Сравнение индукционной и ультразвуковой стимуляции дефектов в активном тепловом контроле для обнаружения трещин в объектах из электропроводящих материалов

AbstractSequential order statistics have been introduced to model sequential k-out-of-n systems which, as an extension of k-out-of-n systems, allow the failure of some components of the system to influence the remaining ones. Based on an independent sample of vectors of sequential order statistics, the maximum likelihood estimators of the model parameters of a sequential k-out-of-n system are derived under order restrictions. Special attention is paid to the simultaneous maximum likelihood estimation of the model parameters and the distribution parameters for a flexible location-scale family. Furthermore, order restricted hypothesis tests are considered for making the decision whether the usual k-out-of-n model or the general sequential k-out-of-n model is appropriate for a given data

The role of digital technologies in supply chain resilience for emerging markets' automotive sector

Purpose: The purpose of this paper is to examine the role of digital supply chain (DSC) technologies in automotive supply chain resilience (SCR) practices to improve the supply chain performance (SC-Perf.) objectives of companies operating in the automotive industry. This study also compares the results of the associated SC-Perf objectives before and after the COVID-19 pandemic outbreak lockdown situation. Design/methodology/approach: We undertook in-depth empirical research using a questionnaire survey to explore the performance of automotive supply chains. Our sample consisted of practitioners from supply chain entities such as automotive original equipment manufacturers (OEMs), Tier-component manufacturers, and lead logistics providers in Asia-Pacific (AP) emerging markets. Research questions, framework, and hypotheses were developed using the literature review. Findings: The research outcome from analysis of the data we collected from an emerging market context, specifically the automotive sector, emphasizes the role of DSC technologies and encourages the firm’s SCR practices which, in turn, supports the SC-Perf objectives. The DSC technologies competency moderates the SCR and SC-Perf objectives relation, and the moderation effect is higher for post-COVID19 pandemic outbreak lockdown situation than at prior state. Research limitations/implications: The scope of the study is restricted to the automotive firms in the AP region. The data were collected from a representative sample of the population through a questionnaire survey. The small size of the sample incurs a certain level of subjectivity. Practical implications: This research provides practical insights for practitioners and academicians on DSC technologies’ influence in SCR practices to improve the firm’s SC-Perf. This research shares the literature insights on use of DSC technologies across the sector to allow the automotive firm to reassess the existing operational practices. Originality/value: The paper adds insights on introducing or implementing DSC technologies across AP automotive firms to increase the operations’ performance by improving SCR practices and sustainability

Прогноз резервуаров в магматических породах доюрского возраста на примере Александровского мегавала (Томская область)

In a multi-sample experiment, we model the parameters of an equal load-sharing system by means of link functions in sequential order statistics models, and then discuss the estimation of these parameters based on a given link function. Different link functions are examined along with the corresponding maximum likelihood estimators, and their properties are studied both analytically and through Monte Carlo simulations

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Flux-free conductance modulation in a helical Aharonov-Bohm interferometer

A novel conductance oscillation in a twisted quantum ring composed of a helical atomic configuration is theoretically predicted. Internal torsion of the ring is found to cause a quantum phase shift in the wavefunction that describes the electron's motion along the ring. The resulting conductance oscillation is free from magnetic flux penetrating inside the ring, which is in complete contrast with the ordinary Aharonov-Bohm effect observed in untwisted quantum rings.Comment: 10 pages, 4 figure

Composite absorbing potentials

The multiple scattering interferences due to the addition of several contiguous potential units are used to construct composite absorbing potentials that absorb at an arbitrary set of incident momenta or for a broad momentum interval.Comment: 9 pages, Revtex, 2 postscript figures. Accepted in Phys. Rev. Let

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown.Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of ~58% and ~ 40% for localization and functions respectively of proteins were determined at a threshold of ~30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k-nearest neighbor classifier confirmed that our results compared favorably.Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest. © 2014 Kuppuswamy et al.; licensee BioMed Central Ltd

Expression of a Cu,Zn superoxide dismutase typical for familial amyotrophic lateral sclerosis increases the vulnerability of neuroblastoma cells to infectious injury

<p>Abstract</p> <p>Background</p> <p>Infections can aggravate the course of neurodegenerative diseases including amyotrophic lateral sclerosis (ALS). Mutations in the anti-oxidant enzyme Cu,Zn superoxide dismutase (EC 1.15.1.1, SOD1) are associated with familial ALS. Streptococcus pneumoniae, the most frequent respiratory pathogen, causes damage by the action of the cholesterol-binding virulence factor pneumolysin and by stimulation of the innate immune system, particularly via Toll-like-receptor 2.</p> <p>Methods</p> <p>SH-SY5Y neuroblastoma cells transfected with the G93A mutant of SOD1 typical for familial ALS (G93A-SOD1) and SH-SY5Y neuroblastoma cells transfected with wildtype SOD1 were both exposed to pneumolysin and in co-cultures with cultured human macrophages treated with the Toll like receptor 2 agonist N-palmitoyl-S-[2,3-bis(palmitoyloxy)-(2RS)-propyl]-[R]-cysteinyl-[S]-seryl-[S]-lysyl-[S]-lysyl-[S]-lysyl-[S]-lysyl-[S]-lysine × 3 HCl (Pam₃CSK₄). Cell viability and apoptotic cell death were compared morphologically and by in-situ tailing. With the help of the WST-1 test, cell viability was quantified, and by measurement of neuron-specific enolase in the culture supernatant neuronal damage in co-cultures was investigated. Intracellular calcium levels were measured by fluorescence analysis using fura-2 AM.</p> <p>Results</p> <p>SH-SY5Y neuroblastoma cells transfected with the G93A mutant of SOD1 typical for familial ALS (G93A-SOD1) were more vulnerable to the neurotoxic action of pneumolysin and to the attack of monocytes stimulated by Pam₃CSK₄ than SH-SY5Y cells transfected with wild-type human SOD1. The enhanced pneumolysin toxicity in G93A-SOD1 neuronal cells depended on the inability of these cells to cope with an increased calcium influx caused by pores formed by pneumolysin. This inability was caused by an impaired capacity of the mitochondria to remove cytoplasmic calcium. Treatment of G93A-SOD1 SH-SY5Y neuroblastoma cells with the antioxidant N-acetylcysteine reduced the toxicity of pneumolysin.</p> <p>Conclusion</p> <p>The particular vulnerability of G93A-SOD1 neuronal cells to hemolysins and inflammation may be partly responsible for the clinical deterioration of ALS patients during infections. These findings link infection and motor neuron disease and suggest early treatment of respiratory infections in ALS patients.</p