Automatic data acquisition and analysis technique for the seismic refraction method

This paper introduces a SAS/IML program to select among the multivariate model candidates based on a few well-known multivariate model selection criteria. Stepwise regression and all-possible-regression are considered. The program is user friendly and requires the user to paste or read the data at the beginning of the module, include the names of the dependent and independent variables (the y's and the x's), and then run the module. The program produces the multivariate candidate models based on the following criteria: Forward Selection, Forward Stepwise Regression, Backward Elimination, Mean Square Error, Coefficient of Multiple Determination, Adjusted Coefficient of Multiple Determination, Akaike's Information Criterion, the Corrected Form of Akaike's Information Criterion, Hannan and Quinn Information Criterion, the Corrected Form of Hannan and Quinn (HQc) Information Criterion, Schwarz's Criterion, and Mallow's PC. The output also constitutes detailed as well as summarized results.

P-wave velocity (Vp) structures in marine areas have been estimated using marine airgun-source seismic surveys and refraction methods. The lateral resolution of Vp structures depends on the number of receivers and shots. In marine environments, Vp resolution is often limited due to the sparse distribution of ocean bottom seismometers (OBSs). Recently, distributed acoustic sensing (DAS) offers a cost-effective alternative, providing high-density strain measurements along optical fiber cables. This study demonstrates that seismic refraction using DAS data is effective for imaging shallow Vp structures with high lateral resolution. A seismic survey was conducted using R/V Hakuho-maru, airgun sources, and DAS and OBS measurements along a seafloor optical fiber cable off Sanriku, Japan. Strong lateral heterogeneities in the Vp structure were observed, due to the dense spatial sampling of DAS data. The two-dimensional Vp structure obtained from the DAS data showed good agreement with the seismic reflection profile. This experiment highlights the unique advantages of DAS data over OBSs for seismic refraction.

Prognostic Scoring Systems in Diffuse Large B Cell Lymphoma Patients - Is Kyoto Prognostic Index an Added Value?

Background: Metastatic regional lymph nodes (LN) is a strong predictor of worse long-term outcome after resection of breast cancer. This study aimed to compare the prognostic performance of American Joint Committee on Cancer (AJCC) N stage relative to lymph node ratio (LNR), log odds of metastatic lymph nodes (LODDS), number of removed lymph nodes (NRLNs), and number of negative lymph nodes (NNLNs) in breast cancer patients. Methods: All of the breast cancer patients who underwent surgery between 2004 and 2012 were identified from the Surveillance, Epidemiology, and End Results database. Restricted cubic spline functions were used to characterize the association between continuous variables and the risk of death and determine the optimal cut-off points. The Cox proportional hazards models were constructed, and the relative discriminative abilities of the different LN staging systems were assessed using the Akaike's Information Criterion (AIC) and the Harrell's concordance index (C-index). Results: A total of 264,096 breast cancer patients were enrolled, and 177,598 (67.2%) had no lymph node metastasis, whereas 86,498 (32.8%) had lymph node metastasis. 187,785 (71.1%) patients had a limited number of LNs harvested (NRLN <10). The median follow-up time was 73 months, and the 8-year overall survival (OS) and breast cancer-specific survival (BCSS) were 82.6% and 90.4%, respectively. LNR, LODDS, NRLNs, and NNLNs were all nonlinearly associated with OS and BCSS. Patients with metastatic LN had an increased risk of OS (hazards ratio: 2.32, 95% confidence interval: 2.27–2.37; P < 0.001) and BCSS (hazards ratio: 4.53, 95% confidence interval: 4.40–4.66; P < 0.001). When LNR was equal to 0 or 1, there was a heterogeneity of outcomes, and LODDS still yielded informative values compared to LNR. Among the entire cohort, LNR modeled as a continuous variable had a somewhat better prognostic performance (AIC: 923231.4 and C-index: 0.722 for OS; AIC: 482962.3 and C-index: 0.817 for BCSS) than any of other LN staging systems. However, a model with AJCC N stage showed the best fit in patients with a limited number of LNs harvested (AIC: 501321.8 and C-index: 0.699 for OS; AIC: 212605.6 and C-index: 0.809 for BCSS). When assessed among patients with metastatic LN, LODDS outperformed other staging systems including AJCC N stage, LNR, NRLNs and NNLNs, whenever assessed using continuous (AIC: 428626.2 and C index: 0.728 for OS; AIC: 296886.8 and C index: 0.770 for BCSS) or categorical (AIC: 429527.5 and C index: 0.722 for OS; AIC: 297796.6 and C index: 0.762 for BCSS) cutoff values. Conclusions: Although LNR assessed as a continuous variable was the most potent method to stratify patients regardless of LN status, the prognostic superiority of LNR is confounded by a limited LN harvest. LODDS was a better and more powerful predictor of survival when patients were LN positive, especially among those patients with either very low or high LNR. Citation Format: Gong Y, Ji P, Jiang Y-Z, Hu X, Shao Z-M. Comparing prognostic performance of different lymph node staging systems among patients with breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P2-08-36.

The seismic refraction method has been widely used in Japan for engineering purposes such as investigations for tunnel and dam construction. The following features of the seismic refraction survey are considered as reasons why it has been widely used. (1) It is feasible to determine subsurface structure (in seismic velocity) with enough resolution and accuracy even though at a site with complex topography and subsurface structure. (2) It is feasible to detect faults and fracture zones where careful design will be required. (3) A lot of data relating with mechanical properties of rock, including seismic velocity, have been gathered and are utilized as database. By using the database, mechanical properties of rock can be evaluated with seismic velocity, so that results of the seismic refraction survey bring useful information for designing and execution management of construction. This paper reviews practical technique of the seismic refraction method that is commonly carried out in Japan, including survey planning, data acquisition technique and analysis technique, necessary for getting reliable results. Also, a case study of the seismic refraction survey with survey line 6.5 km long, carried out to investigate tunnel construction site is presented.

Prognostic indices combining several clinical and laboratory parameters have been proposed for prognostication in chronic lymphocytic leukemia (CLL). Recently, international consortium on CLL proposed an international prognostic index (CLL-IPI) integrating clinical, molecular, and genetic parameters. The present study was designed to evaluate the reproducibility of CLL-IPI in Indian CLL cohort. The prognostic ability of CLL-IPI in terms of overall survival (OS) and time to first treatment (TTFT) was investigated in treatment-naive CLL patients and also compared with other existing prognostic scores. For assigning scores, clinical and laboratory details were obtained from medical records, and IGHV gene mutation status, β2-microglobulin levels, and copy number variations were determined using c-DNA, ELISA, and multiplex ligation-dependent probe amplification (MLPA), respectively. The scores were generated as per the weighted grades assigned to each variable involved in score categorization. The predictive value of prognostic models was assessed and compared using Harrell's C-index and Akaike's information criterion (AIC). Stratification of patients according to CLL-IPI yielded significant differences in terms of OS and TTFT (p < 0.001). Comparative assessment of scores for OS suggested better performance of CLL-IPI (C = 0.64, AIC = 740) followed by Barcelona-Brno (C = 0.61, AIC = 754) and MDACC score (C = 0.59, AIC = 759). Comparison of predictive value of prognostic scores for TTFT illustrated better performance of CLL-IPI (C = 0.72, AIC = 726) followed by Barcelona-Brno (C = 0.68, AIC = 743), modified GCLLSG (C = 0.66, AIC = 744), and O-CLL1 index (C = 0.55, AIC = 773). The results suggest better performance of CLL-IPI in terms of both OS and TTFT as compared to other available scores in our cohort.

Statistically distinguishing density‐dependent from density‐independent populations and selecting the best demographic model for a given population are problems of primary importance. Traditional approaches are PBLR (parametric bootstrapping of likelihood ratios) and Information criteria (IC), such as the Schwarz information criterion (SIC), the Akaike information criterion (AIC) or the Final prediction error (FPE). While PBLR is suitable for choosing from a couple of models, ICs select the best model from among a set of candidates. In this paper, we use the Structural risk minimization (SRM) approach. SRM is the model selection criterion developed within the Statistical learning theory (SLT), a theory of great generality for modelling and learning with finite samples. SRM is almost unknown in the ecological literature and has never been used to analyze time series.First, we compare SRM with PBLR in terms of their ability to discriminate between the Malthusian and the density‐dependent Ricker model. We rigorously repeat the experiments described in a previous study and find out that SRM is equally powerful in detecting density‐independence and much more powerful in detecting density‐dependence.Then, we compare SRM against ICs in terms of their ability to select one of several candidate models; we generate, via stochastic simulation, a huge amount of artificial time series both density‐independent and dependent, with and without exogenous covariates, using different dataset sizes, noise levels and parameter values. Our findings show that SRM outperforms traditional ICs, because generally a) it recognizes the model underlying the data with higher frequency, and b) it leads to lower errors in out‐of‐samples predictions. SRM superiority is specially apparent with short time series.We finally apply SRM to the population records of Alpine ibex Capra ibex living in the Gran Paradiso National Park (Italy), already investigated by other authors via traditional statistical methods; we both analyze their models and introduce some novel ones. We show that models that are best according to SRM show also the lowest leave‐one‐out cross‐validation error.

Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices

Time series study is of paramount importance especially in developing countries such as India. The objective of this study is to forecast the number of dengue cases by applying time series models. Furthermore, this study also aims to uncover the correlation of ecological variables with dengue cases using a p value test. The ecological variables considered are the rainfall (with a 2-month lag period) and average relative humidity. An evaluation of host factors such as age and gender is done with the number of cases to determine the most affected strata. Monthly data on dengue cases from 2014 to 2017 are taken from the integrated disease surveillance programme, Government of India, and data on ecological variables, humidity and rainfall are taken from www.Indiastat.com . A 2-month lag period is considered for rainfall. These data were analysed by the autoregressive integrated moving average (ARIMA) and exponential smoothing model (ES). Predictive performance of these techniques was assessed by evaluation parameters such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute error and mean absolute scaled error. Applying time series models was useful in the prediction. There were many more males than females according to the data for 2014–2017. The Akaike information criterion (AIC), Bayesian information criterion (BIC) and mean absolute scaled error for ARIMA are 48.6526, 448.333333, 449.30008, 453.08 and 0.507826, respectively. Based on model indices such as the Akaike information criterion (AIC), Bayesian information criterion (BIC), mean absolute error and mean absolute scaled error, the ARIMA performed better than the ES model. Rainfall at the 2-month lag was correlated with the number of cases.

The seismic refraction method from the ground surface has routinely been applied to planning of tunneling as a preliminary survey. A tunnel support pattern is designed based on elastic wave velocity of the mountain obtained by seismic refraction survey. Where overburden on a tunnel is thick, the resolution in velocity structure obtained by this technique becomes poor. A survey ahead of the tunnel from the tunnel face while boring is considered to be effective compared with a ground seismic survey. Two methods are generally used in the survey ahead of the tunnel from tunnel face: one by seismic reflection such as the Tunnel Seismic Prediction (TSP) and Horizontal Seismic Profiling (HSP) methods and the other by drilling data called the Drilling Survey System (DRISS). The authors applied a new exploration method by the use of drilling vibration data called TSPD (Tunnel Seismic Probe Drilling) to estimate elastic wave velocity distribution ahead of the tunnel face. Unlike Seismic While Drilling (SWD) commonly used for ahead of drilling in the petroleum exploration, this method investigates the nature of the rocks between the bit of pilot drilling and the cutting edge of the tunnel. The field tests of TSPD were carried out at a road tunnel. The first test helped to identify and correct the problems in data acquisition. The second field test confirmed improvement of S/N ratio of the waveform, and evaluated the elastic wave velocity structure ahead of the tunnel face.

The shape of the dose-response relation between particulate matter air pollution and mortality is crucial for public health assessment, and departures of this relation from linearity could have important regulatory consequences. A number of investigators have studied the shape of the particulate matter-mortality dose-response relation and concluded that the relation could be adequately described by a linear model. Some of these researchers examined the hypothesis of linearity by comparing Akaike's Information Criterion (AIC) values obtained under linear, piecewise linear, and spline alternative models. However, at the current time, the efficacy of the AIC in this context has not been assessed. The authors investigated AIC as a means of comparing competing dose-response models, using data from Cook County, Illinois, for the period 1987-2000. They found that if nonlinearities exist, the AIC is not always successful in detecting them. In a number of the scenarios considered, AIC was equivocal, picking the correct simulated dose-response model about half of the time. These findings suggest that further research into the shape of the dose-response relation using alternative model selection criteria may be warranted.

This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to estimate the power spectral density (PSD) of heart rate variability (HRV), and accurate order selection is essential for model stability and reliable spectral estimation. Although the AIC is designed to balance model fit and complexity, it suffers from the problem of arbitrary model selection. This study provides a quantitative robustness analysis of information-criterion-based AR order selection under controlled expansion of the search space. Specifically, we investigated the behavior of the AIC using the PhysioNet database (N = 1257) under conditions where the maximum search order was set to an excessively high value (p = 50), far exceeding the commonly recommended range. Our analysis suggested that the AR model began to capture subtle noise and nonstationary components rather than the intrinsic HRV structure, leading to overfitting and excessive order selection, resulting in false peaks in the PSD and reduced robustness. In conclusion, order decisions based solely on information criteria such as the AIC become unstable when the search range is too large. To ensure robustness, it is recommended to complement the AIC with more stringent criteria such as the Bayesian Information Criterion (BIC) or Final Prediction Error (FPE), in addition to the traditional maximum order restriction.

When inferring phylogenies, one important decision is whether and how nucleotide substitution parameters should be shared across different subsets or partitions of the data. One sort of partitioning error occurs when heterogeneous subsets are mistakenly lumped together and treated as if they share parameter values. The opposite kind of error is mistakenly treating homogeneous subsets as if they result from distinct sets of parameters. Lumping and splitting errors are not equally bad. Lumping errors can yield parameter estimates that do not accurately reflect any of the subsets that were combined whereas splitting errors yield estimates that did not benefit from sharing information across partitions. Phylogenetic partitioning decisions are often made by applying information criteria such as the Akaike information criterion (AIC). As with other information criteria, the AIC evaluates a model or partition scheme by combining the maximum log-likelihood value with a penalty that depends on the number of parameters being estimated. For the purpose of selecting an optimal partitioning scheme, we derive an adjustment to the AIC that we refer to as the AIC$^{(p)}$ and that is motivated by the idea that splitting errors are less serious than lumping errors. We also introduce a similar adjustment to the Bayesian information criterion (BIC) that we refer to as the BIC$^{(p)}$. Via simulation and empirical data analysis, we contrast AIC and BIC behavior to our suggested adjustments. We discuss these results and also emphasize why we expect the probability of lumping errors with the AIC$^{(p)}$ and the BIC$^{(p)}$ to be relatively robust to model parameterization.

The detection of multiple outliers can be interpreted as a model selection problem. Models that can be selected are the null model, which indicates an outlier free set of observations, or a class of alternative models, which contain a set of additional bias parameters. A common way to select the right model is by using a statistical hypothesis test. In geodesy data snooping is most popular. Another approach arises from information theory. Here, the Akaike information criterion (AIC) is used to select an appropriate model for a given set of observations. The AIC is based on the Kullback-Leibler divergence, which describes the discrepancy between the model candidates. Both approaches are discussed and applied to test problems: the fitting of a straight line and a geodetic network. Some relationships between data snooping and information criteria are discussed. When compared, it turns out that the information criteria approach is more simple and elegant. Along with AIC there are many alternative information criteria for selecting different outliers, and it is not clear which one is optimal.

A variety of different lymph node (LN) staging systems have been developed to describe the lymph node status accurately. We aim to compare the prognostic accuracy of American Joint Committee on Cancer seventh N stage relative to negative number of lymph node (nLN), lymph node ratio (LNR) and log odds of metastatic lymph nodes (LODDS) in rectal adenocarcinoma (RC). A total of 19 167 Stage II-III rectal cancer patients who underwent surgical resection of rectal adenocarcinoma were identified from Surveillance, Epidemiology and End Results database. Akaike's Information Criterion (AIC) and the Harrell's concordance index (c statistic) were used to evaluate the relative discriminative power of the different LN staging systems. Of the 19 167 patients, 10 958 received preoperative radiotherapy (pre-RT cohort) and 8209 patients were treated with surgical resection directly (SURG cohort). When assessed using categorical cutoff values, LNR has a somewhat better prognostic accuracy both in pre-RT (c-index: 0.62; AIC: 2988.6) and SURG groups (c-index: 0.60; AIC: 3359.8). Further analysis based on different total number of lymph node (TNLN) suggested that when less than 10 lymph nodes were retrieved, LNR exhibited significant superiority (pre-RT: c-index: 0.597, AIC: 1006.8; SURG: c-index: 0.560, AIC: 810.5). When analyzed as a continuous variable, the LODDS system performed the best and was not impacted by TNLN. When assessed as a categorical variable, LNR was the most powerful method to predict survival for Stage II-III RC patients with limited TNLN. Rather, LODDS was the most accurate staging system regardless of the TNLN when LN status was modeled as continuous variable.