Future Point In Time Research Articles

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes.

Employing Deep-Learning Approach for the Early Detection of Mild Cognitive Impairment Transitions through the Analysis of Digital Biomarkers.

Mild cognitive impairment (MCI) is the precursor to the advanced stage of Alzheimer's disease (AD), and it is important to detect the transition to the MCI condition as early as possible. Trends in daily routines/activities provide a measurement of cognitive/functional status, particularly in older adults. In this study, activity data from longitudinal monitoring through in-home ambient sensors are leveraged in predicting the transition to the MCI stage at a future time point. The activity dataset from the Oregon Center for Aging and Technology (ORCATECH) includes measures representing various domains such as walk, sleep, etc. Each sensor-captured activity measure is constructed as a time series, and a variety of summary statistics is computed. The similarity between one individual's activity time series and that of the remaining individuals is also computed as distance measures. The long short-term memory (LSTM) recurrent neural network is trained with time series statistics and distance measures for the prediction modeling, and performance is evaluated by classification accuracy. The model outcomes are explained using the SHapley Additive exPlanations (SHAP) framework. LSTM model trained using the time series statistics and distance measures outperforms other modeling scenarios, including baseline classifiers, with an overall prediction accuracy of 83.84%. SHAP values reveal that sleep-related features contribute the most to the prediction of the cognitive stage at the future time point, and this aligns with the findings in the literature. Findings from this study not only demonstrate that a practical, less expensive, longitudinal monitoring of older adults' activity routines can benefit immensely in modeling AD progression but also unveil the most contributing features that are medically applicable and meaningful.

A comparison of machine learning and econometric models for pricing perpetual Bitcoin futures and their application to algorithmic trading

AbstractBitcoin (BTC) perpetual futures contracts are highly leveraged speculative trading instruments with daily market trading of $45 Billion. BTC perpetual futures are derivative contracts, which depend upon the underlying BTC SPOT (current) price. Pricing perpetual futures fairly is hard, using traditional arbitrage arguments, because of the volatile nature of the so called funding rate, which is used as the replacement of risk free rate in the Cryptocurrency market. This work presents a novel technique for pricing BTC futures contracts using conditional volatility and mean models. Intra‐day high‐frequency futures' return volatility and mean are modelled using different ML and econometric techniques. A comparison is made using statistical measures to find the model that best captures the intra‐day conditional mean and volatility. Exponential generalized autoregressive conditional heteroskedasticity is shown to be an almost unbiased predictor of intra‐day volatility, while a constant autoregressive moving average (0, 0) model best captures the conditional mean of the returns. A market directional high frequency trading algorithm is developed using the volatility and mean models. The algorithm first prices the futures contract at some future point of time using the volatility and mean regression models. Next, the slope between the current futures price and the expected price are used to predict the market direction. A long or short position is taken depending upon the expected market direction movement. Extensive back‐testing results show absolute returns of 1500%–8000% depending upon the transaction fees and leverage used. On average, the market direction is predicted correctly 85% of the time by the best model. Finally, the trading technique is market neutral, in that it gives large positive returns, with low SD, in both bull and bear markets.

The evaluation of emotional maltreatment's effect on family dynamics and suicidal behaviors

BackgroundEmotional maltreatment and poor family functioning are known risks for youth suicide, but few studies have examined these issues as prospective predictors of future attempts. ObjectivesExamine family functioning and suicide risk associated with emotional maltreatment in youth with a lifetime history of major depressive disorder (MDD) and the prospective association of emotional maltreatment and family functioning with future suicide attempts. Participants and settingParticipants included 321 youth aged 12–15 years (251 with emotional maltreatment; 70 with no emotional maltreatment) recruited from a metropolitan children's hospital from 2011 to 2018. Prospective analyses included 280 youths (221 with emotional maltreatment; 59 without emotional maltreatment). MethodsSemi-structured interviews and self-reports assessed family functioning and suicidal thoughts and behaviors in youth with and without emotional maltreatment at baseline, 6-month, 1-year, and 2-year follow-up. Multivariate analyses examined whether emotional maltreatment predicted future suicide attempts, beyond the effect of prior suicide attempts. ResultsEmotionally maltreated youth reported significantly lower scores for family adaptability, cohesion, and family alliance, and higher rates of suicidal ideation and suicide attempts, compared to youth without emotional maltreatment. Youth experiencing multiple forms of abuse were significantly more likely to attempt suicide at future timepoints, however this association was attenuated after controlling for prior suicide attempts. ConclusionYouth who experienced emotional maltreatment had a significantly higher percentage of past suicidal thoughts and behaviors and significantly less favorable scores for family functioning associated with an increased suicide risk. Findings support family-focused suicide prevention strategies as a promising approach to reduce youth suicide.

The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins

Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.

Steering clear of Akrasia: An integrative review of self-binding Ulysses Contracts in clinical practice.

In many jurisdictions, legal frameworks afford patients the opportunity to make prospective medical decisions or to create directives that contain a special provision forfeiting their own ability to object to those decisions at a future time point, should they lose decision-making capacity. These agreements have been described with widely varying nomenclatures, including Ulysses Contracts, Odysseus Transfers, Psychiatric Advance Directives with Ulysses Clauses, and Powers of Attorney with Special Provisions. As a consequence of this terminological heterogeneity, it is challenging for healthcare providers to understand the terms and uses of these agreements and for ethicists to engage with the nuances of clinical decision-making with such unique provisions surrounding patient autonomy. In theory, prospective self-binding agreements may safeguard patient's "authentic" wishes from future "inauthentic" changes of mind. In practice, it is unclear what may be comprised within these agreements or how-and to what effect-they are used. The primary focus of this integrative review is to curate the existing literature describing Ulysses Contracts (and analogous decisions) used in the clinical arena, in order to empirically synthesize their shared essence and provide insights into the traditional components of these agreements when used in practice, the requirements of their consent processes, and the outcomes of their utilization.

Ten questions concerning prospective LCA for decision support for the built environment

It is essential to provide quantitative decision support when pursuing environmental impact mitigation efforts, particularly when considering resource and energy-demanding sectors such as the built environment. Life cycle assessment (LCA) provides widely recognized quantitative decision support regarding environmental performance. However, for long-lived products such as buildings, the usefulness of conventional LCA is limited as it relies on databases that only give a current or past representation of industrial processes. The emerging field of prospective LCA (pLCA) allows us to evaluate the results at a future point in time based on technological and socio-economic projections. This article builds on a systematic literature screening, ongoing discussions in the pLCA academic community, and hands-on experimentation with available software. The goal of the study is to 1) understand implications of how pLCA is conducted, and how it relates to the built environment; 2) Improve the documentation and credibility of pLCA and applied scenarios; And 3) identify practical tools and workflows that can make pLCA more accessible to practitioners. The study raises ten relevant questions when considering how to use pLCA for decision support in the built environment. This list of questions is not exhaustive nor definite, and recommendations are possible answers suggested by the authors. Using scenario narratives from energy and Integrated Assessment Models allows for systematic and consistent transformation of LCA databases to represent possible futures. However, there is a need for pLCA practitioners to improve documentation to ensure that the goal and scope of the LCA are compatible with the chosen future scenario. In the case of built environment, it is relevant to consider different projection years when modeling construction, operation, renovation, and demolition phases, respectively, as they span several decades. Not doing so can misestimate the effects changing socio-economic and technological contexts have on the life-cycle impact of buildings.

MSR80 A Dynamic Panel Binary Data Model for Personalized Patient Engagement Prediction

Recently, pharmaceutical companies offer “nurse ambassadors” to assist patients continuing the use of medications, however, the patient engagement is one of the main issues during treatment. To investigate the impact of in-person or virtual assistance and better engage patients, we develop a dynamic panel binary data model to forecast the patients’ engaged probability at a future time point.

An application of land use, transport, and economy interaction model

Governments need to assess the benefits of transport projects to prioritize investments. It is imperative for governments to have tools that can closely estimate the actual benefits and impacts of investments in significant transport projects.Traditionally, the benefits of a transport project have been estimated by using a transport model, assuming fixed land use for the base and project case in the future. However, this approach cannot measure the impact of land-use changes as residents and businesses relocate to take advantage of lower travel and/or freight costs resulting from the project. Consequently, the benefits of the project may be under or overestimated, depending on its position within the transport network and how it reshapes land use patterns in the future. To overcome this drawback in existing models, this paper firstly presents a literature review of advances in the land use transport interaction model (LUTI), then discusses the development of a LUTI model for Victoria, Australia (VLUTI) by integrating the Victorian Integrated Transport Model with a Spatial Computable General Equilibrium model (SCGE). The land use component (SCGE) was developed to represent a highly resolved zone system, detailed classifications of occupations and industries, and spatial interactions via commuting, and trade in goods and services, and thus enables VLUTI to simulate a detailed and nuanced picture of land use and transport interactions in a complete economic environment.The literature review also indicates limited research in applying a LUTI to estimate land use change benefits. This paper presents an application of VLUTI by looking at differences in the conventional transport benefits under both static and dynamic land use scenarios. In the static method, the land use in the project case is unchanged from the base case. In the dynamic case, the land use in the project case, at a future point in time, is endogenously adjusted within VLUTI. It presents a method to correct, in the dynamic case, the benefits as estimated by the rule of a half, which usually assumes static land use. The paper concludes with suggestions for further developments and improvements of VLUTI such as incorporating an activity-based transport model and an environmental model to progress toward an integrated land-use, transport, economy, and environment model capable of assessing the full impact of a transport project in a holistic way.

P039 The association between gout and psychological co-morbidity: a five-year prospective cohort study

Abstract Background/Aims Gout is associated with physical co-morbidities and a reduction in physical health-related quality of life. However, the association between gout and psychological comorbidity (depression and anxiety) has received less attention. A secondary analysis of data from a five-year prospective cohort study was performed to determine the prevalence and incidence of anxiety and depression in people with gout, and gout characteristics associated with psychological co-morbidity. Methods Adults aged ≥18 years with gout were identified via Read codes or allopurinol prescriptions from 20 general practices across the West Midlands. Questionnaires were mailed at baseline and at 12-, 36- and 60-months. Questionnaire data included socio-demographics, gout-related characteristics (flare frequency, oligo- or poly-articular gout, gout duration and allopurinol use) and validated anxiety (GAD-7) and depression (PHQ-9) questionnaires. Current anxiety and depression were defined as scores>10 on the GAD-7 and PHQ-9 respectively. The prevalence and incidence of new-onset anxiety or depression were described at 12, 36 and 60 months in those without anxiety and depression at baseline. Binary logistic regression used to determine baseline gout characteristics associated with developing new-onset anxiety or depression at each time point (odds ratios (OR), 95% confidence intervals (CI), adjusted for age, gender and deprivation). Results Of 1184 baseline responders, 721, 605 and 411 responded at 12-, 36- and 60-months, respectively. The peak prevalence of both depression and anxiety was at baseline at 12.6% and 10.0%, respectively. Responders with anxiety (GAD-7 ≥10) or depression (PHQ-9 ≥10) at baseline were excluded from new-onset analysis. Over five years, one in thirteen (7.5%) developed new-onset anxiety, and one in eleven (9.2%) developed new-onset depression at any future time point. Developing anxiety was associated with a history of oligo- or poly-articular gout flares at baseline (OR 2.31, 95% CI:1.26 to 4.23) but not flare frequency, gout duration, or allopurinol use. New-onset depression was associated with allopurinol use at baseline (OR 1.93, 95% CI: 1.01 to 3.69) but not flare frequency, gout duration, or oligo- or poly-articular gout flares. Conclusion Psychological co-morbidity is common in people living with gout; clinicians should be aware of this when assessing and treating people with gout. Disclosure J.J.R. Higgs: None. L. Clarson: None. R. Bajpai: None. S. Muller: None. E. Roddy: None.

Prediction of future curve angle using prior radiographs in previously untreated idiopathic scoliosis: natural history from age 6 to after the end of growth (SOSORT 2022 award winner)

PurposeTreatment selection for idiopathic scoliosis is informed by the risk of curve progression. Previous models predicting curve progression lacked validation, did not include the full growth/severity spectrum or included treated patients.The objective was to develop and validate models to predict future curve angles using clinical data collected only at, or both at and prior to, an initial specialist consultation in idiopathic scoliosis.MethodsThis is an analysis of 2317 patients with idiopathic scoliosis between 6 and 25 years old. Patients were previously untreated and provided at least one prior radiograph prospectively collected at first consult. Radiographs were re-measured blinded to the predicted outcome: the maximum Cobb angle on the last radiograph while untreated. Linear mixed-effect models were used to examine the effect of data from the first available visit (age, sex, maximum Cobb angle, Risser, and curve type) and from other visits while untreated (maximum Cobb angle) and time (from the first available radiograph to prediction) on the Cobb angle outcome. Interactions of the first available angle with time, of time with sex, and time with Risser were also tested.ResultsWe included 2317 patients (83% of females) with 3255 prior X-rays where 71% had 1, 21.1% had 2, and 7.5% had 3 or more. Mean age was 13.9 ± 2.2yrs and 81% had AIS. Curve types were: 50% double, 26% lumbar/thoracolumbar-lumbar, 16% thoracic, and 8% other. Cobb angle at the first available X-ray was 20 ± 10° (0–80) vs 29 ± 13° (6–122) at the outcome visit separated by 28 ± 22mths.In the model using data at and prior to the specialist consult, larger values of the following variables predicted larger future curves: first available Cobb angle, Cobb angle on other previous X-ray, and time (with Time2 and Time3) to the target prediction. Larger values on the following variables predicted a smaller future Cobb angle: Risser and age at the first available X-ray, time*Risser and time*female sex interactions. Cross-validation found a median error of 4.5o with 84% predicted within 10°.Similarly, the model using only data from the first specialist consult had a median error of 5.5o with 80% of cases within 10° and included: maximum Cobb angle at first specialist consult, Time, Time2, age, curve type, and both interactions.ConclusionsThe models can help clinicians predict how much curves would progress without treatment at future timepoints of their choice using simple variables. Predictions can inform treatment prescription or show families why no treatment is recommended. The nonlinear effects of time account for the rapid increase in curve angle at the beginning of growth and the slowed progression after maturity. These validated models predicted future Cobb angle with good accuracy in untreated idiopathic scoliosis over the full growth spectrum.

Time series KSTAR PF superconducting coil temperature forecasting using recurrent transformer model

In this paper, we propose a KSTAR PF superconducting coil temperature forecasting model using a recurrent transformer model to protect the PF coil during operation. We developed a Recurrent Transformer model (RTransformer) that recurrently forecasts future time points using current time input data and latent variables from previous time steps based on the perceiver autoregressive model. The RTransformer model is computationally and memory efficient, requiring lower overhead than the Transformer model as it only computes forecasting points using a subset of input sequence data instead of the entire sequence of time windows. The RTransformer model was trained using the KSTAR PF coil temperature dataset obtained from the PF coil monitoring system. We compared the performance of the proposed model with Long–Short-Term memory (LSTM), Transformer model, and TransformerXL, based on R2 score, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). We also compared the inference time for each model. The experimental results showed that the proposed model outperformed other deep learning models, with lower error rates and similar inference times to the Transformer model. For most performance metrics, our proposed model scored among the top 1 and top 2 scoring models.

Frequency reserves and inertia in the transition to future electricity systems

AbstractThe transition towards an electricity system that is dominated by asynchronous and non-dispatchable generators, such as wind and solar power, entails challenges related to balancing the load and, thereby, keeping the grid frequency stable. Many technologies can contribute to load balancing and frequency control. This study investigates the interactions between electricity generation and frequency control in terms of investments and operation, using cost-minimizing, linear optimization modeling. The model is applied in three different geographic cases and for four future time-points, starting off with the already existing transmission and generation capacities, so as to yield insights into different systems and different stages along the energy transition. The results show that frequency control constraints in the optimization model have a weak impact on the system composition and cost, and that batteries are important for minimizing the impact. Furthermore, inertia requirements without a reserve demand show no impact on the cost or system composition. When allowing for vehicle-to-grid from battery electric vehicles, a large proportion of stationary grid battery investments is displaced, and the impact on system cost from adding frequency control constraints is removed.

Community diversity is associated with intra-species genetic diversity and gene loss in the human gut microbiome.

How the ecological process of community assembly interacts with intra-species diversity and evolutionary change is a longstanding question. Two contrasting hypotheses have been proposed: Diversity Begets Diversity (DBD), in which taxa tend to become more diverse in already diverse communities, and Ecological Controls (EC), in which higher community diversity impedes diversification. Previously, using 16S rRNA gene amplicon data across a range of microbiomes, we showed a generally positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with the predictions of DBD (Madi et al., 2020). However, this positive 'diversity slope' plateaus at high levels of community diversity. Here we show that this general pattern holds at much finer genetic resolution, by analyzing intra-species strain and nucleotide variation in static and temporally sampled metagenomes from the human gut microbiome. Consistent with DBD, both intra-species polymorphism and strain number were positively correlated with community Shannon diversity. Shannon diversity is also predictive of increases in polymorphism over time scales up to ~4-6 months, after which the diversity slope flattens and becomes negative - consistent with DBD eventually giving way to EC. Finally, we show that higher community diversity predicts gene loss at a future time point. This observation is broadly consistent with the Black Queen Hypothesis, which posits that genes with functions provided by the community are less likely to be retained in a focal species' genome. Together, our results show that a mixture of DBD, EC, and Black Queen may operate simultaneously in the human gut microbiome, adding to a growing body of evidence that these eco-evolutionary processes are key drivers of biodiversity and ecosystem function.

Long-Term Durability of Stand-Alone Lateral Lumbar Interbody Fusion.

The long-term durability of stand-alone lateral lumbar interbody fusion (LLIF) remains unknown. To evaluate whether early patient-reported outcome measures after stand-alone LLIF are sustained on long-term follow-up. One hundred and twenty-six patients who underwent stand-alone LLIF between 2009 and 2017 were included in this study. Patient-reported outcome measures included the Oswestry Disability Index (ODI), EuroQOL-5D (EQ-5D), and visual analog score (VAS) scores. Durable outcomes were defined as scores showing a significant improvement between preoperative and 6-week scores without demonstrating any significant decline at future time points. A repeated measures analysis was conducted using generalized estimating equations (model) to assess the outcome across different postoperative time points, including 6 weeks, 1 year, 2 years, and 5 years. ODI scores showed durable improvement at 5-year follow-up, with scores improving from 46.9 to 38.5 ( P = .001). Improvements in EQ-5D showed similar durability up to 5 years, improving from 0.48 to 0.65 ( P = .03). VAS scores also demonstrated significant improvements postoperatively that were durable at 2-year follow-up, improving from 7.0 to 4.6 ( P < .0001). Patients undergoing stand-alone LLIF were found to have significant improvements in ODI and EQ-5D at 6-week follow-up that remained durable up to 5 years postoperatively. VAS scores were found to be significantly improved at 6 weeks and up to 2 years postoperatively but failed to reach significance at 5 years. These findings demonstrate that patients undergoing stand-alone LLIF show significant improvement in overall disability after surgery that remains durable at long-term follow-up.

Towards integration of time-resolved confocal microscopy of a 3D in vitro microfluidic platform with a hybrid multiscale model of tumor angiogenesis.

The goal of this study is to calibrate a multiscale model of tumor angiogenesis with time-resolved data to allow for systematic testing of mathematical predictions of vascular sprouting. The multi-scale model consists of an agent-based description of tumor and endothelial cell dynamics coupled to a continuum model of vascular endothelial growth factor concentration. First, we calibrate ordinary differential equation models to time-resolved protein concentration data to estimate the rates of secretion and consumption of vascular endothelial growth factor by endothelial and tumor cells, respectively. These parameters are then input into the multiscale tumor angiogenesis model, and the remaining model parameters are then calibrated to time resolved confocal microscopy images obtained within a 3D vascularized microfluidic platform. The microfluidic platform mimics a functional blood vessel with a surrounding collagen matrix seeded with inflammatory breast cancer cells, which induce tumor angiogenesis. Once the multi-scale model is fully parameterized, we forecast the spatiotemporal distribution of vascular sprouts at future time points and directly compare the predictions to experimentally measured data. We assess the ability of our model to globally recapitulate angiogenic vasculature density, resulting in an average relative calibration error of 17.7% ± 6.3% and an average prediction error of 20.2% ± 4% and 21.7% ± 3.6% using one and four calibrated parameters, respectively. We then assess the model's ability to predict local vessel morphology (individualized vessel structure as opposed to global vascular density), initialized with the first time point and calibrated with two intermediate time points. In this study, we have rigorously calibrated a mechanism-based, multiscale, mathematical model of angiogenic sprouting to multimodal experimental data to make specific, testable predictions.

Consumer product prediction using machine learning

Time-series forecasting is an approach that uses historical and current data to project future values over time or at a given point in time, while forecasting and prediction are often synonymous, there is one interesting detail. In some professions, forecasting may refer to data at a specific future point in time, whereas prediction refers to future data in general. Most widely used to determine the nature of stock prices. A series of analyses and modeling by a finance committee is to guide investors, professors of legal sciences, and processes. And that is why he proposes that this series argument not include a sliding window; they were wise to back then, and they gave up everything, anticipating stock values relative to her. The system presents the (GUI) Graphical User Interface as a stand-alone application. The proposed findings demonstrate a highly predicted accurate approach for nonlinear time series models that are difficult to obtain from traditional models.

Young Children’s Saving and Their Episodic Future Thinking

ABSTRACT Children’s ability to save emerges during the preschool years, but little is known about the different forms saving takes (and whether these relate) and the mechanisms driving its development. Because research with adults suggests that different aspects of future orientation increase adults’ propensity to save, we explored whether, in a sample of 71 3- to 5-year-olds tested in a university laboratory in Ottawa, Canada, the ability to mentally pre-experience the future (or “episodic future thinking”) predicted saving in two different contexts. In the first, using a “Saving marbles” task, we assessed children’s capacity to save for a larger reward in the near future. In the second, using a newly developed “Saving candies” task, we assessed children’s capacity to save a certain amount of resource for a more remote future time point, without necessarily reaping a larger future reward. Children were also given two delay of gratification tasks to determine whether these related to saving. Performance on both saving tasks was significantly related after controlling for age in months and verbal ability (r = .25, p = .041), a finding that suggests some coherence in early saving behaviors. However, we detected no significant associations between saving and delay of gratification. A series of regression analyses showed that episodic future thinking, as measured by three different tasks, did not predict saving. Our discussion focuses on why the capacity to think about the future may not predict saving in early development, and suggests viable avenues for future research in this area.

Automation Is Coming! Exploring Future(s)-Oriented Methods in Education

Emerging automated-decision making (ADM) technologies invite scholars to engage with future points in time and contexts that have not yet arisen. This particular state of not knowing yet implies the methodological challenge of examining images of the future and how such images will materialize in practice. In this respect, we ask the following: what are appropriate research methods for studying emerging ADM technologies in education? How do researchers explore sociotechnical practices that are in the making? Guided by these questions, we investigate the increasing adoption of ADM in teachers’ assessment practices. This constitutes a case in point for reflecting on the research methods applied to address the future of assessment in education. In this context, we distinguish between representational methods oriented to recounting past experiences and future(s) methods oriented to making futures. Studying the literature on speculative methods in digital education, we illustrate four categories of future(s)-oriented methods and reflect on their characteristics through a backcasting workshop conducted with teachers. We conclude by discussing the need to reconsider the methodological choices made for studying emerging technologies in critical assessment practices and generate new knowledge on methods able to contribute to alternative imaginaries of automation in education.

Task-based co-activation patterns reliably predict resting state canonical network engagement during development

Neurodevelopmental research has traditionally focused on development of individual structures, yet multiple lines of evidence indicate parallel development of large-scale systems, including canonical neural networks (i.e., default mode, frontoparietal). However, the relationship between region- vs. network-level development remains poorly understood. The current study tests the ability of a recently developed multi-task coactivation matrix approach to predict canonical resting state network engagement at baseline and at two-year follow-up in a large and cohort of young adolescents. Pre-processed tabulated neuroimaging data were obtained from the Adolescent Brain and Cognitive Development (ABCD) study, assessing youth at baseline (N = 6073, age = 10.0 ± 0.6 years, 3056 female) and at two-year follow-up (N = 3539, age = 11.9 ± 0.6 years, 1726 female). Individual multi-task co-activation matrices were constructed from the beta weights of task contrasts from the stop signal task, the monetary incentive delay task, and emotional N-back task. Activation-based predictive modeling, a cross-validated machine learning approach, was adopted to predict resting-state canonical network engagement from multi-task co-activation matrices at baseline. Note that the tabulated data used different parcellations of the task fMRI data (“ASEG” and Desikan) and the resting-state fMRI data (Gordon). Despite this, the model successfully predicted connectivity within the default mode network (DMN, rho = 0.179 ± 0.002, p < 0.001) across participants and identified a subset of co-activations within parietal and occipital macroscale brain regions as key contributors to model performance, suggesting an underlying common brain functional architecture across cognitive domains. Notably, predictive features for resting-state connectivity within the DMN identified at baseline also predicted DMN connectivity at two-year follow-up (rho = 0.258). These results indicate that multi-task co-activation matrices are functionally meaningful and can be used to predict resting-state connectivity. Interestingly, given that predictive features within the co-activation matrices identified at baseline can be extended to predictions at a future time point, our results suggest that task-based neural features and models are valid predictors of resting state network level connectivity across the course of development. Future work is encouraged to verify these findings with more consistent parcellations between task-based and resting-state fMRI, and with longer developmental trajectories.