An innovative method of fault detection in power transformers

The Bayesian classifier is a priori the optimal solution for minimizing the total error in problems of statistical pattern recognition. The article suggests using the classifier as a regular tool to increase the reliability of defect recognition in power oil-filled transformers based on the results of the analysis of gases dissolved in oil. The wide application of the Bayesian method for solving tasks of technical diagnostics of electrical equipment is limited by the problem of the multidimensional distribution of random parameters (features) and the nonlinearity of classification. The application of a generalized feature of a defect in the form of a nonlinear function of the transformer state parameters is proposed. This simultaneously reduces the dimension of the initial space of the controlled parameters and significantly improves the stochastic properties of the random distribution of the generalized feature. A special algorithm has been developed to perform statistical calculations and the procedure for recognizing the current technical condition of the transformer using the generated decision rule. The presented research results illustrate the possibility of the practical application of the developed method in the conditions of real operation of power transformers.

AsTAR: Sustainable Energy Harvesting for the Internet of Things through Adaptive Task Scheduling

Battery-free Internet-of-Things devices equipped with energy harvesting hold the promise of extended operational lifetime, reduced maintenance costs, and lower environmental impact. Despite this clear potential, it remains complex to develop applications that deliver sustainable operation in the face of variable energy availability and dynamic energy demands. This article aims to reduce this complexity by introducing AsTAR, an energy-aware task scheduler that automatically adapts task execution rates to match available environmental energy. AsTAR enables the developer to prioritize tasks based upon their importance, energy consumption, or a weighted combination thereof. In contrast to prior approaches, AsTAR is autonomous and self-adaptive, requiring no a priori modeling of the environment or hardware platforms. We evaluate AsTAR based on its capability to efficiently deliver sustainable operation for multiple tasks on heterogeneous platforms under dynamic environmental conditions. Our evaluation shows that (1) comparing to conventional approaches, AsTAR guarantees Sustainability by maintaining a user-defined optimum level of charge, and (2) AsTAR reacts quickly to environmental and platform changes, and achieves Efficiency by allocating all the surplus resources following the developer-specified task priorities. (3) Last, the benefits of AsTAR are achieved with minimal performance overhead in terms of memory, computation, and energy.

Bayesian Surface Warping Approach for Rectifying Geological Boundaries Using Displacement Likelihood and Evidence from Geochemical Assays

This article presents a Bayesian framework for manipulating mesh surfaces with the aim of improving the positional integrity of the geological boundaries that they seek to represent. The assumption is that these surfaces, created initially using sparse data, capture the global trend and provide a reasonable approximation of the stratigraphic, mineralization, and other types of boundaries for mining exploration, but they are locally inaccurate at scales typically required for grade estimation. The proposed methodology makes local spatial corrections automatically to maximize the agreement between the modeled surfaces and observed samples. Where possible, vertices on a mesh surface are moved to provide a clear delineation, for instance, between ore and waste material across the boundary based on spatial and compositional analysis using assay measurements collected from densely spaced, geo-registered blast holes. The maximum a posteriori (MAP) solution ultimately considers the chemistry observation likelihood in a given domain. Furthermore, it is guided by an a priori spatial structure that embeds geological domain knowledge and determines the likelihood of a displacement estimate. The results demonstrate that increasing surface fidelity can significantly improve grade estimation performance based on large-scale model validation.

The face is central to primate multicomponent signals

A wealth of experimental and observational evidence suggests that faces have become increasingly important in the communication system of primates over evolutionary time and that both the static and moveable aspects of faces convey considerable information. Therefore, whenever there is a visual component to any multicomponent signal the face is potentially relevant. However, the role of the face is not always considered in primate multicomponent communication research. We review the literature and make a case for greater focus on the face going forward. We propose that the face can be overlooked for two main reasons: first, due to methodological difficulty. Examination of multicomponent signals in primates is difficult, so scientists tend to examine a limited number of signals in combination. Detailed examination of the subtle and dynamic components of facial signals is particularly hard to achieve in studies of primates. Second, due to a common assumption that the face contains “emotional” content. A priori categorisation of facial behavior as “emotional” ignores the potentially communicative and predictive information present in the face that might contribute to signals. In short, we argue that the face is central to multicomponent signals (and also many multimodal signals) and suggest future directions for investigating this phenomenon.

What is the current state of knowledge and distribution of evidence on established policies and programs to regulate international wildlife trade? A systematic map protocol

Background: The international trade of wildlife (animals and plants) provides critical resources for human communities worldwide and contributes to local, national, and international economies. However, increasing demand presents a significant threat to both species and ecosystems as well as wildlife-centered livelihoods. Concerns regarding illicit trade of wildlife and unsustainable harvest has propelled international wildlife trade regulation to the top of political and conservation agendas. Consequently, a broad range of interventions have been established to regulate the trade and address biodiversity decline. To gain a more comprehensive understanding of the impacts of international wildlife trade interventions, this protocol sets out the parameters for a systematic map which will comprehensively collate and describe the extent and distribution of the evidence base. The resulting map aims to provide insight to guide future research and inform practice. Methods: This systematic map will identify, map, and characterize the available evidence on the impacts of established policies and programs to address international wildlife trade. Specifically, the systematic map will describe: (1) the volume and distribution of studies that have examined impacts of various interventions on conservation, biological, and socioeconomic outcomes; (2) research methodologies that have been used to evaluate impacts; (3) distribution of studies on particular taxa and geographical areas; and (4) identify evidence gaps in need of more research. We will search two publication databases and several organizational and topical websites for relevant published articles and grey literature. In addition, a call for literature will be issued among relevant research networks. The titles, abstracts, and full texts of captured studies will be assessed against inclusion criteria. Double screening will be carried out on a subset of studies to ensure consistency. Relevant information from studies will be extracted using an a priori codebook. The resulting map will consist of descriptive statistics, a heat map in the form of a matrix, and a narrative synthesis describing characteristics of included studies.

Assessment of Risks and Threats of "Green" Entrepreneurship based on Cross-Factor Modeling

The article is a continuation of a number of scientifc studies conducted by the authors within the framework of government assignments and grants, dedicated to identifying the relationship between the economic security of territories and the development of "green" entrepreneurship on them.Purpose: of the study is to model the assessment of the state of economic security of territories under various conditions of the transition to "green" entrepreneurship, taking into account the action of external and internal factors. "Green" entrepreneurship is a priority area for the development of Russian regions, consistent with the achievement of Russia's national strategic goals for the period up to 2030.Methods: in this work, cross-factor modeling and the method of integral analysis are used to calculate the composite index of the state of economic security and its structural components, taking into account the development of "green" entrepreneurship, supplemented by the method of a priori ranking of factors to assess the contribution of each component and justify the priority the selected factors affecting the level of economic security of the regions.Results: a three-component cross-factor model for assessing the state of economic security in certain regions of the Russian Federation (Moscow Region, Leningrad Region, Sverdlovsk Region, Novosibirsk Region, Altai Territory, Krasnodar Territory) had been calculated and tested. The regions were ranked according to the value of each of the components, as well as separately according to the aggregate level of economic security. The necessity of stimulating the development of "green" entrepreneurship in each of the analyzed regions. Have been substantiated and stimulating and discouraging factors that have an impact on the level of economic security, taking into account "green" entrepreneurship, pointwise in each subject, have been identifed.Conclusions and Relevance: the proposed approach to assessing the impact of the identifed factors on the level of economic security of the regions, taking into account the development of "green" entrepreneurship on the basis of the developed cross-factor model, made it possible to build a rating of territories and identify stimulating and discouraging factors in their development. On the basis of which to determine the tools for influencing the economic security in a separate entity.

Identification of periodic attractors in Boolean networks using a priori information

Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts.

Dosimetric Risk Factors for Acute Radiation Pneumonitis in Patients With Prior Receipt of Immune Checkpoint Inhibitors

PurposeDosimetric parameters (e.g., mean lung dose (MLD), V20, and V5) can predict radiation pneumonitis (RP). Constraints thereof were formulated before the era of combined immune checkpoint inhibitors (ICIs) and radiotherapy, which could amplify the RP risk. Dosimetric predictors of acute RP (aRP) in the context of ICIs are urgently needed because no data exist thus far.Methods and MaterialsAll included patients underwent thoracic intensity-modulated radiotherapy, previously received ICIs, and followed-up at least once. Logistic regression models examined predictors of aRP (including a priori evaluation of MLD, V20, and V5), and their discriminative capacity was assessed by receiver operating characteristic analysis.ResultsMedian follow-up of the 40 patients was 5.3 months. Cancers were lung (80%) or esophageal (20%). ICIs were PD-1 (85%) or PD-L1 (15%) inhibitors (median 4 cycles). Patients underwent definitive (n=19), consolidative (n=14), or palliative (n=7) radiotherapy; the median equivalent dose in 2 Gy fractions (EQD2) was 60 Gy (IQR, 51.8-64 Gy). Grades 1-5 aRP occurred in 25%, 17.5%, 15%, 2.5%, and 5%, respectively. The only variables associated with any-grade aRP were V20 (p=0.014) and MLD (p=0.026), and only V20 with grade ≥2 aRP (p=0.035). Neither the number of prior ICI cycles nor the delivery of concurrent systemic therapy significantly associated with aRP risk. Graphs were constructed showing the incrementally increasing risk of aRP based on V20 and MLD (continuous variables).ConclusionsThis is the first study illustrating that V20 and MLD may impact aRP in the setting of prior ICIs. However, these data should not be extrapolated to patients without pre-radiotherapy receipt of prior ICIs, or to evaluate the risk of chronic pulmonary effects. If these results are validated by larger studies with more homogeneous populations, the commonly accepted V20/MLD dose constraints could require revision if utilized in the setting of ICIs.

Diabetes Self-Care Behaviors and Barriers to Clinical Care During COVID-19 Pandemic for Marshallese Adults

Purpose: The purpose of the study was to explore experiences of Marshallese adults related to diabetes self-care behaviors during the COVID-19 pandemic. Methods: A qualitative descriptive design was utilized to understand participants’ diabetes self-care behaviors during the pandemic. Nine focus groups with 53 participants were held via videoconference and conducted in English, Marshallese, or a mixture of both languages. A priori codes based on diabetes self-care behaviors provided a framework for analyzing and summarizing participant experiences. Results: Both increases and decreases in healthy eating and exercise were described, with improvements in health behaviors attributed to health education messaging via social media. Participants reported increased stress and difficulty monitoring and managing glucose. Difficulty obtaining medication and difficulty seeing their health care provider regularly was reported and attributed to health care provider availability and lack of insurance due to job loss. Conclusions: The study provides significant insight into the reach of health education campaigns via social media and provides important information about the reasons for delays in care, which extend beyond fear of contracting COVID-19 to structural issues.