14-R. Khalil.indd

A b s t r a c t This is a pioneer study in Egypt that provides some assessment of the microbiological quality of conventional and organic leafy green vegetables that constitute an essential component of the Egyptians' daily diet. A total of 380 samples of unpackaged whole conventional and 84 packaged whole organic leafy greens were collected from retail markets in Alexandria, and analyzed for total aerobic mesophilic count (AMC) and total E. coli count (ECC) using the standard spread plate method. Mean AMC values for organic samples were statistically less (p<0.05) than those of the corresponding conventional samples. Conventional radish and organic parsley samples had the highest AMC of 7.17 and 7.68 log CFU/g respectively, while conventional green cabbage and organic basil had the lowest AMC of 3.63 and 3.23 log CFU/g respectively. The presence of E. coli in 100% of the studied leafy greens was indicative of potential fecal contamination, in view of open and unhygienic environmental and unsanitary handling conditions, as leafy green items are available for sale by street-vendors. Unsatisfactory AMC and ECC levels encountered in the studied samples, warrant future investigations to determine the potential prevalence of foodborne pathogens, and to identify sources of dominating microorganisms, which could make a contribution to the field of food safety. K e y w o r d s: Aerobic counts, E. coli, leafy green vegetables, microbiological quality, organic produc

Novel pectin-based nanocomposite film for active food packaging applications

Abstract Novel pectin-based films reinforced with crystalline nanocellulose (CNC) and activated with zinc oxide nanoparticles (ZnO NPs) were prepared by solvent-casting method. Film ingredients enhanced UV-blocking, thermal, and antibacterial properties of active films against well-known foodborne pathogens. Optimal active films exhibited higher mechanical, water vapor barrier properties compared to pristine pectin films. SEM confirmed the even distribution of CNC and ZnO NPs in pectin matrix and their interactions were proven using FTIR. Wrapping hard cheese samples artificially contaminated with Staphylococcus aureus and Salmonella enterica with the ternary nanocomposite film at 7 °C for 5 days significantly reduced the total population counts by at least 1.02 log CFU/g. Zn2+ migrating to wrapped cheese samples was below the specific limit (5 mg/kg), confirming their safety for food contact. Overall, ZnO/CNC/pectin nanocomposite films represent promising candidates for active food packaging as safe, eco-friendly alternatives for synthetic packaging materials

IoT and ICT based Smart Water Management, Monitoring and Controlling System: A Review

Water is a basic human need in all economic operations. Farmland, renewable energy, the industrial industry, and mining are all critical economic areas. Water supplies are under severe strain. With the population increase, the requirement for water from competing economic sectors is increased. So, there is not enough water left to meet human needs and maintain environmental flows that maintain the integrity of our ecosystems. Underground water is becoming depleted in many sectors, making now and future generations near the point of being deprived of protection from the increasing climate variability. Therefore, the critical role that information technology methods and internet communication technologies (ICT) play in water resources managing to limit the excessive waste of fresh water and to control and monitor water pollution. In this paper, we have to review research that uses the internet of things (IoT) as a communication technology that controls the preservation of the available amount of water and not wastes it by homeowners and farmers. In contrast, they use water, and we have also reviewed some researches that preserve water quality and reduce its pollution

A Comprehensive Study of Caching Effects on Fog Computing Performance

The rapid advancement in the Internet of things applications generates a considerable amount of data and requires additional computing power. These are serious challenges that directly impact the performance, latency, and network breakdown of cloud computing. Fog Computing can be depended on as an excellent solution to overcome some related problems in cloud computing. Fog computing supports cloud computing to become nearer to the Internet of Things. The fog's main task is to access the data generated by the IoT devices near the edge. The data storage and data processing are performed locally at the fog nodes instead of achieving that at cloud servers. Fog computing presents high-quality services and fast response time. Therefore, Fog computing can be the best solution for the Internet of things to present a practical and secure service for various clients. Fog computing enables sufficient management for the services and resources by keeping the devices closer to the network edge. In this paper, we review various computing paradigms, features of fog computing, an in-depth reference architecture of fog with its various levels, a detailed analysis of fog with different applications, various fog system algorithms, and also systematically examines the challenges in Fog Computing which act as a middle layer between IoT sensors or devices and data centers of the cloud

A Comprehensive Study of Malware Detection in Android Operating Systems

Android is now the world's (or one of the world’s) most popular operating system. More and more malware assaults are taking place in Android applications. Many security detection techniques based on Android Apps are now available. The open environmental feature of the Android environment has given Android an extensive appeal in recent years. The growing number of mobile devices are incorporated in many aspects of our everyday lives. This  paper gives a detailed comparison that summarizes and analyses various detection techniques. This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices. Android has a huge number of apps that may be downloaded and used for free. Consequently, Android phones are more susceptible to malware. As a result, additional research has been done in order to develop effective malware detection methods. To begin, several of the currently available Android malware detection approaches are carefully examined and classified based on their detection methodologies. This study examines a wide range of machine-learning-based methods to detecting Android malware covering both types dynamic and static