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This study focuses on a rescue mission problem, particularly enabling agents/robots to navigate efficiently in unknown environments. Technological advances, including manufacturing, sensing, and communication systems, have raised interest in using robots or drones for rescue operations. Effective rescue operations require quick identification of changes in the environment and/or locating the victims/injuries as soon as possible. Several techniques have been developed in recent years for autonomy in rescue missions, including motion planning, adaptive control, and more recently, reinforcement learning techniques. These techniques rely on full knowledge of the environment or the availability of simulators that can represent real environments during rescue operations. However, in practice, agents might have little or no information about the environment or the number or locations of injuries, preventing/limiting the application of most existing techniques. This study provides a probabilistic/Bayesian representation of the unknown environment, which jointly models the stochasticity in the agent's navigation and the environment uncertainty into a vector called the belief state. This belief state allows offline learning of the optimal Bayesian policy in an unknown environment without the need for any real data/interactions, which guarantees taking actions that are optimal given all available information. To address the large size of belief space, deep reinforcement learning is developed for computing an approximate Bayesian planning policy. The numerical experiments using different maze problems demonstrate the high performance of the proposed policy.

Abstract Background Despite the fact that studies indicate that earthquake trauma is associated with numerous psychological consequences, the mediating mechanisms leading to these outcomes have not been well-studied. Therefore, this study investigates the relationship between trauma exposure with substance use tendency, depression, and suicidal thoughts, with the mediating role of peritraumatic dissociation and experiential avoidance. Methods The descriptive-correlational approach was employed in this study. The participants were people who had experienced the Kermanshah earthquake in 2017. A total of 324 people were selected by convenient sampling method. The Traumatic Exposure Severity Scale, the Peritraumatic Dissociative Experiences Questionnaire, the Acceptance and Action Questionnaire, the Iranian Addiction Potential Scale, Beck’s Depression Inventory [BDI-II], and Beck’s Suicidal Thoughts Scale were used to collect data. The gathered data was analyzed‌ using structural equation modeling in ‌SPSS Ver. 24 and LISREL Ver. 24. Results The study findings indicated that the intensity of the trauma exposure is directly and significantly associated with depression symptoms, peritraumatic dissociation, and experiential avoidance. The severity of exposure to trauma had a significant indirect effect on the tendency to use substances through experiential avoidance. This is while the severity of the trauma experience did not directly correlate with substance use and suicidal thoughts. In addition, peritraumatic dissociation did not act as a mediator in the relationship between the severity of trauma exposure with substance use, depression, and suicidal thoughts. Conclusions The severity of exposure to the earthquake was associated with symptoms of depression and these findings indicate the importance of experiential avoidance in predicting the tendency to use drugs. Hence, it is essential to design and implement psychological interventions that target experiential avoidance to prevent drug use tendencies and to establish policies that lower depression symptoms following natural disasters.

Aim and Background: In addition to inflicting distress and functional impairment, the Cyberchondria phenomenon could cause a heavy burden to healthcare professionals by increasing in number of visitors to hospitals and medical centers. Cyberchondria can be helpful in choosing and designing psychological interventions. Therefore, this study aimed to explore relationships between two components of psychological flexibility (Mindfulness and Cognitive diffusion) to Cyberchondria and the possible moderator effect of COVID-19 Anxiety in this relationship. Methods and Materials: The method used in this study was descriptive and correlational. The statistical population included all students who were studying at Shiraz University in the academic year 2021-2022. Convenience Sampling was used to choose participants. Data gathering was done on the Internet because of social distancing obligations. 262 participants completed study questionnaires. The tools used in this research were the Cyberchondria severity scale (CSS), Freiburg Mindfulness Inventory (FMI), Cognitive Fusion Questionnaire, and Corona Disease Anxiety Scale (CDAS). The structural equation modeling method and Lisrel software were used to explore the relationship between study variables. Findings: The presented model has a good fit. No significant relationship was found between mindfulness and cyberchondria. Cognitive Diffusion also had direct and indirect (through COVID-19 Anxiety) effects on Cyberchondria. Therefore, a significant statistical relationship was confirmed between study variables. Conclusions: In sum, it seems cognitive defusion significantly contributes to cyberchondria symptoms, and COVID-19, as a mediant variable, plays a significant part in individuals' vulnerability against cyberchondria.

Advances in technology have enabled the use of sensors with varied modalities to monitor different parts of systems, each providing diverse levels of information about the underlying system. However, resource limitations and computational power restrict the number of sensors/data that can be processed in real-time in most complex systems. These challenges necessitate the need for selecting/scheduling a subset of sensors to obtain measurements that guarantee the best monitoring objectives. This paper focuses on sensor scheduling for systems modeled by hidden Markov models. Despite the development of several sensor selection and scheduling methods, existing methods tend to be greedy and do not take into account the long-term impact of selected sensors on monitoring objectives. This paper formulates optimal sensor scheduling as a reinforcement learning problem defined over the posterior distribution of system states. Further, the paper derives a deep reinforcement learning policy for offline learning of the sensor scheduling policy, which can then be executed in real-time as new information unfolds. The proposed method applies to any monitoring objective that can be expressed in terms of the posterior distribution of the states (e.g. state estimation, information gain, etc.). The performance of the proposed method in terms of accuracy and robustness is investigated for monitoring the security of networked systems and the health monitoring of gene regulatory networks.

The increasing interconnectivity in our infrastructure poses a significant security challenge, with external threats having the potential to penetrate and propagate throughout the network. Bayesian attack graphs have proven to be effective in capturing the propagation of attacks in complex interconnected networks. However, most existing security approaches fail to systematically account for the limitation of resources and uncertainty arising from the complexity of attacks and possible undetected compromises. To address these challenges, this paper proposes a partially observable Markov decision process (POMDP) model for network security under uncertainty. The POMDP model accounts for uncertainty in monitoring and defense processes, as well as the probabilistic attack propagation. This paper develops two security policies based on the optimal stationary defense policy for the underlying POMDP state process (i.e., a network with known compromises): the estimation-based policy that performs the defense actions corresponding to the optimal minimum mean square error state estimation and the distribution-based policy that utilizes the posterior distribution of network compromises to make defense decisions. Optimal monitoring policies are designed to specifically support each of the defense policies, allowing dynamic allocation of monitoring resources to capture network vulnerabilities/compromises. The performance of the proposed policies is examined in terms of robustness, accuracy, and uncertainty using various numerical experiments.

Abstract Early attack detection is essential to ensure the security of complex networks, especially those in critical infrastructures. This is particularly crucial in networks with multi-stage attacks, where multiple nodes are connected to external sources, through which attacks could enter and quickly spread to other network elements. Bayesian attack graphs (BAGs) are powerful models for security risk assessment and mitigation in complex networks, which provide the probabilistic model of attackers’ behavior and attack progression in the network. Most attack detection techniques developed for BAGs rely on the assumption that network compromises will be detected through routine monitoring, which is unrealistic given the ever-growing complexity of threats. This paper derives the optimal minimum mean square error (MMSE) attack detection and monitoring policy for the most general form of BAGs. By exploiting the structure of BAGs and their partial and imperfect monitoring capacity, the proposed detection policy achieves the MMSE optimality possible only for linear-Gaussian state space models using Kalman filtering. An adaptive resource monitoring policy is also introduced for monitoring nodes if the expected predictive error exceeds a user-defined value. Exact and efficient matrix-form computations of the proposed policies are provided, and their high performance is demonstrated in terms of the accuracy of attack detection and the most efficient use of available resources using synthetic Bayesian attack graphs with different topologies.

Abstract Dual‐energy X‐ray absorptiometry (DXA) scans are one of the most frequently used imaging techniques for calculating bone mineral density, yet calculating fracture risk using DXA image features is rarely performed. The objective of this study was to combine deep neural networks, together with DXA images and patient clinical information, to evaluate fracture risk in a cohort of adults with at least one known fall and age‐matched healthy controls. DXA images of the entire body as, well as isolated images of the hip, forearm, and spine (1488 total), were obtained from 478 fallers and 48 non‐faller controls. A modeling pipeline was developed for fracture risk prediction using the DXA images and clinical data. First, self‐supervised pretraining of feature extractors was performed using a small vision transformer (ViT‐S) and a convolutional neural network model (VGG‐16 and Resnet‐50). After pretraining, the feature extractors were then paired with a multilayer perceptron model, which was used for fracture risk classification. Classification was achieved with an average area under the receiver‐operating characteristic curve (AUROC) score of 74.3%. This study demonstrates ViT‐S as a promising neural network technique for fracture risk classification using DXA scans. The findings have future application as a fracture risk screening tool for older adults at risk of falls. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Background: The objective of the current study was to further explore the mediating role of psychological flexibility and self-compassion in the relationship between traumatic memories of shame and the severity of depression and anxiety symptoms. The psychological processes that may have an impact on this connection make it difficult to understand. Methods: The research method was structural equation modeling. A sample of 296 university students from Tehran, Iran, was chosen through random cluster sampling for online research. The study data collection tools included the Early Life Experiences Scale (ELES), Impact of ‎Event Scale-Revised (IES-R), Depression Anxiety Stress Scales (DASS), Cognitive Fusion ‎Questionnaire (CFQ), Acceptance and Action Questionnaire-II (AAQ-II), Self ‎Experiences Questionnaire (SEQ‎), and Self-Compassion Scale (SCS).‎ Based on the partial least squares (PLS) approach, structural equation modeling, SPSS, and SmartPLS‎ software were used to evaluate the generated data. A statistical investigation revealed a clear correlation between the intensity of depression and anxiety symptoms and painful recollections of shame. Results: The results showed that traumatic memories of shame had a significantly favorable impact on anxiety (P < 0.001; β = 0.30) and depression (P < 0.001; β = 0.33), and a significantly negative impact on self-compassion (P < 0.001; β = 0.31) and psychological flexibility (P < 0.001; β = 0.47). Self-compassion significantly decreased sadness and anxiety (P < 0.001; = 0.25), and psychological flexibility significantly decreased sadness (P < 0.001; =0.54) and anxiety (P < 0.001; =0.37). Conclusion: These results suggest that the effects of experiential avoidance, cognitive fusion, and self-conceptualization, which together make up the overall construct of psychological resilience and are linked to the aggravation of depression and anxiety symptoms in people with traumatic shame memories, may be significantly reduced by self-compassion.

Background and Objectives: Metacognition as a process involved in “thinking about own thinking”, has an important role in various psychopathologies in children. This study aims to evaluate the psychometric properties of the Persian version of Metacognitions Questionnaire for Children (MCQ-C) for Iranian children. Methods: Participants were 338 children aged 9-13 years who were randomly selected from primary schools in Qom, Iran. First the MCQ-C was translated to Persian and then back translated into English. After confirming translation, the final draft was administered to 350 children. psychometric properties including construct validity using confirmatory factor analysis (CFA), convergent validity, internal consistency, and test-retest reliability were measured. Data analysis was done in SPSS v.21 and Amos v.24 applications, Results: The results of CFA showed the relative fitness of the model. Correlation coefficients indicated favorable convergent validity of the questionnaire. Cronbach’s alpha coefficient for the whole questionnaire was 0.77 and the three-week test-retest reliability was 0.70, indicating the good reliability of the questionnaire. Conclusion: The Persian MCQ-C has appropriate psychometric properties for Iranian children. It can be used in research and psychotherapy in the field of Iranian children’ psychopathologies, especially anxiety disorders.

Background: Bipolar disorder is one of the most complicated psychiatric disorders. On the one hand, different theoretical models emphasize different structures and there are serious defects in this area and on the other hand, psychological therapies for bipolar disorder are only added as adjunctive therapy to biological therapies. Thus, our knowledge about this disorder is limited. Aims: The purpose of this study is the extraction of the effective therapeutic factors based on the lived experiences of bipolar patients. Methods: In this experiment, a phenomenological-based qualitative method was used. The data was collected by the semi structured interview and inconvenient sampling. Nine participants from the bipolar spectrum (regardless of the type) were interviewed and through the extraction of the lived experiences, the concepts were extracted. Results: In this study 5 organizer concepts were extracted. The organizer concepts were: lifestyle (sleeping and awakening, having routines in life, goals and values in life and medicinal adherence), social communications (assigning one’s responsibility to the others, not being understood by the others, being rejected and being humiliated), cognition and non-functional believes (perfectionism, competitiveness, needlessness to others, catastrophic and dependent self-respect), life skills (problem solving, anger control and stress management) and psychological education (disease symptoms, disease causes and following up the disease treatment). These organizer concepts are originally the main factors of an effective therapy. Conclusion: In order to have an effective therapy for a bipolar person, a multi-dimensional approach should be taken and the interventions should be done in multiple aspects. Such a therapy needs the active participation of both the therapist and the patient. We recommend further longitudinal, qualitative and experimental studies..

Background: COVID-19 pandemic has brought the world many different challenges. One of them is the increase in the traits of mental disorders, especially obsessive-compulsive disorder (OCD). OCD is one of the most disabling disorders for patients and families. One of the factors associated with OCD is self-regulation strategies. Investigating the associations between selfregulation strategies and OCD symptoms can help us find better ways in order to prevent and treat OCD. Additionally, in this critical period, preventive behaviors play a significant role in reducing COVID-19 cases worldwide. The present research aimed to investigate the association between self-regulation strategies, OCD traits, and preventive behaviors during COVID-19 pandemic.Methods: This cross-sectional study aimed to determine the relationship between OCD traits and self-regulation strategies, including expressive suppression, cognitive reappraisal, self-control, integrative self-knowledge, self-compassion, and preventive behaviors during COVID-19 pandemic in 2022. We recruited 380 Iranian adolescents aged 12 to 18 (15.32±2.13) through multistage sampling method and among the students in Shiraz schools in 2022. The sample size was determined based on the study primary objective. In order to gather data, Padua obsession and compulsion inventory, integrative self-knowledge questionnaire, self-control scale, preventive behaviors scale, self-compassionate scale, and emotion regulation questionnaire were utilized as research instruments. The data were analyzed by Pearson correlation coefficient and stepwise regression analysis.Results: The obtained findings shed light on negative relationships between OCD (65.55±20.61) and self-compassion (35.38±6.35, P=0.001), self-control (39.78±10.83, P=0.001), integrative self-knowledge (38.74±6.72, P=0.01), cognitive reappraisal (27.93±6.01, P=0.01). Moreover, positive relationships were observed between OCD, preventive behaviors (5.95±0.92), and expressive suppression (15.04±4.52, P=0.03). The results also revealed that self-regulation strategies account for 62% of preventive behaviors.Conclusion: Self-regulation strategies were found to be important predictors for adolescents’ preventive behaviors during the pandemic. Therefore, paying further attention to self-regulation strategies in this critical period of life is of great necessity.

The aim of present study was to investigate the correspondence of Alternative Model of Personality Disorders (AMPD) dimensions with vulnerable and grandiose narcissism. The resaerch method was a discriptive and correlatinal study. To this end 261 students from Tabriz University were selected by random cluster sampling and completed the Personality Inventory for DSM-5 (PID-5) and Pathological Narcissism Inventory. The Pearson correlation coefficient and multiple regression were used to analyze data. Results revealed that all dimensions and total score of vulnerable narcissism were correlated significantly with five pathological personality traits; but dimensions and total score of grandiose narcissism were mostly correlated with antagonism and psychoticism significantly. Regression analysis of vulnerable narcissism showed that four pathological personality trait, including negative affect, antagonism, disinhibition and psychoticism were significant predictor of total score of vulnerable narcissism, and with regard to dimension of vulnerable narcissism the same four aforementioned dimensions were predictor of contingent self-esteem; detachment and psychoticism were significant predictor of hiding the self; psychoticism was significant predictor of devaluing; and all pathological traits were significant predictor of entitlement rage. With respect to grandiose narcissism regression analysis revealed that antagonism and psychoticism were significant predictor of grandiose narcissism total score; and with respect to its dimensions, antagonism was significant predictor of exploitativeness; and psychoticism was significant predictor of Self-Sacrificing Self-Enhancement, and also significant predictor of grandiose fantasy. It can be concluded that DSM-5 AMPD relatively corresponds with grandiose and not with vulnerable narcissism.

Background & Objective: People with Illness Anxiety Disorder experience excessive worry and in this regard, they have a lot in common with Generalized Anxiety Disorder. Emotion Regulation Therapy (ERT) is a multifaceted and integrated treatment that is widely used for Generalized Anxiety Disorder and its effectiveness in anxious factors has been established in many studies. The aim of the present study is to examine Effectiveness of Emotion Regulation Group Therapy in symptom reduction and worry in individuals with Illness Anxiety Disorder through a Randomized Clinical Trial (RCT). Materials & Methods: This is a Randomized Clinical Trial (RCT) experimental study with pre-test and post-test and control group. Population of the of the present study included all people with Illness Anxiety Disorder in Shiraz in 2019 who went to the psychiatric clinics of Shiraz University of Medical Sciences. The sample was 16 people with Illness Anxiety Disorder who were randomly assigned to the experimental group and the control group. The subjects in the experimental group and the control group were assessed in both pre-test and post-test periods using Structured Clinical Interview for DSM-5 (SCID-5-5), Anxiety and Related Disorders Interview Schedule for DSM-5 (ADIS-5) and The Penn State Worry Questionnaire (PSWQ). Results: Results showed that group format of emotion regulation therapy significantly improved symptoms of anxiety and worry of individuals with illness anxiety disorder. Conclusion: Emotion Regulation Therapy throughout intervention in worry and emotion regulation construct which is known as mediators in the treatment of many disorders, significantly improve symptoms and worry of people with Illness Anxiety Disorder.

Abstract Background Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. Results The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. Conclusion The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data.

Background: According to research, ones well-being is an important factor for mental health and satisfaction in one’s personal life and people with more well-being are more satisfied with their lives. Objectives: The purpose of this study is to find a relationship between self-compassion, emotional quotient, and maternal styles with the well-being in Shiraz school students. Methods: Elementary school students and high school students in the academic year of 2018 were the research population. A total of 510 students who were chosen by multistage sampling method consisted this study sample so that based on the number of schools in Shiraz, 24 primary and high schools were chosen. Two classes from each school were selected and 9 to 11 students from each class participate in study. The tools used were maternal style questionnaire, self-compassion scale (SCS), emotional quotient scale (TMMS), and subjective well being inventory (SWB). Pearson correlation coefficient and multiple regression were applied for analyzing data. Results: According to the analysis, authoritative maternal style, self-compassion, and emotional quotient were important predictors of ones well-being and they can predict 81% of well-being in this study (P = 0.003). In addition, significant correlations were found between authoritative maternal style and self-compassion (correlation coefficient = 0.88, P = 0.007), authoritative maternal style and emotional quotient (correlation coefficient = 0.86, P = 0.008), and authoritative maternal style and subjective well-being (correlation coefficient = 0.94, P = 0.005). Conclusions: According to this study, maternal style, emotional quotient, and self-compassion play an important role in the society and individuals mental health and well-being, therefore, these factors should be attended, especially the authoritative maternal style, which has the most correlation with well-being.

Abstract Background Advancements in gene expression technology allow acquiring cheap and abundant data for analyzing cell behavior. However, these technologies produce noisy, and often correlated, measurements on the transcriptional states of genes. The Boolean network model has been shown to be effective in capturing the complex dynamics of gene regulatory networks (GRNs). It is important in many applications, such as anomaly detection and optimal intervention, to be able to track the evolution of the Boolean states of a gene regulatory network using noisy time-series transcriptional measurements, which may be correlated in time. Results We propose efficient estimators for the Boolean states of GRNs using correlated time-series transcriptional measurements, where the nature of the correlation and of the measurements themselves are entirely arbitrary. More specifically, we propose new algorithms based on a hypothesis tree to compute optimal minimum mean square error (MMSE) filtering and smoothing state estimators for a Partially-Observed Boolean Dynamical System (POBDS) with correlated measurements. The algorithms are exact but may be computationally expensive for large state spaces or long time horizons, in which case a process for pruning the hypothesis tree is employed to obtain an approximation of the optimal MMSE estimators, while keeping computation tractable. Performance is assessed through a comprehensive set of numerical experiments based on the p53-MDM2 negative-feedback loop Boolean regulatory network, where the standard Boolean Kalman Filter (BKF) and Boolean Kalman Smoother (BKS) for uncorrelated measurements are compared to the corresponding new estimators for correlated measurements, called BKF-CORR and BKS-CORR, respectively.

Abstract Background Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. Results BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. Conclusions We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays.

Scientists are attempting to use models of ever-increasing complexity, especially in medicine, where gene-based diseases such as cancer require better modeling of cell regulation. Complex models suffer from uncertainty and experiments are needed to reduce this uncertainty. Because experiments can be costly and time-consuming, it is desirable to determine experiments providing the most useful information. If a sequence of experiments is to be performed, experimental design is needed to determine the order. A classical approach is to maximally reduce the overall uncertainty in the model, meaning maximal entropy reduction. A recently proposed method takes into account both model uncertainty and the translational objective, for instance, optimal structural intervention in gene regulatory networks, where the aim is to alter the regulatory logic to maximally reduce the long-run likelihood of being in a cancerous state. The mean objective cost of uncertainty (MOCU) quantifies uncertainty based on the degree to which model uncertainty affects the objective. Experimental design involves choosing the experiment that yields the greatest reduction in MOCU. This article introduces finite-horizon dynamic programming for MOCU-based sequential experimental design and compares it with the greedy approach, which selects one experiment at a time without consideration of the full horizon of experiments. A salient aspect of the article is that it demonstrates the advantage of MOCU-based design over the widely used entropy-based design for both greedy and dynamic programming strategies and investigates the effect of model conditions on the comparative performances.