bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the Induced Mild Heating by Airborne Ultrasound Application on the Convective Drying of Pork Liver
Authors: Sánchez-Torres Eduardo A. , Giacomozzi Anabella S. , Abril Blanca , Benedito Jose , Bon Jose , García-Pérez Jose V. 
Published: January 2025

AbstractEfficient use of meat by-products, such as pork liver, may entail a previous stage of dehydration for their stabilization, which involves significant energy and time investments. Airborne ultrasound application has been reported as a promising technique to accelerate the air drying of food materials. In this context, the present study addresses, for the first time, the thermal effect associated with ultrasound application on a meat by-product. For that purpose, drying experiments were conducted at 40 and 60 °C on pork liver cylinders at 2 m⋅s−1 with (US) and without (AIR) airborne ultrasound application. The modeling process was based on the principles of heat conduction and moisture diffusion, taking into account the external convection. The results showed that the use of ultrasound reduced the drying time by around 30% at 40 °C, although its impact was less pronounced at 60 °C. With the application of ultrasound, both the sample and air flow temperatures rose by about 4.5 and 2.5 °C, respectively, which partly explains the improvement of drying rate. Due to this low heating effect, airborne ultrasound application must be considered a non-thermal intensification strategy for convective drying of pork liver.
Widely targeted metabolomics analysis reveals differences in volatile metabolites among four Angelica species
Authors: Ji Jiaojiao , Zang Lanlan , Lu Tingting , Li Cheng , Han Xiaoxu , Lee Soo-Rang , Wang Li 
Published: January 2025

AbstractAngelica L. has attracted global interest for its traditional medicinal uses and commercial values. However, few studies have focused on the metabolomic differences among the Angelica species. In this study, widely targeted metabolomics based on gas chromatography-tandem mass spectrometry was employed to analyze the metabolomes of four Angelica species (Angelica sinensis (Oliv.) Diels (A. sinensis), Angelica biserrata (R.H.Shan & Yuan) C.Q.Yuan & R.H.Shan (A. biserrata), Angelica dahurica (Hoffm.) Benth. & Hook.f. ex Franch. & Sav. (A. dahurica) and Angelica keiskei Koidz. (A. keiskei)). A total of 698 volatile metabolites were identified and classified into fifteen different categories. The metabolomic analysis indicated that 7-hydroxycoumarin and Z-ligustilide accumulated at significantly higher levels in A. sinensis, whereas bornyl acetate showed the opposite pattern. Furthermore, a high correspondence between the dendrogram of metabolite contents and phylogenetic positions of the four species. This study provides a comprehensive biochemical map for the exploitation, application and development of the Angelica species as medicinal plants or health-related dietary supplements.
Graphical Abstract
Neural network approaches for sea surface height predictability using sea surface temperature
Authors: Ollier Luther , Thiria Sylvie , Mejia Carlos E. , Crépon Michel , Charantonis Anastase 
Published: January 2025

Abstract
Sea Surface Height Anomaly (SLA) is a signature of the mesoscale dynamics of the upper ocean. Sea surface temperature (SST) is driven by these dynamics and can be used to improve the spatial interpolation of SLA fields. In this study, we focused on the temporal evolution of SLA fields. We explored the capacity of deep learning (DL) methods to predict short-term SLA fields using SST fields. We used simulated daily SLA and SST data from the Mercator Global Analysis and Forecasting System, with a resolution of (1/12)° in the North Atlantic Ocean (26.5–44.42°N, −64.25–41.83°E), covering the period from 1993 to 2019. Using a slightly modified image-to-image convolutional DL architecture, we demonstrated that SST is a relevant variable for controlling the SLA prediction. With a learning process inspired by the teaching-forcing method, we managed to improve the SLA forecast at 5 days by using the SST fields as additional information. We obtained predictions of 12 cm (20 cm) error of SLA evolution for scales smaller than mesoscales and at time scales of 5 days (20 days) respectively. Moreover, the information provided by the SST allows us to limit the SLA error to 16 cm at 20 days when learning the trajectory.
Cloudy with a chance of uncertainty: autoconversion rates forecasting via evidential regression from satellite data
Authors: Novitasari Maria Carolina , Quaas Johannes , Rodrigues Miguel R. D. 
Published: January 2025

Abstract
High-resolution simulations such as the ICOsahedral Non-hydrostatic Large-Eddy Model (ICON-LEM) can be used to understand the interactions among aerosols, clouds, and precipitation processes that currently represent the largest source of uncertainty involved in determining the radiative forcing of climate change. Nevertheless, due to the exceptionally high computing cost required, this simulation-based approach can only be employed for a short period within a limited area. Despite the potential of machine learning to alleviate this issue, the associated model and data uncertainties may impact its reliability. To address this, we developed a neural network (NN) model powered by evidential learning, which is easy to implement, to assess both data (aleatoric) and model (epistemic) uncertainties applied to satellite observation data. By differentiating whether uncertainties stem from data or the model, we can adapt our strategies accordingly. Our study focuses on estimating the autoconversion rates, a process in which small droplets (cloud droplets) collide and coalesce to become larger droplets (raindrops). This process is one of the key contributors to the precipitation formation of liquid clouds, crucial for a better understanding of cloud responses to anthropogenic aerosols and, subsequently, climate change. We demonstrate that incorporating evidential regression enhances the model’s credibility by accounting for uncertainties without compromising performance or requiring additional training or inference. Additionally, the uncertainty estimation shows good calibration and provides valuable insights for future enhancements, potentially encouraging more open discussions and exploration, especially in the field of atmospheric science.
Deep learning based bias correction of TRMM precipitation estimates using IMD-gridded precipitation as ground observation
Authors: Mishra Sharma Sumanta Chandra , Mitra Adway 
Published: January 2025

Abstract
Bias correction is a critical aspect of data-centric climate studies, as it aims to improve the consistency between observational data and simulations by climate models or estimates by remote sensing. Satellite-based estimates of climatic variables like precipitation often exhibit systematic bias when compared to ground observations. To address this issue, the application of bias correction techniques becomes necessary. This research work examines the use of deep learning to reduce the systematic bias of satellite estimations at each grid location while maintaining the spatial dependency across grid points. More specifically, we try to calibrate daily precipitation values of tropical rainfall measuring mission based TRMM_3B42_Daily precipitation data over Indian landmass with ground observations recorded by India Meteorological Department (IMD). We have focused on the precipitation estimates of the Indian Summer Monsoon Rainfall (ISMR) period (June–September) since India gets more than 75% of its annual rainfall in this period. We have benchmarked these deep learning methods against standard statistical methods like quantile mapping and quantile delta mapping on the above datasets. The comparative analysis shows the effectiveness of the deep learning architecture in bias correction.
MLP-mixer-based deep learning network for pedestrian-level wind assessment
Authors: Clarke Adam , Giljarhus Knut Erik Teigen , Oggiano Luca , Saddington Alistair , Depuru-Mohan Karthik 
Published: January 2025

Abstract
This article addresses the challenges of assessing pedestrian-level wind conditions in urban environments using a deep learning approach. The influence of large buildings on urban wind patterns has significant implications for thermal comfort, pollutant transport, pedestrian safety, and energy usage. Traditional methods, such as wind tunnel testing, are time-consuming and costly, leading to a growing interest in computational methods like computational fluid dynamics (CFD) simulations. However, CFD still requires a significant time investment for such studies, limiting the available time for design modification prior to lockdown. This study proposes a deep learning surrogate model based on a MLP-mixer architecture to predict mean flow conditions for complex arrays of buildings. The model is trained on a diverse dataset of synthetic geometries and corresponding CFD simulations, demonstrating its effectiveness in capturing intricate wind dynamics. The article discusses the model architecture and data preparation and evaluates its performance qualitatively and quantitatively. Results show promising capabilities in replicating key wind features with a mean error of 0.3 m/s and rarely exceeding 0.75 m/s, making the proposed model a valuable tool for early-stage urban wind modelling.
Machine learning for stochastic parametrization
Authors: Christensen Hannah M. , Kouhen Salah , Miller Greta , Parthipan Raghul 
Published: January 2025

Abstract
Atmospheric models used for weather and climate prediction are traditionally formulated in a deterministic manner. In other words, given a particular state of the resolved scale variables, the most likely forcing from the subgrid scale processes is estimated and used to predict the evolution of the large-scale flow. However, the lack of scale separation in the atmosphere means that this approach is a large source of error in forecasts. Over recent years, an alternative paradigm has developed: the use of stochastic techniques to characterize uncertainty in small-scale processes. These techniques are now widely used across weather, subseasonal, seasonal, and climate timescales. In parallel, recent years have also seen significant progress in replacing parametrization schemes using machine learning (ML). This has the potential to both speed up and improve our numerical models. However, the focus to date has largely been on deterministic approaches. In this position paper, we bring together these two key developments and discuss the potential for data-driven approaches for stochastic parametrization. We highlight early studies in this area and draw attention to the novel challenges that remain.
Adaptive decision-making: Bayesian Network Modeling for blue–green infrastructure selection in dynamic climate and land use context
Authors: Seker Dima , Orak Nur H. 
Published: January 2025

Abstract
Nature-based solutions are becoming increasingly recognized as effective tools for addressing various environmental problems. This study presents a novel approach to selecting optimal blue–green infrastructure (BGI) solutions tailored to the unique environmental and climatic challenges of Istanbul, Türkiye. The primary objective is to utilize a Bayesian Belief Network (BBN) model for assisting in the identification of the most effective BGI solutions, considering the city’s distinct environmental conditions and vulnerabilities to climate change. Our methodology integrates comprehensive data collection, including meteorological and land use data, and employs a BBN model to analyze and weigh the complex network of factors influencing BGI suitability. Key findings reveal the model’s capacity to effectively predict BGI applicability across diverse climate scenarios, with quantitative results demonstrating a notable enhancement in decision-making processes for urban sustainability. Quantitative results from our model reveal a significant improvement in decision-making accuracy, with a predictive accuracy rate of 82% in identifying suitable BGI solutions for various urban scenarios. This enhancement is particularly notable in densely populated districts, where our model predicted a 25% greater efficiency in stormwater management and urban heat island mitigation compared to traditional planning methods. The study also acknowledges the limitations, such as data scarcity and the need for further model refinement. The results highlight the model’s potential for application in other complex urban areas, making it a valuable tool for improving urban sustainability and climate change adaptation. This study shows the importance of incorporating detailed meteorological and local climate zones data into urban planning processes and suggests that similar methodologies could be beneficial for addressing environmental challenges in diverse urban settings.
TB or not TB? Development and validation of a clinical decision support system to inform airborne isolation requirements in the evaluation of suspected tuberculosis
Authors: Dugdale Caitlin M. , Zachary Kimon C. , Craig Rebecca L. , Doms Alexandra , Germaine Lindsay , Green Chloe V. , Gulbas Eren , Hurtado Rocio M. , Hyle Emily P. , Jerry Michelle S. , Lazarus Jacob E. , Maxfield Stephen , Paras Molly , Swanson Katherine , Shenoy Erica S. 
Published: January 2025

AbstractBackground:The study objective was to develop and validate a clinical decision support system (CDSS) to guide clinicians through the diagnostic evaluation of hospitalized individuals with suspected pulmonary tuberculosis (TB) in low-prevalence settings.Methods:The “TBorNotTB” CDSS was developed using a modified Delphi method. The CDSS assigns points based on epidemiologic risk factors, TB history, symptoms, chest imaging, and sputum/bronchoscopy results. Below a set point threshold, airborne isolation precautions are automatically discontinued; otherwise, additional evaluation, including infection control review, is recommended. The model was validated through retrospective application of the CDSS to all individuals hospitalized in the Mass General Brigham system from July 2016 to December 2022 with culture-confirmed pulmonary TB (cases) and equal numbers of age and date of testing-matched controls with three negative respiratory mycobacterial cultures.Results:104 individuals with TB (cases) and 104 controls were identified. Prior residence in a highly endemic country, positive interferon release assay, weight loss, absence of symptom resolution with treatment for alternative diagnoses, and findings concerning for TB on chest imaging were significant predictors of TB (all P < 0.05). CDSS contents and scoring were refined based on the case–control analysis. The final CDSS demonstrated 100% sensitivity and 27% specificity for TB with an AUC of 0.87.Conclusions:The TBorNotTB CDSS demonstrated modest specificity and high sensitivity to detect TB even when AFB smears were negative. This CDSS, embedded into the electronic medical record system, could help reduce risks of nosocomial TB transmission, patient-time in airborne isolation, and person-time spent reviewing individuals with suspected TB.
A sentinel survey in remote Western Thailand indicates that school-aged children and reproductive-aged women of the Indigenous Pwo Karen community are iodine sufficient
Authors: Joompa Pattamaporn , Sukboon Pornpan , Schultink Werner , Zimmermann Michael B. , Gowachirapant Sueppong 
Published: January 2025

Abstract
Indigenous peoples are often not routinely included in iodine programmes because of language barriers and remote access and may thus be at higher risk of iodine deficiency disorders, which could adversely impact their quality of life. We conducted this cross-sectional study in the remote Pwo Karen community of Thailand to determine the urinary iodine concentration of school-aged children and women of reproductive age and investigate the iodine content in household salt. We measured urinary iodine concentration in spot urine samples from healthy school-aged children and women of reproductive age, administered a questionnaire, estimated daily iodine intake and collected household salt samples to determine salt iodine concentration. The median urinary iodine concentration (range) of school-aged children (n 170) was 192 (136–263) µg/l, which was significantly higher than women of reproductive age (n 306) (147 (89–233) µg/l) (P < 0·001). The estimated daily iodine intake in school-aged children and women of reproductive age were 135 and 195 μg/d, respectively. The median (range) iodine concentration in rock and granulated salts consumed in the households were 2·32 (0·52–3·19) and 26·64 (20·86–31·01) ppm, respectively. Surprisingly, the use of iodised salt and the frequency of seafood consumption were NS predictors of urinary iodine concentration in these two groups. Our data suggest that school children and women of the Pwo Karen community have sufficient iodine intake, indicating the Thai salt iodisation programme is effectively reaching even this isolated Indigenous community. Sentinel surveys of remote vulnerable populations can be a useful tool in national iodine programmes to ensure that programme coverage is truly universal.
Ambient PM2.5 exposure and tuberculosis reactivation: a cross-sectional study in an intermediate burden city
Authors: Lau Leonia Hiu Wan , Wong Ngai Sze , Leung Chi Chiu , Chan Chi Kuen , Tai Lai-bun , Lau Alexis Kai Hon , Lin Changqing , Lee Shui Shan 
Published: January 2025

Abstract
Hong Kong is an intermediate tuberculosis (TB) endemicity city dominated by reactivation diseases. A cross-sectional study on the clinical and epidemiologic data of newly diagnosed TB cases was conducted in such a setting, to examine the association between ambient PM2.5 and TB reactivation. After the exclusion of cases most likely resulting from recent infection, four distinct TB population phenotypes were delineated by latent class analysis based on their reactivation risk and clinical profiles (N = 2,153): ‘Elderly male’ (26%), ‘Otherwise healthy younger adult’ (34%), ‘Older female’ (19%) and ‘Male smoker’ (21%). Overall, exposure to high concentrations of ambient PM2.5 6 and 12 months before the notification was significantly associated with ‘Otherwise healthy younger adults’ membership (OR = 1.07 and 1.11, respectively) compared with ‘Elderly male’. Such association was less evident for other phenotypes. The differential pattern of association between ambient PM2.5 exposure and TB population phenotypes suggested the role of ambient PM2.5 in TB reactivation.
Resolving Peer Disagreement about the Law
Authors: Houghton Alexander 
Published: January 2025

AbstractLegal experts—lawyers, judges, and academics—typically resist changing their beliefs about what the law is or requires when they encounter disagreement from those committed to different jurisprudential or interpretive theories. William Baude and Ryan Doerfler are among the most prominent proponents of this view, holding it because fundamental differences in methodological commitments severs epistemic peerhood. This dominant approach to disagreement, and Baude and Doerfler’s rationale, are both wrong. The latter is committed to an overly stringent account of epistemic peerhood that dogmatically excludes opponents. The former violates the conjunction of three plausible epistemic principles: Complete Evidence, considering all epistemically permissible evidence; Independence, in which only dispute-independent evidence is epistemically permissible; and Peer Support, which involves epistemically permissible evidence. Instead, I argue for jurisprudential humility—we ought to be more willing to admit we do not know what the law is or requires, and take seriously conflicting views.
The Impacts of Erosion on the Carbon Cycle
Authors: Zheng Haiyan , Miao Chiyuan , Huntingford Chris , Tarolli Paolo , Li Dongfeng , Panagos Panos , Yue Yao , Borrelli Pasquale , Van Oost Kristof 
Published: January 2025

AbstractPhysical and chemical erosion associated with water both affect land–atmosphere carbon exchanges. However, previous studies have often addressed these processes separately or used oversimplified mechanisms, leading to ongoing debates and uncertainties about erosion‐induced carbon fluxes. We provide an overview of the on‐site carbon uptake fluxes induced by physical erosion (0.05–0.29 Pg C yr−1, globally) and chemical erosion (0.26–0.48 Pg C yr−1). Then, we discuss off‐site carbon dynamics (during transport, deposition, and burial). Soil organic carbon mineralization during transport is nearly 0.37–1.20 Pg C yr−1 on the globe. We also summarize the overall carbon fluxes into estuaries (0.71–1.06 Pg C yr−1) and identify the sources of different types of carbon within them, most of which are associated with land erosion. Current approaches for quantifying physical‐erosion‐induced vertical carbon fluxes focus on two distinct temporal scales: short‐term dynamics (ranging from minutes to decades), emphasizing net vertical carbon flux, and long‐term dynamics (spanning millennial to geological timescales), examining the fate of eroded carbon over extended periods. In addition to direct chemical measurement and modeling approaches, estimation using indicators of riverine material is popular for constraining chemical‐erosion‐driven carbon fluxes. Lastly, we highlight the key challenges for quantifying related fluxes. To overcome potential biases in future studies, we strongly recommend integrated research that addresses both physical and chemical erosion over a well‐defined timescale. A comprehensive understanding of the mechanisms driving erosion‐induced lateral and vertical carbon fluxes is crucial for closing the global carbon budget.
Future Extreme Climate Events Threaten Alpine and Subalpine Woody Plants in China
Authors: Wu Yongru , Shen Jian , Deane David C. , Yu Haibin , Yu Fangyuan , Wang Xuerong , Cao Zheng , Yu Rong , Xiao Fuan , Wang Tiejun , Wu Zhifeng 
Published: January 2025

AbstractIncreases in the frequency, intensity, and duration of extreme climate events (ECEs) are already impacting ecosystems, with many of the strongest effects associated with high‐elevation areas. Most research on the ecological impacts of climate change has focused on climatic averages, which might differ from ECEs. Rhododendron, a diverse genus of alpine and subalpine woody plant, plays a crucial role in ecosystem stability and biodiversity in the biodiversity hotspots of the Himalayas and Hengduan Mountains. Here, we compared the predicted impacts of average climate with those including ECEs on 189 Rhododendron species in China for the historical period (1981–2010) and the future period (2071–2100) under two emissions scenarios (SSP2‐4.5 and SSP5‐8.5). We analyzed changes in suitable habitat and patterns of species richness, weighted endemism, and phylogenetic diversity, identifying areas of coinciding high‐risk as priority conservation areas. Inclusion of ECEs altered the projected areas of suitable habitat across all species from an increase of over 3% to a decrease exceeding 10%, with the distribution of most Rhododendron species strongly influenced by extremes of drought and high temperatures. We found fewer than 18% of high‐risk areas of diversity loss were currently protected, with priority conservation areas mainly located in the Daxue, Daliang, Wumeng, and Jade Dragon Snow Mountains, as well as in the Nyingchi. We suggest inclusion of ECEs is critical when projecting changes in alpine and subalpine species distributions for effective conservation planning for climate change.