bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improving quantification accuracy of a nuclear Overhauser enhancement signal at −1.6 ppm at 4.7 T using a machine learning approach
Authors: Yin Leqi , Viswanathan Malvika , Kurmi Yashwant , Zu Zhongliang 
Published: January 2025

Abstract

Objective. A new nuclear Overhauser enhancement (NOE)-mediated saturation transfer MRI signal at −1.6 ppm, potentially from choline phospholipids and termed NOE(−1.6), has been reported in biological tissues at high magnetic fields. This signal shows promise for detecting brain tumors and strokes. However, its proximity to the water peak and low signal-to-noise ratio makes accurate quantification challenging, especially at low fields, due to the difficulty in separating it from direct water saturation and other confounding signals. This study proposes using a machine learning (ML) method to address this challenge. Approach. The ML model was trained on a partially synthetic chemical exchange saturation transfer dataset with a curriculum learning denoising approach. The accuracy of our method in quantifying NOE(−1.6) was validated using tissue-mimicking data from Bloch simulations providing ground truth, with subsequent application to an animal tumor model at 4.7 T. The predictions from the proposed ML method were compared with outcomes from traditional Lorentzian fit and ML models trained on other data types, including measured and fully simulated data. Main results. Our tissue-mimicking validation suggests that our method offers superior accuracy compared to all other methods. The results from animal experiments show that our method, despite variations in training data size or simulation models, produces predictions within a narrower range than the ML method trained on other data types. Significance. The ML method proposed in this work significantly enhances the accuracy and robustness of quantifying NOE(−1.6), thereby expanding the potential for applications of this novel molecular imaging mechanism in low-field environments.
Spatio-temporal modeling and simulation of a mode-locked tapered semiconductor diode laser
Authors: Wohlfeil Shulin , Koester Jan-Philipp , Wenzel Hans , Knigge Andrea 
Published: January 2025

Abstract
We present a theoretical model and numerical simulation results of the dynamics of a monolithic edge-emitting mode-locked tapered quantum well laser. The comprehensive simulation employs a

(
2
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1
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dimensional traveling wave model and incorporates models for lateral current spreading and carrier diffusion, while also addressing thermal effects in the laser cavity. Our investigation of the pulse generation by passive mode-locking demonstrates good agreement with experimental observations. Furthermore, we perform a study of the intra-cavity propagation dynamics of the laser, which provides valuable insights for a better understanding of the laser’s behavior.
Divergent impacts of soil desiccation on atmospheric water vapor–temperature responses regulated by evapotranspiration
Authors: Wang Ruonan , Fan Xingwang , Liu Yongwei , Zhao Xiaosong , Wang Rong , Liu Yuanbo 
Published: January 2025

Abstract
Climate warming induces temporally varying atmospheric water vapor (WV), yet the spatial distribution of opposing trends across global land remains elusive. Here, we use the monthly European Centre for Medium-range Weather Forecasts Reanalysis v5 dataset to discern the responses of WV changes to the rising air temperature from 1982 to 2020. Simultaneous increases in both the WV and air temperature are observed over approximately three-quarters of global land, with a median of 0.21 mm·K–1, particularly evident in the tropics. Strong positive responses are primarily influenced by increasing trends in evapotranspiration (ET) and low-elevation areas. About one-fifth of global land shows a decline in WV with a median of −0.62 mm·K–1, predominantly in southeastern South America and southwestern North America. Negative responses are also driven by ET trends, where strong ET enhances these effects that are less pronounced in high-altitude regions. The prevalence of a positive response is highest during September–October–November (81%), while a negative response was observed most in December–January–February (35%). The spatial distribution of negative responses generally aligns with soil desiccation patterns; soil desiccation exacerbates negative responses in humid regions due to evaporative cooling but mitigates them in arid regions due to intensified warming. This study enhances our comprehension regarding the divergent responses of atmospheric WV toward global warming.
Generalised Ornstein–Uhlenbeck process: memory effects and resetting
Authors: Trajanovski P , Jolakoski P , Kocarev L , Metzler R , Sandev T 
Published: January 2025

Abstract
In this work we consider a generalised Ornstein–Uhlenbeck (O–U) process for a stochastically driven particle in an harmonic potential which is governed by a Fokker–Planck equation in the presence of a memory kernel. We analyse the probability density function, the mean and the mean squared displacement (MSD) by employing the subordination approach connecting the operational time of the process with the (generalised) laboratory time. We provide analytical results for the mean and the MSD in case of a power-law memory kernel which corresponds to the fractional O–U process. The generalised O–U process in the presence of Poissonian resetting is also investigated by using the renewal equation approach, and the nonequilibrium stationary state approached in the long time limit is obtained. The analytical results are confirmed by numerical simulations based on the coupled Langevin equations.
Probability theory in ghost imaging with thermal light: progress and applications
Authors: Zhao Yi-Ning , Chen Lin-Shan , Kong Lingxin , Zhang Su-Heng , Wang Chong , Ren Cheng , Cao De-Zhong 
Published: January 2025

Abstract
Ghost imaging reconstructs images by using two separate, but correlated beams. One beam illuminates the object and then is collected by a bucket detector. The other beam is captured by a space-resolved (pixelated) reference detector. The object information is retrieved in the correlation measurements of the two detectors. Ghost imaging can be implemented with entangled photon pairs, or with intensity correlation of thermal light (TL). For the latter, recently, a theory of joint probability density function has been proposed to investigate the high-order correlation functions of TL. The probability theory is based on two assumptions. The intensity of the reference beam inside the image resolution is assumed to be a stochastic variable, which meets a certain probability density function. The second assumption is that the bucket signals are the sums of the stochastic variables from the object. Then the joint probability density function of the bucket and reference signals can then be established. As a result, the probability theory can predict the nonlocal behaviors of ghost imaging with TL (GITL). The progress and applications of the probability theory in the GITL experiments were reviewed in this paper.
The validity of physical optics for microwave analysis: accuracy through simplicity
Authors: Mesa Francisco , Quevedo-Teruel Oscar 
Published: January 2025

Abstract
Traditionally, the optics and microwave communities have been separated not only by the THz band but also by differences in technical knowledge, analytical and computational approaches, design processes, and measurement methodologies. In this contribution, we aim to bridge this divide by leveraging a computational asymptotic technique from optics, known as physical optics, which holds significant potential for the microwave/antenna community. This technique allows for precise microwave calculations for radiation patterns, reflection coefficients, material losses, and mutual coupling between antennas.
Si doping enhances the tribocorrosion resistance of DLC films in acidic, alkaline and salt solutions
Authors: Zhou Hongwei , Yao Yifan , Niu Weiming , Xiao Yuping , Shang Lunlin , Li Hao , Zhu Minhao 
Published: January 2025

Abstract
The a-C:H and a-C:H:Si were fabricated by plasma-enhanced chemical vapor deposition. The microstructures, surface morphologies, mechanical properties, corrosive properties and tribological properties of the a-C:H and a-C:H:Si films were systematically investigated. The findings revealed that silicon doping reduces the residual stress, hardness and modulus of as-fabricated films while increasing the sp3 content. As a result, silicon doping enhances the thickness and densification of a-C:H:Si films. This enhancement serves to prevent corrosive solutions from penetrating the film-substrate interface through cracks and averting the risk of catastrophic failure of the films due to accumulation of corrosion products at the interface. The composition of the boundary lubrication film formed in NaCl, HCl, and NaOH solutions was discussed by examining different reactions between samples and ZrO2 spheres with corrosive solutions at varying pH levels. Additionally, the a-C:H:Si films exhibited favorable tribological performances in corrosive solutions, as evidenced by low friction coefficients and low wear rates. In 96 h neutral salt spray experiments, it was noted that a-C:H:Si films exhibit limited spalling, indicating consistent protection for SUS 304 stainless steel. These findings highlight the significance of doped Si atoms in carbon films for enhancing their tribocorrosion resistance properties.
Novel approach for quality control testing of medical displays using deep learning technology
Authors: Maruyama Sho , Mizutani Fumiya , Watanabe Haruyuki 
Published: January 2025

Abstract

Objectives: In digital image diagnosis using medical displays, it is crucial to rigorously manage display devices to ensure appropriate image quality and diagnostic safety. The aim of this study was to develop a model for the efficient quality control (QC) of medical displays, specifically addressing the measurement items of contrast response and maximum luminance as part of constancy testing, and to evaluate its performance. In addition, the study focused on whether these tasks could be addressed using a multitasking strategy. Methods: The model used in this study was constructed by fine-tuning a pretrained model and expanding it to a multioutput configuration that could perform both contrast response classification and maximum luminance regression. QC images displayed on a medical display were captured using a smartphone, and these images served as the input for the model. The performance was evaluated using the area under the receiver operating characteristic curve (AUC) for the classification task. For the regression task, correlation coefficients and Bland–Altman analysis were applied. We investigated the impact of different architectures and verified the performance of multi-task models against single-task models as a baseline. Results: Overall, the classification task achieved a high AUC of approximately 0.9. The correlation coefficients for the regression tasks ranged between 0.6 and 0.7 on average. Although the model tended to underestimate the maximum luminance values, the error margin was consistently within 5% for all conditions. Conclusion: These results demonstrate the feasibility of implementing an efficient QC system for medical displays and the usefulness of a multitask-based method. Thus, this study provides valuable insights into the potential to reduce the workload associated with medical-device management the development of QC systems for medical devices, highlighting the importance of future efforts to improve their accuracy and applicability.
Impact of green finance on poverty reduction: evidence from China
Authors: Xu Wei , Wang Xiaoning , Zhang Yun , Wang Feng 
Published: January 2025

Abstract
Integrating environmental sustainability within the financial sector, known as green finance, offers a promising approach to poverty reduction in China. This study investigates the impact of green finance on poverty reduction in China. We construct a comprehensive index of green finance, which includes green credit, green insurance, and green investment, to evaluate its development across China’s provinces. We uncover the intricate relationship between green finance and poverty reduction using panel data regression analysis on a dataset spanning 30 provinces from 2010 to 2021. Our findings indicate a positive and significant association between the advancement of green finance and poverty reduction. Notably, this effect is most pronounced in Eastern China, intermediate in Central China, and the least in Western China. This regional disparity underscores the need for geographically sensitive policy interventions to harness green finance’s poverty-reducing potential effectively. Furthermore, we identify economic growth and industrial structure upgrading as primary mediators between green finance and poverty reduction. While environmental improvement plays a role, its impact as a mediator is relatively small, suggesting a more nuanced approach to integrating environmental sustainability with poverty reduction strategies. The study contributes to the literature by providing empirical evidence on the effectiveness of green finance in poverty reduction. It offers insights for policymakers to enhance green finance strategies, emphasizing economic growth, industrial advancement, and sustainable development.
Toward zero-excess lithium sulfur batteries: a systematic cell parameter study
Authors: Cruddos Joshua H , Robinson James B , Shearing Paul R , Rettie Alexander J E 
Published: January 2025

Abstract
Zero-excess lithium (ZEL) or ‘anode-free’ batteries aim to minimize negative electrode material and address the challenges associated with handling thin lithium metal foils during fabrication. To date, most studies in the field of ZEL cells have primarily focused on lithium-ion chemistry, with considerably fewer systematic investigations into ZEL-sulfur (ZELiS) cell fabrication and optimization. Here we develop a ZELiS battery, comprising a Li2S-based composite positive electrode on carbon paper paired with a Ni foil current collector (CC) and evaluate the effects of various CC materials, electrolyte volume to Li2S mass ratio and C-rate. The developed cells reproducibly achieve an average Coulombic efficiency of 99% from cycles 2 to 200, and a final capacity of 272 mAh g−1
Li2S at a C/10 rate. Furthermore, we employ x-ray computed tomography to elucidate the morphological changes and degradation processes occurring within the positive electrode composite, revealing the irreversible loss of Li2S/S8 during cycling, which is exacerbated at high rates. These results should be useful in the development of commercially viable ZEL energy storage devices.
ADL-Net: attention-driven densenet-LSTM network for underwater images classification
Authors: Asam Yusuf , Zhou Zhiyu 
Published: January 2025

Abstract
Imaging in marine environment is a challenging task due to several reasons, including light variations, color distortions, noise, and limited datasets, which causes inaccurate target classification problems. This paper presents a methodology for image classification in marine biology and environmental research. We proposed the Attention-Driven DenseNet-LSTM Network (ADL-Net), aiming to improve classification accuracy of underwater imagery. Initially, feature extraction is performed using multi-levels of DenseNet201, which excels in hierarchical feature extraction, offering stability and rapid convergence. In the next stage, two modified attention mechanisms are designed: the squeeze and excitation operations are used to refine channel-specific feature responses, while the convolutional block attention module refines attention for both channels and spatial dimensions. These attention mechanisms help the model to highlight important features and minimize distractions. Additionally, a Bi-directional Long Short-Term Memory layer is integrated to capture spatial dependencies and improve classification robustness. Various training strategies are used to find the best parameter tuning. Our method demonstrates excellent performance when tested on fish datasets: LifeCLEF 2015 and Fish4Knowledge, achieving accuracies of 98.02%, and 99.52%, respectively. These findings underscore ADL-Net’s potential for enhancing automated underwater image classification, advancing research in marine ecology.
A method to investigate the influence of temperature and working conditions on the pressure distribution of hydrodynamic bearings
Authors: Pham Van Hung , Le Duc Bao , Nguyen Thuy Duong 
Published: January 2025

Abstract
This paper investigates the influence of oil temperature and operating conditions on the pressure distribution in a hydrodynamic bearing using an innovative measurement method. The method employs a single pressure sensor mounted on the bearing bush, which is gradually rotated around the bearing circumference using a differential mechanism. Experiments were performed at oil inlet temperatures of 40 °C, 60 °C, and 80 °C, with rotational speeds of 1100, 1500, and 1900 rpm. The findings reveal that the maximum pressure (Pmax​) increases by approximately 48%–56% as the rotational speed rises from 1100 to 1900 rpm. Conversely, Pmax​ decreases by about 19%–22% when the oil temperature increases from 40 °C to 80 °C. The study concludes that the effect of rotational speed on Pmax​ is significantly greater than that of oil temperature.
An investigation from raw components to composite: 3D printed Voronoi lattice core structured sandwich composite with woven glass fiber-epoxy outer layer
Authors: Gür Yılmaz , Benlikaya Ruhan , Çelik Sare 
Published: January 2025

Abstract
A key emerging trend in mechanical engineering design involves adopting strategies that build lightweight lattice geometries, particularly for sandwich structures. These structures are characterized by low mass, a significant surface area to volume ratio, high porosity, and suitable mechanical behavior. This study focuses on the fabrication and investigation of thermoplastic polyurethane (TPU) Voronoi lattice core (V-core) structured sandwich composite with woven glass fiber (WGF)-Epoxy outer layer. The TPU V-Core structure was fabricated using the Fused Filament Fabrication (FFF) three dimensional (3D) printing method. Fourier Transform Infrared Spectroscopy (FTIR), Raman Spectroscopy, and Thermogravimetric Fourier Transform Infrared Spectroscopy (TG-FTIR) analyses were used to examine the interactions between the components within the sandwich composite structure. The experimental results demonstrated that the investigated structure has intricate interactions at the interface between the WGF-Epoxy outer layer and the V-core structure. The thermal stability and thermal degradation pathway of the composite and its components were also examined. In addition, three-point bending (TPB) tests were conducted on the sandwich composite and also on the core structures with plain rectangular prism and Voronoi lattice to assess their mechanical behavior.
Research on heating temperature control method of VOC gas sensor and development of new sensor module
Authors: Wang Bo , Ma Minghao 
Published: January 2025

Abstract
Sensors made of various new materials exhibit high sensitivity and accuracy in detecting volatile organic compounds (VOCs). However, they also demand higher working temperatures to unleash their outstanding performance within a certain temperature range. Furthermore, the fluctuation of ambient temperature throughout the year significantly impacts the heating temperature of the sensors. Conventional commercial sensor modules utilizing constant voltage heating are no longer able to meet the precise temperature control requirements. Therefore, our research team conducted a heat transfer analysis to establish the mathematical relationship between heating temperature and voltage. This was subsequently validated through finite element analysis, leading to the development of a multivariate linear regression model for predicting voltage based on the desired target temperature and ambient temperature. To address this, we proposed an approach to automatically adjust the heating temperature by controlling the heating voltage duty cycle using a microcontroller based on the ambient temperature. As a result, a novel adaptive temperature-controlled sensor module was developed. Experimental tests demonstrated that within the heating temperature range of 263 °C–464 °C, the substrate temperature could be controlled with an average error of 4.36%, meeting the precision requirements for sensor heating temperature control. In comparison with the commercial module, the temperature-controlled module exhibited a notable improvement in sensitivity when responding to different ambient temperatures. At 0 °C, the average sensitivity enhancement for the MQ3 and MQ7 sensors compared to the commercial module was 12.77% and 31.5%, respectively, and at 40 °C, the average enhancements were 5.94% and 19.8%. This sensor module and temperature control method provide theoretical and technical references for the accurate measurement of VOC parameters.
Dynamic configuration optimization of FPGA accelerators through reinforcement learning for enhanced performance and resource utilization
Authors: Pal Sandipan , Upadhyaya Bijoy Kumar , Majumder Tanmoy , Dasgupta Sudeb , Das Narottam , Bhattacharjee Abhishek 
Published: January 2025

Abstract
Deploying Sparse Ternary Neural Networks on edge devices is in the areas of computational efficiency and energy optimization is a challenging task. This work presents a new FPGA-based accelerator integrating reinforcement learning and neural architecture search to dynamically optimize Sparse Ternary Neural Networks (Sparse TNN) for real-time applications. The design adopts adaptive pruning and quantization techniques for computational complexity and power consumption with the desired accuracy. Experimental evaluation on the Xilinx ZCU102 platform achieves up to 16.46 × speedup compared to dense models at less than 1% accuracy loss and achieves state-of-the-art performance on benchmarks such as Google Net and MobileNetV2. This work holds promise for resource-constrained high-throughput applications, bringing FPGA-based deep learning closer to efficiency and scalability.
Synthesis of biomass-derived carbon quantum dots/polyaniline nanocomposite for photocatalytic degradation of brilliant green dye and bioimaging applications
Authors: Qurtulen Qurtulen , Mujahid Mohammad , Al-Hartomy Omar A , Alzahrani Ahmed Obaid M , Ahmad Asrar 
Published: January 2025

Abstract
Currently, highly efficient nano photocatalysts driven by visible light are being developed for organic pollutant removal and bioimaging applications. Carbon quantum dots (CQDs) have been shown to enhance the photocatalytic activity of nanocomposites. In this work, we report the surface modification of a polyaniline (PANI) photocatalyst using hydrothermally produced CQDs. The synthesized nanocomposite (CQDs/PANI) was characterized using advanced analytical techniques. These studies confirmed the presence of PANI and CQDs and demonstrated their roles in improving electron-hole pair separation and reducing the band gap of PANI. The photocatalytic activity of the CQDs/PANI (5 wt%) nanocomposite was evaluated under visible light irradiation for the degradation of Brilliant Green dye in aqueous solution. The synthesized photocatalyst achieved nearly 100% dye removal after 35 min of irradiation. The kinetics of the dye degradation was predicted using the Langmuir–Hinshelwood model, with a rate constant (kapp) of 0.0351. The study also investigated the effects of initial dye concentration, pH, and photocatalyst dosage on the photocatalytic activity of CQDs/PANI (5 wt%). The optimal conditions were 15 mg of photocatalyst per 50 ml of a 10-ppm dye solution, with a neutral pH of 7. The photocatalyst demonstrated excellent stability and reusability over four reaction cycles. The exact cell line of human breast cells used in this work is MCF-7. Additionally, the nanocomposite was evaluated for cell imaging in human breast cancer cells (MFC-7), showing excellent biocompatibility and low cytotoxicity.