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Complex problem solving (CPS) has emerged over the past several decades as an important construct in education and in the workforce. We examine the relationship between CPS and general fluid ability (Gf) both conceptually and empirically. A review of definitions of the two factors, prototypical tasks, and the information processing analyses of performance on those tasks suggest considerable conceptual overlap. We review three definitions of CPS: a general definition emerging from the human problem solving literature; a more specialized definition from the “German School” emphasizing performance in many-variable microworlds, with high domain-knowledge requirements; and a third definition based on performance in Minimal Complex Systems (MCS), with fewer variables and reduced knowledge requirements. We find a correlation of 0.86 between expert ratings of the importance of CPS and Gf across 691 occupations in the O*NET database. We find evidence that employers value both Gf and CPS skills, but CPS skills more highly, even after controlling for the importance of domain knowledge. We suggest that this may be due to CPS requiring not just cognitive ability but additionally skill in applying that ability in domains. We suggest that a fruitful future direction is to explore the importance of domain knowledge in CPS.

The implementation of environmental flows (e-flows) aims to reduce the negative impacts of hydrological alteration on freshwater ecosystems. Despite the growing attention to the importance of e-flows since the 1970s, actual implementation has lagged. Therefore, we explore the limitations in e-flows implementation, their systemic reasons, and solutions. We conducted a systematic review and a bibliometric analysis to identify peer-reviewed articles published on the topic of e-flows implementation research in the last two decades, resulting in 68 research and review papers. Co-occurrence of terms, and geographic and temporal trends were analyzed to identify the gaps in environmental water management and propose recommendations to address limitations on e-flows implementation. We identify the underlying causes and potential solutions to such challenges in environmental water management. The limitations to e-flow implementation identified were categorized into 21 classes. The most recognized limitation was the competing priorities of human uses of water ( n = 29). Many secondary limitations, generally co-occurring in co-causation, were identified as limiting factors, especially for implementing more nuanced and sophisticated e-flows. The lack of adequate hydrological data ( n = 24) and ecological data ( n = 28) were among the most mentioned, and ultimately lead to difficulties in starting or continuing monitoring/adaptive management ( n = 28) efforts. The lack of resource/capacity ( n = 21), experimentation ( n = 19), regulatory enforcement ( n = 17), and differing authorities involved ( n = 18) were also recurrent problems, driven by the deficiencies in the relative importance given to e-flows when facing other human priorities. In order to provide a clearer path for successful e-flow implementation, system mapping can be used as a starting point and general-purpose resource for understanding the sociohydrological problems, interactions, and inherited complexity of river systems. Secondly, we recommend a system analysis approach to address competing demands, especially with the use of coupled water-energy modeling tools to support decision-making when hydropower generation is involved. Such approaches can better assess the complex interactions among the hydrologic, ecological, socioeconomic, and engineering dimensions of water resource systems and their effective management. Lastly, given the complexities in environmental water allocation, implementation requires both scientific rigor and proven utility. Consequently, and where possible, we recommend a move from simplistic flow allocations to a more holistic approach informed by hydroecological principles. To ease conflicts between competing water demands, water managers can realize more ‘pop per drop’ by supporting key components of a flow regime that include functional attributes and processes that enhance biogeochemical cycling, structural habitat formation, and ecosystem maintenance.

A robust adaptive neural control scheme is addressed for a generic flexible air-breathing hypersonic vehicle, capable of guaranteeing velocity and altitude tracking errors with desired transient performance. Different from the back-stepping design, a novel neural approximation controller is explored for the altitude subsystem based on a quite simple normal output-feedback formulation rather than a strict-feedback one, while there is no need of the complex recursive design procedure of virtual control laws. Furthermore, on the basis of the minimal learning parameter technique, the updating parameters are reduced greatly. Thus, the exploited strategy exhibits good low-complexity computation. In particular, a new finite-time-convergent differentiator is devised to estimate the newly generated states and it is also employed to provide the necessary high-order time derivatives of reference commands, based on which the proposed control methodology becomes achievable. Finally, the effectiveness of the design is confirmed by simulation results.

Simple cycle reservoir is a classic work in reservoir structure design, and has good performance in tasks such as discrete dynamical system prediction and time series classification. However, the overly simple reservoir structure weakens its ability to model the complex systems such as chaotic systems. A minimum complexity interaction echo state network (MCI-ESN) is proposed in this paper to overcome the shortcomings of simple cycle reservoir. MCI-ESN consists of two identical simple cycle reservoirs which are interconnected by only two neurons for reducing the connection redundancy and improve connection efficiency. A sufficient condition is given to guarantee that the MCI-ESN model has the echo state property. Several numerical experiments, including multivariable chaotic time series prediction and time series classification, are used to verify the effectiveness of the proposed method.

This article deals with the existence, multiplicity, minimal complexity, and global structure of the subharmonic solutions to a class of planar Hamiltonian systems with periodic coefficients, being the classical predator-prey model of V. Volterra its most paradigmatic example. By means of a topological approach based on techniques from global bifurcation theory, the first part of the paper ascertains their nature, multiplicity and minimal complexity, as well as their global minimal structure, in terms of the configuration of the function coefficients in the setting of the model. The second part of the paper introduces a dynamical system approach based on the theory of topological horseshoes that permits to detect, besides subharmonic solutions, “chaotic-type” solutions. As a byproduct of our analysis, the simplest predator-prey prototype models in periodic environments can provoke chaotic dynamics. This cannot occur in cooperative and quasi-cooperative dynamics, as a consequence of the ordering imposed by the maximum principle.

The minimum mean square error (MMSE) signal detection algorithm is near-optimal for uplink multi-user large-scale multiple-input–multiple-output (MIMO) systems, but involves matrix inversion with high complexity. It is firstly proved that the MMSE filtering matrix for large-scale MIMO is symmetric positive definite, based on which a low-complexity near-optimal signal detection algorithm by exploiting the Richardson method to avoid the matrix inversion is proposed. The complexity can be reduced from 𝒪(K3) to 𝒪(K2), where K is the number of users. The convergence proof of the proposed algorithm is also provided. Simulation results show that the proposed signal detection algorithm converges fast, and achieves the near-optimal performance of the classical MMSE algorithm.

The broad learning system (BLS) based on the minimum mean square error (MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning tasks. However, when data are polluted by non-Gaussian noise, the stability of BLS may be destroyed because the MMSE criterion is sensitive to outliers. Different from the MMSE criterion, the minimum error entropy (MEE) criterion utilizes the kernel function to capture high-dimensional information and decrease the negative influence of outliers, which will make BLS more discriminative and robust. Nevertheless, the computational complexity of MEE is high due to a double summation of the data size. To solve these issues, this paper proposes a new robust BLS variant based on the quantized minimum error entropy (QMEE) criterion, in which a quantization operation is used to reduce the computational complexity of MEE. The proposed model BLS-QMEE is optimized by the fixed-point iterative method, and a sufficient condition for its convergence is provided. Compared with the standard BLS and other existing robust variants of BLS, BLS-QMEE performs more satisfactorily without consuming too much time. The desirable performance of BLS-QMEE is verified by various experiments on function approximation, several public datasets, and a practical application.

Various efforts to integrate biological knowledge into networks of interactions have produced a lively microbial systems biology. Putting molecular biology and computer sciences in perspective, we review another trend in systems biology, in which recursivity and information replace the usual concepts of differential equations, feedback and feedforward loops and the like. Noting that the processes of gene expression separate the genome from the cell machinery, we analyse the role of the separation between machine and program in computers. However, computers do not make computers. For cells to make cells requires a specific organization of the genetic program, which we investigate using available knowledge. Microbial genomes are organized into a paleome (the name emphasizes the role of the corresponding functions from the time of the origin of life), comprising a constructor and a replicator, and a cenome (emphasizing community-relevant genes), made up of genes that permit life in a particular context. The cell duplication process supposes rejuvenation of the machine and replication of the program. The paleome also possesses genes that enable information to accumulate in a ratchet-like process down the generations. The systems biology must include the dynamics of information creation in its future developments.

The assessment of reliability in non-repairable subsystems of mining electronic equipment represents a computationally challenging problem, particularly for complex and highly connected structures. This study presents a systematic comparative analysis of several deterministic approaches for reliability estimation, focusing on their computational efficiency, accuracy, and applicability. The investigated methods include classical boundary techniques (minimal paths and cuts), analytical decomposition based on the Bayes theorem, the logic–probabilistic method (LPM) employing triangle–star transformations, and the algorithmic Structure Convolution Method (SCM), which is based on matrix reduction of the system’s connectivity graph. The reliability problem is formally represented using graph theory, where each element is modeled as a binary variable with independent failures, which is a standard and practically justified assumption for power electronic subsystems operating without common-cause coupling. Numerical experiments were carried out on canonical benchmark topologies—bridge, tree, grid, and random connected graphs—representing different levels of structural complexity. The results demonstrate that the SCM achieves exact reliability values with up to six orders of magnitude acceleration compared to the LPM for systems containing more than 20 elements, while maintaining polynomial computational complexity. Qualitatively, the compared approaches differ in the nature of the output and practical applicability: boundary methods provide fast interval estimates suitable for preliminary screening, whereas decomposition may exhibit a systematic bias for highly connected (non-series–parallel) topologies. In contrast, the SCM consistently preserves exactness while remaining computationally tractable for medium and large sparse-to-moderately dense graphs, making it preferable for repeated recalculations in design and optimization workflows. The methods were implemented in Python 3.7 using NumPy and NetworkX, ensuring transparency and reproducibility. The findings confirm that the SCM is an efficient, scalable, and mathematically rigorous tool for reliability assessment and structural optimization of large-scale non-repairable systems. The presented methodology provides practical guidelines for selecting appropriate reliability evaluation techniques based on system complexity and computational resource constraints.

This article is concerned with the exponential almost sure stability and stabilization of variable dual switching time-delay systems (VDSTDSs). Firstly, a novel state-dependent switching strategy named minimum state expectation (MSE) is proposed. Under the MSE switching strategy, the VDSTDSs switch to the next Markov jump subsystem with a smaller state expectation. This switching approach fully considers the interaction between deterministic and stochastic switching signals, which is more general for VDSTDSs than dwell time, average dwell time, and persistent dwell time switching. Subsequently, considering the complex switching dynamics and delay dynamics, a mode-dependent Lyapunov-Krasovskii functional (LKF) that contains an exponential term and a triple integral term is constructed to balance the conservativeness and computational complexity. The generalized free-matrix-based integral inequality is used to estimate the integral term in the LKF derivative. Sufficient conditions are established in the form of linear matrix inequalities to ensure the delay-dependent exponential almost sure stability and stabilization of the VDSTDSs. Finally, a numerical example and a multi-loop networked control system model are provided to illustrate the effectiveness and applicability of the proposed methods.