Future State Research Articles

Climate variability, heat distribution, and polar amplification in the warm unipolar “icehouse” of the Oligocene

Abstract. The Oligocene (33.9–23.03 Ma) had warm climates with flattened meridional temperature gradients, while Antarctica retained a significant cryosphere. These may pose imperfect analogues to distant future climate states with unipolar icehouse conditions. Although local and regional climate and environmental reconstructions of Oligocene conditions are available, the community lacks synthesis of regional reconstructions. To provide a comprehensive overview of marine and terrestrial climate and environmental conditions in the Oligocene, and a reconstruction of trends through time, we review marine and terrestrial proxy records and compare these to numerical climate model simulations of the Oligocene. Results, based on the present relatively sparse data, suggest temperatures around the Equator that are similar to modern temperatures. Sea surface temperatures (SSTs) show patterns similar to land temperatures, with warm conditions at mid- and high latitudes (∼60–90°), especially in the Southern Hemisphere (SH). Vegetation-based precipitation reconstructions of the Oligocene suggest regionally drier conditions compared to modern times around the Equator. When compared to proxy data, climate model simulations overestimate Oligocene precipitation in most areas, particularly the tropics. Temperatures around the mid- to high latitudes are generally underestimated in models compared to proxy data and tend to overestimate the warming in the tropics. In line with previous proxy-to-model comparisons, we find that models underestimate polar amplification and overestimate the Equator-to-pole temperature gradient suggested from the available proxy data. This further stresses the urgency of solving this widely recorded problem for past warm climates, such as the Oligocene.

Compatible effects of adopting imaginary future generations and systems thinking in exploring future challenges: Evidence from a deliberation experiment

AbstractWe adopted an innovative methodology that combines systems thinking with “imaginary future generations” (IFGs), a method for activating “futurability” in people, to discussions about the issues and needs of a future society, and we verified the effectiveness and value of this methodology. We conducted a series of five debate experiments in which groups comprised of company employees and university students worked to formulate a vision of the future state, social issues, and social needs of society in 2050, and to investigate policies that should be adopted in the years ahead. The results of a text analysis of group debates and questionnaire surveys of debate participants showed that (1) adopting IFGs facilitates the exploration of new issues and needs when depicting the images of the future state of society; (2) adopting IFGs gives rise to recognized cognitive changes in debate participants; and (3) combining the IFG methodology with causal loop diagrams (CLDs), a systems thinking tool, makes it possible to generate the effects of systems thinking while simultaneously maintaining a “future generation” perspective. Most importantly, the results show that the IFG methodology and CLDs could be compatible. These findings demonstrate that a combination of IFGs and systems thinking can effectively be used in discussions and decision‐making that deal with complex issues related to the future of society.

Plugging Public Health Data into the Health IT Ecosystem to Protect National Health

The Covid-19 pandemic exposed many challenges in the United States’ public health systems, and in particular the antiquated data ecosystem that hinders the nation’s ability to use timely data to detect and respond to emerging public health threats. One key challenge of the public health data ecosystem is the limited use of electronic, standardized, and interoperable ways for data to be readily shared between health care delivery and public health systems. At the start of the Covid-19 pandemic, most data exchange between health care and public health, and across public health, was manual, relying on faxes, labor-intensive data entry, and other outdated technology. Although significant progress has been made over the last several years to introduce more standardization and connectivity between public health and health care IT systems, more work is needed. This commentary describes the benefits and potential of robust, modern interoperability between health care and public health systems, defines key policies and technology activities that will drive toward a future state of a highly connected public health and health care data ecosystem, and summarizes potential challenges that must be overcome.

Re-imagining the future state of the ventricular assist device controller interface through human-centered design.

Ventricular assist devices (VADs) are effective therapy for patients with end-stage heart failure. Current VAD controllers offer improved interactivity, yet limitations of the visual, tactile, and auditory interface persist that impact patient experience and quality of life (QoL). This study explores how VAD controllers can be redesigned using a human-centered design approach to enhance the emotional and functional experience of the device for patients. VAD patients (n = 21), caregivers (n = 4) and healthcare practitioners (n = 24) were interviewed to uncover design opportunities. From this, a series of realistic scenarios to design for emerged. A "design by analogy" method took inspiration from existing consumer products to ideate new functionality for the VAD wearable system, creating concepts for a controller interface and paired wearable device. An additional 15 patients and 2 caregivers were engaged to explore current VAD controller experiences and evaluate the future-state concepts. This research validated the need for increased automation and emergency functionality in VAD controllers, including remote monitoring of data, accurate communication of battery status, and automated medical alerts for critical device alarms. "Manage my health," "Feeling normal," "Social belonging," "Feeling safe," and "Sense of control" emerged as key patient concerns to be met by future VAD controller designs. The study demonstrated an innovative and relevant approach to improve usability of future VAD peripherals. By considering both emotional and functional perspectives in the design of lifesaving medical devices such as VADs, device manufacturers can uncover new opportunities to improve patient QoL through improved user experiences.

Invited commentary: A future of data-rich pharmacoepidemiology studies- transitioning to large-scale linked EHR+claims data.

Electronic health record (EHR) data are seen as an important source for Pharmacoepidemiology studies. In the US healthcare system, EHR systems often only identify fragments of patients' health information across the care continuum, including primary care, specialist care, hospitalizations, and pharmacy dispensing. This leads to unobservable information in longitudinal evaluations of medication effects causing unmeasured confounding, misclassification, and truncated follow-up times. A remedy is to link EHR data with longitudinal claims data which record all encounters during a defined enrollment period across all care settings. We evaluate EHR and claims data sources in three aspects relevant to etiologic studies of medical products: data continuity, data granularity, and data chronology. Reflecting on the strengths and limitations of EHR and insurance claims data, it becomes obvious that they complement each other. The combination of both will improve the validity of etiologic studies and expand the range of questions that can be answered. As the research community transitions towards a future state with access to large-scale combined EHR+claims data, we outline analytic templates to improve the validity and broaden the scope of pharmacoepidemiology studies in the current environment where EHR data are available only for a subset of patients with claims data.

Predictive control of a series-connected two five-phase synchronous magnet permanent machines

The work presented in this paper focuses on studying the concept of two five-phase permanent magnet synchronous machines connected in series, powered by a single five-arm converter, in order to considerably minimize circulating currents. Firstly, we modeled a system composed of two identical five-phase synchronous machines, whose stator windings are connected in series. This modeling aims to demonstrate that the control of each machine in the group can be independent of the other, even though both machines are powered by a single five-phase converter. Then, we carried out the predictive control of the two five-phase machines connected in series. This control is based on the prediction of future switching states, which are managed via the discrete model of the system. The optimal switching state is obtained according to a predetermined cost function. To demonstrate the features of the presented control algorithm, the results of the simulation conducted using MATLAB/Simulink are presented and discussed to evaluate the performance and efficiency of the independent control of the two five-phase synchronous machines connected in series.

Electrochemical Circuit Model Based State of Health Prognostics for Evaluation of Reusability of Lithium-Ion Batteries from Electric Vehicle.

For the purpose of predicting the state of health of already used lithium-ion batteries from 85 kWh electric vehicles, a simplified equivalent circuit model is utilized to estimate the electrochemical time constant from constant current discharge profiles. The grading process among as-obtained LIB cells is classified into three level types according to the remaining capacity and direct current resistance. Theoretically, the logarithmic equation describing cycling behavior is derived and utilized in the prediction of the state of health of the used cells. After the selection of the electrochemical time constant obtained from the best-fitting results in constant current discharge data, the suitable cycle number until the 20th cycle was selected for the prediction of the state of health after the 250th cycling data, which revealed that a narrow error range below 5% was for high and medium battery grades. Also, this error range became abruptly wider in lowest grade batteries, indicating that our proposed model for cycling behavior was highly useful in the prediction of the future state of health of the used batteries.

Environmental drivers of food webs in charr and trout‐dominated cold‐water lakes

AbstractCold‐water lakes situated in high latitudes and altitudes have pivotal socio‐ecological importance both globally and locally. However, they are increasingly threatened by multiple anthropogenic stressors, such as climate change, hydropower and invasive species. The development of efficient management strategies is therefore urgently needed and requires a comprehensive understanding of the factors influencing the biodiversity and ecological processes of these ecosystems. We provide a holistic knowledge base for informed future research and management by addressing the interplay between local and global environmental drivers of food webs in Arctic charr (Salvelinus alpinus, Salmonidae) and brown trout (Salmo trutta, Salmonidae) dominated cold‐water lakes in Fennoscandia. The trophic niche and population dynamics of these generalist top consumers provide extensive insights into the effects of natural and anthropogenic drivers on food webs in intensively studied Fennoscandian cold‐water lakes, covering marked biogeographical gradients in abiotic and biotic conditions. Drawing on a synthesis of existing literature, our focus is on three pivotal drivers: (1) lake location and connectivity, (2) lake area and morphometry and (3) fish community composition. These drivers significantly influence the complexity and the origin and flow of energy in lake food webs, and ultimately the size structure of the charr and trout populations. Furthermore, we highlight ongoing environmental changes in Fennoscandian cold‐water lakes caused by hydropower and invasive species. Finally, we identify crucial knowledge gaps and propose management actions for improving the future state of Fennoscandian cold‐water lake ecosystems and their charr and trout populations.

From 2008–2011 Great Recession to COVID-19 pandemic: an analysis of resilience metrics in European regions

AbstractWe aim to clarify the usefulness of different measures of economic resilience in the context of global shocks. In relation to the Great Recession, Oil& European migrant crises, and COVID-19, we compute and validate from a statistical point of view, then characterize five metrics on 317 NUTS2 regions and 21 years. ROC curves and Cox regression compare them by the capacity to predict the post-shock state. Heterogenous behavior characterizes resistance, recovery, and loss. Furthermore, the non-linear resistance performs best in future state prediction, while the composite index has the lowest efficiency as a predictor of the regions’ recovery.

New Horizons of Model Informed Drug Development in Rare Diseases Drug Development.

Model-informed approaches provide a quantitative framework to integrate all available nonclinical and clinical data, thus furnishing a totality of evidence approach to drug development and regulatory evaluation. Maximizing the use of all available data and information about the drug enables a more robust characterization of the risk-benefit profile and reduces uncertainty in both technical and regulatory success. This offers the potential to transform rare diseases drug development, where conducting large well-controlled clinical trials is impractical and/or unethical due to a small patient population, a significant portion of which could be children. Additionally, the totality of evidence generated by model-informed approaches can provide confirmatory evidence for regulatory approval without the need for additional clinical data. In the article, applications of novel quantitative approaches such as quantitative systems pharmacology, disease progression modeling, artificial intelligence, machine learning, modeling of real-world data using model-based meta-analysis and strategies such as external control and patient-reported outcomes as well as clinical trial simulations to optimize trials and sample collection are discussed. Specific case studies of these modeling approaches in rare diseases are provided to showcase applications in drug development and regulatory review. Finally, perspectives are shared on the future state of these modeling approaches in rare diseases drug development along with challenges and opportunities for incorporating such tools in the rational development of drug products.

Introduction: Revolution and Solidarity in Myanmar

Myanmar is in a protracted revolutionary situation. State military authority has been greatly weakened but has not collapsed in the face of an array of armed and unarmed forces, new and established, which have fought against the junta that seized power in 2021. What forms of solidarity have contributed to the making of a revolutionary situation in Myanmar? How have they been sustained? Where and why has solidarity been hindered or broken? These questions animate the contributions to this Special Issue. To introduce them, this article explains the adoption of “revolution” as a category to describe and interpret events in Myanmar since the military coup. It does this by juxtaposing the period of transition in the 2010s, and interpretations of it, with the post-coup situation. It argues that as the transition paradigm became commonsense, it constrained understanding about happenings in Myanmar. Everything was debated in reference to an anticipated future state. Against that way of proceeding, the article advocates for description and interpretation of the revolutionary situation that turns on its radical contingency and eventfulness.

Generation of optical dromions to generalized stochastic nonlinear Schrödinger equation with Kerr effect and higher order nonlinearity

Stochastic nonlinear Schrödinger equation is a mathematical model, used in the study of complex systems especially nonlinear optics and quantum mechanics. It explains the evolution of a quantum field with stochastic and nonlinear terms. A stochastic or random process is a mathematical representation that outlines the progression of a system in a way that incorporates probability. This is in contrast to a deterministic process, where the future states are strictly defined by initial conditions. In mathematical model, stochastic term is usually represent by variable or a function that involves random variable or a noise. In this paper, we study the generalized nonlinear Schrödinger equation by adding stochastic term for the first time to retrieve different types of optical solitons. To the best of our knowledge, no one work on this model with stochastic term. Our aim is to find optical solitons like bright and dark solitons, also solitary wave solutions for this model like bell type, hyperbolic functions, Jacobi Elliptic Function, Weierstrass Elliptic Function, periodic and rational solutions. These solutions will be obtained using the sub-ode method, subject to certain constraint conditions. Finally, we will use Jupyter as a machine learning tool and also mathematica to plot some obtained solutions in various dimensions like 3D, 2D and contour plots by using some libraries of PYTHON like numpy, scipy.integrate and matplotlib.pyplot. For the first time, we use this approach for this model and our results are new and novel [1].

Body MRI in pediatrics: where we are and what the future holds.

Body magnetic resonance imaging (MRI) is increasingly used for disease diagnosis, characterization, and monitoring in children of all ages. MRI has numerous advantages when compared to other imaging modalities, including a lack of ionizing radiation, superior soft tissue image contrast, and ability to provide objective, quantitative assessments. As MRI continues to evolve, pediatric body MRI examinations of the future will certainly be different than our current and past protocols. In this review article, we will discuss the present and likely future states of pediatric body MRI, including the increasing application of quantitative MRI methods, faster imaging techniques and implementation of abbreviated targeted protocols, and the growing use of artificial intelligence methods.

Machine learning analysis of pressure fluctuations in a gas-solid fluidized bed

Pressure fluctuations in a gas-solid fluidized bed involve much information of the dynamic system. To uncover the value and significance of the pressure fluctuations time series, two meaningful tasks, i.e., the fluidization regime classification and the future state prediction, are investigated using Machine Learning (ML) algorithms, including Back Propagation neural network (BP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), Radial Basis Function network (RBF), Random Forest (RF) and Support Vector Machine (SVM). Findings indicate that the six ML methods rank their performance for the classification task from high to low as: BP ∼ RF > SVM > CNN ∼ LSTM ∼ RBF. For the one-step-ahead future state prediction, the accuracy goes from high to low as: BP ∼ RBF > CNN ∼ LSTM > SVM ∼ RF, whilst for the multistep-ahead prediction the LSTM approach shows the best performance. Additionally, the sampling frequency of pressure time series has significant effects on both tasks. Overall, this study demonstrates the capability of machine learning methods for the value analysis of nonlinear time series and the better operation of gas-solid fluidization systems.

Application of sulfur hexafluoride in electrical engineering. Historical past, current state and prospects in future

A pressing issue today is the analysis of the possibilities of using SF6 gas and its possible limitations of use from the point of view of harmful effects on the environment. The article, based on free sources of information, systematizes the historical facts of the development of SF6 gas, the main stages of the formation of the SF6 gas environment as insulation for electrical equipment. Comparative characteristics of electrical strength for various insulating substances are presented. Research has been carried out on modern examples of the use of SF6 gas in medium and high voltage circuit breakers. Examples of the use of SF6 gas and its mixtures as gas insulation for high-voltage power lines are analyzed. It is shown that a promising direction for the development of SF6 gas is its use in gas-insulated power lines. Design features and examples of practical application of high-voltage gas-insulated power lines are presented. The features of using SF6 insulation in electrical equipment of different voltage classes are determined. Current environmental problems associated with the use of SF6 gas are analyzed and possible directions for overcoming them are established. The elements of SF6 gas decomposition products that are toxic and pose the greatest danger are considered. The characteristics of SF6 gas as a greenhouse gas are given and promising ways to reduce its use are shown. It has been established that by using not pure SF6 gas, but gas mixtures based on it, it is possible to significantly reduce the percentage of SF6 gas use. Promising directions for further development of gas insulating medium for electrical equipment without the use of SF6 gas have been established.

A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation

Deploying Lagrangian drifters that facilitate the state estimation of the underlying flow field within a future time interval is practically important. However, the uncertainty in estimating the flow field prevents using standard deterministic approaches for designing strategies and applying trajectory-wise skill scores to evaluate performance. In this paper an information measurement is developed to quantitatively assess the information gain in the estimated flow field by deploying an additional set of drifters. This information measurement is derived by exploiting causal inference. It is characterized by the inferred probability density function of the flow field, which naturally considers the uncertainty. Although the information measurement is an ideal theoretical metric, using it as the direct cost makes the optimization problem computationally expensive. To this end, an effective surrogate cost function is developed. It is highly efficient to compute while capturing the essential features of the information measurement when solving the optimization problem. Based upon these properties, a practical strategy for deploying drifter observations to improve future state estimation is designed. Due to the forecast uncertainty, the approach exploits the expected value of spatial maps of the surrogate cost associated with different forecast realizations to seek the optimal solution. Numerical experiments justify the effectiveness of the surrogate cost. The proposed strategy significantly outperforms the method by randomly deploying the drifters. It is also shown that, under certain conditions, the drifters determined by the expected surrogate cost remain skillful for the state estimation of a single forecast realization of the flow field as in reality.

Motion extrapolation in sport expertise: Representational momentum and representational gravity in volleyball athletes

When people indicate the vanishing location of a moving target that suddenly disappears, systematic errors forward (in the direction of motion) and downward (in the direction of gravity) emerge. These spatial displacements were coined, respectively, Representational Momentum and Representational Gravity, and are believed to reflect internalized ecologically relevant physical invariants useful for the anticipation of future states of an event. Previous research has shown that sports athletes exhibit increased Representational Momentum, indicating enhanced motion extrapolation and anticipation, albeit it is still not clear up to what degree this effect is specific for the expertise context or if it generalizes to other dynamic events. Furthermore, the influence of expertise on Representational Gravity, particularly in contexts where anticipation of vertically moving objects is crucial, remains understudied. This study aimed to address these gaps by focusing on Volleyball as a context of expertise due to the prevalence of fast vertically moving balls. Volleyball athletes and non-athletes indicated the perceived offset location of a smoothly moving target, which moved at a constant speed or was subjected to acceleration/deceleration, embedded either in a Volleyball or neutral context. Outcomes revealed that for the Volleyball context, athletes, but not non-athletes, revealed a significant trend to misperceive targets moving along the left diagonal to be further displaced forward beyond what would be expected due to Representational Momentum alone. This finding is discussed in relation to the natural statistics of Volleyball games, where crossed ball trajectories, particularly by the outside hitter, are more prevalent, fast, and offensive, requiring better anticipation to be efficiently dealt with.

МАТЕМАТИКА САБАГЫНДА БАШТАЛГЫЧ КЛАССТАГЫ ОКУУЧУЛАРДЫН ЛОГИКАЛЫК ОЙ ЖҮГҮРТҮҮЛӨРҮН ӨЗГӨЧӨ ТАПШЫРМАЛАР АРКЫЛУУ ӨНҮКТҮРҮҮ

In this article, the author examined the problems of developing logical thinking in primary school students through special tasks. One of the main issues in Kyrgyzstan is improving the quality of education, building a future state, achieving prosperous economic development, and providing decent education and training to future generations of its society. Therefore, given the economic level of our country, in order to ensure a decent education and quality education for future generations in the field of education, the formation of logical thinking, basic and subject competence of primary school students is considered a pressing issue. Methods of teaching mathematics in primary school is one of the most important sections of teaching methodology in primary school. Today, the science of teaching in secondary schools in our country, including primary schools, is faced with the most important tasks. Among them are the main tasks, such as the formation of students’ thinking, logic of thinking, activity, the development of new methods and methods of teaching, and increasing the scientific and methodological level of education.

Applications of the Fourier neural operator in a regional ocean modeling and prediction

In this paper, we apply the Fourier neural operator (FNO) paradigm to ocean circulation and prediction problems. We aim to show that the complicated non-linear dynamics of an ocean circulation can be captured by a flexible, efficient, and expressive structure of the FNO networks. The machine learning model (FNO3D and the recurrent FNO2D networks) trained by simulated data as well as real data takes spatiotemporal input and predicts future ocean states (sea surface current and sea surface height). For this, the double gyre ocean circulation model driven by stochastic wind stress is considered to represent an ideal ocean circulation. In order to generate the training and test data that exhibits rich spatiotemporal variability, the initial states are perturbed by Gaussian random fields. Experimental results confirm that the trained models yield satisfactory prediction accuracy for both types of FNO models in this case. Second, as the training set, we used the HYCOM reanalysis data in a regional ocean. FNO2D experiments demonstrated that the 5-day input to 5-day prediction yields the averaged root mean square errors (RMSEs) of 5.0 cm/s, 6.7 cm/s, 7.9 cm/s, 8.9 cm/s, and 9.4 cm/s in surface current, calculated consecutively for each day, in a regional ocean circulation of the East/Japan Sea. Similarly, the RMSEs for sea surface height were 2.3 cm, 3.5 cm, 4.2 cm, 4.6 cm, and 4.9 cm, for each day. We also trained the model with 15-day input and 10-day prediction, resulting in comparable performance. Extensive numerical tests show that, once learned, the resolution-free FNO model instantly forecasts the ocean states and can be used as an alternative fast solver in various inference algorithms.

Environmental factors affecting phenology and distribution of Tentyria species (Coleoptera: Tenebrionidae) in Doñana National Park (Southern Iberian Peninsula).

This research focuses on the effect of environmental factors on the phenology and distribution of the Tentyria species (Coleoptera: Tenebrionidae) from Doñana National Park (SW Iberian Peninsula). Data are derived from the results of a project carried out 20 years ago, aimed at inventorying the coleopteran of the park. This information provides a framework for comparison with current or future states since the time elapsed is long enough to detect variations. As the classification of Tentyria species is complex and controversial, the first aspect to be addressed was the taxonomical verification of the species. Indeed, they were T. platyceps Steven., T. subcostata Solier., T. bifida Bujalance, Cárdenas, Ferrer and Gallardo, and T. donanensis Bujalance, Cárdenas, Ferrer and Gallardo. Sampling consisted of 2 years of monthly pitfall trapping, encompassing the surface of the park and adjacent areas. Data on adult seasonal activity and spatial distribution of the species were obtained from the specific abundance in each sampling plot. Phenologically, the 4 species were mainly summer species, with unimodal or bimodal curves depending on the species. The distribution of the species was quite uneven: while T. donanensis was ubiquitous, T. subcostata was restricted to the southern coastal area of the park, and T. platyceps and T. bifida were recorded in the northern half, in marshes or inland forests, respectively. Our results also suggest that extreme temperatures may impose major constraints on the spatial distribution of Tentyria species, which could affect Doñana's biodiversity in the future scenery of thermal rise linked to climate change.