Modeling and Verification of Simulation-Oriented Digital Selves

Networked life has now become one of our major life forms. In social networks, each individual has its own attributes and certain functions, which makes the current network present characteristics that the previous network did not have. The existing research believes that the structure and attributes of individuals in a network are the same, and they are in a single network at the same time. However, individuals in any social network may be in different networks at the same time and thus exhibit different behaviors, and such individuals are called digital selves. In this paper, we propose a simulation-oriented modeling method for digital selves, which allows them to be in multiple networks at the same time and to have their own decision-making mechanisms. The model consists of six parts, namely, pattern, affecter, decider, executor, monitor, and connector. After the verification of three simulation experiments, namely coevolutions, ecological structure evolution of an e-commerce market, and multi-information coevolution spreading, the model can be well applied in various scenarios, which verifies its feasibility and applicability

A Community Detection Algorithm Based on Topology Potential and Spectral Clustering

Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus

BACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD. METHODS: From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D. RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance ( P<5.0×10 -8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10 -9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10 -9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10 -8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction ( P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase ( P=1.0×10 -16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D

Метод оптимизации демпфирования на основе анализа рабочих режимов для подавления низкочастотных шумов оборудования

С целью снижения низкочастотного шума крупногабаритного оборудования предложен метод оптимизации демпфирования, основанный на анализе рабочих мод (Operational Mode Analysis) - OMA. Благодаря стабильности частот и конфигураций мод применение демпфирования может обеспечить эффективное шумоподавление, не вызывая новых проблем по сравнению со структурной оптимизацией, что делает этот метод одним из наиболее результативных для готовых конструкций. На примере моторного отсека экскаватора, выбранного в качестве объекта исследования, предложен метод оптимизации демпфирования на основе теста ОМА, который позволяет повысить эффективность шумоподавления для крупногабаритного оборудования. Результаты моделирования и реальных испытаний показывают, что на основе метода применения демпфирования с помощью ОМА уровень низкочастотной звуковой мощности значительно снижается, приближаясь к требованиям национальных стандартов

Spectral Characteristics of Unique Species of Burmese Amber

Special species of Burmese amber are highly valued within the gemological market due to their fancy optical characteristics. However, some ordinary amber species are misidentified as precious species, which has disrupted consumers’ purchasing behavior and the market order. In this study, seven Burmese amber species (golden, golden-blue, blood-tea, black-tea, green-tea, brownish-red, and ‘chameleon’ amber) were collected and investigated. By using conventional gemological tests, Fourier transform infrared (FTIR), three-dimensional (3D) fluorescence, and photoluminescence (PL) spectrometers, detailed analyses were performed on unique species. The FTIR spectra identified that there are three groups of peaks that can distinguish Burmese amber from any other origin. Additionally, the ‘Chameleon’ amber exhibited special patterns in the third group, which might be due to its internal aromatic hydrocarbons structures that are different from any other species. The 3D fluorescence spectra displayed that all seven species presented similar fluorescence behavior—the 334 or 347 nm emission wavelength could be optimally excited by 240 or 294 nm excitation wavelength in the ultraviolet region and the 380 ± 10 nm or 400 ± 10 nm excitation wavelength optimally excited the 430 nm emission wavelength in the violet region. In the red region, green-tea amber, black-tea amber, and brownish-red amber presented totally different fluorescence behavior, which could be regarded as a reference feature for differentiation. Obvious pink fluorescence on the surface of the tea amber was efficiently found under PL spectra, and we firstly suggest this test could be used as an effective way to distinguish black-tea amber from green-tea amber and some ordinary species (such as blood-tea amber). Both the PL and 3D fluorescence measurements demonstrated the different luminescence behavior of tea amber in the red region, which might be related to the type and content of red fluorescent substances in the tea amber

A Novel Local Maximum Potential Point Search Algorithm for Topology Potential Field

Topology potential field is a novel model to describe interaction and association of network nodes, which has attracted plenty of attention in community detection, node importance evaluation and network hot topics detection. The local maximum potential point search is a critical step for this research. Hill-climbing is a traditional algorithm for local maximum point search, which may leave out some local maximum potential points, and search performance is greatly influenced by initial node sequence. Based on the detailed analysis of local maximum potential points&apos; characteristics, this paper presents a novel local maximum potential point search algorithm. The results of simulation experiments showed that the new algorithm has better performance than the traditional hill-climbing method. It can find all local maximum potential points with high search efficiency