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Reversal learning measures the ability to form flexible associations between choice outcomes with stimuli and actions that precede them. This type of learning is thought to rely on several cortical and subcortical areas, including the highly interconnected orbitofrontal cortex (OFC) and basolateral amygdala (BLA), and is often impaired in various neuropsychiatric and substance use disorders. However, the unique contributions of these regions to stimulus- and action-based reversal learning have not been systematically compared using a chemogenetic approach particularly before and after the first reversal that introduces new uncertainty. Here, we examined the roles of ventrolateral OFC (vlOFC) and BLA during reversal learning. Male and female rats were prepared with inhibitory designer receptors exclusively activated by designer drugs targeting projection neurons in these regions and tested on a series of deterministic and probabilistic reversals during which they learned about stimulus identity or side (left or right) associated with different reward probabilities. Using a counterbalanced within-subject design, we inhibited these regions prior to reversal sessions. We assessed initial and pre-/post-reversal changes in performance to measure learning and adjustments to reversals, respectively. We found that inhibition of the ventrolateral orbitofrontal cortex (vlOFC), but not BLA, eliminated adjustments to stimulus-based reversals. Inhibition of BLA, but not vlOFC, selectively impaired action-based probabilistic reversal learning, leaving deterministic reversal learning intact. vlOFC exhibited a sex-dependent role in early adjustment to action-based reversals, but not in overall learning. These results reveal dissociable roles for BLA and vlOFC in flexible learning and highlight a more crucial role for BLA in learning meaningful changes in the reward environment.

OBJECTIVE: Deep brain stimulation (DBS) studies have shown that stimulation of the motor segment of the thalamus based on probabilistic tractography is predictive of improvement in essential tremor (ET). However, probabilistic methods are computationally demanding, requiring the need for alternative tractography methods for use in the clinical setting. The purpose of this study was to compare probabilistic vs deterministic tractography methods for connectivity-based targeting in patients with ET. METHODS: Probabilistic and deterministic tractography methods were retrospectively applied to diffusion-weighted data sets in 36 patients with refractory ET. The thalamus and precentral gyrus were selected as regions of interest and fiber tracking was performed between these regions to produce connectivity-based thalamic segmentations, per prior methods. The resultant deterministic target maps were compared with those of thresholded probabilistic maps. The center of gravity (CG) of each connectivity map was determined and the differences in spatial distribution between the tractography methods were characterized. Furthermore, the intersection between the connectivity maps and CGs with the therapeutic volume of tissue activated (VTA) was calculated. A mixed linear model was then used to assess clinical improvement in tremor with volume of overlap. RESULTS: Both tractography methods delineated the region of the thalamus with connectivity to the precentral gyrus to be within the posterolateral aspect of the thalamus. The average CG of deterministic maps was more medial-posterior in both the left (3.7 ± 1.3 mm3) and the right (3.5 ± 2.2 mm3) hemispheres when compared to 30 %-thresholded probabilistic maps. Mixed linear model showed that the volume of overlap between CGs of deterministic and probabilistic targeting maps and therapeutic VTAs were significant predictors of clinical improvement. CONCLUSIONS: Deterministic tractography can reconstruct DBS thalamic target maps in approximately 5 min comparable to those produced by probabilistic methods that require > 12 h to generate. Despite differences in CG between the methods, both deterministic-based and probabilistic targeting were predictive of clinical improvement in ET.

Under the best circumstances, achieving or sustaining optimum ecological conditions in estuaries is challenging. Persistent information gaps in estuarine data make it difficult to differentiate natural variability from potential regime shifts. Long-term monitoring is critical for tracking ecological change over time. In the United States (US), many resource management programs are working at maximum capacity to address existing state and federal water quality mandates (e.g., pollutant load limits, climate impact mitigation, and fisheries management) and have little room to expand routine sampling efforts to conduct periodic ecological baseline assessments, especially at state and local scales. Alternative design, monitoring, and assessment approaches are needed to help offset the burden of addressing additional data needs to increase understanding about estuarine system resilience when existing monitoring data are sparse or spatially limited. Research presented here offers a pseudo-probabilistic approach that allows for the use of found or secondary data, such as data on hand and other acquired data, to generate statistically robust characterizations of ecological conditions in estuaries. Our approach uses a generalized pseudo-probabilistic framework to synthesize data from different contributors to inform probabilistic-like baseline assessments. The methodology relies on simple geospatial techniques and existing tools (R package functions) developed for the US Environmental Protection Agency to support ecological monitoring and assessment programs like the National Coastal Condition Assessment. Using secondary estuarine water quality data collected in the Northwest Florida (US) estuaries, demonstrations suggest that the pseudo-probabilistic approach produces estuarine condition assessment results with reasonable statistical confidence, improved spatial representativeness, and value-added information. While the pseudo-probabilistic framework is not a substitute for fully evolved monitoring, it offers a scalable alternative to bridge the gap between limitations in resource management capability and optimal monitoring strategies to track ecological baselines in estuaries over time. [ABSTRACT FROM AUTHOR]

We present a statistical characterisation of fetal anatomies in obstetric ultrasound video sweeps where the transducer follows a fixed trajectory on the maternal abdomen. Large-scale, frame-level manual annotations of fetal anatomies (head, spine, abdomen, pelvis, femur) were used to compute common frame-level anatomy detection patterns expected for breech, cephalic, and transverse fetal presentations, with respect to video sweep paths. The patterns, termed statistical heatmaps, quantify the expected anatomies seen in a simple obstetric ultrasound video sweep protocol. In this study, a total of 760 unique manual annotations from 365 unique pregnancies were used. We provide a qualitative interpretation of the heatmaps assessing the transducer sweep paths with respect to different fetal presentations and suggest ways in which the heatmaps can be applied in computational research (e.g. , as a machine learning prior). The heatmap parameters are freely available to other researchers (https://github.com/agleed/calopus_statistical_heatmaps). [ABSTRACT FROM AUTHOR]

This paper addresses the critical gap of uncertainty quantification in existing geotechnical models predicting bearing capacity for shallow foundations on cohesionless soils. The developed Bayesian regression model improves point estimate accuracy by 30% compared to traditional models while introducing a robust method for quantifying prediction uncertainty. Utilizing extensively studied literature data, we demonstrate the model's closed-form structure, allowing the direct calculation of both the most probable bearing capacity and its uncertainty. This transparency distinguishes the proposed model from black-box machine learning alternatives. Furthermore, we illustrate its practical applicability by integrating it into reliability-based design, specifically showcasing its utility in incorporating applied load distributions. In essence, this research enhances predictive accuracy and establishes a comprehensive framework for addressing uncertainty in bearing capacity models, with direct implications for reliability-based design practices. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In the realm of Wireless Sensor Networks (WSN), effective data dissemination is vital for applications like traffic alerts, necessitating innovative solutions to tackle challenges such as broadcast storms. OBJECTIVES: This paper proposes a pioneering framework that leverages probabilistic data aggregation to optimize communication efficiency and minimize redundancy. METHODS: The proposed adaptable system extracts valuable insights from the knowledge base, enabling dynamic route adjustments based on application-specific criteria. Through simulations addressing bandwidth limitations and local broadcast issues, we establish a robust WSN-based traffic information system. RESULTS: By employing primal-dual decomposition, the proposed approach identifies optimal packet aggregation probabilities and durations, resulting in reduced energy consumption while meeting latency requirements. CONCLUSION: The efficacy of proposed method is demonstrated across various traffic and topology scenarios, affirming that probabilistic data aggregation effectively mitigates the local broadcast problem, ultimately leading to decreased bandwidth demands. [ABSTRACT FROM AUTHOR]

Treatment wetlands (TWs) effectively remove target pollutants and enhance urban water circularity and resilience. They constitute a prominent solution for urban wastewater treatment, thanks to their adaptability across various types of wastewater, scales and climatic conditions. However, the disparity in TW designs and the focus on a restricted set of variables applicable to research studies impede any comprehensive evaluation and comparison of TW performance. Our study introduces a methodology for data validation, in concurrently establishing a workflow specific to TW. This approach is aimed at defining the scope and relationships within the data, implementing checks and concatenating them into a quality flag, as an initial step towards building reliable statistical models. We underscore the importance of both mobilising comprehensive knowledge and identifying customary, yet implicit, choices intertwined in data processing. As for the application workflow, we collected and analysed data sourced from peer-reviewed papers on horizontal and vertical flow TW. Deficiencies were noted in key data elements like dimensions, concentrations and operational conditions. For the data analysis, relationships are highlighted between variables introduced for modelling purposes. These methodologies and workflows assess the quality of the data, in paving the way towards more dependable statistical models for TW design and implementation. HIGHLIGHTS We provide a six-step data validation methodology highlighting the key elements to consider, regardless of the field.; We develop data quality checks, in proposing a probabilistic output that can be used in data-driven modelling.; We analyse a worldwide-scale dataset on treatment wetlands that can serve as a ground reference for data-driven modelling.;

This research focuses on the dynamic landscape of today’s competitive market, where production management must meet clients’ expectations for high-quality products and shorter lead times while managing internal disruptions due to inevitable defects and unpredictable equipment failures. Achieving these operational goals without compromising product quality, missing deadlines, or experiencing production disruptions is essential for minimizing operational expenses. The study examines the dynamics of the system’s operating costs through the development of models, mathematical formulations, optimization techniques, and algorithm proposals. It demonstrates the system’s convexity and establishes the optimal batch time for implementing the proposed methodologies. The research results show relevant failure costs of 3.51 %, overtime added costs of 4.57 %, outsourcing setup costs of 0.73 %, outsourcing variable costs of 41.82 %, quality-related costs of 2.98 %, in-house variable costs of 40.42 %, and in-house holding costs of 3.55 %. The study develops strategies for optimal overtime use to meet production targets without excessive labor costs and provides a structured framework for making informed outsourcing decisions that balance cost savings with quality and reliability considerations. Overall, the research provides a robust framework for reducing operational costs while maintaining or improving the quality and reliability of manufacturing processes [ABSTRACT FROM AUTHOR]

Anticipating and managing the impacts of sea‐level rise for nations astride active tectonic margins requires understanding of rates of sea surface elevation change in relation to coastal land elevation. Vertical land motion (VLM) can either exacerbate or reduce sea‐level changes with impacts varying significantly along a coastline. Determining rate, pattern, and variability of VLM near coasts leads to a direct improvement of location‐specific relative sea level (RSL) estimates for coastal hazard risk assessment. Here, we utilize vertical velocity field from interferometric synthetic aperture radar (InSAR) data, calibrated with campaign and continuous Global Navigation Satellite System data, to determine the VLM for the entire coastline of New Zealand. Guided by available knowledge of the seismic cycle, the VLM data infer secular, interseismic rates of land surface deformation. Using the Framework for Assessing Changes to Sea‐level (FACTS), we build probabilistic RSL projections using the same emissions scenarios employed in IPCC Assessment Report 6 and local VLM data at 8,179 sites, thereby enhancing spatial coverage that was previously limited to four tide gauges. We present ensembles of probability distributions of RSL for each scenario to 2150, and for low confidence sea‐level processes to 2300. Where land subsidence is occurring at rates >2 mm/y VLM makes a significant contribution to RSL projections for all scenarios out to 2150. Our approach can be applied to similar locations across the world and has significant implications for adaptation planning, as timing of threshold exceedance for coastal inundation can be brought forward (or delayed) by decades. Plain Language Summary: This study outlines an approach for deriving probabilistic projections of relative sea‐level change that account for changes in land surface elevation continuously along a coastline. Previous sea‐level projections that included vertical land movements (VLMs) were restricted to tide gauge locations. To provide spatial‐resolution required by practitioners for effective adaptation planning, we have combined elevations measured using satellite radar data with measurements from land‐based Global Navigation Satellite System (GNSS) receivers to build a continuous VLM database showing land uplift and subsidence (sinking) for the entire coastline of New Zealand. We integrate these data into probabilistic sea‐level projection methodology used in Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6) for the range of future climate scenarios. Our approach could be applied to any region of the world where the coastline is affected by VLM due to secular tectonic deformation. Downward land movement >2 mm/y makes a significant contribution in sea‐level projections for all climate scenarios out to the end of this century. This means that adaptation planning decision thresholds, such as those linked to the impacts of coastal flooding and inundation, may be brought forward by decades. Key Points: Anticipating impacts of sea‐level rise for active tectonic margins requires location‐specific knowledge of vertical land movement (VLM)VLM from geodetic measurements integrated with IPCC AR6 projections to provide probabilistic RSL estimates for dynamic coastlinesDownward VLM > 2 mm/y makes a significant contribution to RSL projections bringing forward adaptation decision thresholds by decades [ABSTRACT FROM AUTHOR]

Anchor-free object-detection methods achieve a significant advancement in field of computer vision, particularly in the realm of real-time inferences. However, in remote sensing object detection, anchor-free methods often lack of capability in separating the foreground and background. This paper proposes an anchor-free method named probability-enhanced anchor-free detector (ProEnDet) for remote sensing object detection. First, a weighted bidirectional feature pyramid is used for feature extraction. Second, we introduce probability enhancement to strengthen the classification of the object's foreground and background. The detector uses the logarithm likelihood as the final score to improve the classification of the foreground and background of the object. ProEnDet is verified using the DIOR and NWPU-VHR-10 datasets. The experiment achieved mean average precisions of 61.4 and 69.0 on the DIOR dataset andNWPU-VHR-10 dataset, respectively. ProEnDet achieves a speed of 32.4 FPS on the DIOR dataset, which satisfies the real-time requirements for remote-sensing object detection. [ABSTRACT FROM AUTHOR]

Environmental contaminant exposure from traditional food consumption presents a risk for Indigenous youth (i.e., children and adolescents) who live in northern Canada; however, there are few studies targeting this demographic. Essential (Co, Cr, Mn, and Se) and non-essential (As, Cd, Hg, Ni, Pb, and Sb) metals and metalloids, and fourteen polychlorinated biphenyl (PCBs) congeners in 229 tissue samples of traditionally consumed foods were analyzed (i.e., birds, fish, and large mammals) using the Arctic Monitoring and Assessment Programme protocol. Deterministic and probabilistic Monte Carlo risk assessments were used to estimate the risk of exposure to contaminants via the consumption of traditional foods; a maximum cumulative ratio calculation (MCR) to assess the risk of a varied diet of traditional foods for metals and metalloids was also carried out. There was a probabilistic risk of exposure to Hg from the consumption of pike, sucker, and walleye for children; the MCR supported the result that the risk was due mainly to Hg. Dietary exposure to PCB congener 153 (PCB-153) was primarily due to the consumption of pike and sucker. There was also a risk of exposure to three other PCB congeners (PCB-101, -180, -187) from the consumption of sucker. Our assessment indicates a risk of exposure to Hg and certain PCBs; however, the actual risk of exposure varies with season, differences in traditional food consumption, and local levels of the toxicant. Therefore, monitoring and communicating information to affected communities on an annual basis is recommended. [ABSTRACT FROM AUTHOR]

This paper discusses various strategies for probabilistic analysis, with a focus on typical engineering applications. The emphasis is on sampling methods and sensitivity analysis. A new sampling method, Latinized particle sampling, is introduced and compared to existing sampling methods. While it can increase the quality of surrogate models, an optimized Latin hypercube sampling is mostly preferable as it shows slightly better results. In sensitivity analysis, the difficulty lies in correlated input variables, which are typical in engineering applications. First, the Sobol indices and the Shapley values are explained using an intuitive example. Then, the modified coefficient of importance is introduced as a new sensitivity measure, which can be used to reliably identify input variables without functional influence. Finally, these results are applied to a turbomachinery test case. In this case, the flow field of a compressor row is investigated, where the blades are subjected to geometric variability. The profile parameters used to describe the geometric variability are correlated. It is shown that the variability of the maximum camber and the thickness of the leading edge have a decisive influence on the variability of the isentropic efficiency. [ABSTRACT FROM AUTHOR]

Human health risk assessment currently uses the reference dose or reference concentration (RfD, RfC) approach to describe the level of exposure to chemical hazards without appreciable risk for non-cancer health effects in people. However, this "bright line" approach assumes that there is minimal risk below the RfD/RfC with some undefined level of increased risk at exposures above the RfD/RfC and has limited utility for decision-making. Rather than this dichotomous approach, non-cancer risk assessment can benefit from incorporating probabilistic methods to estimate the amount of risk across a wide range of exposures and define a risk-specific dose. We identify and review existing approaches for conducting probabilistic non-cancer risk assessments. Using perchloroethylene (PCE), a priority chemical for the U.S. Environmental Protection Agency under the Toxic Substances Control Act, we calculate risk-specific doses for the effects on cognitive deficits using probabilistic risk assessment approaches. Our probabilistic risk assessment shows that chronic exposure to 0.004 ppm PCE is associated with approximately 1-in-1,000 risk for a 5% reduced performance on the Wechsler Memory Scale Visual Reproduction subtest with 95% confidence. This exposure level associated with a 1-in-1000 risk for non-cancer neurocognitive deficits is lower than the current RfC for PCE of 0.0059 ppm, which is based on standard point of departure and uncertainty factor approaches for the same neurotoxic effects in occupationally exposed adults. We found that the population-level risk of cognitive deficit (indicating central nervous system dysfunction) is estimated to be greater than the cancer risk level of 1-in-100,000 at a similar chronic exposure level. The extension of toxicological endpoints to more clinically relevant endpoints, along with consideration of magnitude and severity of effect, will help in the selection of acceptable risk targets for non-cancer effects. We find that probabilistic approaches can 1) provide greater context to existing RfDs and RfCs by describing the probability of effect across a range of exposure levels including the RfD/RfC in a diverse population for a given magnitude of effect and confidence level, 2) relate effects of chemical exposures to clinical disease risk so that the resulting risk assessments can better inform decision-makers and benefit-cost analysis, and 3) better reflect the underlying biology and uncertainties of population risks.

The integration of distributed energy resources (DERs) and, therefore, power electronic devices into distribution networks leads to harmonic distortion injection. However, studying harmonic distortion solely through deterministic approaches presents challenges due to the inherent random behavior of DERs. This study introduced a strategy that leverages PowerFactory's harmonic load flow tool. By combining it with Python co-simulation, probabilistic load flows can be developed. These load flows utilize current sources to represent harmonic distortion emitters with predefined harmonic spectra. The proposed strategy was implemented on a real network, where two different capacities of DERs were integrated at various locations within the network. The distributions for the total harmonic distortion of voltage ($ THD_{v} $) and the total harmonic distortion of current ($ THD_{i} $) were obtained 24 hours a day in nodes and lines of the network. The procedure allowed considering the uncertainty associated to the DERs integration in distribution networks in the study of harmonic distortion, which, speaking from a simulation approach, is scarce in the literature.

Abstract Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk.

Infrastructure outages after disasters can cause contrasting impacts throughout a community due to differing vulnerabilities. The identification of critical social impact nodes for use in infrastructure risk mitigation planning and restoration will allow for overall disaster impact reduction and lessening of impact disparity across a community. To achieve criticality identification, this paper presents a probabilistic methodology to quantify the service outage impact of every node. Modelling of impact due to outages is accomplished by fixating outage occurrence in existing impact models and randomly sampling other input variables. The impact variability due to each node's outage is captured through conditional impact probability density functions (CIPDFs) that are first developed with Monte Carlo simulation at base nodes. Characterization of upstream CIPDFs is achieved through convolution of downstream nodes' CIPDFs. Mean impact curves/surfaces are developed from CIPDF and nodal vulnerability information for all components to further characterize the impact expected at a certain hazard level. Criticality identification using CIPDFs and mean impact curves/surfaces is presented for both pre-event and post-event situations. The methodology is applied for electric power network criticality identification for household dislocation impact in Galveston, TX, USA in a hurricane event. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) are expected to be vital in transitioning to a low-carbon energy system. However, integrating EVs into the power grid poses significant challenges for grid operators and energy suppliers, especially regarding the uncertainty and variability of EV charging demand. Accurate forecasting of EV charging demand is essential for optimal power system integration, yet previous studies have often only considered point predictions that are inadequate for risk assessment. Therefore, this paper compares different probabilistic forecasting models for the short-term prediction of EV charging demand at various aggregation levels, using a large and novel dataset of over 350,000 charging processes at more than 500 locations across Germany. The performance of both machine learning and deep learning methods is evaluated against a naïve benchmark model, and the impact of data availability on the forecasting models is investigated. Further, the paper examines the effects of forecast accuracy on energy procurement, which has so far received minor attention in the literature. The results show that machine learning methods such as Ada Boosting and Random Forest yield robust results with a normalized root mean square error of 0.42 and 0.41 and a mean absolute scaled error of 0.36 and 0.34 at the highest aggregation level. Furthermore, the results show the influence of different site compositions on the forecast quality and how many charging points are likely to yield a robust forecast. Energy and fleet managers can use the described method to reliably predict the required energy quantities for fleets of sufficient size and procure them at low risk. [ABSTRACT FROM AUTHOR]

Study on variation of concrete compression strength in the construction of new bridges In the design of new structures and the assessment of existing buildings or bridges, factors with variation such as material properties and component dimensions are represented by representative calculation values. It has long been known that concrete compressive strength is not a constant value but a variable factor. Smaller variations allow for the use of more favorable material properties due to reduced uncertainties. About 50 years ago, the variability characteristics were extensively researched and the results were subsequently incorporated into national and international standards. Many involved in construction have reported about a personal feeling of significant increases in actual concrete strength and more consistent properties in recent decades. Because of improved production conditions and the urgent need for resource conservation, this article examines, based on several recent bridge construction projects in southern Germany, to what extent the variability of concrete compressive strength has changed compared to previous assumptions. To do this, the results of compressive strength tests on nearly 600 specimens are statistically analyzed. The analysis reveals a much smaller variability than previously assumed for current bridge construction projects. Finally, this result is placed in historical context and potential reasons for this results are explained. [ABSTRACT FROM AUTHOR]

Purpose: To determine the sensitivity profiles of probabilistic and deterministic DTI tractography methods in estimating geometric properties in arm muscle anatomy. Methods: Spin‐echo diffusion‐weighted MR images were acquired in the dominant arm of 10 participants. Both deterministic and probabilistic tractography were performed in two different muscle architectures of the parallel‐structured biceps brachii (and the pennate‐structured flexor carpi ulnaris. Muscle fascicle geometry estimates and number of fascicles were evaluated with respect to tractography turning angle, polynomial fitting order, and SNR. The DTI tractography estimated fascicle lengths were compared with measurements obtained from conventional cadaveric dissection and ultrasound modalities. Results: The probabilistic method generally estimated fascicle lengths closer to ranges reported by conventional methods than the deterministic method, most evident in the biceps brachii (p > 0.05), consisting of longer, arc‐like fascicles. For both methods, a wide turning angle (50º–90°) generated fascicle lengths that were in close agreement with conventional methods, most evident in the flexor carpi ulnaris (p > 0.05), consisting of shorter, feather‐like fascicles. The probabilistic approach produced at least two times more fascicles than the deterministic approach. For both approaches, second‐order fitting yielded about double the complete tracts as third‐order fitting. In both muscles, as SNR decreased, deterministic tractography produced less fascicles but consistent geometry (p > 0.05), whereas probabilistic tractography produced a consistent number but altered geometry of fascicles (p < 0.001). Conclusion: Findings from this study provide best practice recommendations for implementing DTI tractography in skeletal muscle and will inform future in vivo studies of healthy and pathological muscle structure. [ABSTRACT FROM AUTHOR]

This research conducted a probabilistic life-cycle assessment (pLCA) into the greenhouse gas (GHG) emissions performance of nine combinations of truck size and powertrain technology for a recent past and a future (largely decarbonised) situation in Australia. This study finds that the relative and absolute life-cycle GHG emissions performance strongly depends on the vehicle class, powertrain and year of assessment. Life-cycle emission factor distributions vary substantially in their magnitude, range and shape. Diesel trucks had lower life-cycle GHG emissions in 2019 than electric trucks (battery, hydrogen fuel cell), mainly due to the high carbon-emission intensity of the Australian electricity grid (mainly coal) and hydrogen production (mainly through steam–methane reforming). The picture is, however, very different for a more decarbonised situation, where battery electric trucks, in particular, provide deep reductions (about 75–85%) in life-cycle GHG emissions. Fuel-cell electric (hydrogen) trucks also provide substantial reductions (about 50–70%), but not as deep as those for battery electric trucks. Moreover, hydrogen trucks exhibit the largest uncertainty in emissions performance, which reflects the uncertainty and general lack of information for this technology. They therefore carry an elevated risk of not achieving the expected emission reductions. Battery electric trucks show the smallest (absolute) uncertainty, which suggests that these trucks are expected to deliver the deepest and most robust emission reductions. Operational emissions (on-road driving and vehicle maintenance combined) dominate life-cycle emissions for all vehicle classes. Vehicle manufacturing and upstream emissions make a relatively small contribution to life-cycle emissions from diesel trucks (<5% each), but these are important aspects for electric trucks (5% to 30%). [ABSTRACT FROM AUTHOR]

The Hamiltonian Monte Carlo algorithm is known to be highly efficient when sampling high‐dimensional model spaces due to Hamilton's equations guiding the sampling process. For weakly non‐linear problems, linearizing the forward problem enhances this efficiency. This study integrates this linearization with geological prior knowledge for optimal results. We test this approach to estimate the source parameters of a 3.4 magnitude induced event that originated in the Groningen gas field in 2019. The source parameters are the event's centroid (three components), its moment tensor (six components), and its origin time. In terms of prior knowledge, we tested two sets of centroid priors. The first set exploits the known fault geometry of the Groningen gas field, whereas the second set is generated by placing initial centroid priors on a uniform horizontal grid at a depth of 3 km (the approximate depth of the gas reservoir). As for the forward problem linearization, we use an approach in which the linearization is run iteratively in tandem with updates of the centroid prior. We demonstrate that, in the absence of a sufficiently accurate initial centroid prior, the linearization of the forward model necessitates multiple initial centroid priors. Eventually, both prior sets yield similar posteriors. Most importantly, however, they agree with the geological knowledge of the area: the posterior peaks for model vectors containing a centroid near a major fault and a moment tensor that corresponds to normal faulting along a plane with a strike almost aligning with that of the major fault. Plain Language Summary: Earthquake source parameters, such as depth, time, and type of faulting, can be estimated using the recordings (or seismograms) of this (induced) earthquake. Being able to do this such that the uncertainty of the estimated parameters is also quantified is particularly valuable. This, however, requires the use of a probabilistic algorithm. A disadvantage of probabilistic algorithms is their computational cost. In this study, we simplify the relationship between the earthquake source parameters and the seismograms to significantly reduce computational costs. Specifically, we demonstrate that the simplified relation between the earthquake source parameters and the earthquake recordings requires the probabilistic algorithm to be provided with a sufficiently accurate (prior) estimate of these very earthquake source parameters. By means of a magnitude 3.4 induced event that originated in the Groningen gas field in 2019, we show that a geologically inspired prior can be helpful to partly overcome this: we use (known) existing faults in the reservoir to kick start the probabilistic algorithm. As such, we recover earthquake source parameters that are in line with subsurface geological information. Key Points: A modified Hamiltonian Monte Carlo (HMC) algorithm is applied to the recordings of a 3.4 magnitude induced event in the Groningen gas fieldMeaningful prior information is required to ensure the modified HMC algorithm samples the correct mode of the posterior probabilityExisting knowledge of the subsurface geology can be incorporated into the prior information of the algorithm [ABSTRACT FROM AUTHOR]

The Eastern Anatolia Observatory (DAG), to accommodate a 4-m telescope, is under construction in the city of Erzurum at Karakaya Peak of the Palandöken Mountain Range. Owing to the vulnerability of the telescope and its optical components to earthquakes and the high demand for pointing accuracy for quality astronomical observations, the seismic hazard needs to be reliably determined before any design and construction work begins. A relevant rectangular zone, extending 250 km from the observatory in each direction, is selected as the study area. Then, the seismic hazard at the DAG site is estimated by using the probabilistic hazard analysis method. Using different magnitude scales, several earthquake catalogues are recompiled by converting them into a uniform moment magnitude (Mw) scale, and the seismic sources that could affect the study area are determined. The results are aggregated for epistemic uncertainties using the logic-tree method. Finally, peak ground acceleration (PGA) values are obtained for two recurrence periods corresponding to two different design levels (operating basis and design basis earthquakes) using two different ground motion attenuation models. The disaggregation of the seismic hazard permits association of the PGA estimates with various faults. Disaggregation also provides source information for synthetic ground motion generation. Effects of rupture directivity and topographical amplification are discussed. [ABSTRACT FROM AUTHOR]

The integration of distributed energy resources (DERs) and, therefore, power electronic devices into distribution networks leads to harmonic distortion injection. However, studying harmonic distortion solely through deterministic approaches presents challenges due to the inherent random behavior of DERs. This study introduced a strategy that leverages PowerFactory's harmonic load flow tool. By combining it with Python co-simulation, probabilistic load flows can be developed. These load flows utilize current sources to represent harmonic distortion emitters with predefined harmonic spectra. The proposed strategy was implemented on a real network, where two different capacities of DERs were integrated at various locations within the network. The distributions for the total harmonic distortion of voltage (THDv) and the total harmonic distortion of current (THDi) were obtained 24 hours a day in nodes and lines of the network. The procedure allowed considering the uncertainty associated to the DERs integration in distribution networks in the study of harmonic distortion, which, speaking from a simulation approach, is scarce in the literature. [ABSTRACT FROM AUTHOR]

Abstract Anticipating and managing the impacts of sea‐level rise for nations astride active tectonic margins requires understanding of rates of sea surface elevation change in relation to coastal land elevation. Vertical land motion (VLM) can either exacerbate or reduce sea‐level changes with impacts varying significantly along a coastline. Determining rate, pattern, and variability of VLM near coasts leads to a direct improvement of location‐specific relative sea level (RSL) estimates for coastal hazard risk assessment. Here, we utilize vertical velocity field from interferometric synthetic aperture radar (InSAR) data, calibrated with campaign and continuous Global Navigation Satellite System data, to determine the VLM for the entire coastline of New Zealand. Guided by available knowledge of the seismic cycle, the VLM data infer secular, interseismic rates of land surface deformation. Using the Framework for Assessing Changes to Sea‐level (FACTS), we build probabilistic RSL projections using the same emissions scenarios employed in IPCC Assessment Report 6 and local VLM data at 8,179 sites, thereby enhancing spatial coverage that was previously limited to four tide gauges. We present ensembles of probability distributions of RSL for each scenario to 2150, and for low confidence sea‐level processes to 2300. Where land subsidence is occurring at rates >2 mm/y VLM makes a significant contribution to RSL projections for all scenarios out to 2150. Our approach can be applied to similar locations across the world and has significant implications for adaptation planning, as timing of threshold exceedance for coastal inundation can be brought forward (or delayed) by decades.

This study emphasizes the role of active faults as seismogenic sources in probabilistic seismic hazard assessments (PSHA) in the United Arab Emirates (UAE). An extensive PSHA analysis has been carried out in this study, which incorporates faults alongside area seismic sources within a logic tree-oriented framework. Seismicity is evaluated in terms of spectral acceleration (SA) and Peak ground acceleration (PGA) for return periods of 2,475, 975, and 475 years at 5 % damping. The results are compared with seismic hazard projections derived from previous PSHA studies that utilized only area sources. A key observation is the pronounced impact of the Dibba and Oman range frontal faults on the UAE's northeastern region due to the proximity of the faults. This contributes to higher seismic hazard in this region, with some return periods showing greater hazard levels than those documented in earlier studies. In order to aid seismic hazard design in the UAE, contour maps depicting the SAs at 0.2 and 1.0 s, along with PGA, are developed. The analysis reveals that the highest seismic hazard is predicted along the northeastern borders of the UAE, particularly the coastal area of Ras-Al Khaimah, with a PGA of 0.22 g for a return period of 2,475 years.

Accurate robot localization relative to orchard row centerlines is essential for autonomous guidance where satellite signals are often obstructed by foliage. Existing sensor-based approaches rely on various features extracted from images and point clouds. However, any selected features are not available consistently, because the visual and geometrical characteristics of orchard rows change drastically when tree types, growth stages, canopy management practices, seasons, and weather conditions change. In this study, we introduce a novel localization method that does not rely on features; instead, it relies on the concept of a row-sensing template, which is the expected observation of a 3D sensor traveling in an orchard row, when the sensor is anywhere on the centerline and perfectly aligned with it. First, the template is built using a few measurements, provided that the sensor's true pose with respect to the centerline is available. Then, during navigation, the best pose estimate (and its confidence) is estimated by maximizing the match between the template and the sensed point cloud using particle-filtering. The method can adapt to various orchards and conditions by rebuilding the template. Experiments were performed in a vineyard, and in an orchard in different seasons. Results showed that the lateral mean absolute error (MAE) was less than 3.6% of the row width, and the heading MAE was less than 1.72°. Localization was robust, as errors did not increase when less than 75% of measurement points were missing. The results indicate that template-based localization can provide a generic approach for accurate and robust localization in real-world orchards.

This editorial titled "Novel applications of Bayesian and other models in translational neuroscience" explores the use of statistical models, particularly Bayesian models, in understanding brain function and analyzing neural data. It emphasizes the importance of Bayesian networks in calculating interrelated variables and their applications in perception, cognition, and action. The editorial also discusses specific studies that utilize Bayesian models to predict brain function and behavioral outcomes. It concludes by highlighting the potential of Bayesian models in generating precise behavioral predictions and designing adaptive experiments. The document further discusses the use of Bayesian models in analyzing behavioral discounting probabilities, predicting neurological outcomes and cognitive impairment, studying working memory, and neural circuit computation in autism spectrum disorder. The authors hope that this research will lay the groundwork for future studies in translational neuroscience. [Extracted from the article]

Prediction of the potentially devastating impact of landfalling tropical cyclones (TCs) relies substantially on numerical prediction systems. Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios, it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings. RSMCs, TCWCs, and other forecast centers value probabilistic guidance for TCs, but the International Workshop on Tropical Cyclones (IWTC-9) found that the “pull-through” of probabilistic information to operational warnings using those forecasts is slow. IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products (TC-PFP) project, which is also endorsed as a WMO Seamless GDPFS Pilot Project. The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts. TC-PFP is being implemented in 3 phases: Phase 1 (TC formation and position); Phase 2 (TC intensity and structure); and Phase 3 (TC related rainfall and storm surge). This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position. There is considerable variability in the nature and interpretation of forecast products based on ensemble information, making it challenging to transfer knowledge of best practices across forecast centers. Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices. Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts. Finally, forecast centers need timely access to ensemble information that has consistent, user-friendly ensemble information. Greater consistency across forecast centers in data accessibility, probabilistic forecast products, and warnings and their communication to users will produce more reliable information and support improved outcomes.

The permanent deformation of granular materials is an important indicator in making decisions associated with the traffic of superheavy vehicles on pavements. The PBD model, a recently developed mechanistic-empirical model, analyzes the effect of superheavy load vehicles using the plastic strain rate as an indicator. The plastic strain rates of materials subjected to repeated cyclic stresses are defined by two states: (1) the post-compaction phase and (2) the shakedown state, with a probable range of permanent deformation being defined from each of these states. This paper explores the limits of applicability of the PBD model at different times in the stress history of granular materials. An accelerated pavement test with typical pavement structures from Quebec (Canada) is used as a reference. The changes in the plastic strain rate to variables related to the stress history in the granular material were studied, and a probabilistic relationship is proposed to improve the analytical predictions of the PBD model. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) genesis prediction is a major scientific challenge to the TC operation and research community. This report surveys the current status of TC genesis forecasts by a number of major operational centers covering the key ocean basins across both hemispheres. Since IWTC-9, we see an emergence of probabilistic TC genesis forecast products by operational centers, typically supported by the statistical processing of a combination of ensemble prediction and satellite analysis, covering time periods of couple of days to weeks ahead. The prevalence of multi-center grand ensemble approach highlights the uncertainties involved and the forecast challenges in quantitative genesis prediction. While operational practice might differ across agencies, verification efforts generally report a steady or slightly improving skill level in terms of reliability, which likely results from the continual improvement in global numerical weather prediction capability. [ABSTRACT FROM AUTHOR]

Prediction of the potentially devastating impact of landfalling tropical cyclones (TCs) relies substantially on numerical prediction systems. Due to the limited predictability of TCs and the need to express forecast confidence and possible scenarios, it is vital to exploit the benefits of dynamic ensemble forecasts in operational TC forecasts and warnings. RSMCs, TCWCs, and other forecast centers value probabilistic guidance for TCs, but the International Workshop on Tropical Cyclones (IWTC-9) found that the "pull-through" of probabilistic information to operational warnings using those forecasts is slow. IWTC-9 recommendations led to the formation of the WMO/WWRP Tropical Cyclone-Probabilistic Forecast Products (TC-PFP) project, which is also endorsed as a WMO Seamless GDPFS Pilot Project. The main goal of TC-PFP is to coordinate across forecast centers to help identify best practice guidance for probabilistic TC forecasts. TC-PFP is being implemented in 3 phases: Phase 1 (TC formation and position); Phase 2 (TC intensity and structure); and Phase 3 (TC related rainfall and storm surge). This article provides a summary of Phase 1 and reviews the current state of the science of probabilistic forecasting of TC formation and position. There is considerable variability in the nature and interpretation of forecast products based on ensemble information, making it challenging to transfer knowledge of best practices across forecast centers. Communication among forecast centers regarding the effectiveness of different approaches would be helpful for conveying best practices. Close collaboration with experts experienced in communicating complex probabilistic TC information and sharing of best practices between centers would help to ensure effective decisions can be made based on TC forecasts. Finally, forecast centers need timely access to ensemble information that has consistent, user-friendly ensemble information. Greater consistency across forecast centers in data accessibility, probabilistic forecast products, and warnings and their communication to users will produce more reliable information and support improved outcomes. [ABSTRACT FROM AUTHOR]

A wind turbines' power curve is an easily accessible form of damage-sensitive data, and as such is a key part of structural health monitoring (SHM) in wind turbines. Power curve models can be constructed in a number of ways, but the authors argue that probabilistic methods carry inherent benefits in this use case, such as uncertainty quantification and allowing uncertainty propagation analysis. Many probabilistic power curve models have a key limitation in that they are not physically meaningful – they return mean and uncertainty predictions outside of what is physically possible (the maximum and minimum power outputs of the wind turbine). This paper investigates the use of two bounded Gaussian processes (GPs) in order to produce physically meaningful probabilistic power curve models. The first model investigated was a warped heteroscedastic Gaussian process, and was found to be ineffective due to specific shortcomings of the GP in relation to the warping function. The second model – an approximated GP with a Beta likelihood was highly successful and demonstrated that a working bounded probabilistic model results in better predictive uncertainty than a corresponding unbounded one without meaningful loss in predictive accuracy. Such a bounded model thus offers increased accuracy for performance monitoring and increased operator confidence in the model due to guaranteed physical plausibility. [ABSTRACT FROM AUTHOR]

Manufacturing long-chain branched polymers enhances the quality of products at the cost of production time, translating into high manufacturing costs. In this manuscript, we study the cost-versus-quality trade-off while optimizing the operating conditions for large-scale industrial production of Polyvinyl acetate (PVAc). PVAc polymerization is emulated using a large system of stiff differential equations resulting in time-expensive function evaluation. Thus, we aim to achieve the Pareto optimality using Multi-Objective Bayesian Optimization (MOBO) that introduces q-Expected Hyper-Volume Improvement (qEHVI) as the novel acquisition function. Comparison with Non-dominated Sorting Genetic Algorithm-II (NSGA-II), applied to the high-fidelity model, resulted in a similar Pareto front with only 1% of the high-fidelity calls required by NSGA-II. The results indicate the efficiency of MOBO for PVAc optimization and present a real-world application of a generic method that can be implemented to solve time-expensive multi-objective optimization in manufacturing processes [ABSTRACT FROM AUTHOR]

The Caspian Sea level (CSL) is strongly sensitive to climate‐induced changes in its water balance components, especially evaporation from its surface as the main expenditure component of the Caspian Sea's (CS) water budget. Projecting evaporation and determining associated uncertainties obtained from such studies are critical for reliably predicting future CSL fluctuations and developing mitigation and adaptation strategies. This paper studies the projected changes in evaporation from the CS using 18 global climate models (GCMs) from the latest Coupled Model Intercomparison Project phase 6 (CMIP6) and Meyer's semi‐empirical formula. Future evaporation projections are constructed employing a weighted combination of the top‐ranked GCMs, including all ensemble members of selected individual models in a probabilistic framework. This study estimates late 21st century median evaporation of 945, 1016, 1105 and 1173 mm under the low‐emission, medium‐emission, medium‐to‐high emission and high‐emission scenarios, respectively. The weighted multimodel ensemble suggests CS's annual mean evaporation is projected to substantially increase by 3.9%, 13.2%, 20% and 27.9% under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5, respectively, for the late 21st century against the reference period. According to the spatial distribution of evaporation, arid eastern regions of CS experience higher evaporation than semi‐arid western and southwestern temperate regions. Additionally, the northwestern continental regions experience the least evaporation over the CS. [ABSTRACT FROM AUTHOR]

According to Bayesian/predictive coding models of autism, autistic individuals may have difficulties learning probabilistic cue-outcome associations, but empirical evidence has been mixed. The target cues used in previous studies were often straightforward and might not reflect real-life learning of such associations which requires learners to infer which cue(s) among many to track. Across two experiments, we compared adult learners with varying levels of autistic traits on their ability to infer the correct cue to learn probabilistic cue-outcome associations when explicitly instructed to do so or when exposed implicitly. We found no evidence for the effect of autistic traits on probabilistic learning accuracy, contrary to the predictions of Bayesian/predictive coding models. Implications for the current Bayesian/predictive coding models are discussed. [ABSTRACT FROM AUTHOR]

Creep behavior is susceptible to uncertainty. Replicated creep data for steel can exhibit logarithm decades of uncertainty, which necessitates the development of probabilistic creep models for reliability‐based design. The deterministic Wilshire–Cano–Stewart (WCS) model is transformed into a probabilistic creep deformation, damage, and rupture prediction model via injecting the uncertainty of test conditions, pre‐existing damage, and material constants. The WCS model is calibrated to replicated quintuplicate creep data from a "single heat" of 304 stainless steel. The Markov chain Monte Carlo (MCMC) method with the Metropolis–Hastings (MH) algorithm is employed to introduce the sources of uncertainties into the model via calibrated probability distribution functions (pdfs). The probabilistic predictions encapsulate most of the experimental outliers across isostresses‐isotherms. Interpolative and extrapolative assessments reveal that minimum‐creep‐strain‐rate (MCSR) and stress rupture (SR) predictions are consistent across isotherms. The WCS probabilistic model captures the range of creep behavior of a single heat of material using short‐term data. Highlights: A probabilistic creep‐damage model is developed by introducing uncertainties of creep.Probabilistic creep curves replicate observed uncertainty in creep data.Interpolative and extrapolative prediction by probabilistic model exhibits physical realism.Probabilistic WCS model exhibits expected range of scatter in MCSR, SR, and ductility. [ABSTRACT FROM AUTHOR]

Probabilistic seismic hazard analysis (PSHA) has become a fundamental tool in assessing seismic hazards and for estimating seismic design and seismic safety evaluation of ground motions. This report describes a study to test and verify the numerical approaches and software used in PSHA on Semnan, one of the important cities of Iran country. The geological and seismological data are integrated into a probabilistic seismic hazard model for the region. Historical and instrumental earthquake data, geology, tectonics, fault activity, and seismic source models associated with seismic events are taken into account. A probabilistic approach within a logic-tree framework is used to calculate seismic hazard for Semnan. The results were also displayed in terms of uniform hazard spectra for rock sites for return periods of 100, 475 and 2475 years, which is deemed appropriate for structural design standards in the Iran building code. As the results of this study have been displayed on the map for different return periods and different natural periods, it can provide the basis for the preparation of new seismic risk maps. [ABSTRACT FROM AUTHOR]

Objective: Deep brain stimulation (DBS) studies have shown that stimulation of the motor segment of the thalamus based on probabilistic tractography is predictive of improvement in essential tremor (ET). However, probabilistic methods are computationally demanding, requiring the need for alternative tractography methods for use in the clinical setting. The purpose of this study was to compare probabilistic vs deterministic tractography methods for connectivity-based targeting in patients with ET. Methods: Probabilistic and deterministic tractography methods were retrospectively applied to diffusion-weighted data sets in 36 patients with refractory ET. The thalamus and precentral gyrus were selected as regions of interest and fiber tracking was performed between these regions to produce connectivity-based thalamic segmentations, per prior methods. The resultant deterministic target maps were compared with those of thresholded probabilistic maps. The center of gravity (CG) of each connectivity map was determined and the differences in spatial distribution between the tractography methods were characterized. Furthermore, the intersection between the connectivity maps and CGs with the therapeutic volume of tissue activated (VTA) was calculated. A mixed linear model was then used to assess clinical improvement in tremor with volume of overlap. Results: Both tractography methods delineated the region of the thalamus with connectivity to the precentral gyrus to be within the posterolateral aspect of the thalamus. The average CG of deterministic maps was more medial-posterior in both the left (3.7 ± 1.3 mm3) and the right (3.5 ± 2.2 mm3) hemispheres when compared to 30 %-thresholded probabilistic maps. Mixed linear model showed that the volume of overlap between CGs of deterministic and probabilistic targeting maps and therapeutic VTAs were significant predictors of clinical improvement. Conclusions: Deterministic tractography can reconstruct DBS thalamic target maps in approximately 5 min comparable to those produced by probabilistic methods that require > 12 h to generate. Despite differences in CG between the methods, both deterministic-based and probabilistic targeting were predictive of clinical improvement in ET.

Abstract The Hamiltonian Monte Carlo algorithm is known to be highly efficient when sampling high‐dimensional model spaces due to Hamilton's equations guiding the sampling process. For weakly non‐linear problems, linearizing the forward problem enhances this efficiency. This study integrates this linearization with geological prior knowledge for optimal results. We test this approach to estimate the source parameters of a 3.4 magnitude induced event that originated in the Groningen gas field in 2019. The source parameters are the event's centroid (three components), its moment tensor (six components), and its origin time. In terms of prior knowledge, we tested two sets of centroid priors. The first set exploits the known fault geometry of the Groningen gas field, whereas the second set is generated by placing initial centroid priors on a uniform horizontal grid at a depth of 3 km (the approximate depth of the gas reservoir). As for the forward problem linearization, we use an approach in which the linearization is run iteratively in tandem with updates of the centroid prior. We demonstrate that, in the absence of a sufficiently accurate initial centroid prior, the linearization of the forward model necessitates multiple initial centroid priors. Eventually, both prior sets yield similar posteriors. Most importantly, however, they agree with the geological knowledge of the area: the posterior peaks for model vectors containing a centroid near a major fault and a moment tensor that corresponds to normal faulting along a plane with a strike almost aligning with that of the major fault.

BackgroundNeurocognitive mechanisms underlying developmental dyslexia (dD) remain poorly characterized apart from phonological and/or visual processing deficits. Assuming such deficits, the process of learning complex tasks like reading requires the learner to make decisions (i.e., word pronunciation) based on uncertain information (e.g., aberrant phonological percepts)-a cognitive process known as probabilistic decision making, which has been linked to the striatum. We investigate (1) the relationship between dD and probabilistic decision-making and (2) the association between the volume of striatal structures and probabilistic decision-making in dD and typical readers.MethodsTwenty four children diagnosed with dD underwent a comprehensive evaluation and MRI scanning (3T). Children with dD were compared to age-matched typical readers (n = 11) on a probabilistic, risk/reward fishing task that utilized a Bayesian cognitive model with game parameters of risk propensity (γ+) and behavioral consistency (β), as well as an overall adjusted score (average number of casts, excluding forced-fail trials). Volumes of striatal structures (caudate, putamen, and nucleus accumbens) were analyzed between groups and associated with game parameters.ResultsdD was associated with greater risk propensity and decreased behavioral consistency estimates compared to typical readers. Cognitive model parameters associated with timed pseudoword reading across groups. Risk propensity related to caudate volumes, particularly in the dD group.ConclusionDecision-making processes differentiate dD, associate with the caudate, and may impact learning mechanisms. This study suggests the need for further research into domain-general probabilistic decision-making in dD, neurocognitive mechanisms, and targeted interventions in dD.