ライフサイエンスコーパス: forecast

1 her to arrive at an ultimately more accurate forecast.

2 and a Richards model in the context of early forecast.

3 ways that have been previously difficult to forecast.

4 udies by using water demand and water supply forecast.

5 beliefs as opposed to anchoring on the model forecast.

6 erical models provides increased accuracy to forecasts.

7 earning" model), was trained to produce risk forecasts.

8 travel can improve the quality of influenza forecasts.

9 tion effort that complements existing public forecasts.

10 of how this diversity modifies agricultural forecasts.

11 rates as a key area for research to improve forecasts.

12 trust and under-utilize such models in their forecasts.

13 ocesses may hinder long-term epidemiological forecasts.

14 tion, crustal stress evolution, and eruption forecasts.

15 of evolution, regulation, and computational forecasting.

16 , planning healthcare capacity, and epidemic forecasting.

17 hquakes is required for effective earthquake forecasting.

18 on dynamics models - what we call structural forecasting.

19 arking; and data assimilation and ecological forecasting.

20 have not yet been used for seasonal hypoxia forecasting.

21 in tissue and may also be useful for seizure forecasting.

22 Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-di

23 lence of diabetes mellitus and heart failure forecast a growing burden of disease and underscore the

24 onfronted this epidemic, as well as in those forecasting a possible second outbreak.

25 be challenging to identify and hence, limit forecast abilities.

26 nternet data has shown promise for improving forecast accuracy and timeliness in controlled settings,

27 However, our forecast accuracy improved in absolute terms and relativ

28 e of human intervention is likely to improve forecast accuracy in the medium-term in parallel with th

29 1) ), were evaluated for their usability for forecasting adult heat tolerance, measured as the vegeta

30 thogen, and how monitoring and computational forecasting affect protocols and efficiency of control.

31 tions with limited data, was used to fit and forecast age profiles of mortality.

32 s well as whether group brain activity could forecast aggregate video view frequency and duration out

33 Markov Chain Monte Carlo (MCMC) method upon forecast aggregation.

34 In this work, we present a forecasting algorithm that exploits the dimensionality o

35 able indicator that could be used in seizure forecasting algorithms.

36 an observable and identifiable precursor to forecast an impending earthquake within a period of less

37 The official forecast and computer models were unable to predict rapi

38 laws to reduce data dependence in ecological forecasting and accurately predict extreme events beyond

39 e, we detail three regional-scale models for forecasting and assessing the course of the pandemic.

40 yme could be acted as a unique biomarker for forecasting and diagnosing certain diseases.

41 high VPD on plant function, improvements in forecasting and long-term projections of climate impacts

42 improving the predictive ability of seasonal forecasting and modelling of long-range spatial connecti

43 hallenging issue of AMD and, more generally, forecasting and optimization of mineral leaching in indu

44 arly vaccinated populations and thus improve forecasting and vaccination strategies to combat seasona

45 s essential for developing reliable outbreak forecasts and informing stakeholders on mitigation and p

46 Crowdsourcing human forecasts and machine learning models each show promise

47 l community should be hesitant in developing forecasts and prevention strategies for COVID-19 in the

48 in our understanding, reducing confidence in forecasts and projections.

49 new prescription launches a vision of surer forecasts and stands versatile enough to be applicable t

50 an exposure to cyanotoxins is challenging to forecast, and perhaps the least understood exposure rout

51 es and improving the processes of diagnosis, forecasting, and tracking of normal and pathological agi

52 ribution studies, 2) subseasonal to seasonal forecasts, and 3) decadal predictions.

53 dynamic boundary shape suggests that current forecasting approaches assuming a constant shape could b

54 Such forecasts are hampered by ecological uncertainties assoc

55 Yet, epidemic forecasts are rarely evaluated during or after the event

56 st of sepsis care for Medicare beneficiaries forecast arise approximately 13% over 2 years owing the

57 arameters for solar desiccant driven AWC and forecast atmospheric water harvesting potential (L/m(2)/

58 ith Recurrent Neural Networks in the task of forecasting bees' level of activity taking into account

59 ch models have limited skill for longer-term forecasts beyond half a year.

60 Results suggest that (a) we can forecast biological dynamics while applying delta-correc

61 To understand and forecast biological responses to climate change, scienti

62 Forecasting 'Black Swan' events in ecosystems is an impo

63 80s; analyses based on global climate models forecast bleaching will become an annual event for most

64 When we forecast both temperature and EWL into the 2080s, both p

65 in only a subset of these regions, however, forecasted both aggregate view frequency and duration (i

66 e individual level and improved chance-level forecast by 60%.

67 o reduce the local biases of the NSCS marine forecasting by as much as 28-31% (19-36%) in 24 h to 120

68 rformance, and the Brier score, a measure of forecast calibration.

69 ametric statistical approach based on analog forecasting, called kernel analog forecasting (KAF), whi

70 Better forecasts can be expected to help prevent catastrophic c

71 We find that accurate and well-calibrated forecasts can be generated for countries in temperate re

72 of tailored deterministic and probabilistic forecasts can inform key prevention and control strategi

73 asily accessible to medical practitioners to forecast caries at 2 and 3 y of age.

74 We also propose a method for forecasting case counts, which takes advantage of the co

75 ur participation in a weekly prospective ILI forecasting challenge for the United States for the 2016

76 ntion (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season.

77 rces, atmospheric and oceanic transport, and forecasting climate change impacts through modeling.

78 gion previously unrecognised by the seasonal forecast community.

79 ld probabilistic problems such as diagnosis, forecasting, computer vision, etc.

80 n western Micronesia. Our novel approach for forecasting coral growth into the future may be applicab

81 is concave shape has a significant impact on forecasted COVID-19 cases.

82 trate that similar approaches can be used to forecast CRISPR/Cas9 gene editing outcomes in Xenopus tr

83 from past data inputs, model estimation, and forecast data distributions.

84 study shows that seizure probability can be forecasted days in advance by leveraging multidien IEA c

85 ive sea-level rise, hampering the ability to forecast delta response to global climate change.

86 administrative and policy decision makers to forecast demand for hospital resources, to understand ho

87 uncertainty when they are used to predict or forecast ecosystem responses to global change.

88 An ensemble machine learning model forecasted ED visits and inpatient admissions with out-o

89 The proposed model is able to forecast EEG signals 5.76 s in the future.

90 However, forecasting ENSO is one of the most difficult problems i

91 The average forecasting error was 9.22%.

92 elevation, and the process is complicated by forecast errors and sparse wind measurements.

93 future peatland development is important to forecast feedbacks on the global C cycle and help inform

94 el is able to provide an accurate short-term forecast for the numbers and age distribution of cases a

95 Improved forecasts for biodiversity must also integrate the conne

96 eded to quantify the clinical value of these forecasts for patients.

97 as much as 28-31% (19-36%) in 24 h to 120 h forecasts for temperature (salinity) from sea surface to

98 ien IEA cycles alone generated daily seizure forecasts for the next calendar day with IoC in 15 (83%)

99 Probabilistic forecasts for the occurrence of El Nino/La Nina events a

100 Forecasts from climate models and oceanographic observat

101 y was to develop a machine learning model to forecast future circumpapillary retinal nerve fiber laye

102 COPD trends may help healthcare providers to forecast future disease burden.

103 our ability to assess transmission dynamics, forecast future incidence, and estimate the impact of co

104 evaluating how accurately a built model can forecast future outcomes.

105 Using the emerging mechanisms, we forecast future trajectories of spring arrival and evalu

106 and use two-sex matrix population models to forecast future trends.

107 configures sperm with a chromatin state that forecasts gene expression in the next generation.

108 .11-2.81) individuals younger than 20 years, forecasted globally in 2100.

109 on-makers to frequently update (and consider forecasting) grid emissions factors.

110 Forecasting healthcare utilization has the potential to

111 ated how this methodology can be utilized to forecast highway network conditions.

112 The forecasting horizon could be extended up to 3 days while

113 gradients on population differentiation and forecasted how this genetic legacy may limit the persist

114 effects of threatening human processes, and forecasting how threatened species might be distributed

115 us types 1-2 and 3), and develop a method to forecast ILI by aggregating these predictions.

116 across all scales and trophic levels, and to forecast impact thresholds beyond which irreversible cha

117 ponders, and other decision-makers for flood forecast in road networks.

118 ws, and use this model to generate influenza forecasts in conjunction with incidence data from the Wo

119 Global change forecasts in ecosystems require knowledge of within-spec

120 a chance forecaster, and provided meaningful forecasts in the majority of patients.

121 we observed state-of-the-art performances in forecasting individual CKD onsets with different machine

122 ven noisy and potentially incomplete data at forecast initialization.

123 This forecast is immediately relevant to any hospital system

124 Forecasting is beginning to be integrated into decision-

125 Eruption forecasting is inherently challenging in cascading scena

126 water contribution is modeled to explain and forecast its effect as a function of its concentration i

127 athematical models play an important role in forecasting its future dynamics.

128 on analog forecasting, called kernel analog forecasting (KAF), which avoids assumptions on the under

129 to evaluate how model uncertainties affected forecasted landscape outcomes.

130 arid ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shift

131 nd diverging methodologies of estimation and forecasting, leading to important differences in global

132 We use forecasts made in the absence of machine models as prior

133 inferred from past RSV hospitalisations, and forecasts made over a 10-year horizon.

134 Our results have important implications for forecasting mangrove carbon dynamics and the persistence

135 Our forecasting method enables researchers and industry to e

136 pH variations a year in advance over a naive forecasting method, with the potential for skillful pred

137 In study 2, this "local forecasting" methodology outperformed participant evalua

138 r, we show that cLV is as accurate as gLV in forecasting microbial trajectories in terms of relative

139 A linear forecasting model based on a logic matrix decision tree

140 ty experiments with the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) and

141 hemical transport model-Weather Research and Forecasting model coupled with Chemistry version 3.5 (WR

142 The performance of the proposed forecasting model for cpRNFL is consistent across glauco

143 The best forecasting model of cpRNFL was obtained using 3 visits

144 temporal sampling, regardless of the adopted forecasting model.

145 The search for more accurate forecast models can aid both in the understanding of the

146 Empirical forecast models suggested that flowering durations will

147 rly understood and parameterized in existing forecast models.

148 The forecasting models used multimodal patient information i

149 access should be incorporated into COVID-19 forecasting models when applied to low-income countries.

150 n this study, we developed novel methods for forecasting mortality, fertility, migration, and populat

151 On a variety of prediction tasks-forecasting new infections, the popularity of topics in

152 We applied an ensemble forecasting niche modelling approach to project the clim

153 close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation.

154 he spread of COVID-19 allows a more accurate forecast of disease spread when NPIs are partially loose

155 tion cycle, which allowed for the successful forecast of its 2015 eruption.

156 The forecast of population growth over the next 3 decades is

157 Early forecasting of COVID-19 virus spread is crucial to decis

158 time data are valuable in the monitoring and forecasting of epidemics and outbreaks, it is evident th

159 and monitoring of Kilauea enabled successful forecasting of hazardous events.

160 by age and sex to improve real-time targeted forecasting of hospitalization and critical care needs.

161 is study proposes a method for probabilistic forecasting of the disease incidences in extended range

162 our approach could lead to more informative forecasting of the seismic activity in seismogenic areas

163 Accurate understanding and forecasting of traffic is a key contemporary problem for

164 can provide accurate, noninvasive, and early forecasting of ultimate outcomes for NSCLC patients rece

165 e where system nonlinearities limit accurate forecasting of unprecedented events due to poor extrapol

166 ive tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales th

167 s, the ability to generate useful short-term forecasts of Adelie penguin breeding abundance will be e

168 We demonstrate good 3 week model forecasts of deaths with low error and good coverage of

169 With the categorical probabilistic forecasts of disease incidences, this early health warni

170 emiology and cyber-security require accurate forecasts of dynamic phenomena, which are often only par

171 esholds of drought survivorship will improve forecasts of forest and agroecosystem die-off.

172 e drivers of false spring risk, complicating forecasts of future false springs, and potentially resha

173 Forecasts of future forest change are governed by ecosys

174 Reliable forecasts of ILI can support better preparation for pati

175 In terrestrial ecosystems, climate change forecasts of increased frequencies and magnitudes of wet

176 patient's cEEG data (both cohorts) generated forecasts of seizure probability that were tested on sub

177 r conceptual perspectives that are improving forecasts of semi-arid biome shifts.

178 rate these perspectives will rapidly improve forecasts of semi-arid biome shifts.

179 on forces in the pai-dimers lead to improved forecasts of sigma- vs pai-dimerization mode, and sugges

180 om general human movement models can improve forecasts of spatio-temporal transmission patterns in pl

181 nderstanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.

182 Our study warns against precise forecasts of the evolution of epidemics based on mean-fi

183 ccine strain selection through more accurate forecasts of the evolution of the virus.

184 Ecological forecasts of the extent and impacts of invasive species

185 surface data are needed to provide realistic forecasts of the fate of such organisms under anthropoge

186 warning signals should provide more accurate forecasts of the future state of biological systems.

187 seasonal, spatially explicit, time-evolving forecasts of the hypoxic zone that combines statistical

188 Forecasts of the impacts of interventions carried out in

189 sensing are improving current estimates and forecasts of the risks to forest stability.

190 We present maps and forecasts of the spatiotemporal dynamics of large carniv

191 veraged to improve "nowcasts" and short-term forecasts of U.S. influenza activity.

192 flow model, this article provides the first forecasts of water levels in the study area up to the ye

193 Efforts to improve sea level forecasting on a warming planet have focused on determin

194 included in models and studies that monitor, forecast, or manage expansions in natural systems.

195 ple with epilepsy might benefit from seizure forecasting over long horizons.

196 rts have shown improvements by "hybridizing" forecasts-pairing human forecasters with machine models.

197 ld remain below capacity thresholds over the forecast period (from mid-to-late April).

198 During the forecast period (unseen data, 2014-2017), both approache

199 By the end of the forecast period, the inclusion of thermal anomalies was

200 ore educational inequality since 1970 and to forecast progress towards the education-related 2030 SDG

201 ffers no improvement over isolated models in forecast quality.

202 We forecast rates of new cases for COVID-19 under different

203 esearch has utilized clinical information to forecast readmissions, analyzing digital footprints from

204 data can overestimate predictive skill when forecasting recruitment is part of the assessment proces

205 o explore novel approaches to monitoring and forecasting regional outbreaks as they happen or even be

206 redictions of crop yields than official USDA forecasts released midseason.

207 the relationships among estimate precision, forecast reliability and model complexity.

208 nd found that data from preinfusion products forecasted response in CLL successfully in discovery and

209 licy contributions on mobility reduction, we forecast scenarios for relaxing various types of NPIs.

210 dependent and quantitative tool to spatially forecast seismicity.

211 to model produced the outstanding result for forecasting seizure EEG with an error of 11.21%.

212 robability, and we tested the feasibility of forecasting seizures days in advance.

213 nd sleep quality (efficiency) provide future forecasting sensitivity to the rate of Abeta accumulatio

214 outcome was the percentage of patients with forecasts showing improvement over chance (IoC).

215 transmission empirically, our model improves forecast skill over recent, state-of-the-art models for

216 ion owing to their simplicity and comparable forecast skill to first-principles models at short lead

217 iments, and thus their utility to accurately forecast species' responses.

218 nowledge about migration potential is key to forecasting species distributions in changing environmen

219 ints to potential fundamental limitations in forecasting species shifting ranges without considering

220 etic variation is commonly ignored in models forecasting species vulnerability and biogeographical sh

221 iches are thus central for understanding and forecasting species' geographic distributions.

222 Forecasting storm impacts on these ecosystems requires c

223 Although reduction of forecast subjectivity should be a long-term goal, some d

224 The AI produced a valid forecast superior to a chance forecaster, and provided m

225 We show that the forecast system skillfully predicts observed surface pH

226 then compared to the East Asian Seas Nowcast/Forecast System, and it was found that daily temperature

227 Here, we build an accurate battery forecasting system by combining electrochemical impedanc

228 The hierarchical forecasting system can generate predictions for each vir

229 We present a next-generation monitoring and forecasting system for [Formula: see text]-borne disease

230 S national and regional levels, the proposed forecasting system generates improved predictions of bot

231 search trends based 'nowcasts' in influenza forecast systems, and encourage reevaluation of the util

232 However, forecasting systems are uncommon in most countries, with

233 nd attack rates, most existing process-based forecasting systems treat ILI as a single infectious age

234 nditions to improve high-resolution regional forecasting systems.

235 k will likely have reduced recruitment given forecasted temperatures over future decades.

236 brain uses a hippocampal prospective code to forecast temporally structured learned associations.

237 function network (AR-RBFN) provides a better forecast than that obtained using other model-free appro

238 in each Australian state since mid-March and forecast that clinical demand would remain below capacit

239 It is forecast that the adoption of 3D printing could pave the

240 In particular, our model forecasts that the number of COVID-19 cases would only h

241 roposed as a surrogate indicator to mine and forecast the average housing prices in the inland capita

242 at filter masks and dispersal simulations to forecast the distribution of 349 species of forest- and

243 anates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also t

244 This paper shows how to forecast the electoral vote in 2020 taking into account

245 Further, we are able to forecast the equilibrium post-vaccine population composi

246 At failure onset, it may be difficult to forecast the final eruption volume; pressure in a magma

247 c responses need disentangling to accurately forecast the impacts of climate change on animal populat

248 rt of a set of biomarkers that statistically forecast the longitudinal trajectory of cortical Abeta d

249 nstrumental record can better understand and forecast the mechanisms regulating forest sensitivity to

250 tate-of-the-art machine learning methods can forecast the occurrence of a global gap or learn effecti

251 The simulation models forecast the potential extent and impact of a buffelgras

252 Our algorithm was then challenged to forecast the slowly manifesting, spatially replicated re

253 Moreover, our findings offer a framework to forecast the spread and evolvability of MGE-encoded gene

254 factors in Jordan between 1990-2050, and to forecast the T2DM-related costs.

255 iming of their adoption, opening the way for forecasting the effectiveness of future interventions.

256 Forecasting the El Nino-Southern Oscillation (ENSO) has

257 d utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

258 Capable of measuring and forecasting the impacts of social distancing, these mode

259 Yet, forecasting the propagation of these predator-induced tr

260 Forecasting the spatiotemporal spread of infectious dise

261 Nonetheless, modeling and forecasting the spread of COVID-19 remains a challenge.

262 Forecasting the state of health and remaining useful lif

263 coral morphological traits may contribute to forecasting the structure of reef fish communities on no

264 tive interactions are rarely considered when forecasting the success or speed of expansion, in part b

265 for accelerated research to improve seasonal forecasts through multidecadal climate projections.

266 f video viewing (but not initial choice) can forecast time allocation out of sample in an internet at

267 the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictab

268 The global TFR in the reference scenario was forecasted to be 1.66 (95% UI 1.33-2.08) in 2100.

269 n 1990, 21.1% in 2020, and 25.2% in 2050 was forecasted to be spent on T2DM.

270 China was forecasted to become the largest economy by 2035 but in

271 0% from 2017 to 2100; China's population was forecasted to decline by 48.0% (-6.1 to 68.4).

272 e replacement level (TFR <2.1), and 183 were forecasted to have a TFR lower than replacement by 2100.

273 By 2050, 151 countries were forecasted to have a TFR lower than the replacement leve

274 , including Japan, Thailand, and Spain, were forecasted to have population declines greater than 50%

275 ce stays consistent with observed trends are forecasted to increase 756% by midcentury; this is an or

276 With climate change forecasted to increase SSTs and the frequency of extreme

277 5 but in the reference scenario, the USA was forecasted to once again become the largest economy in 2

278 We use nonlinear forecasting to analyse 747 datasets of 228 species to fi

279 a incidence in 2008-2017, then used Bayesian forecasting to examine an extensive range of scenarios.

280 ng the opening of new shipping lines and for forecasting trade volume on links.

281 free, data-driven approach for analyzing and forecasting traffic dynamics.

282 re need of a robust method for analyzing and forecasting traffic.

283 e model is parameterized with these data, we forecast treatment response with and without HER2-target

284 ling is used to understand disease dynamics, forecast trends, and inform public health prioritization

285 that current numerical models can accurately forecast tsunami hazards from these events.

286 y will occur is limited because biodiversity forecasts typically focus on individual snapshots of the

287 Forecast uncertainty intervals (UIs) incorporated uncert

288 ped a human exposure likelihood model (7-day forecast) using general aerosol characteristics and meas

289 earson's correlation coefficient between the forecasted value and the measured thickness was rho = 0.

290 These forecasted values may be useful to personalize patient c

291 element method, is combined with time-series forecasting via auto regressive integrated moving averag

292 Forecasted warming is predicted to increase the spatial

293 568 km transect of field shrub sampling, and forecasted warming scenarios with regional downscaling t

294 A rate-matched random (RMR) forecast was compared to the AI.

295 Forecasts were made from a sliding window of 3-month dia

296 f the hypoxic zone that combines statistical forecasting with information from a 3D biogeochemical mo

297 Forecasts with a shorter horizon of 1 h, possible only f

298 suite of retrospective, initialized ensemble forecasts with an Earth system model (ESM) to predict th

299 Augmenting forecasts with internet data has shown promise for impro

300 Here we use the Weather Research and Forecasting (WRF) model over the western Gobi Desert to