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As technology progresses, automation gains importance. The automation might be in a large-scale industry with more employees and heavy capital investments, or maybe a small-scale industry in which manufacturing, providing the services, and productions are performed on a smaller scale or micro scale, everywhere the automation gains importance. Similar to this is the food processing industry, where fruits and vegetables are some of the most popular products that enhance our health and help us to stay fit. In this approach, we have developed a framework that classifies fruits and vegetables using computer vision and deep learning-based methods. We test the proposed framework on Kaggle's fresh/stale images of fruits and vegetables dataset and IEEEDataPort's FruitsGB dataset. Experiments were conducted in multiple trials to extract model parameters and were analyzed to classify the fruits and vegetables as fresh/stale. The classification depends on the selection of the optimizer and varying the hyperparameter value like batch size, learning rate, kernel size, number of kernels, patch size, etc. The proposed custom CNN model achieves the highest classification accuracy of 97.65% and 95.86% using Kaggle's and FruitsGB datasets, respectively. Similarly, in the second approach, the vision transform (ViT) achieves the highest classification accuracy of 98.34% and 96.75% on the same datasets, respectively. The results of these methods outperform the results of the baseline algorithm used in the classification of the images. [ABSTRACT FROM AUTHOR]

In order to explore the applicability of the hardening soil constitutive model for numerical analysis of soft soil underground engineering in the Pearl River Delta region, and based on four different stress path triaxial tests, the mechanical properties and variation of constitutive model parameters of hardening soil were analyzed for the typical silty soil in this region. Research results have shown that lateral unloading and drainage conditions have significantly impact on the partial parameters of constitutive model, such as the strength parameters of c' and φ ', the stiffness parameters such as Eref 50, E ref ur and m, but the impact on the failure ratio Rf is relatively small. For the consolidated undrained lateral unloading condition, the main parameters of the constitutive model for typical silty soil in the Pearl River Delta region are shown as the reference secant modulus Eref 50 is 2.34 MPa, with reference to tangent modulus Eref oed is 1.59 MPa, with reference to the loading unloading modulus E ref ur is 11.67 MPa, with a failure stress ratio of Rf is 0.98, and the power exponent m related to stiffness stress level is 1.84; Among them: Eref 50 is 1.47 times of Eref oed, E ref ur is 7.34 times of Eref oed. The practice of a deep foundation pit of soft soil in the Pearl River Delta has proven that the constitutive model of hardening soil is suitable for simulating the mechanical properties of soft soil in this region. [ABSTRACT FROM AUTHOR]

Lemna L. sp. is a free‐floating aquatic macrophyte that plays a key role as a standard test species in aquatic risk assessment for herbicides and other contaminants. Population modeling can be used to extrapolate from laboratory to field conditions. However, there are insufficient data on longer‐term seasonal dynamics of this species to evaluate such models. Therefore, several long‐term growth experiments were conducted in outdoor microcosms (surface area 0.174 m2). Monitoring parameters included biomass, frond numbers, water parameters, and weather data. Three different datasets were generated: frond numbers and biomass from weekly to monthly destructively sampled microcosms; a year‐round dataset of frond numbers from five continuously monitored microcosms; and seasonal growth rates without the effect of density dependence over 1–2 weeks in freshly inoculated microcosms. Lemna sp. reached a maximum of approximately 500 000 fronds m−2 and 190 g dry weight m−2. During the first winter, the microcosms were covered by ice for approximately four weeks, and Lemna sp. populations collapsed. The second winter was warmer, without any ice cover, and Lemna sp. populations maintained high abundance throughout the winter. Dry weight per frond was not constant throughout the year but was highest in autumn and winter. Growth rates without density dependence under outdoor environmental conditions reached 0.29 day−1 for frond number, 0.43 day−1 for fresh weight, and 0.39 day−1 for dry weight. In linear regressions, these growth rates were best explained by water temperature. For the populations continuously monitored throughout a year, the nitrogen‐to‐phosphorus ratio best explained the growth rate of frond numbers. This study yielded a relevant dataset for testing and refining Lemna population models used in chemical risk assessment as well as for managing ecosystems and combating the effects of eutrophication. Integr Environ Assess Manag 2024;20:1625–1638. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: Growth rates of Lemna sp. in the field can reach values close to those required in standard laboratory tests designed for high exponential growth but that are affected by light, temperature, nutrient availability, and density dependence.Water temperature was found to be a major driver in the short‐term experiments without density dependence, and the nitrogen‐to‐phosphorus ratio was the factor that best explained the growth rate of Lemna sp. in continuously monitored microcosms.Dry‐weight‐to‐frond ratios of Lemna sp. were highest in autumn and winter, probably the result of the storage of reserves; these ratios are not constant, as assumed in the Lemna sp. model, but vary over the seasons.This study generated ecologically relevant, long‐term data series for testing and refining and/or as input parameters for Lemna sp. population models to be applied in the higher tiers of the risk assessment framework for plant protection products. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate the biokinematic characteristics of the triple jump technique in qualified athletes. Material and methods. The technique of the final attempts of qualified triple jumpers during the 2023 World Athletics Championships was analyzed. The following methods were used in the work: analysis and synthesis of scientific and methodological literature, analysis of video materials, methods of mathematical statistics. The video of the final attempts was used, provided for public use by the International Athletics Federation (World Athletics). The attempts were recorded by four video cameras installed on the side of the treadmill at a distance of 2,5 m, the height of the video camera mounted on a tripod was 1,5 m. Two of the four video cameras recorded video at a frequency of 25 frames per second, the other two video cameras recorded video at a frequency of 1300 frames per second. Video analysis of the final attempts and processing of biokinematic parameters of the technique were determined using the Dartfish and Kinovea programs. All statistical data were processed in the statistical analysis program IBM SPSS Statistics version 28.0.1.0. Results. The metric, time, angular, and speed characteristics of the take-off technique are determined; push-off; jump; step; jump for highly skilled triple jumpers. Based on the analysis of the triple jump technique indicators, the optimal biokinematic parameters of the technique that athletes need to have in order to achieve high competitive results were obtained. Conclusions. To achieve high competitive results in the triple jump, the following technical parameters must be used. The take-off speed should reach 10.40 m s-1 or more, the length of the last take-off step should be 2,20-2,40 m; the pace of the last step is 4-5 steps/s. The push-off; from the bar should last 0.100-0.133 s; the push-off; angle reaches 65-70 ̊; the speed of departure of the general center of mass of the body is 9.60 m s-1 and more. The jump should last no more than 0,660 s. During the jump, the high height of the overall center of body mass should be 1.30-1.45 m; upon landing, the speed of the general center of mass of the body is 9,20 m s-1 or more. The second push-off should last 0,160-0,180 s, the push-off angle is 60-65°. The take-off speed of the general center of body mass in a step should be 8,80 m s-1 or more. The optimal step duration is 0.450-0.520 s, step length is 5.20-5.50 m, the maximum height of the overall center of body mass is 1.24-1.30 m; upon landing, the speed of the general center of mass of the body is 8.50 m s-1 or more. The third push-off should last 130-166 s. The push-off angle is 63-70°. The take-off speed of the general center of mass of the body in a jump is from 8 m s-1 and more. The optimal duration of the jump is 0.750-0.780 s; maximum height of the overall center of body mass 1,10-1,22 m; upon landing, the speed of the general center of mass of the body is 7 m s-1 or more. When taking off and landing, the angle in the supporting leg throughout all three jumps should be 163-170 ̊.

The applications of building energy simulation (BES) in designing heating, ventilation, and air conditioning (HVAC) systems are limited by the high costs of developing simulation models and the lack of references for determining the model parameters. This paper presents a software framework for selecting designs for rooftop unit HVAC (RTU) systems with BES. Specifically, this framework reduces the cost of using BES by automating the generation of EnergyPlus models. It also employs a systematic method for determining model parameters based on well-accepted datasets. We applied this framework in a comprehensive assessment of an advanced design of RTU systems in which 478 EnergyPlus models were developed without human involvement. The assessment reveals that replacing a constant-speed fan/coil with a multiple-speed fan/coil may not guarantee better overall performance. It also suggests the benefits of replacing furnace coils with heat pumps are subject to utility cost, weather conditions, and heating load profiles. [ABSTRACT FROM AUTHOR]

Actual evapotranspiration constitutes a vital component of the exchange of energy and water vapour between the soil‐vegetation and atmospheric systems on terrestrial terrain. Nevertheless, the Tibetan Plateau, owing to its austere environmental conditions, harbours a scarcity of terrestrial monitoring stations. This circumstance presents a formidable challenge in attaining precise estimations of actual evapotranspiration. The complementary relationship method is a potential approach because it requires only routine meteorological data to estimate actual evapotranspiration on a regional or global scale. However, the suitability of the complementary relationship model across diverse ecosystems on the Tibetan Plateau necessitates further investigation. In this study, we scrutinized the simulation of daily and monthly actual evapotranspiration across 18 observation sites spanning eight distinct land use categories on the Tibetan Plateau. We employed the polynomial generalized complementary function introduced by Brutsaert (B2015), alongside its enhanced rendition proposed by Szilagyi (S2017) and Crago (C2018). The outcomes reveal that all three models adeptly replicate the fluctuations in actual evapotranspiration, irrespective of land use category or temporal scale—whether daily or monthly. This is true regardless of whether original or calibrated parameter values are applied. However, there exist significant variations in the performance of these models. In general, the C2018 model demonstrates superior performance across most ecosystems when original parameters are employed. Following parameter calibration, the simulation efficacy of the models experienced marked enhancement. Post parameter calibration, the B2015 model outperforms the other two models notably in desert and wetland environments. Furthermore, the simulation outputs from all three models display heightened sensitivity to parameter α, particularly in the context of the C2018 and S2017 models. These findings suggest that accurate estimation of parameter values is critical to improving the accuracy of estimating actual evapotranspiration. Calibrated parameter values, contingent on a fusion of vegetation, meteorology and surface roughness, exhibit variability across diverse ecosystems. [ABSTRACT FROM AUTHOR]

The high metabolic flexibility and biodiversity of microalgae make them promising systems for the production of chemicals and high-value metabolites to be utilized in various industrial applications. Currently, microalgae are primarily cultivated in phototrophic processes or in fermenters using glucose as substrate. However, such configurations are often too costly for the majority of potential applications and require improvements. The use of acetate as substrate to enhance biomass productivity and reduce cost and environmental impacts is a promising solution. In a future bio-based economy, acetate can serve as an excellent intermediate to link many industrial facilities, as it can be synthesized using different technologies from renewable resources as CO2 and waste. This work provides a detailed description of acetate synthesis processes alternative to the conventional methanol carbonylation, including the pros and cons of each: aerobic and anaerobic fermentations; thermochemical treatments; C1 gas fermentation; microbial electrosynthesis and artificial photosynthesis. Additionally, the utilization of acetate as substrate for microalgae growth in mixotrophic and heterotrophic conditions is reviewed, covering key metabolic and engineering aspects (strains, yields, growth rate, inhibition, productivity, process configuration). These aspects serve as guidelines for a rationale design of an algal cultivation process based on acetate as a carbon source. Finally, the review critically assesses the state of the art of coupling of acetate-rich streams with algal biomass production, highlighting the pros and cons and addressing the main knowledge gaps to be filled through future research. [ABSTRACT FROM AUTHOR]

Purpose: To develop a deep learning model to distinguish rheumatoid arthritis (RA) from osteoarthritis (OA) using hand radiographs and to evaluate the effects of changing pretraining and training parameters on model performance. Materials and methods: A convolutional neural network was retrospectively trained on 9714 hand radiograph exams from 8387 patients obtained from 2017 to 2021 at seven hospitals within an integrated healthcare network. Performance was assessed using an independent test set of 250 exams from 146 patients. Binary discriminatory capacity (no arthritis versus arthritis; RA versus not RA) and three-way classification (no arthritis versus OA versus RA) were evaluated. The effects of additional pretraining using musculoskeletal radiographs, using all views as opposed to only the posteroanterior view, and varying image resolution on model performance were also investigated. Area under the receiver operating characteristic curve (AUC) and Cohen's kappa coefficient were used to evaluate diagnostic performance. Results: For no arthritis versus arthritis, the model achieved an AUC of 0.975 (95% CI: 0.957, 0.989). For RA versus not RA, the model achieved an AUC of 0.955 (95% CI: 0.919, 0.983). For three-way classification, the model achieved a kappa of 0.806 (95% CI: 0.742, 0.866) and accuracy of 87.2% (95% CI: 83.2%, 91.2%) on the test set. Increasing image resolution increased performance up to 1024 × 1024 pixels. Additional pretraining on musculoskeletal radiographs and using all views did not significantly affect performance. Conclusion: A deep learning model can be used to distinguish no arthritis, OA, and RA on hand radiographs with high performance. [ABSTRACT FROM AUTHOR]

In addressing the design challenges for constant-stress accelerated life testing in non-rectangular experimental domains, we aim to optimize the precision in estimating parameters for the product reliability statistical model. Following the principles of regression orthogonal design theory to determine the combinations of stress levels, we constrain the maximum stress levels of each experimental stress along the boundary curve of the non-rectangular experimental domain. The remaining stress levels and the allocation ratios of specimens for each test serve as design variables in the optimization process. We establish a mathematical model for the optimal design of constant-stress accelerated life testing in non-rectangular experimental domains. The results of the optimized design for comprehensive stress accelerated life testing in non-rectangular experimental regions of aerospace electrical connectors indicate that, with the same sample size, the optimized testing scheme not only enhances the precision of model parameter estimation but also reduces the number of required tests. At an equivalent number of tests and testing duration, the optimization scheme proposed in this study demonstrates an improvement of over 63% in the precision of model parameter estimation compared to the EM-optimized testing scheme in non-rectangular experimental regions. Using the mean, standard deviation, and coefficient of variation of the determinant values of the information matrix as criteria for evaluating the precision and robustness of experimental designs, a simulated evaluation was conducted for the optimized experimental design, a conventional experimental design, and an EM experimental design. The results indicate that the optimal experimental design outperforms both the conventional experimental design and the EM experimental design in terms of precision and robustness. [ABSTRACT FROM AUTHOR]

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Within the large Additive Manufacturing (AM) process family, Directed Energy Deposition (DED) can be used to create low-cost prototypes and coatings, or to repair cracks. In the case of M4 HSS (High Speed Steel), a reliable computed temperature field during DED process allows the optimization of the substrate preheating temperature value and other process parameters. Such optimization is required to avoid failure during the process, as well as high residual stresses. If 3D DED simulations provide accurate thermal fields, they also induce huge computation time, which motivates simplifications. This article uses a 2D Finite Element (FE) model that decreases the computation cost through dividing the CPU time by around 100 in our studied case, but it needs some calibrations. As described, the identification of a correct data set solely based on local temperature measurements can lead to various sets of parameters with variations of up to 100%. In this study, the melt pool depth was used as an additional experimental measurement to identify the input data set, and a sensitivity analysis was conducted to estimate the impact of each identified parameter on the cooling rate and the melt pool dimension. [ABSTRACT FROM AUTHOR]

A comprehension of the erosion processes and sediment transport in the watershed is essential for the sustainable management of the water resources and soil fertility. In this study, the Soil and Water Assessment Tool (SWAT) model was applied to demonstrate its ability to modeling the suspended sediment yield in the Wadi K'sob basin located in the northeast of Algeria. The data used to set up the SWAT model are the Digital Elevation Model (DEM), land use, soil types and weather data. The results obtained after calibration oscillating between good and satisfactory where (NSE=0.67 and R²=0.73) in the first calibration period and (NSE=0.65 b and R²=0.67) in the second period. In the validation, the performance of the SWAT model was very good (NSE =0.78 and R2 =0.79) in the first period while in the second period the prediction of the model was satisfactory (NSE=0.52 and R²=0.54). In addition, the validation process revealed that some parameters are stable and related on watershed characteristics while other unstable parameters depend on soil properties especially soil permeability and soil erodibility. [ABSTRACT FROM AUTHOR]

This article presents a model for organizing railway freight transportation between two node stations based on the interaction of neighboring stations depending on their technical capabilities and the demand for freight transportation. The main objective of such interaction is to synchronize the inbound and outbound flow at the stations, specifically reducing the degree of inconsistency between the receipt and dispatch of goods at the stations. This characteristic represents the imbalance between the volume of incoming and outgoing goods at the stations per unit of time and is described by a nonnegative function bounded above by unity. Its dynamics are described by a system of differential equations containing a set of parameters characterizing the infrastructure of the stations, the mode of distribution of goods from the final node station, and the demand for freight transportation. The range of parameter variations for which the specified system has a solution has been determined, as well as such a set of these parameters for which the task of synchronizing the inbound and outbound flow at the stations is best solved. It has been found that regardless of the initial value, starting from a certain point in time, the degree of inconsistency between the receipt and dispatch of goods at all stations except the initial node station becomes zero. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate the biokinematic characteristics of the triple jump technique in qualified athletes. Material and methods. The technique of the final attempts of qualified triple jumpers during the 2023 World Athletics Championships was analyzed. The following methods were used in the work: analysis and synthesis of scientific and methodological literature, analysis of video materials, methods of mathematical statistics. The video of the final attempts was used, provided for public use by the International Athletics Federation (World Athletics). The attempts were recorded by four video cameras installed on the side of the treadmill at a distance of 2,5 m, the height of the video camera mounted on a tripod was 1,5 m. Two of the four video cameras recorded video at a frequency of 25 frames per second, the other two video cameras recorded video at a frequency of 1300 frames per second. Video analysis of the final attempts and processing of biokinematic parameters of the technique were determined using the Dartfish and Kinovea programs. All statistical data were processed in the statistical analysis program IBM SPSS Statistics version 28.0.1.0. Results. The metric, time, angular, and speed characteristics of the take-off technique are determined; push-off; jump; step; jump for highly skilled triple jumpers. Based on the analysis of the triple jump technique indicators, the optimal biokinematic parameters of the technique that athletes need to have in order to achieve high competitive results were obtained. Conclusions. To achieve high competitive results in the triple jump, the following technical parameters must be used. The take-off speed should reach 10.40 m s-1 or more, the length of the last take-off step should be 2,20-2,40 m; the pace of the last step is 4-5 steps/s. The push-off; from the bar should last 0.100-0.133 s; the push-off; angle reaches 65-70 ̊; the speed of departure of the general center of mass of the body is 9.60 m s-1 and more. The jump should last no more than 0,660 s. During the jump, the high height of the overall center of body mass should be 1.30-1.45 m; upon landing, the speed of the general center of mass of the body is 9,20 m s-1 or more. The second push-off should last 0,160-0,180 s, the push-off angle is 60-65°. The take-off speed of the general center of body mass in a step should be 8,80 m s-1 or more. The optimal step duration is 0.450-0.520 s, step length is 5.20-5.50 m, the maximum height of the overall center of body mass is 1.24-1.30 m; upon landing, the speed of the general center of mass of the body is 8.50 m s-1 or more. The third push-off should last 130-166 s. The push-off angle is 63-70°. The take-off speed of the general center of mass of the body in a jump is from 8 m s-1 and more. The optimal duration of the jump is 0.750-0.780 s; maximum height of the overall center of body mass 1,10-1,22 m; upon landing, the speed of the general center of mass of the body is 7 m s-1 or more. When taking off and landing, the angle in the supporting leg throughout all three jumps should be 163-170 ̊. [ABSTRACT FROM AUTHOR]

The gabion mesh is an essential part of gabion-protection structures, which is extensively used in the fields of civil engineering and hydraulic engineering. The quasi-static tensile tests were numerically implemented by establishing a three-dimensional model of the gabion mesh and based on the Abaqus finite element software. The numerical implementation process was described, the simulation results were discussed and analyzed, and verified with physical test results. Firstly, the constitutive model parameters of the mesh material were explored and calibrated to ensure the applicability to numerical simulations. Further, the energy history of the mesh system was analyzed and the reliability of the numerical results was demonstrated. The comparison between numerical simulation and physical test results show that the former well reflects the mechanical characteristics of the gabion mesh under the corresponding working conditions, which lays the foundation for the subsequent in-depth study of the gabion structure. [ABSTRACT FROM AUTHOR]

Social networks provide a fairly wide range of data that allows one way or another to evaluate the effect of the dissemination of information. This article presents the results of a study that describes methods for determining the key parameters of the model needed to analyze and predict the dissemination of information in social networks. An approach based on the analysis of statistical data on user behavior in social networks is proposed. The process of evaluating the main features of the model is described, including the mathematical methods used for data analysis and information dissemination modeling. The study aims to understand the processes of information dissemination in social networks and develop recommendations for the effective use of social networks as a communication and brand promotion tool, as well as to consider the analytical properties of the classical susceptible-infected-removed (SIR) model and evaluate its applicability to the problem of information dissemination. The results of the study can be used to create algorithms and techniques that will effectively manage the process of information dissemination in social networks. [ABSTRACT FROM AUTHOR]

A growing body of research on energy harvesting has revealed the need for effective tools, models, and methods for designing indoor light harvesting systems. In general, PV modules are represented electrically by circuits which include Single Diode Models (SDM), Double Diode Models (DDM), and Triple Diode Models (TDM). PV modules' performance can be understood by appropriately determining the parameters of these models. In order to estimate these parameters, datasets are usually measured experimentally by electrically lighting PV modules or placing them under artificial light. The main goal of this study is to analyse the effects of the nature of light source and illumination level on different model parameters of PV modules. Three PV modules of same power and different cell technology are placed under three commonly used lamps at same illumination levels. The data measured under these conditions is used with the Grey Wolf Optimization algorithm to estimate different model parameters. The results indicate that different values are obtained for shunt and series resistances for the same PV module placed under different lamps and at same illumination level. These parameters are equally seen to vary differently when the illumination level is changed.

Forests worldwide are experiencing increasingly intense biotic disturbances; however, assessing impacts of these disturbances is challenging due to the diverse range of organisms involved and the complex interactions among them. This particularly applies to invasive species, which can greatly alter ecological processes in their invaded territories. Here we focus on the pine wood nematode (PWN, Bursaphelenchus xylophilus), an invasive pathogen that has caused extensive mortality of pines in East Asia and more recently has invaded southern Europe. It is expected to expand its range into continental Europe with heavy impacts possible.Given the unknown dynamics of PWN in continental Europe, we reviewed laboratory and field experiments conducted in Asia and southern Europe to parameterize the main components of PWN biology and host-pathogen interactions in the Biotic Disturbance Engine (BITE), a model designed to implement a variety of forest biotic agents, from fungi to large herbivores. To simulate dynamically changing host availability and conditions, BITE was coupled with the forest landscape model iLand. The potential impacts of introducing PWN were assessed in a Central European forest landscape (40,928 ​ha), likely within PWN’s reach in future decades.A parameter sensitivity analysis indicated a substantial influence of factors related to dispersal, colonization, and vegetation impact, whereas parameters related to population growth manifested a minor effect. Selection of different assumptions about biological processes resulted in differential timing and size of the main mortality wave, eliminating 40%–95% of pine trees within 100 years post-introduction, with a maximum annual carbon loss between 1.3% and 4.2%. PWN-induced tree mortality reduced the Gross Primary Productivity, increased heterotrophic respiration, and generated a distinct legacy sink effect in the recovery period. This assessment has corroborated the ecological plausibility of the simulated dynamics and highlighted the need for new strategies to navigate the substantial uncertainty in the agent’s biology and population dynamics.

On March 12, 2020, the WHO declared the COVID-19 pandemic, in response to widespread infections and thousands of fatalities resulting from the outbreak. Researchers worldwide have paid close attention to this pandemic. This situation has been aided by mathematical models that guide public policy. In Rwanda, this problem involves uncertainty and incomplete information, and a stochastic approach is appropriate for capturing a broad range of possible outcomes. However, many epidemiological models do not account for preventive measures using a stochastic approach to analyze COVID-19 in Rwanda. Therefore, the aim of the paper is to build and analyze a COVID-19 pandemic stochastic model with preventive measures implemented in Rwanda. A deterministic model is formulated and transformed into stochastic equations. The numerical solutions of both models were computed and compared. The stochastic model is considered to be the best to use in the Rwanda context because of the model’s fluctuations, which capture even the residuals and uncertainties. The results indicate that the pandemic could spread faster if awareness-raising strategies are not effectively implemented, and preventive actions for the pandemic are considered crucial tools for effective control and mitigation.

The utilization rate of reclaimed asphalt pavement is significantly low in the sustainable design process of asphalt roads. Numerous researchers have extensively investigated the recycling and utilization of reclaimed asphalt pavements from various perspectives. Molecular dynamics simulations could elucidate the diffusion phenomenon occurring at the molecular scale between virgin and aged asphalt interfaces. This review provides a comprehensive summary of the simulation methods and applications of molecular dynamics simulation in the interface diffusion problem between virgin and aged asphalt. Diffusion theory and model testing methods are discussed. The review proposes the basic steps of molecular dynamics simulation and summarizes the molecular models with the corresponding parameter settings of virgin asphalt and aged asphalt. Moreover, the current influencing factors on the interfacial diffusion problem of virgin and aged asphalt are discussed. The paper explores the validation parameters including density, viscosity, radial distribution function (RDF), glass transition temperature, and solubility parameters based on the existing research. Molecular dynamics simulation could simulate interface diffusion at a micro-scale and clarify the diffusion depth and influence range of different asphalts. The purpose of the study of molecular dynamics is to solve interface issues and advance optimization of reclaimed materials. [ABSTRACT FROM AUTHOR]

Using the extended physics-informed neural network with twin subnets to study the coupled mixed derivative nonlinear Schrödinger equation (NLSE), seven types of vector solitons including vector single soliton, vector double solitons, anti-dark vector single soliton, anti-dark vector double solitons, vector rogue wave, vector bright–dark single soliton and vector bright–dark double solitons are predicted. The prediction results from seven types of vector solitons with different angles confirm that the physical neural network can be used to effectively solve the coupled mixed derivative NLSE. The error on the two sides and the falling point of the vector rogue wave solution is significantly greater than the middle, while the error of other six types of vector soliton solution mainly reflects in the peak or valley value of bright or dark soliton, and increases along the transmission distance. In addition, how to improve the prediction of model parameters from two aspects of data set and loss function is also studied. These results have certain reference value for studying the optical soliton transmission process by the machine learning. [ABSTRACT FROM AUTHOR]

The uncertainty of climatic or weather variations makes human adaptation a challenging task. Though lots of techno developments have taken place addressing the need of predictions and forecasting of climatic behavior, the uncertainty of atmospheric and geoprocesses poses a severe challenge to the effectivity of efforts in dealing with disasters. It has become very crucial to understand the future atmospheric uncertainty process and further predict and forecast for analysis of different activities based on geographical locations. Climate parameters are mandatorily required to improve system activities and output analysis of acquired data. For a sustainable adaptation, this is very important on how the uncertainty of variations in climatic parameters is dealt and the decisions based upon appropriate forecasting model are taken. The work has primarily focused on the development of an appropriate model, training the model with massive monthly rainfall and temperature data which can be used to analyze any climatic predictions and provide support to deal with adverse future situations. This model determines the appropriate amount of rainfall and temperature varied during the dry season and helps set a relationship with other input variables like humidity and maximum and minimum temperature. The model has been trained and tested using the obtained data and performed well with better accuracy. The model developed using long short-term memory algorithm of deep learning for predicting rainfall and temperature climatic variables is supported with the study of the main factors affecting monthly climatic change analysis. Monthly precipitation, rainfall intensity, and temperature have been used in the multipurpose prediction model to analyze the monthly and yearly climatic change prediction. The model has also been optimized for precision, accuracy, and computational speed, which are affected by number of iterations, neurons of a hidden layers, epochs, and batch size. [ABSTRACT FROM AUTHOR]

Teaching–learning-based optimization (TLBO) is a meta-heuristic algorithm that simulates the process of teacher and student (or learner) interaction in transmitting knowledge. The algorithm is relatively simple to implement, with free-tuning parameters for balancing exploration and exploitation of the solution space. TLBO contains two phases, namely, teaching and learning. In this paper, self-adaptive Gaussian bare-bones TLBO (SABBTLBO) is proposed for improving TLBO and Gaussian bare-bones TLBO (BBTLBO) performance. In the SABBTLBO, Gaussian bare-bones and the original teaching phase in TLBO become more adaptive by a mechanism based on the learner's rank. For the new learning phase, an adaptive scaling factor based on the rank mechanism is used to modify the neighborhood search strategy. A restarted mutation approach is also added in the learning phase. The developed SABBTLBO is compared with six state-of-the-art TLBO variant algorithms for inversion of synthetic multiple self-potential (SP) anomaly sources. The proposed SABBTLBO algorithm is also tested and compared with several algorithms applied for field SP data from different locations in the world including India, Portugal, and Indonesia, using the assumption that SP data are sourced by idealized bodies (simple geometric model or thin sheet model). The inversion of multiple SP anomaly sources using SABBTLBO is used not only for determining the best model parameters, but also their uncertainties. The latter is estimated from the equivalence region of the set of possible solutions via cost function topography evaluation. Significant results were obtained and can be associated with the geology of studied area. [ABSTRACT FROM AUTHOR]

Power battery performance plays a pivotal role in the comprehensive performance of electric vehicles, and thus accurate identification of the parameters of the lithium-ion battery model is crucial for subsequent state-of-charge estimation and state-of-health prediction of the battery system. In order to improve the accuracy of parameter identification algorithm of lithium-ion battery model, a second-order RC equivalent circuit model of the battery is established, and a least-squares algorithm based on variable forgetting factor is used to identify the parameters of the lithium-ion battery model online. By building a test platform for charge and discharge experiments, based on the experimental data of two different operating conditions, the proposed algorithm, recursive least squares algorithm and traditional forgetting factor least squares algorithm are used to identify the parameters, and the accuracy of the proposed algorithm is described based on the comparison of the error between the estimated port voltage and the actual value obtained from the experimental test. The experimental results show that the recursive least squares algorithm based on the variable forgetting factor shows fast convergence and high estimation accuracy in the identification of lithium-ion battery parameters.

Early stages of diabetic kidney disease (DKD) are difficult to diagnose in patients with type 2 diabetes. This work was aimed at identifying contrast-enhanced ultrasound (CEUS) perfusion parameters, a microcirculatory biomarker indicative of early DKD progression. CEUS kidney flash-replenishment data were acquired in control, insulin resistant and diabetic vervet monkeys (N = 16). By use of a mono-exponential model, time-intensity curve parameters related to blood volume (A), velocity (β) and flow rate (perfusion index [PI]) were extracted from 10 concentric kidney layers to study spatial perfusion patterns that could serve as strong indicators of disease. Mean squared error (MSE) was used to assess model performance. Features calculated from the perfusion parameters were inputs for the linear regression models to determine which features could distinguish between cohorts. The mono-exponential model performed well, with average MSEs (±standard deviation) of 0.0254 (±0.0210), 0.0321 (±0.0242) and 0.0287 (±0.0130) for the control, insulin resistant and diabetic cohorts, respectively. Perfusion index features, with blood pressure, were the best classifiers between cohorts (p < 0.05). CEUS has the potential to detect early microvascular changes, providing insight into disease-related structural changes in the kidney. The sensitivity of this technique should be explored further by assessing various stages of DKD. [ABSTRACT FROM AUTHOR]

In addressing the design challenges for constant-stress accelerated life testing in non-rectangular experimental domains, we aim to optimize the precision in estimating parameters for the product reliability statistical model. Following the principles of regression orthogonal design theory to determine the combinations of stress levels, we constrain the maximum stress levels of each experimental stress along the boundary curve of the non-rectangular experimental domain. The remaining stress levels and the allocation ratios of specimens for each test serve as design variables in the optimization process. We establish a mathematical model for the optimal design of constant-stress accelerated life testing in non-rectangular experimental domains. The results of the optimized design for comprehensive stress accelerated life testing in non-rectangular experimental regions of aerospace electrical connectors indicate that, with the same sample size, the optimized testing scheme not only enhances the precision of model parameter estimation but also reduces the number of required tests. At an equivalent number of tests and testing duration, the optimization scheme proposed in this study demonstrates an improvement of over 63% in the precision of model parameter estimation compared to the EM-optimized testing scheme in non-rectangular experimental regions. Using the mean, standard deviation, and coefficient of variation of the determinant values of the information matrix as criteria for evaluating the precision and robustness of experimental designs, a simulated evaluation was conducted for the optimized experimental design, a conventional experimental design, and an EM experimental design. The results indicate that the optimal experimental design outperforms both the conventional experimental design and the EM experimental design in terms of precision and robustness.

Within the large Additive Manufacturing (AM) process family, Directed Energy Deposition (DED) can be used to create low-cost prototypes and coatings, or to repair cracks. In the case of M4 HSS (High Speed Steel), a reliable computed temperature field during DED process allows the optimization of the substrate preheating temperature value and other process parameters. Such optimization is required to avoid failure during the process, as well as high residual stresses. If 3D DED simulations provide accurate thermal fields, they also induce huge computation time, which motivates simplifications. This article uses a 2D Finite Element (FE) model that decreases the computation cost through dividing the CPU time by around 100 in our studied case, but it needs some calibrations. As described, the identification of a correct data set solely based on local temperature measurements can lead to various sets of parameters with variations of up to 100%. In this study, the melt pool depth was used as an additional experimental measurement to identify the input data set, and a sensitivity analysis was conducted to estimate the impact of each identified parameter on the cooling rate and the melt pool dimension.

The Crop Environment Resource Synthesis-Maize (CERES-Maize) mechanistic model, included in the Decision Support System for Agrotechnology Transfer (DSSAT), is a useful and powerful tool that simulates the growth and grain yield of maize in different environments. The qualitative and quantitative information provided to the CERES-Maize model guarantees reliability in the simulations obtained. However, it requires a lot of information, including soil characteristics, daily climate, crop characteristics and management, and six genetic coefficients. The objective of this research was to assess a non-destructive methodology for estimating the six genetic coefficients P1, P2, P5, G2, G3 (associated with plant maturity stages) and Phyllochron interval (PHINT), based on the maize physiology and measured by the Growing Degree Days (GDD), base 10. Two experiments were established at the International Maize and Wheat Improvement Center (CIMMYT) experimental station in Tlaltizapan, Morelos, Mexico, where 27 white and 14 yellow maize hybrids were manually sown in an irrigation conservation tillage system. Once the maize growth and grain yield simulations were obtained with CERES-Maize model, the genetic coefficients were calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE). After calibration of the six genetic coefficients for all hybrids, average values of P1, G2, and G3 were within the typical range, while P2 and P5 were greater than the typical range and PHINT was below typical range. However, the simulation model showed good performance after calibration, with an average R2 of 0.9809 and 0.9730 between measured and simulated grain yields for white and yellow hybrids, respectively. The coefficients estimated in this study can be used in the CERES-Maize model to simulate grain yields for the hybrids used in different regions of the country. [ABSTRACT FROM AUTHOR]

A computer simulation of the model liquid flow and liquid nitrogen in a centrifugal cryogranulator was performed. This work presents a good convergence of the results of computer modeling on a model liquid (water) and a full-scale experiment with model parameters, namely, the SST k–w-turbulence model together with equilibrium wall FlowVision functions. The field of liquid nitrogen flow velocities in the volume of the centrifugal cryogranulator is determined. [ABSTRACT FROM AUTHOR]

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A complex investigation was performed for a polluted area using both experimental and computer modelling methods. Among the experimental methods the adsorption and desorption isotherms were measured to estimate the concentration dependent equilibrium in the soil--groundwater system. A new calculation method was worked out for determining the transport parameters from results of laboratory tests. Heavy metal solution was leached through a soil column continuously. The effluent fluidum was collected, and the heavy metal concentration of the collected fractions was measured by atomic absorption spectrophotometer. As the result of the analytic process breakthrough curves were measured in laboratory scale. Due to the applied initial and boundary conditions the transport equation can be solved analytically. Using the OGATA and BANKS (1961) solution of the transport equation a new curve fitting method was introduced. After several transformations of the equation a theoretical function was fitted to the measured concentration vs. time and to the concentration vs. effluent volume data. The parameters of the fitted curve could be used as the dispersion and retardation parameters of a transport model. The water chemistry of the system controls the rate of adsorption and desorption of metals to and from sediment. Adsorption removes the metal from the water column and stores the metal in the substrate. Desorption returns the metal to the water column, where recirculation and bioassimilation may occur. Metals are probably desorbed from the soil if the salt concentration of the water increases, and in case of some metals decreases with the redox potential and with pH. Parallel to determining the basic transport parameters of the system using the column study, the maximal equilibrium concentration of chromium-containing compounds with different oxidation states were calculated with the MINTEQ model with two variable functions (pH and redox potential). As a result of the calculations a non-liner relation was established, as at specific points the maximal equilibrium concentration of chromium increases with a high gradient. This means that there are combinations of pH and redox potential values in the case of which chromium has a high solubility. It is advisable to avoid these points in the pH--Eh field if we want to stabilize the contaminant. This state is to be reached when the goal is the mobilization of the pollutant to make the soil cleaning process possible. With the introduced calculation method areas on the pH--redox potential field (at high pH and Eh values) are found in which the concentration of pollutants may reach a critical value. The introduced calculation method is quick and gives results accurate enough for a pilot test. [ABSTRACT FROM AUTHOR]

Taking coal as the research subject, the damage mechanical properties of coal were studied by grading cyclic loading and unloading tests. Using the Weibull statistical distribution and the introduction of damage variable D, a damage constitutive model suitable for coal at loading and unloading stages was established, which conforms to Mises yield criterion based on the coal microelement strength. According to the test data, the model parameters of each cycle grade were determined by nonlinear fitting method, and the physical significance of the model parameters was analyzed. The results show that the damage constitutive model based on Mises yield criterion can reflect the stress-strain relationship of coal under grading cyclic loading and unloading accurately. With the increase of loading and unloading levels, the residual strain accumulates, and the elastic modulus increases first and then decreases gradually. The hysteresis loop shape shows the characteristics of “fat”-“thin”-“fat”. The constitutive model parameters have clear physical significance.

Induction motors are the horsepower in the industrial environment, and among them, 3-phase induction motors (3PIMs) stand out for their robustness and standard 3-phase power supply. In the literature, there are many approaches to diagnose faults for the nonlinear 3PIM model, and the vast majority focus on a single motor fault, although others address more faults but at the cost of greater computational complexity. In this sense, one of the methods with less computational load and early detection is the parity equation approach, which is based on analyzing the discrepancy between the input and output signals of a real process and a linear mathematical model to generate a residual signal, which contains important information about the fault and is obtained through a suitable selection of a weighting matrix W to isolate the faults as much as possible. The problem in this case study is that the 3PIM model is a nonlinear system. In this work, the fault detection method based on the parity equations approach applied in the 3PIM is explored using a simplified and proposed model of the 3PIM working in the D-Q synchronous reference frame, which is matched with the direct current motor model to guarantee both the existence of the parity space and to ensure a large set of detectable faults in the 3PIM parameters. Simulation and experimental results validate the proposed scheme and confirm a very simple set of residual equations to guarantee both early detection and a large set of detectable faults in: Stator and rotor resistances, stator and rotor inductances, as well as current, voltage, and speed sensors. Additionally, development of human machine interface (HMI) is implemented to validate the proposed scheme. [ABSTRACT FROM AUTHOR]

This article is devoted to the study of the model of cargo transportation organization between two nodal stations. The main characteristic of an arbitrary station is the degree of inconsistency between receiving and sending cargo, which is the difference between the volume of incoming and outgoing cargo per unit of time. The initial node station accepts goods depending on the demand for them within its technical potential, which is determined by the maximum allowable increase in the degree of inconsistency between the reception and dispatch of goods per unit of time. The movement of goods from one station to another is carried out within the framework of their technical potentials. The distribution of goods from the final node station is carried out in a certain mode. Such a model is described by a system of differential equations with a number of parameters that define the characteristics of the demand for cargo transportation, the degree of use of the technical potential of the stations and the mode of cargo distribution from the final node station. When changing the parameters of the model, a bifurcation effect occurs and multiple stationary solutions appear, among which the most acceptable from the point of view of economic feasibility are identified. [ABSTRACT FROM AUTHOR]

The learn ability of animals to behavioral decisions in a specific environment is an important basis for their survival, therefore, how to accurately evaluate the learning ability between using past experience and valuing future reward of animals in Markov decision-making tasks is important for animal behavior and psychology. A Markov decision task with state transition probability was designed, pigeons were trained to choose between two options in different states and consider future benefits to maximize cumulative rewards. At the end of the experiment, Q-learning model of pigeons' behavioral decision-making was modeled, and the learning rate was used to evaluate their ability to make choices based on past experience, and the discount factor was used to evaluate their emphasis on future rewards. The results shows that the learning ability between using past experience and valuing future reward of pigeons in Markov decision-making tasks could be evaluated by Q-learning model parameters. [ABSTRACT FROM AUTHOR]

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin'anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models. [ABSTRACT FROM AUTHOR]

Myriophyllum spicatum is a sediment‐rooted, aquatic macrophyte growing submerged, with a wide geographical distribution and high ecological relevance in freshwater ecosystems. It is used in testing and risk assessment for pesticides in water and sediment. Population models enable effects measured under laboratory conditions to be extrapolated to effects expected in the field with time‐variable environmental factors including exposure. These models are a promising tool in higher‐tier risk assessments. However, there is a lack of data on the seasonal dynamics of M. spicatum, which is needed to test model predictions of typical population dynamics in the field. To generate such data, a two‐year study was set up in outdoor experimental systems from May 2017 to May 2019. The growth of M. spicatum was monitored in 0.2025 m2 plant baskets installed in an experimental ditch. Parameters monitored included biomass (fresh weight [FW] and dry weight [DW]), shoot length, seasonal short‐term growth rates of shoots, relevant environmental parameters, and weather data. The results showed a clear seasonal pattern of biomass and shoot length and their variability. M. spicatum reached a maximum total shoot length (TSL) of 279 m m−2 and a maximum standing crop above‐ground DW of 262 g m−2. Periodical growth rates reached up to 0.072, 0.095, and 0.085 day−1 for total length, FW, and DW, respectively. Multivariate regression revealed that pH (as a surrogate for the availability of carbon species) and water temperature could explain a significant proportion of the variability in M. spicatum growth rates (p < 0.05). This study has provided an ecologically relevant data set on seasonal population dynamics representative of shallow freshwater ecosystems, which can be used to test and refine population models for use in chemical risk assessment and ecosystem management. Integr Environ Assess Manag 2022;18:1375–1386. © 2021 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points: This study has generated a time‐series of seasonal dynamics for the growth of M. spicatum over two years under environmental conditions found in temperate regions to be used to develop and test population models for Myriophyllum spicatum.Myriophyllum spicatum showed a clear seasonal pattern of biomass and shoot length and of their variability (increasing in summer and decreasing in winter).Multiple regression modeling revealed that water temperature and pH (the latter as surrogate for the available carbon species) explained a significant part of the variability in Myriophyllum growth rates (p‐values < 0.05).Over the first four experimental months in summer, exponential functions yielded a better fit for the growth of Myriophyllum spicatum than linear growth functions. [ABSTRACT FROM AUTHOR]