bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wavelet‐Forward Family Enabling Stitching‐Free Full‐Field Fourier Ptychographic Microscopy
Authors: Wu Hao , Wang Jiacheng , Pan Haoyu , Lyu Jifu , Zhang Shuhe , Zhou Jinhua 
Published: October 2024

AbstractFourier ptychographic microscopy (FPM) breaks through the resolution limitations of conventional optical systems, which offer a full‐field view and high resolution without additional mechanical scanning. However, conventional image‐domain optimizations require trade‐offs between correction efficacy, data redundancy, and reconstruction accuracy. Furthermore, the existing linear time‐invariant model for actual nonlinear, time‐varying optical systems leads to forward model mismatch, complicating the corrections of the vignetting effect. To overcome these challenges and achieve stitching‐free FPM, a family of forward wavelet‐transform models (WL‐FPM) is proposed. WL‐FPM employs the reversibility of the wavelet transform for high‐fidelity reconstruction in the multiscale feature domain. The wavelet loss function is updated in each iteration, and non‐convex optimization is solved by complex back diffraction. WL‐FPM offers stitching‐free, high‐resolution, and robust reconstruction under various challenging conditions, including vignetting effects, LED position mismatch, intensity fluctuations, and high‐level noise environments, which outperform conventional FPM methods. Under a 4X objective with NA 0.1, WL‐FPM achieves a 435‐nm resolution and stitching‐free full‐field reconstruction of a 3.328 × 3.328 mm2 pathological section with distinct subcellular organelles. In live cell imaging, it provides a full‐field observation with distinct lipids in a single cell. A large number of simulation and experimental results demonstrate its potential for biomedical applications.
Prediction of MRI R2*$$ {\mathrm{R}}_2^{\ast } $$ relaxometry in the presence of hepatic steatosis by Monte Carlo simulations
Authors: Ma Mengyuan , Cheng Junying , Li Xiaoben , Fan Zhuangzhuang , Wang Changqing , Reeder Scott B. , Hernando Diego 
Published: October 2024

AbstractTo develop Monte Carlo simulations to predict the relationship of with liver fat content at 1.5 T and 3.0 T. For various fat fractions (FFs) from 1% to 25%, four types of virtual liver models were developed by incorporating the size and spatial distribution of fat droplets. Magnetic fields were then generated under different fat susceptibilities at 1.5 T and 3.0 T, and proton movement was simulated for phase accrual and MRI signal synthesis. The synthesized signal was fit to single‐peak and multi‐peak fat signal models for and proton density fat fraction (PDFF) predictions. In addition, the relationships between and PDFF predictions were compared with in vivo calibrations and Bland–Altman analysis was performed to quantitatively evaluate the effects of these components (type of virtual liver model, fat susceptibility, and fat signal model) on predictions. A virtual liver model with realistic morphology of fat droplets was demonstrated, and and PDFF values were predicted by Monte Carlo simulations at 1.5 T and 3.0 T. predictions were linearly correlated with PDFF, while the slope was unaffected by the type of virtual liver model and increased as fat susceptibility increased. Compared with in vivo calibrations, the multi‐peak fat signal model showed superior performance to the single‐peak fat signal model, which yielded an underestimation of liver fat. The ‐PDFF relationships by simulations with fat susceptibility of 0.6 ppm and the multi‐peak fat signal model were (, ) at 1.5 T and (, ) at 3.0 T. Monte Carlo simulations provide a new means for ‐PDFF prediction, which is primarily determined by fat susceptibility, fat signal model, and magnetic field strength. Accurate ‐PDFF calibration has the potential to correct the effect of fat on quantification, and may be helpful for accurate measurements in liver iron overload.
Bio‐inspired graphene oxide/epoxy coating with ordered stacking for anticorrosion/wear protection of Mg alloy
Authors: Ju Meng , Tong Libo , Li Miaomiao , Li Xiangjun , Wang Wei , Wang Kuaishe 
Published: October 2024

AbstractThe random arrangement of graphene oxide (GO) nanosheets within the organic coating forms a conductive network, thereby accelerating galvanic corrosion and exacerbating metal deterioration. Inspired by ordered layered structure of natural nacre, this study has successfully fabricated a biomimetic GO/epoxy coating on Mg alloy to prevent corrosion and reduce wear. By polymerizing polyaniline (PANI) and dopamine (PDA) on the GO, GO/PANI/PDA sheets with good dispersibility and impermeability are obtained. Subsequently, the bio‐inspired coating (I‐GO/PANI/PDA‐WEP) with “nacre‐like” structure is prepared via alternately spraying GO/PANI/PDA layers with epoxy layers. The anisotropic electrical conductivity of the coating is derived from the ordered distribution of GO/PANI/PDA layers to the Mg substrate, which can conduct electricity more efficiently in‐plane while suppressing longitudinal electron transport. The enhanced physical barrier effect by the “nacre‐like” structure and the electron suppression effect can improve anticorrosion performance. The coating reduces the corrosion rate by 6.3 × 105 times in comparison to Mg alloy. Moreover, this coating shows excellent wear resistance, which protects Mg alloy from friction and wear with a reduction in wear rate by approximately 96%. This bio‐inspired approach offers a unique strategy for fabricating GO composite coatings with exceptional performance.Highlights
The corrosion/wear resistance of I‐GO/PANI/PDA has greatly improved.
GO/PANI/PDA coating exhibits excellent anisotropic conductivity.
Galvanic corrosion is inhibited due to the addition of GO/PANI/PDA.
“Nacre‐like” structure shows high labyrinth effect.
The ordered orientation of GO/PANI/PDA suppresses out‐plane electron transport.
Surface treatment effects on morphological and property enhancements of poly (3‐hydroxybutyrate‐co‐3‐hydroxyvalerate)/Diss fibers (Ampelodesmos mauritanicus) biocomposites
Authors: Remila Brahim , Zembouai Idris , Zaidi Lynda , Kaci Mustapha , Kervoelen Antoine , Bruzaud Stéphane 
Published: October 2024

AbstractThe present research investigates the influence of chemical modifications on the surface of Diss fibers (Ampelodesmos mauritanicus) as an effective reinforcing agent for biocomposites based on poly (3‐hydroxybutyrate‐co‐3‐hydroxyvalerate) (PHBV). The Diss fiber surface underwent three different chemical treatments: alkaline, alkaline/peroxide, and alkaline/silane. Changes in morphology, mechanical properties, thermomechanical behavior, and contact angle measurements of the biocomposite materials were studied according to the following weight ratio PHBV/Diss fibers 80/20. Scanning electron microscopy (SEM) analysis of the fractured surface of the biocomposite samples revealed improved adhesion between Diss fibers and the PHBV matrix after surface modification, compared with the unmodified sample. The study demonstrated that the incorporation of Diss fibers leads to an enhancement in the mechanical performances of PHBV‐based biocomposites. The tensile properties of the modified biocomposites showed a significant increase in Young's modulus compared with the biocomposites with untreated fiber. Similar trends were observed in the data obtained from dynamic mechanical analysis (DMA) and contact angle measurement. Overall, the study highlights the beneficial effects of Diss fibers modification, particularly with the alkali–silane combination, in enhancing the properties of PHBV biocomposites, there by broadening their potential application fields.Highlights
The study explores the potential of Diss fibers as an effective reinforcement for PHBV‐based biocomposites.
Fully biodegradable biocomposites based on PHBV/Diss fibers are melt‐compounded.
Diss fibers are chemically modified by alkaline, alkaline/peroxide, and alkaline/silane.
The chemical treatment has considerably enhanced the properties and morphology of the biocomposites, being however much higher for alkaline/silane treatment.
The method can be used for specific applications and large‐scale production.
The preparation of polyaniline/graphene/polypropylene nanocomposite and its novel antibacterial activity
Authors: Ao Xaiofang , Liu Xue , Dai Zheyu , Zhu Aiping 
Published: October 2024

AbstractThe demand for polymers with antibacterial properties is increasing in food packaging industry and medical supplies. Antibacterial additives without toxic metal ion release are crucial for health and safety but still have challenges. Herein, polypropylene (PP) nanocomposite was prepared with polyaniline/graphene nanocomposite (PANI/GNP) as a green antibacterial additive due to its electrostatic, free radical, and nanoknife effect synergistic antibacterial mechanisms. To improve the dispersibility of PANI/GNP, nanosilica coating on PANI/GNP was constructed successfully with in situ condensation polymerization of epoxy functionalized ethyl orthosilicate to obtain EP‐nSiO2‐PANI/GNP. EP‐nSiO2‐PANI/GNP with a thin nanosilica coating has a significantly improved thermal stability as compared with that of PANI/GNP characterized by TGA. The EP‐nSiO2‐PANI/GNP presents a nanoscale disperse state in PP nanocomposite and thus enhances the melt viscosity and storage modulus accordingly characterized by TEM and Rheology. The EP‐nSiO2‐PANI/GNP/PP filled with 0.5 wt% of EP‐nSiO2‐PANI/GNP demonstrates antibacterial activity of 100% against both Escherichia coli and Staphylococcus aureus.Highlights
In situ condensation polymerization to prepare EP‐nSiO2‐PANI/GNP.
EP‐nSiO2‐PANI/GNP has enhanced dispersion stability.
EP‐nSiO2‐PANI/GNP with a thin nanosilica coating has a good thermal stability.
PP nanocomposite with 0.5 wt% filler has excellent antibacterial activity.
The electrostatic, free radical, and nanoknife effect antibacterial mechanism.
Performance of glass epoxy composites under combined effect of in‐plane fiber waviness with circular cutout through tensile test, and image processing technique
Authors: Muddebihal Aravind , Gouda P. S. Shivakumar , Uppin Vinayak S. , Edacherian Abhilash , Patil Santosh , Joshi Prahlad , M A Somashekara 
Published: October 2024

AbstractPresent study comprehensively explores the phenomenon of in‐plane fiber waviness in glass/epoxy fiber reinforced polymer (GFRP) composites. Utilizing a pioneering technique that employs a semi‐circular bar to induce controlled fiber waviness, coupled with image processing using OpenCV library in Python coding for precise measurement and analysis of in‐plane fiber waviness. This defect is commonly found in components fabricated with fiber‐reinforced composites. During the process of manufacturing fibers may deviate from their intended orientation due to process variables or resin flow dynamics. Understanding in‐plane fiber waviness is crucial due to its impact on mechanical properties of composites. It is essential for optimizing manufacturing processes and ensuring enhanced structural performance in diverse engineering applications through analysis of strength and failure mechanism. A novel study was conducted by introducing a circular cutout within the fiber waviness region to investigate the combined effect of multiple defects. The experimental tensile test reveals that as waviness ratio (WR) increases a significant decrease in its tensile strength and vice versa. The study incorporates scanning electron microscopy (SEM) image analysis to examine composite failure. By advancing the understanding of in‐plane fiber waviness and its implications, this research significantly contributes to ongoing efforts in composite design and optimization.Highlights
Adopting a cutting‐edge method to create controlled fiber waviness that makes use of a semi‐circular bar.
Image processing with Python code that makes use of the OpenCV package to precisely measure and analyze in‐plane fiber waviness.
A new investigation was carried out to examine the combined effect of multiple flaws by creating a circular cutout within the fiber waviness zone.
Investigation of acetyl‐CoA carboxylase‐inhibiting herbicides that exhibit soybean crop selectivity
Authors: Lee Stephanie , Payne China , Rees Shaun , Ahrens Hartmut , Arve Lars , Asmus Elisabeth , Bojack Guido , Arsequell Estel.la Buscató , Gatzweiler Elmar , Helmke Hendrik , Kallus Christopher , Laber Bernd , Lange Gudrun , Lehr Stefan , Menne Hubert , Rosinger Christopher H. , Schulte Wolfgang , Sommer Kai , Barber David M. 
Published: October 2024

AbstractThe sustainable control of weed populations, particularly resistant species, is a significant challenge in agriculture around the world. The α‐aryl‐keto‐enol (aryl‐KTE) class of acetyl‐CoA carboxylase (ACCase)‐inhibiting herbicides represent a possible solution for the control of resistant grasses even though achieving crop selectivity remains a challenge. Herein, we present some of our investigations into identifying the most promising structural features within the aryl‐KTE class that give the highest chance of achieving soybean crop selectivity, whilst also maintaining strong and broad efficacy against problematic weed species. We further examined our results by preparing new aryl‐KTE molecules which were evaluated in glasshouse screening assays for their herbicidal efficacy as well as their soybean selectivity. We consider that uniting this approach with other optimization criteria, such as toxicological and environmental safety profiles, will enable the streamlining of crop protection optimizations programmes, ultimately delivering safer and more sustainable solutions to farmers and consumers. © 2024 The Author(s). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Characterization of CYP303A1 and its potential application based on ZIF‐8 nanoparticle‐wrapped dsRNA in Nilaparvata lugens (Stål)
Authors: Du Zuyi , Zhang Guijian , Yu Chang , Qin Yao , He Shun , Li Jianhong , Guo Le , Wan Hu 
Published: October 2024

AbstractBACKGROUNDRNA interference (RNAi) technology has been put forward as a promising method for pest control and resistance management. Mining highly efficient lethal genes and constructing stable double‐stranded RNA (dsRNA) delivery systems are of great significance to improve the application potential of RNAi technology.RESULTSIn this study, we characterized a molting‐related gene, NlCYP303A1, in Nilaparvata lugens that was highly expressed in the cuticle and at the end stages of each instar in nymphs. Silencing the expression of NlCYP303A1 in N. lugens resulted in a deformed phenotype and a significant increase in mortality. Furthermore, interfering with NlCYP303A1 changed the relative expression of key genes in the chitin synthesis and degradation pathway. Finally, we used the nanocarrier zeolitic imidazolate framework‐8 (ZIF‐8) to load dsNlCYP303A1, forming a complex denoted as dsNlCYP303A1@ZIF‐8. The results of both feeding and rice‐seedling dip experiments indicated that the expression of NlCYP303A1 was dramatically and persistently suppressed by the dsNlCYP303A1@ZIF‐8 treatment, compared with treatment with dsNlCYP303A1, suggesting that ZIF‐8 can enhance the interference efficiency as well as the stability of dsNlCYP303A1.CONCLUSIONSOur results demonstrate that the lethal gene NlCYP303A1 can be employed as an excellent target for RNAi technology by loading onto a nano‐delivery system, and provide new insights into the creation of innovative pest control approaches. © 2024 Society of Chemical Industry.
Combining satellite and field data reveals Congo's forest types structure, functioning and composition
Authors: Picard Juliette , Nungi‐Pambu Dembi Maïalicah M. , Barbier Nicolas , Cornu Guillaume , Couteron Pierre , Forni Eric , Gibbon Gwili , Lim Felix , Ploton Pierre , Pouteau Robin , Tresson Paul , van Loon Tom , Viennois Gaëlle , Réjou‐Méchain Maxime 
Published: October 2024

AbstractTropical moist forests are not the homogeneous green carpet often illustrated in maps or considered by global models. They harbour a complex mixture of forest types organized at different spatial scales that can now be more accurately mapped thanks to remote sensing products and artificial intelligence. In this study, we built a large‐scale vegetation map of the North of Congo and assessed the environmental drivers of the main forest types, their forest structure, their floristic and functional compositions and their faunistic composition. To build the map, we used Sentinel‐2 satellite images and recent deep learning architectures. We tested the effect of topographically determined water availability on vegetation type distribution by linking the map with a water drainage depth proxy (HAND, height above the nearest drainage index). We also described vegetation type structure and composition (floristic, functional and associated fauna) by linking the map with data from large inventories and derived from satellite images. We found that water drainage depth is a major driver of forest type distribution and that the different forest types are characterized by different structure, composition and functions, bringing new insights about their origins and successional dynamics. We discuss not only the crucial role of soil–water depth, but also the importance of consistently reproducing such maps through time to develop an accurate monitoring of tropical forest types and functions, and we provide insights on peculiar forest types (Marantaceae forests and monodominant Gilbertiodendron forests) on which future studies should focus more. Under the current context of global change, expected to trigger major forest structural and compositional changes in the tropics, an appropriate monitoring strategy of the spatio‐temporal dynamics of forest types and their associated floristic and faunistic composition would considerably help anticipate detrimental shifts.
Deep Neural Network‐Based Accelerated Failure Time Models Using Rank Loss
Authors: Kim Gwangsu , Park Jeongho , Kang Sangwook 
Published: October 2024

ABSTRACTAn accelerated failure time (AFT) model assumes a log‐linear relationship between failure times and a set of covariates. In contrast to other popular survival models that work on hazard functions, the effects of covariates are directly on failure times, the interpretation of which is intuitive. The semiparametric AFT model that does not specify the error distribution is sufficiently flexible and robust to depart from the distributional assumption. Owing to its desirable features, this class of model has been considered a promising alternative to the popular Cox model in the analysis of censored failure time data. However, in these AFT models, a linear predictor for the mean is typically assumed. Little research has addressed the non‐linearity of predictors when modeling the mean. Deep neural networks (DNNs) have received much attention over the past few decades and have achieved remarkable success in a variety of fields. DNNs have a number of notable advantages and have been shown to be particularly useful in addressing non‐linearity. Here, we propose applying a DNN to fit AFT models using Gehan‐type loss combined with a sub‐sampling technique. Finite sample properties of the proposed DNN and rank‐based AFT model (DeepR‐AFT) were investigated via an extensive simulation study. The DeepR‐AFT model showed superior performance over its parametric and semiparametric counterparts when the predictor was nonlinear. For linear predictors, DeepR‐AFT performed better when the dimensions of the covariates were large. The superior performance of the proposed DeepR‐AFT was demonstrated using three real datasets.
Testing Similarity of Parametric Competing Risks Models for Identifying Potentially Similar Pathways in Healthcare
Authors: Möllenhoff Kathrin , Binder Nadine , Dette Holger 
Published: October 2024

ABSTRACTThe identification of similar patient pathways is a crucial task in healthcare analytics. A flexible tool to address this issue are parametric competing risks models, where transition intensities may be specified by a variety of parametric distributions, thus in particular being possibly time‐dependent. We assess the similarity between two such models by examining the transitions between different health states. This research introduces a method to measure the maximum differences in transition intensities over time, leading to the development of a test procedure for assessing similarity. We propose a parametric bootstrap approach for this purpose and provide a proof to confirm the validity of this procedure. The performance of our proposed method is evaluated through a simulation study, considering a range of sample sizes, differing amounts of censoring, and various thresholds for similarity. Finally, we demonstrate the practical application of our approach with a case study from urological clinical routine practice, which inspired this research.
Modulating Aggregation Behavior by Ternary Strategies for Efficient and Stable Thick‐Film Organic Solar Cells
Authors: Xu Jie , Wang Lei , Jeong Seonghun , Xia Shuangshuang , Zhao Xiaohong , Yang Changduk , Hu Yu , Yuan Zhongyi 
Published: October 2024

AbstractFunctional third components targeted to improve a specific property of organic solar cells is an effective strategy. However, introducing a third component to simultaneously improve efficiency and stability and achieve good performance in thick‐film devices has rarely been reported. Herein, low diffusion third components IDCN and ID2CN are reported to achieve a power conversion efficiency (PCE) of 18.08% and a high short‐circuit current (J SC) of 27.82 mA cm−2, one of the highest values based on PM6:Y6. They increase light harvesting in the range of 400–500 nm while enhancing energy transfer via Förster resonance energy transfer (FRET). A tightly ordered molecular arrangement is achieved by modulating the preaggregation and film formation kinetics of Y6, which enhance exciton dissociation and charge transport. Moreover, the low‐diffusion third component can effectively restrict the diffusion of Y6 to improve the morphology stability, and the T90 lifetime is increased from 689 to 1545 h. In 300 nm thick‐film devices, PM6:ID2CN:Y6 achieves a PCE of 15.01%, much higher than PM6:Y6's 12.83%, demonstrating the great potential of ID2CN in thick‐film devices.
11.88% Efficient Flexible Ag‐Free CZTSSe Solar Cell: Spontaneously Tailoring the Alkali Metal Level
Authors: Xu Han , Meng Rutao , Xu Xuejun , Liu Yue , Sun Yali , Zhang Yi 
Published: October 2024

AbstractAlkali metal is the requirement for highly efficient Cu2ZnSn(S, Se)4 (CZTSSe) solar cells, thus it is crucial to additionally incorporate alkali metal into the absorber layer for flexible solar cells. However, the efficiency of flexible CZTSSe devices reported to date, based on the conventional alkali incorporation strategies, still lags behind those made on rigid substrates. One of the main issues is the inability to control the alkali content and distribution in the absorber layer. Here, a facile alkaline incorporation approach is proposed, effectively regulating the content and distribution of alkali metals in the film. Such a method can spontaneously tailor the alkali metal content to a proper level, thus leading to the suppression of non‐radiative recombination and a better carrier transport through the enhanced film quality and the optimized band binding structure. Finally, a champion flexible CZTSSe solar cell with an efficiency of 11.88% is achieved, the highest reported efficiency for a CZTSSe solar cell without noble Ag doping. This study affords an innovative spontaneous alkali‐doping design for the preparation of high‐performance flexible CZTSSe solar cells and provides a deeper insight into the extent of alkali metal doping.