Substrate-tuned boson localization in superfluid (4)He films

We have studied the influence of the substrate on superfluidity in thin 4He films. The inert coverage, the amount of nonsuperfluid 4He localized by the substrate, is found to scale with the well depth of the 4He-substrate interaction potential for six different substrates. Although the inert coverages for these substrates differ by more than a factor of 4, the phase boundary in the temperature-coverage plane is found to have a universal shape.     

Radicals in skew polynomial and skew Laurent polynomial rings

We provide a general procedure for characterizing radical-like functions of skew polynomial and skew Laurent polynomial rings under grading hypotheses. In particular, we are able to completely characterize the Wedderburn and Levitzki radicals of skew polynomial and skew Laurent polynomial rings in terms of ideals in the coefficient ring. We also introduce the T-nilpotent radideals, and perform similar characterizations.     

Underwater cytometer for in situ measurement of marine phytoplankton by a technique combining laser-induced fluorescence and laser Doppler velocimetry

We present a new type of flow cytometer that can operate underwater for a long time, as long as days, for measuring the size distribution, concentration, and biomass of marine phytoplankton. The major improvement of the instrument over existing techniques is the elimination of sample preparation, which is achieved with a laser Doppler crossed-beam arrangement for both defining a measurement volume and measuring the speed of the particle traversing it. By simultaneously sampling the laser-induced fluorescence signal and the Doppler signals, the technique can discriminate sizes of phytoplankton.     

A novel optical method without phase unwrapping for subsurface flaw detection

This paper presents a novel optical method without phase unwrapping for subsurface flaw detection, based on the computer-vision reflection grating technique. In the setup, a computer-generated fringe pattern displayed on a computer-monitor is placed in front of the test object surface and forms a mirror image behind the reflective surface. The fringe pattern is perturbed according to the surface slope distribution. Unlike the current fringe pattern analysis techniques, which require one/several images, the analysis algorithm of the present method involves a sequence of fringe patterns having multiple frequencies to deduce the ‘pixel-slope’ information which is directly related to the surface slope of the test object. The novel method offers independent analysis for each pixel of the data array, and thus the problem of phase discontinuity in phase unwrapping can be eliminated. In this paper, experimental demonstration of the novel optical method to subsurface flaw detection is presented.     

A two-level hedging point policy for controlling a manufacturing system with time-delay,demand uncertainty and extra capacity

This paper focuses on the production control of a manufacturing system with time-delay, demand uncertainty and extra capacity. Time-delay is a typical feature of networked manufacturing systems (NMS), because an NMS is composed of many manufacturing systems with transportation channels among them and the transportation of materials needs time. Besides this, for a manufacturing system in an NMS, the uncertainty of the demand from its downstream manufacturing system is considered; and it is assumed that there exist two-levels of demand rates, i.e., the normal one and the higher one, and that the time between the switching of demand rates are exponentially distributed. To avoid the backlog of demands, it is also assumed that extra production capacity can be used when the work-in-process (WIP) cannot buffer the high-level demands rate. For such a manufacturing system with time-delay, demand uncertainty and extra capacity, the mathematical model for its production control problem is established, with the objective of minimizing the mean costs for WIP inventory and occupation of extra production capacity. To solve the problem, a two-level hedging point policy is proposed. By analyzing the probability distribution of system states, optimal values of the two hedging levels are obtained. Finally, numerical experiments are done to verify the effectiveness of the control policy and the optimality of the hedging levels.     

On exchange rings with primitive factor rings artinian

Abstract

In this paper we introduce generalized ideal-stable regular rings. It is shown that if a regular ring R is a generalized I-stable ring, then every square matrix over I is the product of an idempotent matrix and an generalized invertible matrix and admits a diagonal reduction by some generalized invertible matrices.     

Stokes phenomena and non-perturbative completion in the multi-cut two-matrix models

The Stokes multipliers in the matrix models are invariants in the string-theory moduli space and related to the D-instanton chemical potentials. They not only represent non-perturbative information but also play an important role in connecting various perturbative string theories in the moduli space. They are a key concept to the non-perturbative completion of string theory and also expected to imply some remnant of strong coupling dynamics in M theory. In this paper, we investigate the non-perturbative completion problem consisting of two constraints on the Stokes multipliers. As the first constraint, Stokes phenomena which realize the multi-cut geometry are studied in the Zk symmetric critical points of the multi-cut two-matrix models. Sequence of solutions to the constraints are obtained in general k-cut critical points. A discrete set of solutions and a continuum set of solutions are explicitly shown, and they can be classified by several constrained configurations of the Young diagram. As the second constraint, we discuss non-perturbative stability of backgrounds in terms of the Riemann-Hilbert problem. In particular, our procedure in the 2-cut (1,2) case (pure-supergravity case) completely fixes the D-instanton chemical potentials and results in the Hastings-McLeod solution to the Painlevé II equation. It is also stressed that the Riemann-Hilbert approach realizes an off-shell background independent formulation of non-critical string theory.     

Phosphine‐mediated Highly Enantioselective Spirocyclization with Ketimines as Substrates

Phosphine‐catalyzed enantioselective annulation reactions involving ketimines are a daunting synthetic challenge owing to the intrinsic low reactivity of ketimine substrates. A highly enantioselective [3+2] cycloaddition reaction that makes use of isatin‐derived ketimines as reaction partners was developed. Notably, both simple and γ‐substituted allenoates could be utilized, and various 3,2′‐pyrrolidinyl spirooxindoles with a tetrasubstituted stereocenter were obtained in excellent yields and with nearly perfect enantioselectivity (>98 % ee in all cases).     

High-throughput study of poly(ethylene glycol)/ibuprofen formulations under controlled environment using FTIR imaging

Simultaneous analysis of many samples under identical conditions improves the effectiveness of research and accelerates product design. A novel spectroscopic imaging approach using a multichannel detector has been developed for parallel analysis of pharmaceutical formulations under controlled environments. Samples of formulations of ibuprofen in poly(ethylene glycol) have been prepared with ibuprofen concentrations ranging from 0 to 100% using a microdroplet deposition approach. The concentration of ibuprofen in PEG at which dimerization of ibuprofen molecules can be avoided has been determined via simultaneous measurement of all samples using in situ FTIR spectroscopic imaging. FTIR spectra from all samples have been analyzed to assess the molecular state of the drug and the degree of polymer swelling as a function of drug concentration. The effect of elevated temperature on the stability of all formulations was also studied. This high-throughput approach identified the concentration range for stable formulations and provided evidence that hydrogen bonding between ibuprofen and the polymer is responsible for enhanced stability at higher temperatures. This high-throughput imaging approach, based on a miniature sampling system, significantly reduces the experimental time by allowing many (potentially a few thousand) experiments to be run in parallel and increases the accuracy by minimizing variations between experiments.     

Multiplex metal-detection based assay (MMDA) for COVID-19 diagnosis and identification of disease severity biomarkers

The ongoing COVID-19 pandemic caused by SARS-CoV-2 highlights the urgent need to develop sensitive methods for diagnosis and prognosis. To achieve this, multidimensional detection of SARS-CoV-2 related parameters including virus loads, immune response, and inflammation factors is crucial. Herein, by using metal-tagged antibodies as reporting probes, we developed a multiplex metal-detection based assay (MMDA) method as a general multiplex assay strategy for biofluids. This strategy provides extremely high multiplexing capability (theoretically over 100) compared with other reported biofluid assay methods. As a proof-of-concept, MMDA was used for serologic profiling of anti-SARS-CoV-2 antibodies. The MMDA exhibits significantly higher sensitivity and specificity than ELISA for the detection of anti-SARS-CoV-2 antibodies. By integrating the high dimensional data exploration/visualization tool (tSNE) and machine learning algorithms with in-depth analysis of multiplex data, we classified COVID-19 patients into different subgroups based on their distinct antibody landscape. We unbiasedly identified anti-SARS-CoV-2-nucleocapsid IgG and IgA as the most potently induced types of antibodies for COVID-19 diagnosis, and anti-SARS-CoV-2-spike IgA as a biomarker for disease severity stratification. MMDA represents a more accurate method for the diagnosis and disease severity stratification of the ongoing COVID-19 pandemic, as well as for biomarker discovery of other diseases.

A MMDA platform is developed by using metal-tagged antibodies as reporting probes combined with machine learning algorithms, as a general strategy for highly multiplexed biofluid assay.