Group-Level Ranking-Based Hubness Analysis of Human Brain Connectome Reveals Significant Interhemispheric Asymmetry and Intraparcel Heterogeneities

Objective: Graph theory applications are commonly used in connectomics research to better understand connectivity architecture and characterize its role in cognition, behavior and disease conditions. One of the numerous open questions in the field is how to represent inter-individual differences with graph theoretical methods to make inferences for the population. Here, we proposed and tested a simple intuitive method that is based on finding the correlation between the rank-ordering of nodes within each connectome with respect to a given metric to quantify the differences/similarities between different connectomes.Methods: We used the diffusion imaging data of the entire HCP-1065 dataset of the Human Connectome Project (HCP) (n = 1,065 subjects). A customized cortical subparcellation of HCP-MMP atlas (360 parcels) (yielding a total of 1,598 ROIs) was used to generate connectivity matrices. Six graph measures including degree, strength, coreness, betweenness, closeness, and an overall “hubness” measure combining all five were studied. Group-level ranking-based aggregation method (“measure-then-aggregate”) was used to investigate network properties on population level.Results: Measure-then-aggregate technique was shown to represent population better than commonly used aggregate-then-measure technique (overall rs: 0.7 vs 0.5). Hubness measure was shown to highly correlate with all five graph measures (rs: 0.88–0.99). Minimum sample size required for optimal representation of population was found to be 50 to 100 subjects. Network analysis revealed a widely distributed set of cortical hubs on both hemispheres. Although highly-connected hub clusters had similar distribution between two hemispheres, average ranking values of homologous parcels of two hemispheres were significantly different in 71% of all cortical parcels on group-level.Conclusion: In this study, we provided experimental evidence for the robustness, limits and applicability of a novel group-level ranking-based hubness analysis technique. Graph-based analysis of large HCP dataset using this new technique revealed striking hemispheric asymmetry and intraparcel heterogeneities in the structural connectivity of the human brain.

ANALYSIS OF THE STRUCTURE AND PREDICTION OF THE MORBIDITY OF CHILDREN IN THE KAMENSKY DISTRICT

В статье приведены анализ и прогнозирование основных статистических показателей, характеризующих распространенность различных нозологических форм заболеваний среди детского населения Каменского района. Для определения качества медицинской помощи, предоставляемой в детской поликлинике, среди жителей района был проведен опрос, в ходе которого была выявлена частота посещения данного амбулаторно-поликлинического учреждения по поводу заболевания и с целью профилактики, оценен уровень оказываемой помощи по различным критериям, определены как положительные, так и отрицательные аспекты деятельности, а также предложены методы повышения эффективности работы поликлиники. С целью предвидения основных показателей заболеваемости был построен прогноз. В качестве данных для прогнозирования были использованы показатели заболеваемости детского населения прошлых лет. Прогнозирование осуществляется с помощью метода экспоненциального сглаживания с использованием линейного тренда и выбором оптимальных параметров сглаживания. Экспоненциальное сглаживание является интуитивным методом, который взвешивает наблюдаемые временные ряды неравномерно. Последние наблюдения взвешиваются более интенсивно, чем отдаленные наблюдения. Основной целью анализа и прогнозирования является выявление основных тенденций в изменении структуры заболеваемости, а также определение влияния качества и доступности оказываемых медицинских услуг в поликлинике на здоровье детского населения Каменского района The article presents the analysis and prediction of the main statistical indicators characterizing the prevalence of various nosological forms of diseases among the children of the Kamensky district. To determine the quality of medical care provided in the children's polyclinic, a survey was conducted among the residents of the district, during which the frequency of visits to this outpatient clinic for the disease and for the purpose of prevention was revealed, the level of care provided was assessed according to various criteria, both positive and negative aspects of activity were identified, and methods of improving the efficiency of the polyclinic were proposed. In order to anticipate the main indicators of morbidity, a forecast was built. The indicators of morbidity of the child population of previous years were used as data for forecasting. Forecasting is carried out using the exponential smoothing method using a linear trend and the choice of optimal smoothing parameters. Exponential smoothing is an intuitive method that weighs the observed time series unevenly. Recent observations are weighed more intensively than distant observations. The main purpose of the analysis and forecasting is to identify the main trends in the change in the structure of morbidity, as well as to determine the impact of the quality and availability of medical services provided in the polyclinic on the health of the children's population of the Kamensky district

Chemical hardness-driven interpretable machine learning approach for rapid search of photocatalysts

Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the ML models. Therefore, we devised an intuitive method of interpreting such opaque ML models through SHapley Additive exPlanations (SHAP) values and coupling them with the HT approach for finding efficient 2D water-splitting photocatalysts. We developed a new database of 3099 2D materials consisting of metals connected to six ligands in an octahedral geometry, termed as 2DO (octahedral 2D materials) database. The ML models were constructed using a combination of composition and chemical hardness-based features to gain insights into the thermodynamic and overall stabilities. Most importantly, it distinguished the target properties of the isocompositional 2DO materials differing in bond connectivities by combining the advantages of both elemental and structural features. The interpretable ML regression, classification, and data analysis lead to a new hypothesis that the highly stable 2DO materials follow the HSAB principle. The most stable 2DO materials were further screened based on suitable band gaps within the visible region and band alignments with respect to standard redox potentials using the GW method, resulting in 21 potential candidates. Moreover, HfSe2 and ZrSe2 were found to have high solar-to-hydrogen efficiencies reaching their theoretical limits. The proposed methodology will enable materials scientists and engineers to formulate predictive models, which will be accurate, physically interpretable, transferable, and computationally tractable.

Study on the assessment and reduction technology of carbon dioxide from cementing material manufacturing sector

For the purposes of simplifying the calculation task, adjusting production processes in time and solving the inconsistent requirements for carbon emissions, this paper investigates the calculation methods of carbon dioxide emissions from cement production, for example IPCC, WBCSD-CSI, MEE-CBMA, CNIS and BNU. Then a simplification and intuitive method is proposed. Based on the intuitive method, CO2 emission of 21 cement plants in China are calculated and analyzed, of which the error between the calculation results and those obtained by HJ 2519-2012 is less than 0.5%. About the carbon reduction technology in cement industry, there is limited reduction space that rely on energy efficiency improvements and clinker substitution. The technology of alternative fuels still needs to be further expanded. China has operated the first demonstration production line of CCUS technology at the Anhui Baimashan Conch cement plant with a capacity of 20,000 tons/year of industrial-grade liquid CO2 products and 30,000 tons/year of food-grade liquid CO2 products. Alternative raw material technology may be one developing direction to cut carbon emission; only 6.18% of steel slag was added to the raw meal at a 2500t/d production line, CO2 emission from process emissions were reduced by nearly 10%.

Detecting malware samples with similar image sets

This paper proposes a method for identifying and visualizing similarity relationships between malware samples based on their embedded graphical assets (such as desktop icons and button skins). We argue that analyzing such relationships has practical merit for a number of reasons. For example, we find that malware desktop icons are often used to trick users into running malware programs, so identifying groups of related malware samples based on these visual features can highlight themes in the social engineering tactics of today’s malware authors. Also, when malware samples share rare images, these image sharing relationships may indicate that the samples were generated or deployed by the same adversaries.To explore and evaluate this malware comparison method, the paper makes two contributions. First, we provide a scalable and intuitive method for computing similarity measurements between malware based on the visual similarity of their sets of images. Second, we give a visualization method that combines a force- directed graph layout with a set visualization technique so as to highlight visual similarity relationships in malware corpora. We evaluate the accuracy of our image set similarity comparison method against a hand curated malware relationship ground truth dataset, finding that our method performs well. We also evaluate our overall concept through a small qualitative study we conducted with three cyber security researchers. Feedback from the researchers confirmed our use cases and suggests that computer network defenders are interested in this capability.

Selection of Output Voltage Compensators Gains in Two Cascaded Boost Converters with Input Filters by Means of the \({\mathfrak{D}}\)-Decomposition Technique

In this paper, the D-decomposition technique is investigated as an intuitive method for finding the non-linear trajectories of PI-compensator gains. The trajectories reflect the desired dynamic properties at a system level specified by the gain and the phase margin (GMPM) in the frequency domain. They are presented as parametric curves in the proportional and the integral gains coordinates in form of KI=f(KP) functions. The curves are inscribed into global stability boundaries (GSB). The corresponding Nyquist plots are included for comparison. The analysis is based on a system consisting of two serial-connected boost converters. Each converter has its input filter. The major parasitic components of the system are taken into account during the mathematical and simulation modelling. The control circuit time delays and non-linear semiconductors characteristics are also included. A complete set of practically useful system-level transfer functions in form of mathematical formulas is included. Selected aspects, such as the control-to-output voltage and the control-to-input current of one sub-system of the simulation model, have been verified experimentally. The presented results clearly indicate the need for interactions between the sub-systems of a system to be taken into account during controller gains selection.

Galaxy assembly bias and large-scale distribution: a comparison between IllustrisTNG and a semi-analytic model

In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo paramaters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy-halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher-order statistics, such as cumulants of the density field, help to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.

Analysis of Capacitor Voltage Imbalance in Hybrid Converters and Inherently Balanced Operation Using Symmetric Architecture

<p>This paper investigates the origin of the flying capacitor voltage imbalance in hybrid converters. By observation and logical deduction, an intuitive voltage-charge relationship is established which can give a general explanation of the flying capacitor voltage balance in hybrid converters. This relationship can establish a relatively simple and intuitive method to identify the difference of balance performance in hybrid converters for Vout<(V_in /N) cases. Converter with even number of inductor charging intervals, are shown to be susceptible to flying capacitor voltage imbalance, while flying capacitors in hybrid converters with inductors having odd charging intervals have inherently balanced operations. As a direct result of the analysis, a new symmetric operation of FCML converters is introduced to achieve an inherent balance of flying capacitor voltages. Hardware implementations and experiments have been carried out for verifications of the analytical analysis and the new symmetric operation.</p>

Analysis of Capacitor Voltage Imbalance in Hybrid Converters and Inherently Balanced Operation Using Symmetric Architecture

<p>This paper investigates the origin of the flying capacitor voltage imbalance in hybrid converters. By observation and logical deduction, an intuitive voltage-charge relationship is established which can give a general explanation of the flying capacitor voltage balance in hybrid converters. This relationship can establish a relatively simple and intuitive method to identify the difference of balance performance in hybrid converters for Vout<(V_in /N) cases. Converter with even number of inductor charging intervals, are shown to be susceptible to flying capacitor voltage imbalance, while flying capacitors in hybrid converters with inductors having odd charging intervals have inherently balanced operations. As a direct result of the analysis, a new symmetric operation of FCML converters is introduced to achieve an inherent balance of flying capacitor voltages. Hardware implementations and experiments have been carried out for verifications of the analytical analysis and the new symmetric operation.</p>

Analysis of Uncertainties and Levels of Foreknowledge in Relation to Major Features of Emerging Technologies—The Context of Foresight Research for the Fourth Industrial Revolution

One of the key roles in the development of Industry 4.0 systems is played by “emerging technologies” as new tools with promising—though with a high level of uncertainty—capabilities. The management of such systems should be based on a comprehensive—future-oriented—research approach. Such activities are enabled by the foresight methodology. The main purpose of this publication is to attempt to answer the following research question: “What levels of foreknowledge and knowledge in the context of the development of emerging technologies—in relation to their features in Industry 4.0—should be taken into account during the analysis of uncertainties in the sense of foresight research based on different anticipated options?” In detail, the examination covered the relationship of classes of research foresight methods with regard to types of future, scopes of uncertainty, cycles of knowledge and original levels of foreknowledge in the field of the development of emerging technologies in Industry 4.0. Emerging technologies combined with the research on foreknowledge and uncertainties is an interesting research area with many theoretical and practical potential implications. The study uses the results of the analysis and criticism of the literature, mental experiments, and the intuitive method as the main research methods. This provides a basis for performing conceptual modeling.