Complex Parameters Research Articles

Machine learning prediction models for individualized technical success of chronic total occlusion revascularization using anatomical input

Abstract Introduction Chronic total occlusion revascularization (CTO PCI) remains a challenging procedure in the realm of percutaneous coronary interventions. Efforts such as implementing the hybrid algorithm for CTO crossing have been made. Interventional techniques are evolving, and complexity scores such as the J-CTO score might get outdated. Machine learning models can be used to overcome this limitation by constantly providing new data and improving the models by continuous data feeding. In this study, we introduced machine learning models to predict individualized technical success of CTO PCI using anatomical coronary information and baseline information. Methods Consecutive patients undergoing percutaneous coronary intervention of chronic total occlusions were enrolled in this study. Angiographic data and lesion characteristics from coronary angiography were extracted. Machine learning models were applied to predict technical success. A total of 271 patients were used to validate and train the models and 50 separate patients for testing. Data was normalized and fed to several classifying machine learning models including decision trees (DT), random forest (RF), extreme gradient boosting (XGB), CatBoost (CB) classifiers, and light gradient boosting (LGB). Finally, accuracy, precision, recall and F1-score of the selected hyperparameters and cross-validation was performed. Results CB was the best performing model with an accuracy of 0.81, precision of 0.83 and recall of 0.98. The F1 score was 0.90. RF was the second-best performing model with accuracy of 0.77, precision of 0.82, recall of 0.92 and F1 score 0.87. The tuned models performed modestly with RF having a low F1 score of 0.44, due to small sample size. In the tuned models. LGB had a higher F1 score and a relatively high accuracy (0.782) and precision (0.508). The feature importance matrix found age, CTO length, reference diameter to have the highest value among the lesion complexity parameters. Discussion: Overall, the normal-run CB was the best model for predicting the technical success after a CTO PCI with a high accuracy. The fine-tuned and cross-validated models performed modestly because of the relatively small, unbalanced dataset and overfitting. However, data showed promising first results in implementing machine learning into predicting interventional outcomes. Further studies like data augmentation and providing a weighted dataset would increase the model performance and provide a very precise statement whether a patient would have increased chances to have a successful PCI based on baseline anatomic information. Conclusion This is one of the first studies to predict technical success of CTO PCI using angiographic baseline parameters using machine learning. Future studies with larger patient cohorts are needed to involve deploying the selected model in larger patient cohorts including external validation and assessing its performance with new, unseen data.Figure 1.Machine learning modelsFigure 2.Feature importance matrix

A simplified bedside model for acute kidney injury prediction after percutaneous coronary intervention

Abstract Background Concern for acute kidney injury (AKI) after percutaneous coronary intervention (PCI) is a major determinant in the decision to pursue cardiac catheterization if otherwise indicated. A bedside AKI risk prediction model derived from the National Cardiovascular Data Registry (NCDR), most recently updated in 2023, employs relatively complex parameters for AKI prediction. Purpose To test the efficacy of a simplified pre-procedural 5-variable bedside score for post-PCI AKI prediction based on the NCDR model. Methods We analyzed our institution’s NCDR-reported catheterization laboratory registry between Q2 2022 and Q1 2023. Inclusion criteria, variable selection, and definitions were based on the NCDR model. A bedside prediction model was validated, including 5 pre-procedural variables: anemia (hemoglobin <10 mg/dl), history of heart failure, stage 4 or 5 chronic kidney disease (glomerular filtration rate <30 mg/dl), cardiac arrest, and shock status. Results We included 840 patients, of which 593 (70.6%) were men. Mean age was 69 (±12.5). Most procedures were performed on an urgent basis (444, 52.9%). AKI developed in 107 (12.7%) patients. Compared to the non-AKI group, patients who developed AKI were older (age 74±11.5 vs 68.3 ±12.5), and more frequently had anemia (23.6% vs 7.4%), diabetes (51.4% vs 35.2%), severe frailty (45.8% vs 23.7%), heart failure (62.6% vs 29.7%), cardiac arrest (13.1% vs 2.9%), CKD (30.8% vs 6%), shock (12.1% vs 1.5%), and mechanical circulatory support (7.5% vs 2.9%), p<0.001. Utilizing integer scores, the simplified bedside model employing 5 pre-procedural variables demonstrated discrimination similar to the bedside NCDR model, with a C-statistic of 0.77 (0.60-0.88) and excellent calibration (intercept of 0.31 and slope of 1.03) (Figure), indicating the robust performance of the model (compared to bedside NCDR model with a C-statistic 0.793, intercept of -0.197, and slope of 0.957). Conclusion A simplified NCDR-based bedside score using 5 pre-procedural variables is an effective tool for predicting AKI as a complication of PCI, with comparable performance to the 2023 updated bedside NCDR AKI risk model.

Tooth eruption status and bite force determine dental microwear texture gradients in albino rats (Rattus norvegicus forma domestica).

Dental microwear texture analysis (DMTA) is widely applied for inferring diet in vertebrates. Besides diet and ingesta properties, factors like wear stage and bite force may affect microwear formation, potentially leading to tooth position-specific microwear patterns. We investigated DMTA consistency along the upper cheek tooth row in young adult female rats at different growth stages, but with erupted adult dentitions. Bite forces for each molar (M) position were determined using muscle cross-sectional areas and lever arm mechanics. Rats were categorized into three size classes based on increasing skull length. Maximum bite force increased with size, while across all size classes, M3 bite force was almost 1.4 times higher than M1 bite force. In size class 1, M1 and M2 showed higher values than M3 for DMTA complexity, height, and volume parameters, while in size class 3, M1 had the lowest values. Comparing the same tooth position between size classes revealed opposing trends: M1 and M2 showed, for most parameters, decreasing roughness and complexity from size class 1-3, while M3 displayed the opposite trend, with size class 1 showing lowest, and either size class 2 or 3 the highest roughness and complexity values. This suggests that as rats age and M3 fully occludes, it becomes more utilized during mastication. DMTA, being a short-term diet proxy, is influenced by eruption and occlusion status changes. Our findings emphasize the importance of bite force and ontogenetic stage when interpreting microwear patterns and advise to select teeth in full occlusion for diet reconstruction.

Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding.

The influence maximization problem has several issues, including low infection rates and high time complexity. Many proposed methods are not suitable for large-scale networks due to their time complexity or free parameter usage. To address these challenges, this article proposes a local heuristic called Embedding Technique for Influence Maximization (ETIM) that uses shell decomposition, graph embedding, and reduction, as well as combined local structural features. The algorithm selects candidate nodes based on their connections among network shells and topological features, reducing the search space and computational overhead. It uses a deep learning-based node embedding technique to create a multidimensional vector of candidate nodes and calculates the dependency on spreading for each node based on local topological features. Finally, influential nodes are identified using the results of the previous phases and newly defined local features. The proposed algorithm is evaluated using the independent cascade model, showing its competitiveness and ability to achieve the best performance in terms of solution quality. Compared with the collective influence global algorithm, ETIM is significantly faster and improves the infection rate by an average of 12%.

Serial batching to minimize the weighted number of tardy jobs

AbstractThe $$1\vert \text {s-batch}(\infty ),r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j scheduling problem takes as input a batch setup time $$\Delta $$ Δ and a set of n jobs, each having a processing time, a release date, a weight, and a due date; the task is to find a sequence of batches that minimizes the weighted number of tardy jobs. This problem was introduced by Hochbaum and Landy (Oper Res Lett 16(2):79–86, 1994); as a wide generalization of Knapsack, it is $$\textsf{NP}$$ NP -hard. In this work, we provide a multivariate complexity analysis of the $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j problem with respect to several natural parameters. That is, we establish a classification into fixed-parameter tractable and $$\textsf{W}[1]$$ W [ 1 ] -hard problems, for parameter combinations of (i) $$\#p$$ # p = number of distinct processing times, (ii) $$\#w$$ # w = number of distinct weights, (iii) $$\#d$$ # d = number of distinct due dates, (iv) $$\#r$$ # r = number of distinct release dates. Thereby, we significantly extend the work of Hermelin et al. (Ann Oper Res 298:271–287, 2018) who analyzed the parameterized complexity of the non-batch variant of this problem without release dates. As one of our key results, we prove that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is $$\textsf{W}[1]$$ W [ 1 ] -hard parameterized by the number of distinct processing times and distinct due dates. To the best of our knowledge, these are the first parameterized intractability results for scheduling problems with few distinct processing times. Further, we show that $$1\vert \text {s-batch}(\infty ), r_j\vert \sum w_jU_j$$ 1 | s-batch ( ∞ ) , r j | ∑ w j U j is fixed-parameter tractable parameterized by $$\#d + \#p + \#r$$ # d + # p + # r , and parameterized by $$\#d + \#w$$ # d + # w if there is just a single release date. Both results hold even if the number of jobs per batch is limited by some integer b.

The complexity of optimizing atomic congestion

Atomic congestion games are a classic topic in network design, routing, and algorithmic game theory, and are capable of modeling congestion and flow optimization tasks in various application areas. While both the price of anarchy for such games as well as the computational complexity of computing their Nash equilibria are by now well-understood, the computational complexity of computing a system-optimal set of strategies—that is, a centrally planned routing that minimizes the average cost of agents—is severely understudied in the literature. We close this gap by identifying the exact boundaries of tractability for the problem through the lens of the parameterized complexity paradigm. After showing that the problem remains highly intractable even on extremely simple networks, we obtain a set of results which demonstrate that the structural parameters which control the computational (in)tractability of the problem are not vertex-separator based in nature (such as, e.g., treewidth), but rather based on edge separators. We conclude by extending our analysis towards the (even more challenging) min-max variant of the problem.

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.

Locating a black hole in a dynamic ring

In networked environments supporting mobile agents, a pressing problem is the presence of network sites harmful for the agents. In this paper we consider the danger posed by a node that destroys any incoming agent without leaving any trace. Such a dangerous node is known in the literature as a black hole (Bh). The problem of a team of system agents determining its location, known as black hole search (Bhs ), has been extensively studied in the literature under a variety of assumptions, both in synchronous and asynchronous settings. The main complexity parameter of Bhsis the number of system agents (called size) needed to solve the problem; other parameters are the number of moves (called cost) performed by the agents, and the time until termination.In the existing literature, with only a couple of exceptions, all results are based on a common assumption that the network is static, i.e. its topology does not change in time. We consider instead the Bhswhen the network is dynamic: the link structure of the graph changes over time. While time-varying graphs have been the focus of intense research in the last two decades, very little is known on the problem of locating the Bh in such networks.In this paper, we contribute to fill this research gap by studying Bhsin dynamic ring networks, focusing on the 1-interval connectivity adversarial dynamics. Feasibility and complexity of the problem depend on many factors, specifically on the size n of the ring, whether or not n is known, and the type of inter-agent communication (whiteboards, tokens, face-to-face, visual). In this paper, we provide a complete feasibility characterization presenting size optimal algorithms. Furthermore, we establish lower bounds on the cost and time of size-optimal solutions and show that our algorithms achieve those bounds.

Why English Legal Discourse is Difficult to Understand

To date, language complexity has come to the fore of modern linguistic research, which is related to scientific discussion of similarity / difference in the degree of complexity between various languages. There is general agreement among various linguists that it rests on a great number of parameters, which is the result of the lack of uniform knowledge about this relatively novel linguistic phenomenon. Under any circumstances, though the dichotomy principle underlies several approaches to the definition of complexity, i.e. it can be absolute (objective) and relational (subjective); systemic (paradigmatic) and structural (syntagmatic); the universal three-dimensional nature of language can serve as the guiding principle for the analysis of complexity parameters in any type of discourse. Thus, the paper highlights the lexical, syntactic and pragmatic parameters of English legal discourse (LD) language complexity based on the generally accepted semiotic triad. These elaborations are also complemented by the deployment of the research framework consisting of three models “language-communicator”, “language-language” and “language-discourse” which, though initially designed for different purposes, can as well be successfully applied to the study of language parameters as factors stipulating the language complexity of LD. The given models are illustrated by the relevant examples of legal and juridical nature.

Unsupervised Denoising in Spectral CT: Multi-Dimensional U-Net for Energy Channel Regularisation

Spectral Computed Tomography (CT) is a versatile imaging technique widely utilized in industry, medicine, and scientific research. This technique allows us to observe the energy-dependent X-ray attenuation throughout an object by using Photon Counting Detector (PCD) technology. However, a major drawback of spectral CT is the increase in noise due to a lower achievable photon count when using more energy channels. This challenge often complicates quantitative material identification, which is a major application of the technology. In this study, we investigate the Noise2Inverse image denoising approach for noise removal in spectral computed tomography. Our unsupervised deep learning-based model uses a multi-dimensional U-Net paired with a block-based training approach modified for additional energy-channel regularization. We conducted experiments using two simulated spectral CT phantoms, each with a unique shape and material composition, and a real scan of a biological sample containing a characteristic K-edge. orangeMeasuring the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) for the simulated data and the contrast-to-noise ratio (CNR) for the real-world data, our approach not only outperforms previously used methods—namely the unsupervised Low2High method and the total variation-constrained iterative reconstruction method—but also does not require complex parameter tuning.

Photophysiological and Oxidative Responses of the Symbiotic Estuarine Anemone Anthopleura hermaphroditica to the Impact of UV Radiation and Salinity: Field and Laboratory Approaches.

The estuarine anemone Anthopleura hermaphroditica and its symbiont Philozoon anthopleurum are continuously exposed to intense fluctuations in solar radiation and salinity owing to tidal changes. The aim of this study was to evaluate the effects of the tidal cycle, solar radiation, and salinity fluctuations on the photosynthetic and cellular responses (lipid peroxidation, total phenolic compounds, and antioxidant activity) of the symbiont complex over a 24 h period in the Quempillén River Estuary. Additionally, laboratory experiments were conducted to determine the specific photobiological responses to photosynthetically active radiation (PAR), ultraviolet radiation (UVR), and salinity. Our field results showed that the photosynthetic parameters of the symbiont complex decreased with increasing ambient radiation; however, no relationship was observed with changes in salinity. Increased peroxidative damage, total phenolic compound levels, and antioxidant activity were mainly related to increased UVR and, to a lesser extent, PAR. During the dark period, only PAR-exposed organisms returned to the basal levels of photosynthesis and cell damage. Laboratory exposure confirmed the deleterious effects of UVR on the photosynthetic response. The present study suggests that the ability of A. hermaphroditica to acclimate to natural radiation stress is mediated by the concerted action of various physiological mechanisms that occur at different times of the day, under varying levels of environmental stress.

Detection of Impedance Inhomogeneity in Lithium-Ion Battery Packs Based on Local Outlier Factor

The inhomogeneity between cells is the main cause of failure and thermal runaway in Lithium-ion battery packs. Electrochemical Impedance Spectroscopy (EIS) is a non-destructive testing technique that can map the complex reaction processes inside the battery. It can detect and characterise battery anomalies and inconsistencies. This study proposes a method for detecting impedance inconsistencies in Lithium-ion batteries. The method involves conducting a battery EIS test and Distribution of Relaxation Times (DRT) analysis to extract characteristic frequency points in the full frequency band. These points are less affected by the State of Charge (SOC) and have a strong correlation with temperature, charge/discharge rate, and cycles. An anomaly detection characteristic impedance frequency of 136.2644 Hz was determined for a cell in a Lithium-ion battery pack. Single-frequency point impedance acquisition solves the problem of lengthy measurements and identification of anomalies throughout the frequency band. The experiment demonstrates a significant reduction in impedance measurement time, from 1.05 h to just 54 s. The LOF was used to identify anomalies in the EIS data at this characteristic frequency. The detection results were consistent with the actual conditions of the battery pack in the laboratory, which verifies the feasibility of this detection method. The LOF algorithm was chosen due to its superior performance in terms of FAR (False Alarm Rate), MAR (Missing Alarm Rate), and its fast anomaly identification time of only 0.1518 ms. The method does not involve complex mathematical models or parameter identification. This helps to achieve efficient anomaly identification and timely warning of single cells in the battery pack.

Quantum radiation reaction: Analytical approximations and obtaining the spectrum from moments

We derive analytical χ≪1 approximations for spin-dependent quantum radiation reaction for locally constant and locally monochromatic fields. We show how to factor out fast spin oscillations and obtain the degree of polarization in the plane orthogonal to the magnetic field from the Frobenius norm of the Mueller matrix. We show that spin effects lead to a transseries in χ, with powers χk, logarithms (lnχ)k, and oscillating terms, cos(…/χ) and sin(…/χ). In our approach we can obtain each moment, ⟨(kP)m⟩, of the light-front longitudinal momentum independently of the other moments and without considering the spectrum. We show how to obtain a low-energy expansion of the spectrum from the moments by treating m as a continuous, complex parameter and performing an inverse Mellin transform. We also show how to obtain the spectrum, without making a low-energy approximation, from a handful of moments using the principle of maximum entropy. Published by the American Physical Society 2024

Spectral fluctuations of multiparametric complex matrix ensembles: evidence of a single parameter dependence

Abstract We numerically analyze the spectral statistics of the multiparametric Gaussian ensembles of complex matrices with zero mean and variances with different decay routes away from the diagonals. As the latter mimics different degree of effective sparsity among the matrix elements, such ensembles can serve as good models for a wide range of phase transitions e.g. localization to delocalization in non-Hermitian systems or Hermitian to non-Hermitian one. Our analysis reveals a rich behavior hidden beneath the spectral statistics e.g. a crossover of the spectral statistics from Poisson to Ginibre universality class with changing variances for finite matrix size, an abrupt transition for infinite matrix size and the role of complexity parameter, a single functional of all system parameters, as a criteria to determine critical point. We also confirm the theoretical predictions in \cite{psgs, psnh}, regarding the universality of the spectral statistics in non-equilibrium regime of non-Hermitian systems characterized by the complexity parameter.

A Review Paper on Memory Fault Models and its Algorithms

The significance of testing semiconductor memories has grown significantly in the semiconductor industry due to the increased density of modern memory chips. This paper aims to investigate and analyze different types of functional faults present in today's memory technology. These faults include stuck-at faults, transition faults, coupling faults, address decoder faults, and neighborhood pattern-sensitive faults. The paper also delves into the techniques utilized to identify and detect these faults. In particular, the focus is placed on the importance of zero-one, checkerboard, and March pattern tests, which are widely employed to assess functional memory defects at different levels, such as the chip level, array level, and board level. Furthermore, the study provides an in-depth exploration of various test algorithms and thoroughly examines their fault coverage capabilities. Overall, this review paper provides valuable insights into the challenges posed by the dense nature of modern memory chips and offers a comprehensive analysis of functional faults in memory technology. By emphasizing the importance of testing and presenting a detailed exploration of fault detection methods and test algorithms, this study contributes to the advancement of reliable and high-performance memory devices in the electronic industry suggesting that MARCH algorithms outperform others when considering factors like fault coverage, power efficiency, area optimization, and time complexity parameters, making them the preferable choice for reliable and high-performance memory devices in the electronic industry.

Exceptional stability of the sugammadex-solasodine complex: Insights from experimental and theoretical studies

Sugammadex (SGM) is the first cyclodextrin (CD)-based selective relaxant binding agent. We investigated its ability to capture natural aminosteroid phytotoxins, and assessed its potential as an antidote for intoxication. Solasodine (SS), a toxic alkaloid from the Solanaceae family, was chosen as the model compound. Complexation was studied using nuclear magnetic resonance (NMR) spectroscopy, molecular modelling, and isothermal titration calorimetry (ITC). NMR in various D2O/DMSO‑d6 media revealed a particularly stable inclusion-type complex, identifying a slow exchange process between the CD and the aminosteroid along with a less significant fast exchange between DMSO and SGM. Using various NMR techniques, the structure and kinetic/thermodynamic parameters of the inclusion complex were explored. Theoretical calculations showed the secondary amino head of SS near the carboxylate ends of the SGM sidechains, facilitating intermolecular ionic interactions. ITC experiments in an aqueous environment provided Ka stability constants of 7.03 × 106 M−1 and 4.17 × 106 M−1 at 25 °C and 37 °C, respectively, similar to previously reported SGM complexes with aminosteroid neuromuscular blockers. Finally, SGM significantly increased cell survival and reduced SS toxicity in mHippoE-14 mouse hippocampal embryonic cells, supporting the hypothesis that SGM could act as an antidote to SS's toxic effects.

Network-Based Modeling of Lean Implementation Strategies and Planning in Prefabricated Construction

Abstract: Prefabricated construction (PC) is increasingly promoted in the construction sector for its potential benefits, including reduced resource assumption and improved quality. Accordingly, Lean methods are popularly applied to PC projects for optimizing operational processes and enhancing their performance in line with strategic objectives. A key factor in effectively implementing Lean to improve strategic control is developing specific strategies and planning that consider their complex interactions. Thus, this paper aims to propose a quantitative network-based model by integrating Interpretive Structural Modeling (ISM) and Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) under complex network theory to develop a Lean implementation framework for effective strategy formulation. Specifically, 17 Lean implementation strategies for PC in the context of the Chinese prefabrication industry were identified via an extensive literature review and expert interviews. Then, ISM-MICMAC quantitatively identifies the direct and indirect relationships among strategies, while subsequent analysis of Topological Structure Weight (TSW) and Structural Degree Weight (SDW), as complex network parameters, is used to evaluate the importance of each strategy. The findings show that the strategic planning for Lean implementation in PC consists of four levels, i.e., foundation, organizational, technical, and control. Selecting appropriate Lean tools and technologies is crucial for PC implementation, which must be built on a top-level management team and foster a Lean culture. Moreover, it involves building a standardized system of processes and activities, enhancing both internal and external collaboration, and continuously improving processes in response to changes. On one hand, this in-depth network-based analysis offers practical insights for PC stakeholders, particularly in China, on Lean implementation in line with PC performance and strategic control and objectives. On the other hand, the network-based model can be future-implemented globally. Additionally, this study expands the current body of knowledge on Lean in PC by exploring the interrelationships of Lean implementation strategies.

Estimation of gross calorific value of coal based on the cubist regression model.

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization-artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R2, mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

Fitness-guided particle swarm optimization with adaptive Newton-Raphson for photovoltaic model parameter estimation

This study introduces a new approach for parameter optimization in the four-diode photovoltaic (PV) model, employing a Dynamic Fitness-Guided Particle Swarm Optimization (DFGPSO) algorithm and Enhanced Newton-Raphson (ENR) method. The new DFGPSO algorithm is specifically designed to address the intrinsic challenges in PV modelling, such as local optima entrapment and slow convergence rates that typically hinder traditional optimization methods. By integrating a dynamically evolving fitness function derived from advanced swarm intelligence, the proposed approach significantly enhances global search capabilities. This new fitness function adapts continuously to the search landscape, facilitating rapid convergence towards optimal solutions and effectively navigating the complex, non-linear, and multi-modal parameter space of the PV model. Moreover, the robustness of the DFGPSO algorithm is substantially improved through the strategic incorporation of the ENR method. This integration not only provides accurate initial guesses for the particle positions, thus expediting the convergence process, but also minimizes computational burden, making the method more efficient. Comprehensive simulation studies across various case scenarios demonstrate that the proposed method markedly outperforms existing state-of-the-art optimization algorithms. It delivers faster convergence, enhanced accuracy, and robust performance under diverse environmental conditions, establishing a reliable and precise tool for optimizing PV system performance. This advancement promises significant improvements in energy yield and system reliability for the PV industry.

Systematic simulation of tumor cell invasion and migration in response to time-varying rotating magnetic field.

Cancer invasion and migration play a pivotal role in tumor malignancy, which is a major cause of most cancer deaths. Rotating magnetic field (RMF), one of the typical dynamic magnetic fields, can exert substantial mechanical influence on cells. However, studying the effects of RMF on cell is challenging due to its complex parameters, such as variation of magnetic field intensity and direction. Here, we developed a systematic simulation method to explore the influence of RMF on tumor invasion and migration, including a finite element method (FEM) model and a cell-based hybrid numerical model. Coupling with the data of magnetic field from FEM, the cell-based hybrid numerical model was established to simulate the tumor cell invasion and migration. This model employed partial differential equations (PDEs) and finite difference method to depict cellular activities and solve these equations in a discrete system. PDEs were used to depict cell activities, and finite difference method was used to solve the equations in discrete system. As a result, this study provides valuable insights into the potential applications of RMF in tumor treatment, and a series of in vitro experiments were performed to verify the simulation results, demonstrating the model's reliability and its capacity to predict experimental outcomes and identify pertinent factors. Furthermore, these findings shed new light on the mechanical and chemical interplay between cells and the ECM, offering new insights and providing a novel foundation for both experimental and theoretical advancements in tumor treatment by using RMF.