The aim of this study is to propose a stochastic radial basis (RB) artificial neural network (ANN) and the scale conjugate gradient (SCG) called as RB-ANN-SCG for the the diarrhea disease model including treatment and vaccination (DDMTV). The diarrhea disease system is basically a susceptible, infected and recovered model that includes the factor of treatment and vaccination. The dataset is obtained by using the Adam solver, which lessens the mean square error with the distribution of testing (12%), authentication (13%), and training (75%). A transfer radial basis function together with sixteen neurons is used in the hidden layers for solving the DDMTV, while the optimization is performed by the SCG. The scheme’s correctness is obtained via overlapping of the reference and achieved results. The insignificant calculated absolute error and best validation performances present the accuracy of the solver. Furthermore, the constancy and dependability of the RB-ANN-SCG is pragmatic through the histogram curves, function fitness, and correlation/regression for solving the DDMTV.

This paper presents a comprehensive review of the Social Laser Theory (SLT) as a natural extension of the broader framework of Quantum-Like Modeling (QLM). While QLM applies the mathematical formalism of quantum theory — such as Hilbert space representations, interference, and non-commutative observables — to model context-dependent and non-classical phenomena in cognition, decision-making, and social behavior, SLT advances this approach by integrating concepts from quantum field theory. The theory conceptualizes social systems as ensembles of “social atoms” capable of absorbing and emitting quantized units of social energy. Under conditions analogous to population inversion in physical lasers, external informational stimuli (e.g. media signals or mobilizing rhetoric) can trigger coherence across the population, resulting in large-scale, synchronized collective actions such as protests or ideological shifts. SLT thus provides a formal framework for understanding the amplification and coherence of social energy leading to abrupt phase-like transitions in collective behavior. Beyond its metaphorical appeal, the theory proposes measurable quantities and predictive parameters that may support empirical diagnostics of sociopolitical dynamics. By bridging micro-level psychological processes with macro-level sociological phenomena, SLT extends QLM into the domain of complex social systems, offering a mathematically grounded paradigm for interpreting rapid transformations in contemporary societies.

Coal Consumption Rate (CCR) is one of the most important economic indicators of thermal power generating units, which, to some extent, affects the market competitiveness of the thermal power plants and determines whether the emissions are up to standard. However, due to the influence of many coupling factors on CCR, how to reduce coal consumption has become an outstanding technical problem. This paper presents generalized predictive models of CCR based on the ant colony optimization algorithm and hybrid modeling strategies. The data collected from two thermal power generating units rated 300[Formula: see text]MW and 600[Formula: see text]MW, are used to build the forecasting models and the Pearson correlation coefficient is used to determine the input parameters. 4 neural networks are optimized adaptively by the optimization algorithm and used to build the CCR Single Models (SMs). Compared with the neural networks that are not optimized and other optimized algorithms and models, the ACO-CCR models have higher forecasting accuracy. To further improve the prediction accuracy, two hybrid modeling strategies are proposed to build the CCR Hybrid Models (HMs). The predictive results show that the two hybrid modeling strategies could effectively improve the prediction accuracy of the single models. So, it gives a way out for building the CCR HM, especially when the forecasting accuracy of the CCR SM is generally very low.

In permanent magnet synchronous motors (PMSMs) sensorless control, the sliding mode observer (SMO) has emerged as a solution for rotor position estimation due to its advantages of high robustness, low cost, and superior dynamic performance. However, the inevitable aperiodic and periodic disturbances present in the control system can induce harmonics in the back electromotive force (EMF), which affect the accuracy of position estimation. Conventional sliding mode observer with low-pass filter (SMO-LPF) inevitably introduces phase lag, parameter sensitivity, and signal discontinuity issues. This paper proposes a novel composite controller that combines enhanced repetitive control (ERC) and SMO. The method utilizes the periodic signal suppression capability of repetitive control (RC) to compensate for the harmonics of current, thereby reducing the interference of low-order harmonics on position observation. Additionally, approach ensures the stability of the sliding mode while retaining the strong robustness of the SMO. The simulation results demonstrate that the proposed controller exhibits excellent harmonic suppression capability in terms of magnitude-frequency characteristics. Furthermore, comprehensive simulation experiments including dynamic response analysis, robustness testing, and speed estimation accuracy verification have conclusively validated the effectiveness of the novel approach.

Soft set theory offers a systematic method for addressing imprecision and uncertainty by classifying set elements based on specific parameters. In the realm of semigraph theory, soft semigraphs employ this method, providing a parametrized perspective that has greatly enhanced the field through efficient parameter management. Building on this foundation, disemigraphs extend semigraphs by incorporating directional relationships among vertices, making them ideal for modeling situations where the sequence and direction of connections are essential. However, existing models such as fuzzy graphs or soft graphs often fall short in capturing both the directionality and parameter-based uncertainty simultaneously, especially in networks involving multiple interaction types and layered relational structures. This paper introduces the concept of soft disemigraphs, a novel framework that integrates the flexibility of soft set theory with the directional rigor of disemigraphs. This integration enables a more nuanced, structured, and context-aware representation of complex systems, particularly where directionality, hierarchy, and imprecision coexist. We explore various operations on soft disemigraphs, including extended union, restricted union, extended intersection, and restricted intersection, and analyse their structural properties. These operations provide deeper insights into the dynamics of parametrized, directed networks and offer a clear advantage in modeling real-world systems. Furthermore, we demonstrate the practical applicability of this framework through ecological network modeling, particularly in the representation of food webs, where species interactions are inherently directional, hierarchical, and context-sensitive.

This work proposes a hierarchical clustering algorithm for high-dimensional datasets using the cyclic space of reversible finite cellular automata. In cellular automaton (CA)-based clustering, if two objects belong to the same cycle, they are closely related and considered as part of the same cluster. However, if a high-dimensional dataset is clustered using the cycles of one CA, closely related objects may belong to different cycles. This paper identifies the relationship between objects in two different cycles based on the median of all elements in each cycle so that they can be grouped in the next stage. Further, to minimize the number of intermediate clusters which in turn reduces the computational cost, a rule selection strategy is taken to find the best rules based on information propagation and cycle structure. After encoding the dataset using frequency-based encoding such that the consecutive data elements maintain a minimum Hamming distance in encoded form, our proposed clustering algorithm iterates over three stages to finally cluster the data elements into the desired number of clusters given by user. When verified over standard benchmark datasets with various performance metrics, our algorithm is at par with the existing algorithms with quadratic time complexity.

Heterogeneous networks have multiple types of nodes and edges. Single-layer stochastic block model (SBM), bipartite SBM, and multiplex SBM have been proposed as a tool for detecting community structure in networks and generating synthetic networks for use as benchmarks. Yet, any SBM has not been introduced specifically for detecting community in heterogeneous networks. In this paper, we introduce heterogeneous multilayer SBMs for detecting communities in heterogeneous networks. According to these models, we look at heterogeneous networks as multilayer networks, which means each edge type shows one layer. We can categorize these models into two broad groups, those based on the independent degree principle and other based on the shared degree principle. According to our results, in general, the independent degree model has better performance in networks that have less common communities between nodes types. In contrast, the shared degree model has better performance in networks which have more common communities between nodes types. Also, we show that our models outperform in real-world networks. If we put aside the exception case, simulation results and real data applications show the effectiveness of these proposed models compared to single-layer models that are applied to heterogeneous networks.

The research on efficient routing strategies holds paramount importance in mitigating network congestion and enhancing the transmission capacity of complex networks. A Hierarchical Routing Optimization (HRO) strategy is proposed for dual-layer networks featuring both logical and physical layer structures. This strategy employs distinct methods for packet transmission at the logical and physical layers to optimize the utilization of network resources. Comparison with the improved static-weighted routing strategy (ISWR) and the improved effective routing strategy (IER) involves analyzing the relationship between the transmission capacity of the dual-layer network and the coupling method under these three routing strategies. This analysis aims to determine the optimal coupling method for the routing strategy, thereby enhancing overall network capacity. The simulation results reveal that the random coupling method proves most effective when employing the ISWR strategy, the assortative coupling method excels with the IER strategy, and the dissortative coupling method stands out when implementing the HRO strategy. In evaluating the ISWR strategy, IER strategy, and HRO strategy, it becomes evident that the HRO strategy substantially amplifies the transmission capacity of the dual-layer network. Furthermore, the HRO strategy exhibits heightened robustness against both random and deliberate attacks compared to the IER strategy.

Google employs PageRank to rank web pages, determining the order in which search results are presented to users based on their queries. PageRank is primarily utilized for directed networks, although there are instances where it is also applied to undirected networks. In this paper, we have applied PageRank to undirected networks, showing that a vertex’s PageRank relies on its initial value, often referred to as an intrinsic, non-network contribution. We have analytically proved that when the initial value of vertices is either proportional to their degrees or set to zero, the PageRank values of the vertices become directly proportional to their degrees. Simulated and empirical data are employed to bolster our research findings. Additionally, we have investigated the impact of initial values on PageRank localization.

The rapid development of 3D scanning has allowed us to digitally capture cultural heritage objects as large-scale point clouds, recording their complex internal and external structures. We have developed a 3D multi-view transparent visualization using edge highlighting as visual guide, improving accuracy of perceived depth for reliable visual analyses of the 3D scanned point clouds. When transparently visualizing a 3D object on a 3D multi-view display, the depth information tends to become less distinct. Therefore, we aimed to explore whether emphasizing the statistically extracted 3D edges of the scanned object can enhance the perceived depth of transparently visualized 3D objects. We conducted psychophysical experiments where participants were asked to report their perceived magnitudes of depth for multiple types of 3D structures of cultural heritage presented on a multi-view 3D display. The results indicated that our edge highlighting reduced the underestimation of depth from 46.8% to 34.0% on average. This improvement persisted even when the 3D structures produced an atypical linear perspective, normally caused by reverse depths, where the front and back are reversed. These findings suggest that edge highlighting brings us an effective visual guide to improve the accuracy of perceived depths on a 3D multi-view display.