State Dimension Research Articles

A feature extractor for temporal data of electronic nose based on parallel long short-term memory network in flavor discrimination of Chinese vinegars

Volatile flavor is a key indicator of food quality which can directly affect consumer preference and purchase intention. Electronic nose is considered as a promising intelligent sensory analysis tool for food flavor assessment, however, extracting effective features from the gas sensor array is still a major challenge, which largely determines the performance of subsequent classifiers. Here, a parallel long short-term memory (LSTM) network is proposed as a feature extractor for automatically extracting features from the whole time series of sensor responses in flavor discrimination of five Chinese vinegars. The network was trained by the temporal data from the sensor array and yielded different feature patterns corresponding to different vinegars, which were then fed to other conventional classifiers for pattern recognition. We also evaluated the influence of the extracted feature dimension that is related to the dimension of the hidden state of the LSTM layer on the classification performance. The results indicate that a larger dimension of extracted feature is unnecessary for promoting classification accuracy, instead, the optimum dimension 4 of the hidden state gives the highest accuracy of 95.8% in this application under the softmax evaluator. Moreover, much higher accuracies were obtained when combined with other sophisticated classifiers such as support vector machine. The results demonstrate that the proposed network is competent to extract features directly and automatically from the temporal data of the electronic nose.

Neuropsychological and psychometric parameters in the patients with duodenal ulcer depending on Helicobacter pylori presence

Neuropsychological and psychometric parameters in the patients with duodenal ulcer depending on Helicobacter pylori presence

Associations between day-level affect profiles and same-day substance use among young adults.

Emotions play a critical role in health risk behaviors, including substance use. However, current research often focuses exclusively on average levels of positive and negative affect, neglecting the complexity of daily emotional patterns. By capturing multiple dimensions of affect, including arousal and discrete states, we can improve our understanding of proximal predictors of substance use. The present study demonstrates the utility of a novel methodological approach for assessing affect patterns in daily life in relation to alcohol and cannabis use. Daily diary data from N = 154 young adults who reported recent heavy episodic drinking and simultaneous use of alcohol and cannabis were analyzed using a mixed-indicator latent profile analysis to identify and describe day-level affective patterns and outcomes. Results revealed five distinct day-level profiles of affect: undifferentiated negative affect days, undifferentiated positive affect days, high-arousal positive affect days, mixed affect days, and low reactivity days. Undifferentiated positive affect days, high-arousal positive affect days, and low reactivity days were associated with significantly greater odds of same-day alcohol use compared to days characterized by undifferentiated negative affect (χ² = 10.55, p = .032). Findings suggest that daily affect patterns differentially impact alcohol use and can inform the development of interventions for problematic substance use. Additionally, the innovative methodological approach employed herein could be applicable for investigating the role of emotion in other health behaviors. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

TACO: A Turkish database for abstract concepts.

The organization of abstract concepts reflects different dimensions, grounded in the brain regions coding for the corresponding experience. Normative measures of linguistic stimuli offer noteworthy insights into the organization of conceptual knowledge, but studies differ in the dimensions and classes of concepts considered. Additionally, most of the available information has been collected in English, without considering possible linguistic and cultural differences. Here, we aimed to create a comprehensive Turkish database for abstract concepts (TACO), including rarely investigated classes such as political concepts. We included 503 words-78 concrete (fruits, animals, tools) and 425 abstract (emotions, social, mental states, theoretical, quantity, space, political)-rated by 134 Turkish speakers for familiarity, imageability, age of acquisition, valence, arousal, quantity, space, theoretical, social, mental state, and political dimensions. We calculated dominance and exclusivity, indicating the dimension receiving the highest mean score for each word, and the position of the word along the unidimensional-multidimensional continuum, respectively. A principal component analysis (PCA) was conducted on the semantic dimensions. The results showed that mental state was the dominant dimension for most concepts. Moderate to low levels of exclusivity indicated that the concepts were multidimensional. PCA revealed three components: Component 1 captured the juxtaposition between social/mental state and magnitude polarities, Component 2 highlighted affective components, and Component 3 grouped together political and theoretical dimensions. The introduction of political concepts provided insights into the multidimensional nature of this unexplored class, closely intertwined with the theoretical dimension. TACO constitutes the first comprehensive Turkish database covering several abstract dimensions, paving the way for cross-linguistic and cross-cultural studies of semantic representations.

Hierarchical control of linear systems using extended robust approximate simulation

This paper develops a hierarchical control structure for linear systems based on extended robust approximate simulation. In numerous real-world scenarios, systems are subject to external disturbances, and there is a restriction on accessibility to all of their states. Furthermore, these systems are modeled using linear dynamics with high state dimensions. Hence, the control synthesis problem for these concrete systems is computationally challenging, and constructing an abstract model with smaller state dimensions to design the controller would be beneficial. Therefore, we introduce the notion of a extended robust approximate simulation function between the concrete system and the abstract model under a new hierarchical control structure. With this respect, a controller is designed for the abstract model and refined for the concrete system by formulating an observer-based robust interface controller based on H∞ while guaranteeing the desired performance for the concrete system. It is worth noting that an Unknown Input Observer (UIO) is employed in the controller, which circumvents the full-state accessibility of the concrete system. The applicability of the presented approach is demonstrated by controlling bus voltages on a smart grid.

Fast Quantum State Preparation and Bath Dynamics Using Non-Gaussian Variational Ansatz and Quantum Optimal Control.

Fast preparation of quantum many-body states is essential for myriad quantum algorithms and metrological applications. Here, we develop a new pathway for fast, nonadiabatic preparation of quantum many-body states that combines quantum optimal control with a variational Ansatz based on non-Gaussian states. We demonstrate our approach on the spin-boson model, a single spin interacting with the bath. We use a multipolaron Ansatz to prepare near-critical ground states. For one mode, we achieve a reduction in infidelity of up to ≈60 (≈10) times compared to linear (optimized local adiabatic) ramps; for many modes, we achieve a reduction in infidelity of up to ≈5 times compared to nonadiabatic linear ramps. Further, we show that the typical control quantity, the leakage from the variational manifold, provides only a loose bound on the state's fidelity. Instead, in analogy to the bond dimension of matrix product states, we suggest a controlled convergence criterion based on the number of polarons. Finally, motivated by the possibility of realizations in trapped ions, we study the dynamics of a system with bath properties going beyond the paradigm of (sub- and/or super-) Ohmic couplings.

The Block-Correlated Pseudo Marginal Sampler for State Space Models

Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimensional state vectors than previous approaches. The improvement is achieved by the following innovations. First, a novel block version of PMMH that works with multiple particle filters is proposed. Second, the trimmed mean of the unbiased likelihood estimates of the multiple particle filters is used. Third, the article develops an efficient auxiliary disturbance particle filter, which is necessary when the bootstrap disturbance filter is inefficient, but the state transition density cannot be expressed in closed form. Fourth, a novel sorting algorithm, which is as effective as previous approaches but significantly faster than them, is developed to preserve the correlation between the logs of the likelihood estimates at the current and proposed parameter values. The performance of the sampler is investigated empirically by applying it to nonlinear Dynamic Stochastic General Equilibrium models with relatively high state dimensions and with intractable state transition densities and to multivariate GARCH diffusion-driven volatility in the mean models. Although we only apply the method to state space models, the approach will be useful in a wide range of applications such as large panel data models and stochastic differential equation models with mixed effects.

Driving factors and key paths of greenhouse gas and air pollutants synergistic control using hybrid multiple-criteria decision-making

Humanity has consumed a large amount of energy and resources to maintain the rapid development of the economy and society, causing greenhouse gas and air pollutants to rise continuously, generating enormous pressures for the sustainable development of many cities. It is economical to control greenhouse gas and air pollutants from the synergy perspective. To identify the key driving factors involved in synergistic control, this paper uses the pressure-state-response (PSR) model to design a performance evaluation model of greenhouse gas and air pollutants synergistic control (GASC) utilizing pressure, state, and response dimensions. The performance evaluation factor system of GASC comprises three primary aspects and 18 criteria. The analytic hierarchy process (AHP) was used to determine the weight of each factor in the evaluation system. The technique for order preference by similarity to an ideal solution (TOPSIS) method was used to calculate the ranking of the synergistic control effects of the four representative provinces in China. We use Importance-performance analysis (IPA) to analyze the performance of driving factors of synergistic control in the province with the lowest ranking from 2016 to 2020. The research shows that in Northeast China, represented by Liaoning province, the government’s response should include changing the support strategy for the new energy consumer, introducing synergistic control standards and policies, and making flexible adjustments to the supply chain. The research provides a scientific basis for the performance evaluation of GASC and decision-making support for lean response strategies.

Role of executive functions in the relations of state- and trait-math anxiety with math performance.

The detrimental effect of math anxiety on math performance is thought to be mediated by executive functions. Previous studies have primarily focused on trait-math anxiety rather than state-math anxiety and have typically examined a single executive function rather than comprehensively evaluating all of them. Here, we used a structural equation modeling approach to concurrently determine the potential mediating roles of different executive functions (i.e., inhibition, switching, and updating) in the relationships between both state- and trait-math anxiety and math performance. A battery of computer-based tasks and questionnaires were administered to 205 university students. Two relevant results emerged. First, confirmatory factor analysis suggests that math anxiety encompassed both trait and state dimensions and, although they share substantial variance, trait-math anxiety predicted math performance over and above state-math anxiety. Second, working memory updating was the only executive function that mediated the relationship between math anxiety and math performance; neither inhibition nor switching played mediating roles. This calls into question whether some general proposals about the relationship between anxiety and executive functions can be extended specifically to math anxiety. We also raise the possibility that working memory updating or general cognitive difficulties might precede individual differences in math anxiety.

Associations between Sleep Disturbances, Personality Traits and Self-Regulation in a Sample of Healthy Adults.

Background: Scientific evidence and everyday experience show that sleep disturbances and self-regulation as a proxy of stress reactivity are linked. Particular personality traits such as neuroticism, internalizing and externalizing problems are also associated with sleep disturbances. Here, we combined self-regulation and personality traits and associated these variables with subjective sleep disturbances. Methods: A total of 846 adults (mean age: 33.7 years; 78.7% females) completed questionnaires covering sleep disturbances, self-regulation and personality traits. Results: Higher scores for sleep disturbances were associated with higher scores for externalization, internalization, and instability and with lower scores for stability (all trait variables) and with poorer self-regulation (state variable). The regression model showed that higher scores for externalization and internalization (traits), and lower scores for self-regulation (state) predicted higher scores for sleep disturbance. Next, self-regulation had both a direct effect on sleep disturbance, and an indirect effect via personality traits. Conclusions: Sleep disturbances were related to both state (i.e., self-regulation) and trait (e.g., internalization and instability) dimensions. The current data analysis leapfrogs the state-trait dichotomy discussion and reconciles the state-and-trait approach in the prediction of poor sleep, though self-regulation appeared to have both direct and indirect effects on sleep disturbances.

Dynamic Functional Hyperconnectivity After Psilocybin Intake Is Primarily Associated With Oceanic Boundlessness

BackgroundPsilocybin is a widely studied psychedelic substance that leads to the psychedelic state, a specific altered state of consciousness. To date, the relationship between the psychedelic state’s neurobiological and experiential patterns remains undercharacterized because they are often analyzed separately. We investigated the relationship between neurobiological and experiential patterns after psilocybin by focusing on the link between dynamic cerebral connectivity and retrospective questionnaire assessment. MethodsHealthy participants were randomized to receive either psilocybin (n = 22) or placebo (n = 27) and scanned for 6 minutes in an eyes-open resting state during the peak subjective drug effect (102 minutes posttreatment) in ultrahigh field 7T magnetic resonance imaging. The 5-Dimensional Altered States of Consciousness Rating Scale was administered 360 minutes after drug intake. ResultsUnder psilocybin, there were alterations across all dimensions of the 5-Dimensional Altered States of Consciousness Rating Scale and widespread increases in averaged brain functional connectivity. Time-varying functional connectivity analysis unveiled a recurrent hyperconnected pattern characterized by low blood oxygen level–dependent signal amplitude, suggesting heightened cortical arousal. In terms of neuroexperiential links, canonical correlation analysis showed higher transition probabilities to the hyperconnected pattern with feelings of oceanic boundlessness and secondly with visionary restructuralization. ConclusionsPsilocybin generates profound alterations at both the brain and the experiential levels. We suggest that the brain’s tendency to enter a hyperconnected-hyperarousal pattern under psilocybin represents the potential to entertain variant mental associations. These findings illuminate the intricate interplay between brain dynamics and subjective experience under psilocybin, thereby providing insights into the neurophysiology and neuroexperiential qualities of the psychedelic state.

Spatial-temporal evolution and influencing factors of ecological resilience in urban agglomerations: a case study of Shanxi section of the Yellow River Basin

Scientific assessment of the development status and factors influencing the urban ecological resilience of the Yellow River Basin (YRB) is highly significant for promoting its development. This study constructed an evaluation index system for urban ecological resilience considering the four dimensions of pressure, state, response, and innovation. The spatiotemporal ecological resilience of the urban agglomeration (UA) in the Shanxi section of the YRB from 2012 to 2021 was studied using kernel density estimation, Dagum Gini coefficient, and standard deviation ellipse, and the influencing factors of urban ecological resilience were analyzed using a geographic detector. This research revealed that (1) the ecological resilience of cities in the Shanxi section of the YRB experienced a fluctuation process of rise—fall—rise. The urban ecological resilience generally reflected a gradient decreasing spatial pattern of Central Shanxi UA > South Shanxi UA > North Shanxi UA, and gradually changed from the dual core of “Taiyuan—Jincheng City” to the single core of Taiyuan City. (2) The migration trajectory of urban ecological resilience center of gravity fluctuated in the direction of “northwest-southeast,” and moved 12.63 km to the southeast overall. (3) The water supply per ten thousand Yuan GDP, occupied area of construction land per ten thousand Yuan GDP, green coverage rate in built-up areas, ratio of research and experimental development funds (R&D) to GDP, proportion of science and technology expenditure in local fiscal expenditure, and patent licensing quantity index have a high influence on urban ecological toughness at all stages. This influence was further strengthened by the interaction between factors. This study provides an important scientific basis for shaping high-quality development advantages in the YRB and creating a resilient and livable environment.

Development and Democracy: Society, State, and Market in the Development Process

This article tries to analyze the relationship between development and democracy from the vantage point of prior well-informed and substantive participation of stakeholders in development processes. It focuses on the processes of prior well-informed and substantive participation of stakeholders that are followed (or not followed) in the land development projects. Land development projects focus on accumulating stakeholders’ odd- or irregular-sized non-serviced parcels of land, and re-arrange them in smaller plots with road, drainage, open space, and other necessary infrastructure facilities. The issue of ‘development and democracy’ has been assessed from the dimensions of society, state, and market relations. Moreover, how the market is capitalizing on the situation created by the inadequate national policy as well as the ineffective roles of development agents have also been assessed.

Effect of ultrasound-guided bilateral superficial cervical plexus block versus perioperative intravenous lidocaine infusion on postoperative quality of recovery in patients undergoing thyroidectomy: A randomised double-blind comparative trial.

Recent studies have found that ultrasound-guided (USG) bilateral superficial cervical plexus block (BSCPB) and intravenous infusion of lidocaine (IVL) have the potential to improve the quality of postoperative recovery. This study aimed to investigate and compare their effects on postoperative quality of recovery in patients undergoing thyroidectomy. A total of 135 patients were randomised to Group N: BSCPB with 10 mL 0.75% ropivacaine on each side, Group L: intravenous lidocaine (1.5 mg/kg for 10 min, followed by 1.5 mg/kg/h) and Group C: intravenous saline combined with BSCPB saline. The primary objective was quality of recovery-40 (QoR-40). Other parameters compared were numeric rating pain scale (NRS) score, haemodynamic data, opioid dosage and incidence of adverse effects. Statistical analysis was performed using the one-way analysis of variance (ANOVA), the Kruskal-Wallis test and the Chi-square test. Compared to Group C, both groups N and L had higher QoR-40 total scores as well as scores indicating physical comfort, emotional state and pain dimensions on postoperative day (POD) 1 and POD2 (P < 0.001). The QoR-40 total and pain dimension scores in Group N were higher on POD1 and POD2 (P < 0.05). The NRS scores and the change in haemodynamics were lower in Group N compared to groups L and C (P < 0.05). The results of other parameters were lower in groups N and L than in Group C (P < 0.05). USG BSCPB and IVL are comparable in improving the quality of postoperative recovery in patients undergoing thyroidectomy.

Dictionary-based online-adaptive structure-preserving model order reduction for parametric Hamiltonian systems

Classical model order reduction (MOR) for parametric problems may become computationally inefficient due to large sizes of the required projection bases, especially for problems with slowly decaying Kolmogorov n-widths. Additionally, Hamiltonian structure of dynamical systems may be available and should be preserved during the reduction. In the current presentation, we address these two aspects by proposing a corresponding dictionary-based, online-adaptive MOR approach. The method requires dictionaries for the state-variable, non-linearities, and discrete empirical interpolation (DEIM) points. During the online simulation, local basis extensions/simplifications are performed in an online-efficient way, i.e., the runtime complexity of basis modifications and online simulation of the reduced models do not depend on the full state dimension. Experiments on a linear wave equation and a non-linear Sine-Gordon example demonstrate the efficiency of the approach.

Global inverse optimality for a class of recurrent neural networks with multiple proportional delays

This paper formulates two novel theoretical designs of input-to-state stabilizing control for a class of recurrent neural networks with multiple proportional delays. The analysis tool developed in this paper is based on Lyapunov function and inverse optimality method, which does not require solving Hamilton-Jacobi-Bellman equations. Two inverse optimal feedback laws are constructed via the dimensions of state and input, which ensure the input-state stability for the considered system. When the dimensions of state and input are different, we establish a scalar function and give one of the control laws by Sontag's formula. Furthermore, the designs of inverse optimal control reach both global inverse optimality and global asymptotic stability of the system for some meaningful cost functional. Four numerical examples are provided to show the effectiveness of the inverse optimal control.

Prescribed time bipartite output consensus tracking for heterogeneous multi-agent systems with external disturbances

This paper investigates the bipartite output consensus tracking problem for disturbed agent networks where the agents have different state dimensions. A prescribed time robust coordination control scheme is proposed, which consists of a leader-state observer and an output-regulation-based robust controller that both converge within a preset time. Time-scaling functions with preset parameters T1 and T2 respectively take effect in the observer and the controller, and by computing the output regulation matrix which turns the output errors of followers tracking the heterogeneous leader into their state errors, the model-mismatched agents could reach bipartite output tracking consensus within a total duration of T1+T2. Moreover, neural networks are deployed to estimate and eliminate the unknown disturbances. Thorough Lyapunov-based derivations approve the preset-time convergence and robustness for the controller. Numerical simulations are performed to verify the efficacy of the proposed control scheme.

Water resource security evaluation and barrier analysis in Henan Province utilizing the DPSIR framework

Water resource health is one of the necessary conditions for society to achieve sustainable development. Due to the predominant focus of most studies on relatively short time spans, with limited attention to long time series and spatial trends, this study, using various regions of Henan Province as a case study, constructs a water resource security assessment framework based on the DPSIR model encompassing Drivers (D), Pressures (P), State (S), Impact (I), and Response (R) dimensions, with a selection of 19 evaluation indicators. Based on this evaluation index system, the CRITIC-TOPSIS evaluation method is formulated by integrating the CRITIC (Criteria Importance Through Intercriteria Correlation) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) models. This method is employed to assess the degree of water resource security in Henan Province from 2013 to 2022. And the Obstruction Degree Model is introduced to diagnose the water resource security levels in various regions of Henan Province. The assessment results indicate that over the past decade, the overall level of water resource security in various regions of Henan Province has shown an increasing trend. Irrigated area, per capita water resources, water consumption per unit of industrial value added, per acre water consumption for agricultural irrigation, the ratio of river length meeting water quality standards, groundwater supply proportion, and sewage treatment rate are identified as the primary obstacles influencing the water resource security levels in different regions of Henan Province. The research outcomes of this study can serve as theoretical foundations to enhance urban water resource security globally, ultimately facilitating sustainable development.

Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Monitoring the correctness of distributed cyber-physical systems is essential. Detecting possible safety violations can be hard when some samples are uncertain or missing. We monitor here black-box cyber-physical system, with logs being uncertain both in the state and timestamp dimensions: that is, not only the logged value is known with some uncertainty, but the time at which the log was made is uncertain too. In addition, we make use of an over-approximated yet expressive model, given by a non-linear extension of dynamical systems. Given an offline log, our approach is able to monitor the log against safety specifications with a limited number of false alarms. As a second contribution, we show that our approach can be used online to minimize the number of sample triggers, with the aim at energetic efficiency. We apply our approach to three benchmarks, an anesthesia model, an adaptive cruise controller and an aircraft orbiting system.

Enhancing urban flood resilience: A coupling coordinated evaluation and geographical factor analysis under SES-PSR framework

Urban flooding has emerged as a significant urban issue in cities worldwide, with China being particularly affected. To effectively manage and mitigate urban floods, a holistic examination of the interaction between urban subsystems is required to improve urban flood resilience. However, the interactions and mechanisms between urban subsystems under flood disaster haven't been addressed adequately in previous studies. Therefore, this paper established a conceptual framework for illustrating the interactions between urban natural-ecological and social-economic subsystem considering urban pressure, state, and response within flood cycle. The objective is to investigate the coupling coordination degree (CCD) between these subsystems and identify the driving factors with a geographical detector model, and the cities in Yangtze River Delta are selected as an empirical example. The findings reveal an overall upward trend towards coordination for the whole area with notable variability among the cities. The resilience of the state dimension emerges as a crucial aspect in determining the CCD of the urban flood resilience of the area. Key driving factors for the coordinated development of urban flood resilience are identified as air pollution, global warming, technological innovation, governance power, financial strength, and urbanization. Based on the findings and the interactions among the driving factors, this paper presents potential implications that can serve as effective guidance and offer insights for policymakers, planners, and researchers in their efforts to enhance urban flood resilience for sustainable development in the future.