Future State Research Articles

Multi-world Model in Continual Reinforcement Learning

World Models are made of generative networks that can predict future states of a single environment which it was trained on. This research proposes a Multi-world Model, a foundational model built from World Models for the field of continual reinforcement learning that is trained on many different environments, enabling it to generalize state sequence predictions even for unseen settings.

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.

DynAMO: Multi-agent reinforcement learning for dynamic anticipatory mesh optimization with applications to hyperbolic conservation laws

We introduce DynAMO, a reinforcement learning paradigm for Dynamic Anticipatory Mesh Optimization. Adaptive mesh refinement is an effective tool for optimizing computational cost and solution accuracy in numerical methods for partial differential equations. However, traditional adaptive mesh refinement approaches for time-dependent problems typically rely only on instantaneous error indicators to guide adaptivity. As a result, standard strategies often require frequent remeshing to maintain accuracy. In the DynAMO approach, multi-agent reinforcement learning is used to discover new local refinement policies that can anticipate and respond to future solution states by producing meshes that deliver more accurate solutions for longer time intervals. By applying DynAMO to discontinuous Galerkin methods for the linear advection and compressible Euler equations in two dimensions, we demonstrate that this new mesh refinement paradigm can outperform conventional threshold-based strategies while also generalizing to different mesh sizes, remeshing and simulation times, and initial conditions.

NCI Cancer Research Data Commons: Lessons Learned and Future State.

More than ever, scientific progress in cancer research hinges on our ability to combine datasets and extract meaningful interpretations to better understand diseases and ultimately inform the development of better treatments and diagnostic tools. To enable the successful sharing and use of big data, the NCI developed the Cancer Research Data Commons (CRDC), providing access to a large, comprehensive, and expanding collection of cancer data. The CRDC is a cloud-based data science infrastructure that eliminates the need for researchers to download and store large-scale datasets by allowing them to perform analysis where data reside. Over the past 10 years, the CRDC has made significant progress in providing access to data and tools along with training and outreach to support the cancer research community. In this review, we provide an overview of the history and the impact of the CRDC to date, lessons learned, and future plans to further promote data sharing, accessibility, interoperability, and reuse. See related articles by Brady et al., p. 1384, Wang et al., p. 1388, and Pot et al., p. 1396.

The phenology of the spring phytoplankton bloom in the North Atlantic does not trend with temperature

Climate change is anticipated to alter the phenology of phytoplankton blooms in the ocean, making their recent dynamics of interest to inform models of future ocean states. We characterized temperature change in the North Atlantic using metrics that track the patterns of sea surface water temperature (SST) defined by quantiles. To complement these thermal indicators, we estimated a thermal phenology index in the form of the date of the spring transition, taken as the date that temperature achieved the long-term mean at a specific location. We then used ocean color data (1998–2022) and characterized spring bloom phenology using change point methods to derive bloom initiation, duration, magnitude, and intensity. The North Atlantic has warmed over recent decades, averaging a rate of increase of 0.27°C decade−1, yet throughout most of the basin, spring transition timing has remained constant, with the exception of small areas with either delayed or advanced transitions. There were no clear trends in bloom start or duration in the North Atlantic, indicating that spring bloom phenology was independent of climate-driven temperature change. Bloom magnitude and intensity trended downward in some North Atlantic continental shelf seas, indicating that increased temperatures may have had negative effects on overall bloom productivity. However, exclusive of the areas where the bloom parameters were trending, there was a decrease in magnitude and intensity with warmer winter temperatures, suggesting that the inter-annual variability of these parameters may be affected by thermal conditions at the onset of the bloom. While temperature has increased in the North Atlantic, vernal light availability has remained unchanged, which may explain why spring bloom phenology has remained resistant to climate change. Consequently, it seems plausible that future climate change may have limited effects on spring bloom phenology, but could have substantial effects on overall phytoplankton production.

Predicting the Displacement Variation of Rehabilitated Foundation of Onshore Wind Turbines Using Machine Learning Models

The rehabilitation of wind turbine foundations after damage is increasingly common. However, limited research exists on the deformation of wind turbine foundations after rehabilitation. Artificial intelligence methods can be used to analyze future deformation state and predict post-rehabilitation deformation of foundations. This paper focuses on analyzing the stability of damaged wind turbine foundations after rehabilitation, as well as establishing and evaluating machine learning models. Specifically, Decision Tree (DT), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), and Long Short-Term Memory Network (LSTM) models are utilized to predict the vertical displacement of the rehabilitated foundation. Hence, the stability of the rehabilitated foundation is discussed in correlation with the measured wind speed, based on the foundation vertical displacement data. During the development of the machine learning model, the most suitable combination of hyperparameters is determined. The prediction performance of the SVR and LSTM models, which exhibit good performance, is compared to further evaluate their effectiveness. Furthermore, the models are analyzed and validated. The results indicate that the vertical displacements of the rehabilitated foundations gradually get close to a state of steady fluctuation over time. The SVR model is identified as the most effective in predicting the vertical displacements of wind turbine foundations after rehabilitation. This study aims to analyze and predict the vertical displacement of wind turbine foundations after rehabilitation based on extensive field monitoring data and powerful machine learning models.

Reduction of Lean Wastes by Using Value Stream Mapping: A Case Study of Textile Company in Pakistan

Purpose: Lean manufacturing, originating from the Toyota Production System (TPS), aims to reduce waste and optimize resources. Developed countries must adopt Lean practices for performance improvements while developing countries often use just-in-case approaches. Value Stream Mapping (VSM) is a crucial tool for diagnosing, implementing, and maintaining Lean Manufacturing, helping identify improvement opportunities and eliminating waste. The research at the company aims to eliminate Lean wastes and line unbalancing issues to improve lead time and the value-added ratio (VAR), enhancing production efficiency. The research aligns with Sustainable Development Goal 12 (Responsible Production & Consumption) by focusing on waste reduction.
 Methodology: The research uses time study, Edaw max, and Visio software to analyze tasks, create VSMs of current and future states, and control charts to examine data variations over time. Data collection involves cycle time, batch size, packet size, and the number of workers required for each activity.
 Findings: The research objectives include studying the existing scenario of production units via VSM, identifying, and eliminating Lean wastes, and comparing proposed and existing scenarios for improvement opportunities. The literature review highlights the importance of Lean manufacturing in eliminating unnecessary processes, reducing lead time, and fostering positive stakeholder relationships.
 Unique Contribution to Theory, Practice and Policy: This study suggests the better way to reduce the lean wastages and balance the line by using VSM technique. The future state VSM is created to conceptualize potential improvements and gather feedback, focusing on reducing non-value-adding tasks, work-in-process inventory, workforce, and overall process time.

A Koopman operator-based prediction algorithm and its application to COVID-19 pandemic and influenza cases

Future state prediction for nonlinear dynamical systems is a challenging task. Classical prediction theory is based on a, typically long, sequence of prior observations and is rooted in assumptions on statistical stationarity of the underlying stochastic process. These algorithms have trouble predicting chaotic dynamics, “Black Swans” (events which have never previously been seen in the observed data), or systems where the underlying driving process fundamentally changes. In this paper we develop (1) a global and local prediction algorithm that can handle these types of systems, (2) a method of switching between local and global prediction, and (3) a retouching method that tracks what predictions would have been if the underlying dynamics had not changed and uses these predictions when the underlying process reverts back to the original dynamics. The methodology is rooted in Koopman operator theory from dynamical systems. An advantage is that it is model-free, purely data-driven and adapts organically to changes in the system. While we showcase the algorithms on predicting the number of infected cases for COVID-19 and influenza cases, we emphasize that this is a general prediction methodology that has applications far outside of epidemiology.

Navigating the Digital Neurolandscape: Analyzing the Social Perception of and Sentiments Regarding Neurological Disorders through Topic Modeling and Unsupervised Research Using Twitter

Neurological disorders represent the primary cause of disability and the secondary cause of mortality globally. The incidence and prevalence of the most notable neurological disorders are growing rapidly. Considering their social and public perception by using different platforms like Twitter can have a huge impact on the patients, relatives, caregivers and professionals involved in the multidisciplinary management of neurological disorders. In this study, we collected and analyzed all tweets posted in English or Spanish, between 2007 and 2023, referring to headache disorders, dementia, epilepsy, multiple sclerosis, spinal cord injury or Parkinson’s disease using a search engine that has access to 100% of the publicly available tweets. The aim of our work was to deepen our understanding of the public perception of neurological disorders by addressing three major objectives: (1) analyzing the number and temporal evolution of both English and Spanish tweets discussing the most notable neurological disorders (dementias, Parkinson’s disease, multiple sclerosis, spinal cord injury, epilepsy and headache disorders); (2) determining the main thematic content of the Twitter posts and the interest they generated temporally by using topic modeling; and (3) analyzing the sentiments associated with the different topics that were previously collected. Our results show that dementias were, by far, the most common neurological disorders whose treatment was discussed on Twitter, and that the most discussed topics in the tweets included the impact of neurological diseases on patients and relatives, claims to increase public awareness, social support and research, activities to ameliorate disease development and existent/potential treatments or approaches to neurological disorders, with a significant number of the tweets showing negative emotions like fear, anger and sadness, and some also demonstrating positive emotions like joy. Thus, our study shows that not only is Twitter an important and active platform implicated in the dissemination and normalization of neurological disorders, but also that the number of tweets discussing these different entities is quite inequitable, and that a greater intervention and more accurate dissemination of information by different figures and professionals on social media could help to convey a better understanding of the current state, and to project the future state, of neurological diseases for the general public.

An Approach to Reduce Production Lead Time in the Valve Manufacturing Industry

Lean manufacturing aids improved productivity and product quality by identifying and eliminating waste. The present work aims to utilize Value Stream Mapping (VSM), a lean manufacturing technique to minimize the cycle time in the production of valves. A current state map is developed with available information gathered from the production unit, to identify the time-consuming factors like waiting time, improper inspection, and the lack of material and resources for manufacturing. Problem-solving techniques like 5s, JIT, KANBAN, and KAIZEN are used to reduce time-consuming activities. Value-Added and Non-value-added activities are identified through Current State Map. Activities such as placing a purchase order, drawing, assembling, testing, packaging, and shipping are Value-added activities. Non-Value-added activities are identified as rework, inspection, movement, waiting for the next process, and over-processing. The job waiting time between the next process is reduced by 70% using the future state map and the productivity is enhanced.

COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic based on historical situations. This problem has received ever-increasing attention in various scenarios and facilitated the development of numerous downstream applications such as urban planning and transportation management. However, the efficacy of existing methods remains sub-optimal due to their tendency to model temporal and spatial relationships independently, thereby inadequately accounting for complex high-order interactions of both worlds. Moreover, the diversity of transitional patterns in traffic forecasting makes them challenging to capture for existing approaches, warranting a deeper exploration of their diversity. Toward this end, this paper proposes Conjoint Spatio-Temporal graph neural network (abbreviated as COOL), which models heterogeneous graphs from prior and posterior information to conjointly capture high-order spatio-temporal relationships. On the one hand, heterogeneous graphs connecting sequential observation are constructed to extract composite spatio-temporal relationships via prior message passing. On the other hand, we model dynamic relationships using constructed affinity and penalty graphs, which guide posterior message passing to incorporate complementary semantic information into node representations. Moreover, to capture diverse transitional properties to enhance traffic forecasting, we propose a conjoint self-attention decoder that models diverse temporal patterns from both multi-rank and multi-scale views. Experimental results on four popular benchmark datasets demonstrate that our proposed COOL provides state-of-the-art performance compared with the competitive baselines.

Combined discrete Fourier transform method for azimuthal acoustic mode analysis

Modal analysis is an effective and prevalent method for analysing and diagnosing oscillatory systems. In this study, a combined discrete Fourier transform (cDFT) method was developed in addition to the discrete Fourier transform (DFT) method and solving equation (SE) method for azimuthal acoustic mode analysis to utilize the advantages of both methods. cDFT transforms the original sampling matrix equation into a sparse matrix equation by using the aliasing property of the DFT and obtains the modal spectrum results by solving the sparse matrix equation. In this study, the relative accuracy and computational complexity of modal analysis are further demonstrated. The cDFT method improves the calculation speed by grouping the DFT and solving the sparse matrix while simultaneously influencing the relative accuracy of the analysis results by changing the condition number of the sampling matrix. Applications of the cDFT method in terms of robustness to sensor failure and constrained sampling scheme optimisation are also presented. The cDFT method broadens the sampling limitations of DFT and enhances the sampling flexibility and robustness to sensor failure. Further, it can be used to determine the optimal sampling and analysis scheme according to the actual constraints, accuracy, and speed requirements, as well as the expectations of the future state of the measurement system.

Mdpg: a novel multi-disease diagnosis prediction method based on patient knowledge graphs.

Diagnosis prediction, a key factor in enhancing healthcare efficiency, remains a focal point in clinical decision support research. However, the time-series, sparse and multi-noise characteristics of electronic health record (EHR) data make it a great challenge. Existing methods commonly address these issues using RNNs and incorporating medical prior knowledge from medical knowledge bases, but they neglect the local spatial characteristics and spatial-temporal correlation of the data. Consequently, we propose MDPG, a diagnosis prediction model based on patient knowledge graphs. Initially, we represent the electronic visit records of patients as a patient-centered temporal knowledge graph, capturing the local spatial structure and temporal characteristics of the visit information. Subsequently, we design the spatial graph convolution block, temporal self-attention block, and spatial-temporal synchronous graph convolution block to capture the spatial, temporal, and spatial-temporal correlations embedded in them, respectively. Ultimately, we accomplish the prediction of patients' future states through multi-label classification. We conduct comprehensive experiments on two real-world datasets independently and evaluate the results using visit-level precision@k and code-level accuracy@k metrics. The experimental results demonstrate that MDPG outperforms all baseline models, yielding the best performance.

Curriculum Goal-Conditioned Imitation for Offline Reinforcement Learning

Offline reinforcement learning (RL) enables learning policies from pre-collected datasets without online data collection. Although it offers the possibility to surpass the performance of the datasets, most existing offline RL algorithms struggle to compete with behavior cloning policies in many dataset settings due to trading off policy improvement and additional regularization to address the distributional shift issue. In many cases, if one can imitate a sequence of sub-optimal sub-trajectories in data and properly <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“stitch”</i> them toward reaching an ideal future state, it may potentially result in a more reliable policy while avoiding the difficulties that present in typical value-based offline RL algorithms. We borrow the idea of curriculum learning to embody the above intuition. We construct a curriculum that progressively imitates a sequence of sub-optimal trajectories conditioned on a series of carefully constructed future states and cumulative rewards as goals. The sub-optimal trajectories gradually guide policy learning toward reaching the ideal goal states. We name our algorithm Curriculum Goal-conditioned Imitation (CGI). Experimental results show that CGI achieves competitive performance against state-of-the-art offline RL algorithms, especially for challenging tasks with long horizons and sparse rewards.

Roll Prediction and Parameter Identification of Marine Vessels Under Unknown Ocean Disturbances

Abstract This paper deals with two topics: roll predictions of marine vessels with machine-learning methods and parameter estimation of unknown ocean disturbances when the amplitude, frequency, offset, and phase are difficult to estimate. This paper aims to prevent the risky roll motions of marine vessels exposed to harsh circumstances. First of all, this study demonstrates complex dynamic phenomena by utilising a bifurcation diagram, Lyapunov exponents, and a Poincare section. Without any observers, an adaptive identification applies these four parameters to the globally exponential convergence using linear second-order filters and parameter estimation errors. Then, a backstepping controller is employed to make an exponential convergence of the state variables to zero. Finally, this work presents the prediction of roll motion using reservoir computing (RC). As a result, the RC process shows good performance for chaotic time series prediction in future states. Thus, the poor predictability of Lyapunov exponents may be overcome to a certain extent, with the help of machine learning. Numerical simulations validate the dynamic behaviour and the efficacy of the proposed scheme.

The current and future states of MCDM methods in sustainable supply chain risk assessment

The current and future states of MCDM methods in sustainable supply chain risk assessment

Putting the coach in the game: Current and future state of surgical coaching in pediatric urology

Surgical coaching has been proposed as a mechanism to fill gaps in proficiency and encourage continued growth following formal surgical training. Coaching benefits have been demonstrated in other surgical fields; however, have not been evaluated within pediatric urology. The aims of this study were to survey members of The Societies for Pediatric Urology (SPU) to assess the current understanding and utilization of surgical coaching while gauging interest, potential barriers and personal goals for participation in a coaching program. Following IRB approval, members of the SPU were invited to electronically complete an anonymous survey which assessed 4 domains: 1) understanding of surgical coaching principles, 2) current utilization, 3) interest and potential barriers to participation, and 4) personal surgical goals. To evaluate understanding, questions with predefined correct answers on the key principles of coaching were posed either in multiple choice or True/False format to the SPU membership. Of the 674 pediatric urologists invited, 146 completed the survey (22%). Of those, 46% correctly responded the definition of surgical coaching. Coaching utilization was reported in 27% of respondents currently or having previously participated in a surgical coaching program. Despite current participation rates, only 6 surgeons (4%) have completed training in surgical coaching, despite 79% expressing interest to participate in a surgical coaching program. The most influential barrier to participating in a coaching program was time commitment. Respondents largely prioritized technical and cognitive skill improvement as their primary goals for coaching (see figure below). While interest in surgical coaching is high among pediatric urologists, the principles of surgical coaching were not universally understood. Furthermore, formal coach training is markedly deficient, representing a gap in our profession and an opportunity for significant avenues for improvement, especially for technical and cognitive skills. Development of a coaching model based on these results would best suit the needs of pediatric urologists providing that the time commitment barrier for these endeavors can be mitigated and/or reconciled.

A probabilistic successor representation for context-dependent learning.

Two of the main impediments to learning complex tasks are that relationships between different stimuli, including rewards, can be uncertain and context-dependent. Reinforcement learning (RL) provides a framework for learning, by predicting total future reward directly (model-free RL), or via predictions of future states (model-based RL). Within this framework, "successor representation" (SR) predicts total future occupancy of all states. A recent theoretical proposal suggests that the hippocampus encodes the SR in order to facilitate prediction of future reward. However, this proposal does not take into account how learning should adapt under uncertainty and switches of context. Here, we introduce a theory of learning SRs using prediction errors which includes optimally balancing uncertainty in new observations versus existing knowledge. We then generalize that approach to a multicontext setting, allowing the model to learn and maintain multiple task-specific SRs and infer which one to use at any moment based on the accuracy of its predictions. Thus, the context used for predictions can be determined by both the contents of the states themselves and the distribution of transitions between them. This probabilistic SR model captures animal behavior in tasks which require contextual memory and generalization, and unifies previous SR theory with hippocampal-dependent contextual decision-making. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

The malarial blood transcriptome: translational applications.

The blood transcriptome of malaria patients has been used extensively to elucidate the pathophysiological mechanisms and host immune responses to disease, identify candidate diagnostic and prognostic biomarkers, and reveal new therapeutic targets for drug discovery. This review gives a high-level overview of the three main translational applications of these studies (diagnostics, prognostics, and therapeutics) by summarising recent literature and outlining the main limitations and future directions of each application. It highlights the need for consistent and accurate definitions of disease states and subject groups and discusses how prognostic studies must distinguish clearly between analyses that attempt to predict future disease states and those which attempt to discriminate between current disease states (classification). Lastly it examines how many promising therapeutics fail due to the choice of imperfect animal models for pre-clinical testing and lack of appropriate validation studies in humans, and how future transcriptional studies may be utilised to overcome some of these limitations.

One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization.

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China's largest lake, Qinghai, over 1 to 12months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477-594 km2) and R2 (88-92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10months, 11months, and 12months). Based on the normalized RMSE criterion (0.095-0.125), the models' performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February-March) than in the wet months (October-November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.