bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection of land surface albedo changes over Iran using remote sensing data
Authors: Kefayat Motlagh Omid Reza , Darand Mohammad 
Published: August 2024

AbstractAlbedo is one of the key parameters in climatic studies. Investigating its temporal and spatial behavior can be a tool for understanding environmental changes. The MODIS sensor continuously produces the land surface albedo on a global scale and with the appropriate spatial resolution and makes it available to researchers. In this study, to analyze Iran's surface albedo trend, first, the daily albedo data of the MODIS on Iran in the period from January 1, 2001 to December 30, 2021 with a spatial resolution of 500 m were prepared from the NASA website. After the necessary pre‐processing, the long‐term seasonal and annual trend of Iran's albedo was calculated at the 90% confidence level using the non‐parametric Mann–Kendall test. The findings showed that the albedo trend is positive in the lowland interior areas of Iran and negative in the highland areas. Since the decreasing trend of albedo in highland areas indicates the reduction of snow cover in these areas, this issue can challenge the life and water resources of these areas that rely on the accumulation of snow.
Detection of defects in composite insulators based on laser‐induced plasma combined with machine learning
Authors: Xu Shuaiqi , Che Changjin 
Published: August 2024

AbstractDue to Prolonged operation and external environmental factors, composite insulators may develop various defects, which can potentially lead to serious accidents in power systems. Detecting and analyzing these defects is critically important. In this study, we combine machine learning with laser‐induced breakdown spectroscopy (LIBS) to identify defects in composite insulators and obtain spectral data from both defective and nondefective samples. The Uniform Manifold Approximation and Projection algorithm was employed to reduce the dimensionality of the data, thereby enhancing detection accuracy and efficiency. Decision tree, random forest (RF), K‐nearest neighbor, and support vector machine algorithms were used to classify the dimensionality‐reduced data. The results indicate that the RF algorithm achieved the best classification performance, with accuracies of 100% and 95.46% for the training and test sets, respectively. Furthermore, the precision, recall, and F1 scores, which reflect model performance, also showed superior results with the RF algorithm. These findings suggest that the combination of LIBS technology and machine learning can rapidly and accurately detect and analyze defects in composite insulators, offering new insights and methods for defect detection in composite insulators.
Adapting standardized trout monitoring to a changing climate for the upper Yellowstone River, Montana, USA
Authors: Briggs Michelle A. , Glassic Hayley C. , Guy Christopher S. , Opitz Scott T. , Rotella Jay J. , Schmetterling David A. 
Published: August 2024

Abstract

Objective
Long-term standardized monitoring programs are fundamental to assessing how fish populations respond to anthropogenic stressors. Standardized monitoring programs may need to adopt new methods to adapt to rapid environmental changes that are associated with a changing climate. In the upper Yellowstone River, Montana, biologists have used a standardized, mark–recapture monitoring protocol to annually estimate the abundance of trout since 1978 to assess population status and trends. However, within the past two decades, climate change has caused changes in discharge timing that have prevented standardized monitoring from occurring annually.

Methods
We investigated the feasibility of using two analytical methods, N-mixture models and mean capture probability, for estimating the abundance of three trout species in the upper Yellowstone River using the historical long-term data set; these methods allow abundance to be estimated when a mark–recapture estimate cannot be obtained due to hydrologic conditions.

Result
When compared with abundance estimates from mark–recapture methods, N-mixture models most often resulted in negatively biased abundance estimates, whereas mean capture probability analyses resulted in positively biased abundance estimates. Additionally, N-mixture models produced negatively biased estimates when tested against true abundance values from simulated data sets. The bias in the N-mixture model estimates was caused by poor model fit and variation in capture probability. The bias in the mean capture probability estimates was caused by heterogeneity in capture probability, likely caused by variable environmental conditions, which were not accounted for in the models.

Conclusion
N-mixture models and mean capture probability are not viable alternatives for estimating abundance in the upper Yellowstone River. Thus, exploring additional adaptations to sampling methodologies and analytical approaches, including models that require individually marked fish, will be valuable for this system. Climate change will undoubtedly necessitate changes to standardized sampling methods throughout the world; thus, developing alternative sampling and analytical methods will be important for maintaining the utility of long-term data sets.
Cold blood in warming waters: Effects of air temperature, precipitation, and groundwater on Gulf Sturgeon thermal habitats in a changing climate
Authors: Carlson Andrew K. , Gaffey Bethany M. 
Published: August 2024

Abstract

Objective
In a changing climate, the effects of air temperature, precipitation, and groundwater on water temperature and thermal habitat suitability for Gulf Sturgeon Acipenser desotoi, listed as threatened under the U.S. Endangered Species Act, are not well understood. Hence, we incorporated these factors into thermal habitat models to forecast how Gulf Sturgeon may be affected by wide-ranging climate change scenarios in 2024–2074.

Methods
Using data from the Choctawhatchee River, Florida, we developed precipitation- and groundwater-corrected air–water temperature models, compared their accuracy with that of conventional air–water temperature models used in fisheries management, and projected future Gulf Sturgeon thermal habitat suitability for normal physiological functioning and fieldwork (i.e., population sampling and telemetry surgeries) in summer (May–August) under 16 climate change scenarios.

Result
Precipitation- and groundwater-corrected models were more accurate than conventional air–water temperature models (mean improvement in adjusted R2 = +0.45; range = +0.09 to +0.75). Water temperature was projected to warm at widely variable rates across climate change scenarios encompassing different air temperature, precipitation, and groundwater regimes. Importantly, Gulf Sturgeon summer aggregation areas were cooler and influenced more by precipitation and groundwater and less by air temperature than were non-aggregation areas. If precipitation and groundwater—as drivers of cooling—become warm in a changing climate, summer aggregation areas were projected to exhibit thermal habitat degradation equivalent to or greater than that of non-aggregation areas.

Conclusion
Our results add hydrological context to the premise that aggregation areas provide cool water and energetic savings for Gulf Sturgeon during summer, underscoring the importance of protecting these habitats through groundwater conservation, water quality monitoring, and riparian/watershed habitat management. Our findings indicate that identifying thermally appropriate times for fieldwork activities will be increasingly important and time-restricted as climate change intensifies. However, our research provides managers with a portfolio of water temperature models and an accurate, cost-effective, management-relevant approach to forecasting thermal habitat conditions for Gulf Sturgeon and other species in a changing climate.
Preparative temperature rising elution fractionation of polyolefin elastomer and its chain microstructure
Authors: Liu Peng , He Shuang , Yang Tao , Zhang Zhenkang , Xue Yanhu 
Published: August 2024

AbstractPolyolefin elastomer (POE) has very weak crystalline ability, consequently, applying the conventional preparative temperature‐rising elution fractionation (P‐TREF) to separate is challenging. Here, a unique, home‐built P‐TREF apparatus with an extensive range of temperatures from −80°C to 150°C is applied to first fractionate POE depending on its crystallizability. The main fractions are eluted at 0°C, 8°C, 15°C, 20°C, 25°C, 30°C, and 35°C. The corresponding weight percentages of fractions are 8.31, 13.38, 15.59, 12.05, 13.39, 17.30, and 10.53 wt%, respectively. The chain structures of fractions are further analyzed by high‐temperature gel permeation chromatography (HT‐GPC), 13C‐nuclear magnetic resonance spectroscopy (13C‐NMR), differential scanning calorimetry (DSC), and successive self‐nucleation and annealing (SSA). The crystallinity of the fraction grows continually as the elution temperature rises. The 1‐octene comonomer concentrations within the fractions decreases from 13.8 to 7.9 mol% when the elution temperature rises from −10°C to 40°C. These findings enable for the detailed recognition of the chain microstructure of POE resin and the extension of the TREF approach to POE resins. This lays the groundwork for fundamental studies and practical uses in industry.
Evaluation and optimization of sample size of neonates and infants for pediatric clinical studies on cefiderocol using a model‐based approach
Authors: Yamaguchi Daichi , Katsube Takayuki , Wajima Toshihiro 
Published: August 2024

AbstractWhen planning pediatric clinical trials, optimizing the sample size of neonates/infants is essential because it is difficult to enroll these subjects. In this simulation study, we evaluated the sample size of neonates/infants using a model‐based optimal approach for identifying their pharmacokinetics for cefiderocol. We assessed the usefulness of data for estimation performance (accuracy and variance of parameter estimation) from adults and the impact of data from very young subjects, including preterm neonates. Stochastic simulation and estimation were utilized to assess the impact of sample size allocation for age categories in estimation performance for population pharmacokinetic parameters in pediatrics. The inclusion of adult pharmacokinetic information improved the estimation performance of population pharmacokinetic parameters as the coefficient of variation (CV) range of parameter estimation decreased from 4.9%–593.7% to 2.3%–17.3%. When sample size allocation was based on the age groups of gestational age and postnatal age, the data showed 15 neonates/infants would be necessary to appropriately estimate pediatric pharmacokinetic parameters (<20%CV). By using the postmenstrual age (PMA), which is theoretically considered to be associated with the maturation of organs, the number of neonates/infants required for appropriate parameter estimation could be reduced to seven (one and six with <32 and >32 weeks PMA, respectively) to nine (three and six with <37 and >37 weeks PMA, respectively) subjects. The model‐based optimal design approach allowed efficient evaluation of the sample size of neonates/infants for estimation of pediatric pharmacokinetic parameters. This approach to assessment should be useful when designing pediatric clinical trials, especially those including young children.
Comparative Analyses of Bioequivalence Assessment Methods for In Vitro Permeation Test Data
Authors: Leon Sami , Rantou Elena , Kim Jessica , Choi Sungwoo , Choi Nam Hee 
Published: August 2024

ABSTRACTFor topical, dermatological drug products, an in vitro option to determine bioequivalence (BE) between test and reference products is recommended. In particular, in vitro permeation test (IVPT) data analysis uses a reference‐scaled approach for two primary endpoints, cumulative penetration amount (AMT) and maximum flux (J
max), which takes the within donor variability into consideration. In 2022, the Food and Drug Administration (FDA) published a draft IVPT guidance that includes statistical analysis methods for both balanced and unbalanced cases of IVPT study data. This work presents a comprehensive evaluation of various methodologies used to estimate critical parameters essential in assessing BE. Specifically, we investigate the performance of the FDA draft IVPT guidance approach alongside alternative empirical and model‐based methods utilizing mixed‐effects models. Our analyses include both simulated scenarios and real‐world studies. In simulated scenarios, empirical formulas consistently demonstrate robustness in approximating the true model, particularly in effectively addressing treatment–donor interactions. Conversely, the effectiveness of model‐based approaches heavily relies on precise model selection, which significantly influences their results. The research emphasizes the importance of accurate model selection in model‐based BE assessment methodologies. It sheds light on the advantages of empirical formulas, highlighting their reliability compared to model‐based approaches and offers valuable implications for BE assessments. Our findings underscore the significance of robust methodologies and provide essential insights to advance their understanding and application in the assessment of BE, employed in IVPT data analysis.
Malignant Transformation of Post-Radiation Induced Erosive Lichen Planus to Squamous Cell Carcinoma
Authors: Regester R. Maxwell , Kwan Kevin R. , Fernandez Jennifer M. , Sutton Adam , Arthur Megan 
Published: August 2024

Abstract
Radiation therapy is commonly used to treat various types of malignancies during or after radiation. Approximately 95% percent of patients develop common skin manifestations including dermatitis, atrophy and fibrosis. Rare manifestations, including non-melanoma skin cancers, morphea, cutaneous angiosarcoma and bullous pemphigoid, have been reported post-treatment. The development of lichen planus (LP) from radiation therapy is exceedingly rare, with only 14 previous cases reported. Of these, none were associated with malignant transformation. Malignant transformation from LP is uncommon, with reported cases mainly in oral manifestations of LP at rates of ∼1%–2%. Classic cutaneous manifestations of LP have not been associated with an increased risk of malignancy. We report a unique case of erosive cutaneous LP with malignant transformation in a previously radiated site. Our case highlights a novel cutaneous adverse event to radiation treatment and emphasises the importance of considering erosive LP on the differential when evaluating recalcitrant erosions in a previously radiated area and to monitor closely for transformation to squamous cell carcinoma.
Angstrom‐Scale 2D Channels Designed For Osmotic Energy Harvesting
Authors: Ding Zhengmao , Gu Tiancheng , Zhang Minghao , Wang Kaiqiang , Sun Daoheng , Li Jinjin 
Published: August 2024

AbstractConfronting the impending exhaustion of traditional energy, it is urgent to devise and deploy sustainable clean energy alternatives. Osmotic energy contained in the salinity gradient of the sea‐river interface is an innovative, abundant, clean, and renewable osmotic energy that has garnered considerable attention in recent years. Inspired by the impressively intelligent ion channels in nature, the developed angstrom‐scale 2D channels with simple fabrication process, outstanding design flexibility, and substantial charge density exhibit excellent energy conversion performance, opening up a new era for osmotic energy harvesting. However, this attractive research field remains fraught with numerous challenges, particularly due to the complexities associated with the regulation at angstrom scale. In this review, the latest advancements in the design of angstrom‐scale 2D channels are primarily outlined for harvesting osmotic energy. Drawing upon the analytical framework of osmotic power generation mechanisms and the insights gleaned from the biomimetic intelligent devices, the design strategies are highlighted for high‐performance angstrom channels in terms of structure, functionalization, and application, with a particular emphasis on ion selectivity and ion transport resistance. Finally, current challenges and future prospects are discussed to anticipate the emergence of more anomalous properties and disruptive technologies that can promote large‐scale power generation.
Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage
Authors: Huang Qi , Hu Chengmin , Qin Yang , Jin Yaowei , Huang Lu , Sun Yaojie , Song Ziyang , Xie Fengxian 
Published: August 2024

AbstractCarbon superstructures with exquisite morphologies and functionalities show appealing prospects in energy realms, but the systematic tailoring of their microstructures remains a perplexing topic. Here, hydrangea‐shaped heterodiatomic carbon superstructures (CHS) are designed using a solution phase manufacturing route, wherein machine learning workflow is applied to screen precursor‐matched solvent for optimizing solvent‐precursor interaction. Based on the established solubility parameter model and molecular growth kinetics simulation, ethanol as the optimal solvent stimulates thermodynamic solubilization and growth of polymeric intermediates to evoke CHS. Featured with surface‐active motifs and consecutive charge transfer paths, CHS allows high accessibility of zincophilic sites and fast ion migration with low energy barriers. A anion−cation hybrid charge storage mechanism of CHS cathode is disclosed, which entails physical alternate uptake of Zn2+/CF3SO3− ions at electroactive sites and chemical bipedal redox of Zn2+ ions with carbonyl/pyridine motifs. Such a beneficial electrochemistry contributes to all‐round improvement in Zn‐ion storage, involving excellent capacities (231 mAh g−1 at 0.5 A g−1; 132 mAh g−1 at 50 A g−1), high energy density (152 Wh kg−1), and long‐lasting cyclability (100 000 cycles). This work expands the design versatilities of superstructure materials and will accelerate experimental procedures during carbon manufacturing through machine learning in the future.
Enhancing the Efficiency and Stability of Perovskite Solar Cells Using Chemical Bath Deposition of SnO2 Electron Transport Layers and 3D/2D Heterojunctions
Authors: Tian Shun , Gao Xiao‐Xin , Reyes David , Syzgantseva Olga A. , Baytemirov Milorad M. , Shibayama Naoyuki , Kanda Hiroyuki , Schouwink Pascal A. , Fei Zhaofu , Zhong Liping , Tiranito Farzaneh Fadaei , Fang Yanyan , Dyson Paul J. , Nazeeruddin Mohammad Khaja 
Published: August 2024

AbstractChemical bath deposition (CBD) is an effective technique used to produce high‐quality SnO2 electron transport layers (ETLs) employed in perovskite solar cells (PSCs). By optimizing the CBD process, high‐quality SnO2 films are obtained with minimal oxygen vacancies and close energy level alignment with the perovskite layer. In addition, the 3D perovskite layers are passivated with n‐butylammonium iodide (BAI), iso‐pentylammonium iodide (PNAI), or 2‐methoxyethylammonium iodide (MOAI) to form 3D/2D heterojunctions, resulting in defect passivation, suppressing ion migration and improving charge carrier extraction. As a result of these heterojunctions, the power conversion efficiency (PCE) of the PSCs increased from 21.39% for the reference device to 23.70% for the device containing the MOAI‐passivated film. The 2D perovskite layer also provides a hydrophobic barrier, thus enhancing stability to humidity. Notably, the PNAI‐based device exhibited remarkable stability, retaining approximately 95% of its initial efficiency after undergoing 1000‐h testing in an N2 environment at room temperature.
Recent advances in self‐immobilizing fluorescent probes for in vivo imaging
Authors: Zhang Yong , Lv Xuemei , Wang Yafu , Chen Xueqian , Zhang Jinchao , Su Dongdong 
Published: August 2024

AbstractIn situ precise detection of bioactive molecules with high sensitivity and spatiotemporal resolution is essential for studying physiological events and disease diagnosis. The utilization of versatile fluorescent probes in fluorescence imaging offers a powerful tool for in vivo imaging of biomarkers closely associated with pathological conditions. However, the dynamic behavior leading to rapid clearance of small molecule probes from regions of interest severely compromises their potential for precise imaging. Notably, self‐immobilizing fluorescent probes that selectively recognize diseased tissues while improving in situ retention and enrichment enable accurate high‐fidelity fluorescence imaging. In this review, we aim to summarize the strategies employed for recent advances in the performance and precision of in vivo fluorescence imaging using self‐immobilizing techniques. Lastly, we discuss the prospects and potential challenges associated with self‐immobilizing fluorescent probes to promote further development and application of more delicate fluorescent probes.
Thermal radiation effect in electroosmosis regulated peristalsis transport of Williamson hybrid nanofluid via an asymmetric tapered channel
Authors: Chaudhary Santosh , Chouhan Kiran Kunwar 
Published: August 2024

AbstractElectroosmosis effects in a peristaltic transport of nanofluids are significant for developing the biomimetic pumping structure at a microscopic extent in physiological medications, for instance, ocular drug delivery systems. The present article addresses the numerical assessment of a peristaltically driven electro‐osmotic flow of a Williamson hybrid nanofluid. The flow is intended to be two‐dimensional, incompressible, unsteady, and subjected to an asymmetric tapered micro‐channel. The characteristics of hybrid nanofluid, which consists of silver (Ag) and copper (Cu) as nanoparticles with base fluid‐blood, are explored in a relative manner with regular nanofluid Ag‐blood. Further, the study includes the impact of linear thermal radiation, energy dissipation through viscosity and resistance phenomena with an externally applied consistent magnetic field. The mathematical model is simplified using dimensionless similarity transformations and numerically solved via MATLAB software. Variations in momentum, thermal energy, and entropy generation against various emerging physical parameters are deliberated through graphical results. Longitudinal velocity towards the center line and heat transfer rate is also analyzed through numerical data illustrated in table form. This study introduces a novel mathematical model for the peristaltically driven electroosmosis flow of Ag‐Cu/blood hybrid nanofluid in a tapered asymmetric microchannel, incorporating external electric and magnetic field effects.