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Successful computer-supported collaborative learning (CSCL) relies on collaborative group engagement, a complex construct characterized by multifaceted, dynamic, socio-emotional, and socio-cognitive processes. This paper provides a detailed analysis of collaborative group engagement, with a particular focus on nonverbal behaviors as indicators of high versus low group engagement. Using video observations and a multi-method approach, we examine in detail the relationships between different dimensions of group engagement (i.e., behavioral, social, cognitive, and conceptual-to-consequential engagement) and nonverbal behaviors. Using qualitative case analysis, we also provide a rich examination of selected cases to identify the role that nonverbal behaviors play in high-engagement sequences compared with low-engagement sequences. Our findings shed light on specific nonverbal behaviors, including nodding, laughing, and eye contact, as significant indicators of high versus low collaborative group engagement. Notably, more of these nonverbal behaviors are evident in high-engagement sequences, suggesting mutual reinforcement on each dimension of engagement—with the exception of behavioral engagement, where increased laughing or smiling is evident in low-engagement sequences, but still serves a productive group process. The paper concludes with a discussion and implications of the findings. By demonstrating the role of nonverbal behaviors as important indicators, this work contributes to the understanding of the complex, dynamic, and contextualized nature of collaborative group engagement in CSCL settings. [ABSTRACT FROM AUTHOR]

Families of children with developmental disabilities in rural and underserved areas may lack access to direct service providers. Global pandemics such as COVID-19 have further affected access to service providers for most children, often shifting services online and increasing the role of technology and involvement of parents and caregivers in intervention. Thus, there is a need to examine the characteristics and effects of caregiver implemented technology-based interventions for children with developmental disabilities. The present synthesis evaluates the research on caregiver implemented technology-based interventions for children with autism spectrum disorder and other developmental disabilities aged 21 or younger. A systematic search of four electronic databases resulted in 14 single-case design studies which encompassed 58 participants with a developmental disability, along with 62 caregivers. Studies used a variety of technologies and involved strategies such as computer assisted instruction, self-management, and variations of video modeling. Interventions targeted a variety of outcomes and most incorporated behavioral strategies such as prompting or positive reinforcement. Results indicate positive outcomes without overly time intensive parent training; however, it is important to consider these findings within context of methodological strength, as only four included studies utilized strong experimental methods according to the evaluative framework provided by Reichow et al. (2008). Implications for practice and recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

Recent advancements in quadrupedal robotics have explored the motor potential of these machines beyond simple walking, enabling highly dynamic skills such as jumping, backflips, and even bipedal locomotion. While reinforcement learning has demonstrated excellent performance in this domain, it often relies on complex reward function tuning and prolonged training times, and the interpretability is not satisfactory. Riemannian motion policies, a reactive control method, excel in handling highly dynamic systems but are generally limited to fully actuated systems, making their application to underactuated quadrupedal robots challenging. To address these limitations, we propose a novel framework that treats each leg of a quadrupedal robot as an intelligent agent and employs multi-agent reinforcement learning to coordinate the motion of all four legs. This decomposition satisfies the conditions for utilizing Riemannian motion policies and eliminates the need for complex reward functions, simplifying the learning process for high-level motion modalities. Our simulation experiments demonstrate that the proposed method enables quadrupedal robots to learn stable locomotion using three, two, or even a single leg, offering advantages in training speed, success rate, and stability compared to traditional approaches, and better interpretability. This research explores the possibility of developing more efficient and adaptable control policies for quadrupedal robots. [ABSTRACT FROM AUTHOR]

The expected huge number of connected devices in Internet of Things (IoT) applications characterizes the massive machine-type communication (mMTC) scenario, one prominent use case of beyond fifth-generation (B5G) systems. To meet mMTC connectivity requirements, grant-free (GF) random access (RA) protocols are seen as a promising solution due to the small amount of data that MTC devices usually transmit. In this paper, we propose a GF RA protocol based on a multi-agent reinforcement learning approach, applied to aid IoT devices in selecting the least congested RA pilots. The rewards obtained by the devices in collision cases resemble the congestion level of the chosen pilot. To enable the operation of the proposed method in a realistic B5G network scenario and aiming to reduce signaling overheads and centralized processing, the rewards in our proposed method are computed by the devices taking advantage of a large number of base station antennas. Numerical results demonstrate the superior performance of the proposed method in terms of latency, network throughput, and per-device throughput compared with other protocols. [ABSTRACT FROM AUTHOR]

This study presents a method based on active preference learning to overcome the challenges of designing reward functions for autonomous navigation. Results obtained from training with artificially designed reward functions may not accurately reflect human intentions. We focus on the limitations of traditional reward functions, which often fail to facilitate complex tasks in continuous state spaces. We propose the adoption of active preference learning to resolve these issues and to generate reward functions that align with human preferences. This approach leverages an individual's subjective preferences to guide an agent's learning process, enabling the creation of reward functions that reflect human desires. We utilize mutual information to generate informative queries and apply information gained to balance the agent's uncertainty with the human's response capacity, encouraging the agent to pose straightforward and informative questions. We further employ the No-U-Turn Sampler (NUTS) method to refine the belief model, which outperforms that constructed using the Metropolis algorithm. Subsequently, we retrain the agent using reward weights derived from active preference learning. As a result, our autonomous driving vehicle can navigate between random starting and ending points without dependence on high-precision maps or routing, relying solely on its forward vision. We validate our approach's performance within the CARLA simulation environment. Our algorithm significantly improved the success rate of autonomous driving navigation tasks that originally failed due to artificially designed rewards, increasing it to approximately 60%. Experimental results show significant improvement over the baseline algorithm, providing a solid foundation for enhancing navigation capabilities in autonomous driving systems and advancing the field of autonomous driving intelligence. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the self-perception, difficulties, and mathematical performance of junior high school students at Datu Mitmug Memorial High School in Mapantao Lumba-Bayabao, Lanao del Sur, during the academic year 2022-2023. To investigate the association between these characteristics and junior high school students' mathematics performance, the study used a descriptive survey correlational methodology. Two hundred twenty-five learners in grades 7 through 10 made up the sample. The results showed that the students' overall proficiency in mathematics was satisfactory. Additionally, the students exhibited a positive self-concept in learning mathematics, particularly in the dimensions of learned, organized, and dynamic. Notably, the organized self-concept significantly influenced their performance in mathematics. Moreover, the study identified anxiety and lack of confidence as the main challenges faced by the learners, which significantly impacted their academic performance. As a recommendation, teachers were encouraged to implement positive reinforcement techniques in the classroom to address the mathematics learning gap and enhance students' self-concept. These interventions had the potential to improve students' performance and contribute to their overall mathematical achievement. [ABSTRACT FROM AUTHOR]

Background and aims: This study was conducted in a clinical setting with the aim of replicating previously used procedures for teaching receptive vocabulary. Researchers increased the number of vocabulary words and maintained use of match-to-sample (MtS), prompting, and reinforcement procedures. Researchers were also interested in the efficacy of the intervention from caregivers' perspectives. Methods: Using a concurrent multiple baseline design, two autistic preschoolers with receptive language impairment were taught to identify 30 common objects. MtS, prompting, and reinforcement procedures were individualized to support each child. Maintenance checks and generalization probes were completed after a predetermined number of intervention sessions (i.e. three or four clinic sessions). A social validity questionnaire was completed by parents following the final maintenance check. Results: Receptive object identification improved significantly for both participants. Despite exposure to vocabulary targets for only three or four sessions, they generalized the vocabulary targets to non-identical pictures and maintained words at maintenance checks. Participants were most successful when researchers individualized prompting and reinforcement. Conclusion: MtS, prompting, and reinforcement were effective procedures for improving object identification, even with a limited number of intervention sessions. To support varying learner profiles, modifying prompting and reinforcement procedures was necessary. Caregivers of both participants reported positive improvements in areas such as communication, attention, and behaviors. Implications: This replicated study provides support for MtS, prompting, and reinforcement as means of teaching receptive vocabulary to autistic preschoolers in a clinical setting. The materials used were simple and cost-effective. Overall, this study outlines and supports a flexible and effective evidence-based practice to teach receptive language to autistic children. [ABSTRACT FROM AUTHOR]

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]

The following paper describes a steady-state model of concurrent choice, termed the active time model (ATM). ATM is derived from maximization principles and is characterized by a semi-Markov process. The model proposes that the controlling stimulus in concurrent variable-interval (VI) VI schedules of reinforcement is the time interval since the most recent response, termed here "the active interresponse time" or simply "active time." In the model after a response is generated, it is categorized by a function that relates active times to switch/stay probabilities. In the paper the output of ATM is compared with predictions made by three other models of operant conditioning: melioration, a version of scalar expectancy theory (SET), and momentary maximization. Data sets considered include preferences in multiple-concurrent VI VI schedules, molecular choice patterns, correlations between switching and perseveration, and molar choice proportions. It is shown that ATM can account for all of these data sets, while the other models produce more limited fits. However, rather than argue that ATM is the singular model for concurrent VI VI choice, a consideration of its concept space leads to the conclusion that operant choice is multiply-determined, and that an adaptive viewpoint–one that considers experimental procedures both as selecting mechanisms for animal choice as well as tests of the controlling variables of that choice–is warranted. [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]

Introduction: Women in India experience high rates of postpartum depression (PPD), with minimal availability of screening or treatment. India has an extensive network of community health workers, known as accredited social health activists (ASHAs). While they are knowledgeable about most maternal-childhealthproblems, they have minimal knowledge about PPD. We trained ASHAs to deliver a simple home-based intervention, behavioral activation (BA), which involves individuals in activities that are sources of positive reinforcement to counter depression. The research questions guiding this study were as follows: 1) What are the feasibility and acceptability of ASHAs screening for and delivering a brief behavioral activation intervention addressing PPD among women in Belagavi, South India? 2) What impact did the brief behavioral activation intervention have on PPD? Methods: The mixed methods evaluation used interviews with participants and interventionists, and depression scores were assessed before and after the evaluation. After a 2-day training with 17 ASHAs that focused on understanding PPD, screening using the Edinburgh Postnatal Depression Scale (EPDS), and implementing the BA protocol, ASHAs and researcher supervisors screened the mothers 6-12 weeks postpartum presenting at pediatric immunization clinics. Mothers who screened positive were invited to participate in an ASHA-led 5-week BA intervention, with ASHAs visiting the mothers' homes. We assessed post-intervention EPDS scores and conducted satisfaction assessments and individual interviews. Results: All 26 women who screened positive on the EPDS agreed to be enrolled in the study. All participants had a significant reduction (p < 0.001) in PPD scores. Both ASHAs and mothers had high enthusiasm for the intervention methods and activities. Discussion: This ASHA-delivered BA intervention was found to be feasible, acceptable, and effective in treating PPD in rural Indian mothers. This corroborates literature that demonstrates the efficacy of a BA intervention among individuals with generalized depression in South Asia. In communities with minimal mental health resources, interventions led by trained community workers have the potential to address PPD. [ABSTRACT FROM AUTHOR]

Introduction: Robotic assembly tasks require precise manipulation and coordination, often necessitating advanced learning techniques to achieve efficient and effective performance. While residual reinforcement learning with a base policy has shown promise in this domain, existing base policy approaches often rely on hand-designed full-state features and policies or extensive demonstrations, limiting their applicability in semi-structured environments. Methods: In this study, we propose an innovative Object-Embodiment-Centric Imitation and Residual Reinforcement Learning (OEC-IRRL) approach that leverages an object-embodiment-centric (OEC) task representation to integrate vision models with imitation and residual learning. By utilizing a single demonstration and minimizing interactions with the environment, our method aims to enhance learning efficiency and effectiveness. The proposed method involves three key steps: creating an object-embodiment-centric task representation, employing imitation learning for a base policy using via-point movement primitives for generalization to different settings, and utilizing residual RL for uncertainty-aware policy refinement during the assembly phase. Results: Through a series of comprehensive experiments, we investigate the impact of the OEC task representation on base and residual policy learning and demonstrate the effectiveness of the method in semi-structured environments. Our results indicate that the approach, requiring only a single demonstration and less than 1.2 h of interaction, improves success rates by 46% and reduces assembly time by 25%. Discussion: This research presents a promising avenue for robotic assembly tasks, providing a viable solution without the need for specialized expertise or custom fixtures [ABSTRACT FROM AUTHOR]

Tourette disorder (TD) is poorly understood, despite affecting 1/160 children. A lack of animal models possessing construct, face, and predictive validity hinders progress in the field. We used CRISPR/Cas9 genome editing to generate mice with mutations orthologous to human de novo variants in two high-confidence Tourette genes, CELSR3 and WWC1. Mice with human mutations in Celsr3 and Wwc1 exhibit cognitive and/or sensorimotor behavioral phenotypes consistent with TD. Sensorimotor gating deficits, as measured by acoustic prepulse inhibition, occur in both male and female Celsr3 TD models. Wwc1 mice show reduced prepulse inhibition only in females. Repetitive motor behaviors, common to Celsr3 mice and more pronounced in females, include vertical rearing and grooming. Sensorimotor gating deficits and rearing are attenuated by aripiprazole, a partial agonist at dopamine type II receptors. Unsupervised machine learning reveals numerous changes to spontaneous motor behavior and less predictable patterns of movement. Continuous fixed-ratio reinforcement shows that Celsr3 TD mice have enhanced motor responding and reward learning. Electrically evoked striatal dopamine release, tested in one model, is greater. Brain development is otherwise grossly normal without signs of striatal interneuron loss. Altogether, mice expressing human mutations in high-confidence TD genes exhibit face and predictive validity. Reduced prepulse inhibition and repetitive motor behaviors are core behavioral phenotypes and are responsive to aripiprazole. Enhanced reward learning and motor responding occur alongside greater evoked dopamine release. Phenotypes can also vary by sex and show stronger affection in females, an unexpected finding considering males are more frequently affected in TD. [ABSTRACT FROM AUTHOR]

Introduction: Locomotor adaptation is a motor learning process used to alter spatiotemporal elements of walking that are driven by prediction errors, a discrepancy between the expected and actual outcomes of our actions. Sensory and reward prediction errors are two different types of prediction errors that can facilitate locomotor adaptation. Reward and punishment feedback generate reward prediction errors but have demonstrated mixed effects on upper extremity motor learning, with punishment enhancing adaptation, and reward supporting motor memory. However, an in-depth behavioral analysis of these distinct forms of feedback is sparse in locomotor tasks. Methods: For this study, three groups of healthy young adults were divided into distinct feedback groups [Supervised, Reward, Punishment] and performed a novel locomotor adaptation task where each participant adapted their knee flexion to 30 degrees greater than baseline, guided by visual supervised or reinforcement feedback (Adaptation). Participants were then asked to recall the new walking pattern without feedback (Retention) and after a washout period with feedback restored (Savings). Results: We found that all groups learned the adaptation task with external feedback. However, contrary to our initial hypothesis, enhancing sensory feedback with a visual representation of the knee angle (Supervised) accelerated the rate of learning and short-term retention in comparison to monetary reinforcement feedback. Reward and Punishment displayed similar rates of adaptation, shortterm retention, and savings, suggesting both types of reinforcement feedback work similarly in locomotor adaptation. Moreover, all feedback enhanced the aftereffect of locomotor task indicating changes to implicit learning. Discussion: These results demonstrate the multi-faceted nature of reinforcement feedback on locomotor adaptation and demonstrate the possible different neural substrates that underly reward and sensory prediction errors during different motor tasks. [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]

Assessments of the hosting capacity of electricity distribution networks are of paramount importance, as they facilitate the seamless integration of rooftop photovoltaic systems into the grid, accelerating the transition towards a more carbon neutral and sustainable system. This paper employs a deep reinforcement learning-based approach to evaluate the real-time hosting capacity of low voltage distribution networks in a model-free manner. The proposed approach only requires real-time customer voltage data and solar irradiation data to provide a fast and accurate estimate of real-time hosting capacity at each customer connection point. This study addresses the imperative for accurate electrical models, which are frequently unavailable, in evaluating the hosting capacity of electricity distribution networks. To meet this challenge, the proposed approach utilizes a deep neural network-based, data-driven model of a low-voltage electricity distribution network. This proposed methodology incorporates model-free elements, enhancing its adaptability and robustness. In addition, a comparative analysis between model-based and model-free hosting capacity assessment methods is presented, highlighting their respective strengths and weaknesses. The utilization of the proposed hosting capacity estimation model enables distribution network service providers to make well-informed decisions regarding grid planning, leading to cost minimization. [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]

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]

The emotions attributed to an event can change from occurrence to recall. Autobiographical memories (AMs) exhibit fixed affect (i.e., no change in emotionality), fading affect (i.e., a decrease in emotional intensity), flourishing affect (i.e., an increase in emotional intensity), and flexible affect (i.e., change of valence). Mixed-effects multinomial models were used to predict the likelihood of the different affect change categories. Mixed-effects regression models were used to predict the amount of emotional change within each category. Predictors at the event-level were initial intensity, social rehearsal, and recall frequency. Predictors at the participant-level were components of subjective wellbeing. Analyses were based on 1,748 AMs reported by 117 young participants in response to 16 event cues. Frequency biases, and biases in the amount of change across positive and negative emotionality, were found for all types of emotional change. Specifically, there was more fading of negative (29.98%) than positive affect (11.90%), more flourishing of positive (34.27%) than negative affect (9.61%), and more AMs changing from negative to positive valence (13.33%) than vice versa (3.95%). These biases were also evident in the amount of change within the categories. Moreover, slightly more AMs remained fixed in positive (49.89%) than negative affect (47.08%). Both event and participant level predictors were significantly associated with the likelihood of different affect change categories and the amount of emotional change within the categories. The present findings highlight the importance of considering the different ways in which AMs change emotionally from occurrence to later recall. [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]

Purpose: Using data from a randomized clinical trial evaluating cognitive behavioral therapy (CBT) for children with autism and co-occurring anxiety, this study examined the relationship between autism features and anxiety symptoms throughout CBT. Methods: Two multilevel mediation analyses were run which examined the mediating role of changes in anxiety for changes in two core features of autism, (a) repetitive and restrictive behaviors (RRBs) and (b) social communication/interaction impairments, between pre- and post-treatment. Results: Indirect effects between time and autism characteristics were significant for both models, indicating that as anxiety changes, so do RRBs and social communication/interaction as the outcomes respectively. Conclusion: Findings suggest a bidirectional relationship between anxiety and autism features. Implications of these findings are discussed. [ABSTRACT FROM AUTHOR]

In this paper, we study the optimal stopping problem in the so-called exploratory framework, in which the agent takes actions randomly conditioning on the current state and a regularization term is added to the reward functional. Such a transformation reduces the optimal stopping problem to a standard optimal control problem. For the American put option model, we derive the related HJB equation and prove its solvability. Furthermore, we give a convergence rate of policy iteration and compare our solution to the classical American put option problem. Our results indicate a trade-off between the convergence rate and bias in the choice of the temperature constant. Based on the theoretical analysis, a reinforcement learning algorithm is designed and numerical results are demonstrated for several models. [ABSTRACT FROM AUTHOR]

Premise of research. Reinforcement is the process through which prezygotic reproductive barriers evolve in sympatry owing to selection against hybridization between co-occurring, closely related species. The role of self-fertilization in reinforcement and reproductive isolation is uncertain, in part because its efficiency as a barrier against heterospecific mating can depend on the timing of autonomous selfing. Methodology. To investigate whether increased autonomous selfing has evolved as a mechanism for reinforcement, we compared Phlox cuspidata populations across their native Texas range using both estimates of genetic diversity and experimental manipulation with morphological measurements. Specifically, we investigated patterns of variation in floral traits and timing of selfing between individuals from allopatric populations of P. cuspidata and from populations sympatric with the closely related species Phlox drummondii. Pivotal results. We infer intermediate rates of selfing across field-collected individuals with no significant difference between allopatric and sympatric populations. Among greenhouse-grown plants, we find no differences in timing of selfing or other floral traits, including anther dehiscence timing, anther-stigma distance, autonomous selfing rate, and self-seed count, between allopatric and sympatric populations. However, our statistical analyses indicate that P. cuspidata individuals sympatric with P. drummondii seem to have generally larger flowers compared with allopatric individuals. Conclusions. Despite strong evidence of costly hybridization with P. drummondii , we find no evidence of trait divergence due to reinforcement in P. cuspidata. Although we document nearly complete autonomous self-seed set in the greenhouse, estimates of selfing rates from genetic data imply that realized selfing is much lower in nature, suggesting an opportunity for reinforcing selection to act on this trait. [ABSTRACT FROM AUTHOR]

This study was aimed to find out what effect the maternal methyl-enriched diet (MED) in the perinatal period exerts on cognitive functions in adult offspring of WAG/Rij rats, a genetic model of absence epilepsy with comorbid depression. The cognitive functions (learning and memory) were evaluated in the novel object recognition (NOR) and fear conditioning (for fear memory) tests. The experiments were carried out on 6-month-old male WAG/Rij rats. The mothers of the experimental group fed a MED, while the mothers of the control group fed a control diet. The NOR test was used to assess episodic memory. This test is based on innate exploratory (novelty preference) motivation, which is usually reduced in depression. The conditioning of fear memory is the classical Pavlovian defensive conditioned reflex, manifested as freezing in response to negative reinforcement (electric footshock). In the NOR test, in rats of the experimental group, the novel object recognition index (a measure of episodic memory) and the number of entries to the center of the arena (a measure of exploratory motivation) were significantly higher compared to the corresponding measures in the control group. However, no intergroup differences were revealed in the manifestation of conditioned fear memory (% of freezing responses) to a context and a sound cue. The results indicate that the maternal MED improves episodic memory in the NOR test but does not affect the conditioned fear memory associated with a context and sound cue in the adult offspring of WAG/Rij rats. [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]

There is growing interest in the role of Social Prescribing (SP) to help promote mental well-being and support individuals with mental health difficulties. Yet, implementation of SP to children and young people (CYP) has proved slow and underdeveloped compared with adult populations. Understanding the barriers and facilitators will help key stakeholders to better embed SP for CYP into practice. Using the Theoretical Domains Framework (TDF), a comprehensive, theoretical-led framework, underpinned by 33 behaviour change theories and 128 constructs, perceived barriers and facilitators to SP were investigated. The sample comprised of 11 Link Workers and 9 individuals involved in facilitating SP with CYP, who took part in semi-structured interviews. Transcripts were analysed using a deductive thematic analysis, and themes were coded under each theoretical domain. Overall, 33 barriers and facilitators for SP were identified across 12 domains of the TDF. Under capability, barriers and facilitators were found for knowledge, skills, memory/attention/decision making processes, and behavioural regulation. For opportunity, barriers and facilitators were found for social/professional influences, as well as environmental context and resources. Finally, for motivation, domains covered included: beliefs about consequences, beliefs about capabilities, optimism, motivations/goals, reinforcement, and emotions. Findings suggest that a wide range of barriers and facilitators affect the implementation of CYP SP to improve mental health and well-being. Interventions which target different domains related to capability, opportunity and motivation should be developed to better facilitate CYP SP. [ABSTRACT FROM AUTHOR]

Neuronal ensembles in the medial prefrontal cortex mediate cocaine self‐administration via projections to the nucleus accumbens. We have recently shown that neuronal ensembles in the prelimbic cortex form rapidly to mediate cocaine self‐administration. However, the role of neuronal ensembles within the nucleus accumbens in initial cocaine‐seeking behaviour remains unknown. Here, we sought to expand the current literature by testing the necessity of the cocaine self‐administration ensemble in the nucleus accumbens core (NAcCore) 1 day after male and female rats acquire cocaine self‐administration by using the Daun02 inactivation procedure. We found that disrupting the NAcCore ensembles after a no‐cocaine reward‐seeking test increased subsequent cocaine seeking, while disrupting NAcCore ensembles following a cocaine self‐administration session decreased subsequent cocaine seeking. We then characterized neuronal cell type in the NAcCore using RNAscope in situ hybridization. In the no‐cocaine session, we saw reduced dopamine D1 type neuronal activation, while in the cocaine self‐administration session, we found preferential dopamine D1 type neuronal activity in the NAcCore. [ABSTRACT FROM AUTHOR]

• The maximum corrosion inhibition efficiency (97.84 %) was achieved when the MBI/B72 ratio was 3:7. • After MBI/B72 treatment, a denser and smoother membrane layer structure is formed. • The ability of MBI/B72 solution to enhance hardness is somewhere between B72 and MBI. • The penetration properties of this solution are advantageous for the treatment of heavily patinated bronzes. • The corrosion inhibition mechanism was inferred based on the microscopic Raman, IR, and XPS data. The corrosion inhibition effect, reinforcement effect and penetration effect of rusted bronze before and after the compounding of 2-mercaptobenzimidazole (MBI) with B72 (Paraloid B72) were investigated using surface analysis and electrochemical techniques. Fourier transform infrared spectroscopy (FT-IR), Raman spectroscopy (Raman), X-ray photoelectron spectroscopy (XPS) and quantum chemical computing techniques were used to explore the mechanism of corrosion mitigation further. In addition, an electronic universal testing machine was used to test the ability of the material to reinforce different rust compositions before and after compounding. The dynamic potential polarization method was used to determine the composite scheme's optimal corrosion inhibition ratio and study the corrosion inhibition effect. The experimental results showed that the best corrosion inhibition effect was achieved at the ratio of MBI/B72 ratio 3:7, and the corrosion inhibition efficiency was 97.84 % under these conditions. Most of the MBI molecules used by us presented prominent permeation adsorption behavior. They formed uneven films of Cu(I)-MBI and Cu(II)-MBI coordination compounds on the surface of the rust layer. Finally, B72 combines with MBI molecules through hydrogen bonding to form a compact and smooth film, which helps improve corrosion inhibition efficiency and plays a certain role in reinforcement. [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]

Sexual revictimization can have a negative impact on many facets of women's wellbeing, yet limited evidence exists regarding specific interventions that support healing and the reduction of further revictimization. This paper will explore regional and rural women's experience of a group-based empowerment program, the Shark Cage program, in Victoria, Australia. The "Shark Cage" program aims to address revictimization by empowering women and girls to build personal boundaries and assertiveness within the context of gender equality and human rights. Data were collected via participant observations across the 8-week program, in combination with semi-structured interviews with participants (N = 11) pre and post intervention. All participants had access to therapeutic support outside of the program. Findings indicate that the program fostered connections among women with shared experiences of sexual revictimization, reducing feelings of isolation. Participants detailed the benefit of developing and practicing skills in reducing revictimization, such as assertiveness and boundary setting. Program learning and recovery was embedded within a network of embodied emotions, social connections, cultural norms and place-based relations that influenced how participants recovery could be understood, processed and addressed. [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]

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]

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]

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]

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]

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]

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]

Verbal reinforcement stands as a pivotal procedure implicated in behavioral change across various therapeutic modalities. It constitutes a fundamental tenet of behavioral principles, yet its verbal manifestation in clinical contexts remains relatively underexplored. This study aims to comprehensively examine this phenomenon and contribute insights into how therapists employ positive verbal reinforcement throughout different phases of psychological interventions. An analysis of 60 clinical sessions in behavioral therapy employs a systematic categorization of therapist and client verbal behavior designed to scrutinize therapeutic interaction. The findings suggest that therapists employ verbal reinforcement by expressing nuanced levels of approval contingent on the nature of client verbalizations. This research underscores the efficacy of observational methodology in probing processes that elucidate clinical change, shedding light on the role of verbal reinforcement procedures and their interplay with other potential functions of therapist verbal behavior. [ABSTRACT FROM AUTHOR]

Older adults are at increased risk of pressure injuries (PIs) due to age‐related changes. Traditionally, PI knowledge and education have been delivered in hospitals and residential aged care facilities, however, there remains a critical gap in understanding how PI knowledge on prevention and management is shared with older adults and their carers living in the community. We aimed to describe the nature and characteristics of structured and unstructured PI education programs available to community‐dwelling older adults and their carers. As coping review was undertaken. We searched five databases: CINAHL, Medline, Scopus, Cochrane Library and ProQuest from 2009 to August 2023. The review was guided by Arksey and O'Malley's six‐step framework and adhered to the PRISMA‐ScR guidelines. It included primary peer‐reviewed papers published in English, which focus on PI education for older adults and/or their carers living in community settings. Data extraction was organised in a table, and findings presented as a narrative summary. One‐hundred and thirty‐six papers were screened and four included in the review. Results indicate that consideration was placed on literacy levels and cognitive status of older adults and their carers when designing PI education materials. Educational materials such as leaflets/brochures, in‐person training sessions or a combination of both were used. However, duration of these interventions varied, lasting for 1–4 weeks while others were completed over 12 months. Some improvements in PI knowledge such as how to treat PI, dietary requirements and importance of mobility were noted. However, information retention and its translation into effective long‐term behaviour change remained unclear. In conclusion, adopting a multifaceted educational approach increases the effectiveness of PI knowledge translation. Continuous education, support and reinforcement on PIs over time are necessary when interacting with older adults and caregivers to ensure long‐term management and prevention success. Conversations on PIs should start at the primary care levels when older adults and carers are visiting their GP clinics and accessing support services for other healthcare needs. Understanding older adults' and carers' literacy levels, cognitive status and cultural background can assist clinicians in designing and delivering fit‐for‐purpose PI educational interventions that are accessible, relatable and effective in promoting knowledge transfer and behaviour change. Carers are vital conduits in the care continuum. These factors will lead to a more informed, collaborative and person‐centred approaches to PI management and prevention. [ABSTRACT FROM AUTHOR]

Purpose: Multiple falls preventions exercise programs have been rolled out globally, however, few studies have explored the factors necessary for their implementation. This study aimed to investigate the factors influencing the implementation of "Steady Feet" (SF), a 12-week community fall prevention exercise intervention, for older adults living in Singapore. Material and Methods: This study utilized purposive sampling to recruit two participant groups: (i) older adults who declined or withdrew from the program and (ii) providers of the program (eg, instructors). We conducted 22 semi-structured interviews, recordings were transcribed and translated, followed by thematic analysis. Data collection and analysis were informed by the PRECEDE-PROCEED framework, focusing on predisposing, enabling, and reinforcing factors. Results: Findings revealed two predisposing, four enabling, and two reinforcing themes. Predisposing themes encompassed (i) knowledge, attitudes, and practices of older adults towards exercises and falls prevention, and (ii) perceptions and attitudes of providers towards SF. Both older adults and providers identified several enabling elements in implementing SF, emphasizing the significance of (i) accessibility, availability, and affordability. Providers highlighted (ii) tools and structural support for continual engagement, (iii) minimizing variations in capabilities through a competency development program, and (iv) fostering synergistic partnerships. Positive reinforcement included (i) the role of providers in engaging and promoting participation, (ii) family support, social networks, and (iii) incentives for older adults. Conversely, both groups highlighted negative reinforcements, including (iv) communication issues and (v) repetitive exercises, while providers specifically identified (vi) labor constraints as a deterrent for implementation. Conclusion: Findings indicate that effective implementation necessitates a multifaceted approach. Promoting participation involves engaging instructors, emphasizing social bonds and family involvement, offering incentives, and providing subsidized or free classes. A competency development program proved effective in reducing variations in providers' capabilities. Strengthening community partnerships, with management support, was crucial for ensuring the availability and accessibility of falls prevention programs. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low accuracy of unsupervised learning anomaly detection algorithm, a dual-input anomaly detection method based on deep reinforcement learning was proposed. The proposed model mainly consists of a feature extractor and anomaly detector. Based on the deep reinforcement learning framework, the feature extractor uses a dual-input deep neural network to form the current value network and the target value network, which are used to extract the low-dimensional feature vectors. Based on the 3 σ principle, the reward function of reinforcement learning is designed to reward and punish the output results of the model during training. The model was trained only with the normal data, and the extracted feature vector of the normal class was used as the input of the anomaly detector to complete the learning of the detector. During the test, the input anomaly detection was realized based on the dual-input convolutional neural network, and the anomaly detector was completed by learning. To illustrate the generality and generalization performance of the proposed method, four sets of image data and two sets of rolling bearing fault data in different fields were verified respectively. At the same time, the proposed method is applied to the fault detection of a real aero-engine rolling bearing.The results show that the proposed model has high anomaly detection accuracy, which is superior to the current optimal method. [ABSTRACT FROM AUTHOR]

Efficient spectrum sharing is essential for maximizing data communication performance in Vehicular Networks (VNs). In this article, we propose a novel hybrid framework that leverages Multi-Agent Reinforcement Learning (MARL), thereby combining both centralized and decentralized learning approaches. This framework addresses scenarios where multiple vehicle-to-vehicle (V2V) links reuse the frequency spectrum preoccupied by vehicle-to-infrastructure (V2I) links. We introduce the QMIX technique with the Deep Q Networks (DQNs) algorithm to facilitate collaborative learning and efficient spectrum management. The DQN technique uses a neural network to approximate the Q value function in high-dimensional state spaces, thus mapping input states to (action, Q value) tables that facilitate self-learning across diverse scenarios. Similarly, the QMIX is a value-based technique for multi-agent environments. In the proposed model, each V2V agent having its own DQN observes the environment, receives observation, and obtains a common reward. The QMIX network receives Q values from all agents considering individual benefits and collective objectives. This mechanism leads to collective learning while V2V agents dynamically adapt to real-time conditions, thus improving VNs performance. Our research finding highlights the potential of hybrid MARL models for dynamic spectrum sharing in VNs and paves the way for advanced cooperative learning strategies in vehicular communication environments. Furthermore, we conducted an in-depth exploration of the simulation environment and performance evaluation criteria, thus concluding in a comprehensive comparative analysis with cutting-edge solutions in the field. Simulation results show that the proposed framework efficiently performs against the benchmark architecture in terms of V2V transmission probability and V2I peak data transfer. [ABSTRACT FROM AUTHOR]

This research addresses multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we used reinforcement learning-based methods to simulate traffic engineering in ship networks. We focused on three aspects—traffic balance, instruction priority, and complex network structure—to evaluate reinforcement learning performance in these scenarios. Performance: We developed a reinforcement learning framework for ship network traffic engineering that treats the routing policy as the state and the network state as the environment. The agent generates routing changes and uses actions to optimize traffic services. The experimental results show that reinforcement learning optimizes network traffic balance, reasonably arranges instruction priorities, and copes with complex network structures, greatly improving the network's quality of service (QoS). Through an in-depth analysis of the experimental data, we noticed that network consumption was reduced by 9.1% under reinforcement learning. Reinforcement learning effectively implemented priority routing of high-priority instructions while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%. [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]

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]