Exact Decision Research Articles

Commentary-A Military Health Care Ethics Framework.

Ethical practice within military health care is a significant topic of professional and academic debate. The term "military health care ethics" enfranchises the entire health care team. Military health care professionals are subject to tension between their duties as military personnel, and their ethical duties as health care professionals, so-called "Dual Loyalty." Some military health care practitioners have suffered moral injury because of the psychological stress associated with ethical challenges on military operations. It is important to define military health care ethics and also to consider how it should be taught. The essence of ethical practice is ethical decision-making. It has become self-evident from our experience of teaching military health care ethics that a simple and agreed framework for analyzing an ethical problem is required. This paper describes the development of the King's Military Healthcare Ethics Framework in support of a military health care ethics policy on behalf of the NATO Military Healthcare Working Group. There is logic to using a stepped approach to analyze an ethical problem in military health care. These steps are: "Identify" the problem, "Analyze" the problem including consideration of perspectives, "Fuse" the analysis, and "Decide". Step 1-Identify-is intended to orientate the decision-making group, and to articulate the problem specifically and clearly in order to determine the exact ethical issue and the secondary issues that arise. Step 2-Analyse-considers the problem from 4 perspectives: patient, clinical, legal, and societal/military. These reflect the breadth of perspectives that impact on health care practice within a military context. Step 3-Fuse-is the culminating step. The conclusions from the analysis of perspectives should be summarized and key references cited. This will determine the exact decision(s) to be made. Step 4-Decide-clearly articulates the decision made and provides the record of the key reasons for making that decision. This may include areas of enduring uncertainly and any planned review of the decision. The King's Military Healthcare Ethics Analytical Framework has been evaluated for content validity through iterative discussion at 4 meetings of the NATO MHCWG and a specific workshop on military health care ethics over 2022/2023. It is included within the draft NATO Standardization Agreement on Military Healthcare Ethics.

Constructing two-stream input matrices in a convolutional neural network for photovoltaic power prediction

To enhance the prediction accuracy of photovoltaic (PV) power and provide a more exact decision basis for power system management, an end-to-end reinforced two-stream convolutional neural network (RTS-CNN) is proposed. A temporal data converter is firstly devised to adaptively transform the input series into two different input matrices for improving the representational ability of the input data. One matrix is obtained through the tensor dimension adjustment, while the other uses Fast Fourier Transform (FFT) to acquire periodic information to reconstruct the shape of raw data. Next, to fully extract features from these two input matrices, the RTS-CNN model selects the powerful ConvNeXt block as the foundational building block of the encoder. Subsequently, the PV power prediction is realized using a decoder augmented with the Convolutional Block Attention Module (CBAM). Finally, for better performance and learning speed, the model is trained using the Second-order Clipped Stochastic Optimization (Sophia). Three sets of comparative experiments are conducted on the dataset obtained from the Desert Knowledge Australia Solar Centre (DKASC), and the model performance is further validated using the Diebold-Mariano (DM) test and external validation. The experimental results show that the proposed RTS-CNN model has outstanding performance in predicting PV power under different temporal resolutions and seasonal conditions. In the spring season, with time intervals of 15 min, 30 min, and 1 h, the RTS-CNN model achieves root mean square errors (RMSE) of 0.5113, 0.5984, and 0.7801, respectively.

A data-driven robust optimization model for integrated network design solar photovoltaic to micro grid

In recent years, energy experts have paid considerable attention to renewable resource energy. The most widely renewable energy resource, in the world is solar energy. Therefore, optimizing the sustainable supply chain of solar energy production like photovoltaic helps policy-makers in this field. This paper proposes a two-phase method to elaborate a sustainable supply network design model for the solar photovoltaic supply chain. The first phase selects the suitable land for setting up solar photovoltaic power plants by the robust best–worst method, and the second phase develops the photovoltaic supply network design. Additionally, this work deals with the most significant concerns for policy-makers and investors, which are the uncertainty of radiation and demand parameters by a data-driven robust optimization model. As a significant result, it can be obtained with actualized data which the proposed uncertainty approach versus classical closed uncertainty convex sets has less conservatism. This advantage can impose less cost on the system due to its robustness. For validation of the proposed model, an in-depth case study is conducted. Its results show that solar power plants in Yazd, South Khorasan and Kerman are located as strategic decisions, and material flow between suppliers and wafer and ingot plants are determined as operational decisions. Also, on the tactical level, the number of vehicles required to transfer the flow of goods in the supply chain layers based on different time intervals and various transportation modes has been optimized. • Dealing with the intrinsic uncertainty nature of the photovoltaic supply chain to micro grid using a Data-Driven robust optimization approach. • Using a robust best–worst method for selecting potential points of solar plant that has low need to exact decision maker’s opinion than other MCDM approaches. • Comparing the solution of the proposed model in two cases for showing performance of the DDRO approach, these modes are: (1) Box close convex uncertainty set (the highest level of conservatism) (2) Deterministic approach with nominal data • Developing a sustainable photovoltaic supply chain for DC electricity grids and pay attention to the environmental effects analyzes by Simapro. • Implementing a case study in order to validate the applicability of the proposed mathematical model.

Semantics modulo satisfiability with applications: function representation, probabilities and game theory

AbstractIn the context of propositional logics, we apply semantics modulo satisfiability—a restricted semantics which comprehends only valuations that satisfy some specific set of formulas—with the aim to efficiently solve some computational tasks. Three possible such applications are developed.We begin by studying the possibility of implicitly representing rational McNaughton functions in Łukasiewicz Infinitely-valued Logic through semantics modulo satisfiability. We theoretically investigate some approaches to such representation concept, called representation modulo satisfiability, and describe a polynomial algorithm that builds representations in the newly introduced system. An implementation of the algorithm, test results and ways to randomly generate rational McNaughton functions for testing are presented. Moreover, we propose an application of such representations to the formal verification of properties of neural networks by means of the reasoning framework of Łukasiewicz Infinitely-valued Logic.Then, we move to the investigation of the satisfiability of joint probabilistic assignments to formulas of Łukasiewicz Infinitely-valued Logic, which is known to be an NP-complete problem. We provide an exact decision algorithm derived from the combination of linear algebraic methods with semantics modulo satisfiability. Also, we provide an implementation for such algorithm for which the phenomenon of phase transition is empirically detected.Lastly, we study the game theory situation of observable games, which are games that are known to reach a Nash equilibrium, however, an external observer does not know what is the exact profile of actions that occur in a specific instance; thus, such observer assigns subjective probabilities to players actions. We study the decision problem of determining if a set of these probabilistic constraints is coherent by reducing it to the problems of satisfiability of probabilistic assignments to logical formulas both in Classical Propositional Logic and Łukasiewicz Infinitely-valued Logic depending on whether only pure equilibria or also mixed equilibria are allowed. Such reductions rely upon the properties of semantics modulo satisfiability. We provide complexity and algorithmic discussion for the coherence problem and, also, for the problem of computing maximal and minimal probabilistic constraints on actions that preserves coherence.Abstract prepared by Sandro Márcio da Silva Preto.E-mail: spreto@ime.usp.brURL:https://doi.org/10.11606/T.45.2021.tde-17062021-163257

Quality Evaluation of Rock Mass Using RMR14 Based on Multi-Source Data Fusion.

The uncertainties in quality evaluations of rock mass are embedded in the underlying multi-source data composed by a variety of testing methods and some specialized sensors. To mitigate this issue, a proper method of data-driven computing for quality evaluation of rock mass based on the theory of multi-source data fusion is required. As the theory of multi-source data fusion, Dempster–Shafer (D-S) evidence theory is applied to the quality evaluation of rock mass. As the correlation between different rock mass indices is too large to be ignored, belief reinforcement and Murphy’s average belief theory are introduced to process the multi-source data of rock mass. The proposed method is designed based on RMR14, one of the most widely used quality-evaluating methods for rock mass in the world. To validate the proposed method, the data of rock mass is generated randomly to realize the data fusion based on the proposed method and the conventional D-S theory. The fusion results based on these two methods are compared. The result of the comparison shows the proposed method amplifies the distance between the possibilities at different ratings from 0.0666 to 0.5882, which makes the exact decision more accurate than the other. A case study is carried out in Daxiagu tunnel in China to prove the practical value of the proposed method. The result shows the rock mass rating of the studied section of the tunnel is in level III with the maximum possibility of 0.9838, which agrees with the geological survey report.

Neural network method of the decision Of the nonlinear problem of optimum distribution of the non-uniform resource

Given article is devoted features of the decision of a problem of integer nonlinear programming, by means of developed neural network method and algorithm of nonlinear optimization of means «decision Search» tabular processor Microsoft Excel. In offered neural network method the task in view decision is made by means of a recurrent neural network (RNN) matrix architecture with m neurons in each line and n neurons in each column. All neurons such network are connected with each other by communications, and the signal from an exit neuron can move on its input. Neural network method is characterized by that on inputs mentioned RNN the entrance vector of values of parameters of optimized nonlinear criterion function of a problem of distribution of a non-uniform resource moves, calculation of values of weight factors connected among themselves neurons is carried out and signal RNN is formed. This signal by means of nonlinear function will be transformed to the discrete target signal characterizing values quasi-optimal of the decision of the mentioned problem which size changes from 0 to 1. The estimation of efficiency of the decision of a considered problem was carried out at its various values of an indicator of efficiency on the basis of developed imitating model RNN. As indicators of efficiency of application offered neural network method were used – an average relative error and time of the decision of a problem. The value received by means of algorithm of nonlinear optimization of means was accepted to the exact decision «decision Search» tabular processor Microsoft Excel. The analysis of the received results of the experimental researches, offered neural network method, has allowed to make the conclusion that in comparison with an existing method of nonlinear optimization of tabular processor Microsoft Excel use offered neural network method allows essentially (in 9,4 times) to lower time of the decision of a problem dimension 10 × 8 (m ×n) and thus to provide accuracy of its decision not less than 99,8 %.

Determination of the possible quantum numbers for the newly observed \u039eb(6227)0 state

The LHCb Collaboration recently reported the observation of a new excited bottom baryon Ξb(6227)0 and announced an improvement in the measurements related to the previously observed Ξb(6227)− state. We conduct an analysis for Ξb(6227)0 state considering it as isospin partner of the Ξb(6227)− resonance and possibly 1P or 2S excited state with spin J = frac{3}{2} . The corresponding masses for both possibilities have consistent results with the experimental data, indicating that only with the mass sum rules, one can not make exact decision on the nature and quantum numbers of this state. To go further, the decays of these possible excited states to {Xi}_b^{-}{pi}^{+} final state are also considered and the relevant strong coupling constants are extracted from the light cone sum rules. The obtained decay width values support the possibility of Ξb(6227)0 to be the 1P excited state of Ξb(5945)0 baryon.

Application of Geometric Modeling in Visualizing the Medical Image Dataset

Geometric modeling is a research based on computational geometry for processing the 3D objects in computer graphics or image processing. Rendering the graphical objects in medical applications is an interesting research that can help doctors have an exact decision in their diagnostic method. Application of geometric modeling in visualization of medical data is considered as a tool to support treatment. In the field of big data analysis and data science, the methods, tools and techniques are essential and important to analyze a massive amount of information and make data-driven decisions. These data are first analyzed and computed depending on the criteria and purpose of users. They are then visualized or simulated using visual elements like charts, graphs or maps to explore, understand their characteristics and data structure. Which method or solution is best for rendering and visualizing the analyzed data? That is still a question and even a challenge to the researchers. In this article, we apply geometric modeling to build a web-based application for visualizing a medical image dataset. This is an improved version of the selected paper in the previous work [1]. We first review several researches in the fields of computer graphics, images processing, geometric modeling and their applications; then we combine all of them and propose a method to develop our application for reconstructing the medical data objects. The input data are a Dicom (Digital Imaging and Communications in Medicine) dataset captured from CT (computed tomography) scanner or MRI (Magnetic Resonance Imaging). The output are 2D and 3D models that can be displayed and handled in different ways on the web interface. In comparison with other existing applications, our method shown the advantages of its using in practice.

Application of Specific Mathematical Methods in the Context of Revitalization of Defunct Intermodal Transport Terminal: A Case Study

The article deals with a specific research study discussing the possibility to revitalize a specific non-functional intermodal transport terminal. The structure of the manuscript consists of five sections. The first part is focused on the analysis of the subject being investigated in important scientific publications. The second part of the article presents a brief analysis of the current situation concerning intermodal transport in the Czech Republic and emphasizes the need to build an intermodal transport terminal in the South Bohemia region. The third section describes the contemporary state of the Nemanice transshipment station, provides a calculation of the terminal transport potential, which is thereafter used in the next paper section in order to design individual terminal elements. The objective of this study is to propose an approach methodology regarding the terminal connection to railway and road infrastructure, necessary quantification of the transport potential, which subsequently defines the operational need for handling equipment, and other parameters of the terminal fundamental elements. This methodological guideline is based on variant recommendations to equip the terminal with various handling devices, which are then evaluated, applying the exact decision making methods. Such an evaluation is included in the fourth part of the article. Here, the authors try to find out which terminal equipment is the most appropriate in terms of overall terminal efficiency. The last part of the manuscript is devoted to the final discussion of the key findings and conclusion containing an insight into potential future projects or studies to be addressed.

Modeling Design and Implementation of an Embeds System Real Time Over a Network of Wireless Sensors to Environmental Monitoring

Artificial Neurons Network (ANN) is used in the decision and control of dynamic systems which can be with a lack of superfluous information.it forces the use of fuzzy logic.
 
 For this reason, several methods and monitoring techniques have been implemented. This article presents a technique based on artificial neural networks implanted at the level of a multisensor surveillance system. It is a statistical learning method that displays optimal training and generalization performance in several domains, including the recognition domain of forms.
 
 In this case ANN based on raspberry PI card for decision node and arduino for the input and hidden nodes, in order to develop a complete platform environmental monitoring system. and hence enhance multi-Sensor wireless signals aggregation via multi-bit decision fusion. 
 
 The back-propagation algorithm generates a weight for all nodes in the networks, with aim of minimizing absolute error committed in fusion data and economics of electrical energy using artificial intelligence techniques. This algorithm is more efficient than the human being since it can reason and learn from its errors so as not to repeat them.
 
 Its main applications include a variety of data monitoring parameters (such as : temperature, humidity, gas sensor, … etc), that can be found in factory automation, for instance : home automation, remote monitoring and home device control, or it may be used in environment to make an exact decision in short time.

Acceleration methods for modelling the static modes of electronic devices

The modelling of the static modes of complicated electronic devices on the basis of the solution of the differential equations is their long integration by known numerical methods until establishment of model periodically repeating decisions. For slowly fading transition processes it leads to the increase of modelling time. The article considers the algorithms of the accelerated calculation of the set modes of the electro technical devices described by the differential equations with periodically changing coefficients. The first algorithm based on the solution of the state equation with previously defined values of the initial state vector is transferring the decision to the established values area. The second algorithm is based on a finding of an entry conditions vector for formulas of the exact solution of the state equations transferring the decision to the established values area by use for this purpose of exact decision formulas.

Feature Selection for Breast Cancer Detection using Machine Learning Algorithms

Cancer has been portrayed as a heterogeneous disease comprising of a wide range of subtypes. The early diagnosis of a cancer type is very important to determine the course of medical treatment required by the patient. The significance of classifying cancerous cells into benign or malignant has driven many research studies, in the biomedical and the bioinformatics field. In the past years researchers have been encouraged to use different machine learning (ML) techniques for cancer detection, as well as prediction of survivability and recurrence. What's more, ML instruments can be used to distinguish key highlights from complex datasets and uncover their significance. An assortment of these procedures, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Random Forest Methods (RVMs) and Decision Trees (DTs) has been usually used in cancer research for the development of predictive models, resulting in successful and exact decision making. Although it is obvious that the usage of machine learning techniques can enhance our comprehension of cancer detection, progression, recurrence and survivability, a proper level of accuracy is required for these strategies to be considered in the ordinary clinical practice. The predictive models talked about here depend on different administered ML strategies and on various input features and data samples. We have used Naïve-Bayes classifier, Neural Networks method, Decision Tree and Logistic Regression algorithm to detect the type of breast cancer (Benign or Malignant) and selection of features which are more relevant for prediction. We have made a comparative study to find out the best algorithm of the above four, for prediction of cancer type. With a high level of accuracy, any of these methods can be used to predict the type of breast cancer of any particular patient.

Multiple-attribute decision making based on equivalence consistency under probabilistic linguistic dual hesitant fuzzy environment

Due to the complexity of the real problems and the limitations of expert’s knowledge, it is often difficult for decision makers to give the exact decision information. How to accurately and adequately express the evaluation information of decision makers becomes the primary problem of multi-attribute decision making(MADM). Therefore, a large number of fuzzy sets(such as linguistic hesitant fuzzy sets(LHFS), probabilistic hesitant fuzzy sets(PHFS), dual hesitant fuzzy sets(DHFS)) that express decision makers’ judgment information have been widely used in multi-attribute decision making. Inspired by these fuzzy sets, this paper proposes a new set called probabilistic linguistic dual hesitant fuzzy sets(PLDHFS). Based on the PLDHFS, the probabilistic linguistic dual hesitant fuzzy preference relation(PLDHFPR) is further given. In order to obtain a reasonable ranking weight vector, the equivalence consistency between the PLDHFPR and the interval-valued fuzzy preference relation(IVFPR) is proposed, and we present the objective programming model(OPM) based on the multiplicative consistency of the IVFPR. Moreover, we establish a consistency improvement model for inconsistent PLDHFPR. Finally, an example of venture capital is analyzed to demonstrate the feasibility and effectiveness of the proposed method.

Application of Exact Methods in Employee Selection in Accordance with the Age Management Concept

The decision making process is an integral part of every management activity. Different methods can be used for decision making. Each of these methods is appropriate in other conditions that may occur in the enterprise. One of the decision making methods is also the exact decision making. Mentioned method is based on general models and procedures. The exact decision making is appropriate to use for complex problems. The analytic hierarchy process (AHP) is one of the methods used in the exact decision making. The AHP can be used in the Smart City concept, specifically within the Smart People. This method will be used to solve employee selection in accordance with the Age management concept. The main objective of the paper is to select the right employee in accordance with the Age management concept and on the basis of defined criteria. The partial objective is to set the order of importance of the individual criteria on which the selection will take place.

Decision Statistics for Incoherent Signal Detection in Multi-Element Antenna Arrays

We present a detailed study of the characteristics of the exact decision statistic for the optimal incoherent signal detection in a multielement antenna array. The exact decision statistic is derived from a rigorous expression for the likelihood ratio in the case where the useful-signal amplitudes at the antenna-array elements are identical, whereas the phases are random. The analysis is carried out in the space of the variables which are observed at the outputs of the incoherent matched filters performing the initial signal processing at each antenna-array element. Variation in the signal-to-noise ratio is shown to deform the boundary of partitioning of the multidimensional observation space, which results in a variation in the threshold values of the exact decision statistic when the Neyman–Pearson criterion is used. The characteristics of the exact decision statistic and various approximate decision statistics are compared in detail. For the antenna arrays with a large number of elements, the influence of the used decision statistic on the target-missing probability is shown to become rather significant. A new combined decision statistic with the characteristics that are close to those of the exact decision statistic is introduced for a wide signal-to-noise ratio range.

Построение точного стационарного решения системы уравнений газовой динамики в условиях действия сил тяжести и Кориолиса

Построение точного стационарного решения системы уравнений газовой динамики в условиях действия сил тяжести и Кориолиса

A Hash Based Method for Large Scale Nonparallel Support Vector Machines Prediction

Recent years have witnessed more and more success of hash methods for building efficient classifiers, but less for prediction in machine learning. In this paper, we propose a hash based method for large scale nonparallel support vector machine prediction(HNPSVM). Our key idea of this method is that we use an approximal decision function instead of exact decision function by computing the Hamming distance between hashing the normal to the hyperplane of the classifier and the features. This method benefits nonparallel support vector(NPSVM) prediction in three aspects. First, it enhances the prediction accuracy using an flexible and general method. Second, the proposed HNPSVM reduce storage cost owing to the compact binary hash representation. Last, HNPSVM can speed up the computation of classification function. Moreover, we prove that the classification results of a hash based NPSVM classifier converge to the results of the exact NPSVM classifier as the number of binary hash functions tends to infinity. Several experiments on large scale data sets show the efficient of our method.

Factors Associated with Lethal Outcome in Patients with Severe Form of Influenza

Clinical manifestations of influenza range from relatively mild and self-limiting respiratory infections to severe clinical manifestations with significant morbidity and mortality. The awareness of predictive indicators for the lethal outcome of influenza is of particular significance in making timely and exact decision for adequate treatment. The aim of this study was to identify the factors in patients with a severe form of influenza, resulting in lethal outcome. The investigation was a prospective group comparison conducted at the University Clinic for Infectious Diseases in Skopje, R. Macedonia in the period from January 01, 2012 to January 01, 2015. The study included adult patients with a severe form of influenza who were further categorized into a group of either survived patients or a group of deceased patients. Demographic, clinical and biochemical data were noted in all patients included in the study on admission. The variables of the univariate analysis that showed a significant difference in terms of the outcome were used for creating multivariate logistic and regression analysis of the outcome as dependent factors. The independent predictors for lethal outcome in severe cases of influenza were identified by using logistic regression. The study included 87 patients with a severe form of clinical and laboratory confirmed influenza. The patients were divided in two groups: survived (n = 75) and deceased (n = 75). The overall mortality was 13.79%. Multivariate analysis conducted on admission to hospital identified cardiovascular comorbid diseases (p = 0.014), urea values higher than 8.3 U/L (p = 0.045) and SAPS score (p = 0.048) as independent predictors of the outcome in patients with severe form of influenza. Influenza patients with cardiovascular diseases had 2.024 times greater risk of death from influenza in comparison to the patients having influenza without history of such a disease (OR = 2.024 95% CI 1.842-17.337). Patients with serum urea values higher than 8.3 U/L had 1.89 times higher chance of death compared to patients with normal values (OR = 1.89 95% CI 1.091-11.432). The increase of the SAPS score in one point increased the chance of death in patients with influenza by 1.2% (OR = 1.12 95% CI 1.01-2.976). The ROC analysis indicated that cardiovascular diseases, increased urea values and SAPS score in combination act as a good prognostic model for the fatal outcome. The global authenticity of this predictive model to foresee lethal outcome amounts to 80%, sensitivity being 82%, and specificity 70%. Cardiovascular diseases, increased values of urea over 8.3 mmol/l and SAPS score are independent predictive indicators for lethal outcome in severe influenza. Early identification of the outcome predictors in patients with severe influenza will allow implementation of adequate medical treatment and will contribute to decreasing of mortality in patients with severe form of influenza.

Development of the Preventive Diagnosis Algorithm on the Partial Discharges Using Current and Ultrasonic Sensors

In this paper, the development of a novel diagnosis algorithm for partial discharges in electric power apparatus was carried out by current and ultrasonic sensors. 6 models for simulating partial discharge in electric power apparatus were used. It was classified into the training and test procedures, and PCA in dimension reduction and kNN classifier were used for the exact decision on partial discharge faults. The cD3 coefficients through wavelet transform in the feature extraction was best matched with the characteristics of partial discharges. The final results of the developed diagnosis algorithm showed the very high accuracy on each partial discharge faults.

Приложение теории искусственного интеллекта в задачах моделирования устойчивости грунтового массива

Paper is devoted to a problem of ensuring stability of the soil massif by use of an automatic control system. Today the main problem of the soil-cultivating units used on slopes and also in the conditions of a difficult relief and existence of obstacles is uncontrollable change of a trajectory of their movement. It is possible to solve this problem by introduction of the computer control and management systems having possibility of fast adaptation in the course of work. The carried-out analysis of literature showed that now there are no computer monitoring systems of stability of the soil massif (landslide) that would allow units to react quickly to extreme conditions and to adapt for them. Authors offer the concept of a control system, the main on creation of automatic indistinct system. This approach is realized by means of a program complex, data of supervision move on an entrance of system which makes the decision on danger of emergence of instability. Thus this system is adjusted optimum for obtaining more exact decision by means of genetic algorithms. Creation of model of forecasting of stability of the soil massif (embankment) is considered, that is the necessary condition of emergence of a landslide is considered. For the forecast of the coefficient influencing stabil-ity methods of fuzzy logic are offered. We construct linguistic variable that takes some value for the terms of the membership function is constructed, built a knowledge base, fuzzification and defuzzification is performed, obtained some prognostic value that specified by the genetic algorithm. The algorithm of emergence of instability pays off and fast response of the operator to this phenomenon is predicted, that is the sequence of the actions necessary in order to avoid danger is specified.The development of stability ground mass worked out. The fuzzy logic methods are considered for predication of development stability ground mass.