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We introduce a general framework for regression in the errors-in-variables regime, allowing for full flexibility about the dimensionality of the data, observational error probability density types, the (nonlinear) model type and the avoidance of ad-hoc definitions of loss functions. In this framework, we introduce model fitting for partially unpaired data, i.e., for given data groups the pairing information of input and output is lost (semi-supervised). This is achieved by constructing mixture model densities, which directly model the loss of pairing information allowing inference. In a numerical simulation study linear and nonlinear model fits are illustrated as well as a real data study is presented based on life expectancy data from the world bank utilizing a multiple linear regression model. These results show that high quality model fitting is possible with partially unpaired data, which opens the possibility for new applications with unfortunate or deliberate loss of pairing information in data. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chinese Institute of Food Science & Technology / Zhongguo Shipin Xuebao is the property of Journal of Chinese Institute of Food Science & Technology Periodical Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by K. xylinus in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for Acetobacter or Gluconacetobacter were fitted to published data and compared using the Akaike Information Criterion (AIC). A stepwise fitting procedure was proposed for model selection to reduce computation effort, including a first calibration in which only the biomass and substrate were simulated, a selection of the three most effective models in terms of AIC, and a calibration of the three selected models with the simulation of biomass, substrate, and product. Also, an uncoupled product equation involving a modified Monod substrate function is proposed for a 500 rpm agitation rate, leading to an improved prediction of BC productivity. The M2c and M1c models were the most efficient for biomass growth and substrate consumption for the combined AIC, under 700 rpm and 500 rpm agitation rates, respectively. The average coefficients of determination for biomass, substrate, and product predictions were 0.981, 0.994, and 0.946 for the 700 rpm agitation rate, and 0.984, 0.991, and 0.847 for the 500 rpm agitation rate. It is shown that the prediction of BC productivity is improved through the proposed substrate function, whereas the computation effort is reduced through the proposed model fitting procedure. [ABSTRACT FROM AUTHOR]

Abstract This article addresses the critical need for understanding the dynamics of COVID-19 transmission and the role of booster vaccinations in managing the pandemic. Despite widespread vaccination efforts, the emergence of new variants and the waning of immunity over time necessitate more effective strategies. A fractional-order mathematical model using Caputo-Fabrizio derivatives was developed to analyze the impact of booster doses, symptomatic and asymptomatic infections, and quarantine measures. The model incorporates real epidemic data from Thailand and includes a sensitivity analysis of parameters influencing disease spread. Numerical results indicate that booster vaccinations significantly reduce transmission rates, and the model’s predictions align well with the observed data. The basic reproduction number was determined to evaluate disease control, showing that a sustained vaccination campaign, including booster doses, is essential to maintaining immunity and controlling future outbreaks. The findings underscore the importance of ongoing vaccination efforts and provide a robust framework for policymakers to design effective strategies for pandemic control.

Abstract Quantitative experiments are essential for investigating, uncovering, and confirming our understanding of complex systems, necessitating the use of effective and robust experimental designs. Despite generally outperforming other approaches, the broader adoption of model-based design of experiments (MBDoE) has been hindered by oversimplified assumptions and computational overhead. To address this, we present PARameter SEnsitivity Clustering (PARSEC), an MBDoE framework that identifies informative measurable combinations through parameter sensitivity (PS) clustering. We combined PARSEC with a new variant of Approximate Bayesian Computation-based parameter estimation for rapid, automated assessment and ranking of experiment designs. Using two kinetic model systems with distinct dynamical features, we show that PARSEC-based experiments improve the parameter estimation of a complex system. By its inherent formulation, PARSEC can account for experimental restrictions and parameter variability. Moreover, we demonstrate that there is a strong correlation between sample size and the optimal number of PS clusters in PARSEC, offering a novel method to determine the ideal sampling for experiments. This validates our argument for employing parameter sensitivity in experiment design and illustrates the potential to leverage both model architecture and system dynamics to effectively explore the experimental design space.

Due to the scarcity of water in many parts of the world, wastewater generated by hydrometallurgical processes has been recognized as a valuable resource for recovery or reuse. A method for reusing hydrometallurgical raffinate in the leaching of low-grade oxide copper ore in a counter-current leaching process was evaluated. In the first stage of the investigation, the influential factors on copper leaching, such as the amount of acid consumed (200–400 g/kg of ore), the initial iron concentration of the raffinate (5–25 g/L), the liquid-to-solid ratio (2–10 mL/g), and the duration of the leaching process (15–75 min), were examined using response surface methodology coupled with central composite design. Two responses, copper recovery and final copper concentration, were selected to determine the optimal conditions for the leaching process. Based on the statistical model, it was found that using 325 g/kg of ore of consumed acid, an initial iron concentration of 10 g/L, a liquid-to-solid ratio of 4 mL/g, and a leaching time of 60 min would result in higher values for both responses. Confirmation runs were conducted to validate the selected conditions and the suitability of the statistical model. Additionally, compositional and morphological characterization of samples before and after the leaching process was conducted using field diffusion scanning electron microscopy and X-ray diffraction analysis, which showed the dissolution of compounds such as tenorite and cuprite. In the second stage, a two-unit counter-current leaching system was adopted, as hydrometallurgical processes are usually carried out in multiple stages on an industrial scale. This leaching system was designed to maximize copper dissolution by considering the information obtained from the response surface methodology. The use of this leaching system increased the overall leaching efficiency by up to 80%. By characterizing the solid samples, it was found that the copper extraction percentage in a counter-current leaching system could be increased by simultaneously precipitating iron-bearing compounds. [ABSTRACT FROM AUTHOR]

Sustained release microspheres loaded with goserelin are regarded as a promising candidate for treating prostate cancer and other sex hormone diseases. However, their widespread adoption has been hindered by issues such as wide particle size distribution and unstable release characteristics. To address these challenges, we employed a combination of the solid-in-oil-in-water microspheres preparation approach (S/O/W) and innovative premix membrane emulsification technology and deeply investigated the effects of four key parameters on the loaded performance of microspheres and the microscopic mechanisms behind them. With this approach, we successfully produced goserelin-loaded sustained release microspheres of narrow particle size distribution (Span 0.642), remarkable encapsulation efficiency (DL = 4.23 %, EE = 93.98 %), low initial burst release (about 0.50 % within 2 h), and compatibility with small injection needles (23-G, inner diameter 0.33 mm, outer diameter 0.64 mm, maximal force 59 N). In the animal model(administered dose, 2.4 mg·Kg−1), goserelin long-acting sustained release microspheres sustained release for over 32 days, maintaining effective concentrations above 2 ng·mL−1, and effectively reduced serum testosterone concentrations to castration levels (<1.0 ng·mL−1) by day 4, maintaining this inhibition for up to 21 days, exhibiting comparable efficacy to the positive control group. In vivo release kinetics analysis revealed that goserelin-loaded sustained release microspheres exhibited a release pattern dominated by diffusion with corrosion assistance in vivo. In summary, the systematic and comprehensive evaluation of uniform-sized goserelin-loaded sustained release microspheres has highlighted their excellent translational potential, and the study herein may provide new strategies and ideas for the development of microsphere dosage forms. [Display omitted] • Developed uniform-sized goserelin microspheres (GOS-M) with high encapsulation efficiency. • Achieved low initial burst release and sustained release for over 32 days in vivo. • Uniform-sized GOS-M adapted to 23-G needles, ideal for low-pain administration. • Offers New strategies for microsphere release regulation and prescription screening. [ABSTRACT FROM AUTHOR]

The incorporation of modern animal models become imperative for planning breeding strategies of dairy populations. The present study was designed to compare between single-trait and multi-traits animal models in estimating genetic parameters and breeding values for some milk yield and reproductive traits of Holstein-Friesian dairy cattle. A total of 9450 records of dairy cows calved in the period between 2007 and 2018 were included in the analyses by using the datasets of the first four lactations. Mixed model methodologies have been applied through applications of restricted maximum likelihood estimation algorithms. The heritability estimates for investigated traits were found be 0.26 for lactation milk yield (LMY), 0.42 (0.45) for milk fat percentage, 0.41 (0.44) for milk protein percentage, 0.13 (0.14) for lactation length (LL), 0.14 (0.15) for age at first calving, 0.17 (0.18) for calving interval, and 0.10 (0.13) for days open as denoted by single-trait (multi-traits) models, respectively. The genetic correlations between lactation milk yield and fertility traits ranged from 0.41 to -0.74. The highest genetic correlation was found between LMY and LL which was 0.84. The Akaike Information Criteria (AIC), the model evaluation measure were estimated for both models and its values were 59245.56 and 58598.23 for single-trait model and multi-traits model, respectively. Therefore, the multi-traits model, the model with the lowest value of AIC was selected as the model of choice for model’s evaluation and preference. In conclusion, the current estimates and results indicate the possibility of genetic improvements for studied traits of Holstein-Friesian cows. Moreover, the multi-traits models are highly recommended for future analyses of complex livestock traits and for construction of selection plans. [ABSTRACT FROM AUTHOR]

This study is aimed at investigating the crystallization kinetics of two structurally related polymers, Nylon 6,6 (PA66) and Nylon 11 (PA11), by differential scanning calorimetry (DSC) in the scope of a logistic-based model using a model fitting approach. By this method, the values of the rate parameters for each specific temperature are obtained from fitting all points of the crystallization exotherm that were accurately recorded at that temperature. This method differs from Arrhenius-based model fitting approaches, in which the initial and final parts of the exotherm do not usually match the shape of Arrhenius-based models and are therefore discarded for fitting. Furthermore, in other kinetic approaches that fall outside the scope of this article, kinetic parameters are typically obtained from specific points in the crystallization exotherm, and good fits cannot generally be obtained nor is that the goal of those approaches. The DSC curves of both polymers obtained at different temperatures are analysed to determine the crystallization kinetics. One of the most insightful parameters of the model is the crystallization rate. Its dependence on temperature is analysed for both polymers and compared to others. The other parameters can also help to better understand some of the crystallization features of these polymers. In addition, the information retrieved from this study can be useful to adjust processing conditions. [ABSTRACT FROM AUTHOR]

Nanoplastics (NPs) are usually formed by the decomposition of large plastics, which will cause water pollution after entering the water body. Carbon filter column is used to adsorb and remove polystyrene nanoparticles (PSNPs). The influence of experimental conditions on adsorption was investigated and fitted by kinetic model. The results show that increasing the height of carbon filter column and decreasing the initial concentration of PSNPs and water flow rate can prolong the breakthrough time of carbon filter column. When the initial concentration of PSNPs is 0.8 mg L−1, the influent flow rate is 4 mL min−1 and the height of carbon filter bed is 8.5 cm, the removal effect is the best, and the depletion point of carbon filter column is extended to 48 h. Adams-Bohart model is suitable for describing the initial stage of adsorption. Thomas and Yoon-Nelson models can well describe the whole dynamic adsorption process of PSNPs, and Yoon-Nelson model can accurately predict the time required for 50% PSNPs to penetrate the carbon column. The adsorption mechanism of NPs by carbon filter column is mainly through the attachment sites and pore retention provided by particles on the surface of activated carbon. This study can provide new technical and theoretical support for the removal of NPs. [ABSTRACT FROM AUTHOR]

The in situ characterization of materials is a crucial challenge in room acoustics, as laboratories measurement cannot always be applied in consultancy practices. In particular, there is a lack of method to characterize in situ systems with perforated facings, which are commonly encountered systems in room acoustics. In this paper, the in situ characterization of a rigidly-backed porous material behind a rigid perforated facing by means of pressure–velocity measurements is presented. The method includes an inverse impedance model fitting based on measurement in a limited frequency range. The applicability of this method was studied by measuring a variety of perforated facings, whether in front of an air cavity or backed by a porous layer, and comparing the obtained impedance model parameters to reference values. Good agreement was observed between the retrieved parameters and the references, with the errors in all retrieved parameters moving mass, facing thickness, cavity depth, porous layer thickness and porous layer flow resistivity not exceeding 15%. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of the Chinese Ceramic Society is the property of Bulletin of the Chinese Ceramic Society Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A simulation experiment was conducted on the water infiltration of coal gangue columns under indoor waterlogging conditions to study the effect of different concentrations of W-OH (0%, 1.5%, 2.5%, and 3.5%) spraying treatment on the water infiltration of coal gangue in high-altitude mining areas. Three infiltration models were used to fit the infiltration process, and a one-dimensional algebraic model was used to predict the distribution characteristics of the volume water content of coal gangue profiles, and the applicability of the model was evaluated. The results indicate that: (1) The cumulative infiltration amount and the distance of wetting front advance gradually increases with the increase of infiltration time, and there is a negative correlation with the concentration of W- OH. At the same infiltration time, the higher the concentration of W- OH, the lower the infiltration rate and wetting front advance rate. Compared with the control (0% W-OH), the initial infiltration rates of the three W-OH concentrations (1.5%, 2.5%, 3.5%) decreased by 1.12%, 3.59%, and 9.64%, respectively. The stable infiltration rates decreased by 16.92%, 78.46%, and 89.23%, respectively, and the average infiltration rates decreased by 11.35%, 58.26%, and 71.02%, respectively. (2) The three infiltration models can all fit the water infiltration process of coal gangue well treated with different concentrations of W-OH. The coefficient of determination (R2) mean values of the Philip, Kostiakov, and Horton model are 0.962, 0.957, and 0.967, respectively. Among them, the Horton model has a good fitting effect. (3) During the process of water infiltration, the larger the burial depth of the same W- OH concentration, the longer it takes for water to infiltrate to each monitoring point. At the same depth, the higher the W-OH concentration, the longer it takes for water to infiltrate to each monitoring point. (4) The one-dimensional algebraic model can effectively simulate the distribution characteristics of volumetric water content in coal gangue profiles after infiltration. The root mean squared error (RMSE) and mean absolute error (MAE) between the simulated and measured values is 2.574%-3.326% and 2.308%-2.707%, respectively, with compliance index (D) values above 0.92. The research results provide theoretical guidance for the application of W- OH solidifying agent in the reconstruction of frozen soil profiles in coal gangue mountains in high-altitude and cold mining areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Tuberculosis (TB) remains a significant global health challenge, claiming over 2 million lives annually, predominantly among adults. Existing TB models often neglect seasonal variations, optimal control, and reinfection, limiting their accuracy in predicting disease dynamics. This study presents a novel data‐driven SVEITRS mathematical model incorporating these factors to analyze TB transmission dynamics. Employing the next‐generation matrix approach, a basic reproduction number R0 of 1.005341 was calculated, suggesting that without robust public health interventions, TB disease may persist in Kenya. The model equations were solved numerically using fourth‐ and fifth‐order Runge–Kutta methods, with the forward–backward sweep technique applied to the optimal control problem. The model was fitted to historical TB incidence data for Kenya from 2000 to 2022 using lsqcurvefit algorithm in MATLAB software. The fitting algorithm yielded a mean absolute error (MAE) of 0.0069, demonstrating a close alignment between simulated and observed data. The optimized parameter values were used to project future TB dynamics. Key findings indicate that a 20% decrease in transmission rate coupled with a 5% increase in vaccine efficacy, while maintaining other parameters constant, would result in a 32.60% reduction in TB transmission in Kenya. Moreover, the incidence of TB in Kenya is expected to decrease to an estimated 17 cases per 100,000 people by 2045 with sustained efforts in vaccine development and public awareness campaigns. The development of highly efficacious vaccines emerges as the most cost‐effective strategy in combating TB transmission in Kenya. Policymakers should prioritize investing in the development and deployment of highly efficacious vaccines to achieve optimal public health outcomes and economic benefits, aligning with Kenya's Vision 2030.

Long historical records and free accessibility have made Landsat data valuable for time-series analysis. However, Landsat time-series analysis is restricted for coastal areas due to the lack of sufficient numbers of clear images. The generation of synthetic Landsat images using model-fitting methods is accepted as an effective means of overcoming this problem. But for coastal areas, available observations are typically sparse and irregular, leading to ill-fitted models that distort synthetic Landsat images. In this study, we propose a linear harmonic model with different orders and implement it on an annual basis to generate synthetic Landsat images based on the Google Earth Engine (GEE) platform. First, we incorporated the available observations from adjacent years and filtered them by season to address the problems of data sparsity and irregularity. Then, we combined the threshold segmentation and ridge regression approaches to address the fake cloud problem, which sometimes contaminates synthetic Landsat images in summer. All cloud-free real Landsat images from five typical sites along China’s coasts during 1986–2020 were used to evaluate the accuracy and robustness of the synthetic Landsat images generated using our proposed methods. The principal results are as follows: (1) the R2 value of the linear harmonic model averaged across different Landsat bands and land cover types was 0.640, and the model was especially successful in simulating the surface reflectance in near NIR bands in forest and grassland areas; (2) 81.1% of the synthetic Landsat images covered by fake clouds were effectively restored, and there was the particular need to remove fake clouds for the synthetic Landsat images at low latitudes; (3) The mean absolute error for our synthetic Landsat images was 0.015, with the rate of 0.124; this was achieved under the clear-sky probability of only 36% and the average number of annual observations below 9, indicating a good performance. Compared with the continuous change detection and classification (CCDC) and seasonal median composite (SMC) methods, our proposed method offers advantages in both the accuracy and integrity of synthetic Landsat images. Our proposed method for synthesizing images also has potential for application to global coastal areas and other satellite datasets.

Abstract A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm—termed generalised Firing-to-Parameter (gFTP)—provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics.

A main goal in neuroscience is to understand the computations carried out by neural populations that give animals their cognitive skills. Neural network models allow to formulate explicit hypotheses regarding the algorithms instantiated in the dynamics of a neural population, its firing statistics, and the underlying connectivity. Neural networks can be defined by a small set of parameters, carefully chosen to procure specific capabilities, or by a large set of free parameters, fitted with optimization algorithms that minimize a given loss function. In this work we alternatively propose a method to make a detailed adjustment of the network dynamics and firing statistic to better answer questions that link dynamics, structure, and function. Our algorithm—termed generalised Firing-to-Parameter (gFTP)—provides a way to construct binary recurrent neural networks whose dynamics strictly follows a user pre-specified transition graph that details the transitions between population firing states triggered by stimulus presentations. Our main contribution is a procedure that detects when a transition graph is not realisable in terms of a neural network, and makes the necessary modifications in order to obtain a new transition graph that is realisable and preserves all the information encoded in the transitions of the original graph. With a realisable transition graph, gFTP assigns values to the network firing states associated with each node in the graph, and finds the synaptic weight matrices by solving a set of linear separation problems. We test gFTP performance by constructing networks with random dynamics, continuous attractor-like dynamics that encode position in 2-dimensional space, and discrete attractor dynamics. We then show how gFTP can be employed as a tool to explore the link between structure, function, and the algorithms instantiated in the network dynamics. [ABSTRACT FROM AUTHOR]

Traditional feature detection and description methods, such as scale-invariant feature transform, are susceptible to nonlinear radiation distortions (NRDs) and geometric distortions (GDs), which in turn generate a large number of outliers or incorrect correspondences. To address this issue, this paper proposes a simple yet effective heterogeneous model fitting (MIMF) for multi-source image correspondences. First, a multi-orientation phase consistency model is constructed, which fuses phase consistency, image amplitude and orientation to detect the correct correspondences of feature points. This model effectively reduces the influence of NRDs. Second, sub-region grids and orientation histograms are exploited to construct the log-polar descriptors with variable-size bins, which are robust to GDs. Finally, a heterogeneous model fitting method is proposed, which can effectively estimate the parameters of the transformation model for alleviating the influence of outliers. Experiments are performed on six public datasets and one constructed dataset containing ten types of multi-source images, and the experimental results show that the proposed MIMF method outperforms several state-of-the-art competing methods in terms of matching performance. [ABSTRACT FROM AUTHOR]

Purpose: Joint analysis of flow‐compensated (FC) and non‐flow‐compensated (NC) diffusion MRI (dMRI) data has been suggested for increased robustness of intravoxel incoherent motion (IVIM) parameter estimation. For this purpose, a set of methods commonly used or previously found useful for IVIM analysis of dMRI data obtained with conventional diffusion encoding were evaluated in healthy human brain. Methods: Five methods for joint IVIM analysis of FC and NC dMRI data were compared: (1) direct non‐linear least squares fitting, (2) a segmented fitting algorithm with estimation of the diffusion coefficient from higher b‐values of NC data, (3) a Bayesian algorithm with uniform prior distributions, (4) a Bayesian algorithm with spatial prior distributions, and (5) a deep learning‐based algorithm. Methods were evaluated on brain dMRI data from healthy subjects and simulated data at multiple noise levels. Bipolar diffusion encoding gradients were used with b‐values 0–200 s/mm2 and corresponding flow weighting factors 0–2.35 s/mm for NC data and by design 0 for FC data. Data were acquired twice for repeatability analysis. Results: Measurement repeatability as well as estimation bias and variability were at similar levels or better with the Bayesian algorithm with spatial prior distributions and the deep learning‐based algorithm for IVIM parameters D$$ D $$ and f$$ f $$, and for the Bayesian algorithm only for vd$$ {v}_d $$, relative to the other methods. Conclusion: A Bayesian algorithm with spatial prior distributions is preferable for joint IVIM analysis of FC and NC dMRI data in the healthy human brain, but deep learning‐based algorithms appear promising. [ABSTRACT FROM AUTHOR]

In addressing the global challenges posed by COVID-19, this study introduces a mathematical model aimed at investigating the transmission dynamics of COVID-19 and forwarding strategies for controlling it. By employing Lyapunov functions, we perform a thorough stability analysis of both disease-free and endemic equilibria. We calibrated the model using daily COVID-19 data from early 2022 in Ethiopia, after vaccination initiation. A global sensitivity analysis confirmed the robustness of the model. In addition, we extended the model to address optimal control by incorporating vaccination, public health education, and treatment. Our findings highlight the effectiveness of individual control measures and reveal that vaccination, public health educational campaign and treatment is the most cost-effective method for mitigating COVID-19 spread.

Malaria, a devastating disease caused by the Plasmodium parasite and transmitted through the bites of female Anopheles mosquitoes, remains a significant public health concern, claiming over 600,000 lives annually, predominantly among children. Novel tools, including the application of Wolbachia, are being developed to combat malaria-transmitting mosquitoes. This study presents a modified susceptible-exposed-infectious-recovered-susceptible (SEIRS) compartmental mathematical model to evaluate the impact of awareness-based control measures on malaria transmission dynamics, incorporating mosquito interactions and seasonality. Employing the next-generation matrix approach, we calculated a basic reproduction number (R0) of 2.4537, indicating that without robust control measures, the disease will persist in the human population. The model equations were solved numerically using fourth and fifth-order Runge-Kutta methods. The model was fitted to malaria incidence data from Kenya spanning 2000 to 2021 using least squares curve fitting. The fitting algorithm yielded a mean absolute error (MAE) of 2.6463 when comparing the actual data points to the simulated values of infectious human population (Ih). This finding indicates that the proposed mathematical model closely aligns with the recorded malaria incidence data. The optimal values of the model parameters were estimated from the fitting algorithm, and future malaria dynamics were projected for the next decade. The research findings suggest that social media-based awareness campaigns, coupled with specific optimization control measures and effective management methods, offer the most cost-effective approach to managing malaria.

In this work, kinetic models are assessed to describe bacterial cellulose (BC) production, substrate consumption, and biomass growth by K. xylinus in a batch-stirred tank bioreactor, under 700 rpm and 500 rpm agitation rates. The kinetic models commonly used for Acetobacter or Gluconacetobacter were fitted to published data and compared using the Akaike Information Criterion (AIC). A stepwise fitting procedure was proposed for model selection to reduce computation effort, including a first calibration in which only the biomass and substrate were simulated, a selection of the three most effective models in terms of AIC, and a calibration of the three selected models with the simulation of biomass, substrate, and product. Also, an uncoupled product equation involving a modified Monod substrate function is proposed for a 500 rpm agitation rate, leading to an improved prediction of BC productivity. The M2c and M1c models were the most efficient for biomass growth and substrate consumption for the combined AIC, under 700 rpm and 500 rpm agitation rates, respectively. The average coefficients of determination for biomass, substrate, and product predictions were 0.981, 0.994, and 0.946 for the 700 rpm agitation rate, and 0.984, 0.991, and 0.847 for the 500 rpm agitation rate. It is shown that the prediction of BC productivity is improved through the proposed substrate function, whereas the computation effort is reduced through the proposed model fitting procedure.

Using the residual sludge from a sewage treatment plant as raw material, sludge biochar with different temperature gradients (300,500,700,750 °C) was prepared by one-step pyrolysis under anoxic conditions, and the adsorption performance of the sludge biochar prepared w让h optimal pyrolysis tempera・ tures on methylene blue was determined, and characterized and analyzed by BET, FTIR, and morphology methods・ The results showed that the sludge biochar prepared by pyrolysis at 700 °C for 1 h under anoxic conditions could remove methylene blue efficiently, and the removal rate could reach 97. 9% ・ The theoret・ ical adsorption capacity of SB700 for methylene blue reached 9・ 87 mg/g at the reaction temperature of 35 °C, biochar dosage of 10 g/L and adsorption equilibrium time of 12 h. The correlation coefficient of the proposed secondrder kinetic model for the adsorption of methylene blue by SB700 was 0・ 987 8, which was higher than that of the proposed first-order kinetic model, indicating that the adsorption process was dominated by the chemical adsorption of monomolecules. [ABSTRACT FROM AUTHOR]

Various published COVID-19 models have been used in epidemiological studies and healthcare planning to model and predict the spread of the disease and appropriately realign health measures and priorities given the resource limitations in the field of healthcare. However, a significant issue arises when these models need help identifying the distribution of the constituent variants of COVID-19 infections. The emergence of such a challenge means that, given limited healthcare resources, health planning would be ineffective and cost lives. This work presents a universal neural network (NN) computational instrument for predicting the mainstream symptomatic infection rate of COVID-19 and models of the distribution of its associated variants. The NN is based on a mixture density network (MDN) with a Gaussian mixture model (GMM) object as a backbone. Twelve use cases were used to demonstrate the validity and reliability of the proposed MDN. The use cases included COVID-19 data for Canada and Saudi Arabia, two date ranges (300 and 500 days), two input data modes, and three activation functions, each with different implementations of the batch size and epoch value. This array of scenarios provided an opportunity to investigate the impacts of epistemic uncertainty (EU) and aleatoric uncertainty (AU) on the prediction model's fitting. The model accuracy readings were in the high nineties based on a tolerance margin of 0.0125. The primary outcome of this work indicates that this easy-to-use universal MDN helps provide reliable predictions of COVID-19 variant distributions and the corresponding synthesized profile of the mainstream infection rate. [ABSTRACT FROM AUTHOR]

Diffusion models have been widely used to obtain information about cognitive processes from the analysis of responses and response-time data in two-alternative forced-choice tasks. We present an implementation of the seven-parameter diffusion model, incorporating inter-trial variabilities in drift rate, non-decision time, and relative starting point, in the probabilistic programming language Stan. Stan is a free, open-source software that gives the user much flexibility in defining model properties such as the choice of priors and the model structure in a Bayesian framework. We explain the implementation of the new function and how it is used in Stan. We then evaluate its performance in a simulation study that addresses both parameter recovery and simulation-based calibration. The recovery study shows generally good recovery of the model parameters in line with previous findings. The simulation-based calibration study validates the Bayesian algorithm as implemented in Stan. [ABSTRACT FROM AUTHOR]

Cholera is an acute diarrheal disease caused by Vibrio cholera, its prevalence occurs in almost all the continents of the world, annually there are about 1.3 to 4.0 million cases of cholera and 21, 000 to 143, 000 deaths worldwide. In this paper, we propose a deterministic model for the transmission dynamics of cholera to assess the impact of vaccines in decreasing the spread of cholera infection in Nigeria. Moreover, we develop an optimal control strategy, in which we consider personal hygiene a control strategy on infection class, with u(t) as the control function. The best values of the fitting parameters have been obtained using least square minimization to validate the model with the help of experimental data obtained from Nigeria. We perform sensitivity analysis to determine the key parameters that have impacts on the control of the spread of cholera infections in the population. In addition, the numerical simulation of the model reveals that the use of vaccines and personal hygiene will effectively control the spread of cholera infection. [ABSTRACT FROM AUTHOR]

Soccer player performance is influenced by multiple unpredictable factors. During a game, score changes and pre-game expectations affect the effort exerted by players. This study used GPS wearable sensors to track players' energy expenditure in 5-min intervals, alongside recording the goal timings and the win and lose probabilities from betting sites. A mathematical model was developed that considers pre-game expectations (e.g., favorite, non-favorite), endurance, and goal difference (GD) dynamics on player effort. Particle Swarm and Nelder–Mead optimization methods were used to construct these models, both consistently converging to similar cost function values. The model outperformed baselines relying solely on mean and median power per GD. This improvement is underscored by the mean absolute error (MAE) of 396.87 ± 61.42 and root mean squared error (RMSE) of 520.69 ± 88.66 achieved by our model, as opposed to the B 1 MAE of 429.04 ± 84.87 and RMSE of 581.34 ± 185.84 , and B 2 MAE of 421.57 ± 95.96 and RMSE of 613.47 ± 300.11 observed across all players in the dataset. This research offers an enhancement to the current approaches for assessing players' responses to contextual factors, particularly GD. By utilizing wearable data and contextual factors, the proposed methods have the potential to improve decision-making and deepen the understanding of individual player characteristics. [ABSTRACT FROM AUTHOR]

Malaria, a devastating disease caused by the Plasmodium parasite and transmitted through the bites of female Anopheles mosquitoes, remains a significant public health concern, claiming over 600,000 lives annually, predominantly among children. Novel tools, including the application of Wolbachia, are being developed to combat malariatransmitting mosquitoes. This study presents a modified susceptible-exposed-infectious-recovered-susceptible (SEIRS) compartmental mathematical model to evaluate the impact of awareness-based control measures on malaria transmission dynamics, incorporating mosquito interactions and seasonality. Employing the next-generation matrix approach, we calculated a basic reproduction number (R0) of 2.4537, indicating that without robust control measures, the disease will persist in the human population. The model equations were solved numerically using fourth and fifth-order Runge-Kutta methods. The model was fitted to malaria incidence data from Kenya spanning 2000 to 2021 using least squares curve fitting. The fitting algorithm yielded a mean absolute error (MAE) of 2.6463 when comparing the actual data points to the simulated values of infectious human population (Ih). This finding indicates that the proposed mathematical model closely aligns with the recorded malaria incidence data. The optimal values of the model parameters were estimated from the fitting algorithm, and future malaria dynamics were projected for the next decade. The research findings suggest that social media-based awareness campaigns, coupled with specific optimization control measures and effective management methods, offer the most cost-effective approach to managing malaria. [ABSTRACT FROM AUTHOR]

ABSTRACT: The present study aimed to apply a sinusoidal model to duck body weight records in order to introduce it to the field of poultry science. Using 8 traditional growth functions as a guide (Bridges, Janoschek, logistic, Gompertz, Von Bertalanffy, Richards, Schumacher, and Morgan), this study looked at how well the sinusoidal equation described the growth patterns of ducks. By evaluating statistical performance and examining model behavior during nonlinear regression curve fitting, models were compared. The data used in this study came from 3 published articles reporting 1) body weight records of Kuzi ducks aged 1 to 70 d, 2) body weight records for Polish Peking ducks aged 1 to 70 d, and 3) average body weight of Peking ducks aged 1 to 42 d belonging to 5 different breeds. The general goodness-of-fit of each model to the various data profiles was assessed using the adjusted coefficient of determination, root mean square error, Akaike's information criterion (AIC), and Bayesian information criterion. All of the models had adjusted coefficient of determination values that were generally high, indicating that the models generally fit the data well. Duck growth dynamics are accurately described by the chosen sinusoidal equation. The sinusoidal equation was found to be one of the best functions for describing the age-related changes in body weight in ducks when the growth functions were compared using the goodness-of-fit criteria. To date, no research has been conducted on the use of sinusoidal equations to describe duck growth development. To describe the growth curves for a variety of duck strains/lines, the sinusoidal function employed in this study serves as a suitable substitute for conventional growth functions.

Nowadays, along with the rise of digital human system, 3D animation, intelligent medical and other industries, 3D face reconstruction technology has become a popular research direction in computer vision and computer graphics. Traditional 3D face reconstruction techniques are affected by face expression, occlusion, and ambient light, resulting in poor accuracy and robustness of the reconstructed model, etc. With the rise of deep learning, all of the above problems have been greatly improved. Focusing on 3D face reconstruction techniques based on deep learning, this paper categorizes the existing research works into 3D face reconstruction based on hybrid learning and explicit regression. The first category of research work fits 2D faces to 3D models, which is a pathological process that requires solving the basis vector coefficients of the 3D face statistical model. The second type of research work, instead of Model Fitting, represents 3D faces with multiple data types in the display space and directly regresses 2D faces through deep networks. This review provides the latest advances in single-image-based 3D face reconstruction techniques in recent years, summarizing some commonly used face datasets, evaluation metrics, and applications. Finally, we discuss the main challenges and future trends of the single-image 3D face reconstruction task.

Cholera is an acute diarrhoeal illness that affects humanity globally, especially in areas where there is limited access to clean water and adequate sanitation. A Nigerian cholera outbreak from January 2021 to January 2022 resulted in many cases and deaths. A mathematical model that takes into consideration the challenges that affected effective implementation of control measures for this 2021 cholera outbreak is developed. Important epidemiological features of the model such as the basic reproduction number (R0), the disease-free equilibrium, and the endemic equilibrium are determined and analysed. The disease-free equilibrium is shown to be asymptotically stable provided R0 < 1. The model is shown to undergo forward bifurcation at R0 = 1 using the Centre Manifold Theorem. Sensitivity analysis is used to determine the parameters that have the highest influence on transmission. Fitting the model to data from the 2021 Nigerian cholera outbreak, important parameters of the model are estimated. The impact of control measures as well as challenges that affected the effective implementation of these control measures are considered.

In this study, the damping responses of uniform soil, equi-proportional fly ash, and local soil as a single unit were investigated. The large-strain cyclic triaxial tests were performed for the specimen compacted at the desired density (95%–99% of maximum dry density). The compacted specimens were tested under the loading frequency of 0.3–1 Hz with medium confinement of 70–100 kPa. Also, the unsymmetrical behavior of the hysteresis loop was analyzed using three different damping estimation approaches, i.e. symmetric hysteresis loop (SHL), asymmetric hysteresis loop (ASHL), and the modified American Society for Testing and Materials (ASTM) method. The outcome of the study shows for fly ash, local soil, and layered soil-ash, the ASHL technique has the highest damping value, followed by ASTM and then the SHL approach. The specimens prepared under high density and subjected to high confinement show low damping values. However, the specimens tested at high frequency exhibits high damping behavior. Similarly, the damping value of fly ash determined using the SHL and ASHL methods has a similar profile and reaches a maximum at 1% shear strain value before decreasing. The composite stratified deposit exhibits more dependency on relative compaction, confining pressure, and less on loading frequency. Based on the results, it is highly recommended to use the ASHL approach, especially under large strain conditions irrespective of soil type. The maximum damping ratio of stratified deposits is always in between the damping ratio of local soil and fly ash. The damping ratio of stratified soil and local soil is slightly larger than that of the other soils, although the damping ratio of fly ash is equivalent to that of the sand and clayey soil. These results may be helpful in the accurate determination of the damping properties of the layered soil-ash system that is required in the seismic response analysis.

Biohydrogen production is influenced by various parameters such as production formation rate, substrates degradation, and biomass utilization. Therefore, it is crucial to predict the kinetics of proper yield. This study was designed to perform a comprehensive kinetic analysis based on several linear or non-linear models. The experimental data analyzed was different co-culture ratios of microalgae and activated sludge with hexitols (glucose, sorbitol, mannitol) as carbon substrates. Results suggest that modified Gompertz model best predicts biohydrogen potential and acetate production in different co-cultures with high R2 values (>0.977 and > 0.982), respectively. A maximum of 9.8 % energy conversion efficiency is achieved in 10 g/L glucose supplemented co-culture, equivalent to traditional fuels. The linearized Luedeking–Piret model also indicates that biohydrogen production is influenced more by substrate degradation/utilization than microalgal biomass. These tools can be utilized to optimize the operational parameters and access the best fit of experimental data for enhanced yield. [Display omitted] • Excellent data fittings of parameters involved in algal activated sludge co-culture. • Biohydrogen and acetate yield can be best predicted by Modified Gompertz model. • Michaelis–Menten model best described kinetic of substrate degradation/utilization. • Relationship of products, substrate, and biomass predicted by Luedeking–Piret model. • Maximum energy conversion efficiency (9.8 %) was achieved in 1:1.5 co-culture ratio. [ABSTRACT FROM AUTHOR]

Indoor 3D reconstruction is particularly challenging due to complex scene structures involving object occlusion and overlap. This paper presents a hybrid indoor reconstruction method that segments the room point cloud into internal and external components, and then reconstructs the room shape and the indoor objects in different ways. We segment the room point cloud into internal and external points based on the assumption that the room shapes are composed of some large external planar structures. For the external, we seek for an appropriate combination of intersecting faces to obtain a lightweight polygonal surface model. For the internal, we define a set of features extracted from the internal points and train a classification model based on random forests to recognize and separate indoor objects. Then, the corresponding computer aided design (CAD) models are placed in the target positions of the indoor objects, converting the reconstruction into a model fitting problem. Finally, the indoor objects and room shapes are combined to generate a complete 3D indoor model. The effectiveness of this method is evaluated on point clouds from different indoor scenes with an average fitting error of about 0.11 m, and the performance is validated by extensive comparisons with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity of yarn structures in terms of twisting and migration characteristics of the involved fibers can be better encountered in terms of ensembles of networks that focus on individual characteristics. We analyze the effect of different loss functions including a parameter loss to penalize the deviation of inferred parameters to ground truth annotations, a reconstruction loss to enforce similar statistics of the image generated for the estimated parameters in comparison to training images as well as an additional regularization term to explicitly penalize deviations between latent codes of synthetic images and the average latent code of real images in the encoder's latent space. We demonstrate that the combination of a carefully designed parametric, procedural yarn model with respective network ensembles as well as loss functions even allows robust parameter inference when solely trained on synthetic data. Since our approach relies on the availability of a yarn database with parameter annotations and we are not aware of such a respectively available dataset, we additionally provide, to the best of our knowledge, the first dataset of yarn images with annotations regarding the respective yarn parameters. For this purpose, we use a novel yarn generator that improves the realism of the produced results over previous approaches. [Display omitted] • We present the first dataset of synthetic yarn images with yarn parameter annotations • We present an advanced yarn generator for the synthesis of natural looking yarns • We present a novel approach for yarn parameter inference from a single image [ABSTRACT FROM AUTHOR]

The world has been fighting against the COVID-19 Coronavirus which seems to be constantly mutating. The present wave of COVID-19 illness is caused by the Omicron variant of the coronavirus. The vaccines against the five variants (α, β, γ, δ, and ω) have been quickly developed using mRNA technology. The efficacy of the vaccine developed for one of the strains is not the same as the efficacy of the vaccine developed for the other strains. In this study, a mathematical model of the spread of COVID-19 was made by considering asymptomatic population, symptomatic population, two infected populations and quarantined population. An analysis of basic reproduction numbers was made using the next-generation matrix method. Global asymptotic stability analysis was made using the Lyapunov theory to measure stability, showing an equilibrium point's stability, and examining the model with the fact of COVID-19 spread in Thailand. Moreover, an analysis of the sensitivity values of the basic reproduction numbers was made to verify the parameters affecting the spread. It was found that the most common parameter affecting the spread was the initial number in the population. Optimal control problems and social distancing strategies in conjunction with mask-wearing and vaccination control strategies were determined to find strategies to give better control of the spread of disease. Lagrangian and Hamiltonian functions were employed to determine the objective function. Pontryagin's maximum principle was employed to verify the existence of the optimal control. According to the study, the use of social distancing in conjunction with mask-wearing and vaccination control strategies was able to achieve optimal control rather than controlling just one or another. [ABSTRACT FROM AUTHOR]