bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reinforcement Learning of Multi‐Timescale Forecast Information for Designing Operating Policies of Multi‐Purpose Reservoirs
Authors: Zanutto D. , Ficchì A. , Giuliani M. , Castelletti A. 
Published: February 2025

AbstractHydrological forecasts have significantly improved in skill over recent years, encouraging their systematic exploitation in multipurpose reservoir operations to improve reliability and resilience to extreme events. Despite the growing availability of multi‐timescale forecasts, there is still a lack of transparent and integrated methods for selecting the most suitable forecast products, variables, and lead times for specific operational challenges. In this work, we propose a holistic approach based on Reinforcement Learning (RL) to design multipurpose dam operating policies informed by available multi‐timescale forecast products. Our approach extends the traditional Evolutionary Multi‐Objective Direct Policy Search method by parametrizing both the operating policy and the forecast information extraction process. We compare our RL approach with a state‐of‐the‐art two‐step procedure in which the forecast selection and processing are performed before the policy optimization. We demonstrate the value of the method for the multipurpose operation of Lake Como (Italy) by considering multi‐timescale forecasts from short to seasonal lead times to manage flood‐ and drought‐related operational objectives. Our approach identifies solutions achieving an 18% improvement in hypervolume indicator compared to policies not informed by forecasts and a 6% improvement over those designed using the two‐step reference methodology. These improvements are accompanied by increased flexibility in policy design and trade‐off analysis by directly extracting forecast information within the multi‐objective optimization. This study demonstrates the feasibility and benefits of integrating policy design with forecast information extraction, particularly when multiple operational forecasts are available.
Future Patterns of Compound Dry and Hot Summers and Their Link to Soil Moisture Droughts in Europe
Authors: Böhnisch Andrea , Felsche Elizaveta , Mittermeier Magdalena , Poschlod Benjamin , Ludwig Ralf 
Published: February 2025

AbstractCompound dry and hot extreme (CDHE) summers in Europe, like 2015, 2018 and 2022, have wide ranging impacts: heat exacerbates moisture shortages during dry periods whereas water demand rises. Current studies of CDHE are mostly conducted in observations or coarse‐resolution global climate model large ensembles. While the latter allow for the assessment of rare CDHE against the backdrop of internal variability, global ensembles fail in providing robust climate change signals at impact‐relevant scales. To overcome this issue, we exploit a regional 50‐member single‐model initial condition large ensemble (SMILE). The SMILE provides an extensive database of CDHE in a current climate and at two global warming levels (+2°C, +3°C) across Europe in high geographical detail. We identify Northern France, Southern Germany, Switzerland, Southern Ireland, and the western coasts of the Black Sea with currently low CDHE frequency as emerging hotspots. These regions experience a tenfold increase of CDHE under global warming conditions, in parts resulting in yet unseen heat and dryness. Temperature is the dominant driver of frequency increases, except for western Europe. Additionally, tail dependence strengthens in regions with large increases in CDHE frequency. In European agricultural areas, soil moisture shows stronger negative correlations with CDHE intensity than with precipitation or temperature. Finally, our results indicate fewer CDHE summers in a +2°C world compared to a +3°C world, highlighting the importance of climate mitigation to reduce the frequency of these multi‐hazard events.
Paleo‐Earthquake Fingerprints and Along‐Strike Slip Variation of the Silent Mt. Morrone Normal Fault (Central Italy): A Structural‐Geochemical Approach
Authors: Bello S. , Galli P. , Perna M. G. , Peronace E. , Messina P. , Rosatelli G. , Andrenacci C. , Lavecchia G. , Pietrolungo F. , Consalvo A. , Mouslopoulou V. , Brozzetti F. 
Published: February 2025

AbstractThis study provides insights into the tectonic evolution of the normal Mt Morrone Fault System (MMFS) in Central Italy and highlights the utility of multidisciplinary approaches in reconstructing the seismic history of dormant fault systems. The MMFS comprises two parallel normal faults that traverse the western slope of Mt. Morrone, and although the system can produce M > 6 earthquakes, it has been aseismic in post Roman times. Here, we combine geochemical analysis of carbonate fault‐scarp samples with new structural fault data and Lidar‐based topographic analysis to provide new constraints on fault geometries and kinematics, the paleo‐earthquake history of MMFS since the Last Glacial Maximum and its slip rates. Structural analysis reveals kinematic similarities between the two parallel strands, reflecting their response to the same stress regime. Rare Earth Elements analyses on 53 limestone samples reveal a minimum of eight concentration fluctuations upscarp, here interpreted as tectonic exhumation of the fault scarp due to post LGM earthquakes. Slip per event ranges from 30 to 110 cm typical of earthquakes with 6.3 ≤ M ≤ 6.8. Lidar analysis reveals triangular slip profiles on both fault strands. We estimate that an earthquake with an average M = 6.5–6.6 would have a recurrence interval of ∼2,125 ± 125 years. Slip rates were calculated to be 0.5–0.65 mm/yr on the lower and 0.65–0.7 mm/yr on the upper fault strand, with the combined system having slip rates of 0.62–0.69 mm/yr. Our findings indicate that both strands of the MMFS are active and accumulate slip interdependently, a finding that is critical for seismic hazard assessment.
Carbon Emissions Through the Weathering of Petrogenic Organic Carbon and the Net Geological Carbon Budget of the Tibetan Plateau
Authors: Wang Ye , Chen Yang , Li Shilei , Hedding David William , Chen Jun 
Published: February 2025

AbstractMountain building, alongside the associated erosion and weathering, plays a key role in mediating the long‐term carbon cycle, thus impacting Earth’s climate. Certain processes, such as the weathering of petrogenic organic carbon (OCpetro), still lack comprehensive quantitative research owing to the interference of human activities, hindering a full understanding of the net budget and temporal changes of mountain building. This study explores OCpetro weathering within the Tibetan Plateau by utilizing rhenium as a tracer to quantify OCpetro weathering rates and elucidate carbon fluxes. The overall OCpetro weathering rate and net carbon budget of the Tibetan Plateau are 1.95(±0.60) tC km−2 yr−1 and 1.81+0.34/−0.49 tC km−2 yr−1, indicating the Tibetan Plateau currently serves as a carbon source. This study not only refines our understanding of the OCpetro weathering but also reveals a dynamic transformational impact on the geological carbon cycle from mountain building at different stages.
Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks
Authors: Rastogi Deeksha , Niu Haoran , Passarella Linsey , Mahajan Salil , Kao Shih‐Chieh , Vahmani Pouya , Jones Andrew D. 
Published: February 2025

AbstractDespite advancements in Artificial Intelligence (AI) methods for climate downscaling, significant challenges remain for their practicality in climate research. Current AI‐methods exhibit notable limitations, such as limited application in downscaling Global Climate Models (GCMs), and accurately representing extremes. To address these challenges, we implement an AI‐based methodology using super‐resolution convolutional neural networks (SRCNN), trained and evaluated on 40 years of daily precipitation data from a reanalysis and a high‐resolution dynamically downscaled counterpart. The dynamical downscaled simulations, constrained using spectral nudging, enable the replication of historical events at a higher resolution. This allows the SRCNN to emulate dynamical downscaling effectively. Modifications, such as incorporating elevation data and data pre‐processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. Future work will expand this methodology to downscale additional variables for future climate projections.
Observations of Fast Local Loss of High‐Energy Ring Current Protons Associated With Deepening Local Minimum in Phase Space Density
Authors: Chen Rui , Zhao Hong , Califf Sam 
Published: February 2025

AbstractThe development of a deepening local minimum in phase space density (PSD)‐ profile indicates fast local loss potentially caused by wave‐induced scattering. The identification and characterization of proton PSD deepening minima are important for investigating the ring current loss and overall dynamics. Using multiyear Van Allen Probes observations, we analyze ∼10–100s keV proton PSD and report >100 keV proton deepening PSD minima for the first time. The overall occurrence rates of proton deepening local minimum peaks at ∼3%, mainly located at = 4.5–5.0 near the plasmapause. The occurrence rate increases with the decrease of AL index and increase of solar wind dynamic pressure. The theoretical resonance energy of protons with typical He‐band electromagnetic ion cyclotron (EMIC) waves agrees with the energy of protons with deepening PSD minima. Thus, EMIC waves are the likely cause of the deepening PSD minimum and contribute to the fast local loss of ring current protons.
Relationships Between Mesoscale Convective System Properties and Midlevel Dynamic Perturbations
Authors: Marquis James N. , Feng Zhe , Lubis Sandro W. , Zhang Zhixiao , Leung L. Ruby , Hu Huancui 
Published: February 2025

AbstractPast studies implicate dynamic anomalies operating on subsynoptic scales as a possible initiation source of summertime (July–August) mesoscale convective systems (MCSs) in the central United States during northwesterly flow regimes. To improve our understanding of warm season MCSs occurring over a variety of flow regimes, we track midlevel (600 hPa) vorticity perturbations (“MPs”) as 2D objects comprising wavelengths of 500–2,500 km over the central US from May–August of 2004–2021. We perform statistical analysis of relationships between metrics of MP objects (e.g., duration, size, intensity, and origin) and high‐resolution MCS precipitation characteristics (e.g., duration, total rainfall, rain coverage area, and motion) that occur while collocated with or in the absence of MPs to discern predictive capability of background dynamic features on storm precipitation potential. Although the majority of MPs collocated with MCS initiation occur during July–August, a significant number (40%) occur between May and June. Northwesterly flow MPs comprise a relative minority of our events, suggesting that MPs can affect MCSs across a variety of warm season flow regimes. MPs affecting MCSs initiated primarily over the high plains near the central Rockies. Only approximately 20% of tracked MCS initiation events were collocated with MPs, but these storms produced ∼25% greater lifetime rainfall and coverage area, and ∼29% more stratiform rain than non‐MP‐induced MCSs. In general, larger and more vigorous MPs resulted in more hydrologically impactful MCSs. The most directly attributable benefit to MCS initiation was from MP‐enhanced background vertical motion and thermodynamic instability (e.g., increased CAPE).
Blue Carbon Mapping Using Temporally Optimized Satellite Remote Sensing Imagery: A Regional Study of Northeast US Salt Marshes
Authors: Teng Wenxiu , Yu Qian , Yellen Brian , Turek Bonnie , Woodruff Jonathan D. 
Published: February 2025

AbstractCoastal wetlands store three to five times more carbon per unit area than tropical rainforests in continually accreting peat soils, collectively referred to as “Blue Carbon.” However, variability in soil carbon density within and between sites leads to large uncertainty when estimating carbon stocks and sequestration rates. Salt marsh carbon sequestration is mainly driven by nonlinear ecogeomorphic feedback between tidal inundation, bioproductivity, and sediment supply—all of which can be observed by satellites. In this study, we used soil bulk density and soil organic content from 410 soil samples collected across 15 sites in the Northeast US to relate soil properties to remotely sensed spectral observations. We tested model fits using Landsat 5, 7, 8, and Sentinel 2 images from 1984 to 2022 to determine the optimal season and tidal conditions for relating remote sensing indices to soil properties. We explored the roles of sediment supply and tidal range in regional prediction models. The study found that (a) spatial patterns of remote sensing indices correlate well with soil properties; (b) at the marsh scale, remote sensing indices capture the spatial variability of soil properties with image acquired at high tide and vegetation phenology specific to geomorphic setting; (c) at the regional scale, tidal range improves the prediction model in barrier marshes, while sediment supply improves the prediction model in fluvial marshes. The considerable spatial variation of SOC within marshes and across regional gradients highlights the need for high resolution maps of salt marsh soil properties.
Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns
Authors: Agrawal Tarun , Goodwell Allison , Kumar Praveen 
Published: February 2025

AbstractSurface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture.
A Terrestrial Ecosystem Carbon Sink Assessment Model Considering Forest Age Dynamics (CEVSA‐AgeD)
Authors: Zhang Mengyu , He Honglin , Zhang Li , Yu Guirui , Ren Xiaoli , Huang Yuanyuan , Yuan Wenping , Niu Zhong'en 
Published: February 2025

AbstractThe large variation in net ecosystem productivity (NEP) with forest age was dominated by the dynamics of net primary productivity (NPP)–which in turn was determined by the different response slopes of gross primary productivity (GPP) and autotrophic respiration (Ra) with forest age. However, only few models can comprehensively represent the impacts of forest age and global changes including land‐use change, climate change, nitrogen deposition, and atmospheric CO2 from the perspective of ecological processes. Based on a process‐based model (CEVSA‐ES) that included these global changes, we developed an ecosystem carbon sink assessment model considering forest age dynamics (CEVSA‐AgeD) using satellite‐based relationships between GPP (or Ra) and forest age to constrain photosynthesis and autotrophic respiration processes. Subsequently, we used a model data‐fusion framework combined with carbon flux observations to calibrate the model. The calibrated CEVSA‐AgeD model performed well in simulating seasonal (R2 values for GPP, ecosystem respiration, and NEP were 0.86, 0.79, and 0.66, respectively) and annual carbon flux changes (R2 of GPP, ecosystem respiration, and NEP were 0.83, 0.77, and 0.67, respectively). The magnitude of average NEP in China estimated using this model was 0.35 ± 0.005 TgC/yr from 2001 to 2021, which was close to previous estimates, and the dynamics of forests age increased NEP by 87–92 TgC/yr. These results indicate that the CEVSA‐AgeD model performed well in simulating carbon fluxes at the site and regional scales and that it was necessary to incorporate the effect of forest age dynamics on carbon cycling processes into process‐based models.
A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia
Authors: Zhao Xiukuan , Li Guozhu , Xie Haiyong , Hu Lianhuan , Sun Wenjie , Li Yi , Dai Guofeng , Liu Jianfei , Li Yu , Ning Baiqi , Nishioka Michi , Perwitasari Septi , Kenpankho Prasert 
Published: February 2025

AbstractEquatorial plasma bubble (EPB) irregularities can significantly impact satellite‐based communication and navigation systems. Accurate prediction of EPB occurrence is essential for mitigating these impacts. Using the GNSS receiver network and ionosonde data from East and Southeast Asia during 2010–2021, and the rate of TEC change index to characterize the occurrence of EPB irregularities, we developed a novel Spatio‐Temporal deep learning model for regional EPB irregularities short‐term Prediction (STEP). The model integrates the convolutional neural network and long short‐term memory (LSTM) network, together with attention mechanisms, to capture both spatial and temporal features of regional ionospheric irregularities. The results show that for 5‐min forecast, the STEP model achieves a root mean square error (RMSE) of 0.062 TECU/min and an R2 of 0.818, reducing RMSE by 19.48% compared to LSTM and 27.06% compared to gated recurrent unit model. For 60‐min prediction, the STEP model can still achieve reasonable accuracy with an RMSE of 0.110 TECU/min and an R2 of 0.482, showing significant improvement over traditional models. The equatorial F layer height and regional TEC fluctuations were identified as the most critical factors for predicting the generation and duration of EPB irregularities, respectively. The spatial and temporal distributions of EPB irregularities, including their latitudinal variation and delayed onset after sunset, and the occurrence across different days in East and Southeast Asia, were well predicted by the STEP. It is expected that the STEP model would provide a valuable tool for improving the resilience of GNSS against ionospheric scintillations induced by EPB irregularities.
Assessing pain and analgesic consumption after the use of Kinesio tape in web strip technique comparing with cryotherapy after impacted third molar extraction: a randomized clinical split-mouth study
Authors: Alhourani Belal , Zenati Mazen , Alnada Ahmad 
Published: February 2025

Abstract

Background and objectives
There are various methods to improve the patient’s quality of life and alleviate complications after surgical extraction of the lower third molar. The use of Kinesiotape is one of the most important methods used recently. This study aimed to assess the effectiveness of Kinesiotape in alleviating complications after surgical extraction of the lower third molar compared to cryotherapy.

Methods
The study was designed as a randomized controlled clinical study according to the split-mouth technique and included 25 patients who had radially symmetrical lower third molars. The patients were divided randomly into two groups, and Kinesiotape (KT) using the Web-strip technique was applied in the study group (KT), while cold packs were applied in the control group Non-Kinesiotape (N-KT). The studied variables were pain, and analgesic pills consumption. These measurements were evaluated in the first five days after surgery using the visual analog scale (VAS) and the number of consumed analgesic pills.

Results
The pain index value in the KT group was (60.4 ± 19.8) on the first day, and (60.4 ± 17.8) in the N-KT group. The number of analgesic pills consumed in the KT group was (3.3 ± 1.3) on the first day, and (3.4 ± 1.3) in the N-KT group. No significant statistical difference was found in pain scores according to the VAS index and the number of consumed analgesic pills between the two groups.

Conclusion
The current study concluded that pain and analgesic pills consumed were similar between the two study groups, without one being superior to the other.
PHGDH inhibition and FOXO3 modulation drives PUMA-dependent apoptosis in osteosarcoma
Authors: Oyama Toshinao , Brashears Caitlyn B. , Rathore Richa , Benect-Hamilton Heather , Caldwell Katharine E. , Dirckx Naomi , Hawkins William G. , Van Tine Brian A. 
Published: February 2025

Abstract
Osteosarcoma is a bone cancer that has been found to be metabolically dependent on the conversion of glucose to serine through the rate-limiting enzyme 3-phosphoglycerate dehydrogenase (PHGDH). The upregulation of PHGDH has been correlated with poor patient survival, and the inhibition of the serine synthesis pathway using targeted small-molecule inhibition of PHGDH induces a rapid metabolic adaptation that prevents cell death due to pro-survival signaling through the mammalian target of rapamycin complex 1 (mTORC1) pathway. Here, PHGDH inhibition in combination with mTORC1 signaling modulation for the treatment of osteosarcoma was evaluated. When combined with PHGDH inhibition, several non-rapalog inhibitors of mTORC1 activated Forkhead box O (FOXO) transcription factor 3 (FOXO3), a transcription factor associated with various cellular processes driving apoptosis. The activation of FOXO3 led to transcriptional activation of the pro-apoptotic gene p53 upregulated modulator of apoptosis (PUMA), inducing apoptosis when combined with PHGDH inhibition. These data suggest a path for the clinical development of PHGDH inhibitors in conjunction with mTORC1 pathway modulators in osteosarcoma.
Navigating the path to corneal healing success and challenges: a comprehensive overview
Authors: Shadmani Athar , Wu Albert Y. 
Published: February 2025

Abstract
The cornea serves to protect the eye from external insults and refracts light to the retina. Maintaining ocular homeostasis requires constant epithelial renewal and an efficient healing process following injury. Corneal wound healing is a dynamic process involving several key cell populations and molecular pathways. Immediately after a large corneal epithelial injury involving limbal stem cells, conjunctival epithelial cells migrate toward the center of the wound guided by the newly formed electrical field (EF). Proliferation and transdifferentiation play a critical role in corneal epithelial regeneration. Corneal nerve endings migrate through the EF, connect with the migrating epithelial cells, and provide them with multiple growth factors. Finally, the migrated epithelial cells undergo differentiation, which is also regulated by corneal nerve endings. All these processes require energy and effective cellular cross-talk between different cell lines and extracellular matrix molecules. We provide an overview of the roles and interactions between corneal wound regeneration components that may help develop fascinating new targeted therapeutic strategies to enhance corneal wound healing with less injury-related corneal opacity and neovascularization.