Water sorption and mechanical properties of starch/chitosan nanoparticle films

The usage of biopolymer as food packaging material has been limited due to high water sorption and poor mechanical properties of the biopolymer. Thus, this study is aimed at improving the water sorption and mechanical properties of biopolymer particularly starch films by incorporation of a natural filler particularly chitosan nanoparticle (CNP) and investigating the properties of starch/CNP films at different storage conditions (relative humidity: 23, 50, and 75%; temperature: 4, 30, and 40°C). The water sorption behavior and isotherms of the films were investigated by fitting the water sorption data to the Peleg model and Guggenheim, Anderson, and de Boer model. Both the models were well fitted to the experimental data, thus proving the reliability of water sorption behavior prediction. It was found that different storage conditions of the films significantly affected the mechanical properties of the films due to the sensitivity of the films towards moisture. Water sorption and mechanical properties of the films were best improved at relative humidity of 23% and temperature of 30°C. The water sorption and mechanical properties of the films are worth to be investigated because the properties affected the stability, shelf life, and application of the films in the food packaging field

Business Process Modeling in Production Logistics : Complementary Use of BPMN and UML

This paper investigates the suitability and sufficincy of BPMN language in business processmodelling. Production logistics system is modeled at the highest domain level using case study in carcomponent industry to examine the language in every aspect. Modelling is carried out in a modular basis forthree main sub-systems generally using BPMN 2.0. Complementary use of other modeling tool to cover all thecritical flows of objects and associate data in modeling was seen necessary and addressed using UML activitydiagrams employing the specific capability of modeling the material flow and semantics. Modeling the flow ofobjects provides the comprehensive perspective of the whole system and fulfill the need of capturing allundepicted object flows as well as processes and data which may not be sheerly covered using the BPMN. Theproposed models provides great beneficial referentiality for modelers, analyists and actual practitioners oflogistics activities. The significant explanatory description and strategy are the key feature of the paper tofollow using state-of-the- art modeling tool of BPMN 2.0 and UML.QC 20150714</p

Effect of ball milling and ultrasonication on particle size of chitosan nanoparticle for potential nanofiller in food packaging application

Incorporation of chitosan nanoparticle (CNP) into food packaging film is very promising due to the many advantages such as improving mechanical and barrier properties of the film. Recently, ionic gelation of chitosan with addition of sodium tripolyphosphate (TPP) has been a popular method to synthesize chitosan nanoparticle (CNP). This study aims to investigate the effect of ball milling and ultrasonication on CNP particle size produced using ionic gelation method. Ball milling time (0, 12, 18 h) and ultrasonication time (15, 30 min) were varied. Combination of ball milling and ultrasonication were found to produce smaller and more uniform size of CNP compared to ball milling or ultrasonication alone. It was found that particle sizes of CNP that underwent ultrasonication decreased with the increase in ball milling time. This may be due to the shear stress during ball milling which led to fragmentation of CNP into smaller and uniform particle size. Meanwhile, particle sizes of CNP underwent ultrasonication without ball milling decreased with the increase in ultrasonication time due to the longer exposure time to cavitation effect produced by ultrasonic probe. The optimum ball milling and ultrasonication time were found to be 18 h and 15 min, respectively

A systematic literature review on outlier detection in wireless sensor networks

Obesity is considered a principal public health concern and ranked as the fifth foremost reason for death globally. Overweight and obesity are one of the main lifestyle illnesses that leads to further health concerns and contributes to numerous chronic diseases, including cancers, diabetes, metabolic syndrome, and cardiovascular diseases. The World Health Organization also predicted that 30% of death in the world will be initiated with lifestyle diseases in 2030 and can be stopped through the suitable identification and addressing of associated risk factors and behavioral involvement policies. Thus, detecting and diagnosing obesity as early as possible is crucial. Therefore, the machine learning approach is a promising solution to early predictions of obesity and the risk of overweight because it can offer quick, immediate, and accurate identification of risk factors and condition likelihoods. The present study conducted a systematic literature review to examine obesity research and machine learning techniques for the prevention and treatment of obesity from 2010 to 2020. Accordingly, 93 papers are identified from the review articles as primary studies from an initial pool of over 700 papers addressing obesity. Consequently, this study initially recognized the significant potential factors that influence and cause adult obesity. Next, the main diseases and health consequences of obesity and overweight are investigated. Ultimately, this study recognized the machine learning methods that can be used for the prediction of obesity. Finally, this study seeks to support decision-makers looking to understand the impact of obesity on health in the general population and identify outcomes that can be used to guide health authorities and public health to further mitigate threats and effectively guide obese people globally