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Flaxseed is considered as a source of functional ingredients because it contains alpha-linolenic acid and high amount of dietary fiber which provides potential health benefits. Therefore, this paper aims to evaluate the effect of flaxseed flour on the quality of mutton nuggets.

Mutton nuggets were incorporated with three different levels of hydrated flaxseed flour (1:1), namely, 4, 8 and 12 per cent to optimize the level of incorporation. Detailed profile analysis was carried out to study various physico-chemical parameters, texture profile analysis and color values in the product with optimum level and compared with control product.

There was no significant difference (p > 0.05) in sensory attributes of product with 4 and 8 per cent level of incorporation except for binding; however, significantly lower (p < 0.01) scores were observed for 12 per cent incorporation level. On the basis of sensory scores, 8 per cent level of incorporation was adjudged as optimum. Incorporation of flaxseed flour resulted in significantly lower (p < 0.01) moisture content, but higher (p < 0.01) fat and dietary fiber content. Texture profile analysis showed increased hardness of the product but resulted in decreased springiness and chewiness parameters. Incorporation of flaxseed flour significantly increased (p < 0.01) redness and yellowness of the product which improved overall acceptability.

The research work is original.

The purpose of this paper is to investigate the free vibration behavior of a functionally graded (FG) rectangular plate under asymmetric boundary conditions using the coupled displacement field method (CDF), which is based on first-order shear deformation theory (FSDT).

The material properties of the FG rectangular plate vary continuously in the direction of thickness based on the power-law exponent form. The approach utilizes the CDF method, where the admissible trail functions are assumed for total rotations in both directions that satisfy various boundary conditions. Based on the coupling equations, the function for lateral displacement is derived in terms of total rotations. By utilizing the energy formulations, the undetermined coefficients are derived. The frequency parameters are obtained by minimizing the Lagrangian with respect to the undetermined coefficients.

The frequencies with respect to different parameters like aspect ratios (a/b), thickness ratio (h/a) and power-law (k) for all edges SCSS and SCSF boundary conditions are found. The effect of the parameters on the obtained frequencies is observed, and these frequency parameters are compared with other literature to validate the derived frequencies. The findings of the present formulation are in excellent accord when compared to other methods in the literature.

The energy formulations in the proposed method contain half the number of undetermined coefficients when compared to the Rayleigh–Ritz (RR) method, which simplifies the vibration problem significantly. The efficacy of the proposed method in finding the frequency parameters lies in reduced computational procedure when compared to other methods.

Meal ordering apps (MOAs) have transformed the customers' dining habits, particularly during mobility restrictions of the COVID-19 pandemic. Under the theoretical cover of the extended stimulus–organism–response (SOR) model, this paper attempts to explore the critical antecedents and outcomes of customer MOA engagement which predict the continuous purchase intentions using these apps. A multigroup analysis is conducted to investigate the difference between the hypothesized relationships between the Chinese and Indonesian consumers.

A mixed-method approach, including a systematic literature review, an open-ended essay (qualitative) with 139 MOA users and an online survey (quantitative) with 1,207 MOA users in total, was used for hypotheses testing.

The structural equation model results revealed that customer MOA experience factors such as mobile online reviews (MR), food quality (FQ), restaurant reputation (RR), service quality and system quality (SyQ) are the absolute positive factors that influence customer MOA cognitive, affective and behavioral engagement, which in turn affect continuous purchase intentions. The multigroup analysis results reveal that Chinese customers prioritized MR and FQ for customer MOA engagement (cognitive, affective and behavioral). Comparatively, Indonesian customers placed most importance on RR and SyQ.

Considering a market-specific setting and based on the extended SOR framework, this study is one of the first to take a comprehensive look at the critical antecedents and outcome of multidimensional customer MOA engagement in the developing countries’ (China and Indonesia) online to offline meal delivery context. Further, this study investigates the customer continuous purchase intentions as an outcome of MOA engagement during the COVID-19 pandemic. The findings also reveal the differences in consumer behavior across the two developing but culturally diverse countries samples during the pandemic.

Ubiquitous health-care monitoring systems can provide continuous surveillance to a person using various sensors, including wearables and implantable and fabric-woven sensors. By assessing the state of many physiological characteristics of the patient’s body, continuous monitoring can assist in preparing for the impending emergency. To address this issue, this study aims to propose a health-care system that integrates the treatment of the impending heart, stress and alcohol emergencies. For this purpose, this study uses readings from sensors used for electrocardiography, heart rate, respiration rate, blood alcohol content percentage and blood pressure of a patient’s body.

For heart status, stress level and alcohol detection, the parametric values obtained from these sensors are preprocessed and further divided into four, five and six phases, respectively. A final integrated emergency stage is derived from the stages that were interpreted to examine at a person’s state of emergency. A thorough analysis of the proposed model is carried out using four classification techniques, including decision trees, support vector machines, k nearest neighbors and ensemble classifiers. For all of the aforementioned detections, four metrics are used to evaluate performance: classification accuracy, precision, recall and fmeasure.

Eventually, results are validated against the existing health-care systems. The empirical results received reveal that the proposed model outperforms the existing health-care models in the context of metrics above for different detections taken into consideration.

This study proposes a health-care system capable of performing data processing using wearable sensors. It is of great importance for real-time systems. This study assures the originality of the proposed system.

The purpose of this research is to study the reflection and transmission of elastic waves at the interface of an elastic half‐space and initially stressed thermoelastic diffusion with voids half‐space.

Two‐dimensional model has been considered of an isotropic elastic half‐space (Medium I) lying over a homogeneous isotropic generalized initially stressed thermoelastic diffusion with voids half‐space (Medium II). There exist two waves, P‐wave and SV‐wave, in isotropic elastic half‐space and four quasi‐longitudinal waves, namely, quasi‐longitudinal wave (QP‐mode), quasi‐longitudinal mass diffusive wave (QMD‐mode), quasi‐longitudinal thermal wave (QPT‐mode) and quasi‐longitudinal volume fractional wave (QPV‐mode), and one quasi‐transverse wave (QSV‐mode) exists in initially stressed thermoelastic diffusion with voids half‐space.

The energy ratios of these waves are computed along various directions of incident wave, and it is found that the sum of all energy ratios is exactly unity at each value of incident angle. The amplitude ratios of various waves have been obtained numerically.

Reflection and transmission of an elastic medium is of great practical importance. Since valuable organic and inorganic deposits beneath the earth surface are difficult to detect by drilling randomly, wave propagation is the simplest and most economic technique for these and does not require any drilling through the earth. Almost all the oil companies rely on seismic interpretation for selecting the sites for exploratory oil wells because seismic wave methods have higher accuracy, have higher resolution and are more economical, as compared to drilling which is expansive and time consuming.

This study aims to explore the impact of systematic liquidity risk on the averaged cross-sectional equity return of the Indian equity market. It also examines the effects of illiquidity and decomposed illiquidity on the conditional volatility of the equity market.

The present study employs the Liquidity Adjusted Capital Asset Pricing Model (LCAPM) for pricing systematic liquidity risk using the Fama & MacBeth cross-sectional regression model in the Indian stock market from January 1, 2012, to March 31, 2021. Further, the study employed an exponential generalized autoregressive conditional heteroscedastic (1,1) model to observe the impact of decomposed illiquidity on the equity market’s conditional volatility. The study also uses the Ordinary Least Square (OLS) model to illuminate the return-volatility-liquidity relationship.

The study’s findings indicate that the commonality between individual security liquidity and aggregate liquidity is positive, and the covariance of individual security liquidity and the market return negatively affects the expected return. The study’s outcome specifies that illiquidity time series analysis exhibits the asymmetric effect of directional change in return on illiquidity. Further, the study indicates a significant impact of illiquidity and decomposed illiquidity on conditional volatility. This suggests an asymmetric effect of illiquidity shocks on conditional volatility in the Indian stock market.

This study is one of the few studies that used the World Uncertainty Index (WUI) to measure liquidity and market risks as specified in the LCAPM. Further, the findings of the reverse impact of illiquidity and decomposed higher and lower illiquidity on conditional volatility confirm the presence of price informativeness and its immediate effects on illiquidity in the Indian stock market. The study strengthens earlier studies and offers new insights into stock market liquidity to clarify the association between liquidity and stock return for effective policy and strategy formulation that can benefit investors.

The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances.

COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO₂). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test.

IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO₂), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials.

The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).

The purpose of this research is to study the reflection and refraction of elastic waves at the interface of an elastic half‐space and initially stressed thermoelastic with voids half‐space.

A two‐dimensional model was considered of an isotropic elastic half‐space (medium I) lying over a homogeneous isotropic generalized initially stressed thermoelastic with voids half‐space(medium II). There exist two waves, P‐wave and SV‐wave in isotropic elastic half‐space and three quasi‐longitudinal waves namely, quasi‐longitudinal wave (QP‐mode), quasi‐longitudinal thermal wave (QPT‐mode), quasi‐longitudinal volume fractional wave (QPV‐mode) and one quasi‐transverse wave (QSV‐mode) exists in initially stressed thermoelastic with voids half‐space.

The energy ratios of these waves are computed along various directions of incident wave, and it is found that the sum of all energy ratios is exactly unity at each value of incident angle. The amplitude ratios of various waves were obtained numerically.

Reflection and refraction of an elastic medium is of great practical importance. Since valuable organic and inorganic deposits beneath the earth surface are difficult to detect by drilling randomly, wave propagation is the simplest and most economic technique to these and does not require any drilling through the earth. Almost all the oil companies rely on seismic interpretation for selecting the sites for exploratory oil wells because seismic wave methods have higher accuracy, higher resolution and more economical, as compared to drilling which is expansive and time consuming.

Technology uptake in supply chains frequently encounters challenges when immediate, tangible benefits are not evident. Therefore, this article's central objective is to pinpoint the risks affecting the integration of supply chain technologies for enduring outcomes. Subsequently, these risks will be organized into a hierarchical structure, facilitating a clearer comprehension of their direct and indirect interconnections.

A combined TISM and quantitative approach has been used to build the hierarchal structure and to validate the direct, and indirect relationship among risks adopting supply chain technologies. A total of 41 respondents participated in the TISM survey, which resulted in creating a level hierarchical structure. Further, 233 responses are used for the quantitative study using SEM to validate the model obtained from TISM.

The study's findings indicated the social risks come out with the highest driving power. This includes the fear of job displacement, community disparity and change in the work culture of an organization. At the same time, technological risks can cause system integration, scalability issues, obsolesce compatibility issues, system failures and supply chain sustainability issues.

The novelty of the study can be found in the developed framework and its subsequent confirmation via quantitative evaluation. TISM provides the theoretical foundation, while a quantitative investigation is carried out to verify this theory.

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.