bluuebunny/crossref_metadata_2025_split huggingface dataset:
Regime shift in secondary inorganic aerosol formation and nitrogen deposition in the rural United States
Authors: Pan Da , Mauzerall Denise L. , Wang Rui , Guo Xuehui , Puchalski Melissa , Guo Yixin , Song Shaojie , Tong Daniel , Sullivan Amy P. , Schichtel Bret A. , Collett Jeffrey L. , Zondlo Mark A. 
Published: June 2024

AbstractSecondary inorganic aerosols play an important role in air pollution and climate change, and their formation modulates the atmospheric deposition of reactive nitrogen (including oxidized and reduced nitrogen), thus impacting the nitrogen cycle. Large-scale and long-term analyses of secondary inorganic aerosol formation based on model simulations have substantial uncertainties. Here we improve constraints on secondary inorganic aerosol formation using decade-long in situ observations of aerosol composition and gaseous precursors from multiple monitoring networks across the United States. We reveal a shift in the secondary inorganic aerosol formation regime in the rural United States between 2011 and 2020, making rural areas less sensitive to changes in ammonia concentrations and shortening the effective atmospheric lifetime of reduced forms of reactive nitrogen. This leads to potential increases in reactive nitrogen deposition near ammonia emission hotspots, with ecosystem impacts warranting further investigation. Ammonia (NH3), a critical but not directly regulated precursor of fine particulate matter in the United States, has been increasingly scrutinized to improve air quality. Our findings, however, show that controlling NH3 became significantly less effective for mitigating fine particulate matter in the rural United States. We highlight the need for more collocated aerosol and precursor observations for better characterization of secondary inorganic aerosols formation in urban areas.
A global atlas of soil viruses reveals unexplored biodiversity and potential biogeochemical impacts
Authors: Graham Emily B. , Camargo Antonio Pedro , Wu Ruonan , Neches Russell Y. , Nolan Matt , Paez-Espino David , Kyrpides Nikos C. , Jansson Janet K. , McDermott Jason E. , Hofmockel Kirsten S. ,   the Soil Virosphere Consortium, Blanchard Jeffrey L. , Liu Xiao Jun A. , Rodrigues Jorge L. Mazza , Freedman Zachary B. , Baldrian Petr , Stursova Martina , DeAngelis Kristen M. , Lee Sungeun , Godoy-Vitorino Filipa , Yeoh Yun Kit , Cadillo-Quiroz Hinsby , Tringe Susannah G. , Chauhan Archana , Cowan Don A. , Van Goethem Marc W. , Woyke Tanja , Dove Nicholas C. , Konstantinidis Konstantinos T. , Juenger Thomas E. , Hart Stephen C. , Myrold David D. , Onstott Tullis C. , Bohannan Brendan J. M. , Schmer Marty R. , Palmer Nathan A. , Nüsslein Klaus , Makhalanyane Thulani P. , Dynarski Katherine A. , Taş Neslihan , Nicol Graeme W. , Hazard Christina , Scully Erin D. , Jain Kunal R. , Madamwar Datta , Bissett Andrew , Constant Philippe , Oliveira Rafael S. , Takacs-Vesbach Cristina , Cregger Melissa A. , Carrell Alyssa A. , Klingeman Dawn M. , Pietrasiak Nicole 
Published: June 2024

AbstractHistorically neglected by microbial ecologists, soil viruses are now thought to be critical to global biogeochemical cycles. However, our understanding of their global distribution, activities and interactions with the soil microbiome remains limited. Here we present the Global Soil Virus Atlas, a comprehensive dataset compiled from 2,953 previously sequenced soil metagenomes and composed of 616,935 uncultivated viral genomes and 38,508 unique viral operational taxonomic units. Rarefaction curves from the Global Soil Virus Atlas indicate that most soil viral diversity remains unexplored, further underscored by high spatial turnover and low rates of shared viral operational taxonomic units across samples. By examining genes associated with biogeochemical functions, we also demonstrate the viral potential to impact soil carbon and nutrient cycling. This study represents an extensive characterization of soil viral diversity and provides a foundation for developing testable hypotheses regarding the role of the virosphere in the soil microbiome and global biogeochemistry.
Theoretical investigation of electrocatalytic activity of Pt-free dual atom-doped graphene for O2 reduction in an alkaline solution
Authors: Shaldehi Tahereh Jangjooye , Rowshanzamir Soosan 
Published: June 2024

AbstractNon-precious electrocatalysts as the alternative to Pt have become a hot research area in the last decade due to the suitable catalytic activity in Oxygen reduction reaction (ORR) in electrochemical systems. In this work, the density functional theory calculations were investigated to explore the activity of Fe, Cu, and Fe-Cu atoms supported by N-doped graphene as the ORR electrocatalyst for Oxygen-depolarized cathodes (ODCs). To this end, the ORR mechanism was surveyed in detail in the gas and solvent phases. The results show that the solvent phase leads to a higher overpotential and thermodynamic limiting potential. According to the density of states curves, there are strong interactions between metal atom and substrate that can effectively tune the electronics of catalysts. Bader's analysis confirms that, in addition to the single metal atoms, nitrogen atoms have also played a critical role in charge transfer between substrates and oxygen molecules in ORR. It is also predicted that Fe-Cu@NC SAC exhibits the highest catalytic activity which is consistent with thermodynamic limiting potential and theoretical overpotential of − 0.26 and 0.66 (V vs. SHE), respectively, indicating that this type of catalyst may be a suitable candidate instead of precious metals in oxygen-depolarized cathodes in electrochemical devices.
Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection
Authors: Jenber Belay Abebech , Walle Yelkal Mulualem , Haile Melaku Bitew 
Published: June 2024

AbstractAlzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.
Assessing the instantaneous social dilemma on social distancing attitudes and vaccine behavior in disease control
Authors: Ariful Kabir K. M. , Tanimoto Jun 
Published: June 2024

AbstractIn the face of infectious disease outbreaks, the collective behavior of a society can has a profound impact on the course of the epidemic. This study investigates the instantaneous social dilemma presented by individuals' attitudes toward vaccine behavior and its influence on social distancing as a critical component in disease control strategies. The research employs a multifaceted approach, combining modeling techniques and simulation to comprehensively assess the dynamics between social distancing attitudes and vaccine uptake during disease outbreaks. With respect to modeling, we introduce a new vaccination game (VG) where, unlike conventional VG models, a 2-player and 2-strategy payoff structure is aptly embedded in the individual behavior dynamics. Individuals' willingness to adhere to social distancing measures, such as mask-wearing and physical distancing, is strongly associated with their inclination to receive vaccines. The study reveals that a positive attitude towards social distancing tends to align with a higher likelihood of vaccine acceptance, ultimately contributing to more effective disease control. As the COVID-19 pandemic has demonstrated, swift and coordinated public health measures are essential to curbing the spread of infectious diseases. This study underscores the urgency of addressing the instantaneous social dilemma posed by individuals' attitudes. By understanding the intricate relationship between these factors, policymakers, and healthcare professionals can develop tailored strategies to promote both social distancing compliance and vaccine acceptance, thereby enhancing our ability to control and mitigate the impact of disease outbreaks in the future.
Assessment of noninvasive brain stimulation interventions in Parkinson’s disease: a systematic review and network meta-analysis
Authors: Wang Yueying , Ding Yi , Guo Chenchen 
Published: June 2024

AbstractA network meta-analysis of randomized controlled trials was conducted to compare and rank the effectiveness of various noninvasive brain stimulation (NIBS) for Parkinson's disease (PD). We searched PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and Chinese Biomedical Literature Service System (SinoMed) databases from the date of database inception to April 30th, 2024. Two researchers independently screened studies of NIBS treatment in patients with PD based on inclusion and exclusion criteria. Two researchers independently performed data extraction of the included studies using an Excel spreadsheet and assessed the quality of the literature according to the Cochrane Risk of Bias Assessment Tool (RoB2). Network meta-analysis was performed in StataMP 17.0. A total of 28 studies involving 1628 PD patients were included. The results showed that HF-rTMS over the SMA (SMD = − 2.01; 95% CI [− 2.87, − 1.15]), HF-rTMS over the M1 and DLPFC (SMD = − 1.80; 95% CI [− 2.90, − 0.70]), HF-rTMS over the M1 (SMD = − 1.10; 95% CI [− 1.55, − 0.65]), a-tDCS over the DLPFC (SMD = − 1.08; 95% CI [− 1.90, − 0.27]), HF-rTMS over the M1 and PFC (SMD = − 0.92; 95% CI [− 1.71, − 0.14]), LF-rTMS over the M1 (SMD = − 0.72; 95% CI [− 1.17, − 0.28]), and HF-rTMS over the DLPFC (SMD = − 0.70; 95% CI [− 1.21, − 0.19]) were significantly improved motor function compared with sham stimulation. The SUCRA three highest ranked were HF-rTMS over the SMA (95.1%), HF-rTMS over the M1 and DLPFC (89.6%), and HF-rTMS over the M1 (73.0%). In terms of enhanced cognitive function, HF-rTMS over the DLPFC (SMD = 0.80; 95% CI [0.03,1.56]) was significantly better than sham stimulation. The SUCRA three most highly ranked were a-tDCS over the M1 (69.8%), c-tDCS over the DLPFC (66.9%), and iTBS over the DLPFC (65.3%). HF-rTMS over the M1 (SMD = − 1.43; 95% CI [− 2.26, − 0.61]) and HF-rTMS over the DLPFC (SMD = − 0.79; 95% CI [− 1.45, − 0.12)]) significantly improved depression. The SUCRA three highest ranked were HF-rTMS over the M1 (94.1%), LF-rTMS over the M1 (71.8%), and HF-rTMS over the DLPFC (69.0%). HF-rTMS over the SMA may be the best option for improving motor symptoms in PD patients. a-tDCS and HF-rTMS over the M1 may be the NIBS with the most significant effects on cognition and depression, separately.Trial registration: International Prospective Register of Systematic Review, PROSPERO (CRD42023456088)
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches
Authors: Fawad Muhammad , Alabduljabbar Hisham , Farooq Furqan , Najeh Taoufik , Gamil Yaser , Ahmed Bilal 
Published: June 2024

AbstractGraphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing to the development of highly efficient composites and the advancement of non-destructive structural health monitoring techniques. However, the complexities involved in these nanoscale cementitious composites are markedly intricate. Conventional regression models encounter limitations in fully understanding these intricate compositions. Thus, the current study employed four machine learning (ML) methods such as decision tree (DT), categorical boosting machine (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), and light gradient boosting machine (LightGBM) to establish strong prediction models for compressive strength (CS) of graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature for model development. The majority portion (70%) of the database was utilized for training the model while 30% was used for validating the model efficacy on unseen data. Different metrics were employed to assess the performance of the established ML models. In addition, SHapley Additve explanation (SHAP) for model interpretability. The DT, CatBoost, LightGBM, and ANFIS models exhibited excellent prediction efficacy with R-values of 0.8708, 0.9999, 0.9043, and 0.8662, respectively. While all the suggested models demonstrated acceptable accuracy in predicting compressive strength, the CatBoost model exhibited exceptional prediction efficiency. Furthermore, the SHAP analysis provided that the thickness of GrN plays a pivotal role in GrNCC, significantly influencing CS and consequently exhibiting the highest SHAP value of + 9.39. The diameter of GrN, curing age, and w/c ratio are also prominent features in estimating the strength of graphene nanoplatelets-based cementitious materials. This research underscores the efficacy of ML methods in accurately forecasting the characteristics of concrete reinforced with graphene nanoplatelets, providing a swift and economical substitute for laborious experimental procedures. It is suggested that to improve the generalization of the study, more inputs with increased datasets should be considered in future studies.
Bifocal diffractive lenses based on the aperiodic Kolakoski sequence
Authors: Garmendía-Martínez Adrián , Muñoz-Pérez Francisco M. , Furlan Walter D. , Ferrando Vicente , Monsoriu Juan A. 
Published: June 2024

AbstractIn this work, we present a new family of Zone Plates (ZPs) designed using the self-generating Kolakoski sequence. The focusing and imaging properties of these aperiodic diffractive lenses coined Kolakoski Zone Plates (KZPs) are extensively studied. It is shown that under monochromatic plane-wave illumination, a KZP produces two main foci of the same intensity along the axial axis. Moreover, one of the corresponding focal lengths is double the other, property correlated with the involved aperiodic sequence. This distinctive optical characteristic is experimentally confirmed. We have also obtained the first images provided by these bifocal new diffractive lenses.
Coupled CFD-FEM analysis of the damage causes of the retention bunker: a case study at hard coal mine
Authors: Janoszek Tomasz , Rotkegel Marek 
Published: June 2024

AbstractUnderground coal storage bunkers serve as crucial infrastructural components in the coal mining industry, providing secure and accessible locations for the storage of mined coal. The interaction between stored coal and underground water in coal storage bunkers indeed poses significant challenges due to the unpredictable nature of the resulting coal-water mixture. This phenomenon is particularly prevalent in coal mines operating under water hazards, where groundwater infiltration into storage areas can lead to the formation of coal-water mixtures, altering the physical properties of the stored coal. The interaction between coal and water can result in the formation of coal-water mixtures (hydromixture), which exhibit complex rheological properties. These mixtures may vary in viscosity, density, and particle size distribution, making their behavior difficult to predict. Underground water may exert hydrostatic pressure on the stored coal, influencing its mechanical behavior and compaction properties. Changes in pressure can result in coal compaction or expansion, affecting bunker stability and the integrity of surrounding rock strata. The main goal of the paper was to determine the values of pressure field variations exerted by the flowing hydromixture within underground coal storage bunkers. This objective reflects a critical aspect of understanding the dynamic behavior of coal-water mixtures (hydromixture) under varying conditions, particularly in environments where water hazards pose significant challenges to storage and operational stability. The paper utilized computational fluid dynamics (CFD) methods to examine the changes in pressure within underground coal storage bunkers induced by the flow of coal-water mixtures. The examination of damage to an underground coal storage bunker due to stress distribution was conducted using the finite element method (FEM). This computational technique is widely utilized in engineering and structural analysis to model complex systems and predict the behavior of materials under various loading conditions The results of the CFD numerical simulation were compared with the mathematical models.
Learning From Research: Thinking About Why and How to Promote Word Reading in Children With Speech Sound Disorder
Authors: Ehrhorn Anna M. 
Published: June 2024

Purpose:
The purpose of this clinical focus article is to review why speech sound disorder (SSD) puts children at risk for word reading difficulties and how speech-language pathologists can integrate foundational word reading skills to reduce this risk.

Method:
The triangle model was reviewed to identify phonological awareness, orthographic knowledge, and language as integrated foundational word reading skills for skilled word reading. Reading research in SSD suggests that children with SSD often exhibit poor phonological awareness, limited orthographic knowledge, and may have potential language deficits increasing risk of word reading difficulties. Recent evidence suggests that one or more reduced foundational word reading skills may decrease the interconnected strength among these three skills, negatively impacting accurate and fluent word reading development.

Results:
Informed by theory and SSD research, clinical recommendations were provided to guide assessment and intervention for children with SSD. Two hypothetical case studies illustrated the practical implementation of these recommendations in both assessment and intervention planning.

Conclusions:
Although SSD elevates the risk of word reading difficulties, not all children with SSD present with these difficulties. Recognizing the interplay of strengths and weaknesses between phonological awareness, orthographic knowledge, and language skills may explain word reading variability in children with SSD. Integrating these foundational word reading skills into SSD assessment and intervention is essential, as recent evidence indicates that deficits in these skill areas may reduce the connections crucial for accurate and fluent word reading. This comprehensive approach, outlined in the clinical recommendations, not only enhances speech sound production for effective communication but also holds significant potential to promote word reading. Understanding and supporting the dynamic relationship between speech and foundational word reading skills in children with SSD can help optimize their reading achievement.
Properties of flare events based on light curves from the TESS survey
Authors: Zhang Liyun , Yang Zilu , Su Tianhao , Han Xianming L. , Misra Prabhakar 
Published: June 2024

Aims. Stellar flares are sudden bursts of energy and are the result of magnetic activity. We used light curves from the TESS 20-second cadence survey from 2020 to 2023 to detect flare events and determine their properties.
Methods. By means of repeated fitting to distinguish stellar background light curves and flare events, we detected 32 978 flare events associated with 5463 flaring stars. Furthermore, we cross-matched our samples with the Gaia and SDSS surveys, obtaining additional stellar parameters that we used to determine the relationships between stellar and flare properties.
Results. We find that the durations of 55% of the studied flares were less than 8 minutes. The flare energies of the TESS 20-second cadence data are typically lower than those obtained from TESS 2-minute cadence data. We identify 28 425 flare events associated with 4784 flaring stars. The relationships between the flare energy and duration for both giant and main sequence stars display a consistent V-shaped distribution, with 1034 erg the midway point. Stars with lower effective temperatures and masses generate more frequent flare events. In summary, it is necessary to detect more flare events with a higher time resolution, and our flare samples with 20-second cadences allowed us to discover additional new properties.
X Persei: A study on the origin of its high-energy emission
Authors: Rodi J. , Natalucci L. , Fiocchi M. 
Published: June 2024

Aims. The origin of the hard X-ray emission in the Be/X-ray binary system X Persei has long been debated as its atypical ‘two-hump’ spectrum can be modelled in multiple ways. The main debate focuses on the the high-energy hump, which is fit as either a cyclotron resonance scatter frequency (CRSF) or inverse Comptonization due to bulk Comptonization.
Methods. Using INTEGRAL/JEM-X and ISGRI data, we studied the temporal and spectral variability in the 3 − 250 keV energy range during observations over ∼15 years. A NuSTAR observation was also included in a joint spectral fit with the INTEGRAL spectrum.
Results. We find that the joint spectrum can be described well by a low-energy component due to thermal Comptonization and a high-energy component due to bulk Comptonization, a CRSF, or a cyclotron emission line. The three models begin to diverge above ∼120 keV, where statistics are low.
Conclusions. We compare our results with observations of other Be/X-ray binaries that show similar ‘two-hump’ spectra while in a low-luminosity state. As the sources are in a low accretion state, the bulk Comptonization process is likely inefficient, and thus not an explanation for the high-energy component. The broad CRSF (27 ± 2 keV) in X Persei suggests that the high-energy emission is not due to a CRSF. Thus, the high-energy component is potentially due to cyclotron emission, though other scenarios are not definitively excluded.
Annulation Reactions of Isoquinolinium Ylides with Sulfamate-Derived Cyclic Imines toward Polycyclic 1,3-Benzoxazepine Heterocycles
Authors: Baidya Mahiuddin , Patra Koushik , Mulani Sana 
Published: June 2024

AbstractA dearomatization-guided and (3+2) cycloaddition-triggered annulation reaction of in situ formed isoquinolinium ylides with sulfamate-derived cyclic imines is reported, offering a cogent synthesis of tetracyclic 1,3-benzoxazepine frameworks in high yields. The protocol features successive ring-forming and ring-breaking events in a cascade fashion and is also scalable.