Designing efficient policies to slow global warming requires an approach that combines economic tools with relations from the natural sciences. The dynamic integrated climate-economy (DICE) model presented here, an intertemporal general-equilibrium model of economic growth and climate change, can be used to investigate alternative approaches to slowing climate change. Evaluation of five policies suggests that a modest carbon tax would be an efficient approach to slow global warming, whereas rigid emissions- or climate-stabilization approaches would impose significant net economic costs.
There are difficult problems in materials science where the general concepts might be understood but which are not as yet amenable to scientific treatment. We are at the same time told that good engineering has the responsibility to reach objectives in a cost and time-effective way. Any model which deals with only a small part of the required technology is therefore unlikely to be treated with respect. Neural network analysis is a form of regression or classification modelling which can help resolve these difficulties whilst striving for longer term solutions. This paper begins with an introduction to neural networks and contains a review of some applications of the technique in the context of materials science.
Jorge Martinez-Palomera, Amy Tuson, TESS Science Support Center
3I/ATLAS is the third known interstellar object to pass through our Solar System. NASA's Transiting Exoplanet Survey Satellite (TESS) made dedicated observations of 3I/ATLAS between 15 -- 22 January 2026 (Sector 1751), capturing high-cadence observations at 200s and 20s cadence. We present two High Level Science Products (HLSPs): (1) comet-centered image time series, corrected for background scattered light and stars; and (2) aperture light curves extracted from the corrected images. We created these data products using the official TESS products and they are publicly available at the Mikulski Archive for Space Telescopes (MAST). TESS's high-precision, near-continuous photometry will provide unique insights into the comet's activity following its closest approach to the Sun. The TESS Science Support Center (TSSC) has created these data products to facilitate scientific analyses by the TESS and Solar System communities.
The concept of integrated pest management (IPM) has been accepted and incorporated in public policies and regulations in the European Union and elsewhere, but a holistic science of IPM has not yet been developed. Hence, current IPM programs may often be considerably less efficient than the sum of separately applied individual crop protection actions. Thus, there is a clear need to formulate general principles for synergistically combining traditional and novel IPM actions to improve efforts to optimize plant protection solutions. This paper addresses this need by presenting a conceptual framework for a modern science of IPM. The framework may assist attempts to realize the full potential of IPM and reduce risks of deficiencies in the implementation of new policies and regulations.
ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximators (FAs) have been predominantly employed in previous studies proposing RL‐based controllers, leaving a significant interpretability issue due to their black‐box nature. In this work, the use of the linear FA for a value‐based RL agent in traffic signal control problems is investigated along with the least‐squares Q‐learning method, abbreviated as LSTDQ. The interpretable linear FA was found to be adequate for the RL agent to learn an optimal policy. This leads to the proposal to replace a non‐linear ANN FA with the linear FA counterpart, resolving the interpretability issue. Moreover, the LSTDQ learning method shows superior behaviour convergence compared to a gradient descent method. In a low‐intensity arrival pattern scenario, the control by the RL agent cuts about half of the average delay resulting from the pretimed control. Owing to the conciseness of the linear FA, a direct interpretation analysis of the converged linear‐FA parameters is presented. Lastly, two online relearning tests of the agents under non‐stationary arrivals are conducted to demonstrate the online performance of LSTDQ. In conclusion, the linear‐FA specification and the LSTDQ method are together proposed to be used for its control algorithm interpretability property, superior convergence quality, and lack of hyperparameters.
Recent advancements in artificial intelligence, particularly with the emergence of large language models (LLMs), have sparked a rethinking of artificial general intelligence possibilities. The increasing human-like capabilities of AI are also attracting attention in social science research, leading to various studies exploring the combination of these two fields. In this survey, we systematically categorize previous explorations in the combination of AI and social science into two directions that share common technical approaches but differ in their research objectives. The first direction is focused on AI for social science, where AI is utilized as a powerful tool to enhance various stages of social science research. While the second direction is the social science of AI, which examines AI agents as social entities with their human-like cognitive and linguistic capabilities. By conducting a thorough review, particularly on the substantial progress facilitated by recent advancements in large language models, this paper introduces a fresh perspective to reassess the relationship between AI and social science, provides a cohesive framework that allows researchers to understand the distinctions and connections between AI for social science and social science of AI, and also summarized state-of-art experiment simulation platforms to facilitate research in these two directions. We believe that as AI technology continues to advance and intelligent agents find increasing applications in our daily lives, the significance of the combination of AI and social science will become even more prominent.
Astronomical solutions provide calculated orbital and rotational parameters of solar system bodies based on the dynamics and physics of the solar system. Application of astronomical solutions in the Earth sciences has revolutionized our understanding in at least two areas of active research. (i) The Astronomical (or Milankovic) forcing of climate on time scales > ~10 kyr and (ii) the dating of geologic archives. The latter has permitted the development of the astronomical time scale, widely used today to reconstruct highly accurate geological dates and chronologies. The tasks of computing vs. applying astronomical solutions are usually performed by investigators from different backgrounds, which has led to confusion and recent inaccurate results on the side of the applications. Here we review astronomical solutions and Milankovic forcing in the Earth sciences, primarily aiming at clarifying the astronomical basis, applicability, and limitations of the solutions. We provide a summary of current up-to-date and outdated astronomical solutions and their valid time span. We discuss the fundamental limits imposed by dynamical solar system chaos on astronomical calculations and geological/astrochronological applications. We illustrate basic features of chaotic behavior using a simple mechanical system, i.e., the driven pendulum. Regarding so-called astronomical "metronomes", we point out that the current evidence does not support the notion of generally stable and prominent metronomes for universal use in astrochronology and cyclostratigraphy. We also describe amplitude and frequency modulation of astronomical forcing signals and the relation to their expression in cyclostratigraphic sequences. Furthermore, the various quantities and terminology associated with Earth's axial precession are discussed in detail. Finally, we provide some suggestions regarding practical considerations.
We provide a brief review of human mobility science and present three key areas where we expect to see substantial advancements. We start from the mind and discuss the need to better understand how spatial cognition shapes mobility patterns. We then move to societies and argue the importance of better understanding new forms of transportation. We conclude by discussing how algorithms shape mobility behaviour and provide useful tools for modellers. Finally, we discuss how progress in these research directions may help us address some of the challenges our society faces today.
Generative Artificial Intelligence (GenAI, generative AI) has rapidly become available as a tool in scientific research. To explore the use of generative AI in science, we conduct an empirical analysis using OpenAlex. Analyzing GenAI publications and other AI publications from 2017 to 2023, we profile growth patterns, the diffusion of GenAI publications across fields of study, and the geographical spread of scientific research on generative AI. We also investigate team size and international collaborations to explore whether GenAI, as an emerging scientific research area, shows different collaboration patterns compared to other AI technologies. The results indicate that generative AI has experienced rapid growth and increasing presence in scientific publications. The use of GenAI now extends beyond computer science to other scientific research domains. Over the study period, U.S. researchers contributed nearly two-fifths of global GenAI publications. The U.S. is followed by China, with several small and medium-sized advanced economies demonstrating relatively high levels of GenAI deployment in their research publications. Although scientific research overall is becoming increasingly specialized and collaborative, our results suggest that GenAI research groups tend to have slightly smaller team sizes than found in other AI fields. Furthermore, notwithstanding recent geopolitical tensions, GenAI research continues to exhibit levels of international collaboration comparable to other AI technologies.
Pierre Joanne, Yeranuhi Hovhannisyan, Alexandre Simon
et al.
Myofibrillar myopathy (MFM) is a rare genetic disorder characterized by muscular dystrophy that is often associated with cardiac disease. This disease is caused by mutations in several genes, among them DES (encoding desmin) is the most frequently affected. Peripheral blood mononuclear cells from 5 different MFM patients with different DES mutations were reprogrammed into induced pluripotent stem cells (IPSC) using non-integrative vectors. For each patient, one IPSC clone was selected and demonstrated pluripotency hallmarks without genomic abnormalities. SNP profiles were identical to the cells of origin and all the clones have the capacity to differentiate into all three germ layers.
Fatima Al Dhaheri, Jens Thomsen, Dean Everett
et al.
The United Arab Emirates has very little data on the incidence or prevalence of fungal diseases. Using total and underlying disease risk populations and likely affected proportions, we have modelled the burden of fungal disease for the first time. The most prevalent serious fungal conditions are recurrent vulvovaginitis (~190,000 affected) and fungal asthma (~34,000 affected). Given the UAE’s low prevalence of HIV, we estimate an at-risk population of 204 with respect to serious fungal infections with cryptococcal meningitis estimated at 2 cases annually, 15 cases of <i>Pneumocystis</i> pneumonia (PCP) annually, and 20 cases of esophageal candidiasis in the HIV population. PCP incidence in non-HIV patients is estimated at 150 cases annually. Likewise, with the same low prevalence of tuberculosis in the country, we estimate a total chronic pulmonary aspergillosis prevalence of 1002 cases. The estimated annual incidence of invasive aspergillosis is 505 patients, based on local data on rates of malignancy, solid organ transplantation, and chronic obstructive pulmonary disease (5.9 per 100,000). Based on the 2022 annual report of the UAE’s national surveillance database, candidaemia annual incidence is 1090 (11.8/100,000), of which 49.2% occurs in intensive care. Fungal diseases affect ~228,695 (2.46%) of the population in the UAE.
Path planning is one of the key issues in the research of unmanned aerial vehicle technology. Its purpose is to find the best path between the starting point and the destination. Although there are many research recommendations on UAV path planning in the literature, there is a lack of path optimization methods that consider both the complex flight environment and the performance constraints of the UAV itself. We propose an enhanced version of the Chimp Optimization Algorithm (TRS-ChOA) to solve the UAV path planning problem in a 3D environment. Firstly, we combine the differential mutation operator to enhance the search capability of the algorithm and prevent premature convergence. Secondly, we use improved reverse learning to expand the search range of the algorithm, effectively preventing the algorithm from missing high-quality solutions. Finally, we propose a similarity preference weight to prevent individuals from over-assimilation and enhance the algorithm’s ability to escape local optima. Through testing on 13 benchmark functions and 29 CEC2017 complex functions, TRS-ChOA demonstrates superior optimization capability and robustness compared to other algorithms. We apply TRS-ChOA along with five well-known algorithms to solve path planning problems in three 3D environments. The experimental results reveal that TRS-ChOA reduces the average path length/fitness value by 23.4%/65.0%, 8.6%/81.0%, and 16.3%/41.7% compared to other algorithms in the three environments, respectively. This indicates that the flight paths planned by TRS-ChOA are more cost-effective, smoother, and safer.