Identification Of Different States Research Articles

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

Research on Vehicle Congestion Group Identification for Evaluation of Traffic Flow Parameters

The traffic flow parameters of the road network are most often evaluated through volumes, which are compared with its maximum volume (capacity) or speed and density. Capacity assessment was performed, considering horizontal and vertical orientation and characteristics of the traffic stream. This article presents the results of research on the identification of different states of creating congestion groups and their relationship to road capacity or decrease in speed. The following hypothesis was verified: when the capacity of the road is exceeded or almost reached, there is “always” a significant drop in the flow of traffic compared to when the capacity is not exceeded. The analysis showed that the average travel speed drops by 30% for the condition where groups of 25 or more vehicles are formed with a time interval of up to 4 s. The results make it possible to set traffic models in short time intervals according to real spatial conditions and to use them in the analysis of the environmental and safety impacts of road transport.

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

Research on the health status evaluation method of rolling bearing based on EMD‐GA‐BP

AbstractTo more accurately evaluate the health state of rolling bearings, this paper proposes a health status evaluation method based on empirical pattern decomposition, genetic algorithm and BP neural network. Firstly, the vibration signal is decomposed by empirical mode decomposition (EMD) and the time domain features of each intrinsic mode function (IMF) component are extracted, and the signal‐to‐noise ratio (Snr) of the signal is improved effectively. Then, the initial threshold and weight of BP neural network are optimized by genetic algorithm, which effectively improves the Snr of the signal. Finally, the extracted features are input into the optimized BP neural network to realize the identification of different states of the bearing. The effectiveness of the method has been effectively verified in the bearing data of Case Western Reserve University bearing dataset and it has higher accuracy and robustness than other common evaluation methods.

Work break scheduling using wrist wearable devices: a conceptual and practical model

In recent years, the use of wearable devices, designed for monitoring the psycho-physical state of the user over time, has undergone a growing trend in the industrial field. The ability to monitor parameters, such as heart rate, electroencephalography, and motion activity, allows the identification of different states of the operator, such as stress, physical fatigue, or the level of attention. Therefore, these devices have the potential for use in working environments. This study investigated the possibility of using wearable wrist devices for optimal management of work breaks. Thanks to the monitoring of the operator's status in terms of heart rate, number of steps, and time, the scheduling of work breaks can be based on the real physiological needs of the operators rather than a fixed schedule. A preliminary conceptual and practical model was developed, and a series of experimental campaigns were presented to illustrate its functioning and potentialities.

Моніторинг хвойних лісів з використанням даних дистанційного зондування (на прикладі Тухлянського лісгоспу)

The purpose of the work is to consider the state of coniferous forests of the Tukhlyanske forestry of the Precarpathian region. Changes in land cover, pollution of air, water and soil, and deterioration of their quality, loss of biological diversity occur for forest ecosystems at the regional and global levels. Climate change, rising temperatures and declining rainfall are provoking the development of pests that are most common in coniferous forests. Remote sensing technologies allow to create forest monitoring systems, including determination of plantation structure, detection of changes in forests due to fires, deforestation, environmental problems, in particular forest drying. The method of detecting changes in forests is based on the use of high-resolution space imagery and on the processing of images obtained from unmanned aerial vehicles to identify healthy, dried and partially damaged by drying conifers in test areas. The result of the study is an image obtained by the method of controlled classification. The accuracy of the classification depends on the choice of signatures, and for that the UAV images are used. Scientific novelty and practical significance. A method for the identification of different states of coniferous forests using the method of controlled classification by the algorithm of maximum probability is proposed. The choice of class signatures is fundamental to solving the problem. The technique can be applied in various structures of forestry

Simulations of droplet combustion under gas turbine conditions

In various applications with recirculation, liquid droplets can be immersed in gases that may have a wide range of possible compositions, from pure air to hot combustion products. In order to gain fundamental understanding of the behaviour of individual droplets in vitiated air, numerical simulations of kerosene single droplet evaporation, autoignition, and combustion in conditions relevant to gas turbines have been performed. The droplet autoignition behaviour has been analysed in both physical and mixture fraction space for a wide range of vitiated air compositions and initial droplet diameters. Results show that the autoignition time delay decreases with increasing level of dilution with hot combustion products and decreasing initial droplet diameter. Chemistry is confined up to a radius of almost 10 initial droplet diameters and the location of autoignition is influenced by both the initial droplet diameter and the level of dilution. The time evolution of species in the gaseous phase after autoignition shows similar trends for all the diameters and dilution levels investigated here with the peak of temperature and OH mass fraction moving towards the droplet surface as a consequence of the balance between fuel production and consumption. In mixture fraction space, the location of the peaks of temperature and OH mass fraction after autoignition do not change in time whereas other intermediate species such as CH2O and pyrolysis products still exhibit a quite variable behaviour. The long-time flame structure has been compared with gaseous laminar counterflow simulations and, although qualitatively similar, the flame structure in the two configurations has differences with implications for flamelet combustion models used in spray combustion. The droplet evaporation, autoignition, and combustion behaviour has been summarized through a regime diagram showing the evaporation and autoignition time delays as a function of both initial droplet diameter and vitiated air dilution. This allows the identification of different states in the droplet combustion scenario and the introduction of critical values of dilution and initial droplet diameter beyond which single droplet rather than cloud combustion can occur, which can be exploited in the design of lean burn gas turbine combustion systems. The approach presented here can be easily extended to other conditions and fuels allowing the generation of regime diagrams for various operating conditions.

Generalized classification modeling of activated sludge process based on microscopic image analysis

ABSTRACTThe state of activated sludge wastewater treatment process (AS WWTP) is conventionally identified by physico-chemical measurements which are costly, time-consuming and have associated environmental hazards. Image processing and analysis-based linear regression modeling has been used to monitor the AS WWTP. But it is plant- and state-specific in the sense that it cannot be generalized to multiple plants and states. Generalized classification modeling for state identification is the main objective of this work. By generalized classification, we mean that the identification model does not require any prior information about the state of the plant, and the resultant identification is valid for any plant in any state. In this paper, the generalized classification model for the AS process is proposed based on features extracted using morphological parameters of flocs. The images of the AS samples, collected from aeration tanks of nine plants, are acquired through bright-field microscopy. Feature-selection is performed in context of classification using sequential feature selection and least absolute shrinkage and selection operator. A support vector machine (SVM)-based state identification strategy was proposed with a new agreement solver module for imbalanced data of the states of AS plants. The classification results were compared with state-of-the-art multiclass SVMs (one-vs.-one and one-vs.-all), and ensemble classifiers using the performance metrics: accuracy, recall, specificity, precision, F measure and kappa coefficient (κ). The proposed strategy exhibits better results by identification of different states of different plants with accuracy 0.9423, and κ 0.6681 for the minority class data of bulking.

Unoccupied electronic states of Ru(0001)

This report presents a combined theoretical and experimental description of the unoccupied electronic states of Ru(0001), along the Γ̅M̅ high symmetry direction of the Brillouin zone. A direct comparison between angle-resolved inverse photoemission spectroscopy and ab initio calculations of the 3-dimensional (3D) electronic structure of Ru(0001) have been used to determine the energy dispersion and the identification of different states and surface resonances. Both, measurements and calculations, complement previous reports regarding the electronic structure of Ru.

Identification of different tumor states in nasopharyngeal cancer using surface-enhanced Raman spectroscopy combined with Lasso-PLS-DA algorithm

Identification of different states in cancer is of vital importance for cancer treatment and management.

Pyrolysis–gas chromatography/mass spectrometry applied to the identification of different states of polyaniline

Pyrolysis–gas chromatography/mass spectrometry (Py–CG/MS) has been applied to study and compare the composition of two electrochemically synthesized polyaniline (PANI) forms: fully reduced leucoemeraldine (LE) and intermediately oxidized emeraldine (EM). The different PANI forms were electrochemically synthesized using cyclic voltammetry. They were dedoped by stirring for 24 h in ammoniacal solutions. Leucoemeraldine and emeraldine pyrograms presented differences in the relative intensity of their fragment signals. Emeraldine pyrograms presented a higher relative intensity of the signals associated to oxidized fragments (i.e. quinone imine) than leucoemeraldine pyrograms. On the other hand, polymer samples synthesized under different anodic potentials showed small differences, consistent with a higher degree of crosslinking at higher anodic potentials. The results obtained indicate that Py–GC/MS may be used for identification among different oxidation states in aryl amine polymers.

Identification of different states of hepatitis B virus infection with a quantitative PCR assay.

The level of hepatitis B virus (HBV) DNA in serum reflects the replicative activity of HBV. To compare serum HBV DNA levels in different states of hepatitis B, 47 sera of patients with HBeAg-positive chronic hepatitis B, 4 sera of patients with HBeAg-negative chronic hepatitis B, 40 samples of patients after HBeAg seroconversion during alpha interferon treatment, 57 sera of inactive HBsAg carriers, and 42 sera of patients who had recovered from chronic hepatitis B more than 12 months prior to blood collection were checked for the presence of HBV DNA with the Amplicor HBV Monitor Test. In patients with HBeAg-positive chronic hepatitis B, the median of serum HBV DNA levels (8.3 x 10(8) copies/ml) was significantly higher than that for patients after HBeAg seroconversion (6.2 x 10(3) copies/ml) and than that for inactive HBsAg carriers (5.6 x 10(3) copies/ml). None of the patients who had recovered from hepatitis B had detectable HBV DNA in serum. Quantitative PCR proved to be a valuable tool for identification of different states of HBV infection. This technique was found to be a good method for determination of serum HBV DNA levels both for patients with HBeAg seroconversion and for inactive carriers who showed low viremia not detectable by conventional hybridization assays.

An Overview of Microwave Resonance Therapy and EEG Correlates of Microwave Resonance Relaxation and Other Consciousness Altering Techniques

Microwave Resonance Therapy (MRT) is a novel medical treatment representing a synthesis of the ancient Chinese traditional knowledge in medicine (acupuncture) and recent breakthroughs in biophysics. MRT affects the appropriate acupuncture points by the generation of high-frequency microwaves (52–78 GHz); remarkable clinical results are being achieved. In this paper, the quantum-like macroscopic biophysical bases of MRT are elaborated, offering new insights into the mechanisms of the assembling gap junction hemichannels on the internal microwave (MW) electromagnetic field spatiotemporal maxima at the temporary position of the acupuncture system, and hence insights into the biophysical nature of temporary psychosomatic health or disease. Such a picture also supports an electromagnetic/ionic “optical” ultra-low-frequency modulated MW quantum holographic neural network-like function of the acupuncture system (similar to complex-valued oscillatory holographic, Hopfield-like neural networks), and its essential relation to consciousness, as strongly suggested from biophysical modeling of altered states of consciousness. Finally, our electroencephalographic (EEG) investigations showed more or less characteristic EEG features in many subjects when relaxation was induced by MRT applied to corresponding acupuncture points, as well as by some other activities that may alter consciousness (transcendental meditation, musicogenic states, healer/healee noncontact interaction, drowsiness), demonstrating that EEG may permit rapid and reproducible identification of different states of consciousness, which could be useful in evaluating the effectiveness of consciousness altering techniques and related stress reduction.

Methods And Technology For the Characterization of the Pore Structure And Fluid Flow Properties of Soils In Reference to Contaminant Transport

Abstract Two key problems in studying the interaction of organic contaminants with soils in environmental remediation are (1) the monitoring of the contaminant evolution in the soil matrix and (2) the understanding of soil/fluid interactions. To study these two problems we have developed a combination of routine analysis (density and grain size analysis techniques) and novel instrumental monitoring techniques. These monitoring techniques are X--ray computer assisted tomography (CAT) scanning, nuclear magnetic resonance (NMR), and X-ray diffraction (XRD). This paper shows how these methods can be combined to study contaminant interaction with soil, as well as the identification of different states of an aqueous phase in the soil pore space. From this combination of techniques, we have been able to monitor qualitative changes in the soil matrix induced by the presence of an aqueous phase and a contaminant phase. Our results indicate that the contaminant (that happened to be a pesticide in these tests) interacts with the soil matrix and reduces the ability of water to interact with the soil. Introduction Environmental-contaminant and soil interaction phenomena are of great importance in remediation problems, and have received a large amount of attention in the literature(1). The understanding of these interactions will allow for a better modelling of contaminant transport, biodegradability, and toxicity. To study the interactions of a contaminant with a given soil, one must first have a full understanding of the soil properties (stationary phase). The reason for this is that soils are of a complex heterogeneous nature(2). In this study we have used the Armadale soil (also known as Mossy Point soil), which has been extensively studied at the University of Calgary(1, 3, 12). This soil has been analysed in its native state both chemically and physically. The chemical analysis consisted of conventional wet chemistry and XRD for mineral determination. The physical analysis consisted of density and grain size distribution measurements. The next step was to study the water/soil interaction with water being considered as a single mobile phase. To do this type of analysis, the preferred technology must be nondestructive and must have the ability to perform time-controlled measurements. There are two techniques that are well suited for this work and have the above properties (1) NMR imaging (MRI) and/or NMR relaxometry, and (2) CAT scanning. The imaging via MRI of water in soils has been shown to be feasible if the soil is low in iron (especially ferromagnetic iron) and other paramagnetic species(4, 5). These same conditions hold for any type of NMR study on soils. X-ray CAT scanning has been shown to be a very powerful tool in soil characterization(2, 17, 18). In parallel to NMR and CAT scanning testing, capillary pressure measurements were also made. The final step was to measure contaminant properties in soil in the presence of water. In this paper, a high-concentration pesticide solution was used as the contaminant. The developed methodology is presented through a series of preliminary tests.