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The mesoscale spectrum describes the distribution of kinetic energy in the Earth's atmosphere between length scales of 10 and 400 km. Since the first observations, the origins of this spectrum have been controversial. At synoptic scales, the spectrum follows a −3 spectral slope, consistent with two‐dimensional turbulence theory, but a shallower −5/3 slope was observed at the shorter mesoscales. The cause of the shallower slope remains obscure, illustrating our lack of understanding. Through a novel coarse‐graining methodology, we are able to present a spatio‐temporal climatology of the spectral slope. We find convection and orography have a shallowing effect and can quantify this using "conditioned spectra." These are typical spectra for a meteorological condition, obtained by aggregating spectra where the condition holds. This allows the investigation of new relationships, such as that between energy flux and spectral slope. Potential future applications of our methodology include predictability research and model validation. Plain Language Summary: The kinetic energy spectrum describes how much energy is at different spatial scales in the atmosphere, from km‐scale atmospheric waves to large‐scale weather systems 1,000 km across. This distribution may influence predictability. Edward Lorenz argued that the spectrum can determine whether a fluid can be forecast arbitrarily far into the future or not. In this paper, we employ a novel method to reveal how the spectrum varies in different locations on Earth. In addition, we generate the first "conditioned spectra," which are the aggregated spectra for different levels of orography, convection and energy transfer. We are able to demonstrate the tendency of convection and orography to increase small‐scale energy and show their effect on the classic global spectrum. Spectra are vital for model validation and predictability research; therefore, these results and the methods used to obtain them are of interest to meteorology practitioners, theorists and those in neighboring fields. Key Points: Global maps of spectral slope are produced through a novel coarse‐graining methodOrography and precipitation shallow the spectral slope in the troposphere significantlyConditioned spectra quantify the relationship between slope, orography, precipitation and energy flux [ABSTRACT FROM AUTHOR]

A Bayesian Model proposed by Kehler et al. ([2008]. Coherence and coreference revisited. Journal of Semantics, 25(1), 1–44. https://doi.org/10.1093/jos/ffm018) suggests that pronoun production and interpretation are driven by a different set of factors following the Bayes’ rule. Evidence suggests that the Bayesian Model makes better predictions on pronoun interpretation compared to other models that assume that pronoun production and interpretation are influenced by the same set of factors. Yet, it remains unclear precisely to what extent the Bayesian Model can capture this relationship. The current study examines the validity of the Bayesian Model by comparing its performance across three different contexts using a variety of evaluation methods. Our results demonstrate that the Bayesian Model’s performance varied across contexts and consistently underestimated the subject bias in interpretation. We suggest that the underestimation is likely because the subject bias in pronoun production is not sufficient to account for the subject bias in actual interpretation, contra to the assumption of the Bayesian Model. We discuss potential sources of the additional subject bias in interpretation. [ABSTRACT FROM AUTHOR]

Special combinations of variables can have more predictability than any single variable in the combination. What is the maximum possible predictability that can be achieved through such combinations? Recently, this question was answered in the context of a linear stochastic model with fixed dynamics, where a sharp upper bound on predictability time was derived. However, the precise maximum is a complicated function of the entire spectrum of dynamical eigenvalues, obscuring any simple relation between predictability and eigenmodes. Based on numerical solutions of specific cases, it is conjectured here that the predictability of a stochastic model with a given least damped mode is bounded above by the predictability of a model in which all dynamical eigenvalues coalesce to the value corresponding to the least damped mode. Furthermore, it is shown that in this limit the maximum predictability time is determined by the largest root of a Laguerre polynomial. This result is used to prove the following simple bound: The maximum predictability time in the limit of coalesced eigenvalues is at most 4D-6 times the predictability time of the least damped mode, where D is the number of dynamical eigenmodes and D exceeds 3. [ABSTRACT FROM AUTHOR]

This paper scrutinizes the conventionalization of the Spanish expression la de que ('the amount of that'), a reduced variant of la cantidad de que. The study seeks to determine the diachrony of and mechanisms underlying the emergence and diffusion of the la de que expression and whether it has conventionalized to develop into an independent form-function pairing. A Bayesian mixed-effects logistic regression analysis of approximately 2000 observations of diachronic corpus data tests the influence of the conditional probability of lexemes in the noun slot and the register, which both turn out to have a meaningful effect. It is argued that the initial omission of cantidad can be accounted for by appealing to the notion of probabilistic reduction, whereby omission is feasible in contexts involving a high degree of constructional predictability. In the mapping out of change, conventionalization of the innovative la de que is most observable in contexts involving high constructional predictability and is least prominent in contexts of low constructional predictability. On the grounds that, over time, the la de que progressively has become stylistically divergent from the longer expression, the two constructions are claimed to be functionally distinct. [ABSTRACT FROM AUTHOR]

Restoration outcomes are notoriously unpredictable and this challenges the capacity to reliably meet goals. To harness ecological restoration's full potential, significant advances to predictive capacity must be made in restoration ecology. We outline a process for predicting restoration outcomes, based on the model of iterative forecasting. We then describe six challenges that impede predictive capabilities in restoration and, for each, an agenda for overcoming the challenge. Key challenges include the lack of clear goals, insufficient knowledge of why restoration outcomes vary, difficulty quantifying known drivers of variation prior to initiation of restoration projects, model uncertainty, the need to scale up local understanding to guide large‐scale restoration efforts, and temporally variable conditions that hinder long‐term forecast accuracy. Meeting these challenges will require research to resolve key drivers of variation in restoration outcomes; however, there is also a critical need to begin forecasting efforts in restoration ecology immediately. Although early efforts may be of limited practical utility, iterating between model development and evaluation will resolve data needs, minimize uncertainty, and lead to predictions that practitioners can confidently embrace. In turn, a robust predictive capacity will help to reliably meet goals, enhance cost‐effectiveness, and guide policy decisions to help see out the promise of the Decade on Ecosystem Restoration. [ABSTRACT FROM AUTHOR]

Social environments impose a number of constraints on individuals' behavior. These constraints have been hypothesized to generate behavioral variation among individuals, social responsiveness, and within-individual behavioral consistency (also termed "predictability"). In particular, the social niche specialization hypothesis posits that higher levels of competition associated with higher population density should increase among-individual behavioral variation and individual predictability as a way to reduce conflicts. Being predictable should hence have fitness benefits in group-living animals. However, to date empirical studies of the fitness consequences of behavioral predictability remain scarce. In this study, we investigated the associations between social behavior, its predictability, and fitness in the eastern water dragon (Intellagama lesueurii), a wild gregarious lizard. Since this species is sexually dimorphic, we examined these patterns both between sexes and among individuals. Although females were more sociable than males, there was no evidence for sex differences in among-individual variation or predictability. However, females exhibited positive associations between social behavior, its predictability, and survival, while males exhibited only a positive association between mean social behavior and fitness. These findings hence partly support predictions from the social niche specialization hypothesis and suggest that the function of social predictability may be sex dependent. [ABSTRACT FROM AUTHOR]

The European North-West shelf seas (NWS) support economic interests and provide environmental services to adjacent countries. Expansion of offshore activities, such as renewable energy infrastructure, aquaculture, and growth of international shipping, will place increasingly complex demands on the marine environment over the coming decades. Skilful forecasting of NWS properties on seasonal timescales will help to effectively manage these activities. Here we quantify the skill of an operational large-ensemble ocean-atmosphere coupled global forecasting system (GloSea), as well as benchmark persistence forecasts, for predictions of NWS sea surface temperature (SST) at 2–4 months lead time in winter and summer. We identify sources of and limits to SST predictability, considering what additional skill may be available in the future. We find that GloSea NWS SST skill is generally high in winter and low in summer. GloSea outperforms simple persistence forecasts by adding information about atmospheric variability, but only to a modest extent as persistence of anomalies in the initial conditions contributes substantially to predictability. Where persistence is low – for example in seasonally stratified regions – GloSea forecasts show lower skill. GloSea skill can be degraded by model deficiencies in the relatively coarse global ocean component, which lacks dynamic tides and subsequently fails to robustly represent local circulation and mixing. However, "atmospheric mode matched" tests show potential for improving prediction skill of currently low performing regions if atmospheric circulation forecasts can be improved. This underlines the importance of coupled atmosphere-ocean model development for NWS seasonal forecasting applications. [ABSTRACT FROM AUTHOR]

Lacking predictability of enforcement hinders the deterrent function of competition law. This article shows that academic analyses of optimal competition rules do not always treat this factor adequately, paying instead excessive attention to the problem of error. Sometimes, predictability is completely ignored as a relevant factor. At other times, it is taken into account but its effects are framed in a way that undermines their significance. This article further discusses three possible reasons why a part of competition law and economics scholarship engages in such mistreatment of predictability. First, it may be a result of writing convenience. Secondly, the role of predictability in selecting the optimal competition rule may simply be misunderstood. Thirdly, the role of predictability may be belittled intentionally in order to advocate rules benefiting the interests of competition practitioners and/or defendants. This article also briefly explores how problematic each reason is and what solutions might be available. [ABSTRACT FROM AUTHOR]

Skill in predicting where damaging convective storms will occur is limited, particularly in the tropics. In principle, near‐surface soil moisture (SM) patterns from previous storms provide an important source of skill at the mesoscale, yet these structures are often short‐lived (hours to days), due to both soil drying processes and the impact of new storms. Here, we use satellite observations over the Sahel to examine how the strong, locally negative, SM‐precipitation feedback there impacts rainfall patterns over subsequent days. The memory of an initial storm pattern decays rapidly over the first 3–4 days, but a weak signature is still detected in surface observations 10–20 days later. The wet soil suppresses rainfall over the storm track for the first 2–8 days, depending on aridity regime. Whilst the negative SM feedback initially enhances mesoscale rainfall predictability, the transient nature of SM likely limits forecast skill on sub‐seasonal time scales. Plain Language Summary: Early warning of severe weather is particularly important in Africa, where resilience to storm hazards such as flash flooding is weak. Given large‐scale atmospheric conditions favorable for convective activity, understanding where storms will occur is challenging for conventional weather prediction models. In semi‐arid regions such as the Sahel, the spatial distribution of SM provides additional predictability of convective rain, via its impact on heating and moistening of the atmosphere. Given that convection is favored over drier soils and that storms create new SM patterns every few days during the wet season, the extent to which knowledge of today's SM aids rainfall prediction in future days is unclear. Here we use 17 years of satellite observations to document how surface properties evolve over 20 days after a storm, and how the surface influences subsequent rainfall patterns. We find that even in regions of West Africa where storms are frequent, the suppression of rain over recently‐wetted soils is evident out to 2 days. In climatologically drier regions, this predictability extends out to 8 days. Overall, the feedback between SM and rainfall enhances rainfall predictability in the short‐term (days), but effectively degrades the skill of longer‐term (weeks) forecasts. Key Points: Satellite observations over the Sahel reveal how the land surface evolves in the 20 days after a Mesoscale Convective System (MCS)After an MCS, rainfall is suppressed over wet soils for 2 days in humid regions and up to 8 days in drier areasInitially soil moisture enhances rainfall predictability, but the strong land feedback degrades skill at longer lead times [ABSTRACT FROM AUTHOR]

One of the most reliable features of natural systems is that they change through time. Theory predicts that temporally fluctuating conditions shape community composition, species distribution patterns, and life history variation, yet features of temporal variability are rarely incorporated into studies of species–environment associations. In this study, we evaluated how two components of temporal environmental variation—variability and predictability—impact plant community composition and species distribution patterns in the alpine tundra of the Southern Rocky Mountains in Colorado (USA). Using the Sensor Network Array at the Niwot Ridge Long‐Term Ecological Research site, we used in situ, high‐resolution temporal measurements of soil moisture and temperature from 13 locations (“nodes”) distributed throughout an alpine catchment to characterize the annual mean, variability, and predictability in these variables in each of four consecutive years. We combined these data with annual vegetation surveys at each node to evaluate whether variability over short (within‐day) and seasonal (2‐ to 4‐month) timescales could predict patterns in plant community composition, species distributions, and species abundances better than models that considered average annual conditions alone. We found that metrics for variability and predictability in soil moisture and soil temperature, at both daily and seasonal timescales, improved our ability to explain spatial variation in alpine plant community composition. Daily variability in soil moisture and temperature, along with seasonal predictability in soil moisture, was particularly important in predicting community composition and species occurrences. These results indicate that the magnitude and patterns of fluctuations in soil moisture and temperature are important predictors of community composition and plant distribution patterns in alpine plant communities. More broadly, these results highlight that components of temporal change provide important niche axes that can partition species with different growth and life history strategies along environmental gradients in heterogeneous landscapes. [ABSTRACT FROM AUTHOR]

In this paper, we propose utilizing machine learning methods to determine the expected aggregated stock market risk premium based on online investor sentiment and employing the multifold forward-validation method to select the relevant hyperparameters. Our empirical studies provide strong evidence that some machine learning methods, such as extreme gradient boosting or random forest, show significant predictive ability in terms of their out-of-sample performances with high-dimensional investor sentiment proxies. They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. Moreover, this predictability based on online investor sentiment has a better economic value, so it improves portfolio performance for investors who need to decide the optimal asset allocation in terms of the certainty equivalent return gain and the Sharpe ratio. [ABSTRACT FROM AUTHOR]

Aims: We intended to characterize the superiority of triglyceride glucose‐body mass index (TyG‐BMI) in predicting type 2 diabetes mellitus (T2DM) compared with triglyceride glucose (TyG) and homeostatic model assessment for insulin resistance (HOMA‐IR). Methods: A total of 699 nondiabetic participants in the Da Qing IGT and Diabetes Study were involved in the present analysis and classified according to the median of baseline TyG‐BMI, namely the G1 (low TyG‐BMI) and G2 (high TyG‐BMI) groups. Information on developing diabetes was assessed from 1986 to 2020. Results: During the 34‐year follow‐up, after adjustment for confounders, the G2 group had a higher risk of developing type 2 diabetes than the G1 group (hazard ratio [HR]: 1.92, 95% confidence interval [CI]: 1.51–2.45, p < 0.0001). Restricted cubic spline analyses showed that increased TyG‐BMI was linearly related to higher risks of type 2 diabetes (p for non‐linearity>0.05). Time‐dependent receiver operator characteristics curves suggested that TyG‐BMI exhibited higher predictive ability than TyG (6‐year: area under the curve [AUC]TyG‐BMI vs. AUCTyG, 0.78 vs. 0.70, p = 0.03; 34‐year: AUCTyG‐BMI vs. AUCTyG, 0.79 vs. 0.73, p = 0.04) and HOMA‐IR (6‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.78 vs. 0.70, p = 0.07; 34‐year: AUCTyG‐BMI vs. AUCHOMA‐IR, 0.79 vs. 0.71, p = 0.04) in both short and long terms, and the thresholds of TyG‐BMI to predict type 2 diabetes were relatively stable (195.24–208.41) over the 34‐year follow‐up. Conclusions: In this post hoc study, higher TyG‐BMI was associated with an increased risk of type 2 diabetes and demonstrated better predictability than TyG and HOMA‐IR, favoring the application of TyG‐BMI as a potential tool for evaluating the risk of type 2 diabetes in clinical practice. [ABSTRACT FROM AUTHOR]

Assemblages in seasonal ecosystems undergo striking changes in species composition and diversity across the annual cycle. Despite a long‐standing recognition that seasonality structures biogeographic gradients in taxonomic diversity (e.g., species richness), our understanding of how seasonality structures other aspects of biodiversity (e.g., functional diversity) has lagged. Integrating seasonal species distributions with comprehensive data on key morphological traits for bird assemblages across North America, we find that seasonal turnover in functional diversity increases with the magnitude and predictability of seasonality. Furthermore, seasonal increases in bird species richness led to a denser packing of functional trait space, but functional expansion was important, especially in regions with higher seasonality. Our results suggest that the magnitude and predictability of seasonality and total productivity can explain the geography of changes in functional diversity with broader implications for understanding species redistribution, community assembly and ecosystem functioning. [ABSTRACT FROM AUTHOR]

The ocean surface mixed layer plays a crucial role as an entry or exit point for heat, salt, momentum, and nutrients from the surface to the deep ocean. In this study, we introduce a framework to assess the evolution of the mixed layer depth (MLD) for realistic forcings and preconditioning conditions. Our approach involves a physically‐based parameter space defined by three dimensionless numbers: λs representing the relative contribution of the buoyancy flux and the wind stress at the air‐sea interface, Rh the Richardson number which characterizes the stability of the water column relative to the wind shear, and f/Nh which characterizes the importance of the Earth's rotation (ratio of the Coriolis frequency f and the pycnocline stratification Nh). Four MLD evolution regimes ("restratification," "stable," "deepening," and "strong deepening") are defined based on the values of the normalized temporal evolution of the MLD. We evaluate the 3D parameter space in the context of 1D simulations and we find that considering only the two dimensions (λs, Rh) is the best choice of 2D projection of this 3D parameter space. We then demonstrate the utility of this two‐dimensional λs − Rh parameter space to compare 3D realistic ocean simulations: we discuss the impact of the horizontal resolution (1°, 1/12°, or 1/60°) and the Gent‐McWilliams parameterization on MLD evolution regimes. Finally, a proof of concept of using observational data as a truth indicates how the parameter space could be used for model calibration. Plain Language Summary: Vertical mixing of water near the ocean surface occurs when cold air temperatures create dense cold water at the surface that tends to sink in the ocean or when a strong wind induces turbulence at the ocean surface. These processes mix heat and salt and create a layer at the top of the ocean that has a uniform temperature and salinity and that is called the "mixed layer." This mixed layer plays a fundamental role in the Earth climate system, and the representation of its evolution in ocean models hence needs to be assessed. For this purpose, we propose to map the mixed layer evolution in a three‐dimensional space where the first axis is related to the wind and the surface heat flux, the second axis to the stability of the water column, and the third axis to the Earth's rotation. We show that this tool performs statistically well and we present how to use it in the context of realistic ocean models. Key Points: A parameter space is proposed to assess the evolution of the mixed layer depth for realistic forcings and preconditioning conditionsAn evaluation of a collection of 1D simulations shows a statistically good performance of the parameter spaceTwo applications demonstrate the utility of the parameter space for assessing and comparing realistic 3D simulations [ABSTRACT FROM AUTHOR]

Recently, the coronavirus (COVID-19) pandemic has affected the economic situation all over the world. The objective of this research is to examine the effect of coronavirus spreading and vaccination rate on the stock market index in Turkey. To do that, we have applied several statistical methods, namely ridge, lasso, principal components, and partial least squares (PLS) regression versus elastic-net regression based on empirical mode decomposition, which can overcome the non-stationary problem and nonlinearity characteristics. The result of using the elastic net regression method based on empirical mode decomposition shows significant effects of coronavirus spreading on the stock market, and it varies based on the intrinsic mode function coefficients and frequencies. The findings of this research could assist practitioners and policymakers to design important strategies in the light of varying stock market dynamics during the coronavirus pandemic. [ABSTRACT FROM AUTHOR]

In this study, we develop the first daily news-based Economic Policy Uncertainty (EPU) index for Nigeria, which was previously not covered in recent EPU indices. The need to track economic uncertainties in Nigeria becomes crucial for investment and policy, especially with the renewed interest in the country as an important investment destination. To construct the EPU index, we use relevant keywords from articles in prominent newspapers in the country, covering the aftermath of the global financial crisis and the COVID pandemic, with a data scope of January 2010 to November 2022. We evaluate the predictability of the index by examining its connection with economic and financial variables like exchange rates, stock prices, and inflation in Nigeria. The results are robust to alternative model specifications, data frequencies, and multiple forecast horizons. We hope to extend this exercise to other useful indices, including Geopolitical Risk, Financial Stress Indicators, and Monetary Policy Uncertainty, which are not readily available for Africa, including Nigeria. [ABSTRACT FROM AUTHOR]

Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The 'intrinsic predictability' of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise.We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (Pygoscelis adeliae) and emperor penguin (Aptenodytes forsteri) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species.We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%).rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance. [ABSTRACT FROM AUTHOR]

Summary The present study aims at identifying sleep patterns in insomnia in a clinical sample using three strategies to define poor nights. Sleep diaries and self‐reported questionnaires were collected from 77 clinical patients with insomnia. The conditional probabilities of observing a poor night after 1, 2, or 3 consecutive poor nights were computed according to three strategies with same criteria for sleep onset latency, wake after sleep onset, and sleep efficiency, but varying criterion for total sleep time. Latent profile analyses were conducted to derive sleep patterns. Uni‐ and multivariate analyses were conducted to characterise the sleep patterns identified. A total of 1586 nights were analysed. The strategy used significantly influenced the average percentage of reported poor nights. Two to three sleep patterns were derived per strategy. Within each strategy, sleep patterns differed from each other on sleep variables and night‐to‐night variability. Results suggest the existence of sleep patterns in insomnia among individuals consulting in psychological clinics. Adding a total sleep time of 6‐h cut‐off as a criterion to define poor nights increases the accuracy of the strategy to define poor night and allows to identify sleep patterns of poor nights in insomnia. [ABSTRACT FROM AUTHOR]

Humans display a remarkable tendency to cooperate with strangers; however, identifying prospective cooperation partners accurately before entering any new relationship is essential to mitigate the risk of being exploited. Visual appearance, as inferrable, for example, from facial images on job portals and dating sites, may serve as a potential signal of cooperativeness. This experimental study examines whether static images enable the correct detection of an individual's propensity to cooperate. Participants first played the Prisoner's Dilemma (PD) game, a standard cooperation task. Subsequently, they were asked to predict the cooperativeness of participants from a prior PD study relying solely on their static facial photographs. While our main results indicate only marginal accuracy improvements over random guessing, a more detailed analysis reveals that participants were more successful at identifying cooperative tendencies similar to their own. Despite no detectable main effect in our primary treatment variations (time pressure versus time delay), participants exhibited increased accuracy in identifying male cooperators under time pressure. These findings point towards a limited yet nuanced role of static facial images in predicting cooperativeness, advancing our understanding of non-behavioral cues in cooperative interactions. [ABSTRACT FROM AUTHOR]

Binomials have been relatively understudied compared to other types of multiword expressions (MWEs) in second language research, such as collocations and idioms. This study investigated English as a Foreign Language (EFL) learners' processing of English binomials and how it is influenced by interlexical factors (L1-L2 congruency and L1-lexicalization) and intralexical factors (word and binomial frequency, binomial reversibility, and binomial predictability). Forty Chinese EFL learners participated in a phrase acceptability judgment task of 64 target binomials (16 congruent L1-lexicalized, 16 congruent L1-nonlexicalized, and 32 incongruent) and 64 non-binomial controls. Results revealed that learners experienced difficulty judging the formulaicity of binomials. They processed binomial stimuli significantly faster than non-binomial baselines, demonstrating a binomial phrase effect. They also processed L1-L2 congruent items faster and more accurately than incongruent items, showing a robust congruency effect. The congruent items which are lexicalized in the L1 showed further processing advantage than the non-lexicalized items, indicating a graded congruency effect. Moreover, binomial reversibility and binomial predictability (measured with cloze probability) also showed significant effects. These findings highlight the need to distinguish and investigate different types of congruency, explore appropriate measures for MWE predictability, and to examine binomials focusing on their unique features. [ABSTRACT FROM AUTHOR]

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]

Introduction: The attentional boost effect, characterized by better memory for background scenes coinciding with a detection target than a nontarget, is believed to stem from a temporary increase in attentional capacity at the time of an acute behavior‐related event occurring. Sisk and Jiang's study found that the attentional boost effect also occurs when the target's appearance was predictable. Unfortunately, the duration of the predictive interval in Sisk and Jiang's study was fixed. Since different predictive intervals had different weakening degrees to the acuteness of the target, this fixed duration hindered further investigation into the impact of different levels of predictability on the attentional boost effect. Method: Using the encoding‐recognition paradigm and the remembering/knowing paradigm, and setting target stimuli with different predictive interval in target detection tasks, the current study aimed to explore the influence of varying the duration of the predictive interval on the attentional boost effect. Results: The attentional boost effect was observed only in the short and medium predictive duration conditions, but not in the long predictive duration condition. Moreover, as the duration of the predictive interval increased, participants' memory performance on target‐paired words gradually declined, while their memory performance on distractor‐paired and baseline‐paired words gradually improved. Conclusions: Predictability may alter the task demands, allowing participants to more effectively allocate attentional resources to the two tasks at hand. [ABSTRACT FROM AUTHOR]

Turbulence has associated chaotic features. In the past couple of decades, there has been growing interest in the study of these features as an alternative means of understanding turbulent systems. Our own input to this effort is in contributing to the initial studies of chaos in Eulerian flow using direct numerical simulation (DNS). In this review, we discuss the progress achieved in the turbulence community in understanding chaotic measures including our own work. A central relation between turbulence and chaos is one by Ruelle that connects the maximum Lyapunov exponent and the Reynolds number. The first DNS studies, ours amongst them, in obtaining this relation have shown the viability of chaotic simulation studies of Eulerian flow. Such chaotic measures and associated simulation methodology provides an alternative means to probe turbulent flow. Building on this, we analyze the finite-time Lyapunov exponent (FTLE) and study its fluctuations; we find that chaotic measures could be quantified accurately even at small simulation box sizes where for comparative sizes spectral measures would be inconclusive. We further highlight applications of chaotic measures in analyzing phase transition behavior in turbulent flow and two-dimensional thin-layer turbulent systems. This work shows that chaotic measures are an excellent tool that can be used alongside spectral measures in studying turbulent flow. [ABSTRACT FROM AUTHOR]

Subjective time perception can change based on a stimulus's valence and expectancy. Yet, it is unclear how these two factors might interact to shape our sense of how long something lasts. Here, we conducted two experiments examining the effects of temporal and probabilistic expectancy on the perceived duration of images with varying emotional valence. In Experiment 1, we varied the temporal predictive cue with varying stimulus-onset asynchronies (SOAs), while in Experiment 2, we manipulated the cue-emotion probabilistic associations. Our results revealed that stimuli appearing earlier than anticipated were perceived as shorter, whereas less infrequent stimuli seemed to last longer. In addition, negative images were perceived longer than neural ones. However, no significant interaction between expectancy and stimulus valence was observed. We interpret these using the internal clock model, suggesting that while emotional stimuli primarily affect the pacemaker's rhythm through arousal, expectation steers attention, influencing how we register time's passage. [ABSTRACT FROM AUTHOR]

Variability of the Surface Air Temperature (SAT) over the Western South Asia (WSA) region leads to frequent heatwaves during the early summer (May-June) season. The present study uses the European Centre for Medium-Range Weather Forecast's fifth-generation seasonal prediction system, SEAS5, from 1981 to 2022 based on April initial conditions (1-month lead) to assess the SAT predictability during early summer season. The goal is to evaluate the SEAS5's ability to predict the El Niño-Southern Oscillation (ENSO) related interannual variability and predictability of the SAT over WSA, which is mediated through upper-level (200-hPa) geopotential height anomalies. This teleconnection leads to anomalously warm surface conditions over the region during the negative ENSO phase, as observed in the reanalysis and SEAS5. We evaluate SEAS5 prediction skill against two observations and three reanalyses datasets. The SEAS5 SAT prediction skill is higher with high spatial resolution observations and reanalysis datasets compared to the ones with low-resolution. Overall, SEAS5 shows reasonable skill in predicting SAT and its variability over the WSA region. Moreover, the predictability of SAT during La Niña is comparable to El Niño years over the WSA region. [ABSTRACT FROM AUTHOR]

Winter precipitation anomalies in South China (SC) frequently result in severe disasters. However, the evaluation of prediction performance and distinctions between positive precipitation anomaly events (PPA, wet condition) and negative precipitation anomaly events (NPA, dry condition) in current operational models remains incomplete. This study employed the Climate Forecast System version 2 (CFSv2) to assess winter precipitation prediction accuracy in SC from 1983 to 2021. Differences in predicting PPA and NPA events and the underlying physical mechanisms were explored. The results indicate that CFSv2 can effectively predict interannual variations in winter precipitation in SC, as there is a significant time correlation coefficient of 0.68 (0.62) between observations and predictions, with a lead time of 0 (3) months. The model revealed an intriguing asymmetry in prediction skills: PPA outperformed NPA in both deterministic and probabilistic prediction. The higher predictability of PPA, as indicated by the perfect model correlation and signal-to-noise ratio, contributed to its superior prediction performance when compared to NPA. Physically, tropical signals from the ENSO and extratropical signals from the Arctic sea ice anomaly, were found to play pivotal roles in this asymmetric feature. ENSO significantly impacts PPA events, whereas NPA events are influenced by a complex interplay of factors involving ENSO and Arctic sea ice, leading to low NPA predictability. The capability of the model to replicate Arctic sea ice signals is limited, but it successfully predicts ENSO signals and reproduces their related circulation responses. This study highlights the asymmetrical features of precipitation prediction, aiding in prediction models improvement. [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 study analyses climate variability on the north coast of Peru to understand how the local weather is coupled with anomalous ocean conditions. Using high-resolution satellite reanalysis, statistical outcomes are generated via composite analysis and point-to-field regression. Daily time series data for 1979–2023 for Moche area (8S, 79W) river discharge, rainfall, wind, sea surface temperature (SST) and potential evaporation are evaluated for departures from the average. During dry weather in early summer, the southeast Pacific anticyclone expands, an equatorward longshore wind jet ~10 m/s accelerates off northern Peru, and the equatorial trough retreats to 10N. However, most late summers exhibit increased river discharge as local sea temperatures climb above 27 °C, accompanied by 0.5 m/s poleward currents and low salinity. The wet spell composite featured an atmospheric zonal overturning circulation comprised of lower easterly and upper westerly winds > 3 m/s that bring humid air from the Amazon. Convection is aided by diurnal heating and sea breezes that increase the likelihood of rainfall ~ 1 mm/h near sunset. Wet spells in March 2023 were analyzed for synoptic weather forcing and the advection of warm seawater from Ecuador. Although statistical correlations with Moche River discharge indicate a broad zone of equatorial Pacific ENSO forcing (Nino3 R~0.5), the long-range forecast skill is rather modest for February–March rainfall (R2 < 0.2). [ABSTRACT FROM AUTHOR]

This study introduces a robust model that captures wheat futures' volatility dynamics, influenced by seasonality, time to maturity, and storage dynamics, with minimal calibratable parameters. Our approach reduces error-proneness and enhances plausibility checks, offering a reliable alternative to models that are difficult to calibrate. Transferring estimated parameters from liquid to illiquid markets is feasible, which is challenging for models with numerous parameters. This is of practical importance as it improves the modeling of volatility in illiquid markets, where price discovery is less efficient. In liquid markets, on the other hand, where speculative activity is high, we find that implied volatility is usually the best measure. Additionally, the introduced volatility model is suitable for pricing options on wheat futures as a risk-neutral measure. [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]

Autonomous driving systems (ADS) operate in an environment that is inherently complex. As these systems may execute a task without the permission of a human agent, they raise major safety and responsibility issues. To identify the relevant issues for information systems, we conducted a critical and scoping review of the literature from many disciplines. The innovative methodology we used combines bibliometrics techniques, grounded theory and a critical conceptual framework to analyse the structure and research themes of the field. Our findings show that there are certain ironies in the way in which responsibility for apparently safe autonomous systems is apportioned. These ironies are interconnected and reveal that there remains significant uncertainty and ambiguity regarding the distribution of responsibility between stakeholders. The ironies draw attention to the challenges of safety and responsibility with ADS and possibly other cyber-physical systems in our increasingly digital world. We make seven recommendations related to (1) value sensitive design and system theory approaches; (2) stakeholders' interests and interactions; (3) task allocation; (4) deskilling; (5) controllability; (6) responsibility (moral and legal); (7) trust. We suggest five areas for future IS research on ADS. These areas are related to socio-technical systems, critical research, safety, responsibility and trust. [ABSTRACT FROM AUTHOR]

Abstract Many ecological systems dominated by stochastic dynamics can produce complex time series that inherently limit forecast accuracy. The ‘intrinsic predictability’ of these systems can be approximated by a time series complexity metric called weighted permutation entropy (WPE). While WPE is a useful metric to gauge forecast performance prior to model building, it is sensitive to noise and may be biased depending on the length of the time series. Here, we introduce a simple randomized permutation test (rWPE) to assess whether a time series is intrinsically more predictable than white noise. We apply rWPE to both simulated and empirical data to assess its performance and usefulness. To do this, we simulate population dynamics under various scenarios, including a linear trend, chaotic, periodic and equilibrium dynamics. We further test this approach with observed abundance time series for 932 species across four orders of animals from the Global Population Dynamics Database. Finally, using Adélie (Pygoscelis adeliae) and emperor penguin (Aptenodytes forsteri) time series as case studies, we demonstrate the application of rWPE to multiple populations for a single species. We show that rWPE can determine whether a system is significantly more predictable than white noise, even with time series as short as 10 years that show an apparent trend under biologically realistic stochasticity levels. Additionally, rWPE has statistical power close to 100% when time series are at least 30 time steps long and show chaotic or periodic dynamics. Power decreases to ~10% under equilibrium dynamics, irrespective of time series length. Among four classes of animal taxa, mammals have the highest relative frequency (28%) of time series that are both longer than 30 time steps and indistinguishable from white noise in terms of complexity, followed by insects (16%), birds (16%) and bony fishes (11%). rWPE is a straightforward and useful method widely applicable to any time series, including short ones. By informing forecasters of the inherent limitations to a system's predictability, it can guide a modeller's expectations for forecast performance.

Abstract Humans display a remarkable tendency to cooperate with strangers; however, identifying prospective cooperation partners accurately before entering any new relationship is essential to mitigate the risk of being exploited. Visual appearance, as inferrable, for example, from facial images on job portals and dating sites, may serve as a potential signal of cooperativeness. This experimental study examines whether static images enable the correct detection of an individual’s propensity to cooperate. Participants first played the Prisoner’s Dilemma (PD) game, a standard cooperation task. Subsequently, they were asked to predict the cooperativeness of participants from a prior PD study relying solely on their static facial photographs. While our main results indicate only marginal accuracy improvements over random guessing, a more detailed analysis reveals that participants were more successful at identifying cooperative tendencies similar to their own. Despite no detectable main effect in our primary treatment variations (time pressure versus time delay), participants exhibited increased accuracy in identifying male cooperators under time pressure. These findings point towards a limited yet nuanced role of static facial images in predicting cooperativeness, advancing our understanding of non-behavioral cues in cooperative interactions.

Protein engineering increasingly relies on machine learning models to computationally pre-screen promising novel candidates. Although machine learning approaches have proven effective, their performance on prospective screening data leaves room for improvement; prediction accuracy can vary greatly from one protein variant to the next. So far, it is unclear what characterizes variants that are associated with large prediction error. In order to establish whether structural characteristics influence predictability, we created a novel high-order combinatorial dataset for an enzyme spanning 3,706 variants, that can be partitioned into subsets of variants with mutations at positions exclusively belonging to a particular structural class. By training four different supervised variant effect prediction (VEP) models on structurally partitioned subsets of our data, we found that predictability strongly depended on all four structural characteristics we tested; buriedness, number of contact residues, proximity to the active site and presence of secondary structure elements. These dependencies were also found in several single mutation enzyme variant datasets, albeit with dataset specific directions. Most importantly, we found that these dependencies were similar for all four models we tested, indicating that there are specific structure and function determinants that are insufficiently accounted for by current machine learning algorithms. Overall, our findings suggest that improvements can be made to VEP models by exploring new inductive biases and by leveraging different data modalities of protein variants, and that stratified dataset design can highlight areas of improvement for machine learning guided protein engineering.

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]

For humans to effectively work with robots, they must be able to predict the actions and behaviors of their robot teammates rather than merely react to them. While there are existing techniques enabling robots to adapt to human behavior, there is a demonstrated need for methods that explicitly improve humans' ability to understand and predict robot behavior at multi-task timescales. In this work, we propose a method leveraging the innate human propensity for pattern recognition in order to improve team dynamics in human–robot teams and to make robots more predictable to the humans that work with them. Patterns are a cognitive tool that humans use and rely on often, and the human brain is in many ways primed for pattern recognition and usage. We propose pattern-aware convention-setting for teaming (PACT), an entropy-based algorithm that identifies and imposes appropriate patterns over a robot's planner or policy over long time horizons. These patterns are autonomously generated and chosen via an algorithmic process that considers human-perceptible features and characteristics derived from the tasks to be completed, and as such, produces behavior that is easier for humans to identify and predict. Our evaluation shows that PACT contributes to significant improvements in team dynamics and teammate perceptions of the robot, as compared to robots that utilize traditionally 'optimal' plans and robots utilizing unoptimized patterns. [ABSTRACT FROM AUTHOR]

This study introduces a robust model that captures wheat futures’ volatility dynamics, influenced by seasonality, time to maturity, and storage dynamics, with minimal calibratable parameters. Our approach reduces error-proneness and enhances plausibility checks, offering a reliable alternative to models that are difficult to calibrate. Transferring estimated parameters from liquid to illiquid markets is feasible, which is challenging for models with numerous parameters. This is of practical importance as it improves the modeling of volatility in illiquid markets, where price discovery is less efficient. In liquid markets, on the other hand, where speculative activity is high, we find that implied volatility is usually the best measure. Additionally, the introduced volatility model is suitable for pricing options on wheat futures as a risk-neutral measure.

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.

This study analyses climate variability on the north coast of Peru to understand how the local weather is coupled with anomalous ocean conditions. Using high-resolution satellite reanalysis, statistical outcomes are generated via composite analysis and point-to-field regression. Daily time series data for 1979–2023 for Moche area (8S, 79W) river discharge, rainfall, wind, sea surface temperature (SST) and potential evaporation are evaluated for departures from the average. During dry weather in early summer, the southeast Pacific anticyclone expands, an equatorward longshore wind jet ~10 m/s accelerates off northern Peru, and the equatorial trough retreats to 10N. However, most late summers exhibit increased river discharge as local sea temperatures climb above 27 °C, accompanied by 0.5 m/s poleward currents and low salinity. The wet spell composite featured an atmospheric zonal overturning circulation comprised of lower easterly and upper westerly winds > 3 m/s that bring humid air from the Amazon. Convection is aided by diurnal heating and sea breezes that increase the likelihood of rainfall ~ 1 mm/h near sunset. Wet spells in March 2023 were analyzed for synoptic weather forcing and the advection of warm seawater from Ecuador. Although statistical correlations with Moche River discharge indicate a broad zone of equatorial Pacific ENSO forcing (Nino3 R~0.5), the long-range forecast skill is rather modest for February–March rainfall (R2 < 0.2).

Expeditious strides in the fields of biomaterials, computer-aided design, and manufacturing have catapulted clear aligner therapy (CAT) to become a comprehensive orthodontic treatment modality. The efficiency of achieving planned tooth movement with clear aligners is a significant consideration while setting up the final treatment goals, as well as calculating treatment times and costs based on the available evidence. Contemporary research outcomes confirm that one of the most commonly reported clinical concerns with CAT is the discrepancy between the prescribed outcome in the digital treatment plan and the clinically achieved outcome from a given series of aligners. Inaccurate prediction of tooth movements may not only lead to a prolonged duration of aligner treatment with an additional need for refinement strategies; but it may also cause other concerns, such as patient burnout and increased potential for relapse. The authors of this paper have elucidated some of the critical elements that may help address this discrepancy between digitally prescribed and clinical outcomes based on an evidence-based approach with regard to the predictability and accuracy of CAT. A strong diagnostic acumen, judicious case selection, solid biomechanical understanding of various types of orthodontic tooth movements, a research framework that keeps pace with technological and material developments and provides evidence-based knowledge of the limitations of CAT; and above all, the ability of the clinician to continually innovate as per different clinical scenarios, all contribute to attaining treatment predictability, efficacy, and efficiency with CAT.

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.

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]