bluuebunny/crossref_metadata_2025 huggingface dataset:
A pentatomomorpha-specific salivary protein activates plant immunity and is critical for insect feeding
Authors: Hu Biao , Feng Xiuli , Xu Manru , Huang Yue , Guo Chunyun , Yuan Ruikun , Li Yiyuan , Wei Zhongyan , Chen Jianping , Sun Zongtao
Published: January 2025

The stinkbug
Riptortus pedestris
, notorious for inducing soybean staygreen-like syndrome, employs a range of salivary proteins to manipulate the host plant for its benefit. Here, we show that RpSP1, a salivary protein specific to Pentatomomorpha, triggers plant defense responses in multiple plant species. RpSP1 interacts with and stabilizes a HSP40 family protein GmSPIP1 and is dependent on GmSPIP1 to induce cell death. We show that a critical 22-amino acid peptide within RpSP1 acts as an intracellular insect-derived elicitor. Furthermore, RpSP1 enhances insect-feeding efficiency. The dual functionality of RpSP1 is highlighted by the significant reduction of soybean staygreen-like syndrome following its overexpression in soybean plants or knockdown in insects. Our findings elucidate the complex molecular interactions between plants and herbivores, positioning RpSP1 as a crucial target for developing advanced pest management strategies with broad implications for agricultural biology.
Keeping loneliness on a short leash: Reducing university student stress and loneliness through a canine-assisted intervention
Authors: Binfet John-Tyler , Green Freya L.L. , Godard Rebecca J.P. , Szypula Madisyn M. , Willcox Amelia A.
Published: January 2025

Despite often crowded campuses and the corresponding opportunities to meet new people, undergraduate students often experience high rates of loneliness as they adjust to life away from home and establish new social networks while adapting to the demands of university coursework. One low-cost intervention that has proven beneficial in reducing stress and overall well-being has seen therapy dog-handler teams brought to campus affording students an opportunity to interact with dogs. This study assessed the effects of spending time with dogs and their handlers on students’ self-reports of stress and loneliness using one-item visual analogue scales. Participants (
N
= 1006; 63% women, 40% first-year) interacted 32 minutes on average with the dog-handler teams and reported significant pre-to-post session reductions in stress and loneliness. Greater stress was associated with significantly greater loneliness at both pre- and post-interaction. Compared to domestic students, international students experienced slightly greater reductions in stress and moderately greater reductions in loneliness. Findings from our study suggest that creating opportunities for university students to interact with certified therapy dog-handler teams can be effective in reducing both stress and loneliness – the latter an understudied outcome variable in the field of canine-assisted interactions. The methodology of this study that saw participants determine the length of their visit combined with the findings attesting to therapy dogs reducing student loneliness can inform the broader contexts of human-animal interactions and the delivery of interventions in support of post-secondary student mental health.
“Can’t imagine having one without the other now”: Maternal perceptions of pets and attachment relationships in the perinatal period
Authors: Cyr Kathryn , Hawkins Roxanne
Published: January 2025

Maternal mental wellbeing and secure attachment relationships are critical for an infant’s socioemotional and psychological development, with research focusing on environmental influences on these elements. Simultaneously, studies are probing how pets provide a bidirectional bond for owners across the lifespan. This article broadens the intersection of these fields by exploring how mothers perceive their own and their infants’ perinatal relationship with their pet. Applying thematic analysis to online survey responses, three themes were developed: (1) Manifestation & perception of changing attachments, (2) Caregiver prioritization, and (3) Patterns of potential baby-pet attachment. Findings indicate that the mother-pet relationship was unique to each individual, with some mothers reporting changing perinatal pet behaviors (e.g., increased closeness) that they interpreted as indicative of an attachment bond, and others (e.g., avoidance) being interpreted as making a bond less secure after birth. Mothers also reported that feelings of attachment were dependent on the extent to which they prioritized a pet. Finally, some mothers felt that observed interactions indicated an attachment-like bond between baby and pet. Additional research is needed to understand these relationships and changes, their implications for infant development, and how they may be used as a protective perinatal factor.
Spatially-distributed parameter identification by physics-informed neural networks illustrated on the 2D shallow-water equations
Authors: Boulenc Hugo , Bouclier Robin , Garambois Pierre-André , Monnier Jérôme
Published: January 2025

Recent advancements in Physics-Informed Neural Networks (PINNs) offer promising opportunities for the identification of parameters of physical models based on ODEs and PDEs. This work revisits two representative PINN-based approaches for inverse problems, and applies them to fluid dynamics simulations. The first approach, here referred to as Fully-Parameterized PINN (FP-PINN), develops a parameter-differentiable surrogate model through an offline training phase, followed by a rapid online parameter identification phase. This method treats physical parameters as Neural Network (NN) inputs, making it prone to the curse of dimensionality. Conversely, the second approach, termed Semi-Parameterized PINN (SP-PINN), integrates physical parameters as NN parameters, allowing for efficient inference regardless of dimension using automatic differentiation. The performance of these two methods is first assessed through numerical experiments using an ODE-based model (the Backwater equation). Then, the SP-PINN is tested in a more representative scenario for identifying a

O

(

10
3

)

-dimensional spatial friction parameter in a 2D Shallow-Water Equations model. Comparisons with the well-established and precision-validated Variational Data Assimilation method are performed. The latter, even based on the adjoint technique, remains computationally expensive for high-dimensional parameter identification and can be complex to implement. This work highlights the attractiveness of SP-PINN in terms of simplicity and efficiency, thus establishing this strategy as a complementary approach or even a compelling alternative in parameter identification of real-world flow models.
Electron beam irradiation induced structural transformation in TEM study
Authors: Zeng Yuan , Zhang Zhihong , Ye Huanyu , Qiu Hongmei , Wang Rongming
Published: January 2025

In-situ transmission electron microscopy (TEM), enabling the introduction of various stimuli into the TEM sample chamber, is a powerful tool to observe the dynamic structural transformation under external stimuli at the atomic scale, and thus has attracted extensive attentions in the research of materials science and nanotechnology. In particular, massive studies using imaging electron beams as a particular type of stimuli in TEM have emerged up, as it requires no additional experimental equipment or special specimen holders. This review provides a comprehensive summary of e-beam stimulated in-situ TEM experiments, and discusses the underlying mechanisms based on the various interaction between electrons and matter. Specifically, it covers electron beam-induced nanostructure sculpture, phase transformation, and chemical reaction driven structural transformation, where the effects of e-beam irradiation are focused. Finally, we summarize current developments in this field and provide insights into its future.
Erasure by friction: an over-scales-manifesting estimation of the nanoscale (quantum) coefficient of friction
Authors: Gadomski Adam , Karpiński Karol
Published: January 2025

The substantial need to introduce a measure of the properly defined coefficient of friction (COF) appears to be a greatly profitable nanotechnological challenge. Since the nanoscale constitutes a passage between classical and quantum physical descriptions, the proposed COF measure should involve its signature. To this end, we wish to propose a simple construction of the COF, bearing the classical (thermodynamic) and quantum (Heisenberg uncertainty involving) counterparts. Thus, while performing the task, we offer a guided tour from meso- (to some extent, from a ‘small’ macroscale even) to nanoscale. As a motivation for building our simple theoretical construct, we have based it on a number of sophisticated experiments involving by-light-trapped atoms and/or ions, atomic force microscopy (AFM) involving the twisting of graphene (bi)layers, and certain atomistic simulations of friction. The derived formula for the COF at the nanoscale consists of two competing terms: one of them contains an energetic contribution, engaging the thermal energy expenditure for an atom/ion erasure. The second, in turn, includes a quantum contribution based on the energetic Heisenberg uncertainty principle, also involving a short but measurable interval for kinetic friction to commence. Both terms are weighted by the same energy term stemming from a very weak gravitational impact with ultralight AFM-type nanosensors (or, nanoindenters) enabled to operate. As a result, the obtained COF is of ultrasmall value, including a tuning friction-reaction time parameter, suggesting a nonlinear kinetic origin of the so-constructed COF. As demonstrated, this gives a principal and ready-for-application novelty to our work. The time parameter can accurately reveal the superlubricity condition of classical-quantum origin assigned to the erasure-by-friction nanoscale event.
Inferring traits of hyperuniformity from local structures via persistent homology
Authors: Milor Abel H G , Salvalaglio Marco
Published: January 2025

Hyperuniformity refers to the suppression of density fluctuations at large scales. Typical for ordered systems, this property also emerges in several disordered physical and biological systems, where it is particularly relevant to understand mechanisms of pattern formation and to exploit peculiar attributes, e.g. interaction with light and transport phenomena. While hyperuniformity is a global property, ideally defined for infinitely extended systems, several disordered correlated systems have finite size. It has been shown in Salvalaglio et al (2024 Phys. Rev. Res.
6 023107) that global hyperuniform (HU) characteristics systematically correlate with distributions of topological properties representative of local arrangements. In this work, building on this information, we explore and assess the inverse relationship between hyperuniformity and local structures in point patterns as described by persistent homology. Standard machine learning algorithms trained on persistence diagrams are shown to detect hyperuniformity of periodic point patterns with high accuracy. Therefore, we demonstrate that the information on patterns’ local structures allows for characterizing whether finite size arrangements are analogous to those realized in HU patterns. Then, addressing more quantitative aspects, we show that parameters defining hyperuniformity globally can be reconstructed by comparing persistence diagrams of targeted patterns with reference ones. We also explore the generation of patterns entailing given topological properties. The results of this study pave the way for advanced analysis of HU patterns including local information, and introduce basic concepts for their inverse design.
Semi-supervised generative adversarial network to estimate the depth map of underwater targets in acoustic images for 3D reconstruction
Authors: Bai Boming , Yan Weisheng , Cui Rongxin
Published: January 2025

Owing to the unique imaging formulation principle of acoustic cameras, certain dimensions of the target may be lost in acoustic images. Some studies have been conducted to retrieve these missing details from a single pseudo front depth that contains 3D information about the target. Nevertheless, the use of a single pseudo front depth may face ambiguity problems in recovering the 3D information of acoustic targets. Therefore, this paper presents a novel semi-supervised generative adversarial network (SGAN) for estimating the depth of two adjacent acoustic images at know relative sonar poses, which generates high-accuracy reconstruction results, while reducing the dependence on the labeled ground truth of acoustic targets. Specifically, we design a semi-supervised depth estimation framework that utilizes image warping for semi-supervised training, resulting in denser depth maps. Various datasets containing underwater targets and terrain were generated to simulate real underwater scenarios. Experiments with simulator data and real images verify the feasibility of using SGAN for depth estimation in 3D target reconstruction.
Higgs boson in condensed matter system
Authors: Kaga Hiroyuki
Published: January 2025

We show that Higgs amplitude mode is possible only when a collective boson state (Higgs state) starts to exist at the onset of condensation from its previous non-collective state in the high-temperature phase before the system undergoes a phase transition to the low-temperature broken symmetry condensed phase. Thus, Higgs boson can exist not only as an excitation mode but as a physical particle. Such phase transition is a first order transition from superconductor (superfluid) to insulator which is described by the Ginzburg-Landau (GL) free energy of second-order-transition type augmented with the Higgs boson state and the entropy term of the insulator phase. Higgs boson excitation is a collective excitation of non-condensed collective boson from the condensate which makes a collective amplitude oscillation with the remaining condensate. Therefore, Higgs mode energy in the long-wavelength limit has the same magnitude as one boson condensation-resolving energy. The experimental observations of Higgs modes in charge density wave superconductor NbSe3 and in disordered superconductor films NbN and InO near the superconductor-to-insulator quantum phase transition are in good accord with the present phenomenological Higgs boson theory. Especially the dynamical conductivity of the latter strongly disordered superconductors provides the evidence that Higgs mode energy is the condensation-resolving energy of one boson.
Cost-effective synthesis of the InO(OH) thin film and exploration of its electrochemical properties as supercapacitor electrode
Authors: Sharma Suneel Kumar , Maurya Priyanka , Nidya M , Pradhan Nilam Sherestha , Pandey S N
Published: January 2025

We present the synthesis of a binder-free InO(OH) electrode by cost-effective successive ionic layer adsorption and reaction (SILAR) method for supercapacitive application. The synthesized material was characterized by x-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Mapping, and energy dispersive x-ray (EDX) analyses. The electrochemical performance of the electrode was evaluated through cyclic voltammetry (CV), galvanostatic charge/discharge (GCD) analysis, cycling stability, and Bode phase angle analyses. The electrode exhibited pseudocapacitive behaviour, achieving a maximum charge storage capacity of 119 F g−1 at 5 mVs−1 scan rate. Additionally, the electrode retained ~ 64% of its initial capacitance after 5,000 GCD cycles. Bode phase angle analysis further confirmed the capacitive nature of the electrode. The energy and power densities were estimated to be 5.7 Wh kg−1 and 172.9 W kg−1 respectively. This is a basic study and the capacitive performance of InO(OH) can be significantly improved in forthcoming researches by dopants or through compositing, for practical application in supercapacitors.
Investigating spin states, oxygen vacancies, and hybridization in bimetallic α-FeCo2(OH)6 electrode material
Authors: Arya Rajkamal , Dagar Tanya , Sinha A K
Published: January 2025

α-phase transition metal hydroxide (TMH) systems show superior electrochemical properties compared to its β-phase, because of the layered crystal structure, in which the layers are separated by the water molecules. TMHs are also the better option for energy storage devices mainly supercapacitors, compared to TM oxides, because of their higher electronic conductivity and superior theoretical capacity. However, for these systems experimentally achieved specific capacity is much lower than its theoretical capacity. Morphology and electronic properties are important inputs to improve electrochemical performance further. The electronic properties of these materials have not been reported and to fill this gap in literature, we report the electronic properties of bimetallic α-FCH (FeCo2(OH)6), using K and L3,2 edge x-ray absorption spectroscopy. We find that average oxidation state for Co is 1.9 resulting in oxygen vacancies at Co sites. The average oxidation state for Fe is more than +2, indicative of a +3-oxidation state for some Fe atoms. The average crystal field splitting (CFS) is 1.3 eV with a core hole contribution of 0.2 and 0.3 eV for Co and Fe, respectively. Satellite peaks in both Co and Fe L3,2 edge spectra, attributed to the core hole transitions, show significant hole doping. TM-O6 octahedra are asymmetric (c > a), as observed from higher FWHM of eg peak than t2g peak. Fe is found to be in a low spin state, whereas Co is in a mixed spin state in FCH. Significant pre-edge in Fe XANES indicates preferential hybridization between Fe 3d and oxygen 2p orbitals.
An analytically tractable marked power spectrum
Authors: Ebina Haruki , White Martin
Published: January 2025

The increasing precision of cosmology data in the modern era is calling for methods to allow the extraction of non-Gaussian information using tools beyond two-point statistics.
The marked power spectrum has the potential to extract beyond two-point information in a computationally efficient way while using much of the infrastructure already available for the power spectrum.
In this work we explore the marked power spectrum from an analytical perspective. In particular, we explore a low-order polynomial for the mark that allows us to better control the theoretical uncertainties and we show that with minimal new degrees of freedom the analytical results match measurements from N-body simulations for both the matter field and biased tracers in redshift space.
Finally, we show that even within the limited forms of mark that we consider, there are degeneracies that can be broken by inclusion of the marked auto-spectrum or the cross-spectrum with the unmarked field. We discuss future theoretical developments that would enable us to apply this approach to survey data.
IntelliMIK: a novel intelligent quench detection method for fusion devices
Authors: Yan Qing , Hu Yanlan
Published: January 2025

The timely and accurate quench detection system is an important guarantee for the safe operation of fusion devices. For the superconducting magnets with rapidly changing current in the fusion device, the core challenge is to compensate for the complex induced voltage to obtain the quench voltage signal. EAST adopts a two-stage induced voltage compensation method of Co-wound Wire primary compensation and Inductive Noise Realtime Calculation (MIK) secondary compensation for quench detection. However, even after compensation, the induced voltage noise may still exceed the quench detection threshold, resulting in system misjudgment. In order to meet the demands of increasingly complicated operating environments of fusion devices and the application of High Temperature Superconductivity, we proposed a new neural network-based induced voltage compensation method IntelliMIK. It is used to replace MIK for secondary compensation. IntelliMIK has been trained using historical data from EAST and then applied on EAST. In the recent actual operation of 2000 shots (shot 141001 - shot 143000), IntelliMIK demonstrated significant advantages over MIK. The results show that IntelliMIK significantly decreases the induced voltage noise of the quench signal, can accurately detect the quench, and can effectively avoid quench misjudgment caused by peak noise.
The removal of ammonia nitrogen from wastewater by using modified Zeolite particles under ultrasonic cavitation
Authors: He Pengde , Liu Fengbin , Yan Hongjuan , Dou Zhaoliang , Chen Wenbin
Published: January 2025

The modified natural zeolite and artificial zeolite were used to treat ammonia nitrogen solution under the combination of aeration and ultrasonic cavitation. The adsorption process was analyzed by means of kinetic model, adsorption isothermal model and internal diffusion model. In addition, the surface microstructures of the modified zeolites before and after the experiment was characterized. The results showed that the kinetic processes of the two modified zeolites under aeration and ultrasonic processes can be better described with the pseudo-second-order kinetic model. And, the adsorption process conforms to the Freundlich isothermal equation better. The modified zeolite can easily adsorb ammonia nitrogen for its stronger affinity for NH4
+ compared to the untreated zeolite. At the same time, the physical adsorption capacity and ion exchange capacity of the modified artificial zeolites under aeration and ultrasonic cavitation processes are stronger than that of the modified natural zeolites under the same conditions, which contribute to the higher removal fraction of the ammonia nitrogen. The different adsorption capacity and ion exchange capacity between the modified artificial zeolites and modified natural zeolites can be attributed to their different microstructures.
Development and optimization of a CMOS-based energy management circuit for piezoelectric energy harvesting in IoT applications
Authors: Vázquez-Valdés S E , Woo-García Rosa M , Herrera‑May Agustín L. , Osorio‑de‑la‑Rosa E , Lopez‑Huerta Francisco
Published: January 2025

Advances in remote and continuous health monitoring using the internet of things (IoT) have highlighted the critical need for uninterrupted and long-term power supplies for these devices. Consequently, the study of energy harvesting has gained paramount importance to provide energy solutions. Energy harvesting involves multiple stages to recover energy from the surrounding environment. This article focuses on the energy management stage, where harvested energy is adapted to power low power devices. Specifically, we propose a reconfigurable rectifier with cross-gate coupling and a low-dropout voltage regulator (LDO converter) using 0.18μm TSMC CMOS technology for a piezoelectric power harvesting system. This circuit handles voltage ranges from 0.5V to 3.3 V with power output in the order of microwatts (μW) and occupies an area of 0.00577702125 mm2 (5777.021 μm2).
Influence of geometric and material parameters on sound insulation of locally resonant phononic crystal plates
Authors: Zhang Mengyang , Zhu Congyun , Ding Guofang
Published: January 2025

The sound isolation of locally resonant phononic crystal panels is significantly affected by geometrical and material parameters. In this paper, the variation of sound insulation is analyzed and observed by designing phononic crystal plates with different geometrical and material parameters. The research results indicate that in the structure of locally resonant phononic crystal plates, the density of scatterers and matrix materials has the most significant impact on sound insulation performance. As the density increases, the maximum sound insulation initially increases and then decreases. The density, elastic modulus, and Poisson’s ratio of the coating layer material also affect the sound insulation performance. Additionally, the filling rate of the scatterer structure influences the performance to some extent. Therefore, when designing locally resonant phononic crystal plates, the material density of scatterers and the matrix should be prioritized, followed by changes in the scatterer structure, and finally, the selection of an appropriate coating material.
Design analysis of supercritical helium circulating pump impeller for CRAFT superconducting magnet platform cooling
Authors: Zhu Yingqiu , Huang Guoquan , Zhang Qiyong , Cheng Anyi
Published: January 2025

The supercritical helium circulating pump in the CRAFT supercritical helium-assisted distribution cooler provides high flow rates of supercritical helium to the superconducting magnets, and the impellers of the supercritical helium pump body are impeller-designed according to functional requirements. In this paper, the impeller of a helium circulation pump with 350 g/s is taken as the research object, and the N-S equation and SST k-ε model are used to numerically solve and analyze the impeller field, and the preliminary design parameters of the impeller are determined by the impeller circulation pressure field and velocity field. The results show that the pressure field of the impeller fluid domain is uniformly distributed, the total pressure reaches 0.67 Mpa at maximum, the velocity of the trailing edge at the back of the blade reaches 40 m/s at maximum, and no vortex is generated in the flow area.
Simulated near-field radioactive source localization in 3D with Coded Aperture and Convolutional Neural Networks
Authors: Karafasoulis K. , Kaissas I. , Papadimitropoulos C. , Potiriadis K. , Lambropoulos C.P.
Published: January 2025

Coded Aperture γ-cameras have been extensively used in applications ranging
from astrophysics to nuclear medicine for imaging radioactive source distributions. These devices
allow the identification of the direction of γ-emitters by analyzing the shadow
patterns projected onto pixelated detectors. In this work, we propose a novel approach based on
Convolutional Neural Networks (CNNs) for accurately localizing radioactive sources in 3D using one
coded aperture gamma camera. The CNN is trained using simulated shadowgrams generated by a custom
simulation tool, with sources placed at various positions within the near-field, ranging from 20 cm to 120 cm from the detector. Unlike previous methods that focused on estimating angular
coordinates, our model also accurately estimates the distance of the source, achieving a distance
estimation accuracy of 10 mm, in addition to determining the polar and azimuthal angles. This
capability is particularly relevant for medical imaging applications, where precise localization
of radioactive sources is crucial. The results of our study demonstrate the potential of CNN-based
algorithms in improving the accuracy and reliability of single-detector systems in near-field
radioactive source localization.
A combined approach of performance evaluation and position distortion correction for a multitube position-sensitive neutron detector
Authors: Zuo Jingxin , Hu Sen , Sun Liangwei , Zhou Rong , Yang Chaowen , Zhang Xiang
Published: January 2025

A new multitube position-sensitive detector (PSD) for
small-angle neutron scattering spectrometer at the Chinese Mianyang
Research Reactor was assembled in 2021. This paper presents the
experimental design and implementation aimed at noise reduction,
spatial resolution testing, and position correction for the
multitube PSD. In addition, the analysis results indicate that the
deviation of the linear fitting correction increases linearly with
the gain difference between the two ends of the PSD, ultimately
hindering effective position correction. To address this issue, a
new position correction method for multitube PSDs has been proposed
that is simpler, more accurate, and broadly applicable to similar
detectors.
Preparation and characterization of mesoporous core–shell bioactive glass-based cements for direct pulp capping
Authors: Xu Xiaoting , He Yehong , Chen Xiaochen , Fan Zhen , Wang Deping
Published: January 2025

Direct pulp capping is crucial for maintaining dental pulp vitality. The materials employed for this purpose should possess properties such as easy-handling, excellent biocompatibility, infection resistance, and the ability to stimulate the formation of reparative dentin. Mineral trioxide aggregate (MTA) is commonly used for pulp capping. However, certain limitations, including its long setting time, insufficient anti-washout ability, high initial curing pH, and handling difficulties, restrict MTA from meeting a broader range of clinical demands. Bioactive glass (BG) is known for its osteo-inductive and bone restoration properties. This study aims to develop a novel BG-based cement (BG-x/SA) for pulp capping applications, using BG microspheres (BG-x) as the solid phase and sodium alginate solution (SA) as the liquid phase. The solid phase has core–shell microspheres made by in-situ transformation, with BG in the core and hydroxyapatite in the shell. The study focuses on how the in-situ synthesis reaction parameters affect the cement’s properties. Ultimately, BG-6/SA cement was identified as the optimal formulation, and a comparison with MTA shows BG-6/SA cement has a short setting time, good anti-washout performance, can adjust pH to mildly alkaline, promotes dentin formation, and has antibacterial effects. Thus, BG-6/SA cement has significant research value and good prospects as a new pulp-capping material.
Current trends in global quantum metrology
Authors: Mukhopadhyay Chiranjib , Montenegro Victor , Bayat Abolfazl
Published: January 2025

Quantum sensors are now universally acknowledged as one of the most promising near-term quantum technologies. The traditional formulation of quantum sensing introduces a concrete bound on ultimate precision through the so-called local sensing framework, in which a significant knowledge of prior information about the unknown parameter value is implicitly assumed. Moreover, the framework provides a systematic approach for optimizing the sensing protocol. In contrast, the paradigm of global sensing aims to find a precision bound for parameter estimation in the absence of such prior information. In recent years, vigorous research has been pursued to describe the contours of global quantum estimation. Here, we review some of these emerging developments. These developments are both in the realm of finding ultimate precision bounds with respect to appropriate figures of merit in the global sensing paradigm, as well as in the search for algorithms that achieve these bounds. We categorize these developments into two largely mutually exclusive camps; one employing Bayesian updating and the other seeking to generalize the frequentist picture of local sensing towards the global paradigm. In the first approach, in order to achieve the best performance, one has to optimize the measurement settings adaptively. In the second approach, the measurement setting is fixed, however the challenge is to identify this fixed measurement optimally.
The impact of copper additive content on the synthesis of SiC from rice husks
Authors: Trung Kien Kieu Do , Ngoc Minh Huynh
Published: January 2025

Silicon carbide is a compound that is usually synthesized by sintering silicon dioxide and carbon in an arc furnace at high temperatures. This method requires sophisticated technology and equipment, which restricts the widespread application of silicon carbide. As a result, researchers have turned to biomass materials for its synthesis, with rice husk being the primary source. However, the efficiency of synthesizing SiC from rice husks tends to be relatively low. This study investigates the effect of copper additives on the synthesis efficiency of SiC. A mixture of rice husks, silica gel, and copper was prepared in varying ratios and then sintered at 1100 °C in a CO2 atmosphere to promote SiC formation. The SiC content, mineralogical composition, and microstructure of the products were analyzed using ISO 21068-2:2008, Fourier Transform Infrared Spectroscopy, x-ray Diffraction, Scanning Electron Microscopy, and Energy Dispersive x-ray Spectroscopy. The results indicate that a rice husks-to-copper ratio of 1:2 significantly improved SiC production. Despite the enhancement, copper introduced unwanted impurities into the final products. These impurities were successfully removed by washing the SiC with hydrofluoric acid, resulting in a SiC content of 76.25% by weight. The synthesized SiC exhibited both α-SiC and β-SiC allotropic forms, contributing to improved mechanical strength and heat resistance of the material.
Optimizing compressive strength prediction of pervious concrete using artificial neural network
Authors: Wijekoon Sathushka Heshan Bandara , Janarth Asoharasa , Dharmar Joseph , Vinojan Perinparasa , Sathiparan Navaratnarajah , Subramaniam Daniel Niruban
Published: January 2025

The prediction of compressive strength is crucial, as it is influenced by various mix parameters such as aggregate size, aggregate-to-cement ratio, and compaction. Accurate forecasting of strength ensures optimized mix designs, enhancing both performance and material efficiency in construction projects. The novelty of this study lies in integrating machine learning techniques to predict the compressive strength of pervious concrete, incorporating these key parameters to improve predictive accuracy and facilitate more precise and sustainable mix design choices. For the experimental study, 600 samples were prepared with varying aggregate-to-cement ratios (3.0–5.0), compaction (0–60 blows from standard proctor rammer), and aggregate size (4.75–25 mm) and monitored for porosity and compressive strength. A modified Ryshkewitch model is assessed alongside experimental evaluations to improve mix design optimization. The effect of parameter variability on compressive strength is assessed to investigate the uncertainty propagation. Key uncertainties are highlighted by sensitivity analysis, and output distributions are produced by Monte Carlo simulations, reducing uncertainty is essential for practical applications, as it guarantees that compressive strength forecasts remain constant across a range of materials and environmental circumstances. In addition, machine learning and neural network models were analyzed for accuracy. Incorporating porosity in compressive strength prediction enhanced the R2 of empirical and neural network models from 0.63 and 0.78 to 0.92, respectively, while uncertainty was comparable to experimental observations. Aggregate size-based models improved performance by more than 0.95 for all cases, insisting on the dominant impact of aggregate size on prediction models. The research concludes that the optimized mix designs not only improve compressive strength but also promote sustainability by reducing material waste and enhancing the durability of pervious concrete. These findings provide valuable insights for more efficient and environmentally friendly concrete mix designs in urban infrastructure projects.
Conclusion
Authors: Ju Angela
Published: January 2025

This final chapter ties together the main findings and broader impacts of the book in Brazil, as well as in other similar immigrant-receiving countries. It pays special attention to theoretical and policy implications of the study for immigration as Brazil seeks to enhance its image on the world stage and addresses the impact of events that have occurred since my fieldwork in São Paulo, such as the presidency of Jair Bolsonaro and the COVID-19 pandemic. This chapter concludes the book by discussing recommendations for future research, especially in the context of Luiz Inácio Lula da Silva’s return to the Brazilian presidency, and the limitations of the study.
Tidy-Mindedness
Authors: Queloz Matthieu
Published: January 2025

This chapter assesses the merits of the main non-foundationalist rival to the needs-based approach to concept appraisal, which also escapes the trilemma and revives something of the spirit of the Vienna Circle: the tidy-minded approach that seeks conceptual authority by eliminating theoretical vices from our conceptual repertoire. Various kinds of theoretical vices—including superficiality, inconsistency, incoherence, and incongruence—are explicated, thereby setting the stage for later chapters. Different forms of the tidy-minded approach are then considered, but they are all found wanting as general answers to the authority question. To supplement more theoretically virtuous concepts with the grounds on which they can claim more authority, and to discriminate contexts in which these grounds are given from contexts in which they are lacking, a more comprehensive approach is needed—one that considers not just how concepts relate to each other, but how they relate to us and our concerns.
Boosting GPT models for genomics analysis: generating trusted genetic variant annotations and interpretations through RAG and Fine-tuning
Authors: Lu Shuangjia , Cosgun Erdal
Published: January 2025

Motivation

Large language models (LLMs) have acquired a remarkable level of knowledge through their initial training. However, they lack expertise in particular domains such as genomics. Variant annotation data, an important component of genomics, is crucial for interpreting and prioritizing disease-related variants among millions of variants identified by genetic sequencing. In our project, we aimed to improve LLM performance in genomics by adding variant annotation data to LLMs by retrieval-augmented generation (RAG) and fine-tuning techniques.

Results

Using RAG, we successfully integrated 190 million highly accurate variant annotations, curated from five major annotation datasets and tools, into GPT-4o. This integration empowers users to query specific variants and receive accurate variant annotations and interpretations supported by advanced reasoning and language understanding capabilities of LLMs. Additionally, fine-tuning GPT-4 on variant annotation data also improved model performance in some annotation fields, although the accuracy across more fields remains suboptimal. Our model significantly improved the accessibility and efficiency of the variant interpretation process by leveraging LLM capabilities. Our project also revealed that RAG outperforms fine-tuning in factual knowledge injection in terms of data volume, accuracy, and cost-effectiveness. As a pioneering study for adding genomics knowledge to LLMs, our work paves the way for developing more comprehensive and informative genomics AI systems to support clinical diagnosis and research projects, and it demonstrates the potential of LLMs in specialized domains.

Availability and implementation

We used publicly available datasets as detailed in the paper, which can be provided upon request.
A regularized Bayesian Dirichlet-multinomial regression model for integrating single-cell-level omics and patient-level clinical study data
Authors: Guo Yanghong , Yu Lei , Guo Lei , Xu Lin , Li Qiwei
Published: January 2025

The abundance of various cell types can vary significantly among patients with varying phenotypes and even those with the same phenotype. Recent scientific advancements provide mounting evidence that other clinical variables, such as age, gender, and lifestyle habits, can also influence the abundance of certain cell types. However, current methods for integrating single-cell-level omics data with clinical variables are inadequate. In this study, we propose a regularized Bayesian Dirichlet-multinomial regression framework to investigate the relationship between single-cell RNA sequencing data and patient-level clinical data. Additionally, the model employs a novel hierarchical tree structure to identify such relationships at different cell-type levels. Our model successfully uncovers significant associations between specific cell types and clinical variables across three distinct diseases: pulmonary fibrosis, COVID-19, and non-small cell lung cancer. This integrative analysis provides biological insights and could potentially inform clinical interventions for various diseases.
Coronary microvascular dysfunction by positron emission tomography and outcomes in patients after cardiac transplantation
Authors: Abadie Bryan , Elghoul Yasmine , Prakash Sakthi Surya , Besir Besir , Ziada Khaled , Jacob Miriam , Bhattacharya Sanjeeb , Bhat Pavan , Starling Randall C , Tang W H Wilson , Jaber Wael
Published: January 2025

Aims

Cardiac allograft vasculopathy (CAV) affects both epicardial and microvascular coronary arteries, however, few studies have characterized microvascular dysfunction in this population. Several prior studies have shown that positron emission tomography/computed tomography (PET/CT) can be used to screen for epicardial CAV, however, the clinical implications of abnormal blood flow in the absence of epicardial CAV are unknown. Our study sought to assess the prognostic implications of microvascular dysfunction and its subtypes, endogen/functional and classical/structural, using PET/CT in cardiac transplant patients without epicardial CAV.

Methods and results

Transplant patients with no prior history of CAV and normal myocardial perfusion imaging were included. Patients were then classified by the presence of coronary microvascular dysfunction (CMD) (myocardial flow reserve < 2.0); patients with CMD were further subcategorized into endogen/functional (stress myocardial blood flow ≥ 1.7 mL/min/g) and classical/structural (stress myocardial blood flow < 1.7 mL/min/g). The primary outcomes were all-cause mortality and a composite of all-cause mortality, heart failure hospitalization, acute coronary syndrome, revascularization, and re-transplantation. Three hundred fifty-six patients met the inclusion criteria. CMD was present in 141 (39.6%) patients, of which 112 (31.4%) had endogen/functional CMD and 29 (8.1%) had classical/structural CMD. After multivariable adjustment, endogen/functional CMD was associated with a higher rate of the composite outcome (HR 2.39, 95% CI 1.32–4.29, P = 0.004) and all-cause mortality (HR 2.98, 95% CI 1.34–6.64, P = 0.008). Classical/structural CMD was not associated with the primary composite outcome (HR 0.92, 95% CI 0.27–3.17, P = 0.893) or all-cause mortality (HR 1.22, 95% CI 0.263–5.69, P = 0.797).

Conclusion

In cardiac transplant patients with no history of CAV and normal myocardial perfusion, an endogen/functional pattern of CMD is associated with higher rate of adverse events and death. This association was not present in patients with a classical/structural CMD pattern. Incorporating endogen/microvascular dysfunction assessment in PET/CT reporting may identify a higher-risk group hereto now considered low risk.
Yellow Pages: Experimental Evidence on Household-Firm Connections
Authors: Dillon Brian , Aker Jenny C , Blumenstock Joshua E
Published: January 2025

We study the impact of a low-cost intervention to reduce information frictions in rural markets by randomly assigning small and medium enterprises in Tanzania to be listed in a telephone directory. The listed firms expand their communication networks, increase sales, and make greater use of mobile money, with positive spillovers to firms in the same village. Estimated effects are larger and more statistically precise for firms that are more productive at baseline, consistent with consumers seeking out better firms when search costs fall. We find no evidence of negative between-village spillovers on unlisted firms.
Meeting the challenges of online teacher education in Madagascar
Authors: Raminoarivony Mirany
Published: January 2025

This study examines attitudes and engagement with online teaching among English teacher educators in a Malagasy pre-service teacher training institute during and after the coronavirus disease (COVID) epidemic. It explores how teachers responded to the COVID-related challenges and opportunities independently, showing various attitudes and approaches toward online teaching. Using a qualitative questionnaire, the data collected were analyzed implementing the General Inductive Approach. Findings reveal individual teachers’ varied usages of social media and Moodle in reconnecting with teaching and students over distances, coping with a relative unpreparedness in a normal situation, exacerbated by the epidemic. The study identifies key challenges such as uncertainties during COVID, insufficient training, material and human resources in educational technology, and high internet costs. They demonstrated a lack of a community of practice that develops teachers’ online teaching competences. It offers recommendations for similar low-resource contexts in Madagascar and the global South.
3-D variational inference-based double-difference seismic tomography method and application to the SAFOD site, California
Authors: Yang Hao , Zhang Xin , Zhang Haijiang
Published: January 2025

Seismic tomography is used to image subsurface structures at various scales, accomplished by solving a nonlinear and non-unique inverse problem. It is therefore important to quantify velocity model uncertainties for accurate earthquake locations and geological interpretations. Monte Carlo sampling techniques are usually used for this purpose, but those methods are computationally intensive, especially for large data sets or high-dimensional parameter spaces. In comparison, Bayesian variational inference provides a more efficient alternative by delivering probabilistic solutions through optimization. The method has been proven to be efficient in 2-D tomographic problems. In this study, we apply variational inference to solve 3-D double-difference (DD) seismic tomographic system using both absolute and differential traveltime data. Synthetic tests demonstrate that the new method can produce more accurate velocity models than the original DD tomography method by avoiding regularization constraints, and at the same time provides more reliable uncertainty estimates. Compared to traditional checkerboard resolution tests, the resulting uncertainty estimates provide a better measure for the reliability of the solution. We further apply the new method to data recorded by a local dense seismic array around the San Andreas Fault Observatory at Depth (SAFOD) site along the San Andreas Fault (SAF) at Parkfield. Similar to previous studies, the obtained velocity models show significant velocity contrasts across the fault. More importantly, the new method produces velocity uncertainties of less than 0.34 km s−1 for ${{{{V}}}_p}$ and 0.23 km s−1 for ${{{{V}}}_s}$. We therefore conclude that variational inference provides an effective tool for solving 3-D seismic tomographic problems and quantifying model uncertainties.
Resting-State Functional Connectivity of the Cerebellum Differs Between Persons With Inflammatory Bowel Disease and Healthy Controls in Relation to Executive Function
Authors: Kornelsen Jennifer , McIver Theresa A , Marrie Ruth Ann , Patel Ronak , Bernstein Charles N
Published: January 2025

Lay Summary

Persons with inflammatory bowel disease exhibit a weaker relationship between executive function and functional connectivity of cerebellar regions (VIIa Crus I and VIIa Crus II) to cortical areas involved in visual processing compared to healthy counterparts with comparable performance.