Abstract: The disclosure relates generally to methods and systems for predicting water quality of a river having a varying river ecosystem. Due to multiple and diverse factors, understanding and estimating the water quality of the river stream (river itself) is extremely and technically challenging. The present disclosure discloses a development of a river digital twin model utilizing a multi-modeling approach to comprehensively model the river and its varying ecosystems. The agents encompass entities that directly or indirectly introduce effluents or withdraw water from the river. Agents and their interactions are defined using a combination of behavior rules, correlations, and physics principles, creating the digital twin model that closely mimics the real river system. Physics-based equations are also employed in the present disclosure to capture the dynamics of the river, while relationships between different agents are established.

Abstract: This disclosure relates generally to the field of retrieval and extraction for designing formulated products by constructed ingredient-functionality lists for formulated products using search engine result excerpts in an automated manner. Many ingredients used in formulated products are multi-functional. Conventionally, in a given category of formulated product, only a single functionality or subset of functionalities of an ingredient is used ignoring other functionalities of which the ingredient is representative. However, there is a need to put together representative ingredients before proceeding further with process steps of making and testing the formulated product. The present disclosure addresses unresolved problems of the conventional methods by obtaining information regarding the functionalities of ingredients used in formulated products using search engines based on a loop processing involving query formation, and construction of a list of ingredients and functionalities till exhaustion.

Abstract: State of the art food quality measurement techniques fail to determine quality of the food item once it is packed and sealed in a container. The disclosure herein generally relates to food quality prediction, and, more particularly, to a system and method for predicting food quality in a non-invasive manner. A Color Changing Indicator (CCI) in a biosensor strip forming a component of the enclosed package in which the liquid food item is packed, changes color when came in contact with the liquid food item. For different quality of the liquid food item the CCI has different color. Based on the color of the CCI, and ambient temperature and relative humidity at the time the color of the CCI is determined, a machine learning model determines rate of deterioration of the liquid food item, and then predicts remaining shelf life, which in turn provided as output to a user.

Abstract: Conventionally, mechanical methods of measuring linear position over a scale required physical coupling with system(s), such as potentiometer-based feedback etc. Such kinds of mechanical feedback mechanisms need geared coupling. And along with a mechanical system comes problems of wear and tear of parts which can lead to increased inaccuracy of the setup over time. With increased capabilities of computer vision-based techniques, a non-contact image-based measurement of linear position has become of prime importance. Embodiments of the present disclosure provide system and method that implement direct visual measurement of liquid level inside a linear measurement setup (syringe here) by employing various techniques such as computer vision, machine learning techniques, and the like, wherein various features are extracted that in turn allow to have a measurement of a liquid level identified or indicator (plug/meniscus) position in a syringe/container.

Abstract: This disclosure relates generally to shelf life of produce and, more particularly, for predicting and enhancing shelf life of produce in storage facility. A significant quantity of produce such as fresh fruits and vegetable are lost before reaching the consumer, during its long-term storage in a warehouse or a storage facility. Many techniques have been employed to preserve-enhance the shelf life. However, the existing techniques do not explicitly consider factors such as air circulation, stacking of container, and respiration of the produce during shelf-life prediction. The disclosed techniques predict and enhance the shelf life of produce in storage facilities in several steps including determining a set of modelling parameters, determining a plurality of shelf-life parameters, predicting a shelf life of the produce based on generating a shelf-life prediction model, predicting a quality index and finally, enhancing the shelf-life of the produce based on an optimization technique.

Abstract: State of the art milk quality estimation techniques fail to determine quality of the milk non-invasively. This disclosure relates generally to a system and method for non-invasive estimation of milk quality within targeted packaging. A voltage signal is applied at a first capacitor plate amongst a plurality of capacitor plates placed on either side of a targeted packaging containing milk, wherein the targeted packaging is placed inside an electrode receptacle. The resulting voltage signal is measured at a second capacitor plate of the plurality of capacitor plates. A capacitance value is calculated from the measured voltage signal and a value of pH corresponding to the calculated capacitance value is then determined using a regression-based model. A correlation of the determined value of pH with one or more pre-defined quality indices is performed and the quality of milk within the targeted packaging estimated based on the correlation.

Abstract: Graphene metasurface based linear to circular polarization converters in THz band plays a crucial role in beyond 5G/THz wireless systems. However, conventional converters suffer a trade-off between “high bandwidth”, “compact size” and “high field confinement”. The present disclosure provides a graphene metasurface based tunable linear to circular polarization converter to minimize the trade-off by operating at a large bandwidth of 800 GHz with considerable compact size and high field confinement. The linear to circular polarization converter of the present disclosure includes a metasurface comprising two-dimensional periodic array of unit cells along x-axis and y-axis, wherein each unit cell is a three-layered stacked structure with a predefined periodicity. The three-layered stacked structure includes a top layer, a middle layer and a bottom layer. The top layer is a single layer graphene. The middle layer is a dielectric substrate, and the bottom layer comprises thin layer of gold.

Abstract: This disclosure relates generally to a sunscreen composition containing visibly transparent biocompatible ultraviolet blocking polymer nanoparticles. The sunscreen composition comprising polydimethylsiloxane (PDMS) nanoparticles, at least one flavoring agent, at least one coloring agent, at least one stabilizer, and at least one preservative, in a defined form, wherein size of the PDMS nanoparticles ranging from 200 to 1200 nm. The size of the PDMS nanoparticles ranging from 200 to 500 nm for air medium and from 800 to 1200 nm for water medium. The PDMS nanoparticles comprise a PDMS polymer having terminal functional group selected from alkyl, alkoxy, vinyl, hydroxyl, or a combination thereof. The sunscreen composition is available in the defined form selected from a group consisting of a skin cream, a skin lotion, a powder, a gel, and a sprayable liquid.

Abstract: Multispectral laser speckle analysis techniques have been widely used for applications such as the analysis of seed qualities, detection of fungal infections in fresh produce, estimation of blood flow velocities, and the like. However, the conventional methods fail to address color bias in images which is a major concern in terms of accuracy in applications like activity prediction. and the present disclosure provides a novel image processing algorithm which performs activity prediction without any color bias. Here, an RGB input image is segmented, and mask is generated. Distance based clustering is performed on the masked image to obtain cluster map. Simultaneously, color coded activity map is generated for each color component of the image. Further, the color coded activity map is multiplied with cluster contrast values and thereby an optimal color coded activity region is selected for activity map generation.

Abstract: Health of perishable commodities such as eatables deteriorate over time. State of art systems for health monitoring of perishable commodities rely on measurement of limited parameters and also fail to consider effect of environment on the health of the perishable commodities. Disclosed herein is an apparatus and method for multimodal sensing and monitoring of perishable commodities. The apparatus allows to change environment within a closed chamber in which the perishable commodity being monitored is kept, and in turn allows to generate health data in different environment settings. This data is used to generate a health model. Data collected in real-time are processed with the health model to establish a correlation with at least one image, wherein each of such images in the health model represents certain health state. Based on the established correlation, health of the perishable commodity is determined.

Abstract: Conventional approaches are not efficient for the screening of active multi component alloy catalysts. Present disclosure provides systems and method that incorporate elements of materials chemistry, artificial intelligence (AI)/machine learning (ML) and optimization for the screening of such active catalysts for a target chemical reaction. Given a set of elements constituting the alloy and the target reaction, surface atomic structures of several different alloy composition and configurations are constructed. Then, adsorption energy of key reaction intermediates on the surface atomic structures are computed using quantum chemical methods (QCM). Next, an AI/ML model is trained using data obtained from QCM to predict the adsorption energy of the reaction intermediates on the alloy surface. Finally, numerical optimization is used to identify alloy compositions that maximize the activity and selectivity of the catalyst for a target chemical reaction.

Abstract: State of the art mechanisms for edible food coating design have the disadvantage that they fail to accommodate various factors such as type of compounds, chemical composition, and processing parameters such as pH, drying rate, temperature affect the nano-microstructure of the film. The disclosure herein generally relates to edible food coating, and, more particularly, to a method and system for developing and characterizing edible films using molecular dynamic simulations. The molecular dynamic simulations help design the edible food coatings, which maybe further used for practical applications.

Abstract: This disclosure relates generally to the field of retrieval and extraction for designing formulated products by constructed ingredient-functionality lists for formulated products using search engine result excerpts in an automated manner. Many ingredients used in formulated products are multi-functional. Conventionally, in a given category of formulated product, only a single functionality or subset of functionalities of an ingredient is used ignoring other functionalities of which the ingredient is representative. However, there is a need to put together representative ingredients before proceeding further with process steps of making and testing the formulated product. The present disclosure addresses unresolved problems of the conventional methods by obtaining information regarding the functionalities of ingredients used in formulated products using search engines based on a loop processing involving query formation, and construction of a list of ingredients and functionalities till exhaustion.

Abstract: Most techniques to estimate the service life of coatings are experimental in nature. Experimental testing of coatings is expensive (destructive in nature) and time consuming. Present disclosure provides system and method that predict change in chemical composition of the coating using reaction mechanism of the coating components in presence of weathering causing environmental agents. The system partial differential equations wherein chemical model is combined with the physical model. The changes in chemical composition enable determining the change in surface roughness and thickness as the weathering of the coating happens which can be correlated with other parameters such as gloss loss, fracture toughness, and the like. Rate of initiation reaction which leads to micro-randomness in concentration profile is randomized to mimic inherently stochastic (random) nature of coating degradation.