Impacts of Milk Fraud on Food Safety and Nutrition with Special Emphasis on Developing Countries

Milk in its natural form has a high food value, since it is comprised of a wide variety of nutrients which are essential for proper growth and maintenance of the human body. In recent decades, there has been an upsurge in milk consumption worldwide, especially in developing countries, and it is now forming a significant part of the diet for a high proportion of the global population. As a result of the increased demand, in addition to the growth in competition in the dairy market and the increasing complexity of the supply chain, some unscrupulous producers are indulging in milk fraud. This malpractice has become a common problem in the developing countries, which lack strict vigilance by food safety authorities. Milk is often subjected to fraud (by means of adulteration) for financial gain, but it can also be adulterated due to ill-informed attempts to improve hygiene conditions. Water is the most common adulterant used, which decreases the nutritional value of milk. If the water is contaminated, for example, with chemicals or pathogens, this poses a serious health risk for consumers. To the diluted milk, inferior cheaper materials may be added such as reconstituted milk powder, urea, and cane sugar, even more hazardous chemicals including melamine, formalin, caustic soda, and detergents. These additions have the potential to cause serious health-related problems. This review aims to investigate the impacts of milk fraud on nutrition and food safety, and it points out the potential adverse human health effects associated with the consumption of adulterated milk.

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Handbook of International Special Education:Northern Ireland

The intent of the Handbook of International Special Education is to provide a concise overview of special education services in countries across the world using the Article on Education in the United Nations Convention on the Rights of Persons with Disabilities as the analytical frame. The Handbook will provide concise, data-driven contributions from across the globe using two primary frames: the relationship between special and general education in the country and the country’s system as aligned with the Article on Education in the UN Convention on the Rights of Person with Disabilities.