Discrete Behavior Research Articles

A multi-objective model predictive current control with two-step horizon for double-stage grid-connected inverter PEMFC system

The fuel cell has been regarded as one of the most promising renewable energy technologies for various applications such as distributed power generation, transportation, portable power source, and automobile. The output power of a fuel cell is affected by operational parameters such as cell temperature, oxygen partial pressure, and hydrogen partial pressure. This paper deal with a two-stage grid-connected PEMFC system. In the first stage, a fuzzy logic controller is proposed to track the maximum power generated by PEMFC by using a boost converter. In the second stage, a multi-objective FSC-MPC two-step prediction to control of 3L-NPC is proposed. The use of Finite Control Set Model Predictive Control (FSC-MPC) can significantly improve the control performance of a three-level NPC (3L-NPC) inverter. Furthermore, this method uses the inverter's discrete behavior to find optimal switching states that minimize the cost function. The suggested model predictive control method for the 3L-NPC inverter is based on a multi-objective cost function that is meant to regulate inverter currents, dc-link voltage balance, and minimize the number of switch states. The performance of the proposed MO–FSC–MPCTS controlled 3L-NPC inverter is simulated with MATLAB/Simulink. The results show that the suggested method ensures MPP tracking and injecting the current into the grid with a 2% THD.

Effect of chemical strengthening residual stress on the flexural performance and fracture behavior of aluminosilicate glass

In this hybrid experimental, theoretical and numerical study the effect of chemical strengthening residual stress on the flexural strength and fracture behavior of aluminosilicate glass was investigated. Three-point bending tests were conducted on annealed and chemically strengthened glass specimens to obtain the flexural strength of both specimens and with the aid of high-speed cameras, the fracture and failure modes of these two kinds of specimens were analyzed and compared. A theoretical analysis based on the elastic fracture mechanics theory was employed to explain the strengthening effect of chemically strengthened glass. It is shown that the mechanical strength of chemically strengthened glass is highly related to the relation of the chemically strengthened layer depth and the surface flaw depth. Furthermore, this study provides an effective numerical approach to replicate the mechanical strength and failure modes for both annealed and chemically strengthened glass. The proposed finite element numerical method includes modeling techniques which consider the presence of pre-stress, random distributed surface flaws and the fracture onset in glass specimens. The residual stress pattern was defined exploiting the dynamic relaxation process for temperature-induced expansion of the elements before the loading process. Discrete distributed surface flaws were inserted in the model, the brittle fracture of glass was represented by the smeared crack method and the numerical results were compared to experimental data. It’s illustrated that the proposed method can be used to represent both the discrete fracture behavior and the chemically-strengthening effect of silicate glass.

Stability analysis method and application of multi-agent systems from the perspective of hybrid systems

Many multi-agent systems (MASs) can be regarded as hybrid systems that contain continuous variables and discrete events exhibiting both continuous and discrete behavior. An MAS can accomplish complex tasks through communication, coordination, and cooperation among different agents. The complex, adaptive and dynamic characteristics of MASs can affect their stability that is critical for MAS performance. In order to analyze the stability of MASs, we propose a stability analysis method based on invariant sets and Lyapunov’s stability theory. As a typical MAS, an unmanned ground vehicle formation is used to evaluate the proposed method. We design discrete modes and control polices for the MAS composed of unmanned ground vehicles to guarantee that the agents can cooperate with each other to reliably achieve a final assignment. Meanwhile, the stability analysis is given according to the definition of MAS stability. Simulation results illustrate the feasibility and effectiveness of the proposed method.

Service-oriented decomposition and verification of hybrid system models using feature models and contracts

• Simulink services to enable modular development and reuse. • Hybrid contracts as formal interface description. • Feature modeling to capture variability in Simulink services. • Compositional verification of hybrid systems modeled in Simulink. • Semi-automatic verification with KeYmaera X. The design of fault-free hybrid control systems, which combine discrete and continuous behavior, is a challenging task. Their hybrid behavior and further factors make their design and verification challenging: These systems can consist of multiple interacting services, and commonly used design languages, like MATLAB/Simulink do not directly allow for the verification of hybrid behavior. Furthermore, services can be reused in new system designs and sometimes require changes in their structure to fit to the new context. By providing hybrid contracts , which formally define the interface behavior of hybrid system services in differential dynamic logic ( d L ), and providing a decomposition technique, we enable compositional verification of Simulink models with interacting services. This enables us to use the interactive theorem prover KeYmaera X to prove the correctness of hybrid control systems modeled in Simulink. With the inclusion of feature modeling in the design process and the creation of hybrid contracts, we are able to capture a wider range of behavior, while still enabling the use of formal interface description in the verification. We demonstrate the applicability of our approach with a temperature control system and an automotive industrial case study.

Beyond Single Discrete Responses: An Integrative and Multidimensional Analysis of Behavioral Dynamics Assisted by Machine Learning.

Understanding behavioral systems as emergent systems comprising the environment and organism subsystems, include spatial dynamics as a primary dimension in natural settings. Nevertheless, under the standard approaches, the experimental analysis of behavior is based on the single response paradigm and the temporal distribution of discrete responses. Thus, the continuous analysis of spatial behavioral dynamics is a scarcely studied field. The technological advancements in computer vision have opened new methodological perspectives for the continuous sensing of spatial behavior. With the application of such advancements, recent studies suggest that there are multiple features embedded in the spatial dynamics of behavior, such as entropy, and that they are affected by programmed stimuli (e.g., schedules of reinforcement) at least as much as features related to discrete responses. Despite the progress, the characterization of behavioral systems is still segmented, and integrated data analysis and representations between discrete responses and continuous spatial behavior are exiguous in the experimental analysis of behavior. Machine learning advancements, such as t-distributed stochastic neighbor embedding and variable ranking, provide invaluable tools to crystallize an integrated approach for analyzing and representing multidimensional behavioral data. Under this rationale, the present work (1) proposes a multidisciplinary approach for the integrative and multilevel analysis of behavioral systems, (2) provides sensitive behavioral measures based on spatial dynamics and helpful data representations to study behavioral systems, and (3) reveals behavioral aspects usually ignored under the standard approaches in the experimental analysis of behavior. To exemplify and evaluate our approach, the spatial dynamics embedded in phenomena relevant to behavioral science, namely, water-seeking behavior and motivational operations, are examined, showing aspects of behavioral systems hidden until now.

Perturbing dimer interactions and allosteric communication modulates the immunosuppressive activity of human galectin-7

The design of allosteric modulators to control protein function is a key objective in drug discovery programs. Altering functionally essential allosteric residue networks provides unique protein family subtype specificity, minimizes unwanted off-target effects, and helps avert resistance acquisition typically plaguing drugs that target orthosteric sites. In this work, we used protein engineering and dimer interface mutations to positively and negatively modulate the immunosuppressive activity of the proapoptotic human galectin-7 (GAL-7). Using the PoPMuSiC and BeAtMuSiC algorithms, mutational sites and residue identity were computationally probed and predicted to either alter or stabilize the GAL-7 dimer interface. By designing a covalent disulfide bridge between protomers to control homodimer strength and stability, we demonstrate the importance of dimer interface perturbations on the allosteric network bridging the two opposite glycan-binding sites on GAL-7, resulting in control of induced apoptosis in Jurkat T cells. Molecular investigation of G16X GAL-7 variants using X-ray crystallography, biophysical, and computational characterization illuminates residues involved in dimer stability and allosteric communication, along with discrete long-range dynamic behaviors involving loops 1, 3, and 5. We show that perturbing the protein–protein interface between GAL-7 protomers can modulate its biological function, even when the overall structure and ligand-binding affinity remains unaltered. This study highlights new avenues for the design of galectin-specific modulators influencing both glycan-dependent and glycan-independent interactions.

Information behavior patterns: A new theoretical perspective from an empirical study of naturalistic information acquisition

AbstractThis empirical study offers a new theoretical perspective in information behavior research by identifying interrelationships between certain information behaviors. While previous work recognizes the iterative nature of information acquisition, information behavior research has so far been dominated by the identification and conceptual elaboration of discrete behaviors. We introduce the theoretical concept of “information behavior patterns” to characterize the intricate connectedness of information interaction in an arts and crafts context. A qualitative study comprising naturalistic observation and semi‐structured interviews with 20 arts and crafts hobbyists was conducted in two “browse‐first” information environments that support various forms of active and passive information acquisition: Pinterest and a brick‐and‐mortar crafts store. Findings revealed a variety of information behavior patterns across both environments. We illustrate several of these through in‐depth discussions of two specific information acquisition sessions. We visualize observed patterns from these sessions to illustrate the interweaving of active, passive acquisition, and personal goals. Our findings demonstrate the complex interconnectedness of human information behavior, highlighting the importance of going beyond compartmentalizing behaviors into “buckets” when trying to understand the complex, dynamic, and evolving nature of information interaction.

The photon exhibits dualism, constant velocity and nonlocality: What do they have in common?

The photon is typically regarded as a unitary object that is both particle-discrete and wave-continuous. This is a paradoxical position and we live with it by making dualism a fundamental feature of radiation. It is argued here that the photon is not unitary; rather it has two identities, one supporting discrete behavior and the other supporting continuous (wave) behavior. There is photon kinetic energy that is always discrete/localized on arrival; it never splits (on half-silvered mirrors) or diffracts (in pinholes or slits). Then there is the photon’s probability wavefront that is continuous and diffractable. Acknowledging that the photon has two identities explains the photon’s dual nature. And wave-particle duality is central to quantum mechanics. Understanding it leads to new insights into the photon’s constant velocity and its entanglement with another photon.

An Evaluation of the Quality of Parent-Child Interactions in Vulnerable Families That Are Followed by Child Protective Services: A Latent Profile Analysis.

In the current study, an observational procedure, recorded in video, was used to evaluate the quality of parent-child interactions in a sample of vulnerable Portuguese families (n = 47) with school-aged children followed by Child Protective Services (CPS). The study sought to explore if the families presented different profiles of parent-child interaction quality, and to characterize such profiles in terms of discrete behaviors observed, parenting outcome variables, and families’ sociodemographic and CPS referral characteristics. The parent-child dyads took part in a 15 minutes structured task and parents completed self-report measures (affection, parenting behaviors, and stress). Discrete behaviors of parents and children during interactions were coded with a micro-analytic coding procedure. The global dimensions of the parents’ interactions were coded with a global rating system. A latent profile analysis, estimated with global dimensions, identified two subgroups, one subgroup in which parents displayed higher quality interactions (n = 12), and another subgroup in which parents displayed lower quality interactions (n = 35). Further analyses comparing the subgroups determined that the higher quality subgroup presented more positive behaviors, and the lower quality subgroup presented more negative behaviors during the interactions. No further differences or associations were found regarding the parenting outcome variables, and the families’ sociodemographic and CPS referral characteristics. The findings are in line with prior studies, suggesting that vulnerable families may frequently present depleted parent-child interactions. However, given the small sample size, future studies should replicate the described procedures and analyses in larger sample sizes.

Evaluating ultrasonic vocalizations as predictors of social behavior during group interaction

During social interaction, animals integrate sensory information—including auditory cues—to influence future behavioral decisions. For example, animals respond to distinct vocalizations with unique actions (Seyfarth et al., 1980). Mice produce ultrasonic vocalizations (USVs) associated with discrete behaviors (Sangiamo et al., 2020); however, it is unclear if USVs inform subsequent actions. To investigate the predictive power of USVs on behavior, while mice freely interacted (n = 11 groups, 2 males and 2 females per group), we continuously recorded video and audio data using a sound source localization system with an 8-channel microphone array to accurately assign vocalizations to individuals. Using machine learning-based approaches to categorize vocalizations based on acoustic properties and extract behaviors in which two mice play unique roles (actor or recipient), we found a significant interaction between particular social behaviors and preceding vocalizations. A permutation test indicated that, depending on the behavioral role of the vocalizer, specific vocalizations preceded dominant (courtship or aggressive) or submissive (avoidant) behaviors at above-chance levels. Additionally, though classifiers trained to distinguish behavioral role based on preceding vocal emission failed for some behaviors, they were successful for particular dominant behaviors. These results suggest a potentially predictive relationship between acoustic communication and subsequent behaviors.

Supervisory Controller Design for Power System Stability using Hybrid Automata Modeling

Power systems are categorized as nonlinear dynamical systems, and the importance and complexity associated with their stability have dramatically increased. Accordingly, the behavior of power systems can be characterized by interactions between continuous and discrete-event dynamics. This paper proposes a systematic approach to the design and analysis of a supervisory control scheme for power systems using the hybrid automata (HA) model. The proposed model for optimal controller application is derived, and the power system's overall behavior is modeled using HA to enhance its stability. Hybrid systems' formulation incorporates continuous dynamics as well as discrete switching behavior into a modeling and control framework, thus allowing a complete system description while crystallizing the concepts of safety into system design criteria. This study uses a power system HA model as a discrete event system (DES) plant and controller. In the proposed method, to present the hybrid model, the discrete events used include the presence and absence of disturbances and voltage control elements, fault, sudden load increase, capacitor bank, and under-load tap changer (ULTC) transformer. Voltage stability and control are investigated by the generators’ rotor angle, bus voltage, eigenvalues, and the stability theory of the switched linear systems. Applications in voltage control, stability, and dynamic service restoration are presented on two benchmark power systems with 12 discrete states. The simulation results reveal the effective performance of the proposed supervisory controller model to enhance voltage stability in power systems.

Rapid fabrication and interface structure of highly faceted epitaxial Ni-Au solid solution nanoparticles on sapphire

Supersaturated Ni-Au solid solution particles were synthesized by rapid solid-state dewetting of bilayer thin films deposited onto c-plane sapphire single-crystals. Rapid thermal annealing above the miscibility gap of the Ni-Au system followed by quenching to room temperature resulted in textured and faceted submicron-sized particles as a function of alloying content in the range of 0–28 at% Au. Morphologically, the observed kinetic crystal shapes are confined by close-packed planes; in addition, high-index facets are identified as a function of alloying content by TEM cross-sectioning and equilibrium crystal shape simulations. All samples exhibit a distinct 〈111〉 out-of-plane as well as in-plane texture along densely packed directions. Lattice parameters extracted from independent orthogonal X-ray and electron diffraction techniques prove the formation of a solid solution without tetragonal distortion imposed by the sapphire substrate. At the particle-substrate interface of highly alloyed particles segregation of Au atoms as well as dislocations in stand-off position are found. These observations are in-line with a semi-coherent interface, where Au segregation is triggered by the reduction of the overall strain energy due to: (i) a lower shear modulus on the particle side of the interface, (ii) the shifting of misfit dislocations in stand-off position further away from the stiffer substrate and (iii) a reduction of intrinsic misfit dislocation strain energy on the tensile side. In addition, the mechanical properties of pure and alloyed particles were characterized by in situ compression experiments in the SEM. Typical force-displacement data of defect-free single-crystals were obtained, reaching the theoretical strength of Ni for particles smaller than 400 nm. Alloying changes the mechanical response from an intermittent and discrete plastic flow behavior into a homogeneous deformation regime at large compressive strain.

Behavioral Correlates of Depression Among Adults with Visual Impairments

Introduction: This study sought to examine: (a) the associations between physical activity, sedentary time, and sleep duration, as discrete behaviors, with depression among adults with visual impairments; and (b) the impact of meeting none, one, two, or three of the guidelines for these behaviors on depression among adults with visual impairments. Materials: One hundred eighty-two ( Mage = 44.8) adults with visual impairments, recruited via email through two visual impairment organizations in the United States, completed the International Physical Activity Questionnaire–Short Form, a sleep duration question, the Major Depression Inventory, and a demographic questionnaire. Based on results from the questionnaires, dichotomous variables for meeting or not meeting physical activity, sleep, and sitting guidelines were created. Data were analyzed using three components: a descriptive analysis, Pearson product-moment correlation analyses, and hierarchical regression analyses. Results: Overall, 14.8% of participants were categorized as having some degree of depression. Meeting the sleep guideline was a significant negative predictor of depression scores in the hierarchical regression analyses. The number of guidelines met was a negative predictor for depression score controlling for other variables. Discussion: Adequate sleep, as well as meeting all three guidelines synergistically, was meaningful in influencing depression among this population. The current study’s results should prompt the continued examination of health-behaviors among adults with visual impairment using a more holistic 24-hour activity cycle framework. Implications for practitioners: This study supports the utilization of multi-behavioral interventions to reduce the risk of depression by enhancing physical activity and sleep, while reducing sitting time, among this population.

Continuous decision‐making for autonomous driving at intersections using deep deterministic policy gradient

Intersections have been identified as the most complex and accident-prone traffic scenarios on road. Making appropriate decisions at intersections for driving safety, efficiency, and comfort become a challenging task for autonomous vehicles (AVs). The existing research on AV decision-making at intersections either considers a single scenario only with discrete behaviour outputs or ignores the requirements for driving efficiency and comfort. To address these problems, this study proposed a deep reinforcement learning based continuous decision-making method to make AVs drive through intersections. The proposed method establishes an end-to-end decision-making framework by using a convolutional neural network to map the relationship between traffic images and vehicle operations. The interaction between the AV and other vehicles was modelled as a Markov decision process (MDP), and a deep deterministic policy gradient algorithm was employed to solve the MDP problem and obtain the optimal driving policy. The top three accident-prone crossing path crash scenarios at intersections were realized in CARLA to verify the effectiveness of the proposed method. The experimental results demonstrated that the developed method could provide effective policies to ensure driving safety and efficiency while considering driving comfort for autonomous driving at intersections in all the examined scenarios.

Flame spread predictions over linear discrete fuel arrays using an empirical B-number model and stagnation point flow

A stagnation-point flow model based in part on the mass transfer B-number was adapted to approximate the heat flux and time to ignition of dowel arrays subject to forced convection flame spread. Previous work on forced flow flame spread along a dowel array has characterized the existence of discretized flame behavior. In deployment-scale wildland fire models, small fuels are smaller than the grid resolution. Sub-grid modeling then relies on empirical relationships, such as Nusselt number–Reynolds number (Nu-Re) models, to estimate the fire spread. Current Nu-Re models utilized to calculate the heat flux and ignition times predict higher heat transfer and faster ignition times with increasing flow speeds. However, analysis of the flame spread revealed that the ignition time has a parabolic relationship with the flow speed, with the fastest ignition time occurring around the onset of the discrete flame behavior. Nu-Re models cannot capture the upward inflection of ignition times beyond the onset of the discrete flame spread, where the ignition times increase with greater flow speeds. A boundary layer solution to the plume heat flux impinging on the next unignited dowel was employed to develop a B-number empirical model. The B-number model captures the changes to mass and heat transfer induced by differences in the flow speed and array spacing. The proposed empirical B-number model together with heat flux approximation generates more accurate predictions for ignition times than the existing approach.

How to Be RAD: Repeated Acquisition Design Features that Enhance Internal and External Validity.

The Repeated Acquisition Design (RAD) is a type of single-case research design (SCRD) that involves repeated and rapid measurement of irreversible discrete skills or behaviors through pre-and postintervention probes across different sets of stimuli. Researchers interested in the study of learning in animals and humans have used the RAD because of its sensitivity to detect immediate changes in rate or accuracy. Despite its strengths, critics of the RAD have cautioned against its use due to reasonable threats to internal validity like pretest effects, history, and maturation. Furthermore, many methodologists and researchers have neglected the RAD in their SCRD standards (e.g., What Works Clearinghouse [WWC], 2020; Horner et al., 2005). Unless given guidance to address threats to internal validity, researchers may avoid the design altogether or continue to use a weak version of the RAD. Therefore, we propose a set of 15 quality RAD indicators, comprising foundational elements that should be present in all RAD studies and additional features that enhance causal inference and external validity. We review contemporary RAD use and describe how the additional features strengthen the rigor of RAD studies. We end the article with suggested guidelines for interpreting effects and the strength of the evidence generated by RAD studies. We invite researchers to use these initial guidelines as a jumping off point for a more RAD future.

Angry Women Are More Trusting: The Differential Effects of Perceived Social Distance on Trust Behavior.

Accumulating evidence suggests that anger can have a strong impact on discrete trust behaviors. However, the mechanisms underlying how anger influences trust are still unclear. Based on the appraisal tendency framework, we hypothesized that perceived social distance would positively mediate the effect of anger on trust, and that gender would moderate this mediation. In Study 1, a 2 (Anger vs. Control) × 2 (Men vs. Women) factorial design was used to investigate this hypothesis. Results supported our predictions that anger drove women, but not men, to perceive smaller social distance, and thus sent more money to their counterparts in a trust game as compared to controls. In Study 2, social distance was manipulated, and a 2 (Low social distance vs. Control) × 2 (Men vs. Women) factorial design was used to critically test the causal role of the mediator, namely to examine the effect of perceived social distance on trust. Results showed that women, but not men, sent more money to their counterparts in the low social distance condition than in the control condition. Results of both studies indicate that the high certainty, higher individual control, and approach motivation associated with anger could trigger optimistic risk assessment, and thus more trust toward others in women, via perceiving smaller social distance to others.

The multiple faces of the oxytocin and vasopressin systems in the brain.

Classically, hypothalamic neuroendocrine cells that synthesise oxytocin and vasopressin were categorised in two major cell types: the magnocellular and parvocellular neurones. It was assumed that magnocellular neurones project exclusively to the pituitary gland where they release oxytocin and vasopressin into the systemic circulation. The parvocellular neurones, on the other hand, project within the brain to regulate discrete brain circuitries and behaviours. Within the last few years, it has become evident that the classical view of these projections is outdated. It is now clear that oxytocin and vasopressin in the brain are released extrasynaptically from dendrites and from varicosities in distant axons. The peptides act principally to modulate information transfer through conventional synapses (such as glutamate synapses) by actions at respective receptors that may be preferentially localised to synaptic regions (on either side of the synapse) to alter the 'gain' of conventional synapses.

A Systematic Review of General Educator Behavior Management Training

The purpose of this review was to systematically analyze the literature on behavior management training for general educators (Pre–K-12). We identified 74 articles in which general educators were trained to implement a behavior management strategy. General educators were most commonly trained to implement behavior-specific praise ( n = 12), Class-Wide Function-Related Intervention Teams ( n = 8), or a multi-component intervention package (i.e., a student-level intervention that included a number of strategies; n = 21). The two most common training components were initial training provided in a one-on-one format ( n = 30) and the inclusion of ongoing coaching ( n = 29). Thirty-nine articles included measures of practitioner fidelity or discrete behaviors (e.g., behavior-specific praise) within the context of an experimental design. We evaluated methodological rigor and evidence of effectiveness of these 39 articles using What Works Clearinghouse standards. Eleven articles included group design studies, nine (81.82%) of which met standards with or without reservations, and four designs had promising evidence of effectiveness. Twenty-eight articles included a total of 49 single-case research designs, of which 27 designs (55.10%) met standards with or without reservations, and 23 designs provided moderate to strong evidence of effectiveness. Directions for future research and implications for the field are provided.

Distinct globus pallidus circuits regulate motor and cognitive functions

A recent article by Lilascharoen et al. identified two distinct pathways in the globus pallidus (GPe) that are associated with discrete behaviors. Dysfunctions in these pathways were shown to underlie Parkinsonian motor and cognitive deficits in mice, and selective manipulation of these circuits rescued locomotor deficits and improved behavioral flexibility.