University of Allahabad

The study analyzed the relationships between the terminal and instrumental values on attitude and behavioral intentions for organic food products in the context of Behavioral Reasoning Theory and Cognitive Hierarchical Model, i.e., Value-Attitude-Behavior. The conceptual model is based on a comprehensive review of the past research. A two-step approach was employed to evaluate the measurement and structural models with SmartPLS software for partial least square structural equation modeling. The findings revealed that for organic food products, both terminal and instrumental values influence attitude and consecutively, attitude influences behavioral intentions. However, the instrumental value exhibited greater influence on both attitude and behavioral intentions in comparison to the terminal value. The research findings may help organic food marketers to develop strategies by promotions that are aligned to the terminal and instrumental values.

Surface plasma waves (SPW) are electromagnetic waves formed by the action of laser on metal. These waves propagate at the surface of metal. The interaction of SPW with other materials like semiconductor has been studied in the present manuscript. The comparative study of efficiency of harmonic generation between metal–vacuum interface and metal–semiconductor interface has also been done. Studies reveal that under considered parameters, harmonic generation at metal–semiconductor interface is more efficient than metal–vacuum interface.

The assessment of soil erosion holds paramount significance in sustainable land management and environmental conservation. In this context, the integration of advanced technologies such as the Revised Universal Soil Loss Equation (RUSLE), Google Earth Engine (GEE), and geospatial techniques presents a formidable approach for evaluating soil erosion dynamics. This integrated methodology proves particularly valuable when applied to the Rel River watershed, where factors such as terrain, land use, and precipitation patterns intricately influence erosion processes. The collective use of two methods, the quantitative method focused on RUSLE to assess soil under various circumstances of erosion and sediment yield, whereas the qualitative approach focused on spectral indices of soil erosion in GEE to generate degradation map. This study was aimed at evaluating soil erosion and land degradation across the Rel River watershed in the western region of Gujarat, India. Soil loss has been estimated using soil loss models, i.e., RUSLE and geoinformation datasets, which were extracted from GEE. The degraded area was prepared using GEE and mapped using geographical information system (GIS). The results demonstrate that estimated value for erosion due to rainfall is 37 to 40 MJ mm h⁻¹ ha⁻¹ year⁻¹, soil erodibility is 0.01 to 0.05 ton h MJ⁻¹ mm⁻¹, topographic variables lies in a range from 0 to 20, and crop management factor is 0.001 to 1. The findings also demonstrate that the total annual soil loss for flood events in 2017 is 35.36 t/ha/year, which is categorized into the severe zone of degradation. According to the soil degradation map created using GEE, the majority of the study region falls into the low and medium degradation zones, while the northeastern part and river fall into the high degradation zone. The findings will be helpful in implementing soil management and conservation techniques to arrest soil erosion in the Rel River watershed.

The green synthesis of plasmonic metal nanoparticles (NPs) has gained considerable attention among researchers as it is cost-effective, environmentally friendly, energy-saving, and nontoxic. We have synthesized silver NPs (Ag NPs) with Oscimum sanctum (holy Tulsi) medicinal plant leaf extract by green synthesis methods. Further, we investigate the antibacterial, antioxidant, and antidiabetic activities of the synthesized Ag NPs. Oscimum sanctum leaf extract has secondary metabolites such as phenolic and flavonoid compounds, which play a significant role in the synthesis of Ag NPs. Subsequently, these bioactive molecules get adsorbed on the large surfaces of the synthesized NPs. Spectroscopic techniques such as X-ray diffraction (XRD), UV−visible absorption, Fourier-transform infrared, and scanning electron microscopy have been used to study and characterize the phytosynthesized Ag NPs. The XRD pattern confirms the formation of crystalline Ag NPs with a high degree of intensity. UV−visible absorption spectra confirm the surface plasmon resonance peak in the range of 440−450 nm. A scanning electron microscopy picture reveals homogeneous growth of Ag NPs with particle sizes of 200−400 nm; however, crystallite size along different planes has been estimated in the range of 18−23 nm. We have found that these Ag NPs synthesized with Oscimum sanctum leaf extract show inhibitory activity against α-amylase and α-glucosidase enzymes in vitro. Our findings further reveal that these Ag NPs are more effective in inhibiting the growth of Salmonella typhi bacteria as compared to other bacterial strains.

Second-harmonic generation (SHG) induced by surface plasma waves (SPW) on three different metal–semiconductor interfaces has been analyzed using Kretschmann attenuated total reflection configuration. The interaction between laser and metal generates surface plasma waves (SPW) which could further influence the charge carriers of semiconductor, coated above metal. The nonlinear interactions governed by ponderomotive force lead to harmonic generation. The presence of density ripple on metal surface supports the interactions and helps in phase matching. Under given conditions, Cu–InSb gives better output. This model could provide a cost-effective method for efficient second-harmonic generation which could be useful for medical purposes.

This paper is devoted to studying the thermal characteristics of a completely developed electrokinetic flow of micropolar fluid through a cylindrical microtube when a static electric field is applied to it. Due to the constant heat flux applied, the microtube wall is supposed to get heated continuously. In addition to this, the local thermal equilibrium (LTE) model is taken into account while analyzing the heat transfer phenomenon. Under low Reynolds numbers and long channel length approximations, the partial differential equations that describe the electrothermal flow of non-Newtonian micropolar fluid have been switched to ordinary differential equations. The finite difference method (FDM) is used to calculate velocity and temperature with second-order precision using uniform grids along the microtube’s radial direction. The Cavalieri–Simpson technique for numerical integration was used to get numerical values for the mean velocity, bulk mean temperature, and mean entropy/Bejan number. Variations in the Nusselt number for changes in velocity and temperature fields and fluctuations in the Bejan number due to heat transfer irreversibility have been presented. Moreover, a comprehensive study has been performed to discuss the impact of pertinent factors on the optimization of the system’s irreversibility through mean entropy generation analysis. Thermofluidic micropumps for chemical mixing/separation and biomicrofluidic devices for diagnostics may be designed using the results obtained from this study.

Chloride-based ionic liquids, exemplified by 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−), possess the capacity to dissolve cellulose, a predominant constituent of biomass. However, their inherently high viscosity poses a hindrance to efficient biomass dissolution for biofuel production. An intriguing solution emerges through the utilization of chloride-based ionic liquids in combination with solvents like water and DMSO, offering a promising avenue to reduce ionic liquid viscosity and enhance the efficacy of biomass dissolution. Utilizing constrained molecular dynamics simulations, we have conducted an extensive exploration of the potentials of mean force (PMFs) governing the behavior of the 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−) ion pair within dimethyl sulfoxide (DMSO)-water mixtures. Analysis of the BMIM+ − Cl− ion pair PMFs has revealed a noteworthy trend: with increasing DMSO mole fraction, there is a conspicuous augmentation in the depths of the minima associated with both the contact ion pair (CIP) and the solvent-assisted ion pair (SAIP). Notably, the CIP minimum exhibits a more pronounced increment relative to the SAIP minimum. This compelling observation underscores the heightened thermodynamic favorability of ion pairing as the DMSO mole fraction elevates. The credibility of the PMFs is corroborated through the meticulous computation of ion pair residence times for various inter-ionic separations. Thermodynamic assessments discern an intriguing trend: within the range of DMSO mole fractions (xDMSO) spanning 0.10, 0.21, 0.35, and 0.48, the stabilization of both CIPs and SAIPs is driven by entropy. In contrast, for xDMSO values of 0.0, 0.91, and 1.00, enthalpy plays a pivotal role in stabilizing the CIP and SAIP states. Further insights emerge from the meticulous analysis of radial distribution functions (RDFs) characterizing the arrangement of water and DMSO molecules surrounding the BMIM+ − Cl− ion pair. This scrutiny reveals the propensity of water molecules to form hydrogen bonds with the chloride ion, while DMSO molecules preferentially engage in hydrogen bonding with the BMIM+ ion, both within the CIP and SAIP states. Re- markably, water emerges as the preferred solvent for the solvation of the BMIM+− Cl− ion pair, superseding the affinity of DMSO. A notable transition surfaces as the DMSO mole fraction transitions from 1.0 to 0.91, resulting in a pronounced diminishment in the stability of both CIP and SAIP states, attributed to a substantial amplification in the local water density. Significantly, the preferential binding coefficients (γ) values for DMSO consistently show negativity, indicating its preferential exclusion from the BMIM+ − Cl− ion pair in DMSO-water mixtures. The calculated decay times for the survival probabilities of water and dimethyl sulfoxide (DMSO) molecules in the vicinity of the BMIM+ − Cl− ion pair suggest that the water cluster surrounding the Cl− ion exhibits greater stability compared to the DMSO cluster around the Cl− ion, while conversely, the trend is reversed for the BMIM+ ion. These findings advance our understanding of ion pairing kinetics in DMSO-water mixtures, providing valuable insights applicable to a wide spectrum of endeavors, notably including the incorporation of BMIM+ − Cl− ionic liquids in the conversion of biomass into biofuels.

Chloride-based ionic liquids, exemplified by 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−), possess the capacity to dissolve cellulose, a predominant constituent of biomass. However, their inherently high viscosity poses a hindrance to efficient biomass dissolution for biofuel production. An intriguing solution emerges through the utilization of chloride-based ionic liquids in combination with solvents like water and DMSO, offering a promising avenue to reduce ionic liquid viscosity and enhance the efficacy of biomass dissolution. Utilizing constrained molecular dynamics simulations, we have conducted an extensive exploration of the potentials of mean force (PMFs) governing the behavior of the 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−) ion pair within dimethyl sulfoxide (DMSO)-water mixtures. Analysis of the BMIM+ − Cl− ion pair PMFs has revealed a noteworthy trend: with increasing DMSO mole fraction, there is a conspicuous augmentation in the depths of the minima associated with both the contact ion pair (CIP) and the solvent-assisted ion pair (SAIP). Notably, the CIP minimum exhibits a more pronounced increment relative to the SAIP minimum. This compelling observation underscores the heightened thermodynamic favorability of ion pairing as the DMSO mole fraction elevates. The credibility of the PMFs is corroborated through the meticulous computation of ion pair residence times for various inter-ionic separations. Thermodynamic assessments discern an intriguing trend: within the range of DMSO mole fractions (xDMSO) spanning 0.10, 0.21, 0.35, and 0.48, the stabilization of both CIPs and SAIPs is driven by entropy. In contrast, for xDMSO values of 0.0, 0.91, and 1.00, enthalpy plays a pivotal role in stabilizing the CIP and SAIP states. Further insights emerge from the meticulous analysis of radial distribution functions (RDFs) characterizing the arrangement of water and DMSO molecules surrounding the BMIM+ − Cl− ion pair. This scrutiny reveals the propensity of water molecules to form hydrogen bonds with the chloride ion, while DMSO molecules preferentially engage in hydrogen bonding with the BMIM+ ion, both within the CIP and SAIP states. Re- markably, water emerges as the preferred solvent for the solvation of the BMIM+− Cl− ion pair, superseding the affinity of DMSO. A notable transition surfaces as the DMSO mole fraction transitions from 1.0 to 0.91, resulting in a pronounced diminishment in the stability of both CIP and SAIP states, attributed to a substantial amplification in the local water density. Significantly, the preferential binding coefficients (γ) values for DMSO consistently show negativity, indicating its preferential exclusion from the BMIM+ − Cl− ion pair in DMSO-water mixtures. The calculated decay times for the survival probabilities of water and dimethyl sulfoxide (DMSO) molecules in the vicinity of the BMIM+ − Cl− ion pair suggest that the water cluster surrounding the Cl− ion exhibits greater stability compared to the DMSO cluster around the Cl− ion, while conversely, the trend is reversed for the BMIM+ ion. These findings advance our understanding of ion pairing kinetics in DMSO-water mixtures, providing valuable insights applicable to a wide spectrum of endeavors, notably including the incorporation of BMIM+ − Cl− ionic liquids in the conversion of biomass into biofuels.

Recently, machine learning models have become a key methodology in detection of cardiovascular diseases (CVD). This gives medical practitioners diagnostic support and indicators. In this work, we compare various machine learning (ML) classification algorithms, apply them to disease dataset and examine how these algorithms perform when subjected to either of the classes to aid in the study and investigation of CVD through computer-aided diagnosis (CAD). Our two main goals in this work are to first offer an automated machine learning ensemble model for categorizing cardiovascular malignancies and second to compare the performance of several classification algorithms to find the best classifier for the task. The proposed technique is specifically developed as a potential support for clinical care based on patient diagnostic data. The proposed approach exhibits an accuracy of 94.28% in the detection of cardiac illnesses when a thorough examination of binary classification is performed and averaged over numerous model training iterations. We believe that incorporating the suggested ensemble methods would produce stable and dependable CAD systems.

From the last few decades, a huge volume of financial data has been generated from the various heterogeneous sources of financial institutions. This data contains valuable or interesting information not easy or nearly impossible to get manually or using traditional approaches. This key information drives the business, marketing, and financial services very effectively, more economically, and with high growth in less time. This can be achieved through the fusion of data analytics (DA) techniques with finance. The rise of the DA approach from a few decades has had a great impact on the way of research or new findings. This study analyzes the literature available on various applications of DA in finance and develops an understanding for the researchers. This knowledge can utilize to resolve typical issues in the financial and decentralized financial domains and explored new ideas with their models.

Nowadays, Vehicular Ad-hoc networks (VANETs) have become a developing technology that is mainly used for transmission of secret messages between Vehicles to provide information related to road traffic, climate, and road conditions. Secure message transmission in VANETs is essential to provide a comfortable and safe driving environment for vehicle users. To enhance security of message transmission and traffic efficiency, efficient and intelligent management of large number of vehicles in VANETs has become a problem. To ensure secure message transmission, the authentication of vehicles must be carefully considered when deploying VANETs. An efficient and robust authentication protocol for VANETs is presented in this paper. The proposed protocol has significantly minimized the computation cost. Theoretical and experimental analysis shows that our protocol performs better comparing with other existing protocols. The proposed technique also provides protection against various cryptographic attacks.

Vehicular Ad hoc Networks (VANETs) is an emergent paradigm of smart transportation system which is used for ensuring reliable transmission between vehicles, exchanging messages of real-time traffic congestion, road conditions and weather conditions. The transmission of these messages in an open environment result in privacy and security challenges in the VANET. In this paper, we propose a key distribution scheme based on Elliptic Curve Cryptography (ECC) for secure VANETs communication. The proposed scheme significantly reduces the cost of computation. The performance analysis based on computational cost is also presented which shows that our scheme gives efficient results. Security analysis clearly shows that our proposed scheme provides protection against various cryptographic attacks.

This work offers a quite sensitive SPR-based sensor with a new hetero-structure based on Platinum and ITO using silver as the plasmonic metal to examine the detection of sulfolane, ethylene glycol, diethylene glycol, Polyethylene Glycol (PEG-200 and PEG-600) in water. The widely utilized transfer matrix technique (TMM) was employed to evaluate the performance of the suggested sensor. The noble plasmonic material silver (Ag) with a thickness of 40 nm is utilized to induce surface plasmons. As an adhesive layer, cytop is used between the ITO and sensing layer; the thickness of 0.5nm has been taken. The sensor's performance was evaluated in terms of reflectance, full width at half maximum (FWHM), detection accuracy, sensitivity, and figure of merit. These parameters are also evaluated by varying the Platinum (P) and ITO (I) layers. The suggested sensor has a maximum sensitivity of 144.1988 degree/RIU (for P = 1, I = 9), DA of 1.8867 degree-1 , and FoM of 98.66 RIU-1 (for P = 3, I = 1). The operating wavelength of 633 nm is used here for this numerical analysis.

Climate change and shifts in land use/ land cover (LULC) are critical factors affecting the environmental, societal, and health landscapes, notably influencing the spread of infectious diseases. This study delves into the intricate relationships between climate change, LULC alterations, and the prevalence of vector-borne and waterborne diseases in Coimbatore district, Tamil Nadu, India, between 1985 and 2015. The research utilised Landsat-4, Landsat-5, and Landsat-8 data to generate LULC maps, applying the maximum likelihood algorithm to highlight significant transitions over the years. This study revealed that built-up areas have increased by 67%, primarily at the expense of agricultural land, which was reduced by 51%. Temperature and rainfall data were obtained from APHRODITE Water Resources, and with a statistical analysis of the time series data revealed an annual average temperature increase of 1.8 °C and a minor but statistically significant rainfall increase during the study period. Disease data was obtained from multiple national health programmes, revealing an increasing trend in den-gue and diarrhoeal diseases over the study period. In particular, dengue cases surged, correlating strongly with the increase in built-up areas and temperature. public health, urban planning, and climate change mitigation. Amidst limited research on the intercon-nections among infectious diseases, climate change, and LULC changes in India, our study serves as a significant precursor for future management strategies in Coimbatore and analogous regions.

Chloride-based ionic liquids, exemplified by 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−), possess the capacity to dissolve cellulose, a predominant constituent of biomass. However, their inherently high viscosity poses a hindrance to efficient biomass dissolution for biofuel production. An intriguing solution emerges through the utilization of chloride-based ionic liquids in combination with solvents like water and DMSO, offering a promising avenue to reduce ionic liquid viscosity and enhance the efficacy of biomass dissolution. Utilizing constrained molecular dynamics simulations, we have conducted an extensive exploration of the potentials of mean force (PMFs) governing the behavior of the 1-butyl-3-methylimidazolium chloride (BMIM+ − Cl−) ion pair within dimethyl sulfoxide (DMSO)-water mixtures. Analysis of the BMIM+ − Cl− ion pair PMFs has revealed a noteworthy trend: with increasing DMSO mole fraction, there is a conspicuous augmentation in the depths of the minima associated with both the contact ion pair (CIP) and the solvent-assisted ion pair (SAIP). Notably, the CIP minimum exhibits a more pronounced increment relative to the SAIP minimum. This compelling observation underscores the heightened thermodynamic favorability of ion pairing as the DMSO mole fraction elevates. The credibility of the PMFs is corroborated through the meticulous computation of ion pair residence times for various inter-ionic separations. Thermodynamic assessments discern an intriguing trend: within the range of DMSO mole fractions (xDMSO) spanning 0.10, 0.21, 0.35, and 0.48, the stabilization of both CIPs and SAIPs is driven by entropy. In contrast, for xDMSO values of 0.0, 0.91, and 1.00, enthalpy plays a pivotal role in stabilizing the CIP and SAIP states. Further insights emerge from the meticulous analysis of radial distribution functions (RDFs) characterizing the arrangement of water and DMSO molecules surrounding the BMIM+ − Cl− ion pair. This scrutiny reveals the propensity of water molecules to form hydrogen bonds with the chloride ion, while DMSO molecules preferentially engage in hydrogen bonding with the BMIM+ ion, both within the CIP and SAIP states. Re- markably, water emerges as the preferred solvent for the solvation of the BMIM+− Cl− ion pair, superseding the affinity of DMSO. A notable transition surfaces as the DMSO mole fraction transitions from 1.0 to 0.91, resulting in a pronounced diminishment in the stability of both CIP and SAIP states, attributed to a substantial amplification in the local water density. Significantly, the preferential binding coefficients (γ) values for DMSO consistently show negativity, indicating its preferential exclusion from the BMIM+ − Cl− ion pair in DMSO-water mixtures. The calculated decay times for the survival probabilities of water and dimethyl sulfoxide (DMSO) molecules in the vicinity of the BMIM+ − Cl− ion pair suggest that the water cluster surrounding the Cl− ion exhibits greater stability compared to the DMSO cluster around the Cl− ion, while conversely, the trend is reversed for the BMIM+ ion. These findings advance our understanding of ion pairing kinetics in DMSO-water mixtures, providing valuable insights applicable to a wide spectrum of endeavors, notably including the incorporation of BMIM+ − Cl− ionic liquids in the conversion of biomass into biofuels.

A novel heterostructure of surface plasmon resonance (SPR) sensor has been proposed in the current study for detecting various cancer cells. The proposed sensor is highly sensitive to the refractive index change of the sensing medium. The suggested sensor consists of Ag, PtSe 2 , and graphene. For the single layer of PtSe 2 and graphene layer, maximum sensitivity and figure of merit for breast type II cancer are 235 Deg/RIU and 41.14 RIU −1 and have been achieved, respectively. The highest value of detection accuracy is 0.194 Deg −1 for basal cells, which causes skin cancer. This is a simulation-based study utilizing the finite element method. To analyze the electric field strength close to the interface between PtSe 2 and the sensing medium, the proposed structure is simulated using the COMSOL Multiphysics system.

The theoretical assessment of mechanical and elastic properties is used to analyze the distinctive properties of high entropy alloys (HEAs) at room temperature. Using Lennard–Jones potential model, the second order elastic constants (SOECs) and third order elastic constants (TOECs) have been determined for the HEAs Hf 0.25 Ti 0.25 Zr 0.25 Sc 0.25− x Al x ( x ≤ 15 %) in their hexagonal close-packed (hcp) phases. SOECs have been used to calculate mechanical constants, Poisson’s ratio, Pugh’s ratio, Kleinman’s parameter. In order to determine the anisotropic behaviour of the selected HEAs, the elastic anisotropy has also been computed at room temperature. All the HEAs under consideration have anisotropy parameters that are not equal to one, indicating anisotropic behaviour. Later, the Grüneisen parameters were estimated for the chosen HEAs Hf 0.25 Ti 0.25 Zr 0.25 Sc 0.25− x Al x ( x ≤ 15 %) along longitudinal and shear modes of wave propagation. Analysis of the research results reveals the inherent properties of HEAs.

IoT-enabled electrocardiogram (ECG) devices are used to identify abnormal heart activity in the ECG signals and predict heart disease. Patients wear these devices, which send the ECG data to a healthcare provider for analysis. To investigate the information and forecast the likelihood of heart ailment, deep learning (DL) algorithms are used. DL-based approach for heart disease calculation using ECG signals would involve the following steps: Initially, IoT-based ECG signals from both healthy individuals and individuals with heart disease would be collected. Then, in preprocessing, the ECG signals are preprocessed via finite impulse response. Next, for feature extraction, the features such as P-wave, ST-segment, R-peak locations, heart rate variability, ST-segment, PQ-segment, T-wave, and QRS complex duration, and continuous wavelet transform and improved mutual information are extracted from the preprocessed signals. Then, for feature selection, the optimal structures are selected from the extracted features using the new hybrid optimization model—alpha spider customized dwarf mongoose optimizer, which is the combination of the standard tunicate swarm algorithm and slime mould algorithm. Finally, the heart disease is detected using the new three-layer framework, which encloses the “convolutional neural networks, bidirectional long-short term memory, and recurrent neural networks”. The overall performance of the proposed methodology is assessed by means of the performance metrics such as “accuracy, sensitivity, specificity, precision, recall, FPR, and FNR”. The suggested methodology is implemented in the platform of MATLAB.

This study aimed to assess the predominant predictors of functional disability in terms of involvement of activity of daily living (ADL) of urban geriatric subjects of Varanasi, India. This community-based cross-sectional study was undertaken on 616 urban geriatric subjects, selected through a multistage sampling procedure. The functional disability of subjects was assessed by using Barthel’s Index of ADL. Statistical analysis was done using Statistical Package for Social Sciences. Chi-square test and logistic regression analysis were done. Among all sociodemographic variables, age and marital status of subjects were significantly associated with their ADL status. In logistic regression, only age was a significant predictor of ADL involvement. Adjusted odds ratio for functional disability in the age group ≥80 years and 70–79 years were 19.82 [95% confidence interval (CI): 4.5–86.58] and 2.86 (95% CI: 1.91–4.28), respectively. Despite the several influencing factors, ageing and functional ability of geriatric subjects share an unbreakable bond.