bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating soil solarization and mustard seed meal as preplant treatments for weed control in annual hill plasticulture strawberry production
Authors: Aljawasim Baker D. , Johnson Charles , Manchester Mikel , Samtani Jayesh B. 
Published: November 2024

Abstract
Alternative strategies to fumigation are needed to manage weeds and improve strawberry fruit yield in annual hill plasticulture production systems. Field experiments were conducted in Blackstone, VA, for two consecutive growing seasons, 2013/14 and 2014/15, to assess the efficacy of 4 wk and 8 wk soil solarization (SS) and application of mustard seed meal (MSM) at 1,121 kg ha−1, alone and in combination, for weed control efficacy and crop yield estimation in this production system. These treatments were compared to the use of 1,3-dichloropropene (1,3-D) + chloropicrin (Pic) as a fumigation standard at 188 kg ha−1 and an untreated control (UTC). Over both growing seasons, compared to 1,3-D+ Pic, the SS-MSM-8wk and SS-8wk treatments provided equivalent or reduced cumulative weed count, including weed count of several dominant weed species such as annual ryegrass, speedwell, common chickweed, and cudweed. The SS-4wk and MSM-4wk treatments did not affect weed density compared with the UTC. The MSM-8 wk and 4-wk treatments reduced cumulative weed counts over that of the UTC. In the second growing season, the total yield was significantly higher after the 1,3-D + Pic fumigation treatment compared with yield after other treatments. The SS-4wk, MSM-4wk, and MSM-8wk treatments did not improve the total or marketable yield compared with the UTC. The marketable yield after the SS-MSM-8wk treatment was similar to that of the 1,3-D + Pic treatment. In conclusion, the SS-8wk and SS-MSM-8wk treatments may be effective weed management strategies for organic growers, small farms, or growers who cannot use chemical fumigants due to new regulations and potential risks to human health.
Depth Mapping in Turbid and Deep Waters Using AVIRIS‐NG Imagery: A Study in Wax Lake Delta, Louisiana, USA
Authors: Kwon Siyoon , Passalacqua Paola , Soloy Antoine , Jensen Daniel , Simard Marc 
Published: November 2024

AbstractRemote sensing has been widely applied to investigate fluvial processes, but depth retrievals face significant constraints in deep and turbid conditions. This study evaluates the potential for depth retrievals under such challenging conditions using NASA's Airborne Visible/Infrared Imaging Spectrometer‐Next Generation (AVIRIS‐NG) imagery. We employ interpretable machine learning to construct a hyperspectral regressor for water depth and explore the spectral characteristics of deep and turbid waters in Wax Lake Delta (WLD), Louisiana, USA. The reflectance spectra of WLD show minor effects from depth differences due to turbidity. Nevertheless, a Random Forest with Recursive Feature Elimination (RF‐RFE) effectively generalizes high and low turbidity cases in a single model, achieving a of . Moreover, this model shows a maximum detectable depth of approximately 30 m, outperforming other methods. A spectral analysis using Shapley additive explanations (SHAP) points out the importance of learning various spectral bands and non‐linear relationships between depth and reflectance. Specifically, the short blue and Near‐InfraRed (NIR) bands, with high attenuation coefficients, play a crucial role. This finding highlights attenuation as the key process for deep‐depth retrievals. The depth maps of WLD produced by this model accurately capture the spatial distribution of deep river and shallow delta regions. However, the high dependency on short blue and NIR bands leads to discontinuous areas due to the noise sensitivity of these bands. This result highlights a drawback of remote sensing using empirical models. Future research will focus on correcting such discontinuities by integrating data from multiple remote sensing sources.
Advancing Sustainable Groundwater Management With a Hydro‐Economic System Model: Investigations in the Harney Basin, Oregon
Authors: Jaeger W. K. , Antle J. , Gingerich S. B. , Bigelow D. 
Published: November 2024

AbstractGroundwater resources frequently trend toward unsustainable levels because, absent effective institutions, individual water users generally act independently without considering the impacts on other users. Hydro‐economic models (HEMs) of human‐natural systems can play a positive role toward successful groundwater management by yielding valuable knowledge and insight. The current study explores how an HEM that captures essential physical and economic characteristics of a system can shed light on the system's processes and dynamics to benefit stakeholders, managers, and also researchers. These propositions are illustrated using the Harney Basin, Oregon, which has seen large groundwater declines in the past 20 years. The HEM shows that: (a) although current groundwater pumping rates will gradually raise costs and reduce well yields, irrigators gain the highest aggregate economic return by continuing current pumping; (b) lowland areas of the basin are hydrologically connected, which limits the efficacy of remedies focused on regulations only in some portions of the basin; (c) community expectations regarding the efficacy of several proposed solutions are overly optimistic; and (d) the study's scenarios identify interventions that would stabilize the groundwater system and prevent additional adverse impacts on residential and livestock wells and groundwater‐dependent ecosystems. These interventions would require limiting groundwater pumping by nearly half and reducing annual profits by $7.5–$9.0M. The HEM also demonstrated its value to researchers: its insights shifted attention toward questions about Oregon's existing groundwater institutions and their inability to adaptively manage the transition from abundant groundwater to scarce groundwater in a timely manner.
On the Contribution of Latitude‐Dependent ULF Waves to the Radial Transport of Off‐Equatorial Relativistic Electrons in the Radiation Belts
Authors: Sarris Theodore E. , Li Xinlin , Zhao Hong , Tu Weichao , Papadakis Kostis , Tourgaidis Stelios , Liu Wenlong , Yan Li , Rankin Robert , Xiang Zheng , Mei Yang , O’Brien Declan , Hogan Benjamin , Brennan David , Ergun Robert E. , Angelopoulos Vassilis , Baloukidis Dimitris , Pirnaris Panagiotis 
Published: November 2024

AbstractUltra‐low frequency (ULF) waves radially diffuse hundreds‐keV to few‐MeV electrons in the magnetosphere, as the range of drift frequencies of such electrons overlaps with the wave frequencies, leading to resonant interactions. Theoretically this process is described by analytic expressions of the resonant interactions between electrons and ULF wave modes in a background magnetic field. However, most expressions of the radial diffusion rates are derived for equatorially mirroring electrons and are based on estimates of the power of ULF waves that are obtained either from spacecraft close to the equatorial plane or from the ground but mapped to the equatorial plane. Based on recent statistical in situ observations, it was found that the wave power of magnetic fluctuations is significantly enhanced away from the magnetic equator. In this study, the distribution of the wave amplitudes as a function of magnetic latitude is compared against models simulating the natural modes of oscillation of magnetospheric field lines, with which they are found to be consistent. Energetic electrons are subsequently traced in 3D model fields that include a latitudinal dependence that is similar to measurements and to the natural modes of oscillation. Particle tracing simulations show a significant dependence of the radial transport of relativistic electrons on pitch angle, with off‐equatorial electrons experiencing considerably higher radial transport, as they interact with ULF wave fluctuations of higher amplitude than equatorial electrons. These findings point to the need for incorporating pitch‐angle‐dependent radial diffusion coefficients in global radiation belt models.
Seismic Response of Hectometer‐Scale Fracture Systems to Hydraulic Stimulation in the Bedretto Underground Laboratory, Switzerland
Authors: Obermann Anne , Rosskopf Martina , Durand Virginie , Plenkers Katrin , Bröker Kai , Rinaldi Antonio Pio , Gholizadeh Doonechaly Nima , Gischig Valentin , Zappone Alba , Amann Florian , Cocco Massimo , Hertrich Marian , Jalali Mohammadreza , Junker Jonas Simon , Kästli Philipp , Ma Xiaodong , Maurer Hansruedi , Meier Men‐Andrin , Schwarz Miriam , Selvadurai Paul , Villiger Linus , Wiemer Stefan , Dal Zilio Luca , Giardini Domenico 
Published: November 2024

AbstractWe performed a series of hydraulic stimulations at 1.1 km depth in the Bedretto underground laboratory, Switzerland, as part of an overall research strategy attempting to understand induced seismicity on different scales. Using an ultra‐high frequency seismic network we detect seismic events as small as Mw < −4, revealing intricate details of a complex fracture network extending over 100 m from the injection sites. Here, we outline the experimental approach and present seismic catalogs as well as a comparative analysis of event number per injection, magnitudes, b‐values, seismogenic index and reactivation pressures. In our first‐order seismicity analysis, we could make the following observations: The rock volume impacted by the stimulations in different intervals differs significantly with a lateral extent from a few meters to more than 150 m. In most intervals multiple fractures were reactivated. The seismicity typically propagates upwards toward shallower depth on parallel oriented planes that are consistent with the stress field and seem to a large extent associated with preexisting open fractures. This experiment confirms the diversity in seismic behavior independent from the injection protocol. The overall seismicity patterns demonstrate that multi‐stage stimulations using zonal isolation allow developing an extended fracture network in a 3D rock volume, which is necessary for enhanced geothermal systems. Our stimulations covering two orders of magnitude in terms of injected volume will give insights into upscaling of induced seismicity from underground laboratory scale to field scale.
Recent Freshening of the Subpolar North Atlantic Increased the Transport of Lighter Waters of the Irminger Current From 2014 to 2022
Authors: Fried Nora , Biló Tiago C. , Johns William E. , Katsman Caroline A. , Fogaren Kristen E. , Yoder Meg , Palevsky Hilary I. , Straneo Fiammetta , de Jong M. Femke 
Published: November 2024

AbstractStarting in 2012, the eastern subpolar North Atlantic experienced the strongest surface freshening in the past 120 years. It is yet unknown whether this salinity anomaly propagated downward into the water column and affected the properties of the boundary currents of the subpolar gyre, which could slow down the overturning. Here, we investigate the imprint of this salinity anomaly on the warm and saline Irminger Current (IC) in the decade thereafter. Using daily mooring data from the IC covering the period 2014–2022 combined with hydrographic sections across the adjacent basins from 1990, the evolving signal of the salinity anomaly over the water column and its imprint on the transport variability is studied. We find that due to the salinity anomaly, the northward freshwater transport of the IC increased by 10 mSv in summer 2016 compared to summer 2015. In 2018, the salinity anomaly covered the water column down to 1,500 m depth. Hydrographic sections across the basin showed that this recent freshening signal spread across the Irminger Sea. Overall, the freshwater transport of the IC increased by a factor of three between 2014–2015 and 2021–2022. The associated density decrease over the upper 1,500 m of the water column resulted in an increase in the northward transport of waters lighter than σ0 = 27.55 kg m−3 from 1.7 to 4.2 Sv. This change in northward IC transport by density class may impact the characteristics of the overturning in the Northeastern Atlantic, its strength and the density at which it peaks.
Comparative Analysis of TPA‐LSTM and Transformer Models for Forecasting GEO Radiation Belt Electron Fluxes
Authors: Tan Mengli , Si Xu , Teng Shangchun , Wu Xinming , Tao Xin 
Published: November 2024

AbstractThe geosynchronous orbit (GEO) is a region filled with energetic electrons and it hosts hundreds of satellites. Electron fluxes at GEO can change sharply within hours, making high‐time‐resolution prediction crucial. In this study, we develop and compare two neural networks for persistent high‐time‐resolution prediction: long short‐term memory with temporal pattern attention (TPA‐LSTM) and Transformer. Unlike most previous models, which only output electron fluxes, our models output the same parameters as the inputs, including magnetic local time, solar wind speed, solar wind dynamic pressure, AE, Kp, Dst, the north‐south component of the interplanetary magnetic field, and electron flux data from GOES‐15. The models are trained on approximately six years of data (2012–2016) and validated using about one year of data (2017–2018). We compare the TPA‐LSTM and Transformer models using 0.8 MeV electron fluxes and find that while the Transformer model performs slightly better, the difference is not statistically significant. Considering the Transformer's higher computational cost, we use the TPA‐LSTM model to develop prediction models for electron fluxes of 275, 475, 0.8 MeV, and 2 MeV with a 5‐min resolution at GEO, up to 3 days. The prediction efficiencies (PE) for 275, 475, 0.8 and 2 MeV electron fluxes based on about one year of test data (2018–2019) are 0.799, 0.831, 0.849, 0.881 (1‐day prediction); and 0.551, 0.618, 0.663 and 0.710 (3‐day prediction), respectively. Our high‐time‐resolution persistent models should be useful for both protecting satellites at GEO and serving as boundary conditions for physics‐based radiation belt models.
Comparative metabolic profiling and quantitative analysis of metabolites in different tissues of Ajuga turkestanica by ESI-UHPLC-QqTOF-MS and NMR
Authors: Mamadalieva Nilufar Z. , Šoral Michal , Kysil Elana , Stark Pauline , Frolov Andrej , Wessjohann Ludger A. 
Published: November 2024

AbstractAjuga turkestanica preparations are used as anti-aging cosmeceuticals and for medicinal purposes. Herein we describe the characterization and quantification of its metabolites in different organs using UHPLC-MS and NMR spectroscopy. A total of 51 compounds belonging to various phytochemical classes (11 flavonoids, 10 ecdysteroids, 9 diterpenes, 6 fatty acids, 5 iridoids, 3 phenylpropanoids, 3 sugars, 2 phenolics, 1 coumarin, 1 triterpene) were annotated and tentatively identified by UHPLC-ESI-QqTOF-MS/MS of methanolic extracts obtained separately from the organs. 1D and 2D NMR spectroscopy independently confirmed the identity of six major compounds. The abundances of these main constituents in flowers, fruits, leaves, roots, seeds, and stems were compared and quantified using 1H NMR. The results showed that 8-O-acetylharpagide, 20-hydroxyecdysone (ecdysterone) and ajugachin B were the most abundant constituents in the species. The two major compounds, 8-O-acetylharpagide and 20-hydroxyecdysone, were chosen as the markers for the quality assessment of A. turkestanica material. The methanolic extract of the aerial parts of A. turkestanica showed no noteworthy anthelmintic (antihelmintic), antifungal, or cytotoxic effect in in vitro assays.
Fermatean fuzzy score function and distance measure based group decision making framework for household waste recycling plant location selection
Authors: Mishra Arunodaya Raj , Rani Pratibha , Saeidi Parvaneh , Deveci Muhammet , Alrasheedi Adel Fahad 
Published: November 2024

AbstractThe household waste (HW) disposal and recycling have become a significant challenge due to increasing quantities of generated household wastes and increased levels of urbanization. Selecting locations/sites for building new HW recycling plant comprises numerous sustainability dimensions, thus, this work aims to develop new decision-making model for evaluating and prioritizing the HW recycling plant locations. This paper is categorized into three phases. First, we propose new improved score function to compare the Fermatean fuzzy numbers. Moreover, an example is presented to validate the effectiveness of proposed score function over the extant ones. Second, we introduce new distance measure to estimate the discrimination degree between Fermatean fuzzy sets (FFSs) and further discuss its advantages over the prior developed Fermatean fuzzy distance measures. Third, we introduce an integrated methodology by combining the method with the removal effects of criteria (MEREC), the stepwise weight assessment ratio analysis (SWARA) and the measurement alternatives and the ranking according to compromise solution (MARCOS) approaches with Fermatean fuzzy (FF) information, and named as the “FF-MEREC-SWARA-MARCOS” framework. In this method, the FF-distance measure is used to find the weights of involved decision-making experts. Moreover, an integrated criteria weighting method is presented with the combination of MEREC and SWARA models under the context of FFSs, while the combined FF-MEREC-SWARA-MARCOS model is applied to evaluate and prioritize the locations for HW recycling plant development, which illustrates its feasibility of the developed framework. Comparative study and sensitivity assessment are conducted to validate the obtained outcomes. This work provides a hybrid decision analysis approach, which marks a significant impact to the HW recycling plant location selection process with uncertain information.
Optimization of heat transfer in a double lid-driven cavity with isoperimetric heated blocks using GFEM
Authors: Refaie Ali Ahmed , Mahmood Rashid , Ishfaq Maria , Rehman Nusrat , Majeed Afraz Hussain 
Published: November 2024

AbstractThis article is concerned with the examination of flow dynamics and heat transfer characteristics in a 1:4 double lid driven cavity in presence of isoperimetric heated blocks of various shapes. The focus is to identify the optimal shape that enhances the heat transfer in a tall cavity. The parametric settings are chosen in such a way that all the convection regimes including natural, forced and mixed convection could be generated. This cavity has lids positioned at the top and bottom, moving in opposite directions along the x-axis. The physical system is represented as a set of coupled partial differential equations incorporating the rheological properties of the power-law fluids (PL). The governing equations in conjunction with various non-dimensional physical parameters are simulated via Galerkin’s Finite Element Method (GFEM) on a very fine hybrid grid. The study includes the computation of the Kinetic Energy and Average Nusselt number to determine the optimal shape. It is concluded that the circular block is superior to the other two in terms of heat transmission efficiency.
Learning spatial‐frequency interaction for generalizable deepfake detection
Authors: Zhai Tianbo , Lu Kaiyin , Li Jiajun , Wang Yukai , Zhang Wenjie , Yu Peipeng , Xia Zhihua 
Published: November 2024

AbstractIn recent years, face forgery detection has gained significant attention, resulting in considerable advancements. However, most existing methods rely on CNNs to extract artefacts from the spatial domain, overlooking the pervasive frequency‐domain artefacts present in deepfake content, which poses challenges in achieving robust and generalized detection. To address these issues, we propose the dual‐stream frequency—spatial fusion network is proposed for deepfake detection. The dual‐stream frequency‐spatial fusion network consists of three components: the spatial forgery feature extraction module, the frequency forgery feature extraction module, and the spatial–frequency feature fusion module. The spatial forgery feature extraction module employs spatial‐channel attention to extract spatial domain features, targeting artefacts in the spatial domain. The frequency forgery feature extraction module leverages the focused linear attention to detect frequency domain anomalies in internal regions, enabling the identification of generated content. The spatial–frequency feature fusion module then fuses forgery features extracted from both the spatial and frequency domains, facilitating accurate detection of splicing artefacts and internally generated forgeries. This approach enhances the model's ability to more accurately capture forgery characteristics. Extensive experiments on several widely‐used benchmarks demonstrate that our carefully designed network exhibits superior generalization and robustness, significantly improving deepfake detection performance.
Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power
Authors: Wang Guibin , Huang Xinlong , Li Yiqun , Wang Hong , Zhang Xian , Qiu Jing 
Published: November 2024

AbstractAccurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short‐term forecasting, such as 60‐min predictions. This article introduces a hybrid data‐driven framework that employs an ensemble deep learning model to provide highly precise short‐term wind power predictions. The framework leverages a data‐driven approach to identify the intrinsic components of wind power data, including high‐frequency and low‐frequency components. A convolutional layer‐based feature fusion network is then established to properly extract important information from irrelevant wind energy features. Subsequently, an ensemble of long short‐term memory (LSTM) networks is developed to forecast wind power using the fused features, thereby mitigating the disadvantage of a single prediction model. The numerical experiment is carried out based on two different real‐life datasets. The results demonstrate the effectiveness of the proposed method in forecasting short‐term wind power compared to five benchmarks.
Effect of Post Sizes and Citric Acid Treatment on the Bond Strength of Fiber Posts Using Self-Etch Resin Cement in Calcium Silicate–Based Sealer Treated Teeth
Authors: Klanliang Kittipit , Dewi Anat , Tangwattanachuleeporn Pradtana , Louwakul Phumisak 
Published: November 2024

Abstract
Objectives This study evaluated the effects of different post sizes and citric acid (CA) treatment on the bond strength of fiber posts cemented with self-etch resin cement in teeth obturated with calcium silicate–based sealer.
Materials and Methods Seventy mandibular premolars were collected and randomly distributed to either a control group (no sealer) or experimental groups obturated with calcium silicate–based sealer (iRoot SP). The experimental groups were classified by post sizes—1.25 mm (no. 1), 1.375 mm (no. 2), and 1.50 mm (no. 3)—and the irrigants used (distilled water [DW] or CA). Prefabricated fiber posts were fixed using NX3 self-etch resin cement. Push-out bond strength was tested in the coronal and middle sections of the roots.
Statistical Analysis The data were analyzed using a one-way analysis of variance (ANOVA), followed by a post hoc Duncan test.
Results In the coronal section, post size no. 1 with DW showed significantly lower bond strength compared to the other experimental groups (p < 0.05). In the middle section, the larger post sizes (nos. 2 and 3) with CA treatment resulted in a significant increase in bond strength compared to the control group (p < 0.05).
Conclusions iRoot SP negatively affected bond strength in the middle section of the canal. However, using larger post sizes (nos. 2 and 3) with CA treatment improved bond strength in the middle section.