Search

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]

Ocean memory is crucial for improving climate models and enhancing the accuracy of climate predictions. Despite its importance, the evolving influence of ocean memory over recent decades on monsoon predictions remains ambiguous. The persistence of sea surface temperature (SST) anomalies, a critical indicator of ocean memory, governs local air-sea coupling processes affecting the Asian-Australian monsoon (A-AM), thereby significantly shaping climate predictions for Asia, Australia, and the broader Indo-Pacific region. Drawing on observational and numerical modeling evidence, the study elucidates that within the context of interdecadal variation in ocean memory, the seasonal persistence of Maritime Continent (MC) SST anomalies was more pronounced during the epoch of robust ocean memory (1982-1999). This persistence sustained the anomalous western North Pacific anticyclone (WNPAC) through an intensified Matsuno-Gill response in the decaying phase of El Niño-Southern Oscillation (ENSO), thereby enhancing its linkage with the A-AM system throughout the monsoon year, contributing to improved prediction skills of the A-AM. In contrast, these air-sea coupling processes have weakened during the weak memory epoch (2000-2017), making it more difficult to capture the characteristics of the A-AM. The decline in MC ocean memory at the onset of the twenty-first century has undermined the prediction skills of the leading modes of the A-AM. Overall, this study underscores the profound influence of ocean memory on monsoon prediction skills while highlighting the significant challenges in addressing A-AM variability and predictability against the backdrop of global warming. [ABSTRACT FROM AUTHOR]

Conducting predictability studies is essential for tracing the source of forecast errors, which not only leads to the improvement of observation and forecasting systems, but also enhances the understanding of weather and climate phenomena. In the past few decades, dynamical numerical models have been the primary tools for predictability studies, achieving significant progress. Nowadays, with the advances in artificial intelligence (AI) techniques and accumulations of vast meteorological data, modeling weather and climate events using modern data-driven approaches is becoming trendy, where FourCastNet, Pangu-Weather, and GraphCast are successful pioneers. In this perspective article, we suggest AI models should not be limited to forecasting but be expanded to predictability studies, leveraging AI's advantages of high efficiency and self-contained optimization modules. To this end, we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies. AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way. Then, we highlight several specific predictability issues with well-determined nonlinear optimization formulizations, which can be well-studied using AI models, holding significant scientific value. In addition, we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm. Comprehensive predictability studies have the potential to transform "big data" to "big and better data" and shift the focus from "AI for forecasts" to "AI for science", ultimately advancing the development of the atmospheric and oceanic sciences. [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]

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]

Traffic condition forecasting is fundamental for Intelligent Transportation Systems. Besides accuracy, many services require an estimate of uncertainty for each prediction. Uncertainty quantification must consider the inherent randomness in traffic dynamics, the so-called aleatoric uncertainty, and the additional distrust caused by data shortage, the so-called epistemic uncertainty. They together depict how predictable macroscopic traffic is. This study uses deep ensembles of graph neural networks to estimate both types of uncertainty in network-level speed forecasting. Experimental results given by the used model reveal that, although rare congestion patterns arise randomly, the short-term predictability of traffic states is mainly restricted by the irreducible stochasticity in traffic dynamics. The predicted future state bifurcates into congested or free-flowing cases. This study suggests that the potential for improving prediction models through expanding speed and flow data is limited while diversifying data types is crucial. [ABSTRACT FROM AUTHOR]

The rapid growth in air traffic has led to increasing congestion at airports, creating bottlenecks that disrupt ground operations and compromise the efficiency of air traffic management (ATM). Ensuring the predictability of ground operations is vital for maintaining the sustainability of the ATM sector. Flight efficiency is closely tied to adherence to assigned airport arrival and departure slots, which helps minimize primary delays and prevents cascading reactionary delays. Significant deviations from scheduled arrival times—whether early or late—negatively impact airport operations and air traffic flow, often requiring the imposition of Air Traffic Flow Management (ATFM) regulations to accommodate demand fluctuations. This study leverages a data-driven machine learning approach to enhance the predictability of in-block and landing times. A Bidirectional Long Short-Term Memory (BiLSTM) neural network was trained using a dataset that integrates flight trajectories, meteorological conditions, and airport operations data. The model demonstrated high accuracy in predicting landing time deviations, achieving a Root-Mean-Square Error (RMSE) of 8.71 min and showing consistent performance across various long-haul flight profiles. In contrast, in-block time predictions exhibited greater variability, influenced by limited data on ground-level factors such as taxi-in delays and gate availability. The results highlight the potential of deep learning models to optimize airport resource allocation and improve operational planning. By accurately predicting landing times, this approach supports enhanced runway management and the better alignment of ground handling resources, reducing delays and increasing efficiency in high-traffic airport environments. These findings provide a foundation for developing predictive systems that improve airport operations and air traffic management, with benefits extending to both short- and long-haul flight operations. [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]

Our paper has two main objectives. First, we aim to investigate the relationship between global economic contraction (GECON) and the return volatility of gold. Second, we examine the role of climate change as a mediator in this connection. To achieve this, we use the GARCH‐MIDAS model, which accommodates data in different frequencies in the same model. This prevents the loss of important information when aggregating high‐frequency data to lower‐frequency data. We also use alternative measures of GECON from Kilian and Zhou (Journal of International Money and Finance, 2018; 88, 54–78) and Baumeister et al. (Review of Economics and Statistics, 2020;104(4), 828–844) to ensure the robustness of our findings. Our results show that global economic contraction positively impacts the return volatility of gold. Additionally, our findings confirm that the increased uncertainty caused by climate change makes gold a safe haven for investors. This means that gold's return volatility is not negatively impacted by the rising level of uncertainty caused by climate‐induced contraction. Moreover, we note that the index of GECON that accommodates more dynamics can produce more accurate predictability for gold market volatility. Our analysis of stock market volatility further confirms that gold has a safe haven potential relative to the stock market. [ABSTRACT FROM AUTHOR]

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]

Background/Objectives: This paper presents an analysis of birth rate statistics, specifically focusing on recorded births in Scotland. The main research objective focuses on investigating the influence of geopolitical concerns on birth rate forecasts. Specifically, we examine whether individuals may choose to postpone or abstain from having children during times of conflict or political turmoil due to concerns about personal safety, the welfare of their children, or uncertainty about the future caused by geopolitical risks. Additionally, this study examines how disruptions to healthcare services, such as limited access to prenatal care and maternal health facilities, can affect birth outcomes and lead to changes in birth rates. Methods: To approach the research objective both machine learning algorithms and classical statistical procedures. Also, as part of the current analysis, the Geopolitical Risk Index has been applied as an extra factor to predict the birth rate. Results: The results of our study demonstrate the effectiveness of machine learning in producing precise predictions in this field, while emphasizing the significant influence of geopolitical risk on comprehending the dynamics of birth rates in Scotland. Conclusions: This study examines the effectiveness of several machine learning regression models in accurately predicting the number of births in Scotland using data that is not included in the model training process. Findings show promising outcomes in predicting births, while geopolitical instability has been indicated as a substantial influence on birth rates and fertility rates. [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]

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.

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.

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.

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]

This study investigates the medium‐range predictability of warm and cold extremes in the Northern Hemisphere and the role that upper‐tropospheric circulation biases play in this regard. Deterministic ERA5 reforecasts for the period 1979–2019 are evaluated based on the ERA5 reanalysis of the respective period, thus providing a large sample for verification and bias identification. The predictability of temperature extremes at 850 hPa is assessed based on the Gilbert Skill Score and other metrics and is shown to exhibit regional and seasonal variations. Summer is generally characterized by lower forecast skill scores than winter for both warm and cold extremes. Moreover, cold extremes in summer have slightly lower skill scores than warm extremes, while the opposite is true in winter. Biases in the frequency of temperature extremes are, to some extent, consistent with biases in mean temperature and indicate an underestimation in the total amount of extremes for much of the hemisphere in summer. Associated with the latter, biases also emerge in the standard deviation of the daily temperature distribution, with the summer values being largely underestimated over most of the hemisphere. The role of upper‐tropospheric circulation in these biases is then assessed by verifying the representation of Rossby‐wave packet (RWP) properties. It is found that the amplitude of RWPs is systematically underestimated in most of the hemisphere in summer, while it is overestimated in many parts of the midlatitudes in winter. Overall, the results suggest that the underestimation of RWP amplitude in summer hinders the medium‐range predictability of temperature extremes in the explored retrospective and operational forecasts. Although operational European Centre for Medium‐Range Weather Forecasts (ECMWF) forecasts gradually improve between 2013 and 2022 in terms of the 850‐hPa temperature and 300‐hPa RWP amplitude absolute errors, the aforementioned summer biases remain qualitatively similar. [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]

The Law Society of England and Wales, as well as English politicians and judges, claim that English (contract) law is admirable, settled and predictable, and non-English legal systems are "laxer systems" whose judges are not as exceptionally knowledgeable as English judges. These claims of legal superiority attract foreign litigants such as African businesspeople to use English laws and forums to resolve their contractual disputes. This article aims to disprove these claims by rigorously assessing them from an Afrocentric lens, as well as from the prediction theory of law, English case law, and rules of contractual interpretation. It finds that English contract law and dispute resolution mechanisms are far from predictable and settled. Also, there is a staggering level of disagreement among English judges regarding their interpretation of commercial contracts. The article exposes the defects of English contract laws to enable foreign businesses to make informed decisions about their choice of laws and forums to resolve their contractual disputes. [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 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]