Real-­time parametric estimation of periodic wake-­foil interactions using bioinspired pressure sensing and machine learning

Periodic wake-­foil interactions occur in the collective swimming of bio­inspired robots. Wake interaction pattern estimation (and control) is crucial to thrust enhancement and propulsive efficiency optimization. In this paper, we study the wake interaction pattern estimation of two flapping foils in tandem configurations. The experiments are conducted at a Reynolds number of 1.41×10^4 in a water channel. A modified wake-­foil phase parameter Φ, which unifies the influences of inter­foil distance Lx, motion phase difference ∆φ and wake convection velocity Uv, is introduced to describe the wake interaction patterns parametrically. We use a differential pressure sensor on the downstream foil to capture wake interaction characteristics. Data sets at different tandem configurations are collected. The wake-­foil phase Φ is used to label the pressure signals. A one ­dimensional convolutional neural networks (1D-CNN) model is used to learn an end­to­end mapping between the raw pressure measurements and the wake-­foil phase Φ. The trained 1D-­CNN model shows accurate estimations (average error 3.5%) on random wake interaction patterns and is fast enough (within 40 ms). Then the trained 1D ­CNN model is applied to online thrust enhancement control of a downstream foil swimming in a periodic wake. Synchronous force monitoring and flow visualization demonstrate the effectiveness of the 1D-­CNN model. The limitations of the model are discussed. The proposed approach can be applied to the online estimation and control of wake interactions in the collective swimming and flying of biomimetic robots.

Geographically Explicit Network Analysis of Urban Living and Working Interaction Pattern in Shenzhen City, South China

Human daily mobility plays an important role in urban research. Commuting of urban residents is an important part of urban daily mobility, especially in working days. However, the characteristic of the mobility network formed by the commuting of urban residents and its impact on the internal structure of the city are still an important work that needs to be explored further. Aiming to study the living–working interaction pattern of meta-populations over urban divisions within cities, a fine-grained dataset of living–working tracking of Shenzhen is curated and used to construct an urban living–working mobility network, and the living–working interaction pattern is analyzed through the community structures of the network. The results show that human daily mobility plays an important role in understanding the formation of urban structure, the administrative divisions of the city affect human daily mobility, and human daily mobility reacts on the formation of urban structure.

Investigating the mutational landscape of the SARS-CoV-2 Omicron variant via ab initio quantum mechanical modeling

SARS-CoV-2 variant “Omicron” B1.1.529 was first identified in South Africa in November 2021. Given the large number of mutations in Omicron’s spike protein compared to the original Wuhan strain, its binding efficacy to the ACE2 receptor and its potential to escape antibodies are in the spotlight. Recently, we presented an ab initio quantum mechanical model to characterize the interactions of spike protein’s Receptor Binding Domain (RBD) with select antibodies and ACE2 variants. The model identified weak links among the residues constituting interactions with the human ACE2 receptor (hACE2), and also enabled us to characterize in silico mutated RBDs to identify potential Variants of Concern (VOC). In particular, we focused on the role of RBD residue 484 in the interaction of the Delta variant with ACE2 and neutralizing antibodies (nAbs). In this report, we apply our model to the Omicron VOC, and characterize its interaction pattern with hACE2. Our results show that (i) binding affinity with hACE2, compared to Delta, is considerably increased, possibly contributing to increased infectivity. (ii) The interaction pattern between B1.1.529 and hACE2 differs from previous variants by shifting the hot-spot interaction residues on hACE2, and potentially affecting nAbs efficacy. (iii) A K mutation in the RBD residue 484 can further improve Omicron’s binding of hACE2 and evasion of nAbs. Finally, we argue that a library of hot-spots for point-mutations can predict binding interaction energies of complex variants.

An interaction effect of norm violations on causal judgment

Existing research has shown that norm violations influence causal judgments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interaction pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N = 1000) and norms of proper functioning (N = 3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.

Effectiveness of Guidance Programme on Impact of Parental Alcoholism among Adolescents in Selected Schools, Mangaluru, Karnataka

Alcoholism is a commonest growing disease of today’s modern society and affected to millions of Indians. Living with an alcoholic parent can have significant impact on adolescent leaving them into risk. The adolescent of alcoholic parents develop a sort of negligence from their parent make them to be isolated. They grow up with low self esteem, dysfunctional family interaction, insecurity and maladaptive coping is higher risk for them become an alcoholic. The present study aimed to find the effectiveness of guidance programme on impact of parental alcoholism among adolescents in selected schools, Mangaluru, Karnataka. An evaluative approach with one group pre-test - post test design was used. Purposive sampling method was used to select the adolescents of alcoholics. Out of 1420 adolescents screened by CAST and a total of 350 adolescents aged 12 – 15 years were identified as adolescents of alcoholic parent/s. The instruments such as Rosenberg self esteem scale, family interaction pattern scale, security inventory rating scale and coping check list was used for the data collection. The computed inferential statistics revealed that the guidance programme has made significant improvement in the self esteem (t = 60.149), family interaction (t = 30.082), security feeling (t = 30.082), and in coping (t =11.679) of adolescents of alcoholic parents. Computed Karl Pearson’s coefficient of correlation revealed that there was a low negative correlation(r = -0.041) between self esteem and family interaction, low negative correlation (r = -0.068) between security feelings with family interaction and low positive correlation between the levels of security feeling with self esteem (r = 0.116). Computed Chi Square showed that there was significant association between the selected demographic variable religion ,self esteem, sex, type of family and incomes with pre test scores of family interaction. The study concluded that the guidance program was highly effective in improving the self esteem, family interaction pattern, security felling and coping of adolescents of alcoholic parents.

SNPxE: SNP-environment interaction pattern identifier

Background Interactions of single nucleotide polymorphisms (SNPs) and environmental factors play an important role in understanding complex diseases' pathogenesis. A growing number of SNP-environment studies have been conducted in the past decade; however, the statistical methods for evaluating SNP-environment interactions are still underdeveloped. The conventional statistical approach with a full interaction model with an additive SNP mode tests one specific interaction type, so the full interaction model approach tends to lead to false-negative findings. To increase detection accuracy, developing a statistical tool to effectively detect various SNP-environment interaction patterns is necessary. Results SNPxE, a SNP-environment interaction pattern identifier, tests multiple interaction patterns associated with a phenotype for each SNP-environment pair. SNPxE evaluates 27 interaction patterns for an ordinal environment factor and 18 patterns for a categorical environment factor. For detecting SNP-environment interactions, SNPxE considers three major components: (1) model structure, (2) SNP’s inheritance mode, and (3) risk direction. Among the multiple testing patterns, the best interaction pattern will be identified based on the Bayesian information criterion or the smallest p-value of the interaction. Furthermore, the risk sub-groups based on the SNPs and environmental factors can be identified. SNPxE can be applied to both numeric and binary phenotypes. For better results interpretation, a heat-table of the outcome proportions can be generated for the sub-groups of a SNP-environment pair. Conclusions SNPxE is a valuable tool for intensively evaluate SNP-environment interactions, and the SNPxE findings can provide insights for solving the missing heritability issue. The R function of SNPxE is freely available for download at GitHub (https://github.com/LinHuiyi/SIPI).

Interaction analysis on social-education of Indonesian school students: A literacy activity

Purpose: The purpose of this study is to analyze the socio-educational interactions between teachers and students. This article also looks at a literacy activity utilized by the teachers and students and their interaction patterns. Research methodology: This research applied a naturalistic approach and descriptive qualitative research design. The data were collected through observation, interviews, and documentation. The data then were analyzed descriptively using the Miles and Huberman analysis model, namely data condensation, data presentation, and concluding. Results: The research results show that to improve student literacy, there were two patterns of interaction: first, a one-way interaction pattern centered on the teacher as a facilitator, corrector, evaluator in literacy activities: second, a multi-directional interaction pattern occurred. Literacy activities aim to train the learners’ ability in reading, understanding and commenting. During the core activities, several aspects of the interactions occur between teachers and students, such as conveying information, explaining, motivating, and proposing to students. At the closing stage of literacy, several aspects of the interaction between teachers and students occur. Making summaries and reading about the literacy activities have provided directions for further literacy activities and conducting evaluations that have been carried out. Limitations: There are limitations to the researcher, so this research is limited to one school only with the object of research on all students and teachers assigned to literacy activities at SMA Negeri 1 Latambaga. Contribution: The study is expected to assist the schools in monitoring the literacy activities and developing student literacy, whereas, for students, this study is expected to improve their literacy and broaden their insights about technological and scientific developments.

DEVELOPMENT OF A METHOD FOR THE INTERACTIVE CONSTRUCTION OF EXPLANATIONS IN INTELLIGENT INFORMATION SYSTEMS BASED ON THE PROBABILISTIC APPROACH

Subject: the use of the apparatus of temporal logic and probabilistic approaches to construct an explanation of the results of the work of an intelligent system in order to increase the efficiency of using the solutions and recommendations obtained. Purpose: development of a method for constructing explanations in intelligent systems with the ability to form and evaluate several alternative interpretations of the results of the operation of such a system. Tasks: justification for the use of the black box principle for interactive construction of explanations; development of a pattern explanation model that provides for probabilistic estimation; development of a method of interactive construction of explanations on the basis of the probabilistic approach. Methods: methods of data analysis, methods of system analysis, methods of constructing explanations, models of knowledge representation. Results: A model of the explanation pattern is proposed, which contains temporal regulations reflecting the sequence of user interaction with an intelligent system, which allows the formation of explanations based on a comparison of the actions of the current user and other well-known users. An interactive method for constructing explanations based on a probabilistic approach has been developed; the method uses patterns of user interaction with an intelligent system and contains phases of constructing patterns of explanations and forming explanations using the obtained patterns. The method organizes the received explanations according to the likelihood of use, which makes it possible to form target and alternative explanations for the user. Conclusions: The use of the black box principle for the development of a probabilistic approach to the construction of explanations in intelligent systems has been substantiated. A model of a pattern of explanations based on temporal regulations is proposed. The model reflects the sequence of user interaction with the intelligent system when receiving decisions and recommendations and contains an interaction pattern as part of temporal regulations that have weight, and also determines the likelihood of using the user interaction pattern. An interactive method for constructing explanations has been developed, considering the interaction of the user with the intelligent system. The method includes phases and stages of the formation of regulations and patterns of user interaction with the determination of the probability of their implementation, as well as the ordering of patterns according to the probability of their implementation. The implementation of the method was carried out when constructing explanations for recommender systems.