There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Economic Control of Quality of Manufactured Product.

In this chapter, we will restrict our attention to the ferrites and a few other closely related materials. The great interest in ferrites stems from their unique combination of a spontaneous magnetization and a high electrical resistivity. The observed magnetization results from the difference in the magnetizations of two non-equivalent sub-lattices of the magnetic ions in the crystal structure. Materials of this type should strictly be designated as “ferrimagnetic” and in some respects are more closely related to anti-ferromagnetic substances than they are to ferromagnetics in which the magnetization results from the parallel alignment of all the magnetic moments present. We shall not adhere to this special nomenclature except to emphasize effects, which are due to the existence of the sub-lattices.

Ferromagnetic materials

Rapid improvements in sequencing and array-based platforms are resulting in a flood of diverse genome-wide data, including data from exome and whole-genome sequencing, epigenetic surveys, expression profiling of coding and noncoding RNAs, single nucleotide polymorphism (SNP) and copy number profiling, and functional assays. Analysis of these large, diverse data sets holds the promise of a more comprehensive understanding of the genome and its relation to human disease. Experienced and knowledgeable human review is an essential component of this process, complementing computational approaches. This calls for efficient and intuitive visualization tools able to scale to very large data sets and to flexibly integrate multiple data types, including clinical data. However, the sheer volume and scope of data pose a significant challenge to the development of such tools.

Integrative Genomics Viewer

A fundamental equation is derived for the structure of dislocations in solids. With the interaction potential that can be obtained properly from the first principle, the equation provides a complete basis for a comprehensive study of the dislocation structure. In particular, the full structural feature and related properties of dislocation can be predicted theoretically, which makes it possible to compare precisely the theoretical results and what is produced by experiments or numerical simulations.

The dislocation equation as a generalization of Peierls equation

Accurate prediction of TBM performance is very important for efficient completion of TBM construction tunnel project. This paper aims to predict the advance rate (AR) of tunnel boring machine (TBM) using three hybrid models by combining three swarm intelligence optimization algorithm (Ant Lion Optimizer (ALO), Loin swarm optimization (LSO) and Seagull optimization algorithm (SOA)) and the Extreme learning machine (ELM) model, namely ELM-ALO, ELM-LSO and ELM-SOA model respectively. The dataset consists of 1, 286 samples from the Pahang Selangor Raw Water Transfer (PSRWT) tunnel project in Malaysia, and containing six parameters (rock quality designation (RQD), uniaxial compressive strength (UCS), rock mass rating (RMR), Brazilian tensile strength (BTS), thrust force per cutter (TFC) and revolution per minutes (RPM)) from rock mass and machines. In order to evaluate the prediction performance of different hybrid models, the root mean square error (RMSE), the mean absolute error (MAE), the mean square error (MSE), the determination coefficient (R2), the sum of square error (SSE), and the variance accounted for (VAF) were adopted as the performance indicators. The results show that ELM-LSO is the best model to predict AR. Sensitivity analysis shows the importance of all considerations to AR. TFC, RPM and RMR are the three most important parameters. But this is not absolute, more parameters need to be taken into account in AR prediction. Meanwhile, the ELM-LSO model proposed in this paper can be used as a new method to predict AR. 

Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Ab initio calculations on the third-order elastic constants for selected B2–MgRE (RE = Y, Tb, Dy, Nd) intermetallics

Stress conditions are critical in deep hard rock mining and significantly influence hard rock cuttability. The peak cutting force (PCF), cutting work (CW), and specific energy (SE) can reflect rock cuttability and determine the feasibility and saving of mechanized mining to some extent. In this paper, the influence of uniaxial lateral stress on rock cuttability was investigated by an indentation experiment on cuboid rock using a conical pick, and a theoretical model was proposed to analyze the PCF and associated factors. The PCF, CW, and SE were used as indices to measure hard rock cuttability. The regression analyses show that rock cuttability presents as decreasing followed by increasing as uniaxial lateral stresses increases. The theoretical model was established by simplifying rock fragments into three-dimensional ellipse cones, and a formula was derived based on the elastic fracture mechanics theory. The error between the calculated and experimental values is 3.8%, which confirms the accuracy of the prediction formula. Finally, rock fragmentation by using conical picks was successfully applied on the field mining stope by inducing high geostresses to promote adjustments in stress and improve ore-rock cuttability. 

/pdf/experimental-investigation-and-theoretical-analysis-of-1j7wrosi.pdf

Experimental investigation and theoretical analysis of indentations on cuboid hard rock using a conical pick under uniaxial lateral stress

Autoimmune diseases develop through the exposure of genetically susceptible hosts to environmental triggers. Advances in high-throughput genotyping and sequencing technologies coupled with comprehensive databases of human genetic variation and the assembly of large cohorts of case and control subjects have led to substantial progress in defining the genetic risk factors that underlie autoimmune diseases (1). The workhorse statistical methodology has been the genome-wide association study (GWAS). Studies using the GWAS approach have convincingly and reproducibly identified ∼700 genomic regions in 183 published studies of autoimmune diseases at the level of genome-wide statistical significance (P < 10−8) (www.genome.gov/gwastudies). The majority of these genetic associations are near genes that map to critical immunoregulatory pathways, illuminating genetic effects that are shared across multiple autoimmune diseases and other genetic effects that are restricted to only a few (1).

GWAS studies detect most causal variants indirectly, by leveraging linkage disequilibrium (LD) throughout the human genome. Classically defined, LD is the nonrandom association of two or more loci, resulting in segments of the genome being inherited as haplotype “blocks.” Knowledge of the allele at one variant predicts with high likelihood the alleles at the other variants on the same haplotype block (2). Though LD makes locus discovery by GWAS very efficient, because only one or two variants per haplotype block need to be genotyped in order to detect association, the high correlation of variants on associated haplotypes confounds the ability of genetic association methods to distinguish causal from noncausal variants. The UBE2L3 locus associated with multiple autoimmune diseases, including systemic lupus erythematosus (SLE) (3), illustrates the problem vividly, where 34 SLE-associated variants are located within a 67-kb haplotype, but we assume that only a few are likely to be causal (3). With a situation such as this, which variants are we to choose for functional screening? No specific guidelines exist on how to answer this question.

In general, the approach to overcoming the LD problem is to first comprehensively understand the genetic architecture at a given locus. Doing this in multiple ethnic populations when possible, adds significant power to our ability to discern causal variants by allowing the comparison of haplotypes across populations. Comprehensive characterization of a locus may include the following activities: 1) locus enrichment—capture for analysis all available genetic variation present on the risk haplotype, 2) locus refinement—winnow down the associated variants within a locus to a prioritized list for functional testing, and 3) functional testing—identify allele-specific differences in biologic function that support the variant’s role(s) in causality. In this review, we discuss each of these steps and describe in more detail the available molecular methods that can provide the functional evidence required to assign causality to variants associated with autoimmune diseases.

Shaofeng Wang

Papers

The dislocation equation as a generalization of Peierls equation

Developing hybrid ELM-ALO, ELM-LSO and ELM-SOA models for predicting advance rate of TBM

Ab initio calculations on the third-order elastic constants for selected B2–MgRE (RE = Y, Tb, Dy, Nd) intermetallics

Experimental investigation and theoretical analysis of indentations on cuboid hard rock using a conical pick under uniaxial lateral stress

Disease Mechanisms in Rheumatology—Tools and Pathways: Defining Functional Genetic Variants in Autoimmune Diseases