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We measure the two-point correlation function (CF) of 1357 galaxy clusters with a mass of log10M200 ≥ 13.6 h−1 M⊙ and at a redshift of z ≤ 0.125. This work differs from previous analyses in that it utilizes a spectroscopic cluster catalogue, $\tt {SDSS-GalWCat}$, to measure the CF and detect the baryon acoustic oscillation (BAO) signal. Unlike previous studies which use statistical techniques, we compute covariance errors directly by generating a set of 1086 galaxy cluster light-cones from the GLAM N-body simulation. Fitting the CF with a power-law model of the form ξ(s) = (s/s0)−γ, we determine the best-fitting correlation length and power-law index at three mass thresholds. We find that the correlation length increases with increasing the mass threshold while the power-law index is almost constant. For log10M200 ≥ 13.6 h−1 M⊙, we find s0 = 14.54 ± 0.87 h−1 Mpc and γ = 1.97 ± 0.11. We detect the BAO signal at s = 100 h−1 Mpc with a significance of 1.60σ. Fitting the CF with a Lambda cold dark matter model, we find $D_\mathrm{V}(z = 0.089)\mathit{r}^{\mathrm{ fid}}_\mathrm{ d}/\mathit{r}_\mathrm{ d} = 267.62 \pm 26$ h−1 Mpc, consistent with Planck 2015 cosmology. We present a set of 108 high-fidelity simulated galaxy cluster light-cones from the high-resolution Uchuu N-body simulation, employed for methodological validation. We find DV(z = 0.089)/rd = 2.666 ± 0.129, indicating that our method does not introduce any bias in the parameter estimation for this small sample of galaxy clusters.

 

The cluster mass–richness relation (MRR) is an observationally efficient and potentially powerful cosmological tool for constraining the matter density Ω_mand the amplitude of fluctuationsσ₈using the cluster abundance technique. We derive the MRR relation usingGalWCat19, a publicly available galaxy cluster catalog we created from the Sloan Digital Sky Survey-DR13 spectroscopic data set. In the MRR, cluster mass scales with richness as$\log M_{200}=\alpha +\beta \log N_{200}$. We find that the MRR we derive is consistent with both the IllustrisTNG and mini-Uchuu cosmological numerical simulations, with a slope ofβ≈ 1. We use the MRR we derived to estimate cluster masses from theGalWCat19catalog, which we then use to set constraints on Ω_mandσ₈. Utilizing the all-member MRR, we obtain constraints of Ω_m=${0.31}_{-0.03}^{+0.04}$andσ₈=${0.82}_{-0.04}^{+0.05}$, and utilizing the red member MRR only, we obtain Ω_m=${0.31}_{-0.03}^{+0.04}$andσ₈=${0.81}_{-0.04}^{+0.05}$. Our constraints on Ω_mandσ₈are consistent and very competitive with the Planck 2018 results.

 

This study evaluates the level of service of shared transportation facilities through mining geotagged data from social media and analyzing the perceptions of road users. An algorithm is developed adopting a text classification approach with contextual understanding to filter out relevant information related to users’ perceptions toward active mobility. Using a heuristic-based keyword matching approach produces about 75% tweets that are out of context, so that approach is deemed unsuitable for information extraction from Twitter. This study implements six different text classification models and compares the performance of these models for tweet classification. The model is applied to real-world data to filter out relevant information, and content analysis is performed to check the distribution of keywords within the filtered data. The text classification model “term frequency-inverse document frequency” vectorizer-based logistic regression model performed best at classifying the tweets. To select the best model, the performances of the models are compared based on precision, recall, F1 score (geometric mean of precision and recall), and accuracy metrics. The findings from the analysis show that the proposed method can help produce more relevant information on walking and biking facilities as well as safety concerns. By analyzing the sentiments of the filtered data, the existing condition of biking and walking facilities in the DC area can be inferred. This method can be a critical part of the decision support system to understand the qualitative level of service of existing transportation facilities. 

The presence of contaminants of emerging concerns (CECs) such as pharmaceuticals and personal care products, endocrine disrupting compounds (EDCs), per/poly‐fluorinated substances (PFAS), pesticides, and nanomaterials poses significant challenges to the environment and human health. This review discusses the current status of electrochemical sensing methods and their potential as low‐cost analytical platforms for the detection and characterization of emerging contaminants. Recent developments in advanced materials and fabrication techniques such as electrophoretic deposition, layer‐by‐layer deposition, roll‐to‐roll and 3D printing techniques, and the scalable manufacturing of low‐cost portable electrochemical devices are discussed. Examples of detection mechanisms, electrode modification procedures, device configuration, and their performance along with recent developments in fundamental electrochemistry, particularly nanoimpact methods, are provided to demonstrate the capabilities of these methods for the environmental monitoring of CECs. Finally, a critical discussion of future research needs, detection challenges, and opportunities is provided to demonstrate how electrochemistry can be used to advance field monitoring of these chemicals. These methods can be used as complementary or alternative methods to the currently used laboratory‐based analytical instrumentation to facilitate large‐scale studies and manage risks associated with the presence of CECs in the environment and other matrices.

 

Nanoelectrochemistry allows for the investigation of the interaction of per‐ and polyfluoroalkyl substances (PFASs) with silver nanoparticles (AgNPs) and the elucidation of the binding behaviour of PFASs to nanoscale surfaces with high sensitivity. Mechanistic studies supported by single particle collision electrochemistry (SPCE), spectroscopic and density functional theory (DFT) calculations indicate the capability of polyfluorooctane sulfonic acid (PFOS), a representative PFAS, to selectively bind and induce aggregation of AgNPs. Single‐particle measurements provide identification of the “discrete” AgNPs agglomeration (e.g. 2–3 NPs) formed through the inter‐particles F−F interactions and the selective replacement of the citrate stabilizer by the sulfonate of the PFOS. Such interactions are characteristic only for long chain PFAS (‐SO₃⁻) providing a means to selectively identify these substances down to ppt levels. Measuring and understanding the interactions of PFAS at nanoscale surfaces are crucial for designing ultrasensitive methods for detection and for modelling and predicting their interaction in the environment.

 

Nanoelectrochemistry allows for the investigation of the interaction of per‐ and polyfluoroalkyl substances (PFASs) with silver nanoparticles (AgNPs) and the elucidation of the binding behaviour of PFASs to nanoscale surfaces with high sensitivity. Mechanistic studies supported by single particle collision electrochemistry (SPCE), spectroscopic and density functional theory (DFT) calculations indicate the capability of polyfluorooctane sulfonic acid (PFOS), a representative PFAS, to selectively bind and induce aggregation of AgNPs. Single‐particle measurements provide identification of the “discrete” AgNPs agglomeration (e.g. 2–3 NPs) formed through the inter‐particles F−F interactions and the selective replacement of the citrate stabilizer by the sulfonate of the PFOS. Such interactions are characteristic only for long chain PFAS (‐SO₃⁻) providing a means to selectively identify these substances down to ppt levels. Measuring and understanding the interactions of PFAS at nanoscale surfaces are crucial for designing ultrasensitive methods for detection and for modelling and predicting their interaction in the environment.

 

We report the discovery of MAGAZ3NE J095924+022537, a spectroscopically confirmed protocluster at$z={3.3665}_{-0.0012}^{+0.0009}$around a spectroscopically confirmedUVJ-quiescent ultramassive galaxy (UMG;$M_{\star }={2.34}_{-0.34}^{+0.23}\times {10}^{11}{M}_{\odot }$) in the COSMOS UltraVISTA field. We present a total of 38 protocluster members (14 spectroscopic and 24 photometric), including the UMG. Notably, and in marked contrast to protoclusters previously reported at this epoch that have been found to contain predominantly star-forming members, we measure an elevated fraction of quiescent galaxies relative to the coeval field (${73.3}_{-16.9}^{+26.7}\mathrm{\%}$versus${11.6}_{-4.9}^{+7.1}\mathrm{\%}$for galaxies with stellar massM_⋆≥ 10¹¹M_⊙). This high quenched fraction provides a striking and important counterexample to the seeming ubiquitousness of star-forming galaxies in protoclusters atz> 2 and suggests, rather, that protoclusters exist in a diversity of evolutionary states in the early universe. We discuss the possibility that we might be observing either “early mass quenching” or nonclassical “environmental quenching.” We also present the discovery of MAGAZ3NE J100028+023349, a second spectroscopically confirmed protocluster, at a very similar redshift of$z={3.3801}_{-0.0281}^{+0.0213}$. We present a total of 20 protocluster members, 12 of which are photometric and eight spectroscopic including a poststarburst UMG ($M_{\star }={2.95}_{-0.20}^{+0.21}\times {10}^{11}{M}_{\odot }$). Protoclusters MAGAZ3NE J0959 and MAGAZ3NE J1000 are separated by 18′ on the sky (35 comoving Mpc), in good agreement with predictions from simulations for the size of “Coma”-type cluster progenitors at this epoch. It is highly likely that the two UMGs are the progenitors of Brightest Cluster Galaxies seen in massive virialized clusters at lower redshift.