A Theoretical Perspective to Study the Optical Properties of Tetrafluoroborates.

In this study, a series of tetrafluoroborates with non-π-conjugated [BF4 ] tetrahedra are investigated systematically by first-principles calculations. Theoretical studies demonstrate that tetrafluoroborates with alkali and/or alkaline-earth metals are more favorable for deep-ultraviolet transmission and are comparable to the classical deep-ultraviolet (deep-UV) material, MgF2 . Furthermore, bandgap decrease with the increasing of ionic radii in alkali and/or alkaline-earth metals. Introducing highly polarizable cations with d10 -configuration or cations with lone pair electrons into the structure will decrease the bandgaps. The birefringence and second harmonic generation effects are not large enough in tetrafluoroborates because polarizability anisotropy and hyperpolarizability in non-π-conjugated [BF4 ] tetrahedra are much smaller than those in π-conjugated groups. However, the second harmonic generation effect for [BF4 ] tetrahedra has a higher contribution in comparison with that due to birefringence. To effectively synthesize the borate fluorides or fluorooxoborates in the deep-UV region, raw materials with B-F bonds are preferred.

Acoustic Multi-Parameter Early Warning Method for Transformer DC Bias State.

The acoustic signal in the operation of a power transformer contains a lot of transformer operation state information, which is of great significance to the detection of DC bias state. In this paper, three typical parameters used for DC bias state detection are selected by comparing the acoustic variation of a 500 kV Jingting transformer substation No. 2 transformer with that of the core model built in the laboratory; then, acoustic samples of the 162 EHV normal state transformers are collected, and the distribution regularity of three typical parameters in normal state is given. Finally, according to the distribution regularity, clear warning threshold of typical parameters are given, and the DC bias cases from the 500 kV Jingting transformer substation are used to verify the effectiveness of the threshold.

A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis.

Collecting and analyzing massive data generated from smart devices have become increasingly pervasive in crowdsensing, which are the building blocks for data-driven decision-making. However, extensive statistics and analysis of such data will seriously threaten the privacy of participating users. Local differential privacy (LDP) was proposed as an excellent and prevalent privacy model with distributed architecture, which can provide strong privacy guarantees for each user while collecting and analyzing data. LDP ensures that each user's data is locally perturbed first in the client-side and then sent to the server-side, thereby protecting data from privacy leaks on both the client-side and server-side. This survey presents a comprehensive and systematic overview of LDP with respect to privacy models, research tasks, enabling mechanisms, and various applications. Specifically, we first provide a theoretical summarization of LDP, including the LDP model, the variants of LDP, and the basic framework of LDP algorithms. Then, we investigate and compare the diverse LDP mechanisms for various data statistics and analysis tasks from the perspectives of frequency estimation, mean estimation, and machine learning. Furthermore, we also summarize practical LDP-based application scenarios. Finally, we outline several future research directions under LDP.

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems.

Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles' location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

How good are my data and what is the resolution?

Following integration of the observed diffraction spots, the process of `data reduction' initially aims to determine the point-group symmetry of the data and the likely space group. This can be performed with the program POINTLESS. The scaling program then puts all the measurements on a common scale, averages measurements of symmetry-related reflections (using the symmetry determined previously) and produces many statistics that provide the first important measures of data quality. A new scaling program, AIMLESS, implements scaling models similar to those in SCALA but adds some additional analyses. From the analyses, a number of decisions can be made about the quality of the data and whether some measurements should be discarded. The effective `resolution' of a data set is a difficult and possibly contentious question (particularly with referees of papers) and this is discussed in the light of tests comparing the data-processing statistics with trials of refinement against observed and simulated data, and automated model-building and comparison of maps calculated with different resolution limits. These trials show that adding weak high-resolution data beyond the commonly used limits may make some improvement and does no harm.

Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health.

Predictive models play a crucial role in RBMs to analyze performance indicator results to manage unexpected events and make timely decisions to resolve them. Their use in Mexico is deficient, and monitoring and evaluation are among the weakest pillars of the model. In response to these needs, the aim of this study was to perform a comparative analysis of three predictive models to analyze 10 medical performance indicators and cancer data related to children with cancer. To accomplish these purposes, a comparative and retrospective study with nonprobabilistic convenience sampling was conducted. The predictive models were exponential smoothing, autoregressive integrated moving average, and linear regression. The lowest mean absolute error was used to identify the best model. Linear regression performed best regarding nine of the ten indicators, with seven showing p < 0.05. Three of their assumptions were checked using the Shapiro-Wilk, Cook's distance, and Breusch-Pagan tests. Predictive models with RBM are a valid and relevant instrument for monitoring and evaluating performance indicator results to support forecasting and decision-making based on evidence and must be promoted for use with cancer data statistics. The place numbers obtained by cancer disease inside the main causes of death, morbidity and hospital outpatients in a National Institute of Health were presented as evidence of the importance of implementing performance indicators associated with children with cancer.

All genera of the infraorder Gryllidea of the world: a full list and biodiversity information on the April 2020 release of the Orthoptera Species File (OSF).

According to the Orthoptera species file, there are 765 genera of the infraorder Gryllidea in the world, belonging to 2 superfamilies, 8 families, 37 subfamilies and 6159 species or subspecies in total. A complete list of all genera in the world is provided, including type species, synonyms and species richness. Through statistical analysis, there are 26 extant subfamilies, accounting for 70.27%, 692 extant genera, accounting for 90.46%, 6010 extant species or subspecies, accounting for 97.58%. At the same time, the composition characteristics of the extant genera and species or subspecies in all subfamilies: subfamily Gryllinae has the largest number of genera and species or subspecies, reaching 125 genera and 1221 species or subspecies, with the proportion of 16.34% and 20.34%, followed by subfamily Podoscrtinae, with the number of 99 genera, 770 species or subspecies, with the proportion of 12.94% and 12.83%; subfamily Gryllomiminae with the least number, the number and ratio is 1 genus, 2 species and 0.13%, 0.03%. Among all fossil genera, subfamily †Cearagryllinae is the largest, with 7 genera, 15 species. There are 21 free genera in infraorder Gryllidea, accounting for 2.75%, and 268 monotypic genera (only 1 species), accounting for 35.03%. The largest genus is Hapithus Uhler, 1864, with 209 species or subspecies.

Covid-19 Dataset: Worldwide spread log including countries first case and first death.

The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.

Ultra-low-dose CT image denoising using modified BM3D scheme tailored to data statistics.

PURPOSE: It is important to enhance image quality for low-dose CT acquisitions to push the ALARA boundary. Current state-of-the-art block-matching three-dimensional (BM3D) denoising scheme assumes white Gaussian noise (WGN) model. This study proposes a novel filtering module to be incorporated into the BM3D framework for ultra-low-dose CT denoising, by accounting for its specific power spectral properties. METHODS: In the current BM3D algorithm, the Wiener filtering is applied in the transform domain to a post-thresholding signal for enhanced denoising. However, unlike most natural/synthetic images, low-dose CTs do not obey the ideal Gaussian noise model. Based on the specific noise properties of ultra-low-dose CT, we derive the optimal transform-domain coefficients of Wiener filter based on the minimum mean-square-error (MMSE) criterion, taking the noise spectrum and the signal/noise cross spectrum into consideration. In the absence of ground-truth signal, the hard-thresholding denoising module in the previous stage is used as a plug-in estimator. We evaluate the denoising performance on thoracic CT image datasets containing paired full-dose and ultra-low-dose images simulated by a well-validated clinical engine (or pipeline). We also assess its clinical implication by applying the denoising methods to the emphysema quantification task. Our modified BM3D method is compared with the current one, using peak signal-to-noise ratio (PSNR) and emphysema scoring results as evaluation metrics. RESULTS: The noise in ultra-low-dose CT presented distinct non-Gaussian characteristics and was correlated with image intensity. Performance evaluation showed that the current Wiener filter in basic BM3D algorithm yielded little denoising enhancement on ultra-low-dose CT images. In contrast, the proposed Wiener filter achieved (1.46, 1.91) dB performance gain in mean and median peak signal-to-noise ratio (PSNR) for 5%-dose image denoising and (0.93, 0.95) dB improvement for 10% dose. A paired t-test of the PSNRs between denoising using the current and the proposed Wiener filters demonstrated statistically significant improvement, yielding P-values of 1.45E-12 and 1.34E-7 on 5% and 10%-dose images, respectively. In addition, emphysema quantification on the denoised images using the modified BM3D method also had statistically significant advantage over that using the current BM3D scheme, resulting in a P-value of 6.30E-5 with the commonly used measure. CONCLUSIONS: This work tailors the Wiener filter in BM3D algorithm to data statistics and demonstrates statistically significant performance improvement on ultra-low-dose CT image denoising and a subsequent emphysema quantification task. Such performance gain is more pronounced with a lower dose level. The development and rationale are generally enough for other image denoising tasks when the WGN assumption is violated.

Diagnostic relevance of free light chains in cerebrospinal fluid - The hyperbolic reference range for reliable data interpretation in quotient diagrams.

BACKGROUND: Free light chains, type kappa (FLC-K), in cerebrospinal fluid (CSF) were compared to oligoclonal IgG in many studies for sensitive detection of immune reactions in brain. The missing consensus about CSF data interpretation prevents reliable conclusions. This can be overcome by a theory-based hyperbolic reference range in CSF/serum quotient diagrams. METHODS: Mean Quotients for FLC-K, QKappa, and albumin, QAlb, of grouped, biochemically defined controls (N = 433) are fitted with the hyperbolic function QKappa(mean) = a/b (QAlb2 + b2)0.5 - c by a generally applicable procedure excluding outliers. RESULTS: With QKappa(mean), the coefficient of variation CV (22.5%) and the reference range (QKappa(mean) ±â€¯3 CV) we got the discrimination line QKappa(lim) = (3.27(QAlb2 + 33)0.5-8.2) ×10-3 in a FLC-K Reibergram. Intrathecal FLC-K was found in 8% of another control group without OCB (N = 388) but was missed in 7% of patients with definite Multiple sclerosis (N = 95). In MS the mean intrathecal fraction was threefold larger for FLC-K (95%) compared to total IgG (36%). Similar mean quantities of intrathecal FLC-K contradict an immunological conversion between a Clinically isolated syndrome and MS. DISCUSSION: The hyperbolic reference range is superior to linear FLC-K Index (10 to 15% false negatives) and exponential curves (30% false positive interpretations for controls) in the analytical range of MS data, with excellent data fit for up to ten-fold larger QAlb values. Dynamics of the small molecule FLC-K contribute to the understanding of molecular size dependent barrier functions.

Those ubiquitous fertility trends: United States, 1945-1979.

PIP: 1970-79 US fertility trends among differnet racial, regional, age, educational, parity, and socioeconomic subgroups in the population were examined, using own children data from the 1976 Survey of Income and Education (SIE) and the March Current Population Surveys (CPS) from 1968-80. In addition, cross-sectional differences in fertility for the subgroups were compared for 1970 and 1976, using multiple regression analysis. 1st, the appropriateness of using fertility rates obtained from own children data was assessed by comparing fertility rates obtained from the SIE data with those derived from vital statistic and census data. The comparative analysis confirmed that the SIE data yielded an accurate estimate of period fertility rates for currently married women, provided the subgroup samples were sufficiently large. CPS fertility estimates were also judged to be accurate if data from 3 adjacent survey years was pooled to increase sample size. Fertility trends for 5 educational groups were assessed separately for 1967-73. During this periold, there was a marked decline in fertility for all 5 groups; for the group with 5-8 years of education the decline was only 14%, but for the other 4 groups, which included women with 9-16 or more years of education, the decline in fertility ranged from 26-29%. In assessing the 1970-76 trends, the sample was restricted to own children, aged 3 years or less, of currently married women, under 40 years of age. Among whites, there was an overall 20% decline in fertility between 1970-76 and an overall fertility increase of about 2% between 1976-79. These trends were observed in all 28 white subgroups. A similar pattern was observed for blacks. There was an overall fertility decline of 24% between 1970-76, and this decline was apparent for all subgroups except women with college degrees. Betwen 1976-79, black fertility rates, unlike white rates, continued to decline, but the rate of decline was only 3%. Furthermore, the decline in almost all the black subgroups was markedly less than in the 1970-76 periold, and for many of the subgroups the trend was reversed and fertility increased. In summary, the fertility trends noted for 1970-79 were pervasive for almost all the subgroups for both blacks and whites; i.e., there was a marked decline in fertility between 1970-76 and than a reversal or slowing down of the decline during the 1976-79 for all black and white subgroups. Cross-sectional fertility differences in the subgroups in 1970 and in 1979 were quite similar, and fertility rates differed markedly for the separate subgroups. These differences do not, of course, explain the pervasive trends observed in the analysis of the fertility rates over time. A similar study assessing fertility trends among subgroups for the early 1940's through the late 1960s also revealed the pervasive nature of period fertility trends. Demographers have not as yet been able to explain these shifts in fertility that cut across all subgroups in the US and which also characterize the period fertility rates in other developed countries. Tables provided information on 1) total fertility rates by educational level and by geographical region for 1945-1975; 2) % change in number of own children less than 3 years of age among women under age 40 by maternal age, maternal education, initial parity, geographical region, and husband's income; and 3) mean number of own children less than 3 years of age among women under age 40 by maternal age, education, parity, region, and husband's income.^ieng

Optimization of service in Outpatient Pharmacy and Emergency Pharmacy by operational research and data statistics / 第二军医大学学报

Objective: To investigate the actualities of service in Outpatient Pharmacy and Emergency Pharmacy. Methods: With queuing theory of operational research and data statistics, the specialities and varities of service in both Pharmacies were investigated before and after the application of computer network. Results: (1)The distribution of the patients number arriving the Pharmacies was unequal.(2) The time of service was extended after using network.(3)The average individual time of service in Emergency Pharmacy was 74 s, and 48 s in Outpatient Pharmacy. The reasons for the average individual time of service in Emergency Pharmacy more than that in Outpatient Pharmacy was related to the formulation of drugs in 2 pharmacies and the number of drugs on prescriptions. (4)The number of windows for service should be increased in Emergency Pharmacy after using network. Conclusion: Operational research and data statistics will provide the data assisting the manager in making decisions. [