bluuebunny/crossref_metadata_2025 huggingface dataset:
Photon-counting-detector CT outperforms state-of-the-art cone-beam and energy-integrated-detector CT in delineation of dental root canals
Authors: Rau Stephan , Pichotka Martin Peter , Rau Alexander , Reisert Marco , Altenburger Markus Jörg , Schmelzeisen Rainer , Cieplik Fabian , Bamberg Fabian , Russe Maximilian Frederik , Semper-Hogg Wiebke
Published: January 2025

This experimental phantom study investigates current standard of care protocols in cone beam computed tomography (CBCT), energy-integrating-detector (EID) CT, and photon-counting-detector (PCD) CT regarding their potential in delineation of dental root canals. Artificial accessory canals (diameters: 1000, 600, 400, 300 and 200 μm) were drilled into three bovine teeth mounted on a bovine rib as a jaw substitute. The phantom was scanned in two dental CBCTs, two EID-CTs and a PCD-CT using standard clinical protocols. Scans from a micro-CT served as reference standard. Spatial resolution was evaluated via line profiles through the canals, whereby visibility compared to surrounding noise and compared to the ground truth were assessed. PCD-CT was able to delineate all artificial canals down to 200 μm diameter. In CBCT and EID-CT canals could only be reliably detected down to 300 μm. Also, PCD-CT showed a considerably smaller width-divergence from the ground trough with 4.4% at 1000 μm and 35.1% at 300 μm compared to CBCT (13.5 and 72.9%) and EID-CT (10.1 and 115.7%). PCD-CT provided superior resolution, accurate size measurement, and enhanced detection of small dental root canals, thereby offering improvements in diagnostic capabilities compared to CBCT and EID-CT systems.
Comparison of outcomes with and without intrastent placement during PMS surgery
Authors: Miura Yusaku , Fukuda Ken , Yamashiro Kenji
Published: January 2025

To assess the efficacy of using a nylon suture as a stent in the PreserFlo MicroShunt (PMS) lumen to prevent postoperative hypotony, 59 eyes that underwent PMS implantation with follow-up for > 6 months were analyzed. Patients were divided into no intrastenting (NST) and intrastenting (ST) groups, with the ST group subdivided into 9 − 0 nylon suture fully placed (9 F), 9 − 0 nylon suture placement in only half of the lumen (9 H), 10 − 0 nylon suture fully placed (10 F), and 10 − 0 nylon suture placement in only half of the lumen (10 H). The distribution was as follows: 23 eyes in the NST group, 10 in the 9 F group, 9 in the 9 H group, 11 in the 10 F group, and 6 in the 10 H group. No significant differences were observed in preoperative and 6-month postoperative intraocular pressure, number of glaucoma medications, or cumulative survival rate between groups. Postoperative hypotony occurred in 13 eyes (56.5%) in the NST group, one (2.78%) in the ST group (p = 0.00014). Post-intrastent removal, hypotony occurred in 6 eyes (16.7%) in the ST group. These findings suggest that intrastent placement effectively prevents postoperative hypotony, regardless of nylon suture diameter or insertion length; however, timing is crucial as hypotony may occur after removal.
Associations of inferior frontal sulcal hyperintensities on brain MRI with cerebral small vessel disease, cognitive function, and depression symptoms
Authors: Dörner Marc , Pfister Malte , Tyndall Anthony , von Känel Roland , Neumann Katja , Schreiber Frank , Arndt Philipp , Fuchs Erelle , Garz Cornelia , Glanz Wenzel , Butryn Michaela , John Anna-Charlotte , Hildebrand Annkatrin , Euler Sebastian , Hofmann Andreas B. , Machetanz Lena , Kirchebner Johannes , Tacik Pawel , Grimm Alexander , Jansen Robin , Pawlitzki Marc , Henneicke Solveig , Perosa Valentina , Labeit Bendix , Düzel Emrah , Meuth Sven G. , Vielhaber Stefan , Mattern Hendrik , Bernal Jose , Schreiber Stefanie
Published: January 2025

Inferior frontal sulcal hyperintensities (IFSH) observed on fluid-attenuated inversion recovery (FLAIR) MRI have been proposed as indicators of elevated cerebrospinal fluid waste accumulation in cerebral small vessel disease (CSVD). However, to validate IFSH as a reliable imaging biomarker, further replication studies are required. The objective of this study was to investigate associations between IFSH and CSVD, and their potential repercussions, i.e., cognitive impairment and depression. We prospectively recruited 47 patients with CSVD and 29 cognitively normal controls (NC). IFSH were rated visually based on FLAIR MRI. Using different regression models, we explored the relationship between IFSH, group status (CSVD vs. NC), CSVD severity assessed with MRI, cognitive function, and symptoms of depression. Patients with CSVD were more likely to have higher IFSH scores compared to NC (OR 5.64, 95% CI 1.91–16.60), and greater CSVD severity on MRI predicted more severe IFSH (OR 1.47, 95% CI 1.14–1.88). Higher IFSH scores were associated with lower cognitive function (-0.96, 95% CI -1.81 to -0.10), and higher levels of depression (0.33, 95% CI 0.01–0.65). CSVD and IFSH may be tightly linked to each other, and the accumulation of waste products, indicated by IFSH, could have detrimental effects on cognitive function and symptoms of depression.
Determination of eight fungicides in tanned leather by liquid chromatography with mass spectrometry and with diode array spectrophotometric detection
Authors: Acevedo-Aguilar Francisco Javier , Enciso Donis Israel , Wrobel Kazimierz , Corrales Escobosa Alma Rosa , Magaña Maldonado Luis Mario , Wrobel Katarzyna
Published: January 2025

Two liquid chromatography procedures with PPO as IS were set up and implemented for the determination of eight fungicides in tanned leather; both were suitable for the analytical control of industrial products.
The outputs of molecular sensors detectable by human senses
Authors: Rubel Maria S. , Zemerova Tatiana , Kolpashchikov Dmitry M.
Published: January 2025

Among the outputs of molecular sensors sensed directly by humans, visual outputs dominate. Tactile outputs are accessible to visually impaired people. Gas release is particularly useful as it can be detected quantitatively both visually and by touch.
Aqueous solution as a playground of {MoO4}, {Mo4O12}, {Mo8O26}, {Mo8V5O40} and {V7Mo2O27} species in the presence of carboxylic acids and [Co(C2O4)(NH3)4]+ or [Co(en)3]3+ cations
Authors: Kuzman D. , Damjanović V. , Cindrić M. , Vrdoljak V.
Published: January 2025

Nine tetraammineoxalatocobalt(iii) salts built of different oxomolybdate anions, i.e. {β-Mo8O26}4−, {Na2Mo8O29(H2O)4}n4m−, {[β-Mo8O26(H2O)2][γ-Mo8O26]}8−, and {γ-Mo8O26}n4m− were isolated.
Defect physics investigations in bulk NaBiO3 photocatalysts via Heyd–Scuseria–Ernzerhof hybrid density functional theory calculations
Authors: Ling Song , Wang Jingcheng , Kong Bo , Zeng Ti-xian , Wang Wentao
Published: January 2025

The Na1+i and Bi2+Na defects are two dominant intrinsic defects in NaBiO3, which can act as the photocatalytic reaction active sites rather than as the recombination centers of the photo-generated electrons and holes.
Half-metallic and ferromagnetic phases in CrSH monolayers using DFT+U and BO-MD calculations
Authors: Sukserm Akkarach , Seeyangnok Jakkapat , Pinsook Udomsilp
Published: January 2025

We present an investigation of the structural, electronic, magnetic, and vibrational properties of CrSH monolayers in the 1T and 1H phases using density functional theory with U value of 5.52 eV and Born–Oppenheimer molecular dynamics simulations.
Microscopic time-resolved spectroscopy of organic crystals at the nanometer and micrometer scale
Authors: Liu Xi , Li Minjie , Wan Yan
Published: January 2025

Microscopic time-resolved spectroscopy can study the morphology-dependent photoexcitation dynamics and energy spatial transport processes in organic crystals at the nanometer and micrometer scale.
Synthetic structure effect of CuO/NaZSM-5 on the catalytic oxidation of chlorinated aromatic hydrocarbon
Authors: Xie Man , Wang Jiasheng , Zhou Hangyu , Zhang Hao , Sun Pengfei , Dong Xiaoping
Published: January 2025

Petrochemical, tobacco, and pharmaceutical processing industries extensively use chlorinated volatile organic compounds (CVOCs) as solvents and lubricants, which cause significant environmental hazards.
Theoretical prediction of metallomimetic stable cyclic group 13 carbenoids
Authors: Phukan Ashwini K. , Bora Barsha Rani , Chakraborty Barsha
Published: January 2025

Quantum chemical calculations suggest that cyclic intramolecular base-stabilized group 13 carbenoids are capable of exhibiting metallomimetic behavior and are capable of activating small molecules.
Integrated polyanion-layered oxide cathodes enabling 100 000 cycle life for sodium-ion batteries
Authors: Zou Zhiyu , Mu Yongbiao , Han Meisheng , Chu Youqi , Liu Jie , Zheng Kunxiong , Zhang Qing , Song Manrong , Jian Qinping , Wang Yilin , Hu Hengyuan , Yu Fenghua , Li Wenjia , Wei Lei , Zeng Lin , Zhao Tianshou
Published: January 2025

A schematic illustration of the NVFP-VO cathode with a two-phase intergrown heterostructure of NVFP and V2O3.
Detection and quantification of nanoparticles in runoff from a highly trafficked urban motorway
Authors: Dia Malak , Peyneau Pierre-Emmanuel , Courtier-Murias Denis , Bechet Béatrice
Published: January 2025

Among the elements analyzed, Ti emerged as the most dominant element detected in runoff water analysis. Simplifying the steps of the sample preparation method is recommended.
Protective association between dietary phytosterol intake and cardiovascular health: an analysis of the UK Biobank cohort
Authors: Qiao Wanning , Feng Hanxiao , Zhang Yi-Feng , Zhang Zhilan , Yang Jinzhao , Wu Manni , Xie Jiyu , Huang Juan , Zhou Tao , Zhang Yang
Published: January 2025

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, with dietary phytosterols intake showing the cardioprotective effects.
Synthesis of CO2-based biomass derived non-isocyanate polyurethane hybrid adhesives with excellent mechanical properties and water resistance
Authors: Zhang Ping , Qin Chen , Yuan Hao , Wang Yu , Cao Yizhong , Wang Zhe , Jin Chunde
Published: January 2025

The environmentally friendly, non-toxic, non-isocyanate polyurethane (NIPU) adhesives derived from CO2 and green resources were synthesized.
Determination of Ru and Os with enhanced sensitivity and Rh, Pd, Pt and Ir at sub ng g−1 levels in geological samples by ICP-MS
Authors: Huang Qishuai , Sun Yali , Zhao Shouqian
Published: January 2025

Volatile RuO4 and OsO4: a distillation procedure separates and preconcentrates Ru and Os from sample solutions, improving sensitivity for ICP-MS analysis. This method could be applied to PGE analysis of low abundance samples.
A pumpless microfluidic co-culture system to model the effects of shear flow on biological barriers
Authors: Lino Marsel , Persson Henrik , Paknahad Mohammad , Ugodnikov Alisa , Farhang Ghahremani Morvarid , Takeuchi Lily E. , Chebotarev Oleg , Horst Caleb , Simmons Craig A.
Published: January 2025

Organ-on-a-chip modeling of biological barriers, like the vascular endothelium, blood-brain barrier, and gut, is enabled by VitroFlo, a co-culture microfluidic device that generates physiological unidirectional shear flow without the need for pumps.
The physics-AI dialogue in drug design
Authors: Vargas-Rosales Pablo Andrés , Caflisch Amedeo
Published: January 2025

What are the potential impacts and limitations of machine learning models for ligand identification and optimization? We review the field and compare data-hungry models with physics-based methods.
The pivotal role of the carbonyl group in methoxy chalcones: comprehensive analyses of the structure and computational insights into binding affinity towards monoamine oxidase enzymes
Authors: Harish Keshav Kumar , Khamees Hussien Ahmed , Venkatesha Keerthikumara , Nagaraja Omantheswara , Madegowda Mahendra
Published: January 2025

Chalcone scaffolds having carbonyl groups with conjugated double bonds are promising monoamine oxidase inhibitors. Structural and computational studies reveal their potential to regulate brain neurotransmitters and promote emotional well-being.
Immobilization of ultrafine bimetallic nanocatalysts on metal–organic framework-derived N-doped nanoporous carbon/manganese oxide for efficient hydrous hydrazine decomposition
Authors: Yang Ruoyu , Ding Xiang , Han Ruqu , Xu Linlin , Liu Tong , Xiang Jun
Published: January 2025

The obtained MnOx–Ni0.4Rh0.6@NC catalytic nanosystem demonstrates a prominent catalytic activity of selectively catalyzing N2H4·H2O decomposition and the corresponding overall turnover frequency value could achieve to 1363 h−1.
Cerium-doped Mn2O3 microspheres: a high-performance cathode material for aqueous zinc-ion batteries
Authors: Li Xin , Wang Wenyu , Li Linwen , Xue Chengyu , Chen Yang , Zhu Tiantian , Wei Fuxiang , Sui Yanwei , He Jie , Zhang Zunyang
Published: January 2025

Aqueous zinc-ion batteries (AZIBs) have received considerable attention due to their advantages of being safe, environmentally friendly, and cost-effective.
Small-signal capacitance in ferroelectric hafnium zirconium oxide: mechanisms and physical insights
Authors: Koduru Revanth , Saha Atanu K. , Frank Martin M. , Gupta Sumeet K.
Published: January 2025

Phase-field simulation study of the mechanisms governing small-signal capacitance in ferroelectric hafnium zirconium oxide and correlation to polarization domain configurations and switching mechanisms.
Altermagnetism in two-dimensional Ca2RuO4 perovskite
Authors: González J. W. , León A. M. , González-Fuentes C. , Gallardo R. A.
Published: January 2025

We propose a novel two-dimensional altermagnetic material, Ca2RuO4. DFT calculations reveal an enhanced altermagnetism via symmetry breaking and dimensionality reduction, offering a new platform for spintronics and topological phenomena.
Elastomer-assisted graphene transfer technique for enhanced resonance characteristics in chemisorption-based graphene resonant mass sensors
Authors: Kato Motoki , Arano Ken , Saito Masato , Fujie Toshinori , Imaizumi Yuki , Goda Tatsuro , Choi Yong-Joon , Noda Toshihiko , Sawada Kazuaki , Takahashi Kazuhiro
Published: January 2025

We develop a graphene-based resonant sensor to enhance the resonant properties by applied strain. An aptamer-modified sensor exhibited a frequency shift in response to inactivated SARS-CoV-2 virus, demonstrating a mass sensitivity of 0.99 zg Hz−1.
3D-printable liquid metal-based hydrogel for use as a multifunctional epidermal sensor
Authors: Wei Jingjiang , Chen Hao , Pan Fei , Zhang Hongming , Yuan Tianyu , Fang Yuanlai , Bai Zhongxiang , Yang Kun , Li Yidi , Ping Hang , Wang Yanqing , Wang Qingyuan , Fu Zhengyi
Published: January 2025

The liquid metal-based hydrogels coated with polyacrylic acid have the combination of conductivity, plasticity, printability, self-healing and skin-like mechanical properties, are therefore used as an epidermal sensor for human health monitoring.
Combination of the structural components of amino acids with fatty acids: access to an unknown class of “fatty” α-amino acids by palladium-catalyzed amidocarbonylation
Authors: Asadpour Mohammad , Scarpa de Souza Edson Leonardo , Spannenberg Anke , Correia Carlos Roque Duarte , Neumann Helfried , Beller Matthias
Published: January 2025

A new class of fatty amino acids, integrating fatty acids motifs, is synthesized in one step via palladium-catalyzed amidocarbonylation.
Interfacial modulation and optimization of the electrical properties of ZrGdOx composite films prepared using a UVO-assisted sol–gel method
Authors: Guo Chaozhong , Tuokedaerhan Kamale , Shen Xiangqian , Sagidolda Yerulan , Azamat Zhambyl
Published: January 2025

Structural diagram of ZrGdOx gate-dielectric films based on ultraviolet ozone (UVO)-assisted treatment, and dielectric characteristics exhibited by MOS capacitors based on ZrGdOx gate-dielectric films.
Tetrazolium iridium complexes as potential antibacterial agents
Authors: Lu Yifei , Zhang Xiujuan , Song Minmin , Xie Hua , Chen Shuhua , Zhou Yuyang , Jia Junli , Tang Huamin
Published: January 2025

Three iridium(iii) tetrazolato complexes have been used in antibacterial, biofilm removal and for other bioactivities for the first time.
Unveiling the photophysical mechanistic mysteries of tetrazine-functionalized fluorogenic labels
Authors: Shen Tianruo , Liu Xiaogang
Published: January 2025

This review highlights the mechanisms underlying the unique fluorogenic properties of tetrazine-functionalized labels and proposes new strategies to enhance fluorogenicity in the near-infrared region.
Microrheological model for Kelvin–Voigt materials with micro-heterogeneities
Authors: Azevedo T. N. , Oliveira K. M. , Maia H. P. , Teixeira A. V. N. C. , Rizzi L. G.
Published: January 2025

We introduce a generalization of the Kelvin–Voigt model in order to include and characterize micro-heterogeneities in viscoelastic semisolid materials.
Control of self-propagating reactions and phases in Ni/Si reactive multilayers
Authors: Chen Yi-Chen , Chang Yuan-Wei , Hsiang Cheng-Chih , Chiu Yi-Pang , Su Kuan-Wei , Chou Yi-Chia
Published: January 2025

Selecting freestanding bilayer thickness of Ni/Si reactive multilayers below 50 nm or above 170 nm could better control the final phases for practical applications.
Expression of concern: Electrochemical genosensor based on gold nanostars for the detection of Escherichia coli O157:H7 DNA
Authors: Razmi Nasrin , Hasanzadeh Mohammad , Willander Magnus , Nur Omer
Published: January 2025

Expression of concern for ‘Electrochemical genosensor based on gold nanostars for the detection of Escherichia coli O157:H7 DNA’ by Nasrin Razmi et al., Anal. Methods, 2022, 14, 1562–1570, https://doi.org/10.1039/D2AY00056C.
Correction: Neuroprotective effects of fermented yak milk-derived peptide LYLKPR on H2O2-injured HT-22 cells
Authors: Jiang Yunlong , Qi Yuan , Liu Xiaoting , Fang Li , Gao Yawen , Liu Chunlei , Wu Dan , Wang Xiyan , Zhao Fanrui , Wang Ji , Min Weihong
Published: January 2025

Correction for ‘Neuroprotective effects of fermented yak milk-derived peptide LYLKPR on H2O2-injured HT-22 cells’ by Yunlong Jiang et al., Food Funct., 2022, 13, 12021–12038, https://doi.org/10.1039/D2FO02131E.
Showcasing the technological advancements of carbon dioxide conversion: a pathway to a sustainable future
Authors: Jiang Xiao
Published: January 2025

Xiao Jiang introduces RSC Sustainability’s themed collection: CO2 Conversion.
Robust multi‐objective optimisation of an axial‐radial flux hybrid excitation permanent magnet synchronous motors based on improved sequential Taguchi method
Authors: Xu Naixi , Li Ning , Sun Xiaodong , Wang Yong , Wan Bingkuan
Published: January 2025

This paper proposes a novel axial‐radial flux hybrid excitation permanent magnet synchronous motor (ARFHE‐PMSM), which has better space utilisation and torque density than traditional radial flux PMSM (RFPMM) and axial flux PMSM (AFPMSM). Due to the complex mechanical structure of ARFHE‐PMSM, the computation burden is higher and larger than RFPMM and AFPMSM. Therefore, the sequential Taguchi optimisation method is proposed to improve the performance of ARFHE‐PMSM and reduce computation burden. The torque, torque ripple, core loss and permanent magnet volume of ARFHE‐PMSM are optimisation targets. In the optimisation process, the design parameters are divided into non‐sensitive and sensitive parameters based on the sensitivity analysis results. The non‐sensitive parameters are first optimised through the finite element model and then the sensitive parameters are optimised. The motor's manufacturing errors are considered in the optimisation process, and the Gaussian membership function and characteristic index are utilised to transform the multi‐objective optimisation problem into a single‐objective optimisation problem. The iterative Taguchi method narrows the optimisation scope until the convergence conditions are met. The optimal combination of different design parameters of the motor is obtained by analysing the experimental results. Compared with the initial design, the electromagnetic performance and robustness of the ARFHE‐PMSM have been significantly increased.
SpaGraphCCI: Spatial cell–cell communication inference through GAT‐based co‐convolutional feature integration
Authors: Zhang Han , Cui Ting , Xu Xiaoqiang , Sui Guangyu , Fang Qiaoli , Yang Guanghao , Gong Yizhen , Yang Sanqiao , Lv Yufei , Shang Desi
Published: January 2025

Spatially resolved transcriptomics technologies potentially provide the extra spatial position information and tissue image to better infer spatial cell–cell interactions (CCIs) in processes such as tissue homeostasis, development, and disease progression. However, methods for effectively integrating spatial multimodal data to infer CCIs are still lacking. Here, the authors propose a deep learning method for integrating features through co‐convolution, called SpaGraphCCI, to effectively integrate data from different modalities of SRT by projecting gene expression and image feature into a low‐dimensional space. SpaGraphCCI can achieve significant performance on datasets from multiple platforms including single‐cell resolution datasets (AUC reaches 0.860–0.907) and spot resolution datasets (AUC ranges from 0.880 to 0.965). SpaGraphCCI shows better performance by comparing with the existing deep learning‐based spatial cell communication inference methods. SpaGraphCCI is robust to high noise and can effectively improve the inference of CCIs. We test on a human breast cancer dataset and show that SpaGraphCCI can not only identify proximal cell communication but also infer new distal interactions. In summary, SpaGraphCCI provides a practical tool that enables researchers to decipher spatially resolved cell–cell communication based on spatial transcriptome data.
Multiband embeddings of light curves
Authors: Becker I. , Protopapas P. , Catelan M. , Pichara K.
Published: January 2025

In this work, we propose a novel ensemble of recurrent neural networks (RNNs) that considers the multiband and non-uniform cadence without having to compute complex features. Our proposed model consists of an ensemble of RNNs, which do not require the entire light curve to perform inference, making the inference process simpler. The ensemble is able to adapt to varying numbers of bands, tested on three real light curve datasets, namely Gaia, Pan-STARRS1, and ZTF, to demonstrate its potential for generalization. We also show the capabilities of deep learning to perform not only classification, but also regression of physical parameters such as effective temperature and radius. Our ensemble model demonstrates superior performance in scenarios with fewer observations, thus providing potential for early classification of sources from facilities such as Vera C. Rubin Observatory’s LSST. The results underline the model’s effectiveness and flexibility, making it a promising tool for future astronomical surveys. Our research has shown that a multitask learning approach can enrich the embeddings obtained by the models, making them instrumental to solve additional tasks, such as determining the orbital parameters of binary systems or estimating parameters for object types beyond periodic ones.
Multi-wavelength study of 1eRASS J085039.9−421151 with eROSITA, NuSTAR, and X-shooter
Authors: Zainab A. , Avakyan A. , Doroshenko V. , Thalhammer P. , Sokolova-Lapa E. , Ballhausen R. , Zalot N. , Stierhof J. , Hämmerich S. , Diez C. M. , Weber P. , Dauser T. , Berger K. , Kretschmar P. , Pottschmidt K. , Pradhan P. , Islam N. , Maitra C. , Coley J. B. , Blay P. , Corbet R. H. D. , Rothschild R. E. , Wood K. , Santangelo A. , Heber U. , Wilms J.
Published: January 2025

The eROSITA instrument on board Spectrum-Roentgen-Gamma (SRG) has completed four scans of the X-ray sky, leading to the detection of almost one million X-ray sources in eRASS1 alone, including multiple new X-ray binary candidates. We report on analysis of the X-ray binary 1eRASS J085039.9−421151, using a ∼55 ks long NuSTAR observation, following its detection in each eROSITA scan. An analysis of the eROSITA and NuSTAR X-ray spectra in combination with X-shooter data of the optical counterpart provide evidence of an X-ray binary with a red supergiant (RSG) companion, confirming previous results. However, we did determine a cooler spectral type for M2–3, owing to the presence of TiO bands in the optical and near-infrared spectra. The X-ray spectrum is well-described by an absorbed power law with a high-energy cutoff typically applied for accreting high mass X-ray binaries. In addition, we detected a strong fluorescent neutral iron line with an equivalent width of ∼700 eV and an absorption edge, the latter indicating strong absorption by a partial covering component. It is unclear whether the partial absorber is ionised. There is no significant evidence of a cyclotron resonant scattering feature. We did not detect any pulsations in the NuSTAR light curves, possibly on account of a large spin period that has gone undetected due to insufficient statistics at low frequencies or potentially large absorption that causes pulsations to be smeared out. Even so, the low persistent luminosity, the spectral parameters observed (photon index, Γ < 1.0), and the minuscule likelihood of detection of RSG-black hole systems suggest that the compact object is a neutron star.
Revealing the hidden cosmic feast: A z = 4.3 galaxy group hosting two optically dark, efficiently star-forming galaxies
Authors: Brinch Malte , Jin Shuowen , Gobat Raphael , Sillassen Nikolaj B. , Algera Hiddo , Gillman Steven , Greve Thomas R. , Gomez-Guijarro Carlos , Gullberg Bitten , Hodge Jacqueline , Lee Minju , Liu Daizhong , Magdis Georgios , Valentino Francesco
Published: January 2025

We present the confirmation of a compact galaxy group candidate, CGG-z4, at z = 4.3 in the COSMOS field. This structure was identified by two spectroscopically confirmed z = 4.3 Ks-dropout galaxies with ALMA 870 μm and 3 mm continuum detections, surrounded by an overdensity of near infrared-detected galaxies with consistent photometric redshifts of 4.0 < z < 4.6. The two ALMA sources, CGG-z4.a and CGG-z4.b, have been detected with both CO(4–3) and CO(5–4) lines, whereby [CI](1–0) has been detected on CGG-z4.a, and H2O(11, 0–10, 1) absorption detected on CGG-z4.b. We modeled an integrated spectral energy distribution (SED) by combining the far-infrared-to-radio photometry of this group and estimated a total star formation rate of ∼2000 M⊙ yr−1, making it one of the most star-forming groups known at z > 4. Their high CO(5–4)/CO(4–3) ratios indicate that each respective interstellar medium (ISM) is close to thermalization, suggesting either high gas temperatures, high densities, and/or high pressure; whereas the low [CI](1–0)/CO(4–3) line ratios indicate high star formation efficiencies. With the [CI]-derived gas masses, we found the two galaxies have extremely short gas depletion times of 99 Myr and < 63 Myr, respectively, suggesting the onset of quenching. With an estimated halo mass of log(Mhalo [M⊙]) ∼ 12.8, we find that this structure is likely to be in the process of forming a massive galaxy cluster.
Dust evolution by chemisputtering during protostellar formation
Authors: Borderies Antonin , Commerçon Benoît , Bourdon Bernard
Published: January 2025

Context. Dust grains play a crucial role in the modeling of protostellar formation, particularly through their opacity and interaction with the magnetic field. The destruction of dust grains in numerical simulations is currently modeled primarily by temperaturedependent functions. However, a dynamical approach could be necessary to accurately model the vaporization of dust grains.

Aims. We focused on modeling the evolution of dust grains during star formation, specifically on the vaporization of the grains by chemisputtering. We also investigated the evolution of non-ideal magnetohydrodynamic resistivities and the Planck and Rosseland mean opacities influenced by the grain evolution.

Methods. We modeled the evolution of the dust by considering spherical grains at thermal equilibrium with the gas phase, composed only of one kind of material for each grain. We then took into account the exchange processes that can occur between the grains and the gas phase and that make the grain size evolve. We considered three materials for the grains: carbon, silicate, and aluminum oxide. Given a temporal evolution in temperature and density of the gas phase, we computed the evolution of a dust grain distribution. This evolution was then used to compute the non-ideal magnetohydrodynamic resistivities and the Planck and Rosseland mean opacities.

Results. We observed a significant dependence of the sublimation temperature of the carbon grains on the dynamical evolution of the gas phase. The application of our method to trajectories where the temperature and density of the gas decrease after the sublimation of a portion of the grain distribution highlights the limitations of current vaporization prescriptions in simulations.

Conclusions. The dynamical approach leads to more accurate results for the carbon grain quantity when the temperature and density of the gas evolve quickly. The dynamical approach application to collapse and disk evolution is then foreseen with its integration into hydrodynamic simulations.
Blue monsters at z > 10: Where all their dust has gone
Authors: Ferrara A. , Pallottini A. , Sommovigo L.
Published: January 2025

The properties of luminous, blue, super-early galaxies (a.k.a. blue monsters) at redshift z > 10 have been successfully explained by the attenuation-free model (AFM), in which dust is pushed to kiloparsec scales by radiation-driven outflows. As an alternative to AFM, here we assess whether “attenuation-free” conditions can be replaced by a “dust-free” scenario in which dust is produced in very limited amounts and/or later destroyed in the interstellar medium. To this aim, we compare the predicted values of the dust-to-stellar mass ratio, ξd, with those measured in 15 galaxies at z > 10 from James Webb Space Telescope (JWST) spectra, when outflows are not included. Our model constrains ξd as a function of several parameters by allowing wide variations in the initial mass function (IMF), dust and metal production, and dust destruction for a set of supernova (SN) progenitor models and explosion energies. We find that log ξd ≈ −2.2 for all systems, which is indicative of the dominant role of SN dust production over destruction in these early galaxies. Such a value is strikingly different from the data, which instead indicates log ξd ≲ −4. We conclude that dust destruction alone can hardly explain the transparency of blue monsters. Other mechanisms, such as outflows, might be required.
Study energy consumption, evaporation rate of the blue swimming crab hatchery system
Authors: Thongplu Suwapitchakorn , Jiamrittiwong Prayut , Puangngernmak Nutdechatorn
Published: January 2025

The rising demand for blue swimming crab products has led to a decrease in their coastal populations. The Blue Crab Bank Project was launched to counteract this decline. This study aims at the energy consumption, seawater evaporation rate, and dissolved oxygen levels at different airflow rates of 2 l/min and 3 l/min. The results indicate that the airflow rates lead to a reduction in evaporation rates by 6.39% and 4.53%, respectively, without significantly affecting salinity levels. The study also finds that energy consumption for incubating crab eggs at 2 l/min is 0.025 kWh per crab, which is significantly lower (24.24% reduction) than at 3 l/min. Additionally, the dissolved oxygen level at the lower flow rate is 7.0 mg/l. These findings highlight the effectiveness of lower flow rates in reducing energy consumption and maintaining optimal environmental conditions for crab egg hatching, which can help in sustaining the population of blue swimming crabs and support the livelihoods of coastal fishermen.
Effect of tray drying temperature and drying time on properties of cricket powder
Authors: Simasatitkul Lida , Thongprom Kittisak , Jaroensang Thunyapisit , Im-orb Karittha
Published: January 2025

This research studied the production of cricket powder via tray drying and powdering processes by investigating the effect of drying temperature of 80, 90, 100oC and drying time ranging of 30 - 240 min on the properties of cricket powder. The drying rate increased while the moisture content decreased as drying temperature and drying time increased causing it easy to be ground and had good solubility. The cricket powder had dark brown color and its lightness decreased with increasing drying temperature. The produced cricket powder could reach equilibrium moisture content of approximately 5% and the water activity was decreased with increasing drying temperature. Regarding the nutrient content, the change in drying temperature in a range of 80-100oC had no significant effect on the nutrient content. The suitable drying condition of crickets offering the highest protein content of 70.35% was achieved at drying temperature of 80oC and drying time of 240 min. At this condition, 55.57% yield of cricket powder satisfying dry food specification (moisture content of 5.32% and aw of 0.3539) was obtained.
Comparative Study on Traffic Prediction Using Different Models
Authors: Jiao Zhtiofan
Published: January 2025

Traffic flow prediction (TFP) is a complex and critical field that is of great significance for urban planning, management, and resource allocation. This paper discusses the development history and optimization strategies of TFP models. This paper first introduces the importance of TFP and outlines the basic concepts and characteristics of traditional and modern TFP models. Subsequently, through comparative analysis, the prediction accuracy and applicability of the two models were discussed in depth. On this basis, the key factors affecting TFP accuracy are further analyzed, and the corresponding model optimization strategy is proposed. This paper proposes an improved method for fusion prediction by combining multiple data sources. Through these optimizations, the model is better able to respond to sudden traffic changes and improve the robustness and real-time prediction of the forecast. Finally, the research results are summarized and the future research directions are prospected. Through the systematic study of TFP models, this paper provides theoretical support and practical guidance for traffic management and planning, which has important academic value and application prospects.
Application and Comparative Study of Large Language Model in Early Prediction of Glaucoma
Authors: Xu Manyi
Published: January 2025

Glaucoma is an important problem in global public health. However, the symptoms of glaucoma are not obvious in the early stages, and the correct diagnosis of glaucoma is very challenging. This paper aims to explore and compare the application effects of several advanced large language models in the early prediction of glaucoma. The study selected a variety of large language models, including Chat Generative Pre-trained transformer (ChatGPT), to build a glaucoma prediction model and analyze clinical data, genetic information, and lifestyle data of patients. The results of empirical studies show that ChatGPT and other models show high accuracy and good generalization ability in predicting the risk of glaucoma, especially in identifying high-risk patients. In addition, the performance of different models on specific data sets is different, suggesting that model selection should be optimized according to actual application scenarios and data characteristics. This study not only provides a new technical means for the early screening of glaucoma but also expands a new direction for the application of large language models in the medical field.
A LiDAR - camera fusion detection method based on weight allocation
Authors: Kang Haotian , Wang Tianshu
Published: January 2025

In automatic driving target detection problem, the neural network is applied to two methods, vision, and LiDAR. These two methods have some relatively mature models based on neural networks. Combining the two to complement each other has become a hot topic. At present, most autonomous driving sensor fusion methods focus on fusion strategy and feature alignment, and there are few studies on the weight ratio of the two sensors after fusion in different environments. In this paper, a fusion target detection model of camera and LiDAR is proposed based on the weighted weight allocation method. The weighted fusion method is adopted, image feature points are extracted by Fast RCNN, and then LiDAR point cloud data is fused into the model by the weighted method, environment variables are introduced, and different weight allocation methods are output under different environments through full connection layer preprocessing. The results on the Nuscenes dataset show that compared with the results without weight assignment, the model can effectively achieve targeted weight assignment in different situations, and the performance is due to the single-sensor method.
Traffic Sign Recognition in Rainy Conditions Based on Federated Learning
Authors: Chen Yilin
Published: January 2025

The challenge of traffic sign recognition in rainy conditions poses significant difficulties for autonomous driving systems, primarily due to obscured visibility and altered sign characteristics. To tackle this issue, this paper simulated rainy environments to improve the recognition accuracy of traffic signs in real world. This paper utilized OpenCV to preprocess images by adding a rain effect, thereby enhancing the dataset’s realism. Subsequently, this study implemented a LeNet model within a Federated Learning framework, which enables decentralized training while preserving data privacy. The approach involved leveraging the Belgium Traffic Sign Classification Benchmark dataset, achieving an impressive accuracy of approximately 93% in recognizing traffic signs despite the simulated rainy conditions. The federated learning model effectively aggregated knowledge from multiple clients, resulting in a more resilient and efficient recognition system. The proposed method is demonstrated by experimental results to enhance performance in challenging weather conditions while also maintaining data privacy in machine learning applications. Overall, this paper underscores the potential of integrating federated learning with CNNs to improve traffic sign recognition capabilities.
Enhancing Sentiment Analysis with a CNN-Stacked LSTM Hybrid Model
Authors: Shao Shuaijie
Published: January 2025

This paper focuses on developing a new hybrid model to solve sentiment analysis problems in Natural language processing. Sentiment analysis is a key branch of Natural language processing (NLP) and new models with better performance can boost the development of machine learning. The new model mentioned in this research is a hybrid model containing convolutional neural network (CNN), stacked multi-layer long short-term memory (LSTM) and max pooling layers. This model uses CNN for its advantage of capturing local features in the sequence after the embedding process, and LSTM for its advantage of capturing long-term dependencies in such sequential data after CNN layer. The global max pooling layer can better organize the entire sequence. This model has been tested to show that it has a better performance than previously mentioned models when solving the sentiment analysis task based on IMDB dataset provided by TensorFlow. Introducing this new model in sentiment analysis may open new avenues for research. The performance of the model can be further improved, offering valuable insights for future hybrid model development in machine learning tasks.
Research on image generation technology based on deep learning
Authors: Li Jinchen
Published: January 2025

In the realm of image creation, deep learning stands out as an effective and valuable machine learning technique. Deep learning can automatically learn the intrinsic features of images， reaching the goal of generating high-quality images by utilizing multi-layer neural network models. In recent years, deep learning-based image generation technology has made significant progress. This paper mainly introduces two main methods: generating adversarial network (GAN) and variational autoencoder (VAE). GAN has been widely used in image generation, image repair and other aspects. VAE has a good performance in image generation, image classification and so on. However, current image generation technologies still face problems such as diversity and insufficient authenticity. Based on the above problems, this paper analyzes the methods of improving and optimizing the mainstream image generation algorithm from the perspectives of improving and optimizing the loss function, improving the space modeling, revising the structure of both the generator and discriminator, while speeding up the training process. Furthermore, the performance of these methods in image generation tasks is compared, and the strengths and weaknesses of each approach are evaluated. Image generation has emerged as a prominent research area in contemporary academia, with a high possibility of exploration and practice.
Particle Swarm Optimization Algorithm and Its Application in Image Segmentation
Authors: Yu Liyang
Published: January 2025

The complexity and scale of image data are growing due to the fast advancement of medical imaging technology. As a result, traditional image segmentation methods are finding it difficult to handle these data, as they typically perform poorly when faced with complex structures and noise. In order to greatly increase the segmentation accuracy and resilience, this study presents the Particle Swarm Optimization (PSO) technique, which optimizes the segmentation parameters through global search, and K-means in image segmentation for fatty liver level recognition and their optimization strategies. The paper concludes that heuristic optimization algorithms such as the PSO have gained considerable focus. This paper provides insight into the application of the PSO algorithm, Otsu’s Method, and the Watershed within the field of image segmentation for their global search capability and adaptability to complex problems. Meanwhile, classical segmentation methods such as OSTU, Watershed and K-means are also widely used in medical image processing due to their simplicity and effectiveness. The PSO simulates information sharing and collaboration between individuals in a population to optimize the process of image segmentation. This paper concludes that the PSO outperforms traditional methods such as OSTU, Watershed Algorithm and K-means clustering in image segmentation for liver fat level identification, especially in both complex images and lighting conditions, and shows strong accuracy and stability.
Advancing Computational Humor: LLaMa-3 Based Generation with DistilBert Evaluation Framework
Authors: He Jinliang , Mei Aohan
Published: January 2025

Humor generation presents significant challenges in the field of natural language processing, primarily due to its reliance on cultural backgrounds and subjective interpretations. These factors contribute to the variability of human-generated humor, necessitating computational models capable of mastering diverse comedic styles with minimal subjectivity and maximal generalizability. This study introduces a novel approach to humor generation by fine-tuning the LLaMA-3 language model with Low-Rank Adaptation (LoRA). The study developed a comprehensive dataset sourced from diverse online platforms, supplemented by non-humorous content from scientific literature and press conferences to enhance the model's discriminative capabilities. Utilizing DistilBERT for efficient evaluation, the fine-tuned LLaMA-3 achieved an impressive accuracy of 95.6% and an F1-score of 97.75%, surpassing larger models such as GPT-4o, and Gemini. These results demonstrate the model's exceptional capability in generating humor, offering a more efficient and scalable solution for applications such as conversational agents and entertainment platforms. This research advances the field by showcasing the benefits of comprehensive dataset preparation and targeted fine-tuning, providing a foundation for future developments in humor-related artificial intelligence applications.
Analyzing the Application of Machine Learning in Anemia Prediction
Authors: Li Yuxi
Published: January 2025

This paper explores the applications of machine learning in the prediction of anemia, highlighting its potential to revolutionize clinical diagnosis and management. Anemia, a prevalent condition affecting millions globally, is often underdiagnosed due to traditional diagnostic methods that rely on clinical judgment and standard laboratory tests. Machine learning techniques provide innovative solutions by analyzing complex datasets that incorporate questionnaire, clinical features, demographic information, and laboratory results, thereby enhancing the accuracy of anemia predictions. This paper examines decision trees, random forests, support x'ector machines, and neural networks. emphasizing their efficacy in identifying patterns and risk factors associated with anemia. Obstacles such as data quality, feature selection, and model interpretability continue to hinder clinical adoption. The review identifies future research directions aimed at improving model generalizability and interpretability, ensuring that these technologies can be effectively integrated into healthcare practice. This paper advocates for the systematic adoption of machine learning methodologies in anemia management, positing that such innovations are crucial for advancing public health and optimizing resource allocation in clinical settings.
Evaluating the Performance of SVM, Isolation Forest, and DBSCAN for Anomaly Detection
Authors: Lu Haowen
Published: January 2025

With the advancement of computer technologies, various data models and algorithms have been integrated into industrial processes, significantly improving the efficiency of anomaly detection in datasets while reducing time and energy consumption. Identifying the most effective algorithm for anomaly detection is essential for enhancing nidustrial productivity. This study evaluates the suitability of three algorithms—Support Vector Machine (SVM), Isolation Forest, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by comparing their accuracy and time efficiency in detecting outliers in different types of datasets. The algorithms are tested across various datasets, and their performance is systematically compared. The results are then analysed in relation to the structure of each algorithm to identify their respective advantages and disadvantages. The study finds that each algorithm performs differently depending on the dataset type. Specifically, SVM demonstrates superior performance in detecting point anomalies, while DBSCAN is more effective when the dataset is pre-processed. Additionally. Isolation Forests are most efficient at identifying collective anomalies within the dataset.
Forecasting the Treasury Yield Spread for FRED T10Y2Y Data Based on Multiple Approaches
Authors: Leu Ying Chen
Published: January 2025

As a matter of fact, price prediction for financial underlying assets always been a hot topic in financial fields in recent years with high volatility. With this in mind. this study looks into the usage of machine learning models to predict the yield spread between 10-year and 2-year US Treasury bonds (T10Y2Y). Based on the data from the Federal Reserve Economic Data (FRED) database (1976-2024), this study assesses the performance of four different models: multi-layer perceptron (MLP) regression, LSTM, ARIMA model and Facebook Prophet model. Each model’s performance is measured using MAE, MSE, RMSE as well as F2 score. The results show that both MLP regression and LSTM models achieve high accuracy in predicting the yield spread. However, MLP regression outperforms LSTM in terms of producing more reasonable future predictions, particularly over longer time periods. ARIMA and Prophet, while effective for linear forecasts, were confused by the data and made unreasonable and incorrect predictions