bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gravimetric inhibition efficiency prediction model of AA7075-T7351 alloy using Treculia africana extract in 1.0 M HCl through input feature optimization
Authors: Udensi S. C. , Ejelonu B. O. 
Published: August 2024

AbstractThe applications of four machine learning (ML) algorithms, namely: Support Vector Regressor (SVR), Extreme Gradient Boosting (XGBoost), Least absolute shrinkage and selection operator (Lasso), and Ridge, in predicting the corrosion inhibition efficiency (IE) of Treculia africana (TA) leaves extract on AA7075-T7351 alloy, in corrosive 1.0 M HCl environment, with a small (42) sample space, have been studied. Time and resource constraints in traditional corrosion study methods have been avoided through feature engineering to expedite prediction process. The dominant features, which affected the IE, were done through feature importance and selection processes using pair plot matrix of features and Kendall correlation etc., to remove redundant features. The results in the form of data visualization, feature importance, and the performance of each algorithm on the test set were explicitly depicted. The evaluation metrics, including coefficients of determination (R2) and root mean square error (RMSE), validated the efficacy of the models in predicting the IE of TA on AA7075-T7351 in 1.0 M HCl environments. Ridge model demonstrated superior accuracy, with R2 score of 0.972, particularly in handling the highly correlated dataset used in this study. SVR followed closely in performance (0.969). XGBoost proved reliable at R2 score of 0.953. Lasso with R2 of 0.952 was the least of the four models, due to its random feature selection method. The RMSE scores corroborated the prediction accuracies with values; 4.145, 4.408, 5.138 and 5.462 respectively. This study revealed the viability of using the four machine learning algorithms in potential generalization ability of IE prediction accuracy, while offering an efficient and accurate alternative to traditional methods.
Graphical Abstract
Construction biotechnology: improving mortar properties through calcium carbonate precipitation using a novel strain of the bacterium Neisseria perflava
Authors: Syarif Ridwan , Chaerun Raudhatul Islam , Chaerun Siti Khodijah , Prassetyo Simon Heru , Wattimena Ridho Kresna 
Published: August 2024

AbstractIn construction technology, there are significant efforts to reduce environmental emissions, particularly NH3 and other pollutants. This study marks the first application of CaCO3 biomineralization biotechnology in microbially induced calcium carbonate precipitation (MICCP) to enhance mortar properties using the non-pathogenic Neisseria perflava strain SKC/VA-3, which employs carbonic anhydrase mechanisms. The results demonstrated that N. perflava could significantly improve the physical and mechanical characteristics of mortar. Incorporating N. perflava and calcium lactate pentahydrate resulted in a 20% increase in compressive strength and a 14% rise in indirect tensile strength of the mortar. Examination through scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM–EDS) revealed calcite formation within the microstructure of the bio-mortar. Additionally, self-healing assessments indicated that calcite precipitation, driven by bacterial metabolism, also occurred on the cracked surfaces of the bacterial mortar, suggesting potential for reduced maintenance and increased material longevity. This study provides the first report on the use of N. perflava for bio-mortar enhancement and represents a novel biotechnological approach to improving the properties of mortar and other cementitious materials. The utilization of N. perflava in bio-mortar represents a groundbreaking biotechnological advance, potentially enhancing mortar and other cement-based materials. This development contributes to sustainable, durable, and environmentally friendly construction technologies.
Elaboration and outlook for metal hydride applications in future hydrogen-powered aviation
Authors: Franke F. , Kazula S. , Enghardt L. 
Published: August 2024

AbstractHydrogen is a promising energy carrier to decarbonise aviation. However, many challenges regarding its storage or handling still have to be solved to successfully utilise hydrogen in aircraft and at airport infrastructures. The increasing use of hydrogen also generates opportunities for disruptive improvements, like the possibility to integrate metal hydrides (MHs) into the hydrogen powertrain and its infrastructure. Besides their ability to store hydrogen, MHs enable a wide range of potential secondary functions such as high-power thermal applications or compression. This way, MHs may contribute to achieve the goal of sustainable hydrogen-powered aviation. Hence, potential MH application options and their current state-of-the-art are presented. Based on that overview, the following seven use cases for aviation are selected for evaluation: ‘hydrogen emergency storage’, ‘cabin air-conditioning’, ‘thermal management of fuel cells’, ‘gas gap heat switches’, ‘hydrogen boil-off recovery’, ‘onboard hydrogen compression’ and ‘hydrogen safety sensors’. Four of these use cases are investigated to achieve comparable degrees of detail to avoid misevaluations in the subsequent weighted point rating. The results reveal the high potential of MHs for ‘hydrogen boil-off recovery’, ‘hydrogen safety sensors’ and ‘cabin air-conditioning’. For the three most promising use cases, outlooks to their potential future implementation are provided in order to outline the ability of MHs to empower sustainable aviation. These investigations highlight the huge potential of MHs for boil-off treatment.
Predictive Understanding of Links Between Vegetation and Soil Burn Severities Using Physics‐Informed Machine Learning
Authors: Seydi Seyd Teymoor , Abatzoglou John T. , AghaKouchak Amir , Pourmohamad Yavar , Mishra Ashok , Sadegh Mojtaba 
Published: August 2024

AbstractBurn severity is fundamental to post‐fire impact assessment and emergency response. Vegetation Burn Severity (VBS) can be derived from satellite observations. However, Soil Burn Severity (SBS) assessment—critical for mitigating hydrologic and geologic hazards—requires costly and laborious field recalibration of VBS maps. Here, we develop a physics‐informed Machine Learning model capable of accurately estimating SBS while revealing the intricate relationships between soil and vegetation burn severities. Our SBS classification model uses VBS, as well as climatological, meteorological, ecological, geological, and topographical wildfire covariates. This model demonstrated an overall accuracy of 89% for out‐of‐sample test data. The model exhibited scalability with additional data, and was able to extract universal functional relationships between vegetation and soil burn severities across the western US. VBS had the largest control on SBS, followed by weather (e.g., wind, fire danger, temperature), climate (e.g., annual precipitation), topography (e.g., elevation), and soil characteristics (e.g., soil organic carbon content). The relative control of processes on SBS changes across regions. Our model revealed nuanced relationships between VBS and SBS; for example, a similar VBS with lower wind speeds—that is, higher fire residence time—translates to a higher SBS. This transferrable model develops reliable and timely SBS maps using satellite and publicly accessible data, providing science‐based insights for managers and diverse stakeholders.
The impact of breastfeeding on the preterm infant’s microbiome and metabolome: a pilot study
Authors: Schulkers Escalante Keriann , Bai-Tong Shiyu S. , Allard Sarah M. , Ecklu-Mensah Gertrude , Sanchez Concepcion , Song Se Jin , Gilbert Jack , Bode Lars , Dorrestein Pieter , Knight Rob , Gonzalez David J. , Leibel Sydney A. , Leibel Sandra L. 
Published: August 2024

Abstract
Background
Human milk is unquestionably beneficial for preterm infants. We investigated how the transition from tube to oral/breastfeeding impacts the preterm infants’ oral and gut microbiome and metabolome.

Methods
We analyzed stool, saliva, and milk samples collected from a cohort of preterm infants enrolled in the MAP Study, a prospective observational trial. The microbiome and metabolome of the samples were analyzed from 4 longitudinal sample time points, 2 during tube feeds only and 2 after the initiation of oral/breastfeeding.

Results
We enrolled 11 mother-infant dyads (gestational age = 27.9 (23.4–32.2)) and analyzed a total of 39 stool, 44 saliva, and 43 milk samples over 4 timepoints. In saliva samples, there was a shift towards increased Streptococcus and decreased Staphylococcus after oral feeding/breastfeeding initiation (p < 0.05). Milk sample metabolites were strongly influenced by the route of feeding and milk type (p < 0.05) and represented the pathways of Vitamin E metabolism, Vitamin B12 metabolism, and Tryptophan metabolism.

Conclusion
Our analysis demonstrated that the milk and preterm infant’s saliva microbiome and metabolome changed over the course of the first four to 5 months of life, coinciding with the initiation of oral/breastfeeds.

Impact

The microbiome and metabolome is altered in the infant’s saliva but not their stool, and in mother’s milk when feeds are transitioned from tube to oral/breastfeeding.

We assessed the relationship between the gut and oral microbiome/metabolome with the milk microbiome/metabolome over a longitudinal period of time in preterm babies.

Metabolites that changed in the infants saliva after the initiation of oral feeds have the potential to be used as biomarkers for disease risk.
UBE2M forms a positive feedback loop with estrogen receptor to drive breast cancer progression and drug resistance
Authors: Lin Xiongzhi , Sun Dongsheng , Yang Shuhan , Cheng Kai , Wang XingYi , Meng Weijia , Wu Haowei , Liu Wenlin , Wu Xiaoyu , Yang Hui , Wang Xiaojun , Zhou Lisha 
Published: August 2024

AbstractUBE2M, a NEDD8-conjugating enzyme, is dysregulated in various human cancers and promotes tumor cell proliferation. However, its role in estrogen receptor-positive (ER+) breast cancer remains unknown. We found that UBE2M expression was significantly higher in ER+ breast cancer tissues than in ER-negative (ER-) breast cancer tissues. Higher expression of UBE2M indicated a poorer prognosis in patients with ER+ breast cancer but not in those with ER- breast cancer. Of interest, a positive feedback loop was observed between UBE2M and ERα. Specifically, ERα enhanced the HIF-1α-mediated transcription of UBE2M. In turn, UBE2M maintained ERα expression by inhibiting its ubiquitination and degradation through UBE2M-CUL3/4A-E6AP-ERα axis. Functionally, silencing of UBE2M suppressed the growth of breast cancer cells by inducing cell cycle arrest and apoptosis and improved their sensitivity to fulvestrant both in vitro and in vivo. Altogether, our findings reveal that the UBE2M-ERα feedback loop drives breast cancer progression and fulvestrant resistance, suggesting UBE2M as a viable target for endocrine therapy of ER+ breast cancer.
Chiral topological light for detection of robust enantiosensitive observables
Authors: Mayer Nicola , Ayuso David , Decleva Piero , Khokhlova Margarita , Pisanty Emilio , Ivanov Misha , Smirnova Olga 
Published: August 2024

AbstractThe topological response of matter to electromagnetic fields is a highly demanded property in materials design and metrology due to its robustness against noise and decoherence, stimulating recent advances in ultrafast photonics. Embedding topological properties into the enantiosensitive optical response of chiral molecules could therefore enhance the efficiency and robustness of chiral optical discrimination. Here we achieve such a topological embedding by introducing the concept of chiral topological light—a light beam which displays chirality locally, with an azimuthal distribution of its handedness described globally by a topological charge. The topological charge is mapped onto the azimuthal intensity modulation of the non-linear optical response, where enantiosensitivity is encoded into its spatial rotation. The spatial rotation is robust against intensity fluctuations and imperfect local polarization states of the driving field. Our theoretical results show that chiral topological light enables detection of percentage-level enantiomeric excesses in randomly oriented mixtures of chiral molecules, opening a way to new, extremely sensitive and robust chiro-optical spectroscopies with attosecond time resolution.
Investigation into the variation characteristics and influencing factors of coalbed methane gas content in deep coal seams
Authors: Zhu Qianya , Du Xuejia , Zhang Tong , Yu Haiming , Liu Xiaobo 
Published: August 2024

AbstractGas saturation is a critical parameter for the selection and development of coalbed methane, as well as a key indicator reflecting the challenges in coalbed methane development and productivity evaluation of coalbed methane wells. As one of the significant factors influencing gas saturation, gas content plays a vital role in comprehensively investigating coal pore properties to fully comprehend the process and conditions of methane adsorption and desorption. In this study, 3# and 15# coals from Qinshui Basin, China was selected as research subjects. The experimental evaluation encompassed an examination of composition, pore characteristics, permeability characteristics of coal, rock mechanical parameters while discussing the impact of temperature and pressure on coal's adsorption and desorption capacity. The mineral characteristics analysis revealed that vitrinite is the main component with varying percentages and reflectance values in both 3# and 15# coal seams. The gas content and methane concentration in the 15# coal seam are higher than those in the 3# coal seam. The relationship between gas content within a coal seam and burial depth depends on achieving a balance between positive pressure effects caused by overburden stress exertion on gases trapped within pores under high pressures during burial history versus negative temperature effects due to cooling during geological processes over time. Predictions were made regarding deep-coal gas content which holds significant implications for accurately understanding variations in desorption behavior along with optimizing fracturing engineering.
Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms
Authors: Vach Marius , Wolf Luisa , Weiss Daniel , Ivan Vivien Lorena , Hofmann Björn B. , Himmelspach Ludmila , Caspers Julian , Rubbert Christian 
Published: August 2024

AbstractThis study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing various fully automatic pre-processing pipelines, and (3) rigorously validate the model’s transferability on independent, external test-datasets. A convolutional neural network was trained on 235 TOF-MRAs acquired on local scanners from a single vendor to segment intracranial aneurysms. Different pre-processing pipelines including bias field correction, resampling, cropping and intensity-normalization were compared regarding their effect on model performance. The models were tested on independent, external same-vendor and other-vendor test-datasets, each comprised of 70 TOF-MRAs, including patients with and without aneurysms. The best-performing model achieved excellent results on the external same-vendor test-dataset, surpassing the results of the previous publication with an improved sensitivity (0.97 vs. ~ 0.86), a higher Dice score coefficient (DSC, 0.60 ± 0.25 vs. 0.53 ± 0.31), and an improved false-positive rate (0.87 ± 1.35 vs. ~ 2.7 FPs/case). The model further showed excellent performance in the external other-vendor test-datasets (DSC 0.65 ± 0.26; sensitivity 0.92, 0.96 ± 2.38 FPs/case). Specificity was 0.38 and 0.53, respectively. Raising the voxel-size from 0.5 × 0.5×0.5 mm to 1 × 1×1 mm reduced the false-positive rate seven-fold. This study successfully replicated core principles of a previous approach for detecting and segmenting cerebral aneurysms in TOF-MRAs with a robust, fully automatable pre-processing pipeline. The model demonstrated robust transferability on two independent external datasets using TOF-MRAs from the same scanner vendor as the training dataset and from other vendors. These findings are very encouraging regarding the clinical application of such an approach.
Floods across the eastern United States are projected to last longer
Authors: Kim Hanbeen , Villarini Gabriele 
Published: August 2024

AbstractWhile there is growing attention toward the changes in flood magnitude and frequency, little is known about the way climate change could impact flood duration. Here we focus on 378 streamgages across the eastern United States to develop statistical models that allow the description of the year-to-year changes in flood duration above two National Weather Service (NWS) flood severity levels (i.e., minor and moderate). We use climate-related variables (i.e., basin- and season-averaged precipitation and temperature) as predictors, and show that they can be used to describe the inter-annual variability in seasonal flood durations for both NWS flood severity levels. We then use the insights from the understanding of the historical changes to provide an assessment of the projected changes in flood durations using global climate models from the Coupled Model Intercomparison Project Phase 6 and multiple shared socio-economic pathways. Our results show that the eastern United States is projected to experience longer flood durations, especially in winter (i.e., the main flood season) and under higher emission scenarios.
Design of a Ka‐band low‐cost microstrip line phase shifter and a sum‐difference beam antenna based on liquid crystal technology
Authors: Sima Boyu , Li ZiLing , Li Huangyan , Wang Xiang , Qiu Li , Wang Jianpeng , Wu Wen 
Published: August 2024

AbstractThis letter presents a Ka‐band low‐cost microstrip line phase shifter based on liquid crystal (LC) technology. Furthermore, a sum‐difference beamforming antenna array based on the LC phase shifter is designed. The phase shifter is composed of two coaxial connectors, a metal base, an intermediate dielectric layer, a gradient metal sheet and a top glass layer. The simulated results show that a nearly 360° phase shift coverage can be achieved with the effective permittivity of LC varying from 2.5 to 3.5. The sum‐difference beam antenna array is composed of a circulator, a sum‐difference network, a one‐to‐eight power‐division network, eight liquid crystal phase shifters, and a serious fed antenna array. The simulation results show that the antenna array can achieve sum‐difference beam scanning range of ±30°. A prototype of LC phase shifter is fabricated and tested. The results show that the designed LC phase shifter can achieve a 360° phase coverage by applying a direct current (DC) voltage ranging from 0 to 12 V. Compared to traditional phase shifters, the proposed design offers a simple structure at a lower cost. When applied in the design of a sum‐difference beam antenna array, it can reduce the producing cost and enable integrated design.
Spatial guided image captioning: Guiding attention with object's spatial interaction
Authors: Du Runyan , Zhang Wenkai , Li Shuoke , Chen Jialiang , Guo Zhi 
Published: August 2024

AbstractNowadays relational position embedding is widely used in many large multi‐modal models. It begins with relational captioning (a branch of image captioning) and contains two procedures: geometric modelling and prior attention. However, there are some problems that remain unsolved in the conventional procedures. This paper reviews the shortcomings of geometric modelling and prior attention. Then, a new framework called relational guided transformer (RGT) is proposed to verify the authors' conclusion from the origin of relational position embedding—relational captioning. Specifically, RGT has two simple but effective improvements in geometric modelling and prior attention: (1) A machine‐learned geometric modelling strategy called multi‐task geometric modelling (MTG) is used under multi‐task learning, replacing the original hand‐made geometric feature. (2) The effectiveness of multiple kinds of prior attention is discussed and preserved in a better form, which is called spatial guided attention (SGA) to integrate the geometric prior knowledge. Extensive experiments on MSCOCO and Flickr30k have been performed to investigate the effectiveness of each module and prove our argument. The superiority of the model comparing to the authors' baseline has also been proven on the offline evaluation with the “Karpathy” test split of both datasets.
Prediction of the vehicle lane‐changing distance in an urban inter‐tunnel weaving section based on wavelet transform and dual‐channel neural network
Authors: Zhu Changfeng , An Chun , He Runtian , Zhang Chao , Cheng Linna 
Published: August 2024

AbstractVehicle lane‐changing behaviour is often regarded as transient traffic behaviour while ignoring behavioural characteristics of the lane‐changing process. A combined prediction model based on wavelet transform (WT) and dual‐channel neural network (DCNN) is proposed to explore the selection behaviour of lane‐changing distance by taking lane‐changing behaviour in an urban inter‐tunnel weaving section. Firstly, the extracted lane‐changing data are analysed for correlation and noise reduction, and the main factors affecting lane‐changing distance are taken as input variables of the model. The trajectory data of the inter‐tunnel weaving section of the “Jiuhuashan‐Xi'anmen” tunnel in Nanjing, China, are used to improve the prediction of vehicle lane‐changing distance by training the model. The results show that the proposed WT‐DCNN model has high prediction performance when compared with existing artificial neural network (ANN), DCNN and wavelet neural network (WNN) models. The characterization and study of the typical lane‐changing behaviour in the weaving section can lay the theoretical foundation for the development of an urban inter‐tunnel weaving section management scheme.
Facile Synthesis of Silanols via Cesium Carbonate Catalyzed Hydrosilanes with Water
Authors: Bai Yunfeng , Chai Miaomiao , Ma Cong , Han Zhengang 
Published: August 2024

AbstractThe demand for green and efficient methods for preparing silanols is significant. In this study, we employed inexpensive cesium carbonate as a catalyst to facilitate the hydrolysis of hydrosilanes for silanol production. This approach offers numerous advantages, including mild reaction conditions, broad substrate compatibility, straightforward post-treatment procedures, high yields, and scalability to gram-level synthesis. Our method demonstrated compatibility with diverse organosilanes bearing alkyl, aryl, alkynyl, and heterocyclic substituents, including sterically hindered variants. The significance of these findings extends beyond scientific inquiry, offering practical utility in the synthesis of silanols.
Catalytic Asymmetric Approach to the Naturally Occurring Clavine Alkaloid (+)-Lysergine
Authors: Das Mrinal K. , Shaw Kundan , Bisai Alakesh , Chaudhuri Saikat , Mondal Debabrata 
Published: August 2024

AbstractAn efficient asymmetric approach to the ergot alkaloids has been achieved via a highly regioselective Heck cyclization. Asymmetric induction of the key intermediate was achieved via a catalytic enantioselective α-aminoxylation catalyzed by d-proline (98% ee). Utilizing the aforementioned strategy, formal total synthesis of the ergot alkaloids (+)-lysergine and (+)-isolysergine has been achieved. Importantly, an asymmetric approach to the unnatural analogues (–)-lysergine and (–)-isolysergine has also been achieved via catalytic enantioselective α-aminoxylation using l-proline.