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The majority of previous studies made on Recycled Concrete Aggregates (RCA) are limited to the utilisation of non-structural grade concrete due to unfavourable physical characteristics of RCA including the higher absorption of water, tending to increased water requirement of concrete. This seriously limits its applicability and as a result it reduces the usage of RCA in structural members. In the present study, the impact of hybrid fibres on cracking behaviour of RCA concrete beams along with the inclusion of reinforcing steel bars under two-point loading system exposed to different sustained elevated temperatures are being investigated.

RCA is substituted for Natural Coarse Aggregates (NCA) at 0, 50 and 100 percentages. The study involves testing of 150 mm cubes and beams of size (700 × 150 × 150) mm, i.e. with steel reinforcing bars along with the addition of 0.35% Steel fibres+ 0.15% polypropylene fibres. The specimens are being exposed to temperatures from 100° to 500°C with 100° interval for 2 h. Studies were made on the post crack analysis, which includes the measurement of crack width, crack length and load at first crack. The crack patterns were analysed in order to understand the effect of fibres and RCA at sustained elevated temperatures.

The result shows that ultimate load carrying capacity of reinforced concrete beams and load at first crack decreases with the raise in temperatures and increased percentage of RCA content in the mix. Further that 100% RCA replacement specimens showed lesser cracks when compared to the other mixes and the inclusion of fibres enhances the flexural capacity of members highlighting the importance of fibres.

RCA can be used for structural purposes and the study can be projected for assessing the performance of real structures with the extent of fire damage when recycled aggregates are used.

Most of recycled materials can be used in the regular concrete which solves two problems namely avoiding the dumping of C&D waste and preventing the usage of natural aggregates. Hence the study provides sustainable option for the production of concrete.

The reduction in capacity of flexural members due to the utilisation of recycled aggregates can be negated by the usage of fibres. Hence improved flexural performance is observed for specimens with fibres at sustained elevated temperatures.

This paper aims to explore the application of drumstick (Moringa oleifera) flower (DF) as a functional antioxidative ingredient in goat meat product.

Dried DF was included in the product formulation at 1% (Treatment I) and 2% (Treatment II) levels. The physicochemical, colour, textural and sensory quality as well as storage stability of nuggets with DF were determined against control.

The dried DF was found to be rich source of protein and dietary fibre, possessing good antioxidant potential. Chromatographic analysis of DF extract showed presence of 14 active principles known to have antioxidative properties. Inclusion of dried DF decreased pH values of emulsion (p = 0.005) as well as nuggets (p < 0.001) and increased (p < 0.001) the ash, dietary fibre and phenolic contents. The added DF affected the product’s lightness (p = 0.017), yellowness (p < 0.001, hardness (p < 0.001), adhesiveness (p = 0.032), cohesiveness (p = 0.006), gumminess and chewiness (p < 0.001). Sensory characteristics of control and product with DF were statistically similar except low (p = 0.002) flavour score for Treatment II. DF inclusion lowered (p < 0.001) thiobarbituric acid reactive substances number and total plate count.

DF can be used as a source of antioxidants and dietary fibre in goat meat nuggets to enhance their health value, functionality and storage stability.

Foods including goat meat nuggets enriched with goodness of functional ingredients like dietary fibre and natural antioxidants are gaining consumer’s preference globally. Inclusion of drumstick flower in goat meat nuggets significantly increases the dietary fibre and antioxidants making such products healthier and more stable. Consumption of goat meat nuggets added with drumstick flower is expected to improve consumer’s well-being as well.

The present study was conducted to evaluate the effect of banana peel flour as fat replacer on rheological, physico-chemical, textural, mineral content and sensory properties of chicken patties.

Ultra low fat chicken patties were prepared with incorporation of banana peel flour at 0% (C), 1% (BP1), 2% (BP2) and 3% (BP3) levels separately to replace 50% externally added vegetable fat in formulation and evaluated for various quality characteristics and sensory attributes.

Highest G' and G''? modulus were observed in banana peel powder incorporated emulsion. No cross-point was observed at all ranges of frequency in meat emulsions prepared with banana peel. Among physico-chemical properties, control had significantly (p < 0.05) higher emulsion pH, emulsion stability, product pH, water activity values, fat and cholesterol content; however, cooking yield, moisture and ash content, fat retention and moisture retention values increased significantly (p < 0.05) in treatment patties. Mineral, textural and colour parameters had a significant (p < 0.05) effect except on manganese content and a* values. Various sensory scores decreased significantly (p < 0.05) with increased level of banana peel flour.

Sensory scores of 3% banana peel powder incorporated patties were significantly (p < 0.05) lower than other treatments. There was no significant difference between 1 and 2% banana peel incorporated chicken patties. Therefore, an ultra low fat chicken patties incorporated with 2.0% banana peel flour to replace 50% vegetable fat were selected as the best treatment.

Present global trend and life style are currently driving ready-to-eat healthy meat products and factors include extended working hours, increasing number of single-person households and perception of food as reward. Fat is an important component of meat products and imparts tenderness, improving flavor and mouth feel to processed meat products, like chicken patties. However intake of excess energy in form of saturated and unsaturated fat may lead to various life style diseases in consumers. Hence development of ultra low fat chicken patties with incorporation of fruit waste without adverse effect on sensory properties may be a significant challenge.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

Digital technologies emerged as innovative avenues for launching new products, advertising brands, increasing customer awareness and thus leaving a remarkable impact on the online marketplace. The present study analyzed the effects of crucial antecedents of AR interactive technology on customers' behavior toward AR-based e-commerce websites.

Convenience sampling was used to collect primary data from 357 iGen respondents aged 16–22 years; residing in New Delhi and the NCR region of India and examined using the structural equation modeling technique.

Results revealed that technology anxiety and virtuality significantly influence customers' attitudes and behavioral intentions toward AR-based e-commerce websites. However, interactivity and innovativeness remain non-significant. Additionally, non-significant moderating effects were identified for the moderators, i.e. trust and need for touch. At the same time, gender has a significant moderating effect only for the association between technology anxiety and attitude toward AR-based e-commerce websites.

The study summarizes numerous theoretical and managerial implications for AR-based website designers and policymakers, followed by the crucial limitations and directions for future research.

The present research provides a significant understanding of the e-commerce industry by providing valuable insights about young iGen consumers' perceptions of AR-based e-commerce websites.

This paper has used account-level data of corporate and retail borrowers, assessed their credit risk through the risk-neutral principle and examined its implication on loan pricing.

It derives the capital charge and credit risk-premium for expected and unexpected losses through a risk-neutral approach. It estimates the risk-adjusted return on capital as the pricing principle for loans. Using GMM regression, the article has assessed the determinants of risk-based pricing.

It has been found that risk-premium is not reflected in the current loan pricing policy as per Basel II norms. However, the GMM estimation on RAROC can price risk premium and probability of default, LGD, risk weight, bank beta and capital adequacy, which are the prime determinants of loan pricing. The average RAROC for retail loans is more than that of corporate loans despite the same level of risk capital requirement for both categories of loans. The robustness tests indicate that the RAROC method of loan pricing and its determinants are consistent against the time and type of borrowers.

The RAROC method of pricing effectively assesses the inherent risk associated with loans. Though the empirical findings are confined to the sample bank, the model can be used for any bank implementing the Basel principle of risk and capital assessments.

The article has developed and validated the model for estimating RAROC, as per Basel II guidelines, for loan pricing that any bank can use.

It has developed the risk-based loan pricing model for retail and corporate borrowers. It has significant practical utility for banks to manage their risk, reduce their losses and productively utilise the public deposits for societal developments.

The article empirically validated the risk-neutral pricing principle using a unique 1,520 retail and corporate borrowers dataset.

The case illustrates an entrepreneurial voyage and venture creation and through it helps in identifying the reasons and causes for that venture's failure. It also enables discussion on the importance of planning a venture, more importantly; financing, managing, growing, and ending a venture and on how to avoid the pitfalls that befall such enterprises. This case can be used in Entrepreneurship courses as well as MBA, PGP and Executive Education programmes on Entrepreneurship.

This paper aims to discuss the successful 3D printing of styrene–ethylene–butylene–styrene (SEBS) block copolymers using solvent-cast 3D printing (SC-3DP) technique.

Three different Kraton grade SEBS block copolymers were used to prepare viscous polymer solutions (ink) in three different solvents, namely, toluene, cyclopentane and tetrahydrofuran. Hansen solubility parameters (HSPs) were taken into account to understand the solvent–polymer interactions. Ultraviolet–visible spectroscopy was used to analyze transmittance behavior of different inks. Printability of ink samples was compared in terms of shape retention capability, solvent evaporation and shear viscosity. Dimensional deviations in 3D-printed parts were evaluated in terms of percentage shrinkage. Surface morphology of 3D-printed parts was investigated by scanning electron microscope. In addition, mechanical properties and rheology of the SC-3D-printed SEBS samples were also investigated.

HSP analysis revealed toluene to be the most suitable solvent for SC-3DP. Cyclopentane showed a strong preferential solubility toward the ethylene–butylene block. Microscopic surface cracks were present on tetrahydrofuran ink-based 3D-printed samples. SC-3D-printed samples exhibited high elongation at break (up to 2,200%) and low tension set (up to 9%).

SC-3DP proves to be an effective fabrication route for complex SEBS parts overcoming the challenges associated with fused deposition modeling.

To the best of authors’ knowledge, this is the first report investigating the effect of different solvents on physicomechanical properties of SC-3D-printed SEBS block copolymer samples.

In a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.

The present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.

Empirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.

As a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.

Based on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

The present study aims to develop and offer a model to evaluate the customers' attitude and intention to adopt solar net metering systems (commonly called solar NMS) in a developing economy. Therefore, the research examines different factors affecting the Indian households' attitudes and intention to adopt solar NMS.

The data were collected from 247 solar NMS users from India. The structural equation modeling (SEM) technique was applied using SmartPLS 3.3.2 software to analyze the impact of various factors on their adoption intention. The conceptual model comprises environmental concern, perceived ease of use (PEOU), subjective norms, perceived usefulness (PU), attitude and behavioral intention to adopt solar NMS.

Subjective norms and environmental concerns significantly influence the PU and PEOU of solar NMS. Also, PU and PEOU significantly influence their attitude and intentions toward adopting solar NMS. Thus, the perceived social pressure and environmental concern affect their perception of solar NMS's usefulness and ease of use, leading to favorable attitudes and adoption intentions. Additionally, the solar NMS benefits the customers, society and the environment by enhancing environmental quality, compatibility with the modern lifestyle, and reducing dependency on the power grid and electricity bills. These benefits shape the customers' overall perception and increase the adoption of solar technologies.

The present research helps bridge the gaps in the existing literature by identifying (1) factors affecting customers' intention toward solar technologies in developing nations and (2) describing the significant prediction of environmental concern and subjective norms to increase solar technologies adoption.