bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Muon Puzzle in cosmic-ray induced air showers and its connection to the Large Hadron Collider
Authors: Albrecht Johannes , Cazon Lorenzo , Dembinski Hans , Fedynitch Anatoli , Kampert Karl-Heinz , Pierog Tanguy , Rhode Wolfgang , Soldin Dennis , Spaan Bernhard , Ulrich Ralf , Unger Michael 
Published: March 2022

AbstractHigh-energy cosmic rays are observed indirectly by detecting the extensive air showers initiated in Earth’s atmosphere. The interpretation of these observations relies on accurate models of air shower physics, which is a challenge and an opportunity to test QCD under extreme conditions. Air showers are hadronic cascades, which give rise to a muon component through hadron decays. The muon number is a key observable to infer the mass composition of cosmic rays. Air shower simulations with state-of-the-art QCD models show a significant muon deficit with respect to measurements; this is called the Muon Puzzle. By eliminating other possibilities, we conclude that the most plausible cause for the muon discrepancy is a deviation in the composition of secondary particles produced in high-energy hadronic interactions from current model predictions. The muon discrepancy starts at the TeV scale, which suggests that this deviation is observable at the Large Hadron Collider. An enhancement of strangeness production has been observed at the LHC in high-density events, which can potentially explain the puzzle, but the impact of the effect on forward produced hadrons needs further study, in particular with future data from oxygen beam collisions.
Data assimilation with soft constraints (DASC) through a generalized iterative ensemble smoother
Authors: Luo Xiaodong , Cruz William C. 
Published: March 2022

AbstractThis work investigates an ensemble-based workflow to simultaneously handle generic, nonlinear equality and inequality constraints in reservoir data assimilation problems. The proposed workflow is built upon a recently proposed umbrella algorithm, called the generalized iterative ensemble smoother (GIES), and inherits the benefits of ensemble-based data assimilation algorithms in geoscience applications. Unlike the traditional ensemble assimilation algorithms, the proposed workflow admits cost functions beyond the form of nonlinear-least-squares, and has the potential to develop an infinite number of constrained assimilation algorithms. In the proposed workflow, we treat data assimilation with constraints as a constrained optimization problem. Instead of relying on a general-purpose numerical optimization algorithm to solve the constrained optimization problem, we derive an (approximate) closed form to iteratively update model variables, but without the need to explicitly linearize the constraint systems. The established model update formula bears similarities to that of an iterative ensemble smoother (IES). Therefore, in terms of theoretical analysis, it becomes relatively easy to transit from an ordinary IES to the proposed constrained assimilation algorithms, and in terms of practical implementation, it is also relatively straightforward to implement the proposed workflow for users who are familiar with the IES, or other conventional ensemble data assimilation algorithms like the ensemble Kalman filter (EnKF). Apart from the aforementioned features, we also develop efficient methods to handle two noticed issues that would be of practical importance for ensemble-based constrained assimilation algorithms. These issues include localization in the presence of constraints, and the (possible) high dimensionality induced by the constraint systems. We use one 2D and one 3D case studies to demonstrate the performance of the proposed workflow. In particular, the 3D example contains experiment settings close to those of real field case studies. In both case studies, the proposed workflow achieves better data assimilation performance in comparison to the choice of using an original IES algorithm. As such, the proposed workflow has the potential to further improve the efficacy of ensemble-based data assimilation in practical reservoir data assimilation problems.
Fecal indicator organisms in northern oligotrophic rivers: An explorative study on Escherichia coli prevalence in a mountain region with intense tourism and reindeer herding
Authors: Maes Sharon , Odlare Monica , Jonsson Anders 
Published: March 2022

AbstractIncreasing pollution levels in waters from remote mountain areas in northern Sweden have been observed. To support a sustainable water quality management, it is necessary to know which environmental and antrophogenic factors influence the water quality. The purpose of this study was to map the Escherichia coli prevalence in the catchment area of the upper part of a large northern Scandinavian river and investigate the controlling factors of microbial contamination. A total of 112 water samples were collected from various locations in the research area between July 2020 and December 2020. These samples were analyzed for microbial and chemical characteristics, and information about tourism and reindeer herding was compiled. Additionally, microbial and physicochemical water characteristics collected by Indalsälven Water Conservation Association (IWCA, 1993–2020) and Swedish Meteorological and Hydrological Institute (SMHI, 2004–2020) were analyzed. The results showed that E. coli enumerations ranged between 0 and 500 CFU/100 ml. There was generally no obvious relation between suspected point sources, e.g., sewage treatment plants at mountain stations, and E. coli levels at downstream sampling points. Principal component analysis showed that E. coli was correlated to coliforms, total heterotrophic count, river discharge, CODMn and river color. Since microbial analyses are time-consuming, expensive and difficult to perform in remote areas, it is important to find more easily extracted water parameters that can serve as a proxy for E. coli. In particular, river color and discharge are promising parameters that may serve as an early indication of bacterial outbreak and fecal contamination in mountain waters.
Green preferences
Authors: Busato Francesco , Chiarini Bruno , Cisco Gianluigi , Ferrara Maria 
Published: March 2022

AbstractThis paper studies how interaction between economic decision-making and environmental awareness affects US business cycle and GHG emissions in a two-sector DSGE model. We emphasize the mechanisms that relate carbon emissions dynamics, consumer behavior, and environmental awareness in a framework incorporating two classes of goods (i.e., “clean” and “dirty”). This paper offers three main results. First, green consumption preferences play a key role in emissions reduction when they internalize emissions concentrations. Second, a green preference shock is the second source of fluctuation in many sectoral variables and stabilizes the business cycle. Third, a pollutant supply shock leads to sustainable consumption procyclicality documented in US data, only if households are environmentally aware.
Multiple imputation of multibeam angular response data for high resolution full coverage seabed mapping
Authors: Misiuk Benjamin , Brown Craig J. 
Published: March 2022

AbstractAcoustic data collected by multibeam echosounders (MBES) are increasingly used for high resolution seabed mapping. The relationships between substrate properties and the acoustic response of the seafloor depends on the acoustic angle of incidence and the operating frequency of the sonar, and these dependencies can be analysed for discrimination of benthic substrates or habitats. An outstanding challenge for angular MBES mapping at a high spatial resolution is discontinuity; acoustic data are seldom represented at a full range of incidence angles across an entire survey area, hindering continuous spatial mapping. Given quantifiable relationships between MBES data at various incidence angles and frequencies, we propose to use multiple imputation to achieve complete estimates of angular MBES data over full survey extents at a high spatial resolution for seabed mapping. The primary goals of this study are (i) to evaluate the effectiveness of multiple imputation for producing accurate estimates of angular backscatter intensity and substrate penetration information, and (ii) to evaluate the usefulness of imputed angular data for benthic habitat and substrate mapping at a high spatial resolution. Using a multi-frequency case study, acoustic soundings were first aggregated to homogenous seabed units at a high spatial resolution via image segmentation. The effectiveness and limitations of imputation were explored in this context by simulating various amounts of missing angular data, and results suggested that a substantial proportion of missing measurements (> 40%) could be imputed with little error using Multiple Imputation by Chained Equations (MICE). The usefulness of imputed angular data for seabed mapping was then evaluated empirically by using MICE to generate multiple stochastic versions of a dataset with missing angular measurements. The complete, imputed datasets were used to model the distribution of substrate properties observed from ground-truth samples using Random Forest and neural networks. Model results were pooled for continuous spatial prediction and estimates of confidence were derived to reflect uncertainty resulting from multiple imputations. In addition to enabling continuous spatial prediction, the high-resolution imputed angular models performed favourably compared to broader segmentations or non-angular data.
Group awareness and regulation in computer-supported collaborative learning
Authors: Schnaubert Lenka , Bodemer Daniel 
Published: March 2022

AbstractGroup awareness is of critical relevance for collaborative learning and interaction and is thus often referred to in CSCL research. However, the concept is only vaguely defined as some kind of understanding or perception of characteristics of learning partners or the collaborating group. Most CSCL research activities concerned with group awareness aim at modifying learners' awareness using so-called group awareness tools. However, there are much less attempts to measure group awareness and to conceptualize its formation. Thus, building on existing group awareness research, this article derives a conceptualization with six defining aspects of group awareness: (1) group awareness is cognitive, (2) group awareness is conscious, (3) group awareness is current, (4) group awareness is individual, (5) group awareness is social, and (6) group awareness is perceived as valid. Additionally, while it is often assumed that group awareness builds on self-regulatory skills, its role in regulating behavior and cognition within a social context is seldom explored. Thus, this article aims at defining and analyzing the concept of group awareness, specifying its relation to regulatory processes, and sketching possible research paths whilst building on, complementing, and informing tool-driven research.
Configuration and Delivery of Primary Care in Rural and Urban Settings
Authors: Fraze Taressa K. , Lewis Valerie A. , Wood Andrew , Newton Helen , Colla Carrie H. 
Published: March 2022

Abstract
Background
There are concerns about the capacity of rural primary care due to potential workforce shortages and patients with disproportionately more clinical and socioeconomic risks. Little research examines the configuration and delivery of primary care along the spectrum of rurality.

Objective
Compare structure, capabilities, and payment reform participation of isolated, small town, micropolitan, and metropolitan physician practices, and the characteristics and utilization of their Medicare beneficiaries.

Design
Observational study of practices defined using IQVIA OneKey, 2017 Medicare claims, and, for a subset, the National Survey of Healthcare Organizations and Systems (response rate=47%).

Participants
A total of 27,716,967 beneficiaries with qualifying visits who were assigned to practices.

Main Measures
We characterized practices’ structure, capabilities, and payment reform participation and measured beneficiary utilization by rurality.

Key Results
Rural practices were smaller, more primary care dominant, and system-owned, and had more beneficiaries per practice. Beneficiaries in rural practices were more likely to be from high-poverty areas and disabled. There were few differences in patterns of outpatient utilization and practices’ care delivery capabilities. Isolated and micropolitan practices reported less engagement in quality-focused payment programs than metropolitan practices. Beneficiaries cared for in more rural settings received fewer recommended mammograms and had higher overall and condition-specific readmissions. Fewer beneficiaries with diabetes in rural practices had an eye exam. Most isolated rural beneficiaries traveled to more urban communities for care.

Conclusions
While most isolated Medicare beneficiaries traveled to more urban practices for outpatient care, those receiving care in rural practices had similar outpatient and inpatient utilization to urban counterparts except for readmissions and quality metrics that rely on services outside of primary care. Rural practices reported similar care capabilities to urban practices, suggesting that despite differences in workforce and demographics, rural patterns of primary care delivery are comparable to urban.
Rural smartness: Its determinants and impacts on rural economic welfare
Authors: Mukti Iqbal Yulizar , Henseler Jörg , Aldea Adina , Govindaraju Rajesri , Iacob Maria E. 
Published: March 2022

AbstractSolving urbanization problems, especially in developing countries, solely through the adoption of smartness in urban areas is insufficient as urbanization is mostly driven by the wide urban-rural economic gap. To narrow this gap, the adoption of smartness needs to be extended into rural areas. However, studies in that direction are still lacking. Therefore, we developed a theoretical model that explains the determinants of rural smartness and its subsequent consequences on rural economic welfare. We validated the model with survey data from 179 villages in West Java Province, Indonesia. The results suggest that rural smartness is determined by the interplay of organizational, environmental, and technological readiness, and has a strong positive impact on innovativeness which, in turn, improves the competitiveness of the rural business ecosystem. This model can serve as a reference for further studies of rural smartness and as the foundation for the design of information platforms supporting it.
A GIS-based framework using fuzzy relation equation system solutions in urban planning
Authors: Cardone Barbara , Di Martino Ferdinando 
Published: March 2022

AbstractSeveral fuzzy decision models are proposed in literature to solve urban planning problems. In this research we present a novel GIS-based framework to solve decision problems in urban planning based on a System of Fuzzy Relation Equations in which the unknowns represent characteristics affecting observable facts constituting the input variables. Aim of this research is to partition the urban study area into subzones, each of which identifies a sub-area of the study area within which the set of analyzed characteristics are homogeneous. The study area is initially decomposed in atomic urban areas called microzones; for each microzone are calculated the greatest and lowest solutions of a System of Fuzzy Relation Equations by using the Universal solution Algorithm and are calculated and fuzzified the values of the output variables. Spatially adjoining microzones with same output variables are dissolved forming homogeneous urban areas with reference to the problem analyzed, called Urban Contexts. For each output variable a thematic map is constructed; in addition, a thematic map of its reliability is created. This framework is tested on a study area given by the district of Ponticelli in the municipality of Naples (Italy); comparison tests performed with respect to a previous GIS-based framework based on a System of Fuzzy Relation Equations show that our method provides a more detailed knowledge of the characteristics of the urban study area with reference to the problem dealt with.
Synthesis of CuxCo3−xO4 nanocatalyst for degradation of nitrogenous organic wastewater in Fenton-like membrane reactor
Authors: Zhou Ming , Li Wenyue , Chen Min , Zhong Zhaoxiang , Xing Weihong 
Published: March 2022

AbstractMembrane Fenton-like process with cobalt–copper spinel catalyst is proposed to degrade nitrogenous organic hazardous in wastewater. Cu-auxiliary cobalt oxide (CuxCo3–xO4) was synthesized with the dispersion co-precipitation method. As-prepared CuCo2O4 performed as the best catalyst for Fenton degradation on nitrogen-containing organic compound as methylene blue (C16H18N3ClS) in water and N,N-dimethylacetamide [CH3CON(CH3)2] in industrial wastewater. Lattice substation of Cu2+ to Co2+ ion increased the oxygen vacancies and catalytic activity. The catalyst stability was improved owing to hybrid polarization confinement (total metal elution less than 0.8 mg/L). Methylene blue in water (C0 = 100 mg/L) was completely decolorized in 50 min using 0.6 g/L CuCo2O4 and 0.09 M H2O2 at intrinsic pH 6.7. Degradation reaction rate constant was increased by two folds as k = 0.076 min−1 catalyzed by CuCo2O4 over k = 0.039 min−1 by Co3O4. DMAc-contaminant wastewater of high CODs 19,080 mg/L was treated using 2.4 g/L CuCo2O4 and 2.4 M H2O2 at intrinsic pH = 7.6. Removal rate of DMAc was enhanced from 81 to 99% in the ceramic membrane reactor when H2O2 was activated, and self-sweeping effect was suppressed. As a result, Total Organic Nitrogen (2900 mg/L) in the wastewater was mineralized by 99.9% with a fast transformation to inorganic NO3− ion. Biodegradability of the resistant organic wastewater was remarkably improved, and BOD/COD was increased by 18 folds.
Groundwater potential mapping using multi-criteria decision, bivariate statistic and machine learning algorithms: evidence from Chota Nagpur Plateau, India
Authors: Hasanuzzaman Md , Mandal Mehedi Hasan , Hasnine Md , Shit Pravat Kumar 
Published: March 2022

AbstractIncreased consumption of water resource due to rapid growth of population has certainly reduced the groundwater storage beneath the earth which leads certain challenges to human being in recent time. For optimal management of this vital resource, exploration of groundwater potential zone (GWPZ) has become essential. We have applied Analytical Hierarchy Process (AHP), Frequency Ratio (FR) and two machine learning techniques specifically Random Forest (RF) and Naïve Bayes (NB) here to delineate GWPZ in Gandheswari River Basin in Chota Nagpur Plateau, India. To achieve the goal of the study, twelve factors that determine occurrence of groundwater have been selected for inter-thematic correlations and overlaid with location of wells. These factors include elevation, drainage density, slope, lithology, geomorphology, topographical wetness index (TWI), distance from the river, rainfall, lineament density, Normalized Difference Vegetation Index (NDVI), soil, and Land use and Land cover (LULC). A total 170 points including 85 in well site and 85 in non-well site have been selected randomly and allocated into two parts: training and testing at the share of 70:30. The implemented methods have significantly provided five GWPZs specifically Very Good (VG), Good (G), Moderate (M), Poor (P) and Very Poor (VP) with high and acceptable accuracy. The study also finds that geomorphology, slope, rainfall and elevation have greater importance in shaping GWPZs than LULC, NDVI, etc. Model performance has been tested with receiver operator characteristics (ROC), Accuracy (ACC), Kappa Coefficient, MAE, RMSE, etc., methods. Area under curve (AUC) in ROC curve has revealed that accuracy level of AHP, FR, RF and NB is 78.8%, 81%, 85.3% and 85.5, respectively. The machine learning techniques coupled with AHP and FR unveil effective delineation of groundwater potential area in said river basin which by genetically offers low primary porosity due to lithological constrains. Therefore, the study can be helpful in watershed management and identifying appropriate location wells in future.
Comparative analysis of groundwater potentiality zone using fuzzy AHP, frequency ratio and Bayesian weights of evidence methods
Authors: Maity Biswajit , Mallick Suraj Kumar , Das Pritiranjan , Rudra Somnath 
Published: March 2022

AbstractGroundwater resource management in the urban area is one of the important aspects because of growing population demand and having inadequate water supply. So, proper information is needed to manage the future urban planning for such kinds of areas. This study emphasizes groundwater potentiality zone (GPZ) assessment in the Asansol urban agglomeration (AUA) region, West Bengal, India. For this purpose, we have incorporated eight conditioning factors namely LULC, lithology, slope, elevation, rainfall, drainage density, lineaments density, and soil map using conventional and remote sensing data in GIS software. All these conditioning factors have been reclassified in ArcGIS and processed by the fuzzy analytical hierarchy process (FAHP), frequency ratio (FR), and Bayesian weights of evidence (BWOE) statistical methods. Then, the groundwater potential index has been formulated, and finally, GPZ maps are generated based on of selected three models. The result shows that very high area of GPZ, e.g. 9.13%, 11.62% and 7.43% are under BWOE, FR and FAHP models, respectively. The receiver operating characteristic validation curves show that FR method (AUC = 96.4%) is well obtained for GPZ in comparison with both BWOE (AUC = 83.8%) and FAHP methods (AUC = 82.9%). Therefore, this statistical method is highly recommended for the study of groundwater potential assessment and this outcome is very suitable for the groundwater resources management in future land use planning. Precautionary works in low potential areas should be given priority for long-term planning. Thus, this study can be considered as a good document for decision support in water exploitation planning and sustainable groundwater management in AUA region.
Decontamination of cationic dye brilliant green from the aqueous media
Authors: Singh Santosh , Gupta Himanshu , Dhiman Soniya , Sahu Nawal Kishore 
Published: March 2022

AbstractBrilliant green is a synthetic and toxic dye that is currently being utilized for various purposes, such as dying paper, leather, wool, and silk. The present study demonstrates the activated carbon preparation from waste banana peels as well as its usefulness to remove cationic dye brilliant green from aqueous medium. The dye removal was examined under a set of diverse conditions. The obtained results indicate that dye adsorption was maximum after 60 min of contact time. The removal of brilliant green dye enhances due to a rise in adsorbent dosage and becomes quantitative at 15 min of adsorbent dose. At a pH of 2, the uptake of dye by adsorbent was maximum, which decreases with the rise in pH. Langmuir isotherm was slightly better fitted than Freundlich model at varying temperatures. The experimental value of adsorption capacity was > 900 mg/g, which was observed quite close with pseudo-second-order model for brilliant green adsorption on the prepared adsorbent based on banana peel. Thermodynamic studies suggested exothermic, spontaneous, and favorable adsorption process for brilliant green dye. The adsorbent prepared in the present study can be incorporated for the treatment of wastewater contaminated with brilliant green as well as other toxic pollutants.