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This study investigates the effects of incorporating halloysite nanotubes (HNTs) into polyetherimide (PEI) adhesive for carbon fiber/polyetherketoneketone (CF/PEKK) composite joints. The CF/PEKK substrate is fabricated through an oven consolidation process, while PEI adhesive films with varying HNTs loadings (0.5, 1 and 3 wt.%) are prepared using solvent casting with γ -butyrolactone. Homogeneous dispersion of HNTs in PEI is achieved through magnetic stirring and IR heating. The resulting film is hot pressed to form a 100 μ m thick film, and adhesive lap shear joint specimens are manufactured through oven consolidation. Morphology and dispersion of HNTs in PEI are analyzed using SEM and EDS, revealing favorable dispersion at 0.5 wt.% HNTs and aggregation at higher loadings. Differential scanning calorimetry (DSC) is employed to study the glass transition temperature (Tg) of PEI, showing decreased Tg for solvent-cast PEI and increased Tg with HNTs incorporation. This increase is attributed to the constrained mobility of polymer chains due to HNTs' influence. Lap shear strength (LSS) is evaluated, demonstrating enhancement with HNTs incorporation: 0.5 wt.% HNTs (+12.5%) > 1 wt.% HNTs (+8.33%) > Pure PEI > 3 wt.% HNTs (−12.5%). The study attributes this enhancement to HNTs' reinforcement and chemical interactions with PEKK. Aggregation tendencies at 3 wt.% HNTs lead to decreased LSS. SEM-EDS analysis of fractured specimens indicates a multifaceted bonding joint interface involving PEKK, PEI and HNTs. CF/PEKK composites show adhesive, cohesive and adherend failure modes, with adherend failure being dominant. Notably, fabric-like textures and plastic deformations in PEI with 0.5 wt.% HNTs contribute to enhanced LSS, while higher HNTs content leads to cohesive failure due to aggregation. This study provides insights into optimizing composite adhesive systems by harnessing nanotube reinforcement and controlling dispersion, thereby influencing interfacial strength. [ABSTRACT FROM AUTHOR]

Securing confidential data against unauthorized users leads to access control policies with the rapid progression of Internet of Things (IoT) devices. Because of high mobility subscribers, the dynamic IoT environment is subjected to high signaling overhead which remains a challenging issue to guarantee data dissemination to legitimate users. The group's key management schemes are the central mechanism to deal with dynamic environments. But they are centralized concepts that cause scalability issues and suffer in handling large numbers of subscribers. Therefore, this paper proposes a modified deep reinforcement oppositional wolf learning-based group key management (MDROWL-GKM) system to monitor the data obtained in IoT properly. It does not maximize the network traffic as well as computational overhead when a group member leaves or joins. With the inclusion of an opposition-based learning gray wolf optimization algorithm, the overload issue of the modified deep reinforcement method is eliminated and the performance is enhanced. The efficacy of the proposed MDROWL-GKM system is investigated using different measures namely storage overhead, computation overhead, access response time, space complexity, re-evaluation time, policy adjustment accuracy, and communication overhead. The experimental analysis proves that the proposed MDROWL-GKM system is superior to other state-of-the-art techniques, particularly with high policy adjustment accuracy (96%), less communication overhead (8 μs) and area under curve (AUC) rate (0.982). [ABSTRACT FROM AUTHOR]

Drugs like opioids are potent reinforcers thought to co-opt value-based decisions by overshadowing other rewarding outcomes, but how this happens at a neurocomputational level remains elusive. Range adaptation is a canonical process of fine-tuning representations of value based on reward context. Here, we tested whether recent opioid exposure impacts range adaptation in opioid use disorder, potentially explaining why shifting decision making away from drug taking during this vulnerable period is so difficult. Participants who had recently (<90 days) used opioids (n = 34) or who had abstained from opioid use for ≥ 90 days (n = 20) and comparison control participants (n = 44) completed a reinforcement learning task designed to induce robust contextual modulation of value. Two models were used to assess the latent process that participants engaged while making their decisions: 1) a Range model that dynamically tracks context and 2) a standard Absolute model that assumes stationary, objective encoding of value. Control participants and ≥90-days-abstinent participants with opioid use disorder exhibited choice patterns consistent with range-adapted valuation. In contrast, participants with recent opioid use were more prone to learn and encode value on an absolute scale. Computational modeling confirmed the behavior of most control participants and ≥90-days-abstinent participants with opioid use disorder (75%), but a minority in the recent use group (38%), was better fit by the Range model than the Absolute model. Furthermore, the degree to which participants relied on range adaptation correlated with duration of continuous abstinence and subjective craving/withdrawal. Reduced context adaptation to available rewards could explain difficulty deciding about smaller (typically nondrug) rewards in the aftermath of drug exposure. [ABSTRACT FROM AUTHOR]

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China's green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes. [ABSTRACT FROM AUTHOR]

The optimal control issue of discrete‐time nonlinear unknown systems with time‐delay control input is the focus of this work. In order to reduce communication costs, a reinforcement learning‐based event‐triggered controller is proposed. By applying the proposed control method, closed‐loop system's asymptotic stability is demonstrated, and a maximum upper bound for the infinite‐horizon performance index can be calculated beforehand. The event‐triggered condition requires the next time state information. In an effort to forecast the next state and achieve optimal control, three neural networks (NNs) are introduced and used to approximate system state, value function, and optimal control. Additionally, a M NN is utilized to cope with the time‐delay term of control input. Moreover, taking the estimation errors of NNs into account, the uniformly ultimately boundedness of state and NNs weight estimation errors can be guaranteed. Ultimately, the validity of proposed approach is illustrated by simulations. [ABSTRACT FROM AUTHOR]

Summary: Sleep disturbances in children with epilepsy are prevalent, and are associated with substantial adverse medical and psychosocial consequences. This study is a 5‐year follow‐up of a clinic‐based sleep intervention study that randomized 100 toddlers and preschool‐age children with epilepsy to a usual care group or a sleep intervention group. The intervention group received standard paediatric neurology care plus three education sessions during the child's routine clinic visit. The outcomes measured were: (1) child sleep by actigraphy and parental report; and (2) maternal sleep and depression. We aimed to evaluate the long‐term benefits of a clinic‐based sleep intervention for paediatric epilepsy. In total, 42 families (42.0%) participated. The average child's age at follow‐up was 9.55 years. Thirty‐eight (90.5%) children were not obtaining sufficient sleep at baseline, and 40 (95.2%) at the 5‐year follow‐up. The numbers of children with clinically significant sleep disturbances were 40 (95.2%) at baseline and 36 (85.7%) at the 5‐year follow‐up. Fourteen mothers (33.3%) had poor sleep quality and high depressive symptoms at both assessment time points. There were no differences (P > 0.05) in the child and maternal outcomes between the two trial arms. Overall, there was no evidence that a clinic‐based sleep intervention that effectively improved multiple aspects of sleep in toddlers and preschool‐age children with epilepsy had long‐lasting beneficial effects. Our findings suggest that sleep interventions for families of children with epilepsy require ongoing reinforcement and monitoring during routine paediatric neurology care to prevent sleep problems from persisting or recurring. [ABSTRACT FROM AUTHOR]

Efficient methods for assessing the relative aversiveness of stimuli are sparse and underresearched. Having access to efficient procedures that can identify aversive stimuli would benefit researchers and practitioners alike. Across three experiments, 13 participants helped to pilot, refine, and test two approaches to identifying negative reinforcers. The first experiment presented two conditions, one in which computerized button pressing started or stopped one of two recorded infant cries (or silence, when the control button was selected). Choices were presented either in a modified observing‐response procedure (i.e., simultaneous observing) or in a modified progressive‐ratio procedure (i.e., committed concurrent progressive ratio; CCPR). Results were favorable though not conclusive on their own. A second experiment, using more distinct stimuli (i.e., one likely aversive, one likely not aversive), replicated the first, and clearer results emerged. Finally, the third experiment tested the stimuli from the second experiment in a CCPR arrangement where sound was terminated contingent on responding and idiosyncratic negative reinforcement hierarchies emerged. The utility of these two procedures is discussed, and future work that addresses the limitations is outlined. [ABSTRACT FROM AUTHOR]

We developed and examined a laboratory preparation with adult humans that pits shorter term avoidance over longer term positive reinforcement and may serve as a useful laboratory functional analogue of problematic behavior. Participants were exposed to choices between (1) avoiding an aversive sound and acquiring no money or (2) listening to an aversive sound for a set duration and then receiving money. The first choice, avoiding an aversive sound and acquiring no money, was conceptualized as immediate negative reinforcement and no positive reinforcement, whereas the latter choice, listening to an aversive sound for a set duration and then receiving money, was conceptualized as a potential positive punisher paired with a larger later positive reinforcer. We manipulated the duration of the sound and the magnitude of money to identify the point at which individual participants' choices changed from avoiding the sound to choosing the sound plus money. As the sound duration increased, the choice of listening to the sound and receiving money decreased. Similar functions were observed with two different monetary magnitudes. The model has potential applicability to real‐world problems such as smoking, addiction, gambling, anxiety disorders, and other impulse control disorders. [ABSTRACT FROM AUTHOR]

Discrimination performance in perceptual choice tasks is known to reflect both sensory discriminability and nonsensory response bias. In the framework of signal detection theory, these aspects of discrimination performance are quantified through separate measures, sensitivity (d′) for sensory discriminability and decision criterion (c) for response bias. However, it is unknown how response bias (i.e., criterion) changes at the single‐trial level as a consequence of reinforcement history. We subjected rats to a two‐stimulus two‐response conditional discrimination task with auditory stimuli and induced response bias through unequal reinforcement probabilities for the two responses. We compared three signal‐detection‐theory‐based criterion learning models with respect to their ability to fit experimentally observed fluctuations of response bias on a trial‐by‐trial level. These models shift the criterion by a fixed step (1) after each reinforced response or (2) after each nonreinforced response or (3) after both. We find that all three models fail to capture essential aspects of the data. Prompted by the observation that steady‐state criterion values conformed well to a behavioral model of signal detection based on the generalized matching law, we constructed a trial‐based version of this model and find that it provides a superior account of response bias fluctuations under changing reinforcement contingencies. [ABSTRACT FROM AUTHOR]

The current study explored the influence of different levels of speaker coherence on rule following and speaker preference. In Experiment 1, rules provided by three different speakers were either 100% accurate, 0% accurate, or 50% accurate/inaccurate. Experiment 2 was similar to Experiment 1 except that the speaker's coherence was adjusted to 80% accurate, 20% accurate, and 50% accurate/inaccurate, respectively. Overall, participants tended to follow coherent speaker rules and avoid following incoherent speaker rules during training and testing phases. The results also indicated that following and not following rules provided by speakers may be generalizable to novel stimuli and maintained in the absence of differential reinforcement (i.e., in experimental test phases). Additionally, in a preference test, participants tended to prefer coherent over incoherent and partially coherent speakers. Furthermore, participants tended to prefer the relatively more incoherent speaker (i.e., 0% or 20% accurate) over the 50% accurate coherent speaker in both experiments. Finally, a comparison of the results of both experiments indicated that different levels of relational coherence affected the variability of rule‐following and speaker preference behaviors. These findings are discussed in the context of the complexities that appear to be involved in rule‐following behaviors and speaker preference. [ABSTRACT FROM AUTHOR]

The bolt-grouting reinforcement method can reduce water inflow and reinforce the surrounding rock in a tunnel. However, analytical theories on bolt-grouting reinforcements that consider nonlinear seepage in water-rich tunnels are few. This study proposes an elastic–brittle–plastic semi-analytical solution that considers nonlinear seepage for surrounding rockbolt grouting reinforcement under axisymmetric conditions based on Izbash's law, Terzaghi's effective stress principle, the Mohr–Coulomb criterion, and a plastic constitutive model in terms of incremental plastic strain. The results of the proposed semi-analytical solutions were consistent with those of the finite element model, and the effects of bolt grouting and other design parameters on the maximum displacement of the tunnel wall and radius of the plastic region are discussed. As the grouting circle thickness increases, the deformation and plastic region radius decrease. The rockbolt length should be more than 0.3 times the diameter of the tunnel to ensure its effectiveness in the absence of support pressure. The data calculated using the proposed model also suggest that blindly increasing the length of the rockbolt and grouting circle thickness is not beneficial. The increase in the tunnel diameter and impermeability pressure hinder the stress state control and deformation of the host rock. The empirical coefficient of Izbash's law increases the plastic region radius of the surrounding rock; however, an increase in the empirical coefficient could reduce the effective stress values. Highlights: A closed-form elastic solution for a cylindrical tunnel reinforced by both rockbolts and grouting under nonlinear seepage was proposed. Brittle-plastic semi-analytical solutions was obtained based the proposed elastic solution. The residual force of the rockbolt was considered in the brittle-plastic solution. A coefficient describing the degree of brittleness of the rock mass was introduced in the boundary conditions of the proposed model. [ABSTRACT FROM AUTHOR]

Label accuracy plays an important role in supervised hyperspectral image classification problems. However, labelling can be prone to various types of noise, mainly including human errors and incorrect pseudo-label predictions. Learning from datasets with noisy labels is a critical challenge in practice. To address this issue, we first propose a deep reinforcement learning method named HyperRefine to tackle the label noise problem for hyperspectral image classification. The method utilizes an agent to learn the accuracy of each sample label and determine its suitability to be used in training the classifier model. The agent is trained using a reinforcement signal based on the reward obtained from a small validation set that reflects performance on the target task, where the validation set is generated from the training set by a momentum-based recruiter. Compared to complex hand-crafted methods of label noise cleaning, HyperRefine is easy to implement, yet highly effective. The robustness of the model to different types of label noises is significantly improved by further combining a novel label refinement approach. Experiments on artificially added label noise and pseudo-label noise hyperspectral datasets show that the proposed method can achieve promising performance. [ABSTRACT FROM AUTHOR]

Persuasive gamified systems are effective tools for motivating behaviour change using various persuasive strategies. In line with the reinforcement theory, some persuasive gamified systems employ reward and punishment in their design to achieve the intended behavioural outcome. Research has argued both in favour and against using these strategies in behaviour change applications due to mixed results with respect to their effectiveness. However, there is a lack knowledge about how interventions using these strategies could motivate or demotivate behaviours. Therefore, this paper explores the mechanism through which Reward and Punishment motivate or demotivate behaviours with respect to their strengths and weaknesses. The results of large-scale exploratory studies (N = 1768) uncover important strengths and weaknesses that could facilitate or hinder the effectiveness of Reward and Punishment at motivating behaviour change. These include their ability to engage users and make behaviour fun, reinforce commitments to goals, and reveal some consequences of bad behaviour. We also compared the perceived effectiveness of reward and punishment quantitatively. [ABSTRACT FROM AUTHOR]

• Habit Reversal Therapy (HRT) is an effective behavioral intervention for reducing tics. • The hypothesized premonitory urge-tic cycle and its role in HRT lack support. • A dynamical systems framework can improve tests of the mechanism underlying HRT. • Findings were inconsistent with existing hypotheses regarding HRT change mechanism. • Tic suppression did not disrupt the urge-tic cycle consistently across participants. • Most participants did not experience urge reduction patterns consistent with habituation. Tic disorders are a class of neurodevelopmental disorders characterized by involuntary motor and/or vocal tics. It has been hypothesized that tics function to reduce aversive premonitory urges (i.e., negative reinforcement) and that suppression-based behavioral interventions such as habit reversal training (HRT) and exposure and response prevention (ERP) disrupt this process and facilitate urge reduction through habituation. However, previous findings regarding the negative reinforcement hypothesis and the effect of suppression on the urge-tic relationship have been inconsistent. The present study applied a dynamical systems framework and within-subject time-series autoregressive models to examine the temporal dynamics of urges and tics and assess whether their relationship changes over time. Eleven adults with tic disorders provided continuous urge ratings during separate conditions in which they were instructed to tic freely or to suppress tics. During the free-to-tic conditions, there was considerable heterogeneity across participants in whether and how the urge-tic relationship followed a pattern consistent with the automatic negative reinforcement hypothesis. Further, little evidence for within-session habituation was seen; tic suppression did not result in a reduction in premonitory urges for most participants. Analysis of broader urge change metrics did show significant disruption to the urge pattern during suppression, which has implications for the current biobehavioral model of tics. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia is associated with hypoactivation of reward sensitive brain areas during reward anticipation. However, it is unclear whether these neural functions are similarly impaired in other disorders with psychotic symptomatology or individuals with genetic liability for psychosis. If abnormalities in reward sensitive brain areas are shared across individuals with psychotic psychopathology and people with heightened genetic liability for psychosis, there may be a common neural basis for symptoms of diminished pleasure and motivation. Methods: We compared performance and neural activity in 123 people with a history of psychosis (PwP), 81 of their first-degree biological relatives, and 49 controls during a modified Monetary Incentive Delay task during fMRI. Results: PwP exhibited hypoactivation of the striatum and anterior insula (AI) during cueing of potential future rewards with each diagnostic group showing hypoactivations during reward anticipation compared to controls. Despite normative task performance, relatives demonstrated caudate activation intermediate between controls and PwP, nucleus accumbens activation more similar to PwP than controls, but putamen activation on par with controls. Across diagnostic groups of PwP there was less functional connectivity between bilateral caudate and several regions of the salience network (medial frontal gyrus, anterior cingulate, AI) during reward anticipation. Conclusions: Findings implicate less activation and connectivity in reward processing brain regions across a spectrum of disorders involving psychotic psychopathology. Specifically, aberrations in striatal and insular activity during reward anticipation seen in schizophrenia are partially shared with other forms of psychotic psychopathology and associated with genetic liability for psychosis. [ABSTRACT FROM AUTHOR]

The purpose of this research is to examine gender differences in promotion/prevention self‐regulatory focus, a dispositional motivational orientation with major implications for human functioning. First, a review of literature using social cognitive theory as a framework suggests that, driven by socialization processes, (1) women may on average be more prevention focused than men – meaning more vigilant to maintain a secure status quo, whereas (2) men may on average be more promotion focused than women – meaning more eager to advance to a better situation than their status quo. Second, we provide data to examine these possible gender differences in self‐regulatory focus with secondary analyses of (a) our own existing data on dispositional regulatory focus and of (b) a large scale, representative panel study (LISS Survey). The data suggest a highly consistent difference with women being more prevention focused than men, while the difference in promotion focus is much smaller and is only found in European samples. Auxiliary data suggest promotion‐focused women hold less traditional gender role beliefs as well as showing that regulatory focus partially explains examples of behavioural differences between men and women. The analysis of gender difference in regulatory focus sheds new light on gender differences and biases already known, and on regulatory focus, and as such opens up many new and important areas of future inquiry. [ABSTRACT FROM AUTHOR]

Background and aims: Despite the high prevalence and negative effects of hazardous substance use, few young adults enter treatment. Community Reinforcement and Family Training (CRAFT), a support programme for concerned significant others of people with substance use disorders, has proved efficacious in promoting treatment entry. The aim of the current trial was to compare the efficacy of CRAFT with an active control for parents of young adults (aged 18–24 years) with hazardous substance use. Design: This was a randomized controlled superiority trial comparing CRAFT (n = 58) with an active control group receiving manualized counselling (n = 55), with outcome assessments at 6, 12 and 24 weeks (primary end‐point). A sequential design with a stopping rule was added post recruitment commencement. Setting: The study took place in two outpatient clinics for young adults in Stockholm, Sweden, and subsequently via video‐conference due to COVID‐19. Participants: Between October 2018 and May 2021, 113 participants (92% female) who were parents of young adults (87% male) were recruited. Recruitment was discontinued when 70% of the planned sample had been recruited, following an interim analysis of the primary outcome showing no difference between conditions. Intervention and comparator: Participants were randomized (ratio 1 : 1) to eight manual‐based individual CRAFT sessions or five individual manual‐based counselling sessions + one voluntary psychoeducative group session, delivered over maximum 14 weeks. Measurements The primary outcome measure was the rate of young adult entry in substance use treatment during the trial period (24 weeks). Findings At the 24 weeks follow‐up, 19 (33%) of CRAFT participants and 17 (31%) of counselling participants had reported young adult treatment entry, with no difference between conditions (odds ratio CRAFT versus counselling 0.84, 95% confidence interval = 0.35; 1.99, P = 0.700). Both conditions reported clinically relevant reductions in young adult substance use, but no change in participants' levels of depression, anxiety or stress. Conclusions: This trial showed no statistically significant evidence that Community Reinforcement and Family Training (CRAFT), a support programme for concerned significant others of people with substance use disorders, is more efficacious than manual‐based counselling regarding treatment entry for young adults. [ABSTRACT FROM AUTHOR]

Modularity is an approach to simplify systems and reduce complexity. However, existing research suggests that a mono-dimensional modularity strategy, focusing solely on one dimension, such as product, process, or organization, might not fully achieve these goals in design activities. This research investigates how combining strategies from various dimensions of modularity can reduce the complexity of large-scale engineering design. The Huoshenshan Hospital, a 1,000-bed hospital designed and built in 10 days, provided an extreme case study of the first emergency hospital to address COVID-19. The research identified 10 different aspects, termed 'proximities', which relate to how people perceive the four dimensions of modularity, specifically across organization–process–product–supply-chain dimensions. Additionally, it identified three types of reinforcement relationships aimed at diminishing complexity in design activities: modular alignment (i.e., synchronized alignment and asynchronous alignment), modular complementarity (i.e., subtraction complement and addition complement), and modular incentive relationships. This research highlights that these three types of reinforcement relationships between different dimensions of modularity can reduce complexity, allowing subsystems to support the system in working as a whole. [ABSTRACT FROM AUTHOR]

The central amygdala (CeA) and bed nucleus of the stria terminalis (BNST) are reciprocally connected nodes of the extended amygdala thought to play an important role in alcohol consumption. Studies of immediate-early genes indicate that BNST and CeA are acutely activated following alcohol drinking and may signal alcohol reward in nondependent drinkers, while stress signaling in the extended amygdala following chronic alcohol exposure drives increased drinking via negative reinforcement. However, the temporal dynamics of neuronal activation in these regions during drinking behavior are poorly understood. In this study, we used fiber photometry and the genetically encoded calcium sensor GCaMP6s to assess acute changes in neuronal activity during alcohol consumption in BNST and CeA before and after a chronic drinking paradigm. Activity was examined in the pan-neuronal population and separately in dynorphinergic neurons. BNST and CeA showed increased pan-neuronal activity during acute consumption of alcohol and other fluid tastants of positive and negative valence, as well as highly palatable chow. Responses were greatest during initial consummatory bouts and decreased in amplitude with repeated consumption of the same tastant, suggesting modulation by stimulus novelty. Dynorphin neurons showed similar consumption-associated calcium increases in both regions. Following three weeks of continuous alcohol access (CA), calcium increases in dynorphin neurons during drinking were maintained, but pan-neuronal activity and BNST-CeA coherence were altered in a sex-specific manner. These results indicate that BNST and CeA, and dynorphin neurons specifically, are engaged during drinking behavior, and activity dynamics are influenced by stimulus novelty and chronic alcohol. • BNST and CeA calcium activity increases during drinking of alcohol and other fluids. • BNST and CeA dynorphin neurons are engaged during alcohol consumption. • BNST and CeA activity is higher when taste stimuli are novel. • BNST and CeA calcium activity during drinking is reduced after chronic alcohol. • BNST-CeA coherence is increased during alcohol drinking bouts in male mice. [ABSTRACT FROM AUTHOR]

Available hospital evacuation simulation models usually focus on the movement of the evacuees while ignoring the crucial behavioural factors of the evacuees' which impact the simulation results. For instance, the issue of patient prioritization behaviour during evacuation simulation is often overlooked and oversimplified in these models. Furthermore, to control the movement of the evacuees, almost all these models utilize rule-based artificial intelligence to develop navigation systems, which sometimes do not guarantee realistic and optimal movement behaviour. This research aims to address these problems by modelling feasible and novel solutions. In this research, we propose to develop a hospital evacuation simulation model which utilizes a hybrid of fuzzy logic and reinforcement learning to simulate assisted hospital evacuation using the Unity3D game engine. We propose a novel and effective approach to model patient prioritization using a fuzzy logic controller; a reinforcement learning based navigation system to tackle the issues related to the rule-based navigation system by proposing novel reward formulation, observation formulation, action formulation and training procedure. The results and findings exhibited by the proposed model are found to be in line with the available literature. For instance, available literature suggests that an increased number of patients increases the evacuation time, and an increased number of staff or exits decreases the evacuation time. The proposed model also demonstrates similar findings. Moreover, the proposed navigation system is found to take a "near shortest distance" to reach the target as the mean difference between "shortest vector distance" and "distance covered" is approximately 1.73 m. The proposed simulation model simulates the repeated patient collection more realistically and can be used to estimate the Required Safe Egress Time, which enables the establishment of safety performance levels. The evacuation performance of different scenarios can be compared using the proposed model. This research can play a vital role in future developments of hospital evacuation simulation models. [ABSTRACT FROM AUTHOR]

In this paper, we consider an integrated sensing, communication, and computation (ISCC) system to alleviate the spectrum congestion and computation burden problem. Specifically, while serving communication users, a base station (BS) actively engages in sensing targets and collaborates seamlessly with the edge server to concurrently process the acquired sensing data for efficient target recognition. A significant challenge in edge computing systems arises from the inherent uncertainty in computations, mainly stemming from the unpredictable complexity of tasks. With this consideration, we address the computation uncertainty by formulating a robust communication and computing resource allocation problem in ISCC systems. The primary goal of the system is to minimize total energy consumption while adhering to perception and delay constraints. This is achieved through the optimization of transmit beamforming, offloading ratio, and computing resource allocation, effectively managing the trade-offs between local execution and edge computing. To overcome this challenge, we employ a Markov decision process (MDP) in conjunction with the proximal policy optimization (PPO) algorithm, establishing an adaptive learning strategy. The proposed algorithm stands out for its rapid training speed, ensuring compliance with latency requirements for perception and computation in applications. Simulation results highlight its robustness and effectiveness within ISCC systems compared to baseline approaches. [ABSTRACT FROM AUTHOR]

Network dismantling is one of the most challenging problems in complex systems. This problem encompasses a broad array of practical applications. Previous works mainly focus on the metrics such as the number of nodes in the Giant Connected Component (GCC), average pairwise connectivity, etc. This paper introduces a novel metric, the accumulated 2-core size, for assessing network dismantling. Due to the NP-hard computational complexity of this problem, we propose SmartCore, an end-to-end model for minimizing the accumulated 2-core size by leveraging reinforcement learning and graph neural networks. Extensive experiments across synthetic and real-world datasets demonstrate SmartCore's superiority over existing methods in terms of both accuracy and speed, suggesting that SmartCore should be a better choice for the network dismantling problem in practice. [ABSTRACT FROM AUTHOR]

Background: Laboratory paradigms are widely used to study fear learning in posttraumatic stress disorder (PTSD). Recent basic science models demonstrate that, during fear learning, patterns of activity in large neuronal ensembles for the conditioned stimuli (CS) begin to reinstate neural activity patterns for the unconditioned stimuli (US), suggesting a direct way of quantifying fear memory strength for the CS. Here, we translate this concept to human neuroimaging and test the impact of post-learning dopaminergic neurotransmission on fear memory strength during fear acquisition, extinction, and recall among women with PTSD in a re-analysis of previously reported data. Methods: Participants (N = 79) completed a context-dependent fear acquisition and extinction task on day 1 and extinction recall tests 24 h later. We decoded activity patterns in large-scale functional networks for the US, then applied this decoder to activity patterns toward the CS on day 1 and day 2. Results: US decoder output for the CS+ increased during acquisition and decreased during extinction in networks traditionally implicated in human fear learning. The strength of US neural reactivation also predicted individuals skin conductance responses. Participants randomized to receive L-DOPA (n = 43) following extinction on day 1 demonstrated less US neural reactivation on day 2 relative to the placebo group (n = 28). Conclusion: These results support neural reactivation as a measure of memory strength between competing memories of threat and safety and further demonstrate the role of dopaminergic neurotransmission in the consolidation of fear extinction memories. [ABSTRACT FROM AUTHOR]

Maze-solving is a classical mathematical task, and is recently analogously achieved using various eccentric media and devices, such as living tissues, chemotaxis, and memristors. Plasma generated in a labyrinth of narrow channels can also play a role as a route finder to the exit. In this study, we experimentally observe the function of maze-route findings in a plasma system based on a mixed discharge scheme of direct-current (DC) volume mode and alternative-current (AC) surface dielectric-barrier discharge, and computationally generalize this function in a reinforcement-learning model. In our plasma system, we install two electrodes at the entry and the exit in a square lattice configuration of narrow channels whose cross section is 1×1 mm2 with the total length around ten centimeters. Visible emissions in low-pressure Ar gas are observed after plasma ignition, and the plasma starting from a given entry location reaches the exit as the discharge voltage increases, whose route converging level is quantified by Shannon entropy. A similar short-path route is reproduced in a reinforcement-learning model in which electric potentials through the discharge voltage is replaced by rewards with positive and negative sign or polarity. The model is not rigorous numerical representation of plasma simulation, but it shares common points with the experiments along with a rough sketch of underlying processes (charges in experiments and rewards in modelling). This finding indicates that a plasma-channel network works in an analog computing function similar to a reinforcement-learning algorithm slightly modified in this study. [ABSTRACT FROM AUTHOR]

In order to cope with ever-evolving and increasing cyber threats, intrusion detection systems have become a crucial component of cyber security. Compared with signature-based intrusion detection methods, anomaly-based methods typically employ machine learning techniques to train detection models and possess the capability to discover unknown attacks. However, intrusion detection methods face the challenge of low detection rates for minority class attacks due to imbalanced data distributions. Traditional intrusion detection algorithms address this issue by resampling or generating synthetic data. Additionally, reinforcement learning, as a machine learning method that interacts with the environment to obtain feedback and improve performance, is gradually being considered for application in the field of intrusion detection. This paper proposes a reinforcement-learning-based intrusion detection method that innovatively uses adaptive sample distribution dual-experience replay to enhance a reinforcement learning algorithm, aiming to effectively address the issue of imbalanced sample distribution. We have also developed a reinforcement learning environment specifically designed for intrusion detection tasks. Experimental results demonstrate that the proposed model achieves favorable performance on the NSL-KDD, AWID, and CICIoT2023 datasets, effectively dealing with imbalanced data and showing better classification performance in detecting minority attacks. [ABSTRACT FROM AUTHOR]

Twenty years ago, pioneer Doctor of Nursing Practice (DNP) program students completed the first DNP projects. Today, DNP projects remain a requirement for graduation. This article illustrates how the DNP project can be integrated throughout the DNP curriculum to maximize effectiveness and promote the introduction, reinforcement, and evaluation of competencies in the new American Association of Colleges of Nursing Essentials. [ABSTRACT FROM AUTHOR]

Idiopathic toe walking (ITW) refers to ambulation with a toe‐to‐toe gait in the absence of a known cause. Although it is exhibited by typically developing children, it is more common among children with autism spectrum disorder (ASD). Persistent ITW can cause several long‐term medical problems, including ankle deformities and balance and posture abnormalities. In the current study, we first conducted a series of no interaction or alone conditions to verify that ITW exhibited by four children with ASD persisted in the absence of social consequences. We then evaluated a hands‐on‐shoulder plus praise procedure in which gentle pressure is applied to participants' shoulders to prompt their heels to contact the ground, along with praise for appropriate steps in order to reduce ITW. Results suggest that the procedure was effective with all participants, and measures of social validity indicate that caregivers approved of the procedure. We discuss the results in terms of the putative mechanisms responsible for the effects of the procedure as well as the conditions under which the procedure might be used. [ABSTRACT FROM AUTHOR]

Mobile edge computing requires more and more high-performance servers, resulting in increased energy consumption. As an effective means to reduce energy consumption, virtual machine placement (VMP) has been widely studied. In the edge computing environment, as the number of terminal device requests continues to increase, the scale of VMP becomes larger and larger, and existing research algorithms may take a long time to converge. The reason is that as the number of VMs increases, the search space of the policy becomes larger and the agent needs to interact with the environment for a longer time to make the best decision. In addition, existing research methods only consider reducing energy consumption, rarely consider the response latency of virtual machines, and almost ignore the dynamic changes of the edge environment. To overcome these drawbacks, we propose a virtual machine placement algorithm based on meta-reinforcement learning, which consists of an inner and outer loop. The inner loop designs a deep reinforcement learning algorithm combined with the order exchange and migration mechanism to generate the best decision, and the outer loop provides meta-strategy parameters for the inner loop based on meta-learning to accelerate the convergence capability of the inner loop, thereby obtaining efficient virtual machine placement decisions quickly from a new environment. Through simulation experiments, we demonstrate that our approach effectively reduces the energy consumption of the edge server and the response latency of VMs at different problem sizes compared to the three baseline algorithms. At the same time, it quickly adapts to the new environment with only a small number of gradient updates. [ABSTRACT FROM AUTHOR]

The spontaneously hypertensive rat (SHR) is a selectively bred animal strain that is frequently used to model attention‐deficit hyperactivity disorder (ADHD) because of certain genetically determined behavioural characteristics. To test the hypothesis that the characteristically altered response to positive reinforcement in SHRs may be due to altered phasic dopamine response to reward, we measured phasic dopamine signals in the SHRs and Sprague Dawley (SD) rats using in vivo fast‐scan cyclic voltammetry. The effects of the dopamine reuptake inhibitor, methylphenidate, on these signals were also studied. Phasic dopamine signals during the pairing of a sensory cue with electrical stimulation of midbrain dopamine neurons were significantly smaller in the SHRs than in the SD rats. Over repeated pairings, the dopamine response to the sensory cue increased, whereas the response to the electrical stimulation of dopamine neurons decreased, similarly in both strains. However, the final amplitude of the response to the sensory cue after pairing was significantly smaller in SHRs than in the SD rats. Methylphenidate increased responses to sensory cues to a significantly greater extent in the SHRs than in the SD rats, due largely to differences in the low dose effect. At a higher dose, methylphenidate increased responses to sensory cues and electrical stimulation similarly in SHRs and SD rats. The smaller dopamine responses may explain the reduced salience of reward‐predicting cues previously reported in the SHR, whereas the action of methylphenidate on the cue response suggests a potential mechanism for the therapeutic effects of low‐dose methylphenidate in ADHD. [ABSTRACT FROM AUTHOR]

Children with autism spectrum disorder (ASD) who are also minimally verbal may require specialized interventions to promote the development of vocalizations. Preliminary research supports an innovative procedure called response contingent stimulus-stimulus pairing (SSP) as a potentially effective procedure to promote vocalizations. The primary aim of this study was to demonstrate the feasibility of delivering a randomized trial comparing response contingent SSP to a control condition. Secondary aims were to demonstrate the feasibility of deploying to the home environment the Digital Language Processor (DLP), a voice recorder by LENA®, and to examine preliminary efficacy of the intervention. Sixteen minimally verbal children with ASD (ages 2.0 to 3.9 years) were randomly assigned to the intervention or control condition with access to delayed treatment. Feasibility metrics were met in that 14 participants (88%) finished the study and attendance to all assessment time points and treatment sessions was over 90%. The research team conducted treatment sessions with 99% fidelity and collected 83.3% of voice recorder data. Nine of 16 caregiver satisfaction surveys were returned, and scores averaged 5.68 out of a possible 6.00 across all participants. Preliminary treatment data results showed that 8/14 participants (57%) showed significant improvement in target sounds and participants receiving the intervention were 3.5 times as likely to improve compared to control. [ABSTRACT FROM AUTHOR]

Certified registered nurse anesthetists (CRNAs) who are responsible for airway management, may lack adequate continuing education for emergency front of neck access (EFONA), an advanced skill necessary in situations when a patient cannot be intubated and cannot be oxygenated (CICO). The purpose of this study was to improve CRNA knowledge and confidence when performing a scalpel-bougie cricothyrotomy for EFONA in a CICO event through the implementation of a spaced learning intervention. Thirteen CRNAs at a 160-bed community hospital participated in a 3-week educational intervention. Week 1: online preintervention survey followed by an educational video. Week 2: video review and skills component practiced on a cricothyrotomy trainer. Week 3: skills component practiced on a cricothyrotomy trainer followed by postintervention survey. This was a single-arm study and Wilcoxon sign ranked tests and a paired t-test were utilized to monitor for change in CRNA knowledge, confidence, and skill in performing EFONA. Implementation of a 3-week spaced learning program for educating CRNAs to perform a scalpel-bougie cricothyrotomy significantly increased CRNA knowledge, confidence, and skill when performing EFONA. Utilizing a spaced learning program may therefore improve provider skills, resulting in optimized patient care during a CICO event, leading to improved patient safety and outcomes. [ABSTRACT FROM AUTHOR]

Background: Achievement of evidence-informed decision making (EIDM) requires the integration of evidence into all practice decisions by identifying and synthesizing evidence, then developing and executing plans to implement and evaluate changes to practice. This rapid systematic review synthesizes evidence for strategies for the implementation of EIDM across organizations, mapping facilitators and barriers to the COM-B (capability, opportunity, motivation, behaviour) model for behaviour change. The review was conducted to support leadership at organizations delivering public health services (health promotion, communicable disease prevention) to drive change toward evidence-informed public health. Methods: A systematic search was conducted in multiple databases and by reviewing publications of key authors. Articles that describe interventions to drive EIDM within teams, departments, or organizations were eligible for inclusion. For each included article, quality was assessed, and details of the intervention, setting, outcomes, facilitators and barriers were extracted. A convergent integrated approach was undertaken to analyze both quantitative and qualitative findings. Results: Thirty-seven articles are included. Studies were conducted in primary care, public health, social services, and occupational health settings. Strategies to implement EIDM included the establishment of Knowledge Broker-type roles, building the EIDM capacity of staff, and research or academic partnerships. Facilitators and barriers align with the COM-B model for behaviour change. Facilitators for capability include the development of staff knowledge and skill, establishing specialized roles, and knowledge sharing across the organization, though staff turnover and subsequent knowledge loss was a barrier to capability. For opportunity, facilitators include the development of processes or mechanisms to support new practices, forums for learning and skill development, and protected time, and barriers include competing priorities. Facilitators identified for motivation include supportive organizational culture, expectations for new practices to occur, recognition and positive reinforcement, and strong leadership support. Barriers include negative attitudes toward new practices, and lack of understanding and support from management. Conclusion: This review provides a comprehensive analysis of facilitators and barriers for the implementation of EIDM in organizations for public health, mapped to the COM-B model for behaviour change. The existing literature for strategies to support EIDM in public health illustrates several facilitators and barriers linked to realizing EIDM. Knowledge of these factors will help senior leadership develop and implement EIDM strategies tailored to their organization, leading to increased likelihood of implementation success. Review registration: PROSPERO CRD42022318994. [ABSTRACT FROM AUTHOR]

Scientists increasingly apply concepts from reinforcement learning to affect, but which concepts should apply? And what can their application reveal that we cannot know from directly observable states? An important reinforcement learning concept is the difference between reward expectations and outcomes. Such reward prediction errors have become foundational to research on adaptive behavior in humans, animals, and machines. Owing to historical focus on animal models and observable reward (e.g., food or money), however, relatively little attention has been paid to the fact that humans can additionally report correspondingly expected and experienced affect (e.g., feelings). Reflecting a broader "rise of affectivism," attention has started to shift, revealing explanatory power of expected and experienced feelings—including prediction errors—above and beyond observable reward. We propose that applying concepts from reinforcement learning to affect holds promise for elucidating subjective value. Simultaneously, we urge scientists to test—rather than inherit—concepts that may not apply directly. [ABSTRACT FROM AUTHOR]

Pedagogical practices use support of empirical research conducted on students learning in-person, but traditional methods of teaching might not work in times of rapid changes. Globally, courses have switched to online/remote learning and the transition has created challenges to student achievement. The unexpected public safety hazards shifted research focus on the impact of remote learning during COVID-19. Previous interventions including positive reinforcement, negative punishment, perceived control, optimism, and factors of self-determination as single constructs have been used to increase student motivation and engagement. The literature reviewed provides an overview of the impact of previous motivation interventions and the theories used to examine students' motivation. Future implications to design courses incorporating these key five focuses are ideal for student achievement in remote learning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a frame-part-activated deep reinforcement learning (FPA-DRL) for action prediction. Most existing methods for action prediction utilize the evolution of whole frames to model actions, which cannot avoid the noise of the current action, especially in the early prediction. Moreover, the loss of structural information of human body diminishes the capacity of features to describe actions. To address this, we design a FPA-DRL to exploit the structure of the human body by extracting skeleton proposals and reduce the redundancy of frames under a deep reinforcement learning framework. Specifically, we extract features from different parts of the human body individually, activate the action-related parts in features and the action-related frames in videos to enhance the representation. Our method not only exploits the structure information of the human body, but also considers the attention frame for expressing actions. We evaluate our method on three popular action prediction datasets: UT-Interaction, BIT-Interaction and UCF101. Our experimental results demonstrate that our method achieves the very competitive performance with state-of-the-arts. • We design the part-activated module to enhance the action-related parts of features. • We design the frame-activated module to reduce the redundancy of frames. • We achieved very competitive results of state-of-the-arts on three datasets. [ABSTRACT FROM AUTHOR]

Objectives: Ethnic discrimination and acculturative stress play an important role in sexual risk behaviors for Latinx emerging adults, who are at disproportionate risk for sexually transmitted infections. Factors such as familism support and ethnic identity may be protective, yet research is limited. This study is guided by a culturally adapted stress and coping framework to examine associations of ethnic discrimination and acculturative stress with sexual risk behaviors (i.e., multiple sex partners, alcohol or drug use before sex, and condomless sex with a primary or casual partner), and examine the moderating roles of familism support and ethnic identity among Latinx emerging adults. Method: Participants were recruited from Arizona and Florida and were primarily female (51.3%) with a mean age of 21.48 years (SD = 2.06). Using cross-sectional data from 158 sexually active Latinx emerging adults, this study employed multiple logistic regression and moderation analyses. Results: Higher levels of ethnic discrimination and pressure to acculturate were associated with fewer sex partners, and higher levels of pressure against acculturation were associated with increased condomless sex with a casual partner. The moderation effect of higher levels of familism support on pressure to acculturate was associated with fewer sex partners, and the moderation effect of higher levels of ethnic identity on pressure against acculturation was associated with decreased condomless sex with casual partners. Conclusions: Examining the results within a culturally informed theoretical framework supports that protective factors may help mitigate sexual risk factors among Latinx emerging adults experiencing acculturative stress. Public Significance Statement: Ethnic discrimination and acculturative stress can be linked with sexual risk behaviors among Latinx emerging adults. This study found that while ethnic discrimination and pressure to acculturate were linked with fewer sex partners, pressure against acculturation was linked to increased condomless sex with a casual partner. Family support and ethnic identity can be protective in the prevention of sexual risk behaviors among Latinx emerging adults who experience acculturative stress. [ABSTRACT FROM AUTHOR]

Reliability analysis of reinforced slopes is crucial in geotechnical engineering. In this study, a procedure is proposed for calculating the reliability of three-dimensional reinforced slopes, taking into consideration the spatial variability of soil strength parameters. An ellipsoidal sliding surface is utilized as an approximate substitute for the actual sliding surface. The Karhunen–Loève (K–L) expansion method is employed to generate random fields. The safety factor calculations are based on the Bishop method. The reliability is evaluated through the Monte Carlo simulation. Based on the procedure, the effects of different reinforcement parameters and random parameters on the mean safety factor and failure probability of three-dimensional slopes are studied and compared with the results of two-dimensional slopes. Notably, the implementation of an upper sparse and lower dense reinforcement scheme leads to a remarkable enhancement in slope reliability, resulting in a substantial 38.4% reduction in failure probability and a 2.4% increase in the safety factor. Additionally, an effective means of enhancing slope reliability is found to be the increase in the length and number of reinforcement layers. The reliability of 3D reinforced slopes is notably influenced by the spatial variability of soil strength parameters. The degree of influence of autocorrelation distance on the failure probability is ranked as lz > lx > ly. When lz increases from 1 to 5 m, the failure probability is increased by 221.85%, from 5.4 to 17.38%. As the correlation coefficient is increased from −0.7 to −0.3, the failure probability increased by 26.7%, from 23.2% to 29.4%. The comparison with 2D reinforced slopes reveals that 3D reinforced slopes demonstrate a higher safety factor and a lower failure probability. As a result, slope reliability is tended to be underestimated by the 2D slope analysis. [ABSTRACT FROM AUTHOR]

Federated learning is a distributed machine learning approach that enables a large number of edge/end devices to perform on-device training for a single machine learning model, without having to share their own raw data. We consider in this paper a federated learning scenario wherein the local training is carried out on IoT devices and the global aggregation is done at the level of an edge server. One essential challenge in this emerging approach is IoT devices selection (also called scheduling), i.e., how to select the IoT devices to participate in the distributed training process. The existing approaches suggest to base the scheduling decision on the resource characteristics of the devices to guarantee that the selected devices would have enough resources to carry out the training. In this work, we argue that trust should be an integral part of the decision-making process and therefore design a trust establishment mechanism between the edge server and IoT devices. The trust mechanism aims to detect those IoT devices that over-utilize or under-utilize their resources during the local training. Thereafter, we introduce DDQN-Trust, a double deep Q learning-based selection algorithm that takes into account the trust scores and energy levels of the IoT devices to make appropriate scheduling decisions. Finally, we integrate our solution into four federated learning aggregation approaches, namely, FedAvg, FedProx, FedShare and FedSGD. Experiments conducted using a real-world dataset show that our DDQN-Trust solution always achieves better performance compared to two main benchmarks: the DQN and random scheduling algorithms. The results also reveal that FedProx outperforms the competitor aggregation models in terms of accuracy when integrated into our DDQN-Trust solution. [ABSTRACT FROM AUTHOR]

Motivation to seek social interactions is inherent to all social species. For instance, even with risk of disease transmission in a recent pandemic, humans sought out frequent in-person social interactions. In other social animals, socialization can be prioritized even over water or food consumption. Zebra finches, Taeniopygia guttata , are highly gregarious songbirds widely used in behavioural and physiological research. Songbirds, like humans, are vocal learners during development, which rely on intense auditory learning. Aside from supporting song learning, auditory learning further supports individual identification, mate choice and outcome associations in songbirds. To study auditory learning in a laboratory setting, studies often employ operant paradigms with food restriction and reinforcement and require complete social isolation, which can result in stress and other unintended physiological consequences for social species. Thus, in this work, we designed an operant behavioural method leveraging the sociality of zebra finches for goal-directed behaviours. Our approach relies on visual social reinforcement, without depriving the animals of food or social contact. Using this task, we found that visual social reinforcement was a strong motivational drive for operant behaviour. Motivation was sensitive to familiarity towards the stimulus animal and higher when engaging with a familiar versus a novel individual. We further show that this tool can be used to assess auditory discrimination learning using either songs or synthetic pure tones as stimuli. As birds gained experience in the task, they developed a strategy to maximize reward acquisition in spite of receiving more punishment, i.e. liberal response bias. Our operant paradigm provides an alternative to tasks using food reinforcement and could be applied to a variety of highly social species, such as rodents and nonhuman primates. • We used a novel social reward-based operant task for zebra finches. • Birds were more motivated to obtain access to familiar over novel conspecifics. • Social motivation reinforced auditory discrimination learning in a Go/No-go task. • Birds with more experience in the Go/No-go task developed liberal response bias. [ABSTRACT FROM AUTHOR]

Positive effects of Positive Behavior Intervention and Supports (PBIS) frameworks have been reported in juvenile justice facilities but have not yet been extended to adult prisons. As part of initial PBIS development in a large prison in the United Kingdom, this study utilized focus groups to investigate the valued outcomes considered most important to prison officers and prisoners. We found that although prisoners and staff largely shared the same values, there were meaningful differences in their relative priorities, reflecting the disparate roles that each play in the functioning of the prison. Focus groups also revealed possible contextual barriers to PBIS implementation in this setting. We conclude that input from both parties is essential in designing values-based support strategies that are aligned across key stakeholders. We discuss how focus groups might inform PBIS intervention planning and how our findings point to specific research gaps in applying PBIS in adult prisons. [ABSTRACT FROM AUTHOR]

Check-in/check-out (CICO) is an evidence-based multicomponent intervention widely implemented in elementary and middle schools. The purpose of this study was to evaluate whether an adapted CICO intervention, with CICO components designed to be more developmentally appropriate for adolescents and contextually relevant for high schools, could be implemented with procedural fidelity and improve outcomes for students with and at risk for disabilities. The study included three ninth-grade students (two with Individualized Education Programs) in one U.S. public high school. We used a non-concurrent multiple baseline design conducted over two academic years during the COVID-19 pandemic to experimentally evaluate the effects of CICO-Secondary on student academic engagement. Results demonstrated that students participated with adequate procedural fidelity and that there was a functional relation between implementation of CICO-Secondary and improved student academic engagement. Students and school personnel also perceived CICO-Secondary to be socially valid. Implications for both future research and practice are discussed. [ABSTRACT FROM AUTHOR]

Increasing expression of antagonism toward immigrants has turned immigration into one of the most polarizing issues in many countries, among them Finland, dividing people into those who favor and those who oppose immigration. But while affective polarization of the kind exemplified by widespread responses to immigration has recently received increasing attention from political psychologists, little attention has been paid to how affective polarization develops through the mutual reinforcement of opposing discourses. The application of critical discursive psychology to interviews with lay Finns reveals this mutual reinforcement in progress. In our interviews, Finns across the political spectrum construct five subject positions by attributing blame for immigration‐related challenges. Both sides blame some "other" for the challenges while exempting themselves from blame. Our study makes three contributions to political psychology: exploring how blame attribution helps to generate affective polarization, illustrating the ability of (critical) discursive psychology to illuminate processes of affective polarization in individuals, and bringing the concept of affective polarization drawn from survey research into dialogue with the concept of subject positions constructed by blame attribution drawn from discursive studies of populism. [ABSTRACT FROM AUTHOR]

Simple Summary: Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Twelve bottlenose dolphins (five females, seven males) in managed care were reinforced for exhibiting different behaviors of their choosing in response to a hand gesture. Using a human-based theory of creativity, the dolphins' behaviors were assessed for four aspects: how many different behaviors they could produce in a session or in a row (fluency), how different the behaviors were from each other (flexibility), how simple or complex the behaviors were (elaboration), and how novel or new the behaviors were (originality). The results indicated that dolphins were variable in all aspects measured, with some animals producing more behaviors that were also more complex and variable in type and energy than the other dolphins. Behaviors were also invented by several dolphins. The dolphins were engaged and cognitively challenged by this task, which suggests this task facilitates cognitive welfare while providing a means to study innovative behavior across species. Creative or novel behaviors in bottlenose dolphins (Tursiops truncatus) can be indicators of flexible thinking and problem solving. Over 50 years ago, two rough-tooth dolphins demonstrated creative novel behaviors acquired through reinforcement training in human care. Since this novel training, a variety of species have been trained to respond to this conceptual cue. The current study assessed the creativity of 12 bottlenose dolphins (5 females, 7 males) housed at the Roatan Institute for Marine Sciences (RIMS) in Roatan, Honduras. Individual differences were found across four constructs measured for creativity: fluency, flexibility, elaboration, and originality. Variability in performance occurred across test sessions. Animals with less experience with this task performed fewer "innovative" behaviors as compared to more experienced animals. Despite errors, dolphins continued to attempt the task during test sessions, suggesting the concept of "innovate" was intrinsically rewarding and cognitively engaging. This task may be utilized across species to promote the comparative study of innovative or creative behavior as well as to promote cognitive welfare. [ABSTRACT FROM AUTHOR]

Background: The ability to extinguish a maladaptive conditioned fear response is crucial for healthy emotional processing and resiliency to aversive experiences. Therefore, enhancing fear extinction learning has immense potential emotional and health benefits. Mindfulness training enhances both fear conditioning and recall of extinguished fear; however, its effects on fear extinction learning are unknown. Here we investigated the impact of mindfulness training on brain mechanisms associated with fear-extinction learning, compared to an exercise-based program. Methods: We investigated BOLD activations in response to a previously learned fear-inducing cue during an extinction paradigm, before and after an 8-week mindfulness-based stress reduction program (MBSR, n = 49) or exercise-based stress management education program (n = 27). Results: The groups exhibited similar reductions in stress, but the MBSR group was uniquely associated with enhanced activation of salience network nodes and increased hippocampal engagement. Conclusions: Our results suggest that mindfulness training increases attention to anticipatory aversive stimuli, which in turn facilitates decreased aversive subjective responses and enhanced reappraisal of the memory. [ABSTRACT FROM AUTHOR]

Background: Aberrant reward functioning is implicated in depression. While attention precedes behavior and guides higher-order cognitive processes, reward learning from an attentional perspective – the effects of prior reward-learning on subsequent attention allocation – has been mainly overlooked. Methods: The present study explored the effects of reward-based attentional learning in depression using two separate, yet complimentary, studies. In study 1, participants with high (HD) and low (LD) levels of depression symptoms were trained to divert their gaze toward one type of stimuli over another using a novel gaze-contingent music reward paradigm – music played when fixating the desired stimulus type and stopped when gazing the alternate one. Attention allocation was assessed before, during, and following training. In study 2, using negative reinforcement, the same attention allocation pattern was trained while substituting the appetitive music reward for gazing the desired stimulus type with the removal of an aversive sound (i.e. white noise). Results: In study 1 both groups showed the intended shift in attention allocation during training (online reward learning), while generalization of learning at post-training was only evident among LD participants. Conversely, in study 2 both groups showed post-training generalization. Results were maintained when introducing anxiety as a covariate, and when using a more powerful sensitivity analysis. Finally, HD participants showed higher learning speed than LD participants during initial online learning, but only when using negative, not positive, reinforcement. Conclusions: Deficient generalization of learning characterizes the attentional system of HD individuals, but only when using reward-based positive reinforcement, not negative reinforcement. [ABSTRACT FROM AUTHOR]

Calls for culture change abound. Headlines regularly feature calls to change the "broken" or "toxic" cultures of institutions and organizations, and people debate which norms and practices across society are now defunct. As people blame current societal problems on culture, the proposed fixis "culture change." But what is culture change? How does it work? Can it be effective? This article presents a novel social psychological framework for intentional culture change--actively and deliberately modifying the mutually reinforcing features of a culture. Synthesizing insights from research and application, it proposes an integrated, evidence-based perspective centered around seven core principles for intentional culture change: Principle 1: People are culturally shaped shapers, so they can be culture changers; Principle 2: Identifying,mapping, and evaluating the key levels of culture helps locate where to target change; Principle 3: Culture change happens in both top-down and bottom-up ways and is more effective when the levels are in alignment; Principle 4: Culture change can be easier when it leverages existing core values and harder when it challenges deep-seated defaults and biases; Principle 5: Culture change typically involves power struggles and identity threats; Principle 6: Cultures interact with one another and change can cause backlash, resistance, and clashes; and Principle 7: Timing and readiness matter. While these principles may be broadly used, here they are applied to the issue of social inequality in the United States. Even though culture change feels particularly daunting in this problem area, it can also be empowering--especially when people leverage evidence-based insights and tools to reimagine and rebuild their cultures. [ABSTRACT FROM AUTHOR]

As internet use has become nearly ubiquitous, problems related to internet use have become more pervasive. Identifying factors that promote internet use is an important first step in developing effective interventions. The preliminary Internet Consequences Questionnaire (ICQ-P) is a new functional behavioral assessment that measures the degree to which people's internet use is maintained by six different behavioral functions. In this pilot study, 61 psychology students completed the ICQ-P and a brief measure of problematic internet use, the Young Diagnostic Questionnaire (YDQ). Students with YDQ scores < 3 endorsed items related to attention and escape from demand more than other items. Students with YDQ scores ≥ 3 endorsed sensory reinforcement, demand, social avoidance, and private events more than students with lower YDQ scores. They also endorsed escape from demand and private events more than any other behavioral functions. The variability in responses to the ICQ-P and the systematic differences in responses of students with high and low YDQ scores provide evidence that the ICQ-P can be used to investigate possible functional relations. Thus, the ICQ-P may be useful in informing targeted interventions that address the specific reinforcement dynamics identified in individual cases of people seeking to change their internet use. [ABSTRACT FROM AUTHOR]

Response disequilibrium theory, an extension of the response deprivation hypothesis, proposes that behavior-change is due to disruptions in an organism's baseline levels of responding. This theory challenges the notion of distinct stimuli as reinforcers or punishers and instead emphasizes the role of instrumental and contingent activities. The present study aimed to test the predictions of response disequilibrium theory by replicating Heth and Warren's, Animal Learning & Behavior, 6, 294–300, (1978) experiment using a modernized apparatus. Sixteen undergraduate psychology students participated in the experiment, which included a familiarization period, baseline periods, and a contingency period. During the contingency period, the instrumental activity was the activation of a video that resulted in a deficit or excess of audio relative to baseline. According to response disequilibrium theory, a deficit will produce a reinforcement effect whereas an excess will produce a punishment effect. The results supported the predictions of response disequilibrium theory, as instrumental video activity increased for participants in the deficit condition and decreased for participants in the excess condition. In addition, the study revealed a strong correlation between the size of disequilibrium and the magnitude of behavior change. These findings further support the use of disequilibrium models to a priori identify effective contingency arrangements in both research and practice. Informal applications of the Premack principle may be supplanted with the precision of a formal model of reinforcement and punishment in response disequilibrium theory. [ABSTRACT FROM AUTHOR]

To address the issues of instability and inefficiency that the fluctuating and uncertain characteristics of renewable energy sources impose on low-carbon microgrids, this research introduces a novel Knowledge-Data-Driven Load Frequency Control (KDD-LFC) approach. This advanced strategy seamlessly combines pre-existing knowledge frameworks with the capabilities of deep learning neural networks, enabling the adaptive management and multi-faceted optimization of microgrid functionalities, with a keen emphasis on the symmetry and equilibrium of active power. Initially, the process involves the cultivation of foundational knowledge through established methodologies to augment the reservoir of experience. Following this, a Knowledge-Aggregation-based Proximal Policy Optimization (KA-PPO) technique is employed, which proficiently acquires an understanding of the microgrid's state representations and operational tactics. This strategy meticulously navigates the delicate balance between the exploration of new strategies and the exploitation of known efficacies, ensuring the harmonization of frequency stability, precision in tracking, and the optimization of control expenditures through the strategic formulation of the reward function. The empirical validation of the KDD-LFC method's effectiveness and its superiority are demonstrated via simulation tests conducted on the load frequency control (LFC) framework of the Sansha isolated island microgrid, which is under the administration of the China Southern Grid. [ABSTRACT FROM AUTHOR]

The functional analysis approach described by Iwata et al. (1982/1994) has been used widely to determine the variables evoking and maintaining challenging behavior. However, one potential concern with conducting functional analyses is that repeated exposure to contingencies may induce a novel functional relation. To examine the likelihood of these potential iatrogenic effects, we evaluated social test conditions of the functional analysis for 116 participants and searched for patterns of responding indicative of acquisition. Patterns suggestive of acquisition occurred in 13.70% of tangible reinforcement conditions; however, the prevalence was only slightly lower in the attention condition (8.75%). Much lower prevalence was observed for the escape condition (2.13%). When grouped by quotient score, a pattern of acquisition was just as likely to be observed in the attention condition as in the tangible condition. Additionally, patterns indicative of acquisition were no more likely to be observed with participants who emitted automatically reinforced challenging behavior. [ABSTRACT FROM AUTHOR]