Deterministic Model Research Articles

Improving electric vehicle charging forecasting: A hybrid deep learning approach for probabilistic predictions

AbstractElectric vehicles (EVs) have gained significant attention recently. Despite their advantages, challenges in the power grid, such as providing necessary information for optimal operation, persist. High‐precision forecasting techniques are essential to address the nonlinear and complex behavior of EV charging. A hybrid structure based on deep learning, called LSTLNet, has been proposed. LSTLNet combines convolutional neural networks (CNN), gated recurrent neural networks (GRU), attention mechanisms (AM), and automatic regression (AR) models. This combination improves the deterministic forecasting model and addresses the weaknesses of CNN and GRU. Deterministic prediction, which determines only one point of consumption charge, is prone to error. Therefore, probabilistic forecasting, represented as a probability distribution function (PDF) containing comprehensive statistical information, is preferred. A smooth band limit maximum likelihood (SBLM) estimator is used to indirectly predict the PDF from the data. Comparative results with conventional shallow and deep methods for similar time series forecasting demonstrate the superiority of the proposed method for both deterministic and probabilistic forecasting.

Modeling the impact of screening awareness and vaccination on human papilloma virus transmission

In this work, a two‐sex deterministic model for human papillomavirus (HPV) is developed to assess the impact of screening awareness and vaccination on the transmission dynamics. We provide a detailed analysis of the HPV model and established results for boundedness of the solutions and existence of a positively invariant and attracting set. The basic reproduction number is characterized, and it is shown that the disease‐free equilibrium is locally asymptotically stable if and the system is uniformly persistent if . Sensitivity analysis of reveals that HPV vaccination can mitigate HPV transmission, and HPV vaccination along with screening can further reduce the spread of HPV infection. Numerical simulations showed that the public awareness of screening after being vaccinated is contributing to reduce the spread of epidemic, and the role of screening awareness in controlling the disease was significantly greater among men than women. Besides, the numerical simulations reveal that increasing vaccination rates for male is effective in controlling HPV transmission. Hence, it is necessary to enhance the screening for men. Moreover, increasing vaccination rates is more effective than increasing screening awareness in eliminating the epidemic.

Ecological and epidemiological ramification of fear: Exploring deterministic and stochastic dynamics in a predator–prey system with predator switching and harvesting

In an ecosystem, harvesting infected prey can assist in managing and containing the spread of the illness within the prey species. On the other hand, the harvesting of predators can be beneficial as it regulates their numbers, preventing them from over-consuming prey and subsequently preserving existence of the prey population. This study introduces a predator–prey model that encompasses prey infection, predator–prey interactions influenced by fear, switching and harvesting. We derive an analytic expression for the basic reproduction number, a critical determinant of disease spread. We investigate the global stability of disease-free and endemic equilibria contingent on the basic reproduction number’s value, highlighting the potential for disease eradication by maintaining it below unity. In-depth analysis of the deterministic model is undertaken, with a focus on Hopf bifurcations that delineate thresholds for disease-free and endemic states. Furthermore, the deterministic model is extended to incorporate environmental stochasticity. We obtain the conditions under which population extinction occurs. Our findings elucidate how the intensity of environmental noise influences population dynamics, providing valuable insights into extinction risks under varying noise levels.

Cost-effectiveness of nonavalent vs bivalent HPV vaccine in Polish setting.

Human papillomavirus (HPV) is a prevalent sexually transmitted infection with significant implications for public health. In Poland, a nationwide vaccination program offers a choice between the 9-valent (9v) and 2-valent (2v) HPV vaccines. We aimed to assess the cost-effectiveness of the 9v vs 2v vaccine from the public payer perspective in Poland. A cost-effectiveness analysis was conducted to compare the public health and economic benefits of using 9v vs 2v vaccine in Poland over 100-year horizon using a previously published deterministic dynamic transmission model. A target population of girls and boys aged 12-13 years was considered. The model was populated with local epidemiological inputs, utilities, and costs, including vaccine and administration costs, as well as costs related to medical procedures for HPV-related diseases. The 9v vaccine reduced the prevalence of HPV infections and HPV-related diseases substantially more than 2v vaccine when both are compared to no vaccination strategy. The total discounted cost savings of using the 9v vaccine instead of 2v, excluding the vaccine costs, amounted to EUR 66 million. The incremental cost-effectiveness ratio amounted to 8094 EUR per quality-adjusted life year, much below the official cost-effectiveness threshold in Poland set up at the three times the annual gross domestic product per capita. 9v cost-effectiveness ratio remained unchanged when shorter time-horizons of 20, 40, 60, or 80 years were considered. Using 9v HPV vaccine in Poland is highly cost-effective compared to the 2v vaccine.

Алгоритм для численного решения диффузионно-реакционно-дрейфового уравнения с дробной производной по времени и координате

<p>The paper is devoted to the construction and program implementation of the computational algorithm for modeling a process of diffusion-drift nature based on the fractional diffusion approach. The mathematical model is formulated as an initial-boundary value problem for the time-space fractional diffusion-drift equation in a limited domain. Time and space fractional derivatives are considered in the sense of Caputo and Riemann – Liouville, respectively. A modified implicit finite-difference scheme is constructed. The concept of the considered mathematical problem provides an example of a deterministic model of the charging process of dielectric materials. An application program has been developed that implements the constructed numerical algorithm. The results were verified using the example of solving a test problem.</p>

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

A distributionally robust optimization approach of multi-park integrated energy systems considering shared energy storage and Uncertainty of Demand Response

To enhance the economic efficiency and renewable energy integration capacity of multi-park integrated energy systems (MPIES) and address the issue of insufficient consideration of demand response uncertainty in existing studies, this paper proposes a distributionally robust optimization approach for multi-park integrated energy systems, considering shared energy storage and the uncertainty of demand response. First, models of the shared energy system and demand response are established. Based on these models, a deterministic optimization scheduling model for MPIES is developed, aiming to minimize system costs while considering constraints such as grid power balance. To address uncertainty in demand response, this paper employs Interval Type-2 Fuzzy Theory to construct uncertainty sets and considers system reliability constraints. The original optimization problem is then transformed into an equivalent robust counterpart model, and the optimal distributionally robust solution is obtained through parameter domain decomposition methods. Finally, the proposed method is validated using the IEEE 33-bus system. The results show that considering shared energy storage and demand response individually can reduce total system costs by 4.86 % and 26.46 %, respectively. After accounting for the uncertainty in demand response, the total system cost increases only slightly by 4.36 %, but this improves the system's robustness.

Exploring the influence of refuges and additional foods on predator–prey interactions amidst environmental stochasticity and water level fluctuations

By considering that the predators have some additional foods apart from the focal prey and the prey take refuge, a predator–prey model with the effect of seasonality in a fluctuating environment is investigated in this study. Gaussian white noises, created by random fluctuation of water level, are introduced on the prey’s growth rate and predator’s mortality rate. Both the deterministic and stochastic systems are analyzed mathematically as well as numerically. Our analytical findings show that the intensity of environmental noise, additional foods and prey refuge play significant roles in survival as well as extinction of both prey and predator populations in the aquatic system. Our numerical results show that the effectual level of additional foods, depending on water level, has positive as well as negative effects on the predator and prey populations. We find that both species strongly persist at certain water level and for the low intensity of noise. However, for an increased value of noise intensity, they persist weakly and then go to extinction. Overall, our findings show that the variations of water level together with additional food and white noise plays crucial roles in persistence and extinction of species in the ecosystem.

Efficient prediction uncertainty quantification in dam behavior monitoring with attention-based sequence-to-sequence learning

Displacement is an intuitive monitoring indicator of dam structural behavior. Conventional deterministic modeling methods frequently disregard the inherent uncertainty associated with monitoring data. This study aims to quantify such uncertainties within dam displacement predictions by augmenting a sequence-to-sequence structure with the novel recurrent unit, termed as tiny gated unit (TGU), attention mechanism, as well as quantile regression, resulting in the Att-S2STQ model. Specifically, TGU adopts only one gate to identify nonlinearity in data sequences, while Bahdanau attention mechanism dynamically assigns weights to different parts of the input sequence. The inclusion of quantile regression allows Att-S2STQ to produce prediction intervals (PIs), from which the probability density functions (PDFs) and cumulative distribution functions (CDFs) are further derived via kernel density estimation. PIs, PDFs, and CDFs jointly serve to reveal the prediction uncertainty. The effectiveness of the model is illustrated through comparative experiments using real-world dam monitoring datasets. Results indicate that the proposed model outperforms traditional methods in point, interval, and probability predictions, while also having a simpler structure and faster training. The superiority of both accuracy and efficiency makes it a valuable tool for dam management, aiding in data-driven decision-making and enhancing operational safety.

A deterministic SAIR model with vaccination and treatment: dynamical behaviors and control strategies

A deterministic SAIR model with vaccination and treatment: dynamical behaviors and control strategies

Transition and bifurcation mechanism of firing activities in memristor synapse-coupled Hindmarsh–Rose bi-neuron model

Memristor, especially the locally active one, is widely used in emulating the synapse-inspired activities in neural networks, in which the connection between memristor properties and neuronal electrical activities requires further investigation. In this paper, a bi-neuron model is constructed by bi-directionally connecting a locally active memristor between the membrane potentials of two deterministic 3D HR neuron models. Complex spiking/bursting firing activities and their transitions are disclosed under different memristor control parameters. The bifurcation mechanisms of the revealed synchronous and asynchronous firing activities are explored inside each individual neuron by taking the externally input state variables as modulation parameters. These results demonstrate the crucial effect of the locally active memristor synapse on firing activities of its coupled network. FPGA-based digital circuit experiment is finally performed to verify the numerical results. This research is beneficial to understanding, control and application of firing activities in neural networks.

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon

Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations, which have consequences to ecosystem functioning including biogeochemical cycling from mild to severe disruption. The study investigates the potential of machine learning (ML) and deterministic models to predict future hypoxia events. Employing ML models, e.g. Random Forest and AdaBoost, past hypoxia events (2008–2019) in the Venice Lagoon were classified with an F1 score of around 0.83, based on water quality, meteorological, and spatio-temporal factors. Future scenarios (2050, 2100) were estimated by integrating hydrodynamic-biogeochemical and climate projections. Results suggest hypoxia events will increase from 3.5 % to 8.8 % by 2100, particularly in landward lagoon areas. Summer prediction foresee a rise from 118 events to 265 by 2100, with a longer hypoxia-prone season. This model is a valuable tool for developing hypoxia scenarios, aiding in identifying restoration hotspots for climate-threatened lagoons.

Design and experimental characterization of a propane-based reversible dual source/sink heat pump

The current paper presents the design and energy performance analysis of a propane-based reversible Dual Source/Sink Heat Pump (DSHP). DSHPs offer an alternative to conventional water to water and air to water heat pumps, leveraging the strengths of both technologies in an efficient manner. The developed prototype incorporates an innovative Dual Source/Sink Heat eXchanger (DSHX), enabling the unit operating in various modes, including space heating, space cooling, and domestic hot water production using brine, air or both simultaneously as a source/sink. The DSHX serves as as both a condenser or an evaporator, directly rejecting or absorbing heat from air and/or brine. By eliminating secondary loops and defrost cycles, the DSHX minimizes energy losses. The main novelty of this work lies in the DSHX that integrates external units typically duplicated in DSHPs into a single component, eliminating the need for split refrigerant flow rates, thus avoiding maldistribution, refrigerant charge increase and draining valves. A steady state experimental campaign was conducted in a climatic chamber to characterize the DSHP prototype and validate the DSHX performance models. Heating capacity up to 11.2 kW and COP values up to 4.7 were achieved at nominal compressor speed by supplying hot water at 35 °C with an ambient temperature of 7 °C. Similarly, when producing cold water at 7 °C, cooling capacity and EER reached 9.8 kW and 3.6, respectively, at nominal compressor speed using air as heat sink at 35 °C. The effects of various operating parameters on the overall coefficient of performance and heat duty in both heating and cooling modes, considering air or brine as heat source/sink are analyzed in detail. Results demonstrate enhancements of approximately 15 % in capacity and efficiency compared to earlier work. Moreover, four deterministic models were created in order to predict the behaviour of the DSHX and validated against experimental results, reaching deviation values below 15 %.

Dynamics of two feed forward genetic motifs in the presence of molecular noise

Understanding the function of common motifs in gene regulatory networks is an important goal of systems biology. Feed forward loops (FFLs) are an example of such a motif. In FFLs, a gene (X) regulates another gene (Z) both directly and via an intermediary gene (Y). Previous theoretical studies have suggested several possible functions for FFLs, based on their transient responses to changes in input signals (using deterministic models) and their fluctuations around steady state (using stochastic models). In this paper we study stochastic models of the two most common FFLs, “coherent type 1” and “incoherent type 1”. We incorporate molecular noise by treating DNA binding, transcription, translation, and decay as stochastic processes. By comparing the dynamics of these loops with models of alternative networks (in which X does not regulate Y), we explore how FFLs act to process information in the presence of noise. This work highlights the importance of incorporating realistic molecular noise in studying both the transient and steady-state behavior of gene regulatory networks.

Dynamic airport gate assignment with improved Shuffled Frog-Leaping Algorithm and triangle membership function

he rapid development of the air transportation industry has increased air traffic, posing challenges to the task of airport gate assignment (AGA) for flights. Most past studies have solved the AGA problem (AGAP) using deterministic models, which are incapable of dealing with uncertainty and dynamic conditions at airports. Thus, this research employs fuzzy theory and proposes a triangular membership function to handle flight uncertainty in the AGAP. In addition, an improved metaheuristic, termed the improved Shuffled Frog-Leaping Algorithm (ISFLA), is proposed to circumvent the computationally intractable problems commonly faced by exact approaches when handling large instances. In this research, the AGAP is first formulated as a stochastic Mixed-Integer Linear Programming (MILP) model, with stochastic flight lateness and earliness considered. The objective of this model is to minimize the total cost, which consists of three sub-costs: passenger walking distances, non-preferred gate (NPG) assignments for planes, and fuzzy idle times of gates. These three sub-costs correspond to the major concerns of passengers, airlines, and airports, respectively. The cooperation between the ISFLA and the triangular membership function demonstrates their capability to effectively handle big AGAP instances. Furthermore, the experimental results show that the ISFLA outperforms the standard SFLA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FA).

TRIBOINFORMATICS: MACHINE LEARNING METHODS FOR FRICTIONAL INSTABILITIES

The study of friction is traditionally a data-driven area with many experimental data and phenomenological models governing structure-property relationships. Triboinformatics is a new area combining Tribology with Machine Learning (ML) and Artificial Intelligence (AI) methods, which can help to establish correlations in data on friction and wear. This is particularly relevant to unstable motion, where deterministic models are difficult to build. There are several types of friction-induced instabilities including those caused by the velocity dependency of dry friction, coupling of friction with another process (wear, heat generation, etc.), the elastic Adams instabilities, and others. The onset of sliding is also an unstable process. ML/AI methods, such as Topological Data Analysis and various ML algorithms, which have been already used for various aspects of data analysis on friction, can be applied also to the frictional instabilities.

Effects of doxycycline post-exposure prophylaxis for prevention of sexually transmitted infections on gonorrhoea prevalence and antimicrobial resistance among men who have sex with men in the USA: a modelling study

Doxycycline post-exposure prophylaxis (PEP) has been shown to be efficacious for the prevention of bacterial sexually transmitted infections, but resistance implications for Neisseria gonorrhoeae remain unknown. We aimed to use a mathematical model to investigate the anticipated impact of doxycycline PEP on the burden of gonorrhoea and antimicrobial resistance dynamics in men who have sex with men (MSM) in the USA. Using a deterministic compartmental model, characterising gonorrhoea transmission in a US MSM population comprising three sexual activity groups defined by annual partner turnover rates, we introduced doxycycline PEP at various uptake levels (10-90%) among those with high sexual activity. Infections were stratified by symptom status and resistance profile (ie, susceptible, ceftriaxone-resistant, tetracycline-resistant, or dual-resistant), with ceftriaxone the treatment for active infection. As resistance to tetracycline, not doxycycline, is monitored and reported nationally, we used this as a proxy for doxycycline PEP resistance. We compared the 20-year prevalence, incidence rates, and cumulative incidence of gonococcal infection, resistance dynamics (time to 5% prevalence of ceftriaxone resistance, 5% prevalence of dual resistance, and 84% prevalence of tetracycline resistance), and antibiotic consumption with baseline (ie, no doxycycline PEP). Uptake of doxycycline PEP resulted in substantial reductions in the prevalence and incidence of gonorrhoea, but accelerated the spread of tetracycline resistance. The maximum reduction in prevalence over 20years compared with no uptake ranged from 40·3% (IQR 15·3-83·4) with 10% doxycycline PEP uptake to 77·4% (68·4-84·9) with 90%uptake. Similarly, the maximum reduction in the incidence rate ranged from 38·6% (14·1-83·6) with 10% uptake to 77·6% (68·1-84·7) with 90% uptake. Cumulative gonococcal infections were reduced by a median of 14·5% (IQR8·4-21·6) with 10% uptake and up to 46·2% (26·5-59·9) with 90% uptake after 5years, and by 6·5% (3·4-13·0) with 10% uptake and 8·7% (4·3-36·2) with 90% uptake by 20years. In almost all scenarios explored, doxycycline PEP lost clinical effectiveness (defined as 84% prevalence of tetracycline resistance) within the 20-year period, but itslifespan ranged from a median of 12·1years (IQR 9·9-15·7) with 10% uptake to 1·6years (1·3-1·9) with 90%uptake. Doxycycline PEP implementation had minimal impact on extending the clinical lifespan of ceftriaxone monotherapy (5·0years [IQR 4·0-6·2]), with the median time to 5% prevalence of resistance ranging from 4·8years (3·9-6·0) for 90% uptake to 5·0years (4·1-6·2) for 10% uptake. Similarly, the median time to 5%prevalence of dual resistance to ceftriaxone and tetracycline ranged from 4·8years (3·9-6·0) for 90% uptake to 5·8years (4·8-7·4) for 10% uptake. Median decrease in ceftriaxone consumption for high doxycycline PEP uptake levels compared with baseline ranged from 41·7% (27·0-54·3) for 50% uptake to 50·2% (29·3-62·7) for 90% uptake at 5years, but dropped to 11·8% (6·9-32·0) for 50% uptake and 12·1% (7·0-41·6) for 90% uptake after 20years. Notwithstanding the clear benefits of doxycycline PEP for other sexually transmitted infections, for Ngonorrhoeae, model findings suggest that doxycycline PEP is an effective but impermanent solution for reducing infection burden, given eventual selection for resistant strains. This finding presents a challenge for policy makers considering strategies for doxycycline PEP implementation and oversight: the need to balance the clear, short-term clinical benefits with the risk of harm via antimicrobial resistance. US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases.

Quantitative tracing of typical herbicides and their metabolites in sorghum agrosystems for fate tendency and cumulative risk

Elucidating the combined exposure of agrochemicals is essential for safeguarding human health and agroecosystem safety. A rapid and high-sensitivity UHPLC-MS/MS method was developed for simultaneous quantification of nine compounds in sorghum by an assembly-line optimization process with a limit of quantitation of 0.001 mg/kg. The concentration variation of atrazine, quinclorac, fluroxypyr-meptyl and metabolites was reflected by terminal magnitudes of ≤0.0665 mg/kg. Additionally, atrazine was dealkylated to deethyl atrazine and desethyl desisopropyl atrazine at concentrations of 0.0014–0.0058 mg/kg during the sorghum harvest. Acceptable health hazardous of atrazine and quinclorac for all life cycle populations were comparatively assessed via deterministic and probabilistic models, in which atrazine gained an 83.55 % share of cumulative dietary risks. Rural residents had significantly higher risks than urban residents, and children were the most sensitive group. Despite the low health risks, combined exposure to herbicides and their metabolites should be continuously stressed, given their cumulative amplification effects.

Secure smart grid implementation with automatic data integrity attack location prediction and exalted energy theft detection

Smart grid systems use digital communication technology to efficiently monitor and manage electricity distribution. However, there is a need for improve the robustness of smart grid systems. Hence a novel “Secure Smart Grid Implementation with Automatic Data Integrity Attack Location Prediction and Exalted Energy Theft Detection” has been proposed. Current detection methods are not good at identifying data integrity attacks. So a novel Automatic Deterministic Attack Location Prediction Model detects data integrity attacks using a Stacked Watermarking BiGRU-based Deep Deterministic Q network, extracting system matrix and state time series variation. Moreover, Energy theft detection algorithms, which are intended to detect dishonest clients, provide erroneous findings because of linear interpolation processes and randomised near misses, which ignore important data points. Thus a novel Exalted Energy Theft Detector with Secure Encoding is introduced where Amortized Multicode GAN method secures smart grids by detecting energy theft and preventing firmware code modifications, utilising fractional multi-level quantum encoding to identify injection locations. As a result, the proposed model has an accuracy of 97.1%, recall of 96.25%, error rate is 1.36%, F1-score is 96.20%, sensitivity is 96.10%, specificity is 96.10%, MAPE is 0.48%, MSE is 0.0001 and RMSE is 0.017.

Vehicle platoon safety considering the vehicle cut-in: A coupled reinforcement learning and model predictive control approach

A longitudinal platoon control method based on Twin Delayed Deep Deterministic Policy Gradient (TD3) and Model Predictive Control (MPC) is proposed to solve the problems of low following efficiency and system instability in longitudinal platoon control. Firstly, Dynamic Bayesian Network (DBN) and Long Short-Term Memory (LSTM) network are introduced to identify the driving behavior of bystanders and derive the MPC objective constraint function containing three indicators of following, comfort and fuel consumption according to the platoon dynamics equation. Secondly, the system prediction model and cost function are introduced into the action-critic network of TD3 to solve the problem of no model training in the traditional TD3 algorithm and to speed up the training speed and accuracy of the network. On this basis, a Bellman equation is proposed to calculate the time-domain difference error and the expected loss function to solve the TD3 network overestimation problem. Finally, the joint simulation platform is built to simulate the platoon driving conditions and compare with the DDPG optimization algorithm and the traditional MPC algorithm respectively. The results show that the improved TD3-MPC algorithm satisfies the constraints and the spacing error is controlled within 0.3 m, and the vehicle speed changes more smoothly in the scenario of speed fluctuation in the front vehicle, and the ride comfort of the platoon is improved, and it has better robustness in the scenario of vehicle cut-in. The experimental results show that when the vehicle speeds are 20, 40, and 60 km/h, compared to MPC, the average spacing errors are reduced by 27.56%, 25.64%, and 28.04%, respectively, and compared to DDPG-MPC, the average spacing errors are reduced by 6.59%, 8.77%, and 7.86%, respectively.