bluuebunny/crossref_metadata_2025_split huggingface dataset:
Revision of the Neotropical Obscure Genus Ebenia Macquart 1846 (Diptera, Tachinidae, Dufouriini)
Authors: de Santis Marcelo Domingos 
Published: June 2024

AbstractThe Neotropical genus Ebenia Macquart, 1846, is a member of the tribe Dufouriini (Dexiinae), and before the current work, comprised four species, viz. E. claripennis Macquart 1846, E. fumata (Wulp, 1891), E. neofumata Santis & Nihei, 2022 and E. trichopoda (Wulp, 1891). The present taxonomic revision results in a new generic synonymy: Comyops Wulp, 1891 syn. nov. of Ebenia. The following two new combinations result from this act: E. nigripennis (Wulp, 1891) comb. nov. and E. striaticollis (Wulp, 1891) comb. nov. both originally described in Comyops. In addition, the species originally described as Homodexia spinosa Bigot, 1889 is moved from its current placement in Thelairodes Wulp, 1891 to Ebenia as Ebenia spinosa (Bigot, 1889) comb. nov. A new specific synonymy is proposed for this last species: Morinia trichopoda Wulp, 1891, previously treated as a valid species of Ebenia, becomes a junior synonym of E. spinosa. All valid species are redescribed and photographed with the first description and illustration of the male terminalia for E. claripennis, E. neofumata and E. nigripennis and female terminalia for E. spinosa. Additionally, lectotype fixations are made for E. nigripennis and M. trichopoda. Finally, an updated diagnosis for the genus Ebenia and a key to the six known species are provided.
Non-adhesive and adhesive contacts of an elastic quarter-or eighth-space with freely sliding sides
Authors: Li Qiang , Popov Valentin L. 
Published: June 2024

AbstractThe contact of an elastic quarter- or eighth-space is studied under the condition that the movement of the side surface of the quarter-space is constrained: It can slide freely along the plane of the side surface but its normal movement is blocked (for example, by a rigid wall). The solution of this contact problem can be easily achieved by additionally applying a mirrored load to an elastic half-space. Non-adhesive contact and the Johnson-Kendall-Roberts (JKR)-type adhesive contact between a rigid sphere and an elastic quarter-space under such a boundary condition is numerically simulated using the fast Fourier transform (FFT)-assisted boundary element method (BEM). Contacts of an elastic eighth-space are investigated using the same idea. Depending on the position of the sphere relative to the side edge, different contact behavior is observed. In the case of adhesive contact, the force of adhesion first increases with increasing the distance from the edge of the quarter-space, achieves a maximum, and decreases further to the JKR-value in large distance from the edge. The enhancement of the force of adhesion compared to the half-space-contact is associated with the pinning of the contact area at the edge. We provide the maps of the force of adhesion and their analytical approximations, as well as pressure distributions in the contact plane and inside the quarter-/eighth-space.
Performance Evaluation of Cutting Fluids Formulated with Recycled Toner Waste and Rice Bran Oil in Sustainable Machining of LMD Processed Ti64 Alloy
Authors: Ross Nimel Sworna , Mashinini Peter Madindwa , Vinayagamoorthy Dhanasekar , Rai Ritu , Sivaraman Viswanathan , Ananth Belsam Jeba , Kumar D. Satish , Gupta M. K. 
Published: June 2024

AbstractOne of the greatest environmental and health risks associated with electronic waste is toner waste. Only 20–30% of global toner waste is recycled, and the rest is deposited in landfills. Owing to the desirable endurance of toners, the sophisticated combination of chemicals, carbon black, and plastic particles makes recycling difficult. The present work deals with the recycling of toner waste and its use as a cutting fluid in the machining of Laser Metal deposition (LMD)-processed titanium alloy. Although Ti-6Al-4V (Ti64) is widely utilized in various industries, including aerospace, its machinability is adversely affected by the high amount of heat generated during machining. In this study, the performance of toner waste powder (TWP) dispersed in rice bran oil (RBO) was evaluated for milling operations on LMD-Ti64 workpieces and compared with different cooling conditions. The different wt.% (0–1.00) of TWP are disseminated in RBO with glycerine (GLY) to test the contact angle, dynamic viscosity and thermal conductivity. From the test results, it is clear that 0.75 wt% of TWP with RBO + GLY gives the better results in terms of thermo-physical properties. In addition, the TWP-enriched cutting fluid improves the machining performance and surface quality in milling operations, particularly in difficult-to-cut LMD processed Ti64. The TWP-MQL condition reduced the surface roughness (Ra) by 60–64%, respectively.
What Do Service Users with Psychosis Want from Psychological Interventions Regarding Outcome Priorities and Delivery Mode? A Systematic Review
Authors: Rogerson Shauna , Twomey Conal 
Published: June 2024

AbstractDespite previous investigations of service user preferences for the overall mental health service treatment of psychosis, there is uncertainty surrounding the specific psychological intervention preferences of service users with psychosis. We conducted a systematic review of 10 studies outlining the psychological intervention preferences of service users with psychosis, organising the findings into outcome priorities and delivery mode preferences. Service users generally prioritised various psychosocial outcomes—more specifically emotional, behavioural, self-reflective, and quality of life outcomes—with outcomes related to psychosis symptoms considered less important. Regarding delivery mode, there were indicated preferences for some level of individualised care over the sole provision of group interventions. There were also some indicated preferences for collaborative formulation within psychological interventions and remote delivery options. Notwithstanding various methodological limitations, the findings could be considered as a progressive preliminary step towards the increased accommodation of the psychological intervention preferences of service users with psychosis.
Channel-Enhanced Contrastive Cross-Domain Sequential Recommendation
Authors: Yufang Liu , Shaoqing Wang , Keke Li , Xueting Li , Fuzhen Sun 
Published: June 2024

AbstractSequential recommendation help users find interesting items by modeling the dynamic user-item interaction sequences. Due to the data sparseness problem, cross-domain sequential recommendation (CDSR) are proposed. CDSR explore rich data from a source domain to improve performance of the target domain. However, most of the existing CDSR methods are difficult to capture the temporal context of sequences and only learn user preference based on interactions of single domain, which leads to suboptimal performance. To address these shortcomings, we propose a channel-enhanced contrastive cross-domain sequential recommendation model (C3DSR). To be specific, (1) we design a feature extractor, which extends attention to the channel dimension, to extract the user’s channel feature and capture the temporal contextual relationships between sequences. Then we calculate the weights of each channel by using three SE-Res2Blocks and multiply it with the channel feature to obtain user preference. (2) We concatenate the user’s single-domain representation, the cross-domain representation, and the user features to make CDSR. Contrastive learning is leveraged to enhance mutual information between two domains. Experimental results show that the proposed model achieves the significant improvement of performance compared with other CDSR models on Amazon and HVIDEO datasets.
Noise4Denoise: Leveraging noise for unsupervised point cloud denoising
Authors: Wang Weijia , Liu Xiao , Zhou Hailing , Wei Lei , Deng Zhigang , Murshed Manzur , Lu Xuequan 
Published: June 2024

AbstractExisting deep learning-based point cloud denoising methods are generally trained in a supervised manner that requires clean data as ground-truth labels. However, in practice, it is not always feasible to obtain clean point clouds. In this paper, we introduce a novel unsupervised point cloud denoising method that eliminates the need to use clean point clouds as groundtruth labels during training. We demonstrate that it is feasible for neural networks to only take noisy point clouds as input, and learn to approximate and restore their clean versions. In particular, we generate two noise levels for the original point clouds, requiring the second noise level to be twice the amount of the first noise level. With this, we can deduce the relationship between the displacement information that recovers the clean surfaces across the two levels of noise, and thus learn the displacement of each noisy point in order to recover the corresponding clean point. Comprehensive experiments demonstrate that our method achieves outstanding denoising results across various datasets with synthetic and real-world noise, obtaining better performance than previous unsupervised methods and competitive performance to current supervised methods.
Influential factors on elementary students’ academic performance and identifying the appropriate performance group
Authors: Wickramasinghe Indika , Aragon Regina , Valles James R. 
Published: June 2024

AbstractElementary education is critical as it lays the foundation for learning, critical thinking, social and emotional development, individual growth, and self-confidence. Hence, studying the elementary students’ educational progress is immensely important. This manuscript aims to investigate the factors that impact elementary students’ academic performance, predict their academic performance, and use the above factors to identify the student’s appropriate skilled level group based on their academic performance. In this study, the Dynamic Indicators of Basic Early Literacy Skills (DIBELS) score, a measurement to assess students’ early literacy skills for K-6 graders, is used to quantify students' academic performance. A sample of 185 first and second graders and their features, including student’s BMI (body mass index), amount of time the student spends watching television, student’s gender, number of hours that a student sleeps each night, amount of time that the student spends reading books, student’s race, and amount of time that the student spends on physical activity are used. Based on the regression analysis, second graders’ academic performances are significantly impacted by their BMI values (β = − 4.002, p < 0.05) and the amount of time students spend reading books (β = 29.14, p < 0.05). The first graders’ academic performances are significantly impacted by the amount of sleeping time (β = 41.89, p < 0.01) and their gender (β = − 37.129, p < 0.05). Furthermore, the experimental findings indicate that machine-learning techniques accurately predict the students' appropriate academic group. In the naive Byers classifier, students in the lowest academic performance group can be identified successfully with a sensitivity of 92%, and the students in the highest academic group can be identified with a specificity value of 100%.
Local and Nonlocal Biophysical Effects of Historical Land Use and Land Cover Changes in CMIP6 Models and the Intermodel Uncertainty
Authors: Luo Xing , Ge Jun , Cao Yipeng , Liu Yu , Yang Limei , Wang Shiyao , Guo Weidong 
Published: June 2024

AbstractLand use and land cover changes (LULCCs) can influence surface temperature through local and nonlocal biophysical processes, which remain inadequately addressed. In this study, we separate the local and nonlocal effects of historical (1850–2014) LULCCs based on model outputs from the Coupled Model Intercomparison Project Phase 6. We also attempt to explore the sources of intermodel differences in the effects of LULCCs. The multimodel mean shows a cooling effect of −0.05°C (with an intermodel range of −0.24–0.06°C) at the global scale due to cropland and pastureland expansion, consisting of dominant nonlocal cooling of −0.06°C (with an intermodel range of −0.26–0.06°C) and slight local warming of 0.01°C (with an intermodel range of −0.01–0.05°C). The modeling results show some clear consistency in the effects of LULCCs despite considerable intermodel uncertainties. The local effects cause warming at low latitudes and cooling in boreal regions via changes in upward shortwave radiation and sensible and latent heat fluxes. The nonlocal effects mainly cause cooling via decreases in downward longwave radiation and increases in upward shortwave radiation. Intermodel differences in the total effects are dominated by those in the nonlocal effects, which are further attributed to divergent changes in downward longwave radiation and sensible heat flux across the models. This study highlights the importance of the nonlocal effects of LULCCs in terms of strength and intermodel uncertainty, with implications for designing land‐based solutions aimed at climate change mitigation.
MAGE Model Simulation of the Pre‐Reversal Enhancement and Comparison With ICON and Jicamarca ISR Observations
Authors: Wu Qian , Wang Wenbin , Lin Dong , Qian Liying , Huang Chaosong , Zhang Yongliang 
Published: June 2024

AbstractUsing the latest coupled geospace model Multiscale Atmosphere‐Geospace Environment (MAGE) and observations from Jicamarca Incoherent scatter radar (ISR) and ICON ion velocity meter (IVM) instrument, we examine the pre‐reversal enhancement (PRE) during geomagnetic quiet time period. The MAGE shows comparable PRE to both the Jicamarca ISR and ICON observations. There appears to be a discrepancy between the Jicamarca ISR and ICON IVM with the later showed PRE about two times larger (∼40 m/s). This is the first time that MAGE is used to simulate the PRE. The results show that the MAGE can simulate the PRE well and are mostly consistent with observations.
Simulating the Thermal Regime and Surface Energy Balance of a Permafrost‐Underlain Forest in Mongolia
Authors: Zweigel Robin B. , Dashtseren Avirmed , Temuujin Khurelbaatar , Aalstad Kristoffer , Webster Clare , Stuenzi Simone M. , Aas Kjetil S. , Lee Hanna , Westermann Sebastian 
Published: June 2024

AbstractForests overlap with large parts of the northern hemisphere permafrost area, and representing canopy processes is therefore crucial for simulating thermal and hydrological conditions in these regions. Forests impact permafrost through the modulation of radiative fluxes and exchange of turbulent fluxes, precipitation interception and regulation of transpiration. Forests also feature distinct soil layers of litter and organic matter, which play central roles for the infiltration and evaporation of water, while also providing thermal insulation for deeper ground layers. In this study, we present a new module within the CryoGrid community model to simulate forest ecosystems and their impact on the surface water and energy balance. The module includes a big‐leaf vegetation scheme with adaptations for canopy heat storage and transpiration. Furthermore, we account for the effect of surface litter layers on water and energy transfer. We show that the model is capable of simulating radiation, snow cover and ground temperatures below a deciduous needleleaf forest on a north‐facing slope in the Khentii Mountains in Central Mongolia. A sensitivity analysis of topographic aspect and ecosystem configuration confirms the important role of the litter layers for the energy and water balance of the ground. Furthermore, it suggests that the presence of permafrost is primarily linked to topographic aspect rather than the presence of forest at this site. The presented model scheme can be used to study the development of the ground thermal regime in forests, including the state of permafrost, under different climate, ecosystem, and land use scenarios.
Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis
Authors: Barthélémy Sébastien , Counillon François , Wang Yiguo 
Published: June 2024

AbstractEnsemble data assimilation methods, such as the Ensemble Kalman Filter (EnKF), are well suited for climate reanalysis because they feature flow‐dependent covariance. However, because Earth System Models are heavy computationally, the method uses a few tens of members. Sampling error in the covariance matrix can introduce biases in the deep ocean, which may cause a drift in the reanalysis and in the predictions. Here, we assess the potential of the hybrid covariance approach (EnKF‐OI) to counteract sampling error. The EnKF‐OI combines the flow‐dependent covariance computed from a dynamical ensemble with another covariance matrix that is static but less prone to sampling error. We test the method within the Norwegian Climate Prediction Model, which combines the Norwegian Earth System Model and the EnKF. We test the performance of the reanalyzes in an idealized twin experiment, where we assimilate synthetic sea surface temperature observations monthly over 1980–2010. The dynamical and static ensembles consist respectively of 30 members and 315 seasonal members sampled from a pre‐industrial run. We compare the performance of the EnKF to an EnKF‐OI with a global hybrid coefficient, referred to as standard hybrid, and an EnKF‐OI with adaptive hybrid coefficients estimated in space and time. Both hybrid covariance methods cure the bias introduced by the EnKF at intermediate and deep water. The adaptive EnKF‐OI performs best overall by addressing sampling noise and rank deficiencies issues and can sustain low analysis errors by doing smaller updates than the standard hybrid version.
RaFSIP: Parameterizing Ice Multiplication in Models Using a Machine Learning Approach
Authors: Georgakaki Paraskevi , Nenes Athanasios 
Published: June 2024

AbstractAccurately representing mixed‐phase clouds (MPCs) in global climate models (GCMs) is critical for capturing climate sensitivity and Arctic amplification. Secondary ice production (SIP), can significantly increase ice crystal number concentration (ICNC) in MPCs, affecting cloud properties and processes. Here, we introduce a machine‐learning (ML) approach, called Random Forest SIP (RaFSIP), to parameterize SIP in stratiform MPCs. RaFSIP is trained on 16 grid points with 10‐km horizontal spacing derived from a 2‐year simulation with the Weather Research and Forecasting (WRF) model, including explicit SIP microphysics. Designed for a temperature range of 0 to −25°C, RaFSIP simplifies the description of rime splintering, ice‐ice collisional break‐up, and droplet‐shattering using only a limited set of inputs. RaFSIP was evaluated offline before being integrated into WRF, demonstrating its stable online performance in a 1‐year simulation keeping the same model setup as during training. Even when coupled with the 50‐km grid spacing domain of WRF, RaFSIP reproduces ICNC predictions within a factor of 3 when compared to simulations with explicit SIP microphysics. The coupled WRF‐RaFSIP scheme replicates regions of enhanced SIP and accurately maps ICNCs and liquid water content, particularly at temperatures above −10°C. Uncertainties in RaFSIP minimally impact surface cloud radiative forcing in the Arctic, resulting in radiative biases under 3 Wm−2 compared to simulations with detailed microphysics. Although the performance of RaFSIP in convective clouds remains untested, its adaptable nature allows for data set augmentation to address this aspect. This framework opens possibilities for GCM simplification and process description through physics‐guided ML algorithms.
Understanding Disturbance Regimes From Patterns in Modeled Forest Biomass
Authors: Wang Siyuan , Yang Hui , Koirala Sujan , Forkel Matthias , Reichstein Markus , Carvalhais Nuno 
Published: June 2024

AbstractNatural and anthropogenic disturbances are important drivers of tree mortality, shaping the structure, composition, and biomass distribution of forest ecosystems. Differences in disturbance regimes, characterized by the frequency, extent, and intensity of disturbance events, result in structurally different landscapes. In this study, we design a model‐based experiment to investigate the links between disturbance regimes and spatial biomass patterns. First, the effects of disturbance events on biomass patterns are simulated using a simple dynamic carbon cycle model based on different disturbance regime attributes, which are characterized via three parameters: μ (probability scale), α (clustering degree), and β (intensity slope). 856,800 dynamically stable biomass patterns were then simulated using combined disturbance regime, primary productivity, and background mortality. As independent variables, we use biomass synthesis statistics from simulated biomass patterns to retrieve three disturbance regime parameters. Results show confident inversion of all three “true” disturbance parameters, with Nash‐Sutcliffe efficiency of 94.8% for μ, 94.9% for α, and 97.1% for β. Biomass histogram statistics primarily dominate the prediction of μ and β, while texture features have a more substantial influence on α. Overall, these results demonstrate the association between biomass patterns and disturbance regimes. Given the increasing availability of Earth observation of biomass, our findings open a new avenue to understand better and parameterize disturbance regimes and their links with vegetation dynamics under climate change. Ultimately, at a large scale, this approach would improve our current understanding of controls and feedback at the biosphere‐atmosphere interface in the present Earth system models.
Simulation of Microcapsule Transport in Fractured Media Using Coupled CFD‐DEM
Authors: Zhang Xiaoming , Newell Pania 
Published: June 2024

AbstractGeothermal energy is sustainable and gaining momentum as a solution to energy crises and environmental issues. However, challenges like production temperature and thermal breakthrough can impact geothermal project efficiency. One innovative solution to alleviate the thermal breakthrough is to inject polymer‐based materials that are encapsulated in microcapsules into fractures to modify fracture permeability and prevent preferential flow. In our study, we utilized a coupled computational fluid dynamics and discrete element method to simulate the transport of microcapsules under various scenarios controlled by microcapsule size, microcapsule concentration, and fracture roughness. For a smooth fracture, the results indicate that small microcapsules can travel through a smooth fracture regardless of their concentrations. Large microcapsules can transport through a smooth fracture when present in lower concentrations. However, medium and mixed‐size microcapsules tend to cause the sealing of a smooth fracture, irrespective of their concentrations. For a rough fracture, the transport of microcapsules is complicated by their interactions with the rough fracture walls. The presence of two sealing positions in a rough fracture adds further complexity to this transport phenomenon. The size and concentration of microcapsules control one sealing location, while the rough fracture walls determine the other sealing location. The rough walls substantially affect microcapsule transport, rendering the role of microcapsule size and concentration less significant. The simulation results suggest that complex fracture surfaces significantly elevate the occurrence of sealing behavior. To mitigate sealing behavior within more complex fractures, it would be beneficial to use smaller and lower concentrations of microcapsules.