Revisiting The Trapping of Noble Gases (He – Kr) By The Triatomic H3+ and Li3+ Species: A Density Functional Reactivity Theory Study

Small atomic clusters with exotic stability, bonding, aromaticity and reactivity properties can be made use of for various purposes. In this work, we revisit the trapping of noble gas atoms (He – Kr) by the triatomic H3+ and Li3+ species by using some analytical tools from density functional theory, conceptual density functional theory, and the information-theoretic approach. Our results showcase that though similar in geometry, H3+ and Li3+ exhibit markedly different behaviour in bonding, aromaticity, and reactivity properties after the addition of noble gas atoms. Moreover, the exchange-correlation interaction and steric effect are key energy components in stablizing the clusters. This study also finds that the origin of the molecular stability of these species is due to the spatial delocalization of the electron density distribution. Our work provides an additional arsenal towards better understanding of small atomic clusters capturing noble gases.

Quantitative Characterization of Complex Systems—An Information Theoretic Approach

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic two-stage examination structure for complex systems aimed towards developing an information theory-based approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains.

Understanding Loneliness in Adolescence: A Test of Competing Hypotheses on the Interplay of Extraversion and Neuroticism

Given that adolescents often experience fundamental changes in social relationships, they are considered to be especially prone to loneliness. Meanwhile, theory and research highlight that both extraversion and neuroticism are closely intertwined with individual differences in loneliness. Extant research has explored the linear main effects of these personality traits, yet potential non-linear associations (e.g., exponential effects) and the potential interplay of extraversion and neuroticism (e.g., mutual reinforcement effects) remain unknown. We addressed these open questions using cross-sectional and one-year longitudinal data from two adolescent samples (overall N = 583, Mage = 17.57, 60.55% girls) and an information-theoretic approach combined with polynomial regression. Analyses showed little evidence for interaction effects but revealed non-linear effects in addition to the main effects of extraversion and neuroticism on loneliness. For example, the positive cross-sectional association between neuroticism and loneliness was stronger at higher neuroticism levels (i.e., exponential effect). Results differed across loneliness facets in that both traits predicted emotional loneliness, but only extraversion predicted social loneliness. Longitudinal analyses showed that loneliness changes were mainly related to neuroticism. We discuss results in the light of sample differences, elaborate on the importance to differentiate between emotional versus social aspects of loneliness, and outline implications for adolescent development.

Trading bits in the readout from a genetic network

In the regulation of gene expression, information of relevance to the organism is represented by the concentrations of transcription factor molecules. To extract this information the cell must effectively “measure” these concentrations, but there are physical limits to the precision of these measurements. We use the gap gene network in the early fly embryo as an example of the tradeoff between the precision of concentration measurements and the transmission of relevant information. For thresholded measurements we find that lower thresholds are more important, and fine tuning is not required for near-optimal information transmission. We then consider general sensors, constrained only by a limit on their information capacity, and find that thresholded sensors can approach true information theoretic optima. The information theoretic approach allows us to identify the optimal sensor for the entire gap gene network and to argue that the physical limitations of sensing necessitate the observed multiplicity of enhancer elements, with sensitivities to combinations rather than single transcription factors.

High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program

Background High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single ‘high-need high-cost’ designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations. Methods Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients’ pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability. Results Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3–11.3), hospitalizations (0.3–2.0), and cluster stability (32–91%). Conclusions We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.

Information-Theoretic Channel for Multi-exposure Image Fusion

Multi-exposure image fusion has emerged as an increasingly important and interesting research topic in information fusion. It aims at producing an image with high quality by fusing a set of differently exposed images. In this article, we present a pixel-level method for multi-exposure image fusion based on an information-theoretic approach. In our scheme, an information channel between two source images is used to compute the Rényi entropy associated with each pixel in one image with respect to the other image and hence to produce the weight maps for the source images. Since direct weight-averaging of the source images introduce unpleasing artifacts, we employ Laplacian multi-scale fusion. Based on this pyramid scheme, images at every scale are fused by weight maps, and a final fused image is inversely reconstructed. Multi-exposure image fusion with the proposed method is easy to construct and implement and can deliver, in less than a second for a set of three input images of size 512$\times $340, competitive and compelling results versus state-of-art methods through visual comparison and objective evaluation.