bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of pre‐examination video education in Gd‐EOB‐DTPA‐enhanced liver MRI: A comparative study
Authors: Huang Hongfang , Li Chenhui , Zeng Zisan , Liang Junli 
Published: November 2024

AbstractIntroductionHepatocellular carcinoma (HCC) is a leading cause of cancer‐related mortality, and early diagnosis via gadolinium ethoxybenzyl‐diethylenetriamine pentaacetic acid (Gd‐EOB‐DTPA)‐enhanced magnetic resonance imaging (MRI) significantly impacts patient outcomes. However, patient anxiety during MRI can affect image quality. This study investigates the impact of pre‐examination video education on anxiety, satisfaction and image quality in Gd‐EOB‐DTPA‐enhanced liver MRI.MethodsWe prospectively enrolled 480 patients who underwent Gd‐EOB‐DTPA‐enhanced liver MRI from January 2022 to May 2023 at our hospital. Patients were divided into study and control groups in order of odd and even days, with 240 cases in each group. Before the examination, the radiology staff provided routine verbal guidance and breathing training to the patients in the control group, while the study group was given additional video education. The state anxiety scores, satisfaction scores of the provided information and motion artefact scores of the images before and after the examination were compared between the two groups.ResultsThe state anxiety scores of both groups of patients were lower than before the examination (all P < 0.05), but the change value of the study group was significantly greater than that of the control group (P = 0.004). The satisfaction rate of the information provided before the scan in the study group was significantly higher (P < 0.001). The image quality scores of the arterial phase were similar between the two groups (P = 0.403), but the image quality of the study group in the pre‐contrast, portal phase, transitional phase and hepatobiliary phase was significantly better than that of the control group (all P < 0.05).ConclusionSupplementing routine pre‐scan care with video guidance for Gd‐EOB‐DTPA‐enhanced liver MRI offers several benefits, including reduced patient anxiety, increased satisfaction and improved image quality. These results suggest the potential for widespread application of video‐based interventions to enhance the MRI experience for patients.
Advancing the Commercialization of Perovskite‐Based Radiation Detectors for High‐Resolution Imaging
Authors: Zhang Wenqing , Wang Hu , Chen Zhilong , Shao Yuchuan 
Published: November 2024

AbstractRadiation detectors play an indispensable role in medical diagnostics, industrial non‐destructive inspection and national security. Recently, halide perovskites are considered as the new generation of radiation active materials due to excellent optoelectronic properties such as adjustable bandgap, high absorption coefficient, high carrier mobility and low cost. The radiation detectors based on perovskite show high sensitivity and low detection limit, contributing to excellent spatial resolution for imaging. However, the commercialization of perovskite radiation detectors for high quality imaging still faces many challenges, including ion migration in perovskite, fermi level pinning and electrochemical reaction at the interface of perovskite/electrode, and difficulties of integration with readout circuit. All the issues hinder the further improvement of device performance. This review summarizes the material forms and the optimized growth methods of perovskite for radiation imaging detectors. Further, this work focuses on challenges and improvements of the interface between perovskites and electrodes. Meanwhile, this work outlines the technical routes used to realize array detectors for radiation imaging. The comprehensive review would guide the commercialization of perovskite radiation detectors for high‐quality imaging.
Suppressing the Dark Current Under Forward Bias for Dual‐Mode Organic Photodiodes
Authors: Xiao Jianhua , Wang Yang , Wu Jiaao , Yuan Liu , Tai Huiling , Jiang Yadong 
Published: November 2024

AbstractTremendous research efforts are developed to suppress the reverse dark current (Jd) and enhance the responsivity of organic photodiodes (OPDs). The functional layers of traditional OPDs usually follow the principle of energy level alignment to make unobstructed photo‐carriers transport under reverse bias, but this inevitably leads to a large forward Jd. Herein, a universal strategy is proposed to manipulate the carrier dynamics and effectively suppress the forward Jd of OPDs, that is, tuning the energy level and electron traps of the anode interface layers (AILs). The bandgap and electron traps of typical organometallic chelate AIL (PEIE‐Co) can be well controlled by adjusting the component ratio of PEIE and metal ions. The wide bandgap increases the carrier injection barrier under reverse and forward bias, endowing OPD with a much lower Jd; the electron traps induce hole tunneling injection by capturing photo‐generated electrons under forward bias, thereby enabling the photomultiplication effect. The obtained OPD exhibits photoconductive/photomultiplication working mode at reverse/forward bias and the specific detectivity approaches ≈1013/1012 Jones, showing promise for adaptively detecting faint and strong light. This study presents an intelligent strategy to achieve dual‐mode OPDs, paving the way for the multifunctional development of photodetectors.
Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field
Authors: Xue Hemeng , Shang Mingtao , Zhang Ze , Yu Hongfei , Liang Jinchao , Guan Meiling , Sun Chengming , Wang Huahua , Wang Shufeng , Ye Zhengyu , Gao Feng , Gao Lu 
Published: November 2024

AbstractThe transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount.
Highly Efficient Flexible Antimony Halide Scintillator Films with In Situ Preparation for High‐Resolution X‐Ray Imaging
Authors: Meng Haixing , Li Ying , Wang Ying , Zhu Minqi , Xiao Jiawen , Shen Guozhen 
Published: November 2024

AbstractFlexible scintillators with high light yield, spatial resolution and low light scattering are ideal for X‐ray imaging application. However, conventional scintillators are always prepared by crystallization of functional layer, grinding and mixing with polymers, resulting in serious light scattering. Herein, an in situ fabrication strategy is proposed to prepare a low light scattering flexible scintillator film based on 0D antimony halide C38H36P2SbCl5 (MTP2SbCl5). The prepared scintillator film exhibits bright yellow emission with an outstanding photoluminescence quantum yield (PLQY) of 99.69%, and it demonstrates linear responsiveness to X‐ray dose, achieving an impressive light yield of 39800 photons MeV−1 and a low detection limitation of 78.4 nGyair s−1. The scintillator film possesses strong radiation hardness and stability. In addition, low light scattering greatly inhibits optical crosstalk during X‐ray detection, effectively improving the spatial resolution of MTP2SbCl5 film from 4.5 to 10.2 lp mm−1. On account of the simple preparation method and high performance, this work provides guidance for the preparation of high‐efficiency, large‐area, low‐scattering and high‐resolution flexible scintillator in the future.
A Hyperbranched Phosphorus/Nitrogen/Silicon‐Containing Polymer as a Multifunctional Additive for Epoxy Resins
Authors: Zhong Qian , Huo Siqi , Wang Cheng , Ye Guofeng , Zhang Qi , Wang Hao , Liu Zhitian 
Published: November 2024

AbstractHigh‐performance, versatile epoxy resins (EPs) are used in a variety of fields, but the manufacture of transparent, fireproof, and strong EPs remains a major challenge. The hyperbranched, multifunctional flame retardant (DSi) is prepared by using diethanolamine, polyformaldehyde, diphenylphosphine oxide, and phenyltrimethoxysilane as raw materials in this work. When the additional amount of DSi is only 2 wt.%, the EP‐DSi2 sample reaches a vertical burning (UL‐94) V‐0, and its limiting oxygen index (LOI) is 32.8%. When the content of DSi is 3 wt.%, the peak heat release rate (PHRR) and total smoke production (TSP) of EP‐DSi samples are 43.8% and 21.4% lower than those of EP. The good compatibility of DSi and EP endows EP‐DSi with high transparency, and the hyperbranched structure of DSi makes EP‐DSi have obviously enhanced mechanical strength and toughness. The enhanced fire safety of EP‐DSi is mainly due to the promoting carbonization and radical quenching effects of DSi. This paper offers a comprehensive design concept aimed at creating high‐performance epoxy resins with good optical, mechanical, and flame‐retardant properties, which have broad application prospects.
Recent advances in the application of functional hydrogels in skin wound healing
Authors: Bei Zhongwu , Zheng Jing 
Published: November 2024

AbstractThe effective treatment of skin wounds has long posed a significant challenge in the medical field, impacting patient comfort, quality of life, and the rate and outcome of wound healing. With continuous advancements in science and technology, novel materials have emerged, providing new possibilities for skin wound treatment. Among these, multifunctional hydrogels have shown considerable potential in promoting skin wound healing as a type of wound dressing material. This review systematically examines the progress in the application of multifunctional hydrogels in skin wound healing. Initially, the structure and composition of the skin are introduced. Subsequently, skin wounds are classified, and the wound‐healing process is discussed in detail. Traditional and modern dressings are then categorized, with a particular emphasis on the characteristics and applications of hydrogel dressings. The various functions of hydrogels in skin wound healing, including antibacterial, antioxidant, hemostatic, adhesive, stimulus‐responsive, and wound status monitoring, are reviewed. The paper concludes with a summary of the existing research gaps and provides insights into the future development directions of multifunctional hydrogels. This review aims to guide the preparation of hydrogel wound dressings and offer theoretical references for the exploration of next‐generation functional hydrogels.
Multi‐scale contextual learning for medical image segmentation via dual distillation
Authors: Cui Ruize , Liu Lanqing , Song Youyi , Ren Ge , Hu Xiaowei , Qin Jing 
Published: November 2024

AbstractBackgroundRecently, many studies have explored fusing features extracted from Convolutional neural networks (CNNs) and transformers to integrate multi‐scale representations for better performance in medical image segmentation tasks. Although these hybrid models have achieved better results than previous CNN‐based and transformer‐based methods, they suffer from high computation and space complexities.PurposeThe purpose of this research is to address the prohibitive computation and space complexities of hybrid models, which limit their application in clinical practice where computational resources are usually constrained.MethodsWe propose a novel model equipped with a dual distillation scheme to sufficiently harness the complementary advantages of CNNs and transformers without compromising model efficiency. We further propose a multi‐scale prior‐knowledge distillation (MPD) module to effectively distill multi‐scale knowledge from features extracted from transformers. In addition, to cooperate with the knowledge distillation scheme, we also propose an efficient and robust Selective Fusion module in the student network.ResultsWe extensively evaluate the proposed model against fourteen different network frameworks on two representative datasets: SipakMed and ISIC 2017. In the SipakMed dataset, 3037 Pap smear images are used for training and 1012 for testing. In the ISIC 2017 dataset, 2000 dermoscopic images are used for training, 150 for validation, and 600 for testing. Experimental results demonstrate that our method not only surpasses existing methods by a considerable margin with respect to the evaluation metrics of mean Intersection over Union, mean Dice coefficient, mean average symmetric surface distance, but also requires fewer computational resources in terms of model parameters and floating‐point operations per second.ConclusionsComprehensive comparisons in terms of segmentation accuracy and computational complexity unequivocally confirm that our method effectively and efficiently integrates the advantages of both CNNs and transformers, showing its suitability and significance for clinical applications.
MRI denoising with a non‐blind deep complex‐valued convolutional neural network
Authors: Dou Quan , Wang Zhixing , Feng Xue , Campbell‐Washburn Adrienne E. , Mugler John P. , Meyer Craig H. 
Published: November 2024

AbstractMR images with high signal‐to‐noise ratio (SNR) provide more diagnostic information. Various methods for MRI denoising have been developed, but the majority of them operate on the magnitude image and neglect the phase information. Therefore, the goal of this work is to design and implement a complex‐valued convolutional neural network (CNN) for MRI denoising. A complex‐valued CNN incorporating the noise level map (non‐blind DnCNN) was trained with ground truth and simulated noise‐corrupted image pairs. The proposed method was validated using both simulated and in vivo data collected from low‐field scanners. Its denoising performance was quantitively and qualitatively evaluated, and it was compared with the real‐valued CNN and several other algorithms. For the simulated noise‐corrupted testing dataset, the complex‐valued models had superior normalized root‐mean‐square error, peak SNR, structural similarity index, and phase ABSD. By incorporating the noise level map, the non‐blind DnCNN showed better performance in dealing with spatially varying parallel imaging noise. For in vivo low‐field data, the non‐blind DnCNN significantly improved the SNR and visual quality of the image. The proposed non‐blind DnCNN provides an efficient and effective approach for MRI denoising. This is the first application of non‐blind DnCNN to medical imaging. The method holds the potential to enable improved low‐field MRI, facilitating enhanced diagnostic imaging in under‐resourced areas.
Autonomous Intelligent Monitoring of Photovoltaic Systems: An In‐Depth Multidisciplinary Review
Authors: Aghaei M. , Kolahi M. , Nedaei A. , Venkatesh N. S. , Esmailifar S. M. , Moradi Sizkouhi A. M. , Aghamohammadi A. , Oliveira A. K. V. , Eskandari A. , Parvin P. , Milimonfared J. , Sugumaran V. , Rüther R. 
Published: November 2024

ABSTRACTThis study presents a comprehensive multidisciplinary review of autonomous monitoring and analysis of large‐scale photovoltaic (PV) power plants using enabling technologies, namely artificial intelligence (AI), machine learning (ML), deep learning (DL), internet of things (IoT), unmanned aerial vehicle (UAV), and big data analytics (BDA), aiming to automate the entire condition monitoring procedures of PV systems. Autonomous monitoring and analysis is a novel concept for integrating various techniques, devices, systems, and platforms to further enhance the accuracy of PV monitoring, thereby improving the performance, reliability, and service life of PV systems. This review article covers current trends, recent research paths and developments, and future perspectives of autonomous monitoring and analysis for PV power plants. Additionally, this study identifies the main barriers and research routes for the autonomous and smart condition monitoring of PV systems, to address the current and future challenges of enabling the PV terawatt (TW) transition. The holistic review of the literature shows that the field of autonomous monitoring and analysis of PV plants is rapidly growing and is capable to significantly improve the efficiency and reliability of PV systems. It can also have significant benefits for PV plant operators and maintenance staff, such as reducing the downtime and the need for human operators in maintenance tasks, as well as increasing the generated energy.
A Bayesian Dynamic Model‐Based Adaptive Design for Oncology Dose Optimization in Phase I/II Clinical Trials
Authors: Qiu Yingjie , Li Mingyue 
Published: November 2024

ABSTRACTWith the development of targeted therapy, immunotherapy, and antibody‐drug conjugates (ADCs), there is growing concern over the “more is better” paradigm developed decades ago for chemotherapy, prompting the US Food and Drug Administration (FDA) to initiate Project Optimus to reform dose optimization and selection in oncology drug development. For early‐phase oncology trials, given the high variability from sparse data and the rigidity of parametric model specifications, we use Bayesian dynamic models to borrow information across doses with only vague order constraints. Our proposed adaptive design simultaneously incorporates toxicity and efficacy outcomes to select the optimal dose (OD) in Phase I/II clinical trials, utilizing Bayesian model averaging to address the uncertainty of dose–response relationships and enhance the robustness of the design. Additionally, we extend the proposed design to handle delayed toxicity and efficacy outcomes. We conduct extensive simulation studies to evaluate the operating characteristics of the proposed method under various practical scenarios. The results demonstrate that the proposed designs have desirable operating characteristics. A trial example is presented to demonstrate the practical implementation of the proposed designs.
Temporal cross‐validation in forecasting: A case study of COVID‐19 incidence using wastewater data
Authors: Lai Mallory , Wulff Shaun S. , Cao Yongtao , Robinson Timothy J. , Rajapaksha Rasika 
Published: November 2024

AbstractTwo predominant methodologies in forecasting temporal processes include traditional time series models and machine learning methods. This paper investigates the impact of time series cross‐validation (TSCV) on both approaches in the context of a case study predicting the incidence of COVID‐19 based on wastewater data. The TSCV framework outlined in the paper begins by engineering interpretable features hypothesized as potential predictors of COVID‐19 incidence. Feature selection and hyperparameter tuning are then utilized with TSCV to identify the best features and hyperparameters for optimal model performance given a specific forecast horizon. While evidence supporting the utility of TSCV for auto‐regressive integrated moving average model with exogenous variables (TS‐ARIMAX) forecasts is lacking in this study, such an approach proves advantageous for gradient boosting machine forecasts (TS‐GBM). In Wyoming, for instance, TS‐GBM had a 34.9% improvement compared to naïve predictions, whereas GBM without TSCV only had a 15.6% improvement. However, TSCV also enhances interpretability for both TS‐ARIMAX and TS‐GBM models as this approach selects specific features, such as lagged values of COVID‐19 cases, based on forecast performance and forecast length. Future research should work to explore the influence of stationarity and model averaging on the performance of TSCV in forecasting applications.
Assessment of Fermented Kiwifruit on Morpho‐Physiological and Productive Performances of Fragaria spp Plants, Grown Under Hydroponic Conditions
Authors: Nazeer Samreen , Agosti Anna , Del Vecchio Lorenzo , Leto Leandra , Di Fazio Andrea , Saadoun Jasmine H. , Levante Alessia , Lazzi Camilla , Cirlini Martina , Chiancone Benedetta 
Published: November 2024

ABSTRACTClimate change poses a significant threat to global agriculture by altering weather patterns, increasing the frequency of extreme events, and reducing the availability of arable land. Hydroponic systems offer a sustainable solution allowing efficient resource use, including water and nutrients, and enabling cultivation in areas with poor soil quality or limited space. The incorporation of biostimulants derived from plant byproducts further enhances sustainability by improving plant growth and resilience, reducing the use of synthetic fertilizers and the environmental footprint of agriculture, promoting, at the same time, healtier crop production. This study investigates the effect of biostimulants, derived from fermented kiwifruit byproducts, on the morpho‐physiological and productive performances of Fragaria vesca (L.), cv. Malga, and of Fragaria x ananassa (Duch.), cv. Annabelle, grown in a column hydroponic system. Plants of both species, when treated with the biostimulant, demonstrated significant improvements for all the parameters evaluated, with healthier plants and improved quality features in fruits. These findings suggest that fermented kiwi byproduct could be an effective, sustainable integration to synthetic fertilizers, promoting better growth and fruit quality in strawberry cultivation under hydroponic systems.
Learning and Distributed Expertise in Community‐Based Science
Authors: Jadallah Christopher C. , Ballard Heidi L. 
Published: November 2024

ABSTRACTIn the face of growing social‐ecological challenges, multiple forms of expertise must be brought to bear in environmental problem‐solving. As such, community‐based science has been touted as a potential way to “democratize” scientific knowledge production, allowing for multiple sources of expertise to be harnessed and for learning across stakeholders to occur in new ways. The extent to which this occurs, however, is mediated by a variety of factors. Focusing on the context of a dam removal and river restoration initiative in the Western United States, we leverage community science literacy as a conceptual lens to critically interrogate if and how multiple forms of expertise are taken up through community‐based science toward the goal of watershed revitalization. Based on an analysis of empirical evidence collected from interviews, ethnographic observations, and project artifacts, we found that local residents in the watershed demonstrated robust forms of situated local knowledge that they often leveraged toward the work of science. This occurred through processes of coordination work, which were mediated by opportunities for individuals to shift between roles, the contributions of brokers and boundary spanners, and issues of power, status, and rank. While individuals demonstrated and shared their nuanced local knowledge, dominant science still structured if, when, and how this was taken up in the project. Ultimately, we suggest that leveling hierarchies in community‐based science—and affording broader publics greater epistemic agency in shaping the work of science—is key to fostering social‐ecological transformation through science learning in informal settings.