Active packaging technology: cassava starch/orange essential oil for antimicrobial food packaging

Introduction; Bridging the digital divide requires the provision of affordable, fair and quality ICT. With nearly two-thirds of the world’s population still offline, there is a need to provide affordable web access for everyone. For developing countries, increasing the popularity of information and communication technology has become the most important factor in reducing poverty. The danger of electrical and electronic waste disposal contains hazardous substances, but most of the electrical and electronic equipment is still disposed of in an unhealthy environment in the field development area, affecting the level of contamination in Water, Air and Soil ultimately affecting people’s health. Eliminating E-waste responsibility and protecting the environment is a challenge for countries. Smart cities can solve environmental problems through proper waste management for improving human health, protecting water resources, and reducing pollution. Objective; In this paper, we explore how blockchain technology can help smart cities to manage E-waste by providing consistency, immutability, transparency, and accountability control in a distributed, reliable, and secure manner. We discussed the advantages of blockchain technology in various aspects of E-waste management, such as instant tracking and monitoring, E-waste disposal and E-waste management regulation compliance, proper disposal management, E-waste management, and material handling, etc. All examples of disposal services, but in our study we have found that there is no fool proof system to check the disposal of E-waste whether it has been disposed off Fully or Partially. We mainly focused on the tracking of E-waste management system for 100% safe and eco-friendly disposal from the originating point of E-waste to end disposal point of total disposal. Methods; For this, we have used machine-learning model to find the existing percentage of disposal of E-waste at the end-point which reveals that it is never 100%. And partial disposal of E-waste means we have still partial E-waste around us in different forms, which will be a threat for the society to be indulged in hazardous after effects of randomly dumping E-waste.Results; After this we have modelled a Disposal Tracking System(DTS) using blockchain technology to create an E-waste data storage as Decentralized Shareable Ledger (DSL) which records the quantity and state of E-waste data from its originating point to a different level of disposal unit and finally reflect the balance of E-waste data as NIL at the end of last disposal point.Conclusion; This system will helpful for safe and ecofriendly E-waste management and it provides complete transparency and traceability of E-waste during the life cycle of complete disposal. After implementation of this system any district or Block or Village authority can ensure to its citizens for E-waste hazards free environment and safety of natural resources

E-waste Management Using Blockchain Technology

Kumar,

Al-Fadi Al-Sharif

2024

Optimizing a Novel Tracking System for Living Beings and Objects through Advanced Mathematical Modeling and Graph Theory

Anass,

Imane,

Mabrouki

et al. 2024

This study extends the formulation of a tracking system for both live items and living persons, and gives a thorough theoretical framework for an advanced tracking system. A large number of tracking systems in use today were created inside certain frameworks and designed to monitor in either infinite or restricted spatial contexts. The latter typically makes use of specialized technological instruments designed with tracking objects or living things in mind. Our contribution to this topic is the formulation of a system theory that both formulates and innovates the challenge of monitoring objects and living things. Graphical modeling is widely used in tracking, which is interesting because it makes it easier to create precise relationships between the objects that need to be tracked and other parts of the system. But our study argues that the best way to achieve a high-performing, contextually relevant, and flexible system in a range of scenarios is still to build a tracking system around graphs, both theoretically and practically. We provide a unique tracking method to further the discipline, based on the ideas of hypergraphs and graph learning. This method carefully examines the order between various linkages inside the system, allowing the system to fully use both direct and indirect relations. The way we formulate tracking is as a complex search problem on graphs and hypergraphs. In this case, the system's components—living things or objects—are represented by vertices, and the kinds of relationships that exist between them are indicated by edges. We present a governing law that facilitates different processing tasks, manages shared data across system parts, and defines the connections between vertices. Additionally, we provide illustrated examples covering single and multi-context tracking scenarios to support our work. These illustrations highlight how, in comparison to current tracking technologies, the suggested approach performs better theoretically. In addition to adding to the theoretical conversation, this discovery has potential applicability in a variety of tracking contexts

Optimizing Query Using the FOAF Relation and Graph Neural Networks to Enhance Information Gathering and Retrieval

Mahdi Abdulkadium,

Hadi

2025

A lot of students suffer expressing their desired enquiry about to a search engine (SE), and this, in turn, can lead to ambiguit and insufficient results. A poor expression requires expanding a previous user query and refining it by adding more vocabularies that make a query more understandable through the searching process. This research aims at adding vocabulary to an enquiry by embedding features related to each keyword, and representing a feature of each query keyword as graphs and node visualization based on graph convolution network (GCN). This is achieved following two approaches. The first is by mapping between vertices, adding a negative link, and training a graph after embedding. This can help check whether new information reach-es for retrieving data from the predicted link. Another approach is based on adding link and node embedding that can create the shortest path to reaching a specific (target) node, . Particularly, poor data retrieval can lead to a new concept named graph expansion network (GEN). Query expansion (QE) techniques can obtain all documents related to expanding and refining query. On the other hand, such documents are represented as knowledge graphs for mapping and checking the similarity between the connection of a graph based on two authors who have similar interst in a particular field, or who collaborate in a research publications. This can create paths or edges between them as link embedding, thereby increasing the accuracy of document or pa-per retrieval based on user typing