Necessary Amount Of Data Research Articles

Multi criteria analysis of municipal solid waste management and resource recovery in Poland compared to other EU countries

Statistics show that the inhabitants of Poland are producing increasingly more household waste. This article attempts to determine the current level of development of Poland in the field of waste management concerning other EU countries and partner countries; identify trends in the mass of generated, segregated, and mixed municipal waste; and obtain an idea of the attitude of the Polish population toward sorting waste at the source to bring the country to a higher level of waste management. The empirical base is statistical data published on the website of the EU Data Explorer and the Central Statistical Office. The ranking of countries was determined by the TOPSIS method using a synthetic indicator based on the selected diagnostic features. The significance of the obtained ranks was tested using the non-parametric Friedman test (p < 0.01). We established that Poland has been consistently ranked 16th-17th over the past 5 years. Unfortunately, thus far, no systematic approach has been found to raise citizens' awareness, which may be due to the lack of the necessary amount of data. Researchers recommend investigating the sensitivity of the relationship between the generation of alternative energy from waste and the authorities' action.

Applying national-regional component in teaching a professionally oriented foreign language to undergraduates of agrarian specialties

The article considers proven in practical teaching methodological techniques and strategies that are effective for the development of oral speech skills for undergraduates of agricultural specialties. Individual tasks and group projects based on the national-regional component are offered, facilitating the adaptation of probation specialists in the professional field and contributing to the development of professional communication skills in a foreign language. An integrative approach, methods of general scientific analysis, and a descriptive method are used. The introduction of a national-regional component is proposed using supportive tables and schemes that facilitate the comprehension of professional information. Visualization of agrotechnical information allows a student to select and apply the necessary amount of data for analyzing to discuss in a concise and accessible form, in a systematic and consistent presentation without the need to memorize. The article presents 2 types of tables useful to perform specific tasks aimed to develop professional competencies in the process of learning a foreign language. The data of the survey proposed to the students are given, the most relevant goals are highlighted in the opinion of undergraduates, achieved with the introduction of a regional component. The introduction of a regional component in the process of learning a professional foreign language contributes to the development of general professional competencies. Besides, it allows you to bring the learning process closer to the conditions of practical work and apply the acquired skills and knowledge of a foreign language to solve specific agrotechnical tasks. The ability to systematize information in the form of tables, formulate a related statement based on the data from a table or a diagram also contribute to the development of professional communication skills in a foreign language, useful for students in later professional activities.

Corporate reporting and its role in Business Management

The purpose of the study is to reveal the features and role of corporate reporting in business management. Ideas for its improvement are connected with the developing economic reality and are aimed at meeting the needs of an ever wider audience of interested parties (stakeholders). The article deals with topical issues related to the existing structure of corporate reporting aimed at disseminating the necessary amount of data among stakeholders, as well as the features of integrated reporting, which seeks to combine various components of corporate information. The methodological basis of the study was a system-functional and economic-statistical analysis, comparison. Based on the results of the work, it was concluded that corporate reporting is an important means by which companies communicate with stakeholders in management activities, incl. In fulfillment of their obligations. The results of the study can be used in the collection of data for the preparation and formation of corporate reporting of organizations.

Machine learning-based detection of label-free cancer stem-like cell fate

The detection of cancer stem-like cells (CSCs) is mainly based on molecular markers or functional tests giving a posteriori results. Therefore label-free and real-time detection of single CSCs remains a difficult challenge. The recent development of microfluidics has made it possible to perform high-throughput single cell imaging under controlled conditions and geometries. Such a throughput requires adapted image analysis pipelines while providing the necessary amount of data for the development of machine-learning algorithms. In this paper, we provide a data-driven study to assess the complexity of brightfield time-lapses to monitor the fate of isolated cancer stem-like cells in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. We show that with our experimental system and on two different primary cell lines our optimized deep learning based algorithm outperforms classical computer vision and shallow learning-based algorithms in terms of accuracy while being faster than cutting-edge convolutional neural network (CNNs). With this study, we show that tailoring our deep learning-based algorithm to the image analysis problem yields better results than pre-trained models. As a result, such a rapid and accurate CNN is compatible with the rise of high-throughput data generation and opens the door to on-the-fly CSC fate analysis.

Recurrent neural network-based multiaxial plasticity model with regularization for physics-informed constraints

A recurrent neural network (RNN) based model is developed as a surrogate to predict nonlinear plastic response under multiaxial loading. The RNN-based model is trained and tested on stress versus strain curves generated using a numerical solution based on the classical radial return method. Besides simply learning the basic constitutive relationship, a novel approach is taken to enforce certain physical conditions. Specifically, regularization is employed to maintain non-negative plastic power density throughout the loading history thereby ensuring monotonically increasing plastic work and thermodynamic consistency. Enforcing physics in this manner permits coupling of the data-driven RNN approach with physics-based knowledge and laws. This has the effect of reducing the necessary amount of data and ensuring known physical laws are not violated. Since, once trained, the model need not perform the expensive task of solving nonlinear equations, its efficiency is orders of magnitude greater than its numerical counterpart. The RNN-based model has been trained on varied sets of data and the accuracy on test datasets validated. The developed model is general and robust and has widespread application such as in the simulation of metal forming, large scale plasticity, and part life prediction.

Surrogate-based Bayesian comparison of computationally expensive models: application to microbially induced calcite precipitation

Geochemical processes in subsurface reservoirs affected by microbial activity change the material properties of porous media. This is a complex biogeochemical process in subsurface reservoirs that currently contains strong conceptual uncertainty. This means, several modeling approaches describing the biogeochemical process are plausible and modelers face the uncertainty of choosing the most appropriate one. The considered models differ in the underlying hypotheses about the process structure. Once observation data become available, a rigorous Bayesian model selection accompanied by a Bayesian model justifiability analysis could be employed to choose the most appropriate model, i.e. the one that describes the underlying physical processes best in the light of the available data. However, biogeochemical modeling is computationally very demanding because it conceptualizes different phases, biomass dynamics, geochemistry, precipitation and dissolution in porous media. Therefore, the Bayesian framework cannot be based directly on the full computational models as this would require too many expensive model evaluations. To circumvent this problem, we suggest to perform both Bayesian model selection and justifiability analysis after constructing surrogates for the competing biogeochemical models. Here, we will use the arbitrary polynomial chaos expansion. Considering that surrogate representations are only approximations of the analyzed original models, we account for the approximation error in the Bayesian analysis by introducing novel correction factors for the resulting model weights. Thereby, we extend the Bayesian model justifiability analysis and assess model similarities for computationally expensive models. We demonstrate the method on a representative scenario for microbially induced calcite precipitation in a porous medium. Our extension of the justifiability analysis provides a suitable approach for the comparison of computationally demanding models and gives an insight on the necessary amount of data for a reliable model performance.

Electron dynamics of tip-tunable oxygen species on TiO2 surface

The redox states of oxygen species on the surface of TiO2 can be altered by electron tunneling by varying the applied bias voltage of an atomic force microscope tip. However, tunneling is stochastic in nature and typically requires ultra-low temperatures to obtain statistically significant data. Here, we use a highly sensitive fast atomic force microscopy setup to study redox transitions of oxygen atoms on a TiO2 surface, in the form of reactive oxygen species and single-atom quantum dots, at 78 K. The fast and highly sensitive nature of our experimental setup enables a statistically necessary amount of data to be collected without having to resort to ultra-low temperatures. This enabled us to study multiple dots and provide insight into the electronic structure and correlation between the oxygen species, which are inaccessible by standard atomic force microscopy. We show that single-atom quantum dots exist in two charge states with drastically different conductance, with one being conducting and the other non-conducting.

ENSURING THE QUALITY AND VOLUME OF GEOLOGICAL-TECHNOLOGICAL DATA FOR APPLICATION OF MACHINE LEARNING METHODS KNOWLEDGE-ORIENTED SYSTEM

The article considers the issues of complications arising during the technological processes of drilling related to geological, geophysical and external conditions, urbanization and detection of inconsistencies between the actual drilling conditions from the projected ones due to changes in climate and geological changes, which occur in the period from the end of the project and the actual start of development of the field. The interrelation of the factors that complicate the drilling process with the stages of design and organization of precedents, on the basis of which the technological processes of drilling are modeled, is analyzed. The phases of the cycle of reasoning based on knowledge with a projection on the information cycle of drilling process control are revealed. The architecture of automation of technological processes of drilling with reference to the pyramid of computer-integrated production is presented. The article shows the possibility of applying machine learning methods to data analysis tasks related to the drilling process. The use of a combined approach to adapt the data used for knowledge-based decision-making is proposed. The problem of insufficient precedent for training knowledge-oriented system of intellectual support of decision-making processes is considered and the provision of full-scale simulators with the necessary amount of data for modeling complications of high-risk drilling process is substantiated. The authors determine the level of the expected ratio between the main objects of the knowledge-oriented system of intelligent decision-making on the course of the technological process: the problems of machine learning on the one hand and oil and gas wells on the other.

Relative-perfectness of discrete gradient vector fields and multi-parameter persistent homology

The combination of persistent homology and discrete Morse theory has proven very effective in visualizing and analyzing big and heterogeneous data. Indeed, topology provides computable and coarse summaries of data independently from specific coordinate systems and does so robustly to noise. Moreover, the geometric content of a discrete gradient vector field is very useful for visualization purposes. The specific case of multivariate data still demands for further investigations, on the one hand, for computational reasons, it is important to reduce the necessary amount of data to be processed. On the other hand, for analysis reasons, the multivariate case requires the detection and interpretation of the possible interdepedance among data components. To this end, in this paper we introduce and study a notion of perfectness for discrete gradient vector fields with respect to multi-parameter persistent homology, called relative-perfectness. As a natural generalization of usual perfectness in Morse theory for homology, relative-perfectness entails having the least number of critical cells relevant for multi-parameter persistence. As a first contribution, we support our definition of relative-perfectness by generalizing Morse inequalities to the filtration structure where homology groups involved are relative with respect to subsequent sublevel sets. In order to allow for an interpretation of critical cells in 2-parameter persistence, our second contribution consists of two inequalities bounding Betti tables of persistence modules from above and below, via the number of critical cells. Our last result is the proof that existing algorithms based on local homotopy expansions allow for efficient computability over simplicial complexes up to dimension 2.

Functional binning: improving convergence of Eulerian statistics from Lagrangian particle tracking

In the analysis of Lagrangian particle tracking data, ensemble averaging with spatial bins is used to generate Eulerian flow statistics. Due to the scattered nature of the particles over independent snapshots, the possible spatial resolution is directly dependent on the measured particle position accuracy and the amount of available data. This requires a balance between convergence of the underlying statistic and the bin resolution. Current binning approaches use the velocity information of the particle positions at single time steps directly and do not exploit the additional information available from the temporal filtering of the tracking process. We present a novel functional approach to the binning procedure that extracts all available information from the particle tracks and improves convergence speed. For a given experiment this allows for higher resolution of flow statistics than classical approaches or alternatively to reduce the necessary amount of data required for a given resolution. Furthermore, uncertainty measures from the particles position, velocity and acceleration can be propagated directly by weighting coefficients.

MODELS OF GENERATION OF INPUT DATA OF TRAINING OF NEURAL NETWORK MODULES FOR DIAGNOSTIC OF DISEASES IN UROLOGY

The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of uroflowmetric parameters, in particular, graphs of uroflowrograms of the required volume, used as input data for NNM training. Objective: to develop a mathematical model for the formation of uroflowmetric parameters using a probabilistic approach based on a uniform "white noise". To develop an effective algorithm for the procedure for generating new parameter values and tools for its implementation. Methods used: NNM training methods, mathematical modeling methods, digital signal processing methods, tools for generating and processing random numerical sequences, digital data filtering methods. The following results were obtained: when creating and implementing a mathematical model for generating a large amount of training data, the requirements of randomness are taken into account when obtaining new values of uroflowmetric parameters. And at the same time, the obtained noise values are filtered to values of a given range, which are percentage-wise comparable to the amplitude value of the uroflowmetric parameter. Conclusions. The scientific novelty of the results is as follows: the NNM training method for recognizing diseases in urology has been improved by developing a mathematical model to generate uroflowmetric parameters for NNM training. The presented model allows you to create the necessary amount of data for training neural network modules in the course of experimental research on the recognition of diseases. The generation of uroflowmetric parameters is based on adding noise to the parameter values. This allows you to change the input data of the NNM training in a given range. This ensures the creation of the required input volume of the NNM training procedure. In the future, this contributes to the testing process of trained neural network modules with reliable information on the diagnosis of diseases in urology.

At the interface between domain knowledge and statistical sampling theory: Conditional distribution based sampling for environmental survey (CODIBAS)

In environmental research, there is an extensive knowledge of the underlying processes that cause a distinct spatial landscape pattern of system properties. Statistical sampling theory deals with how a dataset must be constructed that allows for the transferability of the insights from the collected data to the system. Stratified and balanced sampling schemes applied for environmental survey, seek to reduce the necessary amount of data to capture the spatial heterogeneity of a landscape. The particular design’s specification depends on the field of application. Against this background, the author strives to draw the attention to the conceptual shortcomings of conditioned Latin hypercube sampling (cLHS) in the context of soil survey and digital soil mapping. Furthermore, a new sampling design is presented which (1) combines the advantages of both, stratified and balanced designs, (2) shows consistency in the application of pedogenetic theory, and (3) which can be obtained by some simple modifications to the computer code of cLHS. Overall, this manuscript shall promote a vivid discussion in the Pedometrics community concerning the consideration of scientific domain knowledge in statistical sampling theory.

Optimised Geophysical Survey Design for CO2 Monitoring – A Synthetic Study

In this work, we evaluate strategies for design of fit-for-purpose geophysical survey layouts that allow acquisition of the minimum necessary amount of data while maintaining a sufficient level of resolution or accuracy of the subsurface parameters to be recovered. The objective is to determine the optimal survey layout for CO2 monitoring based on our, potentially limited, knowledge of the subsurface prior to data acquisition. The proposed method is based on the analysis of the eigenvalue spectrum of the data misfit Hessian. The spectrum provides a simple and intuitive means to evaluate and compare the quality of given survey designs. Examples from seismic and EM monitoring are included. We perform synthetic studies and assess whether a sequential experimental design can be used to determine if only a small amount of data is sufficient to achieve comparable resolution to when the comprehensive dataset is used. Comparisons between random and optimal data subset selection are performed. The results suggest that the method is quite robust with respect to the starting model used for the analysis. The method offers a powerful means to acquire enough information at a minimal cost and analyse the pattern of the optimal acquisition design for a given tomographic method.

Intelligent System for Identification of Wheelchair User’s Posture Using Machine Learning Techniques

This paper presents an intelligent system aimed at detecting a person's posture when sitting in a wheelchair. The main use of the proposed system is to warn an improper posture to prevent major health issues. A network of sensors is used to collect data that are analyzed through a scheme involving the following stages: selection of prototypes using condensed nearest neighborhood rule (CNN), data balancing with the Kennard-Stone algorithm, and reduction of dimensionality through principal component analysis. In doing so, acquired data can be both stored and processed into a micro controller. Finally, to carry out the posture classification over balanced, pre-processed data, and the K-nearest neighbors algorithm is used. It turns to be an intelligent system reaching a good tradeoff between the necessary amount of data and performance is accomplished. As a remarkable result, the amount of required data for training is significantly reduced while an admissible classification performance is achieved being a suitable trade given the device conditions.

Predicting Sensor Events, Activities, and Time of Occurrence Using Binary Sensor Data From Homes With Older Adults

We present a comprehensive study of state-of-the-art algorithms for the prediction of sensor events and activities of daily living in smart homes. Data have been collected from eight smart homes with real users and 13-17 binary sensors each – including motion, magnetic, and power sensors. We apply two probabilistic methods, namely Sequence Prediction via Enhanced Episode Discovery and Active LeZi, as well as Long Short-Term Memory Recurrent Neural Network, in order to predict the next sensor event in a sequence. We compare these with respect to the required number of preceding sensor events to predict the next, the necessary amount of data to achieve good accuracy and convergence, as well as varying the number of sensors in the dataset. The best-performing method is further improved by including information on the time of occurrence to predict the next sensor event only, and in addition to predict both the next sensor event and the mean time of occurrence in the same model. Subsequently, we apply transfer learning across apartments to investigate its applicability, advantages, and limitations for this setup. Our best implementation achieved an accuracy of 77-87% for predicting the next sensor event, and an accuracy of 73-83% when predicting both the next sensor event and the mean time elapsed to the next sensor event. Finally, we investigate the performance of predicting daily living activities derived from the sensor events. We can predict activities with an accuracy of 61-90%, depending on the apartment.

Using Cox Regression to Forecast of Survival of Women with Multiple Malignant Neoplasms

Recently, an increase in the incidence of multiple primary malignant neoplasms has been observed, specifically, when two or more unrelated tumors originate from different organs and appear in the body simultaneously or sequentially, one after another. During past few years, the interval between the first and second reproductive cancer diagnosis has decreased in 6 times – from 11 to just 2 years while probability of surviving the next 3 years after 8.5 years past initial diagnosis has decreased from 0.995 to 0.562. Using performed analysis, this paper provides details of survival modelling for women with breast cancer with the aim to find the most significant factors affecting the likelihood of survival not by chance alone. The data used for research were obtained from Ukrainian National Institute of Cancer covering 1981–2017 period. The modelling was performed using Cox regression with forward effect selection method and stay in p-value boundary equal to 0.15. The forward method firstly computes the adjusted chi-square statistics for each variable. Then, it examines the largest computed statistics and if particular one is significant, the corresponding variable is added to the model. Once the variable is entered, it is never removed from the model. 3 out of 4 factors that appeared to be significant according to forward selection method were confirmed as the significant ones by stepwise selection method. The results of modelling proved the possibility of prediction the survival using certain set of disease features and subjects’ characteristics. Testing of global hypothesis for Beta resulted in rejecting of null hypothesis (Beta = 0) in favor of the alternative one (Beta ≠ 0) thus it was confirmed that the models make sense and can be used to predict survival in women with breast cancer. According to obtained results, the most significant disease features and subjects characteristics appeared to be: type of multiple processes (synchronous or metachronous), presence of relapse and/or metastasis, type and combination of treatment, stage of disease. Cancer with synchronous processes is characterized by greater aggressiveness and it reduces survival by almost 13 times compared with cancer where metachronous processes take place. Even though chemotherapy significantly increases the survival rate of patients, it also impacts the probability of relapses and metastasis occurrence, which are 16 times more likely to occur if chemotherapy was a part of treatment. This gives grounds for assumption that it has an indirect effect on survival and hence needs to be analyzed considering its negative impact on the relapses and metastasis occurrence probability, which, in turn, reduces survival by 10 times. This fact, in our opinion, introduces the need for further in-depth analysis. The significant difference between survival rates in patients with the first and third stages of cancer has been proved – the chances to survive with the disease at the first stage are almost 12 times higher than with disease at the third stage. At the same time, the difference in the survival rates in women with the disease at the second and the third stages is not so big and it is only 1.6 times. The modern method of conducting surgery compared with the standard one appeared to be capable to reduce the risk of relapses and metastases by 2.6 times, while breast conservative surgery in multiple oncological processes – by 3 times compared with mastectomy, which allows to state that both factors have a positive effect on the survival probability and reduce the risk of mortality. Regarding subgroup models built for patients having synchronous process and patients with metachronous processes separately, an increase in the sample size is needed to assess assumed difference in factors affecting survival and to improve predictive abilities of models. This, in turn, requires additional studies during which the necessary amount of data can be collected.

Monitoring high complex production processes using process capability indices

The increasing demand and the globalization of the market are leading to increasing levels of quality in production processes, and thus, nowadays, multiple product characteristics must be tested because they are considered critical. In this context, decision makers are forced to interpret a huge amount of quality indicators, when monitoring production processes. This fact leads to a misunderstanding as a result of information overload. The aim of this paper is to help practitioners when monitoring the capability of processes with a huge amount of product characteristics. We propose a methodology that reduces the amount of data in capability analysis by structuring hierarchically the multiple quality indicators obtained in the quality tests. The proposed methodology may help practitioners and decision makers of the industry in three aspects of statistical process monitoring: to identify the part of a complex production process that presents capability problems, to detect worsening over the time in multivariate production processes, and to compare similar production processes. Some illustrative examples based on different kinds of production processes are discussed in order to illustrate the methodology. A case of study based on a real production process of the automotive industry is analyzed using the proposed methodology. We conclude that the proposed methodology reduces the necessary amount of data in capability analysis; and thus, that it provides an added value of great interest for managers and decision makers.

On spurious detection of linear response and misuse of the fluctuation–dissipation theorem in finite time series

Using a sensitive statistical test we determine whether or not one can detect the breakdown of linear response given observations of deterministic dynamical systems. A goodness-of-fit statistics is developed for a linear statistical model of the observations, based on results for central limit theorems for deterministic dynamical systems, and used to detect linear response breakdown. We apply the method to discrete maps which do not obey linear response and show that the successful detection of breakdown depends on the length of the time series, the magnitude of the perturbation and on the choice of the observable.We find that in order to reliably reject the assumption of linear response for typical observables sufficiently large data sets are needed. Even for simple systems such as the logistic map, one needs of the order of 106 observations to reliably detect the breakdown with a confidence level of 95%; if less observations are available one may be falsely led to conclude that linear response theory is valid. The amount of data required is larger the smaller the applied perturbation. For judiciously chosen observables the necessary amount of data can be drastically reduced, but requires detailed a priori knowledge about the invariant measure which is typically not available for complex dynamical systems.Furthermore we explore the use of the fluctuation–dissipation theorem (FDT) in cases with limited data length or coarse-graining of observations. The FDT, if applied naively to a system without linear response, is shown to be very sensitive to the details of the sampling method, resulting in erroneous predictions of the response.

HIDDEN MARKOV MODEL BASED SPEECH SYNTHESIS SYSTEM IN SLOVAK LANGUAGE WITH SPEAKER INTERPOLATION

This paper describes the first experiments with speaker interpolation in Slovak speech synthesis system. The interpolation provides an approach to voice characteristic conversion for hidden Markov model based text-to-speech synthesis system. The main idea of this technique is to synthesize an artificial speech with unseen and untrained output speech characteristic by interpolating of the existing sets of the pretrained models. The use of this technique allows to create new voices without the need to add additional data into training procedure. This is a major advantage especially for the low resources languages, such as Slovak language, where it is often difficult to obtain the necessary amount of data. The obtained results shows that the characteristics of the synthesized speech is changed from one male speaker to another one with the help of the interpolation ratio by which the Slovak voices with the new characteristics may be crated.

Bayesian Approach to Dynamically Controlling Data Collection in P300 Spellers

P300 spellers provide a noninvasive method of communication for people who may not be able to use other communication aids due to severe neuromuscular disabilities. However, P300 spellers rely on event-related potentials (ERPs) which often have low signal-to-noise ratios (SNRs). In order to improve detection of the ERPs, P300 spellers typically collect multiple measurements of the electroencephalography (EEG) response for each character. The amount of collected data can affect both the accuracy and the communication rate of the speller system. The goal of the present study was to develop an algorithm that would automatically determine the necessary amount of data to collect during operation. Dynamic data collection was controlled by a threshold on the probabilities that each possible character was the target character, and these probabilities were continually updated with each additional measurement. This Bayesian technique differs from other dynamic data collection techniques by relying on a participant-independent, probability-based metric as the stopping criterion. The accuracy and communication rate for dynamic and static data collection in P300 spellers were compared for 26 users. Dynamic data collection resulted in a significant increase in accuracy and communication rate.