bluuebunny/crossref_metadata_2025_split huggingface dataset:
Green path development: The agency of the City of Vaasa in establishing the battery industry
Authors: Okonkwo Ejike , Berg Petra , Rabetino Rodrigo 
Published: October 2024

AbstractEmpirical studies on agency in green path development (GPD) have underexplored non‐firm actors' roles. Through a semi‐structured interview, this article provides insight into how the City of Vaasa exercises agency toward developing the battery industry and the concomitant effects. The findings reveal that the City of Vaasa provides place‐based leadership, for example, through a local‐based initiative, that is, the GigaVaasa, as a platform to increase participation and coordinate the actions of public and private actors. As an institutional entrepreneur, they govern the issuance of operating licenses to optimize their economic interest; as innovative entrepreneur, they strategically plan and coordinate infrastructure development to increase the region's attractiveness to external investors and talents. The City of Vaasa agency increases the chances for the region to participate in the global battery market and stimulate the region's economic development. Nonetheless, environmental sustainability concerns remain a profound unintended effect. Conceptually, the article introduces the agency cyclicality and synchronization model to enhance the understanding of the interplay and effect of agency in an emerging green industry's development trajectory.
Increased calcification by erythrophagocytosis in aortic valvular interstitial cells
Authors: Qin Zihan , Bäck Magnus , Franco‐Cereceda Anders , Pawelzik Sven‐Christian 
Published: October 2024

AbstractBackgroundCalcific aortic valve disease (CAVD) progresses over time to severe aortic stenosis and eventually heart failure. Recent evidence indicates that intraleaflet haemorrhage (ILH) strongly promotes CAVD progression. However, it remains poorly understood how it mechanistically contributes to valvular calcification.MethodILH was identified as iron deposition by morphological analysis. To elucidate the underlying mechanism, human valvular interstitial cells (VIC) were cultured in the presence of fresh or senescent red blood cells (RBC), simulating ILH in vivo conditions.ResultILH was common in aortic valves derived from patients with severe aortic stenosis. VIC undergo erythrophagocytosis of senescent RBC, leading to intracellular iron accumulation analogous to observed following exposure to extracellular iron. The presence of senescent RBC significantly intensified VIC calcification, which was significantly mitigated by ferroptosis inhibition.ConclusionsOur results identify erythrophagocytosis by VIC, leading to iron accumulation and enhanced calcification through ferroptosis. This may be a crucial component of the pathophysiological mechanisms that links ILH to valvular calcification and accelerated aortic stenosis progression.
Considerations for drug trials in hypertrophic cardiomyopathy
Authors: Farrant John P. , Schmitt Matthias , Reid Anna B. , Garratt Clifford J. , Newman William G. , Malhotra Aneil , Beynon Rhys , Mahmod Masliza , Raman Betty , Cooper Robert M. , Dawson Dana , Green Thomas , Prasad Sanjay K. , Singh Anvesha , Dodd Susanna , Watkins Hugh , Neubauer Stefan , Miller Christopher A. 
Published: October 2024

AbstractHypertrophic cardiomyopathy (HCM) is a heterogeneous condition with potentially serious manifestations. Management has traditionally comprised therapies to palliate symptoms and implantable cardioverter‐defibrillators to prevent sudden cardiac death. The need for disease‐modifying therapies has been recognized for decades. More recently, an increasing number of novel and repurposed therapies hypothesized to target HCM disease pathways have been evaluated, culminating in the recent regulatory approval of mavacamten, a novel oral myosin inhibitor. HCM poses several unique challenges for clinical trials, which are important to recognize when designing trials and interpreting findings. This manuscript discusses the key considerations in the context of recent and ongoing randomized trials, including the roles of genotype, phenotype and symptom status in patient selection, the evidence base for clinical and mechanistic outcome measurements, trial duration and sample size.
Facile Approach for the Fabrication of Vapor Sensitive Spin Transition Composite Nanofibers
Authors: Tran Kevin , Sander Patrick , Seydi Kilic Maximilian , Brehme Jules , Sindelar Ralf , Renz Franz 
Published: October 2024

AbstractIn this work polymer nanofibers were functionalized by incorporation of the spin transition (ST) compound [Fe(H2btm)2(H2O)2]Cl2 (FeH2btm) (H2btm=di(1H‐tetrazol‐5‐yl)methane). FeH2btm is an interesting compound due to its ability to reversibly and sensitively switch between high spin (HS) and low spin (LS) state when exposed to common volatile compounds (VOC) like ammonia and methanol. By using polyvinylidene fluoride (PVDF) as the main compound, inhibiting interactions between the complex and polymer were minimized. By using UV‐Vis spectroscopy, the visible and reversible switching between HS and LS state when exposed to an ammonia or hydrochloric acid atmosphere was confirmed. Powder X‐Ray diffraction (PXRD), scanning electron microscopy (SEM) and energy dispersive X‐Ray spectroscopy (EDX) show a homogenous distribution of FeH2btm with no major crystalline accumulations and a mean fiber diameter of 106±20 nm. The composite fiber has a similarly high thermal stability as the pure FeH2btm, as shown by thermogravimetric analysis (TGA). Mössbauer spectroscopy indicates an incomplete spin transition after exposition to ammonia. This could be due to low permeability of the VOC into the composite fiber.
Chiral Electrochemical Sensing Platform Constructed by Chiral Chitosan Materials for Enhanced Electrochemical Chiral Analysis
Authors: Wang Jianrong , Wang Luhua , Zhao Hongfang , Li Hongxia , Niu Xiaohui , Wang Yi , Wang Kunjie 
Published: October 2024

AbstractPolysaccharide materials have wide applications in the field of chiral recognition. Herein, a new series of chiral materials based on chiral polysaccharide were synthesized for electrochemical enantioselective recognition of tryptophan (Trp) enantiomers. Chiral recognition was investigated via differential pulse voltammetry (DPV). Among poly (L‐lactide) La/hydroxyapatite chitosan (PLLHC‐X) series materials (PLLHC‐0, PLLHC‐1, PLLHC‐3, PLLHC‐7 PLLHC‐9 and PLLHC‐5), PLLHC‐5 has the best recognition efficiency. And the results reveal that the PLLHC‐5 displays a stronger interaction with L‐Trp than to that of D‐Trp. The synergetic effect of chiral recognition from chitosan and signal amplification from poly (L‐lactide) (PLLA) offered an easy‐handling and effective electrochemical approach for isomers discrimination to realize the chiral recognition of Trp enantiomers. The synergetic effect results in highly efficiency and thus opens up new directions in the field of chiral materials.
Halogen‐free solvent processed organic solar sub‐modules (≈55 cm2) with 14.70% efficiency by controlling the morphology of alkyl chain engineered polymer donor
Authors: Gokulnath Thavamani , Kim Hyerin , Song Donghyun , Park Ho‐Yeol , Jee Je‐Sung , Kim Young Yong , Yoon Jinhwan , Kranthiraja Kakaraparthi , Jin Sung‐Ho 
Published: October 2024

AbstractGoals of high efficiency, morphological analysis, and the ability to produce organic solar cell (OSC) sub‐modules using halogen‐free solvents are demanding. In this study, a robust conjugated polymer with thienothiophene π‐spacer with pendant alkyl side chain (NapBDT‐C12) was synthesized and used to fabricate sub‐modules. Excellent efficiencies were demonstrated by a NapBDT‐C12 integrated ternary blend, which was used to produce stable small‐area‐to‐sub‐module devices using O‐xylene. The efficiency of the NapBDT‐C12 added small‐area ternary devices (PM6:NapBDT‐C12:L8‐BO) was 18.71%. Owing to the controlled homogeneity of the blend with favorable nanoscale film morphology, enhanced carrier mobilities, and exciton dissociation/splitting properties, contributed to the efficiencies of small‐area‐to‐sub‐module OSCs. Moreover, a 55 cm2 sub‐module with an efficiency of 14.69% was accomplished by bar coating using O‐xylene under ambient conditions. This study displays the potential of a ternary blend based OSC device to produce high efficiency scalable sub‐modules at ambient conditions.image
Defining a maximum heat release rate probability distribution function for design fires in sprinkler‐protected residential buildings
Authors: Hopkin Charlie 
Published: October 2024

AbstractIn fire safety engineering analysis of sprinkler‐protected residential buildings, the maximum heat release rate is a key parameter requiring consideration. Several documents provide advice for estimating the heat release rate of a sprinkler‐controlled fire, with a prevailing suggestion that it is fixed upon activation of the first sprinkler. When carrying out deterministic analysis, this requires the engineer to assume fixed fire parameters and consider that sprinklers limit fire growth. To explore these assumptions, the study uses three deterministic models to estimate a sprinkler‐controlled maximum heat release rate for a representative apartment layout. The models include Alpert's correlation, a B‐RISK zone model and a computational fluid dynamics model in the Fire Dynamics Simulator. These deterministic models are compared to a probabilistic model in B‐RISK, where Monte Carlo simulations are used to generate a range of maximum heat release rates from distribution functions for fire and sprinkler properties. An output distribution function is generated with a mean of 296.6 kW and a standard deviation of 503.8 kW, with a lognormal distribution (μ = 5.014, σ = 1.165) estimated as a best‐fit. The deterministic models are estimated to sit in the 92–98 percentile range of this function, indicating that common deterministic assumptions are reasonably conservative. The article concludes with suggesting that, for deterministic analysis, a percentile between the 80th and 99th (340–2640 kW) could be qualitatively selected based on the design objectives, building situation and relative consequence of a fire. Further research is needed to establish guidelines for selecting appropriate percentiles across various building scenarios.
Total Synthesis of Tagitinins, Goyazensolide and Related Furanoheliangolides and their Covalent Interaction with Importin‐5 (IPO5)
Authors: Liu Weilong , Patouret Rémi , Peev Elsa , Barluenga Sofia , Winssinger Nicolas 
Published: October 2024

AbstractHerein, we detail an extension of our research on the synthesis of a small library of furanoheliangolides and the characterization of the covalent interaction between goyazensolide and IPO5. Using a build‐couple‐pair strategy, we assembled a small library of germacrene‐type lactones and diversified them into eight groups of structurally different analogues. The germacrene lactones were synthesized using Sonogashira coupling and Barbier‐type macrocyclization, while the furanoheliangolides were further elaborated through gold‐catalyzed transannulation followed by esterification. This synthetic approach enabled the generation of a goyazensolide alkyne‐tagged cellular probe, which was used to identify the selective binding between goyazensolide and the oncoprotein importin‐5 (IPO5). Mass spectrometry analysis of the proteolytic digest from the reaction between the goyazensolide probe and a recombinant IPO5 indicated a covalent engagement at Cys560 of IPO5, which was confirmed by site‐directed mutagenesis.
Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation
Authors: Liu Bao , Sun Yaohua , Gao Lei 
Published: October 2024

ABSTRACTAccurate prediction of groundwater recharge is crucial for the sustainable management of water resources. Existing models, while effective, still have potential for improved accuracy. This study proposed a novel deep forest model—the Feature Selection‐based Deep Forest model (FSDF)—for enhanced groundwater recharge prediction. This model consists of three key essential components: a feature selection layer, a cascade enhancement layer and a decision output layer, all designed to enhance the prediction accuracy of groundwater recharge rates. The feature selection layer effectively filtered out redundant features, ensuring that only relevant features are fed into the subsequent cascade enhancement layer. The cascaded enhancement layer was jointly constructed by random forests and completely random forests, processing the data layer‐by‐layer. Finally, the predictions of groundwater recharge rates were produced through an averaging strategy in the decision output layer. To further enhance the FSDF model's predictive capabilities, Bayesian optimization was applied for fine‐tuning model hyperparameters. The model's performance was evaluated and compared with existing models using a dataset comprising of groundwater recharge rates from 1549 wells in New South Wales, Australia. The FSDF model exhibited exceptional performance, achieving a training accuracy of 95.91% and a testing accuracy of 89.65%. It outperformed the adaptive boosting, categorical boosting, extreme gradient boosting, multiple linear regression and random forests by 2.02%, 6.98%, 9.05%, 17.02% and 2.74% in prediction performance, respectively. This study contributes to both hydrological processes and groundwater management by identifying key factors such as rainfall, surface geology and PET, and refining hydrological models for greater predictive accuracy. The FSDF model offers a powerful tool for accurately forecasting groundwater recharge, outperforming traditional models. The model's adaptability makes it applicable to different geographical regions for managing water resources in the face of challenges such as water scarcity and climate change.
Comprehensive, Multi‐Scale Evaluation of Field Methods for Assessing Stream–Aquifer Interactions Along Channelised Lowland Streams
Authors: Ledesma Benjamin , Villalpando‐Vizcaino Rodrigo , Larsen Daniel , Schoefernacker Scott , Waldron Brian , Meier Claudio 
Published: October 2024

ABSTRACTStream–aquifer interactions (SAIs) play a critical role in effective groundwater management, yet their complex dynamics remain poorly understood in channelized lowland perennial streams. This study presents a multi‐scale, multi‐technique investigation of SAIs along two long‐stream reaches in Tennessee, United States. The goal is to define a suitable methodology for characterising SAIs in this specific hydrological setting, serving as a starting point for developing a more standardised and replicable approach. The methodology includes an initial evaluation of various field techniques, followed by extensive surveys using potentiomanometers, electromagnetic induction (EMI), vertical temperature profilers (VTPs) and complementary methods such as seepage meters, bank tests and well‐data analyses. Results reveal distinct hydrogeomorphic behaviours across and along the streams, challenging the SAI‐homogeneity notions typically assumed in groundwater models. Nonconnah Creek exhibited streambed colmation and negligible hydraulic gradients, resulting in disconnection from the aquifer during low flows, except for a 300‐m losing reach with high downward gradients. In contrast, the Loosahatchie River displayed relatively homogeneous streambed properties and small, upward hydraulic gradients, suggesting uniform SAIs along the surveyed reaches. EMI proved highly effective for mapping streambed sediments quickly, while potentiomanometers accurately measured small head differences critical for understanding SAI dynamics. VTPs were less practical due to the extended data‐collection times required and their vulnerability to flooding. This study emphasises the importance of multi‐scale investigations using diverse techniques to accurately characterise SAIs in lowland streams, highlighting the confounding influences of geological formations, anthropogenic alterations and depositional processes on groundwater–surface water interactions. The findings contribute to refining local water balances, informing groundwater management strategies and underscoring the need for incorporating local‐scale field data into regional groundwater models. The proposed methodology serves as a foundation for developing a standardised approach for characterising SAIs in lowland channelized perennial streams, adaptable for similar stream systems worldwide.
Modelling the Contributions of Riparian Vegetation and Topography to Stream Shade Using LiDAR and Conventional Digital Elevation Data
Authors: Browning B. L. , Moore R. D. 
Published: October 2024

ABSTRACTStream temperature is widely considered the master variable in stream ecosystems. One of the key drivers of diel and seasonal stream temperature variability is the solar radiation received at the stream surface, which can be influenced by shading associated with both larger scale topographic features and riparian vegetation. In this study, a stream shade model was developed that uses LiDAR point cloud data to model shading by riparian vegetation, including canopy overhang, and conventional elevation data to model stream shading by topography. The model was applied to a dominantly north–south oriented river flowing in a floodplain within a mountain valley. When compared with shade interpreted from PlanetScope visual imagery, the model predicted stream shade at the point scale with 92% agreement. Sources of error were attributed to pixel and azimuth band size, which can be refined within the model arguments, although at the cost of increased processing time. The shade model was re‐run after virtually rotating the reach by 90° and 270° clockwise to evaluate the effect of valley orientation. Peak reach‐wide sunlight exposure occurred approximately 2 h later in the day when the stream reach was rotated 90°, and produced greater shading from mid‐morning to mid‐afternoon. Further work should test the model on smaller streams using ground‐based oblique or drone‐based photography to provide ground‐truthing, particularly to assess the accuracy of predicted shade below over‐hanging vegetation.
Spatial Variation of Changes in Extreme Discharge Seasonality Across the Northeastern United States
Authors: Richardson Owen H. , Renshaw Carl E. , Magilligan Francis J. 
Published: October 2024

ABSTRACTThe Northeast United States exhibits significant spatial heterogeneity in flood seasonality, with spring snowmelt‐driven floods historically dominating northern areas, while other regions show more varied flood seasonality. While it is well documented that since 1996 there has been a marked increase in extreme precipitation across this region, the response of flood seasonality to these changes in extreme precipitation and the spatial distribution of these effects remain uncertain. Here we show that, historically, snowmelt‐dominated northern regions were relatively insensitive to changes in extreme precipitation. However, with climate warming, the dominance of snowmelt floods is decreasing and thus the extreme flood regimes in northern regions are increasingly susceptible to changes in extreme precipitation. While extreme precipitation increased everywhere in the Northeastern United States in 1996, it has since returned to near pre‐1996 levels in the coastal north while remaining elevated in the inland north. Thus, the inland north region has and continues to experience the greatest changes in extreme flooding seasonality, including a substantial rise in floods outside the historical spring flood season, particularly in smaller watersheds. Further analysis reveals that while early winter floods are increasingly common, the magnitude of cold season floods (Nov‐May) have remained unchanged over time. In contrast, warm season floods (June‐Oct), historically less significant, are now increasing in both frequency and magnitude in the inland north. Our results highlight that treating the entire Northeast as a uniform hydroclimatic region conceals significant regional variations in extreme discharge trends and, more generally, climate warming will likely increase the sensitivity of historically snowmelt dominated watersheds to extreme precipitation. Understanding this spatial variability in increased extreme precipitation and increased sensitivity to extreme precipitation is crucial for enhancing disaster preparedness and refining water management strategies in affected regions.
How Do Socially Responsible Investment Funds Go Green? The Influence of Investment Styles and managers' Experience
Authors: Mercadier Mathieu , Rannou Yves , Boutabba Mohamed Amine , Chen Jinzhao 
Published: October 2024

ABSTRACTWe provide an holdings‐based analysis of socially responsible investment (SRI hereafter) funds' greenness and its determinants in Europe over 8 years (2015–22). To this end, a segmentation of SRI funds by greenness degree based on k‐means clustering is developed. Our results reveal that SRI funds' greenness depends on specific investment styles and the salient experience of managers in SRI. We verify that greening an SRI fund implies a higher stock selectivity leading managers to underweight their portfolios with fossil fuel stocks and raise the weight of green stocks once SRI market regulations in Europe have been implemented. Also, we find that active investment styles make SRI funds greener. Moreover, we provide evidence of counteracting experience effects provided that the more (resp. less) the manager is experienced in SRI (resp. mutual fund industry), the greener the fund is. In addition to those experience effects, we detect a gender diversity effect provided that a greater gender diversity in fund management teams contributes to increase SRI funds' greenness. Overall, our findings support the argument that the experience and abilities of fund managers are important factors for investors to consider when they choose SRI funds notably because skilled professionals with more experience in SRI are more likely to adapt to regulatory shifts.