bluuebunny/crossref_metadata_2025_split huggingface dataset:
Increasing sustainability by intercropping legume crops with sugar beet under imposed water stress
Authors: Ouda Samiha , Zohry Abd El‐Hafeez 
Published: August 2023

AbstractThe sustainable use of soil and water resources is a goal that should be pursued to attain food security. This investigation was implemented to compare the effects of three legume intercropping systems with sugar beet on (i) enhancing sugar beet yield under imposed water stress conditions, (ii) soil N contents, (iii) land and water use efficiencies, and (iv) farmers' net income. Two field experiments were carried out in the 2018/19 and 2019/20 seasons in the El‐Minia Governorate, Egypt. The treatments included the interaction between three irrigation treatments: required irrigation ((control, 100% ETc, RI) and two levels of imposed water stress (85% ETc (WS1) and 70% ETc (WS2)), and three intercropping systems: faba bean (FIS), chickpea (CIS) and lentil (LIS) intercropped with sugar beet. The results indicate that the application of implementing CIS and with RI attained higher average values of soil N content (SNC = 14.0 mg kg−1) and net income (NI = US$3,974 ha−1) than those obtained by sugar beet alone. Irrigation with WS1 and the implementation of CIS gave a value of SNC (12.3 mg kg−1) close to that obtained under sugar beet alone irrigated with RI. CIS irrigated with WS1 attained higher averaged values of land equivalent ratio (LER = 1.34), area time equivalent ratio (ATER = 1.28), land equivalent coefficient (LEC = 0.38), water equivalent ratio (WER = 1.12), negative value of change in water use (ΔWU < 0) and NI (US$3,602 ha−1) than those obtained under FIS or LIS irrigated with RI. In conclusion, implementing CIS and irrigation with RI can achieve the sustainable use of land and water. However, implementing CIS and irrigation with WS1 can realize soil improvement, conserve irrigation water and attain higher values of LER, ATER, LEC, WER, and NI than those obtained under FIS, LIS and sugar beet alone.
Effects of biochar application on rice physiological growth and water use efficiency under dry direct‐seeded mode
Authors: Cao Xiaoqiang , Liu Jilong , Wu Yu , Wei Yongxia , Zhang Lingling , Li Jizhen , Lv Hang , Idimesheva Olga 
Published: August 2023

AbstractTo investigate the effects of various biochar application levels on the growth physiology and water use efficiency of dry direct‐seeded rice (DDSR) in the black soil region of north‐east China, five biochar application treatments consisting of 2% (BC1), 4% (BC2), 6% (BC3), 8% (BC4) and 10% (BC5) were set, and no biochar application was used as the control treatment (CK). The differences in photosynthetic properties, dry matter accumulation characteristics, water consumption pattern, yield and water use efficiency of DDSR under different biochar application levels were compared and analysed, and the effect of the water use efficiency of leaves (WUEL) at each growth stage on the water use efficiency of yield (WUEy) was also investigated. The results showed that the photosynthetic properties, dry matter accumulation and water consumption of rice increased and then decreased with increasing biochar application level. Compared with CK, the rice yield increased by 1.6%–52% with biochar application, and the 4% biochar application level showed the most significant increase in rice yield (P < 0.05). The WUEy in the BC2, BC3, BC4, BC1, CK and BC5 treatments gradually decreased in that order. Among the WUEy values, significant increases of 27%, 13% and 8% in the BC2, BC3 and BC4 treatments, respectively, compared with the CK were observed (P < 0.05). The effect of WUEL at the prophase tiller, late tiller and milk ripening stages on WUEy enhancement was significant.
MRI‐Based Radiomics and Deep Learning in Biological Characteristics and Prognosis of Hepatocellular Carcinoma: Opportunities and Challenges
Authors: Xia Tianyi , Zhao Ben , Li Binrong , Lei Ying , Song Yang , Wang Yuancheng , Tang Tianyu , Ju Shenghong 
Published: August 2023

Hepatocellular carcinoma (HCC) is the fifth most common malignancy and the third leading cause of cancer‐related death worldwide. HCC exhibits strong inter‐tumor heterogeneity, with different biological characteristics closely associated with prognosis. In addition, patients with HCC often distribute at different stages and require diverse treatment options at each stage. Due to the variability in tumor sensitivity to different therapies, determining the optimal treatment approach can be challenging for clinicians prior to treatment. Artificial intelligence (AI) technology, including radiomics and deep learning approaches, has emerged as a unique opportunity to improve the spectrum of HCC clinical care by predicting biological characteristics and prognosis in the medical imaging field. The radiomics approach utilizes handcrafted features derived from specific mathematical formulas to construct various machine‐learning models for medical applications. In terms of the deep learning approach, convolutional neural network models are developed to achieve high classification performance based on automatic feature extraction from images. Magnetic resonance imaging offers the advantage of superior tissue resolution and functional information. This comprehensive evaluation plays a vital role in the accurate assessment and effective treatment planning for HCC patients. Recent studies have applied radiomics and deep learning approaches to develop AI‐enabled models to improve accuracy in predicting biological characteristics and prognosis, such as microvascular invasion and tumor recurrence. Although AI‐enabled models have demonstrated promising potential in HCC with biological characteristics and prognosis prediction with high performance, one of the biggest challenges, interpretability, has hindered their implementation in clinical practice. In the future, continued research is needed to improve the interpretability of AI‐enabled models, including aspects such as domain knowledge, novel algorithms, and multi‐dimension data sources. Overcoming these challenges would allow AI‐enabled models to significantly impact the care provided to HCC patients, ultimately leading to their deployment for clinical use.Level of Evidence5Technical EfficacyStage 2
Probabilistic modelling of interarrival time of drought for different operational drought indices used in Pakistan
Authors: Mohsin Muhammad , Adnan Shahzada 
Published: August 2023

AbstractAdverse climate changes in Pakistan undermine the water resources and give rise to transient and permanent droughts in different parts of the country. The occurrence of drought cannot be prevented, but its detrimental impacts can be brought down by proper pre‐drought planning. Effective planning owes to using scientific and probabilistic approaches. In this paper, the interarrival time of drought for seven operational drought indices in Pakistan is modelled by using the modified distribution of convolution derived from the Bivariate Affine Linear Exponential (BALE) distribution. A new stochastic variable, interarrival time of drought, is generated from the data of drought indices from 1951 to 2016 used in Pakistan by applying the theory of runs. The proposed model identifies Palmer drought severity index (PDSI) and Reconnaissance drought index (RDI) as the most appropriate meteorological indices for the under‐study country. The quantiles of the model are computed that provide information about the interarrival time of drought and help to anticipate this phenomenon. In addition, return periods for all the seven indices are calculated to report the frequency and duration of drought over a certain period. Finally, some recommendations for the National Drought Monitoring Centre (NDMC) and Pakistan Council of Research in Water Resources (PCRWR) are stated for better future planning to avoid the adverse impacts of drought.
A polysaccharide from salviae miltiorrhizae radix inhibits weight gain of mice with high‐fat diet via modulating intestinal bacteria
Authors: Liu Zhihai , Ma Ce , Gao Hongwei , Huang Xiaoli , Zhang Yaru , Liu Congmin , Hou Ranran , Zhang Qidi , Li Qiu 
Published: August 2023

AbstractBACKGROUNDObesity, a global chronic disease, has been recognized as a severe risk to health. In our study, a novel polysaccharide named ARS was isolated and purified from aerial part of salviae miltiorrhizae radix. Our aim is to investigate the weight‐reducing effect of a polysaccharide from salviae miltiorrhizae radix on mice fed a high‐fat diet.RESULTSThe novel polysaccharide ARS mainly consisted of glucose and galactose with a molar ratio of 0.59:1.00. We found that treatment with ARS could inhibit weight gain of mice fed a high‐fat diet via modulating the intestinal bacteria. Moreover, we surveyed its mechanism in mice, and the gut microbiota sequencing results demonstrated that ARS can reverse or resist high‐fat‐diet‐induced significant weight gain or obesity by increasing the diversity of gut microbiota and optimizing the ratio of Firmicutes to Bacteroidetes. Phylum and species analysis of gut microbiota demonstrated that obesity caused by a high‐fat diet was accompanied by significant changes in the microbial communities, but ARS could reverse the disturbance of gut microbiota induced by the high‐fat diet to maintain homeostasis.CONCLUSIONOverall, our findings suggested a new function of ARS in regulating gut microbiota, which provides a theoretical basis for the development of high‐quality ARS functional foods and the application of dietary supplements. © 2023 Society of Chemical Industry.
Effect of ecological restoration projects on carbon footprint in a grassland ecosystem on the Qinghai‐Tibet Plateau
Authors: Yu Lu , Liu Shiliang , Wang Fangfang , Liu Hua , Liu Yixuan , Wang Qingbo , Zhao Yifei 
Published: August 2023

AbstractInvestigating the cost‐effective and low‐carbon pathways for grassland ecological restoration projects is vital to achieving carbon neutrality and Sustainable Development Goals. To fill the gap of incorporating ecological and socioeconomic systems of ecological restoration projects, this study established a cost‐benefit evaluation framework that integrated the carbon footprint based on life cycle assessment and a carbon price measurement model based on the Slack‐Based Measure‐Data Envelopment Analysis model. Taking a ‘Returning Grazing Land to Grassland Project’ on the Qinghai‐Tibet Plateau as a case, this study quantified the carbon footprint of three different ecological measurements, including fences construction (released 107 kgCO2‐eq·hm−2 and absorbed 322 kgCO2‐eq·hm−2), degenerated grassland improvement (released 1080 kgCO2‐eq·hm−2 and absorbed 580 kgCO2‐eq·hm−2) and artificial grassland planting (released 2540 kgCO2‐eq·hm−2 and absorbed 6540 kgCO2‐eq·hm−2). Raw material production was the largest emitter. In addition, we calculated the carbon price in the grassland ecosystem by the carbon price measurement model, at 1.06 yuan·kgCO2‐eq−1 on average during 2011–2018. In the cost‐benefit analysis, the total carbon sequestration benefits (8.90 × 106 yuan) outweighed the total carbon emission costs (4.26 × 106 yuan). Optimizing the usage and transportation of building materials and the application of fertilizer can benefit carbon emission mitigation and enhancement of carbon sequestration capacity in grassland. This study provided a theoretical basis for ecological restoration projects and grassland carbon pricing mechanisms aiming for sustainable management.
Verification of multiresolution model forecasts of heavy rainfall events from 23 to 26 August 2017 over Nigeria
Authors: Gbode Imoleayo E. , Ajayi Vincent O. , Adefisan Elijah A. , Okogbue Emmanuel C. , Cafaro Carlo , Olaniyan Eniola A. , Ogungbenro Stephen B. , Oluleye Ayodeji , Lawal Kamoru A. , Omotosho Jerome A. , Stein Thorwald 
Published: August 2023

AbstractThe study uses numerical weather prediction models to predict the occurrence of heavy convective rainfall associated with the passage of the African Easterly Wave (AEW) during the period 23–26 August 2017 over Nigeria. Fraction skill score (FSS) and method for object‐based diagnostic evaluation (MODE) verification techniques were applied to verify how well the models predict the high‐impact event and to demonstrate how these tools can support operational forecasting. Ensemble model forecasts at a convective scale from UK Met Office Unified Model (MetUM) and a one‐way nested weather research and forecasting (WRF) model were compared with the integrated multisatellite retrievals for global precipitation measurement (IMERG GPM). The purpose is to examine skills of improved model resolution and ensemble in reproducing rainfall forecasts on useful scales and how the skill varies with spatial scale. WRF 2 and 6 km model forecasts show comparable skill at smaller grid scales. The skill of MetUM improves dramatically when the verification statistics are applied to the ensemble mean of the binary fields of the individual member forecast. The object‐based analysis reveals a similar structure as observed, although displaced eastwards. Most improvement occurred for heavier rainfall events associated with the passage of the AEW. WRF 6 km compares reasonably well with WRF 2 km in terms of shape and structure of rainfall underscoring the ability of the model to reasonably represent convection at 6 km horizontal resolution. The ensemble members in MetUM explicitly reproduce convection at 4 km resolution but are displaced at about 166 km behind observed rainfall.
Simulation of consequences of using nonideal detectors during beam data commissioning measurements
Authors: Wegener Sonja , Sauer Otto A. 
Published: August 2023

AbstractBackgroundBeam data commissioning is a core task of radiotherapy physicists. Despite multiple detectors available, a feasible measurement program compromises between detector properties and time constraints. Therefore, it is important to understand how nonideal measurement data propagates into patient dose calculation.PurposeWe simulated the effects of realistic errors, due to beam commissioning with presumably nonoptimal detectors, on the resulting patient dose distributions. Additionally, the detectability of such beam commissioning errors during patient plan quality assurance (QA) was evaluated.MethodsA clinically used beam model was re‐commissioned introducing changes to depth dose curves, output factors, profiles or combinations of those. Seventeen altered beam models with incremental changes of the modelling parameters were created to analyze dose changes on simplified anatomical phantoms. Additionally, fourteen altered models incorporate changes in the order of signal differences reported for typically used detectors. Eighteen treatment plans of different types were recalculated on patient CT data sets using the altered beam models.ResultsFor the majority of clinical plans, dose distributions in the target volume recalculated on the patient computed tomography data were similar between the original and the modified beam models, yielding global 2%/2 mm gamma pass rates above 98.9%. Larger changes were observed for certain combinations of beam modelling errors and anatomical sites, most extreme for output factor changes in a small target volume plan with a pass rate of 80.6%. Modelling an enlarged penumbra as if measured with a 0.125 cm3 ion chamber had the largest effect on the dose distribution (average pass rate of 96.5%, lowest 85.4%). On different QA phantom geometries, dose distributions between calculations with modified and unmodified models typically changed too little to be detected in actual measurements.ConclusionWhile the simulated errors during beam modelling had little effect on most plans, in some cases changes were considerable. High‐quality penumbra and small field output factor should be a main focus of commissioning measurements. Detecting modelling issues using standard patient QA phantoms is unlikely. Verification of a beam model should be performed especially for plans with high modulation and in different depths or geometries representing the variety of situations expected clinically.
Pre‐whitened matched filter and convolutional neural network based model observer performance for mass lesion detection in non‐contrast breast CT
Authors: Lyu Su Hyun , Abbey Craig K. , Hernandez Andrew M. , Boone John M. 
Published: August 2023

AbstractBackgroundMathematical model observers have been shown to reasonably predict human observer performance and are useful when human observer studies are infeasible. Recently, convolutional neural networks (CNNs) have also been used as substitutes for human observers, and studies have shown their utility as an optimal observer. In this study, a CNN model observer is compared to the pre‐whitened matched filter (PWMF) model observer in detecting simulated mass lesions inserted into 253 acquired breast computed tomography (bCT) images from patients imaged at our institution.PurposeTo compare CNN and PWMF model observers for detecting signal‐known‐exactly (SKE) location‐known‐exactly (LKE) simulated lesions in bCT images with real anatomical backgrounds, and to use these model observers collectively to optimize parameters and understand trends in performance with breast CT.MethodsSpherical lesions with different diameters (1, 3, 5, 9 mm) were mathematically inserted into reconstructed patient bCT image data sets to mimic 3D mass lesions in the breast. 2D images were generated by extracting the center slice along the axial dimension or by slice averaging across adjacent slices to model thicker sections (0.4, 1.2, 2.0, 6.0, 12.4, 20.4 mm). The role of breast density was retrospectively studied using the range of breast densities intrinsic to the patient bCT data sets. In addition, mass lesions were mathematically inserted into Gaussian images matched to the mean and noise power spectrum of the bCT images to better understand the performance of the CNN in the context of a known ideal observer (the PWMF). The simulated Gaussian and bCT images were divided into training and testing data sets. Each training data set consisted of 91 600 images, and each testing data set consisted of 96 000 images. A CNN and PWMF was trained on the Gaussian training images, and a different CNN and PWMF was trained on the bCT training images. The trained model observers were tested, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was the primary performance metric used to compare the model observers.ResultsIn the Gaussian background, the CNN performed essentially identically to the PWMF across lesion sizes and section thicknesses. In the bCT background, the CNN outperformed the PWMF across lesion size, breast density, and most section thicknesses. These findings suggest that there are higher‐order features in bCT images that are harnessed by the CNN observer but are inaccessible to the PWMF.ConclusionsThe CNN performed equivalently to the ideal observer in Gaussian textures. In bCT background, the CNN captures more diagnostic information than the PWMF and may be a more pertinent observer when conducting optimal performance studies in breast CT images.
Optimal design of line replaceable units
Authors: Driessen Joni , de Kruijff Joost , Arts Joachim , van Houtum Geert‐Jan 
Published: August 2023

AbstractA line replaceable unit (LRU) is a collection of connected parts in a system that is replaced when any part of the LRU fails. Companies use LRUs as a mechanism to reduce downtime of systems following a failure. The design of LRUs determines how fast a replacement is performed, so a smart design reduces replacement and downtime cost. A firm must purchase/repair a LRU upon failure, and large LRUs are more expensive to purchase/repair. Hence, a firm seeks to design LRUs such that the average costs per time unit are minimized. We formalize this problem in a new model that captures how parts in a system are connected, and how they are disassembled from the system. Our model optimizes the design of LRUs such that the replacement (and downtime) costs and LRU purchase/repair costs are minimized. We present a set partitioning formulation for which we prove a rare result: the optimal solution is integer, despite a nonintegral feasible polyhedron. Second, we formulate our problem as a binary linear program (BLP). The article concludes by numerically comparing the computation times of both formulations and illustrates the effects of various parameters on the model's outcome.
Boosting quantification accuracy of chemical exchange saturation transfer MRI with a spatial–spectral redundancy‐based denoising method
Authors: Chen Xinran , Wu Jian , Yang Yu , Chen Huan , Zhou Yang , Lin Liangjie , Wei Zhiliang , Xu Jiadi , Chen Zhong , Chen Lin 
Published: August 2023

AbstractChemical exchange saturation transfer (CEST) is a versatile technique that enables noninvasive detections of endogenous metabolites present in low concentrations in living tissue. However, CEST imaging suffers from an inherently low signal‐to‐noise ratio (SNR) due to the decreased water signal caused by the transfer of saturated spins. This limitation challenges the accuracy and reliability of quantification in CEST imaging. In this study, a novel spatial–spectral denoising method, called BOOST (suBspace denoising with nOnlocal lOw‐rank constraint and Spectral local‐smooThness regularization), was proposed to enhance the SNR of CEST images and boost quantification accuracy. More precisely, our method initially decomposes the noisy CEST images into a low‐dimensional subspace by leveraging the global spectral low‐rank prior. Subsequently, a spatial nonlocal self‐similarity prior is applied to the subspace‐based images. Simultaneously, the spectral local‐smoothness property of Z‐spectra is incorporated by imposing a weighted spectral total variation constraint. The efficiency and robustness of BOOST were validated in various scenarios, including numerical simulations and preclinical and clinical conditions, spanning magnetic field strengths from 3.0 to 11.7 T. The results demonstrated that BOOST outperforms state‐of‐the‐art algorithms in terms of noise elimination. As a cost‐effective and widely available post‐processing method, BOOST can be easily integrated into existing CEST protocols, consequently promoting accuracy and reliability in detecting subtle CEST effects.
Modeling of environmentally assisted intergranular crack propagation in polycrystals
Authors: Auth Kim Louisa , Brouzoulis Jim , Ekh Magnus 
Published: August 2023

AbstractPolycrystalline nickel‐based superalloys experience accelerated intergranular crack growth when exposed to dwell times in oxygen‐rich environments and a combination of high temperature and tensile mechanical loading. Increasing crack growth rates are observed for increasing amounts of environmental oxygen in a certain oxygen concentration range, while below and above that range crack growth rates remain approximately constant. A fully coupled chemo‐mechanical modeling framework accounting for the degradation of grain boundaries by oxygen has been presented by the authors. In this work, we expand the framework by a moving boundary condition to capture a realistic oxygen flux in grain boundary cracks for both edge cracks connected to the environment and interior cracks. In numerical simulation results, the behavior of the moving boundary condition is shown for intergranular crack propagation through a polycrystal subjected to cyclic loading. Finally, the capabilities of the modeling framework to qualitatively predict the dependence of the average crack growth rate on the environmental oxygen content, load level, and dwell time are evaluated and it is shown that predictions qualitatively agree with experimental observations for intergranular fracture.