Example of modeling the use of fire-fighting vessels

Introduction. The article considers a systematic approach to assessing the effectiveness of the preparation and use of fire-fighting vessels. Using the Pattern method, a study of the use of a fire-fighting boat for solving problems of extinguishing fires on water transport was conducted. Goal and objectives. The purpose of the study is to improve the methodology for determining the effectiveness of the preparation and use of fire-fighting vessels according to the selected evaluation criteria, with the determination of the impact of each criterion on the overall effectiveness of fire extinguishing. Methods. In the article, the process of evaluating the effectiveness of the preparation and use of fire-fighting vessels can be divided into two stages. At the first stage, a verbal presentation of the research task is carried out with the identification of the most "weak" training measures and during the application of fire-fighting vessels using the Pattern Method. At the second stage of the study, an assessment of the state of the identified "weak" measures is carried out with the definition of measures to improve the technical readiness of the material part or organizational and technical measures during the operation of fire-fighting vessels. Results and discussion. The author obtained a particular analytical solution for improving the efficiency of operation of fire-fighting vessels for the case of using a fire-fighting boat. A method for calculating the evaluation criteria is proposed. Conclusions. Thus, the proposed modification of the model of preparation and application of fire-fighting vessels consists in the representation of organizational and technical processes in the form of a logical "tree of goals". The directions of further research in terms of the development of the results obtained in the analysis of the operation of fire-technical equipment on fire-fighting vessels and fire-fighting vessels themselves are determined. Keywords: model, system approach, analysis, pattern method, diesel, special fire extinguishing means

LOGICAL CLASSIFICATION TREES IN RECOGNITION PROBLEMS

The paper is dedicated to algorithms for constructing a logical tree of classification. Nowadays, there exist many algorithms for constructing logical classification trees. However, all of them, as a rule, are reduced to the construction of a single classification tree based on the data of a fixed training sample. There are very few algorithms for constructing recognition trees that are designed for large data sets. It is obvious that such sets have objective factors associated with the peculiarities of the generation of such complex structures, methods of working with them and storage. In this paper, we focus on the description of the algorithm for constructing classification trees for a large training set and show the way to the possibility of a uniform description of a fixed class of recognition trees. A simple, effective, economical method of constructing a logical classification tree of the training sample allows you to provide the necessary speed, the level of complexity of the recognition scheme, which guarantees a simple and complete recognition of discrete objects.

Tracking post-colonisation geomorphic river sensitivity: A framework for identifying hotspots of river adjustment across a catchment

<p>Contemporary geomorphic river behaviour can only be understood with a sound knowledge of the historical range of river adjustment. This is particularly the case for rivers that have experienced anthropogenic alterations. Using a case study of Richmond River, New South Wales, Australia, we track the history of geomorphic river adjustment from the time of European colonisation in the late 18<sup>th</sup> Century. We use this study to develop a novel framework, called the <em>‘Behavioural sensitivity logical tree’</em> which can be applied to any catchment for assessing and quantifying reach scale behavioural sensitivity, defined as the ease with which geomorphic units and associated water, sediment, vegetation interactions adjust within the expected behavioural regime of a river. We use this framework to develop a behavioural sensitivity index and categorise rivers as <em>Fragile, Active sensitive, Passive sensitive, Insensitive and Resilient</em>. When applied across a catchment, such analyses highlights hotspots of river adjustment and sensitivity.</p><p>Fragile rivers have a behavioural sensitivity index > 0.85 and have the propensity to undergo wholesale river change such that a new river type and behavioural regime is created. For example, change from discontinuous or absent channels (e.g. swamps) to continuous channelised fills. Active sensitive rivers have a behavioural sensitivity index of 0.50-0.85 and have the ability to re-configure within their contemporary behavioural regime. For example, by reducing sinuosity via abrupt chute cut-off or progressive channel straightening. The behavioural sensitivity index of Passive sensitive rivers is between 0.20-0.50. These rivers have the ability to maintain their behavioural regime and withstand adjustment. Insensitive rivers have a behavioural sensitivity index of 0.05-0.20. They do not readily adjust and may contain significant resistance elements such as fine-grained sediments that limit geomorphic adjustment.  Resilient rivers have a behavioural sensitivity index < 0.05 and tend to be confined reaches where the capacity for adjustment is controlled by bedrock or other antecedent controls, such that the river cannot readily adjust.</p><p>We further demonstrate the evolutionary nature of behavioural sensitivity itself. The behavioural sensitivity of a river is not set in space and time, rather, rivers can dynamically evolve and shift to a different sensitivity category over time and in response to different forms of direct and indirect disturbance. Analysing a rivers’ behaviour sensitivity and identifying hotspots of geomorphic adjustment, can help inform process-based river management practice.</p>

Incorporating systemic functional linguistics in the unlock course book: English intermediate level at Birzeit University

This research attempts to develop a supplementary writing teaching method that is compatible with the Unlock intermediate level students at Birzeit University. To achieve this goal, the study identifies four general writing needs and purposes for such students: understanding purpose and genre; using clause and sentence structure for these purposes and text types; developing a sense of audience and writing coherently. This review includes an overview of genre and the three register dimensions: ideational, interpersonal and textual. Writing instruction is provided through two teaching methodologies: Joint Construction and the Logical Tree which will be discussed thoroughly under the Methodology section. The sample of this study is 54 intermediate level students of Unlock English program; 27 of which were the control group and the other 27 were the experimental group. The overall results showed more use of the measured variables in the experimental group than in the control one. Keywords: Systemic functional linguistics, unlock course book, metafunctions, ideational, interpersonal, textual.

FEATURES OF SOFTWARE SOLUTIONS OF MODELS OF LOGICAL CLASSIFICATION TREES BASED ON SELECTION OF SETS OF ELEMENTARY FEATURES

Urgency of the research.Currently there are several independent approaches (concepts) to solve the classification problem in the general setting, and the development of various concepts, approaches, methods, and models that cover the general issues of the theory of artificial intelligence and information systems, all of these approaches in a recognition theory have their advantages and disadvantages and form a single tool to solve applied problems of the theory of artificial intelligence. This study will focus on the current concept of decision trees (classification trees). The general problem of software (algorithmic) construction of logical recognition trees (classification) is considered. The object of this research is logical classification trees (LСT structures). The subject of the research is actual methods and algorithmic schemes for constructing logical classification trees. Target setting.The main existing methods and algorithms for working with arrays of discrete information in the construc-tion of recognition functions (classifiers) do not allow you to achieve a predetermined level of accuracy (efficiency) of the classification system and regulate their complexity in the construction process. However, this disadvantage is absent in meth-ods and schemes for building recognition systems based on the concept of logical classification trees (decision trees). That is, the coverage of the training sample the set of elementary signs in the case of LCT generates a fixed tree data structure (model LCT), which provides compression and conversion initial data TS, and therefore allows significant optimization and savings of hardware resources of the system, and is based on a single methodology – the optimal approximation test sample set of elementary features (attributes) that are included in some schema (operator) constructed in the learning process.Actual scientific researches and issues analysis. The possibility of an effective and economical software (algorithmic) scheme for constructing a logical classification tree (LCT structuremodel) based on the source arrays of training samples (arrays of discrete information) of a large sample.The research objective. Development of a simple and high-quality software method (algorithm and software system) for building models (structures) LCTfor large arrays of initial samples by synthesizing minimal forms of classification and recog-nition trees that provide an effective approximation of educational information with a set of ranked elementary features (at-tributes) is created on the basis of ascheme for branched feature selection in a wide range of applied problems.The statement of basic materials. We propose a general program scheme for constructing structures of logical classifi-cation trees, which for a given initial training sample builds a tree structure (classification model), which consists of a set of elementary features evaluated at each step of building the model for this sample. A method and ready-made software system build logic trees the main idea is to approximate the initial random sampling of the volume set of elementary features. This method provides the selection of the most informative (qualitative) elementary features from the source set when forming the current vertex of the logical tree (node). This approach allows to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent analysis.Conclusions. The developed and proposed mathematical support for constructing LCT structures (classification tree mod-els) allows it to be used for solving a wide range of practical problems of recognition and classification, and the prospectsfor further research may consist in creating a limited method of logical classification tree (LCT structures), which consists in maintaining the criterion for stopping the procedure for constructing a logical tree by the depth of the structure, optimizing its software implementations, as well as experimental studies of this method for a wider range of practicalproblems.

Estimation of the complexity of constructing a logical classification tree for an arbitrary case in conditions of strong class separation of the initial training sample

The paper offers an estimation of the complexity of the constructed logical tree structure for classifying an arbitrary case in the conditions of a strong class division of the initial training sample. The principal solution to this question is of a defining nature, regarding the assessment of the structural complexity of classification models (in the form of tree-like structures of LCT/ACT) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of post-pruning structure. The presented research is relevant not only for constructions (structures) of logical classification trees, but also allows us to extend the scheme of complexity estimation to the General case of algorithmic structures (ACT models) of classification trees (the concept of algorithm trees and trees of generalized features - TGF). Is investigated the actual question of the concept of decision trees (tree recognition) – evaluation of the maximum complexity of the General scheme of constructing a logical tree based classification procedure of stepwise selection of sets of elementary features (they can be diverse sets and combinations) that for given initial training sample (array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) are estimated at each stage of the scheme of the model in this sample for the case of strong separation of classes. Modern information systems and technologies based on mathematical approaches (models) of pattern recognition (structures of logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information, and this is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods, schemes and achieve a fundamentally new result. The research is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features (individual features and their combinations) built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent instrumental analysis (the final decomposition of the model).

Estimation of general complexity of the procedure for constructing a binary logical classification tree for an arbitrary case

We propose an upper estimate of the complexity of the binary logical tree synthesis procedure for classifying an arbitrary case (for conditions of weak and strong separation of classes in the training sample). The solution to this question is of a fundamental nature, regarding the assessment of the structural complexity of classification models (in the form of tree structures) of discrete objects for a wide range of applied classification and recognition problems in terms of developing promising schemes and methods for their final optimization (minimization) of the structure. This research is relevant not only for the constructions of logical classification trees, but also allows us to extend the complexity estimation scheme itself to the general case of algorithmic structures of classification trees (concepts of algorithm trees and generalized feature trees). The current issue of complexity of the general procedure for constructing a logical classification tree based on the concept of step-by-step selection of sets of elementary features (their possible heterogeneous sets and combinations), which for a given initial training sample (an array of discrete information) builds a tree structure (classification model), from a set of elementary features (basic attributes) evaluated at each stage of the model construction scheme for this sample. Thus, modern information technologies based on mathematical models of pattern recognition (logical and algorithmic classification trees) are widely used in socio-economic, environmental and other systems of primary analysis and processing of large amounts of information. This is due to the fact that this approach allows you to eliminate a set of existing disadvantages of well-known classical methods and schemes and achieve a fundamentally new result. The work is devoted to the problems of classification tree models (decision trees), and offers an assessment of the complexity of logical tree structures (classification tree models), which consist of selected and ranked sets of elementary features built on the basis of the General concept of branched feature selection. This method, when forming the current vertex of the logical tree (node), provides the selection of the most informative (qualitative) elementary features from the source set. This approach allows you to significantly reduce the size and complexity of the tree (the total number of branches and tiers of the structure) and improve the quality of its subsequent analysis.

Earthquake Risk Analysis and Qualitative Comparison of Attenuation Relationships in Garmsar City, Iran

Despite the scientists’ wide efforts to determine earthquake risks all around the world, it is not still possible to predict the exact time, location and magnitude of future earthquakes and aftershocks at the ground surface so precise results are not predictable within near future. The most significant reason for this relates to numerous complexities of earthquake mechanism and causal conditions and waves through different ground layers with completely different properties. Logical tree method was used with weights to determine acceleration spectra due to spectral nature of region. Probabilistic analysis of earthquake hazard was done using SEISRISK III program. The analysis results are proposed through spectral acceleration maps for 50 years in Garmsar. Moreover, uniform hazard spectrum and spectrum with constant shape are presented.