The use of online social networks has become a standard medium of social interactions and information spreading. Due to the significant amount of data available online, social network analysis has become apropos to the researchers of diverse domains to study and analyse innovative patterns, friendships, and relationships. Message dissemination through these networks is a complex and dynamic process. Moreover, the presence of reciprocal links intensify the whole process of propagation and expand the chances of reaching to the target node. We therefore empirically investigated the relative importance of reciprocal relationships in the directed social networks affecting information spreading. Since the dynamics of the information diffusion has considerable qualitative similarities with the spread of infections, we analysed six different variants of the Susceptible-Infected (SI) epidemic spreading model to evaluate the effect of reciprocity. By analysing three different directed networks on different network metrics using these variants, we establish the dominance of reciprocal links as compared to the non-reciprocal links. This study also contributes towards a closer examination of the subtleties responsible for maintaining the network connectivity.
This paper represents the development strategies of Internet of Things based flood monitoring and alerting system with weather forecasting through open weather API. This project is based on the open source electronic platform i.e. Arduino. The Arduino Uno R3 is to be set up multiple different devices such as ultrasonic sensor for the water level detection by capturing the time between transmitting and receiving sound waves, temperature and humidity sensor DHT11/AM2302 for analyzing the moisture content and water flow sensor for evaluating the speed of water flow. Further, these values received by different sensors are to be transferred to the Android Application which is developed with the technologies such as Java, XML, Android studio. The final system will be deployed in the flood prone areas for early detection and prevention of flood.
The present study was focused primarily on the optimized extraction of dietary fiber (DF) from pineapple peel (PP) waste using alkaline and combined alkaline ultrasound methods and it was used with antioxidant rich black rice to prepare cookies with tailored physicochemical properties. The extraction of alkaline ultrasound extracted DF (AUEDF) was optimized using response surface methodology (RSM) conducted with central composite design (CCD). The optimum processing conditions for the AUEDF extraction method was solvent: solute (26.15 ml/g), ultrasound amplitude (34.32%) and sonication time (29.91 min), which gave the highest yield of DF (71.88%). Further, AUEDF yielded higher total DF content with well aligned and uniform fibrous structure, better thermal stability, crystallinity, water holding capacity, and oil holding capacity as compared to alkaline extracted dietary fiber (AEDF). The cookies were prepared by substituting wheat flour with extracted DF and black rice powder in flour formulation. The characteristics properties of the cookies in terms of texture, color, and sensory attributes were evaluated. The hardness and lightness of cookies were found to decrease with an increased concentration of black rice powder. Fuzzy logic was used for sensory evaluation, in which the cookie containing 30% black rice powder was found as the best sample. The quality attributes of cookies such as taste, and texture can be modified by the adopted strategy, which, are considered as important attributes for consumer acceptance followed by appearance and flavor. The results of the present investigation evinced that DF of pineapple waste utilization can be an excellent source for preparation of cookies with black rice. Novelty impact statement Dietary fibre (DF) from “Kew” variety pineapple peel was extracted via alkaline‐ultrasound and alkaline extraction methods and comparison revealed that the DF extracted from the former evinced uniform fibrous structure, better thermal stability, crystallinity, and other functional properties over the latter extraction method. Cookies were developed using DF and antioxidant‐rich black rice in the wheat formulation and the texture (hardness) and color of cookies decreased with increased black rice powder contents. Fuzzy logic was used to evaluate the preferred sensory properties of cookies and fortified cookie containing 30% black rice and 6% DF was selected as the best sample.
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