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The red palm weevil is one of the most dangerous pests affecting date palm trees in the Gulf region and the Middle East. This paper investigates the control of the red palm weevil (RPW) in date palms using several techniques, namely stem injection, sex pheromone traps, and the Sterile Insect Technique. A mathematical model describing the spread of the red palm weevil is presented, and the boundedness of the solution for the RPW system is studied. The conditions for local stability, forward bifurcation, and Hopf bifurcation are also obtained. Additionally, this research highlights the significance of optimal control in managing RPW and simulates the model using the Forward-Backward Sweep numerical method. It is observed that the parameters of the sterile insect technique, the pheromone trap, and the mechanical injection play important roles in controlling the RPW. Mechanical Injection may be more effective in controlling the palm weevil compared to the other two methods. [ABSTRACT FROM AUTHOR]

Earth observation satellite (EOS) scheduling is critical for improving the performance of Earth observation system. Most of the existing studies on satellite observation scheduling problems focus on static tasks and real-time dynamic tasks also known as emergency tasks while few attempts have been made for common dynamic tasks yet. The common dynamic tasks are characterized by uncertain arrival times and delayable observations while the emergency tasks require immediate observations. The number of common dynamic tasks is rapidly increasing with the expansion of satellite observation applications, which poses great challenges to EOS observation scheduling. To address this issue, this paper investigates the satellite observation scheduling problem with common dynamic tasks. Firstly, a centralized onboard dynamic scheduling framework based on a periodic-triggered rolling horizontal optimization (RHO) strategy is proposed and a novel two-stage mathematical model is established for the periodic rescheduling problem. Secondly, we propose a low-complexity onboard periodic rescheduling algorithm (OPR), which consists of a greedy-based task allocation algorithm, a pointer network (Ptr-network) based task scheduling algorithm and an iterative local search algorithm. In the greedy-based task allocation algorithm, we define a Task-EOS Fitness Indicator (TEFI) and each task is greedily allocated to the EOS with maximum TEFI. In the Ptr-network based task scheduling algorithm, the allocated task sets of all EOSs are fed into the Ptr-network in parallel to generate the observation scheduling result. Afterward, an iterative local search algorithm is proposed to further improve the quality of the observation scheduling result. Finally, extensive experiments are conducted to demonstrate the superiority of the OPR algorithm for the satellite observation scheduling problem with common dynamic tasks. [ABSTRACT FROM AUTHOR]

• Reviews the mathematics of the EOQ/EPQ-based reverse logistics models. • It provides a condensed review of the works that stemmed from the work of Schrady (1967). • It identifies significant research gaps and provides future research directions. This paper reviews the mathematics of the economic order/production quantity (EOQ/EPQ)-based models in the reverse logistics (RL) literature. It starts with the seminal work of Schrady (A deterministic inventory model for reparable items. Naval Research Logistics Quarterly, 14(3), 1967, 391–398) up until December 31, 2022. It provides researchers with a road map of how the mathematics has evolved since the work of Schrady and saves them time and effort by avoiding the need to review the entire RL literature. As it unifies the notations for all reviewed models, it also provides some research pointers for promising topics to be pursued. [ABSTRACT FROM AUTHOR]

• A new design of an oil-free rotary-vane compressor with a mechanical efficiency exceeding 0.9 has been proposed. • A mathematical model for calculating power losses due to friction for the new design of an oil-free rotary-vane compressor has been developed, with a calculation error not exceeding 10 %. • The power losses due to friction in the working chamber of the new oil-free rotary-vane compressor in the range of rotor speeds from 1000 to 3700 rpm are 4 to 20 times lower than those for the traditional design. Additionally, the frictional power losses in the rotor slot are 20–30 % lower, and on the stator, they are 16–20 times lower. This paper presents the design of an oil-free rotor vane stage with a new tribological scheme. The new scheme differs from existing ones in that the sealing vane contain protrusions that rest on rolling unloading bearings located in the end covers of the cylinder. Experimental results on friction power losses in the new and traditional stages are provided, demonstrating that the implementation of the new stage design allows for an average reduction in friction power losses by a factor of 10. A mathematical model for calculating friction power losses in the rotor vane type compressor stage, implementing the new tribological scheme, has been developed. The analysis based on the mathematical model confirms the possibility of achieving mechanical efficiency values higher than 0.9 in oil-free rotor vane stages with a pressure ratio of 3 and a rotor speed of 3000 rpm, through the use of the new tribological scheme. It has been found that there is a redistribution of the importance and values of the main design parameters recommended for the design of oil-free rotor vane stages with the new tribological scheme, which determine its structural configuration, compared to its traditional design. [ABSTRACT FROM AUTHOR]

The reliable incorporation of hydrogen as a clean energy source necessitates an in-depth comprehension of the dynamics of flammable clouds resulting from potential leakage incidents. This research introduces an innovative model rooted in the principles of the kinetic theory of gases to determine the dimensions of flammable hydrogen clouds during leaks. The model integrates critical variables such as leak rate, leak direction, wind speed, and wind direction, in addition to surrounding atmospheric conditions, offering a precise forecast of hydrogen dispersion and behaviour during leakage scenarios. This modelling approach relies on two dimensionless groups derived through dimensional analysis, and the correlation between these groups has been established. Furthermore, the model incorporates three constants accounting for hydrogen buoyancy, gas dilution, and mixing, which are fine-tuned through a minimal number of Computational Fluid Dynamics (CFD) simulations. This indispensable information is crucial for the formulation of effective safety protocols, evacuation plans, and risk assessment strategies. Validation of the model against simulated data attests to its accuracy and reliability, underscoring its potential as a valuable tool for steering the secure integration of hydrogen technologies. This research significantly contributes to the advancement of safety measures in the utilisation of hydrogen as a clean energy source, providing a robust foundation for the design and implementation of precautionary measures in real-world applications. • A method for predicting hydrogen flammable cloud size based on dimensional analysis and CFD is proposed. • This method allows an improvement in the calculation efficiency while ensuring its accuracy. • A detailed comparative analysis of three prediction methods was presented in this paper. • This method is suitable for explosion risk analysis during the preliminary design phase. [ABSTRACT FROM AUTHOR]

The paper considers a process of a controlled attitude motion of a dual-spin nanosatellite. The main goal of control is to achieve a gravitationally stabilized position in a circular orbit in the shortest possible time. The dual-spin nanosatellite contains a gravitational damper and an internal rotor inside a movable unit. The movable unit can fulfill small controlled inclination relative to the main body of the nanosatellite. The mathematical model of the attitude dynamics of the nanosatellite is built and appropriate control laws are developed. Simulation results confirm the performance of the gravitational damper and efficiency of the control algorithm. [ABSTRACT FROM AUTHOR]

Emerging Industry 5.0 pushes advanced automation towards resilient solutions with enhanced human role. Resilience as an ability to sustain processes in the face of disruptions and adversarial attacks requires careful modelling and simulation. Cellular automata are efficient mathematical models used to simulate the behavior of complex systems, which change their state based on a set of predefined rules. In this paper, we suggest several updates to cellular automata (particularly Conway's "Game of Life") to address resilience. These include "Life and Creation", "War and Peace", and their hybrid "War and Creation" capable of addressing the important components of resilience, such as controllable creativity and adversarial interactions. Inherited in these updates and known advantages of cellular automata, such as simplicity, emergent behavior, parallelism, and adaptability, makes it a powerful simulation tool for a wide range of Industry 5.0 systems that involve humans, smart infrastructure, their complex and adversarial interactions, safety, and resilience. [ABSTRACT FROM AUTHOR]

This research paper focuses on the development of advanced mathematical models for disease diagnosis and prediction, and the subsequent creation of automated systems based on these models. These systems leverage a range of mathematical models and incorporate cutting-edge information technology achievements to provide medical professionals with valuable decision-making support. By amalgamating mathematical rigor and technological innovation, this research endeavors to enhance the accuracy and efficiency of medical diagnoses, thereby improving patient care and healthcare outcomes. This study delves into the persistent need for contemporary information systems, where information plays a crucial role in decision-making. It aims to provide an objective approach to addressing pressing medical challenges, particularly in disease diagnosis and prediction, enhancing the effectiveness of these critical tasks. Automated medical information systems, built on advanced mathematical models, significantly empower physicians. Machine diagnostics, relying on deterministic logic, the phase interval method, and information-probabilistic logic, bolster diagnostic capabilities. Functional entropy enables individuals to handle vague information, aiding decision-making. Assessing imprecision and uncertainty computationally diminishes subjectivity, while employing fuzzy set theory enhances diagnostic modeling. Mathematical models assess diagnostic indicators, and linguistic variables quantify resemblance. The diagnostic model for primary biliary cirrhosis and active hepatitis utilizes a diagnostic table and gradient projection. This comprehensive study advances medical diagnostics through mathematical models and automated systems, addressing critical healthcare challenges. [ABSTRACT FROM AUTHOR]

Southeastern Turkey perished by the M w 7.8, Kahramanmaras earthquake on February 6, 2023, at 01:17 UT (04:17 LT). The Kahramanmaras-centered event, hit by the break of the East Anatolian Fault Line, is felt in Syria, Egypt, Lebanon, Iraq, and Cyprus on the border of Turkey. Together with Syria, it causes loss of lives and severe damage in 10 cities. This work is dedicated to the people of the region. The study discusses the causality of anomalies by conducting ionospheric anomaly research via the interpolated TEC map obtained from the CODE according to the coordinates of the epicenter of the earthquake (latitude, longitude). The TEC amplitudes are observed by evolving the time-domain TEC map into the frequency-domain with the Fourier transform. The peaks of the amplitudes allow preliminary information for the anomaly days. For the anomaly, the TEC (TECU) boundaries are drawn by the statistical specification. The TEC map outside these boundaries is marked as an anomaly. The causality of abnormalities is tried to be read through the triad of the geomagnetic storm(s) , solar activity , and the earthquake effect. One can find seven anomaly days in the paper. January 30, February 2, 3, 11, 15, 16, and 21 are the anomaly days. The essay probably detects the anomaly before 3 days related to the Southeastern earthquake. • Reads the causality of the earthquake in southeast Turkey (Mw 7.8), through the ionosphere-earth crust relationship. • Notices potential anomalies by presenting the time-domain TEC atlas in the frequency domain with Fourier spectral analysis. • Considering all possible TEC anomaly essentials detect the precursors of the earthquake 3 days before the event day. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINN) architectures have been recently explored to accelerate hemodynamics simulations by leveraging mathematical models for blood flow and empirical data. In this paper, a variable separated physics-informed neural networks based on adaptive weighted loss functions (AW-vsPINN) is developed for blood flow model in arteries. In particular, sub-neural networks are proposed to separately predict the unknown scalar state variables by sharing the same input layer. The AW-vsPINN adaptively adjusts the weights of loss terms by the minmax algorithm, which will be updated synchronously along with the network parameters and can balance the contributions of different loss terms during training. The two-stage optimization is implemented to train the neural networks. Specifically, the Adam optimizer is iterated for initial steps with the learning rate generated by the inverse time decay scheduler, and then the L-BFGS optimizer continues to train until the loss converges. Numerical results illustrate that the AW-vsPINN can remarkably improve prediction accuracy and enhance the ability of generalization compared to the conventional PINN. The proposed AW-vsPINN framework has high potential in predicting the blood flow information in cardiovascular disease. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of model fidelity and parameter uncertainty on the performance of a hybrid model-based feedback-feedforward control scheme for attitude tracking of a satellite with flexible appendages. The feedforward component is an inverse model-based term produced through a computational approach known as inverse simulation (InvSim), which works by iteratively solving a discretised reference trajectory. The hybrid controller's feedback is proportional-derivative (PD) based, using body attitude and rate feedback to provide stability and robustness. Furthermore, to ensure that the flexible modes do not trigger instability, the PD control gains are tuned to give a closed-loop response that is significantly slower than the flexible modes. Additionally, excitation of the flexible modes is reduced by minimising jerk through polynomial rest-to-rest manoeuvres, following the shortest quaternion path using spherical–linear-interpolation (SLERP). The effects of the appendage flexing on attitude tracking are then compensated through the feedforward element of the hybrid controller, with performance being compared to a traditional PD tracking law. The effect of the model fidelity on the performance of the hybrid controller is investigated through the use of both rigid body and multiple-fidelity finite-element mathematical models. Additionally, the effect of uncertainties in the model parameters is investigated to determine the accuracy of the model required to obtain significant improvement in attitude tracking. It is found that in the absence of any model parameter uncertainty, the hybrid controller outperforms the PD tracking control law by at least one order of magnitude when the finite-element model is used. Increasing the number of finite elements was found to provide no significant improvement in performance, with one element being sufficient and favourable with its lower computational overhead. It was also found that to ensure good performance compared to the PD tracking controller, the uncertainty in the inertia tensor should be < 1%. Similarly, uncertainty in the first flexible modal frequency should be < 0.5 rad/s. • Inverse simulation (InvSim) used to produce model-based feedforward control. • Combined with PD feedback to improve attitude tracking. • Effects of model uncertainty and fidelity on model-based control investigated. [ABSTRACT FROM AUTHOR]

Exploring special multi-scroll chaotic systems is meaningful work. This paper studies an expanded multi-scroll chaotic system consisting of eight terms with one nonlinearity. It is generated by modifying the nonlinear term of the newly constructed chaotic system by a polynomial function. The unique mathematical model makes the unstable index-2 equilibria increase in four dimensions, which contributes to the number of scrolls expanding in each phase plane unidirectionally. Dynamic analysis finds that the system can yield complex nested multi-scroll attractors and has single-parameter-based synchronization control of amplitude and offset boosting behavior. Moreover, circuit implementation verifies the physical existence of the proposed system. Also, an image encryption algorithm for remote sensing images is established. Permutation and diffusion operations are performed using new pixel coordinates derived from the data of the chaotic matrix, which comes from the same position as the pixel matrix. Relevant tests suggest that the algorithm is secure and able to resist undesirable interference. • Simple multiscroll chaotic system with various signal control features is proposed. • Complex dynamics, circuit implementation of the proposed system is studied. • New chaos-based image encryption algorithm with high security is designed. [ABSTRACT FROM AUTHOR]

This paper studies a cascaded flowshop joint scheduling problem that has critical applications in the electronic information equipment manufacturing industry but has received limited attention in the scheduling field. The cascaded flowshop joint scheduling problem encompasses both a distributed permutation flowshop scheduling problem and a hybrid flowshop scheduling problem. This paper investigates the efficient scheduling of a set of jobs in two heterogeneous flowshops to minimize the makespan. We present a mixed integer linear programming mathematical model and a shifting iterated greedy algorithm, which constantly changes its search space to explore different solution spaces. Based on the specific characteristics of the problem, a hybrid scheduling approach that combines forward and backward scheduling, a step-by-step destruction and reconstruction operator, and three adaptive reconstructive methods that combine coarse-tuning and fine-tuning are proposed to explore the near-optimal solution. Through comprehensive computational comparison and statistical analysis, the results demonstrate that the proposed shifting iterated greedy algorithm performs significantly better in relative deviation index values at the same CPU running time. [ABSTRACT FROM AUTHOR]

Tourist trip design is a fast-growing area of research. Tourist interest, budget, travel style, safety, and the existence of travelers with special needs (for example, wheelchair accessibility) are some of the elements to consider for obtaining personalized routes. Including these elements in a single mathematical model can considerably complicate the solution process. Also, route decision-making is affected by the context (health, social, political, economic, etc.) in which decisions are made. In this paper, the first aim is to propose a three-step methodology to obtain contextualized solutions to a tourist trip design problem (TTDP) with time-dependent recommendation factors. The methodology consists of (1) providing a basic TTDP model that avoids the complexity of including contextual information, (2) obtaining a set of solutions to the problem using a Modeling to Generate Alternatives (MGA) approach, and (3) using a recently developed a posteriori method to include the contextual information through fuzzy propositions. The second aim of the paper is to evaluate three algorithmic strategies for the MGA step. Lastly, considering a context for people with mobility impairments, an example is solved using real data. The results show the usefulness of the proposed methodology in solving the TTDP with contextual information. • A tourist trip design problem with time-dependent recommendations factors is solved. • A methodology to obtain contextualized solutions is developed. • An accessibility context is modeled with fuzzy propositions. • Solutions are obtained by using the modeling-to-generate-alternatives approach. • Best solutions are suitable for the accessibility context. [ABSTRACT FROM AUTHOR]

This paper is the second in a two-part series on the concavity-convexity of the distribution system security region (DSSR). In Part I, the concave-convex characteristics and mechanism of DSSR are revealed by observing many case grids. In Part II (this paper), the concave-convex principle of DSSR is revealed through mathematical deduction. Firstly, the relevant concepts of sub-regions are proposed, and a DSSR model based on the union of sub-regions is developed. Secondly, the concave-convex principle of DSSR is proposed. The proposed criteria show that the security region of a distribution system with a single load transfer path for each feeder is convex, while that with multiple load transfer paths for some feeders can be concave or convex. Thirdly, the proposed criteria are deduced mathematically. Fourthly, based on the criteria, a method is proposed to judge whether a DSSR is concave or convex. Fifthly, the proposed criteria and method are verified through several cases, and the DSSR concavity-convexity of real distribution systems in China is also analyzed. Finally, the applications of DSSR concavity-convexity are illustrated. This work lays a mathematical foundation for the research of the topological properties of DSSR and is also useful for the application of DSSR. • The DSSR mathematical model based on the union of sub-regions is developed. • Three criteria are proposed and proved, revealing the concave-convex principle of DSSR. • A judgment method for DSSR concavity-convexity is proposed based on these criteria. • The DSSR concavity-convexity of real distribution systems in China is analyzed. • The application values of DSSR concavity-convexity are demonstrated. [ABSTRACT FROM AUTHOR]

With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (T T D). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality; (2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution; (3) A knowledge-based destruction and construction is designed to improve the exploration capability; (4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors. • This paper first investigates DAHFSSP-DRC with minimization TTD. • A MILP model and a KBIG are proposed for DAHFSSP-DRC. • The importance level of different knowledge is considered in KBIG. • Experimental results prove the effectiveness of MILP model and KBIG. [ABSTRACT FROM AUTHOR]

In the modern era, mathematical modeling is a promising tool to evaluate the forward osmosis (FO) and pressure retarded osmosis (PRO) systems process feasibility and check the outcome of performance parameters on the overall process without running it. The FO process is an energy-efficient and cost-effective desalination technique, and the PRO process is a promising renewable energy method that can generate energy by allowing different saline solutions to pass through a semipermeable membrane. This review paper critically analyses various mathematical models for FO and PRO processes. The paper provides an initial overview of FO and PRO, such as their operating principles, challenges, and potential applications. Then, it presents a detailed analysis of different mathematical models proposed for FO and PRO, highlighting their strengths and weaknesses. The review covers theoretical and experimental models and discusses the role of parameters and variables in the modeling process. The paper concludes with a discussion on future research directions and the potential for improving FO and PRO through innovative mathematical modeling. This study helps researchers in chemical engineering, membrane science, and renewable energy fields and can serve as a valuable reference for future studies on FO and PRO models to enhance system efficiency. [Display omitted] • Provides an overview of FO and PRO, their operating principles, challenges, and applications • Discusses theoretical and experimental models in FO and PRO processes • Presents the role of parameters and variables in the modeling process • Analyzes the future research directions for improving FO and PRO modeling [ABSTRACT FROM AUTHOR]

• The model allocates safety stocks in supply chains at minimum cost. • An MIQCP reformulation with piecewise approximation greatly improves computational efficiency. • The piecewise function yields an improved estimation for the fill rates. • The GSM is extended to handle non-normally distributed demands. • Real-world case studies solved to optimality within few seconds of computational time. This paper presents a Multi-Echelon Inventory Optimization (MEIO) framework, based on the Guaranteed-Service Model (GSM), to allocate safety stocks across a supply chain with several locations and products, minimizing costs while meeting service level objectives. Extending previous work by Achkar et al. (2023), this paper enhances the Mixed-Integer Quadratically Constrained Program (MIQCP) with a highly efficient solution approach. The model introduces a piecewise linear approximation, significantly improving computational efficiency and the accuracy of the approximation for the fill rate function. It also introduces a different and more efficient approach to account for stochastic lead times using a discrete function. Moreover, an extension of the approach to account for non-normally distributed demands is proposed. The model is applied to several instances of a real-world case study from a pharmaceutical company, with more than 7300 product-location combinations, showing that optimal solutions can be obtained within few seconds of computational time. [ABSTRACT FROM AUTHOR]

In an integrated electricity-gas system (IEGS), gas-fired units with flexibility response capabilities address the uncertainty associated with renewable energy by swiftly adapting their output, which may result in uncertain fluctuations in the gas withdrawal of gas-fired units, potentially posing risks to the security of IEGS. Hence, it is necessary to efficiently evaluate the dynamic security region (DSR) of natural gas systems in IEGS by considering the real-time dynamic characteristics of natural gas systems, which are not satisfactorily handled in state-of-the-art works. To bridge this gap, this paper first conceptionally verifies the necessity of the DSR and establishes its mathematical model. Then, a dimensionality reduction method is proposed for the efficient solution and visualization of the high-dimensional DSR evaluation model. A fast evaluation (FE) algorithm is developed to address the difficulties of the nonconvex dynamic constraints in the reduced DSR model. Finally, the necessity and notable advantages of the proposed DSR model and FE are verified based on small and relatively large test systems in comparison with common security region models and algorithms. To the best of our knowledge, this is the first paper that comprehensively presents models and efficient algorithms regarding the DSR of natural gas systems in IEGS. • This paper proposes the novel dynamic security region (DSR) of the natural gas system with natural gas system dynamics. • A dimensionality reduction method for the efficient solution and visualization of the high-dimensional DSR is proposed. • A fast evaluation (FE) algorithm is developed to address the nonconvex dynamic constraints in the DSR model. • Case studies confirm the necessity and effectiveness of the DSR, dimensionality reduction method, and FE. [ABSTRACT FROM AUTHOR]

Urban building layout has an important influence on the formation of shadows on building façades, and photovoltaic (PV) power generation performance is greatly affected by shadows. However, there is limited research on detailed modeling of the power generation of photovoltaic systems on facades with different building layouts. In this paper, according to different urban building layouts, an annual dynamic shadow model of the building and a mathematical model of PV array power generation were established and validated. Then the model was used to calculate the annual power generation hour by hour, optimize the PV array topology, and analyze the shadow shading ratio and PV power generation performance of different building layouts. The results show that 95 % of the best topologies of all partially shaded façades in the four layouts are Total-Cross-Tied (Vertical and Horizontal); the highest annual shading ratio is layout 4 with a value of 60.161 %, the largest annual power generation is layout 3, but layout 4 has the highest broad generation efficiency, with layouts 1, 2, and 3 lower than it by 4.29 %, 0.20 %, and 0.11 %, respectively. This paper provides new ideas for the early design of building layouts and the performance research of PV systems. • Yearly dynamic shadow and detailed PV models developed for urban layouts. • Optimized PV topologies under year-round dynamic shading. • 95 % optimal topologies are Total-Cross-Tied (Vertical and Horizontal). • Compared annual PV output and broad efficiency across building layouts. • Layout 4 excels in broad generation efficiency, leading by 4.29 %. [ABSTRACT FROM AUTHOR]

Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation has become one of the key technologies to reach energy-saving and carbon reduction targets. However, dust accumulation will significantly affect the electrical, optical, and thermal performance of PV panels and cause some energy loss. For this reason, appropriate cleaning measures are needed to restore their performance and power output. Many researchers have reviewed the effects of dust on the performance of PV panels and cleaning methods, but their coverage is narrow and lacks more in-depth summarization, comparison, and critique of key quantitative results. Using the Web of Science database as the main search source, this paper provides a comprehensive overview of research results on the mechanisms and influencing factors of dust deposition on photovoltaic panels, photovoltaic performance loss and prediction models, cleaning methods, and dirt monitoring systems. The results found that the module power output degradation due to dust deposition is more serious in different regions, ranging from 7% to 98.13%. The automatic cleaning robot, as an emerging intelligent technology, has a better cleaning effect and can increase PV efficiency by up to 49.53%. This paper also proposes a comprehensive strategy for dust prevention on PV panels that integrates "real-time monitoring of dust accumulation - model prediction of losses - and optimization of cleaning solutions", emphasises the development of new intelligent cleaning methods represented by robots and drone cleaning, and suggests promoting the application of AI in the monitoring and cleaning of PV modules to accelerate the process of achieving carbon neutrality. [ABSTRACT FROM AUTHOR]

Periodic cleaning of photovoltaic (PV) panels, such as every three months, is a common industry practice. However, this fixed period may not be optimal for maximizing the profit of a PV power generation system, due to numerous time-variant influencing factors, such as weather, temperature, etc. To increase the overall profit of a solar farm, it is highly desirable to have a flexible cleaning schedule that considers time-variant influencing factors. For this requirement, a rolling-horizon cleaning recommendation system is presented in this paper. Within this cleaning recommendation system, a prediction model and profit model are proposed. The prediction model, called the ensemble long-term and nonlinear autoregressive, can provide a time-variant future horizon by analyzing and compressing the time-variant characteristics in historical information. The profit model based on mathematical constraints, can process time-variant future horizon output from prediction model to generate a flexible optimized recommendation for cleaning schedule. The effectiveness of the proposed system is validated in real farms and all data used in this paper is collected from real world. The two case studies in experiments show that the profit improvement can reach up to 6% and 30%, respectively. [Display omitted] • A rolling-horizon cleaning recommendation system for PV panels. • A novel long-term and nonlinear predictive method. • A concise profit model for easy deployment in industry. • Flexible cleaning schedule considering time-variant characteristics. • Improvements of 6% and 30% in two different real farms, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we address modelling and simulation of dynamics involved in docking of large marine vessels with aid of tugboats, with the aim of providing a tool for improved design of control algorithms for autonomous docking operations. Mathematical models of the docking vessel and tugboats in 3DOF are derived, with a special focus on realistic interactions through towline, contact and friction forces with stick–slip dynamics and time delays, which are typically not considered in previous publications on the topic. We also present the implementation of the developed models in the first step towards a Simulink simulator in which considers the special case of tugboat-assisted docking operations in the Narvik harbour. The developed simulator is available on GitHub (see Ørke et al. (2023)) and encompasses model dynamics, tugboat control and vessel control and guidance, although the scope of this paper is model dynamics. We present simulation results that show that we are indeed able to capture interaction dynamics in a realistic way. Finally, we include a discussion of our results and some ideas for further development, as well as some concluding remarks. [Display omitted] • Mathematical modelling of docking vessel and tugboats in 3DOF. • Modelling of interaction forces in pushing/pulling operations. • Model implementation in Simulink-developed simulator. • Simulation and discussion of interaction forces, and ideas for future work. [ABSTRACT FROM AUTHOR]

In this paper the aerothermal heating of a reusable launch vehicle is reconstructed on the basis of temperature measurements taken in the thermal protection system of this vehicle. The discussed integrated thermal protection system is composed of three layers. Mathematical model, describing the problem, takes into account the dependence on temperature of the material parameters as well as the thermal resistances occurring in the contact zones of the layers, which is a novelty in the proposed approach. For solving the direct problem, the implicit scheme of the finite difference method is applied. Next, by using the solution of the direct problem, the Tikhonov functional is created, which describes the error of the current approximate solution. Whereas for determining the solution of the inverse problem the Levenberg-Marquardt method, modified and adapted to the Tikhonov functional, is used. The paper presents the mathematical model of the problem and the method of solution together with the selected examples illustrating its exactness and stability. In order to better examination of the solution method some various values of parameters are taken in the demonstrated examples. • Reconstructing an aerothermal heating in space vehicle protection system with temperature dependent material properties. • Reconstructing an aerothermal heating taking into account the thermal resistance between layers. • Solving the problem for three layers model of the thermal protection system. • Inverse problem solved for input data taken from NASA measurements. • Heat flux measured by NASA reconstructed with good precision and stability. [ABSTRACT FROM AUTHOR]

An intelligent detection of the communication channel by the transceiver is what is known as cognitive radio (CR), a sort of radio communication. Avoid using channels that are already in use to reduce interference and swiftly direct traffic to open channels. In cognitive wireless networks, spectrum allocation has long been a research hotspot as one of the key problems to increase spectrum efficiency. This article examines the issue of spectrum sharing, in which one primary user (PU) might rent out portions of their spectrum to numerous secondary users (SU) in order to share it. The suggested Fuzzy Ant Colony Optimization based Multiple Scheduling Resource Selection Algorithm (FAMSRSA) chooses the best network while maximising data rate and minimising interference and cost. Here, Nash's balanced multi-antenna-based spectrum sharing model (NE-MASS) is used to optimize and share the spectrum. In this paper, a mathematical model optimizes multiple network selection goals to maximize the overall bandwidth, and the total cost and idle spectrum assignment are determined to a minimum in the context of a comprehensive consideration of differences in the spectral resource characteristics of primary networks that differ in network domain and diversify the user's secondary requirements and analyze the complexity of the problem in the user domain. This approach provides an efficient way to access the spectrum available to both primary and secondary users. As a result of the experiment, this algorithm shows the algorithm's efficiency better than the four conventional algorithms in terms of performance metrics like Maximized Minimum Bandwidth, Maximized Network Efficiency, Maximized Proportion Fairness, Relative error and Spectrum Utilization Efficiency. The performance of the NE spectrum allocation algorithm was evaluated by extensive simulation. The proposed spectrum allocation algorithm has shown superior channel allocation performance than other conventional literature algorithms. [ABSTRACT FROM AUTHOR]

Governments worldwide have promulgated greenness-based electric vehicle subsidy (GEVS) policies to encourage electric vehicle (EV) manufacturers to develop products with higher greenness (i.e., energy saving and emission reduction performance). Normally a GEVS policy would set a subsidy threshold to ensure that only EVs whose greenness meets the subsidy threshold requirement receive the subsidy. By considering consumers' environmental preferences (CEPs), this paper develops game-theoretical models to investigate the impacts of the GEVS policy on EV manufacturers' product development strategies and profits, as well as on the environment. The findings show that the product development strategies highly depend on subsidy thresholds, and three equilibrium product development strategies are obtained in equilibrium. Besides, it is intuitive to find that, in the absence of CEP, a low subsidy threshold can increase both EV manufacturers' profits and reduce the environmental impact of EVs simultaneously. However, the opposite results emerge when consumers have strong environmental preferences; that is, a low subsidy threshold would hamper both EV manufacturers' profits, and meanwhile increase EVs' environmental impacts. Surprisingly, as CEP increases, the GEVS policy is more likely to reduce EV manufacturers' profits and increase EVs' environmental impacts. • The product development strategies of electric vehicle manufacturers highly depend on subsidy thresholds. • Three equilibrium product development strategies are obtained in equilibrium. • A low subsidy threshold can make the environment better off without consumers' environmental preferences. • A low subsidy threshold can hamper profits and environment with consumers' environmental preferences. • A high subsidy threshold is not effective in increasing profits and protecting environment. [ABSTRACT FROM AUTHOR]

• The workpiece coordinate system of face gears is iteratively established. • The measured points are corrected by reconstructing complicated tooth surfaces. • Multi-objective optimization comprehensively compensates for measurement errors. • The proposed measurement method is effective and more robust than the previous ones. In contact coordinate measurement, measurement error sources would affect the establishment of the workpiece coordinate system (WCS) and the exact measurement for the points on the complicated surfaces. This issue would be more complicated for face gears with complex features, especially when having huge machining errors. To address the above problem, this paper proposes a novel precise mathematical model to measure face gears by considering the geometric deviations of multiple teeth, including the deviations of tooth surfaces, pitch, and tooth thickness. An iterative method is presented to establish the WCS on machines by considering multiple reference points on different teeth. Subsequently, the correction model of measured points is established, and the sensitivity analysis is conducted. Further, a multi-objective optimization is proposed to comprehensively compensate for measurement errors, and the algorithm is given. The experiment of a face gear with huge overcut tooth surfaces validates the proposed method is effective and robust. [ABSTRACT FROM AUTHOR]

The paper discusses the nonlinear behavior of a viscoelastic 3D thin film in frictional contact with a flat substrate. The frictional contact is modeled by Coulomb's law of friction at substrate. A function is included for damage caused by strain or excessive film stress to which the materials may be subjected over time. Assumptions and a functional framework are identified to allow for the existence and uniqueness of the weak solution to the problem. In contrast, preliminary estimates of the potential damage and stresses to the system during a specified operating period T are disclosed, the study concluded in the latter by an asymptotic analysis model of the problem explained with mathematical justifications and intended results. [ABSTRACT FROM AUTHOR]

A nonlinear energy-based controller and a Lyapunov-based model predictive control (MPC) technology are constructed in sequence for shipboard boom cranes, while taking full state constraints into account. The mathematical model is firstly transformed to ease the explicit influences of ship rollings on the desired positions and payload swings. Barrier Lyapunov functions (BLFs) are then involved in the energy-based controller to deal with different types of state constraints, in which constraints with positive bounds are also effectively tackled with a modified BLF. By adding a contractive constraint with the energy-based controller in traditional MPC framework, Lyapunov-based MPC is then established, in which the recursive feasibility and stability is ensured effectively and easily. Asymptotical stability of two controllers is analyzed and guaranteed in theory, respectively. Compared with the energy-based controller, control performance is improved and enhanced by the Lyapunov-based MPC through solving the optimal control problem. Simulations are finally implemented, and comparisons are also carried out to demonstrate the effectiveness and features of the established controllers in this paper. • An energy-based controller is independently developed for shipboard boom cranes. The asymmetrical constraints on all states are strictly respected and asymptotic stability is guaranteed by embedding BLFs in the Lyapunov function and controller. • To the best of our knowledge, Lyapunov-based MPC is firstly applied to shipboard boom cranes while tackling full state constraints. The recursive feasibility and asymptotic stability are ensured by taking energy-based controller as initial guesses and contractive constraints. • Two groups of numerical simulations are implemented to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

This paper describes a comprehensive freewheel clutch (FWC) modeling approach. Such an approach can be utilized for the analysis and synthesis of a wide range of FWCs. The proposed approach consists of a multi-leveled mathematical model and an output dashboard. The multibody model considers the components' inertia, internal forces distribution, and the velocity and the deformation of the contacts. To efficiently analyze the results, the simulation output was assembled as a sub-plots dashboard. The dashboard shows a number of output parameters, such as the theoretical speed of the FWC components, transmitted torque, friction coefficient variation, and normal and frictional forces distribution. The validation of the gained theoretical data took place on a custom testing machine equipped with a generic wedge FWC, which confirmed the robustness of the proposed modeling approach. [ABSTRACT FROM AUTHOR]

In this paper we formulate and analyze an elementary model for the propagation of advancing autoignition fronts in reactive co-flow fuel/oxidizer jets injected into an aqueous environment at pressures exceeding the critical point of water. This work is motivated by the experimental studies of autoignition of hydrothermal flames performed at the high pressure laboratory of the NASA Glenn Research Center. Guided by experimental observations, we use several simplifying assumptions that allow the derivation of a simple, still experimentally feasible, mathematical model for the propagation of advancing ignition fronts. We analyze the model by means of asymptotic and numerical techniques and discuss feasible regimes of propagation of advancing ignition fronts. We hope that the present study will be helpful for the interpretation of existing experimental data and guiding of future experiments. [Display omitted] • Model for autoignition of hydrothermal flames was introduced and studied. • Configuration of reactive jet injected into supercritical water was considered. • Critical condition for propagation of autoignition front was formulated. • Parametric regimes for ignition front propagation were identified and studied. • Qualitative comparison of experimental and theoretical studies was presented. [ABSTRACT FROM AUTHOR]

Handwritten mathematical expression recognition (HMER) is a challenging task in the field of computer vision due to the complex two-dimensional spatial structure and diverse handwriting styles of mathematical expressions (MEs). Recent mainstream approach treats MEs as objects with tree structures, modeled by sequence decoders or tree decoders. These decoders recognize the symbols and relationships between symbols in MEs in depth-first order, resulting in long decoding steps that can harm their performance, particularly for MEs with complex structures. In this paper, we propose a novel tree-based model with branch parallel decoding for HMER, which parses the structures of ME trees by explicitly predicting the relationships between symbols. In addition, a query constructing module is proposed to assist the decoder in decoding the branches of ME trees in parallel, thus reducing the number of decoding time steps and alleviating the problem of long sequence attention decoding. As a result, our model outperforms existing models on three widely-used benchmarks and demonstrates significant improvements in HMER performance. • Proposing a tree-based model for handwritten mathematical expression recognition. • Performing explicit symbol recognition and relationship prediction between symbols. • Proposing a query constructing module to decode the branches of trees in parallel. • Reducing the number of decoding steps and improving the recognition performance. [ABSTRACT FROM AUTHOR]

Energy consumed by HVAC (heating, ventilating and air conditioning) systems represents a considerable part of the energy consumed in buildings. This paper focuses on achieving energy efficiency, through automatic control strategies, of HVAC systems in the biopharmaceutical industry, a sector little covered by previous studies, mainly focused on residential and commercial buildings. The system under study is an air handling unit (AHU). The main contributions of this research are the obtaining of a dynamic, multivariable, and non-linear model of the AHU, proposing a relatively simple structure and the procedure to estimate its parameters; a non-linear static model of the power consumption of its bank of electrical resistors, also simple, but useful to guide the PID tuning toward energy efficiency; and the approximation to the model of a PID controller whose control low is unknown. The methods proposed to obtain the models and to perform the simulations are also provided. Results for a close-to-reality simulation scenario that suggests the possibility of reducing the power consumed by the resistor bank by 29 % are presented. The use of an industrial PI control algorithm, instead of the classical textbook algorithm, also distinguishes this work from others. • Energy-efficient control of AHUs with cascade control loops requires energy efficiency and effectiveness of internal loops. • Mathematical models and simulations allow to increase the efficiency of control systems by re-tunning its controllers. • To use Hammertein-Wiener models with a proper experiment design is a simple way to capture the dynamics of complex systems. • It is necessary to validate the control algorithms of industrial controllers with experimental data. • Proper tuning of PID loops in AHUs with electric heating, temperature and humidity control, allows energy saving over 20%. [ABSTRACT FROM AUTHOR]

This paper presents the calibration of the volume of a tank prover using the gravimetric method. A mathematical model of this calibration has been developed and it is non-linear. An evaluation of measurement uncertainty of the tank prover calibration has been carried out using the "Guide to the Expression of Uncertainty in Measurement" (GUM) and the Monte Carlo method. The measurement results have been compared. The influence of calibration conditions on the validation of GUM method by means of the Monte Carlo method (MCM) has been investigated computationally, as well as the behavior of the expanded uncertainty (GUM method) as a function of these calibration conditions. Seven case studies have been analyzed in this research work. In reference case, the tank prover calibration is carried out in the normal laboratory condition. In other case studies, the tank prover calibration is modeled in various hypothetical situations, such as change in tank prover scale quality, variation of calibration water density during calibration, large temperature gradient of water inside the tank prover and combination of the above conditions. In general, GUM method has not been validated by MCM due to one or more of the following conditions: a) the dominant source of uncertainty is a rectangular distribution; b) the uncertainty budget contains large uncertainty and the measurand model is non-linear; c) the measurand probability distribution is not normal. • The tank prover volume has been calibrated using the gravimetric method. • A mathematical model of the measurand has been conceived and it is non-linear. • The analysis of uncertainty of the tank prover calibration has been developed using GUM approach and Monte Carlo method (MCM). • The influence of calibration conditions on the validation of GUM method by MCM has been investigated in case studies. [ABSTRACT FROM AUTHOR]

The paper presents an experimental and simulative study on anisotropic dry friction. Friction force components tangential and normal to sliding velocity are measured for the corrugated cardboard-felt frictional pair. Six different curves with two-fold symmetry are analysed in terms of modelling anisotropic friction. Next, the parameters of the analysed curves are identified based on the experimental results. Friction hodograph is best described by the Gielis' curve and the superellipse, whereas the ellipse seems to be the worst choice. The sliding potential is described best by the oval. It is shown that modelling of the sliding potential can be crucial in capturing the dynamics of the system and the superelliptical sliding rule can lead to qualitatively different results. • Anisotropic friction forces tangential and normal to sliding velocity are measured. • Sliding rule is modelled with an ellipse, a superellipse, a cosine curve and an oval. • Friction hodograph is best described by the Gielis' curve and the superellipse. • Best description for the sliding potential is the oval. • Free sliding and anisotropic friction-induced oscillations are analysed numerically. [ABSTRACT FROM AUTHOR]

The Modular Modeling Methodology (MMM) is a general approach, applicable for holonomic or nonholonomic multibody systems, which allows the integrated use of libraries of already available mathematical models along with general-purpose computational tools. This paper extends the application of the Modular Modeling Methodology to kinematically redundant parallel mechanisms and identifies its relevant advantages for the derivation of inverse dynamics models. A planar kinematically redundant parallel manipulator is adopted for illustrating the application of the proposed approach. Four representative trajectories were selected for the trajectory planning and metrics were proposed to evaluate the proximity to singularities and reduce energy consumption. To this end, two distinct strategies are implemented and their differences are discussed. In comparison with previously proposed structures, the analyzed mechanism demonstrates relative advantages in terms of weight and simplicity. The achieved long distant paths by the end-effector and the singularity-free path with low energy consumption carry practical significance, opening possibilities for enhanced performance and efficiency in practical applications. In this scenario, this study contributes to the field of dynamic analysis and trajectory planning for parallel mechanisms. • Extension of the Modular Modeling Methodology to redundant parallel mechanisms. • Relevant advantages for the derivation of inverse dynamics models. • Two strategies for singularity-free trajectories with low energy consumption. • Contributions to the field of dynamic analysis and trajectory planning for mechanisms. [ABSTRACT FROM AUTHOR]

• The primary circuit mass flow rates with reverse flow in U-tubes are over predicted. • A new single pipe model is established by introducing an additional resistance. • The additional resistance under steady-state conditions is calculated. • A steady-state simulation is carried out using the RELAP code. Under a natural circulation condition, reverse flow can occur in some U-tubes of steam generator (SG), which would have a significant effect on the thermal–hydraulic characteristics of SG. A single pipe model is generally used to analyze the thermal–hydraulic characteristics of SG. However, this model will overpredict the primary circuit mass flow rates with reverse flow in U-tubes. To eliminate the limitation of this single pipe model, the new single pipe model is established by introducing an additional resistance. This paper focuses on the calculation of the additional resistance to provide some help for the simulation of SG with reverse flow in U-tubes. A mathematical model is established to calculate the additional resistance under steady-state conditions. To verify the developed models, a steady-state simulation is carried out using the RELAP code for a test facility and the calculation results are compared with the experimental data. The calculation results show an agreement with the experimental data. [ABSTRACT FROM AUTHOR]

With the extensive application of laser cutting, the proper planning of the cutting path has a significate impact on industrial production, like clothing, metalware, and furniture. This paper proposes heuristic approaches for the two-dimensional cutting path problem, which aims to find the shortest path to cut pieces from the master plate. Two cutting methods, the arc routing method, and the node routing method, are studied based on whether the piece must be cut separately. A two-step heuristic approach is proposed for the arc routing method. A model based on a generalized traveling salesman problem (GTSP) and a variable neighborhood search (VNS) is introduced for the node routing method. The results show that the proposed two-step heuristic quickly finds the optimal or near-optimal solution. CPLEX using the GTSP model can solve small-size instances, while the VNS can find the solution with good quality in a reasonable time for medium-size instances. In addition, the model and approaches are also tested on larger instances with more than 250 pieces and 20,000 nodes. The results show that the proposed heuristics can handle these instances within a reasonable time. • A two-step heuristic is proposed for the arc routing method. • A new model is proposed for the node routing method. • A variable neighborhood search is introduced for the node routing method. • The two-step heuristic quickly finds the optimal or near-optimal solution. [ABSTRACT FROM AUTHOR]

The paper presents the original general model of induction surface hardening based upon analysis of coupled electromagnetic, thermal, mechanical and metallurgical phenomena. The distribution of mechanical strains and stresses in surface layers of steel materials subjected to induction hardening is determined. This distribution is not caused only by the thermoelastic processes, but often, it is also influenced by plastic deformations of the exposed layers and transformation of particular levels of steel. An illustrative example shows the methodology of solution for an axi-symmetric configuration. • Mathematical model of induction surface hardening system was formulated. • Distribution of strains and stresses of thermoelastic, thermoplastic and transformation origins was determined • Computations were provided for exemplary induction surface hardening of a cylinder made of steel AISI 4140. • Next work in the field will be aimed at verification of computations by corresponding experiments. [ABSTRACT FROM AUTHOR]

Multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) is a new approach to quantitative intersectional modelling. Along with an outcome of interest, MAIHDA entails the use of two sets of independent variables. These include group demographics such as race, gender, and poverty status as well as strata which are constructs such as Black female poor, Black female wealthy, and White female poor. These constructs represent the combination of the demographic variables. To operationalize the approach, an initial random intercepts model with strata as a level 2 context is specified. Then, another model is specified that includes the strata as well as the demographic variables as level 1 fixed effects. As such, it is argued that MAIHDA uniquely identifies the additive and intersectional effects for any given outcome. In this paper we show that MAIHDA falls short of this promise: the strata are an individual-level composite variable not a level 2 context. Rather than being analogous to neighborhoods as contexts, strata are analogous to socio-economic status which is a combination of individual-level demographic variables, albeit often presented as a group-level characteristic. The result is that the demographic variables are inserted in both level 2 and 1. This duplication across the levels in MAIHDA means that there is a built-in collinearity across the levels and that the models are mis-specified and, therefore, redundant. We conclude that single-level models with the demographic variables and interactions or with the strata as fixed effects are still the more accurate models for quantitative intersectional analyses. • There has been limited analysis of the MAIHDA method. • We compare MAIHDA to traditional HLM. • We show that strata are a level 1 composite variable not a level 2 context. • We identify duplication and collinearity in this approach. • We advocate for single level models in quantitative intersectionality. [ABSTRACT FROM AUTHOR]

Crystallization fouling is an ongoing concern for many heat transfer systems, and simulating fouling formation under various flow and heat transfer conditions is crucial for predicting fouling resistance and improving energy efficiency. This paper proposes a mesoscopic model for predicting crystallization fouling in narrow rectangle channels accounting for the realistic crystal growth. A hybrid lattice Boltzmann and finite difference model is established to predict the coupled process of fluid flow, heat transfer, concentration diffusion and crystallization fouling. The second-order mass deposition rate is solved and transformed to the surface-reaction boundary condition in lattice Boltzmann (LB) scheme, and the volume of pixel (VOP) method is employed to simulate the dynamic crystal growth. The scheme and the sub-models are verified systematically by the analytical solution of a linear diffusion-reaction problem, the semi-theoretical fouling models, and the experimental data. The results indicate that the present model can effectively predict the crystallization fouling controlled by either surface integration or mass transfer, and show its capability to simulate the non-uniform fouling growth process in heat transfer channels, providing insight towards full understanding and mitigation design of crystallization fouling. • A mesoscopic model of fouling formation under convective heat transfer is built. • Dynamic crystal growth under the diffusion-convection-reaction process is modeled. • Model predictions show good correlation with semi-theoretical model and test data. • The evolving fouling structure with the coupled multi-physics field are analyzed. • A 355-h long term fouling test in narrow rectangle channel is conducted. [ABSTRACT FROM AUTHOR]

The proportion of spot trading and short-term contracts has gradually increased in the rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that significantly challenge LNG shipping decisions. In this paper, a mathematical model is developed to minimize transportation costs from multiple exporting countries to multiple importing countries under demand and price uncertainty, both of which are results of LNG market liberalization. A Gaussian process metamodel based simulation optimization method is proposed to solve the fleet planning problem, accounting for various uncertainties. A case study is given to illustrate the effects of uncertainties on the optimal solutions. The results demonstrate that shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. LNG market liberalization can significantly reduce its transportation costs and carbon emissions. The results suggest the need for further LNG market liberalization and measures to mitigate uncertainties for shipping companies, such as removing destination clauses. • More price and demand uncertainties stemming from LNG market liberalization challenge fleet planning decisions. • Consider a fleet size and composition optimization problem with a mathematical model. • A Gaussian process based simulation optimization method solves the fleet planning problem. • Shipping companies may purchase fewer ships and charter more ships to hedge against the risk of uncertainty. • LNG market liberalization can significantly reduce transportation costs and carbon emissions. [ABSTRACT FROM AUTHOR]

There are many different kinds of microorganisms in the soil, and many of them are biofilms because they can make supracellular compounds. Surface-associated microorganisms in a biofilm are encased in a hydrated extracellular polymeric substance that aids in adherence and survival. Numerous different kinds of microorganisms call the soil home. Strong interactions with and among species are made possible by biofilms; this, in turn, might increase the effectiveness with which organic compounds and poisons in soil are degraded. This encouraged us to take a close look at soil biofilm ecosystems, which we do in this paper. In this research, we will look at how soil biofilms arise and how that affects the composition of microbial communities and their function in the soil. Recent years have seen an uptick in interest in questions about biofilm structure and the social interactions of various bacteria. Many concepts elucidating the underlying mathematics of biofilm growth are also presented. Since biofilms are so widespread, this breakthrough in soil biofilm inquiry might help scientists understand soil microbiomes better. Mathematical models further extrapolate the relationships between microbial communities and gives a more precise information as to what is happening in a biofilm. Biofilms can help plants cope with a variety of environmental challenges. Soil quality, plant nourishment, plant protection, bioremediation, and climate change are all influenced by the interplay of biofilm communities. Thus, biofilms play an important role in the development of environmentally friendly and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Collision avoidance is the key to ensure the safe navigation of unmanned surface vessels (USVs). This paper proposes a collision avoidance and path planning algorithm (CAPP) algorithm for real-time collision avoidance and path planning of USV in the marine environment with multiple obstacle ships. Firstly, a mathematical model of both the encounter and action situations is introduced to address the problem of unifying the quantitative standard for encounter situations. Secondly, a deep deterministic policy gradient algorithm, suitable for continuous action space, is improved. A reward function model integrating international regulations for preventing collisions at sea (COLREGs) and driving habits is proposed to solve the problems of stability, economy, and comfort in the process of sailing for USVs. Thirdly, a compound experience replay mechanism is proposed to increase the training efficiency of the network model by selecting the experience with high priority. Additionally, a sum-tree data extraction method is introduced to improve sampling efficiency. Finally, test results on the simulation and training platform are used to verify the functionality and effectiveness of the proposed CAPP algorithm. • A mathematical model of both the encounter and action situations is established. • A reward function model integrating COLREGs and driving habits is proposed. • A compound experience replay mechanism is proposed to increase the training efficiency. • A sum-tree data extraction method is introduced to improve sampling efficiency. [ABSTRACT FROM AUTHOR]

The perturbed Gerdjikov–Ivanov (pGI) equation, a mathematical model that depicts the behaviour of optical pulses during propagation while accounting for perturbation influences, has been thoroughly studied by us. This equation has significant applications in the field of optical fibres, notably for photonic crystal fibres. In this paper, we combined the Riccati-Bernoulli sub-ODE method with the Backlund transformation to construct travelling wave solutions for the perturbed Gerdjikov–Ivanov (pGI) equation. This technique offered us families of solutions as well as a fresh way of dealing with nonlinear models. The results that emerge could greatly advance our knowledge of the physical consequences that are present in the nonlinear model that we are studying. The resulting solutions broaden the scope of earlier findings made using various approaches by including solitons, trigonometric functions, and rational expressions. The Backlund transformation used in this study is distinguished by its simplicity and brevity, and it produces results that are noticeably more thorough than those often obtained by alternative methods. Additionally, we create graphical representations of our results using the computational capabilities of the Maple software. In order to do this, the proper parameter values must be chosen, allowing for a visual study of the results. Through the display of two-dimensional graphical depictions, these effects have been clearly explained, permitting a thorough understanding of their physical implications. [ABSTRACT FROM AUTHOR]

In this paper, integral equation formulations and field-dependent micromechanical modeling of global nonlinear magneto-mechanical behavior of magneto-elastic composites under high strain and magnetic field are elaborated. The modeling is obtained based on an extension of micro–macro transition inclusion problem to nonlinear behavior using field-dependent and highly nonlinear localization tensors. A methodological procedure is elaborated based on newly introduced strain and magnetic field-dependent Green tensors, strain and magnetic field-dependent integral equations linked to tensors of Eshelby and micromechanical approaches. The field-dependent global moduli are predicted based on the Mori–Tanaka approach and self-consistent method. Iterative incremental schemes based on the Newton–Raphson and fixed-point algorithms are examined and established precise semi-analytic equations of the effective magneto-elastic properties of composites that are dependent on the magnetic-strain field for different types of inclusions. A numerical code is elaborated for numerical predictions and the obtained field-dependent effective magneto-elastic coefficients are obtained and presented for various volume fractions of inclusion, types and shapes of the reinforced nonlinear composites. • Nonlinear micromechanical modeling of fully coupled magneto-elastic under large deformation and high magnetic field. • Magnetic-Strain dependent Green tensors and associated localization tensors. • Iterative incremental schemes for semi-analytical magnetic-strain field dependent effective magneto-elastic properties. • Magneto-mechanical field dependent effective properties for several types of matrix and inclusions phases. [ABSTRACT FROM AUTHOR]