Optimization Ability Research Articles

Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.

Prediction of aviation safety incident risk level based on ECSDNN

Prediction of aviation safety incident risk level is an important means of active risk management. Considering the high-dimensional complexity and class imbalance of massive aviation safety incident data, a prediction method for aviation safety incident risk level based on integrated cost-sensitive deep neural network (ECSDNN) is proposed. The feature representation of aviation safety incident data is realized by using the method of embedded coding of categorical attributes and splicing of numerical attributes; the cost-sensitive matrix and cost-sensitive loss function are designed by comprehensively considering the misclassification ratio and fixed cost, and the base classifier model based on cost-sensitive deep neural network (CSDNN) is constructed; the hard voting method is used to integrate multiple base classifiers with different parameters and performances to construct the integrated prediction model ECSDNN for aviation safety incident risk level. The experimental results on the Aviation Safety Incident Reporting System (ASRS) dataset show that compared with the optimal prediction ability of the baseline algorithm, the prediction accuracy of the ECSDNN model is improved 4.51%; compared with the single CSDNN base classifier, the prediction accuracy is improved 3.17%.

An advanced RIME Optimizer with Random Reselection and Powell Mechanism for Engineering Design

Abstract RIME is a recently introduced optimization algorithm that draws inspiration from natural phenomena. However, RIME has certain limitations. For example, it is prone to falling into Local Optima (LO), thus failing to find the Global Optima (GO), and has the problem of slow convergence. To solve these problems, this paper introduces an improved RIME algorithm (PCRIME), which combines the random reselection strategy and the Powell mechanism. The random reselection strategy enhances population diversity and helps to escape LO, while the Powell mechanism helps to improve the convergence accuracy and thus find the optimal solution. To verify the superior performance of PCRIME, we conducted a series of experiments at CEC 2017 and CEC 2022, including qualitative analysis, ablation studies, parameter sensitivity analysis, and comparison with various advanced algorithms. We used the Wilcoxon signed-rank test and the Friedman test to confirm the performance advantage of PCRIME over its peers. The experimental data show that PCRIME has superior optimization ability and robustness. Finally, this paper applies PCRIME to five real engineering problems and proposes feasible solutions and comprehensive performance index definitions for these five problems to prove the stability of the proposed algorithm. The results show that the PCRIME algorithm can not only effectively solve practical problems, but also has excellent stability, making it an excellent algorithm.

Bi2MoO6/MgIn2S4 S-scheme heterojunction with rich oxygen vacancies for effective organic pollutants degradation: Degradation pathways, biological toxicity assessment, and mechanism research

Designing and constructing novel photocatalysts for organic pollutant degradation is of critical significance for water environment treatment. Constructing a Bi2MoO6/MgIn2S4 (BMO/MIS) S-scheme heterojunction with oxygen vacancies, which efficiently removed RhB within visible light irradiation. Oxygen vacancies regulated the band structure of Bi2MoO6 (BMO) and enhanced the adsorption of RhB on the catalyst's surface. The S-scheme heterojunctions contributed to the separation and transfer of photo-generated charge, retained the optimal redox ability of Bi2MoO6-Vo (BMO-Vo) and MgIn2S4 (MIS) at the same time. X-ray photoelectron spectroscopy (XPS) and electron paramagnetic resonance (EPR) further confirmed the S-scheme charge transfer mechanism and the presence of oxygen vacancies. Within 30 min of visible light irradiation, RhB was completely degraded by BMO/MIS-8%, while the degradation efficiency of RhB by BMO-Vo and MIS were only 39.93 % and 80.87 %, respectively. The kinetics constant is 0.1035 min−1 of BMO/MIS-8%, which was 20.29 and 1.95 times that of BMO-Vo and MIS, respectively. At the same time, BMO/MIS-8% achieved removal efficiency of 24.28 %, 56.53 %, 59.15 % and 100 % for MO, TC,H-TC and MB, respectively. Compared to the previously synthesized Bi-ZFO/BMO-Vo photocatalyst, BMO/MIS-8% demonstrated superior degradation efficiency, exhibiting a degradation kinetic constant that was 2.12 times greater than that of Bi-ZFO/BMO-Vo under visible light irradiation. To improve the practical application of BMO/MIS, the effects of pH, co-existing ions and types of pollutants on degradation efficiency were evaluated. In addition, the biological toxicity assessment showed a significant decrease in the toxicity of RhB degradation products. This work provides a feasible avenue for constructing Bi2MoO6-based heterojunctions to treat dye wastewater.

General Definitions of Information, Intelligence, and Consciousness from the Perspective of Generalized Natural Computing

Atoms themselves have no thoughts and cannot be thinking. Why does the human body, which is composed of atoms, have consciousness? The widely used concepts of information and intelligence in today’s science, which are related to this, do not yet have appropriate general definitions. Answering these interesting questions is a crucial issue for technological development in the historical context of human society entering the era of intelligence. The key lies in how to fully utilize the existing fundamental theories subtly related to information science. Here we attempt to give the definition of general information and general intelligence from the perspective of generalized natural computing, based on the least action principle, Hamilton-Jacobi equation, dynamic programming, reinforcement learning, and point out the relationship between the two. The least action principle for describing conservative systems can be seen as an intelligent manifestation of natural matter, and its equivalent form, the Hamilton-Jacobi equation, can be extended to describe quantum phenomena and is a special case of continuous dynamic programming equations. Dynamic programming is an efficient optimization method under deterministic models, while reinforcement learning, as a manifestation of biological intelligence, is its model-free version. The statement that reinforcement learning is the most promising machine learning method has a profound physical foundation. General information is defined as the degree to which a certain environmental element determines the behavior of the subject. General intelligence is defined as the automatic optimization ability of the action or value function of a system with a certain degree of conservatism. Intelligence is a basic property of material systems, rather than an emergent property that only complex systems possess. Consciousness is an advanced intelligent phenomenon, a reconstruction of quasi conservative systems based on complex systems.

An iteratively optimized downscaling method for city-scale air quality forecast emission inventory establishment

Air quality models (AQMs) are pivotal in forecasting air quality and shaping pollution control strategies. Nonetheless, the effectiveness of AQMs is often compromised in many cities due to the absence of accurate local emission inventories. To address this gap, this study presents a novel AQM-ready emission inventory generation technique with iterative optimization ability for city-scale applications in China. An efficient emission processing tool was introduced in this study, which utilizes the High-Resolution Multi-resolution Emission Inventory for China (HR-MEIC) as input. Using environmental observations and a region map, the tool can justify emissions of different regions iteratively. With the iterative optimization method, the model performance can be notably improved even without local emissions. The optimization was realized by splitting model-ready emissions into different regions and adjusting the emissions using scale factors calculated with the modeling results and the observations of each region. This methodology was applied to the Eight Cities in the Chengdu Plain (CP8C), located in the western margin of Sichuan Basin with complex topography and meteorological conditions, southwestern China, monthly throughout 2023. Air quality modeling was carried out using Weather Forecast and Research Model (WRF) and the Community Multiscale Air Quality Model (CMAQ). The results showed that the optimization acquired a good performance after five cycles for PM2.5 and NO2, with correlation coefficients (R values) surging from 0.62 and 0.37 to 0.77 and 0.73, respectively, while their normalized mean bias (NMB) substantially decreased from 22.8 % and 100.4 % to 3.6 % and 3.3 %. The underestimation on O3 concentration was also improved by the optimization, although enhancements in O3 modeling remained modest. This technique provides an easy-to-copy method to generate reasonable AQM-ready emission files with open emission data and observation data, which would be beneficial for the cities' air quality forecast in cities without local emission inventories.

Heuristic Optimization Algorithm of Black-Winged Kite Fused with Osprey and Its Engineering Application.

Swarm intelligence optimization methods have steadily gained popularity as a solution to multi-objective optimization issues in recent years. Their study has garnered a lot of attention since multi-objective optimization problems have a hard high-dimensional goal space. The black-winged kite optimization algorithm still suffers from the imbalance between global search and local development capabilities, and it is prone to local optimization even though it combines Cauchy mutation to enhance the algorithm's optimization ability. The heuristic optimization algorithm of the black-winged kite fused with osprey (OCBKA), which initializes the population by logistic chaotic mapping and fuses the osprey optimization algorithm to improve the search performance of the algorithm, is proposed as a means of enhancing the search ability of the black-winged kite algorithm (BKA). By using numerical comparisons between the CEC2005 and CEC2021 benchmark functions, along with other swarm intelligence optimization methods and the solutions to three engineering optimization problems, the upgraded strategy's efficacy is confirmed. Based on numerical experiment findings, the revised OCBKA is very competitive because it can handle complicated engineering optimization problems with a high convergence accuracy and quick convergence time when compared to other comparable algorithms.

Hybrid Multi-Objective Chameleon Optimization Algorithm Based on Multi-Strategy Fusion and Its Applications.

Aiming at the problems of chameleon swarm algorithm (CSA), such as slow convergence speed, poor robustness, and ease of falling into the local optimum, a multi-strategy improved chameleon optimization algorithm (ICSA) is herein proposed. Firstly, logistic mapping was introduced to initialize the chameleon population to improve the diversity of the initial population. Secondly, in the prey-search stage, the sub-population spiral search strategy was introduced to improve the global search ability and optimization accuracy of the algorithm. Then, considering the blindness of chameleon's eye turning to find prey, the Lévy flight strategy with cosine adaptive weight was combined with greed strategy to enhance the guidance of random exploration in the eyes' rotation stage. Finally, a nonlinear varying weight was introduced to update the chameleon position in the prey-capture stage, and the refraction reverse-learning strategy was used to improve the population activity in the later stage so as to improve the ability of the algorithm to jump out of the local optimum. Eighteen functions in the CEC2005 benchmark test set were selected as an experimental test set, and the performance of ICSA was tested and compared with five other swarm intelligent optimization algorithms. The analysis of the experimental results of 30 independent runs showed that ICSA has stronger convergence performance and optimization ability. Finally, ICSA was applied to the UAV path-planning problem. The simulation results showed that compared with other algorithms, the paths generated by ICSA in different terrain scenarios are shorter and more stable.

Optimization of building thermal environment and VR industrial heritage landscape design enhanced by computer vision algorithms

With the acceleration of urbanization, the optimization of thermal environment of buildings has become an important topic to improve energy efficiency and environmental sustainability. This paper aims to enhance the optimization of building thermal environment through computer vision algorithm, and explore its application in VR industrial heritage landscape design, so as to achieve the dual goals of building energy conservation and cultural inheritance. In this study, advanced computer vision technology is used to monitor and analyze the thermal performance of buildings in real time. Through the construction of thermal environment model, combined with deep learning algorithm to extract building thermal energy data, and use VR technology for visual display, so as to provide scientific basis for design decisions. The research objects include several representative industrial heritage buildings, and analyze their thermal energy consumption characteristics and optimization potential. The results show that the application of the computer vision algorithm reduces the heat loss of the building and significantly improves the energy efficiency. At the same time, the VR visual model intuitively shows the design effect after thermal optimization, enhancing the communication and understanding between designers and stakeholders. Therefore, the combination of computer vision and VR technology can effectively improve the optimization ability of building thermal environment and promote the sustainable design and operation of traditional industrial heritage.

Flower pollination algorithm with ring topology for multi-solution spaces to solve the disassembly line balancing problem

Abstract The Disassembly Line Balancing Problem (DLBP) is of significant importance in the product recycling process. However, existing DLBP research have primarily proposed improved optimization algorithms for single solution space. To provide decision makers with more efficient disassembly solutions, this paper chooses three classical layouts (straight, U-shaped, and parallel) to broaden the solution space and expands an incomplete DLBP with multi-solution spaces (DLBP-MS). By employing the strategy of incomplete disassembly, only the essential components are retained. In DLBP-MS, the disassembly information from used products is processed and imported into the three types of disassembly spaces to be solved and compared to find suitable disassembly solutions. And a multi-objective mathematical model is developed, this includes factors such as workstation count, free time, disassembly smoothness index, carbon emission, and disassembly revenue. The part constraints are established based on directed graphs, and the encoding and decoding methods for multi-solution spaces disassembly sequences in the random incomplete case are designed respectively. A ring topology based flower pollination algorithm (RTFPA) is introduced to effectively address the DLBP-MS. The solution set obtained during the iterative process is divided into subsets based on the congestion level and the overall diversity is preserved by internal optimization of these subsets. After that, the RTFPA is applied to the DLBP-MS of waste cell phones and computers. The improvement of the algorithm's optimization ability under different solution spaces is verified by comparing the results with those obtained from four other algorithms.

Twenty-Four-Hour Post-Thrombolysis NIHSS Score As the Strongest Prognostic Predictor After Acute Ischemic Stroke: ENCHANTED Study.

This study was conducted to determine optimal predictive ability of National Institutes of Health Stroke Scale (NIHSS) measurements at baseline, 24 hours, and change from baseline to 24 hours after thrombolysis on functional recovery in patients with acute ischemic stroke who participated in the ENCHANTED (Enhanced Control of Hypertension and Thrombolysis Stroke Study). ENCHANTED was an international, multicenter, 2×2 quasifactorial, prospective, randomized open trial of low-dose versus standard-dose intravenous alteplase and intensive versus guideline-recommended blood pressure lowering in thrombolysis-eligible patients with acute ischemic stroke. Absolute (baseline minus 24 hours) and percentage (absolute change/baseline × 100) changes in NIHSS scores were calculated. Receiver operating characteristic curve analyses assessed performance of different NIHSS measurements on 90-day favorable functional recovery (modified Rankin Scale [mRS] score 0-2) and excellent functional recovery (mRS score 0-1). Youden index was used to identify optimal predictor cutoff points. A total of 4410 patients in the ENCHANTED trial were enrolled. The 24-hour NIHSS score had the highest discriminative ability for predicting favorable 90-day functional recovery (mRS score 0-2; area under the curve 0.866 versus 0.755, 0.689, 0.764; P<0.001) than baseline, absolute, and percentage change of NIHSS score, respectively. The optimal cutoff point of 24-hour NIHSS score for predicting favorable functional recovery was ≤4 (sensitivity 66.5%, specificity 87.1%, adjusted odds ratio, 9.44 [95% CI, 7.77-11.48]). The 24-hour NIHSS score (≤3) was the best predictor of 90-day excellent functional recovery (mRS score 0-1). Findings were consistent across subgroups, including sex, race, baseline NIHSS score, stroke subtype, and age. In thrombolysis-eligible patients with acute ischemic stroke, 24-hour NIHSS score (optimal cutpoint of 4) is the strongest predictor of 90-day functional recovery over baseline and early change of NIHSS score. URL: https://clinicaltrials.gov. Unique Identifier: NCT01422616.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

PurposeThis paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.Design/methodology/approachA six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.FindingsInspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.Practical implicationsThe proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.Originality/valueThe CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

A Multi-Strategy Improved Northern Goshawk Optimization Algorithm for Optimizing Engineering Problems.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO's exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.

Harnessing machine learning for enhanced quantitative financial strategies: Applications and case studies

Machine learning algorithms have long been an essential tool for quantitative finance, as they play an important role in the asset pricing, portfolio optimisation and financial risk management. This work discusses how to apply Random Forest, Support Vector Machine (SVM), and Neural Networks to further develop and enhance the quantitative investment research. With automatic feature selection, reconstruction of high-dimensional data and resist against over-fitting, Random Forest has been an efficient ensemble approach with remarkable prediction performance. For this reason, Random Forest is leveraged for the stock price prediction. SVM, particularly known for its robustness in high-dimensional classification task, has a non-linear optimisation ability that can be employed to capture the inherent non-linear dependencies in options prices. Neural Networks, as a learning approach capable of discovering complex relationships, is employed for the portfolio optimisation, with a promise for further improving the risk-adjusted portfolio return. The work is conducted through practical case studies to simulate how Random Forest, SVM and Neural Networks enable us to further improve the financial performance, and to illustrate its advantage, disadvantage and practical application in quantitative finance.

Energy-Saving Optimization of HVAC Systems Using an Ant Lion Optimizer with Enhancements

The complex and time-varying external climate conditions and multi-equipment variable coupling characteristics make it challenging to optimize the Heating, Ventilation, and Air Conditioning (HVAC) systems in existing buildings effectively. Additionally, the intricate energy exchange processes within HVAC systems present difficulties in developing accurate and generalizable energy consumption models. In response to these challenges, this paper proposes an Ant Lion Optimizer with Enhancements (ALOE) that can dynamically adjust the number of populations and the movement trend to improve the convergence speed and optimization ability, and randomly adjust the movement amplitude to enhance the local optimal escape ability. Finally, a case study of an office building in Hangzhou was carried out, and an overall energy consumption model of the HVAC system based on parameter identification and a general mechanism model was established. In this model, the energy-saving optimization effects of various advanced swarm intelligence optimization algorithms were compared. The experimental results demonstrate that under high, medium, and low load conditions, the ALOE algorithm achieves energy-saving rates of 28.16%, 28.26%, and 24.85%, respectively, the overall energy-saving rate for the entire day reaches 29.06%, which indicates the ALOE has significant superiority. This work will contribute to the development of energy-saving and emission-reduction technologies.

An improved sand cat swarm optimization with lens opposition-based learning and sparrow search algorithm

The sand cat swarm optimization (SCSO) is a recently proposed meta-heuristic algorithm. It inspires hunting behavior with sand cats based on hearing ability. However, in the later stage of SCSO, it is easy to fall into local optimality and cannot find a better position. In order to improve the search ability of SCSO and avoid falling into local optimal, an improved algorithm is proposed - Improved sand cat swarm optimization based on lens opposition-based learning and sparrow search algorithm (LSSCSO). A dynamic spiral search is introduced in the exploitation stage to make the algorithm search for better positions in the search space and improve the convergence accuracy of the algorithm. The lens opposition-based learning and the sparrow search algorithm are introduced in the later stages of the algorithm to make the algorithm jump out of the local optimum and improve the global search capability of the algorithm. To verify the effectiveness of LSSCSO in solving global optimization problems, CEC2005 and CEC2022 test functions are used to test the optimization performance of LSSCSO in different dimensions. The data results, convergence curve and Wilcoxon rank sum test are analyzed, and the results show that it has a strong optimization ability and can reach the optimal in most cases. Finally, LSSCSO is used to verify the effectiveness of the algorithm in solving engineering optimization problems.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Double-kernel based Bayesian approximation broad learning system with dropout

Broad learning system (BLS) is an efficient incremental learning machine algorithm. However, there are some disadvantages in such an algorithm. For example, the number of hidden layer nodes needs to be manually adjusted during the training process, meanwhile the large uncertainty will be caused by two random mappings. To solve these problems, based on the optimization ability of the kernel function, a double-kernel broad learning system (DKBLS) is proposed to eliminate the uncertainty of random mapping by using additive kernel strategy. Meanwhile, to reduce the computing costs and training time of DKBLS, a double-kernel based bayesian approximation broad learning system with dropout (Dropout-DKBLS) is further proposed. Ablation experiments show that the output accuracy of Dropout-DKBLS does not decrease even if the node is dropped. In addition, function approximation experiments show that DKBLS and Dropout-DKBLS have good robustness and can accurately predict noise data. The regression and classification experiments on multiple datasets are compared with the latest kernel-based learning methods. The comparison results show that both DKBLS and Dropout-DKBLS have good regression and classification performance. By further comparing the training time of these kernel-based learning methods, we prove that the Dropout-DKBLS can reduce the computational cost while ensuring the output accuracy.

Construction of two-dimensional heterojunctions based on metal-free semiconductor materials and Covalent Organic Frameworks for exceptional solar energy catalysis

Covalent organic frameworks (COFs) are newly developed crystalline substances that are garnering growing interest because of their ultra-high porosity, crystalline nature, and easy modified architecture, showing promise in the field of photocatalysis. However, it is difficult for pure COFs materials to achieve excellent photocatalytic hydrogen production due to their severe carrier recombination problems. To mitigate this crucial issue, establishing heterojunction is deemed an effective approach. Nonetheless, many of the metal-containing materials that have been used to construct heterojunctions with COFs own a number of drawbacks, including small specific surface area and rare active sites (for inorganic semiconductor materials), wider bandgaps and higher preparation costs (for MOFs). Therefore, it is necessary to choose metal-free materials that are easy to prepare. Red phosphorus (RP), as a semiconductor material without metal components, with suitable bandgap, moderate redox potential, relatively minimal toxicity, is affordable and readily available. Herein, a range of RP/TpPa-1-COF (RP/TP1C) composites have been successfully prepared through solvothermal method. The two-dimensional structure of the two materials causes strong interactions between the materials, and the construction of heterojunctions effectively inhibits the recombination of photogenic charge carrier. As a consequence, the 9% RP/TP1C composite, with the optimal photocatalytic ability, achieves a photocatalytic H2 evolution rate of 6.93 mmol g−1 h−1, demonstrating a 10.19-fold increase compared to that of bare RP and a 4.08-fold improvement over that of pure TP1C. This article offers a novel and innovative method for the advancement of efficient COFs-based photocatalysts.

Design optimization method of pipeline parameter based on improved artificial neural network

The rationality of pipeline design is directly related to its energy efficiency, reliability, and safety. Pipeline vibration may lead to negative effects such as mechanical loss and fatigue damage. Therefore, this study utilizes pipeline optimization design to mitigate these effects. Recently, neural networks have been widely used in structure design optimization. In the study, a backpropagation neural network (BP) combined with a variant slime mould algorithm (SMA) is utilized to solve the pipeline structure design optimization problem. Pipeline transport plays a crucial role in the efficient movement of various commodities, including but not limited to gas, oil, water, and other liquid substances. The interaction between liquid and pipeline can cause pipeline vibration and even damage. Therefore, based on the simulation model considering FSI (fluid-structure interaction), machine learning methods such as BP can predict vibration characteristics of fluid-conveying pipelines. However, existing research has shown that BP has insufficient parsing ability in structure mechanics problems, especially in solving the overall characteristics of complex structures (such as maximum structural strain). This study proposes an Arithmetic-based slime mould algorithm (ACSMA) with an adaptive decision strategy and a chaotic mapping strategy. A hybrid algorithm named ACSMA-BP is presented to promote the model's prediction ability. At last, to verify the effectiveness of the proposed pipeline structure design optimization approach, the ACSMA-BP model is utilized to complete a structure design optimization case for a simulated pipeline. The numerical results indicate that compared with AOA, CWOA, ESSAWOA, NGS_WOA, and RSA, the ACSMA has the best optimization ability.