Search Results (42)

The multi-strategy optimized dream optimization algorithm (MSDOA) is proposed to address the challenges of inadequate search capability, slow convergence, and susceptibility to local optima in intelligent optimization algorithms applied to UAV three-dimensional path planning, aiming to enhance the global search efficiency and accuracy of UAV path planning algorithms in 3D environments. First, the algorithm utilizes Bernoulli chaotic mapping for population initialization to widen individual search ranges and enhance population diversity. Subsequently, an adaptive perturbation mechanism is incorporated during the exploration phase along with a lens imaging reverse learning strategy to update the population, thereby improving the exploration ability and accelerating convergence while mitigating premature convergence. Lastly, an Adaptive Individual-level Mixed Strategy (AIMS) is developed to conduct a more flexible search process and enhance the algorithm’s global search capability. The performance of the algorithm is evaluated through simulation experiments using the CEC2017 benchmark test functions. The results indicate that the proposed algorithm achieves superior optimization accuracy, faster convergence speed, and enhanced robustness compared to other swarm intelligence algorithms. Specifically, MSDOA ranks first on 28 out of 29 benchmark functions in the CEC2017 test suite, demonstrating its outstanding global search capability and conver-gence performance. Furthermore, UAV path planning simulation experiments conducted across multiple scenario models show that MSDOA exhibits stronger adaptability to complex three-dimensional environments. In the most challenging scenario, compared to the standard DOA, MSDOA reduces the best cost function fitness by 9% and decreases the average cost function fitness by 12%, thereby generating more efficient, smoother, and higher-quality flight paths. Full article

Background/Objectives: Lucid dreaming (LD), during which the dreamer becomes aware of the dream state, offers a unique opportunity for a variety of applications, including motor practice, personal well-being, and nightmare therapy. However, these applications largely depend on the dreamer’s ability to control their dreams. While LD research has traditionally focused on induction techniques to increase dream frequency, the equally important skill of dream control remains largely underexplored. This study provides an exploration into the mechanisms of LD motor practice, dream control, and its potential influencing factors. We specifically examined whether a complex motor skill—juggling—could be performed during LD, calling for relatively high levels of dream control and access to procedural memory. Methods: Four healthy participants underwent overnight polysomnography (PSG), provided detailed dream reports, and completed questionnaires assessing dream control and self-efficacy. Dream-task success was assessed using predefined in-dream motor performance criteria. Differences between high and low LD control participants were examined, and two detailed case reports of lucid dream juggling attempts provide insight into the challenges of executing complex motor tasks during LD. Results: Dream control varied between and within participants. Both dream control and self-efficacy seemed to predict participants’ ability to execute the LD motor task. Conclusions: Despite the low sample size, this study highlights the potential roles of individual traits like self-efficacy in shaping dream control abilities and motor performance during LD. By using empirical, task-based measures, this study helps build the foundation for future research aimed at optimizing LD applications in clinical and non-clinical fields. Full article

This paper investigates the complex causal relationships within Jain narrative literature (prathamānuyoga/dharmakathānuyoga), specifically focusing on the examination of the Sītā abandonment narrative across Prakrit, Sanskrit, and Apabhramsha textual traditions. By employing textual analysis and philosophical interpretation, the study explores the intricate causative mechanisms in narratives by Vimalasūri, Raviṣeṇa, and Svayambhūdeva. The paper addresses critical research questions examining the multifaceted nature of causality: the root causes of Sītā’s abandonment, the identification of precursory causal signs like dreams, cravings during pregnancy and omens, an analysis of Sītā’s philosophical response to her circumstances, and the complex interplay between external and internal instrumental causes (bahiraṅga- and antaraṅga-nimitta-kāraṇa). Through rigorous textual comparison and philosophical analysis of the Paümacariyaṃ, Padmapuraṇa, and Paümacariü, the study reveals that Jain narrative literature predominantly employs instrumental causality as its primary explanatory framework. The paper demonstrates how external and internal instrumental causes interplay, and explores the role of Sītā’s bad or good karma in shaping her narrative trajectory. The paper contributes to the ongoing scholarship on Jain narratives by analyzing causality in religious narratives, offering philosophical insights into narrative causation, providing an interdisciplinary perspective that bridges literary analysis with philosophical interpretation, and illuminating the ways Jain narratives employ causality to explain complex human experiences and ethical dilemmas, ultimately revealing how narrative structures reflect deeper metaphysical and philosophical concepts within the Jain textual tradition. Full article

The DREAM (dimerization partner, RB-like, E2F, and multi-vulval class B) complex is an evolutionarily conserved transcriptional repression complex that coordinates nearly one thousand target genes, primarily associated with the cell cycle processes. The formation of the DREAM complex consequently inhibits cell cycle progression and induces cellular quiescence. Given its unique role in cell cycle control, the DREAM complex has gained significant interest across various physiological and pathological contexts, particularly in conditions marked by dysregulated cell cycles, such as cancer. However, the specific cancer types most significantly affected by alterations in the DREAM complex are yet to be determined. Moreover, the possibility of restoring or pharmacologically targeting the DREAM complex as a therapeutic intervention against cancer remains a relatively unexplored area of research and is currently under active investigation. In this review, we provide an overview of the latest advances in understanding the DREAM complex, focusing on its role in cancer. We also explore strategies for targeting the DREAM complex as a potential approach for cancer therapeutics. Advances in understanding the precise role of the DREAM complex in cancer, combined with ongoing efforts to develop targeted therapies, may pave the way for new options in cancer therapy. Full article

Bayesian model updating has received considerable attention and has been extensively used in structural damage detection. It provides a rigorous statistical framework for realizing structural system identification and characterizing uncertainties associated with modeling and measurements. The Markov Chain Monte Carlo (MCMC) is a promising tool for inferring the posterior distribution of model parameters to avoid the intractable evaluation of multi-dimensional integration. However, the efficacy of most MCMC techniques suffers from the curse of parameter dimension, which restricts the application of Bayesian model updating to the damage detection of large-scale systems. In addition, there are several MCMC techniques that require users to properly choose application-specific models, based on the understanding of algorithm mechanisms and limitations. As seen in the literature, there is a lack of comprehensive work that investigates the performances of various MCMC algorithms in their application of structural damage detection. In this study, the Differential Evolutionary Adaptive Metropolis (DREAM), a multi-chain MCMC, is explored and adapted to Bayesian model updating. This paper illustrates how DREAM is used for model updating with many uncertainty parameters (i.e., 40 parameters). Furthermore, the study provides a tutorial to users who may be less experienced with Bayesian model updating and MCMC. Two advanced single-chain MCMC algorithms, namely, the Delayed Rejection Adaptive Metropolis (DRAM) and Transitional Markov Chain Monte Carlo (TMCMC), and DREAM are elaborately introduced to allow practitioners to understand better the concepts and practical implementations. Their performances in model updating and damage detection are compared through three different engineering applications with increased complexity, e.g., a forty-story shear building, a two-span continuous steel beam, and a large-scale steel pedestrian bridge. Full article

Digital Twins of the Ocean (DTO) are a rapidly emerging topic that has attracted significant interest from scientists in recent years. The initiative, strongly driven by the EU, aims to create a digital replica of the ocean to better understand and manage marine environments. The Iliad project, funded under the EU Green Deal call, is developing a framework to support multiple interoperable DTO using a federated systems-of-systems approach across various fields of applications and ocean areas, called pilots. This paper presents the results of a Water Quality DTO pilot located in the Trondheim fjord in Norway. This paper details the building blocks of DTO, specific to this environmental monitoring pilot. A crucial aspect of any DTO is data, which can be sourced internally, externally, or through a hybrid approach utilizing both. To realistically twin ocean processes, the Water Quality pilot acquires data from both surface and benthic observatories, as well as from mobile sensor platforms for on-demand data collection. Data ingested into an InfluxDB are made available to users via an API or an interface for interacting with the DTO and setting up alerts or events to support ’what-if’ scenarios. Grafana, an interactive visualization application, is used to visualize and interact with not only time-series data but also more complex data such as video streams, maps, and embedded applications. An additional visualization approach leverages game technology based on Unity and Cesium, utilizing their advanced rendering capabilities and physical computations to integrate and dynamically render real-time data from the pilot and diverse sources. This paper includes two case studies that illustrate the use of particle sensors to detect microplastics and monitor algae blooms in the fjord. Numerical models for particle fate and transport, OpenDrift and DREAM, are used to forecast the evolution of these events, simulating the distribution of observed plankton and microplastics during the forecasting period. Full article

Studying gene regulatory networks (GRNs) is paramount for unraveling the complexities of biological processes and their associated disorders, such as diabetes, cancer, and Alzheimer’s disease. Recent advancements in computational biology have aimed to enhance the inference of GRNs from gene expression data, a non-trivial task given the networks’ intricate nature. The challenge lies in accurately identifying the myriad interactions among transcription factors and target genes, which govern cellular functions. This research introduces a cutting-edge technique, EGRC (Effective GRN Inference applying Graph Convolution with Self-Attention Graph Pooling), which innovatively conceptualizes GRN reconstruction as a graph classification problem, where the task is to discern the links within subgraphs that encapsulate pairs of nodes. By leveraging Spearman’s correlation, we generate potential subgraphs that bring nonlinear associations between transcription factors and their targets to light. We use mutual information to enhance this, capturing a broader spectrum of gene interactions. Our methodology bifurcates these subgraphs into ‘Positive’ and ‘Negative’ categories. ‘Positive’ subgraphs are those where a transcription factor and its target gene are connected, including interactions among their neighbors. ‘Negative’ subgraphs, conversely, denote pairs without a direct connection. EGRC utilizes dual graph convolution network (GCN) models that exploit node attributes from gene expression profiles and graph embedding techniques to classify these. The performance of EGRC is substantiated by comprehensive evaluations using the DREAM5 datasets. Notably, EGRC attained an AUROC of 0.856 and an AUPR of 0.841 on the E. coli dataset. In contrast, the in silico dataset achieved an AUROC of 0.5058 and an AUPR of 0.958. Furthermore, on the S. cerevisiae dataset, EGRC recorded an AUROC of 0.823 and an AUPR of 0.822. These results underscore the robustness of EGRC in accurately inferring GRNs across various organisms. The advanced performance of EGRC represents a substantial advancement in the field, promising to deepen our comprehension of the intricate biological processes and their implications in both health and disease. Full article

It is generally assumed that all estrogen-receptor-positive (ER+) breast cancers proliferate in response to estrogen and, therefore, examples of the estrogen-induced regression of ER+ cancers are paradoxical. This review re-examines the estrogen regression paradox for the Luminal A subtype of ER+ breast cancers. The proliferative response to estrogen is shown to depend on the level of ER. Mechanistically, a window of opportunity study of pre-operative estradiol suggested that with higher levels of ER, estradiol could activate the DREAM-MMB (Dimerization partner, Retinoblastoma-like proteins, E2F4, and MuvB–MYB-MuvB) pathway to decrease proliferation. The response of breast epithelium and the incidence of breast cancers during hormonal variations that occur during the menstrual cycle and at the menopausal transition, respectively, suggest that a single hormone, either estrogen, progesterone or androgen, could activate the DREAM pathway, leading to reversible cell cycle arrest. Conversely, the presence of two hormones could switch the DREAM-MMB complex to a pro-proliferative pathway. Using publicly available data, we examine the gene expression changes after aromatase inhibitors and ICI 182,780 to provide support for the hypothesis. This review suggests that it might be possible to integrate all current hormonal therapies for Luminal A tumors within a single theoretical schema. Full article

Through an immersive qualitative exploration, we delve into the narratives of East Jerusalem’s Palestinian students as they navigate the complexities of an Israeli teacher training college. Workshops and artistic expression unearth their aspirations, challenges, and coping strategies within a labyrinthine educational landscape. Our findings illuminate their indomitable spirit and unwavering pursuit of dreams despite isolation, discrimination, and the ever-present Israeli–Palestinian conflict. This study contributes to understanding minority students’ experiences in higher education, particularly those in conflict-ridden regions. Drawing upon Resilience Theory, Social Identity Theory, Critical Race Theory, and Cross-Cultural Psychology, we weave a multi-dimensional framework elucidating factors shaping academic achievements, identity formation, and psychological well-being. The fruits of this research empower educators and policymakers to nurture resilience and inclusivity among diverse student populations navigating tempestuous waters. Amplifying Palestinian students’ voices, our work stands as a clarion call for equity and social justice in education, even amidst adversity. We underscore the paramount importance of tailored support systems and interventions, illuminating education’s transformative potential as a catalyst for positive change within communities gripped by conflict. Full article

Background: Clinical case illustrations of patients with an impairment of personality functioning (IPF) have repeatedly reported that progress during psychotherapy is reflected by alterations in dream content. However, quantitative studies based on samples of psychotherapy patients are scarce. As a core component of both personality functioning and contemporary psychodynamic dream theory, the construct of affect regulation is of specific significance in this context. Aims: To test if improvement in personality functioning in the course of psychotherapy is associated with an increasing ability to regulate affects in dreams. Method: In a longitudinal design, affect regulation was compared in N = 94 unsolicited dream reports from the first vs. last third of long term psychotherapy of ten patients with initial IPF. Dream reports were transcribed from recordings of the sessions. Expert ratings of the level of personality functioning were obtained using the Scales of Psychological Capacities. The capacity for affect regulation was assessed using the Zurich Dream Process Coding System. Group differences were assessed using linear mixed models, controlling for dream length as well as the nested structure of this data set. Results: Patients demonstrated an increased capacity for affect regulation in dreams that was primarily evident in three core features: the complexity of dream elements (cf., e.g., parameter attributes, p = 0.024); the extent of affective involvement in the dream ego (cf., e.g., parameter subject feeling, p = 0.014); and the flexibility to regulate the dynamics of safety/involvement processes (p ≤ 0.001). This pattern was especially prominent in a subgroup (n = 7) of patients with more pronounced improvements in personality functioning. Conclusion: These findings support the hypotheses that decreasing IPF during psychotherapy is associated with increases in the capacity for affect regulation in dreams. Thus, researchers and therapists can utilize dream reports to illuminate the important aspects of treatment progress in clinical practice. Full article

This paper explores George Eliot’s The Lifted Veil (1859) as an early portrayal of traumatic neurosis, providing a fresh perspective to enhance the existing scholarly attention on trauma in Eliot’s Daniel Deronda. To illustrate potential contemporary diagnoses for Latimer, I examine other prevalent mid-nineteenth-century models of mental pathology, including phrenology, mesmerism, and hemispheric brain disunity. Drawing on Pierre Janet’s trauma theories from the late nineteenth century, I argue that Eliot presents an early portrayal of dissociative trauma through Latimer’s psychological experiences. Latimer’s visions, complex dream-like interactions, and involuntary consciousness splitting provide a framework for understanding dissociation in response to his emotionally traumatic loss of his mother. Eliot’s exploration of dissociation anticipates Pierre Janet’s theories, which underpin contemporary understandings of trauma, revealing a remarkable modernity in Eliot’s approach. Full article

Lucid dreaming (LD) is a physiological state of consciousness that occurs when dreamers become aware that they are dreaming, and may also control the oneiric content. In the general population, LD is spontaneously rare; thus, there is great interest in its induction. Here, we aim to review the literature on neuropsychopharmacological induction of LD. First, we describe the circadian and homeostatic processes of sleep regulation and the mechanisms that control REM sleep with a focus on neurotransmission systems. We then discuss the neurophysiology and phenomenology of LD to understand the main cortical oscillations and brain areas involved in the emergence of lucidity during REM sleep. Finally, we review possible exogenous substances—including natural plants and artificial drugs—that increase metacognition, REM sleep, and/or dream recall, thus with the potential to induce LD. We found that the main candidates are substances that increase cholinergic and/or dopaminergic transmission, such as galantamine. However, the main limitation of this technique is the complexity of these neurotransmitter systems, which challenges interpreting results in a simple way. We conclude that, despite these promising substances, more research is necessary to find a reliable way to pharmacologically induce LD. Full article

Worldwide abatement of untreated sewage discharge into surface water is a challenging task. Sewage discharging into surface waters has a detrimental impact on water quality. This paper presents a MATLAB (R2018b) framework designed to identify sewage flow discharges into rivers from an inverse problem-solving perspective. The computational tool integrates a hydrodynamic model using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS 5.0.0) and an open-source toolbox for Differential Evolution Adaptive Metropolis (DREAM) as the inverse problem method. The proposed framework can effectively infer discharge sources in scenarios of highly transient flow based on hydraulic data at pre-set monitoring sites. To validate its capabilities, one hypothetical case and two real cases of sewage flow discharges entering a river were used to test the developed modeling framework. The results based on three performance metrics showed that this mathematical tool can be extended to simulate complex hydrodynamic flow patterns. This accomplishment underscores its potential as a valuable asset for environmental monitoring and water quality restoration efforts. Full article

The i-DREAMS project established a ‘Safety Tolerance Zone (STZ)’ to maintain operators within safe boundaries through real-time and post-trip interventions, based on the crucial role of the human element in driving behavior. This paper aims to model the inter-relationship among driving task complexity, operator and vehicle coping capacity, and crash risk. Towards that aim, data from 80 drivers, who participated in a naturalistic driving experiment carried out in three countries (i.e., Belgium, Germany, and Portugal), resulting in a dataset of approximately 19,000 trips were collected and analyzed. The exploratory analysis included the development of Generalized Linear Models (GLMs) and the choice of the most appropriate variables associated with the latent variables “task complexity” and “coping capacity” that are to be estimated from the various indicators. In addition, Structural Equation Models (SEMs) were used to explore how the model variables were interrelated, allowing for both direct and indirect relationships to be modeled. Comparisons on the performance of such models, as well as a discussion on behaviors and driving patterns across different countries and transport modes, were also provided. The findings revealed a positive relationship between task complexity and coping capacity, indicating that as the difficulty of the driving task increased, the driver’s coping capacity increased accordingly, (i.e., higher ability to manage and adapt to the challenges posed by more complex tasks). The integrated treatment of task complexity, coping capacity, and risk can improve the behavior and safety of all travelers, through the unobtrusive and seamless monitoring of behavior. Thus, authorities should utilize a data system oriented towards collecting key driving insights on population level to plan mobility and safety interventions, develop incentives for road users, optimize enforcement, and enhance community building for safe traveling. Full article

This cluster randomized controlled trial (RCT) examined the effect of a three year follow up of a multi-tier intervention aiming at improving the psychosocial environment in upper secondary schools in Norway. Two intervention conditions were tested: a universal single-tier intervention focused on improving the psychosocial school climate, the dream school program (DSP), and a multi-tier intervention combining the DSP with a targeted measure, the mental health support team (MHST). A total of 2203 students responded to the baseline survey. Of the 2203 pupils, 1884 responded to the first follow-up survey (year 1), 1287 pupils to the second (year 2), and 756 pupils to the third (year 3). The direct and indirect effects on school completion were analyzed using a multi-level linear mixed model. The results showed no significant effects of either the DSP or the DSP and MHST in combination on support, the school climate, mental health, or academic adjustment. We found no significant effect of the interventions on the proportion who had completed school or were in training (the three groups varied between 76.6% and 77.8%). Future similar studies should be attentive to the potential challenges of implementing RCTs in the school setting. Furthermore, the long-term effects of school interventions on the constructs included in this study could be difficult to capture due to the complexity of the phenomena. The implications of these findings are discussed. Full article