Distribution Of Values Research Articles

Image Feature Extraction Using Symbolic Data of Cumulative Distribution Functions

Symbolic data analysis is an emerging field in statistics with great potential to become a standard inferential technique. This research introduces a new approach to image feature extraction using the empirical cumulative distribution function (ECDF) and distribution function of distribution values (DFDV) as symbolic data. The main objective is to reduce the dimension of huge pixel data by organizing them into more coherent pixel-intensity distributions. We propose a partitioning method with different breakpoints to capture pixel intensity variations effectively. This results in an ECDF representing the proportion of pixel intensities and a DFDV representing the probability distribution at specific points. The novelty of this approach lies in using ECDF and DFDV as symbolic features, thus summarizing the data and providing a more informative representation of the pixel value distribution, facilitating image classification analysis based on intensity distribution. The experimental results underscore the potential of this method in distinguishing image characteristics among existing image classes. Image features extracted using this approach promise image classification analysis with more informative image representations. In addition, theoretical insights into the properties of DFDV distribution functions are gained.

Evaluation of the Condition of the Bottom of the Tanks for Petroleum Products-Forecast of the Remaining Operating Life

Corrosion rate measurements in the tank enable us to develop a new model for predicting the remaining operating period of the tank. The empirical model for the study of bottom plate thicknesses and corrosion pits is more conservative than the standard linear model, as it considers the autocatalytic nature of the corrosion process. We used the double exponential distribution of maximum values (Gumbel's) to evaluate the maximum depth of pits, and the double exponential distribution of minimum values to evaluate the minimum values of the plate thickness. A comparison of the values of the parameters obtained using linear extrapolation and exponential models indicates the unreliability of linear extrapolation, since disregarding dynamic processes underestimates the actual rate of corrosion.

A practical method to draw from multivariate extreme value distributions

Generating random draws from multivariate extreme value (MEV) distributions plays an important role in the microsimulation of travel behaviors, which can effectively avoid heavy computational burdens from simulation based on calculated probability values, particularly in simulations for a large population or choice behaviors from a large choice set. However, there are few practical and effective methods for drawing from MEV distributions. This paper proposes a simple and computationally efficient approach for drawing from MEV distributions in the nested logit (NL), cross-nested logit (CNL), and paired combinatorial logit (PCL) models. The proposed approach to draw from the MEV distribution for a CNL model provides a new perspective to understand the underlying choice mechanism of the CNL model. To our knowledge, this is the first study to draw from an MEV distribution in the PCL model. Random draws from the proposed approach approximately follow the standard Gumbel distribution, which is the marginal distribution of NL/CNL/PCL models, and approximate correlations among alternatives well. Simulation results of NL/CNL/PCL models show that the proposed approach provides high-level accuracy in recovering model parameters with the overall mean absolute percentage bias being less than 3%. The proposed approach is computationally more efficient than similar ones because it only needs to draw from Gumbel distributions. The proposed approach can be used to simulate NL/CNL/PCL models with a large choice set or a multiple discrete-continuous generalized extreme value model in various application settings such as joint destination-mode choices, time use allocations, etc.

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from Climate Models

Abstract Precipitation values produced by climate models are biased due to the parameterization of physical processes and limited spatial resolution. Current bias-correction approaches usually focus on correcting lower-order statistics (mean and standard deviation), which make it difficult to capture precipitation extremes. However, accurate modeling of extremes is critical for policymaking to mitigate and adapt to the effects of climate change. We develop a deep learning framework, leveraging information from key dynamical variables impacting precipitation to also match higher-order statistics (skewness and kurtosis) for the entire precipitation distribution, including extremes. The deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Using climate model simulation data and observations that are climatologically similar but not strictly paired, the UFNet identifies and corrects the climate model biases, significantly improving the estimation of daily precipitation as measured by a broad range of spatiotemporal statistics. In particular, UFNet significantly improves the underestimation of extreme precipitation values seen with current bias-correction methods. Our approach constitutes a generalized framework for correcting other climate model variables which improves the accuracy of the climate model predictions, while utilizing a simpler and more stable training process.

Poster 346: The Statistical Fragility of Lateral Extra-articular Tenodesis Research: A Systematic Review

Objectives: With reinterest in lateral extra-articular tenodesis (LET), there is an imperative need to evaluate the rigor of clinical studies regarding LET with anterior cruciate ligament reconstruction (ACLR). This study aims to present a comprehensive picture of the robustness of evidence from comparative studies regarding LET and to inform evidence-based medical decision-making for current practitioners. We hypothesized that the results of these analyses will show statistical fragility, consistent with similar evidence across the orthopaedic literature. Methods: Using the PubMed database, comparative studies and randomized controlled trials (RCTs) related to LET as an augmentation procedure to ACLR or revision ACLR published between 2000 and 2022 were identified and collected. Studies on the topic were broadly queried for relevance, and after screening, they were included if they were (1) pertaining to LET as an augmentation to ACLR or revision ACLR, and (2) designed as comparative studies or RCTs. Exclusion criteria included (1) cadaveric, nonhuman, in vitro, laboratory, or surgical technique (without patient outcomes); (2) commentary, editorial, letter to the editor, conference reports, future study design/published protocol; (3) only abstract available; (4) non-English; or (5) lacked the statistical basis for a fragility analysis. Manuscripts were independently reviewed by three authors and variables of interest of interest were extracted, including both dichotomous and continuous variables relevant to clinical decision making. Discrepancies were resolved via paired discussions. For each dichotomous outcome, we calculated a Fragility Index (FI). FI was calculated using a 2-by-2 contingency table and the Fisher's exact test using the method outlined in Figure 1. For each continuous outcome, we calculated a Continuous Fragility Index (CFI). CFI was calculated using Welch's t-test and the method proposed by Caldwell et al to expand fragility analysis to continuous variables. The analysis for each outcome was conducted with n = 5 simulations using synthetic, representative data generated from the reported sample mean, standard deviation, and sample size for both the experimental and control arms. For both dichotomous and continuous outcomes, the statistical fragility was reported using median and interquartile range. Comparisons of mean statistical fragility were conducted using a nonparametric t test. Data were analyzed using Python 3.7. Results: Out of the 455 initially identified studies, 178 full texts were screened. Ultimately, 29 studies were included in the final analysis, including 20 comparative studies and 9 RCTs. 18 studies reported dichotomous outcomes and 27 studies reported continuous outcomes, which resulted in a cumulative total of 48 dichotomous outcomes and 265 continuous outcomes for analysis. Figure 4 reports the distribution of FI and CFI values for all dichotomous and continuous outcomes. The median FI was 9.0 (25-75th percentile, 7.0-13.25). The median CFI was 7.8 (25-75th percentile, 4.2-19.6). Commonly reported dichotomous outcomes included clinical failure, graft rupture, and return to sport. The number of outcomes in which loss to follow-up (LTF) exceeded the FI was 13 (27%). Reported significant dichotomous outcomes were significantly more fragile than outcomes that were not significant (fragility quotient [FQ] = 0.02 vs 0.11, Welch t test p < 0.001); however, most dichotomous outcomes were reported insignificant (79.2%) (Table 1). Commonly reported continuous outcomes across different studies included KOOS, IKDC, and Lysholm scores. LTF exceeded the CFI for 20 outcomes (8%). In contrast to dichotomous outcomes, continuous outcomes from RCTs and comparative studies were equivalently robust (continuous fragility quotient [CFQ] = 0.12 vs 0.12). The CFQ of both significant and nonsignificant continuous outcomes were similarly equal (0.12 vs 0.12) (Table 2). Reports on fragility quotients from studies that focused on LET for either primary or revision ACLR clinically are presented in Table 3. These subgroups excluded translational research and imaging studies. The FQ and CFQ for studies focused on revision ACLR were larger (0.117 and 0.113) than those focused on primary ACLR (0.042 and 0.095). Conclusions: Research regarding the usage of LET as an augmentation procedure for ACLR and revision ACLR is more statistically robust than many other topics within orthopaedics. Studies regarding LET with primary ACLR are more fragile than those for revision ACLR. Given the resurgent interest in this procedure and the current mixed evidence regarding its effectiveness and indications, we recommend the future reporting of fragility quotients alongside p-values, to assist clinicians in assessing the robustness of new evidence to inform decision making.

Sex as a predictor of clinical phenotype and determinant of immune response in IgG4-related disease: a retrospective study of patients fulfilling the American College of Rheumatology-European League Against Rheumatism classification criteria.

IgG4-related disease is a multiorgan fibroinflammatory disease considered to have an autoimmune origin. Case series describing individual organ involvement have suggested differences in phenotypic expression between males and females. We aimed to characterise differences in IgG4-related disease manifestations between male and female patients in a large single-centre cohort. In this retrospective, single-centre cohort study, patients were recruited from the Massachusetts General Hospital Rheumatology Clinic (Boston, MA, USA) and classified according to the American College of Rheumatology-European Alliance of Associations for Rheumatology (ACR-EULAR) classification criteria. Only patients satisfying the ACR-EULAR classification criteria were included in the study. Data on age at diagnosis, organ involvement at baseline, treatment status, and pre-treatment laboratory values were collected. Circulating plasmablasts and B-cell subsets were quantitated by flow cytometry. Active disease was defined by an IgG4-related disease Responder Index score of more than 0. Laboratory values were analysed for patients who were untreated at baseline and had active IgG4-related disease. The main outcomes were assessed in all participants with available data. Of the 564 participants enrolled in the Massachusetts General Hospital Rheumatology Clinic IgG4-related disease Registry, 328 fulfilled ACR-EULAR classification criteria and were included between January, 2008, and May, 2023. There was a strong male predominance (male:female ratio 2·2:1) with 226 (69%) males and 102 (31%) females, which contrasted markedly with our general rheumatology clinic population (0·4:1; p<0·001). The male predominance increased with each decade of life starting at age 40 years. On average, male patients were 5·5 years older at diagnosis than female patients (63·7 years vs 58·2 years; p=0·0031). We observed male patients to have higher ACR-EULAR classification criteria scores at baseline with a median score of 35·0 (IQR 28·0-46·0), compared with 29·5 (25·0-39·0) for females (p=0·0010). The proportion of male patients with pancreatic and renal involvement was almost double the proportion observed in female patients (50% of the male patients had pancreatic involvement, compared with about 26% of the female patients; p<0·0001). Male patients were more likely to have serological abnormalities at baseline. The distribution of IgG4 values differed significantly between male an female sexes, favouring higher values in males. We found that male patients with IgG4-related disease were more likely to have active B-cell responses in the blood as defined by plasmablast expansions. IgG4-related disease is unusual among autoimmune diseases in that it is more likely to affect males than females and to present with a striking sex-dependent organ distribution and degree of B-cell response. These findings highlight important variation between IgG4-related disease and other conditions generally believed to have an autoimmune basis. Most autoimmune diseases, by contrast to IgG4-related disease, demonstrate pronounced predilections for affecting females more frequently than males. Hypotheses surrounding the cause and pathophysiology of this condition need to consider this unusual sex distribution among patients with IgG4-related disease. National Institutes of Health, National Institute of Allergy and Infectious Diseases, Rheumatology Research Foundation, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases.

Population study of pupillary unrest in ambient light

IntroductionPupillary unrest in ambient light (PUAL) describes the fluctuation of pupil diameter observed in normal, awake subjects under typical levels of indoor light. PUAL becomes low to absent in young healthy subjects during opioid intoxication. We sought to determine the age-related distribution of PUAL values in a random sample of ambulatory participants. MethodsSubjects ≥18 years of age were recruited. All were identified by age range (18–29, 30–49, 50–69, and ≥70), and surveyed for diabetes, beta-blocker use, and prior 24-hour opioid use. Relationship between mean PUAL, age group, comorbidity and opioid use were examined by Kruskal Wallis test, and PUAL and was modeled using stepwise multilevel linear regression, including diabetes, beta blocker use, prior 24-hour opioid use, autonomic dysfunction, and pupil diameter as fixed effects and subject as random effect. ResultsAmong 150 subjects, 17 reported diabetes, 12 reported beta-blocker use, 14 reported prior 24-hour opioid use, and 120 reported no comorbid conditions. PUAL declined in higher age categories (by 0.0307, P < 0.001), with diabetes (by 0.0481, P = 0.025), and with beta-blocker use (by 0.0616, P = 0.005). Opioid related PUAL decline was observed, but statistical significance varied by model. Among healthy subjects, no PUAL value fell within range indicating high likelihood of opioid toxicity based on previous data from healthy subjects undergoing opioid infusion. ConclusionPUAL declined in higher age groups, diabetes and beta-blocker use, conditions associated with impaired autonomic function, and with opioid use but significance varied depending on the chosen model.

Charged particle reflection in a magnetic mirror

We study the reflection of electrons in a magnetic field mirror. It is a field with a gradient from a lowest value B0 to a largest one Bm. With this purpose, Montecarlo simulations are made. We use a number of 5,000 particles with random pitch angles. The frequency distribution of these random values follows a Gaussian distribution centered at \theta = 0 and with standard deviation \sigma. Various values of \sigma and also different values of the B0 /Bm ratio are used in the simulations. The simulations show that \sigma has an important influence on the confinement of charged particles, for the different B0 /Bm ratios here studied. However, the percent of reflected particles differs from a B0 /Bm ratio to another in the whole range of \sigma, although for \sigma = 10 the differences between different ratios are small (&lt;5%). The number of reflected particles increases very rapidly with \sigma, in the range of 10° to 40°. For the \sigma range from 20° to 40°, the percent of reflected particles is \leq 20% larger for B0 /Bm = 0.10 than for the 0.55 ratio.

"Just forget about it and move on": Stillbirth ruptured and repaired narratives beyond expectant futures.

The critical sociological literature has explored social prescriptions on women's reproductive lives, trajectories, outcomes and aftermaths. However, little attention has been given to how these prescriptions are expressed through temporal negotiations. This article delves into the narratives of Jewish-Israeli women who have experienced stillbirths. In their narratives, these women contest expectations directed towards them in interactions with professionals, family and friends. Within these expectations, a form of dictation over their future comes into being, where a new pregnancy should quickly precede the stillbirth. The focus here lies on how these women navigate and contest these temporal expectations, carving out space for stillbirth as a meaningful and painful event that should be granted attention and for alternative forms of remembering their stillborn and caring for them after their death. They reshape their narratives through what I call "thickening a present tense" and extend care to the stillborn in the aftermath of the stillbirth. This work contributes to scholarly inquiries into reproductive life and probing time as a socially prescribed mechanism for the value and social distribution of care.

Detecting wire breaks in post-tensioned tendons of wind turbines: A signal energy spectrum analysis approach

In the wind industry, recent advancements have led to the research and development of wind turbines featuring hybrid towers, a novel combination of steel tube and precast concrete segments. These concrete modules, which absorb the high stresses caused at the intersection between the tower and its foundation, are commonly joined with post-tensioned tendons. This study focuses on the identification of wire breaks in post-tensioned tendons through the application of acoustic emission testing. Measurements involve the evaluation of wire breaks under controlled laboratory conditions and the analysis of the dynamic operational environment of a functioning wind turbine. The investigation encompasses the development and assessment of a methodology based on principles of discrete Fourier transformation and Parseval’s theorem for analyzing the frequency-dependent energy distribution of acoustic signals. This analytical approach extends to the examination of wire breaks generated under varying stress levels as well as the operational noise within the actual wind turbine. The findings demonstrate a notable concentration of energy within the frequency range of 5-20 kHz in the wire break signals. In contrast, the operational noise signals show a distinct pattern, presenting an extreme value distribution within the 0-2 kHz range. The developed methodology exhibits robustness, even in the presence of high background noise levels, establishing itself as a valuable tool for wire break detection. Additionally, its potential for integration with machine learning algorithms offers the prospect of automated wire break detection, enabling further advancements in structural health monitoring for wind turbines.

Goodness-of-fit, identifiability and extrapolation: Can the Two-Component Extreme Value distribution be used in at-site flood frequency analysis?

When fitting three-parameter flood frequency models to annual maximum (AM) flood series, the lack-of-fit can be mitigated by censoring potentially influential low flows (PILFs). An alternative and less-studied approach is to apply mixture probability models with four or more parameters, which trade off greater flexibility to fit AM series against the need to deal with degeneracy which arises when there is insufficient information to identify mixture components. However, the issue of degeneracy and the lack of a robust inference framework present a significant barrier to adoption. This study investigated the potential of the most parsimonious mixture model, the four-parameter Two-Component Extreme Value (TCEV) model. A Bayesian framework using Markov chain Monte Carlo sampling was developed to robustly characterize parameter uncertainty even in the presence of degeneracy. Two new posterior diagnostics based on the strength of the TCEV components were developed to aid identification of degeneracy. Armed with a robust inference framework, the study evaluated the potential of TCEV using a case study based on 31 catchments in eastern Australia with records exceeding 70 years. The evaluation used short to long records to compare TCEV and Log Pearson III fit and extrapolative uncertainty. The Log Pearson approach (LP3-PILF) censors PILFs following the approach described in Australian Rainfall and Runoff. With respect to goodness-of-fit, we found in most cases that TCEV fitted AM flood peaks well without the need to censor or stratify data, and overall, no clear difference emerged between TCEV and LP3-PILF. However, and contrary to expectation, TCEV produced high flow quantile confidence intervals consistently narrower than LP3-PILF even in the presence of degeneracy. While more case studies in different regions are needed to confirm the potential of TCEV, this study is a reminder that goodness-of-fit is a necessary but not sufficient criterion for selecting the probability model that best represents flood frequency.

Converting estimated breeding values from the observed to probability scale for health traits

Dairy cattle health traits are paramount from a welfare and economic viewpoint; therefore, modern breeding programs prioritize the genetic improvement of these traits. Estimated breeding values for health traits are published as the probability of animals staying healthy. They are obtained using threshold models, which assume that the observed binary phenotype (i.e., healthy or sick) is dictated by an underlying normally distributed liability exceeding or not a threshold. This methodology requires significant computing time and faces convergence challenges as it implies a nonlinear system of equations. Linear models have more straightforward computations and provide a robust approximation to threshold models; thus, they could be used to overcome the mentioned challenges. However, linear models yield estimated breeding values on the observed scale, requiring an approximation to the liability scale analogous to that from threshold models to later obtain the estimated breeding values on the probability scale. In addition, the robustness of the approximation of linear to threshold models depends on the amount of information and the incidence of the trait, with extreme incidence (i.e., ≤ 5%) deviating from optimal approximation. Our objective was to test a transformation from the observed to the liability and then to the probability scale in the genetic evaluation of health traits with moderate and very low (extreme) incidence. Data comprised displaced abomasum (5.1M), ketosis (3.6M), lameness (5M), and mastitis (6.3M) records from a Holstein population with a pedigree of 6M animals, of which 1.7M were genotyped. Univariate threshold and linear models were performed to predict breeding values. The agreement between estimated breeding values on the probability scale derived from threshold and linear models was assessed using Spearman rank correlations and comparison of estimated breeding values distributions. Correlations were at least 0.95, and estimated breeding value distributions almost entirely overlapped for all the traits but displaced abomasum, the trait with the lowest incidence (2%). Computing time was ∼3x longer for threshold than for linear models. In this Holstein population, the approximation was suboptimal for a trait with extreme incidence (2%). However, when the incidence was ≥6%, the approximation was robust, and its use is recommended along with linear models for analyzing categorical traits in large populations to ease the computational burden.

An extreme value analysis of water levels at the Akosombo dam, Ghana

The Akosombo Dam is the largest dam in Ghana and is linked to the world's largest man-made lake by surface area. The top of flood control pool of the dam has been breached a number of times, so it is of interest to know the corresponding probability. The paper fits the generalized extreme value distribution to the extreme water levels – with all three of its parameters (including the shape parameter) accounting for various linear trends, seasonality and cyclic trends with respect to time, the first time such a model has been fitted. The fitted model contains in total 50 parameters. It provided an adequate fit, as evaluated by probability plots, quantile plots, and the Kolmogorov-Smirnov test. It is used to provide return level estimates as well as probabilities of the top of flood control pool of the dam being breached.

An improved interval prediction method for recurrence period wind speed

Based on the improved interval operation theory, an improved expression of the return period wind speed interval prediction is constructed by using an approximate first-order Taylor series expansion. According to the measured wind speed data in Beijing, Jinan, Nanjing, Wuxi, Shanghai and Shenzhen, the improved method and the traditional method are respectively used to predict the interval of the return period wind speed. Furthermore, the interval results predicted by the improved method and the traditional method are compared and analyzed under the same confidence level. Results show that the improved method has good applicability for different parameter estimation methods under the condition of certain extreme value distribution model, and the interval prediction results of the return period wind speed are basically stable. Compared with the interval results predicted by the traditional method, the interval predicted by the improved method is more likely to be close to or contain the exact solution of the return period wind speed, which has higher prediction accuracy. In addition, the calculation process of the improved method is relatively simple and can realize the simplified calculation of interval prediction.

Analysing the variability of non-stationary extreme rainfall events amidst climate change in East Malaysia

ABSTRACT Climate change is intensifying the occurrence of extreme rainfall events, drawing attention to the importance of understanding the return period concept within the realm of extreme weather studies. This study evaluates the stationarity of extreme rainfall series on both monthly and annual series across East Malaysia, employing the Augmented Dickey–Fuller, Phillips Perron, and Kwiatkowski–Phillips–Schmidt–Shin tests. To model these extreme rainfall series, various probability distributions were applied, followed by goodness-of-fit tests to determine their adequacy. The study identified the stationary and non-stationary return values at 25-, 50-, and 100-year return periods. Additionally, maps depicting the spatial distribution for non-stationary increment were generated. The results indicated that extreme monthly rainfall exhibited stationary characteristics, while extreme yearly rainfall displayed non-stationary characteristics. Among the tested probability distributions, the generalised extreme value distribution was found to be superior in representing the characteristics of the extreme rainfall. Furthermore, a significant finding is that the non-stationary rainfall exhibits higher return values than those of stationary rainfall across all return periods. The northeast coast of Sabah highlighted as the most affected area, with notably high return values for extreme rainfall.

INDIVIDUAL CONTROL CHARTS FOR NON-NORMAL DATA

Individual control charts are a suitable option to consider when the processes being studied change slowly. If obtaining standard samples for Shewhart control charts is expensive or in the case of studying statistical characteristics of the technological process, they are used. Control charts are used to control technological processes in the case of a normal distribution of the possible values of the controlled parameter. Sometimes a quasi-normal distribution of the studied parameter is allowed. In practice, it is often necessary to deal with technological processes in which samples are not distributed according to a normal distribution. In such cases, using individual control charts can lead to type I and II errors. This is because the control limits of the charts are calculated for the mean and standard deviation of the normal distribution. Control charts of individual values are most sensitive to the deviation of the distribution from the normal one since individual values are used to construct the limits, to which the central limit theorem does not apply, as in the case of control charts of mean values. Therefore, it is extremely important to develop methods of applying the individual control charts for random samples, the general population of which is not normally distributed. The study examines the application of individual control charts for a random process that is non-normally distributed. This is achieved by using the normalization method to transform samples of the studied parameter into samples that are distributed normally or quasi-normally. The normalization is performed using the Box-Cox method with further construction of control charts based on the transformed samples. The mathematical apparatus of the individual control chart application for samples with non-normal distribution is described. Numerical modeling of the application of individual control charts for the chi-square distributed samples, which have a significant distribution asymmetry for small values of the shape factor, and for the samples obtained by normalizing the chi-square one, was performed. According to the research findings, the utilization of individual control charts for samples exhibiting a significantly asymmetric distribution results in a higher likelihood of type I errors being detected. However, when the method of sample normalization is employed, this issue is almost entirely eliminated. Modeling process disruption, by changing a certain individual value, also demonstrated the emergence of false signals of process disruption.

Geoelectric Investigation for Groundwater in Imo State University Campus and Environs using Direct Current Electrical Resistivity Method

Eighteen, vertical electrical soundings (VES), were carried out in Imo State University and Environs to delineate the potential site for groundwater. Data were acquired using ABEM SAS 300 resistivity meter. Schlumberger array and maximum electrode spread of 700m were adopted. Data were interpreted with IP2WIN software. Contour maps were done using Sulfer 20 software. Geoelectric cross-sections constructed along three profile lines delineated subsurface stratification. Sediments consist of sequence of sandstone/gravel, silty sand/sand and clay with geology representing the Benin Formation. Groundwater was delineated within the zone interpreted to be sand with quartz making up more than 95% of all grains. Resistivity of aquifer varies across study area and ranges from 159Ωm, observed around Library and Information Department (VES 11) to 100000Ωm recorded at IMSU Plaza (VES 10). Depth to water table also varied across study area with higher values of 150m and 130m observed at College of Health Science (VES 3) and Behind Old ETF (VES 4). A relatively shallow aquifer is delineated behind Senate Building and Okwu-Uratta with water table at depths of 23.5m and 26.5m. The transmissivity values are moderately high except at some locations. The variation in longitudinal conductance and transverse resistance values show that part of the study area such as Aso Rock, Female Hostel, Law Faculty and IMSU Plaza are underlain by relatively resistive (low longitudinal conductance) aquifer material. Distribution of hydraulic conductivity and electrical conductivity values indicates that in some of the VES points, the values are fairly constant. This could be seen as areas with similar geologic settings and water quality. Storativity of study area ranges from 0.00003 at VES 1 and 12 to 0.00028 at VES 4. Study area shows a good prospect for groundwater development, except at Library and Information Department, where the zone delineated is an aquitard.

MEASURING THICKNESS OF FLAX FIBRES IN A CLUSTER USING COMPUTER VISION

Authors analyse a method of indirect measurement of flax fibre linear density using its thickness, based on computer vision approach and “distance transform” algorithm in particular. A number of flaws of this method are identified. The main reason for the discrepancy is the effect of crossed fibres within the analysed sample. The secondary reason is the specific shape of fibre’s topological skeletonnear its tips. Based on these observations, a software model of the problem has been created. The software produces a simplified image of crossed fibres with specified properties, and measures this image using the above method. The effect of various factors on the discrepancy in the distribution of thickness values appears to match well with theoretical analysis. Additionally, a number of ways to improve the software model are outlined, related to how typicallyfibres are oriented within the analysed sample.

Randomized algorithms in systems without coordination and centralization

Evaluating the complexity of algorithms based only on the possibility of the worst possible variant of the input data is often not justified. The development of algorithms that would predictably work quickly on all possible inputs is of practical importance. If for the problem there is a reasonable opportunity to model the distributions of input values, then you can use probabilistic analysis as a method of developing effective algorithms. When the information about the distribution of input values is not enough for their numerical modeling, algorithms are developed by giving a part of the algorithm itself a random character - randomized algorithms. The use of randomization ensures the operation of the algorithm with minimal needs to store internal states and events in the past, and the algorithms themselves look compact. The paper studies problems for which there are relatively effective deterministic algorithms for solving. But, as will be shown, the construction of appropriate randomized algorithms leads to effective and efficient parallel computing schemes with linear complexity on average. The advantages of randomization are especially evident in the case of large computer systems and communication networks that function without coordination and centralization. Examples of such distributed systems are, in particular, networks of currently popular cryptocurrencies. The use of randomized heuristics allows the system to adapt to changing operating conditions and minimizes the likelihood of conflicts between processes. The paper shows the advantages of using a randomized algorithm over deterministic algorithms for the problem of routing in a network with a hypercube topology. A theorem on estimating the expected number of steps required by Valiant's randomized algorithm to deliver all messages to an address is proved. The expected linear complexity of Valiant's algorithm is a direct consequence of the proven theorem.

Quantifying regional rainfall dynamics in southern India: Unravelling monsoon characteristics and intense precipitation using satellite and observed data records

Rainfall patterns are unpredictable, which has implications for planning irrigation systems, constructing farm ponds and drainage systems, and conserving water and soil. The research findings have been compiled over 36 years, utilizing gridded daily precipitation data from the IMD and PWD (1984–2019). This study investigates the frequency of consecutive rainy days, the yearly rainfall pattern and the precipitation threshold value that triggers the flood. The Mann-Kendall approach is used to analyze trends in the statistical application XLSTAT. According to the trend analysis, rainfall and wet days during the Northeastern Monsoon (NEM) throughout the year show significant and nonsignificant increasing trends at most stations. Statistical or empirical approaches can be used to establish the threshold value of precipitation that causes a flood by examining historical flood episodes and the precipitation that occurred. The Gumbel extreme value distribution method was used to determine the recurrence intervals of days 1, 2, 3, and 4 that are consecutive and equal to or above 5 days. The study's findings will show that rainfall patterns can be used to forecast floods and estimate their return and recurrence intervals. As a result, implementing this research with these precipitation threshold values could help with flood warning systems and the infrastructure required for emergency responses to flooding disasters.