Васильева А.А., Козлов А.И., Вершубская Г.Г.
et al.
Введение. Пополнение и уточнение геногеографической картины гена FTO (rs9939609) является актуальной задачей, поскольку носительство аллеля A*FTO ассоциировано с повышенным риском развития ожирения, сахарного диабета 2 типа, ишемической болезни сердца. Важно также накопление данных о частотах аллелей FTO в группах населения с различными вариантами традиционного природопользования и питания.
Цель исследования: дать характеристику распределения частот аллеля A*FTO (rs9939609) в популяциях коренного населения Севера Западной Сибири.
Материалы и методы. В общую выборку (N=171) вошли северные ханты (n=90), сосьвинские манси (n=31), ямальские ненцы (n=50). Проведено генотипирование выделенной из образцов крови геномной ДНК по полиморфному локусу гена FTO rs9939609.
Результаты демонстрируют близость западносибирских выборок по частотам аллелей и генотипов. Средняя частота носительства аллеля *A в популяциях Севера Западной Сибири составляет 0,377. Значимых различий между этническими выборками (ханты, манси, ненцы) по частотам аллеля *A и генотипа AA*FTO не выявлено (p>0,05).
Обсуждение. Для обследованных групп характерно невысокое носительство «рисковых» вариантов полиморфизма FTO rs9939609. Выборки хантов, манси и ямальских ненцев не различаются по частотам аллеля A* и генотипа AA*FTO и близки к описанным в группах тазовских ненцев (Батурин с соавт., 2017), калмыков и монголов, хотя значимо (p<0,05) ниже, чем у алтайцев и русских (Бондарева с соавт., 2018).
Заключение. Поскольку распределение аллеля A*FTO в наших выборках отвечает равновесию Харди-Вайнберга, а ранговые корреляции Спирмена между популяционными частотами генотипов FTO с географической широтой и климатическими характеристиками области проживания 16 популяций Евразии не достигают принятого уровня значимости (p>0,2), можно предположить, что в современных популяциях полиморфизм FTO rs9939609 не вовлечён в процессы адаптации к условиям высокоширотных регионов.
Решение вопроса о специфике распределения аллелей и генотипов FTO rs9939609 в популяциях, представляющих различные расовые группы и адаптивные типы, требует расширения географического охвата и привлечения более обширного материала.
Благодарности. Исследование выполнено в рамках государственного задания МГУ имени М.В.Ломоносова (для А.А Васильевой, А.И. Козлова, Г.Г. Вершубской); Программы фундаментальных исследований ФГБОУ ВО НИУ ВШЭ и Государственного задания для ФГБНУ «МГНЦ».
Ethnology. Social and cultural anthropology, Physical anthropology. Somatology
Study region: The Mekong River Basin Study focus: This study reduces the multi-agent bargaining game to a one-to-one model by assuming downstream countries act as coalitions in water allocation scenarios. Each country’s risk level and perception inform its discount factor, which is then aggregated and converted into coalition discount factors through weighted averaging. Then, a Rubinstein bargaining water allocation model with multi-agent participation and multi-stage negotiation is constructed and applied to allocate water in the Mekong River Basin. New hydrological insights for the region: The proposed Multi-stage Rubinstein Bargaining Model produced allocations that were more stable than those generated by traditional bankruptcy rules such as Proportion, Adjusted Proportion, Constrained Equal Loss, Constrained Equal Award, and Shapley. Therefore, this allocation framework can serve as both a theoretical foundation and a practical tool for water allocation in transboundary river basins.
Mihir Prabhudesai, Aryan Satpathy, Yangmin Li
et al.
We have witnessed remarkable advances in LLM reasoning capabilities with the advent of DeepSeek-R1. However, much of this progress has been fueled by the abundance of internet question-answer (QA) pairs, a major bottleneck going forward, since such data is limited in scale and concentrated mainly in domains like mathematics. In contrast, other sciences such as physics lack large-scale QA datasets to effectively train reasoning-capable models. In this work, we show that physics simulators can serve as a powerful alternative source of supervision for training LLMs for physical reasoning. We generate random scenes in physics engines, create synthetic question-answer pairs from simulated interactions, and train LLMs using reinforcement learning on this synthetic data. Our models exhibit zero-shot sim-to-real transfer to real-world physics benchmarks: for example, training solely on synthetic simulated data improves performance on IPhO (International Physics Olympiad) problems by 5-10 percentage points across model sizes. These results demonstrate that physics simulators can act as scalable data generators, enabling LLMs to acquire deep physical reasoning skills beyond the limitations of internet-scale QA data. Code available at: https://sim2reason.github.io/.
<p>Vegetation interacts with both soil moisture and atmospheric conditions, contributing to water flow partitioning at the land surface. Therefore, changes in both climate and land cover with vegetation affect the availability of water resources. This study aimed to determine the differential effects of climate change on the soil water regime of two common Central European montane forest types: Norway spruce (<i>Picea abies</i> L.) and European beech (<i>Fagus sylvatica</i> L.). A unique dataset, including 22 years (2000–2021) of measured soil water potentials, was used with a bucket-type soil water balance model to investigate differences in evapotranspiration and groundwater recharge both between the forest types and across years. Results revealed an accelerating transition from a fully energy-limited state towards water-limitation, with evidence of strict water-limitation in recent outlier years, unprecedented in this system. While long-term column-averaged pressure heads indicated drier soil at the spruce site overall, this was driven by the wettest years in the dataset. Seasonal and interannual variability of meteorological conditions drove complex but robust differences between the flow partitioning of the two forest types, which diverged further with increasing water-limitation. Higher snow interception by spruce (27 mm per season) resulted in drier soil below the spruce canopy in the cold season. Higher transpiration by beech (100 mm per season) led to increasingly drier soils over the warm seasons causing lower ground water recharge (34 mm per season). Low summer precipitation inputs exacerbated soil drying under beech more than under to spruce. These suggest that expected trends in regional climate and forest species composition may interact to produce a disproportionate shift of recharge from the summer to the winter season.</p>
Lidiia L. Chinarova, Ivan L. Andronov, Nina V. Savchuk
et al.
The study guide (textbook) is part of a set of materials designed to support high-quality practical training in physics. It includes a collection of tasks for organizing both in-class and independent work. The guide serves as a foundation for further study in physics-related disciplines and aligns with current educational programs. This textbook presents a curated set of 120 physics problems with detailed solutions, structured according to the first-year bachelor's curriculum. Each section addresses common student questions and emphasizes conceptual understanding. Problem-solving is essential in physics education. It not only tests knowledge but also transforms theory into practical skills. Applying physical laws to real-world scenarios enhances comprehension and fosters analytical thinking. Through solving problems, students gain deeper insight into physical phenomena and develop effective strategies for analysis, and develop solutions to tasks-making the learning process truly comprehensive.
In the last 175 years, the physical understanding of nature has seen a revolutionary change. Until about 1850, Newton's theory and the mechanical world view derived from it provided the dominant view of the physical world, later supplemented by Maxwell's theory of the electromagnetic field. That approach was entirely deterministic and free of probabilistic concepts. In contrast to that conceptual edifice, today many fields of physics are governed by probabilistic concepts. Statistical mechanics in its classical or quantum version and random-matrix theory are obvious examples. Quantum mechanics is an intrinsically statistical theory. Classical chaos and its quantum manifestations also require a stochastic approach. The paper describes how a combination of discoveries and conceptual problems undermined the mechanical world view, led to novel concepts, and shaped the modern understanding of physics.
Ion channels selectively transport ions, yet the underlying mechanisms remain elusive. We propose a physical model based on the Driven Damped Harmonic Oscillator (DDHO), where self-organizing turbulent structures in the ionic flow generate oscillating pressure waves and toroidal vortices. These structures drive aqua-ions into resonance, facilitating the shedding of hydration shells and enabling ion permeation as free ions. To capture the spatiotemporal complexity of this process, we develop a macroscopic continuum model integrating the Navier--Stokes equations, Gauss's law, and convection-diffusion dynamics. Numerical simulations reveal strong oscillations that drive dehydration and ionic jet formation, supporting the DDHO mechanism. Model predictions closely match patch-clamp experimental data. The DDHO framework predicts a frequency-dependent resonance response, effectively acting as a selective filter. Applied to experimental data, the model reveals distinct separation between ion species and hydration states, quantified by a high Mahalanobis distance and oscillator quality factor. Furthermore, the model provides insight into the effects of single nucleotide polymorphisms (SNPs) on ion selectivity. Mutations that alter channel geometry shift resonance peaks, disrupting selective transport and potentially leading to genetic disorders.
Oh Hyun-Bin, Yuhta Takida, Toshimitsu Uesaka
et al.
Recent advances in Video-to-Audio (V2A) generation have achieved impressive perceptual quality and temporal synchronization, yet most models remain appearance-driven, capturing visual-acoustic correlations without considering the physical factors that shape real-world sounds. We present Physics-Aware Video-to-Audio Synthesis (PAVAS), a method that incorporates physical reasoning into a latent diffusion-based V2A generation through the Physics-Driven Audio Adapter (Phy-Adapter). The adapter receives object-level physical parameters estimated by the Physical Parameter Estimator (PPE), which uses a Vision-Language Model (VLM) to infer the moving-object mass and a segmentation-based dynamic 3D reconstruction module to recover its motion trajectory for velocity computation. These physical cues enable the model to synthesize sounds that reflect underlying physical factors. To assess physical realism, we curate VGG-Impact, a benchmark focusing on object-object interactions, and introduce Audio-Physics Correlation Coefficient (APCC), an evaluation metric that measures consistency between physical and auditory attributes. Comprehensive experiments show that PAVAS produces physically plausible and perceptually coherent audio, outperforming existing V2A models in both quantitative and qualitative evaluations. Visit https://physics-aware-video-to-audio-synthesis.github.io for demo videos.
Recent advances in video generation have shown remarkable potential for constructing world simulators. However, current models still struggle to produce physically consistent results, particularly when handling large-scale or complex dynamics. This limitation arises primarily because existing approaches respond isotropically to physical prompts and neglect the fine-grained alignment between generated content and localized physical cues. To address these challenges, we propose ProPhy, a Progressive Physical Alignment Framework that enables explicit physics-aware conditioning and anisotropic generation. ProPhy employs a two-stage Mixture-of-Physics-Experts mechanism for discriminative physical prior extraction, where Semantic Experts infer semantic-level physical principles from textual descriptions, and Refinement Experts capture token-level physical dynamics. This mechanism allows the model to learn fine-grained, physics-aware video representations that better reflect underlying physical laws. Furthermore, we introduce a physical alignment strategy that transfers the physical reasoning capabilities of vision-language models into the Refinement Experts, facilitating a more accurate representation of dynamic physical phenomena. Extensive experiments on physics-aware video generation benchmarks demonstrate that ProPhy produces more realistic, dynamic, and physically coherent results than existing state-of-the-art methods.
Abstract Bromoform (CHBr3) contributes to stratospheric ozone depletion but is not regulated under the Montreal Protocol due to its short lifetime and large natural sources. Here, we show that anthropogenic sources contribute significantly to the amount of CHBr3 transported into the Northern Hemisphere (NH) extratropical stratosphere. We present a new CHBr3 emission inventory comprised of natural and anthropogenic sources, with the latter estimated from ship ballast, power plant cooling and desalination plant brine water. Including anthropogenic sources in the new inventory increases CHBr3 emissions by up to 31.5% globally and 70.5% in the NH. In consequence, atmospheric CHBr3 is also significantly higher, especially over the NH extratropics during boreal winter. Here anthropogenic sources enhance bromine at the tropopause by 0.9 ppt Br, thus doubling natural CHBr3 abundances. For some latitudes, tropopause bromine increases by 2.4 ppt Br suggesting significant contributions of anthropogenic CHBr3 to the NH lowermost stratosphere.
Irene Marzolff, Mario Kirchhoff, Robin Stephan
et al.
Monitoring woody cover by remote sensing is considered a key methodology towards sustainable management of trees in dryland forests. However, while modern very high resolution satellite (VHRS) sensors allow woodland mapping at the individual tree level, the historical perspective is often hindered by lack of appropriate image data. In this first study employing the newly accessible historical HEXAGON KH-9 stereo-panoramic camera images for environmental research, we propose their use for mapping trees in open-canopy conditions. The 2–4 feet resolution panchromatic HEXAGON satellite photographs were taken 1971–1986 within the American reconnaissance programs that are better known to the scientific community for their lower-resolution CORONA images. Our aim is to evaluate the potential of combining historical CORONA and HEXAGON with recent WorldView VHRS imagery for retrospective woodland change mapping on the tree level. We mapped all trees on 30 1-ha test sites in open-canopy argan woodlands in Morocco in the field and from the VHRS imagery for estimating changes of tree density and size between 1967/1972 and 2018. Prior to image interpretation, we used simulations based on unmanned aerial system (UAS) imagery for exemplarily examining the role of illumination, viewing geometry and image resolution on the appearance of trees and their shadows in the historical panchromatic images. We show that understanding these parameters is imperative for correct detection and size-estimation of tree crowns. Our results confirm that tree maps derived solely from VHRS image analysis generally underestimate the number of small trees and trees in clumped-canopy groups. Nevertheless, HEXAGON images compare remarkably well with WorldView images and have much higher tree-mapping potential than CORONA. By classifying the trees in three sizes, we were able to measure tree-cover changes on an ordinal scale. Although we found no clear trend of forest degradation or recovery, our argan forest sites show varying patterns of change, which are further analysed in Part B of our study. We conclude that the HEXAGON stereo-panoramic camera images, of which 670,000 worldwide will soon be available, open exciting opportunities for retrospective monitoring of trees in open-canopy conditions and other woody vegetation patterns back into the 1980s and 1970s.
In recent years, the gap between Deep Learning (DL) methods and analytical or numerical approaches in scientific computing is tried to be filled by the evolution of Physics-Informed Neural Networks (PINNs). However, still, there are many complications in the training of PINNs and optimal interleaving of physical models. Here, we introduced the concept of Physical Activation Functions (PAFs). This concept offers that instead of using general activation functions (AFs) such as ReLU, tanh, and sigmoid for all the neurons, one can use generic AFs that their mathematical expression is inherited from the physical laws of the investigating phenomena. The formula of PAFs may be inspired by the terms in the analytical solution of the problem. We showed that the PAFs can be inspired by any mathematical formula related to the investigating phenomena such as the initial or boundary conditions of the PDE system. We validated the advantages of PAFs for several PDEs including the harmonic oscillations, Burgers, Advection-Convection equation, and the heterogeneous diffusion equations. The main advantage of PAFs was in the more efficient constraining and interleaving of PINNs with the investigating physical phenomena and their underlying mathematical models. This added constraint significantly improved the predictions of PINNs for the testing data that was out-of-training distribution. Furthermore, the application of PAFs reduced the size of the PINNs up to 75% in different cases. Also, the value of loss terms was reduced by 1 to 2 orders of magnitude in some cases which is noteworthy for upgrading the training of the PINNs. The iterations required for finding the optimum values were also significantly reduced. It is concluded that using the PAFs helps in generating PINNs with less complexity and much more validity for longer ranges of prediction.
Anna M. Phillips, Ezra J. Gouvea, Brian E. Gravel
et al.
Computation is intertwined with essentially all aspects of physics research and is invaluable for physicists' careers. Despite its disciplinary importance, integration of computation into physics education remains a challenge and, moreover, has tended to be constructed narrowly as a route to solving physics problems. Here, we broaden Physics Education Research's conception of computation by constructing an epistemic \emph{metamodel} -- a model of modeling -- incorporating insights on computational modeling from the philosophy of science and prior work. The metamodel is formulated in terms of practices, things physicists do, and how these inform one another. We operationalize this metamodel in an educational environment that incorporates making, the creation of shared physical and digital artifacts, intended to promote students' agency, creativity and self-expression alongside doing physics. We present a content analysis of student work from initial implementations of this approach to illustrate the very complex epistemic maneuvers students make as they engaged in computational modeling. We demonstrate how our metamodel can be used to understand student practices, and conclude with implications of the metamodel for instruction and future research.
The carrying capacity of water resources is key to the sustainable development of arid and semi-arid regions. There are important challenges related to the detection of discontinuous and crooked water bodies in the vast Mongolian Plateau, despite the availability of remote sensing technology which has the advantage of facilitating water observations over large areas and timelines. Given the high cost and low coverage of high-resolution images and the low resolution of images with high coverage, this study proposes a pixel-based convolutional neural network (CNN) method for the application of water extracted from the region of interest (ROI) to medium-resolution Landsat images. The pixel-based CNN method combines the texture and spectral features of the ground object by connecting the center pixels of the images to the surrounding pixels. ROI is used instead of full-label datasets, reduce the difficulty of building labels in low-to-medium-resolution images. Taking the Tuul River in Mongolia as a case, the pixel-based CNN method, the normalized difference water index threshold (NDWI) method, the modified normalized difference water index (MNDWI) threshold method, U-net model in deep learning, and the pixel-based deep neural network (DNN) method were used with medium-resolution Landsat 8 images with ROI labels. The pixel-based CNN method shows better water extraction results for the cloud, cloud shadows, and building areas, compared with other methods. The method proposed in this study had the highest verification accuracy (92.07%). It also has the advantages of fewer training parameters and shorter training time. The training accuracies of the pixel-based CNN, pixel-based DNN, and U-net were 99.90%, 96.98%, and 93.70%, respectively. All training models and calling methods were uploaded to GitHub (https://github.com/CaryLee17/Pixel-based-CNN).
A rigorous mathematical theory of dimensional analysis, systematically accounting for the use of physical quantities in science and engineering, perhaps surprisingly, was not developed until relatively recently. We claim that this has shaped current mathematical models of theoretical physics, which generally lack any explicit reference to units of measurement, and we propose a novel mathematical framework to alleviate this. Our proposal is a generalization of the usual categories of algebraic structures used to formulate physical theories (groups, rings, vector spaces...), herein dubbed dimensioned, that can naturally articulate the structure of physical dimension. Our goal in the present work is not so much to define an algebraic theory of physical quantities - this has already been done - but to define a theory of algebra informed by how physical quantities are used in practice. We conclude by studying the dimensioned analogue of Poisson algebras in some detail due to their relevance in Jacobi geometry and classical mechanics. These topics are further explored in sequel papers by the author.
Duality transformations are very important in both classical and quantum physics. They allow one to relate two seemingly different formulations of the same physical realm through clever mathematical manipulations, and offer numerous advantages for the study of many-body physics. In this work, we suggest a method which shall introduce them to the world of quantum simulation too: a feasible scheme for implementing duality transformations as physical operations, mapping between dual quantum states showing the same observable physics, rather than just a mathematical trick. Demonstrating with Abelian lattice models, we show how duality transformations could be implemented in the laboratory as sequences of single- and two-body operations - unitaries and measurements.
The Gardon is a right tributary of the Rhône (France, Gard). This Mediterranean river crosses a karstic piedmont (valley then gorge). Between the hydrometric station of Ners (upstream) and the sources of La Baume (downstream of the gorges) it suffers losses all year round. The water comes from the Cévennes (upstream catchment area on metamorphic rock). In spring and summer the bed of the Gardon dries out from downstream to upstream. A second hydrometric station located at the entrance of the gorges (in Russan) allows to calculate the speed of the floods (measures every 15 mn). A typology of floods for the 2017-2018 cycle is proposed. Floods cover the 16.25 km between Ners and Russan in 100 to 400 mn (average: 250 mn). We then determined all hourly losses for the 2018-2019 cycle after submitting time shifts to Russan's chronicle. The losses are evaluated in m3.s-1 and as a percentage of the flow at Ners. As a percentage, after a flood, the losses increase progressively up to about 80 % if the recession is long enough (3 to 4 decades). These relaxation phases have been modelled. Only the early summer phase reaches 100 % (dewatering). Most of the losses are between 4.8 and 8 m3.s-1 (median = 6.4 m3.s-1). The highest karstic losses are between 10 and 12 m3.s-1 (excluding the major bed filling phenomenon). The average loss is 4.2 m3.s-1. The statistical series (7534 hourly values) is modelled by LAPLACE's law provided that the variable is the logarithm of the flows (double exponential). This work should allow a reasoned exploitation of this vast karstic aquifer which is mainly drained by the La Baume springs.