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.

Pectin-based edible film plasticized with glycerol has been developed, and the effect of pectin and glycerol concentration was optimized using response surface methodology for better mechanical properties and transparency. The upper and lower concentration of pectin (3-5 g) and glycerol (15%-25%) concentration ranges were considered in this study based on the preliminary experiment. The responses of the edible film determined were tensile strength, elongation at break and elastic modulus and opacity. The interaction effects of glycerol and pectin concentrations on edible film properties significantly affected the film properties. Tensile strength and opacity were positively affected by pectin concentrations; however, elastic modulus and elongation at break were negatively affected. Glycerol concentration negatively affected the edible film's tensile strength and elastic modulus. The decrease in the opacity of the biofilm was observed as the pectin concentration increased; however, glycerol had not shown a significant influence on opacity. The numerical optimization provided 4 g of pectin, and 20% of glycerol showed a strong and transparent edible film. The TGA curve showed that the maximum weight loss occurred between the temperatures 250-400 °C due to the loss of polysaccharides. From FTIR analysis, observed peaks around 1037 cm^-1 represented the C-O-C stretching vibrations of the saccharide found in pectin and glycerol.

Air pollution consists of harmful gases and fine Particulate Matter (PM_2.5) which affect the quality of air. This has not only become the key issues in scientific research but also turned to be an important social issues of the public’s life. Therefore, many experts and scholars at different R&Ds, universities, and abroad are involved in lot of research on PM_2.5 pollutant predictions. In this scenario, the authors proposed various machine learning models such as linear regression, random forest, KNN, ridge and lasso, XGBoost, and AdaBoost models to predict PM_2.5 pollutants in polluted cities. This experiment is carried out using Jupyter Notebook in Python 3.7.3. From the results with respect to MAE, MAPE, and RMSE metrics, among the models, XGBoost, AdaBoost, random forest, and KNN models (8.27, 0.40, and 13.85; 9.23, 0.45, and 10.59; 39.84, 1.94, and 54.59; and 49.13, 2.40, and 69.92, respectively) are observed to be more reliable models. The PM_2.5 pollutant concentration (PC_low-PC_high) range observed for these models is 0-18.583 μg/m³, 18.583-25.023 μg/m³, 25.023-28.234 μg/m³, and 28.234-49.032 μg/m³, respectively, so these models can both predict the PM_2.5 pollutant and can forecast the air quality levels in a better way. On comparison between various existing models and proposed models, it was observed that the proposed models can predict the PM_2.5 pollutant with a better performance with a reduced error rate than the existing models.

In the south-west of Ethiopia, enset plant has a major role to play in food security and the ability to drought-resistant and multi-purpose in modern lifestyles. The use of traditional fermented kocho has been increased in south Ethiopia as consumers want a healthy dietary food. The microbiology of kocho fermentation ensures the desirable quality of kocho. Existing papers have been organized and reviewed on the dynamics of growth, survival, and biochemical activity of microbes and their importance in the traditional fermentation process of kocho. The quality of kocho depends on the enset processing, fermentation methods, and periods. Few studies have been published on the importance of microorganisms during the kocho fermentation process. In kocho fermentation, LAB is used for the production of metabolites such as acetic acid, propanoic acid, butanoic acid, pentanoic acid, and hexanoic acid. Some of the species of Lactic acid bacteria in kocho fermentation that impart desired protein content, flavor, odor, and degrading anti-nutritional factor. However, there are limitations of studies on the role of microorganisms in kocho fermentation process. In conclusion, some of species of the microorganisms during kocho fermentation are identified and isolated for the purpose of quality, and safety of kocho, but other suitable microorganisms are not well studied in previous researchers. Thus, further investigations may also be needed on the microbiology of kocho fermentation in order to enhance the nutritional quality, sensory properties, and safety of the kocho product.

Intelligent transportation systems (ITS) are a newer trend in technology that improve the safety and performance in transportation systems. The exchange of information is considered as one of the key elements for ITS since it involves communication between vehicles and other components of ITS on road. On other hand, the data collections via Internet of Things (IoT) sensors play a major role for data collection and transmission between the vehicles and road segments in ITS. The data collection provides the current traffic and weather conditions that is considered necessary for driving in traffic. However, energy savings is one of the predominant objectives of electric vehicle (EV) while it is connected with ITS. In this research, we propose an ontology‐based architecture for EVs using the data collected from IoT sensor network, which is intended to improve the overall driving experience. The system uses IoT sensor data to execute a range of activities in order to ensure the driver safety and comfort while on the road. The simulation is conducted on an Eclipse SUMO simulator, and the performance is reported. The results of simulation shows that the proposed intelligent model on making decisions along with weather and traffic conditions is reported efficient than existing ITS models.

The number of users of current mobile and cellular networks is constantly increasing. The allocation of spectrum to its users is constantly facing various hurdles. This causes users to leave that network and connect to another network. Thus, those telecom companies are constantly struggling to provide high-speed services to the users. As a result, the demand for 5G networks is currently increasing. Considering these, an algorithm has been proposed here to suit the needs of the users. Its main feature is that it easily identifies the primary and secondary users of the 5G network and creates a spectrum allocation system for them accordingly. Generally, all other methods are designed with the primary user in mind. Furthermore, the spectrum hole calculation that is currently being proposed is done accurately so that the spectrum switching processes required for the secondary user can take place here very quickly so that the secondary user can use the spectrum without any hindrance. The proposed model achieved 93.29% of spectrum blocking, 6.71% of spectrum band dropping, 94.03% bandwidth utilization, 1073 ms end-to-end delay, and 17273 bps of throughput. The proposed model effectively handles the spectrum and intelligent approach to resolve the spectrum hole problems. The existing models are practically faced with these problems. The proposed model spectrum utilization and efficiency were increased compared with the existing models.