bluuebunny/crossref_metadata_2025_split huggingface dataset:
A method for analyzing and predicting the influencing factors of carbon emission intensity based on fuzzy comprehensive evaluation algorithm and data mining
Authors: Liu Jia , Zhang Shuang , Ma Rui , Sha Jiangbo , Kang Wenni , Lin Mingxia 
Published: November 2024

Abstract
The current analysis and prediction methods of influencing factors of carbon emission intensity are generally set for a certain region. The deployment of monitoring points in the predicted coverage area is relatively single, which leads to an increase in the relative error of the final prediction. For this reason, a fuzzy synthesis-based fuzzy synthesis method is proposed for designing and analyzing factors affecting carbon emission intensity and prediction methods using evaluation algorithms and data mining. A brief analysis of factors affecting carbon emission intensity decomposes driving factors and adopts a multi-level approach to make node deployment more flexible and achieve hierarchical monitoring point deployment. Based on this, a data mining carbon emission prediction model is designed, and an adaptive correction method is used to achieve prediction processing. The test results show that for the 6 randomly selected test areas, compared with the linear and nonlinear ARDL carbon emission prediction methods, interval variables, and low-carbon emission optimization methods, the fuzzy design of this design performs better. The relative error finally obtained by the carbon emission prediction model of the comprehensive evaluation algorithm and data mining prediction method is relatively small, showing that this design’s prediction results are more flexible and changeable. The prediction coverage and processing efficiency are also significantly improved, and the pertinence is strong and has practical application value and predictive significance.
Filtering algorithm for systems with transmission random failures and packet loss prediction compensation
Authors: Caballero-Águila Raquel , Linares-Pérez Josefa 
Published: November 2024

Abstract
The problem of least-squares linear filtering is explored for a specific type of networked systems, where the measurements are affected by random parameters and experience random delays and packet dropouts. To prevent network congestion, only one packet is transmitted per sampling interval. However, due to these phenomena, either a single packet, two packets, or no data at all may be received at each sampling time. In the latter case, the estimator compensates by utilizing a predicted value. An innovation-based method is used to derive a recursive filter that does not require the signal dynamics, but only some statistical properties (first-order and second-order moments) of the stochastic processes in the observation model.
A reinforcement learning-based algorithm for discrete dynamic stochastic recognition of speech dialog emotions
Authors: Zhao Xin , Wu Jiangbo 
Published: November 2024

Abstract
At present, the most common speech dialogue emotion discrete dynamic random recognition calculation natural language processing is mostly independent processing of object data, and the recognition efficiency is low, resulting in the infinite increase of the final FRP value. According to the current recognition requirements, speech data resource collection and speech recognition are carried out first, and a multi-level method is adopted to improve the recognition efficiency and realize multi-level processing and sentiment analysis of natural speech. Based on this, a discrete dynamic random recognition calculation model of reinforcement learning speech dialogue emotion was constructed, and the multi-cycle automatic synchronous correction method was used to realize the random recognition processing. The test results show that for the six randomly selected test periods, compared with the improved emotion model random recognition method and the artificial intelligence emotion random recognition method, the final FRP value of the reinforcement learning emotion random recognition method designed this time is well controlled 15%, which indicates that with the assistance of reinforcement learning technology, the designed calculation method is more flexible and changeable. Furthermore, its inherent random recognition mechanism is more comprehensive, efficient, and targeted, thereby rendering it highly valuable and significant for applications under complex background conditions.
Optimization of Electron Beams for Ion Bombardment Secondary Emission Electron Gun
Authors: Wang Zebin , Liu Junbiao , Chen Aiguo , Wang Dazheng , Wang Pengfei , Han Li 
Published: November 2024

Abstract
Electron beam fluorescence technology is an advanced non-contact measurement in rarefied flow fields, the fluorescence signal intensity is positively correlated with the electron beam current. The ion bombardment secondary emission electron gun is suitable for the technology. To enhance the beam current, COMSOL simulations and analyses were con-ducted to examine plasma density distribution in the discharge chamber under the effects of various conditions and the electric field distribution between the cathode and the spacer gap. The anode shape and discharge pressure conditions were optimized to increase plasma density. Additionally, an improved spacer structure was designed with the dual purpose of enhancing the electric field distribution between the cathode-spacer gap and improving vacuum dif-ferential effects. This design modification aims to increase the pass rate of secondary elec-trons. Both simulation and experimental results demonstrated that the performance of the optimized electron gun was effectively enhanced. When the electrode voltage remains con-stant and the discharge gas pressure is adjusted to around 8 Pa, the maximum beam current was increased from 0.9mA to 1.6 mA.
The Cosmic Question
Authors: Insole Christopher J 
Published: November 2024

Abstract
The chapter explores Thomas Nagel’s cosmic question, ‘Is there a way to live in harmony with the universe, and not just in it?’. When the cosmic question presents itself, there can be a range of responses, in terms of where meaning and harmony might be identified: from nowhere, to within the natural world, or in human nature, or in something that transcends nature (human and non-human). Criteria are set out for choosing the figures and movements that are explored later in the book. Not the least important criterion is a desire to unearth and bring to light some largely forgotten parts of various traditions, such as William James’ radical empiricism, and absolute idealism. The intention is to put these into play with living and current movements, such as new atheism, humanism, modern paganism and non-metaphysical (but non-naturalistic) moral realism.
Did crop domestication change the fitness landscape of root response to soil mechanical impedance? An in silico analysis
Authors: Rangarajan Harini , Lynch Jonathan P 
Published: November 2024

Abstract

Background and Aims
Root axes with greater penetration ability are often considered to be beneficial in hard soils. We hypothesized that maize root phenotypes with greater plasticity (meaning reduced elongation in response to mechanical impedance, i.e. a ‘stop signal’) have fitness advantages over phenotypes with reduced plasticity (i.e. unimpeded root elongation) in native (virgin, uncultivated) soils, by reallocating root foraging to softer, presumably wetter, soil domains, and that the value of the stop signal reduced with soil cultivation and crop domestication.

Methods
We used OpenSimRoot to simulate native and cultivated soils and evaluated maize root phenotypes with varying axial and lateral root penetration ability in water, nitrogen (N) and impedance regimes associated with Neolithic agriculture.

Key Results
The stop signal was advantageous in native soils but was less beneficial in cultivated, irrigated soils. Reduced root foraging in hard, dry topsoil enabled root growth in deeper domains where water is available, resulting in an improved balance of resource expenditure and acquisition. The value of the stop signal declined during crop domestication with the advent of irrigation, which increased water availability in the topsoil. Soil cultivation reduced N availability, while irrigation increased N leaching, resulting in a shift in the fitness landscape, with greater lateral root length (i.e. reduced plasticity) being advantageous by colocalizing root foraging with N availability. The importance of the stop signal is evident in modern high-input systems in which drought is a limiting factor.

Conclusions
Our results support the hypotheses that the reduction of lateral root growth by mechanical impedance is adaptive in native soil, but became less adaptive with soil cultivation and irrigation associated with Neolithic agriculture.
The usefulness of Caenorhabditis elegans lifespan analysis in screening for functional foods
Authors: Yasuda Kayo 
Published: November 2024

Abstract
Caenorhabditis elegans is a type of nematode that has significantly contributed to aging research as a multicellular animal model because of its high reproductive rate, ease of cultivation, low cost, short generation cycle, body transparency, and eukaryotic nature. Since the discovery of long-lived mutant strains of C. elegans, signaling pathways involved in lifespan have been elucidated. Some of these pathways are shared with mammals, indicating that aging research in C. elegans may be applied to other animals, including humans. Studies on the mechanisms of aging have advanced with the availability of mutants for these pathways. In recent years, C. elegans has also contributed to the discovery of antioxidants and the elucidation of the molecular mechanisms responsible for the anti-aging effects of foods and traditional medicines with lifespan as an indicator. This review summarizes the characteristics of C. elegans for lifespan analysis associated with functional foods.
LIMO-GCN: a linear model-integrated graph convolutional network for predicting Alzheimer disease genes
Authors: Lin Cui-Xiang , Li Hong-Dong , Wang Jianxin 
Published: November 2024

Abstract
Alzheimer’s disease (AD) is a complex disease with its genetic etiology not fully understood. Gene network-based methods have been proven promising in predicting AD genes. However, existing approaches are limited in their ability to model the nonlinear relationship between networks and disease genes, because (i) any data can be theoretically decomposed into the sum of a linear part and a nonlinear part, (ii) the linear part can be best modeled by a linear model since a nonlinear model is biased and can be easily overfit, and (iii) existing methods do not separate the linear part from the nonlinear part when building the disease gene prediction model. To address the limitation, we propose linear model-integrated graph convolutional network (LIMO-GCN), a generic disease gene prediction method that models the data linearity and nonlinearity by integrating a linear model with GCN. The reason to use GCN is that it is by design naturally suitable to dealing with network data, and the reason to integrate a linear model is that the linearity in the data can be best modeled by a linear model. The weighted sum of the prediction of the two components is used as the final prediction of LIMO-GCN. Then, we apply LIMO-GCN to the prediction of AD genes. LIMO-GCN outperforms the state-of-the-art approaches including GCN, network-wide association studies, and random walk. Furthermore, we show that the top-ranked genes are significantly associated with AD based on molecular evidence from heterogeneous genomic data. Our results indicate that LIMO-GCN provides a novel method for prioritizing AD genes.
Cortico-thalamic tremor circuits and their associations with deep brain stimulation effects in essential tremor
Authors: He Shenghong , West Timothy O , Plazas Fernando R , Wehmeyer Laura , Pogosyan Alek , Deli Alceste , Wiest Christoph , Herz Damian M , Simpson Thomas , Andrade Pablo , Baig Fahd , Hart Michael G , Morgante Francesca , FitzGerald James J , Barbe Michael T , Visser-Vandewalle Veerle , Green Alexander L , Pereira Erlick A , Cagnan Hayriye , Tan Huiling 
Published: November 2024

Abstract
Essential tremor (ET) is one of the most common movement disorders in adults. Deep brain stimulation (DBS) of the ventralis intermediate nucleus (VIM) of the thalamus and/or the posterior subthalamic area (PSA) has been shown to provide significant tremor suppression in patients with ET, but with significant inter-patient variability and habituation to the stimulation. Several non-invasive neuromodulation techniques targeting other parts of the central nervous system, including cerebellar, motor cortex, or peripheral nerves, have also been developed for treating ET, but the clinical outcomes remain inconsistent. Existing studies suggest that pathology in ET may emerge from multiple cortical and subcortical areas, but its exact mechanisms remain unclear.
By simultaneously capturing neural activities from motor cortices and thalami, and hand tremor signals recorded via accelerometers in fifteen human subjects who have undergone lead implantations for DBS, we systematically characterized the efferent and afferent cortico-thalamic tremor networks. Through the comparisons of these network characteristics and tremor amplitude between DBS OFF and ON conditions, we further investigated the associations between different tremor network characteristics and the magnitude of DBS effect.
Our findings implicate the thalamus, specifically the contralateral hemisphere, as the primary generator of tremor in ET, with a significant contribution of the ipsilateral hemisphere as well. Although there is no direct correlation between the cortico-tremor connectivity and tremor power or reduced tremor by DBS, the strength of connectivity from the motor cortex to the thalamus and vice versa at tremor frequency predicts baseline tremor power and effect of DBS. Interestingly, there is no correlation between these two connectivity pathways themselves, suggesting that, independent of the subcortical pathway, the motor cortex appears to play a relatively distinct role, possibly mediated through an afferent/feedback loop in the propagation of tremor. DBS has a greater clinical effect in those with stronger cortico-thalamo-tremor connectivity involving the contralateral thalamus, which is also associated with bigger and more stable tremor measured with an accelerometer. Interestingly, stronger cross-hemisphere coupling between left and right thalami is associated with more unstable tremor.
Together this study provides important insights into a better understanding of the cortico-thalamic tremor generating network and its implication for the development of patient-specific therapeutic approaches for ET.
How is health equity considered in policy evaluations employing quasi-experimental methods? A scoping review and content analysis
Authors: Sell Kerstin , Rabbani Setareh , Burns Jacob 
Published: November 2024

Abstract
Public health researchers employ quasi-experimental methods (QEM) to evaluate the effects of policies. Whilst some policies are designed to improve (health) equity, others may intentionally or unintentionally have detrimental effects on disadvantaged populations. We thus sought to investigate how health equity is addressed in policy evaluations which employ QEM. We conducted a content analysis on studies sourced from a scoping review. We drew a random sample of 350 records identified in systematic database searches in Medline, EMBASE, and EconLit (December 2022). Studies that employed QEM labels and examined public policies implemented in the WHO European region were included. We extracted data on study design, policies, and populations; assessed whether outcomes were examined in population sub-groups (as defined by PROGRESS-Plus criteria); and analysed discussion sections for equity-related conclusions. We included 59 studies, of which 39 (66.1%) studies considered health equity—albeit to variable depth. Twenty-five studies were focused exclusively on examining policy outcomes in a disadvantaged population (42.4%), of which 19 studies evaluated policies that targeted disadvantaged groups (e.g. minimum wage, social housing policies). Outcomes were stratified for one or more sub-populations in 22 studies (37.3%), most commonly for gender (n = 15, 25.4%) and a measure of socio-economic status (n = 13, 22%), particularly income and employment. Equity-related results and implications were discussed in 24 studies. While policy evaluations employing QEM have considerable value for informing decision-making in public health and other sectors that influence health, their potential to investigate equity impacts is currently not harnessed.
Chromosome-scale Reference Genome and RAD-based Genetic Map of Yellow Starthistle (Centaurea solstitialis) Reveal Putative Structural Variation and QTL Associated With Invader Traits
Authors: Reatini Bryan , Pelosi Jessie A , Cang F Alice , Jiang Qiuyu , McKibben Michael T W , Barker Michael S , Rieseberg Loren H , Dlugosch Katrina M 
Published: November 2024

Abstract
Invasive species offer outstanding opportunities to identify the genomic sources of variation that contribute to rapid adaptation, as well as the genetic mechanisms facilitating invasions. The Eurasian plant yellow starthistle (Centaurea solstitialis) is highly invasive in North and South American grasslands and known to have evolved increased growth and reproduction during invasion. Here, we develop new genomic resources for C. solstitialis and map the genetic basis of invasiveness traits. We present a chromosome-scale (1N = 8) reference genome using PacBio CLR and Dovetail Omni-C technologies, and functional gene annotation using RNAseq. We find repeat structure typical of the family Asteraceae, with over 25% of gene content derived from ancestral whole-genome duplications (paleologs). Using an F2 mapping population derived from a cross between native and invading parents, with a restriction site-associated DNA (RAD)-based genetic map, we validate the assembly and identify 13 quantitative trait loci underpinning size traits that have evolved during invasion. We find evidence that large effects of quantitative trait loci may be associated with structural variants between native and invading genotypes, including a variant with an overdominant and pleiotropic effect on key invader traits. We also find evidence of significant paleolog enrichment under two quantitative trait loci. Our results add to growing evidence of the importance of structural variants in evolution, and to understanding of the rapid evolution of invaders.
Geometrically nonlinear analysis of composite piezoelectric plates using meshfree RPIM with a new layerwise approach
Authors: Nourmohammadi Hossein , Behjat Bashir 
Published: November 2024

Summary
In this article, nonlinear analysis of piezoelectric plates using Ferreira layerwise theory based on radial point interpolation method (RPIM) meshfree method is presented. This paper develops Ferreira layerwise method for geometrically nonlinear analysis of composite plates considering piezoelectric effects. Based on this layerwise method, the first-order shear deformation theory is used for layers. Nonlinear Von Karman-type strains are considered to predict the geometrically nonlinear behavior of the piezoelectric plates. By a combination of Ferreira layerwise method and meshfree RPIM and using electrical enthalpy, the matrix form of nonlinear equations is obtained. The Newton–Raphson iteration method is used to obtain the solution of governing nonlinear equations. Using this form of formulation and combining it with the meshfree method increase the linear displacements and stresses accuracy. Consequently, the accuracy of the nonlinear results will increase. Linear and nonlinear solutions are compared with some numerical and exact solutions in the literature. The presented results show that there is a good agreement between them and the exact solution. The results presented in this paper show that Ferreira layerwise model has a good ability to predict the nonlinear behavior of thick or thin piezoelectric composite plates.
Does Liquidity Management Induce Fragility in Treasury Prices? Evidence from Bond Mutual Funds
Authors: Huang Shiyang , Jiang Wenxi , Liu Xiaoxi , Liu Xin 
Published: November 2024

Abstract
Mutual funds investing in illiquid corporate bonds actively manage Treasury positions to buffer redemption shocks. This liquidity management practice can transmit non-fundamental fund flow shocks onto Treasuries, generating excess return volatility. Consistent with this hypothesis, we find that Treasury excess return volatility is positively associated with bond fund ownership, and this pattern is more pronounced among funds conducting intensive liquidity management. Causal evidence is provided by exploiting the U.S. Securities and Exchange Commission’s 2017 Liquidity Risk Management Rule. Evidence also suggests that the COVID-19 Treasury market turmoil was attributed to intensified liquidity management, an unintended consequence of the 2017 Liquidity Risk Management Rule.
Human T cells engineered with an HLA-A2-restricted murine T-cell receptor targeting glypican 3 effectively control human hepatocellular carcinoma in mice
Authors: Vercher Enric , Covo-Vergara Ángela , Conde Enrique , Hernández-Rueda Mercedes , Elizalde Edurne , Mancheño Uxua , Glez-Vaz Javier , Tamayo-Uria Ibon , García-García Maritza R. , Ferrer-Roig Marta , Marañón-Lopez Javier , Repáraz David , Ruiz Marta , López-Díaz de Cerio Ascensión , Inogés Susana , Iñarrairaegui Mercedes , Lasarte Juan J. , Sangro Bruno , Sarobe Pablo , Hervas-Stubbs Sandra 
Published: November 2024

Background and Aims:
Glypican-3 (GPC3) is a promising target for T-cell therapy in HCC. While chimeric antigen receptor (CAR) T cells targeting GPC3 have demonstrated therapeutic efficacy, their effectiveness is limited by challenges such as low persistence and shedding of surface GPC3. Natural T-cell receptors (TCRs) may serve as an alternative, though identifying GPC3-specific TCRs within the endogenous repertoire is difficult.

Approach and Results:
We immunized human leucocyte antigen-A2 (HLA-A2) transgenic mice with an adenovirus expressing human GPC3, identifying a panel of TCRs that recognize the GPC3(522-530) epitope. We cloned 3 murine GPC3-TCRs (TCR-A, TCR-B, and TCR-C) and engineered primary human T cells (TCR-T). TCR-T cells effectively recognized GPC3+HLA-A2+ human HCC cells, with recognition diminished by GPC3 silencing and HLA-A2 blockade. TCR-B-T and TCR-C-T cells showed the highest reactivity, with TCR-B-T cells exhibiting superior effector functions, proliferative capacity, and therapeutic efficacy in xenograft HCC models. Notable, TCR-B-T cells outperformed second-generation 41BB GPC3-specific CAR-T cells, attributed to lower exhaustion, enhanced proliferation, greater effector function, and improved resilience. Furthermore, mixed dosing of CAR-T and TCR-B-T cells was significantly more effective than staggered dosing of the same cell type, suggesting potential synergistic effects.

Conclusions:
Transgenic TCRs join forces with CARs, expanding the arsenal of GPC3-targeting receptors for HCC T-cell therapy.