Extending the Möbius Modeling Environment with the Advanced Replication Operator

Mobius is well known as a modeling and evaluation environment for performance and dependability indicators. It has been conceived in a modular and flexible fashion, to be easily expanded to incorporate new features, formalisms and tools. The need of modeling systems characterized by a large population of heterogeneous interacting components, which are nowadays more and more common in a variety of application contexts, provided the opportunity to focus on a new operator to efficiently manage non-anonymous replication, as requested for these systems. This tool paper presents the implementation of a new replication operator, called Advanced Rep, in Mobius. Efficiency of Advanced Rep is evaluated against a recently developed alternative solution.

A review of methods for addressing components of interventions in meta-analysis

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder.

METHODOLOGICAL PARADIGM OF MODERN LEGAL SCIENCE IN UKRAINE

The leading idea of the article is the application of the most appropriate methodology for disclosing the essence and content of law, its origin, evolution, contradictions and their coordination in the context of the relationship between natural and positive law, social and individual paradigms, in particular, in the context of nationalizing the individual, and, on the contrary, individualizing the collective in a wide humanistic plane. This aspect synthesizes approaches to the disclosure of individual and collective through the categories of �self�, where dialectical, phenomenological, existential and other approaches are qualified as communicative and dialogic paradigm, which today finds its practical embodiment in lawmaking and law enforcement. Feeling the controversy of these views, the authors emphasize the deepening of the humanistic content of the legal regulator of social relations. Domestic modern legal science in its development should focus on deepening the humanistic content of the normative regulation of social relations. This actualizes the need to solve such scientific problems as the methodology of research and functioning of law, achieving a harmonious relationship between individual and collective in law, the connection of its natural and positive aspects, etc. A fundamentally new definition of the essence of law is proposed, not as the will of a certain class or majority, but as the will to self-existence, which is expressed in the phenomenon of self as a harmonious synthesis of individual and social. In this context, to investigate the legal phenomena dialectics can be effectively used not as a materialistic or idealistic methodology, but as the most general theory and way of ascending to the truth. After all, opposites in law are not necessarily antipodes, but can act as interacting components of legal reality

Human factors: bridging artificial intelligence and patient safety

The recent launch of complex artificial intelligence (AI) in the domain of healthcare has embedded perplexities within patients, clinicians, and policymakers. The opaque and complex nature of artificial intelligence makes it challenging for clinicians to interpret its outcome. Incorrect interpretation and poor utilization of AI might hamper patient safety. The principles of human factors and ergonomics (HFE) can assist in simplifying AI design and consecutively optimize human performance ensuring better understanding of AI outcome, their interaction with the clinical workflow. In this paper, we discuss the interactions of providers with AI and how HFE can influence these interacting components to patient safety.

Data-driven identification of reliable sensor species to predict regime shifts in ecological networks

Signals of critical slowing down are useful for predicting impending transitions in ecosystems. However, in a system with complex interacting components not all components provide the same quality of information to detect system-wide transitions. Identifying the best indicator species in complex ecosystems is a challenging task when a model of the system is not available. In this paper, we propose a data-driven approach to rank the elements of a spatially distributed ecosystem based on their reliability in providing early-warning signals of critical transitions. The proposed method is rooted in experimental modal analysis techniques traditionally used to identify structural dynamical systems. We show that one could use natural system fluctuations and the system responses to small perturbations to reveal the slowest direction of the system dynamics and identify indicator regions that are best suited for detecting abrupt transitions in a network of interacting components. The approach is applied to several ecosystems to demonstrate how it successfully ranks regions based on their reliability to provide early-warning signals of regime shifts. The significance of identifying the indicator species and the challenges associated with ranking nodes in networks of interacting components are also discussed.

On the essence of systems in general and social systems

The concept of system has been the basis for deepening the analysis of many natural, mechanical and social phenomena. However, its applications and developments have been based on a very simple definition: complex of interacting components; on which free and imaginative interpretations of the meaning and nature of the components and interaction have been used. The exaggerated and lax use of the concept of system has not only led to many false conclusions, but it has deteriorated its true value and usefulness. In this paper a stricter definition arises and accurate picture of what is a system, in order to limit its application and constrain its use to those phenomena that contain the elements that make up a system and its application is analyzed in the study of systems created by human beings, social systems and natural systems.

Link Prediction in Complex Networks using Embedding Techniques and Similarity Measures

Networks have proved to be very helpful in modelling complex systems with interacting components. There are various problems across various domains where the systems can be modelled in the form of a network with links between interacting components. The Problem of Link Prediction deals with predicting missing links in a given network. The application of link prediction ranges across various disciplines including biological networks, transportation networks, social networks, telecommunication networks, etc. In this paper, we use node embedding methods to encode the nodes into low dimensional embeddings and predict links based on the edge embeddings computed by taking the hadamard product of the participating nodes. We further compare the accuracy of the models trained on different dimensions of embeddings. We also study how the introduction of additional features changes the accuracy when introduced to various dimensions of node embeddings. The additional features include overlapping measures such as Jaccard similarity, Adamic-Adar score and dot product between node embeddings as well as heuristic features i.e. Common Neighbors, Resource Allocation, preferential attachment and friend tns score.

Nonlinear Interaction Decomposition (NID): A Method for Separation of Cross-frequency Coupled Sources in Human Brain

Cross-frequency coupling (CFC) is a phenomenon through which spatially and spectrally distributed information can be integrated in the brain. There is, however, a lack of methods decomposing brain electrophysiological data into interacting components. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modeling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 (dB). Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data.

Evolutionary-developmental modeling of neurodiversity and psychopathology

Modeling the extremes of mental/emotional conditions requires explicit accounts of evolutionary-developmental sources of human neurodiversity, not merely psychopathology. The target article's approach could be improved by incorporation of a hierarchical scheme wherein mental/emotional infrastructure interacts across differentiated layers of function. The notion of “symptom networks” thus calls for differentiation into hierarchically interacting components of mental/emotional evolution and development.