Outdoor air pollution is estimated to cause a huge number of premature deaths worldwide. It catalyzes many diseases on a variety of time scales, and it has a detrimental effect on the environment. In light of these impacts, it is necessary to obtain a better understanding of the dynamics and statistics of measured air pollution concentrations, including temporal fluctuations of observed concentrations and spatial heterogeneities. Here, we present an extensive analysis for measured data from Europe. The observed probability density functions (PDFs) of air pollution concentrations depend very much on the spatial location and the pollutant substance. We analyze a large number of time series data from 3544 different European monitoring sites and show that the PDFs of nitric oxide ($ NO $), nitrogen dioxide ($ {NO}_2 $), and particulate matter ($ {PM}_{10} $ and $ {PM}_{2.5} $) concentrations generically exhibit heavy tails. These are asymptotically well approximated by $ q $-exponential distributions with a given entropic index $ q $ and width parameter $ \lambda $. We observe that the power-law parameter $ q $ and the width parameter $ \lambda $ vary widely for the different spatial locations. We present the results of our data analysis in the form of a map that shows which parameters $ q $ and $ \lambda $ are most relevant in a given region. A variety of interesting spatial patterns is observed that correlate to the properties of the geographical region. We also present results on typical time scales associated with the dynamical behavior.

The Northwest Tibet region is defined by several terranes, magmatic belts, basins and sutures, which were primarily shaped by the tectonic activities associated with Proto-, Palaeo- and Neo-Tethys Oceans. However, the basement nature and Precambrian tectonic evolution of the Northwest Tibet region, particularly within the Tashikuergan-Tianshuihai terrane, remain largely unknown. The Hongliutan area, located in the northeastern part of the Tashikuergan-Tianshuihai terrane, contains a critical sequence of Precambrian metamorphic rock strata. Detailed petrological, geochronological, and geochemical analyses of these metamorphic rocks – including plagioclase schist, quartz schist, amphibolite and nearby leucogranite – reveal the intricate processes of tectonic evolution within the Tianshuihai unit. Combining these findings with previous geochronological results is crucial for re-evaluating the nature of the Tashikuergan-Tianshuihai basement and its Precambrian tectonic evolution of the Tashikuergan-Tianshuihai basement. Our results reveal the following: (1) the leucogranite and amphibolite, identified as Cambrian igneous rocks, display distinct geochemical signatures indicative of a continental arc origin. These include calc-alkaline characteristics, enrichment in Th, U, Pb, Zr and Hf and depletion in Ba, Nb, Sr and Ti. Their εNd(t) values, close to zero, further support this tectonic setting, with the leucogranite and amphibolite formed at 506 and 522 Ma, respectively. (2) The plagioclase schist and quartz schist are interpreted to be Neoproterozoic volcaniclastic rocks that formed in a rifted (passive) continental margin setting. The quartz schist is particularly rich in detrital zircons, displaying a broad spectrum of 207Pb/206Pb ages, ranging from 901 to 3364 Ma. (3) A significant subset of detrital zircons within the quartz schist exhibits oscillatory zoning, high Th/U ratios and sharp-edged, anhedral-to-subhedral crystal forms, suggesting a derivation from proximal or deep-seated terranes. The concordant U–Pb zircon ages of 2468 and 974 Ma from the quartz schist, along with the 978 Ma age from the inherited zircons in the amphibolite, and the 1.2–2.1 Ga T2DM(Nd) from leucogranite and metamorphic rocks, collectively suggest that the Tianshuihai unit is likely underpinned by a Palaeoproterozoic basement that indicates Neoproterozoic reworking.

Therefore, our findings suggest the presence of a continuous, northwest-southeast trending Palaeoproterozoic basement underlying the entire Tashikuergan-Tianshuihai terrane. An alternative scenario posits that the ancient basement, currently beneath the Tashikuergan terrane, could extend into the Tianshuihai region, potentially indicating a Cambrian continental margin arc interspersed with remnants of older terranes.

Mastitis in dairy cows is an important factor restricting the healthy development of dairy industry. Natural extracts have become a research hotspot to alleviate and prevent diseases because of their unique properties. The purpose of this study was to investigate the effects of resveratrol (RES) on the mitochondrial biosynthesis, antioxidation, and anti-inflammatory in bovine mammary epithelial cells (BMECs) and its mechanism involved. Blood samples were collected from six healthy cows and six mastitis affected cows, respectively, and lipopolysaccharide (LPS) was used to treat BMECs to construct inflammation models, gene interference is achieved by transfection. The results showed that messenger RNA (mRNA) expression of peroxisome proliferator-activated receptor γ coactivator-1α (PGC-1α) was down-regulated and mitochondrial biogenesis-related gene expression was disrupted in the blood of mastitis cows and LPS-induced BMECs. RES is the best active substance to activate PGC-1α. The addition of RES can effectively alleviate the production of BMECs reactive oxygen species (ROS) and mitochondrial damage induced by LPS, and improve the antioxidation and anti-inflammatory ability, while the alleviation effect of RES is inhibited after interfering with protein kinase AMP-activated catalytic subunit α 1 (PRKAA1). In summary, our study emphasizes that PRKAA1 is a key gene mediating the activation of PGC-1α by RES, which regulates mitochondrial biosynthesis, inhibits ROS release, attenuates mitochondrial damage, and improves mitochondrial antioxidant capacity through the activation of PGC-1α by PRKAA1, thus attenuating the inflammatory response in BMECs.

Conventional survey tools such as weighting do not address non-ignorable nonresponse that occurs when nonresponse depends on the variable being measured. This paper describes non-ignorable nonresponse weighting and imputation models using randomized response instruments, which are variables that affect response but not the outcome of interest. This paper uses a doubly robust estimator that is valid if one, but not necessarily both, of the weighting and imputation models is correct. When applied to a national 2019 survey, these tools produce estimates that suggest there was nontrivial non-ignorable nonresponse related to turnout, and, for subgroups, Trump approval and policy questions. For example, the conventional MAR-based weighted estimates of Trump support in the Midwest were 10 percentage points lower than the MNAR-based estimates.

This article presents a “glocal” method of comparative constitutional interpretation. In the debate on the judicial use of foreign ideas, transnationalists claim to propose a simultaneously global and local approach. However, they perpetuate the methodological nationalism of globalists and localists by assuming nations as their primary units of analysis. In contrast, this article advances a truly glocal theory of judicial interpretation. The glocal is the product of a constant interplay between the global and the local, from the inception of an idea to its practical judicial application. This approach follows a three-step process. First, it provides a multiscale toolkit to demonstrate that ideas may have never been purely national in the first place but are the result of plural hybridizations. Second, it uncovers the units that generate and disseminate constitutional knowledge: trans-territorial networks united by thematically shared beliefs rather than by nationality or a global mission. Third, it equips judges with the ability to glocalize or customize the idea, not as an exercise of national differentiation but as a strategy to make it epistemically familiar and more politically appealing to the network. In this way, the article critically engages with the debate on constitutional transplants, challenging its nationalist bias.

Snow is a crucial element of the sea ice system, affecting the sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have a very idealized representation of snow, often including just one-layer thermodynamics, omitting several processes that shape its properties. Even though sophisticated snow process models exist, they tend to be excluded in climate modeling due to their prohibitive computational costs. For example, SnowModel is a numerical snow process model developed to simulate the evolution of snow depth and density, blowing snow redistribution and sublimation, snow grain size, and thermal conductivity in a spatially distributed, multilayer snowpack framework. SnowModel can simulate snow distributions on sea ice floes in high spatial (1-m horizontal grid) and temporal (1-hour time step) resolution. However, for simulations spanning over large regions, such as the Arctic Ocean, high-resolution runs face challenges of slow processing speeds and the need for large computational resources. To address these common issues in high-resolution numerical modeling, data-driven emulators are often used. However, these emulators have their caveats, primarily a lack of generalizability and inconsistency with physical laws. In our study, we address these challenges by using a physics-guided approach in developing our emulator. By integrating physical laws that govern changes in snow density due to compaction, we aim to create an emulator that is efficient while also adhering to essential physical principles. We evaluated this approach by comparing three machine learning models: long short-term memory (LSTM), physics-guided LSTM, and Random Forest, across five distinct Arctic regions. Our evaluations indicate that all models achieved high accuracy, with the physics-guided LSTM model demonstrating the most promising results in terms of accuracy and generalizability. Our approach offers a computationally faster way to emulate the SnowModel with high fidelity and a speedup of over 9000 times.

Sustainability practices of a company reflect its commitments to the environment, societal good, and good governance. Institutional investors take these into account for decision-making purposes, since these factors are known to affect public opinion and thereby the stock indices of companies. Though sustainability score is usually derived from information available in self-published reports, News articles published by regulatory agencies and social media posts also contain critical information that may affect the image of a company. Language technologies have a critical role to play in the analytics process. In this paper, we present an event detection model for detecting sustainability-related incidents and violations from reports published by various monitoring and regulatory agencies. The proposed model uses a multi-tasking sequence labeling architecture that works with transformer-based document embeddings. We have created a large annotated corpus containing relevant articles published over three years (2015–2018) for training and evaluating the model. Knowledge about sustainability practices and reporting incidents using the Global Reporting Initiative (GRI) standards have been used for the above task. The proposed event detection model achieves high accuracy in detecting sustainability incidents and violations reported about an organization, as measured using cross-validation techniques. The model is thereafter applied to articles published from 2019 to 2022, and insights obtained through aggregated analysis of incidents identified from them are also presented in the paper. The proposed model is envisaged to play a significant role in sustainability monitoring by detecting organizational violations as soon as they are reported by regulatory agencies and thereby supplement the Environmental, Social, and Governance (ESG) scores issued by third-party agencies.

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits.