bluuebunny/crossref_metadata_2025_split huggingface dataset:
A systematic review and Bayesian meta-analysis provide evidence for an effect of acute physical activity on cognition in young adults
Authors: Garrett Jordan , Chak Carly , Bullock Tom , Giesbrecht Barry 
Published: August 2024

AbstractPhysical exercise is a potential intervention for enhancing cognitive function across the lifespan. However, while studies employing long-term exercise interventions consistently show positive effects on cognition, studies using single acute bouts have produced mixed results. Here, a systematic review and meta-analysis was conducted to determine the impact of acute exercise on cognitive task performance in healthy young adults. A Bayesian hierarchical model quantified probabilistic evidence for a modulatory relationship by synthesizing 651 effect sizes from 113 studies from PsychInfo and Google Scholar representing 4,390 participants. Publication bias was mitigated using the trim-and-fill method. Acute exercise was found to have a small beneficial effect on cognition (g = 0.13 ± 0.04; BF = 3.67) and decrease reaction time. A meta-analysis restricted to executive function tasks revealed improvements in working memory and inhibition. Meta-analytic estimates were consistent across multiple priors and likelihood functions. Physical activities were categorized based on exercise type (e.g., cycling) because many activities have aerobic and anaerobic components, but this approach may limit comparison to studies that categorize activities based on metabolic demands. The current study provides an updated synthesis of the existing literature and insights into the robustness of acute exercise-induced effects on cognition. Funding provided by the United States Army Research Office.
Design of a Phase 3, Global, Multicenter, Randomized, Placebo-Controlled, Double-Blind Study of Nipocalimab in Pregnancies at Risk for Severe Hemolytic Disease of the Fetus and Newborn
Authors: Komatsu Yosuke , Verweij E.J.T. Joanne , Tiblad Eleonor , Lopriore Enrico , Oepkes Dick , Agarwal Prasheen , Lam Edwin , Leu Jocelyn H. , Ling Leona E. , Nelson Robert M. , Olusajo Victor , Saeed-Khawaja Shumyla , Tjoa May Lee , Zhou Jie , Amin Umair , Sirah Waheeda , Moise Kenneth J. 
Published: August 2024

Objective Nipocalimab is a neonatal fragment crystallizable (Fc) receptor (FcRn)—blocking monoclonal antibody that inhibits placental immunoglobulin G (IgG) transfer and lowers circulating maternal IgG levels. In an open-label, single-arm, phase 2 study, nipocalimab demonstrated evidence of safety and efficacy that support further investigation in a pivotal phase 3 trial of recurrent hemolytic disease of the fetus and newborn (HDFN). The phase 3 AZALEA study aims to evaluate the efficacy and safety of nipocalimab in a larger population at risk for severe HDFN, defined as HDFN associated with poor fetal outcomes or neonatal death.
Study Design AZALEA is a multicenter, randomized, placebo-controlled, double-blind, phase 3 study enrolling alloimmunized pregnant individuals (N ≈ 120) at risk for severe HDFN based on obstetric history. Participants are randomized 2:1 to receive intravenous 45 mg/kg nipocalimab or placebo weekly from 13–16 to 35 weeks gestational age (GA). During the double-blind treatment period, participants receive standard-of-care weekly monitoring for fetal anemia until planned delivery at 37 to 38 weeks of GA. Postnatal follow-up periods are 24 weeks for maternal participants and 104 weeks for neonates/infants.
Results The primary endpoint is the proportion of pregnancies that do not result in intrauterine transfusion (IUT), hydrops fetalis, or fetal loss/neonatal death from all causes. Key secondary endpoints include the severity of HDFN as measured by a composite HDFN severity index, the earliest time to occurrence of IUT or hydrops fetalis, the modified neonatal mortality and morbidity index in liveborn neonates, and the number of IUTs received. Other endpoints are safety, patient- and caregiver-reported outcomes, pharmacokinetics, pharmacodynamics (e.g., IgG, FcRn receptor occupancy), and immunogenicity of nipocalimab.
Conclusion AZALEA, the first placebo-controlled, randomized, multicenter, prospective trial in severe HDFN, is designed to evaluate the safety and efficacy of nipocalimab, a potential preventive and noninvasive intervention, in at-risk HDFN pregnancies.
Key Points
Recent Progress in (3+3) Cycloadditions of Bicyclobutanes to Access Bicyclo[3.1.1]heptane Derivatives
Authors: Feng Jian-Jun 
Published: August 2024

AbstractThe synthesis of bicyclo[3.1.1]heptane (BCHeps) derivatives, which serve as three-dimensional (3D) bioisosteres of benzenes and are the core skeleton of several terpene natural products, is garnering growing interest. The (3+3) cycloadditions of bicyclobutanes (BCBs) represent an attractive method for efficiently accessing (hetero)BCHep skeletons with 100% atom economy. Herein, we give a brief summary of recent achievements in this approach for the synthesis of diverse BCHep derivatives, emphasizing our recent progress in the initial palladium-catalyzed (3+3) cycloadditions of bicyclobutanes with vinyl oxiranes.1	Introduction2	Radical (3+3) Cycloaddition Reaction3	Polar (3+3) Cycloaddition Reaction4	Palladium-Catalyzed Enantioselective (3+3) Cycloaddition Reaction5	Conclusion
Evolution of Robotics in Endoscopy—Past and Future
Authors: Haas A.J. , Feria Alejandro , Noubani Lema , McCauley Kainoa , Kara Ali M. , El-Hayek Kevin M. 
Published: August 2024

AbstractThe field of flexible endoscopy has evolved rapidly since the introduction of the semiflexible gastroscope. Now, robotic flexible endoscopy is beginning to gain a foothold and mirror the path that laparoscopy has taken. This review describes the development of platforms that have entered the field over the past two decades—from clever enhancements to existing endoscopes to fully robotic platforms with high-tech teleoperational consoles. These advancements improve upon visualization, articulation, and locomotion to reproduce surgery on a miniature scale—both endoluminally and intracorporeally. The field of robotic flexible endoscopy is highly variegated with a multitude of platforms in development, promising a bright future where minimally invasive procedures become safely accessible to patients worldwide.
The wetting of H2O by CO2
Authors: Brookes Samuel G. H. , Kapil Venkat , Schran Christoph , Michaelides Angelos 
Published: August 2024

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO2–H2O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO2–H2O interface, particularly concerning the interfacial tension and phase behavior of CO2 at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO2–H2O interface. Interfacial tensions are predicted from 1 to 500 bars and found to be in close agreement with experiment at pressures for which experimental data are available. Structural analyses indicate the buildup of an adsorbed, saturated CO2 film forming at a low pressure (20 bars) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. The CO2 monolayer buildup coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science.
fg-ORKA: fast and gridless reconstruction of moving and deforming objects in multidimensional data
Authors: Bossmann Florian , Ma Jianwei , Wu Wenze 
Published: August 2024

Abstract
Identifying and tracking objects over multiple observations is a frequent task in many applications. Traffic monitoring requires the tracking of vehicles or pedestrians in video data and geophysical exploration relies on identifying seismic wave fronts from data of multiple sensors, only to mention two examples. In many cases, the object changes its shape or position within the given data from one observation to another. Vehicles can change their position and angle relative to the camera while seismic waves have different arrival times, frequencies, or intensities depending on the sensor position. This complicates the task at hand. In a previous work, the authors presented a new algorithm to solve this problem—object reconstruction using K-approximation (ORKA). This algorithm is hindered by two conflicting limitations: the tracked movement is limited by the sampling grid while the complexity increases exponentially with the resolution. We introduce an iterative variant of the ORKA algorithm that is able to overcome this conflict. We also give a brief introduction on the original ORKA algorithm. Knowledge of the previous work is thus not required. We give theoretical error bounds and a complexity analysis which we validate with several numerical experiments. Moreover, we discuss the influence of different parameter choices in detail. The results clearly show that the iterative approach can outperform ORKA in both accuracy and efficiency. On the example of video processing we show that the new method can be applied where the original algorithm is too time and memory intensive. Furthermore, we demonstrate on seismic exploration data that we are now able to recover much finer details on the wave front movement then before.
The visible magneto-optical response of RE1-xAxMnO3 manganites: relationship with the charge component of the material
Authors: Samoshkina Yulia , Petrov Dmitriy , Telegin Andrei , Neznakhin Dmitry , Kaul Andrey 
Published: August 2024

Abstract
Magnetic circular dichroism (MCD) spectroscopy for manganite films of various compositions and morphologies has been studied in the range of 1.2–3.7 eV. The primary focus was on the temperature behavior of the MCD spectra, as well as the magnetization and resistivity of the films. The data obtained were analyzed in comparison with magneto-optical spectroscopy of the Kerr rotation (KR) on both single crystal and thin film of manganites. It has been established that the MCD response at 2.3 eV is typical for manganites transitioning into a conducting state. Consequently, it reflects a change in the band structure of the material. This response is also observed in the KR spectrum of manganites in the range 2.3–2.6 eV below the metal-insulator transition temperature. These findings complement the understanding of the electronic structure of manganites in general. Moreover, they also provide a basis for the search for new functional materials.
Extraction of complex pipeline features from incomplete point clouds
Authors: Fu Duduo , Xia Renbo , Zhao Hao , Wang Fangyuan , Zhao Liang , Zhang Tianyu , Chen Yueling 
Published: August 2024

Abstract
Control point detection in industrial pipelines, characterized by flanges and multiple passes, is critical for accurate virtual simulations and quality assessments in manufacturing. This paper introduces an innovative method for detecting control points in complex pipelines using incomplete point clouds, significantly streamlining the process. Our approach uniquely requires only the straight sections and end-plane localizations as inputs, markedly reducing both data acquisition and processing times. We develop a robust feature descriptor to align the CAD model with incomplete point clouds, facilitating semantic automatic segmentation despite the lack of explicit semantic information. Following this, geometric primitives are fitted to the segmented clouds, and a cylindrical fitting algorithm tailored for incomplete data is introduced. The control points are computed based on the relative positions and geometric parameters of these primitives. Our method has been validated through experiments on several real-world industrial complex pipelines. The results confirm that our approach achieves a high measurement accuracy of 0.067 mm, even with point cloud incompleteness up to 50%. These findings highlight the effectiveness of our method in accurately determining the geometric parameters of complex pipelines and suggest its considerable potential for practical applications.
Graph semi-supervised soft sensor modeling based on label propagation algorithm
Authors: Gao Shiwei , Li Tianzhen , Dong Xiaohui 
Published: August 2024

Abstract
Data-driven soft sensor modeling methods have become prevalent in the industry. Nonetheless, the complexity of industrial processes often leads to the absence or difficulty in obtaining key labeled data, and existing methods frequently fail to fully utilize the inherent correlations between variables. This paper proposes a novel graph semi-supervised soft sensor modeling method using the label propagation algorithm to address these issues. This method utilizes correlations within the data to assign pseudo-labels to unlabeled data reasonably and employs graph convolutional networks to capture spatial relationships between nodes. Additionally, by embedding a long short-term memory structure, the model can capture temporal dependencies of the data while focusing on spatial structures. Furthermore, the introduction of a residual structure enables the model to directly learn the differences between inputs and outputs, facilitating information transmission, and improving the model’s feature extraction ability. Experiments demonstrate the effectiveness of the method.
Indoor altitude estimation assisted by inertial compensation and online floor modeling
Authors: Xia Ming , Wang Jiale , Shi Chuang , Wen Weisong 
Published: August 2024

Abstract
Indoor pedestrian altitude information plays a key role in such applications as emergency relief and military reconnaissance. However, height errors of inertial positioning grow without boundary because of the altitude channel divergence of the Strap-down Inertial Navigation System. This article proposes a new vertical location method based on the foot-mounted Inertial Measurement Unit and online floor modeling. First, the number of stairs at each step is calculated after the height of each step is corrected by the error compensation model. Second, the prior height is estimated by utilizing the number of stairs and the stair height. Then, the key point library related to the floor is built online. Finally, the error correction of the vertical displacement is carried out by matching the prior height with the key point library. The gait classification shows that the accuracy based on the error compensation model can reach up to 99%. Moreover, the maximum altitude error is less than 1 m and the accumulated vertical positioning errors are eliminated completely when a pedestrian walks up and down inside a multi-story building, and all these have verified accuracy and robustness of vertical indoor positioning.
Complex-plane singularity dynamics for blow up in a nonlinear heat equation: analysis and computation
Authors: Fasondini M , King J R , Weideman J A C 
Published: August 2024

Abstract
Blow-up solutions to a heat equation with spatial periodicity and a quadratic nonlinearity are studied through asymptotic analyses and a variety of numerical methods. The focus is on tracking the dynamics of the singularities in the complexified space domain all the way from the initial time until the blow-up time, which occurs when the singularities reach the real axis. This widely applicable approach gives forewarning of the possibility of blow up and an understanding of the influence of singularities on the solution behaviour on the real axis, aiding the (perhaps surprisingly involved) asymptotic analysis of the real-line behaviour. The analysis provides a distinction between small and large nonlinear effects, as well as insight into the various time scales over which blow up is approached. The solution to the nonlinear heat equation in the complex spatial plane is shown to be related asymptotically to a nonlinear ordinary differential equation. This latter equation is studied in detail, including its computation on multiple Riemann sheets, providing further insight into the singularities of blow-up solutions of the nonlinear heat equation when viewed as multivalued functions in the complex space domain and illustrating the potential intricacy of singularity dynamics in such (non-integrable) nonlinear contexts.
Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression
Authors: Sonbas Cobb Buket , Kolb Stephen J , Rutkove Seward B 
Published: August 2024

Abstract

Objective. To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression. Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression. Main Results. Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment approaches available at the time. LDA scores were also able to track progression effectively, although a multifrequency reactance-based measure also performed very well in this context. Significance. EIM enhanced with ML promises to be effective for providing effective diagnosis and tracking children and adults with SMA treated with currently available therapies. The normative trends identified here may also inform future applications of the technology in very young children. The basic analyses applied here could also likely be applied to other neuromuscular disorders characterized by muscle atrophy.
Ultrafast artificial intelligence: machine learning with atomic-scale quantum systems
Authors: Pfeifer Thomas , Wollenhaupt Matthias , Lein Manfred 
Published: August 2024

Abstract
We train a model atom to recognize pixel-drawn digits based on hand-written numbers in the range 0–9, employing intense light–matter interaction as a computational resource. For training, the images of the digits are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schrödinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states (classification states) that correspond to the input digits. A success rate of about 40% is achieved when using a basic optimization scheme that might be limited by the computational resources for finding the optimal program pulse in a high-dimensional search space. Our key result is the demonstration that the laser-programmed atom is able to generalize, i.e. successful classification is not limited to the training examples, but also the classification of previously unseen images is improved by training. This atom-sized machine-learning image-recognition scheme operates on time scales down to tens of femtoseconds, is scalable towards larger (e.g. molecular) systems, and is readily reprogrammable towards other learning/classification tasks. An experimental implementation of the scheme using ultrafast polarization pulse shaping and differential photoelectron detection is within reach.
Constraining the general oscillatory inflaton potential with freeze-in
dark matter and gravitational waves
Authors: Cembranos Jose A.R. , Karčiauskas Mindaugas 
Published: August 2024

Abstract
The reheating phase after inflation is one of the least observationally constrained epochs
in the evolution of the Universe. The forthcoming gravitational wave observatories will enable us
to constrain at least some of the non-standard scenarios. For example, models where the radiation
bath is produced by the perturbative inflaton decay that oscillates around a minimum of the
potential of the form V ∝ ϕ
2n, with n > 2. In such scenarios a part of the
inflationary gravitational wave spectrum becomes blue tilted, making it observable, depending on
the inflation energy scale and the reheating temperature. The degeneracy between the latter two
parameters can be broken if dark matter in the Universe is produced via the freeze-in
mechanism. The combination of the independent measurement of dark matter mass with gravitational
wave observations makes it possible to constrain the reheating temperature and the energy scale at
the end of inflation, at least within some parameter ranges.
A self-organised partition of the high dimensional plasma parameter space for plasma disruption prediction
Authors: Aymerich Enrico , Fanni Alessandra , Pisano Fabio , Sias Giuliana , Cannas Barbara ,   JET Contributors,   WPTE Team
Published: August 2024

Abstract
This paper introduces a disruption predictor constructed through a fully unsupervised two-dimensional mapping of the high-dimensional JET operational space. The primary strength of this disruption predictor lies in its inherent self-organization capability. Diverging from both supervised disruption predictors and earlier approaches suggested by the same authors, which were based on unsupervised models such as Self-Organizing or Generative Topographic Maps, this predictor eliminates the need for labeling data of disruption terminated pulses during training. In prior methods, labels were indeed required post-mapping to inform the model about the presence or absence of disruption precursors at each time instant during the disrupted discharges. In contrast, our approach in this study involves no labeling of data from disruption-terminated experiments. The Self-Organizing Map, operating without any a priori information, adeptly identifies the regions characterizing the pre-disruptive phase. Moreover, SOM discovers non-trivial relationships and captures the complicated interplay of device diagnostics on the internal plasma states from the experimental data. The provided model is highly interpretable; it allows the visualization of high-dimensional data and facilitates easy interrogation of the model to understand the reasons behind its correlations. Hence, utilizing SOMs across various devices can prove invaluable in extracting rules and identifying common patterns, thereby facilitating extrapolation to ITER of the knowledge acquired from existing tokamaks.
Circular Economy: Sustainable Agricultural Tourism Development Solution in Phu Giao District, Binh Duong Province
Authors: Trang Ngo Thi Thu 
Published: August 2024

Abstract
Agriculture applying circular solutions is an appropriate approach for sustainable agricultural development and delivering high-quality agricultural products to consumers. Additionally, coordinating tourism activities brings benefits in promoting agricultural products and establishing closer connections with consumers. Phu Giao is a district located in the northeastern part of Binh Duong province, known for its favorable land, climate, and water resources for agricultural development. Currently, most ecological agriculture production facilities in Phu Giao district are privately-owned farms with extensive land areas. Identifying potentials, opportunities, and further developing circular agriculture models in conjunction with tourism is a necessary prerequisite for researching sustainable local economic development solutions. Through on-site surveys, in-depth interviews, and questionnaire surveys in the study area, the authors propose solutions for develop circular agriculture tourism model.