909 resultados para Business Intelligence,Data Warehouse,Sistemi Informativi


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Existing models of equilibrium unemployment with endogenous labor market participation are complex, generate procyclical unemployment rates and cannot match unemployment variability relative to GDP. We embed endogenous participation in a simple, tractable job market matching model, show analytically how variations in the participation rate are driven by the cross-sectional density of home productivity near the participation threshold, andhow this density translates into an extensive-margin labor supply elasticity. A calibration of the model to macro data not only matches employment and participation variabilities but also generates strongly countercyclical unemployment rates. With some wage rigidity the model also matches unemployment variations well. Furthermore, the labor supply elasticity implied by our calibration is consistent with microeconometric evidence for the US.

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Business cycles are both less volatile and more synchronized with the world cycle in rich countries than in poor ones. We develop two alternative explanations based on the idea that comparative advantage causes rich countries to specialize in industries that use new technologies operated by skilled workers, while poor countries specialize in industries that use traditional technologies operated by unskilled workers. Since new technologies are difficult to imitate, the industries of rich countries enjoy more market power and face more inelastic product demands than those of poor countries. Since skilled workers are less likely to exit employment as a result of changes in economic conditions, industries in rich countries face more inelastic labour supplies than those of poor countries. We show that either asymmetry in industry characteristics can generate cross-country differences in business cycles that resemble those we observe in the data.

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We study the effects that the Maastricht treaty, the creation of the ECB, andthe Euro changeover had on the dynamics of European business cycles using a panelVAR and data from ten European countries - seven from the Euro area and threeoutside of it. There are changes in the features of European business cycles and in thetransmission of shocks. They precede the three events of interest and are more linkedto a general process of European convergence and synchronization.

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When selecting sites for the industry’s leading data centers, experts and IT leaders such as Google and Microsoft, are increasingly choosing Iowa They’re doing so because they understand that Iowa is a safe zone for data and information storage — tucked away from the major threats and terrorist targets in the U.S. What’s more, there are many infrastructural and financial benefits that only Iowa offers. With all this activity, available research and Iowa’s inherent advantages, it seems the question isn't so much why Iowa, but why not?

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Iowa may be known for some of the world’s most fertile cropland, but in recent years, we’ve become fertile ground for rapid growth in information technology as well. From the recent “plug-in” of Google’s $600-million Council Bluffs data center to Microsoft’s planned half-billion-dollar West Des Moines expansion to IBM’s 1,300-job-creating expansion in Dubuque, technology leaders are finding Iowa a place to grow. And why not? Iowa has a supportive business climate and its infrastructure — physical and human — give our technology companies the competitive advantages that allow them to grow and prosper.

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Iowa may be known for some of the world’s most fertile cropland, but in recent years, we’ve become fertile ground for rapid growth in information technology as well. From the recent “plug-in” of Google’s $600-million Council Bluffs data center to Microsoft’s planned half-billion-dollar West Des Moines expansion to IBM’s 1,300-job-creating expansion in Dubuque, technology leaders are finding Iowa a place to grow. And why not? Iowa has a supportive business climate and its infrastructure — physical and human — give our technology companies the competitive advantages that allow them to grow and prosper.

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Iowa may be known for some of the world’s most fertile cropland, but in recent years, we’ve become fertile ground for rapid growth in information technology as well. From the recent “plug-in” of Google’s $600-million Council Bluffs data center to Microsoft’s planned half-billion-dollar West Des Moines expansion to IBM’s 1,300-job-creating expansion in Dubuque, technology leaders are finding Iowa a place to grow. And why not? Iowa has a supportive business climate and its infrastructure — physical and human — give our technology companies the competitive advantages that allow them to grow and prosper.

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Information technology is a strong and important sector of the Iowa economy employing 30,000 Iowans at more than 2,000 companies, according to a new analysis from Battelle Institute consultants. The strength of Iowa’s IT industry is based in the service-segment. Internet and data services, communications network services, and software and computer services constitute 81 percent of all IT employment. Slightly ahead of U.S. trends, these service sectors compose the backbone of Iowa’s IT industry,

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Medicine counterfeiting is a crime that has increased in recent years and now involves the whole world. Health and economic repercussions have led pharmaceutical industries and agencies to develop many measures to protect genuine medicines and differentiate them from counterfeits. Detecting counterfeit is chemically relatively simple for the specialists, but much more information can be gained from the analyses in a forensic intelligence perspective. Analytical data can feed criminal investigation and law enforcement by detecting and understanding the criminal phenomenon. Profiling seizures using chemical and packaging data constitutes a strong way to detect organised production and industrialised forms of criminality, and is the focus of this paper. Thirty-three seizures of a commonly counterfeited type of capsule have been studied. The results of the packaging and chemical analyses were gathered within an organised database. Strong linkage was found between the seizures at the different production steps, indicating the presence of a main counterfeit network dominating the market. The interpretation of the links with circumstantial data provided information about the production and the distribution of counterfeits coming from this network. This forensic intelligence perspective has the potential to be generalised to other types of products. This may be the only reliable approach to help the understanding of the organised crime phenomenon behind counterfeiting and to enable efficient strategic and operational decision making in an attempt to dismantle counterfeit network.

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Today's approach to anti-doping is mostly centered on the judicial process, despite pursuing a further goal in the detection, reduction, solving and/or prevention of doping. Similarly to decision-making in the area of law enforcement feeding on Forensic Intelligence, anti-doping might significantly benefit from a more extensive gathering of knowledge. Forensic Intelligence might bring a broader logical dimension to the interpretation of data on doping activities for a more future-oriented and comprehensive approach instead of the traditional case-based and reactive process. Information coming from a variety of sources related to doping, whether directly or potentially, would feed an organized memory to provide real time intelligence on the size, seriousness and evolution of the phenomenon. Due to the complexity of doping, integrating analytical chemical results and longitudinal monitoring of biomarkers with physiological, epidemiological, sociological or circumstantial information might provide a logical framework enabling fit for purpose decision-making. Therefore, Anti-Doping Intelligence might prove efficient at providing a more proactive response to any potential or emerging doping phenomenon or to address existing problems with innovative actions or/and policies. This approach might prove useful to detect, neutralize, disrupt and/or prevent organized doping or the trafficking of doping agents, as well as helping to refine the targeting of athletes or teams. In addition, such an intelligence-led methodology would serve to address doping offenses in the absence of adverse analytical chemical evidence.

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Whether for investigative or intelligence aims, crime analysts often face up the necessity to analyse the spatiotemporal distribution of crimes or traces left by suspects. This article presents a visualisation methodology supporting recurrent practical analytical tasks such as the detection of crime series or the analysis of traces left by digital devices like mobile phone or GPS devices. The proposed approach has led to the development of a dedicated tool that has proven its effectiveness in real inquiries and intelligence practices. It supports a more fluent visual analysis of the collected data and may provide critical clues to support police operations as exemplified by the presented case studies.

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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.

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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.

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In this paper, we develop a new decision making model and apply it in political Surveys of economic climate collect opinions of managers about the short-term future evolution of their business. Interviews are carried out on a regular basis and responses measure optimistic, neutral or pessimistic views about the economic perspectives. We propose a method to evaluate the sampling error of the average opinion derived from a particular type of survey data. Our variance estimate is useful to interpret historical trends and to decide whether changes in the index from one period to another are due to a structural change or whether ups and downs can be attributed to sampling randomness. An illustration using real data from a survey of business managers opinions is discussed.

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The Attorney General’s Consumer Protection Division receives hundreds of calls and consumer complaints every year. Follow these tips to avoid unexpected expense and disappointments. This record is about: The Drive to Destroy: Removing data from computer hard drives, storage devices & wireless phones