bluuebunny/crossref_metadata_2025 huggingface dataset:
Spiritual Humanism: Self, Community, Earth, and Heaven
Authors: Tu Weiming
Published: April 2024

This paper summarizes the author’s view and research on the concept of ‘Spiritual Humanism’ as a cross-cultural, historical heritage and theoretical framework for contemporary research in philosophy. It builds on comprehensive scholarship conducted over the last decades within a plurality of leading academic communities. It reflects the author’s commitment to include Chinese Philosophy and intellectual history within the international scholarly canon.
‘A New Prague Spring, but from Below?’ Socialist Dissent in the Last Soviet Generation and the Emergence of Solidarność in Poland, 1980–1981
Authors: Wilson Natasha
Published: April 2024

This article examines the Young Socialists, a left-wing dissident circle of intellectuals from the last Soviet generation, and focuses on their contacts with Solidarność during 1980–81. These dissidents, located in Moscow and Minsk, interpreted the Polish strikes as the possible beginnings of a wider move to socialist reform in the Eastern Bloc. Using oral history and samizdat materials from the Russian and Polish archives and the former archives of Radio Free Europe, the article demonstrates how the Young Socialists’ interactions with Poland developed in the wider context of the transnational history of dissent in the Eastern Bloc at the turn of the 1980s. It argues that a combination of internationalist values and bloc-wide dissident solidarities caused socialist dissidents to view nationalist movements on the Soviet periphery and Eastern Europe as potential drivers of socialist reform on the eve of Perestroika.
Proops’s ‘Nugget of Gold’ in Kant’s Dialectic
Authors: Hogan Desmond P.
Published: April 2024

The Fiery Test of Critique describes Kant’s indirect proof of idealism from the Antinomy of Pure Reason as the ‘nugget of gold’ in the Critique of Pure Reason’s Transcendental Dialectic. Here, I offer critical reflections on Proops’s reading of Kant’s indirect proof.
Influence of Lowering Soot‐Water Contact Angle on Ice Nucleation of Ozone‐Aged Soot
Authors: Gao Kunfeng , Kanji Zamin A.
Published: April 2024

Organic‐lean and organic‐rich size‐selected soot particles were exposed to a varying O3 concentration, progressively decreasing the soot‐water contact angle (θ) to study its impact on ice nucleation (IN). The IN ability of fresh and O3‐aged soot between 218 and 233 K was observed while monitoring the particle mass and size distributions. The properties of fresh and O3‐aged bulk organic‐lean soot samples with a low and high O3‐adsorption were characterized for soot‐water θ, chemical composition, functional groups, soot‐water interaction ability and porosity. By retaining the soot porosity between aged and unaged samples, we demonstrate that a decrease in θ after O3‐aging enhances organic‐lean soot IN via pore condensation and freezing. Fresh organic‐rich soot exhibits suppressed homogeneous freezing, but after O3‐aging it freezes within uncertainty of the homogeneous freezing threshold of solution drops, because of increased hydrophilicity.
Refined Short‐Term Forecasting Atmospheric Temperature Profiles in the Stratosphere Based on Operators Learning of Neural Networks
Authors: Chen Biao , Sheng Zheng , Cui Fei
Published: April 2024

The efficacious forecasting of single‐station atmospheric temperature profiles can provide essential support for the structural design and flight missions of spacecrafts in near space. However, empirical models and reference atmospheric models most are calculations of the average state of the atmosphere profiles. Numerical assimilation models require expensive computational costs to improve the accuracy for medium and long‐term forecasting. It has been still a challenge to refined predict short‐term temperature profiles of near space at a low‐cost. We present a temperature profile operator method for refined modeling in the stratosphere by fusing the ability of Long Short‐Term Memory (LSTM) networks or its variants‐ bidirectional LSTM (BiLSTM) to exploit time series correlated information and deep operator networks (DeepONets) to approximate the solution operator of temperature profiles. It consists of three subnetworks. The first subnetwork is used to approximate the discrete temperature profile function, the second net is applied to represent the spatial information of pressure heights, and the third branch is utilized to encode the time domain of the temperature profile operator. We first use the hourly low latitude temperature data (20–50 km) from ERA5 for training, verification and iterative testing in the next 48 hr. The results denote that the temperature profile operator network has a stable and low error of cumulative generalization, and the BiLSTM operator significantly outperform the other models. We also apply two scenarios to testing the refined applicability of the high latitude temperature profile operator and the mid latitude wind profile operator in the stratosphere. This work provides a novel perspective for us to study the refined single‐station modeling of the upper and middle atmosphere.
Circulating tumor cells shielded with extracellular vesicle-derived CD45 evade T cell attack to enable metastasis
Authors: Yang Chuan , Wang Xueping , To Kenneth K. W. , Cui Caimei , Luo Min , Wu Shaocong , Huang Lamei , Fu Kai , Pan Can , Liu Zeyu , Fan Teng , Yang Caibo , Wang Fang , Fu Liwu
Published: April 2024

Circulating tumor cells (CTCs) are precursors of distant metastasis in a subset of cancer patients. A better understanding of CTCs heterogeneity and how these CTCs survive during hematogenous dissemination could lay the foundation for therapeutic prevention of cancer metastasis. It remains elusive how CTCs evade immune surveillance and elimination by immune cells. In this study, we unequivocally identified a subpopulation of CTCs shielded with extracellular vesicle (EVs)-derived CD45 (termed as CD45+ CTCs) that resisted T cell attack. A higher percentage of CD45+ CTCs was found to be closely correlated with higher incidence of metastasis and worse prognosis in cancer patients. Moreover, CD45+ tumor cells orchestrated an immunosuppressive milieu and CD45+ CTCs exhibited remarkably stronger metastatic potential than CD45− CTCs in vivo. Mechanistically, CD45 expressing on tumor surfaces was shown to form intercellular CD45-CD45 homophilic interactions with CD45 on T cells, thereby preventing CD45 exclusion from TCR-pMHC synapse and leading to diminished TCR signaling transduction and suppressed immune response. Together, these results pointed to an underappreciated capability of EVs-derived CD45-dressed CTCs in immune evasion and metastasis, providing a rationale for targeting EVs-derived CD45 internalization by CTCs to prevent cancer metastasis.
A database of groundwater wells in the United States
Authors: Lin Chung-Yi , Miller Alexandra , Waqar Musab , Marston Landon T.
Published: April 2024

Groundwater wells are critical infrastructure that enable the monitoring, extraction, and use of groundwater, which has important implications for the environment, water security, and economic development. Despite the importance of wells, a unified database collecting and standardizing information on the characteristics and locations of these wells across the United States has been lacking. To bridge this gap, we have created a comprehensive database of groundwater well records collected from state and federal agencies, which we call the United States Groundwater Well Database (USGWD). Presented in both tabular form and as vector points, USGWD comprises over 14.2 million well records with attributes, such as well purpose, location, depth, and capacity, for wells constructed as far back as 1763 to 2023. Rigorous cross-verification steps have been applied to ensure the accuracy of the data. The USGWD stands as a valuable tool for improving our understanding of how groundwater is accessed and managed across various regions and sectors within the United States.
Design and development of PI controller for DFIG grid integration using neural tuning method ensembled with dense plexus terminals
Authors: Hete R. R. , Shrivastava Tarun , Dash Ritesh , Anupallavi L. , Fathima Misba , Reddy K. Jyotheeswara , Dhanamjayalu C. , Mohammad Faruq , Khan Baseem
Published: April 2024

In a DFIG grid interconnected system, the control of real and reactive power relies on various factors. This paper presents an approach to regulate the flow of real and reactive power using a Neural Tuning Machine (NTM) based on a recurrent neural network. The focus is on controlling the flow of reactive power from the rotor-side converter, which is proportional to the grid-side controller through a coupling voltage. The proposed NTM method leverages neural networks to fine-tune the parameters of the PI controller, optimizing performance for DFIG grid integration. By integrating dense plexus terminals, also known as dense connections, within the neural network, the control system achieves enhanced adaptability, robustness, and nonlinear dynamics, addressing the challenges of the grid. Grid control actions are based on the voltage profile at different bus locations, thereby regulating voltage. This article meticulously examines the analysis in terms of DFIG configuration and highlights the advantages of the neural tuning machine in controlling inner current loop parameters compared to conventional PI controllers. To demonstrate the robustness of the control algorithm, a MATLAB Simulink model is designed, and validation is conducted with three different benchmarking models. All calculations and results presented in the article strictly adhere to IEEE and IEC standards. This research contributes to advancing control methodologies for DFIG grid integration and lays the groundwork for further exploration of neural tuning methods in power system control.
Exploring local and global stability of COVID-19 through numerical schemes
Authors: Xiao Nan , Xu Hongyan , Morani Amjid Hussain , Shokri Ali , Mukalazi Herbert
Published: April 2024

Respiratory sensitivity and pneumonia are possible outcomes of the coronavirus (COVID-19). Surface characteristics like temperature and sunshine affect how long the virus survives. This research article analyzes COVID-19 mathematical model behavior based on symptomatic and non-symptomatic individuals. In the reproductive model, the best result indicates the intensity of the epidemic. Our model remained stable at a certain point under controlled conditions after we evaluated a specific element. This approach is in place of traditional approaches such as Euler’s and Runge–Kutta’s. An unusual numerical approach known as the non-standard finite difference (NSFD) scheme is used in this article. This numerical approach gives us positivity. A dependable numerical analysis allowed us to evaluate different approaches and verify our theoretical results. Unlike the widely used Euler and RK4 approaches, we investigated the benefits of implementing NSFD schemes. By numerically simulating COVID-19 in a variety of scenarios, we demonstrated how our theoretical concepts work. The simulation findings support the usefulness of both approaches.
A novel simulation-based analysis of a stochastic HIV model with the time delay using high order spectral collocation technique
Authors: Khan Sami Ullah , Ullah Saif , Li Shuo , Mostafa Almetwally M. , Bilal Riaz Muhammad , AlQahtani Nouf F. , Teklu Shewafera Wondimagegnhu
Published: April 2024

The economic impact of Human Immunodeficiency Virus (HIV) goes beyond individual levels and it has a significant influence on communities and nations worldwide. Studying the transmission patterns in HIV dynamics is crucial for understanding the tracking behavior and informing policymakers about the possible control of this viral infection. Various approaches have been adopted to explore how the virus interacts with the immune system. Models involving differential equations with delays have become prevalent across various scientific and technical domains over the past few decades. In this study, we present a novel mathematical model comprising a system of delay differential equations to describe the dynamics of intramural HIV infection. The model characterizes three distinct cell sub-populations and the HIV virus. By incorporating time delay between the viral entry into target cells and the subsequent production of new virions, our model provides a comprehensive understanding of the infection process. Our study focuses on investigating the stability of two crucial equilibrium states the infection-free and endemic equilibriums. To analyze the infection-free equilibrium, we utilize the LaSalle invariance principle. Further, we prove that if reproduction is less than unity, the disease free equilibrium is locally and globally asymptotically stable. To ensure numerical accuracy and preservation of essential properties from the continuous mathematical model, we use a spectral scheme having a higher-order accuracy. This scheme effectively captures the underlying dynamics and enables efficient numerical simulations.
Neuropsychological task outcomes among survivors of childhood acute lymphoblastic leukemia in Malaysia
Authors: Alias Hamidah , Mohd Ranai Norashikin , Lau Sie Chong Doris , de Sonneville Leo M. J.
Published: April 2024

This study intended to explore the neuropsychological ramifications in childhood acute lymphoblastic leukemia (ALL) survivors in Malaysia and to examine treatment-related sequelae. A case-control study was conducted over a 2-year period. Seventy-one survivors of childhood ALL who had completed treatment for a minimum of 1 year and were in remission, and 71 healthy volunteers were enlisted. To assess alertness (processing speed) and essential executive functioning skills such as working memory capacity, inhibition, cognitive flexibility, and sustained attention, seven measures from the Amsterdam Neuropsychological Tasks (ANT) program were chosen. Main outcome measures were speed, stability and accuracy of responses. Mean age at diagnosis was 4.50 years (SD ± 2.40) while mean age at study entry was 12.18 years (SD ± 3.14). Survivors of childhood ALL underperformed on 6 out of 7 ANT tasks, indicating poorer sustained attention, working memory capacity, executive visuomotor control, and cognitive flexibility. Duration of treatment, age at diagnosis, gender, and cumulative doses of chemotherapy were not found to correlate with any of the neuropsychological outcome measures. Childhood ALL survivors in our center demonstrated significantly poorer neuropsychological status compared to healthy controls.
AI and augmented reality for 3D Indian dance pose reconstruction cultural revival
Authors: Jayanthi J. , Maheswari P. Uma
Published: April 2024

This paper delves into the specialized domain of human action recognition, focusing on the Identification of Indian classical dance poses, specifically Bharatanatyam. Within the dance context, a “Karana” embodies a synchronized and harmonious movement encompassing body, hands, and feet, as defined by the Natyashastra. The essence of Karana lies in the amalgamation of nritta hasta (hand movements), sthaana (body postures), and chaari (leg movements). Although numerous, Natyashastra codifies 108 karanas, showcased in the intricate stone carvings adorning the Nataraj temples of Chidambaram, where Lord Shiva’s association with these movements is depicted. Automating pose identification in Bharatanatyam poses challenges due to the vast array of variations, encompassing hand and body postures, mudras (hand gestures), facial expressions, and head gestures. To simplify this intricate task, this research employs image processing and automation techniques. The proposed methodology comprises four stages: acquisition and pre-processing of images involving skeletonization and Data Augmentation techniques, feature extraction from images, classification of dance poses using a deep learning network-based convolution neural network model (InceptionResNetV2), and visualization of 3D models through mesh creation from point clouds. The use of advanced technologies, such as the MediaPipe library for body key point detection and deep learning networks, streamlines the identification process. Data augmentation, a pivotal step, expands small datasets, enhancing the model’s accuracy. The convolution neural network model showcased its effectiveness in accurately recognizing intricate dance movements, paving the way for streamlined analysis and interpretation. This innovative approach not only simplifies the identification of Bharatanatyam poses but also sets a precedent for enhancing accessibility and efficiency for practitioners and researchers in the Indian classical dance.
Reassessing human MHC-I genetic diversity in T cell studies
Authors: Slieker Roderick C. , Warmerdam Daniël O. , Vermeer Maarten H. , van Doorn Remco , Heemskerk Mirjam H. M. , Scheeren Ferenc A.
Published: April 2024

The Major Histocompatibility Complex class I (MHC-I) system plays a vital role in immune responses by presenting antigens to T cells. Allele specific technologies, including recombinant MHC-I technologies, have been extensively used in T cell analyses for COVID-19 patients and are currently used in the development of immunotherapies for cancer. However, the immense diversity of MHC-I alleles presents challenges. The genetic diversity serves as the foundation of personalized medicine, yet it also poses a potential risk of exacerbating healthcare disparities based on MHC-I alleles. To assess potential biases, we analysed (pre)clinical publications focusing on COVID-19 studies and T cell receptor (TCR)-based clinical trials. Our findings reveal an underrepresentation of MHC-I alleles associated with Asian, Australian, and African descent. Ensuring diverse representation is vital for advancing personalized medicine and global healthcare equity, transcending genetic diversity. Addressing this disparity is essential to unlock the full potential of T cells for enhancing diagnosis and treatment across all individuals.
Genome sequencing identifies “Limestone Canyon virus” as Montaño virus (Hantaviridae: Orthohantavirus montanoense) circulating in brush deermice in New Mexico
Authors: Goodfellow Samuel M. , Nofchissey Robert A. , Morley Valerie J. , Coan Kathryn E. , Schwalm Kurt C. , Cook Joseph A. , Dunnum Jonathan L. , Hanfelt-Goade Diane , Dinwiddie Darrell L. , Domman Daryl B. , Dragoo Jerry W. , Kuhn Jens H. , Bradfute Steven B.
Published: April 2024

Orthohantaviruses infect distinct eulipotyphlan and rodent reservoirs throughout the world; some rodent orthohantaviruses can cause disease in humans. In the United States, a primary rodent reservoir for the human-pathogenic Sin Nombre virus (SNV) is the western deermouse (Peromyscus sonoriensis; formerly included in Peromyscus maniculatus). Deermice (rodents of genus Peromyscus) carry presumably distinct orthohantaviruses but, although deermice of ten species have been recorded in New Mexico, only SNV has been reported in rodents from that state. Using a set of pan-orthohantavirus primers, we discovered a non-SNV orthohantavirus in a brush deermouse (P. boylii), trapped in central New Mexico in 2019. Sequencing enabled the generation of a consensus coding-complete genome sequence, revealing similarity to the known partial sequences of the unclassified “Limestone Canyon virus (LSCV)” in GenBank and aligning with the information in an unpublished study of wild-caught brush deermice trapped in southwestern New Mexico in 2006. Phylogenetic analysis of these combined data revealed geospatial clades and overall identity of “LSCV”, uncovering its association with the classified Montaño virus (MTNV), which is known to infect Aztec and Orizaba deermice in central Mexico. Our work emphasizes the importance of determining coding-complete viral genome sequences as a framework for rigorous virus classification as the basis for epidemiological studies.
Critical Casimir forces in soft matter
Authors: Gambassi A. , Dietrich S.
Published: April 2024

We review recent advances in the theoretical, numerical, and experimental studies of critical Casimir forces in soft matter, with particular emphasis on their relevance for the structures of colloidal suspensions and on their dynamics.
Hydrogen bonding enhanced drug–polymer interaction for efficient drug loading and delivery
Authors: Qu Xiaotian , Li Junran , Yu Yishu , Yang Jie
Published: April 2024

A carrier design strategy of hydrogen bonding enhanced drug–carrier interaction is developed to prepare a polymeric nanomedicine with high drug loading content and superb loading efficiency as well as tunable nanostructures.
Self-healing polyacrylates based on dynamic disulfide and quadruple hydrogen bonds
Authors: Du Longjin , Zhong Yuting , Zhao Linying , Hu Chengzhen , Shen Liang , Yang Yuping , Zhong Jiang
Published: April 2024

Herein, a self-healing polyacrylate system was successfully prepared by introducing crosslinking agents containing disulfide bonds and monomers capable of forming quadruple hydrogen bonds through free radical copolymerization.
Verb Vocabulary Supports Event Probability Use in Developmental Language Disorder
Authors: Kueser Justin B. , Borovsky Arielle , Deevy Patricia , Muezzinoglu Mine , Outzen Claney , Leonard Laurence B.
Published: April 2024

Purpose:

Children with developmental language disorder (DLD) tend to interpret noncanonical sentences like passives using event probability (EP) information regardless of structure (e.g., by interpreting “The dog was chased by the squirrel” as “The dog chased the squirrel”). Verbs are a major source of EP information in adults and children with typical development (TD), who know that “chase” implies an unequal relationship among participants. Individuals with DLD have poor verb knowledge and verb-based sentence processing. Yet, they also appear to rely more on EP information than their peers. This paradox raises two questions: (a) How do children with DLD use verb-based EP information alongside other information in online passive sentence interpretation? (b) How does verb vocabulary knowledge support EP information use?

Method:

We created novel EP biases by showing animations of agents with consistent action tendencies (e.g., clumsy vs. helpful actions). We then used eye tracking to examine how this EP information was used during online passive sentence processing. Participants were 4- to 5-year-old children with DLD (
n
= 20) and same-age peers with TD (
n
= 20).

Results:

In Experiment 1, children with DLD quickly integrated verb-based EP information with morphosyntax close to the verb but failed to do so with distant morphosyntax. In Experiment 2, the quality of children's sentence-specific verb vocabulary knowledge was positively associated with the use of EP information in both groups.

Conclusion:

Depending on the morphosyntactic context, children with DLD and TD used EP information differently, but verb vocabulary knowledge aided its use.

Supplemental Material:

https://doi.org/10.23641/asha.25491805
One size fits all: Insights into extrinsic thermal absorption based on the similarity of supernova remnant radio-continuum spectra
Authors: Abadi Mario G. , Castelletti Gabriela , Supan Leonardo , Kassim Namir E. , Lazio Joseph W.
Published: April 2024

Typically, integrated radio frequency continuum spectra of supernova remnants (SNRs) exhibit a power-law form due to their synchrotron emission. In numerous cases, these spectra show an exponential turnover, which has long been assumed to be due to thermal free-free absorption in the interstellar medium. We used a compilation of Galactic radio continuum SNR spectra, with and without turnovers, to constrain the distribution of the absorbing ionised gas. We introduce a novel parameterisation of SNR spectra in terms of a characteristic frequency, ν* which depends both on the absorption turnover frequency and the power-law slope. Normalising to v* and to the corresponding flux density, S* we demonstrate that the stacked spectra of our sample reveal a similarity in behavior with low scatter (root mean square, rms, of ~15%), and a unique exponential drop-off that is fully consistent with the predictions of a free-free absorption process. Observed SNRs, whether exhibiting spectral turnovers or not, appear to be spatially well-mixed in the Galaxy without any evident segregation between them. Moreover, their Galactic distribution does not show a correlation with general properties such as heliocentric distance or Galactic longitude, as might have been expected if the absorption were due to a continuous distribution of ionised gas. However, it naturally arises if the absorbers are discretely distributed, as suggested by early low-frequency observations. Modelling based on H II regions tracking Galactic spiral arms successfully reproduces the patchy absorption observed to date. While more extensive statistical datasets should yield more precise spatial models of the absorbing gas distribution, our present conclusion regarding its inhomogeneity will remain robust.
Wavelet-based statistics for enhanced 21cm EoR parameter constraints
Authors: Hothi Ian , Allys Erwan , Semelin Benoît , Boulanger François
Published: April 2024

We propose a new approach to improve the precision of astrophysical parameter constraints for the 21cm signal from the epoch of reionisation (EoR). Our method introduces new sets of summary statistics, hereafter ‘evolution compressed’ statistics, which quantify the spectral evolution of the 2D spatial statistics computed at fixed redshift. We defined such compressed statistics for power spectrum (PS), wavelet scattering transforms (WST), and wavelet moments (WM), which also characterise non-Gaussian features. To compare these different statistics, along with the 3D power spectrum, we estimated their Fisher information on three cosmological parameters from an ensemble of simulations of 21cm EoR data, both in noiseless and noisy scenarios using Square Kilometre Array (SKA) noise levels equivalent to 100 and 1000 h of observations. We also compare wavelet statistics, in particular WST, built from standard directional Morlet wavelets, as well as from a set of isotropic wavelets derived from the binning window function of the 2D power spectrum. For the noiseless case, the compressed wavelet statistics give constraints that are up to five times more precise than those obtained from the 3D isotropic power spectrum. At the same time, for 100 h SKA noise, from which it is difficult to extract non-Gaussian features, compressed wavelet statistics still give over 30% tighter constraints. We find that the wavelet statistics with wavelets derived from the power-spectrum binning window function provide the tightest constraints of all the statistics, with the WSTs seemingly performing better than the WMs, in particular when working with noisy data. The findings of this study demonstrate that evolution-compressed statistics extract more information than usual 3D isotropic power-spectra approaches and that our wavelet-based statistics can consistently outmatch power-spectrum-based statistics. When constructing such wavelet-based statistics, we also emphasise the need to choose a set of wavelets with an appropriate spectral resolution concerning the astrophysical process studied.
Machine Learning-Based Predictive Models for Patients with Venous Thromboembolism: A Systematic Review
Authors: Danilatou Vasiliki , Dimopoulos Dimitrios , Kostoulas Theodoros , Douketis James
Published: April 2024

Background Venous thromboembolism (VTE) is a chronic disorder with a significant health and economic burden. Several VTE-specific clinical prediction models (CPMs) have been used to assist physicians in decision-making but have several limitations. This systematic review explores if machine learning (ML) can enhance CPMs by analyzing extensive patient data derived from electronic health records. We aimed to explore ML-CPMs' applications in VTE for risk stratification, outcome prediction, diagnosis, and treatment.

Methods Three databases were searched: PubMed, Google Scholar, and IEEE electronic library. Inclusion criteria focused on studies using structured data, excluding non-English publications, studies on non-humans, and certain data types such as natural language processing and image processing. Studies involving pregnant women, cancer patients, and children were also excluded. After excluding irrelevant studies, a total of 77 studies were included.

Results Most studies report that ML-CPMs outperformed traditional CPMs in terms of receiver operating area under the curve in the four clinical domains that were explored. However, the majority of the studies were retrospective, monocentric, and lacked detailed model architecture description and external validation, which are essential for quality audit. This review identified research gaps and highlighted challenges related to standardized reporting, reproducibility, and model comparison.

Conclusion ML-CPMs show promise in improving risk assessment and individualized treatment recommendations in VTE. Apparently, there is an urgent need for standardized reporting and methodology for ML models, external validation, prospective and real-world data studies, as well as interventional studies to evaluate the impact of artificial intelligence in VTE.
A virtual thermometer for ultrahigh-temperature–pressure experiments in a large-volume press
Authors: Feng Bingtao , Xie Longjian , Hou Xuyuan , Liu Shucheng , Chen Luyao , Zhao Xinyu , Li Chenyi , Zhou Qiang , Hu Kuo , Liu Zhaodong , Liu Bingbing
Published: April 2024

Ultrahigh-temperature–pressure experiments are crucial for understanding the physical and chemical properties of matter. The recent development of boron-doped diamond (BDD) heaters has made such melting experiments possible in large-volume presses. However, estimates of temperatures above 2600 K and of the temperature distributions inside BDD heaters are not well constrained, owing to the lack of a suitable thermometer. Here, we establish a three-dimensional finite element model as a virtual thermometer to estimate the temperature and temperature field above 2600 K. The advantage of this virtual thermometer over those proposed in previous studies is that it considers both alternating and direct current heating modes, the actual sizes of cell assemblies after compression, the effects of the electrode, thermocouple and anvil, and the heat dissipation by the pressure-transmitting medium. The virtual thermometer reproduces the power–temperature relationships of ultrahigh-temperature–pressure experiments below 2600 K at press loads of 2.8–7.9 MN (∼19 to 28 GPa) within experimental uncertainties. The temperatures above 2600 K predicted by our virtual thermometer are within the uncertainty of those extrapolated from power–temperature relationships below 2600 K. Furthermore, our model shows that the temperature distribution inside a BDD heater (19–26 K/mm along the radial direction and <83 K/mm along the longitudinal direction) is more homogeneous than those inside conventional heaters such as graphite or LaCrO3 heaters (100–200 K/mm). Our study thus provides a reliable virtual thermometer for ultrahigh-temperature experiments using BDD heaters in Earth and material sciences.
Improving the pre-medical emergency team: the case for a behavioural theoretical lens
Authors: Currey Judy , Sprogis Stephanie K. , Jones Daryl , Considine AO Julie
Published: April 2024

There is mounting evidence that the pre-medical emergency team (pre-MET) of rapid response systems is underutilised in clinical practice due to suboptimal structures and processes and resource constraints. In this perspective article, we argue for examining the pre-MET through a ‘Behaviour Change Wheel’ lens to improve the pre-MET and maximise the associated patient safety benefits. Using pre-MET communication practices as an example, we illustrate the value of the COM-B model, where clinicians’ ‘capability’, ‘opportunity’, and ‘motivation’ drive ‘behaviour’. Optimising clinicians’ behaviours and establishing failsafe rapid response systems is a complex undertaking; however, examining clinicians’ behaviours through the COM-B model enables reframing barriers and facilitators to develop multifaceted and coordinated solutions that are behaviourally and theoretically based. The COM-B model is recommended to clinical governance leaders and health services researchers to explore the underlying causes of behaviour and successfully enact change in the design, implementation, and use of the pre-MET to improve patient safety.
Surface-enhanced Raman spectroscopy (SERS) substrate based on gold nanostars–silver nanostars for imidacloprid detection
Authors: Abu Bakar Norhayati , Shapter Joseph George
Published: April 2024

Surface-enhanced Raman spectroscopy (SERS) is a powerful molecular spectroscopy technique that combines Raman spectroscopy with nanostructured metallic surfaces to amplify the Raman signals of target molecules by more than 103. The high sensitivity of SERS poses a significant opportunity for pesticide detection in complex matrices at ultralow concentrations. In this study, we improved the SERS sensitivity for imidacloprid (IMD) by employing silver nanostars (AgNs) coated with gold nanostars (AuNs) as the SERS-active substrate. The SERS response towards IMD detection increased based on the combination of AuNs and AgNs on the substrate surface. The intensity of the SERS signal of IMD using the AuNs/AgNs substrate increased compared to using individual metal nanoparticle substrates. The excellent reproducibility of SERS intensity using the AuNs/AgNs substrate was achieved with a low relative standard derivative (RSD) of 4.87% for 20 different spots on the same sample and 5.19% for 20 different samples. This detection system can be used for multiple tests, which is crucial for the advancement of handheld sensors designed for field use, where minimal or no high-level technical support is accessible.
Ruled by construal?
Authors: Romain Laurence , Hanzlíková Dagmar , Milin Petar , Divjak Dagmar
Published: April 2024

In cognitive linguistics, grammatical structure is known to be representative of meaning. This is also true of
English articles. In this paper, we argue that the choice of article, when the grammar allows it, is dependent on the wider
discourse context and most importantly on how the speaker construes this context. Using survey data from 181 native speakers of
English, we show that the choice of article depends on the activation of semantic frames and how speakers may choose to highlight
different elements of a frame to construe the situation differently. We rely on Entropy to measure the restrictiveness of a
context and to identify particular contexts in which choice is allowed or inhibited. We find that some contextual features such as
the specificity of the referent are more restrictive while Hearer Knowledge is more open to construal.
Conformal diagrams for stationary and dynamical strong-field hyperboloidal slices
Authors: Vañó-Viñuales Alex
Published: April 2024

Conformal Carter–Penrose diagrams are used for the visualization of hyperboloidal slices, which are smooth spacelike slices reaching null infinity. The focus is on the Schwarzschild black hole geometry in spherical symmetry, whose Penrose diagrams are introduced in a pedagogical way. The stationary regime involves time-independent slices. In this case, different options are given for integrating the height function—the main ingredient for constructing hyperboloidal foliations. The dynamical regime considers slices changing in time, which are evolved together with the spacetime using the eikonal equation. It includes the relaxation of hyperboloidal Schwarzschild trumpet slices and the collapse of a massless scalar field into a black hole, for which Penrose diagrams are presented.
Inverse design of electromagnetic metamaterials: from iterative to deep learning-based methods
Authors: Ma Chen , Wang Zhenyu , Zhang Hui , Yang Fengyuan , Chen Jianlin , Ren Qinghua , Ma Yiming , Wang Nan
Published: April 2024

In recent years, considerable research advancements have emerged in the application of inverse design methods to enhance the performance of electromagnetic (EM) metamaterials. Notably, the integration of deep learning (DL) technologies, with their robust capabilities in data analysis, categorization, and interpretation, has demonstrated revolutionary potential in optimization algorithms for improved efficiency. In this review, current inverse design methods for EM metamaterials are presented, including topology optimization (TO), evolutionary algorithms (EAs), and DL-based methods. Their application scopes, advantages and limitations, as well as the latest research developments are respectively discussed. The classical iterative inverse design methods categorized TO and EAs are discussed separately, for their fundamental role in solving inverse design problems. Also, attention is given on categories of DL-based inverse design methods, i.e. classifying into DL-assisted, direct DL, and physics-informed neural network methods. A variety of neural network architectures together accompanied by relevant application examples are highlighted, as well as the practical utility of these overviewed methods. Finally, this review provides perspectives on potential future research directions of EM metamaterials inverse design and integrated artificial intelligence methodologies.
Particle physics using reactor antineutrinos
Authors: Akindele O A , Bowden N S , Roca C , Xu J , Zhang X , Berryman J M , Carr R , Conant A J , Fernandez-Moroni G , Huber P , Link J M , Langford T J , Littlejohn B R , Ochoa-Ricoux J P , Strigari L , Schoppmann S , Zhang C
Published: April 2024

Nuclear reactors are uniquely powerful, abundant, and flavor-pure sources of antineutrinos that have played a central role in the discovery of the neutrinos and in elucidation of their properties. This continues through a broad range of experiments investigating topics including Standard Model and short-baseline oscillations, beyond-the-Standard-Model physics searches, and reactor flux and spectrum modelling. This Report will survey the state of the reactor antineutrino physics field and summarize the ways in which current and future reactor antineutrino experiments can play a critical role in advancing the field of particle physics in the next decade.
Advanced digital twin-enabled fault diagnosis framework for unmanned vehicle systems
Authors: Li Junfeng , Wang Jianyu
Published: April 2024

The control system of unmanned vehicles must demonstrate strong capability to promptly diagnose and address system faults. Such a capability can improve transportation efficiency, ensure the smooth execution of production tasks, and to a certain extent, mitigate the risk of human casualties. To ensure the upkeep of unmanned vehicles and address the diagnostic requirements of control systems, this study integrates traditional wheeled vehicle control systems with digital twin (DT) technology to establish a framework for control system fault diagnosis and maintenance, with the primary objective of fulfilling the fault diagnosis task. By this framework, a method for detecting faults in unmanned vehicle control systems based on DT technology has been developed. This method involves the design of a data-driven model using multiple sensors and the application of a DT-improved particle filter fault diagnosis algorithm, utilizing a multi-domain model approach. A case study of the proposed method and simulation results are presented to illustrate its feasibility.
Selection of noise models for GNSS coordinate time series based on model averaging algorithm
Authors: Huan Yueyang , Chang Guobin , Huang Yangjin , Feng Yong , Zhu Yuhua , Yang Shuoqi
Published: April 2024

In the field of global navigation satellite system (GNSS) time series noise analysis, appropriately modeling the noise components plays an important role in determining the velocity of GNSS sites and quantifying the uncertainty associated with the velocity estimation. Over the years, researchers have focused on only one optimal noise model, while other noise models that show similar performance to the optimal model have been ignored. We investigated whether these ignored noise models can be made use of to describe the noise in the GNSS time series after applying a model averaging algorithm. The experimental data were derived from 28 International GNSS Service (IGS) sites in the California region of the United States and 110 IGS sites worldwide. The results showed that for the GNSS time series of 28 IGS sites in the California, 79%, 68%, and 75% of the site components can be applied the model averaging algorithm in the east/north/up (E/N/U) directions, respectively. Based on it, the east direction showed the best performance, with 50% of the site components obtaining more conservative velocity uncertainty after applying the model averaging algorithm compared to the optimal noise model. For GNSS time series of 110 IGS stations worldwide, the model averaging algorithm demonstrates excellent performance in all the E/N/U directions. In the E/N/U directions, 86%, 94%, and 57% of the site components can apply the model averaging algorithm. Building upon this, 77%, 65%, and 62% of the site components achieve more conservative velocity uncertainty in the E/N/U directions compared to the optimal noise model. To fully validate the feasibility of the model averaging algorithm, we also tested GNSS time series of varying lengths and different thresholds of the model averaging algorithm. In summary, the model averaging algorithm performs exceptionally well in the noise analysis of GNSS time series. It helps prevent overly optimistic estimation results.
An existence theory for superposition operators of mixed order subject to jumping nonlinearities
Authors: Dipierro Serena , Perera Kanishka , Sportelli Caterina , Valdinoci Enrico
Published: April 2024

We consider a superposition operator of the form

∫

[
0
,
1
]

(
−
Δ
)

s

u

d
μ

(
s
)

,

for a signed measure µ on the interval of fractional exponents

[
0
,
1
]

, joined to a nonlinearity whose term of homogeneity equal to one is ‘jumping’, i.e. it may present different coefficients in front of the negative and positive parts. The signed measure is supposed to possess a positive contribution coming from the higher exponents that overcomes its negative contribution (if any). The problem taken into account is also of ‘critical’ type, though in this case the critical exponent needs to be carefully selected in terms of the signed measure µ. Not only the operator and the nonlinearity considered here are very general, but our results are new even in special cases of interest and include known results as particular subcases. The possibility of considering operators ‘with the wrong sign’ is also a complete novelty in this setting.
RF antenna helicity dependent particle heating in a helicon source
Authors: Stevenson K J , Gilbert T J , Good T N , Paul M , Shi P , Nirwan R , Srivastav P , Steinberger T E , Scime E E
Published: April 2024

Experiments have demonstrated that ion phenomena, such as the lower hybrid resonance, play an important role in helicon source operation. Damping of the slow branch of the bounded whistler wave at the edge of a helicon source (i.e. the Trivelpiece-Gould mode) has been correlated with the creation of energetic electrons, heating of ions at the plasma edge, and anisotropic ion heating. Here we present ion velocity distribution function measurements, electron density and temperature measurements, and magnetic fluctuation measurements on both sides of an

m
=

|

1
|

helical antenna in a helicon source as a function of the driving frequency, magnetic field strength, and magnetic field orientation relative to the antenna helicity. Significant electron and ion heating (up to two times larger) occurs on the side of the antenna consistent with the launch of the

m
=
+
1

mode. The electron and ion heating occurs within one electron skin depth of the plasma edge, where slow wave damping is expected. The source parameters for enhanced particle heating are also consistent with lower hybrid resonance effects, which can only occur for Trivelpiece-Gould wave excitation.
Enhancing external quantum efficiency of deep ultraviolet micro-leds through geometry design and multi-physics field coupling analysis
Authors: Gao Yujie , Hu Xun , Zhu Lingli , Gao Na , Zhou Rui , Wu Yaping , Huang Kai , Li Shuping , Kang Junyong , Zhang Rong
Published: April 2024

High-efficiency deep-ultraviolet (DUV) micro light-emitting diodes (LEDs) are explored for inspiring development in numerous fields, such as non-line-of-sight solar-blind communication, optical pumping, and maskless lithography. In this study, we performed FDTD and SimuLED calculations to investigate the optimized DUV micro-LED structure geometry for high light extraction efficiency (LEE) by designing different mesa structures, including square, hexagonal, and circular geometries of micro-LEDs emitted at a wavelength of 275 nm. The results showed that a circular mesa of 5 μm diameter achieved a LEE of 27% from the bottom and sidewall emissions of as-prepared DUV micro-LED. And both the near- and far-field transverse magnetic polarized light intensities were enhanced by a factor of 1.5 over the square and hexagonal mesas. Meanwhile, the transverse electric (TE) polarized light of the circular mesa structure was enhanced and concentrated along the normal direction. Moreover, the internal quantum efficiency (IQE) of circular mesas with varied sizes was comprehensively investigated in the interactions of the thermal and electric fields. An AlGaN-based DUV micro-LED with a diameter of 5 μm was found to obtain the highest IQE owing to a high current-density distribution and its self-heating properties, thereby achieving a sufficiently high external quantum efficiency of 26.75%. This study provides a comprehensive technical report, including electrical, thermal, and optical analyses, and a new perspective for developing high-efficiency, high-performance DUV micro-LEDs in practical applications.
Low parasitic carrier reservoir of AlGaN-based DUV-LED via controlled-polarization step-graded superlattice electron blocking layer for high luminescence lighting
Authors: Hairol Aman Mohammad Amirul , Ahmad Noorden Ahmad Fakhrurrazi , Daud Suzairi , Abdul Kadir Muhammad Zamzuri
Published: April 2024

Achieving high luminescence intensity of deep-ultraviolet light-emitting diode (DUV-LED) is generally performed through the implementation of electron blocking layer (EBL) on the chip’s epilayers. However, the issue of parasitic carrier reservoir that originated from the uncontrolled piezoelectric field polarization has restricted the performance of DUV-LED by reducing the radiative recombination in the active region. This work reports on the numerical computation analysis of the DUV-LED with different types of EBL designs which are reference EBL, conventional superlattice EBL and step-graded superlattice EBL. The analysis of the DUV-LED focuses on the band diagram, carrier concentration at the EBL interfaces, current density of the carrier in the active region, radiative recombination rates, and luminescence spectrum. Remarkably, it is found that the DUV-LED step-graded superlattice EBL provides the polarization-controlled band diagram and emits 272 nm UVC-wavelength in which it is superior in performance compared to the other structures, specifically in terms of its radiated intensity. The parasitic electron and hole reservoir have been reduced by 30% and 60%, respectively. The luminescence intensity was also enhanced by 11% compared with the reference EBL and the IQE obtained by the DUV-LED with step-graded superlattice EBL is 50.12%.