Mapping the physiography of Michigan with GIS

Abstract: We present a new physiographic map of Michigan, that is also available interactively, online. Only four, small-scale physiographic maps of Michigan had been previously published. Our mapping project made use of a wide variety of spatial data, in a GIS environment, to visualize and delineate the physical landscape in more detail than has been done previously. We also examined many of the unit boundaries in the field, using a GIS running on a GPS-enabled laptop. Unlike previous physiographic maps, the online version of the map enables users to query the criteria used to define each of the 224 boundaries of its 10 major and 91 minor physiographic units. The interactive nature of the online version of the map is a unique enhancement to physiographic maps and mapping. Our study also provides data on the number and types of criteria used to define each of the 224 unit boundaries within the map. Most of our unit boundaries are based on data derived from 10-m raster elevation data and NRCS soils data, e.g., relief, soil wetness, escarpments, landscape fabric, and parent material characteristics. Data gleaned from NRCS SSURGO county-scale soil maps were a strength of the project

From Too Much to Too Little: How the central U.S. drought of 2012 evolved out of one of the most devastating floods on record in 2011

Table of Contents Section 1: Introduction....................................................................... 1 Section 2: Regional Drought Perspective................................. 2 Section 3: State Drought Perspectives........................................ 3 Section 3.1: Colorado........................................................................... 20 Section 3.2: Illinois.................................................................. 25 Section 3.3: Indiana................................................. 29 Section 3.4: Iowa...................... 36 Section 3.5: Kansas............................................................... 42 Section 3.6: Kentucky............................................................................ 46 Section 3.7: Michigan.............................. 52 Section 3.8: Minnesota............................................................ 58 Section 3.9: Missouri..................................................... 63 Section 3.10: Nebraska................................................. 67 Section 3.11: North Dakota............................................ 73 Section 3.12: Ohio................................................... 79 Section 3.13: South Dakota..................................... 85 Section 3.14: Wyoming........................................... 96 Section 4: Conclusions.............................................................. 9

Heurísticas para balanceamento de carga de máquinas em infraestruturas de nuvem.

Em ambientes de Computação na Nuvem, principalmente os que utilizam o modelo de infraestrutura como um serviço, a característica de elasticidade no provisionamento de recursos traz consigo a necessidade de gerenciar os recursos físicos de forma apropriada para preservar a qualidade de serviço aos seus usuários, e o bom desempenho da infraestrutura. Este trabalho propõe heurísticas que são capazes de auxiliar no balanceamento de carga dos servidores em uma infraestrutura de nuvem, propondo migrações para diminuir a sobrecarga nos servidores que foram identificados como sobrecarregados,visto que, como passar do tempo há uma variação natural na quantidade de recursos em uso. Esta variação é uma consequência da remoção ou adição de aplicações, ou até mesmo de tentativas de melhoramento do desempenho das aplicações através do provisionamento vertical. Uma ferramenta foi implementada para fazer uso dos algoritmos das heurísticas e assim auxiliar nos experimentos para a validação das mesmas. As métricas utilizadas vem diretamente de servidores heterogêneos da nuvem OpenStack do Laboratório de Sistemas Distribuídos. Os resultados obtidos mostram que além da diminuição no consumo de CPU dos servidores dos quais que estavam sobrecarregados, também é possível melhorar o desempenho destes servidores em alguns casos.In CloudComputingenvironments,especiallythoseusingtheinfrastructureasaservice model, theelasticitycharacteristicinresourceprovisioningcomeswiththeneedtomanage resources sothequalityofservicecancontinuetobeguaranteedtousersandalsoto maintain agoodperformanceoftheinfrastructure.Thisworkproposesheuristicsthat are abletoassistintheloadbalancingoftheserversinaCloudinfrastructure,proposing migrations toreducetheoverheadintheserversthatwereidentifiedasoverloaded,since with thepassageoftimethereisanaturalvariationintheamountofresourcesinuse.This variationinaconsequenceofremovaloradditionofapplicationsandevenoftheusageof verticalscalingtoimproveapplication’sperformance.Atoolwasimplementedtomake use oftheheuristicalgorithmsandthustoaidintheexperimentsandtheirvalidation,the metrics usedcomedirectlyfromheterogeneousserversoftheOpenStackCloudofthe DistributedSystemsLaboratory.TheresultsshowthatinadditiontothedecreaseinCPU consumption ofserversthatwereoverloaded,itisalsopossibletoimprovetheperformance of theseserversinsomecases

From Too Much to Too Little: How the central U.S. drought of 2012 evolved out of one of the most devastating floods on record in 2011

Table of Contents Section 1: Introduction....................................................................... 1 Section 2: Regional Drought Perspective................................. 2 Section 3: State Drought Perspectives........................................ 3 Section 3.1: Colorado........................................................................... 20 Section 3.2: Illinois.................................................................. 25 Section 3.3: Indiana................................................. 29 Section 3.4: Iowa...................... 36 Section 3.5: Kansas............................................................... 42 Section 3.6: Kentucky............................................................................ 46 Section 3.7: Michigan.............................. 52 Section 3.8: Minnesota............................................................ 58 Section 3.9: Missouri..................................................... 63 Section 3.10: Nebraska................................................. 67 Section 3.11: North Dakota............................................ 73 Section 3.12: Ohio................................................... 79 Section 3.13: South Dakota..................................... 85 Section 3.14: Wyoming........................................... 96 Section 4: Conclusions.............................................................. 9

Assessing eligibility for non-intensive chemotherapy (IC) randomized clinical trials (RCT) in patients (pts) with newly diagnosed (ND) AML from the Connect Myeloid Disease Registry

7029 Background: Pts with AML in RCTs often do not reflect the population seen in clinical practice due to strict eligibility criteria. This study evaluated criteria from a recent RCT of non-IC against a broad cohort of real-world pts with AML from the Connect ® Myeloid Disease Registry (NCT01688011). Methods: Pts were stratified into 3 groups based on the non-IC phase 3 VIALE-A trial eligibility criteria: 1) “eligible” pts who would have met all VIALE-A inclusion criteria; 2) “unfit” pts who would have been ineligible for VIALE-A due to ≥ 1 of the following: abnormal liver/kidney function, high ECOG, recent prior malignancy, comorbidities score ≥ 2 by ACE-27, hepatic grade ≥ 1, AIDS grade ≥ 1; 3) “fit” pts who would have been ineligible for VIALE-A because they would have qualified for IC (defined as: ≤ 74 y of age, low ECOG, no apparent cardiovascular/renal comorbidities, and did not meet any criteria in #2). Baseline characteristics were summarized by eligibility group. Overall survival (OS) by group was estimated using the Kaplan–Meier method. Induction regimens were categorized as IC (any regimens containing 7+3, MEC, CLAG, FLAG) or venetoclax (VEN)-based. Hazard ratios (HRs) for induction regimens among each group were estimated using Cox models adjusted for age, ELN risk, ECOG, frailty score, and comorbidity index. Results: Of 734 enrolled pts with AML (Dec 2021), most were male (61%) and white (84%); median age 71 y (range 55–97). Only 26% of pts (n = 192) were eligible for a non-IC RCT, 45% (n = 327) were ineligible due to unfitness, and 29% (n = 215) were ineligible due to overall fitness. The main reason for non-IC RCT ineligibility was high overall comorbidity grade (n = 265 [36%]). Fit pts intended to undergo transplant more often compared with unfit pts. Median OS for eligible, unfit, and fit pts were 14, 10, and 22 months, respectively. Among unfit pts, those receiving IC had significantly longer OS compared with pts receiving a VEN-based regimen (median OS 14 vs 6 months, respectively; HR: 0.51, 95% CI: 0.27–0.98, P = 0.042; Table). Eligible pts who received IC tended to have shorter median OS (13 months) vs pts who received VEN-based therapies (23 months; not sig.). Conclusions: The majority of pts with ND AML in the Registry would have been ineligible for a non-IC RCT due to being too fit or unfit. Pts ineligible for an RCT due to unfitness but who received IC maintained an OS benefit vs those receiving VEN-based therapies. This analysis suggests that non-IC RCTs may be excluding pts who appear unfit but can potentially tolerate IC and experience improved survival outcomes. Clinical trial information: NCT01688011. [Table: see text