Discrete Steps Research Articles

Data-specific mask-guided image reconstruction for diffuse optical tomography.

Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l1-regularization based sparse recovery approach.

Stability Results for Two-Dimensional Systems of Fractional-Order Difference Equations

Linear autonomous incommensurate systems that consist of two fractional-order difference equations of Caputo-type are studied in terms of their asymptotic stability and instability properties. More precisely, the asymptotic stability of the considered linear system is fully characterized, in terms of the fractional orders of the considered Caputo-type differences, as well as the elements of the linear system’s matrix and the discretization step size. Moreover, fractional-order-independent sufficient conditions are also derived for the instability of the system under investigation. With the aim of exemplifying the theoretical results, a fractional-order discrete version of the FitzHugh–Nagumo neuronal model is constructed and analyzed. Furthermore, numerical simulations are undertaken in order to substantiate the theoretical findings, showing that the membrane potential may exhibit complex bursting behavior for suitable choices of the model parameters and fractional orders of the Caputo-type differences.

Online identification of mechanistic milling force models

Mechanistic models are commonly used to compute the machining forces in milling operations. The constant coefficients of mechanistic models, also known as Specific Force Coefficients (SFC), are directly linked to the mechanics of shearing, friction, and ploughing actions during chip formation. Because of this direct linkage, online monitoring of them can provide informative metrics for process monitoring. However, SFC are usually determined offline by using the machining forces measured under various cutting conditions (e.g. feedrate and cutting speed). In this paper, the performance of Recursive Least Squares (RLS) and Kalman Filter (KF) algorithms in online monitoring of SFC is studied.In the RLS method, instantaneous milling forces are expressed as linear functions of unknown SFC. The RLS algorithm is then used to recursively identify the SFC from the milling forces measured at discrete time steps during the process. In the Kalman Filter method, the SFC are modelled as constant stochastic processes, and the dynamics of their variation is described by a state-space model in which the SFC are the state variables and the milling forces are outputs. A Kalman Filter state observer is then designed to recursively estimate the SFC from the forces measured at discrete time steps. Numerical simulations and experimental studies are presented to verify the effectiveness of the proposed methods in identifying SFC in various cutting conditions.

A Variable-Gain Low-Noise Transimpedance Amplifier for Miniature Ultrasound Probes

This article presents a low-noise transimpedance amplifier (TIA) designed for miniature ultrasound probes. It provides continuously variable gain to compensate for the time-dependent attenuation of the received echo signal. This time-gain compensation (TGC) compresses the echo-signal dynamic range (DR) while avoiding imaging artifacts associated with discrete gain steps. Embedding the TGC function in the TIA reduces the output DR, saving power compared to prior solutions that apply TGC after the low-noise amplifier. The TIA employs a capacitive ladder feedback network and a current-steering circuit to obtain a linear-in-dB gain range of 37 dB. A variable-gain loop amplifier based on current-reuse stages maintains constant bandwidth in a power-efficient manner. The TIA has been integrated in a 64-channel ultrasound transceiver application-specific integrated circuit (ASIC) in a 180-nm BCDMOS process and occupies a die area of 0.12 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . It achieves a gain error below ±1 dB and a 1.7 pA/√Hz noise floor and consumes 5.2 mW from a ±0.9 V supply. B-mode images of a tissue-mimicking phantom are presented that show the benefits of the TGC scheme.

Fully discrete finite element approximation of the stochastic Cahn–Hilliard–Navier–Stokes system

Abstract In this paper we study the numerical approximation of the stochastic Cahn–Hilliard–Navier–Stokes system on a bounded polygonal domain of $\mathbb{R}^{d}$, $d=2,3$. We propose and analyze an algorithm based on the finite element method and a semiimplicit Euler scheme in time for a fully discretization. We prove that the proposed numerical scheme satisfies the discrete mass conservative law, has finite energies and constructs a weak martingale solution of the stochastic Cahn–Hilliard–Navier–Stokes system when the discretization step (both in time and in space) tends to zero.

A Machine-Learning Tool Concurrently Models Single Omics and Phenome Data for Functional Subtyping and Personalized Cancer Medicine.

Simple SummaryTumours are heterogeneous that reflect variable patient prognosis and treatment responses (phenotypes). Since these variable phenotypes are outcomes of genomics, it is essential to integrate genome and phenome jointly. In this study, we report the development and application of a new machine learning tool (Phenotypic Mapping; PhenMap) to identify unsupervised clinically-relevant (functional) subtypes and biomarkers by simultaneously integrating clinical phenotypes and single omics data, mainly, transcriptome. This integrative analysis provides the opportunity to simultaneously identify robust and context-specific subtypes and associated phenotypes (that biologically explain each subtype) without statistically losing information. We demonstrate its utility using breast cancer cell lines and patient samples to identify functional subtypes associated with specific drug responses (including CD4/6 inhibitor) and prognosis. These subtypes may potentially predict clinical outcomes with further validation. This tool can be applied to other omics data such as methylomics, genomics and radiomics along with any phenotypic data.One of the major challenges in defining clinically-relevant and less heterogeneous tumor subtypes is assigning biological and/or clinical interpretations to etiological (intrinsic) subtypes. Conventional clustering/subtyping approaches often fail to define such subtypes, as they involve several discrete steps. Here we demonstrate a unique machine-learning method, phenotype mapping (PhenMap), which jointly integrates single omics data with phenotypic information using three published breast cancer datasets (n = 2045). The PhenMap framework uses a modified factor analysis method that is governed by a key assumption that, features from different omics data types are correlated due to specific “hidden/mapping” variables (context-specific mapping variables (CMV)). These variables can be simultaneously modeled with phenotypic data as covariates to yield functional subtypes and their associated features (e.g., genes) and phenotypes. In one example, we demonstrate the identification and validation of six novel “functional” (discrete) subtypes with differential responses to a cyclin-dependent kinase (CDK)4/6 inhibitor and etoposide by jointly integrating transcriptome profiles with four different drug response data from 37 breast cancer cell lines. These robust subtypes are also present in patient breast tumors with different prognosis. In another example, we modeled patient gene expression profiles and clinical covariates together to identify continuous subtypes with clinical/biological implications. Overall, this genome-phenome machine-learning integration tool, PhenMap identifies functional and phenotype-integrated discrete or continuous subtypes with clinical translational potential.

Process Mapping Total Knee Arthroplasty: A Comparison of Instrument Designs

BackgroundTotal knee arthroplasty (TKA) is commonly performed with proprietary, manual instrumentation provided by the surgical implant manufacturer. Registry studies and meta-analysis, with few outliers, have consistently shown similar functional outcomes and implant survival after TKA regardless of implant manufacturer, implant design, or surgical technique. We hypothesized that process mapping could identify areas for improvement in TKA instrumentation. MethodsSeventeen TKA implant systems from 10 companies representing over 90% of all TKAs performed in the United States were evaluated. Instrumentation required for femoral, tibial, and patellar preparation was compared. The number of steps including surgical technician assembly steps, instrument handoffs, and surgeon steps were tabulated based off application of a standardized surgical flow, adjusted for manufacturer-recommended steps during completion of a TKA operation. ResultsCruciate-retaining (CR) knee instrumentation in studied systems required 158-225 discrete steps and posterior-stabilized (PS) knees required 181-230 steps. With the fewest steps for femoral, tibial, and patellar instrumentation, CR and PS knee systems could be improved to 145 and 163 steps, respectively. The Arthrex iBalance and the Biomet Vanguard Microplasty required fewest steps among CR systems; the OrthoDevelopment Balanced and the Corin Unity required fewest steps among PS systems. ConclusionsProcess mapping identified potential areas for improved instrumentation in all studied systems, suggesting the possibility to reduce operative steps broadly across the TKA industry. Patient outcomes were not evaluated by system. Future implant system design changes may do well to reduce unnecessary steps and instrumentation.

Simulation approach and code functionality assessment using deterministic and Monte Carlo codes system for U-235 core-follow depletion calculation

Improving the performance of reactor simulation codes and adequately predicting the fuel composition are crucial to increase the accuracy of simulations and the quality of the analysis. McMaster Nuclear Reactor (MNR) follows a strict daily operational and shutdown schedule, except for Sundays. This results in a buildup of neutron poison during the reactor shutdown and hence the Control Rods (CRs) are extracted in the time of the reactor startup. Therefore, tracking MNR core composition using faithful Control Rods (CRs) positions data to estimate the axial fuel inventory profile is not yet established. In this study, nodal diffusion (OSCAR-4) and Monte Carlo (Serpent-2) simulations codes were employed. This study investigates: (1) code-to-code differences in the multiplication factor (keff) and in the axial fuel inventory considering the same burnup step sizes and total energy release with all CRs out; (2) for long cycles of core calculation, Monte Carlo stochastic code is computationally expensive, therefore an adequate temporal discretization steps are studied and identified. The keff values for the fresh reactor when all rods are out is: 1.1339 and 1.1283, and the results at the end of the burnup calculation is: 1.0101 and 1.0049, for Serpent-2 and OSCAR-4, respectively. In addition, the findings showed that averaging the CRs travel distance, for calculation the fuel inventory, can provide results similar to those with most tracked CRs motion.

Discrete dispersion scan setup for measuring few-cycle laser pulses in the mid-infrared.

In this work, we demonstrate a discrete dispersion scan scheme using a low number of flat windows to vary the dispersion of laser pulses in discrete steps. Monte Carlo simulations indicate that the pulse duration can be retrieved accurately with less than 10 dispersion steps, which we verify experimentally by measuring few-cycle pulses and material dispersion curves at 3 and 10 µm wavelength. This minimal measuring scheme using only five optical components without the need for linear positioners and interferometric alignment can be readily implemented in many wavelength ranges and situations.

An evolutionary approach for resource constrained project scheduling with uncertain changes

In Resource Constrained Project Scheduling Problems (RCPSPs), it is usually assumed that the activity durations are known and integers. This assumption helps to conveniently develop a standard mathematical model, using discrete time steps. However, in reality, activity durations may not only be integer, and they may not be known with certainty at the time of project planning. The consideration of real-valued activity durations would increase the complexity in modelling of RCPSPs. In this paper, we consider that activity duration can be either integer or real-valued or both, and they are uncertain. To solve the optimization problem with uncertainty, scenario-based approaches are a popular choice. However, such a solution method is computationally very expensive. Therefore, in this research, we propose a simulation assisted evolutionary framework, that consists of two multi-operator based EAs and two heuristics to deal with the optimization process, and a simulation approach to deal with the uncertainty components. In the simulation, a range of problem instances is evaluated that are generated based on uncertain durations. The framework also proposes a new strategy to reduce the number of simulation runs. In the approach, the solution representation is different from the one required in the mathematical programming approach for RCPSP, and it does not require any discretization of the time periods. More than 1600 test problems, including some industrial problems, with up to 120 activities, have been solved using this proposed approach and the results have been compared with a set of state-of-the-art algorithms. The results obtained by the proposed approach were found to be of acceptable quality with a significant reduction of computational time.

Influence of surface wettability on methane hydrate formation in hydrophilic and hydrophobic mesoporous silicas

The methane hydrate (MH) formation process in confinement was investigated using high-pressure methane sorption experiments on two wet materials with similar pore size distributions, B – PMO (hydrophobic) and MCM – 41 (hydrophilic). Their methane sorption isotherms possess two discrete methane gas consumption steps at ~10 bar and ~ 30 bar at 243 K. A systematic analysis reveals that external water and the so-called ‘core water’ inside the pore is rapidly consumed in the first step to form bulk-like hydrate, whereas adsorbed water is slowly consumed in the second step to form less stable confined hydrates at higher pressures. Synchrotron powder X-Ray results confirm methane hydrate structure I and reveal that bulk ice is swiftly and fully converted to hydrate in MCM – 41, whereas inactive bulk ice co-exists with MH in B – PMO at 6 MPa demonstrating the huge impact of the surface wettability on the water’s behavior during MH formation.

Dynamic behaviours for semi-discrete stochastic Cohen-Grossberg neural networks with time delays

By using semi-discretization technique, a discrete analogue of stochastic Cohen-Grossberg neural networks is formulated. Firstly, the existence of pth mean almost periodic sequence solutions and pth moment global exponential stability of the above semi-discrete stochastic system are investigated with the help of Krasnoselskii’s fixed point theorem and some analysis techniques in stochastic theory. Secondly, the exponential stability and stochastic stabilization for a special discrete stochastic Cohen-Grossberg neural networks are studied. These findings show that stochastic disturbances and small discrete step length have negative effects on the existence of mean almost periodic solutions and moment exponential stability. But they have positive effects on the exponential stability and exponential non-stability for some special models, respectively. Besides, this paper also finds that some unstable neural networks should become exponentially stable by stochastic disturbances. In the end, some examples and computer simulations are given to demonstrate the effectiveness of the theoretical results.

In-situ nanoscale characterization of composition and structure during formation of ultrathin nickel silicide

We characterize composition and structure of ultrathin nickel silicide during formation from 3 nm Ni films on Si(100) using in-situ high-resolution ion scattering and high-resolution transmission electron microscopy. We show the transition to occur in discrete steps, in which an intermediate phase is observed within a narrow range of temperature from 230 °C to 290 °C. The film composition of this intermediate phase is found to be 50% Ni:50% Si, without evidence for long-range structure, indicating the film to be a homogeneous monosilicide NiSi phase. The final phase is resemblant of the cubic disilicide NiSi2, but with slightly off-stoichiometric composition of 38% Ni and 62% Si. Along the [100] axis, the lattices of the film and the substrate are found in perfect alignment. Due to the epitaxial growth of the silicide, a contraction of the c lattice constant of the film by 0.7–1% is detected.

Strong Tolerance and Strong Universality of Interval Eigenvectors in a Max-Łukasiewicz Algebra

The Łukasiewicz conjunction (sometimes also considered to be a logic of absolute comparison), which is used in multivalued logic and in fuzzy set theory, is one of the most important t-norms. In combination with the binary operation ‘maximum’, the Łukasiewicz t-norm forms the basis for the so-called max-Łuk algebra, with applications to the investigation of systems working in discrete steps (discrete events systems; DES, in short). Similar algebras describing the work of DES’s are based on other pairs of operations, such as max-min algebra, max-plus algebra, or max-T algebra (with a given t-norm, T). The investigation of the steady states in a DES leads to the study of the eigenvectors of the transition matrix in the corresponding max-algebra. In real systems, the input values are usually taken to be in some interval. Various types of interval eigenvectors of interval matrices in max-min and max-plus algebras have been described. This paper is oriented to the investigation of strong, strongly tolerable, and strongly universal interval eigenvectors in a max-Łuk algebra. The main method used in this paper is based on max-Ł linear combinations of matrices and vectors. Necessary and sufficient conditions for the recognition of strong, strongly tolerable, and strongly universal eigenvectors have been found. The theoretical results are illustrated by numerical examples.

Precisely Assembled Nanoparticles against Cisplatin Resistance via Cancer-Specific Targeting of Mitochondria and Imaging-Guided Chemo-Photothermal Therapy.

Cisplatin resistance in tumor cells is known mainly due to the reduced accumulation of platinum ions by efflux, detoxification by intracellular GSH, and nucleotide excision repair machinery-mediated nuclear DNA repair. In this work, theranostic Pt(IV)-NPs, which are precisely self-assembled by biotin-labeled Pt(IV) prodrug derivative and cyclodextrin-functionalized IR780 in a 1:1 molecular ratio, have been developed for addressing all these hurdles via mitochondria-targeted chemotherapy solely or chemophotothermal therapy. In these nanoparticles, IR780 as a small-molecule dye acts as a mitochondria-targeting ligand to make Pt(IV)-NPs relocate finally in the mitochondria and release cisplatin. As demonstrated by in vitro and in vivo experiments, Pt(IV)-NPs can markedly facilitate cancer-specific mitochondrial targeting, inducing mitochondrial dysfunction and mitochondrial DNA (mtDNA) damage, thus greatly increasing the Pt accumulation, reducing the GSH levels, and avoiding DNA repair machinery in cisplatin-resistant cancer cells (A549R), finally resulting in significant inhibition of A549R tumor growth on animal models by chemotherapy solely. Upon near-infrared irradiation, mitochondria-targeted chemophotothermal synergistic therapy can be realized, further overcoming cisplatin resistance and even eliminating A549R tumors completely. Moreover, such novel Pt(IV)-NPs integrate multimodal targeting (cancer and mitochondria targeting), imaging (near-infrared imaging and photoacoustic imaging), and therapeutic (chemo- and photothermal therapy) moieties in a constant ratio (1:1:1) into a single, reproducible, and structurally homogeneous entity, avoiding nonuniform drug loading and premature leakage as well as the discrete steps of imaging and therapy, which thus is more beneficial for precise therapeutics and future clinical translation.

Complex phase masks for OH suppression filters in astronomy: part I: design.

The design of a complex phase mask (CPM) for inscribing multi-notch fiber Bragg grating filters in optical fibers for OH suppression in astronomy is presented. We demonstrate the steps involved in the design of a complex mask with discrete phase steps, following a detailed analysis of fabrication constraints. The phase and amplitude of the complex grating is derived through inverse modelling from the desired aperiodic filter spectrum, following which the phase alone is encoded into the surface relief of a CPM. Compared to a complicated "running-light" Talbot interferometer based inscription setup where the phase of the inscribing beam is controlled by electro- or acousto-optic modulators and synchronized to a moving fiber translation stage, CPM offers the well-known convenience and reproducibility of the standard phase mask inscription technique. We have fabricated a CPM that can suppress 37 sky emission lines between 1508 nm to 1593 nm, with a potential of increasing to 99 channels for suppressing near-infrared (NIR) OH-emission lines generated in the upper atmosphere and improving the performance of ground-based astronomical telescopes.

New solutions of hyperbolic telegraph equation

<p style='text-indent:20px;'>We present a new method based on unification of fictitious time integration (FTI) and group preserving (GP) methods. The GP method is applied in numerically discretized ordinary differential equations obtained from application of FTI method to a given partial differential equation (PDE). The algorithm is applied to hyperbolic telegraph equation and utilizes the Cayley transformation and the Pade approximations in the Minkowski space. It avoids unauthentic solutions and ghost fixed points which is one of the advantages of the present method over other related numerical methods in the literature. The technique is tested on three specific examples for various parameter values appearing in the telegraph equation and discretization steps. Such solutions of the telegraph equation are obtained first time in this paper. Illustrative figures are provided. Efficiency of the method is determined by an error analysis which is achieved by comparing numerical solutions with exact solutions.

Researching the efficiency of buck converter synchronous rectifier

The object of study is synchronous buck-voltage converter with digital control system. One of the most problematic things is energy changing and transmission in converters to reach certain numerical range with minimal losses in the components of the electrical circuit. An enormous calculated parameters of electrical scheme. There was advised and described both structure and electrical scheme of synchronous converter, which, thanks to digital system, provides dates with more accuracy connected with an impact on working scheme. There was shown detailed analysis example with a numerical value for the certain elements of electrical scheme. It’s a fundament in order to choose certain parts of electrical scheme according to the certain categories.During research there was used selection of hardware and software tools: elements for buck-converter – key, diode and capacitor; certain voltage and frequency range for microcontroller; control of the power keys of the circuit with the corresponding operating parameters for driver. There was analyzed and calculated all over the possible losses during the process bucking of the voltage to the certain level, an enormous losses in the components of the converter electrical scheme – induction coil, keys and capacitors. It’s an important part of synchronous buck-converter. There was calculated power losses and efficiency through the received graphics of keys commutation in electrical scheme. There were received graphic dependence of converter efficiency on output power; time characteristics of the control signal pulse-width modulation (PWM) and output voltage; dependence on the commutation losses. This is because advised synchronous converter has a set of features. Particularly analog to digital converter in the capacity of feedback, digital regulation system with a discrete step and rectification by replacing diodes with actively controlled switches. There are keys of low-side and high-side levels according to the passing voltage and current values. Therewith provides possibility for receiving more accuracy value. In comparison with analogic buck-converters, this converter has voltage parameter with fractional error.

Crystal structure of bis-(N,N'-di-methyl-thio-urea-κS)bis-(thio-cyanato-κN)cobalt(II).

During systematic investigations on the synthesis of coordination polymers with Co(NCS)2 involving different thio-urea derivatives as coligands, crystals of the title compound Co(NCS)2(N,N'-di-methyl-thio-urea)2, or [Co(C3H8N2S)2(NCS)2], were obtained. These crystals were non-merohedric twins and therefore, a twin refinement using data in HKLF-5 format was performed. In the crystal structure of this compound, the CoII cations are coordinated by two N-terminally bonded thio-cyanate anions as well as two S-bonding N,N'-di-methyl-thio-urea mol-ecules, forming two crystallographically independent discrete complexes each with a strongly distorted tetra-hedral geometry. An intricate network of inter-molecular N-H⋯S and C-H⋯S hydrogen bonds can be found between the complexes. The thermogravimetric curve of the title compound shows two discrete steps in which all coligand mol-ecules have been emitted, which is also accompanied by partial decomposition of the cobalt thio-cyanate. If the measurement is stopped after the first mass loss, only broad reflections of CoS can be found in the XRPD pattern of the residue, which proves that this compound decomposes completely upon heating. However, at lower temperatures an endothermic signal can be found in the DTA and DSC curve, which corresponds to melting, as proven by thermomicroscopy.

Tolerance and weak tolerance of interval eigenvectors in fuzzy algebra

In max-min fuzzy algebra, the study of eigenvectors is important because it can help us to recognize the steady states of systems working in discrete steps. This paper investigates the properties of steady states described by max-min matrices and vectors with interval coefficients. The characteristics of the eigenspace structure and polynomial-time algorithms for recognition of tolerable and weakly-tolerable interval eigenvectors in max-min algebra are described. This research is a continuation of an earlier investigation concerning strongly-tolerable interval eigenvectors.