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In this article, we examine the empirical and theoretical support for the idea that experiences of isolation can intensify substance use, even among those in committed close relationships, and can increase the likelihood of negative conflict behaviors, including intimate partner violence (IPV). Cross-sectional, longitudinal, and laboratory-based research suggest that individuals and couples who report experiences of social exclusion and have fewer social supports experience more mental health problems, more negative emotional experiences, and higher levels of stress. These negative outcomes spur coping efforts to reduce these distressing states, which often involve pain-relieving substances such as alcohol. We describe how dynamic models of relationship conflict and IPV can integrate alcohol and substance use patterns among both individuals and couples to understand and predict contexts leading to conflict escalation and IPV perpetration.

Enforcement of laws that prevent corrupt international business dealings has intensified recently. Firms have paid record-setting fines of hundreds of millions of dollars, and individuals have been tried and convicted. This escalating situation demands effective action from business leaders. Compliance has become increasingly complicated as more countries enact anti-bribery laws. To address this situation, four root causes of corruption are identified, and innovative real-world examples of solutions are presented. Combinations of these solutions can be crafted to create a high compliance work system (HCWS) to avoid corruption. Firms can formulate their own unique, innovative, and dynamic models to achieve high levels of firm success while also avoiding corruption. These models go beyond trade-off thinking, which suggests that compliance must be exchanged for performance. Pivoting away from such trade-off thinking enables innovative solutions to manage corruption risks and offers firms a sustained competitive advantage over their peers.

Microbial communities and their functions are shaped by complex networks of interactions among microbes and with their environment. While the critical roles microbial communities play in numerous environments have become increasingly appreciated, we have a very limited understanding of their interactions and how these interactions combine to generate community-level behaviors. This knowledge gap hinders our ability to predict community responses to perturbations and to design interventions that manipulate these communities to our benefit. Dynamic models are promising tools to address these questions. We review existing modeling techniques to construct dynamic models of microbial communities at different scales and suggest ways to leverage multiple types of models and data to facilitate our understanding and engineering of microbial communities.

Aims Left ventricular outflow tract(LVOT) obstruction after mitral valve replacement can be life-threatening once occur. We simulated mitral valve replacement preoperatively using dynamic, three-dimensional(3D) printed models to help predict LVOT obstruction in this study. Methods 56 patients who underwent mitral valve replacement were included. Prediction of LVOT obstruction in vitro was based on the data from 4 sources: digital, anatomical, flexible, and dynamic model. Digital 3D models were designed based on computed tomography (CT) image dataset and printed with photopolymer resin to create a 3D anatomical model, which contributed to the morphology display. Then, flexible models were made from specialized silicone, which is similar to cardiac tissue in terms of its softness and elasticity. Dynamic function was achieved by coupling flexible models to a mock circulatory system (MCS). Besides, surgery simulation and hemodynamic testing was done using dynamic 3D printed model and patients were regrouped based on hemodynamic change. Finally, different methods for prediction of LVOT obstruction as well as classification based on two-dimensional image data and dynamic model were compared with surgical results as golden standard. Results (1)Qualitatively, the prediction of LVOT obstruction using the dynamic 3D model was the most accurate and was consistent with clinical outcomes. In the four patients who developed LVOT obstruction after surgery, only two were at a high risk based on the other three models. (2)Quantitatively, the area of neo-LVOT predicted by the digital, anatomical, and flexible models was higher compared with the dynamic models and in-vivo after surgery. (3)Classification based on traditional criteria(two-dimensional image data) was different from surgical results. While the difference between dynamic model and surgical results was not statistically different. Conclusions After coupling the flexible model with the mock circulatory system, the dynamic 3D model predicted LVOT obstruction more accurately with hemodynamic testing compared with morphological evaluation. 3D printing can assist surgeons to better plan mitral valve replacement than traditional image data.

International audience; The input into the QIVIVE and Physiologically-Based kinetic and dynamic models of drug metabolising enzymes performance and their inter-individual differences significantly improve the modelling performance, supporting the development and integration of alternative approaches to animal testing. Bayesian meta-analyses allow generating and integrating statistical distributions with human in vitro metabolism data for quantitative in vitro-in vivo extrapolation. Such data are lacking on glutathione-S-transferases (GSTs). This paper reports for the first time results on the human variability of GST activities in healthy individuals, their tissue localisation and the frequencies of their major polymorphic variants by means of extensive literature search, data collection, data base creation and meta-analysis. A limited number of papers focussed on in vivo GST inter-individual differences in humans. Ex-vivo total GST activity without discriminating amongst isozymes is generally reported, resulting in a high inter-individual variability. The highest levels of cytosolic GSTs in humans are measured in the kidney, liver, adrenal glands and blood. The frequencies of GST polymorphisms for cytosolic isozymes in populations of different geographical ancestry were also presented. Bayesian meta-analyses to derive GST-related uncertainty factors provided uncertain estimates, due to the limited database. Considering the relevance of GST activities and their pivotal role in cellular adaptive response mechanisms to chemical stressors, further studies are needed to identify GST probe substrates for specific isozymes and quantify inter-individual differences.

The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perception based on their digital footprints. The research material was social media data. To optimize the analysis, several parallel models were used. The results were obtained using the semantic neural network model; in parallel, statistical analysis of the experimental data was performed, and dynamic models of processes were built. The analysis of the solutions from all models showed that all solutions were consistent. Types of models used for the data analysis are: a neural network-based model, statistical analysis and differential equations.

Artificial intelligence (AI) is the development of computer systems that normally require human intelligence. In the field of acute kidney injury (AKI) AI has led to an evolution of risk prediction models. In the past, static prediction models were developed using baseline (eg, preoperative) data to evaluate AKI risk. Newer models which incorporated baseline as well as evolving data collected during a hospital admission have shown improved predicative abilities. In this review, we will summarize the advances made in AKI risk prediction over the last several years, including a shift toward more dynamic, real-time, electronic medical record-based models. In addition, we will be discussing the role of electronic AKI alerts and decision support tools. Recent studies have demonstrated improved patient outcomes through the use of these tools which monitor for nephrotoxin medication exposures as well as provide kidney focused care bundles for patients at high risk for severe AKI. Finally, we will briefly discuss the pitfalls and implications of implementing these scores, alerts, and support tools.

Rather than being sold several months before a program is aired, more than 20% of TV advertising slots are retained for sale weekly near the program’s broadcast time. Distinct from the literature that mainly focuses on the forecasting of program ratings for advanced sales of advertising slots, we explore approaches that can provide more accurate forecasts for near real-time ratings. We propose two dynamic models that mainly employ individual viewing records for past episodes to forecast viewers’ decisions on episodes in the coming week, and therefore the ratings for these episodes. One is a reduced-form dynamic model that measures the influence of past watching experience by the weighted average of the viewers’ choices of past episodes. The other is a structural dynamic model that goes deeper in its use of previous viewing information by modeling the underlying process of this influence based on the Bayesian updating theory. Using data from the Hong Kong TV industry, we test and compare the two models. Results show that the reduced-form model generally performs better when the variance of ratings across episodes is small, while the structural model generates more accurate forecasts in other cases.

We present an application of the switching linear dynamical model (SLDM) to model the nominal Mexican peso-USD exchange rate time series from January 01, 1970 to January 09, 2019. Two main features exhibited by the time series are observed: dynamical behavior with upward and downward movements after moving to the floating exchange rate regime, and most recently a persistent increasing trend. The SLDM is a flexible method for learning about dynamic models for time series with complex and uncertain behavior patterns, such as those exhibited by the exchange rate. The SLDM allows us to account for four novel results not obtained before: (1) we are able to identify four regimes; (2) we cluster the observations into regimes; (3) we provide the estimated probability of a change point at each time instead of estimating a regime constant persistence; and (4) we find that the change points match with some internal and external events associated with crisis periods.

To address the discrepancy between dynamic models and industrial practice in airline revenue management, we propose a hybrid model for the multi-leg problem that integrates static and dynamic models. We formulate the problem using a stochastic dynamic program and characterize the optimal booking policy as a switching-curve (surface) type.

This paper shows that oil shocks impact economic growth primarily through the conditional variance of growth. Our comparison of models focuses on density forecasts. Over a range of dynamic models, oil shock measures and data, we find a robust link between oil shocks and the volatility of economic growth. We then develop a new measure of oil shocks and show that it is superior to existing measures; it indicates that the conditional variance of growth increases in response to an indicator of the local maximum oil price exceedance. The empirical results uncover a large pronounced asymmetric response of the growth volatility to oil price changes. The uncertainty about future growth is considerably lower than with a benchmark AR(1) model when no oil shocks are present.

In Smart Manufacturing, the Internet of Things technology brings about new possibilities as for the interaction of Cyber-Physical Systems with their virtual models—in Industry 4.0 jargon, Digital Twins. Besides traditional roles in engineering and control design, Digital Twins cab play innovative ones by evolving together with their cyber-physical counterpart: predictive maintenance, fault detection, or fast training of data-based decision aid tools. In this paper we focus on the relevance of dynamic models in this scenario, particularly as for the co-existence of continuous-time and discrete-event models wherever control and planning are involved. We argue that in such cases, models with analysis-specific levels of detail have to co-exist, interact and maintain mutual consistency. We propose a modelling approach to address the problem, and present a supporting example based on an object-oriented modelling language.

The authors examine contemporary integral dynamic models of biological, environmental, and technological systems with heterogeneous components. The models contain two-dimensional control functions, nonlinearities, and delays in system inputs. The paper demonstrates the versatility of integral equations and their importance to various applications. Connections and advantages of integral and differential models are discussed. A survey of optimal control strategies for such models is provided. Keywords: Integral dynamic models, Endogenous delay, Optimal control, Population models, Technological renovation

In rare theoretical treatments of ideas developed by Hamilton (1975, 1976) almost fifty wears ago, recent papers by Barseghyan and Coate (2016) and Calabrese et al. (2007) develop dynamic models to analyze zoning and property taxation in a system of communities. Their analyses led to negative conclusions, contradicting Hamilton’s view, by showing that efficient zoning does not emerge in equilibrium. The purpose of this note is to show that a different, affirmative conclusion can be reached in a simpler, highly stylized model that formalizes Hamilton’s idea.

Tradable credit schemes (or tolling in tokens) are a form of quantity control, which promise to be an appealing alternative to congestion pricing (or tolling in dollars) owing to considerations of revenue neutrality, equity, reduced infrastructure costs, and political acceptability. The comparative performance of the two instruments under uncertainty in demand and supply has only recently received attention in the transportation setting, despite being widely studied for emission markets. In this paper, we add to this literature by considering a tradable credit scheme in a departure time context wherein users are provided an initial endowment of tokens by the regulator and incur a token charge (determined prior to all departures) to travel in a specific time-period. Tokens can be bought and sold within a marketplace at a price determined endogenously by token demand and supply. Two key features of the market model are: (1) the time-of-day dynamics of price is explicitly modeled through a smooth market clearing mechanism in each period, and (2) the selling decisions of users, which determine the distribution of token supply in the market over the day are explicitly modeled. This enables us to study the impact of selling behavior on performance of the credit system. Travel demand is modeled using a logit mixture model and supply consists of static congestion. Extensive experiments under stochastic demand show that when the tolls (in dollars and tokens) are not day-to-day adaptive, tolling in tokens outperforms tolling in dollars when congestion effects are more severe (e.g., realistic BPR models and steep congestion functions, high demand levels and high day-to-day variability). Importantly, we find that this result is robust with respect to selling behavior in the market, although there can be welfare losses in the quantity control system when selling behavior in the market is excessively irrational. These findings underscore the importance of examining disaggregate market behavior when designing tradable credit schemes. Moreover, when the supply of tokens can be adapted from day to day, the credit system was found to be superior in all tested scenarios, provided the selling behavior of individuals is rational. Finally, even in the case when toll revenues in the price instrument are equally redistributed (often difficult in practice), tolling in tokens (when tokens are equally distributed) is marginally more equitable in scenarios where congestion effects are more severe. These findings make a case for tolling in tokens.

The invention of heat pipe solar collectors (HPSCs) is considered as an immense step forward towards solving the challenges of conventional solar thermal systems. Their unique qualities have acted as a great motivation for researchers to focus their studies on HPSCs and their applications. A considerable share of these studies has been allocated to theoretical studies due to several technical and economic reasons. However, to the authors’ knowledge and despite many valuable efforts in this field, there is no review paper available to summarise the relevant proposed and developed theoretical models to date and identifies the research gaps in this field. Therefore, in this review paper, the latest theoretical studies in the field of HPSCs along with their advantages, disadvantages, and contribution have been categorized, reviewed, and discussed. First, the operational principles and structure of HPSCs have been explained to create a background for readers. This is followed by a short section dedicated to the simulation of solar radiation as the most important input for all solar mathematical models. In addition, various mathematical approaches including steady state models (i.e. one-dimensional energy balance and thermal resistance network methods), dynamic models, and models for novel configurations and applications of HPSCs have been reviewed. Moreover, mathematical models to determine the exergy efficiency of HPSCs, which is an effective tool to evaluate the solar systems from a thermodynamic point of view, have been presented. Finally, the challenges, research gaps, and recommendations for future research directions have been provided.

This review evaluates the three dynamic models (biokinetic model: BK, physiologically based pharmacokinetic model: PBPK, and toxicokinetic-toxicodynamic model: TKTD) in our understanding of the key questions in metal ecotoxicology in aquatic systems, i.e., bioaccumulation, transport and toxicity. All the models rely on the first-order kinetics principle of metal uptake and elimination. The BK model basically treats organisms as a single compartment, and is both physiologically and geochemically based. With a good understanding of each kinetic parameter, bioaccumulation of metals in any aquatic organisms can be studied holistically and mechanistically. Modeling efforts are not merely restrained from the prediction of metal accumulation in the tissues, but instead provide the direction of the key processes that need to be addressed. PBPK is more physiologically based since it mainly addresses the transportation, transformation and distribution of metals in the organisms. It can be treated conceptually as a multi-compartmental kinetic model, whereas the physiology is driving the development of any good PBPK model which is no generic for aquatic animals and contaminants. There are now increasingly applications of the PBPK modeling specifically in metal studies, which reveal many important processes that are impossible to be teased out by direct experimental measurements without adequate modeling. TKTD models further focus on metal toxicity in addition to metal bioaccumulation. The TK part links exposure and bioaccumulation, while the TD part links bioaccumulation and toxic effects. The separation of TK and TD makes it possible to model processes, e.g., toxicity modification by environmental factors, interaction between different metals, at both the toxicokinetic and toxicodynamic levels. TKTD models provide a framework for making full use of metal toxicity data, and thus provide more information for environmental risk assessments. Overall, the three models reviewed here will continue to provide guiding principles in our further studies of metal bioaccumulation and toxicity in aquatic organisms.

We examine the dynamic relationship between oil prices and news-based indices of global geopolitical risks (GPRs), as well as a composite measure of the same for emerging economies, which we develop using Dynamic Model Averaging (DMA). In doing so, we train a number of linear and nonlinear probabilistic models to capture the ability of GPRs in forecasting oil returns. Our empirical findings show that global GPRs associated with wars is the most accurate in forecasting oil returns in the short-run, while composite GPRs emanating from the emerging markets, forecasts oil returns relatively better at medium- to longer-horizons. However, differences across the linear and nonlinear models incorporating information of GPRs are not necessarily markedly different. Given an observe negative relationship between GPRs and oil returns, density forecasts show that increases in GPRs from their initial lower levels, which would imply higher conditional oil returns initially, can predict the resulting increases in oil returns thereafter more accurately compared to the lower end of the conditional distribution, which in turn, corresponds to higher initial levels of GPRs.

The ability to perform safe landing is of great concern for reusable launch vehicles and landers; however, sloped terrains severely limit the degree of landing safety. To ensure that the launch vehicle and lander land safely, a novel control strategy was built for them. The control strategy depends on the slopes of terrains, which are measured by laser scanning machines. If the measured slope of a terrain is higher than 18% (10°), the lift of vehicle is increased by its engines to allow it to leave the terrain. When the measured slope of a terrain is lower than 18% (10°), the launch vehicle and lander are allowed to land on the terrain. To ensure that the launch vehicle and lander land on a sloped terrain safely, a new adaptive landing gear was designed for them. In the adaptive landing gear, each landing strut contains a shock absorber and an actuating cylinder. The actuating cylinders are controlled to adjust the angles of landing struts to ensure that all landing struts touch down simultaneously. To analyze the landing responses of landing struts, landing dynamic models were established for them. A set of vertical drop tests were used to validate the landing dynamic models. After the validation, dynamic simulations were used to analyze the influence of the adaptive landing gear on the landing responses of landing struts. The landing struts of the adaptive landing gear touched down simultaneously, and their landing responses were compared with traditional landing struts. The comparison verified that the adaptive landing gear decreased the maximum axial force of the most compressed shock absorber. As the maximum axial force of the shock absorber was decreased, its buffer efficiency was increased to 83%. The adaptive landing gear increased the slope limit of terrain when it was equipped on launch vehicles and landers. The launch vehicle with the adaptive landing gear landed on a 55% (29°) sloped terrain safely, and the lander with the adaptive landing gear landed on a 78% (38°) sloped terrain safely.

The objective of this research is to examine the effect of economic growth, energy consumption and CO2 emissions on each other for a panel of 68 countries. I used panel data from 68 countries over the period 2001–2017 for developed, emerging and the Middle East and North Africa (MENA) countries. I applied seemingly unrelated regression (SUR) and dynamic models estimated through means of the generalized method of moments (GMM) and System generalized method of moments (Sys GMM) for data analysis. The estimated empirical results indicates that economic growth increase with increase in energy consumption in developed and emerging countries while declined in MENA countries; CO2 emissions increase in all countries due to increase in energy consumption. Energy consumption increase in all countries but economic growth increase in all countries except MENA due to increase in CO2 emissions. The CO2 emissions increase while the energy consumption decrease in developed and MENA countries but energy consumption increase and CO2 emissions decrease in emerging countries due to increase in economic growth. Based on the estimated results it is recommended that the policy maker in these countries should adopt such strategies that should focus to adopt environmentally friendly technologies to decrease the CO2 emissions.

Proper site characterisation is vital in the planning stages of a CO2 storage project; but we can also learn a good deal about the reservoir once the injection is underway or has been completed. During CO2CRC Otway Project Stage 2C, sources of valuable information about storage performance have been generated as a consequence of the staged injection of 15,000 t of CO2 rich gas, as well as observations from time-lapse seismic surveys and well monitoring data. Now that injection has ceased for Stage 2C, the geological model is compared against field observations for the period spanning injection and 23 months after injection ended. The post-injection reservoir characterisation has proven important to refine the static and dynamic models for future field development and added assurance about the long-term stabilisation of the CO2 plume. The south-eastern progress of plume development, as seen on the time-lapse seismic data, has led to a review of the structural interpretation and horizon-fault geometry represented in the models. The developing plume has illuminated the extent of splay faults previously unresolved on the baseline seismic data. Saturation profiles interpreted from pulsed-neutron logs at the injection and observation wells show a preference for higher saturations occurring in high permeability distributary channels penetrated by each of the wells. This has reduced the uncertainty in predicting connectivity of this facies between the wells. The pressure data from numerous injection events has been used to refine the characterisation of the average horizontal permeability of the reservoir zone, and the vertical permeability of the intra-formational seal. It has also been used to infer near-field bounding conditions of the interior splay fault, which in turn improves our understanding of containment at the site.

A multi-stack module consisting of a number of stacks connected in series and parallel serves as a basis for installation of MW-scale vanadium flow battery system in grid storage applications. Due to the existence of stack-to-stack variation in resistance, the module performance can be notably limited by an inappropriate module layout that magnifies the impact of stack resistance variation through series and parallel connections. To understand the layout effect on performance, an in-depth investigation is conducted for an eight-stack 250 kW module in this study. Based on experimental measurements, the correlation of module layout to performance is firstly revealed on both the 250 kW module and a laboratory mini-module. Subsequently, 35 different layouts are specified for the 250 kW module and their performance is fully evaluated by means of development of dynamic models for the module. Simulation results prove that the module charging capacity can be effectively improved by grouping stacks with similar resistances into the same branch and be further promoted by optimizing the flow rate for the stack with the largest resistance. The present study offers not only mechanistic insights into the importance of module layout but a cost-effective way to evaluate the module performance as well.

Managing electricity demand has become a key consideration in power grid operations. Industrial demand response (DR) is an important component of demand-side management, and electricity-intensive chemical processes can both support power grid operations and derive economic benefits from electricity price fluctuations. For air separation units (ASUs), DR participation calls for frequent production rate changes, over time scales that overlap with the dominant dynamics of the plant. Production scheduling calculations must therefore explicitly consider process dynamics. We introduce a data-driven approach for learning the DR scheduling-relevant dynamics of an industrial ASU from its operational history, and present a dynamic optimization-based DR scheduling framework. We show that a class of low-order Hammerstein-Wiener models can accurately represent the dynamics of the industrial ASU and its model predictive control system. We evaluate the economic benefits of the proposed scheduling framework, and analyze their sensitivity to electricity price uncertainty.

Reliance on π-diagrams and tangent plots to describe fold geometry has created an unwarranted status for conical folds at the expense of periclines. Analysis of virtual periclines using synthetic stereograms, tangent diagrams, and geologic curvature analysis demonstrates small portions of periclines are “cylindrical.” The more significant “non-cylindrical” portion of periclines allows for a mathematically permissible, but unrealistic, geometrical representation as portions of cones. We demonstrate cones are an extremely poor geometric representation of both the virtual periclines and examples of natural non-cylindrical folds. “Conical folds,” if they exist, terminate at a point; the amplitude to width ratio and the plunge of the crestal line must remain constant towards the fold terminus, with a Gaussian curvature of zero. In contrast, the amplitude to width ratio, plunge along the crestal line, and Gaussian curvature of periclines vary towards the fold terminus. These differences have important implications for the rheological modeling of folds, and while realistic dynamic models for periclines exist, models for conical fold formation remain conceptual. We suggest that in order to advance our understanding of how folds form, it may be “pointless” to continue the misconception of conical folds as an accurate geometric representation of how folds end.

Unmanned wave gliders (UWGs) are a new type of wave-powered unmanned marine vehicle for persistent ocean environmental monitoring. The present paper proposed the dynamic model of a UWG by considering the influences of the flexibility of the umbilical. The dynamic models of surface float body (float) and submerged glider (glider) with slack umbilical were first separately established, and subsequently, the dynamic model of the UWG was formulated when the umbilical was under tension. Furthermore, the judgment principle and the state transfer problem in the switching process were analyzed, and numerical simulations of longitudinal and rotary motions of the UWG were performed. The established dynamic model was found to be reasonable to reflect the dynamic characteristics of the UWG with flexible umbilical.

A novel vision guided intelligent vehicle (IV) trajectory tracking control algorithm based on expected yaw velocity is proposed to compensate for traditional vision guided trajectory tracking controller limitations in robustness and trajectory tracking accuracy. In this paper, the structure and mechanism of trajectory tracking controller based on expected yaw velocity is briefly introduced at first. The designed trajectory tracking controller models of IV system are then derived through the dynamic models and motion models of IV by using Matlab/Simulink. Simulations under two different road conditions and different preview distances are used to validate the superiority of designed trajectory tracking controller in comparison with traditional vision guided trajectory tracking controller. The effectiveness of designed trajectory tracking controller has been verified by simulation results, in which the robustness and trajectory tracking accuracy of designed trajectory tracking controller has been improved significantly. Finally, the novel controller is designed based on Simulink results, and test results reconfirm that the partial performances of controller effectively.

The performance of continuous conveyor systems based on belt conveyors depends on their reliable and trouble-free operation. The most common failures encountered during their operation are mainly those directly related to the conveyor belt itself. However, this is not the only case that may cause their operation to be interrupted. A breakdown may also result from increased wear of the belt conveyor itself. Such systematic failure occurrence then greatly manifests itself in the reduced uptime of the entire belt conveyor. One of the effective solutions to the given problem is to make predictions of their emergence using dynamic models. By appropriately modified dynamic belt conveyor models, it is possible to quickly and accurately establish and predict the cause and location of the conveyor drive wear. This paper presents the belt drive upgrade has been verified under operating conditions. Trouble-free operation time, during which the online belt conveyor's technical condition was monitored, has increased 6 times in comparison to the original life of the drive.

Large vibrating screen is the key equipment to improve the clean and efficient utilization of coal. In recent years, various advanced design techniques and methods have been energetically explored and applied into developing high-performance and long-life vibrating screen. In this paper, firstly, a systematic search of literature published during the last fifteen years was carried out and based on the analysis on these papers, these advanced design techniques and methods were summed up as three issues: the kinetic properties of the screen body by dynamic modeling, the optimal screening operational parameters by discrete element method and the dynamic design of the screen body by finite element method. In detail, the traditional dynamic models of vibrating screen were classified into four types according to the number of degree of freedom. Besides, four kinds of influence factors pertaining to the optimal operation parameters of a vibrating screen were presented. Moreover, two aspects of dynamic design process, i.e., the dynamic analysis and the optimal design strategy of the screen body was introduced. Finally, based on the comprehensive review, some topics such as a concordant optimal method on screening operational parameters, the appropriate control method for stable operation of a large vibrating screen, and the precise kinetic properties of screen body by dynamic modeling were recommended.

A novel dynamic and unified 3D Computational Fluid Dynamics (CFD) model to hydrodynamics studies of waste stabilization ponds is proposed. Two full-scale maturation ponds, at different operational settings, were modelled. This work attempted to consider gaps existing in literature regarding CFD modelling in ponds, such as the real geometric conformation of the accumulated sludge on the bottom; dynamic models representing environmental conditions (wind, solar radiation, air temperature), approximation of the physical properties of the fluid to real conditions, buoyancy etc., all together in a unified model. For validation/calibration purposes, monitoring data were used and experimental tests were performed in the field, using tracers, dyes, temperature and water quality measurements, hydraulic devices and local weather data. Results of field tests agreed well with each other and with the results of the CFD models. From the model proposed, more detailed studies and important analyses regarding the hydrodynamic behavior of ponds could be accomplished, so that interventions to improve treatment efficiency can be better studied and oriented, with low cost and without the need for field or laboratory experiments. The model developed in this study certainly has potential for application in the modelling of ponds in the future, under more realistic environmental conditions.

We consider a stochastic virus model with saturation functional responses, in which the death rate of the uninfected CD4 + T, the infected CD4 + T, and the HIV virus particles are influenced by white noises. We prove the global existence of a positive solution of the system. Although this system does not have any equilibrium points compared with the corresponding deterministic model, by Ito’s formula and by constructing a proper Lyapunov function, we prove that the solutions of the stochastic virus model fluctuate randomly around the uninfected equilibrium of the deterministic virus model if the crucial value R0 1 and that the stochastic perturbations (of standard white noise type) influence the rate of change of the uninfected CD4 + T, the infected CD4 + T, and the HIV virus particles directly by strength proportional to the distances between T ¯ and T, T ¯ * and T*(t), and V ¯ and V(t), respectively. Here ( T ¯ , T ¯ * , V ¯ ) is the infected equilibrium of not only the stochastic system but also the corresponding deterministic system. We obtain the sufficient conditions which guarantee the stochastic asymptotical stability of the infected equilibrium. Finally, we present some numerical simulations to verify our results, and discuss our results.

In practice, carbon tax is an effective supplement to emissions trading system, and is also a key to achieving China's emission reduction targets. Although much research has been done to study the internal mechanism and macro impact of carbon tax, prior literature paid little attention on whether carbon tax affect the strategy and performance of low-carbon technology sharing among enterprise. A differential game model is developed in this paper, which assumed that enterprise low-carbon technology stock is jointly determined by both the efforts of the enterprise low-carbon technology innovation and the external enterprise low-carbon technology sharing. This paper constructs dynamic models under decentralized decision-making with cost-sharing, without cost-sharing, and centralized decision-making respectively, obtain enterprise optimal feedback equilibrium strategies, low-carbon technology stocks and the trajectory of optimal value function of benefits. The results demonstrate that carbon tax can promote enterprise low-carbon technology innovation and sharing to some extent. Meanwhile, this paper found that under the scenario of centralized decision-making, the more the efforts of enterprise low-carbon technology innovation or sharing, the higher the system emission reduction benefits than non-cooperation scenarios. Some factors, such as cost coefficient, cost sharing ratio, etc., will affect the enterprise's decision-making and emission reduction benefits. Finally, the validity of the model is verified through numerical simulation and the sensitivity of the relevant parameters in the scenario of cooperative innovation are analyzed. The results may provide important policy implications for promoting low-carbon technology sharing among enterprises, so as to faciliate the achievement of China's emission reduction targets.

Photovoltaic modeling has attracted researchers’ attention worldwide because of its importance in the photovoltaic system design. Therefore, several photovoltaic models have been introduced as static and dynamic photovoltaic models. Moreover, a novel fractional order dynamic photovoltaic model has been recently developed to enhance the accuracy and flexibility of the conventional integral order one. The unknown parameters of these models should be extracted accurately to achieve a proper photovoltaic system design and operation. In this work, novel Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are introduced, where the Heterogeneous Comprehensive Learning Particle Swarm Optimizer is combined with ten different chaos maps to adapt its parameters. Six Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants are proposed in addition to the standard Heterogeneous Comprehensive Learning Particle Swarm Optimizer version to identify the parameters of both the static and the dynamic models based on different experimental datasets. To demonstrate the superiority of the developed variants, their results are compared to the most recent state-of-the-art algorithms with the aid of statistical analysis methods. The main outcome is that, in both of the static and the dynamic photovoltaic models, the Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variants show their efficiency, accuracy and robustness not only over Heterogeneous Comprehensive Learning Particle Swarm Optimizer but also over recently published algorithms. They provide better fitting relative to the experimental datasets with the least deviation error and the fastest convergence speed as well. In the case of static models, the fourth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with an iterative map for the single diode model, the third variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with singer map for the double diode model of solar cell. On the other hand, for the dynamic models, the second Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer variant with sinusoidal map for the integral order dynamic photovoltaic model and the sixth variant of Chaotic Heterogeneous Comprehensive Learning Particle Swarm Optimizer with Gauss/mouse map for the fractional order dynamic photovoltaic model offer the best performance.

A study is conducted to examine the dynamic performance and electric consumption of an electric bicycle based on the effects of various operating conditions. To simulate the operation of the electric bicycle, this study establishes the simulation models including dynamic models of the electric bicycle and battery models. These simulation models are solved by Matlab-Simulink to provide the operating characteristics of the electric bicycle. The effects of operating conditions such as air density and slope on the dynamics and electric consumption of the electric bicycle are investigated. The simulation results show that the reduction of air density and slope results in an improvement of the dynamic performance and electric consumption. Aside from the simulation study, an experimental study is also conducted to examine the dynamic and electric consumption characteristics of the electric bicycle. This study shows agreements between simulation and experimental results at the same initial conditions.

A fully coupled nonlinear simulation tool using Morison's equation is developed to predict the dynamic response of floating offshore wind turbine (FOWT) system. Water tank tests are conducted to investigate hydrodynamic coefficients and the performance of the simulation tool under different sea states. Three issues are discussed in this paper. First, correction factors for the added mass and drag coefficients are proposed to account for the effects of frequency dependent hydrodynamic coefficients, and the effects of these correction factors on the global matrices are validated by the forced oscillation tests. Then, the effects of the frequency dependent hydrodynamic coefficients and the axial Froude-Krylov force on slender members are clarified by the free decay tests and water tank tests with regular and irregular waves. Finally, the dynamic behavior of mooring system on the fairlead tension is investigated by using quasi-static and dynamic models, respectively. The dynamic responses of FOWT with improved hydrodynamic models agree well with those measured in the water tank tests.

A novel continuous adaptive finite time guidance (CAFTG) law is proposed for homing missiles. Firstly, three-dimensional nonlinear dynamics describing the pursuit situation of the missile and the target are introduced to obtain the mathematical model of engagement. Secondly, in order to improve the accuracy of interception, a nonlinear disturbance observer with finite time convergence is employed to estimate the acceleration of a target and compensate the guidance law. A continuous guidance scheme with robustness is constructed via sliding mode control theory, which guarantees finite time convergence by Lyapunov stability theory. Finally, simulations are conducted on the nonlinear dynamic models and results demonstrate the effectiveness of proposed guidance method.

In this study, the effects of pressure on the optical sensor in an on-line particle measurement system were investigated in detail based on the quantum theory and kinetic theory of gas. The results show that pressure alters the divergent light angle by altering the density and refractive index of the gas, so that the focused beam position is offset from the center of the aerosol tube; this causes the intensity per cross-section area of the optical measurement volume (OMV) to change, creating a significant deviation in the measurement result relative to the actual sample. Theoretical and experimental dynamic models were established based on the geometric optics theory and the geometric mathematical relation to analyze these variations. The results show that OMV size increases as pressure and divergent light angle increase. During an actual particle measurement process, the dynamic models proposed here can be used to effectively adjust and reconstruct the optical sensor, which can substantially improve the precision and accuracy of the measurement results.

The article considers an algorithm for constructing the linear stochastic stationary model in terms of the state space for description of behaviour and labour productivity state prediction and filtration depending on the capital-labour ratio and labour cost instead of the object description given by the three-factor non-linear Cobb-Douglas model. Cobb-Douglas function is converted into a dynamic model for which it is necessary to solve the equations for the Kalman filter scheme. It is shown that the built model provides efficient predictions estimates for labour productivity values. There was made the numerical calculation of the prediction estimates accuracy.

Artificial neural networks (ANNs) are considered efficient tools for modeling complex, non-linear processes with uncertain dynamic models. ANNs were originally applied as effective predictors of diverse processes with static dependence on the input-output information. However, when the ANN must be applied to characterize an approximate model of time-dependent input-output relationships, then it is necessary to introduce the time effect as part of the ANN, yielding to the construction of dynamic ANN or DNN. This review establishes the variants of recurrent and differential forms of DNN, their mathematically formulation as well as the methods to adjust the network weights. The characteristics of DNNs motivate their use to represent the dynamics of decontamination processes. This review details recent findings on the DNN application for the modeling and control of treatment systems based on either biological and chemical processes. The modeling application of DNN for some common methods used in the treatment of wastewater, contaminated soil and atmosphere is described. The major benefits of using the approximate DNN-based model instead of designing the complex mathematical description for each treatment are analyzed in the context of enhancing the efficiency of the decontamination treatment. This review also highlights the remarkable efficiency of DNNs as a keystone tool for modeling and control sequence of treatments. The last section in the review introduces several open researching areas for the application of DNN for decontamination systems based on biochemical and chemical treatments.

The wetting phenomenon of Sn-37Pb alloy solder spreading on Cu substrate under different ultrasonic fields was investigated. Sn-37Pb/Cu wetting systems showed significant precursor film characteristics with the assist of ultrasonic energy. A short-time ultrasonic field evidently enhanced the wettability of Sn-37Pb/Cu compare to a common wetting field which had no introduced ultrasonic. At the temperature of 213 °C, spreading area of a Sn-37Pb solder drop in a 20 kHz, 1000 W ultrasonic field reached maximum at 2 s, and showed significant precursor film characteristics. Atomistic dynamic models showed the relevance between wettability and precursor film formation. The observed dynamic results agreed with the experimental results.

This study presents an analysis of the effect of the transport delay which occurs in solar flat-plate collector fields and how to include its behavior in dynamic models suitable for control purposes. This investigation has been carried out using simplified models based on dynamic energy balances and models based on step response methods and experimental tests. The solar flat-plate collector field encompasses parallel absorber pipes intertwined with pipelines which act as several first-order plus dead time systems in parallel. The effect is an apparent delay observed at the outlet temperature that must be included in the dynamic model in order to reduce the error between the real measurement and the model prediction. The main contribution of this paper is the procedure to evaluate the apparent delay to obtain adequate dynamic models aimed to be used for control purposes.

This study reports a continuous-time experimental test of evolutionary models in coordinated attacker–defender games. It implements three experimental treatment conditions: one with strong coordination incentives, one with weak coordination incentives, and one with zero coordination incentives. Each treatment exhibits identical equilibrium predictions but distinct evolutionary predictions. Observed behavior was tightly clustered around equilibrium under both the zero coordination treatment and the weak coordination treatment but widely dispersed from equilibrium under the strong coordination treatment. This result was anticipated by explicitly dynamic models but not by conventional stability criteria. In contrast to the widely maintained assumption of sign-preservation, subjects frequently switched to lower earning strategies, suggesting that non-sign-preserving evolutionary models may provide a more accurate characterization of human behavior.

Computationally-efficient simulations of structural responses, such as displacements and inter-story drift ratios, are central to performance-based earthquake engineering. Calculating these responses involves potentially time-consuming response history analysis of inelastic structural behavior. To overcome this burden, this paper introduces a new inelastic model condensation (IMC) procedure. The method presented here is non-iterative and uses the modal properties of the full model (in the elastic range) to condense the structural model such that the condensed elastic model preserves the modal properties of the full model at certain modes specified by the analyst. Then, by replacing the inter-story elastic forces with hysteretic forces, the inelastic behavior of the full finite element model is incorporated into the condensed model. The parameters of these hysteretic forces are easily tuned, in order to fit the inelastic behavior of the condensed structure to that of the full model under a variety of simple loading scenarios. The fidelity of structural models condensed in this way is demonstrated via simulation for different ground motion intensities on three different building structures with various heights. The simplicity, accuracy, and efficiency of this approach could significantly alleviate the computational burden of performance-based earthquake engineering.

Recommendation systems rely on the historic data of users' purchases and their feedbacks to profile their preferences and make future recommendations. Most of these systems usually employ Collaborative Filtering (CF) models to analyze users’ ratings and infer the latent factors which represent the user and item features in k-dimensional latent space. However, the historical rating data used for recommendations are usually sparsed and unbalanced. Various approaches have been used to resolve these issues by combining the user’s ratings and reviews to better capture the user’s sentiments and make accurate recommendations. Other challenges comprise changes in users’ preferences and items’ perceptions over time. Therefore, this paper presents a new Sentiment Scoring Model (SSM) based on Long-/Short-Term Memory and a combination function that catches the sentiment bias between user rating and review to relieve the sparsity and unbalanced dataset. Next, we proposed an Adaptive LSTM (ALSTM) method that can model the drifting of user and item features to improve the recommendation accuracy. We show the performance of our model on the three real-world rating datasets from Amazon reviews, which comprises Fine Food, Baby, and Cell-phone & Accessories categories. The result shows the superiority of our proposed model over the existing static and dynamic models. The statistical test shows that all the performance gains are significant at p

What type of reasoning do high school students develop and exhibit when they use a Dynamic Geometry System (GeoGebra) to work on word problems? We report a research study framed under problem-solving principles in which high school students relied on GeoGebra affordances to represent, explore and solve word problems. In this process, they recognized that all families of word problems involve a ratio of quantities or parameters embedded in problem statements that could be represented and explored geometrically. This exploration led them to identify relationships associated with those parameters that were analysed via finding loci and expressing their parametrization to solve word problems both geometrically and algebraically.

Thermochromic glazing and phase-change materials are capable of adapting their optical and thermal properties in response to temperature variation, without consuming additional energy. They are therefore ideal construction materials for passive building envelopes. In this study, the energy-saving potential of combining the two materials into one adaptive glazing system is investigated numerically. Computational fluid dynamic models were constructed to simulate the thermal performance of different adaptive glazing systems consisting of the two materials. The models were tested against two existing experimental studies, and good agreements were found. Based on the models, the applicability of the novel glazing systems for a south office building facade was investigated for summer and winter in Shanghai, China. It was found that PCM-integrated thermochromic triple glazing units outperform normal double-glazing units by reducing total heat gain by up to 32% and 40% for a sunny and cloudy day in summer, respectively. In winter, however, they tend to block more desirable solar radiation and hence are not as effective as normal double-glazing units.

In this study, the possibility of predicting the efficacy of Solar water disinfection (SODIS) for the removal of bacterial pathogens was assessed by the development of a three-level plan: firstly, systematic E. coli inactivation was performed (in vitro) in Lake Geneva water, under otherwise controlled conditions of water temperature (20–50 °C), sunlight intensity (0–1200 W/m2), presence of natural dissolved organic matter (DOM, 0–6 mg/L) and turbidity (0–50 NTU). As a second step a kinetic evaluation led to the selection of the most relevant parameters to be included in a novel static and dynamic model theoretical formulation. The static and dynamic models reliably described the experimental findings (bacterial inactivation under various climatic conditions) and were considered as equally eligible candidates for disinfection modeling. The final step considered ambient temperature, incident radiation and cloud-cover data to forecast (in silico) SODIS efficacy in Africa as a case study. The simulation results were compared with the experimental data and indicated that most African regions are suitable for SODIS processes, but there are areas of risk correlated with climatological conditions (cloud-cover and temperature). The results of this study could be applied for regional in decision-making strategies for application of SODIS or in the search for viable alternatives to SODIS in cases where it is deemed unsuitable.