Search

Alzheimer’s disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals, such as electroencephalography (EEG) recordings, into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the reconstruction of a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases inδ-θcoupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.

In food industries, the usage of eggs in different products plays a very important role. The quality of eggs consists of various aspects of quality, each of which can be related to the quality of interior eggs or the quality of external eggs. Chitin's crisp egg-shell does not bear the collision and extrusion, and is very easy to damage. The external microorganism will reach the shell easily through the crack if the egg-shell has been split, so the decaying of the egg is accelerated or the consistency is decreased. The parameters such as color, thickness, shell strength, eggshell crack, bloodspots, porosity, air cell depth, shell integrity and freshness can also be used to detect the contaminated egg. But the weight loss and change in temperature gives us the variation in quality of the egg gradually and accurately. A simple method is proposed in which the key parameters like weight and temperature is measured using suitable sensor. The analog output from the sensor is fed to the Arduino board as an input signal for further processing. Based on the measured parameters value the quality of the egg and its expiring day will be displayed for consumer.

There is a direct link between the extent of formation damage and the filtration volume of the drilling fluids in hydrocarbon reservoirs. The filtration volume could be diminished by adding different additives to the drilling fluids. Recently, nanoparticles have been extensively used for enhancing the filtration characteristics of the drilling fluids. However, there is no reliable model for investigating the influence of this class of additives on the performance of drilling fluids. Hence in this study, two powerful tools ELM (Extreme Learning Machine) and PSO-LSSVM (Particle Swarm Optimization-Least Square Support Vector Machine) are applied to determine the effect of various nanoparticles on the filtration volume. The assessment of the models is carried out by computing the statistical parameters and it was found that ELM has a greater ability to predict the filtration volumes, while PSO-LSSVM performs satisfactorily too. The model predictions and experimental results are in excellent agreement as suggested by the values of root mean squared error (RMSE = 0.2459), coefficient of determination (R2 = 0.999), and mean relative error (MRE = 2.028%) for the dataset. The statistical analysis showed that the suggested model can predict the filtration volume with great accuracy. Moreover, through sensitivity analysis of the input parameters, it was found that for a specified nanoparticle, the filtration volume is highly influenced by nanoparticle concentration and it is the essential variable for the optimization process.

Many engineering products have more than one failure mode and the evolution of each mode can be monitored by measuring a performance characteristic (PC). It is found that the underlying multi-dimensional degradation often occurs with inherent process stochasticity and heterogeneity across units, as well as dependency among PCs. To accommodate these features, in this paper, we propose a novel multivariate degradation model based on the inverse Gaussian process. The model incorporates random effects that are subject to a multivariate normal distribution to capture both the unit-wise variability and the PC-wise dependence. Built upon this structure, we obtain some mathematically tractable properties such as the joint and conditional distribution functions, which subsequently facilitate the future degradation prediction and lifetime estimation. An expectation-maximization algorithm is developed to infer the model parameters along with the validation tools for model checking. In addition, two simulation studies are performed to assess the performance of the inference method and to evaluate the effect of model misspecification. Finally, the application of the proposed methodology is demonstrated by two illustrative examples.

Vacuum gas oil (VGO) is the most important feedstock for hydrocracking processes in refineries, but its molecular composition cannot be fully acquired by current analysis techniques owing to its complexity. In order to build an accurate and reliable molecular-level kinetic model for reactor design and process optimization, the molecular composition of VGO has to be reconstructed based on limited measurements. In this study, a modified stochastic reconstruction-entropy maximization (SR-REM) algorithm was applied to reconstruct VGOs, with generation of a general molecule library once and for all via the SR method at the first step and adjustment of the molecular abundance of various VGOs via the REM method at the second step. The universality of the molecule library and the effectiveness of the modified SR-REM method were validated by fifteen VGOs (three from the literature) from different geographic regions of the world and with different properties. The simulated properties (density, elemental composition, paraffin-naphthene-aromatics distribution, boiling point distribution, detailed composition of naphthenes and aromatics in terms of ring number as well as composition of S-heterocycles) are in good agreement with the measured counterparts, showing average absolute relative errors of below 10% for each property.

Symbiotic bacteria often navigate complex environments before colonizing privileged sites in their host organism. Chemical gradients are known to facilitate directional taxis of these bacteria, guiding them towards their eventual destination. However, less is known about the role of physical features in shaping the path the bacteria take and defining how they traverse a given space. The flagellated marine bacteriumVibrio fischeri,which forms a binary symbiosis with the Hawaiian bobtail squid,Euprymna scolopes, must navigate tight physical confinement, squeezing through a bottleneck constricting to ~2 μm in width on the way to its eventual home. Using microfluidicin vitroexperiments, we discovered thatV. fischericells alter their behavior upon entry into confined space, straightening their swimming paths and promoting escape from confinement. Using a computational model, we attributed this escape response to two factors: reduced directional fluctuation and a refractory period between reversals. Additional experiments in asymmetric capillary tubes confirmed thatV. fischeriquickly escape from tapered ends, even when drawn into the ends by chemoattraction. This avoidance was apparent down to a limit of confinement approaching the diameter of the cell itself, resulting in a balance between chemoattraction and evasion of physical confinement. Our findings demonstrate that non-trivial distributions of swimming bacteria can emerge from simple physical gradients in the level of confinement. Tight spaces may serve as an additional, crucial cue for bacteria while they navigate complex environments to enter specific habitats.Significance StatementSymbiotic bacteria that navigate to and through specific host tissues often face tight physical confinement. This work reveals that confinement-associated changes in swimming can dramatically alter taxis, shaping bacterial localization in conjuncture with other motility-directing cues. This work helps explain how bacteria can avoid getting stuck in confined areas while transiting to privileged spaces, adding confinement as an environmental cue that symbiotic bacteria use to shape their motility behavior.

N2-methylguanosine is a post-transcriptional modification of RNA that is found in eukaryotes and archaea. The biological function of m2G modification discovered so far is to control and stabilize the three-dimensional structure of tRNA and the dynamic barrier of reverse transcription. To discover additional biological functions of m2G, it is necessary to develop time-saving and labor-saving calculation tools to identify m2G. In this paper, based on hybrid features and a random forest, a novel predictor, RFhy-m2G, was developed to identify the m2G modification sites for three species. The hybrid feature used by the predictor is used to fuse the three features of ENAC, PseDNC, and NPPS. These three features include primary sequence derivation properties, physicochemical properties, and position-specific properties. Since there are redundant features in hybrid features, MRMD2.0 is used for optimal feature selection. Through feature analysis, it is found that the optimal hybrid features obtained still contain three kinds of properties, and the hybrid features can more accurately identify m2G modification sites and improve prediction performance. Based on five-fold cross-validation and independent testing to evaluate the prediction model, the accuracies obtained were 0.9982 and 0.9417, respectively. The robustness of the predictor is demonstrated by comparisons with other predictors.

Enzyme assisted DNA probes are powerful tools in molecular diagnostics for their simplicity, rapidity, and low detection limit. However, cost of probes, difficulty in optimization and disturbance of secondary structure hindered the wider application of enzyme assisted DNA probes. To solve the problems, we designed a new system named shared-probe system. By introducing two unlabeled single stranded DNA named Sh1 and Sh2 as the bridge between probe and the substrate, the same sequence of dually labeled probe with stable performance was shared for different mutations, thus sparing the expense and time cost on designing, synthesizing and optimizing corresponding probes. Besides, the hybridization between Sh1 and the substrate could overcome secondary structures, which guaranteed the detection of different substrates. The performance and generality of the design were tested by low abundance detection in synthetic single DNA samples and the limit of detection was 0.05% for PTENR130Q, EGFR-L858R and 0.02% for BRCA1-NM007294.3. In genomic DNA samples, the limit of detection of 0.1% can be achieved for EGFR-L858R, demonstrating the potential of clinical application in our design.

The cellular response to the complex extracellular microenvironment is highly dynamic in time and type of extracellular matrix. Accurately reconstructing this process and analyzing the changes in receptor conformation on the cell membrane surface and intracellular or intercellular signaling has been a major challenge in analytical chemistry and biophysical methodology. In this paper, a time-coded multi-concentration microfluidic chemical waveform generator was developed for the dynamic signaling probing with single-cell array of high temporal resolution, high throughput, and multi-concentration combination stimulation. Based on innovative microchannel structure, sophisticated external control methods and multiplexing technology, the system not only allowed for temporally sequential permutations of the four concentrations of stimuli (time code), but also generated pulsed and continuous waveforms at different frequencies in a highly controllable manner. Furthermore, the single-cell trap array was set up to efficiently capture cells in suspension, dramatically increasing throughput and reducing experiment preparation time. The maximum frequency of the platform was 1 Hz, and one cell could be stimulated at multiple frequencies. To show the ability of the system to investigate rapid biochemical events in high throughput, pulse stimulation and continuous stimulation of different frequencies and different time codes, combined with four concentrations of histamine (HA), were generated for probing G protein-coupled receptor (GPCR) signaling in HeLa cells. Then, statistical analysis was performed for the mean peak height and mean peak area of the cellular response. We believe that the time-coded multi-concentration microfluidic chemical waveform generator will provide a novel strategy for analytical chemistry, biophysics, cell signaling, and individualized medicine applications.

There are numerous correlations and thermodynamic models for predicting the natural gas hydrate formation condition but still the lack of a simple and unifying general model that addresses a broad ranges of gas mixture is highly felt. This study was aimed to develop a user-friendly universal correlation based on hybrid group method of data handling (GMDH) for prediction of hydrate formation temperature of a wide range of natural gas mixtures including sweet and sour gas. To establish the hybrid GMDH, the total experimental data of 343 were obtained from open articles. The selection of input variables was based on the hydrate structure formed by each gas species. The modeling resulted in a strong algorithm since the squared correlation coefficient (R2) and root mean square error (RMSE) were 0.9721 and 1.2152, respectively. In comparison to some conventional correlation, this model represents not only the outstanding statistical parameters but also its absolute superiority over others. In particular, the result was encouraging for sour gases concentrated at H2S to the extent that the model outstrips all available thermodynamic models and correlations. Leverage statistical approach was applied on datasets to the discovery of the defected and doubtful experimental data and suitability of the model. According to this algorithm, approximately all the data points were in the proper range of the model and the proposed hybrid GMDH model was statistically reliable.

(Bio)pharmaceutical products freeze-dried in vials must meet stringent quality specifications: among these, the residual moisture (RM) is crucial. The most common techniques adopted for measuring the RM are destructive, e.g. Karl Fisher titration, thus few samples from each batch are tested. Being a high intra-batch variability an intrinsic feature of batch freeze-drying, a high number of samples needs to be tested to get a representative measurement. Near-Infrared (NIR) spectroscopy was extensively applied in the past as a non-invasive method to quantify the RM. In this paper, an accurate Partial Least Square (PLS) model was developed and calibrated with a single product, focusing on a small but significative wavelength range of NIR spectra (model SR), characteristic of the water and not of the product. The salient feature of this approach is that the model SR appears to provide fairly accurate estimates with the same product but at a higher concentration, with other excipients and in presence of an amino acid at high concentration, without requiring any additional calibration with KF analysis, as in previous techniques; the irrelevance of the vial shape was also shown. This approach was compared to a simpler one, based on a single-variable linear regression, and to more complex one, using a wider wavelength range or calibrating the PLS model with several products. Model SR definitely ended up as the most accurate, and it appeared to have a great potential as a robust model, suitable also for products that were not involved in the calibration step.

The principle and control for solidification present crucial challenges. Through in situ and real time observation on the solidification of M50 bearing steel, here we discovered that the base of conventional dendritic arms is constituted by a primary semi-solid matrix. Due to the superimposition of temperature fluctuations from the large to the subtle scale, multi-scale dendritic patterns will sequentially emerge and evolve in the primary semi-solid matrix. These findings redefine the essence of multi-scale dendrites as the multi-scale segregation patterns in the primary semi-solid matrix, reconstitute the time and space sequence in the formation of multi-scale dendrites, and reveal the superimposition of temperature fluctuation as the driving principle. These new understandings will fundamentally influence the solidification field. These results also indicate important engineering applications, such as designing multi-scale dendrites, controlling the dendritic segregation and eliminating the detrimental eutectics.

The scale of biological discovery is driven by the vessels in which we can perform assays and analyze results, from multi-well plates to microfluidic compartments. We report on the compatibility of sub-nanoliter single-cell containers or “nanovials” with commercial fluorescence activated cell sorters (FACS). This recent lab on a particle approach utilizes 3D structured microparticles to isolate cells and perform single-cell assays at scale with existing lab equipment. Use of flow cytometry led to detection of fluorescently labeled protein with dynamic ranges spanning 2-3 log and detection limits down to ∼10,000 molecules per nanovial, which was the lowest amount tested. Detection limits were improved compared to fluorescence microscopy measurements using a 20X objective and a cooled CMOS camera. Nanovials with diameters between 35-85 µm could also be sorted with purity from 99-93% on different commercial instruments at throughputs up to 800 events/second. Cell-loaded nanovials were found to have unique forward and side (or back) scatter signatures that enabled gating of cell-containing nanovials using scatter metrics alone. The compatibility of nanovials with widely-available commercial FACS instruments promises to democratize single-cell assays used in discovery of antibodies and cell therapies, by enabling analysis of single cells based on secreted products and leveraging the unmatched analytical capabilities of flow cytometers to sort important clones.

The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling these reactions is difficult when water directly participates in the reaction, since it requires a fully quantum mechanical description of the system. Ab-initio molecular dynamics is the ideal candidate to shed light on these processes. However, its scope is limited by a high computational cost. A popular alternative is to perform molecular dynamics simulations powered by machine learning potentials, trained on an extensive set of quantum mechanical calculations. Doing so reliably for reactive processes is difficult because it requires including very many intermediate and transition state configurations. In this study we used an active learning procedure accelerated by enhanced sampling to harvest such structures and to build a neural-network potential to study the urea decomposition process in water. This allowed us to obtain the free energy profiles of this important reaction in a wide range of temperatures, to discover several novel metastable states, and improve the accuracy of the kinetic rates calculations. Furthermore, we found that the formation of the zwitterionic intermediate has the same probability of occurring via an acidic or a basic pathway, which could be the cause of the insensitivity of reaction rates to the solution pH.

In this work, we investigate deterministic mechanisms that generate and perpetuate flutter and fibrillation in the right atrium of the human heart using mathematical models and high-performance computational simulations. Using a GPU, the solutions of systems of as much as 360 000 cells of 29 variables each were distributed among the available processors to simulate a cell-to-cell electric behavior of heart tissue. Moreover, in a smaller array of 462 cells, we showed that fibrillation, fluttering, and fibrillation-to-fluttering can be modeled even by using the simplest excitable cell models including only a few variables as well as with realistic models of heart cells, simply by implementing aperiodicity and fractality to the connections network. Micro-reentries which may be identified with fibrillation of the systems can be simulated in silico experiments where emerge several self-perpetuating, colliding waves associated with non-periodic and mathematically calculated pseudo-EG.

An extensive data set has been developed and used to further the progress of a model-informed design of controlled drug release. An improved drug-release model with mechanistic modeling of hydrolytic polymer degradation is used and validated by comparing model predictions to in vitro experiments. Combining parameter estimates from the literature with model fits to the data set, this study can aid in achieving a priori design of controlled drug release from a model PLGA release system. A systematic series of model release systems were formulated with FITC-labeled dextran, as a surrogate for biopharmaceuticals, in PLGA rods over a broad range of compositions. While general comparisons between the model and experiments were favorable, important discrepancies were identified for several formulations with significant first-phase drug release. Supported by cross-sectional fluorescence microscopy images of the FITC-dextran distribution within the rods, this first-phase release was attributed to a combination of two main factors: (1) percolation of the drug particles and (2) swelling of and pore formation in the rods due to water uptake. These observations indicate the importance of careful selection of the PLGA polymer grade when designing drug release systems but also reflect a need for better understanding of phenomena such as pore formation. Adapting model parameters, without modifying the physical processes included in the model, enabled accurate fitting of the experimental data for all formulations, highlighting the applicability of the model.

Liquid-liquid phase separation drives the formation of biological condensates that play essential roles in transcriptional regulation and signal sensing. Computational modeling could provide high-resolution structural characterizations of these condensates and help uncover physicochemical interactions that dictate their stability. However, many protein molecules involved in phase separation often contain multiple ordered domains connected with flexible, structureless linkers. Simulating such proteins necessitates force fields with consistent accuracy for both folded and disordered proteins. We provide a critical review of existing coarse-grained force fields for disordered proteins and highlight the challenges in their application to folded proteins. After discussing existing algorithms for force field parameterization, we propose an optimization strategy that should lead to computer models with improved transferability across protein types.

We explore the use of deep artificial neural networks (DNNs) to impute data to the experimental data reported by the methane hydrate crystal growth at a bubble surface reported in the literature (Ma, et al., 2002; Sun et al., 2007). We use a genetic programming symbolic regressor to propose a novel empirical rate equation as a function of the temperature and the pressure from the new data set.

19 p.-8 fig.-1 tab., Long-duration space missions will need to rely on the use of plants in bio-regenerative life support systems (BLSSs) because these systems can produce fresh food and oxygen, reduce carbon dioxide levels, recycle metabolic waste, and purify water. In this scenario, the need for new experiments on the effects of altered gravity conditions on plant biological processes is increasing, and significant efforts should be devoted to new ideas aimed at increasing the scientific output and lowering the experimental costs. Here, we report the design of an easy-to-produce and inexpensive device conceived to analyze the effect of interaction between gravity and light on root tropisms. Each unit consisted of a polystyrene multi-slot rack with light-emitting diodes (LEDs), capable of holding Petri dishes and assembled with a particular filter-paper folding. The device was successfully used for the ROOTROPS (for root tropisms) experiment performed in the Large Diameter Centrifuge (LDC) and Random Positioning Machine (RPM) at ESA's European Space Research and Technology centre (ESTEC). During the experiments, four light treatments and six gravity conditions were factorially combined to study their effects on root orientation of Brassica oleracea seedlings. Light treatments (red, blue, and white) and a dark condition were tested under four hypergravity levels (20 g, 15 g, 10 g, 5 g), a 1 g control, and a simulated microgravity (RPM) condition. Results of validation tests showed that after 24 h, the assembled system remained unaltered, no slipping or displacement of seedlings occurred at any hypergravity treatment or on the RPM, and seedlings exhibited robust growth. Overall, the device was effective and reliable in achieving scientific goals, suggesting that it can be used for ground-based research on phototropism-gravitropism interactions. Moreover, the concepts developed can be further expanded for use in future spaceflight experiments with plants., This work was supported by the ESA-HRE CORA contract #4000127705/19/NL/PG/pt. Financial support was also provided by ESA-TEC contract #4000116223/16/NL/KML/hh to Jack J.W.A. van Loon, by Spanish Agencia Estatal de Investigación (grant RTI2018–099309-B-I00, co-funded by EU-ERDF) to F. Javier Medina, and by NASA (grant 80NSSC17K0546) to John Z. Kiss.

Sterilizing filtration is a common unit operation for the manufacture of parenteral drug products. However, filter performance can be impacted by properties of both the membrane material and the solution being filtered, requiring extensive multi-factor studies to optimize the filtration process for a given drug product. Here, we report the use of a modified bundle of capillaries approximation to predict filter performance. The model is directly applicable for both Newtonian and non-Newtonian solutions and does not require assumptions of steady state. Using a hydrophilic polyvinylidene difluoride (PVDF) filter as a test case, we show that the model fitting parameters align with expected values and both flux and shear are well predicted. Moreover, two case studies are presented to demonstrate the model's utility for filtration process optimization: 1) protein adsorption of an antibody formulation and 2) filter fouling of a 1% (w/v) carboxymethylcellulose (CMC) solution. In both cases, the model was able to accurately identify optimal filtration parameters to reduce the amount of adsorption or improve the filter capacity, respectively. This methodology can be easily extended to alternate filter types and provides an additional predictive tool to speed process development and minimize trial and error during filtration process design.

Flux balance analysis (FBA) and associated techniques operating on stoichiometric genome-scale metabolic models play a central role in quantifying metabolic flows and constraining feasible phenotypes. At the heart of these methods lie two important assumptions: (i) the biomass precursors and energy requirements neither change in response to growth conditions nor environmental/genetic perturbations, and (ii) metabolite production and consumption rates are equal at all times (i.e., steady-state). Despite the stringency of these two assumptions, FBA has been shown to be surprisingly robust at predicting cellular phenotypes. In this paper, we formally assess the impact of these two assumptions on FBA results by quantifying how uncertainty in biomass reaction coefficients, and departures from steady-state due to temporal fluctuations could propagate to FBA results. In the first case, conditional sampling of parameter space is required to re-weigh the biomass reaction so as the molecular weight remains equal to 1 g/mmol, and in the second case, metabolite (and elemental) pool conservation must be imposed under temporally varying conditions. Results confirm the importance of enforcing the aforementioned constraints and explain the robustness of FBA biomass yield predictions.

Microorganisms can be defined as a small living forms of life, even can't be viewable with the naked eyes. The three types of microorganisms named Bacteria, Yeasts and Molds in which almost always there may be confusion with their functions, but they are just as real and alive as we are. They eat, grow, reproduce and die also. The tumour cells has also ability to reproduce themselves, to grow in numbers and die to reduce their density in some specific region. In this work we try show the growth for both tumour cells and microorganism with the help of mathematical model and also try to compare the numerical results with the help of Python. This model consists of a system of partial differential equations incorporated with different factors like migrating cells, proliferating cells and nutrient which effects the growth most. At last the results shows the variation of different classes of cell densities with respect to growth function and as well as with the total cell density.

Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, lack of fundamental knowledge of skin adaptation to mechanical cues, and lack of predictive computational models limit the broader adoption and efficacy of tissue expansion. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by our porcine animal model. Here we use the data from a new set of experiments to calibrate the computational model within a Bayesian framework. Four 10 × 10 cm 2 patches were tattooed in the dorsal skin of four 12 weeks-old minipigs and a total of six patches underwent successful tissue expander placement and inflation to 60cc for expansion times ranging from 1 h to 7 days. Six patches that did not have expanders implanted served as controls for the analysis. We find that growth can be explained based on the elastic deformation. The predicted area growth rate is k ∈ [ 0.02 , 0.08 ] [ h − 1 ]. Growth is anisotropic and reflects the anisotropic mechanical behavior of porcine dorsal skin. The rostral-caudal axis shows greater deformation than the transverse axis, and the time scale of growth in the rostral-caudal direction is given by rate parameters k 1 ∈ [ 0.04 , 0.1 ] [ h − 1 ] compared to k 2 ∈ [ 0.01 , 0.05 ] [ h − 1 ] in the transverse direction. Moreover, the calibration results underscore the high variability in biological systems, and the need to create probabilistic computational models to predict tissue adaptation in realistic settings. Statement of significance Tissue expansion is a widely used technique in reconstructive surgery because it triggers growth of skin for the correction of large skin lesions and for breast reconstruction after mastectomy. Despite of its potential, complications and undesired outcomes persist due to our incomplete understanding of skin mechanobiology. Here we quantify the deformation and growth fields induced by an expander over 7 days in a porcine animal model and use these data to calibrate a computational model of skin growth using finite element simulations and a Bayesian framework. The calibrated model is a leap forward in our understanding skin growth, we now have quantitative understanding of this process: area growth is anisotropic and it is proportional to stretch with a characteristic rate constant of k ∈ [ 0.02 , 0.08 ] [ h − 1 ].

Dissolution testing is widely used to measure the rate of drug release and predict its in-vivo behavior. The release rate can be controlled by adjusting the particle size distribution (PSD). However, experimental investigation of various particle sizes requires many time-consuming experiments. To reduce the need for them, we propose an optimization framework to solve the inverse problem, i.e., design a PSD that results in a prescribed dissolution profile. The framework's computational core predicts a dissolution profile using a population balance model coupled with a mass balance equation, while the optimization algorithm obtains the inverse solution. The model was validated using mono- and multimodal particle populations of a reference compound (KCl). The validation resulted in a good agreement between the simulated and experimental data. This suggests that the usage of the framework can provide a fast determination of the required PSD, reducing the number of experiments needed.

Aim of the study was to predict quality parameters namely protein and carbohydrate content of wheat grain stored for one year at 4 °C and 21 °C using Near Infrared spectroscopy (NIRS) and Chemometrics. The spectra of grains were measured in reflectance mode using lab made filter based pre dispersive NIRS ranging from wavelength 750 nm to 2580 nm. NIR Wavelengths were divided into two sets for all the stored samples depending on its correlation with factors. The reference data and NIR data were analysed by principal component analysis (PCA) scores, partial last squared regression (PLSR) model, Multiple Regression Analysis (MLR) and Support Vector Machine Regression (SVMR). First four factors were sufficient to develop regression models for the above mentioned methods. Prediction performance was compared on the basis of the coefficient of correlation (R2) and Root Mean Square Error (RMSE) for the calibration and validation sets. R2 values for prediction of protein content from PLSR model were 0.955, 0.997 for 4 °C Wavelength Set I and II and 0.903, 0.989 for 21 °C Wavelength Set I and II. For the prediction of carbohydrate content 0.978, 0.951 and 0.946, 0.974 were the R2 values for the 4 °C and 21 °C wheat samples Set I and Set II respectively. MLR model also gave R2 values greater than 0.9 for all the samples. Wavelengths with high β correlation coefficients were defined. SVMR predicted the protein content and carbohydrate content with good accuracy. SVMR and PLS results. All the models developed showed good accuracy for successful prediction of protein and carbohydrate content of stored wheat grains with PLSR and SVMR giving the best results. This study showed that near infrared spectroscopy has potential to distinguish wheat grains stored under different conditions.

Carbonate radical is among the most important environmental relevant reactive species which govern the transformation and fate of pharmaceutical contaminants (PCs). However, reaction rate constants between carbonate radical and most of the PCs have not been experimentally determined, and quantitative structural-activity relationships (QSARs) have not been established for rate estimation. This study applied MaxMin data processing method and used molecular fingerprints (MF) as the input of a deep neural network (DNN) to predict the rate constants between carbonate radical and organic compounds. MF parameters and the hyper-structure of the DNN were adjusted to yield satisfactory accuracy of rate prediction. The vector length of 512 bits with radius of 1 for MF and 5 hidden layers gave the best performance. The optimized MaxMin-MF-DNN model was compared with some of the most commonly used QSARs and machine learning methods, including random data splitting, molecular descriptors, supporting vector machine, decision tree, etc. Results showed that the MF-DNN model out-performed the other methods by more than 10% increase in prediction accuracy. Applying this MF-DNN model, we estimated reaction rates between carbonate radical and pharmaceuticals used in human medicine (1576) and veterinary practice (390). Among them, 46 drugs were identified as fast-reacting compounds, suggesting the important relations of their environmental fate with carbonate radical.

The advent of digital-twin manufacturing in additive manufacturing (AM) is to integrate the physical world of real-time 3D printing with the digital world of a simulated print. This paper contributes to digital-twin manufacturing in laser-based additive manufacturing by combining melt pools’ simulated thermal behavior via finite element analysis (FEA) and melt pools’ empirical thermal behavior via pyrometry-based sensors. Studying the thermal behavior of melt pools based on heat transfer characteristics determines melt pools’ porosity and part quality. FEA uses Godak’s moving heat flux to capture the melt pools’ physically bound temperature profile in three dimensions. Simulated data helps to mitigate the influence of measuring errors from real-world data and provides non-observable data such as gradient changes of thermal behavior at the curvature of the 3D melt pool. The pyrometer captures empirical temperature behavior, including uncertainty and randomness introduced to the process. A significant knowledge gap exists when predicting melt pool porosity accurately with theoretical FEA and empirical in situ evidence alone. The gap is bridged by combining the data sources, specifically, feature engineering via functional principal component analysis (empirical data source) and capturing the melt pool's 3-D temperature shape profile via FEA (simulated data source). A hybrid model predicts melt pool porosity by capturing the strengths of prior simulated and posterior in situ empirical data by matching simulated melt pools to real-world empirical melt pools. Moreover, comparing predicted porosity labels with true porosity labels of Ti–6Al–4V thin-wall structure from laser metal deposition verified the proposed interpretable and robust supervised-learning model's validity. This methodology can apply to other materials and part shapes printed under various additive-manufactured printers.

In this study, the macro-structure of quartz flocs was assessed from X-ray computed tomography images. An image segmentation workflow was established to identify the floc sub-units that comprised each macro-floc, which permitted the macro-structure to be defined in terms of the number, size, and connectivity of the sub-units. A simplified structural model of non-penetrating spheres was used to understand the scaling relationships of the macro-floc properties. It was found that although sub-unit size varied widely, there was a limiting volume (2.8 × 106μm3), above which floc growth occurs only by sub-unit aggregation. This approach permits the quantification of macro-floc structure and composition in 3D on the scale of tens of μm to the mm scale. Knowledge of macro-floc structure allows a deeper understanding of the role of macro-floc breakage and re-arrangement in subsequent dewatering treatments such as thickening or filtration.

Much of the in-silico research in the world has been directed towards rational drug design, environmental risk assessment, toxicity and the forecasting of the characteristics of chemical and pharmaceutical products. The aim of this study was conceived to suggest a 2D-QSAR study of a new series of 26 quinazoline derivatives, acting as antitumor agents by the DFT-B3LYP method with the 6 31G base set. The starting point for such methods is built on the definition of chemical descriptors that accurately reflect the most precise details of molecular structures imported from an experimental reference database. In order to analyze structural data and detect the determining factors for anticancer activity, different types of tools can be used: multiple linear (MLR) and non-linear (MNLR) regressions, principal component regression PCA and partial least square (PLS) regression. The 3 models MLR, MNLR and PLS were developed and validated on a training set of 20 molecules, their robustness is estimated by internal validation methods and their predictive power was assessed by external validation from a set of 6 molecules with predicted correlation coefficients Rtest of 0.93,0.94 and 0.94 respectively. In this work,4 relevant descriptors that can influence the anti-cancer activity, specifically the lowest unoccupied molecular orbital energy, dipole moment, surface tension and the number of H-bond donors. Therefore, on the basis of these descriptors, new molecules belonging to the same structures have been theoretically designed. This work also consists in studying the inhibition of tyrosine kinase activity over expressed by quinazoline derivatives with molecular docking which is one of the molecular modeling methods.

Surface morphology of the components is a key factor that influences the quality and performance of precision systems. The influence of different morphologies on the contact performance of mating surfaces may be studied by simulating a real rough surface and calculating the contact performance. First, the micro-geometric characteristics of the surfaces of the machined parts are measured by a 3D surface profilometer (NV5000 5022S), and the distribution law of the surface micro-geometric characteristics is obtained by statistical and characterization methods. Second, the rough surface conforming to this law is simulated by a non-Gaussian mathematical algorithm, and the contact model between the mating parts with micro-geometric characteristics of the simulated surface is reconstructed using reverse engineering. Finally, the mechanical properties of the mating surfaces are obtained using a numerical algorithm based on Yoshimura's integral method and tests. The comparison of mechanical properties obtained from numerical analysis and test shows that the maximum errors in the contact stiffness obtained by the two methods are 9.4% (grinding surface) and 11.5% (milling surface), respectively. Therefore, it is feasible to use a non-Gaussian simulation method and Yoshimura's integral method to realize an effective simulation of the micro-geometric characteristics of the surface and the analysis of the contact performance of the mating surface respectively. The results obtained from this study is thus important as a reference for the analysis of the surface morphology and mechanical properties between the matching parts.

This paper proposes a new continuous two phase bioreactor (C-TPPB) configuration for the removal and degradation of xenobiotic compounds from wastewaters. The reactor is made of three principal parts: a tube side in which the wastewater flows; a polymeric membrane which constitutes the tube walls; a shell side, set as a continuous stirred tank reactor (CSTR), in which the xenobiotics are degraded by means of a bacterial population. A mathematical model for the system description was developed and validated by means of fitting using literature experimental data obtained from a similar configuration. Since the nature of the fitting parameters did not depend upon the particular set-up, such validation procedure was possible. A dimensionless analysis was also carried out by defining a modified Stenton (St’) and Damkohler (Da’) number in order to detect the C-TTPB design criteria. The removal of 4-NtiroPhenol was reported as a case study and an algorithm for a preliminary design procedure was developed.

The particle size distribution (PSD) and particle morphology of metal powders undoubtedly affects the quality of parts produced by additive manufacturing (AM). It is, therefore, crucial to accurately know the PSD and morphology of these powders. There exist several measurement techniques for these quantities, but since each method is based on different physical phenomena, which are sensitive to different aspects of a particle's shape and size, it is unclear how the measured PSDs and morphology compare to one another. In this study, five different techniques are used: sieve analysis, dynamic imaging analysis, laser diffraction analysis, X-ray computed tomography (XCT), and scanning electron microscopy. The first three are commonly used in the powder metallurgy field while the last two are laboratory-based tools capable of providing robust size and shape data. Nominally identical samples of stainless-steel powders were produced via riffling, and each technique was employed to measure effectively the same PSD and in some cases the morphology. In this paper, the differences among these measurement techniques are explored by a comparison of the measured results. Besides the random variations of the various measurement processes, the difference in the results is partly due to the fact that the particles are not perfectly spherical and that there are many multi-particles present. Each of these affect the principle of each method differently. Three-dimensional particle morphology and size data collected via XCT is used to provide insight regarding the discrepancies among other sizing and morphology measurement techniques. (Official contribution of the National Institute of Standards and Technology; not subject to copyright in the United States.)

Graphical abstract, Environmental structure describes physical structure that can determine heterogenous spatial distribution of biotic and abiotic (nutrients, stressors etc.) components of a microorganism’s microenvironment. This study investigated the impact of micrometre-scale structure on microbial stress sensing, using yeast cells exposed to copper in microfluidic devices comprising either complex soil-like architectures or simplified environmental structures. In the soil micromodels, the responses of individual cells to inflowing medium supplemented with high copper (using cells expressing a copper-responsive pCUP1-reporter fusion) could be described neither by spatial metrics developed to quantify proximity to environmental structures and surrounding space, nor by computational modelling of fluid flow in the systems. In contrast, the proximities of cells to structures did correlate with their responses to elevated copper in microfluidic chambers that contained simplified environmental structure. Here, cells within more open spaces showed the stronger responses to the copper-supplemented inflow. These insights highlight not only the importance of structure for microbial responses to their chemical environment, but also how predictive modelling of these interactions can depend on complexity of the system, even when deploying controlled laboratory conditions and microfluidics.

Model-driven design approaches have great potential to improve current engineering workflows for wet granulation and other particulate processes. The key to model-driven design is a predictive process model. In this paper, a novel predictive model is proposed for high-shear wet granulation using a one-dimensional population balance modelling framework. The wet granulation mechanisms are represented by rate expressions which are based on mechanistic understanding. Material characterisation tests and granulation experiments are designed to verify critical modelling assumptions and determine the modelling parameters. Based on the Sobol’ indices results from a parameter sensitivity analysis, the impactful parameters to estimate are identified: critical pore saturation, and coefficients for consolidation, collision and breakage. Only impactful parameters that cannot be measured are estimated to reduce the experimental effort and improve the model's predictive power. Lab-scale experiments are designed to estimate parameters individually before fine-tuning the results. The model is assessed using a novel model validation workflow, which is based on predictions of experiments at four different scales from lab scale to pilot plant: 2 L to 70 L.

A century after the first attempts of Wentworth to characterise the shape of cobbles, our understanding of particle morphology is still expanding. A plethora of shape indices has been proposed in the literature to characterise the morphology of individual particles. This study aims to shed light on the merits and limitations of the indices currently used to characterise particle elongation, flatness and compactness, adopting a unified classification framework. Second, new indices are proposed to address the identified shortcomings. Third, a new particle classification system derived from the proposed indices is illustrated. It is shown the new system overcomes the misclassification of a range of particles that are incorrectly classified as bladed in the Zingg system.

In order to achieve quality control of nickel-based superalloy powders, it is necessary to quantitatively characterize the particle shape. This paper introduces how the image processing techniques were used to quantitatively evaluate the particle shape and its distribution. Firstly, the appropriate edge extraction operator was determined by multiple comparisons of processing effect of six edge extraction operators. It was found that the morphological processing technology, being similar to the watershed algorithm, demonstrated strong applicability for edge extraction. Secondly, eight shape descriptors were calculated and verified by standard graphics to ensure the accuracy of image processing algorithms. By monitoring single particle, it was found that the image processing algorithms can accurately distinguish the spherical particle from the satellite particle, and the shape descriptors change in an obvious manner. Finally, the probability density distributions of eight shape descriptors were demonstrated and compared. The results show that the image processing techniques can effectively characterize the particle shape and its distribution, which provides a methodological basis for the characterization of superalloy powders and theoretical guidance for the adjustment of atomization process. By mathematically fitting, a relationship was found between the mean shape descriptors and the angle of repose (AOR). Therefore, the method can be used to obtain the average value of shape descriptors, and then establish the relationship between the average shape descriptors and powder flowability, which can predict the process performance of powder and the performance during 3D printing.

The interaction of proteins and peptides with inorganic surfaces is relevant in a wide array of technological applications. A rational approach to design peptides for specific surfaces would build on amino-acid and surface specific interaction models, which are difficult to characterize experimentally or by modeling. Even with such a model at hand, the large number of possible sequences and the large conformation space of peptides make comparative simulations challenging. Here we present a computational protocol, the effective implicit surface model (EISM), for efficient in silico evaluation of the binding affinity trends of peptides on parameterized surface, with a specific application to the widely studied gold surface. In EISM the peptide surface interactions are modeled with an amino-acid and surface specific implicit solvent model, which permits rapid exploration of the peptide conformational degrees of freedom. We demonstrate the parametrization of the model and compare the results with all-atom simulations and experimental results for specific peptides.

The characterization of multidimensional particle property distributions through computed tomography requires an adapted sample preparation strategy. This strategy should both generate as many spatially separated particles as possible in the smallest achievable volumes and also enable mechanically and vacuum-stable samples that are suitable for correlative measurement, for example with high-energy ion beam methods. In the present study an epoxy-based method is presented that minimizes the negative influence of particle sedimentation by adding very low X-ray absorbing graphite nanoparticles as spacer. A machine learning-based method is presented to discretize the particle system. Results are compared with data from 2D SEM validation measurements and data of a previous study.

Theoretical models for food drying commonly utilise an effective diffusivity solved through curve fitting based on experimental data. This creates models with limited predictive capabilities. Multiscale modeling is one approach which can help transition to a more physics-based model minimizing the empirical information required while improving a model's predictive capabilities. However, to enable an accurate scaling operation, multiscale models require diffusivity at a fine scale (microscale). Measuring these properties is experimentally costly and time consuming as they are often temperature and/or moisture dependent. This research conducts an inverse analysis on a multiscale homogenization food drying model to deduce the temporal diffusivity of intracellular water. A representation of the real cellular water breakdown was considered and appropriate assumptions to represent its cellular heterogeneity, in relation to time, were investigated. The work uncovered that a linear decrease in intracellular water content could be assumed and thus a function for its diffusivity was developed. The proposed function is in terms of sample temperature and intracellular water content opening the possibilities to be applied to various food materials.

Molecular sequences are shaped by selection, where the strength of selection relative to drift is determined by effective population size ( N e ). Populations with high N e are expected to undergo stronger purifying selection, and consequently to show a lower substitution rate for selected mutations relative to the substitution rate for neutral mutations ( ω ). However, computational models based on biophysics of protein stability have suggested that ω can also be independent of N e . Together, the response of ω to changes in N e depends on the specific mapping from sequence to fitness. Importantly, an increase in protein expression level has been found empirically to result in decrease of ω , an observation predicted by theoretical models assuming selection for protein stability. Here, we derive a theoretical approximation for the response of ω to changes in N e and expression level, under an explicit genotype-phenotype-fitness map. The method is generally valid for additive traits and log-concave fitness functions. We applied these results to protein undergoing selection for their conformational stability and corroborate out findings with simulations under more complex models. We predict a weak response of ω to changes in either N e or expression level, which are interchangeable. Based on empirical data, we propose that fitness based on the conformational stability may not be a sufficient mechanism to explain the empirically observed variation in ω across species. Other aspects of protein biophysics might be explored, such as protein–protein interactions, which can lead to a stronger response of ω to changes in N e .

This work focuses on a new mathematical model able to describe in a simple manner the intestinal physiology, in order to better study drug absorption and bioavailability. The aim of our model is to overcome the limitations of physiological pharmacokinetics models of the literature, introducing a different modelling approach. The core of the new proposed model is a Discrete-Continuous Approach (DCA): a sequence of boluses travels in the investigated portion of the intestine, in counter-current with blood that flows in continuous mode. No empirical equations are implemented in this model. Simulation results show an excellent correlation between the predicted and experimental concentration profile used to validate our model. Our new approach provides a simple tool, with a good reliability, to analyze a very complex phenomenon, using only few parameters.

Apple is one of the most widely consumed fresh fruit in the world. Simulation of kinetics and physical aspects of fruit could be considered as an emerging technology in most of harvest and postharvest phases. Impact damage of fruit is a common phenomenon during handling and transportation, therefore, in this work, experimental and computational analysis of the free fall of apples was conducted. Discrete element method (DEM) simulations of a free fall drop test were performed for four different apple-shaped models. DEM simulations can lead to promising new insights in phenomena involved in particulate processes and discontinuous systems, in which the shape model used plays a key role. This work demonstrated that shape modelling in DEM simulation could be a potentially helpful tool for analysing collisions, and the behaviour and dynamics of particle–particle (apple–apple) and particle-surface (apple-surface) contacts. The computational time of DEM simulation increases when the complexity of apple shape increased. It was found in this work that an increase in the number of spheres in model apple increased the accuracy of the numerical results until a threshold number of spheres was reached. Exceeding that threshold resulted in a decrease in the accuracy of the model. A model of an apple consisting of 50 spheres in 5 sets predicted the end up locations of apples after free-fall with the least errors. It was therefore considered as the most appropriate method for modelling apples in DEM simulations.

Organ and tissue growth result from an integration of biophysical communication across biological scales, both in time and space. In this review, we highlight new insight into the dynamic connections between control mechanisms operating at different length scales. First, we consider how the dynamics of chemical and electrical signaling in the shape of gradients or waves affect spatiotemporal signal interpretation. Then, we discuss the mechanics underlying dynamic cell behavior during oriented tissue growth, followed by the connections between signaling at the tissue and organismal levels.

In the present paper, we report the effect of nanoparticles in a three-species aquatic food chain model. It is assumed that due to the unavoidable interactions with nanoparticles, the growth rate of basal prey reduces. Fascinating dynamical scenarios in the model are explored in different bi-parametric spaces. We show that the model exhibits coexisting attractors of different kinds (periodic–periodic/periodic–chaotic) in some regions of the bi-parametric space. We also discover well-organized shrimp-shaped periodic structures in the chaotic regime, indicating the coexistence of order (regular pattern) and chaos. Further, a period-bubbling transition to chaos is found near the inner boundaries of the shrimp-shaped structure. It is also observed that managing the depletion rate of the nanoparticles can be used as a control strategy to prevent population extinction in an aquatic food chain system.

In this paper, we study a wide spectrum of dynamical features of a prey–predator model with infection in prey population only. In the model formulation, we consider nonlinear incidence for infection transmission and Holling type II as response function of predator. We analyze the model for local stability and Hopf bifurcation, and the theoretical results are validated and extrapolated by numerical simulations. Also, we perform sensitivity analysis to explore the significance of the crucial parameters influencing the densities of prey and predator populations. Our numerical results show that disease persists in the system even if the basic reproduction number is below unity. The rates of predation on susceptible and infected prey populations are shown to dramatically change the behavior of eco-epidemiological system. We extend our model by considering the fact that the newly infected prey becomes productively infectious after the effective contact between susceptible and infectious preys, and some time lag is involved in this biological process; also the conversion process conceals some time. We analyze our eco-epidemiological model with two time delays (incubation delay and predator’s gestation delay) for stability and bifurcation. From numerical simulations, we observe that both the delay parameters substantially affect the stable configuration of the system.

Finding an accurate method for estimating permeability aside from well logs has been a difficult task for many years. The most commonly used methods targeted towards regression technique to understand the correlation between pore throat radii, porosity and permeability are Winland and Pittman equation approaches. While these methods are very common among petrophysicists, they do not give a good prediction in certain cases. Consequently, this paper investigates the relationship among porosity, permeability, and pore throat radii using three methods such as multiple regression analysis, artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) for application in transition zone permeability modeling. Firstly, a comprehensive mercury injection capillary pressure (MICP) test was conducted using 228 transition zone carbonate core samples from a field located in the Middle-East region. Multiple regression analysis was later performed to estimate the permeability using pore throat and porosity measurement. For the ANN, a two-layer feed-forward neural network with sigmoid hidden neurons and a linear output neuron was used. The technique involves training, validation, and testing of input/output data. However, for the ANFIS method, a hybrid optimization consisting of least-square and backpropagation gradient descent methods with a subtractive clustering technique was used. The ANFIS combines both the artificial neural network and fuzzy logic inference system (FIS) for the training, validation, and testing of input/output data. The results show that the best correlation for the multiple regression technique is achieved for pore throat radii with 35% mercury saturation (R35). However, for both the ANN and ANFIS techniques, pore throat radii with 55% mercury saturation (R55) gives the best result. Both the ANN and ANFIS are later found to be more effective and efficient and thus recommended as compared with the multiple regression technique commonly used in the industry.

Regardless of the bone remodeling type (i.e. targeted or non targeted), disturbances in this process can have serious repercussions in bone diseases’ progression. Therefore, research in this field has been continuous in recent decades, producing clinically relevant results. Regarding non targeted remodeling, distinct computational models were developed to describe the process. However, they only offer a time analysis, lacking mathematical models able to simulate the process in space. Thus, this work combines, for the first time, advanced numerical discretization techniques with non targeted remodeling models. The mathematical formulation used is based on Komarova’s model, correlating the remodeling activity of osteoclasts and osteoblasts with their autocrine and paracrine signaling pathways. For the numerical example, a two-dimensional bone patch was studied, allowing a spatio-temporal analysis of bone mass, cell density of osteoclasts and osteoblasts during an event of bone remodeling. A biological stimulus was applied to trigger remodeling and the obtained bone mass variation is consistent with the applied stimulus. Also, results are supported by the histomorphometric data available in the literature. The numerical methods used in this work are the Finite Element Method (FEM) and the Radial Point Interpolation Method (RPIM). The inclusion of the meshless method RPIM conveys a great advantage for future work since the model will be combined with medical images obtained with CT scans and MRI.

Residual stresses and deformations are one of the main challenges for metal additive manufacturing (AM). The multiscale nature of the metal AM poses certain difficulties for conventional melt pool tracking models to predict the residual stresses and deformations within a reasonable amount of time, thus causing obstacles for further process optimization. The inherent strain method, which incorporates the influence of the process history into the inherent strain and calculates the final stress-strain state in a single mechanical step, is a promising approach to solve the above problems. In the present study, based on a modified inherent strain (MIS) method which is specifically developed for AM, a strategy for considering the variance of the mechanical properties during the AM process (termed as MIS-VM) is proposed. The accuracy of the proposed MIS-VM model is compared with an experiment conducted in literature and a detailed thermo-mechanical model for various process parameters and heat source moving paths. The results show that the mechanical properties are essential for accurate predictions of the stresses and deformations in the MIS method. Compared to the MIS model with constant properties, the accuracy of the MIS-VM model can be improved as large as 10%. The high computational efficiency of the proposed model also suggests it is promising to be incorporated in an optimization method for the topology or process parameters.

Uncertainty about how process factors affect output might lead to waste of resources in laboratory experiments. To address this constraint, a data-driven method might be used to describe the non-linear connection between process parameters and desired output. A Multi-Layer Perceptron-Artificial Neural Network (MLP-ANN) and non-linear response surface method are used to predict hydrogen generation by catalytic steam methane reforming. The impact of training methods (scaled conjugate and gradient descent), hidden layer variation, artificial neuron variation, and activation functions were studied in 80 MLP-ANN combinations (hyperbolic tangent function and sigmoid function). The performance of MLP-ANN models was affected by the training techniques, activation functions, layer count, and number of artificial neurons. The model with the sigmoid function and 3 input layers, 17 artificial neurons in the first layer, 15 artificial neurons in the second layer, and 2 output nodes had the greatest performance among the 40 configurations of scaled conjugate trained ANNs. It projected an 89.55% maximal hydrogen yield with a coefficient of determination (R2) of 0.997 and reduced errors with Mean absolute percentage error (MAPE) and mean squared error (MSE) of 0.199 and 0.121, respectively. Similarly, the gradient descent ANN model with hyperbolic tangent activation function had the greatest performance among the 40 gradient descent trained-ANN configurations. The 3–15–7–2 gradient descent trained ANN model projected a maximum hydrogen output of 89.73% compared to the experimental results of 89.51%. The MLP-ANN models outperformed nonlinear response surface methods, with R2, MAPE, and MSE of 0.231, 0.191, and 0.988, respectively. The updated Garson algorithm indicated that the input parameters impacted the hydrogen production in the sequence reaction temperature>methane partial pressure>steam partial pressure. The sensitivity analysis might assist identify how resources should be spent.