bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pathways to Eviction
Authors: Summers Nicole , Steil Justin 
Published: October 2024

Abstract
Over the past several years, socio-legal researchers have focused attention on the phenomenon of eviction, particularly in low-income communities and communities of color. One major aspect of the eviction phenomenon has been largely overlooked: how and why certain eviction filings result in forced, legally compelled tenant moves and others do not. Through coding of the legal documents associated with eviction filings and multi-level regression analysis, this article advances the analysis of evictions in two crucial ways. First, it identifies and describes the frequency of the distinct legal procedural pathways that result in forced tenant moves once an eviction case has been filed. Second, it identifies the case, tenancy, neighborhood, and property correlates of forced tenant moves and of distinct procedural pathways to forced tenant moves. The article demonstrates that move-out agreements are the primary procedural pathway by which tenants are forcibly moved, yet they have been largely overlooked in previous eviction research because they are not easily analyzable in administrative datasets. The regression analyses advance the growing work examining the role of landlord characteristics in shaping tenants’ housing stability and break new ground in identifying the characteristics of the different pathways through which tenants are forced out of their homes following eviction filing.
A Global Social History of Princes, Courts and Elites
Authors: Duindam Jeroen 
Published: October 2024

AbstractRoyals abound in global history. Kings were served at court by domestic, administrative, and military elites who connected the centre to the provinces. Contemporary observers as well as modern historians have often stressed the contrast between oriental despots and limited monarchs in the West, downplaying structural resemblances. This article moves beyond clichés commonly ascribed to East and West, and asks to what extent social practices of court life were shared across early modern Eurasia. Then it reviews the profound changes in European court life during the long eighteenth century. Can parallel reform movements be found in other parts of Eurasia? Finally, it moves from comparisons to connections, by tracing fundamental shifts in the relationships between European royals and royals across the globe from the sixteenth century to the nineteenth. This longue durée examination questions common views about European exceptionalism and corrects persistent clichés about rising middle classes and declining nobilities.
How Did Bengal Become a Society?
Authors: Sartori Andrew 
Published: October 2024

AbstractStarting in the nineteenth century, ‘society’ emerged as a new object of contemplation, a new conception of historicity, and a new framework of norms in Bengal. This article asks what kind of epistemological project this turn to the social represented, and what its emergence suggests about the historical circumstances that underwrote its conditions of possibility. I suggest that, beyond the narrower framework of colonial knowledge, the social emerged as a reflexive inquiry into the ways in which the conditions of collective and individual life were being transformed by practices of interdependence that defied containment to regional geographies.
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

Abstract

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

AbstractMercury'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

AbstractBionic 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

AbstractWhite 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

AbstractProteins, 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.
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.
Designing L2 pronunciation instruction activities for primary school learners
Authors: Gao Jie 
Published: October 2024

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.
Numerical simulation of vehicle hood underwater active propulsion separation
Authors: Guo Chen , Yu Wenhui , Yao Baotai , Zhang Peng 
Published: October 2024

Abstract
In order to study the separation process of the underwater active propulsion hood, this paper analyzes the main stages of the underwater separation system. By using unstructured dynamic mesh technology, the control equations of computational fluid dynamics are coupled with the six-degree-of-freedom motion equations to calculate the influence of different installation angles of the propulsion device and the distance of thrust deviation from the center of gravity on the separation of the hood. The results show that hydrodynamics will exert a moment on the hood, causing the hood to rotate. Excessive rotation of the hood can lead to collisions with the vehicle. Raising the installation angle of the propulsion device and applying thrust to resist the rotation of the hood can reduce the angular velocity of the hood, improve the effectiveness of the thrust, and optimize the separation performance of the system.