL, intracerebroventricular (ICV), unilateral) induced AD model followed by the histology and quantification of neurotransmitters levels. Results The optimized AAN revealed desired PS (44.1 ± 12.4 nm), ZP (- 47.1 ± 0.017 mv) and EE (73.41 ± 2.53%) for brain targeting delivery of AA. In-vitro, AAN exhibited better neuroprotective potential than AA suspension (AAS). AA content was 1.28 folds and 2.99 folds heightened in plasma and brain respectively after the i.p. administration of AAN as compared to AAS. The results of pharmacodynamic studies manifested the AAN treatment significantly (p < 0.05) ameliorated the cognitive deficits. Conclusions Hence, developed AAN has neuroprotective potential and should be further considered as an unconventional platform in preclinical model for the management of AD. Graphical Abstract
Linear actuator device for gas compression has significant importance in a cryocooler system. The compressor provides acoustic power to drive the cooling phenomenon in the pulse tube cryocooler. In general, the compressor is designed based on electromagnetic effect. But, such compressors have limiting factors, such as heating due to eddy current, large size, and exhibit electromagnetic interference (EMI), which is undesired for space applications. A novel linear-drive compressor actuated using a piezoelectric actuator is presented in this paper. Piezoelectric actuators have an inherent limitation of extremely small elongation as compared to their length. A hydraulic amplification system along with resonance amplification is proposed. Motion transfer is done using different diameter pistons and housing assembly. This paper includes theoretical formulation and numerical study of the amplification system. An equivalent analytical spring-mass-damper model is formulated to determine resonance frequency. Amplitudes of the piezoelectric actuator elongation and piston displacement are shown. Design is aimed to achieve an operating frequency of 100 Hz.KeywordsPiezoelectric actuatorLinear compressorHydraulic amplificationResonance amplification
The financial market integration is important for the investors to have the portfolio diversification of their investment. The investors do the portfolio diversification to the market where they can have higher return with lower risk. The purpose of the paper is to analyse portfolio diversification opportunities among Asian Developed, Emerging and Frontier markets. The study is performed using various methods such as Correlation, Granger causality test, Johansen cointegration test, Portfolio diversification analysis using various diversification strategies. The study examines portfolio diversification opportunities by comparing non-diversified portfolio (home market) with diversified portfolios (Equal Weighted Portfolio, Minimum Variance Portfolio and Maximum Sharpe Portfolio). The gain from the portfolio diversification was also analyzed to measure the benefits of the diversification. The study found that the lack of integration among many markets proves the existence of the portfolio diversification opportunity. Study is unique in a nature that it examines the portfolio diversification benefits for the investors in developed, emerging and frontier markets, as past studies were limited to developed markets only. The study concluded that the investors can gain better return, lower risk and higher Sharpe with portfolio diversification in international market. The researchers can examine in future the portfolio diversification benefits with other frontier and emerging markets for the investors of the developed markets.
Satellite navigation system has come a way long from its initial stages when it was used for military applications to now in mobile devices worldwide. With the advancement in the satellite navigation system, various new navigation satellite constellations have been set up in space. Various data processing and analyzing tools have been developed for the systems. The number of this software, both online and offline, has been increasing due to which it has become essential to have a detailed comparative study on this software to design more efficient ones in the future. To this end, this paper surveys different software for global navigation satellite systems. The selection of the software for the survey is based on their attractiveness among scientists, results published in literature, and noteworthy characteristics and features. The survey work aims to assist scientists, researchers, and software developers in selection of an apt software for their work based on system requirements, supporting constellation, supported data format, price, size, strengths, and weaknesses. Software developers can further identify limitations of the existing software and overcome them.KeywordsSoftware tools for satellite navigation systemBernesegLABGNSSGPSOPUSRTKLIBTeqc
The low voltage ride through (LVRT) is the ability of wind farms to remain connected in synchronism with the power grid during the disturbance in the power system. The literature proposes various techniques for LVRT improvement of grid-connected wind farms by considering the single machine infinite bus (SMIB) system. The behavior of the power system having multiple synchronous generators, asynchronous generators and loads needs to be observed for the system-level fault as the effectiveness of any solution cannot be extrapolated to large systems only by verifying it on the SMIB system. The fault current limiters (FCLs) are widely used in literature to improve the LVRT requirement of wind farms. This paper compares the operation of different types of FCLs by considering a system-level fault at the weak bus in a multi-machine power system. The effects of FCLs on generator buses during the symmetrical fault are presented and analyzed in the paper.KeywordsDoubly fed induction generatorFault current limiterLow voltage ride throughMulti-machine power systemWind farms
An oppositional-based learning approach with a real coded chemical reaction algorithm (ORCCRO) has been considered in this manuscript. The ORCCRO algorithm has been used here to find the near-global optimum solution for multi-objective economic-emission load dispatch problem with having nonlinear constraints. Emission extract from thermal power plants like NOx, inequality constraints like power generation operating limit and equality constraints like power balance consideration are considered here. ORCCRO follows the process to reach a stable state in lower energy with different molecular chemical collision. Oppositional-based learning mechanism has also hybrid here with RCCRO to find out more effective solution. Three different test systems are considered for simulation studies revealed that the ORCCRO method is much superior in comparison with other effective algorithms. The results obtained in these three test systems prove the robustness and the efficiency of ORCCRO.
User mobility represents the movement of either individual or group. In smart cities, detection and prediction of mobility patterns are required for numerous applications like resource distribution, traffic management, and user behavioral analysis. With the increase in the number of smart vehicles, urban mobility detection and prediction have become a critical problem for study. Bike‐sharing ecosystems (BSS) form an integral part of such ecosystems, as it supports the green revolution, ease of access, and solves traffic problems. However, recent schemes have suggested that BSS are challenged by issues of high density, mobility complexity of bikes (stations), large commute cost, uneven distribution, and route imbalances. To address the critical issues, the article proposes a hybrid scheme that combines rebalancing using clustering that addresses the mobility complexity. Once rebalancing is done, we address the uneven distribution among clusters using prediction models. This article is presented a comparative analysis of algorithms like fuzzy C‐means clustering, linear regression, decision tree, and random forest classifiers for predictive analysis performed on weather data and nonweather data. The presented results indicate the viability of the proposed model in real‐world scenarios.
Due to the major applications in various industries, the global demand for Metal Matrix Composites (MMC) is increasing. The characteristics of manufactured composites highly depend upon the manufacturing method. The present research article showcases the manufacturing of three different compositions, namely AA 2014 + 5 wt.% SiC, AA 2014 + 10 wt.% SiC and AA 2014 + 15 wt.% SiC. A stir casting process has been implemented to manufacture these composites. While implementing the casting process one cannot get rid of the defects that are generated during the solidification. Thus, to avoid these defects in as-cast composites, friction stir processing (FSP) has been performed. FSP avoids casting defects and further enhances the distribution of reinforcement particles. The considered combinations of rotational speed and transverse speed are as follows (i) 270 rpm and 78 mm/min (E1 condition) and (ii) 190 rpm and 50 mm/min (E2 condition). Furthermore, the article compares the microstructure, microhardness and tribological properties of the as-received AA 2014 alloy, as-cast and processed composites. Irrespective of the FSP condition, AA 2014 + 10% SiC composites revealed the highest wear resistance among all the variety of specimens considered for the wear test.
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