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AbstractIntroductionMethodsResultsDiscussionConditioned pain modulation (CPM) allows to investigate endogenous pain modulation and its clinical outcomes. Although co-activation of emotions has been shown to affect CPM, the impact of ‘threat,’ which may accompany CPM stimulation itself, has been mostly neglected. A critical factor for the threat level of the conditioning stimulus (CS) may be its predictability.38 healthy participants (18 female) took part in a CPM study with pressure stimulation on the leg (blood-pressure cuff) serving as CS and heat stimulation on the forearm (contact thermode; CHEPS) serving as test stimulus (TS). While CS varied in intensity and –as operationalisation of threat– in temporary predictability, TS was kept constant. CPM effects were studied by EEG parameters (N2P2) and pain ratings.We found a significant CPM effect when considering N2P2, with low CS predictability augmenting CPM inhibition; in contrast, a surprisingly facilitatory CPM effect occurred in pain ratings (in the high CS predictability condition). The threat manipulation was only partially successful because CS intensity increased the threat ratings but not -as intended- CS predictability. Correlations between subjective and psychophysiological CPM responses were low.The differing CPM effects in subjective and psychophysiological responses, with both inhibitory and facilitatory effects, is puzzling but has already been observed earlier. The consideration of the CPM stimulation as major threat that is emotionally active is theoretically clearly justifiable but the operationalisation by means of different levels of CS predictability as in the present study might not have been ideal. Thus, further attempts of experimental verification are warranted. [ABSTRACT FROM AUTHOR]

Employing the nonlinear local Lyapunov exponent (NLLE) technique, this study assesses the quantitative predictability limit of oceanic mesoscale eddy (OME) tracks utilizing three eddy datasets for both annual and seasonal means. Our findings reveal a discernible predictability limit of approximately 39 days for cyclonic eddies (CEs) and 44 days for anticyclonic eddies (AEs) within the South China Sea (SCS). The predictability limit is related to the OME properties and seasons. The long-lived, large-amplitude, and large-radius OMEs tend to have a higher predictability limit. The predictability limit of AE (CE) tracks is highest in autumn (winter) with 52 (53) days and lowest in spring (summer) with 40 (30) days. The spatial distribution of the predictability limit of OME tracks also has seasonal variations, further finding that the area of higher predictability limits often overlaps with periodic OMEs. Additionally, the predictability limit of periodic OME tracks is about 49 days for both CEs and AEs, which is 5–10 days higher than the mean values. Usually, in the SCS, OMEs characterized by high predictability limit values exhibit more extended and smoother trajectories and often move along the northern slope of the SCS. [ABSTRACT FROM AUTHOR]

This paper documents that the ratio of residential-to-nonresidential investment is a strong in-sample and out-of-sample predictor for the dollar up to 12 quarters. The predictability is robust to a battery of additional checks and holds for other G10 currencies. We explain the predictability in an analytical model with time-varying housing preference, productivity, and volatility. In the model, the U.S. housing investment share is higher during periods with higher growth and lower uncertainty, corresponding to lower future nontradable prices, dollar index, and excess returns. We find strong empirical support for the channel. Alternative explanations, including the business and financial cycle, find less empirical support. This paper was accepted by David Sraer, finance. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4932. [ABSTRACT FROM AUTHOR]

We investigate the role of the subpolar North Atlantic (SPNA) for downstream predictability, using two decadal climate prediction systems. We use the subpolar extreme cold and fresh anomaly event developing in winter 2013/2014 as initial conditions and evaluate ensemble predictions of the two systems in the following decade. In addition, we perform ensemble pacemaker experiments where the models are forced toward observed ocean temperature and salinity anomalies in the SPNA from November 2014 through December 2019. The pacemaker experiments show improved skill along the Atlantic Water pathway, compared with the standard decadal predictions, and we therefore conclude that the correct description of the ocean in the SPNA is the key. The enhanced skill is most prominent in subsurface salinity in the form of propagating anomalies. Plain Language Summary: Observations show that ocean anomalies propagate across the North Atlantic and further north along the Norwegian coast. Anomalous ocean temperatures can modify heat exchange between ocean and atmosphere and influence the circulation of the atmosphere. Since such anomalies can persist for months, it potentially opens a door to seasonal prediction of the atmosphere. However, climate models used for seasonal and decadal prediction fail to predict these propagating anomalies. In our study we aim to find causes for this lack of predictability. We use the record low temperatures observed in the North Atlantic in 2015 as a test case. We employ two climate prediction systems and make two types of predictions: one type is a standard prediction initialized with winter 2014/2015 ocean conditions; the other type is initialized in the same way and in addition, the model is kept close to observations in the subpolar gyre during the whole prediction, in the region where the cold blob was formed. Comparing the two types of predictions, we see that keeping the model close to observations in the subpolar gyre increases the predictability along the Norwegian coast. This points to the subpolar gyre as a key area where models need to be improved. Key Points: Knowing temperature and salinity in the subpolar gyre increases skill downstream along the Atlantic Water pathwaySalinity anomalies propagate northward, while temperature anomalies are more difficult to traceA pacemaker experiment has been used for decadal predictions for the first time [ABSTRACT FROM AUTHOR]

The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range. [ABSTRACT FROM AUTHOR]

Naturalistic observations show that animals pre-empt danger by moving to locations that increase their success in avoiding future threats. To test this in humans, we created a spatial margin of safety (MOS) decision task that quantifies pre-emptive avoidance by measuring the distance subjects place themselves to safety when facing different threats whose attack locations vary in predictability. Behavioral results show that human participants place themselves closer to safe locations when facing threats that attack in spatial locations with more outliers. Using both univariate and multivariate pattern analysis (MVPA) on fMRI data collected during a 2 h session on participants of both sexes, we demonstrate a dissociable role for the vmPFC in MOS-related decision-making. MVPA results revealed that the posterior vmPFC encoded for more unpredictable threats with univariate analyses showing a functional coupling with the amygdala and hippocampus. Conversely, the anterior vmPFC was more active for the more predictable attacks and showed coupling with the striatum. Our findings converge in showing that during pre-emptive danger, the anterior vmPFC may provide a safety signal, possibly via foreseeable outcomes, while the posterior vmPFC drives unpredictable danger signals. [ABSTRACT FROM AUTHOR]

In July and August 2022, the Yangtze River basin (YRB) experienced its hottest summer since 1961. The SINTEX‐F2 seasonal prediction system initialized in early May predicted the hotter‐than‐normal summer due to its successful prediction of central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the tropical West Pacific‐East Indian Ocean (TWP_EIO). The common SST forcing explains only about 26% to the heatwave strength, while the internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction account for approximately 65%, based on the analysis of 108 ensemble members. These factors have collectively increased the maximum temperature over the YRB through the enhancement and westward expansion of western North Pacific subtropical high. Our findings quantify the relative contributions of external forcing and internal variations to the unprecedented hot event, offering insights into its forming mechanism and potential predictability. Plain Language Summary: A record‐breaking heatwave event occurred in the YRB in July and August 2022, posing significant risks on human health, power supply, and social economic activities. Recognizing the importance of such an event, our study aims to identify key factors influencing its prediction using the SINTEX‐F2 system. The central Pacific La Niña, negative Indian Ocean Dipole and the resultant warming in the TWP_EIO provide the dominant predictability, but accounts only about 26% of the heatwave strength. However, internal variations in the anomalous warming in the TWP_EIO and Europe, surplus precipitation in Pakistan, and local land‐air interaction collectively explain about 65%. Our results suggest the necessity of large‐ensemble prediction in capturing this kind of unprecedented extreme event. Key Points: The SINTEX‐F2 initialized on 2022 early May captures the warmer‐than‐normal air temperatures in Yangtze River basinThe model successfully predicts co‐occurrences of CP La Niña and negative IOD in 2022However, the direct SST forcing contributes only 26% to the 2022 extreme event in Yangtze River basin [ABSTRACT FROM AUTHOR]

The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are the two leading modes of North Atlantic pressure variability and have a substantial impact on winter weather in Europe. The year‐to‐year contributions to multi‐model seasonal forecast skill in the Copernicus C3S ensemble of seven prediction systems are assessed for the wintertime NAO and EA, and well‐forecast and poorly‐forecast years are identified. Years with high NAO predictability are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Well‐forecast EA winters also generally occurred when there was substantial tropical forcing, although the relationship was less robust than for the NAO. These results support previous findings of the impacts of tropical forcing on the North Atlantic and show this is important from a multi‐model seasonal forecasting perspective. Plain Language Summary: The wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) are two important indicators of atmospheric variability in the North Atlantic. They can have a substantial impact on European winter weather. The ability of seasonal forecast models to forecast the NAO and EA varies from year to year. This intermittency of forecast skill is investigated in seven different seasonal forecast systems from the Copernicus C3S database, by focusing on the most well‐forecast and poorly‐forecast years. Years where the NAO is well‐forecast are associated with substantial tropical forcing, generally from the El Niño Southern Oscillation (ENSO), while poor forecasts of the NAO occur when ENSO forcing is weak. Similar but weaker results hold for the EA. These results are valuable for increasing the usability of seasonal forecasts by identifying conditions under which forecasts are more likely to be skillful. Key Points: Seasonal forecast skill of the wintertime North Atlantic Oscillation (NAO) and East Atlantic Pattern (EA) is intermittentWell‐forecast NAO/EA winters generally occur when there is substantial tropical forcing [ABSTRACT FROM AUTHOR]

Studies of word predictability in context show that words in English tend to be shorter if they are predictable from the next word, and to a lesser extent, if they are predictable from the previous word. Some studies distinguish function and content words, but otherwise have not considered grammatical factors, treating all two-word sequences as comparable. Because function words are highly frequent, words occurring with them have low predictability. Highest predictability occurs within bigrams with two content words. Using the Buckeye corpus, we show that content word bigrams from different constructions vary widely in predictability, with adjective–noun and noun–noun sequences (content words within a noun phrase) having the highest scores. It is known that in adjective–noun sequences, the vowel of the adjective is shorter than in other positions. We study noun–noun sequences within the noun phrase and show that the first noun is shorter than in other contexts. It follows that the shorter duration of the first word when it is predictable from the second in many cases is due to the noun phrase construction and not necessarily the regulation of duration corresponding to predictable versus unpredictable information. We conclude that predictability studies must consider the constructions words occur in. [ABSTRACT FROM AUTHOR]

Atmospheric prediction at 2–6 weeks lead time (so‐called subseasonal‐to‐seasonal timescales) entails large forecast uncertainty. Here we investigate the flow‐dependence of this uncertainty during Boreal winter. We categorize the large‐scale flow using North Atlantic‐European weather regimes. First, we show that forecast uncertainty of near‐surface geopotential height (Z1000) and temperature (T2m) are strongly sensitive to the prevailing regime. Specifically, forecast uncertainty of Z1000 reduces over northern Europe following Greenland Blocking (enhanced predictability) due to a southward shifting eddy‐driven jet. However, due to strong temperature gradients and variable flow patterns, Greenland blocking is linked to increased forecast uncertainty of T2m over Europe (reduced predictability). Second, we show that forecast uncertainty of weather regimes is modulated via the stratospheric polar vortex. Weak polar vortex states tend to reduce regime‐uncertainty, for example, due to more frequent predicted occurrence of Greenland blocking. These regime changes are associated with increased T2m uncertainty over Europe. Plain Language Summary: Weather is chaotic, and forecasts several weeks ahead are quite uncertain. Nevertheless, the degree of uncertainty varies, which can be relevant for long‐term planning in various sectors, including agriculture, energy supply, and public health. Here we show that the degree of uncertainty depends on the weather at the time of forecast start, which we categorize using eight characteristic weather patterns for the North Atlantic and Europe. We analyze a large set of forecasts during winter, with lead times up to 6 weeks. For example, persistent high‐pressure systems over Greenland are known to favor low temperatures over northern Europe. Our results indicate that, in addition, temperatures are highly variable in these cases, leading to unusually high forecast uncertainty. In contrast, uncertainty of near‐surface air pressure tends to decrease due to less frequent storms over the North Atlantic. Furthermore, we show that forecast uncertainty of the weather patterns themselves varies, which is useful when large‐scale flow conditions are more critical than local weather. We analyze forecasts under different circulation conditions in the Arctic stratosphere, as these can have long‐lasting impacts on surface weather. We find forecasts of weather patterns become less uncertain when the circumpolar winds in the Arctic stratosphere are weak. Key Points: Ensemble spread of near‐surface weather in subseasonal forecasts is sensitive to the prevailing North Atlantic‐European weather regimeGreenland blocking is linked to the smallest ensemble spread of geopotential but to the largest spread of temperature over northern EuropeWeak polar vortex states are followed by reduced forecast uncertainty of weather regimes but increased uncertainty of European temperature [ABSTRACT FROM AUTHOR]

Even though most children acquire language effortlessly, not all do. Nowadays, language disorders are difficult to diagnose before 3-4 years of age, because diagnosis relies on behavioral criteria difficult to obtain early in life. Using electroencephalography, I investigated whether differences in newborns' neural activity when listening to sentences in their native language (French) and a rhythmically different unfamiliar language (English) relate to measures of later language development at 12 and 18 months. Here I show that activation differences in the theta band at birth predict language comprehension abilities at 12 and 18 months. These findings suggest that a neural measure of language discrimination at birth could be used in the early identification of infants at risk of developmental language disorders. [ABSTRACT FROM AUTHOR]

The characteristics of the Madden–Julian oscillation (MJO) have changed and are projected to continue changing with the expansion of the Indo–Pacific warm pool, which is the Earth's largest region of warm sea surface temperatures (SSTs). However, the likelihood of a change in MJO predictability following warm pool expansion remains unaddressed. Therefore, this study investigated the effect of warm pool expansion on MJO variability and predictability using the highly idealized aquaplanet configuration of Community Earth System Model 2 (CESM2). By expanding the warm pool in the Indo–Pacific, MJO‐like waves become more regionally confined, short‐lived convective events with weaker magnitude and less robust eastward propagating signals, possibly due to stronger zonal SST gradients and wider meridional widths of the warm pool. Perfect‐model ensemble forecast experiments revealed that the MJO predictability decreased by approximately 5 days, the forecast error proliferated, and the signal rapidly reduced following warm pool expansion. Plain Language Summary: The Madden–Julian oscillation (MJO) is a prominent mode of tropical intra‐seasonal variability in which its convective anomalies drive anomalous circulation within the tropics and remotely in the extratropics, thus providing a "forecast of opportunity" for global weather and climate events. Over the decades, MJO characteristics have changed and are projected to continue changing with the expansion of the Indo–Pacific warm pool—the largest pool with the warmest ocean temperature on Earth. If warm pool expansion changes the MJO characteristics, it is likely that its predictability has changed and will continue to do so in the future. In this study, the change in MJO predictability by expanding the Indo–Pacific warm pool was explored using CESM2 aquaplanet model experiments. The results show that, with warm pool expansion, MJO predictability decreased by approximately 5 days. This decrease is primarily attributed to the change of the MJO‐like waves into more regionally confined, short‐lived convective events with weaker magnitude and less robust eastward propagating signals, owing to the change in SST and circulations. Key Points: A sensitivity experiment was performed using Community Earth System Model 2 aquaplanet with different prescribed sea surface temperaturesExpanding the Indo–Pacific warm pool makes the Madden–Julian oscillation‐like waves less organizedThe warm pool expansion quickens the forecast error increase and predictability decrease [ABSTRACT FROM AUTHOR]

Many bilinguals switch languages in daily-life conversations. Although this usually happens within sentence context and with another speaker, most research on the cognitive mechanisms underlying the production of language switches has studied individual words. Here, we examined how context influences both switching frequency and the temporal cost associated with it. Sixty Bulgarian-English bilinguals named pictures in their language of choice without any context, in a sentence context, and in interaction with another (recorded) bilingual. Switching frequency was lower, and costs higher, when bilinguals switched languages with context than without context. This suggests switching costs were not an artifact of tasks without context. Furthermore, both switching frequency and costs correlated across the tasks. In addition, we examined the potential influence of sentence context and the conversation partner. Predictability in sentence context has previously been argued to reduce language competition, which in turn could influence switching. We therefore compared sentences with a predictable or unpredictable target word. As hypothesized, bilinguals were less likely to switch languages when a word was predictable in its sentence context, potentially because words in the other language were less active. The conversation partner's overall switching behaviour had little impact on a bilingual's general switching rate, showing relatively low global alignment. However, local alignment was observed as switching was influenced by the partner's switching in the immediately preceding utterance. Together, these findings show that while production tasks without context can reliably measure switching costs, studying effects of context is necessary to better capture a bilingual's language-switching behavior. [ABSTRACT FROM AUTHOR]

The accurate prediction of intense precipitation events is one of the main objectives of operational weather services. This task is even more relevant nowadays, with the rapid progression of global warming which intensifies these events. Numerical weather prediction models have improved continuously over time, providing uncertainty estimation with dynamical ensembles. However, direct precipitation forecasting is still challenging. Greater availability of machine‐learning tools paves the way to a hybrid forecasting approach, with the optimal combination of physical models, event statistics, and user‐oriented postprocessing. Here we describe a specific chain, based on a random‐forest (RF) pipeline, specialised in recognising favourable synoptic conditions leading to precipitation extremes and subsequently classifying extremes into predefined types. The application focuses on northern and central Italy, taken as a testbed region, but is seamlessly extensible to other regions and time‐scales. The system is called MaLCoX (Machine Learning model predicting Conditions for eXtreme precipitation) and is running daily at the Italian regional weather service of ARPAE Emilia‐Romagna. MalCoX has been trained with the ARCIS gridded high‐resolution precipitation dataset as the target truth, using the last 20 years of the European Centre for Medium‐Range Weather Forecasts (ECMWF) reforecast dataset as input predictors. We show that, with a long enough training period, the optimal blend of larger‐scale information with direct model output improves the probabilistic forecast accuracy of extremes in the medium range. In addition, with specific methods, we provide a useful diagnostic to convey to forecasters the underlying physical storyline which makes a meteorological event extreme. [ABSTRACT FROM AUTHOR]

Cold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase. [ABSTRACT FROM AUTHOR]

The study of the dynamics of foreign exchange rates is of paramount importance to economic policy makers for developing countries like India that depend on foreign exchange to meet a large part of their import obligations. This paper uses the concepts of entropy to study the regularity/irregularity of the returns from the Indian Foreign exchange (forex) markets particularly during periods of economic instability. We use the Approximate Entropy and Sample Entropy statistics which measure the level of repeatability in the data are used to quantify the randomness in the forex returns for four major currencies namely the US Dollar, the British pound, the EURO and the Japanese Yen with respect to the Indian Rupee from the time period 2006 to 2021. These statistics are chosen as they are both statistical measures for assessing the randomness or regularity of time series data and enable researchers to objectively evaluate the degree of randomness present in the returns series and understand the crucial underlying patters or behaviors. In this research we look at two periods of major financial upheavals, the subprime crisis also known as the Global Financial Crisis (GFC) during 2006-2007 and the recent Covid-19 pandemic during 2020-2021. Our empirical results overwhelmingly confirm our working hypothesis that regularity in the returns of the major Indian foreign exchange rates increases during times of financial crisis. This is evidenced by a decrease in the values of the sample entropy and approximate entropy before and after/during the financial crisis period for the majority of the exchange rates. Our empirical results also show that Sample Entropy is a better measure of regularity than Approximate Entropy for the Indian forex rates which is in agreement with the theoretical predictions. Understanding the returns from foreign exchange rates during financial crises has important policy implications for various stakeholders, including market participants, policymakers, and regulators. It enables them to formulate better risk management and hedging strategies. It also helps policy makers in crisis management and contingency planning and safeguard the stability of the financial system. Exchange rate movements are interconnected across global markets, and crises often have cross-border implications. Collaborating on crisis management strategies across countries can help foster stability in international markets. [ABSTRACT FROM AUTHOR]

In this article, we consider a measure of predictability of a random field following a stationary two-dimensionally indexed autoregressive moving-average (2-D ARMA). The purpose of this article is two-fold. First, we give a characterization of equal predictability processes by providing necessary and sufficient conditions, which highlight the role of the coefficients of the moving average (MA) and the equivalent autoregressive (EAR) representations. Second, we implement a procedure allowing us to carry out a test of equal predictability. A simulation study is presented and an application to real data of wheat yield is reported. [ABSTRACT FROM AUTHOR]

This study investigates the predictability of downslope windstorms located in Santa Barbara County, California, locally referred to as Sundowner winds, from both observed relationships and a high-resolution, operational numerical weather prediction model. We focus on April 2022, during which the Sundowner Winds Experiment (SWEX) was conducted. We further refine our study area to the Montecito region owing to some of the highest wind measurements occurring at or near surface station MTIC1, situated on the coast-facing slope overlooking the area. Fires are not uncommon in this area, and the difficulty of egress makes the population particularly vulnerable. Area forecasters often use the sea-level pressure difference (Δ SLP) between Santa Barbara Airport (KSBA) and locations to the north such as Bakersfield (KBFL) to predict Sundowner windstorm occurrence. Our analysis indicates that Δ SLP by itself is prone to high false alarm rates and offers little information regarding downslope wind onset, duration, or magnitude. Additionally, our analysis shows that the high-resolution rapid refresh (HRRR) model has limited predictive skill overall for forecasting winds in the Montecito area. The HRRR, however, skillfully predicts KSBA-KBFL Δ SLP, as does GraphCast, a machine learning weather prediction model. Using a logistic regression model we were able to predict the occurrence of winds exceeding 9 m s − 1 with a high probability of detection while minimizing false alarm rates compared to other methods analyzed. This provides a refined and easily computed algorithm for operational applications. [ABSTRACT FROM AUTHOR]

The scale-dependent predictability of the devastating 7·20 extreme rainstorm in Zhengzhou, China in 2021 was investigated via ensemble experiments, which were perturbed on different scales using the stochastic kinetic-energy backscatter (SKEB) scheme in the WRF model, with the innermost domain having a 3-km grid spacing. The daily rainfall (RAIN24h) and the cloudburst during 1600–1700 LST (RAIN1h) were considered. Results demonstrated that with larger perturbation scales, the ensemble spread for the rainfall maximum widens and rainfall forecasts become closer to the observations. In ensembles with mesoscale or convective-scale perturbations, RAIN1h loses predictability at scales smaller than 20 km and RAIN24h is predictable for all scales. Whereas in ensembles with synoptic-scale perturbations, the largest scale of predictability loss extends to 60 km for both RAIN1h and RAIN24h. Moreover, the average positional error in forecasting the heaviest rainfall for RAIN24h (RAIN1h) was 400 km (50–60) km. The southerly low-level jet near Zhengzhou was assumed to be directly responsible for the forecast uncertainty of RAIN1h. The rapid intensification in low-level cyclonic vorticity, mid-level divergence, and upward motion concomitant with the jet dynamically facilitated the cloudburst. Further analysis of the divergent, rotational and vertical kinetic spectra and the corresponding error spectra showed that the error kinetic energy at smaller scales grows faster than that at larger scales and saturates more quickly in all experiments. Larger-scale perturbations not only boost larger-scale error growth but are also conducive to error growth at all scales through a downscale cascade, which indicates that improving the accuracy of larger-scale flow forecast may discernibly contributes to the forecast of cloudburst intensity and position. [ABSTRACT FROM AUTHOR]

OBJECTIVE To assess the predictability of the hemangiosarcoma likelihood prediction (HeLP) score and the Tufts Splenic Tumor Assessment Tool (T-STAT) for hemangiosarcoma and malignancy, respectively. ANIMALS 261 dogs undergoing splenectomy for a splenic mass. METHODS Medical records were retrospectively reviewed; variables for the HeLP score and T-STAT were collected, and scores were assigned. Area under the curve (AUC) was calculated for each score. RESULTS The HeLP score included 141 dogs; hemangiosarcoma was diagnosed in 87 (61.7%) dogs. The median cumulative HeLP score was 51 (range, 17 to 82; IQR, 39 to 58) for dogs with hemangiosarcoma and 28 (range, 0 to 70; IQR, 17 to 41) fordogs without hemangiosarcoma. The categorical HeLP score was low (28; 32.2%), medium (31; 35.6%), and high (28; 32.2%) for dogs with hemangiosarcoma and was low (41; 75.9%), medium (9; 16.7%), and high (4; 7.4%) for dogs without hemangiosarcoma. The AUC of the cumulative and categorical HeLP scores for diagnosis of hemangiosarcoma were 0.79 (95% Cl, 0.71 to 0.86) and 0.73 (95% Cl, 0.65 to 0.82), respectively. The T-STAT included 181 dogs. Lesions were benign in 95 (52.5%) and malignant in 86 (47.5%) dogs. The median T-STAT score was 62% (range, 5% to 98%; IQR, 36% to 77%) for dogs with malignant lesions and 38% (range, 5% to 91%; IQR, 24% to 59%) for dogs with benign lesions. The T-STAT had an AUC of 0.68 (0.60 to 0.76) for diagnosis of malignancy. CLINICAL RELEVANCE The HeLP score had acceptable performance, and the T-STAT had poor performance for diagnosis prediction. A tool with excellent or outstanding discrimination is needed to more reliably predict the presence of hemangiosarcoma or a malignant lesion preoperatively. [ABSTRACT FROM AUTHOR]

• A higher perceived predictability was associated with a more positive impact on health-related quality of life. • Predictability in family caregiving should be taken into considerations when assessing conditions of family caregivers of persons living with dementia. • The healthcare interventions that enhance predictability for family caregivers of persons living with dementia could help reduce the impact of high caregiving demands. The purpose of this study was to examine the role of predictability in the relationship between caregiving demands and caregiving consequences. Design. This 2-year longitudinal survey study collected from self-report questionnaires. A convenience sample of family caregivers of older persons living with dementia were recruited from a neurology clinic. A total of 200 family caregivers were recruited to participate. Analysis indicated predictability was a partial mediator between caregiving demand and caregiver consequences of role strain, depressive symptoms and both physical and mental components of health-related quality of life at the 2-year follow. Predictability accounted for 25 %, 28.8 %, 15.3 % and 46.5 % of the relationship between caregiving demand and caregiving consequences of role strain, depressive symptoms, physical- and mental-health related quality of life, respectively. The contributions of caregiving demand to outcomes of caregiver consequences were in part due family caregivers perceived predictability for caregiving. [ABSTRACT FROM AUTHOR]

In this study, we set out to construct a daily frequency climate risk index with the aim to explore the impacts of climate risk on the stock markets of advanced and emerging countries and green stocks. We adopt an approach similar to the Sharpe ratio to demonstrate the pricing of climate risk into stocks and therefore reexamine the (in)efficiency of these markets. We specify an econometric model to evaluate the predictive content of the climate risk index for stock returns. We find compelling evidence of market efficiency when climate risk is priced into the stocks and market inefficiency otherwise. Results from the predictability analyses indicate that about 85% of advanced stock markets, 17% of emerging stock markets, and 100% of green stocks have the capacity to protect investors against climate risk. Forecast evaluations reveal that the climate risk index is a good predictor of the stock returns only after controlling for the macroeconomic environment. We explore several robustness checks and highlight both investment and policy implications. [ABSTRACT FROM AUTHOR]

This paper investigates the extent to which commodity prices can predict economic growth, the growth exposure of the countries to specific commodity group, and whether the country stage of development matters for growth dependency on commodities. We use a novel time‐varying mixed‐frequency vector autoregressive model to jointly estimate causality between monthly commodity prices and quarterly economic growth for the period from January 1980 to March 2020. Our findings suggest that growth dependency on commodities varies over time and across different synthetic measures, but in general solid evidence of predictability is determined. The overall commodity index shows better performance in predicting economic growth than the segregated proxies of either fuel or non‐fuel commodities in developed economies. However, the overall commodity index is as effective as the fuel index in predicting economic growth for their developing counterparts. We develop a new index of global commodity growth connectivity (GCGCI) to determine the economic growth dependency on commodities over time. Our results show that economic growth dependency on commodities has increased at least three times with financialization of commodity markets. The GCGCI for all commodities reveals a higher growth dependency on commodities in developing countries than developed ones in the post‐financialization period. Interestingly, we find that growth decreases its reliance on fuel commodities in both developed and developing countries after financialization of commodity markets. Our findings bear implications for policymakers to design stabilization policies in an attempt to protect economies vulnerable to commodity price shocks and lay the foundation for sustainable economic growth. [ABSTRACT FROM AUTHOR]

Abstract The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range.

When the obligee does not collaborate with the obligor based on good faith in any steps of the contract including formation and execution or even after contract breach and this causes or increases losses, then that situation gives rise to the concept of the fault of the injured party. Therefore, if the obligee and the obligor jointly cause or increase losses, then the concept of contributory negligence, or comparative negligence according to the US legal system, should be considered. The present paper aims to conduct a comparative study on contributory negligence in contract law together with an analysis of relevant domestic and foreign opinions. It is concluded that in the US legal system, contributory negligence is usually dismissed in the field of contracts; but "reasonable reliance" and "predictability" have efficient similarities to the contributory negligence concept and this fact reveals traces of contributory negligence in the US legal system. However, in the Iranian legal system laws indirectly imply this concept and the presence of contributory negligence in the Iranian law can also be confirmed based on analogy, ascertainment, good faith, and purpose of law and the fact that this concept merely mentioned with regard to some laws, can be explained based on the Islamic prevalence rule (Qalabah).

An extreme heavy rainfall event struck the northern part of the Guilin urban area in the early morning of May 22, 2023, breaking local historical records for both hourly and three-hourly precipitation, resulting in severe urban waterlogging. Analysis was conducted using data from routine observation, ground-based dense automatic stations, Doppler weather radar, and ERA5 reanalysis. The results are as follows. (1) This event occurred on the edge of the subtropical high-pressure system, with the continuous strengthening of the southwest jet stream providing abundant moisture and energy for the extreme rainfall under the influence of the low-level shear line and the southward movement of the surface cold front. (2) The echoes of heavy rainfall evolved into a linear convection, which changed the direction and shape when timely met with the surface cold air. Meanwhile, the continuously developing new individual cells on its western side joined to form a "train effect," leading to extreme rainfall. The echo exhibited characteristics of low core and high efficiency. (3) The timely intrusion of weak cold air intensified convective rainfall and enhanced cold pool outflow, triggering new convection in the warm and moist region ahead of it, which favored the development and persistence of heavy rainfall. (4) During the eastward movement of individual convective cells, they merged through cloud bridges and expanded through cloud development, forming new cloud clusters and rapidly intensifying precipitation. The interaction and merging of convective cells were the main mechanisms for the prolonged and intensified heavy rainfall. (5) Various numerical models underestimated the intensity of this event, with biases in forecasting the rainfall center mainly due to deviations in the timing of forecasting the influence of surface cold air.

Abstract Background to evaluate the predictability of expansion achieved in patients in early mixed dentition treated with Clear Aligners (CA), analyzing the efficiency of the expansion at the end of the first set of aligners and at the end of the therapy in the upper and lower arch. Methods 36 patients (20 F, 16 M; mean age 8.3 ± 1.5 years) were selected retrospectively from the Department of Orthodontics of the Hospital of Rome “Tor Vergata”. All subjects were treated with CA with no other auxiliaries than attachments. For each patient a standardized sequential expansion protocol was planned for both arches. Digital dental casts were created at three observation periods from an intraoral scanner: prior to treatment (T0), at the end of the first set of aligners (T1), at the end of treatment (T2). The 3D models in planned position determined by the first Clincheck (CC) were obtained for comparison with T1 and T2. Six linear transversal measurements were used to evaluate the dimensional changes and the predictability of expansion movements, comparing T1-CC and T2-CC. Results a statistically significant increase within the pre-treatment and the final outcomes for all the variables examined was found. In the upper arch, the greatest level of predictability was detected at the level of the first (46.44%) and second deciduous molar width (44.95%) at T1. The analysis of T2-CC changes showed a significant increase in the percentage of predictability of expansion at the level of the first permanent molars, at mesial (54.86%) and distal (58.92%) width. In the lower arch, a higher percentage of predictability than the upper arch was reported at T1-CC and T2-CC, with the greatest values at the level of second (T1-CC: 48.70%; T2-CC: 75.32%) and first deciduous molar width (T1-CC: 45.71%; T2-CC: 72.75%). Conclusions CA can induce significant transversal increments. The predictability of expansion is variable, but it did not exceed the 50% during the first set of aligners. It was necessary to apply refinement set to achieve a good predictability for expansion of about 70%. The expansion in the lower arch was observed to be more predictable than in the upper arch.

Abstract Clear aligners are employed daily for the treatment of several malocclusions. Previous clinical studies indicated low accuracy for the correction of tooth rotations. The aim of this study was to evaluate the predictability of tooth rotations with clear aligners. The sample comprised 390 teeth (190 mandibular; 200 maxillary), measured from the virtual models of 45 participants (21 men, 24 women; mean age: 29.2 ± 6.6 years old). For each patient, pre-treatment (T0) digital dental models (STL files), virtual plan (T1) and post-treatment digital dental models (T2) of both the mandibular and maxillary arches were imported onto Geomagic Control X, a 3D metrology software which allows angular measurements. Rotations were calculated by defining reproducible vectors for all teeth in each STL file and superimposing both T0 with T1 to determine the prescribed rotation, and T0 with T2 to determine the achieved rotation. Prescribed and achieved rotations were compared to assess movement’s accuracy. The Wilcoxon signed-rank test and paired t-test were used to assess differences between the prescribed and achieved movements (P

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein.

In accordance with Sieyès' understanding of sovereignty, later reflected in the text of the Romanian Constitution, the title of sovereignty rests with the Romanian people, who exercise it through their representative organs.Thus, the representatives of the sovereign power undertake to ensure the stability of the legal regulations, so that, frequent changes in legislation and the legal or economic regime of taxes do not put the taxpayer in a situation where he cannot make a fiscal planning, and inherently disrupt its activities, especially economic. Based on these dynamics, the present study also proposes a broadening of the spectrum of analysis of the principle of predictability, which is not limited to the fiscal matter, but considers the entire normative corpus, being even a formal requirement subject to extrinsic examination by the Constitutional Court of Romania on the control of the constitutionality of the laws, lato sensu.

Seasonal hindcasts have previously been demonstrated to show multi‐decadal variability in skill across the twentieth century in indices describing El‐Niño Southern Oscillation (ENSO), which drives global seasonal predictability. Here, we analyze the skill of predicting ENSO events' magnitude and spatial pattern, in the CSF‐20C coupled seasonal hindcasts in 1901–2010. We find minima in the skill of predicting the first (in 1930–1950) and second (in 1940–1960) principal components of sea‐surface temperature (SST) in the tropical Pacific. This minimum is also present in the spatial correlation of SSTs, in 1930–1960. The skill reduction is explained by lower ENSO magnitude and variance in 1930–1960, as well as decreased SST persistence. The SST skill minima project onto surface winds, leading to worse predictions in coupled hindcasts compared to hindcasts using prescribed SSTs. Questions remain about the offset between the first and second principal components' skill minima, and how the skill minima impact the extra‐tropics. Plain Language Summary: Seasonal forecasts use slowly changing elements of the climate system to predict average weather and climate several months ahead, and have applications across the energy, agriculture, and insurance industries. The most important signal is El‐Niño Southern Oscillation (ENSO), an ocean temperature pattern in the tropical Pacific which varies between warm and cold phases every 2–7 years. ENSO's specific pattern, including how warm/cold it is compared to normal, and where heating/cooling is concentrated, influences global weather. Seasonal forecasting models are tested by running 'reforecasts': comparing a 'forecast' of the past with observations. In this study, we investigate how skilfully reforecasts from a state‐of‐the‐art seasonal forecasting model reproduce observations, for sea‐surface temperatures in the tropical Pacific (where ENSO occurs). We analyze reforecast skill in every Northern Hemisphere winter from 1901 to 2010 and find periods of high skill at the start and end of the century, with reduced skill in 1930–1960. This was likely caused by a reduction in the size of ENSO events, and decreased sea‐surface temperature memory. This reduction in ocean skill leads to lower skill for surface‐level winds. These results are important for understanding whether season‐ahead predictability changes over decades/centuries, and how climate change might impact this predictability. Key Points: The skill of predicting the spatial pattern of ENSO events in coupled seasonal hindcasts is low in 1930–1960, compared to before and afterThe skill minimum is attributable to lower ENSO variability and decreased SST persistence in the mid‐century periodDecreased skill in the SST spatial pattern is linked to decreased surface wind skill in coupled hindcasts (vs. atmosphere‐only hindcasts) [ABSTRACT FROM AUTHOR]

With the rapid development of the mobile Internet, online social networks are playing an increasingly vital role in the dissemination of information. Accurately predicting the size of information cascades in advance has become a crucial issue, particularly in the realms of viral marketing, risk management and resource allocation. There are numerous studies that have tackled this prediction task, but the outcomes are unsatisfactory. In this paper, we explore the predictability of information cascade size through the lens of percolation theory. Our investigation reveals that the accuracy of cascade size prediction is notably diminished in the proximity of the threshold, evident in both artificial and empirical networks. Moreover, we observe a degradation and an user-level difference in prediction performance as social media platforms undergo evolution. Our findings underscore the necessity for additional factors to enhance prediction accuracy. [ABSTRACT FROM AUTHOR]

Background: to evaluate the predictability of expansion achieved in patients in early mixed dentition treated with Clear Aligners (CA), analyzing the efficiency of the expansion at the end of the first set of aligners and at the end of the therapy in the upper and lower arch. Methods: 36 patients (20 F, 16 M; mean age 8.3 ± 1.5 years) were selected retrospectively from the Department of Orthodontics of the Hospital of Rome "Tor Vergata". All subjects were treated with CA with no other auxiliaries than attachments. For each patient a standardized sequential expansion protocol was planned for both arches. Digital dental casts were created at three observation periods from an intraoral scanner: prior to treatment (T0), at the end of the first set of aligners (T1), at the end of treatment (T2). The 3D models in planned position determined by the first Clincheck (CC) were obtained for comparison with T1 and T2. Six linear transversal measurements were used to evaluate the dimensional changes and the predictability of expansion movements, comparing T1-CC and T2-CC. Results: a statistically significant increase within the pre-treatment and the final outcomes for all the variables examined was found. In the upper arch, the greatest level of predictability was detected at the level of the first (46.44%) and second deciduous molar width (44.95%) at T1. The analysis of T2-CC changes showed a significant increase in the percentage of predictability of expansion at the level of the first permanent molars, at mesial (54.86%) and distal (58.92%) width. In the lower arch, a higher percentage of predictability than the upper arch was reported at T1-CC and T2-CC, with the greatest values at the level of second (T1-CC: 48.70%; T2-CC: 75.32%) and first deciduous molar width (T1-CC: 45.71%; T2-CC: 72.75%). Conclusions: CA can induce significant transversal increments. The predictability of expansion is variable, but it did not exceed the 50% during the first set of aligners. It was necessary to apply refinement set to achieve a good predictability for expansion of about 70%. The expansion in the lower arch was observed to be more predictable than in the upper arch. [ABSTRACT FROM AUTHOR]

ABSTRACT Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food‐web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator–prey interactions, and a generic, modular model is needed to describe all the possible variation in predator–prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food‐web impacts of well‐studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world. [ABSTRACT FROM AUTHOR]

Aims and objectives: We examined how context is used to facilitate reading in the second language (L2) compared with the first language (L1), and how L2 availability and age modulate these context effects. Methodology: Using self-paced reading, participants read high- and low-constraint Hebrew sentences. In Experiment 1, L1 (n = 45) and L2 (n = 48) Hebrew readers were compared, whereas in Experiment 2, only L2 readers (n = 131) were examined, testing modulations by L2 availability and age. Data and analysis: Reading times of target, post target, and sentence final words were analyzed using linear-mixed-effects models. Findings: In Experiment 1, L2 readers differed from L1 readers in contextual processing, as evident in the significant interaction between context type and language background on the final word measure. In Experiment 2, L2 readers with lower L2 availability scores differed from those with higher scores, and younger readers differed from older ones, in the way high- and low-constraining context affected their reading behavior in the target word and in the final word of the sentence. These differences were indicated by significant interactions between context type and L2 availability as well as between context type and age group. These findings are best understood under a compensatory processing account. Originality: By complementing L1–L2 group comparisons with in-depth examination of the L2 profile, the current study reveals a continuous effect of L2 availability, such that a lower L2 availability is associated with a greater reliance on context. Furthermore, the inclusion of older and younger adults provides converging evidence to the use of contextual support as a compensatory mechanism when lexical processing is more effortful. [ABSTRACT FROM AUTHOR]

: Under the changing environment, global drought occurs frequently, which has a great impact on social economy and ecology. Current researches focus on the research and development of drought forecasting technology, but pay little attention to the theory of drought forecasting, such as the temporal and spatial distribution and driving mechanism of drought predictability. This work took the Loess Plateau as the research object, constructed a support vector machine model to forecast regional drought from 2008 to 2019, and quantified drought predictability with Kling, Gupta Efficiency (KGE). Then the spatial distribution characteristics of drought predictability at different time scales were investigated, and the key factors influencing changes in the distribution of drought predictability were identified. The results showed that the drought predictability of the Loess Plateau showed an upward trend with the increase of the time scale. The KGE average of SPEI12 was 73.8% higher than that of SPEI1. The predictability of seasonal drought is the highest in autumn, followed by summer, spring and winter. The drought predictability showed spatial heterogeneity, showing a high distribution pattern in the north and low distribution pattern in the south, and the drought predictability in the central Loess Plateau in spring and in the northwest desert region of the Loess Plateau in winter was extremely low. There were differences in the main factors affecting the predictability of inter, seasonal drought. The highest explanatory power in autumn was temperature, summer and spring was aridity index(AI), and winter was the coupling of air, sea PRE_AMO. Meanwhile, the influence of both variable factors on drought predictability of the Loess Plateau was significantly higher than that of single factor. This study provided a new insight into the predictability of drought and was helpful to further improve the ability of drought forecasting and early warning in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Intentional binding refers to the subjective temporal compression between a voluntary action and its subsequent sensory outcome. Despite some studies challenging the link between temporal compression and intentional action, intentional binding is still widely used as an implicit measure for the sense of agency. The debate remains unsettled primarily because the experimental conditions used in previous studies were confounded with various alternative causes for temporal compression, and action intention has not yet been tested comprehensively against all potential alternative causes in a single study. Here, we solve this puzzle by jointly comparing participants' estimates of the interval between three types of triggering events with comparable predictability—voluntary movement, passive movement, and external sensory event—and an external sensory outcome (auditory or visual across experiments). The results failed to show intentional binding, that is, no shorter interval estimation for the voluntary than the passive movement conditions. Instead, we observed temporal (but not intentional) binding when comparing both movement conditions with the external sensory condition. Thus, temporal binding appears to originate from sensory integration and temporal prediction, not from action intention. As such, these findings underscore the need to reconsider the use of "intentional binding" as a reliable proxy of the sense of agency. Public Significance Statement: When we press a light switch and observe a bulb lightening, we experience a sense of agency (SoA), a feeling of control over these events. We often perceive the temporal interval between our voluntary action and its consequence as shorter compared to the same interval between two events we are not involved. Such temporal binding has commonly been taken as a measure of the SoA. However, our study reveals that voluntary actions are neither necessary nor sufficient for temporal binding. Instead, temporal binding relies on predicting and integrating information. The SoA can be disturbed in various psychiatric disorders, and its brain mechanisms are currently being actively explored. Our study urges amending how it is measured. [ABSTRACT FROM AUTHOR]

Ensemble forecasts have been shown to better predict observed Atlantic climate variability than that of their own ensemble members. This phenomenon—termed the signal‐to‐noise paradox—is found to be widespread across models, timescales, and climate variables, and has wide implications. The signal‐to‐noise paradox can be interpreted as forecasts underestimating the amplitude of predictable signals on seasonal‐to‐decadal timescales. The cause of this remains unknown. Here, we examine sea level pressure variability from a very large multi‐model ensemble of uninitialized atmosphere‐only simulations, focusing on boreal winter. To assess signal‐to‐noise errors, the ratio of predictable components (RPC) is examined globally, as well as for regional climate indices: the North Atlantic Oscillation, Arctic Oscillation, Southern Annular Mode, and an Arctic index. Our analyses reveal significant correlations between the multi‐model ensemble‐mean and observations over large portions of the globe, particularly the tropics, North Atlantic, and North Pacific. However, RPC values greater than one are apparent over many extratropical regions and in all four climate indices. Higher‐resolution models produce greater observation‐model correlations and greater RPC values than lower‐resolution models in all four climate indices. We find that signal‐to‐noise errors emerge more clearly at higher resolution, but the amplitudes of predictable signals do not increase with resolution, at least across the range of resolutions considered here. Our results suggest that free‐running atmospheric models underestimate predictable signals in the absence of sea surface temperature biases, implying that signal‐to‐noise errors originate in the atmosphere or in ocean–atmosphere coupling. [ABSTRACT FROM AUTHOR]

This article introduces a new type of linear regression model with regularization. Each predictor is conditionally truncated through the presence of unknown thresholds. The new model, called the two-way truncated linear regression model (TWT-LR), is not only viewed as a nonlinear generalization of a linear model but is also a much more flexible model with greatly enhanced interpretability and applicability. The TWT-LR model performs classifications through thresholds similar to the tree-based methods and conducts inferences that are the same as the classical linear model on different segments. In addition, the innovative penalization, called the extremely thresholding penalty (ETP), is applied to thresholds. The ETP is independent of the values of regression coefficients and does not require any normalizations of regressors. The TWT-LR-ETP model detects thresholds at a wide range, including the two extreme ends where data are sparse. Under suitable conditions, both the estimators for coefficients and thresholds are consistent, with the convergence rate for threshold estimators being faster than n . Furthermore, the estimators for coefficients are asymptotically normal for fixed dimension p. It is demonstrated in simulations and real data analyses that the TWT-LR-ETP model illustrates various threshold features and provides better estimation and prediction results than existing models. for this article are available online. [ABSTRACT FROM AUTHOR]

• Unplanned linear and angular movements occur with deepbite correction with Invisalign. • Unplanned movements were more evident posteriorly than anteriorly. • Unplanned angular movements were more frequent than linear movements for all teeth. • Unplanned movements can affect predictability of deepbite correction with aligners. This study aimed to investigate unplanned tooth movements in deepbite correction with Invisalign. The sample consisted of 34 adult patients with deepbite malocclusion treated with Invisalign. Pretreatment and predicted digital models were exported from ClinCheck software, while the post-treatment model was obtained from an intraoral scan taken at the end of the first set of aligners. Digital models' superimposition was done using the eModel "Compare" software. Frequency and percentage of occurrence of unplanned linear and angular movements, their magnitude, and direction were then determined. In addition, comparison of unplanned movements was performed between anterior and posterior teeth as well as between linear and angular measurements. The percentage of unplanned movements ranged from 1.68% to 25.63% in the maxilla and 3.36% to 23.95% in the mandible, being most evident in the first and second molars. Unplanned movements were statistically significantly more frequent in posterior teeth compared with the anterior teeth (P > 0.05). In addition, the frequency of angular movements was statistically significantly higher for each tooth than linear movement (P < 0.05) with a higher mean value. Unplanned mesiodistal movements were the least frequent. Unplanned linear as well as angular movements were evident in almost all teeth. Unplanned faciolingual inclination and mesiodistal angulation movements were the most frequently observed. Unplanned vertical movements can affect the predictability of deepbite correction and thus merit particular attention in those cases. [ABSTRACT FROM AUTHOR]