bluuebunny/crossref_metadata_2025 huggingface dataset:
The Other Fight: Women's Suffrage and Iran's Oil Nationalization
Authors: Kashani-Sabet Firoozeh
Published: October 2024

Iran handed the world a surprise in 1951. That spring, its parliament voted to nationalize the country's lucrative petroleum industry. Euphoria spread as young Iranians tore down Anglo-Iranian Oil Company (AIOC) signs and watched mighty Britain cower (temporarily) before the hero of Iran's oil nationalization, Mohammad Mosaddeq. Stunned by this brazenness, an American summary posed the question on the minds of many diplomats: “How did nine Persian politicians win sufficient power to destroy the concession of the Anglo-Iranian Oil Company?” For the next two years the West tried to figure out how to confine this enormous shock to the petroleum market by focusing on the man it held responsible for the crisis. An aristocrat and seasoned politician, Mohammad Mosaddeq garnered the support he needed to break through the monopoly of the AIOC. With the world's attention on Mosaddeq and oil at the time, it was unclear to many (although not to Mosaddeq himself) that another monumental battle for the future of Iran was taking shape: the fight for women's suffrage.
Communism, Cold War, and the 1953 Coup
Authors: Azizi Arash
Published: October 2024

Even as it passed its seventieth anniversary, the 1953 coup in Iran has remained a hotly debated political topic. This is true in the public spheres of Iran, which saw its last democratically elected prime minister, Mohammad Mosaddeq, overthrown in the coup, and in those of the United States and the United Kingdom, which helped stage the ouster. There also has been an attempt at historical revisionism about the coup, usually by overemphasizing the domestic factors that led to the coup and placing less importance on the role of the CIA or questioning Mossadeq's democratic credentials. This revisionism has been robustly rebutted by the scholarly community, which has held to a general consensus on the basic narrative of the coup: that it overthrew a popular leader and that it took place with significant interventions from London and Washington. The release of the final batch of US documents related to the coup in 2017 (following many years of undue delay) also bolstered evidence for this consensus. But, although most public debates about the coup center on questions such as the constitutional process of Mosaddeq's dismissal or the relative weight given to domestic and international actors behind the coup, there is another historiographical question that has been subject to widely divergent perspectives in the field: the relationship of the coup to the Cold War. In other words, can the 1953 coup be considered a Cold War confrontation, or is this a misleading frame of reference? Both sides of this argument have often focused on the motivations of coup plotters (mostly those in Washington, DC, and London) and whether they are more readily explained by a genuine fear of the communist movement in Iran or whether this was a rhetorical smokescreen, masking the neocolonial drive for the control of Iran's resources. This tension is not limited to scholarship on Iran. Even as new global histories of the Cold War have grown in recent years, some have cautioned against the use of this framework for understanding politics in the Global South. Jeremi Suri, for instance, speaks of a “group of scholars” who have “questioned the very utility of the Cold War as an analytical concept” by pointing to “the ways in which this geopolitical term privileges state actors in the United States and Europe and neglects local forces of change, many of which had little apparent connection to the basic issues and personalities of the Cold War.” In his analysis of the global place of Algeria's drive for independence, Matthew Connelly suggests “taking off the Cold War lens,” arguing that such a lens did less to shape the views of historical actors (such as the Eisenhower administration) than “those of the historians who have studied them.”
Biochemical assessment of the nutritional status of infants, children and adolescents in South Africa (1997–2022): a systematic review
Authors: Malan Linda , Zandberg Lizelle , Visser Marina V , Wicks Mariaan , Kruger H Salome , Faber Mieke
Published: October 2024

Objective:

To conduct a systematic review of the published peer-reviewed articles on the biochemical assessment of nutritional status of South African infants, children and adolescents in 1997–2022.

Design:

Online databases (Pubmed, CINAHL, EbscoHost and SAePublications) were used to identify thirty-nine papers.

Setting:

South Africa, 1997–2022.

Participants:

Infants, children and adolescents.

Results:

Vitamin A deficiency prevalence was 35–67 % before 2001 and mostly below 16 % after 2008. Anaemia ranged from 5·4 to 75·0 %, with 36–54 % of infants below 1 year being anaemic. Among 0- to 6-year-olds, iron deficiency (ID) was 7·2–39·4 % in rural and 16–41·9 % in urban areas. Zn deficiency remained high, especially among 0- to 6-year-olds, at 39–48 %. Iodine insufficiency (UIC < 100 µg/l) was between 0 and 28·8 %, with excessive levels in two areas. Vitamin D deficiency was 5 % for 11- to 17-year-olds in one urban study but 33–87 % in under 10-week-old infants. The 2005 national survey reported sufficient folate status among 0- to 6-year-olds, and vitamin B12 deficiency was 0–21 %. Low-grade inflammation was between 5 % and 42 % depending on the biomarker and cut-offs.

Conclusions:

Vitamin A status may have improved meaningfully during the last 25 years in South Africa to below 16 %, and iodine and folate deficiency appears to be low particularly among 0- to 6-year-olds. However, confirmation is needed by a national survey. Anaemia, Fe and Zn deficiencies still pose severe problems, especially among 0- to 6-year-olds. Sufficient data on vitamin D and B12 status are lacking.
Estimating Interplanetary Magnetic Field Conditions at Mercury's Orbit From MESSENGER Magnetosheath Observations Using a Feedforward Neural Network
Authors: Bowers Charles F. , Jackman Caitríona M. , Azari Abigail R. , Smith Andy W. , Wright Paul J. , Rutala Matthew J. , Sun Weijie , Healy Adam
Published: October 2024

Mercury's small magnetosphere is embedded in the dynamic and intense solar wind environment characteristic of the inner heliosphere. Both the magnitude and orientation of the interplanetary magnetic field (IMF) significantly influence the solar wind‐magnetospheric interaction at Mercury, driving phenomena such as magnetic reconnection. The MErcury Surface, Space Environment, Geochemistry and Ranging (MESSENGER) spacecraft provided in‐situ magnetic field measurements of the solar wind, the magnetosheath, and the magnetosphere along each orbit. However, it is a challenge to directly assess the IMF's impact on Mercury's plasma environment due to the temporal separation between observations within the solar wind and the magnetosphere, especially in the absence of an upstream monitor. Here, we present a feedforward neural network (FNN) trained on a subset of magnetosheath observations to estimate the strength and orientation of the IMF upstream of the bow shock. Utilizing magnetosheath magnetic field, cylindrical spatial coordinates, and heliocentric distance measurements, the FNN predicts upstream IMF conditions with an score of 0.70 and mean averaged error of 5.3 nT, thereby greatly decreasing the temporal separation between IMF estimates and magnetospheric measurements throughout the MESSENGER mission. This approach yields IMF estimates for all magnetosheath data measured by MESSENGER, providing a useful tool for future investigations of the IMF impact on Mercury's magnetosphere. This method will be integrable with the dual‐spacecraft BepiColombo magnetosheath measurements, providing useful estimates of upstream IMF conditions particularly during the extended periods in which neither spacecraft sample the solar wind. Our results demonstrate the utility of machine learning techniques on advancing space science research.
A high-sensitivity flexible bionic tentacle sensor for multidimensional force sensing and autonomous obstacle avoidance applications
Authors: Liu Xinyu , Li Kunru , Qian Shuo , Niu Lixin , Chen Wei , Wu Hui , Song Xiaoguang , Zhang Jie , Bi Xiaoxue , Yu Junbin , Hou Xiaojuan , He Jian , Chou Xiujian
Published: October 2024

Bionic tentacle sensors are important in various fields, including obstacle avoidance, human‒machine interfaces, and soft robotics. However, most traditional tentacle sensors are based on rigid substrates, resulting in difficulty in detecting multidirectional forces originating from the external environment, which limits their application in complex environments. Herein, we proposed a high-sensitivity flexible bionic tentacle sensors (FBTSs). Specifically, the FBTS featured an ultrahigh sensitivity of 37.6 N−1 and an ultralow detection limit of 2.4 mN, which benefited from the design of a whisker-like signal amplifier and crossbeam architecture. Moreover, the FBTS exhibited favorable linearity (R2 = 0.98) and remarkable durability (more than 5000 cycles). This was determined according to the improvement in the uniformity of the sensing layer through a high-shear dispersion process. In addition, the FBTS could accurately distinguish the direction of external stimuli, resulting in the FBTS achieving roughness recognition, wind speed detection and autonomous obstacle avoidance. In particular, the ability of autonomous obstacle avoidance was suitably demonstrated by leading a bionic rat through a maze with the FBTS. Notably, the proposed FBTS could be widely applied in tactile sensing, orientation perception, and obstacle avoidance.
Interhemispheric axonal sprouting occurs after pial removal in mice
Authors: Nikouei Kasra , Garma Leonardo , Memic Fatima , Hjerling-Leffler Jens , Goldschmidt Ezequiel
Published: October 2024

White matter lacks the kind of plasticity that is present in the cortex, and subcortical injuries often result in permanent neurological deficits. Because cortical regions share common subcortical nuclei, creating new intergyral connections may allow for the bypass of subcortical damage. In this manuscript, a surgical interhemispheric bridge is created in mice, providing a model for an intercortical transpial bypass. To model this bypass, a midline craniotomy followed by interhemispheric (IH) pial removal was performed in C57BL/6 mice, allowing for the juxtaposition of the right and left prefrontal cortices. Adeno-associated virus (AAV) expressing tdTomato under a neuronal-specific promoter were injected into the right hemisphere. Animals were sacrificed two and four weeks after surgery, and axonal sprouting and glial changes were assessed in the “bypass” (BP) operation and sham surgery. Surgery did not result in any clear functional impairments. Removing the pia resulted in the formation of a physical connection between the hemispheres and the loss of the normal pial IH barrier. Cortical layer I became thinner with neuronal bodies in closer proximity than in the sham group. New interhemispheric axonal crossings were visible at two and four weeks in the BP group but not in the sham mice. These findings constitute the first step in the development of a cortico–cortico transpial bypass, allowing us to test a new way to surgically restore neurological function.
iDLB-Pred: identification of disordered lipid binding residues in protein sequences using convolutional neural network
Authors: Malebary Sharaf J. , Alromema Nashwan
Published: October 2024

Proteins, nucleic acids, and lipids all interact with intrinsically disordered protein areas. Lipid-binding regions are involved in a variety of biological processes as well as a number of human illnesses. The expanding body of experimental evidence for these interactions and the dearth of techniques to anticipate them from the protein sequence serve as driving forces. Although large-scale laboratory techniques are considered to be essential for equipment for studying binding residues, they are time consuming and costly, making it challenging for researchers to predict lipid binding residues. As a result, computational techniques are being looked at as a different strategy to overcome this difficulty. To predict disordered lipid-binding residues (DLBRs), we proposed iDLB-Pred predictor utilizing benchmark dataset to compute feature through extraction techniques to identify relevant patterns and information. Various classification techniques, including deep learning methods such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Multilayer Perceptrons (MLPs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs), were employed for model training. The proposed model, iDLB-Pred, was rigorously validated using metrics such as accuracy, sensitivity, specificity, and Matthew’s correlation coefficient. The results demonstrate the predictor’s exceptional performance, achieving accuracy rates of 81% on an independent dataset and 86% in 10-fold cross-validation.
Carbon Capture and Storage Optimization with Machine Learning
Authors: Siri Dharmapuri , Sandhya Tuti , Pandey Sakshi , Deorari Rajesh , Kaur Namita , Aneja Aseem , Bansal Saloni , Almusawi Muntather
Published: October 2024

This study examines the potential for enhancing carbon capture and storage (CCS) processes by machine learning to markedly improve performance across diverse capture methods, including as absorption, adsorption, membrane separation, and cryogenic distillation. Through the systematic adjustment of critical operating parameters, including temperature, pressure, flow rates, and sorbent characteristics using machine learning algorithms, we saw significant improvements in CO₂ collection efficiency. The use of optimum operating parameters, namely a temperature range of

40-60°C for absorption and a pressure range of 3-5 bar for adsorption, resulted in a 30% enhancement in capture efficiency. Moreover, machine learning models, namely Random Forest and Support Vector Machines (SVM), achieved a maximum enhancement of 20% in forecasting ideal operating parameters for membrane separation and cryogenic systems. Reduced cycle durations in adsorption processes, facilitated by predictive modeling, resulted in a 15% improvement in CO₂ removal rates. The models’ capacity to forecast sorbent regeneration conditions led to a 10% decrease in energy use. Machine learning algorithms adeptly optimized process-specific parameters, including material composition and flow dynamics, enhancing membrane performance by 18% and cryogenic systems by 12%. These results highlight the significance of using machine learning to customize CCS methods for particular materials and situations, facilitating more sustainable, efficient, and scalable carbon capture systems.
Energy Harvesting from Waste Heat using Thermoelectric Generators
Authors: Bhalla Lalit , Siri Dharmapuri , Kakarla Udaya Sri , Singh Dhiraj , Bisht Deepa , Singh Amanveer , Yadav Kanchan , Alsalami Zaid
Published: October 2024

This research examines the synergistic benefits and impact of Thermoelectric Generators (TEGs) in energy harvesting, emphasizing their contribution to improving system efficiency via appropriate integration with energy management systems. The empirical findings indicate a significant 15% enhancement in energy conversion efficiency via enhanced waste heat recovery using TEGs, coupled with an 8% augmentation in total system performance attributable to sophisticated energy management strategies. Furthermore, there is a notable 10% decrease in energy losses, achieved by meticulous management of temperature gradients and regulation of power output. Total Energy Efficiency Metrics indicate a 12.5% enhancement in energy usage, a 2% decrease in system downtime attributable to the improved dependability of TEG units, and a 4°C reduction in average operating temperature, hence assuring increased lifespan of the devices. These findings underscore the practical advantages of integrating TEGs into waste heat recovery systems, enhancing the ongoing discussion on sustainable energy solutions by offering definitive proof of their effectiveness and the collaborative effect they exert on energy harvesting and system efficiency.
Assessing the Risk of Climate-induced Natural Disasters using Risk Assessment Models
Authors: Bahl Ankur , Karuna Gotlur , Lingam B. , Chandna Manisha , Thakur Gaurav , Madan Praney , Kumar Sunil , Shnain Ammar H.
Published: October 2024

The escalation of climate-induced natural disasters—such as hurricanes, floods, droughts, and wildfires—constitutes an increasing hazard to populations and infrastructure globally. This research examines the incorporation of risk assessment models in forecasting, assessing, and alleviating the effects of severe calamities. We evaluate the susceptibility of different locations to climate-related risks by conducting a thorough analysis of historical climate data and use sophisticated risk modeling methodologies. Catastrophe modeling (CAT), geographic information systems (GIS), and statistical hazard analysis are used to delineate high-risk areas and assess possible losses. The results highlight the significance of early warning systems and sustainable adaption measures, particularly for areas increasingly impacted by severe climatic events. The research underscores the constraints of existing models and stresses the need for cohesive, data-informed strategies to enhance the prediction and management of catastrophe risks.
Solution of the Heat and Mass Transfer Problem for Soil Radiant Heating Conditions Using the Method of Finite Integral Fourier Transform
Authors: Vafaeva Khristina Maksudovna , Karpov Denis Fedorovich , Pavlov Mikhail Vasilyevich , Ismailov Aleksei Marlenovich , Baskar Sivaraman , Kapoor Tarun , Singh Devendra , Bhardwaj Nishant , Rao Perumalla Varaprasada , Gudainiyan Jitendra
Published: October 2024

To achieve high agricultural yields, it is essential to predict the soil temperature and moisture regime, considering the heating technology employed. The research object is soil heated by a ceiling-mounted infrared emitter. The research subject encompasses one-dimensional unsteady fields of soil moisture content and temperature. The research goal is to forecast the soil temperature and moisture regime under radiant heating conditions. The research methods involve the analytical solution of heat and mass transfer differential equations using the method of finite integral Fourier transforms. Research results indicate that the top layer of milled peat, with an initial moisture content of 3.7 kg/kg, will reach a final moisture content of 1.0 kg/kg in approximately 6 hours during infrared drying. As a result of radiant heating, the soil temperature will rise from an initial 5 ℃ to a final 22.6 ℃ in approximately 3 hours. The analytical solution of the mass transfer differential equation can be utilized for theoretical studies of drying capillary-porous materials, such as determining the drying period or the thickness of the material layer that will dry to a specified final moisture content. The analytical solution of the heat transfer differential equation, accounting for both thermal conductivity and the Dufour effect, can be employed to manage the operation of the infrared radiation source, such as determining its operational and shutdown periods when the soil surface temperature reaches its maximum (critical) value. The mathematical solutions discussed in the article do not consider thermodiffusion processes in the soil layer (Soret effect), which presents a promising direction for further scientific research.
Experimental Investigation of Liquid Pressure in a Straight Circular Cross-Section Pipeline
Authors: Vafaeva Khristina Maksudovna , Karpov Denis Fedorovich , Pavlov Mikhail Vasilyevich , Baloji Dharavath , Sharma Pratibha , Deorari Rajesh , Bhatnagar Sanjay , Kalpana Kasapogu , Gudainiyan Jitendra
Published: October 2024

An evaluation of the reliability of a widely accepted method for determining pipeline hydraulic resistance is conducted, based on a comparative analysis of theoretical data with the results of laboratory investigations. The research object is a liquid flowing under pressure in a straight pipeline with a circular cross-section. The subject of the study is the pressure losses of the liquid along the length of the pipeline. The aim of the research is to verify the existing method for determining the hydraulic resistance of a straight pipeline. Research methods include the Darcy-Weisbach law for determining pressure losses due to friction in the pipeline and experimentation. The research findings indicate that the air pressure at the inlet and outlet of the pipeline, as determined by experimentation/calculation, were as follows: for a flow capacity of n = 0.33 (1/3) – 4.48 Pa and 2.11 Pa / 4.87 Pa and 1.70 Pa; for n = 0.66 (2/3) – 9.11 Pa and 3.45 Pa / 9.35 Pa and 3.41 Pa; for n = 1 – 15.4 Pa and 5.10 Pa / 15.7 Pa and 5.92 Pa. The absolute and relative errors of indirect technical measurements of linear pressure losses due to friction in the pipeline were approximately ≈1.04 Pa and ≈10.6 %, respectively. Conclusions drawn from the study indicate that the root mean square deviations of calculated air pressure values along the pipeline from experimental data, with a sample volume N = 9, were as follows: for a flow capacity of n = 0.33 (1/3) – σ = 0.40 Pa; for n = 0.67 (2/3) – σ = 0.36 Pa; for n = 1 – σ = 0.61 Pa. Based on the assessment of the error of indirect technical measurements of pressure losses in the pipeline and deviations of calculated air pressure values from instrumental measurement results, it can be concluded that the existing method of hydraulic pipeline calculation is adequate.
Gliding performance in the inland sugar glider in low-canopy forest
Authors: Goldingay Ross L. , Quin Darren G. , Thomas Karen J.
Published: October 2024

Knowledge of the gliding performance of gliding mammals provides important insight into how these species have evolved to use their environment but it can also be used to guide tree retention and habitat restoration. We investigated the glide performance of the inland sugar glider (Petaurus notatus) in central Victoria. We measured 40 glides from untagged individuals during nest box monitoring. On average, gliders launched into a glide from a height of 14.7 m above the ground and landed at 6.2 m above the ground. The average horizontal glide distance was 18.1 m (range 8–41 m). The glide ratio (horizontal glide distance/height dropped) and glide angle averaged 2.2 and 26.4°, respectively. These values represent a better average glide performance than any previously measured for an Australian gliding mammal. These data are contrasted with those of other gliding mammals to explore the hypothesis that smaller species may be more capable gliders than larger related species.
Perceptions and experiences of health professionals when supporting adults with stroke to engage in physical activity
Authors: Pepar A. , Mahendran N. , Preston E. , Keegan R.
Published: October 2024

Background Understanding health professional perceptions and experiences when supporting post-stroke physical activity may assist with development of strategies targeting low physical activity observed in this group. The aims of this study were to explore health professionals’ perceptions and experiences of post-stroke physical activity, the barriers they experience and potential facilitators when supporting people with stroke to be active. Methods Ten focus groups were conducted with 57 health professionals (physiotherapists, occupational therapists, nurses, exercise physiologists, psychologists and sports scientists) and allied health students. Data were analysed via inductive thematic analysis. Results Health professionals were reluctant to recommend moderate intensity physical activity. Barriers included: (1) post-stroke barriers being varied and individual; (2) resources being under pressure and (3) physical activity goals falling through the cracks. Suggested facilitators included: (1) clearly defined roles, processes and environments which encourage activity; (2) funding for more staff; (3) improving health professional skills and confidence and (4) using internal motivation and social supports after stroke. Conclusions Post-stroke physical activity is a complex goal. Varied and individual barriers require tailored solutions. Health professionals report insufficient time, resources and skills to address these individual barriers as well as limited pathways to access physical activity support. Resource-efficient interventions and care models that allow routine strategies targeting post-stroke physical activity are required.
The Greenland spatial fingerprint of Dansgaard–Oeschger events in observations and models
Authors: Buizert Christo , Sowers Todd A. , Niezgoda Kyle , Blunier Thomas , Gkinis Vasileios , Harlan Margaret , He Chengfei , Jones Tyler R. , Kjaer Helle A. , Liisberg Jesper B. , Menking James A. , Morris Valerie , Noone David , Rasmussen Sune Olander , Sime Louise C. , Steffensen Jørgen P. , Svensson Anders , Vaughn Bruce H. , Vinther Bo M. , White James W.C.
Published: October 2024

Pleistocene Ice Ages display abrupt Dansgaard–Oeschger (DO) climate oscillations that provide prime examples of Earth System tipping points—abrupt transition that may result in irreversible change. Greenland ice cores provide key records of DO climate variability, but gas-calibrated estimates of the temperature change magnitudes have been limited to central and northwest Greenland. Here, we present ice-core δ
15
N-N
2
records from south (Dye 3) and coastal east Greenland (Renland) to calibrate the local water isotope thermometer and provide a Greenland-wide spatial characterization of DO event magnitude. We combine these data with existing records of δ
18
O, deuterium excess, and accumulation rates to create a multiproxy “fingerprint” of the DO impact on Greenland. Isotope-enabled climate models have skill in simulating the observational multiproxy DO event impact, and we use a series of idealized simulations with such models to identify regions of the North Atlantic that are critical in explaining DO variability. Our experiments imply that wintertime sea ice variation in the subpolar gyre, rather than the commonly invoked Nordic Seas, is both a sufficient and a necessary condition to explain the observed DO impacts in Greenland, whatever the distal cause. Moisture-tagging experiments support the idea that Greenland DO isotope signals may be explained almost entirely via changes in the vapor source distribution and that site temperature is not a main control on δ
18
O during DO transitions, contrary to the traditional interpretation. Our results provide a comprehensive, multiproxy, data-model synthesis of abrupt DO climate variability in Greenland.
Higher oxygen content and transport characterize high-altitude ethnic Tibetan women with the highest lifetime reproductive success
Authors: Ye Shenghao , Sun Jiayang , Craig Sienna R. , Di Rienzo Anna , Witonsky David , Yu James J. , Moya Esteban A. , Simonson Tatum S. , Powell Frank L. , Basnyat Buddha , Strohl Kingman P. , Hoit Brian D. , Beall Cynthia M.
Published: October 2024

We chose the “natural laboratory” provided by high-altitude native ethnic Tibetan women who had completed childbearing to examine the hypothesis that multiple oxygen delivery traits were associated with lifetime reproductive success and had genomic associations. Four hundred seventeen (417) women aged 46 to 86 y residing at ≥3,500 m in Upper Mustang, Nepal, provided information on reproductive histories, sociocultural factors, physiological measurements, and DNA samples for this observational cohort study. Simultaneously assessing multiple traits identified combinations associated with lifetime reproductive success measured as the number of livebirths. Women with the most livebirths had distinctive hematological and cardiovascular traits. A hemoglobin concentration near the sample mode and a high percent of oxygen saturation of hemoglobin raised arterial oxygen concentration without risking elevated blood viscosity. We propose ongoing stabilizing selection on hemoglobin concentration because extreme values predicted fewer livebirths and directional selection favoring higher oxygen saturation because higher values had more predicted livebirths.
EPAS1,
an oxygen homeostasis locus with strong signals of positive natural selection and a high frequency of variants occurring only among populations indigenous to the Tibetan Plateau, associated with hemoglobin concentration. High blood flow into the lungs, wide left ventricles, and low hypoxic heart rate responses aided effective convective oxygen transport to tissues. Women with physiologies closer to unstressed, low altitude values had the highest lifetime reproductive success. This example of ethnic Tibetan women residing at high altitudes in Nepal links reproductive fitness with trait combinations increasing oxygen delivery under severe hypoxic stress and demonstrates ongoing natural selection.
Persistently active El Niño–Southern Oscillation since the Mesozoic
Authors: Li Xiang , Hu Shineng , Hu Yongyun , Cai Wenju , Jin Yishuai , Lu Zhengyao , Guo Jiaqi , Lan Jiawenjing , Lin Qifan , Yuan Shuai , Zhang Jian , Wei Qiang , Liu Yonggang , Yang Jun , Nie Ji
Published: October 2024

The El Niño–Southern Oscillation (ENSO), originating in the central and eastern equatorial Pacific, is a defining mode of interannual climate variability with profound impact on global climate and ecosystems. However, an understanding of how the ENSO might have evolved over geological timescales is still lacking, despite a well-accepted recognition that such an understanding has direct implications for constraining human-induced future ENSO changes. Here, using climate simulations, we show that ENSO has been a leading mode of tropical sea surface temperature (SST) variability in the past 250 My but with substantial variations in amplitude across geological periods. We show this result by performing and analyzing a series of coupled time-slice climate simulations forced by paleogeography, atmospheric CO
2
concentrations, and solar radiation for the past 250 My, in 10-My intervals. The variations in ENSO amplitude across geological periods are little related to mean equatorial zonal SST gradient or global mean surface temperature of the respective periods but are primarily determined by interperiod difference in the background thermocline depth, according to a linear stability analysis. In addition, variations in atmospheric noise serve as an independent contributing factor to ENSO variations across intergeological periods. The two factors together explain about 76% of the interperiod variations in ENSO amplitude over the past 250 My. Our findings support the importance of changing ocean vertical thermal structure and atmospheric noise in influencing projected future ENSO change and its uncertainty.
Controlling for polygenic genetic confounding in epidemiologic association studies
Authors: Zhao Zijie , Yang Xiaoyu , Dorn Stephen , Miao Jiacheng , Barcellos Silvia H. , Fletcher Jason M. , Lu Qiongshi
Published: October 2024

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.
Maternal NO
2
exposure and fetal growth restriction: Hypoxia transmission and lncRNAs-proinflammation-mediated abnormal hematopoiesis
Authors: Qin Liyao , Yue Huifeng , Gong Zhihua , Guo Yuqiong , Li Dan , Ma Li , YiXi Zhuoma , He Jing , Li Zhihong , Li Guangke , Yan Wei , Sang Nan
Published: October 2024

Epidemiological studies show a strong correlation between air pollution and fetal growth restriction (FGR), but existing results are controversial due to inherent limitations, such as causality of specific pollutants, developmental origin, and maternal–fetal transmission. To address this controversy, we first conducted a retrospective analysis of 28,796 newborns and revealed that maternal nitrogen dioxide (NO
2
) exposure during the second trimester was positively associated with FGR, with an adjusted odds ratio of 1.075 (95% confidence interval: 1.020-1.133) per 10 μg/m
3
NO
2
increase for small for gestational age. Then, by establishing an animal model of prenatal NO
2
exposure, we confirmed its adverse effects on embryonic growth and hematopoiesis in the yolk sac and fetal liver, primarily affecting the differentiation of hematopoietic stem and progenitor cells and erythroid maturation. By applying internal exposure analyses coupled with
15
N isotope tracing, we found that maternal NO
2
inhalation induced acquired methemoglobinemia through its byproducts and placental hypoxia in pregnant mice. Importantly, by combining transcriptional profiling, bioinformatics analysis, and RNA binding protein immunoprecipitation (RIP)/chromatin immunoprecipitation (CHIP), we clarified that placental-fetal hypoxia transmission activated hypoxia-inducible factors, disturbed hematopoiesis through the hypoxia-inducible factor 1β-long noncoding RNAs-CCAAT/enhancer binding protein alpha-proinflammatory signaling pathway, ultimately contributing to FGR progression. These findings provide insights for risk prevention and clinical intervention to promote child well-being in NO
2
-polluted areas.
Political organization and gender predict violence in the Andean archaeological record
Authors: Snyder Thomas J. , Arkush Elizabeth
Published: October 2024

The relationship between sociopolitical organization and violence remains an enduring question in anthropological research. Less studied is the articulation of gender with violence in societies of different sociopolitical organization. We investigate the frequency and type of violence experienced by adult males and females in pre-Hispanic Andean archaeological contexts, comparing exposure to antemortem (nonlethal) and perimortem (potentially lethal) violence across three categories of sociopolitical organization: autonomous communities, regional cultural formations, and states. Using a database of 8,607 adults from 169 publications and over 155 sites, we construct a multinomial logistic regression using Bayesian Hamiltonian Monte Carlo methods to fit our model. The odds of antemortem and perimortem trauma were low for both sexes. However, the odds of antemortem trauma were consistently higher for males than females in all categories of sociopolitical organization, suggesting that men were more frequently exposed to violence. Males display similar odds of cranial trauma across all forms of sociopolitical organization, while females display slightly reduced odds of cranial trauma in states compared to autonomous communities. Perimortem trauma is especially strongly patterned by sex. In autonomous communities, the odds of potentially lethal cranial trauma are equivalent between the sexes; in states, they are consistent for males, but depressed for females. In the pre-Hispanic Andes, living in states dramatically reduced the chances of encountering lethal violence for females, but not males. Our study complicates the notion that increasing sociopolitical complexity leads to decreasing interpersonal violence and highlights the importance of gender in understanding the human history of violence.
Rapid shift in methane carbon isotopes suggests microbial emissions drove record high atmospheric methane growth in 2020–2022
Authors: Michel Sylvia Englund , Lan Xin , Miller John , Tans Pieter , Clark J. Reid , Schaefer Hinrich , Sperlich Peter , Brailsford Gordon , Morimoto Shinji , Moossen Heiko , Li Jianghanyang
Published: October 2024

The growth rate of the atmospheric abundance of methane (CH
4
) reached a record high of 15.4 ppb yr
−1
between 2020 and 2022, but the mechanisms driving the accelerated CH
4
growth have so far been unclear. In this work, we use measurements of the
13
C:
12
C ratio of CH
4
(expressed as
δ
13
C
CH4
) from NOAA’s Global Greenhouse Gas Reference Network and a box model to investigate potential drivers for the rapid CH
4
growth. These measurements show that the record-high CH
4
growth in 2020–2022 was accompanied by a sharp decline in
δ
13
C
CH4
, indicating that the increase in CH
4
abundance was mainly driven by increased emissions from microbial sources such as wetlands, waste, and agriculture. We use our box model to reject increasing fossil fuel emissions or decreasing hydroxyl radical sink as the dominant driver for increasing global methane abundance.
Methane fueled lake pelagic food webs in a Cretaceous greenhouse world
Authors: Sun Funing , Luo Genming , Pancost Richard D. , Dong Zhengkun , Li Zhiguo , Wang Hongmei , Chen Zhong-Qiang , Xie Shucheng
Published: October 2024

Methane (CH
4
) is a potent greenhouse gas but also an important carbon and energy substrate for some lake food webs. Understanding how CH
4
incorporates into food webs is, therefore, crucial for unraveling CH
4
cycling and its impacts on climate and ecosystems. However, CH
4
-fueled lake food webs from pre-Holocene intervals, particularly during greenhouse climates in Earth history, have received relatively little attention. Here, we present a long-term record of CH
4
-fueled pelagic food webs across the Cretaceous Oceanic Anoxic Event 1a (~120 Mya) that serves as a geological analog to future warming. We show an exceptionally strong expansion of both methanogens and CH
4
-oxidizing bacteria (up to 87% of hopanoid-producing bacteria) during this Event. Grazing on CH
4
-oxidizing bacteria by zooplankton (up to 47% of ciliate diets) within the chemocline transferred substantial CH
4
-derived carbon to the higher trophic levels, representing an important CH
4
sink in the water column. Our findings suggest that as Earth warms, microbial CH
4
cycling could restructure food webs and fundamentally alter carbon and energy flows and trophic pathways in lake ecosystems.
Heat waves may trigger unexpected surge in aerosol and ozone precursor emissions from sedges in urban landscapes
Authors: Wang Hui , Nagalingam Sanjeevi , Welch Allison M. , Leong Christopher , Czimczik C. I. , Guenther Alex B.
Published: October 2024

Biogenic isoprene emissions from herbaceous plants are generally lower than those from trees. However, our study finds widespread isoprene emission in herbaceous sedge plants, with a stronger temperature response surpassing current tree-derived models. We measured and compared isoprene emissions from sedges grown in different climatic zones, all showing an exponential temperature response with a Q10 range of 7.2 to 12, significantly higher than the Q10 of about 3 for other common isoprene emitters. The distinct temperature sensitivity of sedges makes them a hidden isoprene source, significant during heat waves but not easily detected in mild weather. For instance, isoprene emissions from
Carex praegracilis
can increase by 320% with a peak emission of over 100 nmol m
−2
s
−1
compared to preheat wave emissions. During heat waves, the peak isoprene emissions from
C. praegracilis
can match those from
Lophostemon confertus
, a commonly used street tree species which is considered the dominant urban isoprene source due to higher biomass and emission capacities. This surge in isoprene from globally distributed sedges, including those in urban landscapes, could contribute to peak ozone and aerosol pollutants during heat waves.
Designing L2 pronunciation instruction activities for primary school learners
Authors: Gao Jie
Published: October 2024

This study employs a content analysis approach to examine the presentation of pronunciation instruction (PI)
activities in six elementary school English language teaching (ELT) textbook series used in China. Among the textbooks analyzed,
two series are published in English-speaking countries for commercial use, while two are published in China for public schools.
The remaining two series are jointly released by Chinese and UK publishers, and are utilized in schools at Shanghai and Hong
Kong.

Categorizing the PI‑related activities based on the framework by Levis and
Echelberger (2022), the author discovered that PI is predominantly integrated into listening and speaking activities.
The study highlights the evolving design of PI activities in relation to learners’ ages, reflecting the individual preferences and
beliefs of textbook developers. Notably, most of the surveyed textbook series acknowledge the effectiveness of phonics for
teaching pronunciation, yet the use of phonetic alphabet remains minimal. Young learners are encouraged to grasp pronunciation
rules through exposure and observation rather than through direct “top-down” instruction from teachers.

This research provides language educators with additional resources for classroom activities and offers textbook
writers diverse insights for material development work in the future.
Stochastic collapse: how gradient noise attracts SGD dynamics towards simpler subnetworks*
Authors: Chen Feng , Kunin Daniel , Yamamura (山村篤志) Atsushi , Ganguli Surya
Published: October 2024

In this work, we reveal a strong implicit bias of stochastic gradient descent (SGD) that drives overly expressive networks to much simpler subnetworks, thereby dramatically reducing the number of independent parameters, and improving generalization. To reveal this bias, we identify invariant sets, or subsets of parameter space that remain unmodified by SGD. We focus on two classes of invariant sets that correspond to simpler (sparse or low-rank) subnetworks and commonly appear in modern architectures. Our analysis uncovers that SGD exhibits a property of stochastic attractivity towards these simpler invariant sets. We establish a sufficient condition for stochastic attractivity based on a competition between the loss landscape’s curvature around the invariant set and the noise introduced by stochastic gradients. Remarkably, we find that an increased level of noise strengthens attractivity, leading to the emergence of attractive invariant sets associated with saddle-points or local maxima of the train loss. We observe empirically the existence of attractive invariant sets in trained deep neural networks, implying that SGD dynamics often collapses to simple subnetworks with either vanishing or redundant neurons. We further demonstrate how this simplifying process of stochastic collapse benefits generalization in a linear teacher-student framework. Finally, through this analysis, we mechanistically explain why early training with large learning rates for extended periods benefits subsequent generalization.
Efficient training of energy-based models using Jarzynski equality*
Authors: Carbone Davide , Hua Mengjian , Coste Simon , Vanden-Eijnden Eric
Published: October 2024

Energy-based models (EBMs) are generative models inspired by statistical physics with a wide range of applications in unsupervised learning. Their performance is well measured by the cross-entropy (CE) of the model distribution relative to the data distribution. Using the CE as the objective for training is, however, challenging because the computation of its gradient with respect to the model parameters requires sampling of the model distribution. Here, we show how the results for nonequilibrium thermodynamics based on the Jarzynski equality together with tools from sequential Monte Carlo sampling can be used to perform this computation efficiently and avoid the uncontrolled approximations made using the standard contrastive divergence algorithm. Specifically, we introduce a modification of the unadjusted Langevin algorithm (ULA), in which each walker acquires a weight that enables the estimation of the gradient of the CE at any step during gradient descent, thereby bypassing sampling biases induced by slow mixing of the ULA. We illustrate these results with numerical experiments on Gaussian mixture distributions as well as the MNIST and CIFAR-10 datasets. We show that the proposed approach outperforms methods based on the contrastive divergence algorithm in all the considered situations.
Study on the tightening scheme of electronic component screw assemblies based on finite element simulation
Authors: Guan Wei , Yi QingSi , Mo Qiuyun , Jiang Tao , Wang Siyuan , Lei Zhao Ying
Published: October 2024

To improve the fastening effect of electronic components in mechatronic equipment, the tightening sequence and frequency have a significant impact on the structural performance of these components. This study identifies an optimal fastening method through experimental verification, although some measurement errors were noted; the method remains highly reliable. First, theoretical calculations and experimental analyses were conducted to determine the torque values under different conditions. Then, simulations were performed to study the impact on the stress of each screw. Finally, experimental validation of the simulation results was carried out, and a linear relationship (with a value of 0.82) between the measured and theoretical values was explored. Consequently, an optimized screw group fastening method is proposed, and the torque calculation process is simplified, thereby enhancing the reliability and lifespan of electronic components in mechatronic systems.
Density effects on the hydrodynamics and mixing at a large confluence with a compound channel tributary
Authors: Li Kun , Yuan Saiyu , Li Chaoqun , Lu Jun , Chen Cuixia , Xu Lei , Gualtieri Carlo
Published: October 2024

Confluences are marked by converging streamlines, complex hydrodynamics features and mixing processes. Understanding such intricate flow patterns and their effects on the mixing at natural river junctions is difficult, particularly for large confluences with a compound channel tributary, due to the scarcity of field data. This research aims to assess the effects of density differences on hydrodynamics and mixing at the large river confluence between the Yangtze River and its tributary, the Poyang Lake, whose outflow channel has a large inner-side floodplain. Field data were collected during three surveys under various flow conditions to analyse how the alternate flow mechanism (compound channel vs single channel) of the Poyang Lake outflow channel influences the confluence mixing process. In high flow and relatively high flow conditions, the Poyang Lake outflow channel functioned as a compound channel and switched to a single channel in low flow. Acoustic Doppler current profiling (ADCP) was used in all three surveys to explore the features of hydrodynamics and the mixing process at the confluence, supplemented by water quality sampling to characterise the mixing patterns. Secondary flows observed during the field surveys were found to be affected by alterations in the flow mechanism (compound channel vs single channel) of the Poyang Lake outflow channel and the density effects, which were characterised using the densimetric Froude number Frd. Ultimately, the findings obtained in these field surveys confirm the role of density differences between the tributaries in significantly affecting hydrodynamics features and the mixing process at river confluences.