bluuebunny/crossref_metadata_2025_split huggingface dataset:
New class of high-entropy pseudobrookite titanate with excellent thermal stability, low thermal expansion coefficient, and low thermal conductivity
Authors: Wu Jinyu , Ma Xiaohui , Hu Xiaoxia , Yan Liwen , Hou Feng , Liu Jiachen , Guo Anran 
Published: October 2022

AbstractAs a type of titanate, the pseudobrookite (MTi2O5/M2TiO5) exhibits a low thermal expansion coefficient and thermal conductivity, as well as excellent dielectric and solar spectrum absorption properties. However, the pseudobrookite is unstable and prone to decomposing below 1200 °C, which limits the practical application of the pseudobrookite. In this paper, the high-entropy pseudobrookite ceramic is synthesized for the first time. The pure high-entropy (Mg,Co,Ni,Zn)Ti2O5 with the pseudobrookite structure and the biphasic high-entropy ceramic composed of the high-entropy pseudobrookite (Cr,Mn,Fe,Al,Ga)2TiO5 and the high-entropy spinel (Cr,Mn,Fe,Al,Ga,Ti)3O4 are successfully prepared by the in-situ solid-phase reaction method. The comparison between the theoretical crystal structure of the pseudobrookite and the aberration-corrected scanning transmission electron microscopy (AC-STEM) images of high-entropy (Mg,Co,Ni,Zn)Ti2O5 shows that the metal ions (M and Ti ions) are disorderly distributed at the A site and the B site in high-entropy (Mg,Co,Ni,Zn)Ti2O5, leading to an unprecedentedly high configurational entropy of high-entropy (Mg,Co,Ni,Zn)Ti2O5. The bulk high-entropy (Mg,Co,Ni,Zn)Ti2O5 ceramics exhibit a low thermal expansion coefficient of 6.35×10−6 K−1 in the temperature range of 25–1400 °C and thermal conductivity of 1.840 W·m−1·K−1 at room temperature, as well as the excellent thermal stability at 200, 600, and 1400 °C. Owing to these outstanding properties, high-entropy (Mg,Co,Ni,Zn)Ti2O5 is expected to be the promising candidate for high-temperature thermal insulation. This work has further extended the family of different crystal structures of high-entropy ceramics reported to date.
Health conditions of Guiana dolphins facing cumulative anthropogenic impacts
Authors: Soares Estela Dibo , Cantor Mauricio , Bracarense Ana Paula Frederico Rodrigues Loure , Groch Kátia Regina , Domit Camila 
Published: October 2022

AbstractCoastal areas are associated with anthropogenic activities and stressors that can expose the marine fauna to negative cumulative impacts. Apex predators, such as dolphins, can flag the quality of their environment through health parameters such as cutaneous and body conditions. We examined the potential relationship between environmental conditions and health parameters of Guiana dolphins around a port and a conservation unit within the Paranaguá Estuarine Complex, southern Brazil. During boat surveys between July 2018 and April 2019 covering both areas, we measured environmental parameters, photographed dolphins to infer their health condition and the frequency of use of the estuary. In total, 204 individual Guiana dolphins were photo-identified, 52 of which were seen in both years. In general, dolphins showed poor body condition (76% classified as emaciated or thin in 2018, and 79% in 2019), diverse cutaneous conditions (four types of lesions suggestive of an infectious aetiology, two conditions suggestive of traumatic events of anthropogenic origin, and two anomalous pigmentation) and a high prevalence of such cutaneous conditions (85% in 2018, 70% in 2019). Most individuals maintained their body and cutaneous conditions between the two years. There were no clear differences between the port and the conservation areas in terms of environmental conditions, frequency of use and health conditions of individual dolphins, suggesting that dolphins inhabiting this estuarine complex are exposed to potential cumulative impacts, such as pollutants, noise and habitat degradation. This study provides baseline information on some health parameters of Guiana dolphins in southern Brazil and highlights the need for systematic, long-term health assessment of Guiana dolphin populations to guide conservation actions to safeguard this vulnerable species.
Food security analysis and forecasting: A machine learning case study in southern Malawi
Authors: Gholami Shahrzad , Knippenberg Erwin , Campbell James , Andriantsimba Daniel , Kamle Anusheel , Parthasarathy Pavitraa , Sankar Ria , Birge Cameron , Lavista Ferres Juan 
Published: October 2022

Abstract
Chronic food insecurity remains a challenge globally, exacerbated by climate change-driven shocks such as droughts and floods. Forecasting food insecurity levels and targeting vulnerable households is apriority for humanitarian programming to ensure timely delivery of assistance. In this study, we propose to harness a machine learning approach trained on high-frequency household survey data to infer the predictors of food insecurity and forecast household level outcomes in near real-time. Our empirical analyses leverage the Measurement Indicators for Resilience Analysis (MIRA) data collection protocol implemented by Catholic Relief Services (CRS) in southern Malawi, a series of sentinel sites collecting household data monthly. When focusing on predictors of community-level vulnerability, we show that a random forest model outperforms other algorithms and that location and self-reported welfare are the best predictors of food insecurity. We also show performance results across several neural networks and classical models for various data modeling scenarios to forecast food security. We pose that problem as binary classification via dichotomization of the food security score based on two different thresholds, which results in two different positive class to negative class ratios. Our best performing model has an F1 of 81% and an accuracy of 83% in predicting food security outcomes when the outcome is dichotomized based on threshold 16 and predictor features consist of historical food security score along with 20 variables selected by artificial intelligence explainability frameworks. These results showcase the value of combining high-frequency sentinel site data with machine learning algorithms to predict future food insecurity outcomes.
Stress, anxiety and depression in parents of children with autism spectrum disorders in Kazakhstan: prevalence and associated factors
Authors: Alibekova Raushan , Kai Chan Chee , Crape Byron , Kadyrzhanuly Kainar , Gusmanov Arnur , An Sofiya , Bulekbayeva Sholpan , Akhmetzhanova Zulfiya , Ainabekova Assel , Yerubayev Zhanibek , Yessimkulova Fariza , Bekisheva Aislu , Ospanova Zarina , Rakhimova Makhabbat 
Published: October 2022

AbstractIntroductionStudies worldwide reported increased levels of stress among parents of children with autism due to the unique caregiving challenges. While research has shown that parents' and autistic child's demographics and behavioral characteristics are associated with psychological distress among caregivers of children with autism, very few studies have investigated the impact of the caregiver's unmet needs on various aspects of the perceived family burden.MethodsThis cross-sectional study examined the prevalence of stress, anxiety and depressive symptoms among a wide range of 146 parents with different sociodemographic characteristics, social support and unmet needs who care for children with autism spectrum disorder. These parents were recruited from autism non-governmental organizations and the National Children's Rehabilitation Center in Astana, Kazakhstan, a post-Soviet country in Central Asia. Multiple linear regression analyses were utilized to examine the relationship of parental psychological distress variables with social support, unmet needs and parental and child sociodemographic characteristics.ResultsSignificantly higher levels of stress and depression were reported among parents who perceived their needs as being unmet or extremely unmet as addressed by societal acceptance as compared to parents who reported adequate levels of needs met by social acceptance. Employed parents and parents with a higher level of perceived friends' support had less symptoms of stress, anxiety, and depression.ConclusionsIncreasing public awareness about autism and providing early detection and interventions for distressed caregivers of children with autism may be helpful in improving healthy functioning of parents and the entire family.
Graph distances in scale-free percolation: the logarithmic case
Authors: Hao Nannan , Heydenreich Markus 
Published: October 2022

AbstractScale-free percolation is a stochastic model for complex networks. In this spatial random graph model, vertices
$x,y\in\mathbb{Z}^d$
are linked by an edge with probability depending on independent and identically distributed vertex weights and the Euclidean distance
$|x-y|$
. Depending on the various parameters involved, we get a rich phase diagram. We study graph distance and compare it to the Euclidean distance of the vertices. Our main attention is on a regime where graph distances are (poly-)logarithmic in the Euclidean distance. We obtain improved bounds on the logarithmic exponents. In the light tail regime, the correct exponent is identified.
Machine Learning and the Stock Market
Authors: Brogaard Jonathan , Zareei Abalfazl 
Published: October 2022

AbstractPractitioners allocate substantial resources to technical analysis whereas academic theories of market efficiency rule out technical trading profitability. We study this long-standing puzzle by applying a diverse set of machine learning algorithms. The results show that an investor can find profitable technical trading rules using past prices, and that this out-of-sample profitability decreases through time, showing that markets have become more efficient over time. In addition, we find that the evolutionary genetic algorithm’s attitude in not shying away from erroneous predictions gives it an edge in building profitable strategies compared to the strict loss-minimization-focused machine learning algorithms.
Phylogeny, origin and diversification of the Dasylirion genus based on matK and rbcL sequences
Authors: Ortiz-Covarrubias Yadhira C. , Orozco-Sifuentes Martha Monzerrath , Mendoza-Rodríguez Dulce V. , Villlarreal-Quintanilla José A. , Martínez Octavio , Hernández-Godínez Fernando , Jáuregui-González María de Jesús , Reyes-Valdés M. Humberto 
Published: October 2022

AbstractThe Dasylirion genus is highly represented in the arid and semi-arid regions of Mexico and USA, playing important ecological and economical roles. Inferring the evolutionary patterns of this group will eventually facilitate understanding biological phenomena and outlining conservation and usage strategies. We performed a molecular phylogenetic analysis based on two chloroplast DNA regions: maturase-K gene (matK) and the large subunit of ribulose-1,5-bisphosphate carboxylase gene (rbcL). We constructed a phylogenetic tree by maximum likelihood with GTR as the sequence substitution model and a relaxed clock, inferred diversification patterns by lineage through time and explored the diversification rates of Dasylirion by the Yule model. The study included 11 species of the genus, which represent 50% of all its known species. We used two calibration points to date the tree, one based on fossil records of Acorus gramineus, and the other on the estimated stem age of the Yucca genus. The combined sequences of the two partial genes comprised 1455 bp and 18 polymorphic sites. We estimated an average substitution rate of 0.0005 nucleotide per million years for the concatenated DNA sequences. The molecular dating analysis estimated that the Dasylirion genus appeared more than 5.46 million years ago, with a rate of diversification of 0.0466 net speciation events per million years. The estimated age represents a lower bound, since not all Dasylirion species are included. These findings are consistent with other origin and diversification hypotheses for arid-land Asparagaceae in the Mexican highlands as a result of geomorphological events in North America.
Agent‐Based Modeling of Alternative Futures in the British Land Use System
Authors: Brown C. , Seo B. , Alexander P , Burton V. , Chacón‐Montalván E. A. , Dunford R. , Merkle M. , Harrison P. A. , Prestele R. , Robinson E. L. , Rounsevell M. 
Published: October 2022

AbstractSocio‐economic scenarios such as the Shared Socioeconomic Pathways (SSPs) have been widely used to analyze global change impacts, but representing their diversity is a challenge for the analytical tools applied to them. Taking Great Britain as an example, we represent a set of stakeholder‐elaborated UK‐SSP scenarios, linked to climate change scenarios (Representative Concentration Pathways), in a globally‐embedded agent‐based modeling framework. We find that distinct model components are required to account for divergent behavioral, social and societal conditions in the SSPs, and that these have dramatic impacts on land system outcomes. From strong social networks and environmental sustainability in SSP1 to land consolidation and technological intensification in SSP5, scenario‐specific model designs vary widely from one another and from present‐day conditions. Changes in social and human capitals reflecting social cohesion, equality, health and education can generate impacts larger than those of technological and economic change, and comparable to those of modeled climate change. We develop an open‐access, transferrable model framework and provide UK‐SSP projections to 2080 at 1 km2 resolution, revealing large differences in land management intensities, provision of a range of ecosystem services, and the knowledge and motivations underlying land manager decision‐making. These differences suggest the existence of large but underappreciated areas of scenario space, within which novel options for land system sustainability could occur.
From Fossil to Active Hydrothermal Outflow in the Back‐Arc of the Central Apennines (Zannone Island, Italy)
Authors: Curzi M. , Caracausi A. , Rossetti F. , Rabiee A. , Billi A. , Carminati E. , Aldega L. , Bernasconi S. M. , Boschi C. , Drivenes K. , Rizzo A. L. , Sørensen B. E. 
Published: October 2022

AbstractPost‐orogenic back‐arc magmatism is accompanied by hydrothermal ore deposits and mineralizations derived from mantle and crustal sources. We investigate Zannone Island (ZI), back‐arc Tyrrhenian basin, Italy, to define the source(s) of mineralizing hydrothermal fluids and their relationships with the regional petrological‐tectonic setting. On ZI, early Miocene thrusting was overprinted by late Miocene post‐orogenic extension and related hydrothermal alteration. Since active submarine hydrothermal outflow is reported close to the island, Zannone provides an ideal site to determine the P‐T‐X evolution of the long‐lived hydrothermal system. We combined field work with microstructural analyses on syn‐tectonic quartz veins and carbonate mineralizations, X‐ray diffraction analysis, microthermometry and element mapping of fluid inclusions (FIs), C, O, and clumped isotopes, and analyses of noble gases (He‐Ne‐Ar) and CO2 content in FIs. Our results document the evolution of a fluid system of magmatic origin with increasing mixing of meteoric fluids. Magmatic fluids were responsible for quartz veins precipitation at ∼125 to 150 MPa and ∼300°C–350°C. With the onset of extensional faulting, magmatic fluids progressively interacted with carbonate rocks and mixed with meteoric fluids, leading to (a) host rock alteration with associated carbonate and minor ore mineral precipitation, (b) progressive fluid neutralization, (c) cooling of the hydrothermal system (from ∼320°C to ∼86°C), and (d) embrittlement and fracturing of the host rocks. Both quartz and carbonate mineralizations show noble gases values lower than those from the adjacent active volcanic areas and submarine hydrothermal systems, indicating that the fossil‐to‐active hydrothermal history is associated with the emplacement of multiple magmatic intrusions.
What Controls Lake Contraction and Then Expansion in Tibetan Plateau’s Endorheic Basin Over the Past Half Century?
Authors: Chen Wenfeng , Liu Yong , Zhang Guoqing , Yang Kun , Zhou Tao , Wang Jida , Shum C. K. 
Published: October 2022

AbstractThe evolutions of climate‐sensitive lakes on the Tibetan Plateau’s endorheic basin exhibited lake shrinkage before the mid‐1990s, but widespread and rapid lake expansions ever since. However, the quantitative contribution of glacier meltwater to lake changes, and the exact mechanisms behind its dominant drivers remain elusive. Here, a comprehensive examination of glacier mass balance since ∼1975 reveals that excess glacier meltwater compensated for net lake volume loss by 21% ± 5% during its contraction period 1975–1995, whereas promoted net volume gain by 9% ± 1% during expansion period 1995–2020 and by 17% ± 2% over the entire period 1975–2020. Further analyses reveal that the interdecadal variations of sea surface temperatures in the Atlantic, Pacific, and Indian Oceans favor a net moisture inflow to the Tibetan Plateau, leading to increased precipitation since 1995 relative to 1970s–1995, which effectively elucidates the climatic process causing rapid lake volume expansion in the endorheic basin.
Near‐Real‐Time Analysis of the Ionospheric Response to the 15 January 2022 Hunga Tonga‐Hunga Ha'apai Volcanic Eruption
Authors: Maletckii B. , Astafyeva E. 
Published: October 2022

AbstractWe present a near‐real‐time (NRT) scenario of analysis of ionospheric response to the 15 January 2022 Hunga Tonga‐Hunga Ha'apai eruption by using Global Navigation Satellite Systems data in the near‐field (in the vicinity of the volcano), and in the far‐field (Japan, North America, and South America). We introduce a new method to determine instantaneous velocities using an interferometric approach and using the time derivative of the total electron content (TEC). Moreover, for the first time, we propose a novel method that automatically estimates the apparent propagation velocity of ionospheric disturbances from NRT travel‐time diagrams. By using our new methods, we analyzed the dynamics of co‐volcanic ionospheric disturbances generated by the Hunga‐Tonga eruption, and we estimated the first propagation velocity in the near‐field to be ∼800–950 m/s, subsequently decreasing to ∼600 m/s. Based on these values, we conclude that in the near‐field, we detect ionospheric signatures of acoustic waves. In the far field, the apparent velocity of ionospheric disturbances was estimated to be between 277 and 365 m/s, which corresponds to the propagation of the Lamb wave. It is important to note that our new methods can successfully perform at low spatial resolution networks and with 30‐s cadence data. Also, they enable NRT spatio‐temporal analysis of ionospheric TEC response to smaller‐amplitude events.
An ancestral interaction module promotes oligomerization in divergent mitochondrial ATP synthases
Authors: Gahura Ondřej , Mühleip Alexander , Hierro-Yap Carolina , Panicucci Brian , Jain Minal , Hollaus David , Slapničková Martina , Zíková Alena , Amunts Alexey 
Published: October 2022

AbstractMitochondrial ATP synthase forms stable dimers arranged into oligomeric assemblies that generate the inner-membrane curvature essential for efficient energy conversion. Here, we report cryo-EM structures of the intact ATP synthase dimer from Trypanosoma brucei in ten different rotational states. The model consists of 25 subunits, including nine lineage-specific, as well as 36 lipids. The rotary mechanism is influenced by the divergent peripheral stalk, conferring a greater conformational flexibility. Proton transfer in the lumenal half-channel occurs via a chain of five ordered water molecules. The dimerization interface is formed by subunit-g that is critical for interactions but not for the catalytic activity. Although overall dimer architecture varies among eukaryotes, we find that subunit-g together with subunit-e form an ancestral oligomerization motif, which is shared between the trypanosomal and mammalian lineages. Therefore, our data defines the subunit-g/e module as a structural component determining ATP synthase oligomeric assemblies.
Novel concept suppressing plasma heat pulses in a tokamak by fast divertor sweeping
Authors: Horacek J. , Lukes S. , Adamek J. , Havlicek J. , Entler S. , Seidl J. , Cavalier J. , Cikhardt J. , Sedmidubsky V. 
Published: October 2022

AbstractOne of the remaining challenges in magnetic thermonuclear fusion is survival of the heat shield protecting the tokamak reactor vessel against excessive plasma heat fluxes. Unmitigated high confinement edge localized mode (ELM) is a regular heat pulse damaging the heat shield. We suggest a novel concept of magnetic sweeping of the plasma contact strike point fast and far enough in order to spread this heat pulse. We demonstrate feasibility of a dedicated copper coil in a resonant circuit, including the induced currents and power electronics. We predict the DEMO ELM properties, simulate heat conduction, 3D particles motion and magnetic fields of the plasma and coil in COMSOL Multiphysics and Matlab. The dominant system parameter is voltage, feasible 18 kV yields 1 kHz sweeping frequency, suppressing the ELM-induced surface temperature rise by a factor of 3. Multiplied by other known mitigation concepts, ELMs might be mitigated enough to ensure safe operation of DEMO.
Gradient boosting decision tree becomes more reliable than logistic regression in predicting probability for diabetes with big data
Authors: Seto Hiroe , Oyama Asuka , Kitora Shuji , Toki Hiroshi , Yamamoto Ryohei , Kotoku Jun’ichi , Haga Akihiro , Shinzawa Maki , Yamakawa Miyae , Fukui Sakiko , Moriyama Toshiki 
Published: October 2022

AbstractWe sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than $$10^4$$

10
4

. Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes.