Fitting Tool Research Articles

Self-Supervised Three-Dimensional Ocean Bottom Node Seismic Data Shear Wave Leakage Suppression Based on a Dual Encoder Network

Ocean Bottom Node (OBN) is a seismic data acquisition technique, comprising a hydrophone and a three-component geophone. In practice, the vertical component is susceptible to high-amplitude, low-velocity, and low-frequency shear wave noise, which negatively impacts the subsequent processing of dual-sensor data. The most commonly used method is adaptive matching subtraction, which estimates shear wave noise in the vertical component by solving an optimization problem. Neural networks, as robust nonlinear fitting tools, offer superior performance in resolving nonlinear mapping relationship and exhibit computational efficiency. In this paper, we introduce a self-supervised shear wave suppression approach for 3D OBN seismic data, using a neural network in place of the traditional adaptive matching subtraction operator. This method adopts the horizontal components as the input to the neural network, and measures the output and the noisy vertical component to establish a loss function for the network training. The network output is the predicted shear wave noise. To better balance signal leakage and noise suppression, the method incorporates a local normalized cross-correlation regularization term in the loss function. As a self-supervised method, it does not need clean data to serve as labels, thereby negating the tedious work of obtaining clean field data. Extensive experiments on both synthetic and field data demonstrate the effectiveness of the proposed method on shear wave noise suppression for 3D OBN seismic data.

Mechanical Properties and Prediction Based on ANN Model of Basalt and Polypropylene Fiber Reinforced Concrete

Objectives: Utilization of steel fiber increases concrete strength and ductility however, the cost of steel fiber is high and ultimately leads to an increase in the cost of concrete. Basalt fiber is a new type of fiber with high strength and toughness and can effectively replace the place of steel fibers. In this paper, concrete reinforced with polypropylene fibers and basalt fibers was tested for its mechanical properties and compared with the same predicted through a simulated model created by the Artificial Neural Network. Methods: The percentage of polypropylene fiber was kept at 0.25%, and different amounts of basalt fiber were added as 0%, 0.25%, 0.5%, 0.75%, and 1%. Based on traditional testing methods compressive strength, tensile strength, and flexural strength of normal concrete, Fiber reinforced concrete and Hybrid fiber reinforced concrete have been found. To forecast the concrete qualities, the model was simulated after the datasets were trained using a neural network fitting tool. Findings: In comparison to concrete that had only 0.25% polypropylene fiber, a hybrid fiber-reinforced concrete mix including 0.5% basalt fiber and 0.25% polypropylene fiber demonstrated a 9.69% improvement in compressive strength. In comparison to the concrete made only of polypropylene, the mix containing 0.25% polypropylene fiber and 0.75% basalt fiber showed a 26.12% increase in flexural strength and a 39.25% increase in split tensile strength. The error percentage between predicted and experimental values is below 10%. Novelty: Utilization of basalt fiber in concrete is very rare and this proposed work focused on the hybridization of basalt fiber in the place of steel fibers, which is prone to corrosion and reduces the cost of material effectively. The concrete's flexural and tensile strengths were significantly increased by the addition of basalt fibers. The mechanical characteristics of hybrid fiber-reinforced concrete are well predicted by the simulated ANN model. Keywords: Basalt fiber, Polypropylene fiber, Artificial Neural network, Fiber reinforced concrete, Hybridization

Importance of modelling the nebular continuum in galaxy spectra

Neglecting to model stellar and nebular emission has been shown to have a significant impact on the derived physical properties of galaxies experiencing high levels of star formation. This impact has been seen at low redshifts for galaxies in a period of extremely significant star formation, the so-called extreme emission-line galaxies. It has also been suggested as a more general phenomenon among star-forming galaxies at high-redshifts. Even though various studies have approached the issue, a clear limit for the relevant effect of nebular contribution to the total optical emission has not been established. We aim to correlate the nebular contribution in the optical regime with different tracers and to define a threshold, in terms of the nebular contribution, above which there is a significant impact on the estimation of physical properties of galaxies. Additionally, we want to investigate the implication of the results for high-redshift galaxies. We selected a sample of galaxies from SDSS-DR7 with a wide range of star-forming activity levels and analysed their spectra with two conceptually distinct spectral fitting tools: one that self-consistently models stellar and nebular emission and ensures that the best-fitting star formation and chemical enrichment history obtained reproduces the observed nebular characteristics of a galaxy (FADO), and another that lacks such a self-consistency concept (STARLIGHT) and was applied using a purely stellar base. We estimated the nebular contribution and correlate it with different tracers. Then, we compared the stellar properties estimated by the two spectral fitting tools for different degrees of optical nebular contribution. Additionally, we estimated the stellar properties using FADO in pure-stellar mode to further strengthen the robustness of our results. The rest-frame Hα and Hβ equivalent widths (EWs) show a strong linear correlation with the optical nebular contribution and are suitable tracers. We find that for an optical nebular contribution above 8%, which corresponds to EW(Hα)≃500 Å, and EW(Hβ)≃110 Å, there is a significant impact on the estimated physical properties and underlying stellar populations of a galaxy. Given the different definition of FADO for the continuum, this threshold actually corresponds to EW(Hα)≃375 Å, for works considering a pseudo-continuum, which is more commonly used in the literature. These findings were corroborated when considering the results from the application of FADO in pure-stellar mode. Considering the observed redshift evolution of EW(Hα), galaxies in the stellar mass range between M_*=10^7--10^11 M_⊙ will reach, on average, this threshold in the z∼2--6 interval and the optical nebular contribution cannot be neglected. Our results highlight the importance of taking into account both stellar and nebular continuum when analysing the optical spectra of star-forming galaxies. In particular, this is a fundamental aspect for galaxies with a rest-frame EW(Hα)≳500 Å (or the scaled value of 375Å for pseudo-continuum measures). At low redshifts, this mostly impacts extreme emission line galaxies, while at higher redshifts it becomes a dominant aspect given the higher star-forming activity in the younger Universe. In light of current JWST observations and future instruments designed for high-redshift observations, such as MOONS, this reveals a critical issue that ought to be taken into consideration.

Ice inventory towards the protostar Ced 110 IRS4 observed with the James Webb Space Telescope. Results from the Early Release Science Ice Age program

Protostars contain icy ingredients necessary for the formation of potential habitable worlds, therefore, it is crucial to understand their chemical and physical environments. This work is focused on the ice features towards the binary protostellar system Ced 110 IRS4A and IRS4B, separated by 250 au and observed with James Webb Space Telescope (JWST) as part of the Early Release Science (ERS) Ice Age collaboration. This study is aimed at exploring the JWST observations of the binary protostellar system Ced 110 IRS4A and IRS4B primarily to unveil and quantify the ice inventories towards these sources. Finally, we compare the ice abundances with those found for the same molecular cloud. We used data from multiple JWST instruments (NIRSpec, NIRCam, and MIRI) to identify and quantify ice species in the Ced 110 IRS4 system. The analysis was performed by fitting or comparing the laboratory infrared spectra of ices to the observations. Spectral fits are carried out with the ENIIGMA fitting tool that searches for the best fit out of a large number of solutions. The degeneracies of the fits are also addressed and the ice column densities are calculated. In cases where the full nature of the absorption features is not yet known, we explore different laboratory ice spectra to compare them with the observations. We provide a list of securely and tentatively detected ice species towards the primary and the companion sources. For Ced 110 IRS4B, we detected the major ice species H_2O, CO, CO_2, and NH_3. All species are found in a mixture except for CO and CO_2, which have both mixed and pure ice components. In the case of Ced 110 IRS4A, we detected the same major species as in Ced 110 IRS4B, as well as the following minor species: CH_4, SO_2, CH_3OH, OCN^-, NH_4^+, and HCOOH. A tentative detection of N_2O ice (7.75 μm), forsterite dust (11.2 μm), and CH_3^+ gas emission (7.18 μm) in the primary source was also made. Compared with the two lines of sight towards background stars in the Chameleon I molecular cloud, the protostar exhibits similar ice abundances, except in the case of the ions that are higher in IRS4A. The most clear differences are the absence of the 7.2 and 7.4 μm absorption features due to HCOO^- and icy complex organic molecules in IRS4A. There is also evidence of thermal processing in both IRS4A and IRS4B, as probed by the CO_2 ice features. We conclude that the binary protostellar system Ced 110 IRS4A and IRS4B has a large inventory of icy species. The similar ice abundances in comparison to the starless regions in the same molecular cloud suggests that the chemical conditions of the protostar were set at earlier stages in the molecular cloud. It is also possible that the source inclination and complex geometry cause a low column density along the line of sight, which hides the bands at 7.2 and 7.4 μm. Finally, we highlight that a comprehensive analysis using radiative transfer modelling is needed to disentangle the spectral energy distributions of these sources.

An Assessment of Agent-Based Modelling Tools for Community-Based Adaptation to Climate Change

Human-induced climate change has highlighted the need for community-based adaptation (CBA) to build resilience in vulnerable communities. CBA empowers communities to leverage their resources and skills in shaping effective adaptation strategies. Agent-based modelling (ABM) is a suitable tool to develop tailored strategies that account for local capacities, priorities, and cultural contexts. This study assesses ABM tools for their suitability to model CBA, focusing on key criteria such as agent definition, sensitivity analysis, scalability, and experiment design. A comprehensive review of available ABM tools identifies NetLogo as the most fitting tool by its features, due to its flexibility in handling complex community–environment interactions. GAMA and Envision are nearly as suitable, offering robust support for modelling socio-economic and environmental dynamics. This article provides guidance for researchers and practitioners in choosing an appropriate ABM tool aligning with the specific needs of CBA, ensuring contextually relevant and sustainable adaptation solutions.

OilMixProp 1.0: Package for Thermophysical Properties of Oils, Common Fluids, and Their Mixtures

Abstract This work presents the first version of OilMixProp (short for oil mixture properties), which could be the first software package capable of calculating all the core thermophysical properties of fluids involving user-defined oils. Properties needed in thermodynamic cycle analysis are all included: density, phase equilibria, heat capacity, entropy, enthalpy, speed of sound, viscosity, and thermal conductivity. Approximately 632 pure fluids are available, and users can define their oil by determining its fluid constants using embedded fitting tools. Analogous to refpropm (Matlab interface of REFPROP 10.0), a function OilPropm is developed as the only interface for calculating thermophysical properties. Various input combinations (e.g., temperature and pressure, pressure and enthalpy, temperature and vapor fraction, etc.) are available to cater to the needs of thermodynamic cycle analysis. Sophisticated outputs are designed to deliver either complete information (phases and properties in each phase) or selected information about a fluid at the given condition. The package is written in Matlab and will be converted to other languages (e.g., C++) in the future.

Confirming the evolution of the dust mass function in galaxies over the past 5 billion years

ABSTRACT The amount of evolution in the dust content of galaxies over the past 5 billion years of cosmic history is contested in the literature. Here, we present a far-infrared (FIR) census of dust based on a sample of 29 241 galaxies with redshifts ranging from $0 \lt z \lt 0.5$ using data from the Herschel Astrophysical Terahertz Large Area Survey ($H$-ATLAS). We use the spectral energy distribution fitting tool magphys and a stacking analysis to investigate the evolution of dust mass and temperature of FIR-selected galaxies as a function of both luminosity and redshift. At low redshifts, we find that the mass-weighted and luminosity-weighted dust temperatures from the stacking analysis both exhibit a trend for brighter galaxies to have warmer dust. In higher redshift bins, we see some evolution in both mass-weighted and luminosity-weighted dust temperatures with redshift, but the effect is strongest for luminosity-weighted temperature. The measure of dust content in galaxies at $z\lt 0.1$ (the dust mass function) has a different shape to that derived using optically selected galaxies from the same region of sky. We revise the local dust mass density ($z\lt 0.1$) to $\rho _{\rm d} =(1.37\pm 0.08)\times 10^5 {\rm \, {\rm M}_{\odot }\, Mpc^{-3}}\, h_{70}^{-1}$; corresponding to an overall fraction of baryons (by mass) stored in dust of $f_{\rm mb} {(\rm dust)} = (2.22\pm 0.13) \times 10^{-5}$. We confirm evolution in both the luminosity density and dust mass density over the past few billion years ($\rho _{\rm d} \propto (1+z)^{2.6 \pm 0.6}$), with a flatter evolution than observed in previous FIR-selected studies. We attribute the evolution in $\rho _{\rm L}$ and $\rho _{\rm m}$ to an evolution in the dust mass.

A comprehensive comparison of tools for fitting mutational signatures

Mutational signatures connect characteristic mutational patterns in the genome with biological or chemical processes that take place in cancers. Analysis of mutational signatures can help elucidate tumor evolution, prognosis, and therapeutic strategies. Although tools for extracting mutational signatures de novo have been extensively benchmarked, a similar effort is lacking for tools that fit known mutational signatures to a given catalog of mutations. We fill this gap by comprehensively evaluating twelve signature fitting tools on synthetic mutational catalogs with empirically driven signature weights corresponding to eight cancer types. On average, SigProfilerSingleSample and SigProfilerAssignment/MuSiCal perform best for small and large numbers of mutations per sample, respectively. We further show that ad hoc constraining the list of reference signatures is likely to produce inferior results. Evaluation of real mutational catalogs suggests that the activity of signatures that are absent in the reference catalog poses considerable problems to all evaluated tools.

Methodology for improving the physical fitness of qualified goalkeepers in football through fitness means

The article substantiates a methodology for improving the physical fitness of qualified football goalkeepers using health- related fitness. The need for developing alternative training programs for goalkeepers in football has been clarified. The methodology for enhancing the physical fitness of qualified goalkeepers in football includes health-related fitness means and prioritizes an individual approach. The use of fitness tools allows for conducting individual sessions aimed at improving the functional and physical fitness of goalkeepers in football. Thus, the foundation of the methodology consists of exercises using Stick Mobility gymnastic sticks, which have replaced traditional exercises from the Pilates and yoga systems; exercises aimed at activating the myofascial lines of the body; exercises for developing explosive strength; and myofascial release exercises, which have replaced traditional stretching exercises. The author's methodology for the individual training of qualified goalkeepers in football was designed for 4 morphocycles during the preparatory period by the training camp schedule based on the competition calendar. The use of fitness means allowed for conducting individual sessions to improve goalkeepers' functional and physical fitness in football.

WITHDRAWN: Studies on decontamination of cadmium rich water by a novel nano Iron oxide adsorbent

Synthesizing non-toxic adsorbent for abatement of hazardous pollutants from water is important to develop safe environment. In this study, nanocrystalline iron oxide particles were synthesized and utilized as effective nanoadsorbent for removing cadmium through adsorption. The as-prepared nanoadsorbent was characterized using sophisticated techniques. The objective of this study was to improve cadmium adsorption from aqueous solutions under a variety of conditions. With the help of Box-Behnken design, adsorption parameter settings were improved. Response Surface Methodology analysis was conducted to optimize the adsorption conditions, including pH, initial concentration of metal ions, and adsorbent dose. Comparisons were made between the optimal equations for linear and non-linear kinetics, along with isotherm models. Origin's curve fitting tool and the Solver add-in of Microsoft Excel are two non-linear analytical tools. The obtained results showed that the Origin curve fitting tool is superior to solver add in's error analysis method for determining isotherm parameters. The equilibrium data for cadmium removal using nano iron oxide/hydroxide adsorbent was best fitted to Langmuir isotherm model. The adsorption of cadmium was well described by the pseudo-second-order model. The study shows that the system appears to be spontaneous according to the Langmuir constant method and thermodynamic parameter via partition.

Low Socioeconomic Indicators Correlate with Critical Pre-Operative Glenoid Bone Loss and Care Delays

BackgroundChronic and recurrent shoulder dislocations prior to stabilization can increase the risk of glenoid bone loss. Glenoid bone loss exceeding critical levels can lead to further instability and decreased outcomes following arthroscopic labral repair. Indicators of low socioeconomic status (SES), such as high Area Deprivation Index (ADI) and non-commercial insurance, are related to generalized delays to orthopedic care, which can cause recurrent instability and increase glenoid bone loss. Hypothesis/PurposeHigher national ADI and non-commercial insurance would be associated with greater levels of radiographic glenoid bone loss after glenoid instability. MethodsA retrospective study was performed with patients who underwent anterior labral repair. Chart review included demographics, course of care data, preoperative instability data, national ADI, and insurance status. The Neighborhood Atlas Website and patients’ home addresses were used to obtain national ADI. Glenoid bone loss was measured using the Best-fit circle Pico method on three-dimensionally aligned magnetic resonance images (MRIs). Researchers were blinded to SES indicators during radiographic analysis. Glenoid bone loss was compared between SES indicators using one-way ANOVAs. Results146 patients met inclusion criteria and had complete datasets (23.3% female; 22.4±7.0-years-old; national ADI=16.1±15.3). Patients experienced on average 9.12±6.63% glenoid bone loss. A curve fitting tool determined a quadratic non-linear regression best characterized the association of glenoid bone loss and ADI (R2 = 0.392, p < 0.001). Individuals with commercial insurance experienced 8.58%±6.69% glenoid bone loss as compared to 11.78%±6.30% in individuals with Medicaid insurance (p=0.03). Critical bone loss at a threshold of 13.5% was more likely with higher national ADI (p<0.001) and Medicaid insurance (OR=2.49, CI=1.02-6.09). However, only national ADI was predictive of subcritical bone loss at a threshold of 10% (p<0.001). ConclusionPatients with greater national ADI and Medicaid insurance status had greater rates of critical preoperative glenoid bone loss at a threshold of 13.5%. Greater national ADI is also predictive of subcritical glenoid bone loss at a threshold of 10% and overall glenoid bone loss. Further study is needed to assess the postoperative implications of these findings in this population.

A Novel NR-DA-Based ANN for SHEPWM in Cascaded Multilevel Inverters for Renewable Energy Applications

Harmonics is the major power quality problem caused by nonlinear devices, leading to malfunction or operational halt of the system. Low-order harmonics increase vibration and heat generation in motors. Therefore, controlling harmonics in the output waveform is the prime target for industrial applications to avoid economic loss. Selective harmonic elimination pulse width modulation (SHEPWM) is one of the techniques used for eliminating or minimizing selected harmonics in the output voltage waveform. This paper utilizes the Newton-Raphson method and Dragonfly Algorithms to calculate optimum switching angles for a Cascaded H-bridge Multilevel inverter (CHBMLI). The algorithms use non-linear equations to calculate the Switching Angles of MLI. The Dragonfly algorithm requires several iterations to reach an optimum solution. For complex problems, this algorithm becomes computationally expensive and time-consuming. A lookup table addresses the limitation by offline training an Artificial Neural Network (ANN) to generate the optimum switching angle for a given modulation index. Neural Fitting Tool in MATLAB software is used to train the ANN model. The simulation is performed using MATLAB SIMULINK software for both 5-level and 7-level CHBMLI configuration. The Dragonfly algorithm-based ANN achieves THD 8.84% when the modulation index (M) equals 0.8 for a 7-level inverter and THD 14.91% for a 5-level inverter and effectively minimizes third and fifth-order harmonics.

MIRD Pamphlet No. 30: MIRDfit-A Tool for Fitting of Biodistribution Time-Activity Data for Internal Dosimetry.

In nuclear medicine, estimating the number of radioactive decays that occur in a source organ per unit administered activity of a radiopharmaceutical (i.e., the time-integrated activity coefficient [TIAC]) is an essential task within the internal dosimetry workflow. TIAC estimation is commonly derived by least-squares fitting of various exponential models to organ time-activity data (radiopharmaceutical biodistribution). Rarely, however, are methods used to objectively determine the model that best characterizes the data. Additionally, the uncertainty associated with the resultant TIAC is generally not evaluated. As part of the MIRDsoft initiative, MIRDfit has been developed to offer a biodistribution fitting software solution that provides the following essential features and advantages for internal dose assessment: nuclear medicine-appropriate fit functions; objective metrics for guiding best-fit selection; TIAC uncertainty calculation; quality control and data archiving; integration with MIRDcalc software for dose calculation; and a user-friendly Excel-based interface. For demonstration and comparative validation of MIRDfit's performance, TIACs were derived from serial imaging studies involving 18F-FDG and 177Lu-DOTATATE using MIRDfit. These TIACs were then compared with TIAC estimates obtained using other software. In most cases, the TIACs agreed within approximately 10% between MIRDfit and the other software. MIRDfit has been endorsed by the MIRD Committee of the Society of Nuclear Medicine and Molecular Imaging and has been integrated into the MIRDsoft suite of free dosimetry software; it is available for download at no user cost (https://mirdsoft.org/).

An Innovative Deep Neural Network Model for Precise Calorie Burn Prediction from Physical Activity Data

Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.

Efficient Point-spread Function Modeling with ShOpt.jl: A Point-spread Function Benchmarking Study with JWST NIRCam Imaging

With their high angular resolutions of 30–100 mas, large fields of view, and complex optical systems, imagers on next-generation optical/near-infrared space observatories, such as the Near-Infrared Camera (NIRCam) on the James Webb Space Telescope, present new opportunities for science and also new challenges for empirical point-spread function (PSF) characterization. In this context, we introduce ShOpt, a new PSF fitting tool developed in Julia and designed to bridge the advanced features of PSFs in the full field of view (PIFF) with the computational efficiency of PSF Extractor (PSFEx). Along with ShOpt, we propose a suite of nonparametric statistics suitable for evaluating PSF fit quality in space-based imaging. Our study benchmarks ShOpt against the established PSF fitters PSFEx and PIFF using real and simulated COSMOS-Web Survey imaging. We assess their respective PSF model fidelity with our proposed diagnostic statistics and investigate their computational efficiencies, focusing on their processing speed relative to the complexity and size of the PSF models. We find that ShOpt can already achieve PSF model fidelity comparable to PSFEx and PIFF while maintaining competitive processing speeds, constructing PSF models for large NIRCam mosaics within minutes.

Analyzing the Digital Infrastructure Enabling Project Management Success: A Hybrid FAHP-FTOPSIS Approach

This research paper examines the digital infrastructure required to achieve project management success by analyzing the enabling elements of this digital infrastructure in terms of three pillars: digital readiness, digital fitness, and digital tools. A comprehensive literature review was conducted to identify these enabling elements and to develop a list of project management success indicators through which the success of project management can be measured. To evaluate and rank the digital infrastructure enabling elements, a Multi-Criteria Analysis (MCA) was implemented using a hybrid approach combining Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). The study used the digital infrastructure enabling elements as MCA alternatives and the project management success indicators identified in the literature review as MCA criteria. The results indicated that the enabling elements associated with digital tools are the most significant for project management success, with a FTOPSIS closeness coefficient (CCi) of 0.8525, followed by those related to digital fitness (CCi = 0.6481) and digital readiness (CCi = 0.1602). These findings have proven to be robust, as they remained consistent even when weights of the MCA criteria were adjusted in three new scenarios proposed in a scenario analysis. This research highlights the critical role of digital enabling elements in enhancing project management practice and achieving project management success. It also offers a strategic framework for organizations to develop and strengthen their digital infrastructure.

Hindered intermolecular stacking of anti-parallel telomeric G-quadruplexes.

Telomeric G-quadruplexes (G4s) are non-canonical DNA structures composed of TTAGGG repeats. They are extensively studied both as biomolecules key for genome stability and as promising building blocks and functional elements in synthetic biology and nanotechnology. This is why it is extremely important to understand how the interaction between G4s is affected by their topology. We used small-angle x-ray scattering to investigate the end-to-end stacking of antiparallel telomeric G-quadruplexes formed by the sequence AG3(T2AG3)3. To represent the experimental data, we developed a highly efficient coarse-grained fitting tool, which successfully described the samples as an equilibrium mixture of monomeric and dimeric G4 species. Our findings indicate that the antiparallel topology prevents the formation of long multimeric structures under self-crowding conditions, unlike the hybrid/parallel structures formed by the same DNA sequence. This result supports the idea that the stacking of monomeric G-quadruplexes is strongly affected by the presence of diagonal loops.

Not-so-little Red Dots: Two Massive and Dusty Starbursts at z ∼ 5–7 Pushing the Limits of Star Formation Discovered by JWST in the COSMOS-Web Survey

We present the properties of two candidate massive (M ⋆ ∼ 1011 M ⊙) and dusty (A v > 2.5 mag) galaxies at z = 5–7 in the first 0.28 deg2 of the COSMOS-Web survey. One object is spectroscopically confirmed at z spec = 5.051, while the other has a robust z phot = 6.7 ± 0.3. Thanks to their extremely red colors (F277W–F444W ∼ 1.7 mag), these galaxies satisfy the nominal color selection for the widely studied “little red dot” (LRD) population with the exception of their spatially resolved morphologies. The morphology of our targets allows us to conclude that their red continuum is dominated by highly obscured stellar emission and not by reddened nuclear activity. Using a variety of spectral energy distribution fitting tools and star formation histories, we estimate the stellar masses to be log(M⋆)=11.32−0.15+0.07M⊙ and log(M⋆)=11.2−0.2+0.1M⊙ , respectively, with a red continuum emission dominated by a recent episode of star formation. We then compare their number density to the halo mass function to infer stellar baryon fractions of ϵ ⋆ ∼ 0.25 and ϵ ⋆ ∼ 0.5. Both are significantly higher than what is commonly observed in lower-z galaxies or more dust-obscured galaxies at similar redshifts. With very bright ultra-high-z Lyman-Break Galaxies and some non-AGN-dominated LRDs, such “extended” LRDs represent another population that may require very efficient star formation at early times.

Advancing Dyslexia Assessment in Children through Computerized Testing.

The acquisition of reading skills is an intricate process that demands the cultivation of various domain-general and domain-specific abilities. Consequently, it is unsurprising that many children grapple with maintaining proficiency at the grade level, particularly when confronted with challenges spanning multiple abilities across both domains, as observed in individuals with reading difficulties. Strikingly, despite reading difficulties being among the most prevalent neurodevelopmental disorders affecting school-aged children, the majority of available diagnostic tools lack a comprehensive framework for assessing the full spectrum of cognitive skills linked to dyslexia, with minimal computerized options. Notably, there are currently limited tools with these features available for Spanish-speaking children. The aim of this study was to delineate the protocol for diagnosing Spanish-speaking children with reading difficulties using the Sicole-R multimedia battery. This tool for elementary grades focuses on evaluating cognitive skills that are associated with dyslexia as prescribed by the scientific literature. Specifically, it concentrates on assessing a range of cognitive abilities that studies have demonstrated to be linked to dyslexia. This focus is based on the observation that individuals with dyslexia typically exhibit deficits in several of the cognitive areas evaluated by this digital tool. The robust internal consistency and multidimensional internal structure of the battery were demonstrated. This multimedia battery has proven to be a fitting tool for diagnosing children with reading difficulties in primary education, offering a comprehensive cognitive profile that is valuable not only for diagnostic purposes but also for tailoring individualized instructional plans.

Empirically measuring online social influence

Social influence pervades our everyday lives and lays the foundation for complex social phenomena, such as the spread of misinformation and the polarization of communities. A disconnect appears between psychology approaches, generally performed and tested in controlled lab experiments, and quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues; the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work bridges this gap by introducing a human-in-the-loop active learning method that empirically quantifies social influence by crowdsourcing pairwise influence comparisons. We develop simulation and fitting tools, allowing us to estimate the required budget based on the design features and the worker’s decision accuracy. We perform a series of pilot studies to quantify the impact of design features on worker accuracy. We deploy our method to estimate the influence ranking of 500 X/Twitter users. We validate our measure by showing that the obtained empirical influence is tightly linked with agency and communion, the Big Two of social cognition, with agency being the most important dimension for influence formation.