ライフサイエンスコーパス: forecasting

1 n are needed in order to facilitate eruption forecasting.

2 ng Bayesian techniques developed for climate forecasting.

3 ital discrimination, construct validity, and forecasting.

4 me useful purpose, such as long-term seismic forecasting.

5 s of discrimination, construct validity, and forecasting.

6 to provide maximum flexibility and improved forecasting.

7 required for personalized diet and nutrition forecasting.

8 ate sensitivity, and improved extended range forecasting.

9 about the uncertainty in daily and seasonal forecasting.

10 ay provide useful information for earthquake forecasting.

11 nd provides a basis for stochastic mortality forecasting.

12 xtensive post hoc investigation into seizure forecasting.

13 logical invasions that may aid in ecological forecasting.

14 rvational content but supports more accurate forecasting.

15 reproducible research that advanced seizure forecasting.

16 earity, and was the most accurate method for forecasting.

17 s of discrimination, construct validity, and forecasting.

18 se forecasting more challenging than weather forecasting.

19 to the field, setting the stage for exposure forecasting.

20 t better models are needed to improve dengue forecasting.

21 x models with many parameters can compromise forecasting ability.

22 r indirect effects and exercise caution when forecasting abundance patterns from single-species labor

23 The quantitative forecasting accuracy (95% confidence interval [CI]) for

24 ack of a gold-standard tuberculosis DDT, the forecasting accuracy of a completely unreliable tool was

25 er than choice itself.SIGNIFICANCE STATEMENT Forecasting aggregate behavior with individual neural da

26 (area under the curve; AUC > 0.95); however, forecasting and backcasting to alternative time periods

27 These principles inform evolutionary forecasting and have relevance to interpreting the diver

28 TDABC has also improved budget forecasting and informed financing decisions.

29 source of uncertainty both in ash dispersal forecasting and interpretation of eruptions from the geo

30 rgency of incorporating mechanism into range forecasting and invasion management to understand how cl

31 pendence, volatility smiles, etc) to improve forecasting and management of complex adaptive systems.

32 experimental system in which to explore the forecasting and management of tipping points and alterna

33 have implications for flood susceptibility, forecasting and mitigation, including management of grou

34 Targets for improvement include drug forecasting and procurement, and addressing provider rel

35 ing, with implications for numerical weather forecasting and regional climate predictions in dusty re

36 neously improve the quality of economic risk forecasting and reinforce individual government and dono

37 many ecosystems would simplify biodiversity forecasting and represent a rare victory for generality

38 se new inferences are important for eruption forecasting and risk mitigation, and have significant im

39 hysical forcing variables based on nonlinear forecasting and show how the method provides a predictiv

40 their importance for urban planning, traffic forecasting and the spread of biological and mobile viru

41 that such radar observations can be used for forecasting and to study atmospheric dynamics.

42 lity poses greater challenges to operational forecasting and, consequently, greater coastal risk duri

43 h uses including virtual biobanking, disease forecasting, and adaption to other disease outbreaks.

44 ned issues regarding ENSO dynamics, impacts, forecasting, and applications remain unresolved.

45 pen potential practical ways of identifying, forecasting, and controlling complex behaviors in a wide

46 play in advancing theory, improving hind and forecasting, and enabling problem solving and management

47 ent, food security, ecosystem studies, flood forecasting, and geopolitics.

48 including covariates used for farcasting and forecasting, and necessary documentation to the public.

49 d settings, including engineering design and forecasting, and, over the last two decades, have been a

50 eaking countries; it also discusses why past forecasting approaches may have failed.

51 the results in terms of Sclerotinia disease forecasting are discussed.

52 oposes a novel methodology for assessing and forecasting areas of technological innovation.

53 Forecasting assemblage-level responses to climate change

54 ich severely restricts longer-term, accurate forecasting beyond boreal spring.

55 represents a major unresolved challenge for forecasting biosphere responses to global change.

56 Currently, no method exists for forecasting broad biological activity profiles of medici

57 classification curves were well above chance forecasting, but did show a mean 6.54 +/- 2.45% (min, ma

58 nd computational methods for epidemiological forecasting, but here we consider a serious alternative

59 t' highlights the need to improve biological forecasting by detecting early warning signs of critical

60 uld potentially contribute to seismic hazard forecasting by providing a new means to monitor slow sli

61 ted phenomenon, and demonstrate that genetic forecasting can aid preparedness for impending viral inv

62 More accurate forecasting can help officials better respond to and pla

63 ge, in addition to the range set up by ocean forecasting capabilities.

64 al roles of birds and a stochastic model for forecasting change.

65 raits constitute a highly useful concept for forecasting changes in plant communities, and their asso

66 light the value of a multi-model approach in forecasting climate change impacts and uncertainties and

67 at, from the point of view of water resource forecasting, climate model development should prioritize

68 utionary history of a cancer is important in forecasting clinical outlook.

69 sted drugs, and specific predictors aimed at forecasting clinical response to treatment with four spe

70 In the past 3 decades, the weather forecasting community has made significant advances in d

71 A seizure forecasting competition was conducted on kaggle.com usin

72 power, suggesting that advances in long-term forecasting could be exploited to markedly improve manag

73 olera and climate patterns coupled with ENSO forecasting could be used to notify countries in Africa

74 ing crop-stress resistance or avoidance, and forecasting crop performance.

75 Understanding and forecasting current and future consequences of coastal w

76 odels with and without climate variables for forecasting dengue incidence in Mexico.

77 ve of length of stay in real-time accurately forecasting discharge within a 12-hr window: 46% for pat

78 pathogens in the atmosphere is essential for forecasting disease spread and establishing effective qu

79 ent, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation

80 Forecasting ecological responses to climate change, inva

81 The approach has immediate application to forecasting effects of warming on growing season length,

82 Crop forecasting efforts should expand to include EM-MM compa

83 ole in ENSO evolution, and thus important in forecasting El Nino events.

84 namics into the future and characterizes the forecasting error.

85 s that a Gaussian density, estimated on past forecasting errors, gives comparatively accurate uncerta

86 applications include political and economic forecasting, evaluating nuclear safety, public policy, t

87 are only beginning to be revealed, a key to forecasting expected changes in animal communities is an

88 A number of HWRF forecasting experiments were carried out at different ve

89 We also participated in two live forecasting experiments.

90 is important because of the implications for forecasting explosive eruptions and predicting their int

91 ogy in the 21st Century (Tox21) and exposure forecasting (ExpoCast) are generating mechanistic data t

92 ce shifts, and loss of area) that can aid in forecasting extinction and in developing a more comprehe

93 rop growth and yield has important value for forecasting food production and prices and ensuring regi

94 f the familiar tercile framework of seasonal forecasting for the characterization of 21st-century pre

95 roof-of-concept for implementing a circadian forecasting framework, and provide insight into new appr

96 We conclude that models forecasting future biodiversity changes should consider

97 sented here offer considerable potential for forecasting future conditions, highlight regions of conc

98 deas show considerable promise as a means of forecasting future earthquake activity.

99 r lymphatics in prostate tumor and melanoma, forecasting future lymphatic targeting agents for detect

100 ariables were lagged by 2 years to allow for forecasting future rates.

101 Additionally, migrants could benefit from forecasting future wind conditions, crossing on nights w

102 view, breadth of diseases and countries, and forecasting hamper their operational usefulness.

103 Traditional weather forecasting has been built on a foundation of determinis

104 However, progress toward reliable seizure forecasting has been hampered by lack of open access to

105 logarithmic error growing linearly with the forecasting horizon at a typical rate of 2.5% per year.

106 Forecasting how global warming will affect onset of the

107 numerical models can lead to improvements in forecasting hurricane intensity.

108 Forecasting impacts of future climate change is an impor

109 accuracies for nSNPs show opposite patterns, forecasting impediments to building diagnostic tools aim

110 and demonstrates the feasibility of seizure forecasting in canine and human epilepsy.media-1vid110.1

111 Forecasting is different for services with many patients

112 material-based disciplines for which failure forecasting is of central importance.

113 Despite its importance, conventional forecasting is still limited to 6 mo ahead.

114 evastating) impacts around the globe, robust forecasting is still limited to about 6 mo ahead.

115 trol technologies, and (iv) that air quality forecasting is sufficiently accurate to allow EGUs to ad

116 With a successful methodology toward tumor forecasting, it should be possible to integrate large tu

117 Understanding influenza dynamics and forecasting its impact is fundamental for developing pre

118 This paper uses three forecasting machines: (i) data assimilation, a technique

119 ed by assessing their predictive accuracy in forecasting malaria admissions at lead times of one to t

120 Our models open the possibility of forecasting malaria from climate in endemic regions but

121 is centuries-old method of seasonal rainfall forecasting may be based on a simple indicator of El Nin

122 We find that this forecasting method is robust and it outperforms logistic

123 hat prior to the peak of the flu season, our forecasting method produced 50% and 95% credible interva

124 d a modified nonparametric empirical density forecasting method.

125 index of El Nino variability to analyse this forecasting method.

126 rban vital rates and to make improvements to forecasting methods currently in use.

127 e variety of comparatively simple model-free forecasting methods that could be used to predict abunda

128 ing performance of several empirical density forecasting methods, using the continuous ranked probabi

129 ng meteorology from the Weather Research and Forecasting model (WRF).

130 Our forecasting model achieved a 29% increased efficiency in

131 evaluated, such as The Weather Research and Forecasting Model meteorology predictions, emission inve

132 ional Health Interview Survey, we designed a forecasting model to estimate the number of persons in t

133 The authors used a new dynamic forecasting model to show that although the decline may

134 We use the Weather Research and Forecasting Model with atmospheric chemistry (WRF-Chem)

135 nd simulations with the Weather and Research Forecasting Model with Chemistry (WRF-Chem) which indica

136 of model runs using the Weather Research and Forecasting model with Chemistry (WRF/Chem) coupled with

137 n simulations using the Weather Research and Forecasting model with Chemistry based on a new fire emi

138 opic using the regional Weather Research and Forecasting model with European Center for Medium range

139 unt the uncertainty related to the choice of forecasting model.

140 Forecasting models (neural networks) predicted an increa

141 These factors have led to wider use of forecasting models and cultural controls, the developmen

142 Probabilistic forecasting models describe the aleatory variability of

143 The forecasting models for ESRD patient numbers demonstrated

144 ssues regarding the testing of probabilistic forecasting models for ontological errors: the ambiguity

145 nd demonstrates that the predictive power of forecasting models is improved by circadian information.

146 in Saskatchewan, Canada, is used to develop forecasting models of odor using chlorophyll a, turbidit

147 The R(2) values for the forecasting models ranged from 99.09 to 99.98%.

148 d the potential of climate-based statistical forecasting models to predict seasonal incidence of meni

149 We developed an ensemble of 21 forecasting models, all of which probabilistically contr

150 eillance data provide opportunity to develop forecasting models.

151 ractions can be incorporated into ecological forecasting models.

152 fields, such as human behavior, make disease forecasting more challenging than weather forecasting.

153 ing mortality decline, and the difficulty of forecasting mortality are due in part to the pronounced

154 d nuclear accidents is reviewed to assist in forecasting needs of both systems and patients in the ev

155 mathematical framework for understanding and forecasting nonlinear dynamics through time and across s

156 led nutrient cycles, as well as modeling and forecasting nutrient controls over carbon-climate feedba

157 developed Bayesian spatiotemporal models for forecasting of age-specific mortality and life expectanc

158 Statistical forecasting of anesthesia staffing months ahead is condu

159 f S. aureus populations could lead to better forecasting of antibiotic resistance and virulence of em

160 h no other information, this approach allows forecasting of average exposure intake of environmental

161 Accurate forecasting of cardiovascular disease mortality is cruci

162 intensity of tropical cyclones, an accurate forecasting of cyclone evolution and ocean response is b

163 NTIFIC COMMENTARY ON THIS ARTICLE : Accurate forecasting of epileptic seizures has the potential to t

164 allows a confident and biologically informed forecasting of further invasion and range shifting drive

165 nstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable

166 The accurate forecasting of mosquito-borne arboviral epidemics will h

167 Our results make the forecasting of novel herbivore communities feasible in o

168 Experiments suggest that seasonal forecasting of ocean conditions important for fisheries

169 ess of pathogen adaptation, to more accurate forecasting of pathogen evolutionary trajectories.

170 Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful a

171 allow a significant improvement of long-term forecasting of steel demand and scrap availability in em

172 outline for improving ecological models and forecasting of temporal dynamics, while the unique attri

173 rder-specific effects may allow for rational forecasting of the evolutionary dynamics of bacteria giv

174 Skilful seasonal forecasting of the surface climate in both Europe and No

175 ust be taken into account for the successful forecasting of the trajectories and landfall of oil part

176 understanding it allows improved short-term forecasting of timing and eruption style.

177 orest model is also able to provide accurate forecasting of TON levels requiring treatment 12 weeks i

178 ment, national inventories of tOPV, detailed forecasting of tOPV needs, bOPV licensing, scaling up of

179 Such a model could allow more accurate forecasting of virus evolution.

180 rent computer models for long-range ensemble forecasting of weather and short-term climate change.

181 The HFS-TB model is highly accurate at forecasting optimal drug exposures, doses, and dosing sc

182 ortance of RI has been recognized in weather forecasting, our results demonstrate that RI also plays

183 ts with ruptured aneurysms are not useful in forecasting outcome for patients with unruptured aneurys

184 direct implications for improving heavy rain forecasting over the IMR, by developing realistic land c

185 val crevicular fluid (GCF) may be helpful in forecasting patient vulnerability to future attachment l

186 We evaluate the out-of-sample forecasting performance of several empirical density for

187 We report on forecasting performances and statistical significance of

188 tivity (NPP) is of particular relevance in a forecasting perspective.

189 s between temperature and precipitation when forecasting phenology over the coming decades.

190 Models forecasting plant community responses to global change i

191 ection rates makes this type of quantitative forecasting possible.

192 tandard-of-care surgical risk calculators at forecasting postoperative complications.

193 ably to the more complex risk calculators at forecasting postoperative complications.

194 bolic asymmetry in presurgical PET scans for forecasting postsurgical seizure-free clinical outcomes.

195 ns of formation of known deposits as well as forecasting potential exploration targets.

196 her and climate modeling, we submit that the forecasting power of biophysical and biomathematical mod

197 al heterogeneity as a first-order control on forecasting power.

198 In the forecasting problem, prior predictive model checking, ra

199 In forecasting prospective climate changes for the next cen

200 ure-oriented cognitions, including affective forecasting, prospective memory, temporal discounting, e

201 these predictions is limited to the weather forecasting range, in addition to the range set up by oc

202 Yet mainstream macroeconomic forecasting rarely takes account of the risk of potentia

203 Forecasting refugee movements is important, as accurate

204 dels used for climate simulation and weather forecasting require the fluxes of radiation, heat, water

205 ng and guiding agenda setting for ecological forecasting research and development.

206 epresents a powerful approach for accurately forecasting resource demands required for survival by la

207 nd among species, and among communities) for forecasting responses to climate change.

208 nticipatory LCA that incorporates technology forecasting, risk research, social engagement, and compa

209 tic imprinting in any species and imply that forecasting salmon movements is possible using geomagnet

210 developed here provides a model-independent forecasting scheme that relies only on already observed

211 his basis, we can develop an efficient 12-mo forecasting scheme, i.e., achieve some doubling of the e

212 ve enabled development of systems capable of forecasting seasonal influenza epidemics in temperate re

213 esearch has produced a number of methods for forecasting seasonal influenza outbreaks.

214 ct of large earthquakes requires signals for forecasting seismic events.

215 n dynamic environments, which is critical in forecasting species distributions, as well as the divers

216 ) tend to have higher predictive accuracy in forecasting species range shifts than structurally simpl

217 drivers of phenological events is vital for forecasting species' responses to climate change.

218 ntal to understanding resilience properties, forecasting state shifts, and developing effective manag

219 a analysis provides an unbiased new tool for forecasting structure-response relationships and for tra

220 However, current models forecasting such declines do not account for the effects

221 new approaches to assessing cardiac risk and forecasting sudden cardiac death, as well as to monitori

222 We illustrated the method by forecasting suitable habitat for bull trout (Salvelinus

223 pturing and recovering the oil, tracking and forecasting surface oil, protecting coastal and oceanic

224 ese high-risk areas, particularly Natal, the forecasting system did well for previous years (in June,

225 llenges and produce a disease monitoring and forecasting system that is significantly more effective,

226 By assessing the past performance of the forecasting system using observed dengue incidence rates

227 We created the web-based "Epicast" forecasting system which collects and aggregates epidemi

228 elying on integrated physical-biogeochemical forecasting systems.

229 at enable the development of epidemiological forecasting systems.

230 an, for example, solely point predictions of forecasting targets.

231 in significant improvements in the skill for forecasting TC activity at daily and seasonal time-scale

232 nd consistently outperform alternative naive forecasting techniques.

233 Forecasting technological progress is of great interest

234 Despite their importance for eruption forecasting the causes of seismic rupture processes duri

235 sidered as a paramount predictive marker for forecasting the clinical therapeutic response to mTOR in

236 Forecasting the consequences of climate change is contin

237 ticipated that these findings will assist in forecasting the CYP-mediated metabolic fate of phenolic

238 on with long-term water ageing are useful in forecasting the decline in strength of resin-dentine bon

239 thermal environment is largely ignored when forecasting the dynamics of non-native species.

240 ts a threat to marine species worldwide, and forecasting the ecological impacts of acidification is a

241 r results constitute a foundation for better forecasting the effect of climate change on many insect

242 ges in both climate means and variances when forecasting the effects of global change on species dive

243 ction modes: a universal predictor, aimed at forecasting the efficacy of untested drugs, and specific

244 y transitional step to the transverse array, forecasting the faster elongation that follows.

245 The authors have developed a method for forecasting the future burden of occupational cancer to

246 es respond to climate change is critical for forecasting the future dynamics and distribution of pest

247 at single species models may be adequate for forecasting the impacts of climate change in these commu

248 wo-fraction model provides a simple means of forecasting the movement of excess fine sediment supply.

249 have a long history of use in describing and forecasting the movements of people as well as goods and

250 ic coast of the US, a statistical method for forecasting the occurrence of landfalling hurricanes for

251 ics (MD) simulations claim many successes in forecasting the phenomena.

252 A frequently advocated approach for forecasting the population-level impacts of climate chan

253 mpound displayed no neuronal toxicity, thus, forecasting the potential application of this agent and

254 ith the idea of testing this methodology and forecasting the reliability of the biological data as a

255 standing the fate of gravel is important for forecasting the response of rivers to large influxes of

256 and interface, understanding soil mechanics, forecasting the risk of natural calamities, and so on.

257 ers are struggling with these same issues in forecasting the spread of emerging diseases, such as Zik

258 cies richness and N inputs is a hindrance to forecasting the state of the soil N cycle and ecosystem

259 rovide a quantitative physical mechanism for forecasting the strength and duration of bursty seasons

260 variability in iron supply in prediction or forecasting, the variability of light-limited productivi

261 bidity and mortality around the world; thus, forecasting their impact is crucial for planning an effe

262 es, and thus provides a potential method for forecasting these events.

263 nengineering applications such as short-term forecasting, time series, survival analysis, and so on.

264 paving the way for equation-free mechanistic forecasting to be applied in management contexts.

265 ncerted effort to apply lessons from weather forecasting to develop an analogous methodology for pred

266 mmunity is rising to the challenges posed by forecasting to help anticipate and guide the mitigation

267 Despite diverse efforts to develop forecasting tools including autoregressive time series,

268 disease events underscores a need to develop forecasting tools toward a more preemptive approach to o

269 Long-term meteorologic forecasting using El Nino Southern Oscillation (ENSO) ma

270 of-concept yields models with r2 up to 0.92, forecasting value up to the 28 days tested, and several

271 nding this replacement mechanism is vital to forecasting variations in hurricane intensity.

272 predictability simulations, research toward forecasting variations of the climate system now covers

273 onal design of safer vaccine strains and for forecasting virulence of viruses.

274 their Perspective, a new approach--ensemble forecasting--was introduced in the early 1990s.

275 In addition, for forecasting, we estimated a dynamic parametric model of

276 re threats to freshwater ecosystems requires forecasting where land use changes are most likely.

277 ellular and ecosystem level, is critical for forecasting whole-tree responses to altered biogeochemic

278 stem that exhibits a high degree of skill in forecasting wildfire probabilities and drought for 10-23

279 he regional scale using the Weather Research Forecasting (WRF) model.

280 ergy flux method to the Weather Research and Forecasting (WRF) regional atmospheric model equipped wi