Artificial intelligence or AI has a wide range of applications in healthcare and food industries. AI helps in different ways in medical industries, such as analysing the disease progression rate, effective prediction of treatment method, and proper disease diagnosis. Advantages of artificial intelligence in the food business include enhanced customer accessibility, improved technological innovation, readily accessible client requirements and comments, strategic advantage through unique products, and plenty others. Different AI technologies such as “Machine Learning (ML),” “Neural Language Processing (NLP),” “Rule-Based Expert Systems (RESs),” “Deep Learning (DL),” and so on are used in healthcare and food industries for big “medical data” analysis. This study has applied three critical variables to measure the application of AI in enhancing food quality (viz., usage of machine learning models, NLP models, etc.). This study has stated that these models support in enhancing the overall food quality in an effective manner. The present research analyses the importance of these AI technologies in enhancing service quality in healthcare and food industries. A primary survey-based data analysis has been done with 153 individuals taken from healthcare industries. Moreover, statistical analysis has been done in this research with SPSS software. Four independent variables are taken in this research, which are ML, NLP, RES, and DL. The service quality of healthcare has been taken as a dependent variable, and the effect of independent variables on “enhancing healthcare service” has been analysed. Secondary thematic analysis has been done to justify primary data. The results show that 43.79% of the individuals have supported DL and 56.86% have supported the treatment prediction ability AI. 37.9% of the individuals have also supported AI over traditional medications. Further analysis has shown that independent variables ML, DL, NLP, and RES have a strong positive correlation with improving SQ. These results have been justified by secondary journals, and it is proved that AI technologies enhance the service quality in healthcare and food sectors.

The microstructure and mechanical properties of an MMC based on AA 7075 and strengthened through silicon carbide (SiC) as well as boron carbide (B₄C) elements were studied. The (SiC+B₄C) combination was used in various weight percentages of 4, 8, 12, and 16 to create the hybrid composites utilizing the traditional stir casting procedure. XRD and SEM measurements were used to investigate the dispersion of the reinforced particles. For example, microhardness, impact strength, and ultimate tensile strength were measured on hybrid composites at room temperature. The density and porosity of the materials were also studied. The researchers found that increasing the weight percentage of the (SiC+B₄C) mixture resulted in a small drop in elongation. However, hybrid composites comprising 16 (SiC+B₄C) weight reduction showed some decrease in hardness and tensile strength. Equated to unreinforced alloys, the hardness and tensile strength of hybrid composites rise by 8 and 21, respectively. Reinforcement also resulted in a decrease in impact strength and density, as well as an increase in porosity.

Nowadays, Recyclability of huge quantity of waste materials generated in industries to form new materials. The current research is also focuses on utilization of waste materials produced by industry to form new hybrid brick for sustainable the environmental condition, technical aspects, and low production cost in construction field. In this paper hybrid brick specimens are manufactured using waste materials of clay, ceramic waste powder and sugarcane bagasse ash with various mix proportions. These bricks are tested in compression test, soundness test and water absorption test as per Indian Standards. It was observed that clay bricks incorporating ceramic waste powder (10%) and sugarcane bagasse ash (10%) exhibited high compressive strength, less water absorption, less efflorescence, good metallic ringing sound compared to other hybrid bricks leading to protect the environmental stability and keep away from hassle of ash disposal. The combination of ceramic powder and sugarcane bagasse ash residue increases the compressive strength of the clay samples (B2) to 27.2 MPa, and the composition of Brick B2 exhibits decreased water absorption percentage up to 11.4%.

This paper deals with some theoretical aspects of hypergraphs related to hyperpaths and hypertrees. In ordinary graph theory, the intersecting or adjacent edges contain exactly one vertex; however, in the case of hypergraph theory, the adjacent or intersecting hyperedges may contain more than one vertex. This fact leads to the intuitive notion of knots, i.e., a collection of explicit vertices. The key idea of this manuscript lies in the introduction of the concept of the knot, which is a subset of the intersection of some intersecting hyperedges. We define knot-hyperpaths and equivalent knot-hyperpaths and study their relationships with the algebraic space continuity and the pseudo-open character of maps. Moreover, we establish a sufficient condition under which a hypergraph is a hypertree, without using the concept of the host graph.

The normal methods for monitoring environmental pollution with image data have many false positives. Therefore, this study is proposing a single-valued neutrosophic set (SVNS) (a variant of NS)-based method as a decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification in mining area using satellite data. The experimental results show that this decision-making model using intuitionistic fuzzy Hamacher generalized Shapley Choquet integral operators for feature extraction and automatic material classification can better predict the presence of four heavy metals, i.e., vanadium(V), iron (Fe), copper (Cu), and nickel (Ni) in the study area than other methods. For vanadium metal, the determination accuracies, namely, producer accuracy, user accuracy, overall accuracy, and Kappa were 94.5%, 94.1%, 93.88%, and 0.93%, respectively. It was found that the estimated results and the distribution trend of heavy metals are almost the same as in actual ground measurements.

Fashion designers and brands use GANs to create new and unique patterns, styles, and textures. GANs consist of a generator and a discriminator, which work together to produce high-quality, realistic outputs. VAEs are another type of generative model that is applied to generate new fashion designs. VAEs are known for their ability to generate diverse outputs by sampling from a learned latent space. Fashion designers can use VAEs to explore different design variations and styles. StyleGAN and its successor, StyleGAN2, are advancements of GANs that specifically focus on generating high-resolution and realistic images with control over different style elements. These models have been employed in fashion to create detailed and visually appealing designs. These AI generative models have the potential to revolutionize the fashion industry by facilitating creativity and providing new avenues for artistic expression. However, it's essential to consider ethical implications, intellectual property rights, and the responsible use of AI technologies in the context of fashion design.