Mechanical properties of tef starch based edible films: Development and process optimization.

The non-biodegradable synthetic plastic is one of the greatest challenges facing the food packaging business since it seriously harms the environment. To solve this problem, non-biodegradable plastic may be disposed of more affordably and with less harm on the environment by using edible starch-based biodegradable film. Therefore, the present study was focused on the development and optimization of tef starch based edible films based on mechanical properties. In this study response surface methodology was employed by considering 3-5g of tef starch, 0.3-0.5% of agar and 0.3-0.5% of glycerol. The prepared film showed the tensile strength of 17.97-24.25 Mpa, elongation break of 1.21-2.03%, elastic modulus of 17.58-108.69 MPa, puncture force of 2.55-15.02 N, puncture formation of 9.59-14.95 mm. The findings showed that as glycerol concentrations in the film-forming solution increased, the prepared tef starch edible films' tensile strength, elastic modulus, and puncture force declined while their elongation at break and puncture deformation increased. Tef starch edible films' mechanical characteristics, including as tensile strength, elastic modulus, and puncture force, were increased by the increase of agar concentration. The optimized (from 5 gm tef starch, 0.4 g agar and 0.3% glycerol) tef starch edible film exhibited higher tensile strength, elastic modulus, and puncture force while lower elongation at break and puncture deformation. The composite edible film based tef starch with agar exhibited good mechanical properties and can be suggested for application in food industry as food packaging.

Lightweight Artificial Intelligence for Secure Data Communication in Energy-Constrained Healthcare Devices.

Logistics is the transfer of goods from one place to another, mostly from the production house to the customers. A logistics network is a set of operations that involve designing, production, and marketing the goods. Cold-chain logistics are those that needed to be transported in a cold refrigeration right from the production house to the customer. A secured networking model is essential to handle the logistics networks. In this article, we are going to see an intelligent secured networking model to identify the optimal path for cold-chain logistics to hospitals. The optimal pathfinder is used to find the path between point A to point B, which is short and best. It also considers the road traffic and cost of transport. The cold-chain logistics to the hospitals include medicines and vaccines, which are to be stored at a particular temperature. Thus, path optimization is more essential in cold-chain logistics to hospitals than other types of logistics. In this research, the bee-ant optimization algorithm (BAOA) is proposed to perform the intelligent transportation to the hospitals. The proposed algorithm is compared with the existing ant colony optimization (ACO), bee colony optimization (BCO), and neural network model. From the results, it can be observed that the proposed algorithm shows 98.83% for the accurate delivery of logistics to the hospitals.

Expedite Quantification of Landslides Using Wireless Sensors and Artificial Intelligence for Data Controlling Practices.

The power of wireless network sensor technologies has enabled the development of large-scale in-house monitoring systems. The sensor may play a big part in landslide forecasting where the sensor linked to the WLAN protocol can usefully map, detect, analyze, and predict landslide distant areas, etc. A wireless sensor network comprises autonomous sensors geographically dispersed for monitoring physical or environmental variables, comprising temperature, sound, pressure, etc. This remote management service contains a monitoring system with more information and helps the user grasp the problem and work hard when WSN is a catastrophic event tracking prospect. This paper illustrates the effectiveness of Wireless Sensor Networks (WSN) and artificial intelligence (AI) algorithms (i.e., Logistic Regression) for landslide monitoring in real-time. The WSN system monitors landslide causative factors such as precipitation, Earth moisture, pore-water-pressure (PWP), and motion in real-time. The problems associated with land life surveillance and the context generated by data are given to address these issues. The Wireless Sensors Network (WSN) and Artificial Intelligence (AI) give the option of monitoring fast landslides in real-time conditions. A proposed system in this paper shows real-time monitoring of landslides to preternaturally inform people through an alerting system to risky situations.