Producing words during the semantic fluency task (e.g., "name all the animals you can in a minute") involves not only activating the relevant semantic representations but also being able to flexibly utilize phonological information to facilitate retrieval. However, the mechanisms by which individuals effectively use phonology as a latent cue during memory search remain understudied. In this study, we used a computational approach to examine the use of different lexical sources during memory search in two categorical domains (Animals and Foods) within the semantic fluency task. We captured the relative saliency of semantic and acoustic information by creating blended lexical representations obtained from two language models trained on text (word2vec) and speech (speech2vec). To examine process-level mechanisms, we implemented computational models based on optimal foraging principles to characterize transitions within and across clusters of related items. These models were evaluated within a sample of normal hearing children and a naturally occurring control group with limited early auditory and language experience-prelingually deaf children with cochlear implants. Overall, in domains where semantics and phonology were more highly correlated (i.e., Foods), greater use of phonology in within-cluster transitions was associated with better fluency performance for children with normal hearing. In contrast, though children with cochlear implants amplified acoustic structural information in these domains, they were less able to flexibly leverage phonological information to retrieve more items within clusters. Together, this work highlights how early linguistic experience shapes the ability to use semantic and phonological cues to navigate lexical space in a controlled, adaptive manner. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

In this article, we investigate the problem of adaptive safe critic control design for stochastic multiagent systems (MASs) subject to asymmetric state and input constraints. To systematically address asymmetric state constraints, a unified transformation function (UTF) is proposed to convert the constrained consensus control problem into the stability analysis of an unconstrained error system. In addition, a nonquadratic cost function is incorporated to address input limitations effectively. Building upon these developments, a time-varying Hamilton-Jacobi-Bellman equation (HJBE) is formulated by integrating the Bellman optimality principle with Itô's lemma, thereby accommodating stochastic disturbances and enhancing controller robustness. To improve data utilization and eliminate reliance on explicit drift dynamics, an integral reinforcement learning (IRL) algorithm is developed within this framework. Furthermore, a time-varying single-critic network is designed to approximate the solution to the HJBE and generate optimal control policies, thereby considerably reducing computational complexity. To further enhance learning efficiency and relax the persistent excitation (PE) condition, the experience replay (ER) technique is incorporated into the update process of the critic weight. Finally, two simulation examples are provided to verify the feasibility and effectiveness of the proposed approach.

In this article, we explore the optimal portfolio selection problem for an individual who participates in mutual aid insurance and purchases life insurance under an environment where smooth ambiguity and habit formation coexist. The individual invests in a market with one risk-free and two risky assets whose unknown market prices result in ambiguity. Habit formation depends on historical consumption and is state-dependent. We construct a mathematical model for mutual aid insurance based on a three-state (good, bad, and death) process driven by an inhomogeneous Markov chain. Meanwhile, the individual hedges mortality risk via life insurance. We apply an extended Hamilton–Jacobi–Bellman equation with regime switching to solve for the equilibrium strategies that maximize the individual’s lifetime utility. Subsequently, we analyze three combinations of utility and ambiguity preference functions, power–power, exponential–power, and exponential–exponential, and obtain equilibrium strategies under different functional forms. A key finding shows that the Tobin–Markowitz separation theorem is no longer applicable when ambiguity aversion is considered. Finally, we conduct numerical simulations to demonstrate the significance of mutual aid insurance, measure parametric impacts on equilibrium strategies, and provide theoretical guidance for mutual aid product design.

Human-machine shared driving for vehicle collision avoidance based on Hamilton-Jacobi reachability.

Surface drainage of low-permeability coal seams via horizontal wells is critical for safe mining in high-gas outburst mines, but limited understanding of reservoir parameter evolution obscures gas decline and optimization mechanisms. Focusing on the Xintian Coal Mine in Western Guizhou, this study integrated experiments and simulations to invert pore pressure and gas content evolution, diagnose conventional drainage failure, and propose optimization principles. In Well A-1, excessively rapid pore pressure decline during early drainage stages (39.5% drawdown in first 100 days at 14.96 kPa/d) may have induced stress sensitivity, the Jamin effect, and coal fines migration, causing irreversible permeability damage in the strongly stimulated zone. Thus, the desorption area was only 78.7% of the pressure drop area, leaving much adsorbed gas unused. By implementing staged pressure decline control and restraining casing pressure, Well B-1 achieved a pressure drop area (0.67 km2) close to its desorption area (0.65 km2), with 9.8% and 35% expansions in pressure drop and desorption areas. The key to optimization is refined single-phase drainage stage management, realizing synergy of “pressure node identification, staged rate control, dynamic matching of fluid supply capacity” to ensure smooth flow transitions. This principle provides a reference for similar conditions in coal mining areas.

Quantum geometric-entropic optimization for customer lifetime value prediction: convergence theory and an empirical study on transactional retail data

The article presents a scientific and methodological approach to selecting a rational transport and technological scheme for international road freight transportation, taking into account stochastic factors, military risks, and the scale of enterprise activity. The proposed model is based on the principles of multicriteria optimization and considers the structure of logistics costs, the intensity of freight flows, and the number of shippers and consignees. A comparative analysis of four alternative delivery schemes is carried out: direct delivery, delivery using a logistics center in the country of dispatch, delivery with a logistics center in the country of destination, and a combined scheme with two centers. For each scheme, the components of logistics costs are identified, including costs for order processing, shipment formation, transportation, loading and unloading operations, customs clearance, downtime, and addition-al expenses related to military risks. To verify the model, a series of experiments was conducted using a full-factorial experimental methodology, varying key factors such as the intensity of order arrivals, the average order volume, and the number of shippers and consignees. The obtained results showed that for small enterprises, the most efficient scheme is consolidation in the country of dispatch, while for medium and large enterprises, the optimal solution is the scheme with a logistics center in the country of destination. The combined scheme with two centers is advisable only under conditions of complex network supply structures and high flexibility requirements.The proposed model ensures minimization of logistics costs, improvement of efficiency in organizing international road freight transportation, and can serve as a practical tool for managerial decision-making in the field of transport logistics.

Remineralizing therapy is in demand among the population for the prevention of caries due to the safety, simplicity, affordability, and ease of use of modern remineralizing agents. For a long time, scientists have proposed a wide variety of formulations and forms for mineralizing hard dental tissue. Currently, one of the most common options is gel formulations, which combine several mineralizing agents without interaction or mutual deactivation.A modern trend in medicine is the use of biomimetic materials. Hydroxyapatite is such a biomimetic for enamel, which has led to a large number of studies on its use.In addition to the optimal biomimetic principle of medicine, the search is ongoing for innovative remineralizing agents that are as convenient to use as possible. Therefore, the development of innovative formulations is socially in demand, especially among young people. Materials and methods. A comparative prospective clinical study was conducted to evaluate the remineralizing, hyposensitivity, and anti-inflammatory effects of an innovative prophylactic agent in adult patients aged 18–30 years during oral hygiene treatment compared to a control group. Standard clinical indicators were used to evaluate the effectiveness of the hygiene products: the Dental Caries Intensity Index (DMF), the Oral Hygiene Index (OHI-S), the Enamel Resistance Test (ERТ), the Gingival Inflammation Index (PMA), the Sulcus Bleeding Index (SBI), the Schiff Air Scale, and the Visual Analogue Pain Scale (VAS). Various salivary parameters were monitored during the study, including crystal formation, pH, fluoride, Ca, Mg, lactate, and IL-6. The study lasted one month. Results. Clinical and laboratory studies of the GLOBAL WHITE Remineralizing Gel-Pencil confirm the stated consumer properties: an increase in the level of enamel mineralization according to ERT by 36.48 %, an increase in the mineralizing potential of saliva according to microcrystallization data by 15.20 %, according to the level of salivary calcium by 20.72 %, a decrease in hyperesthesia of hard dental tissues according to the Schiff index by 45.16 %, an improvement in the condition of the periodontium during inflammation according to the PMA index by 22.48 %. The overall assessment of the organoleptic properties (including ease of application, taste, texture) was 8.86 ± 0.38 points on a 10-point scale.

This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration.

A BSTRACT Medical imaging utilizing ionizing radiation has become indispensable in modern healthcare, with exponential growth in diagnostic procedures over recent decades contributing to increased population radiation exposure. From a preventive medicine perspective, balancing the diagnostic benefits of radiological examinations against potential long-term cancer risks represents a critical public health challenge. This narrative review examines the epidemiological evidence linking medical radiation exposure to cancer development, quantifies population-level exposure trends, and explores evidence-based strategies for radiation dose optimization and risk reduction. We synthesize data on radiation-induced cancer risks across different age groups, organ systems, and dose levels, with particular attention to vulnerable populations including children, pregnant women, and individuals requiring repeated imaging. The review addresses cumulative radiation exposure from computed tomography, fluoroscopy-guided procedures, nuclear medicine, and other diagnostic modalities. We discuss the principles of justification, optimization, and dose limitation as fundamental components of radiation protection in medical settings. Implementation strategies, including clinical decision support systems, dose tracking programs, standardized protocols, and patient communication approaches, are examined alongside comparative international regulatory frameworks. The review highlights opportunities for dose reduction without compromising diagnostic quality, and provides stakeholder-specific recommendations for preventive medicine specialists, radiologists, healthcare administrators, and policymakers.

This article analyzes the significance of investment flow in managing the equilibrium state within a self-organizing trading system. The research examines the impact of investment flow on economic systems, its various forms, and its influence on the dynamics of the trading system. Additionally, continuous and discrete factors affecting the system are classified, and a model is developed using a stochastic differential equation. A methodology for determining the optimal value of investment flow is proposed based on the Hamilton–Jacobi–Bellman equation. The results obtained are of great importance for forecasting economic systems, optimal resource allocation, and the development of market strategies.

Harmonic Path Integral Diffusion (H-PID) provides an analytically tractable framework for sampling from a target density . H-PID can be viewed as a diffusion bridge model solving a stochastic optimal transport problem from a -density at to the target density at . The dynamics are governed by a controlled stochastic differential equation, and the corresponding variational stochastic optimal transport objective combines a time-dependent quadratic potential, , with a kinetic control cost, . The focus of this paper is the design of the temporal stiffness protocol , which enables explicit control of intermediate sampling dynamics when the terminal density is fixed. We exploit the central advantage of H-PID—its integrability—which yields an explicit representation of the optimal control in terms of the target density and Green functions of the associated linear forward and backward diffusion-in-a-potential problems. Our main contribution is to convert this integrable structure into a practical methodology for protocol optimization. Specializing to piecewise-constant stiffness schedules and Gaussian-mixture targets, we develop two complementary optimization principles: The first is a deterministic one, relying on explicit evaluation of the dynamic marginals, and exemplified on a velocity-gradient-sensitivity objective, which provides a computationally controlled framework for optimizing transport regularity and stiffness. The second is a stochastic one, implemented via sampling, and exemplified on sharpness-based temporal-memory objective regularized to favor transitions within a prescribed time window that targets the temporal organization of the sampling path. These two objectives illuminate different aspects of the same protocol-design problem. The velocity-gradient-sensitivity objective serves as a clean methodological backbone and supports interpretable optimization and scaling studies. The sharpness-based objective reveals that schedule quality is target-dependent, and that the dependence on is not universal: different target geometries may favor different stiffness regimes and qualitatively different transient organizations. Examples with low- and moderate-dimensional Gaussian mixtures demonstrate that the proposed approach can control not only the terminal sampling accuracy but also the transient evolution of probability mass, while remaining computationally light and theoretically transparent.

This paper presents an adaptive optimal consensus tracking control scheme for canonical nonlinear multi-agent systems (MASs) with unknown dynamics, employing an actor–critic reinforcement learning (RL) framework. The scheme integrates a sliding mode mechanism to suppress tracking errors and ensure consensus tracking between the followers and the leader. Additionally, optimal control is designed to find a Nash equilibrium in a graphical game. To address the intractability of obtaining an analytical solution for the coupled Hamilton–Jacobi–Bellman (HJB) equation, a policy iteration algorithm is utilized. Within this algorithm, a critic neural network (NN) approximates the gradient of the optimal value function, while an actor NN approximates the optimal control policy. Together, these networks form a compact actor–critic (AC) architecture that achieves optimal consensus tracking. Furthermore, the proposed method guarantees the boundedness of all closed-loop signals while ensuring consensus tracking. Finally, two simulations are conducted to verify the effectiveness and advantages of the proposed method.

In this paper, we introduce a Euclidean path integral control approach to determine optimal strategies for firms operating under a Walrasian system, Pareto optimality, and a non-cooperative feedback Nash equilibrium. Our framework formulates a Lagrangian control problem with forward-looking stochastic dynamics, eliminating the need for a value function to derive optimal strategies. The method relies on a continuously differentiable Itô process generated by integrating factors, providing a computationally feasible alternative to solving complex market dynamics. Our approach facilitates the analysis of generalized nonlinear market dynamics, where constructing a Hamilton–Jacobi–Bellman (HJB) equation is particularly challenging. Similar to the Feynman–Kac approach, our solutions are not unique. Given the large number of firms considered, our method draws comparisons with mean-field game approach. The primary contribution of this work is the derivation of a non-cooperative feedback Nash equilibrium, offering a comparative perspective against solutions generated by mean-field interactions. We illustrate the effectiveness of our approach through various examples, contrasting it with the Pontryagin maximum principle.

NPK compound fertilizer application and Plant spacing optimization are studied for their impact on the growth and development of Groundnut (Arachis hypogaea L.), to model nutrient management optimization across various agricultural settings. Application of Fertilizer is vital to the economic and biological improvement of crops by adding valuable nutrients like N, P, and K. These same principles of nutrient optimization are essential in Aquaculture and in the culture of highly valuable aquatic resources to advance the desired growth of marine plants and other species. The study was carried out in the southern region of Iraq in clay soil and with various NPK fertilizers (0, 80, 120, 160 kg ha⁻¹) applications and plant spacings (20, 30, and 40 cm). It was found that fertilizer at higher concentrations had a positive effect on growth traits, including plant height, branch number, leaf area, and dry matter yield. That​‍​‌‍​‍‌​‍​‌‍​‍‌ spacing contributed even more to their growth, thus forming a synergistic combination. The findings of the study indicate that nutrient management is necessary in different ecosystems, for example, the marine environment, where the focus should be on aquaculture in the culture of high-value aquatic crops like fish. Through the embracement of technological innovations and the building of their resilience, entrepreneurs are leading the way in a win-win situation that is becoming a more and more digital and competitive ​‍​‌‍​‍‌​‍​‌‍​‍‌‍‌‍‍‌‍‌‍‍‌environment.

Abstract This study employs a systematic numerical investigation framework centered on TCAD simulation to address thermal management challenges in InP-based In0.53Ga0.47As planar Gunn diodes. Through comprehensive two-dimensional thermal-electrical co-simulation, we evaluate substrate materials (InP, Si, diamond) and thermal films (diamond, graphene) on device performance. The simulation results indicate that the diamond substrate can significantly reduce the operating temperature of the device, increase the oscillation frequency, and enhance the output power. Both diamond and graphene thermal films effectively mitigate thermal resistance, improving output characteristics through temperature reduction exceeding 150 K. Furthermore, we propose a novel diamond-graphene composite thermal management structure that achieves a significant temperature reduction from 607.3 K to 373.3 K (ΔT = 234 K), simultaneously enhancing oscillation frequency to 203.6 GHz and power conversion efficiency to 0.80%. These findings establish material-driven thermal optimization principles for high-frequency terahertz sources, suggesting significant potential for device performance enhancement.

The radiological protection principles of justification, optimisation and dose limits are applied through national legislation to safeguard workers, the public and the environment from the short and long-term effects of ionising radiation. This paper analyses how these principles, as implemented in England, align with differing interpretations of sustainable development. This analysis finds that the framework is oriented toward sustaining all natural capital by applying stronger sustainability for human protection and nearly strong sustainability for non-human species. It is argued that the 1 mSv yr-1public dose limit is representative of an environmental limit and is a potential candidate 'safe operating space' for ionising radiation within the novel entities' planetary boundary. Circumstances are also identified during low radiological risk activities such as decommissioning and remediation where very strong sustainability may be applied. Further research is therefore recommended to test the use of the 1 mSv yr-1limit as a global safe operating space and to define clearer criteria to understand and control transitions from stronger to very strong sustainability in low radiological risk activities.

Ultrasonic cavitation is a key mechanism in the dispersion and erosion of solid materials in liquids; however, the influence of processing conditions and medium properties on its efficiency in ultrasonic baths remains poorly systematized. Despite the widespread use of ultrasonic baths in materials processing, general optimization principles are lacking, and operating parameters are typically determined empirically for each system. In this work, cavitation activity was quantitatively assessed using an aluminum foil erosion test, with the foil clamped in a plastic frame to evaluate the mechanical effects of cavitation. The effects of ultrasonic power, frequency, treatment time, temperature, solvent nature, and vessel material on the foil mass loss were systematically investigated. The results demonstrate that both the instrumental parameters and physicochemical properties of the dispersion medium, including viscosity and surface tension, significantly affect the cavitation activity. Solvents with lower cavitation thresholds and favorable acoustic properties promote more intense erosion, while the vessel material and geometry also influence energy transmission to the liquid. This study provides a systematic framework for assessing the cavitation dose in ultrasonic baths and offers practical guidelines for optimizing ultrasonic dispersion processes and improving their reproducibility.

ABSTRACT The optimal control with an ergodic cost function is investigated for a class of stochastic discrete‐time linear systems with unknown system coefficient matrices. We present an online value iteration‐based stochastic adaptive dynamic programming algorithm to obtain the approximation sequence of the optimal state feedback control gain and rigorously prove that the approximation sequence converges to the optimal state feedback control gain under some reasonable conditions. In the framework of direct adaptive control, the diminishing excitation technique is introduced for an unknown stochastic discrete‐time state space model, and we prove that the adaptive controller, which is given by real‐time computation, is optimal. In contrast to existing related results, our method does not need to collect enough data to solve the Bellman equation in each iteration and avoids the requirement to estimate means and variances of controllers or states. A numerical simulation is provided to evaluate the efficacy of the presented approach.

The relevance of developing methods for designing and modeling structural elements of power plants and mechanisms is shown. Attention is focused on the fact that electromechanical energy converters are an integral part of power equipment that should be considered systematically, as one energy complex as part of a gas turbine and steam turbine plant. Electromechanical energy converters of various types (transformers, electric motors, turbogenerators) are the main technological equipment of the power industry and form the basis of the equipment for thermal and nuclear power plants. The principles of system design of power plants are considered, among which the principle of integrity, the principle of hierarchy, the principle of interconnection, the principle of adaptability, and the principle of optimization are highlighted. These principles are proposed to be the basis of multiparametric design of electromechanical energy converters. It is noted that multiparametric design is based on the mathematical apparatus of parametric design. The essence of the principles of integrity and hierarchy is considered in more detail. Other principles of multiparametric design are set out in separate works. The features of these design principles are formulated, functional blocks for performing design work are developed, the sequence of their implementation and the results that should be obtained when they are applied are given. The considered design principles are adapted to the features of the development of a transformer, electric motor and turbogenerator. It is noted that a feature of multiparametric design of electromechanical energy converters of various types and designs is the consideration of multiphysical processes that occur during the operation of these power machines. These are mainly electrodynamic, mechanical and heat-ventilation processes. Performing calculations and modeling of parts and assemblies of power machines taking into account these processes is a complex and extremely important task, because almost all components of a power plant are under the influence of electrical, mechanical and thermal loads.