Keeping California cool: Recent cool community developments

In 2006, California introduced the Global Warming Solutions Act (Assembly Bill 32), which requires the state to reduce greenhouse gas emissions to 1990 levels by 2020. "Cool community" strategies, including cool roofs, cool pavements, cool walls and urban vegetation, have been identified as voluntary measures with potential to reduce statewide emissions. In addition, cool community strategies provide co-benefits for residents of California, such as reduced utility bills, improved air quality and enhanced urban livability. To achieve these savings, Lawrence Berkeley National Laboratory (LBNL) has worked with state and local officials, non-profit organizations, school districts, utilities, and manufacturers for 4 years to advance the science and implementation of cool community strategies. This paper summarizes the accomplishments of this program, as well as recent developments in cool community policy in California and other national and international efforts. We also outline lessons learned from these efforts to characterize successful programs and policies to be replicated in the future

Weighted MCRDR: Deriving Information about Relationships between Classifications in MCRDR.

Multiple Classification Ripple Down Rules (MCRDR) is a knowledge acquisition technique that produces representations, or knowledge maps, of a human expert's knowledge of a particular domain. However, work on gaining an understanding of the knowledge acquired at a deeper meta-level or using the knowledge to derive new information is still in its infancy. This paper will introduce a technique called Weighted MCRDR (WM), which looks at deriving and learning information about the relationships between multiple classifications within MCRDR by calculating a meaningful rating for the task at hand. This is not intended to reduce the knowledge acquisition effort for the expert. Rather, it is attempting to use the knowledge received in the MCRDR knowledge map to derive additional information that can allow improvements in functionality of MCRDR in many problem domains. Preliminary testing shows that there exists a strong potential for WM to quickly and effectively learn meaningful weightings

Portraits of people with dementia : three case studies of creating portraits

Peer reviewedPreprin

Opportunistic linked data querying through approximate membership metadata

Between URI dereferencing and the SPARQL protocol lies a largely unexplored axis of possible interfaces to Linked Data, each with its own combination of trade-offs. One of these interfaces is Triple Pattern Fragments, which allows clients to execute SPARQL queries against low-cost servers, at the cost of higher bandwidth. Increasing a client's efficiency means lowering the number of requests, which can among others be achieved through additional metadata in responses. We noted that typical SPARQL query evaluations against Triple Pattern Fragments require a significant portion of membership subqueries, which check the presence of a specific triple, rather than a variable pattern. This paper studies the impact of providing approximate membership functions, i.e., Bloom filters and Golomb-coded sets, as extra metadata. In addition to reducing HTTP requests, such functions allow to achieve full result recall earlier when temporarily allowing lower precision. Half of the tested queries from a WatDiv benchmark test set could be executed with up to a third fewer HTTP requests with only marginally higher server cost. Query times, however, did not improve, likely due to slower metadata generation and transfer. This indicates that approximate membership functions can partly improve the client-side query process with minimal impact on the server and its interface

Asymptomatic urinary tract infection among pregnant women receiving ante-natal care in a traditional birth home in Benin city, Nigeria

Background: A good proportion of pregnant women patronize traditional birth homes in Nigeria for ante-natal care. This study aimed at determining the prevalence, risk factors, and susceptibility profile of etiologic agents of urinary tract infection among ante-natal attendees in a traditional birth home in Benin City, Nigeria.Methods: Clean-catch urine was collected from 220 pregnant women attending a traditional birth home in Benin City, Nigeria. Urine samples were processed, and microbial isolates identified using standard bacteriological procedures. A cross-sectional study design was used.Results: The prevalence of urinary tract infection among pregnant women was 55.0%, significantly affected by parity and gestational age (P<0.05). Mixed infection was recorded among 13(10.7%) pregnant women, and was unaffected by maternal age, parity, gravidity, gestational age, and educational status. Irrespective of trimester Escherichia coli was the most prevalent etiologic agent of urinary tract infection, followed by Staphylococcus aureus. The flouroquinolones were the most effective antibacterial agents, while Sulphamethoxazole-trimetoprim, Amoxicillin, Nalidixic acid, and Nitrofurantoin had poor activity against uropathogens isolated.Conclusion: The prevalence of urinary tract infection among pregnant women was 55.0% and significantly affected by gestational age and parity. The most prevalent etiologic agent observed was Escherichia coli. With the exception of the flouroquinolones, aminoglycoside, and Amoxicillin-cluvanate, the activity of other antibiotics used on uropathogens were poor. Health education of the traditional birth attendant and her clients by relevant intervention agencies is strongly advocated.Keywords: Urinary tract infection, pregnancy, orthodox birth center, traditional birth center, Nigeri

A new model for classifying DNA code inspired by neural networks and FSA

This paper introduces a new model of classifiers CL(V,E,l,r) designed for classifying DNA sequences and combining the flexibility of neural networks and the generality of finite state automata. Our careful and thorough verification demonstrates that the classifiers CL(V,E,l,r) are general enough and will be capable of solving all classification tasks for any given DNA dataset. We develop a minimisation algorithm for these classifiers and include several open questions which could benefit from contributions of various researchers throughout the world