Extensive Statistical Evaluation Research Articles

Mathematical Modeling and Statistical Analysis of Leach Algorithm Based Cluster Head Selection Approach for Wireless Sensor Networks

In this paper, we present a comprehensive design and analysis of our proposed improved fuzzy logic-based algorithm. The algorithm utilizes a fuzzy logic system to assess node attributes such as residual energy, distance to the base station, and connectivity. By considering these parameters, the algorithm dynamically evaluates the most suitable candidates for cluster head roles, effectively distributing the energy load and promoting equitable cluster formation. An extensive performance evaluation of proposed algorithm has been conducted through simulations and comparisons with the original LEACH protocol. Our results reveal that the improved fuzzy logic-based algorithm outperforms the conventional LEACH in terms of network lifetime, energy consumption, and overall network stability. In this study, we delve into the mathematical modeling and statistical analysis of the proposed algorithm, focusing on its impact on energy efficiency, network stability, and data routing in WSNs. The Improved Fuzzy Logic-Based Cluster Head Selection Algorithm employs fuzzy logic to intelligently select cluster heads, considering parameters like residual energy, distance to the base station, and historical data. We develop mathematical models to describe the algorithm's behavior and analyze its performance through extensive simulations and statistical evaluations. The algorithm demonstrates enhanced adaptability to varying network conditions and node heterogeneity, showcasing its potential to significantly extend the operational duration of WSNs in diverse deployment scenarios. In conclusion, this paper contributes a novel perspective to the domain of WSNs by introducing an advanced fuzzy logic-based cluster head selection algorithm. The algorithm enhances the foundation of the LEACH protocol, effectively addressing energy optimization concerns and promoting prolonged network lifetime. The presented design and analysis substantiate the algorithm's superiority, offering valuable insights for researchers and practitioners working on energy-efficient routing protocols and wireless sensor network management.

Landslide initiation thresholds in data-sparse regions: application to landslide early warning criteria in Sitka, Alaska, USA

Abstract. Probabilistic models to inform landslide early warning systems often rely on rainfall totals observed during past events with landslides. However, these models are generally developed for broad regions using large catalogs, with dozens, hundreds, or even thousands of landslide occurrences. This study evaluates strategies for training landslide forecasting models with a scanty record of landslide-triggering events, which is a typical limitation in remote, sparsely populated regions. We evaluate 136 statistical models trained on a precipitation dataset with five landslide-triggering precipitation events recorded near Sitka, Alaska, USA, as well as > 6000 d of non-triggering rainfall (2002–2020). We also conduct extensive statistical evaluation for three primary purposes: (1) to select the best-fitting models, (2) to evaluate performance of the preferred models, and (3) to select and evaluate warning thresholds. We use Akaike, Bayesian, and leave-one-out information criteria to compare the 136 models, which are trained on different cumulative precipitation variables at time intervals ranging from 1 h to 2 weeks, using both frequentist and Bayesian methods to estimate the daily probability and intensity of potential landslide occurrence (logistic regression and Poisson regression). We evaluate the best-fit models using leave-one-out validation as well as by testing a subset of the data. Despite this sparse landslide inventory, we find that probabilistic models can effectively distinguish days with landslides from days without slide activity. Our statistical analyses show that 3 h precipitation totals are the best predictor of elevated landslide hazard, and adding antecedent precipitation (days to weeks) did not improve model performance. This relatively short timescale of precipitation combined with the limited role of antecedent conditions likely reflects the rapid draining of porous colluvial soils on the very steep hillslopes around Sitka. Although frequentist and Bayesian inferences produce similar estimates of landslide hazard, they do have different implications for use and interpretation: frequentist models are familiar and easy to implement, but Bayesian models capture the rare-events problem more explicitly and allow for better understanding of parameter uncertainty given the available data. We use the resulting estimates of daily landslide probability to establish two decision boundaries that define three levels of warning. With these decision boundaries, the frequentist logistic regression model incorporates National Weather Service quantitative precipitation forecasts into a real-time landslide early warning “dashboard” system (https://sitkalandslide.org/, last access: 9 October 2023). This dashboard provides accessible and data-driven situational awareness for community members and emergency managers.

Cross-Correlated Scenario Generation for Renewable-Rich Power Systems Using Implicit Generative Models

Generation of realistic scenarios is an important prerequisite for analyzing the reliability of renewable-rich power systems. This paper satisfies this need by presenting an end-to-end model-free approach for creating representative power system scenarios on a seasonal basis. A conditional recurrent generative adversarial network serves as the main engine for scenario generation. Compared to prior scenario generation models that treated the variables independently or focused on short-term forecasting, the proposed implicit generative model effectively captures the cross-correlations that exist between the variables considering long-term planning. The validity of the scenarios generated using the proposed approach is demonstrated through extensive statistical evaluation and investigation of end-application results. It is shown that analysis of abnormal scenarios, which is more critical for power system resource planning, benefits the most from cross-correlated scenario generation.

A Heuristic Approach for Telugu Text Summarization with Improved Sentence Ranking

Extracting/abstracting the condensed form of original text document by retaining its information and complete meaning is known as text summarization. The creation of manual summaries from large text documents is difficult and time-consuming for humans. Text summarization has become an important and challenging area in natural language processing. This paper presents a heuristic appraoch to extract a summary of e-news articles of the Telugu language. The method proposes new lexical parameter-based information extraction (IE) rules for scoring the sentences. Event score and Named Entity Score is a novel part in sentence scoring to identify the essential information in the text. Depending on the frequency of occurrence of event/named entites in the sentence and document, sentences are selected for summary. Data is collected from online news sources (i.e., Eenadu, Sakshi,Andhra Jyothi, Namaste Telangana) to experiment. The proposed method is compared with other techniques developed for Telugu text summarization. Evaluation metrics like precision, recall, and F1 score is used to measure the proposed method's performance. An extensive statistical and qualitative evaluation of the system's summaries has been conducted using Recall-Oriented Understudy for Gisting Evaluation (ROUGE), a standard summary evaluation tool. The results showed improved performance compared to other methods.

Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive summary by focusing on a unified presentation model with aspect based Information Extraction (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence generation makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules

This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive sum-mary by focusing on a unified presentation model with aspect based Information Extrac-tion (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence genera-tion makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.

Disentangling the evolution of MEDLINE bibliographic database: A complex network perspective.

Scientific knowledge constitutes a complex system that has recently been the topic of in-depth analysis. Empirical evidence reveals that little is known about the dynamic aspects of human knowledge. Precise dissection of the expansion of scientific knowledge could help us to better understand the evolutionary dynamics of science. In this paper, we analyzed the dynamic properties and growth principles of the MEDLINE bibliographic database using network analysis methodology. The basic assumption of this work is that the scientific evolution of the life sciences can be represented as a list of co-occurrences of MeSH descriptors that are linked to MEDLINE citations. The MEDLINE database was summarized as a complex system, consisting of nodes and edges, where the nodes refer to knowledge concepts and the edges symbolize corresponding relations. We performed an extensive statistical evaluation based on more than 25 million citations in the MEDLINE database, from 1966 until 2014. We based our analysis on node and community level in order to track temporal evolution in the network. The degree distribution of the network follows a stretched exponential distribution which prevents the creation of large hubs. Results showed that the appearance of new MeSH terms does not also imply new connections. The majority of new connections among nodes results from old MeSH descriptors. We suggest a wiring mechanism based on the theory of structural holes, according to which a novel scientific discovery is established when a connection is built among two or more previously disconnected parts of scientific knowledge. Overall, we extracted 142 different evolving communities. It is evident that new communities are constantly born, live for some time, and then die. We also provide a Web-based application that helps characterize and understand the content of extracted communities. This study clearly shows that the evolution of MEDLINE knowledge correlates with the network's structural and temporal characteristics.

Flexural Response Prediction of Reinforced Concrete Members Based on Statistical Observations

Seismic assessment of reinforced concrete (RC) structures requires the determination of inelastic deformation capacity of structural members. Inelastic deformation capacity of members is determined by performing analytical moment–curvature analysis. Due to definition of high amount of data, this type of analysis increases the computational efforts and time when applied to large structures. Considering this situation, this paper proposes simple equations to express inelastic deformation capacity of rectangular RC sections subjected to axial load and uniaxial bending moment. Deformation of members is described in domains of strength and deformations at yield and ultimate. In order to increase applicability and coverage of equations, numerous RC sections were analyzed for various axial load levels by combination of different section aspect ratios, longitudinal and transverse reinforcement configurations and distinct structural materials. During the analysis, nonlinear characteristics of constitutive materials are modeled and some remarkable effects are considered: capacity of concrete in tension, yield elongation and hardening of the longitudinal reinforcement. Equations are then proposed by investigating the effect of sectional and material parameters on the moment–curvature responses and contribution of parameters assigned to prediction equations by extensive statistical evaluations. Reliability of proposed equations is eventually investigated and validated by numerical, experimental and analytical moment–curvature analysis results. It is expected that proposed equations, in close precision, can be helpful for rapid visualization of inelastic capacities of RC members.

Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension

This paper presents a fast mode and partition decision framework for screen content coding (SCC) based on machine learning. Extensive statistical studies and complexity evaluations are conducted to explore the distribution of different coding modes and their complexities. The proposed encoder scheme is designed based on the results from these studies. Firstly, a CU is classified as either a natural image block (NIB) or a screen content block (SCB). An SCB only goes through SCC modes at the current CU level. An NIB is further classified as “ partitioned” or “ non-partitioned”. The partitioned block will bypass current level intra modes. The non-partitioned block is classified as “ directional” or “ non-directional” and only goes through a subset of intra candidates. Decision tree classifiers are designed with chosen features that can distinguish different types of blocks. Furthermore, additional mode/partition checking is terminated once the current mode coding rate is lower than a statistics-based threshold. Compared with HEVC-SCC reference software, our proposed fast encoder can balance the encoding efficiency and complexity by adjusting decision confidence thresholds and rate thresholds. Under all-intra configurations, our “ rate-distortion preserving” setting can achieve 40% complexity reduction with only 1.46% BD-loss. Our “ complexity-reduction boosting” setting can achieve 52% complexity reduction with 3.65% BD-loss.

The statistical properties of gene-set analysis.

The rapid increase in loci discovered in genome-wide association studies has created a need to understand the biological implications of these results. Gene-set analysis provides a means of gaining such understanding, but the statistical properties of gene-set analysis are not well understood, which compromises our ability to interpret its results. In this Analysis article, we provide an extensive statistical evaluation of the core structure that is inherent to all gene- set analyses and we examine current implementations in available tools. We show which factors affect valid and successful detection of gene sets and which provide a solid foundation for performing and interpreting gene-set analysis.

Transformation Invariant On-Line Target Recognition

Transformation invariant automatic target recognition (ATR) has been an active research area due to its widespread applications in defense, robotics, medical imaging and geographic scene analysis. The primary goal for this paper is to obtain an on-line ATR system for targets in presence of image transformations, such as rotation, translation, scale and occlusion as well as resolution changes. We investigate biologically inspired adaptive critic design (ACD) neural network (NN) models for on-line learning of such transformations. We further exploit reinforcement learning (RL) in ACD framework to obtain transformation invariant ATR. We exploit two ACD designs, such as heuristic dynamic programming (HDP) and dual heuristic dynamic programming (DHP) to obtain transformation invariant ATR. We obtain extensive statistical evaluations of proposed on-line ATR networks using both simulated image transformations and real benchmark facial image database, UMIST, with pose variations. Our simulations show promising results for learning transformations in simulated images and authenticating out-of plane rotated face images. Comparing the two on-line ATR designs, HDP outperforms DHP in learning capability and robustness and is more tolerant to noise. The computational time involved in HDP is also less than that of DHP. On the other hand, DHP achieves a 100% success rate more frequently than HDP for individual targets, and the residual critic error in DHP is generally smaller than that of HDP. Mathematical analyses of both our RL-based on-line ATR designs are also obtained to provide a sufficient condition for asymptotic convergence in a statistical average sense.

Segmentation of the carotid intima-media region in B-mode ultrasound images

This paper proposes a new approach for the segmentation of both near-end and far-end intima-media regions of the common carotid artery in ultrasound images. The method requires minimal user interaction and is able to segment the near-end wall in arteries with large, hypoechogenic and irregular plaques, issues usually not considered previously due to the increased segmentation difficulty. The adventitia is detected by searching for the best fit of a cubic spline to edges having features compatible with the adventitia boundary. The algorithm uses a global smoothness constraint and integrates discriminating features of the adventitia to reduce the attraction by other edges. Afterwards, using the information of the adventitia location, the lumen boundary is detected by combining dynamic programming, smooth intensity thresholding surfaces and geometric snakes. Smooth contours that correctly adapt to the intima are produced, even in the presence of deep concavities. Moreover, unlike balloon-based snakes, the propagation force does not depend on gradients and does not require a predefined direction. An extensive statistical evaluation is computed, using a set of 47 images from 24 different symptomatic patients, including several classes, sizes and shapes of plaques. Bland–Altman plots of the mean intima-media thickness, for manual segmentations of two medical experts, show a high intra-observer and inter-observer agreement, with mean differences close to zero (mean between −0.10 mm and 0.18 mm) and with the large majority of differences within the limits of agreement (standard deviation between 0.10 mm and 0.12 mm). Similar plots reveal a good agreement between the automatic and the manual segmentations (mean between −0.07 mm and 0.11 mm and standard deviation between 0.11 mm and 0.12 mm).

Gaussian process modeling of large‐scale terrain

AbstractBuilding a model of large‐scale terrain that can adequately handle uncertainty and incompleteness in a statistically sound way is a challenging problem. This work proposes the use of Gaussian processes as models of large‐scale terrain. The proposed model naturally provides a multiresolution representation of space, incorporates and handles uncertainties aptly, and copes with incompleteness of sensory information. Gaussian process regression techniques are applied to estimate and interpolate (to fill gaps in occluded areas) elevation information across the field. The estimates obtained are the best linear unbiased estimates for the data under consideration. A single nonstationary (neural network) Gaussian process is shown to be powerful enough to model large and complex terrain, effectively handling issues relating to discontinuous data. A local approximation method based on a “moving window” methodology and implemented using k‐dimensional (KD)‐trees is also proposed. This enables the approach to handle extremely large data sets, thereby completely addressing its scalability issues. Experiments are performed on large‐scale data sets taken from real mining applications. These data sets include sparse mine planning data, which are representative of a global positioning system–based survey, as well as dense laser scanner data taken at different mine sites. Further, extensive statistical performance evaluation and benchmarking of the technique has been performed through cross‐validation experiments. They conclude that for dense and/or flat data, the proposed approach will perform very competitively with grid‐based approaches using standard interpolation techniques and triangulated irregular networks using triangle‐based interpolation techniques; for sparse and/or complex data, however, it would significantly outperform them. © 2009 Wiley Periodicals, Inc.

EVALUATION OF THE RED BLOOD CELL CYTOPHEROMETRIC TEST FOR THE DIAGNOSIS OF MULTIPLE SCLEROSIS

The effect of linoleic acid on the electrophoretic mobility of red blood cells from both MS patients and normal subjects was studied. An extensive statistical evaluation of the data clearly demonstrated that there was no difference in behaviour of red blood cells from patients and normal subjects in the presence of linoleic acid. Even a tendency for the mobility of erythrocytes to be decreased in patients and increased in normal subjects after addition of linoleic acid was not observable.

Statistical evaluation of strain-life fatigue crack initiation predictions

Most of the existing methods for estimating εN parameters are based on a relatively limited amount of experimental data. In addition, sound statistical evaluation of the popular rules of thumb used in practice to estimate fatigue properties is scarce, if available. In this work, an extensive statistical evaluation of the existing Coffin–Manson parameter estimates is presented based on monotonic tensile and uniaxial fatigue properties of 845 different metals, including 724 steels, 81 aluminum alloys, and 15 titanium alloys. The studied Coffin–Manson estimates include the methods proposed by Muralidharan and Manson, Bäumel and Seeger, Roessle and Fatemi, Mitchell, Ong, Morrow, Raske, as well as Manson’s universal slope and four-point correlation methods. From the collected data, it is shown that all correlations between the fatigue ductility coefficient ε′ f and the monotonic tensile properties are very poor, and that it is statistically sounder to estimate ε′ f based on constant values for each alloy family. Based on this result, a new estimation method which uses the medians of the individual parameters of the 845 materials is proposed.

A statistical comparison of the stochastic weather generators WGEN and SIMMETEO

Weather generators are frequently used to provide weather data when the length of his- torical weather data is inadequate or when future data are required. The main objective of this study was to evaluate the weather generators WGEN and SIMMETEO as implemented in the Decision Support System for Agrotechnology Transfer (DSSAT) for 5 Iranian locations with contrasting cli- mates. The algorithms for generating weather data are the same in both programs, but they differ with respect to the methods used for parameter estimation. While WGEN requires daily weather data for parameter estimation, SIMMETEO uses monthly summaries. Therefore, the second objective of this study was to evaluate the effect of the parameter-estimation method on the quality of generated weather data. Extensive statistical evaluations, including t-, F- and Kolmogorov-Smirnov tests, were conducted to analyze the differences between observed and generated weather data as well as between generated series. The results showed that the algorithms used to generate precipitation work well. For solar radiation, WGEN showed a poor and SIMMETEO a good performance. WGEN was successful in reproducing maximum and minimum temperatures, and SIMMETEO in reproduc- ing minimum temperature. However, SIMMETEO showed a moderate performance for maximum temperature. SIMMETEO did not generate extreme temperatures well, but WGEN showed a good performance for generating the number of frost days and a moderate performance for the number of hot days. It can be concluded that, when daily weather data are available, WGEN should be pre- ferred for generating weather data. However, when only monthly summaries are available or when resources are limited for preparation of daily weather data, SIMMETEO should be used. It was also found that series generated with identical parameters but different 'seeds' for initialization may be significantly different from each other. More research is, however, needed on this aspect of weather generators.

The ineffective loading process of the embryo transfer catheter alters implantation and pregnancy rates

The ineffective loading process of the embryo transfer catheter alters implantation and pregnancy rates

Assessment of the Reading Skill Improvement in the Computer-Assisted Teaching of a Foreign Language

This article reports the findings of a case study on the influence that the computer-assisted teaching of a foreign language has on the reading skill of elementary school pupils. In fact, it has been investigated the degree to which abilities inferred from the reading process are improved by delivering a typical introductory course of the French language to a Greek speaking target group, based on the use of the generic functions that most of the computer-assisted instructional tools provide. The study was focused on the abilities of perception, information retrieval and concentration and was conducted over a period of three years. It was based on the design and execution of pupil assessment tests on a control and an experimental group per year. At each year, the control group was taught the subject in the traditional textbook-based approach and the experimental group by a computer-assisted approach. The presentation of the textbook teaching material was enhanced with features that only the use of the generic functions of a computer-assisted teaching could offer. An extensive statistical evaluation of the results has disclosed that the use of the computer had definitely a positive influence on the abilities of perception and information retrieval.

Rapid Evaluation of Least-Squares and Minimum-Evolution Criteria on Phylogenetic Trees

We present fast new algorithms for evaluating trees with respect to least squares and minimum evolution (ME), the most commonly used criteria for inferring phylogenetic trees from distance data. These include: an optimal O(N2 ) time algorithm for calculating the branch (edge) lengths on a tree according to ordinary or unweighted least squares (OLS); an O(N3) time algorithm for edge lengths under weighted least squares (WLS) and the FitchMargoliash method; and an optimal O(N4) time algorithm for generalised least squares edge lengths. The Minimum Evolution criterion is based on the sum of edge lengths. Consequently, the edge lengths algorithms presented here lead directly to O(N2), O(N3) and O(N4) time algorithms for ME under OLS, WLS and GLS respectively. These algorithms are substantially faster than all those previously published, and the algorithms for OLS and GLS are the fastest possible (with respect to order of computational complexity). An optimal algorithm for determining path lengths in a tree with given edge lengths is also developed. This leads to an optimal O(N ) algorithm for OLS sums of squares evaluation and corresponding O(N3 ) and O(N4) time algorithms for WLS and GLS sums of squares, respectively. The GLS algorithm is time optimal if the covariance matrix is already inverted. The considerable increases in speed enable far more extensive tree searches and statistical evaluations (e.g. bootstrap, parametric bootstrap or jackknife). Hopefully, the fast algorithms for WLS and GLS will encourage their use for evaluating trees and their edge lengths ( e.g. for approximate divergence time estimates), since they should be more statistically efficient than OLS.

Utilization of ion chromatography and statistics to determine important acids in chromium plating and electropolishing solutions

Inadequate monitoring of sulfuric and chromic acids in chromium plating solutions and phosphoric and sulfuric acids in electropolishing solutions is a serious problem for the industry resulting in poor quality products and wasted resources. This is especially true for the narrow tolerances required to chromium plate cannon systems. Current online and offline instrumental analysis methods either do not result in acceptable precision or are time consuming. Two similar methods, which are improvements on existing ion chromatographic methods, are presented here that provides acceptable analysis and monitoring of these acids in this chromium plating process coupled with an extensive statistical evaluation of the experimental data. For chromium plating solutions, the resultant means and precisions are 2.50 ± 0. 11 g/l sulfuric acid and 250 ± 5 g/l chromic acid. For electropolishing solutions, these values are 685 ± 4 g/l phosphoric acid and 845 ± 5 g/l sulfuric acid.