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Mesenchymal cells employ actin-based membrane protrusions called podosomes and invadopodia for cross-tissue migration during normal human development such as embryogenesis and angiogenesis, and in diseases such as atherosclerosis plaque formation and cancer cell metastasis. The Akt isoforms, downstream effectors of phosphatidylinositol 3 kinase (PI3K), play crucial roles in cell migration and invasion, but their involvement in podosome formation and cell invasion is not known. In this study, we have used Akt1 and/or Akt2 knockout mouse embryonic fibroblasts and Akt3-targeted shRNA to determine the roles of the three Akt isoforms in Src and phorbol ester-induced podosome formation, and extracellular matrix (ECM) digestion. We found that deletion or knockdown of Akt1 significantly reduces Src-induced formation of podosomes and rosettes, and ECM digestion, while suppression of Akt2 has little effect. In contrast, Akt3 knockdown by shRNA increases Src-induced podosome/rosette formation and ECM invasion. These data suggest that Akt1 promotes, while Akt3 suppresses, podosome formation induced by Src, and Akt2 appears to play an insignificant role. Interestingly, both Akt1 and Akt3 suppress, while Akt2 enhances, phorbol ester-induced podosome formation. These data show that Akt1, Akt2 and Akt3 play different roles in podosome formation and ECM invasion induced by Src or phorbol ester, thus underscoring the importance of cell context in the roles of Akt isoforms in cell invasion. [ABSTRACT FROM AUTHOR]

The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur. [ABSTRACT FROM AUTHOR]

Background. While neoadjuvant concurrent chemoradiotherapy has improved outcomes for esophageal cancer patients, surgical complication rates remain high. The most frequent perioperative complications after trimodality therapy were cardiopulmonary in nature. The radiation modality utilized can be a strong mitigating factor of perioperative complications given the location of the esophagus and its proximity to the heart and lungs. The purpose of this study is to make a dosimetric comparison of Intensity-Modulated Radiation Therapy (IMRT), proton and 3D conformal radiotherapy (3D-CRT) with regard to reducing perioperative cardiopulmonary complications in esophageal cancer patients. Materials. Ten patients with esophageal cancer treated between 2010 and 2013 were evaluated in this study. All patients were simulated with contrast-enhanced CT imaging. Separate treatment plans using proton radiotherapy, IMRT, and 3D-CRT modalities were created for each patient. Dose-volume histograms were calculated and analyzed to compare plans between the three modalities. The organs at risk (OAR) being evaluated in this study are the heart, lungs, and spinal cord. To determine statistical significance, ANOVA and two-tailed paired t-tests were performed for all data parameters. Results. The proton plans showed decreased dose to various volumes of the heart and lungs in comparison to both the IMRT and 3D-CRT plans. There was no difference between the IMRT and 3D-CRT plans in dose delivered to the lung or heart. This finding was seen consistently across the parameters analyzed in this study. Conclusions. In patients receiving radiation therapy for esophageal cancer, proton plans are technically feasible while achieving adequate coverage with lower doses delivered to the lungs and cardiac structures. This may result in decreased cardiopulmonary toxicity and less morbidity to esophageal cancer patients. [ABSTRACT FROM AUTHOR]

The regression weights or path coefficients that depict the magnitude of associations of arrows in path diagrams are maximized for the study sample, in keeping with the least-squares criterion. Although the MoCA serves well to predict cognitive impairment and to identify persons having Alzheimer's dementia or mild cognitive impairment [[26]], its meaning becomes unclear when applied to a sample of cognitively intact individuals. The MoCA was developed as a measure of cognitive impairment in elderly persons suspected of developing dementia or mild cognitive impairment and was validated on a sample of three groups of elderly adults: Alzheimer's disease, mild cognitive impairment, or no cognitive impairment [[25]]. The earlier study included 120 males and 63 females, ages 18-64, whereas the more recent study included only 117 males, ages 18-64, and added 58 males, ages 65-91. [Extracted from the article]

The purpose of this in vitro study was to evaluate erosive wear and change in color of high-viscosity glass ionomer cements after pH cycling in two erosive media. There were 3 experimental groups with 22 samples each, (I) EQUIA Forte HT without coat, (II) Fuji IX and (III) Ketac Universal Aplicap. Each group was randomly divided into three subgroups (n = 6–8) further exposed to different environments, (1) distilled water, (2) green tea (pH 3.78) and (3) Aceto balsamico vinegar (pH 3.0). Mass and L* a* b* values were recorded before and after pH cycling. The samples in subgroups 2 and 3 were exposed to the acidic media two times a day for 10 min, over a period of 14 days. The differences among materials and erosive effects of the three media were tested using three-way analyses of variance with post hoc LSD test at the significance level p < 0.05. The effect of pH cycling in Aceto balsamico and green tea was degrading for all three materials. pH cycling in Aceto balsamico caused significantly higher erosive wear than pH cycling in Fuzetea and storage in distilled water, in all materials (p < 0.05). pH cycling in both acidic media and in the control group resulted in a significant change in L* a* and b* (p < 0.05). The L* value decreased significantly and the a* and b* values increased significantly (p < 0.05). [ABSTRACT FROM AUTHOR]

We discuss hypothesis testing and compare different theories in light of observed or experimental data as fundamental endeavors in the sciences. Issues associated with the p-value approach and null hypothesis significance testing are reviewed, and the Bayesian alternative based on the Bayes factor is introduced, along with a review of computational methods and sensitivity related to prior distributions. We demonstrate how Bayesian testing can be practically implemented in several examples, such as the t-test, two-sample comparisons, linear mixed models, and Poisson mixed models by using existing software. Caveats and potential problems associated with Bayesian testing are also discussed. We aim to inform researchers in the many fields where Bayesian testing is not in common use of a well-developed alternative to null hypothesis significance testing and to demonstrate its standard implementation. [ABSTRACT FROM AUTHOR]

Minimum Renyi's pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented. [ABSTRACT FROM AUTHOR]

Determining the complex relationships between diseases, polymorphisms in human genes and environmental factors is challenging. Multifactor dimensionality reduction (MDR) has been proven to be capable of effectively detecting the statistical patterns of epistasis, although classification accuracy is required for this approach. The imbalanced dataset can cause seriously negative effects on classification accuracy. Moreover, MDR methods cannot quantitatively assess the disease risk of genotype combinations. Hence, we introduce a novel weighted risk score-based multifactor dimensionality reduction (WRSMDR) method that uses the Bayesian posterior probability of polymorphism combinations as a new quantitative measure of disease risk. First, we compared the WRSMDR to the MDR method in simulated datasets. Our results showed that the WRSMDR method had reasonable power to identify high-order gene-gene interactions, and it was more effective than MDR at detecting four-locus models. Moreover, WRSMDR reveals more information regarding the effect of genotype combination on the disease risk, and the result was easier to determine and apply than with MDR. Finally, we applied WRSMDR to a nasopharyngeal carcinoma (NPC) case-control study and identified a statistically significant high-order interaction among three polymorphisms: rs2860580, rs11865086 and rs2305806. [ABSTRACT FROM AUTHOR]

Chronic inflammation plays a key tumor-promoting role in lung cancer. Our previous studies in mice demonstrated that neutrophils are critical mediators of tumor promotion in methylcholanthrene (MCA)-initiated, butylated hydroxytoluene (BHT)-promoted lung carcinogenesis. In the present study we investigated the role of neutrophil myeloperoxidase (MPO) activity in this inflammation promoted model. Increased levels of MPO protein and activity were present in the lungs of mice administered BHT. Treatment of mice with N-acetyl lysyltyrosylcysteine amide (KYC), a novel tripeptide inhibitor of MPO, during the inflammatory stage reduced tumor burden. In a separate tumor model, KYC treatment of a Lewis Lung Carcinoma (LLC) tumor graft in mice had no effect on tumor growth, however, mice genetically deficient in MPO had significantly reduced LLC tumor growth. Our observations suggest that MPO catalytic activity is critical during the early stages of tumor development. However, during the later stages of tumor progression, MPO expression independent of catalytic activity appears to be required. Our studies advocate for the use of MPO inhibitors in a lung cancer prevention setting. [ABSTRACT FROM AUTHOR]

Hereditary Tyrosinemia type 1 (HT1) is a metabolic liver disease caused by genetic defects of fumarylacetoacetate hydrolase (FAH), an enzyme necessary to complete the breakdown of tyrosine. The severe hepatic dysfunction caused by the lack of this enzyme is prevented by the therapeutic use of NTBC (2-[2-nitro-4-(trifluoromethyl)benzoyl] cyclohexane-1,3-dione). However despite the treatment, chronic hepatopathy and development of hepatocellular carcinoma (HCC) are still observed in some HT1 patients. Growing evidence show the important role of heat shock proteins (HSPs) in many cellular processes and their involvement in pathological diseases including cancer. Their survival-promoting effect by modulation of the apoptotic machinery is often correlated with poor prognosis and resistance to therapy in a number of cancers. Here, we sought to gain insight into the pathophysiological mechanisms associated with liver dysfunction and tumor development in a murine model of HT1. Differential gene expression patterns in livers of mice under HT1 stress, induced by drug retrieval, have shown deregulation of stress and cell death resistance genes. Among them, genes coding for HSPB and HSPA members, and for anti-apoptotic BCL-2 related mitochondrial proteins were associated with the hepatocarcinogenetic process. Our data highlight the variation of stress pathways related to HT1 hepatocarcinogenesis suggesting the role of HSPs in rendering tyrosinemia-affected liver susceptible to the development of HCC. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) mostly rely on electrophysiological brain signals. Methodological and technical progress has largely solved the challenge of processing these signals online. The main issue that remains, however, is the identification of a reliable mapping between electrophysiological measures and relevant states of mind. This is why BCIs are highly dependent upon advances in cognitive neuroscience and neuroimaging research. Recently, psychological theories became more biologically plausible, leading to more realistic generative models of psychophysiological observations. Such complex interpretations of empirical data call for efficient and robust computational approaches that can deal with statistical model comparison, such as approximate Bayesian inference schemes. Importantly, the latter enable the optimization of a model selection error rate with respect to experimental control variables, yielding maximally powerful designs. In this paper, we use a Bayesian decision theoretic approach to cast model comparison in an online adaptive design optimization procedure. We show how to maximize design efficiency for individual healthy subjects or patients. Using simulated data, we demonstrate the face- and construct-validity of this approach and illustrate its extension to electrophysiology and multiple hypothesis testing based on recent psychophysiological models of perception. Finally, we discuss its implications for basic neuroscience and BCI itself. [ABSTRACT FROM AUTHOR]

Conditional independence tests have received special attention lately in machine learning and computational intelligence related literature as an important indicator of the relationship among the variables used by their models. In the field of probabilistic graphical models, which includes Bayesian network models, conditional independence tests are especially important for the task of learning the probabilistic graphical model structure from data. In this paper, we propose the full Bayesian significance test for tests of conditional independence for discrete datasets. The full Bayesian significance test is a powerful Bayesian test for precise hypothesis, as an alternative to the frequentist's significance tests (characterized by the calculation of the p-value). [ABSTRACT FROM AUTHOR]

A generalized maximum entropy estimator is developed for the linear simultaneous equations model. Monte Carlo sampling experiments are used to evaluate the estimator's performance in small and medium sized samples, suggesting contexts in which the current generalized maximum entropy estimator is superior in mean square error to two and three stage least squares. Analytical results are provided relating to asymptotic properties of the estimator and associated hypothesis testing statistics. Monte Carlo experiments are also used to provide evidence on the power and size of test statistics. An empirical application is included to demonstrate the practical implementation of the estimator. [ABSTRACT FROM AUTHOR]

Bayesian testing of a point null hypothesis is considered. The null hypothesisis that an observation, x, is distributed according to the normal distribution with a mean of zero and known variance σ². The alternative hypothesis is that x is distributed accordingto a normal distribution with an unknown nonzero mean, µ, and variance σ². The testingproblem is formulated as a prediction problem. Bayesian testing based on priors constructedby using conditional mutual information is investigated. [ABSTRACT FROM AUTHOR]

In-vehicle electronic control unit (ECU) communications generally count on private protocols (defined by the manufacturers) under controller area network (CAN) specifications. Parsing the private protocols for a particular vehicle model would be of great significance in testing the vehicle's resistance to various attacks, as well as in designing efficient intrusion detection and prevention systems (IDPS) for the vehicle. This paper proposes a suite of methods for parsing ECU private protocols on in-vehicle CAN network. These methods include an algorithm for parsing discrete variables (encoded in a discrete manner, e.g., gear state), an algorithm for parsing continuous variables (encoded in a continuous manner, e.g., vehicle speed), and a parsing method based on upper-layer protocols (e.g., OBD and UDS). Extensive verifications have been performed on five different brands of automobiles (including an electric vehicle) to demonstrate the universality and the correctness of these parsing algorithms. Some parsing tips and experiences are also presented. Our continuous-variables parsing algorithm could run in a semi-automatic manner and the parsing algorithm from upper-layer protocols could execute in a completely automatic manner. One might view the results obtained by our parsing algorithms as an important indicator of penetration testing on in-vehicle CAN network. [ABSTRACT FROM AUTHOR]

Background: Radioactive contamination and chemical pollution of the environment can affect the processes of carcinogenesis, including the formation of malignant neoplasms of the ovaries in women. We used the data of official state statistics for 2000–2020 to test the hypothesis about the effect of radioactive contamination (following the Chernobyl disaster) and chemical pollutants on the incidence of ovarian malignancies in the female population of the Bryansk region. Methods: A variety of statistical approaches were used to estimate the incidence of ovarian malignancies, including the Shapiro–Wilk test, Mann–Whitney U test, Spearman's rank correlation test and linear regression. Results: We did not establish statistically significant differences in the frequency of primary morbidity of women with malignant neoplasms of the ovaries, regardless of the environmental conditions of living. Furthermore, no significant correlations were found between the frequency of primary morbidity of ovarian malignancies, both with the level of contamination by Cesium-137 and Strontium-90, and air pollution with volatile organic compounds, carbon monoxide, sulfur dioxide and nitrogen oxides. A statistically significant increase in the long-term trend in the frequency of ovarian malignant neoplasms was revealed in the areas of chemical pollution (p = 0.02), however, in other territories, no statistically significant regularities were established. The forecast of the frequency of newly diagnosed malignant neoplasms of the ovaries on average in the Bryansk region shows an increase of 12.4% in 2020 in comparison with the real data for 2020, while the largest increase in predicted values is recorded in the territories of radioactive contamination (by 79.6%), and the least in the combined territories (by 6.9%). Conclusions: The results obtained indicate the need for further work to understand the trends in the presence/absence of independent and combined effects of pollutants and the growth of oncogynecological pathology from the perspective of assessing the distant and regional metastasis, histological and immunohistochemical profile of a specific malignant ovarian neoplasm with levels of environmental contamination. [ABSTRACT FROM AUTHOR]

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas. [ABSTRACT FROM AUTHOR]

Compression creep tests were performed on the ternary 91.84Sn-3.33Ag-4.83Bi (wt.%, abbreviated Sn-Ag-Bi) Pb-free alloy. The test temperatures were: -25 °C, 25 °C, 75 °C, 125 °C, and 160 °C (± 0.5 °C). Four loads were used at the two lowest temperatures and five at the higher temperatures. The specimens were tested in the as-fabricated condition or after having been subjected to one of two air aging conditions: 24 hours at either 125 °C or 150 °C. The strain-time curves exhibited frequent occurrences of negative creep and small-scale fluctuations, particularly at the slower strain rates, that were indicative of dynamic recrystallization (DRX) activity. The source of tertiary creep behavior at faster strain rates was likely to also be DRX rather than a damage accumulation mechanism. Overall, the strain-time curves did not display a consistent trend that could be directly attributed to the aging condition. The sinh law equation satisfactorily represented the minimum strain rate as a function of stress and temperature so as to investigate the deformation rate kinetics: dε/dtmin = Asinhn (ασ) exp (-▵ H/RT). The values of α, n, and ▵ H were in the following ranges (±95% confidence interval): α, 0.010-0.015 (±0.005 1/MPa); n, 2.2-3.1 (±0.5); and ▵ H, 54-66 (±8 kJ/mol). The rate kinetics analysis indicated that short-circuit diffusion was a contributing mechanism to dislocation motion during creep. The rate kinetics analysis also determined that a minimum creep rate trend could not be developed between the as-fabricated versus aged conditions. This study showed that the elevated temperature aging treatments introduced multiple changes to the Sn-Ag-Bi microstructure that did not result in a simple loss ("softening") of its mechanical strength. [ABSTRACT FROM AUTHOR]

Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find an accurate model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation data obtained from Kuosheng NPP between 2006 and 2011 were verified using a linear regression model with a 95% confidence interval. The key parameters of turbine cycle, including turbine throttle pressure, condenser backpressure, feedwater flow rate and final feedwater temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using the commercial software PEPSE® to compare the performance of the ANFIS based turbine cycle model. The results show that the proposed ANFIS based turbine cycle model is capable of accurately estimating turbine-generator output and providing more reliable results than the PEPSE® based turbine cycle models. Moreover, test results show that the ANFIS performed better than the artificial neural network (ANN), which has also being tried to model the turbine cycle. The effectiveness of the proposed neuro-fuzzy based turbine cycle model was demonstrated using the actual operating data of Kuosheng NPP. Furthermore, the results also provide an alternative approach to evaluate the thermal performance of nuclear power plants. [ABSTRACT FROM AUTHOR]

Two goodness-of-fit tests for copulas are being investigated. The first one deals with the case of elliptical copulas and the second one deals with independent copulas. These tests result from the expansion of the projection pursuit methodology that we will introduce in the present article. This method enables us to determine on which axis system these copulas lie as well as the exact value of these very copulas in the basis formed by the axes previously determined irrespective of their value in their canonical basis. Simulations are also presented as well as an application to real datasets. [ABSTRACT FROM AUTHOR]

This paper presents, first, a formal exploration of the relationships between information (statistically defined), statistical hypothesis testing, the use of hypothesis testing in reverse as an investigative tool, channel capacity in a communication system, uncertainty, the concept of entropy in thermodynamics, and Bayes' theorem. This exercise brings out the close mathematical interrelationships between different applications of these ideas in diverse areas of psychology. Subsequent illustrative examples are grouped under (a) the human operator as an ideal communications channel, (b) the human operator as a purely physical system, and (c) Bayes' theorem as an algorithm for combining information from different sources. Some tentative conclusions are drawn about the usefulness of information theory within these different categories. (a) The idea of the human operator as an ideal communications channel has long been abandoned, though it provides some lessons that still need to be absorbed today. (b) Treating the human operator as a purely physical system provides a platform for the quantitative exploration of many aspects of human performance by analogy with the analysis of other physical systems. (c) The use of Bayes' theorem to calculate the effects of prior probabilities and stimulus frequencies on human performance is probably misconceived, but it is difficult to obtain results precise enough to resolve this question. [ABSTRACT FROM AUTHOR]

This paper describes a procedure for the determination of antimony (III) by differential pulse anodic stripping voltammetry using a mercury film screen-printed electrode as the working electrode. The procedure has been optimized using experimental design methodology. Under these conditions, in terms of Residual Standard Deviation (RSD), the repeatability (3.81 %) and the reproducibility (5.07 %) of the constructed electrodes were both analyzed. The detection limit for Sb (III) was calculated at a value of 1.27×10-8 M. The linear range obtained was between 0.99 × 10-8 - 8.26 × 10-8 M. An analysis of possible effects due to the presence of foreign ions in the solution was performed and the procedure was successfully applied to the determination of antimony levels in pharmaceutical preparations and sea water samples. [ABSTRACT FROM AUTHOR]

In this work, a Korean peninsula earthquake network, constructed via event-sequential linking known as the Abe–Suzuki method, was investigated in terms of network properties. A significance test for these network properties was performed via comparisons with those of two random networks, constructed from two approaches, that is, EVENT (SEQUENCE) SHUFFLING and NETWORK (MATRIX) SHUFFLING. The Abe–Suzuki earthquake network has a clear difference from the two random networks. However, the two shuffled networks exhibited completely different functions, and even some network properties for one shuffled datum are significantly high and those of the other shuffled data are low compared to actual data. For most cases, the event-shuffled network showed a functional similarity to the real network, but with different exponents/parameters. This result strongly claims that the Korean peninsula earthquake network has a spatiotemporal causal relation. Additionally, the Korean peninsula network properties are mostly similar to those found in previous studies on the US and Japan. Further, the Korean earthquake network showed strong linearity in a specific range of spatial resolution, that is, 0.20°~0.80°, implying that macroscopic properties of the Korean earthquake network are highly regular in this range of resolution. [ABSTRACT FROM AUTHOR]

Active tectonics is manifested in geomorphological features such as drainage basins and drainage patterns. Geomorphic parameters asymmetry factor (AF) and transverse topography symmetry factor (T) is calculated for 94 third order basins of the Garhwal synform to decipher the tilt-block tectonics based on remote sensing and geographical information system (GIS) techniques. The quantitative analysis of the AF suggests that all the 94 basins are asymmetric and gentle to steeply tilted, indicating active tectonics and early and late stage of development, respectively. The mean vector magnitude (θv) of T suggests the migration of the basin stream towards the south in most basins (60%), suggesting a unidirectional tilting of the tectonic block. The χ2 test for statistical significance indicates that the θv is significant for southern and northern limb basins. The χ2 test affirms that the third order basin position on either side of the main channel of the river basin influences the tilt direction. The regional tectonics suggests migration of the Lansdowne klippe towards the south, as the majority of third order basins show southward tilt. The study provides a quick appraisal of tilting in the tectonic blocks of active margins, such as in the Himalayas. [ABSTRACT FROM AUTHOR]

In the process of extracting hard coal, extensive databases are created on its quality parameters. A statistical assessment was made of the ash, sulfur, and mercury content of commercial coals produced in the Upper Silesian Coal Basin (USCB). The statistical methods applied: non-parametric tests of compatibility for two populations, parametric significance tests, and non-parametric tests of compatibility for the three populations, showed that the distributions of ash and sulfur content in 2014 and 2015 are comparable and the average values are similar. Statistical tests indicated significant differences in the mercury content distributions and their variances. This demonstrates the need for ongoing monitoring of mercury content in commercial coals, as a prediction of mercury content from historical data is hardly possible. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the porosity distribution of BioRoot RCS/single gutta-percha point root-canal fillings performed by a general dental practitioner and endodontist. Fourteen plastic models of maxillary premolars with two roots were selected and shaped with HyFlex EDM instruments to a size 40/0.04 taper and randomly divided into two experimental groups. A total of 14 canals in each group were obturated by two different operators with one HyFlex EDM size 40 gutta-percha point and BioRoot RCS sealer. The specimens were scanned with a high-resolution micro-computed tomography scanner, and the porosity of the fillings at the coronal, middle, and apical thirds of the root canals was qualified and quantified. The differences between groups and root-canal thirds were compared using Mann–Whitney, Friedman, and Wilcoxon tests with the significance level set at p < 0.05. The highest number of pores was observed in the apical third of root-canal fillings in both groups; however, the porosity distribution between the two groups was not significantly different in the apical and middle thirds (p > 0.05). Statistically significant differences were determined only in the coronal third (p < 0.05). The predominance of open pores was detected in all root-canal thirds and groups, with no significant differences in the number of open pores between the coronal and middle thirds (p > 0.05). [ABSTRACT FROM AUTHOR]

Carbon fiber reinforced polymer (CFRP) laminates, as unique multifunctional materials, are widely applied in various aircraft, such as airliners, fighter planes, and space shuttles. To ensure aircraft safety during the production and application of CFRP laminates, it is necessary to improve the accuracy of nonlinear Lamb wave nondestructive testing to assess the damage in CFRP laminates caused by impact, high temperature, friction, corrosion, etc. In this study, the accuracy of nonlinear ultrasonic nondestructive testing was found to highly depend on the cycle number, output level and gain of the nonlinear ultrasonic detection system. Based on a single-factor experiment that considered the cycle number, output level, and gain of the amplifier as independent variables, a regression analysis was carried out on the fundamental wave amplitude value (A1) and second harmonic amplitude value (A2). Two response surface surrogate models were established to improve the accuracy of nonlinear Lamb wave nondestructive testing and to optimize the detection system parameters. The response surface models were verified via an analysis of variance (ANOVA), significance tests and an error statistical analysis. The results revealed the significant influence of these three factors on A1 and A2. Optimization of the response surface was achieved at eight cycles, an output level of 42 and a gain of 32 dB. Moreover, the nonlinear ultrasonic detection system achieved good operational stability, high accuracy and reliability under the above optimal parameter conditions. This approach provides scientific guidance for the accurate assessment of CFRP laminate damage. [ABSTRACT FROM AUTHOR]

Real-time cycle slip detection and repair is one of the key issues in global positioning system (GPS) high precision data processing and application. In particular, when GPS stations are in special environments, such as strong ionospheric disturbance, sea, and high-voltage transmission line interference, cycle slip detection and repair in low elevation GPS observation data are more complicated than those in normal environments. For low elevation GPS undifferenced carrier phase data in different environments, a combined cycle slip detection algorithm is proposed. This method uses the first-order Gauss–Markov stochastic process to model the pseudorange multipath in the wide-lane phase minus narrow-lane pseudorange observation equation, and establishes the state equation of the wide-lane ambiguity with the pseudorange multipath as a parameter, and it uses the Kalman filter for real-time estimation and detects cycle slips based on statistical hypothesis testing with a predicted residual sequence. Meanwhile, considering there are certain correlations among low elevation, observation epoch interval, and ionospheric delay error, a second-order difference geometry-free combination cycle slip test is constructed that takes into account the elevation. By combining the two methods, real-time cycle slip detection for GPS low elevation satellite undifferenced data is achieved. A cycle slip repair method based on spatial search and objective function minimization criterion is further proposed to determine the correct solution of the cycle slips after they are detected. The whole algorithm is experimentally verified using the static and kinematic measured data of low elevation satellites under four different environments: normal condition, high-voltage transmission lines, dynamic condition in the sea, and ionospheric disturbances. The experimental results show that the algorithm can detect and repair cycle slips accurately for low elevation GPS undifferenced data, the difference between the float solution and the true value for the cycle slip does not exceed 0.5 cycle, and the differences obey the normal distribution overall. At the same time, the wide-lane ambiguity and second-order difference GF combination sequence calculated by the algorithm is smoother, which give further evidence that the algorithm for cycle slip detection and repair is feasible and effective, and has the advantage of being immune to the special observation environments. [ABSTRACT FROM AUTHOR]

The upcoming Surface Water and Ocean Topography (SWOT) mission will measure rivers wider than 50–100 m using a 21-day orbit, providing river reach derived discharges that can inform applications like flood forecasting and large-scale hydrologic modelling. However, these discharges will not be uniform in time or coincident with those of neighboring reaches. It is often assumed discharge upstream and downstream of a river location are highly correlated in natural conditions and can be transferred using a scaling factor like the drainage area ratio between locations. Here, the applicability of the drainage area ratio method to integrate, in space and time, SWOT-derived discharges throughout the observable river network of the Mississippi River basin is assessed. In some cases, area ratios ranging from 0.01 to 100 can be used, but cumulative urban area and/or the number of dams/reservoirs between locations decrease the method's applicability. Though the mean number of SWOT observations for a given reach increases by 83% and the number of peak events captured increases by 100%, expanded SWOT sampled time series distributions often underperform compared to the original SWOT sampled time series for significance tests and quantile results. Alternate expansion methods may be more viable for future work. [ABSTRACT FROM AUTHOR]

A novel general method for constructing nonparametric hypotheses tests based on the field of symbolic analysis is introduced in this paper. Several existing tests based on symbolic entropy that have been used for testing central hypotheses in several branches of science (particularly in economics and statistics) are particular cases of this general approach. This family of symbolic tests uses few assumptions, which increases the general applicability of any symbolic-based test. Additionally, as a theoretical application of this method, we construct and put forward four new statistics to test for the null hypothesis of spatiotemporal independence. There are very few tests in the specialized literature in this regard. The new tests were evaluated with the mean of several Monte Carlo experiments. The results highlight the outstanding performance of the proposed test. [ABSTRACT FROM AUTHOR]

The Bayes error rate is the expected risk for the Bayes classifier, which assigns a given feature vector HT ht to the class with the highest posterior probability. The so obtained rho function is proportional to one minus a suitably scaled normal density raised to the power HT ht . Hence, the large deviation principle in the weak topology of HT ht can be derived (more easily) using a standard Gartner-Ellis argument and, utilizing this, one can, in principle, repeat all of the arguments above to derive results that are analogous to Theorems 2.2 and 2.3. [Extracted from the article]

Calculating and monitoring customer churn metrics is important for companies to retain customers and earn more profit in business. In this study, a churn prediction framework is developed by modified spectral clustering (SC). However, the similarity measure plays an imperative role in clustering for predicting churn with better accuracy by analyzing industrial data. The linear Euclidean distance in the traditional SC is replaced by the non-linear S-distance ( S d ). The S d is deduced from the concept of S-divergence (SD). Several characteristics of S d are discussed in this work. Assays are conducted to endorse the proposed clustering algorithm on four synthetics, eight UCI, two industrial databases and one telecommunications database related to customer churn. Three existing clustering algorithms—k-means, density-based spatial clustering of applications with noise and conventional SC—are also implemented on the above-mentioned 15 databases. The empirical outcomes show that the proposed clustering algorithm beats three existing clustering algorithms in terms of its Jaccard index, f-score, recall, precision and accuracy. Finally, we also test the significance of the clustering results by the Wilcoxon's signed-rank test, Wilcoxon's rank-sum test, and sign tests. The relative study shows that the outcomes of the proposed algorithm are interesting, especially in the case of clusters of arbitrary shape. [ABSTRACT FROM AUTHOR]

Perceptual decision-making requires transforming sensory information into decisions. An ambiguity of sensory input affects perceptual decisions inducing specific time-frequency patterns on EEG (electroencephalogram) signals. This paper uses a wavelet-based method to analyze how ambiguity affects EEG features during a perceptual decision-making task. We observe that parietal and temporal beta-band wavelet power monotonically increases throughout the perceptual process. Ambiguity induces high frontal beta-band power at 0.3–0.6 s post-stimulus onset. It may reflect the increasing reliance on the top-down mechanisms to facilitate accumulating decision-relevant sensory features. Finally, this study analyzes the perceptual process using mixed within-trial and within-subject design. First, we found significant percept-related changes in each subject and then test their significance at the group level. Thus, observed beta-band biomarkers are pronounced in single EEG trials and may serve as control commands for brain-computer interface (BCI). [ABSTRACT FROM AUTHOR]

This paper uses the median-of-means (MOM) method to estimate the parameters of the nonlinear regression models and proves the consistency and asymptotic normality of the MOM estimator. Especially when there are outliers, the MOM estimator is more robust than nonlinear least squares (NLS) estimator and empirical likelihood (EL) estimator. On this basis, we propose hypothesis testing Statistics for the parameters of the nonlinear regression models using empirical likelihood method, and the simulation performance shows the superiority of MOM estimator. We apply the MOM method to analyze the top 50 data of GDP of China in 2019. The result shows that MOM method is more feasible than NLS estimator and EL estimator. [ABSTRACT FROM AUTHOR]

In this paper, a digital hydraulic variable control method based on multi-way valve spool displacement feedback is proposed, which combines the advantages of low cost and high reliability of the loader fixed displacement pump hydraulic system, and the distinguished energy saving effect of the loader variable pump hydraulic system. Based on the principle of hydraulic resistance, the mathematical model of flow-pressure of multi-way valve was established. Meanwhile, the theoretical structure of a valve spool suitable for digital hydraulic system was deduced. On this basis, an improvement scheme of the valve spool was proposed in combination with machining feasibility. The simulation model of multi-way valve was established, the correctness of which was verified by experiment with the significance test method, and the valve port parameters of the multi-way valve were optimized by genetic algorithm. The simulation model before and after optimization was analyzed by AMESim software. The simulation results showed that the digital hydraulic system can basically realize the function of a variable system, and the energy consumption was significantly reduced compared with the fixed displacement pump hydraulic system. The optimized valve spool structure of the digital hydraulic system had more stable load characteristics than the fixed displacement pump system and a larger speed range. The increase range accounts for about 27% of the total stroke. [ABSTRACT FROM AUTHOR]

Developing artificial caries lesions with varying characteristics is needed to adequately study caries process in vitro. The objective of this study was to investigate artificial caries lesion characteristics after secondary demineralization protocol containing theobromine and fluoride. Sixty bovine enamel slabs (4 × 3 mm) were demineralized using a Carbopol-containing protocol for 6 days. A baseline area (2 × 3 mm) was protected with acid-resistant nail varnish, after which specimens were exposed for 24 h to a secondary demineralization protocol containing acetic acid plus one of four fluoride/theobromine combinations (n = 15): theobromine (50 or 200 ppm) and fluoride (0 or 1 ppm). Specimens were sectioned and analyzed using transverse microradiography for changes in mineral content, lesion depth, and surface layer mineralization. Data was analyzed using paired t-test and analysis of variance followed by Bonferroni test at 0.05 significance level. After secondary demineralization, fluoride-containing groups had significantly deeper lesions (p = 0.002 and 0.014) compared to the group with 0 ppm fluoride and 50 ppm theobromine. Mineral content and lesion depth were significantly different compared to baseline for all groups. Theobromine did not show an added effect on mineral uptake. Theobromine-containing groups exhibited particularly deep lesions with a more uniform mineral profile in the presence of fluoride. [ABSTRACT FROM AUTHOR]

Recent research suggests that one of the main motivations for mergers and acquisitions is the attempt to acquire companies to incorporate intangible assets. Such assets provide important sources of sustainable competitive advantages and opportunities for growth. This article analyzes the strategies of engineering companies, as well as value creation in acquisition events of multinational companies, by using the study of the events method, providing an innovative way to be applied to this phenomenon. This method is used in our research to study the influence of the announcement of acquisitions on the abnormal accumulated returns of the acquiring companies, and is allowed to confirm that influence. In general, the average accumulated returns were positive and statistically significant in the three windows of the method, according to the significance tests used. The results validate the hypothesis that the events generate synergy gains for market players, emphasizing the importance of growth via acquisitions for the sector under analysis. [ABSTRACT FROM AUTHOR]

Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics. [ABSTRACT FROM AUTHOR]

To perform statistical inference for time series, one should be able to assess if they present deterministic or stochastic trends. For univariate analysis, one way to detect stochastic trends is to test if the series has unit roots, and for multivariate studies it is often relevant to search for stationary linear relationships between the series, or if they cointegrate. The main goal of this article is to briefly review the shortcomings of unit root and cointegration tests proposed by the Bayesian approach of statistical inference and to show how they can be overcome by the Full Bayesian Significance Test (FBST), a procedure designed to test sharp or precise hypothesis. We will compare its performance with the most used frequentist alternatives, namely, the Augmented Dickey–Fuller for unit roots and the maximum eigenvalue test for cointegration. [ABSTRACT FROM AUTHOR]

We introduce a new multivariate regression model based on the generalized Poisson distribution, which we called geographically-weighted multivariate generalized Poisson regression (GWMGPR) model, and we present a maximum likelihood step-by-step procedure to obtain parameters for it. We use the maximum likelihood ratio test to examine the significance of the regression parameters and to define their critical region. [ABSTRACT FROM AUTHOR]

The range kernel of bilateral filter degrades image quality unintentionally in real environments because the pixel intensity varies randomly due to the noise that is generated in image sensors. Furthermore, the range kernel increases the complexity due to the comparisons with neighboring pixels and the multiplications with the corresponding weights. In this paper, we propose a noise-aware range kernel, which estimates noise using an intensity difference-based image noise model and dynamically adjusts weights according to the estimated noise, in order to alleviate the quality degradation of bilateral filters by noise. In addition, to significantly reduce the complexity, an approximation scheme is introduced, which converts the proposed noise-aware range kernel into a binary kernel while using the statistical hypothesis test method. Finally, blue a fully parallelized and pipelined very-large-scale integration (VLSI) architecture of a noise-aware bilateral filter (NABF) that is based on the proposed binary range kernel is presented, which was successfully implemented in field-programmable gate array (FPGA). The experimental results show that the proposed NABF is more robust to noise than the conventional bilateral filter under various noise conditions. Furthermore, the proposed VLSI design of the NABF achieves 10.5 and 95.7 times higher throughput and uses 63.6–97.5% less internal memory than state-of-the-art bilateral filter designs. [ABSTRACT FROM AUTHOR]

Currently, expert systems and applied machine learning algorithms are widely used to automate network intrusion detection. In critical infrastructure applications of communication technologies, the interaction among various industrial control systems and the Internet environment intrinsic to the IoT technology makes them susceptible to cyber-attacks. Given the existence of the enormous network traffic in critical Cyber-Physical Systems (CPSs), traditional methods of machine learning implemented in network anomaly detection are inefficient. Therefore, recently developed machine learning techniques, with the emphasis on deep learning, are finding their successful implementations in the detection and classification of anomalies at both the network and host levels. This paper presents an ensemble method that leverages deep models such as the Deep Neural Network (DNN) and Long Short-Term Memory (LSTM) and a meta-classifier (i.e., logistic regression) following the principle of stacked generalization. To enhance the capabilities of the proposed approach, the method utilizes a two-step process for the apprehension of network anomalies. In the first stage, data pre-processing, a Deep Sparse AutoEncoder (DSAE) is employed for the feature engineering problem. In the second phase, a stacking ensemble learning approach is utilized for classification. The efficiency of the method disclosed in this work is tested on heterogeneous datasets, including data gathered in the IoT environment, namely IoT-23, LITNET-2020, and NetML-2020. The results of the evaluation of the proposed approach are discussed. Statistical significance is tested and compared to the state-of-the-art approaches in network anomaly detection. [ABSTRACT FROM AUTHOR]

In remote sensing, the term accuracy typically expresses the degree of correctness of a map. Best practices in accuracy assessment have been widely researched and include guidelines on how to select validation data using probability sampling designs. In practice, however, probability samples may be lacking and, instead, cross-validation using non-probability samples is common. This practice is risky because the resulting accuracy estimates can easily be mistaken for map accuracy. The following question arises: to what extent are accuracy estimates obtained from non-probability samples representative of map accuracy? This letter introduces the T index to answer this question. Certain cross-validation designs (such as the common single-split or hold-out validation) provide representative accuracy estimates when hold-out sets are simple random samples of the map population. The T index essentially measures the probability of a hold-out set of unknown sampling design to be a simple random sample. To that aim, we compare its spread in the feature space against the spread of random unlabelled samples of the same size. Data spread is measured by a variant of Moran's I autocorrelation index. Consistent interpretation of the T index is proposed through the prism of significance testing, with T values < 0.05 indicating unreliable accuracy estimates. Its relevance and interpretation guidelines are also illustrated in a case study on crop-type mapping. Uptake of the T index by the remote-sensing community will help inform about—and sometimes caution against—the representativeness of accuracy estimates obtained by cross-validation, so that users can better decide whether a map is fit for their purpose or how its accuracy impacts their application. Subsequently, the T index will build trust and improve the transparency of accuracy assessment in conditions which deviate from best practices. [ABSTRACT FROM AUTHOR]

The sustainability of the supply chain is possible only if the profitability of all the tiers participating in that supply chain is guaranteed. The profitability of each of these tiers is ensured if information sharing as well as an effective and seamless coordination system are realized between the tiers. This process reduces the influence of an important risk factor known as the bullwhip effect. The purpose of the current study is to determine the necessary information sharing level to optimize the supply chain that has asymmetric flows of input and output values and to examine the effects of information sharing on the order fill rate (OFR) and total inventory cost (TIC) of the supply chain through analysis of variance (ANOVA) testing. In this work, the supply chain was optimized by using the particle swarm optimization (PSO) technique, with an objective function that assumes the maximization of OFR and minimization of TIC. The proposed method showed excellent results in comparing the mean, variance, and coefficient of variation. In addition, the method used the ANOVA test with a 5% significance level to verify the impact of the information sharing level. [ABSTRACT FROM AUTHOR]

The present study proposes a model to predict the heat transfer coefficient in R134a liquid–vapor two-phase pulsating flow boiling in an evaporator using the experimental data and response surface methodology (RSM). The model is based on the current existing empirical correlation for R134a liquid–vapor two-phase continuous flow with an imposed modification factor. The model for the imposed modification factor is the function of the pulsating period and inlet/outlet vapor quality, which is obtained using the limited experimental data. An analysis of variance (ANOVA) is carried out to test the significance of the model and normal probability of residuals is analyzed as well. Results show that the regression model produces a mean error of −4.3% and a standard deviation of 15.4%, compared to experimental results. Of the data 95.1% is contained inside a ±50% error window, which indicates that the proposed model could predict the heat transfer coefficient of R134a liquid–vapor two-phase pulsating flow boiling well. [ABSTRACT FROM AUTHOR]

Information dynamics and computational mechanics provide a suite of measures for assessing the information- and computation-theoretic properties of complex systems in the absence of mechanistic models. However, both approaches lack a core set of inferential tools needed to make them more broadly useful for analyzing real-world systems, namely reliable methods for constructing confidence sets and hypothesis tests for their underlying measures. We develop the computational mechanics bootstrap, a bootstrap method for constructing confidence sets and significance tests for information-dynamic measures via confidence distributions using estimates of ϵ -machines inferred via the Causal State Splitting Reconstruction (CSSR) algorithm. Via Monte Carlo simulation, we compare the inferential properties of the computational mechanics bootstrap to a Markov model bootstrap. The computational mechanics bootstrap is shown to have desirable inferential properties for a collection of model systems and generally outperforms the Markov model bootstrap. Finally, we perform an in silico experiment to assess the computational mechanics bootstrap's performance on a corpus of ϵ -machines derived from the activity patterns of fifteen-thousand Twitter users. [ABSTRACT FROM AUTHOR]

A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer's accuracy of 63.85% and user's accuracy of 66.89% for eucalyptus plantations. [ABSTRACT FROM AUTHOR]

The reliability of relaxation time measures in synthetic magnetic resonance images (MRIs) of homemade phantoms were validated, and the diagnostic suitability of synthetic imaging was compared to that of conventional MRIs for detecting ischemic lesions. Phantoms filled with aqueous cupric-sulfate (CuSO4) were designed to mimic spin-lattice (T1) and spin-spin (T2) relaxation properties and were used to compare their accuracies and stabilities between synthetic and conventional scans of various brain tissues. To validate the accuracy of synthetic imaging in ischemic stroke diagnoses, the synthetic and clinical scans of 18 patients with ischemic stroke were compared, and the quantitative contrast-to-noise ratios (CNRs) were measured, using the Friedman test to determine significance in differences. Results using the phantoms showed no significant differences in the interday and intersession synthetic quantitative T1 and T2 values. However, between synthetic and referenced T1 and T2 values, differences were larger for longer relaxation times, showing that image intensities in synthetic scans are relatively inaccurate in the cerebrospinal fluid (CSF). Similarly, CNRs in CSF regions of stroke patients were significantly different on synthetic T2-weighted and T2-fluid-attenuated inversion recovery images. In contrast, differences in stroke lesions were insignificant between the two. Therefore, interday and intersession synthetic T1 and T2 values are highly reliable, and discrepancies in synthetic T1 and T2 relaxation times and image contrasts in CSF regions do not affect stroke lesion diagnoses. Additionally, quantitative relaxation times from synthetic images allow better estimations of ischemic stroke onset time, consequently increasing confidence in synthetic MRIs as diagnostic tools for ischemic stroke. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effect of adaptive motion applied to conventional nickel-titanium (NiTi) rotary instruments on torsional stress generation during shaping procedure. One hundred and twenty mesio-buccal canals of molars were randomly assigned to two groups according to the kinetics; adaptive motion (AD) and continuous rotation (CR). Each group was divided into four subgroups (n = 15) according to the NiTi instrument systems: HyFlex EDM, One Curve, Twisted File Adaptive, and ProTaper Next. A glide path was established with PathFile #1, for each file group being used with either of the kinetic movements. During the instrumentation with the designated motion and file system, the generated torque was measured via the control unit and acquisition module. Based on the acquired data, the maximum and total torque were calculated. The data were statistically analyzed using Kruskal–Wallis and Mann–Whitney tests at a significance level of 95%. The maximum and total torque generated by all instruments were significantly reduced by the adaptive motion (p < 0.05). In the CR group, HyFlex EDM generated the highest maximum and total stress. In the AD group, HyFlex EDM showed the highest maximum torsional stress, and One Curve showed the highest total torsional stress (p < 0.05). The TF Adaptive instrument with adaptive movement produced the lowest maximum and total torsional stress (p < 0.05). Under the conditions of this study, the use of adaptive motion would be useful to reduce the torsional stress of instrument and root dentin. The reduction of torsional stress through adaptive motion may enhance the durability of instruments and reduce the potential risk of dentinal cracks. [ABSTRACT FROM AUTHOR]