Environmental Risk Assessment Of Community Acquired Legionellosis From Recycled Water Use In Los Angeles County California

Abstract Legionellosis is caused by Legionella pneumophila, resulting in 8,000-18,000 yearly hospitalizations and 4,000 deaths in the United States (Bouwknegt, 2012) (Toshiaki, 2004) (World Health Organization, 2012). L. pneumophila may persist in the built environment due to unique intracellular characteristics. Although the mode of transmission of L. pneumophila is inhalation of aerosolized contaminated water, this bacterium is not transmitted human to human (World Health Organization, 2012). L. pneumophila infection may be facilitated by the increased use of recycled water in Los Angeles County. In a water stressed area such as Los Angeles County, a limited potable water supply may not sustain the growing metropolitan population. Recycled and reclaimed water may help lessen the demand by supplying non-drinking water to industries such as agriculture, water table restoration, and recreation use. To be more cost effective, recycled water has been mandated to receive less treatment than the treatment used for county potable water. Recycled water treatment has less stringent backflow and purification procedures compared to county potable water treatment. The Environmental Health division prioritizes monitoring and treatment of potable water. Monitoring and treating recycled water will be limited. Mapping the cases of non-hospital acquired Legionellosis cases exposed to recycled water pipeline compared to potable water pipeline will show the difference in risk associated with Legionellosis. There was a statistically significant difference in the number of cases within the recycled water pipe-lined area including the infectious zone, compared to the unexposed recycled water pipeline area. The Chi-squared P-value was 0.0013 comparing the exposed Legionellosis cases to unexposed. Individuals exposed to L. pneumophila from recycled water pipeline had a 1.4806 (1.3088, 1.6735) times greater risk of developing Legionellosis compared to individuals who were not exposed to L. pneumophila from recycled water pipeline. The current standard of recycled water cleanliness may not promote the public health; a more stringent standard involving more concentrated disinfection and filtration steps is needed. Public Water Supply Rule 16-D and Section 2.12 of the Rules Governing Water and Electric Services in the City of Los Angeles must include inhaled exposure of L. pneumophila. The first steps to reduce Legionellosis may include standardize of Filtration, Ozonation, Disinfection, and/or Fluorination for the Los Angeles-Glendale Water Plant, the Donald C. Tillman Water Reclamation, the Hyperion Waste-Water, and the Terminal Island Water Reclamation Plant to the degree that is used for potable water treatment

El centro artesanal de iberorromano de la Maralaga (Sinarcas, Valencia)

En este artículo se ofrece una visión general sobre un horno alfarero iberorromano, perteneciente al territorio de Kelin. El estudio se centra en la caracterización de su producción cerámica. en la precisión de su cronología y en la distribución de sus productos en el territorio

A RBES for Generating Automatically Personalized Menus

Food bought at supermarkets in, for instance, North America or the European Union, give comprehensive information about ingredients and allergens. Meanwhile, the menus of restaurants are usually incomplete and cannot be normally completed by the waiter. This is specially important when traveling to countries with a di erent culture. A curious example is "calamares en su tinta" (squid in its own ink), a common dish in Spain. Its brief description would be "squid with boiled rice in its own (black) ink", but an ingredient of its sauce is flour, a fact very important for celiacs. There are constraints based on religious believes, due to food allergies or to illnesses, while others just derive from personal preferences. Another complicated situation arise in hospitals, where the doctors' nutritional recommendations have to be added to the patient's usual constraints. We have therefore designed and developed a Rule Based Expert System (RBES) that can address these problems. The rules derive directly from the recipes of the di fferent dishes and contain the information about the required ingredients and ways of cooking. In fact, we distinguish: ingredients and ways of cooking, intermediate products (like sauces, that aren't always made explicit) and final products (the dishes listed in the menu of the restaurant). For a certain restaurant, customer and instant, the input to the RBES are: actualized stock of ingredients and personal characteristics of that customer. The RBES then prepares a "personalized menu" using set operations and knowledge extraction (thanks to an algebraic inference engine [1]). The RBES has been implemented in the computer algebra system MapleTM2015. A rst version of this work was presented at "Applications of Computer Algebra 2015" (ACA'2015) conference. The corresponding abstract is available at [2].Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A fast functional approach to personalized menus generation using set operations

The authors developed some time ago a RBES devoted to preparing personalized menus at restaurants according to the allergies, religious constraints, likes and other diet requirements as well as products availability. A first version was presented at the "Applications of Computer Algebra 2015" (ACA'2015) conference and an improved version to the "5th European Seminar on Computing" (ESCO2016). Preparing personalized menus can be specially important when traveling abroad and facing unknown dishes in a menu. Some restaurants include icons in their menu regarding their adequateness for celiacs or vegetarians and vegans, but this is not always a complete information, as it doesn't consider, for instance, personal dislikes or uncommon allergies. The tool previously developed can obtain, using logic deduction, a personalized menu for each customer, according to the precise recipes of the restaurant and taking into account the data given by the customer and the ingredients out of stock (if any). Now a new approach has been followed, using functions and set operations and the speed has been increased by three orders of magnitude, allowing to deal with huge menus instantly. Both approaches have been implemented on the computer algebra system Maple and are exemplified using the same recipes in order to compare their performances.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Computer Algebra-based RBES personalized menu generator

People have many constraints concerning the food they eat. These constraints can be based on religious believes, be due to food allergies or to illnesses, or can be derived just from personal preferences. Therefore, preparing menus at hospitals and restaurants can be really complex. Another special situation arise when travel- ing abroad. It is not always enough to know the brief description in the restaurant menu or the explanation of the waiter. For example, “calamares en su tinta” (squid in its own ink) is a delicious typical Spanish dish, not well-known abroad. Its brief description would be “squid with boiled rice in its own (black) ink”. But an in- gredient (included in a small amount, in order to thicken the sauce) is flour, a fact very important for someone suffering from celiac disease. Therefore, we have con- sidered that it would be very interesting to develop a Rule Based Expert System (RBES) to address these problems. The rules derive directly from the recipes and contain the information about required ingredients and names of the dishes. We distinguish: ingredients and ways of cooking, intermediate products (like “mayon- naise”, that doesn’t always appear explicitly in the restaurants’ menus) and final products (like “seafood cocktail”, that are the dishes listed in the restaurant menu). For each customer at a certain moment, the input to the system are: on one hand, the stock of ingredients at that moment, and on the other, the religion, allergies and restrictions due to illnesses or personal preferences of the customer. The RBES then constructs a “personalized restaurant menu” using set operations and knowl- edge extraction (thanks to an algebraic Groebner bases-based inference engine[1]). The RBES has been implemented in the computer algebra system Maple TM 18(us-ing its convenient Embedded Components) and can be run from computers and tablets using Maple TM or the Maple TM PlayerUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech