Each row shows the composition of the feature sets selected by MI given the sensor size constraint, the columns give the percentage of the total selected feature sets with the labelled sensor contents.

We compare the numerical properties of the different numerical methods for solving the H-infinity optimization problems for linear discrete-time systems. It is shown that the methods based on the solution of the associated discrete-time algebraic Riccati equation may be unstable due to an unnecessary increase in the condition number and that they have restricted application for ill-conditioned and singular problems. The experiments confirm that the numerical solution methods that are based on the solution of a Linear Matrix Inequality (LMI) are a much more reliable although much more expensive numerical technique for solving H-infinity optimization problems. Directions for developing high-performance software for H-infinity optimization are discussed.

Abstract: The Kuznetsov equation is a classical wave model of acoustics that incorpo- rates quadratic gradient nonlinearities. When its strong damping vanishes, it undergoes a singular behavior change, switching from a parabolic-like to a hyperbolic quasilinear evolution. In this work, we establish for the first time the optimal error bounds for its finite element approximation as well as a semi-implicit fully discrete approximation that are robust with respect to the vanishing damping parameter. The core of the new arguments lies in devising energy estimates directly for the error equation where one can more easily exploit the polynomial structure of the nonlinearities and compensate inverse estimates with smallness conditions on the error. Numerical experiments are included to illustrate the theoretical results. TechnicalRemarks: This program is intended to reproduce the results from the preprint "Robust fully discrete error bounds for the Kuznetsov equation in the inviscid limit" by Benjamin Dörich and Vanja Nikoli\'c The codes generates the lines in Figures 2, 3, 4, and 5 Requirements The program is tested with Kubuntu 22.04.5 and Python 3.10.12 and the following version of its modules: numpy - 1.21.5 matplotlib - 3.5.1

In applications such as clinical safety analysis, the data of the experiments usually consist of frequency counts. In the analysis of such data, researchers often face the problem of multiple testing based on discrete test statistics, aimed at controlling family-wise error rate (FWER). Most existing FWER controlling procedures are developed for continuous data, which are often conservative when analyzing discrete data. By using minimal attainable p-values, several FWER controlling procedures have been specifically developed for discrete data in the literature. In this article, by using known marginal distributions of true null p-values, three more powerful stepwise procedures are developed, which are modified versions of the conventional Bonferroni, Holm and Hochberg procedures, respectively. It is shown that the first two procedures strongly control the FWER under arbitrary dependence and are more powerful than the existing Tarone-type procedures, while the last one only ensures control of the FWER in special settings. Through extensive simulation studies, we provide numerical evidence of superior performance of the proposed procedures in terms of the FWER control and minimal power. A real clinical safety data are used to demonstrate applications of our proposed procedures. An R package “MHTdiscrete” and a web application are developed for implementing the proposed procedures.

China Semiconductor Discrete Device: Number of Employee: Average data was reported at 127.932 Person th in Dec 2013. This records an increase from the previous number of 123.990 Person th for Dec 2012. China Semiconductor Discrete Device: Number of Employee: Average data is updated monthly, averaging 101.800 Person th from Dec 1998 (Median) to Dec 2013, with 69 observations. The data reached an all-time high of 127.932 Person th in Dec 2013 and a record low of 69.913 Person th in Dec 2003. China Semiconductor Discrete Device: Number of Employee: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

This data release contains six different datasets that were used in the report SIR 2018-5108. These datasets contain discharge data, discrete dissolved-solids data, quality-control discrete dissolved data, and computed mean dissolved solids data that were collected at various locations between the Hoover Dam and the Imperial Dam. Study Sites: Site 1: Colorado River below Hoover Dam Site 2: Bill Williams River near Parker Site 3: Colorado River below Parker Dam Site 4: CRIR Main Canal Site 5: Palo Verde Canal Site 6: Colorado River at Palo Verde Dam Site 7: CRIR Lower Main Drain Site 8: CRIR Upper Levee Drain Site 9: PVID Outfall Drain Site 10: Colorado River above Imperial Dam Discrete Dissolved-solids Dataset and Replicate Samples for Discrete Dissolved-solids Dataset: The Bureau of Reclamation collected discrete water-quality samples for the parameter of dissolved-solids (sum of constituents). Dissolved-solids, measured in milligrams per liter, are the sum of the following constituents: bicarbonate, calcium, carbonate, chloride, fluoride, magnesium, nitrate, potassium, silicon dioxide, sodium, and sulfate. These samples were collected on a monthly to bimonthly basis at various time periods between 1990 and 2016 at Sites 1-5 and Sites 7-10. No data were collected for Site 6: Colorado River at Palo Verde Dam. The Bureau of Reclamation and the USGS collected discrete quality-control replicate samples for the parameter of dissolved-solids, sum of constituents measured in milligrams per liter. The USGS collected discrete quality-control replicate samples in 2002 and 2003 and the Bureau of Reclamation collected discrete quality-control replicate samples in 2016 and 2017. Listed below are the sites where these samples were collected at and which agency collected the samples. Site 3: Colorado River below Parker Dam: USGS and Reclamation Site 4: CRIR Main Canal: Reclamation Site 5: Palo Verde Canal: Reclamation Site 7: CRIR Lower Main Drain: Reclamation Site 8: CRIR Upper Levee Drain: Reclamation Site 9: PVID Outfall Drain: Reclamation Site 10: Colorado River above Imperial Dam: USGS and Reclamation Monthly Mean Datasets and Mean Monthly Datasets: Monthly mean discharge data (cfs), flow weighted monthly mean dissolved-solids concentrations (mg/L) data and monthly mean dissolved-solids load data from 1990 to 2016 were computed using raw data from the USGS and the Bureau of Reclamation. This data were computed for all 10 sites. Flow weighted monthly mean dissolved-solids concentration and monthly mean dissolved-solids load were not computed for Site 2: Bill Williams River near Parker. The monthly mean datasets that were calculated for each month for the period between 1990 and 2016 were used to compute the mean monthly discharge and the mean monthly dissolved-solids load for each of the 12 months within a year. Each monthly mean was weighted by how many days were in the month and then averaged for each of the twelve months. This was computed for all 10 sites except mean monthly dissolved-solids load were not computed at Site 2: Bill Williams River near Parker. Site 8a: Colorado River between Parker and Palo Verde Valleys was computed by summing the data from sites 6, 7 and 8. Bill Williams Daily Mean Discharge, Instantaneous Dissolved-solids Concentration, and Daily Means Dissolved-solids Load Dataset: Daily mean discharge (cfs), instantaneous solids concentration (mg/L), and daily mean dissolved solids load were calculated using raw data collected by the USGS and the Bureau of Reclamation. This data were calculated for Site 2: Bill Williams River near Parker for the period of January 1990 to February 2016. Palo Verde Irrigation District Outfall Drain Mean Daily Discharge Dataset: The Bureau of Reclamation collected mean daily discharge data for the period of 01/01/2005 to 09/30/2016 at the Palo Verde Irrigation District (PVID) outfall drain using a stage-discharge relationship.

The well-known Gronwall lemma often serves as a major tool for the analysis of time-dependent problems via energy methods. However, there is the need for a similar tool when considering temporal discretizations of evolutionary problems. With the paper in hand, the author wishes to give an overview of some discrete versions of the Gronwall lemma and presents a unified approach. In particular, new discrete versions of the lemma in its differential form and their application, showing decay behaviour for discretized parabolic problems, are studied. These versions give effective tools for the stability and error analysis of the temporal semi-discretization of parabolic problems covering non-homogeneous problems as well as approximations with variable step sizes.

China Semiconductor Discrete Device: Number of Loss Making Enterprise data was reported at 87.000 Unit in Oct 2015. This records a decrease from the previous number of 91.000 Unit for Sep 2015. China Semiconductor Discrete Device: Number of Loss Making Enterprise data is updated monthly, averaging 86.000 Unit from Dec 1998 (Median) to Oct 2015, with 102 observations. The data reached an all-time high of 178.000 Unit in Feb 2009 and a record low of 48.000 Unit in Aug 2011. China Semiconductor Discrete Device: Number of Loss Making Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

China Semicon Discrete Appliance Mfg: Number of Enterprise data was reported at 343.000 Unit in Dec 2017. This records an increase from the previous number of 334.000 Unit for Dec 2016. China Semicon Discrete Appliance Mfg: Number of Enterprise data is updated monthly, averaging 283.500 Unit from Jan 2004 (Median) to Dec 2017, with 80 observations. The data reached an all-time high of 411.000 Unit in Dec 2010 and a record low of 207.000 Unit in Mar 2004. China Semicon Discrete Appliance Mfg: Number of Enterprise data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under Global Database’s China – Table CN.RFK: Electronic Mfg Industry: Electronic Appliance: Monthly: Semiconductor Discrete.

Transparent boundary conditions (TBCs) are an important tool for the truncation of the computational domain in order to compute solutions on an unbounded domain. In this work we want to show how the standard assumption of `compactly supported data' could be relaxed and derive TBCs for a generalized Schrödinger equation directly for the numerical scheme on the discrete level. With this inhomogeneous TBCs it is not necessary that the initial data lies completely inside the computational region. However, an increased computational effort must be accepted.

The major aim of this study was to examine the influence of an embedded viscoelastic-plastic layer at different viscosity values on accretionary wedges at subduction zones. To quantify the effects of the layer viscosity, we analysed the wedge geometry, accretion mode, thrust systems and mass transport pattern. Therefore, we developed a numerical 2D 'sandbox' model utilising the Discrete Element Method. Starting with a simple pure Mohr Coulomb sequence, we added an embedded viscoelastic-plastic layer within the brittle, undeformed 'sediment' package. This layer followed Burger's rheology, which simulates the creep behaviour of natural rocks, such as evaporites. This layer got thrusted and folded during the subduction process. The testing of different bulk viscosity values, from 1 × 10**13 to 1 × 10**14 (Pa s), revealed a certain range where an active detachment evolved within the viscoelastic-plastic layer that decoupled the over- and the underlying brittle strata. This mid-level detachment caused the evolution of a frontally accreted wedge above it and a long underthrusted and subsequently basally accreted sequence beneath it. Both sequences were characterised by specific mass transport patterns depending on the used viscosity value. With decreasing bulk viscosities, thrust systems above this weak mid-level detachment became increasingly symmetrical and the particle uplift was reduced, as would be expected for a salt controlled forearc in nature. Simultaneously, antiformal stacking was favoured over hinterland dipping in the lower brittle layer and overturning of the uplifted material increased. Hence, we validated that the viscosity of an embedded detachment strongly influences the whole wedge mechanics, both the respective lower slope and the upper slope duplex, shown by e.g. the mass transport pattern.

This integer sequence was registered and published in the On-Line Encyclopedia of Integer Sequences (OEIS.org) Database on October 14 - 2024, under the OEIS code: A377045.

This sequence can be expressed with the help of two general formulas that uses the sequences:

1) A000041: a(n) is the number of partitions of n (the partition numbers).

2) A002407: Cuban primes: primes which are the difference of two consecutive cubes.

3) A121259: Numbers k such that (3*k^2 + 1)/4 is prime.

The two aforementioned general formulas are as follows:

a(n) = A000041(A002407(n)). (1)

a(n) = A000041((3*A121259 (n)^2+1) / 4). (2)

Some interesting properties of this sequence are:

◼ Number of partitions of prime numbers that are the difference of two consecutive cubes.

◼ Number of partitions of primes p such that p=(3*n^2 + 1) / 4 for some integer n (A121259).

◼ a(13) = ~1.49910(x10^43).

◼ The last known integer n in A121259 is 341 and corresponds to a(60) = ~1.59114(x10^323).

The numerical data showed on this dataset was generated by the following Mathematica program:

PartitionsP[Select[Table[(3 k^2 + 1)/4, {k, 500}], PrimeQ]]

The previous program was builded on Mathematica v13.3.0.

Note: More mathematical details, graphics and technical information can be found in the notebook (.nb) & pdf files provided in this data pack.

China Semicon Discrete Appliance Mfg: Number of Enterprise: Loss Making data was reported at 52.000 Unit in Dec 2017. This records a decrease from the previous number of 56.000 Unit for Dec 2016. China Semicon Discrete Appliance Mfg: Number of Enterprise: Loss Making data is updated monthly, averaging 70.000 Unit from Mar 2003 (Median) to Dec 2017, with 70 observations. The data reached an all-time high of 178.000 Unit in Feb 2009 and a record low of 49.000 Unit in Dec 2011. China Semicon Discrete Appliance Mfg: Number of Enterprise: Loss Making data remains active status in CEIC and is reported by Ministry of Industry and Information Technology. The data is categorized under Global Database’s China – Table CN.RFK: Electronic Mfg Industry: Electronic Appliance: Monthly: Semiconductor Discrete.

1. Classic natal dispersal studies focus mainly on distance travelled. Although distances capture some of the main selective pressures related to dispersal, this approach cannot easily incorporate the properties of the actual destination vs. the available alternatives. Recently, movement ecology studies have addressed questions on movement decisions in relation to availability of resources and/or availability of suitable habitats through the use of discrete choice models (DCMs), a widely used type of models within econometrics, which explains individual choices as a function of the properties of a finite number of alternatives. 2. In this contribution, we show how the dispersal discrete choice model (DDCM) can be used for analysing natal dispersal data in patchy environments given that the natal and the breeding area of the disperser are observed. We test this method using a case study on Great Tits (Parus major) in an archipelago of small woodlots. 3. Our results show that DDCMs are able to capture the results of classic distance-based approaches and simultaneously allow testing hypotheses on how departure and settlement are affected by variables that characterize the disperser, the natal patch and the breeding area, as well as their interactions. 4. DDCMs can be applied to any other species and system that uses some form of discrete breeding location or a certain degree of discretization can be applied.

This is the dataset for "Identification of Karst Spring Hydrographs Using Laboratory and Numerical Simulations Considering Combined Discrete-Continuum Approaches”.

China Semiconductor Discrete Device: Number of Enterprise data was reported at 326.000 Unit in Oct 2015. This records an increase from the previous number of 324.000 Unit for Sep 2015. China Semiconductor Discrete Device: Number of Enterprise data is updated monthly, averaging 314.000 Unit from Dec 1998 (Median) to Oct 2015, with 102 observations. The data reached an all-time high of 422.000 Unit in Dec 2008 and a record low of 205.000 Unit in Dec 2003. China Semiconductor Discrete Device: Number of Enterprise data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.

Synthetic data used to demonstrate the effectiveness of the MKAD algorithm with respect to detecting anomalies in both the continuous numerical data and binary discrete data.

This integer sequence was registered and published in the On-Line Encyclopedia of Integer Sequences (OEIS.org) Database on August 17 - 2023, under the OEIS code: A363743.

This sequence can be generally expressed as follows: a(n) = floor(sqrt(log_10(n!))), where n is a non-negative integer. It should be noted that the aforementioned formula is written in accordance with OEIS specific style sheet format. On the other hand, it was possible to represent the general formula on another two forms that the following:

1) a(n) = floor(sqrt(A034886(n) - 1)).

2) a(n) = A000196(A034886(n) -1).

This dataset verifies the reported properties in the comments section of the OEIS publication. Our evaluation ranges from 0 to n = 5000, in contrast to the publication which ranges from 0 to n = 92. These mentioned properties are the following:

* Every non-negative integer occurs at least 4-times.

* Each integer k > 14 appears fewer than k times.

* The only integers k that occur exactly k times are 11, 13 and 14.

* This sequence can produce random values between 0 and 1 if we do a(n)/a(n+m) for any non-negative integer m.

The numerical data showed on this dataset was generated by the following Mathematica program: Array[Floor@ Sqrt[Log10[#!]] &, 5000, 0] The previous program was builded on Mathematica v13.3.0.

Note: More mathematical details, graphics and technical information can be found in the notebook or .nb file provided in this dataset.

This data set contains the numerical raw data for "Mode splitting of spin waves in magnetic nanotubes with discrete symmetries" published in Physical Review B. The data has been obtained using our in-house developed finite-element dynamic-matrix approach for propagating spin waves [see AIP Advances 11, 095006 (2021) for details].

• The folder high-res-only-k0/ contains the lateral mode profiles and frequencies of polygonal nanotubes with different number of corners at k = 0. It also contains a python script used to calculate the microwave absorption from the mode profiles.
• The folders poly_*/ contain only the dispersion without lateral mode profiles for the different polygonal tubes as well as a round nanotube
• All folders contain the geometry and mesh files as well as sparam and eparam yaml files containg the material parameters and experimental parameters, respectively. The equilibrium states are saved as m_eq.h5 hdf5 files.

China Semiconductor Discrete Device: YoY: Number of Employee: Average data was reported at 8.080 % in Dec 2012. This records an increase from the previous number of 6.385 % for Nov 2012. China Semiconductor Discrete Device: YoY: Number of Employee: Average data is updated monthly, averaging 9.500 % from Jan 2006 (Median) to Dec 2012, with 55 observations. The data reached an all-time high of 16.620 % in May 2010 and a record low of -7.480 % in May 2009. China Semiconductor Discrete Device: YoY: Number of Employee: Average data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Industrial Sector – Table CN.BIB: Electronic Device: Semiconductor Discrete Device.