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Our research deals with intelligent decision support systems based on rule-based knowledge bases. Decision support systems use rules "If a condition, then a decision" as a form of knowledge representation. In the process of inference, which mirrors the process of human reasoning, we look for rules that confirm the facts and thus generate new knowledge. Such rule-based knowledge bases can (and often do) contain outlier rules. Our goal is to find such unusual rules. Thanks to this, we can influence the completeness of the knowledge base by finding unusual rules and asking domain experts to supplement knowledge in a rare area. To enhance the effectiveness of decision support systems, we conducted separate investigations into two distinct methods. The first method involved the utilisation of the Local Outlier Factor (LOF) algorithm in detecting rule outliers, while the second method employed the Self-Organizing Maps (SOM) algorithm for the same purpose. Our experiments not only confirmed the effectiveness of both the LOF and SOM algorithms but also involved comparing the results obtained from both methods. The discovery of outlier rules can aid knowledge engineers and domain experts in knowledge exploration and enhance the completeness of the knowledge base, which is crucial for decision support systems. [ABSTRACT FROM AUTHOR]

Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring the availability and efficiency of the energy storage systems based on lithium-ion batteries. This paper proposes a method based on a temporal pattern attention (TPA) mechanism and a CNN-LSTM model to improve the SOH estimation accuracy. Firstly, the health factors (HFs) related to capacity degradation are selected based on the charge–discharge curves of lithium-ion batteries, and the local outliers in the health factor data are detected through the local outlier factor (LOF) method. The Lagrange interpolation is then applied to correct these outliers to ensure the continuity of the health factor data. Secondly, the effectiveness of the health factor is evaluated by using the Pearson and Spearman correlation analyses, with the valid health factor forming the HFs dataset. Thirdly, the TPA mechanism is integrated into the CNN-LSTM model to form a CNN-LSTM-TPA model to enhance its ability to capture key information. The whale optimization algorithm (WOA) is employed to optimize the model's hyperparameters. Finally, the proposed method is tested on the NASA and CALCE lithium-ion battery datasets. The experimental results show that on the NASA dataset, the proposed method improves the SOH estimation accuracy by approximately 26.39% to 46.05% compared to the LSTM method; on the CALCE dataset, the accuracy improves by approximately 56.05% to 73.32% compared to the LSTM method, respectively. These experimental results indicate that the proposed method achieves higher accuracy. [ABSTRACT FROM AUTHOR]

The article concerns the detection of outliers in rule-based knowledge bases containing data on Covid 19 cases. The authors move from the automatic generation of a rule-based knowledge base from source data by clustering rules in the knowledge base to optimize inference processes and to detecting unusual rules allowing for the optimal structure of rule groups. The paper presents a two-phase procedure, wherein in the first phase, we look for the optimal structure of rule clusters when there are outlier rules in the knowledge base. In the second phase, we detect outliers in the rules using the LOF (Local Outlier Factor) algorithm. Then we eliminate the unusual rules from the database and check whether the selected cluster quality measures are responded positively to the elimination of outliers, which would indicate that the rules were rightly considered outliers. The performed experiments confirmed the effectiveness of the LOF algorithm and selected cluster quality measures in the context of detecting atypical rules. The detection of such rules can support knowledge engineers or domain experts in knowledge mining to improve the completeness of the knowledge base, which is usually the basis of the decision support system. [ABSTRACT FROM AUTHOR]

The aim of the article is the analysis of using LOF, COF and Kmeans algorithms for outlier detection in rule based knowledge bases. The subject of outlier mining is very important nowadays. Outliers in rules mean unusual rules which are rare in comparison to others and should be explored further by the domain expert. In the research the authors use the outlier detection methods to find a given (1%, 5%, 10%) number of outliers in rules. Then, they analyze which of seven various quality indices, that they used for all rules and after removing selected outliers, improve the quality of rule clusters. In the experimental stage the authors used six different knowledge bases. The results show that the optimal results were achieved for COF outlier detection algorithm as the one for which, among all analyzed quality indices, the cluster quality improved most frequently. [ABSTRACT FROM AUTHOR]

Identifying the abnormal samples from a data set and determining their type are two key tasks of anomaly detection. However, the existing anomaly detection algorithms are generally faced with the defects of weak generalization ability and insufficient interpretation, the core reason of which is that they cannot mine specific features for different abnormal types. In this paper, a new anomaly detection algorithm aiming at feature selection for different abnormal types is developed. Inspired by self-supervised learning, we take the stationarity of variance changes of abnormal score similarity as a pretext task and combine it with wrapped search method. Then, the features and the corresponding parameters for different abnormal types can be screened to apply to the downstream task of anomaly integration detection. To verify the efficiency of the new algorithm, we conduct two sets of experiments to compare the new algorithm with 11 classical anomaly detection and 3 clustering anomaly detection algorithms on the data sets WDBC, WPBC and Wilt from DAMI database with the evaluation measures P@n , Adj- P@n , AP, Adj-AP and AUC. The experiment results show that, both in the identification and classification on abnormal samples, all performance measures of the new algorithm are explicitly better than that of the contrastive algorithms. Also, we apply the new algorithm to the Chinese auto insurance market, and find that the results can help managers to identify the main patterns of fraudulent claims and to summarize the feature combinations of fraud behaviors. In general, the new algorithm developed in this paper has the following advantages compared with traditional algorithms: 1) It can directly capture abnormal features and realize effective recognition of abnormal types, which effectively bridge the gap between abnormal judgement and feature screening. 2) Automatic screening of abnormal features can be completed under the condition of self-updating learning optimal strategy. 3) Only a few features are extracted from all features to reveal the abnormal characteristics, which significantly improves the interpretability and generalization ability of the algorithm and its results. In a word, through the novel self-supervised design method, feature screening is skillfully integrated into the anomaly detection task, which may provide a new way for anomaly detection research. [ABSTRACT FROM AUTHOR]

Highlights • Experimental investigation of accidental scenarios using a water model of a HLM reactor. • Detailed description of the water model facility. • Phenomenological description of four accidental scenarios based on temperature measurements. • Investigation of accidents: LOF, single pump failure, single heat exchanger failure and LOC. Abstract The thermal-hydraulics challenges of a nuclear reactor are numerous and their investigation is crucial for the design and safety of new reactors. Numerical simulation through CFD codes or System thermal hydraulics codes can address a lot of the different challenges; nevertheless the use of water modeling for the study and validation of the thermal hydraulic behavior of a new primary system remains a valuable tool. A water model of the pool-type liquid metal-cooled MYRRHA reactor has been developed at the von Karman Institute in collaboration with SCK•CEN. It is a full Plexiglas model at a geometrical scale 1/5 of the design version 1.2 of MYRRHA. The scaling was performed by respecting as much as possible the Richardson, Euler, Reynolds and Péclet numbers' similarity with MYRRHA. This transparent water model allows the application of optical measurement techniques for the flow characterization. Different transient tests relevant for MYRRHA have been defined by SCK•CEN. These test cases have been studied using the water model facility. They include the study of the transient thermal hydraulic behavior when switching from forced to Loss-of-Flow (LOF), the failure of a single pump, the failure of one of the four heat exchangers and the loss of coolant inventory (LOC). Results of phenomenological behavior are presented as well as the time–dependent variation of temperatures recorded with different arrays of thermocouples immersed inside the upper plenum. [ABSTRACT FROM AUTHOR]

Pronghorn-Subchannel, referred to as Pronghorn-SC throughout this document, is a subchannel code within the Multiphysics Object-Oriented Simulation Environment (MOOSE) developed at the Idaho National Laboratory (INL). Pronghorn-SC was initially designed to model flows in water-cooled, square-lattice, bare subassemblies. Its capability has been extended to model flows in liquid-metal-cooled, triangular-lattice, wire-wrapped subassemblies. To ensure the accuracy of Pronghorn-SC in predicting the behavior of liquid sodium-cooled reactors, the code was validated by comparing calculations with experimental data, obtained from the experimental breeder reactor (EBR-II), Shutdown Heat Removal Tests (SHRT) 17 and 45R. The steady-state calculation at the beginning of the transients was validated using temperature measurements taken at different axial elevations in the instrumented subassembly XX09. The validation exercise was performed in successive stages. First, a comparison between the measured temperature profiles and standalone Pronghorn-SC simulations using a uniform pin power profile was made. The pin power profile was then refined using a Serpent-2 model of the reactor core. Finally, the radial temperature profile was further corrected considering the inter-assembly heat transfer. A Pronghorn Finite-Volume (FV) thermal hydraulic simulation of XX09 and its six neighboring subassemblies; calculated the heat flux on the inner duct surface of the XX09 subassembly to inform the Pronghorn-SC model. Last, a transient validation calculated the peak temperature evolution during the SHRT tests. • Pronghorn-SC code can simulate sodium-cooled, wire wrapped, hexagonal fuel bundles. • Validation of Pronghorn-SC against EBR-II Shutdown Heat Removal Tests. • Pronghorn-SC calculations are enhanced by multi-physics/multi-scale coupling. [ABSTRACT FROM AUTHOR]

This paper describes the work performed within the WP2.1 of the JASMIN project to validate the thermal-hydraulic models of ASTEC-Na code. The experiments used for validation purposes have been: BI1, E8 and EFM1, carried out in the CABRI reactor; BE + 3, APL1 and APL3, carried out in the SCARABEE reactor and N02, conducted in the KNS facility. ASTEC-Na has been also successfully applied (pretest calculations) to assess the performances of the KASOLA sodium loop and to verify its response under different operating conditions. Finally, a PHENIX natural circulation test has also been performed with ASTEC-Na. Besides, these experimental tests have been used as a code benchmarking exercise where thermal-hydraulic codes (CATHARE, RELAP5-Na and RELAP5-3D) and severe accident codes (SIMMER-III and SAS-SFR) have been compared with ASTEC-Na. [ABSTRACT FROM AUTHOR]

Outlier detection becomes more and more important in our real life, such as network intrusion detection and credit card fraud detection, etc. In this paper, a weight-based method is proposed for inside outlier detection. According to the concepts of density and volume information, the weight is defined and introduced to construct a new measure of outlier-ness. Firstly, the total weight of a given object p and its neighbors is computed via their volume and average density. Then the estimated weight of the neighbors is obtained via the neighborhood's volume and p 's density. If the total weight is not close to the estimated weight, p is an outlier. The weight-based method shows more superiority in inside outlier detection than LOF in low dimensions. Moreover, the proposed method performs as well as LOD in a high-dimensional space or when no inside outlier exists. [ABSTRACT FROM AUTHOR]

Luminescent materials have been widely studied due to the increasing number of applications in catalysis, biosensors and electro-optical devices. In this sense, the improvement of luminescent efficiency has been sought and several strategies have been proposed. In this work, experimental and theoretical approaches were used to achieve and understand the improvement of luminescent efficiency of lanthanide organic frameworks through a heating process. In this study, [Eu 2 (MELL)(H 2 O) 6 ] (EuMELL) was synthesized and characterized by scanning electron and fluorescence microscopies, thermogravimetric analysis, powder X-ray diffraction and photoluminescence techniques. In parallel, theoretical simulations of the material in solid phase were carried out using semiempirical approaches. The comparison between experimental and theoretical results indicated that the more accurate structure was calculated using the Sparkle/PM3 model. The temperature effects on the structure as well as the photophysical properties were evaluated by measurements in situ heating and compared to theoretical simulations using the Sparkle/PM3 geometry. The excellent agreement between the computational and experimental results in this study opens up a series of possibilities for studying other systems, particularly when structural details are not easily available. Moreover, that the controlled heating contributes to improve its the quantum efficiency by approximately 45%, suggesting that this approach can be a valuable tool for technological applications. [ABSTRACT FROM AUTHOR]

The discovery in the 1980s that protein kinase C (PKC) is a receptor for the tumor-promoting phorbol esters fueled the dogma that PKC is an oncoprotein. Yet 30+ years of clinical trials for cancer using PKC inhibitors not only failed, but in some instances worsened patient outcome. The recent analysis of cancer-associated mutations, from diverse cancers and throughout the PKC family, revealed that PKC isozymes are generally inactivated in cancer, supporting a tumor suppressive function. In keeping with a bona fide tumor suppressive role, germline causal loss-of-function (LOF) mutations in one isozyme have recently been identified in lymphoproliferative disorders. Thus, strategies in cancer treatment should focus on restoring rather than inhibiting PKC. [ABSTRACT FROM AUTHOR]

Abstract: Discovering outliers in a collection of patterns is a very well known problem that has been studied in various application domains. Density based technique is a popular one for finding outliers in a dataset. This technique calculates outlierness of each pattern using statistics of neighborhood of the pattern. However, density based approaches do not work well with large datasets as these approaches need to compute a large number of distance computations inorder to find neighborhood statistics. In this paper, we propose to utilize triangle inequality based indexing approach to speed up the classical density based outlier detection method LOF. Proposed approach computes less number of distance computations compared to the LOF method. Experimental results demonstrate that our proposed method reduces a significant number of distance computations compared to the LOF method. [Copyright &y& Elsevier]