Artificial-Intelligence-Based Optimization of the Management of Snow Removal Assets and Resources Final Report, October 2002

Geographic information systems (GIS) and artificial intelligence (AI) techniques were used to develop an intelligent snow removal asset management system (SRAMS). The system has been evaluated through a case study examining snow removal from the roads in Black Hawk County, Iowa, for which the Iowa Department of Transportation (Iowa DOT) is responsible. The SRAMS is comprised of an expert system that contains the logical rules and expertise of the Iowa DOT’s snow removal experts in Black Hawk County, and a geographic information system to access and manage road data. The system is implemented on a mid-range PC by integrating MapObjects 2.1 (a GIS package), Visual Rule Studio 2.2 (an AI shell), and Visual Basic 6.0 (a programming tool). The system could efficiently be used to generate prioritized snowplowing routes in visual format, to optimize the allocation of assets for plowing, and to track materials (e.g., salt and sand). A test of the system reveals an improvement in snowplowing time by 1.9 percent for moderate snowfall and 9.7 percent for snowstorm conditions over the current manual system.

The ability model of emotional intelligence: Searching for valid measures

Current measures of ability emotional intelligence (EI)--including the well-known Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT)--suffer from several limitations, including low discriminant validity and questionable construct and incremental validity. We show that the MSCEIT is largely predicted by personality dimensions, general intelligence, and demographics having multiple R's with the MSCEIT branches up to .66; for the general EI factor this relation was even stronger (Multiple R = .76). As concerns the factor structure of the MSCEIT, we found support for four first-order factors, which had differential relations with personality, but no support for a higher-order global EI factor. We discuss implications for employing the MSCEIT, including (a) using the single branches scores rather than the total score, (b) always controlling for personality and general intelligence to ensure unbiased parameter estimates in the EI factors, and (c) correcting for measurement error. Failure to account for these methodological aspects may severely compromise predictive validity testing. We also discuss avenues for the improvement of ability-based tests.

Intelligence-led crime scene processing. Part II : Intelligence and crime scene examination.

A better integration of the information conveyed by traces within intelligence-led framework would allow forensic science to participate more intensively to security assessments through forensic intelligence (part I). In this view, the collection of data by examining crime scenes is an entire part of intelligence processes. This conception frames our proposal for a model that promotes to better use knowledge available in the organisation for driving and supporting crime scene examination. The suggested model also clarifies the uncomfortable situation of crime scene examiners who must simultaneously comply with justice needs and expectations, and serve organisations that are mostly driven by broader security objectives. It also opens new perspective for forensic science and crime scene investigation, by the proposal to follow other directions than the traditional path suggested by dominant movements in these fields.

Representational pseudoneglect and reference points both influence geographic location estimates

Our mental representation of the world is far from objective. For example, western Canadians estimate the locations of North American cities to be too far to the west. This bias could be due to a reference point effect, in which people estimate more space between places close to them than far from them, or to representational pseudoneglect, in which neurologically intact individuals favor the left side of space when asked to image a scene.We tested whether either or both of these biases influence the geographic world representations of neurologically intact young adults from Edmonton and Ottawa, which are in western and eastern Canada, respectively. Individuals were asked to locate NorthAmerican cities on a two-dimensional grid. Both groups revealed effects of representational pseudoneglect in this novel paradigm, but they also each exhibited reference point effects. These results inform theories in both cognitive psychology and neuroscience.

An illustration of the ALLOC 6B location-allocation analysis system, 1984

The programs included in this Discussion Paper no. 17 are Distance, Unravel, Retrench and Alloc 6B that deal with location-allocation analyses first published in 1973 by the Department of Geography, The University of Iowa.

Location referencing system team report : final report, 1998

This report is a culmination of the Location Referencing System (LRS) team. The team was charged with defining a system that coordinates the collection storage, and access to location referencing information by developing an LRS to be used throughout the Iowa DOT.

Intelligence-led crime scene processing. Part I : Forensic intelligence.

Forensic science is generally defined as the application of science to address questions related to the law. Too often, this view restricts the contribution of science to one single process which eventually aims at bringing individuals to court while minimising risk of miscarriage of justice. In order to go beyond this paradigm, we propose to refocus the attention towards traces themselves, as remnants of a criminal activity, and their information content. We postulate that traces contribute effectively to a wide variety of other informational processes that support decision making inmany situations. In particular, they inform actors of new policing strategies who place the treatment of information and intelligence at the centre of their systems. This contribution of forensic science to these security oriented models is still not well identified and captured. In order to create the best condition for the development of forensic intelligence, we suggest a framework that connects forensic science to intelligence-led policing (part I). Crime scene attendance and processing can be envisaged within this view. This approach gives indications abouthowto structure knowledge used by crime scene examiners in their effective practice (part II).

Relationship between estrogen receptor alpha location and gene induction reveals the importance of downstream sites and cofactors.

BACKGROUND: To understand cancer-related modifications to transcriptional programs requires detailed knowledge about the activation of signal-transduction pathways and gene expression programs. To investigate the mechanisms of target gene regulation by human estrogen receptor alpha (hERalpha), we combine extensive location and expression datasets with genomic sequence analysis. In particular, we study the influence of patterns of DNA occupancy by hERalpha on expression phenotypes. RESULTS: We find that strong ChIP-chip sites co-localize with strong hERalpha consensus sites and detect nucleotide bias near hERalpha sites. The localization of ChIP-chip sites relative to annotated genes shows that weak sites are enriched near transcription start sites, while stronger sites show no positional bias. Assessing the relationship between binding configurations and expression phenotypes, we find binding sites downstream of the transcription start site (TSS) to be equally good or better predictors of hERalpha-mediated expression as upstream sites. The study of FOX and SP1 cofactor sites near hERalpha ChIP sites shows that induced genes frequently have FOX or SP1 sites. Finally we integrate these multiple datasets to define a high confidence set of primary hERalpha target genes. CONCLUSION: Our results support the model of long-range interactions of hERalpha with the promoter-bound cofactor SP1 residing at the promoter of hERalpha target genes. FOX motifs co-occur with hERalpha motifs along responsive genes. Importantly we show that the spatial arrangement of sites near the start sites and within the full transcript is important in determining response to estrogen signaling.

De la Analítica al Social Media Intelligence : Una nueva oportunidad de liderar el mercado mediante una importante ventaja competitiva : saber identificar las necesidades del cliente en el Social Media : La reinvención de las investigaciones de mercado

La revolución que significó Internet a finales del siglo XX supone una oportunidad inmensa para el mundo corporativo. Con la aparición del Social Media y, en concreto, de las redes sociales, se han abierto un sinfín de nuevas oportunidades.No existe ningún otro invento que haya penetrado con tanta facilidad como lo han hecho estas plataformas; es el momento de que las empresas saquen provecho de ello.El objetivo principal de este proyecto consiste en identificar los beneficios potenciales para las empresas que conlleva actuar a través del Social Media. No contentos con eso, nos disponemos a mostrar las grandes razones por las que actuar: la posibilidad deidentificar las necesidades de tu público objetivo, huecos en el mercado y, por consiguiente, conseguir una ventaja competitiva clave en tu sector. ¿Cómo? La clave está en saber gestionar grandes volúmenes de información aplicando soluciones de inteligencia competitiva.Teniendo en cuenta que el proyecto gira entorno al Social Media, la estructura del mismo está dividida en dos grandes partes: en primer lugar estudiamos las claves de la investigación de mercados actual y, seguidamente, utilizamos una perspectiva más corporativa para mostrar las principales inquietudes que surgen en las empresas.Mediante ejemplos de casos prácticos muy relevantes, estudios que aporten datos clave y, sobre todo, las opiniones de profesionales del sector dentro y fuera de España, seremos capaces de deducir la importancia que tiene este terreno, así como el largo camino que nos queda a todos por delante.