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Copyrolysis, being an active area of research due to its synergistic impact in utilizing diverse fuel resources, including waste materials, like, peach stone (PS), has been the focal point for this study. PS, produced in vast quantities annually and typically intended for landscaping or insulation purposes, is being studied in combination with low‐grade bituminous coal for energy utilization focusing on thermokinetics and synergistic aspects. Coal‐peach stone (C‐PS) blends were formulated at different ratios and subjected to comprehensive characterization techniques, including ultimate analysis (CHN‐S), gross calorific value (GCV), Fourier transform infrared spectroscopy, and thermogravimetric analyzer (TGA). The ultimate analysis revealed an enhancement in carbon and hydrogen content from 45.38% to 68.08% and from 3.89% to 6.96%, respectively. Additionally, a reduction in sulfur and nitrogen content from 0.54% to 0.11% and from 1.16% to 0.42%, respectively, was observed with an increase in the ratio of PS in the C‐PS blends. The GCV of C‐PS blends ranged from 20.75 to 26.01 MJ kg −1 . The pyrolysis conditions simulated in TGA are pivotal for evaluating thermokinetics and synergistic effects. The 60C:40PS blend shows a positive synergy index (SI) value of 0.0203% concerning total mass loss ( ML T ) indicating a favorable condition for bio‐oil generation. Coats–Redfern model‐fitting method reveals that the activation energy ( Ea ) of C‐PS blends increases in Section II with the addition of PS, and conversely, it decreases in Section III. The Ea for 100PS and 100C was 106.76 and 45.85 kJ mol −1 through (D3) and (F1), respectively, which was improved through the optimal blend 60C:40PS with an Ea of 94.56 and 27.58 kJ mol −1 through (D3) and (F2), respectively. The values obtained from linear regression prove that the kinetic models are effective while the thermodynamic analysis indicates that the pyrolytic behavior of C‐PS blends is characterized as endothermic, nonspontaneous, and capable of achieving thermodynamic equilibrium more rapidly.

This study aims at analyzing the water budget of the unconfined Beauce aquifer (8000 km2) over a 35 year period, by modeling the hydrological functioning and quantifying exchanged water fluxes inside the system. A distributed process‐based model (DPBM) is implemented to model the surface, the unsaturated zone and the aquifer subsystems. Based on an extensive literature review on multiparameter optimization and inverse problem, a pragmatic hybrid fitting method that couples manual and automatic calibration is developed. Three data subsets are used for calibration (10 year), validation (10 year) and test (35 year). The global piezometric head root‐mean‐square error is around 2.5 m for the three subsets and is rather uniformly spatially distributed over 78 piezometers. The sensitivity of the simulation to the different steps of the calibration process is investigated. The transmissivity field permits the fitting of the low‐frequency signal for long‐term filtering of the recharge signal, whereas the storage coefficient filters the signal with a higher frequency. For long‐term insight into aquifer system functioning, the priority is thus to first fit the transmissivity field and to assess the distributed aquifer recharge accurately. The fitted DPBM, coupled with a linear model of coregionalization, is then used to quantify the hydrosystem water mass balance between 1974 and 2009, indicating that there is yet no trend of water resources decrease neither due to climate nor to human activities. Key Points Hybrid fitting method for hydrological DPBM Long term water mass balance No water scarcity due to climate change or anthropogenic activities

Model building into experimental maps is a key element of structural biology, but can be both time consuming and error prone for low-resolution maps. Here we present Namdinator , an easy-to-use tool that enables the user to run a molecular dynamics flexible fitting simulation followed by real-space refinement in an automated manner through a pipeline system. Namdinator will modify an atomic model to fit within cryo-EM or crystallography density maps, and can be used advantageously for both the initial fitting of models, and for a geometrical optimization step to correct outliers, clashes and other model problems. We have benchmarked Namdinator against 39 deposited cryo-EM models and maps, and observe model improvements in 34 of these cases (87%). Clashes between atoms were reduced, and the model-to-map fit and overall model geometry were improved, in several cases substantially. We show that Namdinator is able to model large-scale conformational changes compared to the starting model. Namdinator is a fast and easy tool for structural model builders at all skill levels. Namdinator is available as a web service (https://namdinator.au.dk), or it can be run locally as a command-line tool.

Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.

The drift-diffusion model (DDM) is an important decision-making model in cognitive neuroscience. However, innovations in model form have been limited by methodological challenges. Here, we introduce the generalized drift-diffusion model (GDDM) framework for building and fitting DDM extensions, and provide a software package which implements the framework. The GDDM framework augments traditional DDM parameters through arbitrary user-defined functions. Models are solved numerically by directly solving the Fokker-Planck equation using efficient numerical methods, yielding a 100-fold or greater speedup over standard methodology. This speed allows GDDMs to be fit to data using maximum likelihood on the full response time (RT) distribution. We demonstrate fitting of GDDMs within our framework to both animal and human datasets from perceptual decision-making tasks, with better accuracy and fewer parameters than several DDMs implemented using the latest methodology, to test hypothesized decision-making mechanisms. Overall, our framework will allow for decision-making model innovation and novel experimental designs.

HighlightsThe sensors displayed high sensitivity (8962.7), fast response time (60 ms), outstanding stability and durability (> 10,000 cycles) and widely workable stretching range (0–160%).A theoretical approach was used to analyze mechanical property, and a model based on tunneling theory was modified to describe the relative change of resistance.Two equations were proposed and offered an effective but simple approach to analyze the change of number of conductive paths and distance of adjacent conductive particles.In recently years, high-performance wearable strain sensors have attracted great attention in academic and industrial. Herein, a conductive polymer composite of electrospun thermoplastic polyurethane (TPU) fibrous film matrix-embedded carbon black (CB) particles with adjustable scaffold network was fabricated for high-sensitive strain sensor. This work indicated the influence of stereoscopic scaffold network structure built under various rotating speeds of collection device in electrospinning process on the electrical response of TPU/CB strain sensor. This structure makes the sensor exhibit combined characters of high sensitivity under stretching strain (gauge factor of 8962.7 at 155% strain), fast response time (60 ms), outstanding stability and durability (> 10,000 cycles) and a widely workable stretching range (0–160%). This high-performance, wearable, flexible strain sensor has a broad vision of application such as intelligent terminals, electrical skins, voice measurement and human motion monitoring. Moreover, a theoretical approach was used to analyze mechanical property and a model based on tunneling theory was modified to describe the relative change of resistance upon the applied strain. Meanwhile, two equations based from this model were first proposed and offered an effective but simple approach to analyze the change of number of conductive paths and distance of adjacent conductive particles.

Discovery of electrocatalytic materials for high-performance energy conversion and storage applications relies on the adequate characterization of their intrinsic activity, which is currently hindered by the dearth of a protocol for consistent and precise determination of double layer capacitance ( C DL ). Herein, we propose a seven-step method that aims to determine C DL reliably by scan rate-dependent cyclic voltammetry considering aspects that strongly influence the outcome of the analysis, including (a) selection of a suitable measuring window, (b) the uncompensated resistance, (c) optimization of measuring settings, (d) data acquisition, (e) selection of data suitable for analysis, (f) extraction of the desired information, and (g) validation of the results. To illustrate the proposed method, two systems were studied: a resistor–capacitor electric circuit, and a glassy carbon disk in an electrochemical cell. With these studies, it is demonstrated that when any of the mentioned steps of the procedure are neglected, substantial deviations of the results are observed with misestimations as large as 61% in the case of the investigated electrochemical system. Moreover, we propose allometric regression as a more suitable model than linear regression for the determination of C DL for both the ideal and the non-ideal systems investigated. We stress the importance of assessing the accuracy of not only highly specialized electrochemical methods, but also of those that are well-known and commonly used as it is the case of the voltammetric methods. The procedure proposed herein is not limited to the determination of C DL , but can be effectively applied to any other analysis that aims to deliver quantitative results via voltammetric methods, which is crucial for the study of kinetic and diffusion phenomena in electrochemical systems.

Faces in natural images are often occluded by a variety of objects. We propose a fully automated, probabilistic and occlusion-aware 3D morphable face model adaptation framework following an analysis-by-synthesis setup. The key idea is to segment the image into regions explained by separate models. Our framework includes a 3D morphable face model, a prototype-based beard model and a simple model for occlusions and background regions. The segmentation and all the model parameters have to be inferred from the single target image. Face model adaptation and segmentation are solved jointly using an expectation–maximization-like procedure. During the E-step, we update the segmentation and in the M-step the face model parameters are updated. For face model adaptation we apply a stochastic sampling strategy based on the Metropolis–Hastings algorithm. For segmentation, we apply loopy belief propagation for inference in a Markov random field. Illumination estimation is critical for occlusion handling. Our combined segmentation and model adaptation needs a proper initialization of the illumination parameters. We propose a RANSAC-based robust illumination estimation technique. By applying this method to a large face image database we obtain a first empirical distribution of real-world illumination conditions. The obtained empirical distribution is made publicly available and can be used as prior in probabilistic frameworks, for regularization or to synthesize data for deep learning methods.

Social neuroscience aims to describe the neural systems that underpin social cognition and behaviour. Over the past decade, researchers have begun to combine computational models with neuroimaging to link social computations to the brain. Inspired by approaches from reinforcement learning theory, which describes how decisions are driven by the unexpectedness of outcomes, accounts of the neural basis of prosocial learning, observational learning, mentalizing and impression formation have been developed. Here we provide an introduction for researchers who wish to use these models in their studies. We consider both theoretical and practical issues related to their implementation, with a focus on specific examples from the field.

George Gaylord Simpson famously postulated that much of life's diversity originated as adaptive radiations— more or less simultaneous divergences of numerous lines from a single ancestral adaptive type. However, identifying adaptive radiations has proven difficult due to a lack of broad-scale comparative datasets. Here, we use phylogenetic comparative data on body size and shape in a diversity of animal clades to test a key model of adaptive radiation, in which initially rapid morphological evolution is followed by relative stasis. We compared the fit of this model to both single selective peak and random walk models. We found little support for the early-burst model of adaptive radiation, whereas both other models, particularly that of selective peaks, were commonly supported. In addition, we found that the net rate of morphological evolution varied inversely with clade age. The youngest clades appear to evolve most rapidly because long-term change typically does not attain the amount of divergence predicted from rates measured over short time scales. Across our entire analysis, the dominant pattern was one of constraints shaping evolution continually through time rather than rapid evolution followed by stasis. We suggest that the classical model of adaptive radiation, where morphological evolution is initially rapid and slows through time, may be rare in comparative data.