bluuebunny/crossref_metadata_2025_split huggingface dataset:
Novel dual spectral conversion via Eu2+, Cu+-coactivated core–shell-structured CaS@CaZnOS phosphors for efficient photosynthesis of plants
Authors: Yan Liu , Peng Mei , Song Zirong , He Yue , Fan Yulin , Wang Yuya , Xiong Rumeng , Lian Shixun , Ma Xinxian , Qiu Zhongxian 
Published: February 2024

Core–shell-structured CaS:Eu2+,Cu+@CaZnOS:Cu+ phosphor with excellent dual-excitation and dual-emission performance can efficiently convert solar energy and enhance the photosynthesis efficiency of crops in facility agriculture.
Survey of Wideband Acoustic Immittance Use by Clinical Audiologists
Authors: Al-Salim Sarah , Skretta Delaney , Merchant Gabrielle Ryan 
Published: February 2024

Purpose:
Wideband acoustic immittance (WAI) is a promising measure of middle-ear mechanics. In contrast to standard tympanometry, which is generally measured at a single stiffness-dominated low frequency, WAI detects mechanical effects on both the mass and stiffness properties of the middle ear across a wide range of frequencies, resulting in a more comprehensive assessment of middle-ear mechanics in healthy and pathological ears. Despite a plethora of research demonstrating the clinical utility of this measure, clinical adoption of WAI is still limited. This work explores audiologists' use and perceptions of WAI, with the goal of identifying the barriers to its clinical adoption.

Method:
A survey on the perception and use of WAI by clinical audiologists in the United States was developed and administered using the Research Electronic Data Capture application. The survey was distributed broadly across the United States. Participation was voluntary and anonymous, and no compensation was provided.

Results:
Findings from 132 survey respondents across 32 states were included in the analyses. Overall, findings suggest the largest barriers to clinical adoption of WAI are lack of access to equipment that measures WAI and lack of training and/or confidence in measuring or interpreting WAI.

Conclusions:
Several barriers to clinical adoption of WAI were identified. However, findings also provide optimism in that audiologists utilizing WAI find it more useful than standard tympanometry, and most audiologists who do not currently use WAI are open to implementing the measure in their clinical practice. We proposed steps to address the highest priority issues and increase the clinical viability of WAI.
Efficacy of a topical formulation containing eprinomectin, esafoxolaner and praziquantel (NexGard® Combo) in the treatment of natural respiratory capillariosis of cats
Authors: Di Cesare Angela , Morganti Giulia , Vignoli Massimo , Colombo Mariasole , Veronesi Fabrizia , Bufalari Antonello , Tielemans Eric , Beugnet Frederic , Traversa Donato 
Published: February 2024

Feline pulmonary capillariosis is a significant disorder due to its distribution and clinical impact. This study evaluated the safety and efficacy of two administrations 28 days apart of a topical solution containing esafoxolaner, eprinomectin and praziquantel (NexGard® Combo) in treating Eucoleus aerophilus (syn. Capillaria aerophila) infection in naturally infected cats. Cats were allocated to two groups: G1 cats (n = 23) received two treatments at study days (SDs) 0 and 28 (±2) and were evaluated for 6 weeks, and G2 cats (n = 17) served as a negative control for 6 weeks and were then treated twice on SDs 42 (±2) and 70 (±2), allowing for an additional 6-week assessment of efficacy. Each cat was subjected to McMaster coproscopy at SDs −7/0, 28 (±2) and 42 (±2) for both groups, 70 (±2) and 84 (±2) only for G2. Clinical examination and chest radiographic images were performed at SDs 0, 28 (±2) and 42 (±2) for G1 and G2, 70 (±2) and 84 (±2) only for G2. The comparison of EPG (eggs per gram of feces), clinical (CS), and radiographic scores (RS) at each time-point was used as a criterion. The efficacy based on the EPG reduction was 99.5% (G1) and 100% (G2) after two administrations of NexGard® Combo 2 weeks apart. At SD 0, no significant differences for CS and RS were recorded between G1 and G2, while a significant reduction (p < 0.05) was observed post-treatment for CS, RS, oculo-nasal discharge, auscultation noises, and cough. Two doses of NexGard® Combo 28 days apart stopped egg shedding and significantly improved clinical alterations in cats infected by E. aerophilus.
Diplomatic relationship-building in the age of generative AI: the European Union and China
Authors: Xia Lucie Qian 
Published: February 2024

AbstractThis paper examines the impact of generative AI on international diplomacy through the lens of EU–China diplomatic relationship-building. The first section introduces the broader context of AI’s geopolitical impact by distinguishing two different models—the European and Chinese models of regulating and implementing generative AI. The second discuss or explain how the two AI models contrast one another. The third section of the paper focuses on discussing Generative AI and its possible implications for EU–China relations, the extent to which their efforts are likely to strain or facilitate diplomatic relationship-building. The fourth section takes the analysis forward by examining how generative AI could be an unexpected enabling force for EU–China relationship-building. This paper purports that the distinctive EU and Chinese models of generative AI too often belie the opportunities that could potentially enable the EU and China to build their relationship, since generative AI raises shared concerns for the EU and China, and utilising generative AI could make their communications become more efficient, the EU and China may come to reach some kind of shared framework for generative AI development and governance; this could lead to productive talks in other domains such as the trade deficit issue plaguing the EU–China relations or more sensitive cross-strait issues; moreover, in the realm of public diplomacy generative AI could facilitate the EU and China’s public diplomatic efforts towards each other.
Diet of fallow deer suggests potential for invasion of novel habitats in Tasmania
Authors: Guy Thomas R. , Kirkpatrick Jamie B. , Cunningham Calum X. , Berry Tina E. , Dawkins Kathryn L. , Driessen Michael M. , Johnson Chris N. 
Published: February 2024

Context Understanding the diet of invasive species can inform the potential for their distribution into novel habitats. Fallow deer are well established in the grassy woodlands of central Tasmania, Australia, in environments generally considered to be their optimum habitat. They are also increasing their range. The potential range of fallow deer in Tasmania will depend on their ability to vary their diet to exploit new habitats. Diet flexibility will also determine the ecological impacts that fallow deer might have in novel habitats. Aims We compared the diets of fallow deer in a lowland grassy woodland, where deer have been established for over 150 years, with diets of deer in highland woodlands and forest with less grass cover and higher rainfall, where deer have been established for a shorter time (<50 years). We expected that fallow deer in grassy woodlands would mainly eat grass and forbs, and we wanted to know to what extent the diet of deer differed between habitats. Methods A metagenomic analysis was performed on fallow deer faecal pellets collected at one lowland and three highland study areas. The method was chosen to maximise information on taxonomic composition of diet and identify plant species that might be affected by deer herbivory to the lowest possible taxonomic level. Key results Fallow deer ate a wide variety of plant taxa. Diets varied among study areas. In the lowland study area, deer predominantly ate forbs and grasses. In the highland study area deer were more likely to browse on eucalypts and a variety of shrubs. Conclusions Fallow deer in Tasmania have a broad dietary niche. Availability of specific plant taxa is unlikely to limit fallow deer expansion into most new habitats. Implications Without stronger management strategies, deer are likely to further increase their range in Tasmania, including into areas with high conservation values. The potential impacts on these areas may be high.
Drones as observers and students as data points: a large-scale demonstration of sound waves
Authors: Löfgren Sebastian Kilde , Virta Mathilda , Marmolejo Javier Tello , Moberg Annie Ringvall , Enger Jonas , Hanstorp Dag 
Published: February 2024

Abstract
Sound is central in everyday life, and the physics of sound is present in many parts of physics education. A common way to teach physics concepts is to conduct experiments or show a phenomenon through demonstrations. However, it is far from trivial to get students to experience the wave nature of sound firsthand. This paper presents three experiments where students become part of the demonstration, which allows them to experience the propagation of sound waves, the speed of sound, and wave interference. By having students listen to sound produced by a speaker and filming their reactions with drones from above, properties of sound and its wavelike nature are evident in the students’ collective behaviour. Their experience during the experiment is reinforced later by reviewing the material with their teachers in the classroom.
Probing exotic phases via stochastic gravitational wave spectra
Authors: Berger Joshua , Bhoonah Amit , Padhi Biswajit 
Published: February 2024

Abstract
Stochastic backgrounds of gravitational waves (GWs) from the pre-BBN era offer a unique opportunity to probe the universe beyond what has already been achieved with the Cosmic Microwave Background (CMB). If the source is short in duration, the low frequency tail of the resulting GW spectrum follows a universal frequency scaling dependent on the equation of state of the universe when modes enter the horizon. We demonstrate that the distortion of the equation of state due to massive particles becoming non-relativistic can lead to an observable dip in the GW spectrum. To illustrate this effect, we consider a first order chiral symmetry breaking phase transition in the weak-confined Standard Model (WCSM). The model features a large number of pions and mostly elementary fermions with masses just below the critical temperature for the phase transition.
These states lead to a 20% dip in the GW power. We find potential sensitivity to the distortions in the spectrum to future GW detectors such as LISA, DECIGO, BBO, and μAres.
Global Parameters of Eight W UMa-type Binary Systems
Authors: Poro Atila , Tanriver Mehmet , Michel Raul , Paki Ehsan 
Published: February 2024

Abstract
Multiband photometric investigations for eight binary systems of the W Ursae Majoris-type are presented. Six systems are presented for the first time to analyze their light curves. All the analyzed systems have a temperature below 5000 K and an orbital period of less than 0.28 days. We extracted primary and secondary minima from the ground-based observations of these systems. According to a few observations reported in the literature, linear fits were considered in the O − C diagrams, and new ephemerides were presented. Light curve solutions were performed using the PHysics Of Eclipsing BinariEs code. The results of the mass ratio and fillout factor indicate that the systems are contact binary stars. Six of them showed the O’Connell effect, and a cold starspot on each companion was required for light curve solutions. Their absolute parameters were estimated and evaluated by two other methods. In this study, the empirical relationship between the orbital period and semimajor axis was updated using a sample consisting of 414 contact binary systems and the Monte Carlo Markov Chain approach. Also, using Machine Learning and the Artificial Neural Network model, the relationship between P–T
1–M
1 was updated for a better estimation of the mass of the primary star.
An adaptive energy regulation in a memristive map linearized from a circuit with two memristive channels
Authors: Yang Feifei , Zhou Ping , Ma Jun 
Published: February 2024

Abstract
Nonlinear circuits can show multistability when a magnetic flux-dependent memristor (MFDM) or a charge-sensitive memristor (CSM) is incorporated into a one branch circuit, which helps estimate magnetic or electric field effects. In this paper, two different kinds of memristors are incorporated into two branch circuits composed of a capacitor and a nonlinear resistor, thus a memristive circuit with double memristive channels is designed. The circuit equations are presented, and the dynamics in this oscillator with two memristive terms are discussed. Then, the memristive oscillator is converted into a memristive map by applying linear transformation on the sampled time series for the memristive oscillator. The Hamilton energy function for the memristive oscillator is obtained by using the Helmholtz theorem, and it can be mapped from the field energy of the memristive circuit. An energy function for the dual memristive map is suggested by imposing suitable weights on the discrete energy function. The dynamical behaviors of the new memristive map are investigated, and an adaptive law is proposed to regulate the firing mode in the memristive map. This work will provide a theoretical basis and experimental guidance for oscillator-to-map transformation and discrete map energy calculation.
Systematic study of cluster radioactivity in trans-lead nuclei with various versions of proximity potential formalisms*
Authors: Liu 刘 Xiao 潇 , Jiang 蒋 Jie-Dong 杰栋 , Wu 吴 Xi-Jun 喜军 , Li 李 Xiao-Hua 小华 
Published: February 2024

Abstract
In this study, based on the framework of the Coulomb and proximity potential model (CPPM), we systematically investigate the cluster radioactivity half-lives of 26 trans-lead nuclei by considering the cluster preformation probability, which possesses a simple mass dependence on the emitted cluster according to R. Blendowske and H. Walliser [Phys. Rev. Lett. 61, 1930 (1988)]. Moreover, we investigate 28 different versions of the proximity potential formalisms, which are the most complete known proximity potential formalisms proposed to describe proton radioactivity, two-proton radioactivity, α decay, heavy-ion radioactivity, quasi-elastic scattering, fusion reactions, and other applications. The calculated results show that the modified forms of proximity potential 1977, denoted as Prox.77-12, and proximity potential 1981, denoted as Prox.81, are the most appropriate proximity potential formalisms for the study of cluster radioactivity, as the root-mean-square deviation between experimental data and relevant theoretical results obtained is the least; both values are 0.681. For comparison, the universal decay law (UDL) proposed by Qi et al. [Phys. Rev. C 80, 044326 (2009)], unified formula of half-lives for α decay and cluster radioactivity proposed by Ni et al. [Phys. Rev. C 78, 044310 (2008)], and scaling law (SL) in cluster radioactivity proposed by Horoi et al. [J. Phys. G 30, 945 (2004)] are also used. In addition, utilizing CPPM with Prox.77-12, Prox.77-1, Prox.77-2, and Prox.81, we predict the half-lives of 51 potential cluster radioactive candidates whose cluster radioactivity is energetically allowed or observed but not yet quantified in NUBASE2020. The predicted results are in the same order of magnitude as those obtained using the compared semi-empirical and/or empirical formulae. At the same time, the competition between α decay and cluster radioactivity of these predicted nuclei is discussed. By comparing the half-lives, this study reveals that α decay predominates.
Journey from human hands to robot hands: biological inspiration of anthropomorphic robotic manipulators
Authors: Han Michael Seokyoung , Harnett Cindy K 
Published: February 2024

Abstract
The development of robotic hands that can replicate the complex movements and dexterity of the human hand has been a longstanding challenge for scientists and engineers. A human hand is capable of not only delicate operation but also crushing with power. For performing tasks alongside and in place of humans, an anthropomorphic manipulator design is considered the most advanced implementation, because it is able to follow humans’ examples and use tools designed for people. In this article, we explore the journey from human hands to robot hands, tracing the historical advancements and current state-of-the-art in hand manipulator development. We begin by investigating the anatomy and function of the human hand, highlighting the bone-tendon-muscle structure, skin properties, and motion mechanisms. We then delve into the field of robotic hand development, focusing on highly anthropomorphic designs. Finally, we identify the requirements and directions for achieving the next level of robotic hand technology.
A bat biomimetic model for scenario recognition using echo Doppler information
Authors: Feng Wang , Chunyang Pang , Yuqing Lu , Hao Wang 
Published: February 2024

Abstract
The flying bat can detect the difference in Doppler frequency between its echolocation transmission signal and the echoes in its surroundings, enabling it to distinguish between various scenarios effectively. By examining the bio-sonar biomimetic model of a flying bat that uses echo Doppler information for environmental recognition, it may enhance the scene recognition capability of human ultrasound sonar during movement. The paper establishes a three-dimensional clutter model of the flying state of bat bio-sonar for bats emitting constant frequency signals. It proposes a scene recognition method that combines multi-scale time-frequency feature analysis with a convolutional neural network (CNN). The short-time Fourier transform of different scales extract the Doppler and range dimensions, which are then fused to create a multi-scale feature plane containing both Doppler and range information. Combined with CNN’s powerful image classification and recognition capabilities, extract features from multi-scale feature planes of different clutter scenes to achieve environment recognition based on the differences in Doppler and range dimensions of echoes in various directions. Through computer simulations, this study provides a numerical interpretation of the environmental classification and perception capabilities of bats in flight. The algorithm significantly improves scenario classification and recognition performance according to simulation results, with accuracy exceeding 98% in varied clutter scenarios at 30 dB signal noise ratio. Based on computer simulations, an experimental scene was constructed and actual echo signals were collected and analyzed. The experiments demonstrate that utilizing Doppler information enables the classification and recognition of cluttered environments. The effectiveness of the proposed algorithm was also verified. Ultrasonic sonar systems, such as navigation robots and helicopter obstacle avoidance, can apply this biomimetic model and algorithm for environmental recognition during motion.
Functional data learning using convolutional neural networks
Authors: Galarza J , Oraby T 
Published: February 2024

Abstract
In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of sine and cosine functions, and the magnitudes and widths of curve peaks. We also use it to classify the monotonicity and curvatures of FD, the algebraic versus exponential growth, and the number of peaks of FD. Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson’s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications.
Data-driven Lie point symmetry detection for continuous dynamical systems
Authors: Gabel Alex , Quax Rick , Gavves Efstratios 
Published: February 2024

Abstract
Symmetry detection, the task of discovering the underlying symmetries of a given dataset, has been gaining popularity in the machine learning community, particularly in science and engineering applications. Most previous works focus on detecting ‘canonical’ symmetries such as translation, scaling, and rotation, and cast the task as a modeling problem involving complex inductive biases and architecture design of neural networks. We challenge these assumptions and propose that instead of constructing biases, we can learn to detect symmetries from raw data without prior knowledge. The approach presented in this paper provides a flexible way to scale up the detection procedure to non-canonical symmetries, and has the potential to detect both known and unknown symmetries alike. Concretely, we focus on predicting the generators of Lie point symmetries of partial differential equations, more specifically, evolutionary equations for ease of data generation. Our results demonstrate that well-established neural network architectures are capable of recognizing symmetry generators, even in unseen dynamical systems. These findings have the potential to make non-canonical symmetries more accessible to applications, including model selection, sparse identification, and data interpretability.
ChIP-GPT: a managed large language model for robust data extraction from biomedical database records
Authors: Cinquin Olivier 
Published: February 2024

Abstract
Increasing volumes of biomedical data are amassing in databases. Large-scale analyses of these data have wide-ranging applications in biology and medicine. Such analyses require tools to characterize and process entries at scale. However, existing tools, mainly centered on extracting predefined fields, often fail to comprehensively process database entries or correct evident errors—a task humans can easily perform. These tools also lack the ability to reason like domain experts, hindering their robustness and analytical depth. Recent advances with large language models (LLMs) provide a fundamentally new way to query databases. But while a tool such as ChatGPT is adept at answering questions about manually input records, challenges arise when scaling up this process. First, interactions with the LLM need to be automated. Second, limitations on input length may require a record pruning or summarization pre-processing step. Third, to behave reliably as desired, the LLM needs either well-designed, short, ‘few-shot’ examples, or fine-tuning based on a larger set of well-curated examples. Here, we report ChIP-GPT, based on fine-tuning of the generative pre-trained transformer (GPT) model Llama and on a program prompting the model iteratively and handling its generation of answer text. This model is designed to extract metadata from the Sequence Read Archive, emphasizing the identification of chromatin immunoprecipitation (ChIP) targets and cell lines. When trained with 100 examples, ChIP-GPT demonstrates 90–94% accuracy. Notably, it can seamlessly extract data from records with typos or absent field labels. Our proposed method is easily adaptable to customized questions and different databases.