Search

In the modern educational environment, rational and efficient course scheduling is of great significance in ensuring teaching quality and improving resource utilization. The traditional sports scheduling methods are faced with the challenges of diversified demands and complex constraints. For this reason, the study proposes a new physical education course scheduling model after mathematically modeling the physical education scheduling model and improving the genetic environment based on chaotic genetic algorithm, and then proposes a new physical education course scheduling model. The experiment outcomes denote that the average computing time of the improved chaotic genetic algorithm is 28 s, and when the number of iterations is 175–200, the optimal number of individuals is 35 at most, and the value of the optimal fitness is 9.4. The simulation test outcomes denote that the new scheduling model can reasonably arrange the course in the 5th-6th section, which meets the needs of students. Meanwhile, when the amount of scheduling teachers is 4 and the amount of courses is 5, the average utilization rate of scheduling resources at this time is the highest 82.5%. Compared with the same type of scheduling model, the new model has the highest superiority of 90.7%, the highest stability of 90.1%, and the highest robustness of 91.6%. The proposed method can meet the diversified and dynamically changing teaching needs, and provides an effective optimization tool for physical education scheduling. [ABSTRACT FROM AUTHOR]

In the field of power systems, the optimization challenge of combined heat and power units economic dispatch (CHPUED) holds immense importance. This study presents an improved Aquila optimization technique (IAQT) that effectively tackles the CHPUED. The primary objective of the enhanced IAQT model is to minimize the overall cost of power generation in CHP systems while satisfying demand and operational constraints. However, to achieve more accurate cost estimations and avoid suboptimal solutions, it is crucial to consider transmission losses in the optimization model. By incorporating transmission losses, the IAQT algorithm can allocate power generation resources more effectively, leading to improved system efficiency and reduced operational costs. The proposed IAQT algorithm addresses the limitations of the standard AQT and introduces novel features to enhance its search capabilities. One key limitation of the standard AQT is its heavy reliance on the best solution found during optimization. To overcome this drawback, the enhanced IAQT model eliminates the dependency on the best solution and enables a more thorough exploration of the search space. Moreover, the algorithm incorporates specific limitations and constraints for each dimension of the newly generated solutions, ensuring their feasibility and validity. The standard AQT and proposed IAQT are tested on CEC 20 benchmark functions. Moreover, the proposed approach is extensively evaluated through experimentation and testing on various scenarios, including 7–48-unit and large 96-unit systems with/without losses. Furthermore, the overall costs for the 7 unit-system are considered including the reserve constraint. The results exhibit the remarkable performance and efficiency of the enhanced IAQT model, outperforming the standard version and several previously reported results. This validation underscores the significant contribution of the study in addressing the CHPUED and highlights its potential for real-world applications. [ABSTRACT FROM AUTHOR]

The meshing and limit equations of worm drive usually have strong nonlinearities such as multiple solutions, solution nonexistence and equation singularity. Meanwhile, the tooth surfaces of worm drive are in nonconforming line contact, which often requires mesh refinement of contact region for the loaded contact analysis. These two challenges cause that the modeling of worm drive heavily relies on manual adjustment and the loaded contact analysis of worm drive is still rare especially when edge contact and assembly error are concerned. Focusing on the double enveloping hourglass worm (DEHW) drive with planar generatrix, this work presents procedures to solve meshing and limit equations with global convergence. The instantaneous contact line, meshing limit line, curvature interference limit line and tooth surface grid discretization are adaptively generated, without manual trial or adjustment. On the basis of adaptive mesh refinement of tooth surface, the mortar virtual element method is adopted for loaded contact analysis of DEHW drive with edge contact and center distance error. Under sliding friction, the discrete system of governing equalities and inequalities is solved by semi-smooth Newton algorithm after constraint condensation. Numerical results for meshing theory and loaded contact analysis of DEHW drive with planar generatrix are discussed. [ABSTRACT FROM AUTHOR]

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment. [ABSTRACT FROM AUTHOR]

Reservoir flood control scheduling is a challenging optimization task, particularly due to the complexity of various constraints. This paper proposes an innovative algorithm design approach to address this challenge. Combining the basic walrus optimization algorithm with the adaptive ε-constraint method and introducing the SPM chaotic mapping for population initialization, spiral search strategy, and local enhancement search strategy based on Cauchy mutation and reverse learning significantly enhances the algorithm's optimization performance. On this basis, innovate an adaptive approach ε A New Algorithm for Constraints and Multi Strategy Optimization Improvement (ε-IWOA). To validate the performance of the ε-IWOA algorithm, 24 constrained optimization test functions are used to test its optimization capabilities and effectiveness in solving constrained optimization problems. Experimental results demonstrate that the ε-IWOA algorithm exhibits excellent optimization ability and stable performance. Taking the Taolinkou Reservoir, Daheiting Reservoir, and Panjiakou Reservoir in the middle and lower reaches of the Luanhe River Basin as a case study, this paper applies the ε-IWOA algorithm to practical reservoir scheduling problems by constructing a three-reservoir flood control scheduling system with Luanxian as the control point. A comparative analysis is conducted with the ε-WOA, ε-DE and ε-PSO (particle swarm optimization) algorithms.The experimental results indicate that ε-IWOA algorithm performs the best in optimization, with the occupied flood control capacity of the three reservoirs reaching 89.32%, 90.02%, and 80.95%, respectively. The control points in Luan County can reduce the peak by 49%.This provides a practical and effective solution method for reservoir optimization scheduling models. This study offers new ideas and solutions for flood control optimization scheduling of reservoir groups, contributing to the optimization and development of reservoir scheduling work. [ABSTRACT FROM AUTHOR]

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for both healthy and pathological femurs. [ABSTRACT FROM AUTHOR]

In the area of 3D computer vision, the ability to estimate pose between two cameras under high noise levels while maintaining small reprojection errors reflects the robustness of such pose estimation algorithms. Moreover, maintaining positive depth constraint is another challenging task. Unfortunately, current pose estimation algorithms are often sensitive to noise/outliers and do not always guarantee positive depths. As a standalone task, these algorithms perform a positive sign check and simply discard the points with negative depths after the algorithms are executed. These algorithms do not integrate positive depth constraints into the algorithms themselves. Instead, they do it afterwards. Here, from a comprehensive mathematical derivation, we propose a novel pose estimation algorithm that integrates positive depth constraint into the algorithm itself by estimating the depths directly. The algorithm was competitive in producing small reprojection errors when compared to the state-of-the-art algorithms under both synthetic and real-world tests, while most importantly guaranteeing positive depths. [ABSTRACT FROM AUTHOR]

The energy harvesting capability of a graded metamaterial is maximised via reinforcement learning (RL) under realistic excitations at the microscale. The metamaterial consists of a waveguide with a set of beam-like resonators of variable length, with piezoelectric patches, attached to it. The piezo-mechanical system is modelled through equivalent lumped parameters determined via a general impedance analysis. Realistic conditions are mimicked by considering either magnetic loading or random excitations, the latter scenario requiring the enhancement of the harvesting capability for a class of forcing terms with similar but different frequency content. The RL-based optimisation is empowered by using the physical understanding of wave propagation in a such local resonance system to constrain the state representation and the action space. The procedure outcomes are compared against grading rules optimised through genetic algorithms. While genetic algorithms are more effective in the deterministic setting featuring the application of magnetic loading, the proposed RL-based proves superior in the inherently stochastic setting of the random excitation scenario. [ABSTRACT FROM AUTHOR]

In order to address the low azimuth imaging problem of traditional Range Doppler (RD) algorithm, a high-resolution imaging algorithm with optimal azimuth sampling sequence length (SSL) constraints is hereby proposed. According to this algorithm, efforts are made to design a formula for the calculation of the initial azimuth SSL on the basis of SAR azimuth imaging parameters. Having obtained the initial SSL and the criteria for the evaluation of SAR imaging performance, we continue to figure out the extreme values of the curve within a reasonable range of variation. On the basis of these values, the optimal azimuth SSL can be determined to provide data support for global azimuth imaging. Measurement data from imaging experiments on airborne and spaceborne SARs show that, compared with traditional RD algorithm, the proposed algorithm reveals outstanding advantages as indicated by a number of factors critical for the evaluation of imaging performance, such as resolution, peak sidelobe ratio (PSLR) and integral sidelobe ratio (ISLR). Compared with reference methods with local optimal imaging performance, the proposed algorithm can help to obtain global optimal SAR imaging data. [ABSTRACT FROM AUTHOR]

A demonstration is provided on how algorithmic asymptotic analysis of multi-scale pharmacokinetics (PK) systems can provide (1) system level understanding and (2) predictions on the response of the model when parameters vary. Being algorithmic, this type of analysis is not hindered by the size or complexity of the model and requires no input from the investigator. The algorithm identifies the constraints that are generated by the fast part of the model and the components of the slow part of the model that drive the system within these constraints. The demonstration is based on a typical monoclonal antibody PK model. It is shown that the findings produced by the traditional methodologies, which require significant input by the investigator, can be produced algorithmically and more accurately. Moreover, additional insights are provided by the algorithm, which cannot be obtained by the traditional methodologies; notably, the dual influence of certain reactions depending on whether their fast or slow component dominates. The analysis reveals that the importance of physiological processes in determining the systemic exposure of monoclonal antibodies (mAb) varies with time. The analysis also confirms that the rate of mAb uptake by the cells, the binding affinity of mAb to neonatal Fc receptor (FcRn), and the intracellular degradation rate of mAb are the most sensitive parameters in determining systemic exposure of mAbs. The algorithmic framework for analysis introduced and the resulting novel insights can be used to engineer antibodies with desired PK properties. [ABSTRACT FROM AUTHOR]

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences. [ABSTRACT FROM AUTHOR]

Noble gases seem to have no significant effect on the anesthetic targets due to their simple, spherical shape. However, xenon has strong narcotic efficacy and can be used clinically, while other noble gases cannot. The mechanism remains unclear. Here, we performed molecular dynamics simulations on phospholipid bilayers with four kinds of noble gases to elucidate the difference of their effects on the membrane. Our results showed that the sequence of effects on membrane exerted by noble gases from weak to strong was Ne, Ar, Kr and Xe, the same order as their relative narcotic potencies as well as their lipid/water partition percentages. Compared with the other three kinds of noble gases, more xenon molecules were distributed between the lipid tails and headgroups, resulting in membrane's lateral expansion and lipid tail disorder. It may contribute to xenon's strong anesthetic potency. The results are well consistent with the membrane mediated mechanism of general anesthesia. [ABSTRACT FROM AUTHOR]