Internal State Research Articles

Tailoring the Electronic Structures and Spectral Properties of ZnO with Irradiation Defects Generated Under Intense Electronic Excitation: A Combined Experimental and DFT Approach

AbstractThe relationship between the composition of the internal defect states, spectral properties, and correlated electronic structures of wurtzite zinc oxide (ZnO) crystals under 645 MeV Xe35+ irradiation is systematically investigated, employing experimental characterizations combined with first‐principle calculations. Based on the ion irradiation‐induced thermal expansion and relaxation processes, the high concentration of vacancy/interstitial defects produced from the transient disordered phase in molten track states trigger photoelectric changes, as follows: i) the generation of internal defect states effectively reduces the intrinsic bandgap (3.25 eV → 2.66 eV); ii) a large number of defective active sites inhibits the recombination between electron–hole pairs, causing dark conductance and photoconductance to increase with increasing damage levels until optimal fluence is achieved. Based on the density functional theory (DFT) with the GGA + U (GGA = generalized gradient approximation) method, the defective models associated with the different electronic structures, density of states, formation energy, and the nature of the chemical bonding are established. The narrowing of the bandgap observed experimentally and the enhancement of carrier concentration originating from the internal electron defect states are qualitatively verified, therefore laying the foundation for designing future nanoscale photoelectronic devices and microelectronics applications.

Genetic Underpinnings of Cuticular Hydrocarbon Biosynthesis in the German Cockroach, Blattella germanica (L.): Progress and Perspectives.

Insect cuticular hydrocarbons (CHCs) serve as important waterproofing barriers and as signals and cues in chemical communication. Over the past 30 years, numerous studies on CHCs have been conducted in the German cockroach, Blattella germanica, leading to substantial progress in the field. However, there has not been a systematic review of CHC studies in this species in recent years. This review aims to provide a concise overview of the chemical composition, storage, transport, and physical properties of different CHCs in B. germanica. Additionally, we focus on the biosynthetic pathway and the genetic regulation of HC biosynthesis in this species. A considerable amount of biochemical evidence regarding the biosynthetic pathway of insect CHCs has been gathered from studies conducted in B. germanica. In recent years, there has also been an improved understanding of the molecular mechanisms that underlie CHC production in this insect. In this article, we summarize the biosynthesis of different classes of CHCs in B. germanica. Then, we review CHCs reaction to various environmental conditions and stressors and internal physiological states. Additionally, we review a body of work showing that in B. germanica, CHC profiles exhibit significant sexual dimorphism, specific CHCs act as essential precursors for female contact sex pheromone components, and we summarize the molecular regulatory mechanisms that underlie sexual dimorphism of CHC profiles. Finally, we highlight future directions and challenges in research on the biosynthesis and regulatory mechanisms of CHCs in B. germanica, and also identify potential applications of CHC studies in the pest control.

Development of an Artificial Intelligence-Based Predictive Anomaly Detection System to Nuclear Power Plant

Abstract In a large-scale plant such as a nuclear power plant, thousands of process values are measured for the purpose of monitoring the plant performance and the system health. It is difficult for plant operators to constantly monitor all of the process values. We present a data-driven method to comprehensively monitor a large number of process values and detect early signs of anomalies, including unknown events, with few false positives. In order to learn the complex changing internal state of a nuclear power plant and accurately predict the normal process values, we have developed a two-stage auto-encoder (TSAE), a type of neural network, composed of a time window auto-encoder and a deviation auto-encoder. TSAE realizes to detect anomalous signals during the plant transient conditions by collecting time-series data and learning the nonlinear temporal correlation among them. In the actual plant, some process values which are physically uncorrelated with each other happen to behave similarly (pseudo-correlation). Learning the pseudo-correlation by the algorithm causes false positives because the predicted values of unrelated process values are incorrectly correlated. Therefore, Toshiba has proposed the model classification concept of separating the process values into two groups based on physical correlation and applied a model structure of TSAE. As a result, it becomes possible to learn only with the process values that are physically correlated and enhance the performance of prediction/detection. We assessed the improved TSAE with simulated process values of a nuclear power plant and showed excellent performances with early detection and few false positives.

Predicting transformer temperature field based on physics‐informed neural networks

AbstractThe safe operation of oil‐immersed transformers is critical to the safety and stability of the power grid. As the operating time increases, the failure rate of oil‐immersed transformers shows an increasing trend, posing serious challenges to safe operation. It is necessary to investigate the internal state of the oil‐immersed transformer to improve the digital degree and achieve digitalisation and intelligent operation and maintenance. A physics‐informed neural network (PINN) for oil‐immersed transformers was introduced to reconstruct the temperature distribution inside the transformer. According to the approach, the loss function of the network would be optimised by incorporating physical constraint loss terms including heat transfer equations, initial conditions and boundary conditions. The results show that the method proposed can be used to reconstruct and predict the temperature field of transformers in a few seconds with satisfactory accuracy. In conclusion, the PINN proposed outperforms deep neural networks in terms of accuracy, reliability and interpretability, especially in data‐poor cases.

Visual SLAM for Unmanned Aerial Vehicles: Localization and Perception

Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual–inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Hysteresis, Impedance, and Transients Effects in Halide Perovskite Solar Cells and Memory Devices Analysis by Neuron‐Style Models

Hysteresis, Impedance, and Transients Effects in Halide Perovskite Solar Cells and Memory Devices Analysis by Neuron‐Style Models

Proactive Language Learning Theory

AbstractSecond language acquisition theory has traditionally focused on the cognitive and psycholinguistic processes involved in additional language (L2) learning. In addition, research on learner psychology has primarily centered on learners’ cognitive abilities (e.g., aptitude and working memory) and internal traits or states (e.g., dispositions, motivations, and affect). Language learning behavior, however, has remained largely neglected and under‐theorized. To address this gap, this paper proposes the proactive language learning theory, which delineates the agentic and strategic behaviors that learners employ to learn an additional language. These behaviors include input‐seeking behavior, interaction‐seeking behavior, information‐seeking behavior, and feedback‐seeking behavior. This paper presents theoretical arguments supporting the proposal, describes the four behavioral dimensions of the theory, and outlines general hypotheses concerning the contextual and learner‐related antecedents of these behaviors and their effects on L2 outcomes. Finally, the potential implications of this theory for advancing our understanding of L2 learning and instruction are discussed.

Algebraic side‐channel attacks on Trivium stream cipher

AbstractAlgebraic Side‐Channel Attacks (ASCAs), first proposed by Renauld and Standaert in 2009, are a potent cryptanalysis method against block ciphers. In this paper, the authors initially utilize ASCAs to analyze the security of the Trivium stream cipher, given its concise algebraic structure. Considering its efficiency in both hardware and software implementations, the authors deploy ASCAs to target Trivium implemented both in application specific integrated circuit (ASIC) under the Hamming Distance Leakage Model (HDLM) (noted as CASE 1) and in microcontrollers of various buses (i.e. some common 8‐bit, 16‐bit, and 32‐bit architectures, noted as CASE 2, CASE 3, and CASE 4, respectively) under the Hamming Weight Leakage Model (HWLM). Here, the authors’ attacks are conducted on power‐simulated targets and not on real devices. For a single power consumption trace without measurement errors, this paper presents experimental results using MiniSat 2.0. Unfortunately, the authors were unable to break the ASIC implementation of Trivium under HDLM (CASE 1) with a time complexity of 2109 s or so, which is worse than the exhaustive key attack. For CASEs 2 to 4, the authors can find the complete 288‐bit state of Trivium within a reasonable timeframe. Specially, the success rate can reach 100% with an average solving time of less than 1 s when only measuring the leakages of the first eight consecutive rounds for CASE 2. Furthermore, the authors can still successfully recover the internal state even when obtaining leakages of the first 41 rounds with a random loss rate. In fact, it can tolerate a 74% random loss rate for the first 223 rounds. With regard to the potential errors in the measurements, the authors mitigate them using Tolerant ASCA (TASCA). Similarly, CASE 1 cannot be compromised even in error‐free situations, while the authors can still successfully recover the internal state of CASEs 2 to 4 from a single power trace, even with a high error rate, including 100% incorrect measurements. Surprisingly, for CASEs 2 to 4, the authors can recover the internal state with a 100% success rate, regardless of the error rate. As a result, the security of Trivium will not be enhanced when transitioning from a smaller 8‐bit platform to a larger 32‐bit platform. In the end, the authors will consider some more abstract attack models. The results can provide us with additional insights into the security of Trivium from a different perspective.

Rethinking core affect: the role of dominance in animal behaviour and welfare research

Rethinking core affect: the role of dominance in animal behaviour and welfare research

Emergence of integrated behaviors through direct optimization for homeostasis

Homeostasis is a self-regulatory process, wherein an organism maintains a specific internal physiological state. Homeostatic reinforcement learning (RL) is a framework recently proposed in computational neuroscience to explain animal behavior. Homeostatic RL organizes the behaviors of autonomous embodied agents according to the demands of the internal dynamics of their bodies, coupled with the external environment. Thus, it provides a basis for real-world autonomous agents, such as robots, to continually acquire and learn integrated behaviors for survival. However, prior studies have generally explored problems pertaining to limited size, as the agent must handle observations of such coupled dynamics. To overcome this restriction, we developed an advanced method to realize scaled-up homeostatic RL using deep RL. Furthermore, several rewards for homeostasis have been proposed in the literature. We identified that the reward definition that uses the difference in drive function yields the best results. We created two benchmark environments for homeostasis and performed a behavioral analysis. The analysis showed that the trained agents in each environment changed their behavior based on their internal physiological states. Finally, we extended our method to address vision using deep convolutional neural networks. The analysis of a trained agent revealed that it has visual saliency rooted in the survival environment and internal representations resulting from multimodal input.

Conditioned preferences: Gated by experience, context, and endocrine systems

Central to the navigation of an ever-changing environment is the ability to form positive associations with places and conspecifics. The functions of location and social conditioned preferences are often studied independently, limiting our understanding of their interplay. Furthermore, a de-emphasis on natural functions of conditioned preferences has led to neurobiological interpretations separated from ecological context. By adopting a naturalistic and ethological perspective, we uncover complexities underlying the expression of conditioned preferences. Development of conditioned preferences is a combination of motivation, reward, associative learning, and context, including for social and spatial environments. Both social- and location-dependent reward-responsive behaviors and their conditioning rely on internal state-gating mechanisms that include neuroendocrine and hormone systems such as opioids, dopamine, testosterone, estradiol, and oxytocin. Such reinforced behavior emerges from mechanisms integrating past experience and current social and environmental conditions. Moreover, social context, environmental stimuli, and internal state gate and modulate motivation and learning via associative reward, shaping the conditioning process. We highlight research incorporating these concepts, focusing on the integration of social neuroendocrine mechanisms and behavioral conditioning. We explore three paradigms: 1) conditioned place preference, 2) conditioned social preference, and 3) social conditioned place preference. We highlight nonclassical species to emphasize the naturalistic applications of these conditioned preferences. To fully appreciate the complex integration of spatial and social information, future research must identify neural networks where endocrine systems exert influence on such behaviors. Such research promises to provide valuable insights into conditioned preferences within a broader naturalistic context.

Non-trivial relationship between behavioral avalanches and internal neuronal dynamics in a recurrent neural network.

Neuronal activity gives rise to behavior, and behavior influences neuronal dynamics, in a closed-loop control system. Is it possible then, to find a relationship between the statistical properties of behavior and neuronal dynamics? Measurements of neuronal activity and behavior have suggested a direct relationship between scale-free neuronal and behavioral dynamics. Yet, these studies captured only local dynamics in brain sub-networks. Here, we investigate the relationship between internal dynamics and output statistics in a mathematical model system where we have access to the dynamics of all network units. We train a recurrent neural network (RNN), initialized in a high-dimensional chaotic state, to sustain behavioral states for durations following a power-law distribution as observed experimentally. Changes in network connectivity due to training affect the internal dynamics of neuronal firings, leading to neuronal avalanche size distributions approximating power-laws over some ranges. Yet, randomizing the changes in network connectivity can leave these power-law features largely unaltered. Specifically, whereas neuronal avalanche duration distributions show some variations between RNNs with trained and randomized decoders, neuronal avalanche size distributions are invariant, in the total population and in output-correlated sub-populations. This is true independent of whether the randomized decoders preserve power-law distributed behavioral dynamics. This demonstrates that a one-to-one correspondence between the considered statistical features of behavior and neuronal dynamics cannot be established and their relationship is non-trivial. Our findings also indicate that statistical properties of the intrinsic dynamics may be preserved, even as the internal state responsible for generating the desired output dynamics is perturbed.

위기청소년이 지각하는 부모의 양육태도와 심리상태 간의 관계: 부산광역시 사례를 중심으로

The purpose of this study is to analyze the relationship between parents' parenting attitudes and internal psychological states such as self-esteem, self-efficacy, emotional regulation, depression, anxiety, and life satisfaction perceived by at-risk adolescents. For this purpose, 283 at-risk adolescents(165 females and 117 males) in Busan Metropolitan City were subjected to frequency analysis, descriptive statistics, correlation analysis, and multiple regression analysis. The main results were as follows. First, at-risk adolescents were found to have a positive perception of their parents' parenting attitudes overall, and their perceptions of self-esteem and self-efficacy were positive, while their perceptions of emotional regulation were somewhat negative. In addition, the perception of depression and anxiety was low, but life satisfaction was slightly higher than normal. Second, among parents' parenting attitudes, warmth and autonomy support were found to have a positive relationship with self-esteem, self-efficacy, emotional regulation, and life satisfaction of at-risk adolescents, and were found to have a negative relationship with depression and anxiety. In addition, rejection and coercion among parents' parenting attitudes were found to have a negative relationship with self-esteem, self-efficacy, emotional regulation, and life satisfaction of at-risk adolescents, and were found to have a negative relationship with depression and anxiety. Third, it was found that warmth among parents' parenting attitudes had a positive effect on self-esteem and life satisfaction of at-risk adolescents, and had a negative effect on depression. Coercion among parents' parenting attitudes was also found to have a positive effect on at-risk adolescents' self-esteem, and rejection among parents' parenting attitudes was found to have a negative effect on at-risk adolescents' self-efficacy and life satisfaction, and had a static effect on anxiety. On the other hand, it was found that parents' parenting attitude did not have a statistically significant effect on the emotional regulation of at-risk adolescents. Based on the main results, implications for parent education were presented.

Don't Flip the Table Yet: A Framework for the Analysis of Visual and Cognitive Ergonomics in Board Games

Board games provide immersive and enjoyable experiences for players, but they can also pose usability challenges. This article presents a comprehensive framework for analyzing board game design, focusing on identifying the elements that make play inadvertently difficult or fatiguing. The proposed framework analyses board games as a user interface between the players and the board game mechanics, thereby focusing on the presentation aspects without altering the underlying rules. The intent is to preserve the deliberate design challenges that enhance gameplay while pinpointing the elements that affect the game experience negatively. We employ a methodology that begins with the observation of players’ behavioral cues to infer internal states such as cognitive or visual fatigue. These inferred states serve as a basis to identify problematic features in the game components or presentation. We illustrate the framework through a series of case studies, providing practical examples of the analysis process through an idiographic approach.

A Method of Reliability Assessment Based on Trend Analysis for Open Source Software

Software reliability growth model (SRGM) is used as one of the reliability assessment methods to assess the software reliability. In SRGM, the degree of reliability growth may fluctuate greatly according to change in the internal state of the software. It is called the change point (CP). Several researchers proposed the SRGM considering CP. In the open source software (OSS), there are many projects that continue development even after the software is released. Therefore, major updates with breaking changes may occur in it. The major updates can be a factor that causes a CP because it greatly changes the internal state of the OSS. This paper focuses on the relationship between CP and software updates. We collect OSS fault data from a bug tracking system. Moreover, we examine the behavior of SRGM before and after software updates. Furthermore, we discuss the applicability of SRGM for CP in OSS. Also, we compare the proposed model based on CP with the model without CP. As a result, we have confirmed that the SRGM can evaluate the reliability in the environment with major updates. Moreover, the proposed method performs better than without considering CP model. Especially, the exponential model’s mean value function is the suitable method to assess the OSS reliability for the proposed method.

A Method for State of Charge and State of Health Estimation of LithiumBatteries Based on an Adaptive Weighting Unscented Kalman Filter

This paper considers the estimation of SOC and SOH for lithium batteries using multi-innovation Levenberg–Marquardt and adaptive weighting unscented Kalman filter algorithms. For parameter identification, the second-order derivative of the objective function to optimize the traditional gradient descent algorithm is used. For SOC estimation, an adaptive weighting unscented Kalman filter algorithm is proposed to deal with the nonlinear update problem of the mean and covariance, which can substantially improve the estimation accuracy of the internal state of the lithium battery. Compared with fixed weights in the traditional unscented Kalman filtering algorithm, this algorithm adaptively adjusts the weights according to the state and measured values to improve the state estimation update accuracy. Finally, according to simulations, the errors of this algorithm are all lower than 1.63 %, which confirms the effectiveness of this algorithm.

Microscopic investigation and local standard deviation analysis of liquid propane jets in supercritical environment

To improve the thermal efficiency of engines, the temperature and pressure in the combustion chamber are continually increased, potentially reaching the critical points of the fuels. Additionally, light fuels such as liquid ammonia and propane are utilized to reduce carbon emissions. The relatively low critical temperature of light fuels facilitates their transition into a supercritical state, which greatly change the jet characteristics. To gain a deeper understanding of the supercritical transition of fuels, jets of liquid propane are investigated through high-speed microscopic imaging technology, covering subcritical to supercritical environmental conditions. Liquid propane is injected through a transparent flat nozzle to observe the internal flow states within the nozzle. For image and data processing, local standard deviation (LSD) analysis and a Residual Network (ResNet-50) model are employed to identify the structures within the mixing region. Droplets counting and frequency domain analysis are also performed to reveal the features of supercritical transition. The findings highlight the importance of mask size in the jet feature extraction. Large mask highlights large scale structures including jet surface and mixing region structures, while small mask brings about full-scale feature including droplets and all other features. A criterion for mixing regime classification based on reduced temperature (Tr) and reduced pressure (Pr) is established directly from image data using the ResNet-50 model, where a condition of Tr1.6Pr0.4 ≥ 1.067 indicates a transcritical mixing regime. Both droplet counting and frequency domain analysis corroborate this mixing regime criterion. These insights enhance our understanding of supercritical transitions and are useful for simulation verifications.

Analysis of the Dynamics of a Cubic Nonlinear Five-Dimensional Memristive Chaotic System and the Study of Reduced-Dimensional Synchronous Masking

To improve the complexity of the chaotic system and achieve the effective transmission of image information, in this paper, a five-dimensional memristive chaotic system with cubic nonlinear terms is constructed, which has four pairs of symmetric coordinates. First, the cubic nonlinear memristive chaotic system is analyzed using the Lyapunov exponential map, bifurcation map, and attractor phase diagram. The experimental results show that under four pairs of symmetric coordinates, the system exists not only parameter-dependent symmetric rotational coexisting attractor and transient chaotic phenomena but also exists super-multistationary with alternating chaotic cycles dependent on the initial value of the memristor. Then, it is proposed to add a constant term to the linear state variable to explore the effect of the offset increment of the linear state variable on the system in four pairs of symmetric coordinates, while circuit simulation of the five-dimensional chaotic system is carried out using Simulink to verify its existence and realisability. Finally, the synchronization of the dimensionality reduction system and the confidential transmission of the image are achieved, using the control voltage of the system to replace the internal state variables of the memristor to achieve the one-dimensional reduction process, and an adaptive synchronization controller is designed to synchronize the system before and after the dimensionality reduction. Based on the above, an image to be transmitted is modulated into a one-dimensional array and then subjected to the fractional and cyclic operations and combined with the linear encryption and decryption functions and the chaotic masking technique, the simple encryption and decryption of the image processes are realized.

Construction alignment and closure control of CFST truss arch bridges based on temperature effect

To reveal the temperature deformation laws and achieve the accurate alignment prediction during the installation process of long-span concrete-filled steel tubular arch bridge ribs in complex environments, this paper establishes a three-dimensional refined heat transfer model for temperature field and effect calculation of truss arch ribs. The model uses the Ray Tracing Technology to recognize the member-caused-shading area on arch ribs. And by comparing with the actual measurement results and shadow effects to achieve the accurate calculation of temperature field. Finally, the variation of ribs deformation during construction and the optimal closure time are detailed analyzed. Results show that the computational method used in this paper can achieve accurate recognition of shadows and precise calculation of the complex three-dimensional temperature field of truss arch ribs. After considering the shadows, the maximum temperature difference between the upper chord sunshine area and lower chord shadow areas can reach 25 °C, which is significantly greater than 8 ℃ in the "Specifications for design of highway concrete-filled steel tubular arch bridge" (JTG/T D65–06-2015). Due to the solar incidence variation along the longitudinal direction of the arch ribs, extreme temperature fluctuation of the chord tubes occurs, with a maximum temperature difference of 21 °C. When arch ribs reach the maximum cantilever, the daily vertical displacement variation can reach approximately 87 mm, and the maximum displacement difference between arch ribs of two canyon sides can reach 15.6 mm at the same time. The solar-caused maximum compressive stress is 34 MPa, which can exceed 100 MPa after superimposing the gravity and cable effects. Based on the analysis of the displacement of both sides of the closure joint under the influence of sunlight, this paper identifies the window period for the closure control of the long-span steel truss arch ribs, laying the foundation for the accurate control of the rib construction alignment and the closure internal force state.

Making sense: towards a unified theory of art psychotherapy through the formulation of an aesthetic grid

ABSTRACT In this paper, I discuss how ‘sense’ is made in art therapy regarding three uses of the term: that of ‘meaning’, ‘somatic experience’ and the capacity for ‘sensing’. I suggest that the patient in an art therapy session produces actions and artefacts which come into contact with the mind of the therapist/group. This allows for a dream to be generated between the creator and the observer, that the artwork can tell us something about internal mental states within the creator. I propose a theory of observation by application of an Aesthetic Grid – an adaptation of Bion’s Grid of genetic epistemology. This is an attempt to bring together ‘three waves of art therapy theory and practice’, and incorporates the function of the art object, as well as the phases of art making and looking/talking. The Aesthetic Grid is not intended for use in sessions but rather to be utilized after the event, in order to reflect upon the status of the ‘art-psychotherapy object’ - the totality of the triangular relationship, creator-artwork-viewer, and whether this constitutes development and growth or its inverse.